diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..2e9253e81678c483546b3a141cd51575107366f2 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/teaser.jpg filter=lfs diff=lfs merge=lfs -text
diff --git a/.ipynb_checkpoints/TRAIN_motionstreamer-checkpoint.sh b/.ipynb_checkpoints/TRAIN_motionstreamer-checkpoint.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f78c2ef286c0414204a35ed5e54fc6f5ed7afd54
--- /dev/null
+++ b/.ipynb_checkpoints/TRAIN_motionstreamer-checkpoint.sh
@@ -0,0 +1,15 @@
+NUM_GPUS=${1:-1}  # default: 1 GPU
+
+BATCH_SIZE=$((30 / NUM_GPUS))
+
+echo "Using $NUM_GPUS GPUs, each with a batch size of $BATCH_SIZE"
+
+accelerate launch --num_processes $NUM_GPUS train_motionstreamer.py \
+--batch-size $BATCH_SIZE \
+--lr 0.0001 \
+--total-iter 100000 \
+--out-dir Experiments \
+--exp-name motionstreamer_model \
+--dataname t2m_babel_272 \
+--latent_dir babel_272_stream/t2m_babel_latents \
+--num_gpus $NUM_GPUS
\ No newline at end of file
diff --git a/.ipynb_checkpoints/demo_t2m-checkpoint.py b/.ipynb_checkpoints/demo_t2m-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..61eb5550ab11f9f1a2c59b1d972f806b6bc59cdf
--- /dev/null
+++ b/.ipynb_checkpoints/demo_t2m-checkpoint.py
@@ -0,0 +1,204 @@
+import os
+import torch
+import numpy as np
+from models.llama_model import LLaMAHF, LLaMAHFConfig
+import models.tae as tae
+import options.option_transformer as option_trans
+import warnings
+
+import smplx
+from utils import bvh, quat
+from utils.face_z_align_util import rotation_6d_to_matrix, matrix_to_axis_angle, axis_angle_to_quaternion
+
+
+warnings.filterwarnings('ignore')
+
+comp_device = torch.device('cuda')
+##### ---- Exp dirs ---- #####
+args = option_trans.get_args_parser()
+torch.manual_seed(args.seed)
+
+from sentence_transformers import SentenceTransformer
+t5_model = SentenceTransformer('sentencet5-xxl/')
+t5_model.eval()
+for p in t5_model.parameters():
+    p.requires_grad = False
+
+def save_motion_as_bvh(motion_data, output_path, fps=30):
+    """
+    Saves a motion tensor in the 272-dimensional format to a BVH file.
+    This version is adapted from the official repository script for robustness.
+    """
+    print(f"--- Starting direct conversion to BVH: {os.path.basename(output_path)} ---")
+    try:
+        # --- 1. Ensure data is a 2D NumPy array ---
+        if isinstance(motion_data, torch.Tensor):
+            motion_data = motion_data.detach().cpu().numpy()
+        
+        # This is the key fix: Check dimensions before squeezing
+        if motion_data.ndim == 3 and motion_data.shape[0] == 1:
+            motion_data = motion_data.squeeze(0)
+        elif motion_data.ndim != 2:
+            raise ValueError(f"Input motion data must be 2D or 3D with a batch size of 1, but got shape {motion_data.shape}")
+
+        # --- 2. Recover 85-dim SMPL format from 272-dim format ---
+        # This logic is from the official script's `recover_from_local_rotation`
+        njoint = 22
+        nfrm, _ = motion_data.shape
+        
+        rotations_matrix = rotation_6d_to_matrix(torch.from_numpy(motion_data[:, 8+6*njoint : 8+12*njoint]).reshape(nfrm, -1, 6)).numpy()
+        
+        # Accumulate heading rotations
+        global_heading_diff_rot_6d = torch.from_numpy(motion_data[:, 2:8])
+        global_heading_diff_rot = rotation_6d_to_matrix(global_heading_diff_rot_6d).numpy()
+        global_heading_rot = np.zeros_like(global_heading_diff_rot)
+        global_heading_rot[0] = global_heading_diff_rot[0]
+        for i in range(1, nfrm):
+            global_heading_rot[i] = np.matmul(global_heading_diff_rot[i], global_heading_rot[i-1])
+
+        # Calculate root translation
+        velocities_root_xy = motion_data[:, :2]
+        positions_no_heading = motion_data[:, 8 : 8+3*njoint].reshape(nfrm, -1, 3)
+        height = positions_no_heading[:, 0, 1]
+        
+        inv_global_heading_rot = np.transpose(global_heading_rot, (0, 2, 1))
+        rotations_matrix[:, 0, ...] = np.matmul(inv_global_heading_rot, rotations_matrix[:, 0, ...])
+        
+        velocities_root_xyz = np.zeros((nfrm, 3))
+        velocities_root_xyz[:, 0] = velocities_root_xy[:, 0]
+        velocities_root_xyz[:, 2] = velocities_root_xy[:, 1]
+        velocities_root_xyz[1:, :] = np.matmul(inv_global_heading_rot[:-1], velocities_root_xyz[1:, :, None]).squeeze(-1)
+        root_translation = np.cumsum(velocities_root_xyz, axis=0)
+        root_translation[:, 1] = height
+
+        # Convert rotation matrices to axis-angle
+        axis_angle = matrix_to_axis_angle(torch.from_numpy(rotations_matrix)).numpy()
+        poses_85dim = np.concatenate([axis_angle.reshape(nfrm, -1), np.zeros((nfrm, 6)), root_translation, np.zeros((nfrm, 10))], axis=-1)
+
+        # --- 3. Convert 85-dim SMPL to BVH data ---
+        # This logic is from the official script's `smpl2bvh`
+        rots = poses_85dim[:, :72].reshape(-1, 24, 3)
+        trans = poses_85dim[:, 72:75]
+
+        # Get skeleton from SMPL model
+        model = smplx.create(model_path="body_models/human_model_files", model_type="smpl", gender="NEUTRAL")
+        parents = model.parents.detach().cpu().numpy()
+        rest_pose = model().joints.detach().cpu().numpy().squeeze()[:24,:]
+        offsets = rest_pose - rest_pose[parents]
+        offsets[0] = np.array([0,0,0])
+        
+        rotations_quat = axis_angle_to_quaternion(torch.from_numpy(rots)).numpy()
+        rotations_euler = np.degrees(quat.to_euler(rotations_quat, order="zyx"))
+        
+        positions = offsets[None].repeat(len(rots), axis=0)
+        positions[:, 0] = trans
+
+        joint_names = [
+            "Pelvis", "Left_hip", "Right_hip", "Spine1", "Left_knee", "Right_knee", "Spine2", 
+            "Left_ankle", "Right_ankle", "Spine3", "Left_foot", "Right_foot", "Neck", 
+            "Left_collar", "Right_collar", "Head", "Left_shoulder", "Right_shoulder", 
+            "Left_elbow", "Right_elbow", "Left_wrist", "Right_wrist", "Left_hand", "Right_hand"
+        ]
+
+        # --- 4. Save the final BVH file ---
+        bvh.save(output_path, {
+            "rotations": rotations_euler,
+            "positions": positions,
+            "offsets": offsets,
+            "parents": parents,
+            "names": joint_names,
+            "order": "zyx",
+            "frametime": 1.0 / fps,
+        })
+        print(f"✅ BVH file saved successfully to {output_path}")
+
+    except Exception as e:
+        print(f"❌ BVH Conversion Failed. Error: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+##### ---- Network ---- #####
+clip_range = [-30,20]
+
+net = tae.Causal_HumanTAE(
+                       hidden_size=args.hidden_size,
+                       down_t=args.down_t,
+                       stride_t=args.stride_t,
+                       depth=args.depth,
+                       dilation_growth_rate=args.dilation_growth_rate,
+                       activation='relu',
+                       latent_dim=args.latent_dim,
+                       clip_range=clip_range
+                       )
+
+
+config = LLaMAHFConfig.from_name('Normal_size')
+config.block_size = 78
+trans_encoder = LLaMAHF(config, args.num_diffusion_head_layers, args.latent_dim, comp_device)
+
+print('loading checkpoint from {}'.format(args.resume_pth))
+ckpt = torch.load(args.resume_pth, map_location='cpu')
+net.load_state_dict(ckpt['net'], strict=True)
+net.eval()
+net.to(comp_device)
+
+
+if args.resume_trans is not None:
+    print('loading transformer checkpoint from {}'.format(args.resume_trans))
+    ckpt = torch.load(args.resume_trans, map_location='cpu')
+    new_ckpt_trans = {}
+    for key in ckpt['trans'].keys():
+        if key.split('.')[0]=='module':
+            new_key = '.'.join(key.split('.')[1:])
+        else:
+            new_key = key
+        new_ckpt_trans[new_key] = ckpt['trans'][key]
+    trans_encoder.load_state_dict(new_ckpt_trans, strict=True)
+trans_encoder.eval()
+trans_encoder.to(comp_device)
+
+
+reference_end_latent = np.load('reference_end_latent_t2m_272.npy')
+reference_end_latent = torch.from_numpy(reference_end_latent).to(comp_device)
+
+mean = np.load('humanml3d_272/mean_std/Mean.npy')
+std = np.load('humanml3d_272/mean_std/Std.npy')
+
+# forward inference
+threshold = 0.1
+cfg_scale = 4.0
+print(f"Generating motion with CFG scale: {cfg_scale}")
+motion_latents = trans_encoder.sample_for_eval_CFG_inference(text=args.text, tokenizer=t5_model, device=comp_device, reference_end_latent=reference_end_latent, threshold=threshold, cfg=cfg_scale)
+
+# forward decode
+motion_seqs = net.forward_decoder(motion_latents)
+from visualization.recover_visualize import recover_from_local_position
+import visualization.plot_3d_global as plot_3d
+
+motion = motion_seqs.squeeze(0)
+motion = motion.detach().cpu().numpy()
+
+if not os.path.exists('demo_output'):
+    os.makedirs('demo_output')
+
+if args.mode == 'pos':
+    # Option1: recover from joint position
+    pred_xyz = recover_from_local_position(motion * std + mean, 22)
+    xyz = pred_xyz.reshape(1, -1, 22, 3)
+    pose_vis = plot_3d.draw_to_batch(xyz, [args.text], [f'demo_output/{args.text}.mp4'], fps=30)
+    print(f"Visualized result is saved in demo_output/{args.text}.mp4")
+
+elif args.mode == 'rot':
+    # De-normalize the motion data to its original scale
+    motion = motion * std + mean
+    
+    # Define the output path for the new BVH file
+    output_bvh_path = os.path.join('demo_output', f'{args.text}.bvh')
+    
+    # Call the new function to save the BVH file directly
+    save_motion_as_bvh(motion, output_bvh_path, fps=30)
+            
+else:
+    raise ValueError(f'Invalid mode: {args.mode}')
+
diff --git a/.ipynb_checkpoints/environment-checkpoint.yaml b/.ipynb_checkpoints/environment-checkpoint.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..93b497dad035a07cfd7d2dfa3febec97ee374356
--- /dev/null
+++ b/.ipynb_checkpoints/environment-checkpoint.yaml
@@ -0,0 +1,258 @@
+name: mgpt
+channels:
+  - pytorch
+  - conda-forge
+  - defaults
+  - https://repo.anaconda.com/pkgs/main
+  - https://repo.anaconda.com/pkgs/r
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - _openmp_mutex=4.5=1_gnu
+  - asttokens=3.0.0=pyhd8ed1ab_0
+  - backcall=0.2.0=pyh9f0ad1d_0
+  - blas=1.0=mkl
+  - bzip2=1.0.8=h7b6447c_0
+  - ca-certificates=2025.1.31=hbcca054_0
+  - certifi=2024.8.30=pyhd8ed1ab_0
+  - comm=0.2.2=pyhd8ed1ab_0
+  - cudatoolkit=10.1.243=h6bb024c_0
+  - debugpy=1.4.1=py38h709712a_0
+  - entrypoints=0.4=pyhd8ed1ab_0
+  - executing=2.1.0=pyhd8ed1ab_0
+  - ffmpeg=4.3=hf484d3e_0
+  - freetype=2.10.4=h5ab3b9f_0
+  - gmp=6.2.1=h2531618_2
+  - gnutls=3.6.15=he1e5248_0
+  - intel-openmp=2021.3.0=h06a4308_3350
+  - ipykernel=6.20.2=pyh210e3f2_0
+  - jpeg=9b=h024ee3a_2
+  - jupyter_client=7.1.2=pyhd8ed1ab_0
+  - jupyter_core=5.7.2=pyh31011fe_1
+  - lame=3.100=h7b6447c_0
+  - lcms2=2.12=h3be6417_0
+  - ld_impl_linux-64=2.35.1=h7274673_9
+  - libffi=3.3=he6710b0_2
+  - libgcc-ng=9.3.0=h5101ec6_17
+  - libgomp=9.3.0=h5101ec6_17
+  - libiconv=1.15=h63c8f33_5
+  - libidn2=2.3.2=h7f8727e_0
+  - libpng=1.6.37=hbc83047_0
+  - libsodium=1.0.18=h36c2ea0_1
+  - libstdcxx-ng=13.2.0=hc0a3c3a_7
+  - libtasn1=4.16.0=h27cfd23_0
+  - libtiff=4.2.0=h85742a9_0
+  - libunistring=0.9.10=h27cfd23_0
+  - libuv=1.40.0=h7b6447c_0
+  - libwebp-base=1.2.0=h27cfd23_0
+  - lz4-c=1.9.3=h295c915_1
+  - mkl=2021.3.0=h06a4308_520
+  - mkl-service=2.4.0=py38h7f8727e_0
+  - mkl_fft=1.3.0=py38h42c9631_2
+  - mkl_random=1.2.2=py38h51133e4_0
+  - ncurses=6.2=he6710b0_1
+  - nest-asyncio=1.6.0=pyhd8ed1ab_0
+  - nettle=3.7.3=hbbd107a_1
+  - ninja=1.10.2=hff7bd54_1
+  - olefile=0.46=py_0
+  - openh264=2.1.0=hd408876_0
+  - openjpeg=2.3.0=h05c96fa_1
+  - openssl=1.1.1k=h7f98852_0
+  - packaging=24.2=pyhd8ed1ab_2
+  - pickleshare=0.7.5=py_1003
+  - pillow=8.3.1=py38h2c7a002_0
+  - pip=21.0.1=py38h06a4308_0
+  - platformdirs=4.3.6=pyhd8ed1ab_0
+  - prompt_toolkit=3.0.48=hd8ed1ab_1
+  - ptyprocess=0.7.0=pyhd3deb0d_0
+  - pure_eval=0.2.3=pyhd8ed1ab_0
+  - pygments=2.18.0=pyhd8ed1ab_0
+  - python=3.8.11=h12debd9_0_cpython
+  - python_abi=3.8=5_cp38
+  - pyzmq=22.1.0=py38h2035c66_0
+  - readline=8.1=h27cfd23_0
+  - setuptools=52.0.0=py38h06a4308_0
+  - six=1.16.0=pyhd3eb1b0_0
+  - sqlite=3.36.0=hc218d9a_0
+  - stack_data=0.6.2=pyhd8ed1ab_0
+  - tk=8.6.10=hbc83047_0
+  - torchaudio=0.8.1=py38
+  - torchvision=0.9.1=py38_cu101
+  - tornado=6.1=py38h497a2fe_1
+  - wheel=0.37.0=pyhd3eb1b0_0
+  - xz=5.2.5=h7b6447c_0
+  - zeromq=4.3.4=h9c3ff4c_0
+  - zlib=1.2.11=h7b6447c_3
+  - zstd=1.4.9=haebb681_0
+  - pip:
+      - absl-py==0.13.0
+      - accelerate==1.0.1
+      - aiohappyeyeballs==2.4.3
+      - aiohttp==3.10.11
+      - aiosignal==1.3.1
+      - annotated-types==0.7.0
+      - antlr4-python3-runtime==4.9.3
+      - async-timeout==5.0.1
+      - attrs==24.2.0
+      - beautifulsoup4==4.12.3
+      - blis==0.7.11
+      - cachetools==4.2.2
+      - catalogue==2.0.10
+      - charset-normalizer==2.0.4
+      - chumpy==0.70
+      - click==8.1.7
+      - clip==1.0
+      - cloudpathlib==0.20.0
+      - confection==0.1.5
+      - cycler==0.10.0
+      - cymem==2.0.10
+      - decorator==5.0.9
+      - diffusers==0.31.0
+      - einops==0.8.0
+      - ffmpeg-python==0.2.0
+      - filelock==3.16.1
+      - freetype-py==2.5.1
+      - frozenlist==1.5.0
+      - fsspec==2024.2.0
+      - ftfy==6.1.1
+      - future==1.0.0
+      - fvcore==0.1.5.post20221221
+      - gdown==5.2.0
+      - glfw==2.8.0
+      - google-auth==2.36.0
+      - google-auth-oauthlib==0.4.6
+      - grpcio==1.68.0
+      - h5py==3.11.0
+      - huggingface-hub==0.26.2
+      - human-body-prior==2.2.2.0
+      - idna==3.2
+      - imageio==2.9.0
+      - imageio-ffmpeg==0.5.1
+      - importlib-metadata==8.5.0
+      - iopath==0.1.10
+      - ipdb==0.13.9
+      - ipython==7.26.0
+      - ipython-genutils==0.2.0
+      - jedi==0.18.0
+      - jinja2==3.1.3
+      - joblib==1.0.1
+      - kiwisolver==1.3.1
+      - langcodes==3.4.1
+      - language-data==1.3.0
+      - lightning-utilities==0.11.9
+      - marisa-trie==1.2.1
+      - markdown==3.3.4
+      - markdown-it-py==3.0.0
+      - markupsafe==2.1.5
+      - matplotlib==3.4.3
+      - matplotlib-inline==0.1.2
+      - mdurl==0.1.2
+      - moviepy==0.2.3.1
+      - mpmath==1.3.0
+      - multidict==6.1.0
+      - murmurhash==1.0.11
+      - natsort==8.4.0
+      - networkx==3.0
+      - numpy==1.22.4
+      - nvidia-cublas-cu11==11.11.3.6
+      - nvidia-cublas-cu12==12.1.3.1
+      - nvidia-cuda-cupti-cu11==11.8.87
+      - nvidia-cuda-cupti-cu12==12.1.105
+      - nvidia-cuda-nvrtc-cu11==11.8.89
+      - nvidia-cuda-nvrtc-cu12==12.1.105
+      - nvidia-cuda-runtime-cu11==11.8.89
+      - nvidia-cuda-runtime-cu12==12.1.105
+      - nvidia-cudnn-cu11==9.1.0.70
+      - nvidia-cudnn-cu12==9.1.0.70
+      - nvidia-cufft-cu11==10.9.0.58
+      - nvidia-cufft-cu12==11.0.2.54
+      - nvidia-curand-cu11==10.3.0.86
+      - nvidia-curand-cu12==10.3.2.106
+      - nvidia-cusolver-cu11==11.4.1.48
+      - nvidia-cusolver-cu12==11.4.5.107
+      - nvidia-cusparse-cu11==11.7.5.86
+      - nvidia-cusparse-cu12==12.1.0.106
+      - nvidia-nccl-cu11==2.20.5
+      - nvidia-nccl-cu12==2.20.5
+      - nvidia-nvjitlink-cu12==12.1.105
+      - nvidia-nvtx-cu11==11.8.86
+      - nvidia-nvtx-cu12==12.1.105
+      - oauthlib==3.1.1
+      - omegaconf==2.3.0
+      - orjson==3.10.15
+      - pandas==1.3.2
+      - parso==0.8.2
+      - pexpect==4.8.0
+      - portalocker==3.0.0
+      - preshed==3.0.9
+      - prompt-toolkit==3.0.20
+      - propcache==0.2.0
+      - protobuf==5.28.3
+      - psutil==6.1.0
+      - pyasn1==0.4.8
+      - pyasn1-modules==0.2.8
+      - pydantic==2.10.1
+      - pydantic-core==2.27.1
+      - pydeprecate==0.3.2
+      - pygame==2.6.1
+      - pyglet==2.1.2
+      - pyopengl==3.1.0
+      - pyparsing==2.4.7
+      - pyrender==0.1.45
+      - pysocks==1.7.1
+      - python-dateutil==2.8.2
+      - pytorch-lightning==1.7.0
+      - pytorch3d==0.3.0
+      - pytz==2021.1
+      - pyyaml==5.4.1
+      - regex==2024.11.6
+      - requests==2.26.0
+      - requests-oauthlib==1.3.0
+      - rich==13.9.4
+      - rsa==4.7.2
+      - safetensors==0.4.5
+      - scikit-learn==0.24.2
+      - scipy==1.7.1
+      - sentence-transformers==3.2.1
+      - sentencepiece==0.2.0
+      - shapely==2.0.7
+      - shellingham==1.5.4
+      - sklearn==0.0
+      - smart-open==7.0.5
+      - smplx==0.1.28
+      - soupsieve==2.6
+      - spacy==3.7.5
+      - spacy-legacy==3.0.12
+      - spacy-loggers==1.0.5
+      - srsly==2.4.8
+      - sympy==1.13.1
+      - tabulate==0.9.0
+      - tensorboard==2.12.0
+      - tensorboard-data-server==0.7.2
+      - tensorboard-plugin-wit==1.8.0
+      - termcolor==2.4.0
+      - thinc==8.2.5
+      - threadpoolctl==2.2.0
+      - timm==1.0.12
+      - tokenizers==0.20.3
+      - toml==0.10.2
+      - torch==2.4.1+cu118
+      - torchgeometry==0.1.2
+      - torchmetrics==0.7.0
+      - tqdm==4.62.2
+      - traitlets==5.0.5
+      - transformers==4.46.3
+      - triangle==20250106
+      - trimesh==4.6.2
+      - triton==3.0.0
+      - typer==0.13.1
+      - typing-extensions==4.12.2
+      - urllib3==1.26.6
+      - wasabi==1.1.3
+      - wcwidth==0.2.5
+      - weasel==0.4.1
+      - werkzeug==2.0.1
+      - wrapt==1.17.0
+      - yacs==0.1.8
+      - yarl==1.15.2
+      - zipp==3.20.2
+prefix: /root/miniconda3/envs/mgpt
\ No newline at end of file
diff --git a/.ipynb_checkpoints/requirements-checkpoint.txt b/.ipynb_checkpoints/requirements-checkpoint.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e19598e3d3cc573e2e5dfedd07ef4595fbb4cc25
--- /dev/null
+++ b/.ipynb_checkpoints/requirements-checkpoint.txt
@@ -0,0 +1,17 @@
+smplx==0.1.28
+transformers==4.56.2
+timm==1.0.12
+sentence-transformers==5.1.0
+clip @ git+https://github.com/openai/CLIP.git@main#egg=clip
+human-body-prior @ git+https://github.com/nghorbani/human_body_prior.git@master#egg=human-body-prior
+gdown
+chumpy==0.70
+scipy==1.7.1
+numpy==1.22.4
+tensorboard
+accelerate
+flash_attn
+matplotlib==3.4.3
+matplotlib-inline==0.1.2
+imageio==2.9.0
+imageio-ffmpeg==0.5.1
\ No newline at end of file
diff --git a/.ipynb_checkpoints/train_motionstreamer-checkpoint.py b/.ipynb_checkpoints/train_motionstreamer-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..9839dadbadc92b3e929ad0c5991f3f4bf2fbdc28
--- /dev/null
+++ b/.ipynb_checkpoints/train_motionstreamer-checkpoint.py
@@ -0,0 +1,264 @@
+"""Train streaming motion generation model (MotionStreamer) with llama blocks, Two-Forward strategy and QK-Norm, using the motion latents encoded by the Causal TAE (trained in the first stage)."""
+
+
+import os
+import torch
+import numpy as np
+import random
+from torch.utils.tensorboard import SummaryWriter
+import json
+from accelerate import Accelerator
+from tqdm import tqdm
+from models.llama_model import LLaMAHF, LLaMAHFConfig
+import options.option_transformer as option_trans
+import utils.utils_model as utils_model
+import warnings
+from torch.optim.lr_scheduler import LambdaLR, CosineAnnealingLR
+warnings.filterwarnings('ignore')
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+##### ---- Exp dirs ---- #####
+args = option_trans.get_args_parser()
+torch.manual_seed(args.seed)
+
+# warm-up + cosine decay scheduler
+class WarmupCosineDecayScheduler:
+    def __init__(self, optimizer, warmup_iters, total_iters, min_lr=0):
+        self.optimizer = optimizer
+        self.warmup_iters = warmup_iters
+        self.total_iters = total_iters
+        self.min_lr = min_lr
+        
+        self.warmup_scheduler = LambdaLR(optimizer, lr_lambda=self.warmup_lambda)
+        
+        self.cosine_scheduler = CosineAnnealingLR(optimizer, 
+                                                  T_max=total_iters - warmup_iters, 
+                                                  eta_min=min_lr)
+        
+    def warmup_lambda(self, current_iter):
+        if current_iter < self.warmup_iters:
+            return float(current_iter) / float(max(1, self.warmup_iters))
+        return 1.0
+
+    def step(self, current_iter):
+        if current_iter < self.warmup_iters:
+            self.warmup_scheduler.step()
+        else:
+            self.cosine_scheduler.step()
+
+    def state_dict(self):
+        return {
+            'warmup_iters': self.warmup_iters,
+            'total_iters': self.total_iters,
+            'min_lr': self.min_lr,
+        }
+
+    def load_state_dict(self, state_dict):
+        self.warmup_iters = state_dict['warmup_iters']
+        self.total_iters = state_dict['total_iters']
+        self.min_lr = state_dict['min_lr']
+
+
+
+args.out_dir = os.path.join(args.out_dir, f'{args.exp_name}')
+os.makedirs(args.out_dir, exist_ok = True)
+
+
+##### ---- Accelerator Setup ---- #####
+accelerator = Accelerator()
+comp_device = accelerator.device
+
+##### ---- Logger ---- #####
+logger = utils_model.get_logger(args.out_dir)
+writer = SummaryWriter(args.out_dir)
+logger.info(json.dumps(vars(args), indent=4, sort_keys=True))
+
+##### ---- Dataloader ---- #####
+from humanml3d_272 import dataset_TM_train_motionstreamer
+train_loader = dataset_TM_train_motionstreamer.DATALoader(args.dataname, args.batch_size, unit_length=2**args.down_t, latent_dir=args.latent_dir)
+
+
+##### ---- Network ---- #####
+from sentence_transformers import SentenceTransformer
+t5_model = SentenceTransformer('sentencet5-xxl/')
+t5_model.eval()
+for p in t5_model.parameters():
+    p.requires_grad = False
+
+
+config = LLaMAHFConfig.from_name('Normal_size')
+config.block_size = 78
+trans_encoder = LLaMAHF(config, args.num_diffusion_head_layers, args.latent_dim, comp_device)
+
+if args.resume_trans is not None:
+    print('loading transformer checkpoint from {}'.format(args.resume_trans))
+    ckpt = torch.load(args.resume_trans, map_location='cpu')
+    new_ckpt_trans = {}
+    for key in ckpt['trans'].keys():
+        if key.split('.')[0]=='module':
+            new_key = '.'.join(key.split('.')[1:])
+        else:
+            new_key = key
+        new_ckpt_trans[new_key] = ckpt['trans'][key]
+    trans_encoder.load_state_dict(new_ckpt_trans, strict=True)
+trans_encoder.train()
+trans_encoder.to(comp_device)
+
+
+##### ---- Optimizer & Scheduler ---- #####
+optimizer = utils_model.initial_optim(args.decay_option, args.lr, args.weight_decay, trans_encoder, args.optimizer)
+scheduler = WarmupCosineDecayScheduler(optimizer, args.total_iter//10, args.total_iter)
+
+t5_model, trans_encoder, optimizer, train_loader = accelerator.prepare(t5_model, trans_encoder, optimizer, train_loader)
+train_loader_iter = dataset_TM_train_motionstreamer.cycle(train_loader)
+
+
+diffmlps_batch_mul = 4
+def lengths_to_mask(lengths, max_len):
+    mask = torch.arange(max_len, device=lengths.device).expand(len(lengths), max_len) < lengths.unsqueeze(1)
+    return mask 
+def get_mask_subset_prob(mask, prob):
+    subset_mask = torch.bernoulli(mask, p=prob) & mask
+    return subset_mask
+
+
+def uniform(shape, device=None):
+    return torch.zeros(shape, device=device).float().uniform_(0, 1)
+
+import math
+def cosine_schedule(t):
+    return torch.cos(t * math.pi * 0.5)
+
+
+#--------------2-forward:------------------
+def cosine_decay(step, total_steps, start_value=1.0, end_value=0.0):
+    step = torch.tensor(step, dtype=torch.float32)  
+    total_steps = torch.tensor(total_steps, dtype=torch.float32)  
+    cosine_factor = 0.5 * (1 + torch.cos(torch.pi * step / total_steps))
+    return start_value + (end_value - start_value) * cosine_factor
+
+def replace_with_pred(latents, pred_xstart, step, total_steps):
+    decay_factor = cosine_decay(step, total_steps).to(latents.device)
+    b, l, d = latents.shape
+    num_replace = int(l * decay_factor)  
+    
+    replace_indices = torch.randperm(l)[:num_replace]  
+
+    replace_mask = torch.zeros(b, l, dtype=torch.bool).to(latents.device)
+    replace_mask[:, replace_indices] = 1  
+
+    updated_latents = latents.clone()  
+    updated_latents[replace_mask] = pred_xstart[replace_mask]
+    
+    return updated_latents
+
+def forward_loss_withmask_2_forward_streaming(latents, trans, m_lens, feat_text, step, total_steps, A_token_length):
+    latents = latents.to(comp_device)   
+    feat_text = feat_text.to(comp_device)
+    A_token_length = A_token_length.to(comp_device)
+    conditions = trans(latents, feat_text) 
+    conditions = conditions.contiguous()
+    z = conditions[:,:-1,:]   
+
+    b, l, d = latents.shape
+    mask = lengths_to_mask(m_lens, l)      
+
+    for j in range(b):
+        mask[j, :A_token_length[j].item()] = False   # A_motion token: do not compute loss
+
+    mask = mask.reshape(b * l).repeat(diffmlps_batch_mul)
+
+    target = latents.clone().detach()
+    target = target.reshape(b * l, -1)    
+    z = z.reshape(b * l, -1)              
+    
+    with torch.no_grad():
+        loss, pred_xstart = trans.diff_loss(target=target, z=z)  
+
+    pred_xstart = pred_xstart.clone().detach()
+    pred_xstart = pred_xstart.reshape(b, l, -1)            
+
+    # do not replace A_motion tokens
+    for k in range(b):
+        pred_xstart[k, :A_token_length[k].item(),:] = latents[k, :A_token_length[k].item(),:]
+
+    updated_latents = replace_with_pred(latents, pred_xstart, step, total_steps)    
+    updated_conditions = trans(updated_latents, feat_text)  
+    updated_conditions = updated_conditions.contiguous()
+    updated_z = updated_conditions[:,:-1,:]                 
+
+    updated_target = latents.clone().detach()      
+
+    updated_target = updated_target.reshape(b * l, -1).repeat(diffmlps_batch_mul, 1)    
+    updated_z = updated_z.reshape(b * l, -1).repeat(diffmlps_batch_mul, 1)              
+
+    updated_target = updated_target[mask]                   
+    updated_z = updated_z[mask]                            
+
+    updated_loss, updated_pred_xstart = trans.diff_loss(target=updated_target, z=updated_z)  
+
+    return updated_loss
+
+
+##### ---- Training Loop ---- #####
+avg_loss_cls = 0.
+
+pbar = tqdm(range(1, args.total_iter + 1), desc="Training MotionStreamer")
+for nb_iter in pbar:
+    batch = next(train_loader_iter)
+    caption, m_tokens, m_tokens_len, A_token_length = batch
+    caption = list(caption)
+    m_tokens, m_tokens_len = m_tokens.to(comp_device), m_tokens_len.to(comp_device)
+    A_token_length = A_token_length.to(comp_device)
+    
+    bs = len(caption)
+    num_masked = int(bs * 0.1)  # 10%
+    mask_indices = random.sample(range(bs), num_masked)
+
+    for idx in mask_indices:
+        caption[idx] = ''
+
+    feat_text = torch.from_numpy(t5_model.encode(caption)).float()
+    feat_text = feat_text.to(comp_device)
+
+    # -------gt--------
+    input_latent = m_tokens[:,:-1,:]  # continuous token
+
+    loss_cls = 0.0
+
+    if args.num_gpus > 1:
+        loss_cls = forward_loss_withmask_2_forward_streaming(latents=input_latent, trans=trans_encoder.module, m_lens = m_tokens_len, feat_text=feat_text, step=nb_iter, total_steps=args.total_iter, A_token_length=A_token_length)
+    else:
+        loss_cls = forward_loss_withmask_2_forward_streaming(latents=input_latent, trans=trans_encoder, m_lens = m_tokens_len, feat_text=feat_text, step=nb_iter, total_steps=args.total_iter, A_token_length=A_token_length)
+
+    
+    # backward & optimizer step
+    optimizer.zero_grad()
+    accelerator.backward(loss_cls)
+    optimizer.step()
+    scheduler.step(nb_iter)
+
+    avg_loss_cls = avg_loss_cls + loss_cls.item()
+
+    args.print_iter = 100
+    if nb_iter % args.print_iter ==  0 :
+        if accelerator.is_main_process:
+            avg_loss_cls = avg_loss_cls / args.print_iter
+            lr = optimizer.param_groups[0]['lr']
+            writer.add_scalar('./Loss/train', avg_loss_cls, nb_iter)
+            writer.add_scalar('./LR/train', optimizer.param_groups[0]['lr'], nb_iter)
+            msg = f"Train. Iter {nb_iter} : Loss. {avg_loss_cls:.5f}"
+            tqdm.write(f"Iter {nb_iter} | Loss: {avg_loss_cls:.5f} | LR: {lr:.6f}")
+            logger.info(msg)
+        avg_loss_cls = 0.
+
+
+    args.save_iter = 10000
+    if nb_iter % args.save_iter == 0:
+        # save checkpoint
+        if accelerator.is_main_process:
+            torch.save({
+                'trans': trans_encoder.state_dict(),
+            }, os.path.join(args.out_dir, f'latest.pth'))
+                     
+accelerator.wait_for_everyone()
\ No newline at end of file
diff --git a/EVAL_causal_TAE.sh b/EVAL_causal_TAE.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c8d49a644b5a8186a0925350a324a4a6dab62848
--- /dev/null
+++ b/EVAL_causal_TAE.sh
@@ -0,0 +1,6 @@
+ln -s ../utils ./Evaluator_272/
+ln -s ../humanml3d_272 ./Evaluator_272/
+ln -s ../options ./Evaluator_272/
+ln -s ../models ./Evaluator_272/
+ln -s ../visualization ./Evaluator_272/
+python eval_causal_TAE.py --resume-pth output/causal_TAE/net_last.pth
\ No newline at end of file
diff --git a/EVAL_t2m.sh b/EVAL_t2m.sh
new file mode 100644
index 0000000000000000000000000000000000000000..869c623a043c34d60cdd08edf16d72c2aa3f5d5e
--- /dev/null
+++ b/EVAL_t2m.sh
@@ -0,0 +1,7 @@
+ln -s ../utils ./Evaluator_272/
+ln -s ../humanml3d_272 ./Evaluator_272/
+ln -s ../options ./Evaluator_272/
+ln -s ../models ./Evaluator_272/
+ln -s ../visualization ./Evaluator_272/
+ln -s ../Causal_TAE ./Evaluator_272/
+python eval_t2m.py --resume-pth Causal_TAE/net_last.pth --resume-trans /cpfs03/shared/IDC/wangjingbo_group/motionstreamer/Open_source_Train_AR_16_1024_fps_30_111M_9/latest.pth
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..30a09b77b5c10706a0b6fd8bf8140e8fd49603cd
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 ZJU3DV
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..c618a1878f9404d7ed9cdbed30c636392474b77b
--- /dev/null
+++ b/README.md
@@ -0,0 +1,336 @@
+
+<h2 align="center"<strong>MotionStreamer: Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space</strong></h2>
+  <p align="center">
+    <a href='https://li-xingxiao.github.io/homepage/' target='_blank'>Lixing Xiao</a><sup>1</sup>
+    ·
+    <a href='https://shunlinlu.github.io/' target='_blank'>Shunlin Lu</a> <sup>2</sup>
+    ·
+    <a href='https://phj128.github.io/' target='_blank'>Huaijin Pi</a><sup>3</sup>
+    ·
+    <a href='https://vankouf.github.io/' target='_blank'>Ke Fan</a><sup>4</sup>
+    ·
+    <a href='https://liangpan99.github.io/' target='_blank'>Liang Pan</a><sup>3</sup>
+    ·
+    <a href='https://yueezhou7@gmail.com' target='_blank'>Yueer Zhou</a><sup>1</sup>
+    ·
+    <a href='https://dblp.org/pid/120/4362.html/' target='_blank'>Ziyong Feng</a><sup>5</sup>
+    ·
+    <br>
+    <a href='https://www.xzhou.me/' target='_blank'>Xiaowei Zhou</a><sup>1</sup>
+    ·
+    <a href='https://pengsida.net/' target='_blank'>Sida Peng</a><sup>1†</sup>
+    ·
+     <a href='https://wangjingbo1219.github.io/' target='_blank'>Jingbo Wang</a><sup>6</sup>
+    <br>
+    <br>
+    <sup>1</sup>Zhejiang University  <sup>2</sup>The Chinese University of Hong Kong, Shenzhen  <sup>3</sup>The University of Hong Kong  <br><sup>4</sup>Shanghai Jiao Tong University  <sup>5</sup>DeepGlint  <sup>6</sup>Shanghai AI Lab
+    <br>
+    <strong>ICCV 2025</strong>
+    
+  </p>
+</p>
+<p align="center">
+  <a href='https://arxiv.org/abs/2503.15451'>
+    <img src='https://img.shields.io/badge/Arxiv-2503.15451-A42C25?style=flat&logo=arXiv&logoColor=A42C25'></a>
+  <a href='https://arxiv.org/pdf/2503.15451'>
+    <img src='https://img.shields.io/badge/Paper-PDF-blue?style=flat&logo=arXiv&logoColor=blue'></a>
+  <a href='https://zju3dv.github.io/MotionStreamer/'>
+    <img src='https://img.shields.io/badge/Project-Page-green?style=flat&logo=Google%20chrome&logoColor=green'></a>
+  <a href='https://huggingface.co/datasets/lxxiao/272-dim-HumanML3D'>
+    <img src='https://img.shields.io/badge/Data-Download-yellow?style=flat&logo=huggingface&logoColor=yellow'></a>
+</p>
+
+<img width="1385" alt="image" src="assets/teaser.jpg"/>
+
+## 🔥 News
+
+- **[2025-06]** MotionStreamer has been accepted to ICCV 2025! 🎉
+  
+## TODO List
+
+- [x] Release the processing script of 272-dim motion representation.
+- [x] Release the processed 272-dim Motion Representation of [HumanML3D](https://github.com/EricGuo5513/HumanML3D) dataset. Only for academic usage.
+- [x] Release the training code and checkpoint of our [TMR](https://github.com/Mathux/TMR)-based motion evaluator trained on the processed 272-dim [HumanML3D](https://github.com/EricGuo5513/HumanML3D) dataset.
+- [x] Release the training and evaluation code as well as checkpoint of Causal TAE.
+- [x] Release the training code of original motion generation model and streaming generation model (MotionStreamer).
+- [x] Release the checkpoint and demo inference code of original motion generation model.
+- [ ] Release complete code for MotionStreamer.
+
+## 🏃 Motion Representation
+For more details of how to obtain the 272-dim motion representation, as well as other useful tools (e.g., Visualization and Conversion to BVH format), please refer to our [GitHub repo](https://github.com/Li-xingXiao/272-dim-Motion-Representation).
+
+## Installation
+
+### 🐍 Python Virtual Environment
+```sh
+conda env create -f environment.yaml
+conda activate mgpt
+```
+
+### 🤗 Hugging Face Mirror
+Since all of our models and data are available on Hugging Face, if Hugging Face is not directly accessible, you can use the HF-mirror tools following:
+```sh
+pip install -U huggingface_hub
+export HF_ENDPOINT=https://hf-mirror.com
+```
+
+## 📥 Data Preparation
+To facilitate researchers, we provide the processed 272-dim Motion Representation of:
+> HumanML3D dataset at [this link](https://huggingface.co/datasets/lxxiao/272-dim-HumanML3D).
+
+> BABEL dataset at [this link](https://huggingface.co/datasets/lxxiao/272-dim-BABEL).
+
+❗️❗️❗️ The processed data is solely for academic purposes. Make sure you read through the [AMASS License](https://amass.is.tue.mpg.de/license.html).
+
+1. Download the processed 272-dim [HumanML3D](https://github.com/EricGuo5513/HumanML3D) dataset following:
+```bash
+huggingface-cli download --repo-type dataset --resume-download lxxiao/272-dim-HumanML3D --local-dir ./humanml3d_272
+cd ./humanml3d_272
+unzip texts.zip
+unzip motion_data.zip
+```
+The dataset is organized as:
+```
+./humanml3d_272
+  ├── mean_std
+      ├── Mean.npy
+      ├── Std.npy
+  ├── split
+      ├── train.txt
+      ├── val.txt
+      ├── test.txt
+  ├── texts
+      ├── 000000.txt
+      ...
+  ├── motion_data
+      ├── 000000.npy
+      ...
+```
+
+2. Download the processed 272-dim [BABEL](https://babel.is.tue.mpg.de/) dataset following:
+```bash
+huggingface-cli download --repo-type dataset --resume-download lxxiao/272-dim-BABEL --local-dir ./babel_272
+cd ./babel_272
+unzip texts.zip
+unzip motion_data.zip
+```
+The dataset is organized as:
+```
+./babel_272
+  ├── t2m_babel_mean_std
+      ├── Mean.npy
+      ├── Std.npy
+  ├── split
+      ├── train.txt
+      ├── val.txt
+  ├── texts
+      ├── 000000.txt
+      ...
+  ├── motion_data
+      ├── 000000.npy
+      ...
+```
+
+3. Download the processed streaming 272-dim [BABEL](https://babel.is.tue.mpg.de/) dataset following:
+```bash
+huggingface-cli download --repo-type dataset --resume-download lxxiao/272-dim-BABEL-stream --local-dir ./babel_272_stream
+cd ./babel_272_stream
+unzip train_stream.zip
+unzip train_stream_text.zip
+unzip val_stream.zip
+unzip val_stream_text.zip
+```
+The dataset is organized as:
+```
+./babel_272_stream
+  ├── train_stream
+      ├── seq1.npy
+      ...
+  ├── train_stream_text
+      ├── seq1.txt
+      ...
+  ├── val_stream
+      ├── seq1.npy
+      ...
+  ├── val_stream_text
+      ├── seq1.txt
+      ...
+```
+> NOTE: We process the original BABEL dataset to support training of streaming motion generation. e.g. If there is a motion sequence A, annotated as (A1, A2, A3, A4) in BABEL dataset, each subsequence has text description: (A1_t, A2_t, A3_t, A4_t).
+
+> Then, our BABEL-stream is constructed as:
+
+> seq1: (A1, A2) --- seq1_text: (A1_t*A2_t#A1_length)
+
+> seq2: (A2, A3) --- seq2_text: (A2_t*A3_t#A2_length)
+
+> seq3: (A3, A4) --- seq3_text: (A3_t*A4_t#A3_length)
+
+> Here, * and # is separation symbol, A1_length means the number of frames of subsequence A1.
+
+## 🚀 Training
+1. Train our [TMR](https://github.com/Mathux/TMR)-based motion evaluator on the processed 272-dim [HumanML3D](https://github.com/EricGuo5513/HumanML3D) dataset:
+    ```bash
+    bash TRAIN_evaluator_272.sh
+    ```
+    >After training for 100 epochs, the checkpoint will be stored at: 
+    ``Evaluator_272/experiments/temos/EXP1/checkpoints/``.
+
+    ⬇️ We provide the evaluator checkpoint on [Hugging Face](https://huggingface.co/lxxiao/MotionStreamer/tree/main/Evaluator_272), download it following:
+    ```bash
+    python humanml3d_272/prepare/download_evaluator_ckpt.py
+    ```
+    >The downloaded checkpoint will be stored at: ``Evaluator_272/``.
+2. Train the Causal TAE:
+    ```bash
+    bash TRAIN_causal_TAE.sh ${NUM_GPUS}
+    ```
+    > e.g., if you have 8 GPUs, run: bash TRAIN_causal_TAE.sh 8
+
+    > The checkpoint will be stored at:
+    ``Experiments/causal_TAE_t2m_272/``
+
+    > Tensorboard visualization:
+    ```bash
+    tensorboard --logdir='Experiments/causal_TAE_t2m_272'
+    ```
+
+    ⬇️ We provide the Causal TAE checkpoint on [Hugging Face](https://huggingface.co/lxxiao/MotionStreamer/tree/main/Causal_TAE), download it following:
+    ```bash
+    python humanml3d_272/prepare/download_Causal_TAE_t2m_272_ckpt.py
+    ```
+
+3. Train text to motion model:
+    > We provide scripts to train the original text to motion generation model with llama blocks, Two-Forward strategy and QK-Norm, using the motion latents encoded by the Causal TAE (trained in the first stage).
+    
+    3.1 Get motion latents:
+   ```bash
+   python get_latent.py --resume-pth Causal_TAE/net_last.pth --latent_dir humanml3d_272/t2m_latents
+   ```
+    3.2 Download [sentence-T5-XXL model](https://huggingface.co/sentence-transformers/sentence-t5-xxl/tree/main) on Hugging Face:
+   ```bash
+   huggingface-cli download --resume-download sentence-transformers/sentence-t5-xxl --local-dir sentencet5-xxl/
+   ```
+    3.3 Train text to motion generation model:
+   ```bash
+   bash TRAIN_t2m.sh ${NUM_GPUS}
+   ```
+    > e.g., if you have 8 GPUs, run: bash TRAIN_t2m.sh 8
+
+    > The checkpoint will be stored at:
+    ``Experiments/t2m_model/``
+
+    > Tensorboard visualization:
+    ```bash
+    tensorboard --logdir='Experiments/t2m_model'
+    ```
+
+    ⬇️ We provide the text to motion model checkpoint on [Hugging Face](https://huggingface.co/lxxiao/MotionStreamer/tree/main/Experiments/t2m_model), download it following:
+    ```bash
+    python humanml3d_272/prepare/download_t2m_model_ckpt.py
+    ```
+
+4. Train streaming motion generation model (MotionStreamer):
+    > We provide scripts to train the streaming motion generation model (MotionStreamer) with llama blocks, Two-Forward strategy and QK-Norm, using the motion latents encoded by the Causal TAE (need to train a new Causal TAE using both HumanML3D-272 and BABEL-272 data).
+    
+    4.1 Train a Causal TAE using both HumanML3D-272 and BABEL-272 data:
+    ```bash
+    bash TRAIN_causal_TAE.sh ${NUM_GPUS} t2m_babel_272
+    ```
+    > e.g., if you have 8 GPUs, run: bash TRAIN_causal_TAE.sh 8 t2m_babel_272
+
+    > The checkpoint will be stored at:
+    ``Experiments/causal_TAE_t2m_babel_272/``
+
+    > Tensorboard visualization:
+    ```bash
+    tensorboard --logdir='Experiments/causal_TAE_t2m_babel_272'
+    ```
+
+    ⬇️ We provide the Causal TAE checkpoint trained using both HumanML3D-272 and BABEL-272 data on [Hugging Face](https://huggingface.co/lxxiao/MotionStreamer/tree/main/Causal_TAE_t2m_babel), download it following:
+    ```bash
+    python humanml3d_272/prepare/download_Causal_TAE_t2m_babel_272_ckpt.py
+    ```
+
+    4.2 Get motion latents of both HumanML3D-272 and the processed BABEL-272-stream dataset:
+   ```bash
+   python get_latent.py --resume-pth Causal_TAE_t2m_babel/net_last.pth --latent_dir babel_272_stream/t2m_babel_latents --dataname t2m_babel_272
+   ``` 
+
+    4.3 Train MotionStreamer model:
+   ```bash
+   bash TRAIN_motionstreamer.sh ${NUM_GPUS}
+   ```
+   > e.g., if you have 8 GPUs, run: bash TRAIN_motionstreamer.sh 8
+
+   > The checkpoint will be stored at:
+    ``Experiments/motionstreamer_model/``
+
+    > Tensorboard visualization:
+    ```bash
+    tensorboard --logdir='Experiments/motionstreamer_model'
+    ```
+
+## 📍 Evaluation
+
+1. Evaluate the metrics of the processed 272-dim [HumanML3D](https://github.com/EricGuo5513/HumanML3D) dataset:
+    ```bash
+    bash EVAL_GT.sh
+    ```
+    ( FID, R@1, R@2, R@3, Diversity and MM-Dist (Matching Score) are reported. )
+
+2. Evaluate the metrics of Causal TAE:
+    ```bash
+    bash EVAL_causal_TAE.sh
+    ```
+    ( FID and MPJPE (mm) are reported. )
+
+3. Evaluate the metrics of text to motion model:
+    ```bash
+    bash EVAL_t2m.sh
+    ```
+    ( FID, R@1, R@2, R@3, Diversity and MM-Dist (Matching Score) are reported. )
+
+
+## 🎬 Demo Inference
+
+1. Inference of text to motion model:
+    > [Option1] Recover from joint position 
+    ```bash
+    python demo_t2m.py --text 'a person is walking like a mummy.' --mode pos --resume-pth Causal_TAE/net_last.pth --resume-trans Experiments/t2m_model/latest.pth
+    ```
+    > [Option2] Recover from joint rotation 
+    ```bash
+    python demo_t2m.py --text 'a person is walking like a mummy.' --mode rot --resume-pth Causal_TAE/net_last.pth --resume-trans Experiments/t2m_model/latest.pth
+    ```
+    > In our 272-dim representation, Inverse Kinematics (IK) is not needed.
+    > For further conversion to BVH format, please refer to [this repo](https://github.com/Li-xingXiao/272-dim-Motion-Representation?tab=readme-ov-file#6-representation_272-to-bvh-conversion-optional) (Step 6: Representation_272 to BVH conversion). The BVH format of motion animation can be visualizd and edited in [Blender](https://www.blender.org/features/animation/).
+
+
+
+
+## 🌹 Acknowledgement
+This repository builds upon the following awesome datasets and projects:
+- [272-dim-Motion-Representation](https://github.com/Li-xingXiao/272-dim-Motion-Representation)
+- [AMASS](https://amass.is.tue.mpg.de/index.html)
+- [HumanML3D](https://github.com/EricGuo5513/HumanML3D)
+- [T2M-GPT](https://github.com/Mael-zys/T2M-GPT)
+- [TMR](https://github.com/Mathux/TMR)
+- [OpenTMA](https://github.com/LinghaoChan/OpenTMA)
+- [Sigma-VAE](https://github.com/orybkin/sigma-vae-pytorch)
+- [Scamo](https://github.com/shunlinlu/ScaMo_code)
+
+## 🤝🏼 Citation
+If our project is helpful for your research, please consider citing :
+``` 
+@article{xiao2025motionstreamer,
+      title={MotionStreamer: Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space},
+      author={Xiao, Lixing and Lu, Shunlin and Pi, Huaijin and Fan, Ke and Pan, Liang and Zhou, Yueer and Feng, Ziyong and Zhou, Xiaowei and Peng, Sida and Wang, Jingbo},
+      journal={arXiv preprint arXiv:2503.15451},
+      year={2025}
+    }
+```
+
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=zju3dv/MotionStreamer&type=Date)](https://www.star-history.com/#zju3dv/MotionStreamer&Date)
diff --git a/TRAIN_causal_TAE.sh b/TRAIN_causal_TAE.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ce44d542d69b8f031662fae356beb9d702927a38
--- /dev/null
+++ b/TRAIN_causal_TAE.sh
@@ -0,0 +1,22 @@
+NUM_GPUS=${1:-1}  # default: 1 GPU
+dataset_name=${2:-t2m_272} # default: t2m_272, options: t2m_272, t2m_babel_272
+
+BATCH_SIZE=$((128 / NUM_GPUS))
+
+echo "Using $NUM_GPUS GPUs, each with a batch size of $BATCH_SIZE"
+
+accelerate launch --num_processes $NUM_GPUS train_causal_TAE.py \
+--batch-size $BATCH_SIZE \
+--lr 0.00005 \
+--total-iter 2000000 \
+--lr-scheduler 1900000 \
+--down-t 2 \
+--depth 3 \
+--dilation-growth-rate 3 \
+--out-dir Experiments \
+--dataname $dataset_name \
+--exp-name causal_TAE_${dataset_name} \
+--root_loss 7.0 \
+--latent_dim 16 \
+--hidden_size 1024 \
+--num_gpus $NUM_GPUS
\ No newline at end of file
diff --git a/TRAIN_evaluator_272.sh b/TRAIN_evaluator_272.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f032f627e010952f441538a20b07cb6594d4c0fd
--- /dev/null
+++ b/TRAIN_evaluator_272.sh
@@ -0,0 +1,6 @@
+export HF_ENDPOINT=https://hf-mirror.com
+cd Evaluator_272
+huggingface-cli download --resume-download distilbert/distilbert-base-uncased --local-dir ./deps/distilbert-base-uncased
+ln -s ../humanml3d_272 ./datasets/humanml3d_272
+python -m train --cfg configs/configs_evaluator_272/H3D-TMR.yaml --cfg_assets configs/assets.yaml --batch_size 256 --nodebug
+cd ..
\ No newline at end of file
diff --git a/TRAIN_motionstreamer.sh b/TRAIN_motionstreamer.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4c5bcaa5f47491a99e1f75f137a532e3572d006a
--- /dev/null
+++ b/TRAIN_motionstreamer.sh
@@ -0,0 +1,16 @@
+NUM_GPUS=${1:-1}  # default: 1 GPU
+
+BATCH_SIZE=$((30 / NUM_GPUS))
+
+echo "Using $NUM_GPUS GPUs, each with a batch size of $BATCH_SIZE"
+
+accelerate launch --num_processes $NUM_GPUS train_motionstreamer.py \
+--batch-size $BATCH_SIZE \
+--lr 0.0001 \
+--total-iter 200000 \
+--out-dir Experiments \
+--exp-name motionstreamer_model \
+--dataname t2m_babel_272 \
+--latent_dir babel_272_stream/t2m_babel_latents \
+--num_gpus $NUM_GPUS
+--resume-trans Experiments/motionstreamer_model/100k.pth \
\ No newline at end of file
diff --git a/TRAIN_t2m.sh b/TRAIN_t2m.sh
new file mode 100644
index 0000000000000000000000000000000000000000..769a1d428abe3005318ad144ba37b1e53db8e537
--- /dev/null
+++ b/TRAIN_t2m.sh
@@ -0,0 +1,15 @@
+NUM_GPUS=${1:-1}  # default: 1 GPU
+
+BATCH_SIZE=$((256 / NUM_GPUS))
+
+echo "Using $NUM_GPUS GPUs, each with a batch size of $BATCH_SIZE"
+
+accelerate launch --num_processes $NUM_GPUS train_t2m.py \
+--batch-size $BATCH_SIZE \
+--lr 0.0001 \
+--total-iter 100000 \
+--out-dir Experiments \
+--exp-name t2m_model \
+--dataname t2m_272 \
+--latent_dir humanml3d_272/t2m_latents \
+--num_gpus $NUM_GPUS
\ No newline at end of file
diff --git a/assets/teaser.jpg b/assets/teaser.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b49953140d48925b6066f99a297d3546a986b7b2
--- /dev/null
+++ b/assets/teaser.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7958c8564ae20e48165890a08d21d1b63d2a6ce94fed017fb7b5504286f0b5da
+size 751242
diff --git a/babel_272/.gitattributes b/babel_272/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..1ef325f1b111266a6b26e0196871bd78baa8c2f3
--- /dev/null
+++ b/babel_272/.gitattributes
@@ -0,0 +1,59 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
diff --git a/babel_272/README.md b/babel_272/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..12087fa3f307e58a398c5af32002cbabd97f9306
--- /dev/null
+++ b/babel_272/README.md
@@ -0,0 +1,34 @@
+---
+license: apache-2.0
+---
+## 🚀 Dataset Usage
+To facilitate researchers, we provide the processed 272-dim Motion Representation of [BABEL](https://babel.is.tue.mpg.de/) dataset in this Hugging Face repo.
+
+Motions are resampled into 30 FPS.
+
+NOTE: ``t2m_babel_mean_std/`` contains the joint mean and std of both HumanML3D and BABEL dataset for joint training of the proposed [Causal TAE](https://github.com/zju3dv/MotionStreamer/blob/main/TRAIN_causal_TAE.sh).
+
+❗️❗️❗️ The processed data is solely for academic purposes. Make sure you read through the [BABEL License](https://babel.is.tue.mpg.de/license.html).
+
+## 📖 Paper & Project Page & Code
+* [Arxiv Paper](https://arxiv.org/abs/2503.15451)
+* [Project Page](https://zju3dv.github.io/MotionStreamer/)
+* [Code](https://github.com/zju3dv/MotionStreamer)
+
+## 🏃 Processing script
+For more details of how to obtain the 272-dim motion representation, as well as other useful tools (e.g., Visualization and Conversion to BVH format), please refer to our [GitHub repo](https://github.com/Li-xingXiao/272-dim-Motion-Representation).
+
+## 🌹 Acknowledgement
+This repository builds upon the following awesome datasets and projects:
+- [BABEL](https://babel.is.tue.mpg.de/)
+
+## 🤝🏼 Citation
+If our project is helpful for your research, please consider citing :
+``` 
+@article{xiao2025motionstreamer,
+      title={MotionStreamer: Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space},
+      author={Xiao, Lixing and Lu, Shunlin and Pi, Huaijin and Fan, Ke and Pan, Liang and Zhou, Yueer and Feng, Ziyong and Zhou, Xiaowei and Peng, Sida and Wang, Jingbo},
+      journal={arXiv preprint arXiv:2503.15451},
+      year={2025}
+    }
+```
\ No newline at end of file
diff --git a/babel_272/motion_data.zip b/babel_272/motion_data.zip
new file mode 100644
index 0000000000000000000000000000000000000000..621ef98a7689970a807b98111f5bdaf902516c23
--- /dev/null
+++ b/babel_272/motion_data.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03ecf1eefd24f828e0717dd0d7d05ad2ad139d79fd09d59baeab711895311525
+size 8093667470
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\ No newline at end of file
diff --git a/babel_272/split/val.txt b/babel_272/split/val.txt
new file mode 100644
index 0000000000000000000000000000000000000000..caad3819fa324b07b9dd462bbb2acb4c28e941e4
--- /dev/null
+++ b/babel_272/split/val.txt
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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
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diff --git a/babel_272_stream/README.md b/babel_272_stream/README.md
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+---
+license: apache-2.0
+---
+## 🚀 Dataset Usage
+To facilitate researchers, we provide the processed streaming 272-dim Motion Representation of [BABEL](https://babel.is.tue.mpg.de/) dataset in this Hugging Face repo.
+
+NOTE: We process the original BABEL dataset to support training of streaming motion generation.
+e.g. If there is a motion sequence A, annotated as (A1, A2, A3, A4) in BABEL dataset, each subsequence has text description: (A1_t, A2_t, A3_t, A4_t).
+
+Then, our BABEL-stream is constructed as:
+
+seq1: (A1, A2)  ---  seq1_text: (A1_t*A2_t#A1_length)
+
+seq2: (A2, A3)  ---  seq2_text: (A2_t*A3_t#A2_length)
+
+seq3: (A3, A4)  ---  seq3_text: (A3_t*A4_t#A3_length)
+
+Here, * and # is separation symbol, A1_length means the number of frames of subsequence A1.
+
+Motions are resampled into 30 FPS.
+
+The dataset is organized as:
+```
+./
+  ├── train_stream
+      ├── seq1.npy
+      ...
+  ├── train_stream_text
+      ├── seq1.txt
+      ...
+  ├── val_stream
+      ├── seq1.npy
+      ...
+  ├── val_stream_text
+      ├── seq1.txt
+      ...
+```
+
+❗️❗️❗️ The processed data is solely for academic purposes. Make sure you read through the [BABEL License](https://babel.is.tue.mpg.de/license.html).
+
+## 📖 Paper & Project Page & Code
+* [Arxiv Paper](https://arxiv.org/abs/2503.15451)
+* [Project Page](https://zju3dv.github.io/MotionStreamer/)
+* [Code](https://github.com/zju3dv/MotionStreamer)
+
+## 🏃 Processing script
+For more details of how to obtain the 272-dim motion representation, as well as other useful tools (e.g., Visualization and Conversion to BVH format), please refer to our [GitHub repo](https://github.com/Li-xingXiao/272-dim-Motion-Representation).
+
+## 🌹 Acknowledgement
+This repository builds upon the following awesome datasets and projects:
+- [BABEL](https://babel.is.tue.mpg.de/)
+
+## 🤝🏼 Citation
+If our project is helpful for your research, please consider citing :
+``` 
+@article{xiao2025motionstreamer,
+      title={MotionStreamer: Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space},
+      author={Xiao, Lixing and Lu, Shunlin and Pi, Huaijin and Fan, Ke and Pan, Liang and Zhou, Yueer and Feng, Ziyong and Zhou, Xiaowei and Peng, Sida and Wang, Jingbo},
+      journal={arXiv preprint arXiv:2503.15451},
+      year={2025}
+    }
+```
\ No newline at end of file
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diff --git a/body_models/human_model_files/smpl/VPOSER_CKPT/TR00_004_00_WO_accad.ini b/body_models/human_model_files/smpl/VPOSER_CKPT/TR00_004_00_WO_accad.ini
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--- /dev/null
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+[All]
+adam_beta1 : 0.9
+base_lr : 0.005
+batch_size : 512
+best_model_fname : None
+cuda_id : 0
+data_shape : [1, 21, 3]
+dataset_dir : None
+display_model_gender : male
+expr_code : 004_00_WO_accad
+fp_precision : 32
+ip_avoid : False
+kl_coef : 0.005
+latentD : 32
+log_every_epoch : 2
+model_type : smpl
+n_workers : 10
+num_bodies_to_display : 10
+num_epochs : 100
+num_neurons : 512
+reg_coef : 0.0001
+remove_Zrot : True
+seed : 4815
+sm_coef : 0.01
+test_only : False
+try_num : 0
+use_cont_repr : True
+verbosity : 0
+work_dir : None
diff --git a/body_models/human_model_files/smpl/VPOSER_CKPT/snapshots/._TR00_E096.pt b/body_models/human_model_files/smpl/VPOSER_CKPT/snapshots/._TR00_E096.pt
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diff --git a/body_models/human_model_files/smpl/VPOSER_CKPT/vposer_smpl.py b/body_models/human_model_files/smpl/VPOSER_CKPT/vposer_smpl.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b07d6286690bb67d0e4c0c0b46fcfcb84fb20e1
--- /dev/null
+++ b/body_models/human_model_files/smpl/VPOSER_CKPT/vposer_smpl.py
@@ -0,0 +1,164 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2019 Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG),
+# acting on behalf of its Max Planck Institute for Intelligent Systems and the
+# Max Planck Institute for Biological Cybernetics. All rights reserved.
+#
+# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is holder of all proprietary rights
+# on this computer program. You can only use this computer program if you have closed a license agreement
+# with MPG or you get the right to use the computer program from someone who is authorized to grant you that right.
+# Any use of the computer program without a valid license is prohibited and liable to prosecution.
+# Contact: ps-license@tuebingen.mpg.de
+#
+#
+# If you use this code in a research publication please consider citing the following:
+#
+# Expressive Body Capture: 3D Hands, Face, and Body from a Single Image <https://arxiv.org/abs/1904.05866>
+# AMASS: Archive of Motion Capture as Surface Shapes <https://arxiv.org/abs/1904.03278>
+#
+#
+# Code Developed by:
+# Nima Ghorbani <https://www.linkedin.com/in/nghorbani/>
+# Vassilis Choutas <https://ps.is.tuebingen.mpg.de/employees/vchoutas> for ContinousRotReprDecoder
+#
+# 2018.01.02
+
+'''
+A human body pose prior built with Auto-Encoding Variational Bayes
+'''
+
+__all__ = ['VPoser']
+
+import os, sys, shutil
+
+import torch
+
+from torch import nn
+from torch.nn import functional as F
+
+import numpy as np
+
+import torchgeometry as tgm
+
+class ContinousRotReprDecoder(nn.Module):
+    def __init__(self):
+        super(ContinousRotReprDecoder, self).__init__()
+
+    def forward(self, module_input):
+        reshaped_input = module_input.view(-1, 3, 2)
+
+        b1 = F.normalize(reshaped_input[:, :, 0], dim=1)
+
+        dot_prod = torch.sum(b1 * reshaped_input[:, :, 1], dim=1, keepdim=True)
+        b2 = F.normalize(reshaped_input[:, :, 1] - dot_prod * b1, dim=-1)
+        b3 = torch.cross(b1, b2, dim=1)
+
+        return torch.stack([b1, b2, b3], dim=-1)
+
+
+class VPoser(nn.Module):
+    def __init__(self, num_neurons, latentD, data_shape, use_cont_repr=True):
+        super(VPoser, self).__init__()
+
+        self.latentD = latentD
+        self.use_cont_repr = use_cont_repr
+
+        n_features = np.prod(data_shape)
+        self.num_joints = data_shape[1]
+
+        self.bodyprior_enc_bn1 = nn.BatchNorm1d(n_features)
+        self.bodyprior_enc_fc1 = nn.Linear(n_features, num_neurons)
+        self.bodyprior_enc_bn2 = nn.BatchNorm1d(num_neurons)
+        self.bodyprior_enc_fc2 = nn.Linear(num_neurons, num_neurons)
+        self.bodyprior_enc_mu = nn.Linear(num_neurons, latentD)
+        self.bodyprior_enc_logvar = nn.Linear(num_neurons, latentD)
+        self.dropout = nn.Dropout(p=.1, inplace=False)
+
+        self.bodyprior_dec_fc1 = nn.Linear(latentD, num_neurons)
+        self.bodyprior_dec_fc2 = nn.Linear(num_neurons, num_neurons)
+
+        if self.use_cont_repr:
+            self.rot_decoder = ContinousRotReprDecoder()
+
+        self.bodyprior_dec_out = nn.Linear(num_neurons, self.num_joints* 6)
+
+    def encode(self, Pin):
+        '''
+
+        :param Pin: Nx(numjoints*3)
+        :param rep_type: 'matrot'/'aa' for matrix rotations or axis-angle
+        :return:
+        '''
+        Xout = Pin.view(Pin.size(0), -1)  # flatten input
+        Xout = self.bodyprior_enc_bn1(Xout)
+
+        Xout = F.leaky_relu(self.bodyprior_enc_fc1(Xout), negative_slope=.2)
+        Xout = self.bodyprior_enc_bn2(Xout)
+        Xout = self.dropout(Xout)
+        Xout = F.leaky_relu(self.bodyprior_enc_fc2(Xout), negative_slope=.2)
+        return torch.distributions.normal.Normal(self.bodyprior_enc_mu(Xout), F.softplus(self.bodyprior_enc_logvar(Xout)))
+
+    def decode(self, Zin, output_type='matrot'):
+        assert output_type in ['matrot', 'aa']
+
+        Xout = F.leaky_relu(self.bodyprior_dec_fc1(Zin), negative_slope=.2)
+        Xout = self.dropout(Xout)
+        Xout = F.leaky_relu(self.bodyprior_dec_fc2(Xout), negative_slope=.2)
+        Xout = self.bodyprior_dec_out(Xout)
+        if self.use_cont_repr:
+            Xout = self.rot_decoder(Xout)
+        else:
+            Xout = torch.tanh(Xout)
+
+        Xout = Xout.view([-1, 1, self.num_joints, 9])
+        if output_type == 'aa': return VPoser.matrot2aa(Xout)
+        return Xout
+
+    def forward(self, Pin, input_type='matrot', output_type='matrot'):
+        '''
+
+        :param Pin: aa: Nx1xnum_jointsx3 / matrot: Nx1xnum_jointsx9
+        :param input_type: matrot / aa for matrix rotations or axis angles
+        :param output_type: matrot / aa
+        :return:
+        '''
+        assert output_type in ['matrot', 'aa']
+        # if input_type == 'aa': Pin = VPoser.aa2matrot(Pin)
+        q_z = self.encode(Pin)
+        q_z_sample = q_z.rsample()
+        Prec = self.decode(q_z_sample)
+        if output_type == 'aa': Prec = VPoser.matrot2aa(Prec)
+
+        #return Prec, q_z.mean, q_z.sigma
+        return {'pose':Prec, 'mean':q_z.mean, 'std':q_z.scale}
+
+    def sample_poses(self, num_poses, output_type='aa', seed=None):
+        np.random.seed(seed)
+        dtype = self.bodyprior_dec_fc1.weight.dtype
+        device = self.bodyprior_dec_fc1.weight.device
+        self.eval()
+        with torch.no_grad():
+            Zgen = torch.tensor(np.random.normal(0., 1., size=(num_poses, self.latentD)), dtype=dtype).to(device)
+        return self.decode(Zgen, output_type=output_type)
+
+    @staticmethod
+    def matrot2aa(pose_matrot):
+        '''
+        :param pose_matrot: Nx1xnum_jointsx9
+        :return: Nx1xnum_jointsx3
+        '''
+        batch_size = pose_matrot.size(0)
+        homogen_matrot = F.pad(pose_matrot.view(-1, 3, 3), [0,1])
+        pose = tgm.rotation_matrix_to_angle_axis(homogen_matrot).view(batch_size, 1, -1, 3).contiguous()
+        return pose
+
+    @staticmethod
+    def aa2matrot(pose):
+        '''
+        :param Nx1xnum_jointsx3
+        :return: pose_matrot: Nx1xnum_jointsx9
+        '''
+        batch_size = pose.size(0)
+        pose_body_matrot = tgm.angle_axis_to_rotation_matrix(pose.reshape(-1, 3))[:, :3, :3].contiguous().view(batch_size, 1, -1, 9)
+        return pose_body_matrot
+
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diff --git a/body_models/human_model_files/smplx/smplx_vert_segmentation.json b/body_models/human_model_files/smplx/smplx_vert_segmentation.json
new file mode 100644
index 0000000000000000000000000000000000000000..cb3adb8e6668a67f77e9d163807392ff991e7821
--- /dev/null
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+}
diff --git a/body_models/human_model_files/smplx/smplx_vert_segmentation.npy b/body_models/human_model_files/smplx/smplx_vert_segmentation.npy
new file mode 100644
index 0000000000000000000000000000000000000000..a3e76430f7478e4996e331732cd6cb0e3bc2c22f
--- /dev/null
+++ b/body_models/human_model_files/smplx/smplx_vert_segmentation.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0710425b2b1e787596ab2fb10b6e622e11a79329381e4ee3f7eb03214d47c18a
+size 83928
diff --git a/body_models/human_model_files/smplx/version.txt b/body_models/human_model_files/smplx/version.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ac6ab56466944d45e30add02cd7607b768a66b5a
--- /dev/null
+++ b/body_models/human_model_files/smplx/version.txt
@@ -0,0 +1 @@
+This is "Version 1.0" of SMPL-X.
diff --git a/demo_mixed.py b/demo_mixed.py
new file mode 100644
index 0000000000000000000000000000000000000000..150bfc089d4022d7db794abc174a0aa8d7a8f540
--- /dev/null
+++ b/demo_mixed.py
@@ -0,0 +1,144 @@
+import os
+import torch
+import numpy as np
+from models.llama_model import LLaMAHF, LLaMAHFConfig
+import models.tae as tae
+import options.option_transformer as option_trans
+from sentence_transformers import SentenceTransformer
+import warnings
+from utils.face_z_align_util import rotation_6d_to_matrix, matrix_to_axis_angle, axis_angle_to_quaternion
+from utils import bvh, quat
+import smplx
+
+warnings.filterwarnings('ignore')
+
+# This function converts the 272-dim representation to a BVH file for visualization.
+def save_motion_as_bvh(motion_data, output_path, fps=30):
+    print(f"--- Converting to BVH: {os.path.basename(output_path)} ---")
+    try:
+        if isinstance(motion_data, torch.Tensor):
+            motion_data = motion_data.detach().cpu().numpy()
+        if motion_data.ndim == 3:
+            motion_data = motion_data.squeeze(0)
+
+        njoint = 22
+        nfrm, _ = motion_data.shape
+        
+        # This complex logic correctly interprets the 272-dim vector into rotations and translations
+        rotations_matrix = rotation_6d_to_matrix(torch.from_numpy(motion_data[:, 8+6*njoint : 8+12*njoint]).reshape(nfrm, -1, 6)).numpy()
+        global_heading_diff_rot = rotation_6d_to_matrix(torch.from_numpy(motion_data[:, 2:8])).numpy()
+        
+        global_heading_rot = np.zeros_like(global_heading_diff_rot)
+        global_heading_rot[0] = global_heading_diff_rot[0]
+        for i in range(1, nfrm):
+            global_heading_rot[i] = np.matmul(global_heading_diff_rot[i], global_heading_rot[i-1])
+
+        velocities_root_xy = motion_data[:, :2]
+        height = motion_data[:, 8 : 8+3*njoint].reshape(nfrm, -1, 3)[:, 0, 1]
+        
+        inv_global_heading_rot = np.transpose(global_heading_rot, (0, 2, 1))
+        rotations_matrix[:, 0, ...] = np.matmul(inv_global_heading_rot, rotations_matrix[:, 0, ...])
+        
+        velocities_root_xyz = np.zeros((nfrm, 3))
+        velocities_root_xyz[:, 0] = velocities_root_xy[:, 0]
+        velocities_root_xyz[:, 2] = velocities_root_xy[:, 1]
+        velocities_root_xyz[1:, :] = np.matmul(inv_global_heading_rot[:-1], velocities_root_xyz[1:, :, None]).squeeze(-1)
+        root_translation = np.cumsum(velocities_root_xyz, axis=0)
+        root_translation[:, 1] = height
+
+        axis_angle = matrix_to_axis_angle(torch.from_numpy(rotations_matrix)).numpy().reshape(nfrm, -1)
+        poses_24_joints = np.zeros((nfrm, 72))
+        poses_24_joints[:, :66] = axis_angle
+        
+        model = smplx.create(model_path="body_models/human_model_files", model_type="smpl", gender="NEUTRAL")
+        parents = model.parents.detach().cpu().numpy()
+        rest_pose = model().joints.detach().cpu().numpy().squeeze()[:24,:]
+        offsets = rest_pose - rest_pose[parents]
+        offsets[0] = np.array([0,0,0])
+        
+        rotations_quat = axis_angle_to_quaternion(torch.from_numpy(poses_24_joints.reshape(-1, 24, 3))).numpy()
+        rotations_euler = np.degrees(quat.to_euler(rotations_quat, order="zyx"))
+        positions = np.zeros_like(rotations_quat[..., :3])
+        positions[:, 0] = root_translation
+        
+        joint_names = ["Pelvis", "Left_hip", "Right_hip", "Spine1", "Left_knee", "Right_knee", "Spine2", "Left_ankle", "Right_ankle", "Spine3", "Left_foot", "Right_foot", "Neck", "Left_collar", "Right_collar", "Head", "Left_shoulder", "Right_shoulder", "Left_elbow", "Right_elbow", "Left_wrist", "Right_wrist", "Left_hand", "Right_hand"]
+
+        bvh.save(output_path, {
+            "rotations": rotations_euler, "positions": positions, "offsets": offsets,
+            "parents": parents, "names": joint_names, "order": "zyx", "frametime": 1.0 / fps,
+        })
+        print(f"✅ BVH file saved successfully to {output_path}")
+
+    except Exception as e:
+        print(f"❌ BVH Conversion Failed. Error: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == '__main__':
+    comp_device = torch.device('cuda')
+    args = option_trans.get_args_parser()
+    torch.manual_seed(args.seed)
+
+    # --- Load Models ---
+    print("Loading models for MotionStreamer...")
+    t5_model = SentenceTransformer('sentencetõ-xxl/')
+    t5_model.eval().to(comp_device)
+
+    print("Loading Causal TAE (t2m_babel) checkpoint...")
+    net = tae.Causal_HumanTAE(latent_dim=16)
+    ckpt = torch.load('Causal_TAE_t2m_babel/net_last.pth', map_location='cpu')
+    net.load_state_dict(ckpt['net'], strict=True)
+    net.eval().to(comp_device)
+
+    print("Loading YOUR trained MotionStreamer checkpoint...")
+    config = LLaMAHFConfig.from_name('Normal_size')
+    trans_encoder = LLaMAHF(config, args.num_diffusion_head_layers, args.latent_dim, comp_device)
+    
+    # --- FIX 1: Manually set the missing attribute ---
+    trans_encoder.use_out_proj = True 
+    
+    ckpt = torch.load('Experiments/motionstreamer_model/latest.pth', map_location='cpu')
+    # Handle DataParallel wrapper if present
+    new_ckpt = {k.replace('module.', ''): v for k, v in ckpt['trans'].items()}
+    trans_encoder.load_state_dict(new_ckpt, strict=True)
+    trans_encoder.eval().to(comp_device)
+
+    print("Loading mean/std from BABEL dataset...")
+    mean = np.load('babel_272/t2m_babel_mean_std/Mean.npy')
+    std = np.load('babel_272/t2m_babel_mean_std/Std.npy')
+
+    # --- Inference ---
+    motion_history = torch.empty(0, 16).to(comp_device) # Start with no history
+    cfg_scale = 7.0
+    text_prompt = "a person is running forward"
+    desired_frames = 240 # How many frames of motion to generate
+
+    print(f"Generating motion for '{text_prompt}' with CFG scale: {cfg_scale}")
+    with torch.no_grad():
+        # Use the correct inference function for the streaming model
+        _, motion_latents = trans_encoder.sample_for_eval_CFG_babel_inference_new_demo(
+            B_text=text_prompt, 
+            A_motion=motion_history, 
+            tokenizer='t5-xxl', 
+            clip_model=t5_model,
+            device=comp_device,
+            cfg=cfg_scale,
+            length=desired_frames
+        )
+
+    print("Decoding latents to full motion...")
+    motion_seqs = net.forward_decoder(motion_latents)
+
+    # --- Denormalize, Correct Speed, and Save ---
+    motion_denormalized = motion_seqs.detach().cpu().numpy() * std + mean
+
+    # --- FIX 2: Subsample the frames to correct the speed ---
+    motion_realtimespeed = motion_denormalized.squeeze(0)[::4, :]
+
+    output_dir = 'demo_output_streamer'
+    os.makedirs(output_dir, exist_ok=True)
+    
+    safe_filename = text_prompt.replace(" ", "_").replace("'", "")
+    output_bvh_path = os.path.join(output_dir, f'{safe_filename}_final.bvh')
+    save_motion_as_bvh(motion_realtimespeed, output_bvh_path, fps=30)
\ No newline at end of file
diff --git a/demo_motionstreamer.py b/demo_motionstreamer.py
new file mode 100644
index 0000000000000000000000000000000000000000..9785d99df25c12f02e0723a52d2ddb79593c35e7
--- /dev/null
+++ b/demo_motionstreamer.py
@@ -0,0 +1,157 @@
+import os
+import torch
+import numpy as np
+from models.llama_model import LLaMAHF, LLaMAHFConfig
+import models.tae as tae
+import options.option_transformer as option_trans
+import warnings
+
+import smplx
+from utils import bvh, quat
+from utils.face_z_align_util import rotation_6d_to_matrix, matrix_to_axis_angle, axis_angle_to_quaternion
+from sentence_transformers import SentenceTransformer
+warnings.filterwarnings('ignore')
+
+def save_motion_as_bvh(motion_data, output_path, fps=30):
+    """
+    Saves a denormalized 272-dim motion numpy array to a BVH file.
+    """
+    print(f"--- Starting direct conversion to BVH: {os.path.basename(output_path)} ---")
+    try:
+        if isinstance(motion_data, torch.Tensor):
+            motion_data = motion_data.detach().cpu().numpy()
+        if motion_data.ndim == 3 and motion_data.shape[0] == 1:
+            motion_data = motion_data.squeeze(0)
+        elif motion_data.ndim != 2:
+            raise ValueError(f"Input motion data must be 2D, but got shape {motion_data.shape}")
+
+        njoint = 22
+        nfrm, _ = motion_data.shape
+        
+        rotations_matrix = rotation_6d_to_matrix(torch.from_numpy(motion_data[:, 8+6*njoint : 8+12*njoint]).reshape(nfrm, -1, 6)).numpy()
+        
+        global_heading_diff_rot_6d = torch.from_numpy(motion_data[:, 2:8])
+        global_heading_diff_rot = rotation_6d_to_matrix(global_heading_diff_rot_6d).numpy()
+        global_heading_rot = np.zeros_like(global_heading_diff_rot)
+        global_heading_rot[0] = global_heading_diff_rot[0]
+        for i in range(1, nfrm):
+            global_heading_rot[i] = np.matmul(global_heading_diff_rot[i], global_heading_rot[i-1])
+
+        velocities_root_xy = motion_data[:, :2]
+        height = motion_data[:, 8 : 8+3*njoint].reshape(nfrm, -1, 3)[:, 0, 1]
+        
+        inv_global_heading_rot = np.transpose(global_heading_rot, (0, 2, 1))
+        rotations_matrix[:, 0, ...] = np.matmul(inv_global_heading_rot, rotations_matrix[:, 0, ...])
+        
+        velocities_root_xyz = np.zeros((nfrm, 3))
+        velocities_root_xyz[:, 0] = velocities_root_xy[:, 0]
+        velocities_root_xyz[:, 2] = velocities_root_xy[:, 1]
+        velocities_root_xyz[1:, :] = np.matmul(inv_global_heading_rot[:-1], velocities_root_xyz[1:, :, None]).squeeze(-1)
+        root_translation = np.cumsum(velocities_root_xyz, axis=0)
+        root_translation[:, 1] = height
+
+        axis_angle = matrix_to_axis_angle(torch.from_numpy(rotations_matrix)).numpy().reshape(nfrm, -1)
+        poses_24_joints = np.zeros((nfrm, 72))
+        poses_24_joints[:, :66] = axis_angle
+        
+        model = smplx.create(model_path="body_models/human_model_files", model_type="smpl", gender="NEUTRAL")
+        parents = model.parents.detach().cpu().numpy()
+        rest_pose = model().joints.detach().cpu().numpy().squeeze()[:24,:]
+        offsets = rest_pose - rest_pose[parents]
+        offsets[0] = np.array([0,0,0])
+        
+        rotations_quat = axis_angle_to_quaternion(torch.from_numpy(poses_24_joints.reshape(-1, 24, 3))).numpy()
+        rotations_euler = np.degrees(quat.to_euler(rotations_quat, order="zyx"))
+        positions = np.zeros_like(rotations_quat[..., :3])
+        positions[:, 0] = root_translation
+        
+        joint_names = [
+            "Pelvis", "Left_hip", "Right_hip", "Spine1", "Left_knee", "Right_knee", "Spine2", 
+            "Left_ankle", "Right_ankle", "Spine3", "Left_foot", "Right_foot", "Neck", 
+            "Left_collar", "Right_collar", "Head", "Left_shoulder", "Right_shoulder", 
+            "Left_elbow", "Right_elbow", "Left_wrist", "Right_wrist", "Left_hand", "Right_hand"
+        ]
+
+        bvh.save(output_path, {
+            "rotations": rotations_euler, "positions": positions, "offsets": offsets,
+            "parents": parents, "names": joint_names, "order": "zyx", "frametime": 1.0 / fps,
+        })
+        print(f"✅ BVH file saved successfully to {output_path}")
+
+    except Exception as e:
+        print(f"❌ BVH Conversion Failed. Error: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == '__main__':
+    comp_device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    args = option_trans.get_args_parser()
+    torch.manual_seed(args.seed)
+
+    # --- Load Models ---
+    print("Loading models for MotionStreamer...")
+    t5_model = SentenceTransformer('sentencet5-xxl/')
+    t5_model.eval()
+    for p in t5_model.parameters():
+        p.requires_grad = False
+
+    print("Loading Causal TAE (t2m_babel) checkpoint...")
+    tae_net = tae.Causal_HumanTAE(
+        hidden_size=1024, down_t=2, stride_t=2, depth=3, dilation_growth_rate=3,
+        latent_dim=16, clip_range=[-30, 20]
+    )
+    tae_ckpt = torch.load('Causal_TAE_t2m_babel/net_last.pth', map_location='cpu')
+    # The TAE checkpoint has weights stored under the 'net' key
+    tae_net.load_state_dict(tae_ckpt['net'], strict=True) 
+    tae_net.eval()
+    tae_net.to(comp_device)
+
+    config = LLaMAHFConfig.from_name('Normal_size')
+    config.block_size = 78
+    trans_encoder = LLaMAHF(config, args.num_diffusion_head_layers, args.latent_dim, comp_device)
+    print("Loading your trained MotionStreamer checkpoint from 'Experiments/motionstreamer_model/latest.pth'...")
+    trans_ckpt = torch.load('Experiments/motionstreamer_model/latest.pth', map_location='cpu')
+    trans_encoder.load_state_dict(trans_ckpt['trans'], strict=True)
+    trans_encoder.eval()
+    trans_encoder.to(comp_device)
+
+    print("Loading mean/std from BABEL dataset...")
+    mean = np.load('babel_272/t2m_babel_mean_std/Mean.npy')
+    std = np.load('babel_272/t2m_babel_mean_std/Std.npy')
+
+    # --- Inference ---
+    # The history must be a 2D tensor for the model's unsqueeze operation to work
+    motion_history = torch.empty(0, 16).to(comp_device) 
+    
+    # --- KEY PARAMETER TO TUNE ---
+    # The author often uses a higher CFG scale for more dynamic motions.
+    # Let's try 7.0, which is a common value in their code.
+    cfg_scale = 7.0 
+    
+    print(f"Generating motion for text: '{args.text}' with CFG scale: {cfg_scale}")
+    with torch.no_grad():
+        # REVERTED to the correct streaming-native inference function
+        _, motion_latents = trans_encoder.sample_for_eval_CFG_babel_inference_new_demo(
+            B_text=args.text, 
+            A_motion=motion_history, 
+            tokenizer='t5-xxl', 
+            clip_model=t5_model,
+            device=comp_device,
+            cfg=cfg_scale, # Use the tuned CFG scale
+            length=240 # Generate a longer sequence for "run"
+        )
+        
+        print("Decoding latents to full motion...")
+        motion_seqs = tae_net.forward_decoder(motion_latents)
+
+    motion = motion_seqs.detach().cpu().numpy()
+    motion_denormalized = motion * std + mean
+    
+    # --- Save Output ---
+    output_dir = 'demo_output_streamer'
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    
+    output_bvh_path = os.path.join(output_dir, f'{args.text.replace(" ", "_")}_cfg{cfg_scale}.bvh')
+    save_motion_as_bvh(motion_denormalized, output_bvh_path, fps=30)
\ No newline at end of file
diff --git a/demo_output_final/He_runs_forward.bvh b/demo_output_final/He_runs_forward.bvh
new file mode 100644
index 0000000000000000000000000000000000000000..532fa89772dd83e6433eda7753f032b5d09b4020
--- /dev/null
+++ b/demo_output_final/He_runs_forward.bvh
@@ -0,0 +1,204 @@
+HIERARCHY
+ROOT Pelvis
+{
+	OFFSET 0.000000 0.000000 0.000000
+	CHANNELS 6 Xposition Yposition Zposition Zrotation Yrotation Xrotation 
+	JOINT Left_hip
+	{
+		OFFSET 0.069520 -0.091406 -0.006815
+		CHANNELS 3 Zrotation Yrotation Xrotation
+		JOINT Left_knee
+		{
+			OFFSET 0.034277 -0.375199 -0.004496
+			CHANNELS 3 Zrotation Yrotation Xrotation
+			JOINT Left_ankle
+			{
+				OFFSET -0.013596 -0.397961 -0.043693
+				CHANNELS 3 Zrotation Yrotation Xrotation
+				JOINT Left_foot
+				{
+					OFFSET 0.026358 -0.055791 0.119288
+					CHANNELS 3 Zrotation Yrotation Xrotation
+					End Site
+					{
+						OFFSET 0.000000 0.000000 0.000000
+					}
+				}
+			}
+		}
+	}
+	JOINT Right_hip
+	{
+		OFFSET -0.067670 -0.090522 -0.004320
+		CHANNELS 3 Zrotation Yrotation Xrotation
+		JOINT Right_knee
+		{
+			OFFSET -0.038290 -0.382569 -0.008850
+			CHANNELS 3 Zrotation Yrotation Xrotation
+			JOINT Right_ankle
+			{
+				OFFSET 0.015774 -0.398415 -0.042312
+				CHANNELS 3 Zrotation Yrotation Xrotation
+				JOINT Right_foot
+				{
+					OFFSET -0.025372 -0.048144 0.123348
+					CHANNELS 3 Zrotation Yrotation Xrotation
+					End Site
+					{
+						OFFSET 0.000000 0.000000 0.000000
+					}
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+		CHANNELS 3 Zrotation Yrotation Xrotation
+		JOINT Spine2
+		{
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+			CHANNELS 3 Zrotation Yrotation Xrotation
+			JOINT Spine3
+			{
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+				CHANNELS 3 Zrotation Yrotation Xrotation
+				JOINT Neck
+				{
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+					CHANNELS 3 Zrotation Yrotation Xrotation
+					JOINT Head
+					{
+						OFFSET 0.005152 0.064970 0.051349
+						CHANNELS 3 Zrotation Yrotation Xrotation
+						End Site
+						{
+							OFFSET 0.000000 0.000000 0.000000
+						}
+					}
+				}
+				JOINT Left_collar
+				{
+					OFFSET 0.078845 0.121749 -0.034090
+					CHANNELS 3 Zrotation Yrotation Xrotation
+					JOINT Left_shoulder
+					{
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+						CHANNELS 3 Zrotation Yrotation Xrotation
+						JOINT Left_elbow
+						{
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+							CHANNELS 3 Zrotation Yrotation Xrotation
+							JOINT Left_wrist
+							{
+								OFFSET 0.249234 0.008986 -0.001171
+								CHANNELS 3 Zrotation Yrotation Xrotation
+								JOINT Left_hand
+								{
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+									CHANNELS 3 Zrotation Yrotation Xrotation
+									End Site
+									{
+										OFFSET 0.000000 0.000000 0.000000
+									}
+								}
+							}
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+					}
+				}
+				JOINT Right_collar
+				{
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+					CHANNELS 3 Zrotation Yrotation Xrotation
+					JOINT Right_shoulder
+					{
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+						CHANNELS 3 Zrotation Yrotation Xrotation
+						JOINT Right_elbow
+						{
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+							CHANNELS 3 Zrotation Yrotation Xrotation
+							JOINT Right_wrist
+							{
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+								CHANNELS 3 Zrotation Yrotation Xrotation
+								JOINT Right_hand
+								{
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+									CHANNELS 3 Zrotation Yrotation Xrotation
+									End Site
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+}
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diff --git a/demo_output_streamer/He_runs_forward.bvh b/demo_output_streamer/He_runs_forward.bvh
new file mode 100644
index 0000000000000000000000000000000000000000..7ffb3760a9449505c4e99f71796b755367bbfcdc
--- /dev/null
+++ b/demo_output_streamer/He_runs_forward.bvh
@@ -0,0 +1,384 @@
+HIERARCHY
+ROOT Pelvis
+{
+	OFFSET 0.000000 0.000000 0.000000
+	CHANNELS 6 Xposition Yposition Zposition Zrotation Yrotation Xrotation 
+	JOINT Left_hip
+	{
+		OFFSET 0.069520 -0.091406 -0.006815
+		CHANNELS 3 Zrotation Yrotation Xrotation
+		JOINT Left_knee
+		{
+			OFFSET 0.034277 -0.375199 -0.004496
+			CHANNELS 3 Zrotation Yrotation Xrotation
+			JOINT Left_ankle
+			{
+				OFFSET -0.013596 -0.397961 -0.043693
+				CHANNELS 3 Zrotation Yrotation Xrotation
+				JOINT Left_foot
+				{
+					OFFSET 0.026358 -0.055791 0.119288
+					CHANNELS 3 Zrotation Yrotation Xrotation
+					End Site
+					{
+						OFFSET 0.000000 0.000000 0.000000
+					}
+				}
+			}
+		}
+	}
+	JOINT Right_hip
+	{
+		OFFSET -0.067670 -0.090522 -0.004320
+		CHANNELS 3 Zrotation Yrotation Xrotation
+		JOINT Right_knee
+		{
+			OFFSET -0.038290 -0.382569 -0.008850
+			CHANNELS 3 Zrotation Yrotation Xrotation
+			JOINT Right_ankle
+			{
+				OFFSET 0.015774 -0.398415 -0.042312
+				CHANNELS 3 Zrotation Yrotation Xrotation
+				JOINT Right_foot
+				{
+					OFFSET -0.025372 -0.048144 0.123348
+					CHANNELS 3 Zrotation Yrotation Xrotation
+					End Site
+					{
+						OFFSET 0.000000 0.000000 0.000000
+					}
+				}
+			}
+		}
+	}
+	JOINT Spine1
+	{
+		OFFSET -0.002533 0.108963 -0.026696
+		CHANNELS 3 Zrotation Yrotation Xrotation
+		JOINT Spine2
+		{
+			OFFSET 0.005487 0.135180 0.001092
+			CHANNELS 3 Zrotation Yrotation Xrotation
+			JOINT Spine3
+			{
+				OFFSET 0.001457 0.052922 0.025425
+				CHANNELS 3 Zrotation Yrotation Xrotation
+				JOINT Neck
+				{
+					OFFSET -0.002778 0.213870 -0.042857
+					CHANNELS 3 Zrotation Yrotation Xrotation
+					JOINT Head
+					{
+						OFFSET 0.005152 0.064970 0.051349
+						CHANNELS 3 Zrotation Yrotation Xrotation
+						End Site
+						{
+							OFFSET 0.000000 0.000000 0.000000
+						}
+					}
+				}
+				JOINT Left_collar
+				{
+					OFFSET 0.078845 0.121749 -0.034090
+					CHANNELS 3 Zrotation Yrotation Xrotation
+					JOINT Left_shoulder
+					{
+						OFFSET 0.090977 0.030469 -0.008868
+						CHANNELS 3 Zrotation Yrotation Xrotation
+						JOINT Left_elbow
+						{
+							OFFSET 0.259612 -0.012772 -0.027456
+							CHANNELS 3 Zrotation Yrotation Xrotation
+							JOINT Left_wrist
+							{
+								OFFSET 0.249234 0.008986 -0.001171
+								CHANNELS 3 Zrotation Yrotation Xrotation
+								JOINT Left_hand
+								{
+									OFFSET 0.084042 -0.008162 -0.014945
+									CHANNELS 3 Zrotation Yrotation Xrotation
+									End Site
+									{
+										OFFSET 0.000000 0.000000 0.000000
+									}
+								}
+							}
+						}
+					}
+				}
+				JOINT Right_collar
+				{
+					OFFSET -0.081759 0.118833 -0.038615
+					CHANNELS 3 Zrotation Yrotation Xrotation
+					JOINT Right_shoulder
+					{
+						OFFSET -0.096012 0.032551 -0.009143
+						CHANNELS 3 Zrotation Yrotation Xrotation
+						JOINT Right_elbow
+						{
+							OFFSET -0.253742 -0.013329 -0.021401
+							CHANNELS 3 Zrotation Yrotation Xrotation
+							JOINT Right_wrist
+							{
+								OFFSET -0.255298 0.007772 -0.005559
+								CHANNELS 3 Zrotation Yrotation Xrotation
+								JOINT Right_hand
+								{
+									OFFSET -0.084622 -0.006117 -0.010315
+									CHANNELS 3 Zrotation Yrotation Xrotation
+									End Site
+									{
+										OFFSET 0.000000 0.000000 0.000000
+									}
+								}
+							}
+						}
+					}
+				}
+			}
+		}
+	}
+}
+MOTION
+Frames: 240
+Frame Time: 0.033333
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diff --git a/demo_t2m.py b/demo_t2m.py
new file mode 100644
index 0000000000000000000000000000000000000000..61eb5550ab11f9f1a2c59b1d972f806b6bc59cdf
--- /dev/null
+++ b/demo_t2m.py
@@ -0,0 +1,204 @@
+import os
+import torch
+import numpy as np
+from models.llama_model import LLaMAHF, LLaMAHFConfig
+import models.tae as tae
+import options.option_transformer as option_trans
+import warnings
+
+import smplx
+from utils import bvh, quat
+from utils.face_z_align_util import rotation_6d_to_matrix, matrix_to_axis_angle, axis_angle_to_quaternion
+
+
+warnings.filterwarnings('ignore')
+
+comp_device = torch.device('cuda')
+##### ---- Exp dirs ---- #####
+args = option_trans.get_args_parser()
+torch.manual_seed(args.seed)
+
+from sentence_transformers import SentenceTransformer
+t5_model = SentenceTransformer('sentencet5-xxl/')
+t5_model.eval()
+for p in t5_model.parameters():
+    p.requires_grad = False
+
+def save_motion_as_bvh(motion_data, output_path, fps=30):
+    """
+    Saves a motion tensor in the 272-dimensional format to a BVH file.
+    This version is adapted from the official repository script for robustness.
+    """
+    print(f"--- Starting direct conversion to BVH: {os.path.basename(output_path)} ---")
+    try:
+        # --- 1. Ensure data is a 2D NumPy array ---
+        if isinstance(motion_data, torch.Tensor):
+            motion_data = motion_data.detach().cpu().numpy()
+        
+        # This is the key fix: Check dimensions before squeezing
+        if motion_data.ndim == 3 and motion_data.shape[0] == 1:
+            motion_data = motion_data.squeeze(0)
+        elif motion_data.ndim != 2:
+            raise ValueError(f"Input motion data must be 2D or 3D with a batch size of 1, but got shape {motion_data.shape}")
+
+        # --- 2. Recover 85-dim SMPL format from 272-dim format ---
+        # This logic is from the official script's `recover_from_local_rotation`
+        njoint = 22
+        nfrm, _ = motion_data.shape
+        
+        rotations_matrix = rotation_6d_to_matrix(torch.from_numpy(motion_data[:, 8+6*njoint : 8+12*njoint]).reshape(nfrm, -1, 6)).numpy()
+        
+        # Accumulate heading rotations
+        global_heading_diff_rot_6d = torch.from_numpy(motion_data[:, 2:8])
+        global_heading_diff_rot = rotation_6d_to_matrix(global_heading_diff_rot_6d).numpy()
+        global_heading_rot = np.zeros_like(global_heading_diff_rot)
+        global_heading_rot[0] = global_heading_diff_rot[0]
+        for i in range(1, nfrm):
+            global_heading_rot[i] = np.matmul(global_heading_diff_rot[i], global_heading_rot[i-1])
+
+        # Calculate root translation
+        velocities_root_xy = motion_data[:, :2]
+        positions_no_heading = motion_data[:, 8 : 8+3*njoint].reshape(nfrm, -1, 3)
+        height = positions_no_heading[:, 0, 1]
+        
+        inv_global_heading_rot = np.transpose(global_heading_rot, (0, 2, 1))
+        rotations_matrix[:, 0, ...] = np.matmul(inv_global_heading_rot, rotations_matrix[:, 0, ...])
+        
+        velocities_root_xyz = np.zeros((nfrm, 3))
+        velocities_root_xyz[:, 0] = velocities_root_xy[:, 0]
+        velocities_root_xyz[:, 2] = velocities_root_xy[:, 1]
+        velocities_root_xyz[1:, :] = np.matmul(inv_global_heading_rot[:-1], velocities_root_xyz[1:, :, None]).squeeze(-1)
+        root_translation = np.cumsum(velocities_root_xyz, axis=0)
+        root_translation[:, 1] = height
+
+        # Convert rotation matrices to axis-angle
+        axis_angle = matrix_to_axis_angle(torch.from_numpy(rotations_matrix)).numpy()
+        poses_85dim = np.concatenate([axis_angle.reshape(nfrm, -1), np.zeros((nfrm, 6)), root_translation, np.zeros((nfrm, 10))], axis=-1)
+
+        # --- 3. Convert 85-dim SMPL to BVH data ---
+        # This logic is from the official script's `smpl2bvh`
+        rots = poses_85dim[:, :72].reshape(-1, 24, 3)
+        trans = poses_85dim[:, 72:75]
+
+        # Get skeleton from SMPL model
+        model = smplx.create(model_path="body_models/human_model_files", model_type="smpl", gender="NEUTRAL")
+        parents = model.parents.detach().cpu().numpy()
+        rest_pose = model().joints.detach().cpu().numpy().squeeze()[:24,:]
+        offsets = rest_pose - rest_pose[parents]
+        offsets[0] = np.array([0,0,0])
+        
+        rotations_quat = axis_angle_to_quaternion(torch.from_numpy(rots)).numpy()
+        rotations_euler = np.degrees(quat.to_euler(rotations_quat, order="zyx"))
+        
+        positions = offsets[None].repeat(len(rots), axis=0)
+        positions[:, 0] = trans
+
+        joint_names = [
+            "Pelvis", "Left_hip", "Right_hip", "Spine1", "Left_knee", "Right_knee", "Spine2", 
+            "Left_ankle", "Right_ankle", "Spine3", "Left_foot", "Right_foot", "Neck", 
+            "Left_collar", "Right_collar", "Head", "Left_shoulder", "Right_shoulder", 
+            "Left_elbow", "Right_elbow", "Left_wrist", "Right_wrist", "Left_hand", "Right_hand"
+        ]
+
+        # --- 4. Save the final BVH file ---
+        bvh.save(output_path, {
+            "rotations": rotations_euler,
+            "positions": positions,
+            "offsets": offsets,
+            "parents": parents,
+            "names": joint_names,
+            "order": "zyx",
+            "frametime": 1.0 / fps,
+        })
+        print(f"✅ BVH file saved successfully to {output_path}")
+
+    except Exception as e:
+        print(f"❌ BVH Conversion Failed. Error: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+##### ---- Network ---- #####
+clip_range = [-30,20]
+
+net = tae.Causal_HumanTAE(
+                       hidden_size=args.hidden_size,
+                       down_t=args.down_t,
+                       stride_t=args.stride_t,
+                       depth=args.depth,
+                       dilation_growth_rate=args.dilation_growth_rate,
+                       activation='relu',
+                       latent_dim=args.latent_dim,
+                       clip_range=clip_range
+                       )
+
+
+config = LLaMAHFConfig.from_name('Normal_size')
+config.block_size = 78
+trans_encoder = LLaMAHF(config, args.num_diffusion_head_layers, args.latent_dim, comp_device)
+
+print('loading checkpoint from {}'.format(args.resume_pth))
+ckpt = torch.load(args.resume_pth, map_location='cpu')
+net.load_state_dict(ckpt['net'], strict=True)
+net.eval()
+net.to(comp_device)
+
+
+if args.resume_trans is not None:
+    print('loading transformer checkpoint from {}'.format(args.resume_trans))
+    ckpt = torch.load(args.resume_trans, map_location='cpu')
+    new_ckpt_trans = {}
+    for key in ckpt['trans'].keys():
+        if key.split('.')[0]=='module':
+            new_key = '.'.join(key.split('.')[1:])
+        else:
+            new_key = key
+        new_ckpt_trans[new_key] = ckpt['trans'][key]
+    trans_encoder.load_state_dict(new_ckpt_trans, strict=True)
+trans_encoder.eval()
+trans_encoder.to(comp_device)
+
+
+reference_end_latent = np.load('reference_end_latent_t2m_272.npy')
+reference_end_latent = torch.from_numpy(reference_end_latent).to(comp_device)
+
+mean = np.load('humanml3d_272/mean_std/Mean.npy')
+std = np.load('humanml3d_272/mean_std/Std.npy')
+
+# forward inference
+threshold = 0.1
+cfg_scale = 4.0
+print(f"Generating motion with CFG scale: {cfg_scale}")
+motion_latents = trans_encoder.sample_for_eval_CFG_inference(text=args.text, tokenizer=t5_model, device=comp_device, reference_end_latent=reference_end_latent, threshold=threshold, cfg=cfg_scale)
+
+# forward decode
+motion_seqs = net.forward_decoder(motion_latents)
+from visualization.recover_visualize import recover_from_local_position
+import visualization.plot_3d_global as plot_3d
+
+motion = motion_seqs.squeeze(0)
+motion = motion.detach().cpu().numpy()
+
+if not os.path.exists('demo_output'):
+    os.makedirs('demo_output')
+
+if args.mode == 'pos':
+    # Option1: recover from joint position
+    pred_xyz = recover_from_local_position(motion * std + mean, 22)
+    xyz = pred_xyz.reshape(1, -1, 22, 3)
+    pose_vis = plot_3d.draw_to_batch(xyz, [args.text], [f'demo_output/{args.text}.mp4'], fps=30)
+    print(f"Visualized result is saved in demo_output/{args.text}.mp4")
+
+elif args.mode == 'rot':
+    # De-normalize the motion data to its original scale
+    motion = motion * std + mean
+    
+    # Define the output path for the new BVH file
+    output_bvh_path = os.path.join('demo_output', f'{args.text}.bvh')
+    
+    # Call the new function to save the BVH file directly
+    save_motion_as_bvh(motion, output_bvh_path, fps=30)
+            
+else:
+    raise ValueError(f'Invalid mode: {args.mode}')
+
diff --git a/environment.yaml b/environment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..93b497dad035a07cfd7d2dfa3febec97ee374356
--- /dev/null
+++ b/environment.yaml
@@ -0,0 +1,258 @@
+name: mgpt
+channels:
+  - pytorch
+  - conda-forge
+  - defaults
+  - https://repo.anaconda.com/pkgs/main
+  - https://repo.anaconda.com/pkgs/r
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - _openmp_mutex=4.5=1_gnu
+  - asttokens=3.0.0=pyhd8ed1ab_0
+  - backcall=0.2.0=pyh9f0ad1d_0
+  - blas=1.0=mkl
+  - bzip2=1.0.8=h7b6447c_0
+  - ca-certificates=2025.1.31=hbcca054_0
+  - certifi=2024.8.30=pyhd8ed1ab_0
+  - comm=0.2.2=pyhd8ed1ab_0
+  - cudatoolkit=10.1.243=h6bb024c_0
+  - debugpy=1.4.1=py38h709712a_0
+  - entrypoints=0.4=pyhd8ed1ab_0
+  - executing=2.1.0=pyhd8ed1ab_0
+  - ffmpeg=4.3=hf484d3e_0
+  - freetype=2.10.4=h5ab3b9f_0
+  - gmp=6.2.1=h2531618_2
+  - gnutls=3.6.15=he1e5248_0
+  - intel-openmp=2021.3.0=h06a4308_3350
+  - ipykernel=6.20.2=pyh210e3f2_0
+  - jpeg=9b=h024ee3a_2
+  - jupyter_client=7.1.2=pyhd8ed1ab_0
+  - jupyter_core=5.7.2=pyh31011fe_1
+  - lame=3.100=h7b6447c_0
+  - lcms2=2.12=h3be6417_0
+  - ld_impl_linux-64=2.35.1=h7274673_9
+  - libffi=3.3=he6710b0_2
+  - libgcc-ng=9.3.0=h5101ec6_17
+  - libgomp=9.3.0=h5101ec6_17
+  - libiconv=1.15=h63c8f33_5
+  - libidn2=2.3.2=h7f8727e_0
+  - libpng=1.6.37=hbc83047_0
+  - libsodium=1.0.18=h36c2ea0_1
+  - libstdcxx-ng=13.2.0=hc0a3c3a_7
+  - libtasn1=4.16.0=h27cfd23_0
+  - libtiff=4.2.0=h85742a9_0
+  - libunistring=0.9.10=h27cfd23_0
+  - libuv=1.40.0=h7b6447c_0
+  - libwebp-base=1.2.0=h27cfd23_0
+  - lz4-c=1.9.3=h295c915_1
+  - mkl=2021.3.0=h06a4308_520
+  - mkl-service=2.4.0=py38h7f8727e_0
+  - mkl_fft=1.3.0=py38h42c9631_2
+  - mkl_random=1.2.2=py38h51133e4_0
+  - ncurses=6.2=he6710b0_1
+  - nest-asyncio=1.6.0=pyhd8ed1ab_0
+  - nettle=3.7.3=hbbd107a_1
+  - ninja=1.10.2=hff7bd54_1
+  - olefile=0.46=py_0
+  - openh264=2.1.0=hd408876_0
+  - openjpeg=2.3.0=h05c96fa_1
+  - openssl=1.1.1k=h7f98852_0
+  - packaging=24.2=pyhd8ed1ab_2
+  - pickleshare=0.7.5=py_1003
+  - pillow=8.3.1=py38h2c7a002_0
+  - pip=21.0.1=py38h06a4308_0
+  - platformdirs=4.3.6=pyhd8ed1ab_0
+  - prompt_toolkit=3.0.48=hd8ed1ab_1
+  - ptyprocess=0.7.0=pyhd3deb0d_0
+  - pure_eval=0.2.3=pyhd8ed1ab_0
+  - pygments=2.18.0=pyhd8ed1ab_0
+  - python=3.8.11=h12debd9_0_cpython
+  - python_abi=3.8=5_cp38
+  - pyzmq=22.1.0=py38h2035c66_0
+  - readline=8.1=h27cfd23_0
+  - setuptools=52.0.0=py38h06a4308_0
+  - six=1.16.0=pyhd3eb1b0_0
+  - sqlite=3.36.0=hc218d9a_0
+  - stack_data=0.6.2=pyhd8ed1ab_0
+  - tk=8.6.10=hbc83047_0
+  - torchaudio=0.8.1=py38
+  - torchvision=0.9.1=py38_cu101
+  - tornado=6.1=py38h497a2fe_1
+  - wheel=0.37.0=pyhd3eb1b0_0
+  - xz=5.2.5=h7b6447c_0
+  - zeromq=4.3.4=h9c3ff4c_0
+  - zlib=1.2.11=h7b6447c_3
+  - zstd=1.4.9=haebb681_0
+  - pip:
+      - absl-py==0.13.0
+      - accelerate==1.0.1
+      - aiohappyeyeballs==2.4.3
+      - aiohttp==3.10.11
+      - aiosignal==1.3.1
+      - annotated-types==0.7.0
+      - antlr4-python3-runtime==4.9.3
+      - async-timeout==5.0.1
+      - attrs==24.2.0
+      - beautifulsoup4==4.12.3
+      - blis==0.7.11
+      - cachetools==4.2.2
+      - catalogue==2.0.10
+      - charset-normalizer==2.0.4
+      - chumpy==0.70
+      - click==8.1.7
+      - clip==1.0
+      - cloudpathlib==0.20.0
+      - confection==0.1.5
+      - cycler==0.10.0
+      - cymem==2.0.10
+      - decorator==5.0.9
+      - diffusers==0.31.0
+      - einops==0.8.0
+      - ffmpeg-python==0.2.0
+      - filelock==3.16.1
+      - freetype-py==2.5.1
+      - frozenlist==1.5.0
+      - fsspec==2024.2.0
+      - ftfy==6.1.1
+      - future==1.0.0
+      - fvcore==0.1.5.post20221221
+      - gdown==5.2.0
+      - glfw==2.8.0
+      - google-auth==2.36.0
+      - google-auth-oauthlib==0.4.6
+      - grpcio==1.68.0
+      - h5py==3.11.0
+      - huggingface-hub==0.26.2
+      - human-body-prior==2.2.2.0
+      - idna==3.2
+      - imageio==2.9.0
+      - imageio-ffmpeg==0.5.1
+      - importlib-metadata==8.5.0
+      - iopath==0.1.10
+      - ipdb==0.13.9
+      - ipython==7.26.0
+      - ipython-genutils==0.2.0
+      - jedi==0.18.0
+      - jinja2==3.1.3
+      - joblib==1.0.1
+      - kiwisolver==1.3.1
+      - langcodes==3.4.1
+      - language-data==1.3.0
+      - lightning-utilities==0.11.9
+      - marisa-trie==1.2.1
+      - markdown==3.3.4
+      - markdown-it-py==3.0.0
+      - markupsafe==2.1.5
+      - matplotlib==3.4.3
+      - matplotlib-inline==0.1.2
+      - mdurl==0.1.2
+      - moviepy==0.2.3.1
+      - mpmath==1.3.0
+      - multidict==6.1.0
+      - murmurhash==1.0.11
+      - natsort==8.4.0
+      - networkx==3.0
+      - numpy==1.22.4
+      - nvidia-cublas-cu11==11.11.3.6
+      - nvidia-cublas-cu12==12.1.3.1
+      - nvidia-cuda-cupti-cu11==11.8.87
+      - nvidia-cuda-cupti-cu12==12.1.105
+      - nvidia-cuda-nvrtc-cu11==11.8.89
+      - nvidia-cuda-nvrtc-cu12==12.1.105
+      - nvidia-cuda-runtime-cu11==11.8.89
+      - nvidia-cuda-runtime-cu12==12.1.105
+      - nvidia-cudnn-cu11==9.1.0.70
+      - nvidia-cudnn-cu12==9.1.0.70
+      - nvidia-cufft-cu11==10.9.0.58
+      - nvidia-cufft-cu12==11.0.2.54
+      - nvidia-curand-cu11==10.3.0.86
+      - nvidia-curand-cu12==10.3.2.106
+      - nvidia-cusolver-cu11==11.4.1.48
+      - nvidia-cusolver-cu12==11.4.5.107
+      - nvidia-cusparse-cu11==11.7.5.86
+      - nvidia-cusparse-cu12==12.1.0.106
+      - nvidia-nccl-cu11==2.20.5
+      - nvidia-nccl-cu12==2.20.5
+      - nvidia-nvjitlink-cu12==12.1.105
+      - nvidia-nvtx-cu11==11.8.86
+      - nvidia-nvtx-cu12==12.1.105
+      - oauthlib==3.1.1
+      - omegaconf==2.3.0
+      - orjson==3.10.15
+      - pandas==1.3.2
+      - parso==0.8.2
+      - pexpect==4.8.0
+      - portalocker==3.0.0
+      - preshed==3.0.9
+      - prompt-toolkit==3.0.20
+      - propcache==0.2.0
+      - protobuf==5.28.3
+      - psutil==6.1.0
+      - pyasn1==0.4.8
+      - pyasn1-modules==0.2.8
+      - pydantic==2.10.1
+      - pydantic-core==2.27.1
+      - pydeprecate==0.3.2
+      - pygame==2.6.1
+      - pyglet==2.1.2
+      - pyopengl==3.1.0
+      - pyparsing==2.4.7
+      - pyrender==0.1.45
+      - pysocks==1.7.1
+      - python-dateutil==2.8.2
+      - pytorch-lightning==1.7.0
+      - pytorch3d==0.3.0
+      - pytz==2021.1
+      - pyyaml==5.4.1
+      - regex==2024.11.6
+      - requests==2.26.0
+      - requests-oauthlib==1.3.0
+      - rich==13.9.4
+      - rsa==4.7.2
+      - safetensors==0.4.5
+      - scikit-learn==0.24.2
+      - scipy==1.7.1
+      - sentence-transformers==3.2.1
+      - sentencepiece==0.2.0
+      - shapely==2.0.7
+      - shellingham==1.5.4
+      - sklearn==0.0
+      - smart-open==7.0.5
+      - smplx==0.1.28
+      - soupsieve==2.6
+      - spacy==3.7.5
+      - spacy-legacy==3.0.12
+      - spacy-loggers==1.0.5
+      - srsly==2.4.8
+      - sympy==1.13.1
+      - tabulate==0.9.0
+      - tensorboard==2.12.0
+      - tensorboard-data-server==0.7.2
+      - tensorboard-plugin-wit==1.8.0
+      - termcolor==2.4.0
+      - thinc==8.2.5
+      - threadpoolctl==2.2.0
+      - timm==1.0.12
+      - tokenizers==0.20.3
+      - toml==0.10.2
+      - torch==2.4.1+cu118
+      - torchgeometry==0.1.2
+      - torchmetrics==0.7.0
+      - tqdm==4.62.2
+      - traitlets==5.0.5
+      - transformers==4.46.3
+      - triangle==20250106
+      - trimesh==4.6.2
+      - triton==3.0.0
+      - typer==0.13.1
+      - typing-extensions==4.12.2
+      - urllib3==1.26.6
+      - wasabi==1.1.3
+      - wcwidth==0.2.5
+      - weasel==0.4.1
+      - werkzeug==2.0.1
+      - wrapt==1.17.0
+      - yacs==0.1.8
+      - yarl==1.15.2
+      - zipp==3.20.2
+prefix: /root/miniconda3/envs/mgpt
\ No newline at end of file
diff --git a/eval_causal_TAE.py b/eval_causal_TAE.py
new file mode 100644
index 0000000000000000000000000000000000000000..e08baf784cb449d3bc9d4f27ca369ce928bd5cd0
--- /dev/null
+++ b/eval_causal_TAE.py
@@ -0,0 +1,101 @@
+import os 
+import torch
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+import json
+import models.tae as tae
+import options.option_tae as option_tae
+import utils.utils_model as utils_model
+import utils.eval_trans as eval_trans
+from humanml3d_272 import dataset_eval_tae
+import sys
+import warnings
+warnings.filterwarnings('ignore')
+
+os.chdir('Evaluator_272')
+sys.path.insert(0, os.getcwd()) 
+
+
+comp_device = torch.device('cuda')
+
+##### ---- Exp dirs ---- #####
+args = option_tae.get_args_parser()
+torch.manual_seed(args.seed)
+
+args.out_dir = os.path.join(args.out_dir, f'{args.exp_name}')
+os.makedirs(args.out_dir, exist_ok = True)
+
+##### ---- Logger ---- #####
+logger = utils_model.get_logger(args.out_dir)
+writer = SummaryWriter(args.out_dir)
+logger.info(json.dumps(vars(args), indent=4, sort_keys=True))
+
+val_loader = dataset_eval_tae.DATALoader(args.dataname, True, 32)
+
+##### ---- Network ---- #####
+clip_range = [-30,20]
+
+net = tae.Causal_HumanTAE(
+                       hidden_size=args.hidden_size,
+                       down_t=args.down_t,
+                       stride_t=args.stride_t,
+                       depth=args.depth,
+                       dilation_growth_rate=args.dilation_growth_rate,
+                       activation='relu',
+                       latent_dim=args.latent_dim,
+                       clip_range=clip_range
+                       )
+
+
+print ('loading checkpoint from {}'.format(args.resume_pth))
+ckpt = torch.load(args.resume_pth, map_location='cpu')
+net.load_state_dict(ckpt['net'], strict=True)
+net.eval()
+net.to(comp_device)
+
+
+# load evaluator:--------------------------------
+import torch
+from mld.models.architectures.temos.textencoder.distillbert_actor import DistilbertActorAgnosticEncoder
+from mld.models.architectures.temos.motionencoder.actor import ActorAgnosticEncoder
+
+modelpath = 'distilbert-base-uncased'
+
+textencoder = DistilbertActorAgnosticEncoder(modelpath, num_layers=4, latent_dim=256)
+motionencoder = ActorAgnosticEncoder(nfeats=272, vae = True, num_layers=4, latent_dim=256, max_len=300)
+
+ckpt = torch.load('epoch=99.ckpt')
+
+# load textencoder
+textencoder_ckpt = {}
+for k, v in ckpt['state_dict'].items():
+    if k.split(".")[0] == "textencoder":
+        name = k.replace("textencoder.", "")
+        textencoder_ckpt[name] = v
+textencoder.load_state_dict(textencoder_ckpt, strict=True)
+textencoder.eval()
+textencoder.to(comp_device)
+
+# load motionencoder
+motionencoder_ckpt = {}
+for k, v in ckpt['state_dict'].items():
+    if k.split(".")[0] == "motionencoder":
+        name = k.replace("motionencoder.", "")
+        motionencoder_ckpt[name] = v
+motionencoder.load_state_dict(motionencoder_ckpt, strict=True)
+motionencoder.eval()
+motionencoder.to(comp_device)
+#--------------------------------
+
+evaluator = [textencoder, motionencoder]
+
+fid = []
+mpjpe = []
+
+best_fid, best_mpjpe, writer, logger = eval_trans.evaluation_tae_single(args.out_dir, val_loader, net, logger, writer, evaluator=evaluator, device=comp_device)
+fid.append(best_fid)
+mpjpe.append(best_mpjpe)
+
+logger.info('final result:')
+logger.info(f'fid: {fid}')
+logger.info(f'mpjpe: {mpjpe} (mm)')
\ No newline at end of file
diff --git a/eval_t2m.py b/eval_t2m.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d19a7f835d423498b4e64455538f89dc9569641
--- /dev/null
+++ b/eval_t2m.py
@@ -0,0 +1,143 @@
+import os 
+import torch
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+import json
+import sys
+from models.llama_model import LLaMAHF, LLaMAHFConfig
+import options.option_transformer as option_trans
+import utils.utils_model as utils_model
+import utils.eval_trans as eval_trans
+from humanml3d_272 import dataset_eval_t2m
+import models.tae as tae
+import warnings
+warnings.filterwarnings('ignore')
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+os.chdir('Evaluator_272')
+sys.path.insert(0, os.getcwd()) 
+
+comp_device = torch.device('cuda')
+
+##### ---- Exp dirs ---- #####
+args = option_trans.get_args_parser()
+torch.manual_seed(args.seed)
+
+args.out_dir = os.path.join(args.out_dir, f'{args.exp_name}')
+os.makedirs(args.out_dir, exist_ok = True)
+
+##### ---- Logger ---- #####
+logger = utils_model.get_logger(args.out_dir)
+writer = SummaryWriter(args.out_dir)
+logger.info(json.dumps(vars(args), indent=4, sort_keys=True))
+val_loader = dataset_eval_t2m.DATALoader(args.dataname, True, 32)
+
+##### ---- Network ---- #####
+from sentence_transformers import SentenceTransformer
+t5_model = SentenceTransformer('../sentencet5-xxl/')
+t5_model.eval()
+for p in t5_model.parameters():
+    p.requires_grad = False
+tokenize_model = t5_model
+
+# Causal TAE
+
+clip_range = [-30,20]
+
+net = tae.Causal_HumanTAE(
+                       hidden_size=args.hidden_size,
+                       down_t=args.down_t,
+                       stride_t=args.stride_t,
+                       depth=args.depth,
+                       dilation_growth_rate=args.dilation_growth_rate,
+                       activation='relu',
+                       latent_dim=args.latent_dim,
+                       clip_range=clip_range
+                       )
+
+config = LLaMAHFConfig.from_name('Normal_size')
+config.block_size = 78
+trans_encoder = LLaMAHF(config, args.num_diffusion_head_layers, args.latent_dim, comp_device)
+
+print('loading checkpoint from {}'.format(args.resume_pth))
+ckpt = torch.load(args.resume_pth, map_location='cpu')
+net.load_state_dict(ckpt['net'], strict=True)
+net.eval()
+net.to(comp_device)
+
+
+if args.resume_trans is not None:
+    print('loading transformer checkpoint from {}'.format(args.resume_trans))
+    ckpt = torch.load(args.resume_trans, map_location='cpu')
+    new_ckpt_trans = {}
+    for key in ckpt['trans'].keys():
+        if key.split('.')[0]=='module':
+            new_key = '.'.join(key.split('.')[1:])
+        else:
+            new_key = key
+        new_ckpt_trans[new_key] = ckpt['trans'][key]
+    trans_encoder.load_state_dict(new_ckpt_trans, strict=True)
+trans_encoder.eval()
+trans_encoder.to(comp_device)
+
+# load evaluator:
+import torch
+from transformers import AutoTokenizer, AutoModel
+from mld.models.architectures.temos.textencoder.distillbert_actor import DistilbertActorAgnosticEncoder
+from mld.models.architectures.temos.motionencoder.actor import ActorAgnosticEncoder
+from collections import OrderedDict
+
+modelpath = 'distilbert-base-uncased'
+
+textencoder = DistilbertActorAgnosticEncoder(modelpath, num_layers=4, latent_dim=256)
+motionencoder = ActorAgnosticEncoder(nfeats=272, vae = True, num_layers=4, latent_dim=256, max_len=300)
+
+ckpt_path = 'epoch=99.ckpt'
+print(f'Loading evaluator checkpoint from {ckpt_path}')
+ckpt = torch.load(ckpt_path)
+# load textencoder
+textencoder_ckpt = {}
+for k, v in ckpt['state_dict'].items():
+    if k.split(".")[0] == "textencoder":
+        name = k.replace("textencoder.", "")
+        textencoder_ckpt[name] = v
+textencoder.load_state_dict(textencoder_ckpt, strict=True)
+textencoder.eval()
+textencoder.to(comp_device)
+
+# load motionencoder
+motionencoder_ckpt = {}
+for k, v in ckpt['state_dict'].items():
+    if k.split(".")[0] == "motionencoder":
+        name = k.replace("motionencoder.", "")
+        motionencoder_ckpt[name] = v
+motionencoder.load_state_dict(motionencoder_ckpt, strict=True)
+motionencoder.eval()
+motionencoder.to(comp_device)
+#--------------------------------
+
+evaluator = [textencoder, motionencoder]
+
+fid = []
+div = []
+top1 = []
+top2 = []
+top3 = []
+matching = []
+mpjpe = []
+
+best_fid, best_div, best_top1, best_top2, best_top3, best_matching, logger = eval_trans.evaluation_transformer_272_single(val_loader, net, trans_encoder, tokenize_model, logger, evaluator, 4.0)
+fid.append(best_fid)
+div.append(best_div)
+top1.append(best_top1)
+top2.append(best_top2)
+top3.append(best_top3)
+matching.append(best_matching)
+
+logger.info('final result:')
+logger.info(f'fid: {fid}')
+logger.info(f'div: {div}')
+logger.info(f'top1: {top1}')
+logger.info(f'top2: {top2}')
+logger.info(f'top3: {top3}')
+logger.info(f'MM-dist (matching score) : {matching}')
diff --git a/get_latent.py b/get_latent.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b5a5ffb41ba6c1cd6ba0196862898ea14270e94
--- /dev/null
+++ b/get_latent.py
@@ -0,0 +1,67 @@
+import os 
+import torch
+import numpy as np
+
+from torch.utils.tensorboard import SummaryWriter
+from os.path import join as pjoin
+import json
+import models.tae as tae
+import options.option_tae as option_tae
+import utils.utils_model as utils_model
+from humanml3d_272 import dataset_tae_tokenizer
+import warnings
+from tqdm import tqdm
+warnings.filterwarnings('ignore')
+
+##### ---- Exp dirs ---- #####
+args = option_tae.get_args_parser()
+torch.manual_seed(args.seed)
+
+args.out_dir = os.path.join(args.out_dir, f'{args.exp_name}')
+os.makedirs(args.out_dir, exist_ok = True)
+
+##### ---- Logger ---- #####
+logger = utils_model.get_logger(args.out_dir)
+writer = SummaryWriter(args.out_dir)
+logger.info(json.dumps(vars(args), indent=4, sort_keys=True))
+
+##### ---- Dataloader ---- #####
+train_loader = dataset_tae_tokenizer.DATALoader(args.dataname)
+
+clip_range = [-30,20]
+
+net = tae.Causal_HumanTAE(
+                       hidden_size=args.hidden_size,
+                       down_t=args.down_t,
+                       stride_t=args.stride_t,
+                       depth=args.depth,
+                       dilation_growth_rate=args.dilation_growth_rate,
+                       activation='relu',
+                       latent_dim=args.latent_dim,
+                       clip_range=clip_range
+                       )
+
+logger.info('loading checkpoint from {}'.format(args.resume_pth))
+ckpt = torch.load(args.resume_pth, map_location='cpu')
+net.load_state_dict(ckpt['net'], strict=True)
+net.eval()
+net.cuda()
+
+
+##### ---- get reference end latent ---- #####
+reference_end_pose = torch.zeros(1, 4, 272).cuda()   # impossible pose prior
+reference_end_latent, _, _ = net.encode(reference_end_pose)
+reference_end_latent = reference_end_latent.permute(1,0)
+np.save(f'reference_end_latent_{args.dataname}.npy', reference_end_latent.cpu().detach().numpy())
+
+os.makedirs(args.latent_dir, exist_ok = True)
+
+for batch in tqdm(train_loader):
+    pose, name = batch
+    bs, seq = pose.shape[0], pose.shape[1]
+    pose = pose.cuda().float()
+    latent, _, _  = net.encode(pose)
+    latent = latent.permute(1,0)
+    latent = torch.cat([latent, reference_end_latent], dim=0)
+    latent = latent.cpu().detach().numpy()
+    np.save(pjoin(args.latent_dir, name[0] +'.npy'), latent)
diff --git a/humanml3d_272/__pycache__/dataset_TM_train_motionstreamer.cpython-39.pyc b/humanml3d_272/__pycache__/dataset_TM_train_motionstreamer.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a63b3a25fa8d7a6d298f8e0814b3706633917005
Binary files /dev/null and b/humanml3d_272/__pycache__/dataset_TM_train_motionstreamer.cpython-39.pyc differ
diff --git a/humanml3d_272/__pycache__/dataset_tae_tokenizer.cpython-39.pyc b/humanml3d_272/__pycache__/dataset_tae_tokenizer.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..13a71f6ed92d12bf221274608914b0cc3fcf7ef8
Binary files /dev/null and b/humanml3d_272/__pycache__/dataset_tae_tokenizer.cpython-39.pyc differ
diff --git a/humanml3d_272/dataset_TM_train.py b/humanml3d_272/dataset_TM_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c88f6b6ae2e92686c4e4239dcce6f54e41c5ff9
--- /dev/null
+++ b/humanml3d_272/dataset_TM_train.py
@@ -0,0 +1,141 @@
+import torch
+from torch.utils import data
+import numpy as np
+from os.path import join as pjoin
+import random
+import codecs as cs
+from tqdm import tqdm
+import utils.paramUtil as paramUtil
+from torch.utils.data._utils.collate import default_collate
+import os
+
+def collate_fn(batch):
+    batch.sort(key=lambda x: x[2], reverse=True)
+    return default_collate(batch)
+
+
+'''For use of training text-2-motion generative model'''
+class Text2MotionDataset(data.Dataset):
+    def __init__(self, dataset_name, unit_length = 4, latent_dir=None):
+        
+        self.max_length = 64
+        self.pointer = 0
+        self.dataset_name = dataset_name
+        self.unit_length = unit_length
+
+        if dataset_name == 't2m_272':
+            self.data_root = './humanml3d_272'
+            self.text_dir = pjoin(self.data_root, 'texts')
+            self.joints_num = 22
+            fps = 30
+            self.max_motion_length = 78   
+            dim_pose = 272
+            split_file = pjoin(self.data_root, 'split', 'train.txt')
+
+        else:
+            raise ValueError(f"Dataset {dataset_name} not supported")
+     
+        id_list = []
+        with cs.open(split_file, 'r') as f:
+            for line in f.readlines():
+                id_list.append(line.strip())
+
+        new_name_list = []
+        data_dict = {}
+        for name in tqdm(id_list):
+            try:
+                m_token_list = np.load(pjoin(latent_dir, '%s.npy'%name))
+            except:
+                continue
+
+            # Read text
+            with cs.open(pjoin(self.text_dir, name + '.txt')) as f:
+                text_data = []
+                flag = False
+                lines = f.readlines()
+
+                for line in lines:
+                    text_dict = {}
+                    line_split = line.strip().split('#')
+                    caption = line_split[0]
+                    t_tokens = line_split[1].split(' ')
+                    f_tag = float(line_split[2])
+                    to_tag = float(line_split[3])
+
+                    f_tag = 0.0 if np.isnan(f_tag) else f_tag
+                    to_tag = 0.0 if np.isnan(to_tag) else to_tag
+
+                    text_dict['caption'] = caption
+                    text_dict['tokens'] = t_tokens
+
+                    if f_tag == 0.0 and to_tag == 0.0:
+                        flag = True
+                        text_data.append(text_dict)
+                    else:
+                        if int(f_tag*fps/unit_length) < int(to_tag*fps/unit_length):
+                            m_token_list_new = [m_token_list[int(f_tag*fps/unit_length) : int(to_tag*fps/unit_length)]] 
+
+                        if len(m_token_list_new) == 0:
+                            continue
+
+                        new_name = '%s_%f_%f'%(name, f_tag, to_tag)
+
+                        data_dict[new_name] = {'m_token_list': m_token_list_new,
+                                                    'text':[text_dict]}
+                        new_name_list.append(new_name)
+                    
+            if flag:
+                data_dict[name] = {'m_token_list': m_token_list,
+                                    'text':text_data}
+                new_name_list.append(name)
+
+        self.data_dict = data_dict
+        self.name_list = new_name_list
+
+    def __len__(self):
+        return len(self.data_dict)
+
+    def __getitem__(self, item):
+        data = self.data_dict[self.name_list[item]]
+        m_token_list, text_list = data['m_token_list'], data['text']
+        m_tokens = np.array(m_token_list)
+
+        text_data = random.choice(text_list)
+        caption= text_data['caption']
+
+        if len(m_tokens.shape) == 3:
+            m_tokens = m_tokens.squeeze(0)
+        coin = np.random.choice([False, False, True])
+        if coin:
+            coin2 = np.random.choice([True, False])
+            if coin2:
+                m_tokens = m_tokens[:-1]
+            else:
+                m_tokens = m_tokens[1:]
+        m_tokens_len = m_tokens.shape[0]
+        
+        if m_tokens_len < self.max_motion_length:
+            m_tokens = np.concatenate([m_tokens, np.zeros((self.max_motion_length-m_tokens_len, m_tokens.shape[1]), dtype=int)], axis=0)
+        return caption, m_tokens, m_tokens_len
+
+
+
+
+def DATALoader(dataset_name,
+                batch_size, latent_dir, unit_length=4,
+                num_workers = 8) : 
+
+    train_loader = torch.utils.data.DataLoader(Text2MotionDataset(dataset_name, latent_dir = latent_dir, unit_length=unit_length),
+                                              batch_size,
+                                              shuffle=True,
+                                              num_workers=num_workers,
+                                              drop_last = True)
+    
+    return train_loader
+
+
+def cycle(iterable):
+    while True:
+        for x in iterable:
+            yield x
+
diff --git a/humanml3d_272/dataset_TM_train_motionstreamer.py b/humanml3d_272/dataset_TM_train_motionstreamer.py
new file mode 100644
index 0000000000000000000000000000000000000000..63bf22ef621e41a5e441f1ba213012ed7c37f6a9
--- /dev/null
+++ b/humanml3d_272/dataset_TM_train_motionstreamer.py
@@ -0,0 +1,175 @@
+import torch
+from torch.utils import data
+import numpy as np
+from os.path import join as pjoin
+import random
+import codecs as cs
+from tqdm import tqdm
+from torch.utils.data._utils.collate import default_collate
+import os
+
+def collate_fn(batch):
+    batch.sort(key=lambda x: x[3], reverse=True)
+    return default_collate(batch)
+
+
+class Text2MotionDataset(data.Dataset):
+    def __init__(self, dataset_name, unit_length = 4, latent_dir=None):
+        
+        self.max_length = 64
+        self.pointer = 0
+        self.dataset_name = dataset_name
+        self.unit_length = unit_length
+        
+        if dataset_name == 't2m_babel_272':
+            # Babel-272-stream data dir
+            self.babel_stream_data_root = './babel_272_stream' 
+            self.babel_stream_text_dir = pjoin(self.babel_stream_data_root, 'train_stream_text')
+            fps = 30
+            self.max_motion_length = 78
+
+            # HumanML3D-272 data dir
+            self.hml_data_root = './humanml3d_272'
+            self.hml_text_dir = pjoin(self.hml_data_root, 'texts')
+    
+        else:
+            raise ValueError(f'Invalid dataset name: {dataset_name}')
+        
+        id_list = []
+        
+        for file in os.listdir(latent_dir):
+            if file.endswith('.npy'):
+                id_list.append(file[:-4])   
+                
+        new_name_list = []
+        data_dict = {}
+        for name in tqdm(id_list):
+            m_token_list = np.load(pjoin(latent_dir, '%s.npy'%name))
+
+            if len(m_token_list) > self.max_motion_length:
+                continue
+
+            # Read text
+            if name.split('_')[0] == 'seq':
+                # Babel-272-stream
+                with cs.open(pjoin(self.babel_stream_text_dir, name + '.txt')) as f:
+                    text_data = []
+                    flag = False
+                    lines = f.readlines()
+
+                    for line in lines:
+                        text_dict = {}
+                        B_split = line.strip().split('*')[1].split('#')
+                        B_text = line.strip().split('*')[1].split('#')[0]
+                        if B_text == '':
+                            continue
+                        B_t_tokens = B_split[1].split(' ')
+                        A_motion_length = B_split[-1]
+                        A_token_length = int(A_motion_length) // unit_length
+                        text_dict['caption'] = B_text   
+                        text_dict['tokens'] = B_t_tokens
+                        
+                        flag = True                                
+                        text_data.append(text_dict)
+
+            else:
+                # HumanML3D-272
+                with cs.open(pjoin(self.hml_text_dir, name + '.txt')) as f:
+                    text_data = []
+                    flag = False
+                    lines = f.readlines()
+
+                    for line in lines:
+                        text_dict = {}
+
+                        line_split = line.strip().split('#')
+                        caption = line_split[0]
+                        
+                        t_tokens = line_split[1].split(' ')
+                        f_tag = float(line_split[2])
+                        to_tag = float(line_split[3])
+
+                        A_token_length = 0
+
+                        f_tag = 0.0 if np.isnan(f_tag) else f_tag
+                        to_tag = 0.0 if np.isnan(to_tag) else to_tag
+                        text_dict['caption'] = caption
+                        text_dict['tokens'] = t_tokens
+                        
+                        if f_tag == 0.0 and to_tag == 0.0:
+                            flag = True                                
+                            text_data.append(text_dict)
+                        else:
+                            if int(f_tag*fps/unit_length) < int(to_tag*fps/unit_length):
+                                m_token_list_new = [m_token_list[int(f_tag*fps/unit_length) : int(to_tag*fps/unit_length)]] 
+
+                            if len(m_token_list_new) == 0:
+                                continue
+                            new_name = '%s_%f_%f'%(name, f_tag, to_tag)
+
+                            data_dict[new_name] = {'m_token_list': m_token_list_new,
+                                                        'text':[text_dict],
+                                                        'A_token_length': A_token_length
+                                                        }
+                            new_name_list.append(new_name)
+
+            if flag:
+                
+                data_dict[name] = {'m_token_list': m_token_list,
+                                    'text':text_data,
+                                    'A_token_length': A_token_length
+                                    }
+                new_name_list.append(name)
+
+        self.data_dict = data_dict
+        self.name_list = new_name_list
+
+    def __len__(self):
+        return len(self.data_dict)
+
+    def __getitem__(self, item):
+        data = self.data_dict[self.name_list[item]]
+        m_token_list, text_list = data['m_token_list'], data['text']
+        m_tokens = np.array(m_token_list)
+
+
+        text_data = random.choice(text_list)
+        caption= text_data['caption']
+
+        if len(m_tokens.shape) == 3:
+            m_tokens = m_tokens.squeeze(0)
+
+
+        A_token_length = data['A_token_length']
+        m_tokens_len = m_tokens.shape[0]
+
+        
+        if m_tokens_len < self.max_motion_length:
+            m_tokens = np.concatenate([m_tokens, np.zeros((self.max_motion_length - m_tokens_len, m_tokens.shape[1]), dtype=int)], axis=0)
+
+
+        return caption, m_tokens, m_tokens_len, A_token_length
+
+
+
+
+def DATALoader(dataset_name,
+                batch_size, unit_length=4,
+                num_workers = 8, latent_dir = None) : 
+
+    train_loader = torch.utils.data.DataLoader(Text2MotionDataset(dataset_name, unit_length=unit_length, latent_dir=latent_dir),
+                                              batch_size,
+                                              shuffle=True,
+                                              num_workers=num_workers,
+                                              #collate_fn=collate_fn,
+                                              drop_last = True)
+    
+
+    return train_loader
+
+
+def cycle(iterable):
+    while True:
+        for x in iterable:
+            yield x
+
diff --git a/humanml3d_272/dataset_eval_t2m.py b/humanml3d_272/dataset_eval_t2m.py
new file mode 100644
index 0000000000000000000000000000000000000000..289e2120da74b999f3bf3a39296181dd8f3a8dd1
--- /dev/null
+++ b/humanml3d_272/dataset_eval_t2m.py
@@ -0,0 +1,179 @@
+import torch
+from torch.utils import data
+import numpy as np
+from os.path import join as pjoin
+import random
+import codecs as cs
+from tqdm import tqdm
+import os
+import utils.paramUtil as paramUtil
+from torch.utils.data._utils.collate import default_collate
+
+
+def collate_fn(batch):
+    batch.sort(key=lambda x: x[2], reverse=True)
+    return default_collate(batch)
+
+
+class Text2MotionDataset(data.Dataset):
+    def __init__(self, dataset_name, is_test, max_text_len = 20, unit_length = 4):
+        
+        self.max_length = 20
+        self.pointer = 0
+        self.dataset_name = dataset_name
+        self.is_test = is_test
+        self.max_text_len = max_text_len
+        self.unit_length = unit_length
+        
+
+        if dataset_name == 't2m_272':
+            self.data_root = './humanml3d_272'
+            self.motion_dir = pjoin(self.data_root, 'motion_data')
+            self.text_dir = pjoin(self.data_root, 'texts')
+            self.joints_num = 22
+            self.max_motion_length = 300
+            fps = 30
+            self.meta_dir = './humanml3d_272/mean_std'
+            if is_test:
+                split_file = pjoin(self.data_root, 'split', 'test.txt')
+            else:
+                split_file = pjoin(self.data_root, 'split', 'val.txt')
+
+        mean = np.load(pjoin(self.meta_dir, 'Mean.npy')) 
+        std = np.load(pjoin(self.meta_dir, 'Std.npy'))
+
+        min_motion_len = 60  # 30 fps
+
+        data_dict = {}
+        id_list = []
+
+        
+        with cs.open(split_file, 'r') as f:
+            for line in f.readlines():
+                id_list.append(line.strip())
+
+        new_name_list = []
+        length_list = []
+
+
+        for name in tqdm(id_list):
+            motion = np.load(pjoin(self.motion_dir, name + '.npy'))
+            if (len(motion)) < min_motion_len or (len(motion) >= self.max_motion_length):
+                continue
+
+            text_data = []
+            flag = False
+            with cs.open(pjoin(self.text_dir, name + '.txt')) as f:
+                for line in f.readlines():
+                    text_dict = {}
+                    line_split = line.strip().split('#')
+                    caption = line_split[0]
+                    tokens = line_split[1].split(' ')
+                    f_tag = float(line_split[2])
+                    to_tag = float(line_split[3])
+                        
+                    f_tag = 0.0 if np.isnan(f_tag) else f_tag
+                    to_tag = 0.0 if np.isnan(to_tag) else to_tag
+
+                    text_dict['caption'] = caption
+                    text_dict['tokens'] = tokens
+
+                    if f_tag == 0.0 and to_tag == 0.0:
+                        flag = True
+                        text_data.append(text_dict)
+                    else:
+                        n_motion = motion[int(f_tag*fps) : int(to_tag*fps)]           
+                        if (len(n_motion)) < min_motion_len or (len(n_motion) >= self.max_motion_length):
+                            continue
+                        new_name = random.choice('ABCDEFGHIJKLMNOPQRSTUVW') + '_' + name
+                        while new_name in data_dict:
+                            new_name = random.choice('ABCDEFGHIJKLMNOPQRSTUVW') + '_' + name
+                        data_dict[new_name] = {'motion': n_motion,
+                                                    'length': len(n_motion),
+                                                    'text':[text_dict]}
+                        new_name_list.append(new_name)
+                        length_list.append(len(n_motion))
+                   
+
+            if flag:
+                data_dict[name] = {'motion': motion,
+                                    'length': len(motion),
+                                    'text': text_data}
+                new_name_list.append(name)
+                length_list.append(len(motion))
+
+
+        name_list, length_list = zip(*sorted(zip(new_name_list, length_list), key=lambda x: x[1]))
+        self.mean = mean
+        self.std = std
+        self.length_arr = np.array(length_list)
+        self.data_dict = data_dict
+        self.name_list = name_list
+        self.reset_max_len(self.max_length)
+
+    def reset_max_len(self, length):
+        assert length <= self.max_motion_length
+        self.pointer = np.searchsorted(self.length_arr, length)
+        print("Pointer Pointing at %d"%self.pointer)
+        self.max_length = length
+
+    def inv_transform(self, data):
+        return data * self.std + self.mean
+
+    def forward_transform(self, data):
+        return (data - self.mean) / self.std
+
+    def __len__(self):
+        return len(self.data_dict) - self.pointer
+
+    def __getitem__(self, item):
+        idx = self.pointer + item
+        name = self.name_list[idx]
+        data = self.data_dict[name]
+        motion, m_length, text_list = data['motion'], data['length'], data['text']
+        text_data = random.choice(text_list)
+        caption = text_data['caption']
+
+        if self.unit_length < 10:
+            coin2 = np.random.choice(['single', 'single', 'double'])
+        else:
+            coin2 = 'single'
+
+        if coin2 == 'double':
+            m_length = (m_length // self.unit_length - 1) * self.unit_length
+        elif coin2 == 'single':
+            m_length = (m_length // self.unit_length) * self.unit_length
+
+        idx = random.randint(0, len(motion) - m_length)
+        motion = motion[idx:idx+m_length]
+
+        #"Motion Normalization"
+        motion = (motion - self.mean) / self.std
+
+        if m_length < self.max_motion_length:
+            motion = np.concatenate([motion,
+                                     np.zeros((self.max_motion_length - m_length, motion.shape[1]))
+                                     ], axis=0)
+
+        return caption, motion, m_length
+
+
+
+
+def DATALoader(dataset_name, is_test,
+                batch_size,
+                num_workers = 64, unit_length = 4, drop_last=True) : 
+    
+    val_loader = torch.utils.data.DataLoader(Text2MotionDataset(dataset_name, is_test, unit_length=unit_length),
+                                              batch_size,
+                                              shuffle = True,
+                                              num_workers=num_workers,
+                                              collate_fn=collate_fn,
+                                              drop_last = drop_last)
+    return val_loader
+
+
+def cycle(iterable):
+    while True:
+        for x in iterable:
+            yield x
\ No newline at end of file
diff --git a/humanml3d_272/dataset_eval_tae.py b/humanml3d_272/dataset_eval_tae.py
new file mode 100644
index 0000000000000000000000000000000000000000..966682650c0b81ed54544e6c70bbcea4e0cff36f
--- /dev/null
+++ b/humanml3d_272/dataset_eval_tae.py
@@ -0,0 +1,179 @@
+import torch
+from torch.utils import data
+import numpy as np
+from os.path import join as pjoin
+import random
+import codecs as cs
+from tqdm import tqdm
+import os
+import utils.paramUtil as paramUtil
+from torch.utils.data._utils.collate import default_collate
+
+
+def collate_fn(batch):
+    batch.sort(key=lambda x: x[1], reverse=True)
+    return default_collate(batch)
+
+
+class Text2MotionDataset(data.Dataset):
+    def __init__(self, dataset_name, is_test, max_text_len = 20, unit_length = 4):
+        
+        self.max_length = 20
+        self.pointer = 0
+        self.dataset_name = dataset_name
+        self.is_test = is_test
+        self.max_text_len = max_text_len
+        self.unit_length = unit_length
+        
+
+        if dataset_name == 't2m_272':
+            self.data_root = './humanml3d_272'
+            self.motion_dir = pjoin(self.data_root, 'motion_data')
+            self.text_dir = pjoin(self.data_root, 'texts')
+            self.joints_num = 22
+            self.max_motion_length = 300
+            fps = 30
+            self.meta_dir = './humanml3d_272/mean_std'
+            if is_test:
+                split_file = pjoin(self.data_root, 'split', 'test.txt')
+            else:
+                split_file = pjoin(self.data_root, 'split', 'val.txt')
+        elif dataset_name == 't2m_babel_272':
+            # HumanML3D-272 data
+            self.hml_data_root = './humanml3d_272'
+            self.hml_motion_dir = pjoin(self.hml_data_root, 'motion_data')
+            if is_test:
+                hml_split_file = pjoin(self.hml_data_root, 'split', 'test.txt')
+            else:
+                hml_split_file = pjoin(self.hml_data_root, 'split', 'val.txt')
+
+            self.joints_num = 22
+            self.max_motion_length = 300
+            fps = 30
+
+            # Babel-272 data
+            self.babel_data_root = './babel_272'
+            self.babel_motion_dir = pjoin(self.babel_data_root, 'motion_data')
+            babel_split_file = pjoin(self.babel_data_root, 'split', 'val.txt')
+            self.meta_dir = pjoin(self.babel_data_root, 't2m_babel_mean_std')
+
+        mean = np.load(pjoin(self.meta_dir, 'Mean.npy')) 
+        std = np.load(pjoin(self.meta_dir, 'Std.npy'))
+
+        min_motion_len = 60  # 30 fps
+
+        data_dict = {}
+        id_list = []
+
+        if dataset_name == 't2m_272':
+            with cs.open(split_file, 'r') as f:
+                for line in f.readlines():
+                    id_list.append(line.strip())
+
+        elif dataset_name == 't2m_babel_272':
+            with cs.open(hml_split_file, 'r') as f:
+                for line in f.readlines():
+                    id_list.append(line.strip())
+            with cs.open(babel_split_file, 'r') as f:
+                for line in f.readlines():
+                    id_list.append('b_' + line.strip())
+
+        new_name_list = []
+        length_list = []
+
+
+        for name in tqdm(id_list):
+            try:
+                if dataset_name == 't2m_272':
+                    motion = np.load(pjoin(self.motion_dir, name + '.npy'))
+                elif dataset_name == 't2m_babel_272':
+                    if name.split('_')[0] == 'b':
+                        motion = np.load(pjoin(self.babel_motion_dir, name.split('_')[1] + '.npy'))
+                    else:
+                        motion = np.load(pjoin(self.hml_motion_dir, name + '.npy'))
+
+                if (len(motion)) < min_motion_len or (len(motion) >= self.max_motion_length):
+                    continue
+
+                data_dict[name] = {'motion': motion,
+                                    'length': len(motion)
+                                        }
+                new_name_list.append(name)
+                length_list.append(len(motion))
+            except:
+                pass
+
+
+        name_list, length_list = zip(*sorted(zip(new_name_list, length_list), key=lambda x: x[1]))
+        self.mean = mean
+        self.std = std
+        self.length_arr = np.array(length_list)
+        self.data_dict = data_dict
+        self.name_list = name_list
+        self.reset_max_len(self.max_length)
+
+    def reset_max_len(self, length):
+        assert length <= self.max_motion_length
+        self.pointer = np.searchsorted(self.length_arr, length)
+        print("Pointer Pointing at %d"%self.pointer)
+        self.max_length = length
+
+    def inv_transform(self, data):
+        return data * self.std + self.mean
+
+    def forward_transform(self, data):
+        return (data - self.mean) / self.std
+
+    def __len__(self):
+        return len(self.data_dict) - self.pointer
+
+    def __getitem__(self, item):
+        idx = self.pointer + item
+        name = self.name_list[idx]
+        data = self.data_dict[name]
+        motion, m_length = data['motion'], data['length']
+
+
+        if self.unit_length < 10:
+            coin2 = np.random.choice(['single', 'single', 'double'])
+        else:
+            coin2 = 'single'
+
+        if coin2 == 'double':
+            m_length = (m_length // self.unit_length - 1) * self.unit_length
+        elif coin2 == 'single':
+            m_length = (m_length // self.unit_length) * self.unit_length
+
+        idx = random.randint(0, len(motion) - m_length)
+        motion = motion[idx:idx+m_length]
+
+        #"Motion Normalization"
+        motion = (motion - self.mean) / self.std
+
+        if m_length < self.max_motion_length:
+            motion = np.concatenate([motion,
+                                     np.zeros((self.max_motion_length - m_length, motion.shape[1]))
+                                     ], axis=0)
+
+        return motion, m_length
+
+
+
+
+def DATALoader(dataset_name, is_test,
+                batch_size,
+                num_workers = 64, unit_length = 4, drop_last=True) : 
+    
+    val_loader = torch.utils.data.DataLoader(Text2MotionDataset(dataset_name, is_test, unit_length=unit_length),
+                                              batch_size,
+                                              shuffle = True,
+                                              num_workers=num_workers,
+                                              collate_fn=collate_fn,
+                                              drop_last = drop_last)
+    return val_loader
+
+
+def cycle(iterable):
+    while True:
+        for x in iterable:
+            yield x
\ No newline at end of file
diff --git a/humanml3d_272/dataset_tae.py b/humanml3d_272/dataset_tae.py
new file mode 100644
index 0000000000000000000000000000000000000000..eaa5222196121089f2196f1fd063ca26f4bd131e
--- /dev/null
+++ b/humanml3d_272/dataset_tae.py
@@ -0,0 +1,132 @@
+import torch
+from torch.utils import data
+import numpy as np
+from os.path import join as pjoin
+import random
+import codecs as cs
+from tqdm import tqdm
+
+
+
+class MotionDataset(data.Dataset):
+    def __init__(self, dataset_name, window_size = 64, unit_length = 4):
+        self.window_size = window_size
+        self.unit_length = unit_length
+        self.dataset_name = dataset_name
+
+        if dataset_name == 't2m_272':
+            self.data_root = './humanml3d_272'
+            self.motion_dir = pjoin(self.data_root, 'motion_data')
+            self.text_dir = pjoin(self.data_root, 'texts')
+            self.joints_num = 22
+            self.max_motion_length = 300
+            self.meta_dir = pjoin(self.data_root, 'mean_std')
+            split_file = pjoin(self.data_root, 'split', 'train.txt')
+            
+        elif dataset_name == 't2m_babel_272':
+            self.hml_data_root = './humanml3d_272'
+            self.hml_motion_dir = pjoin(self.hml_data_root, 'motion_data')
+            hml_split_file = pjoin(self.hml_data_root, 'split', 'train.txt')
+            self.joints_num = 22
+            self.max_motion_length = 300
+
+            self.babel_data_root = './babel_272'
+            self.babel_motion_dir = pjoin(self.babel_data_root, 'motion_data')
+            babel_split_file = pjoin(self.babel_data_root, 'split', 'train.txt')
+            self.meta_dir = pjoin(self.babel_data_root, 't2m_babel_mean_std')
+        else:
+            raise ValueError(f'Dataset {dataset_name} not found')
+
+        mean = np.load(pjoin(self.meta_dir, 'Mean.npy'))
+        std = np.load(pjoin(self.meta_dir, 'Std.npy'))
+        
+        self.data = []
+        self.lengths = []
+        id_list = []
+
+        if dataset_name == 't2m_272':
+            with cs.open(split_file, 'r') as f:
+                for line in f.readlines():
+                    id_list.append(line.strip())
+                
+        elif dataset_name == 't2m_babel_272':
+            with cs.open(hml_split_file, 'r') as f:
+                for line in f.readlines():
+                    id_list.append(line.strip())
+            with cs.open(babel_split_file, 'r') as f:
+                for line in f.readlines():
+                    id_list.append('b_' + line.strip())
+
+        for name in tqdm(id_list):
+            try:
+                if dataset_name == 't2m_272':
+                    motion = np.load(pjoin(self.motion_dir, name + '.npy'))
+                elif dataset_name == 't2m_babel_272':
+                    if name.split('_')[0] == 'b':
+                        # Babel-272
+                        motion = np.load(pjoin(self.babel_motion_dir, name.split('_')[1] + '.npy'))
+                    else:
+                        # HumanML3D-272
+                        motion = np.load(pjoin(self.hml_motion_dir, name + '.npy'))
+                else:
+                    raise ValueError(f'Dataset {dataset_name} not found')
+
+                if motion.shape[0] < self.window_size:
+                    continue
+                self.lengths.append(motion.shape[0] - self.window_size)
+                self.data.append(motion)
+            except:
+                pass
+        
+        print(f'Training on {len(self.data)} motion sequences...')
+            
+        self.mean = mean
+        self.std = std
+        
+
+    def inv_transform(self, data):
+        return data * self.std + self.mean
+    
+    def compute_sampling_prob(self) : 
+        
+        prob = np.array(self.lengths, dtype=np.float32)
+        prob /= np.sum(prob)
+        return prob
+    
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, item):
+        motion = self.data[item]
+        
+        idx = random.randint(0, len(motion) - self.window_size)
+
+        motion = motion[idx:idx+self.window_size]
+        # Motion Normalization
+        motion = (motion - self.mean) / self.std
+
+        return motion
+
+def DATALoader(dataset_name,
+               batch_size,
+               num_workers = 64,
+               window_size = 64,
+               unit_length = 4):
+    
+    trainSet = MotionDataset(dataset_name, window_size=window_size, unit_length=unit_length)
+    prob = trainSet.compute_sampling_prob()
+    sampler = torch.utils.data.WeightedRandomSampler(prob, num_samples = len(trainSet) * 1000, replacement=True)
+    train_loader = torch.utils.data.DataLoader(trainSet,
+                                              batch_size,
+                                              shuffle=True,
+                                              #sampler=sampler,
+                                              num_workers=num_workers,
+                                              #collate_fn=collate_fn,
+                                              drop_last = True)
+    
+    return train_loader
+
+def cycle(iterable):
+    while True:
+        for x in iterable:
+            yield x
\ No newline at end of file
diff --git a/humanml3d_272/dataset_tae_tokenizer.py b/humanml3d_272/dataset_tae_tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..628760ed6583c64b9a734c918b5440911c7f87f8
--- /dev/null
+++ b/humanml3d_272/dataset_tae_tokenizer.py
@@ -0,0 +1,132 @@
+import torch
+from torch.utils import data
+import numpy as np
+from os.path import join as pjoin
+import random
+import codecs as cs
+from tqdm import tqdm
+import os
+
+
+class MotionDataset(data.Dataset):
+    def __init__(self, dataset_name, feat_bias = 5, window_size = 64, unit_length = 4):
+        self.window_size = window_size
+        self.unit_length = unit_length
+        self.feat_bias = feat_bias
+
+        self.dataset_name = dataset_name
+        min_motion_len = 40
+        
+        
+        if dataset_name == 't2m_272':
+            self.data_root = './humanml3d_272'
+            self.motion_dir = pjoin(self.data_root, 'motion_data')
+            self.meta_dir = pjoin(self.data_root, 'mean_std')
+            split_file = pjoin(self.data_root, 'split', 'train.txt')
+
+        elif dataset_name == 't2m_babel_272':
+            # HumanML3D-272 data dir
+            self.hml_data_root = './humanml3d_272'
+            self.hml_motion_dir = pjoin(self.hml_data_root, 'motion_data')
+            hml_split_file = pjoin(self.hml_data_root, 'split', 'train.txt')
+
+            # Babel-272-stream data dir
+            self.babel_stream_data_root = './babel_272_stream'
+            self.babel_stream_motion_dir = pjoin(self.babel_stream_data_root, 'train_stream')
+            self.meta_dir = './babel_272/t2m_babel_mean_std'
+
+        else:
+            raise ValueError(f"Invalid dataset name: {dataset_name}")
+            
+    
+        mean = np.load(pjoin(self.meta_dir, 'Mean.npy'))
+        std = np.load(pjoin(self.meta_dir, 'Std.npy'))
+        
+        data_dict = {}
+        id_list = []
+
+        if dataset_name == 't2m_272':
+            with cs.open(split_file, 'r') as f:
+                for line in f.readlines():
+                    id_list.append(line.strip())
+        elif dataset_name == 't2m_babel_272':
+            # HumanML3D-272 data
+            with cs.open(hml_split_file, 'r') as f:
+                for line in f.readlines():
+                    id_list.append(line.strip())
+
+            # Babel-272-stream data
+            for file in os.listdir(self.babel_stream_motion_dir):
+                if file.endswith('.npy'):
+                    id_list.append(file[:-4])   # seq_1, seq_2, ...
+
+        new_name_list = []
+        length_list = []
+        for name in tqdm(id_list):
+            try:
+                if dataset_name == 't2m_272':
+                    motion = np.load(pjoin(self.motion_dir, name + '.npy'))
+                    if (len(motion)) < min_motion_len:
+                        continue
+                elif dataset_name == 't2m_babel_272':
+                    if name.split('_')[0] == 'seq':
+                        # seq_1, seq_2, ... (Babel-272-stream)
+                        motion = np.load(pjoin(self.babel_stream_motion_dir, name + '.npy'))
+                    else:
+                        # (HumanML3D-272)
+                        motion = np.load(pjoin(self.hml_motion_dir, name + '.npy'))
+                        if (len(motion)) < min_motion_len:
+                            continue
+
+                data_dict[name] = {'motion': motion,
+                                   'length': len(motion),
+                                   'name': name}
+                new_name_list.append(name)
+                length_list.append(len(motion))
+            except:
+                pass
+
+
+        self.mean = mean
+        self.std = std
+        self.length_arr = np.array(length_list)
+        self.data_dict = data_dict
+        self.name_list = new_name_list
+
+    def inv_transform(self, data):
+        return data * self.std + self.mean
+
+    def __len__(self):
+        return len(self.data_dict)
+
+    def __getitem__(self, item):
+        name = self.name_list[item]
+        data = self.data_dict[name]
+        motion, m_length = data['motion'], data['length']
+
+        m_length = (m_length // self.unit_length) * self.unit_length
+
+        idx = random.randint(0, len(motion) - m_length)
+        motion = motion[idx:idx+m_length]
+
+        # "Z Normalization"
+        motion = (motion - self.mean) / self.std
+
+        return motion, name
+
+def DATALoader(dataset_name,
+                batch_size = 1,
+                num_workers = 8, unit_length = 4) : 
+    
+    train_loader = torch.utils.data.DataLoader(MotionDataset(dataset_name, unit_length=unit_length),
+                                              batch_size,
+                                              shuffle=True,
+                                              num_workers=num_workers,
+                                              drop_last = True)
+    
+    return train_loader
+
+def cycle(iterable):
+    while True:
+        for x in iterable:
+            yield x
\ No newline at end of file
diff --git a/humanml3d_272/mean_std/Mean.npy b/humanml3d_272/mean_std/Mean.npy
new file mode 100644
index 0000000000000000000000000000000000000000..83b1e4c10c7db0b87e227bc4aeed438c47d62478
--- /dev/null
+++ b/humanml3d_272/mean_std/Mean.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1200c165ac97501676de3a7f4e870ec4de23f579d6b1d63227d08a5c80ad1272
+size 2304
diff --git a/humanml3d_272/mean_std/Std.npy b/humanml3d_272/mean_std/Std.npy
new file mode 100644
index 0000000000000000000000000000000000000000..a8e27387c2bfd45599c6d3cd28e2854bcd78bee1
--- /dev/null
+++ b/humanml3d_272/mean_std/Std.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb699c61755042e3b3c20e64723edf39f4b374cfd71f860efe9a4515f105d99c
+size 2304
diff --git a/humanml3d_272/motion_data.zip b/humanml3d_272/motion_data.zip
new file mode 100644
index 0000000000000000000000000000000000000000..9d30d8ec2400ce6601d6db9c78bb69554f04db29
--- /dev/null
+++ b/humanml3d_272/motion_data.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:39d92b459b83159c1e30bdca319f60389221b68301a1c3f1f70dbe3b3def05e5
+size 8355859664
diff --git a/humanml3d_272/prepare/download_Causal_TAE_t2m_272_ckpt.py b/humanml3d_272/prepare/download_Causal_TAE_t2m_272_ckpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdf21ce46dd00b2ca46a953dc783cfa7a60592b5
--- /dev/null
+++ b/humanml3d_272/prepare/download_Causal_TAE_t2m_272_ckpt.py
@@ -0,0 +1,14 @@
+import os
+os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+  repo_id="lxxiao/MotionStreamer",
+  repo_type="model",
+  local_dir="./",
+  allow_patterns=["Causal_TAE/*"],
+  local_dir_use_symlinks=False,
+  resume_download=True,
+  max_workers=8
+)
\ No newline at end of file
diff --git a/humanml3d_272/prepare/download_Causal_TAE_t2m_babel_272_ckpt.py b/humanml3d_272/prepare/download_Causal_TAE_t2m_babel_272_ckpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfc101f0e11e3852cb76ce9fa1d4053941a3b9eb
--- /dev/null
+++ b/humanml3d_272/prepare/download_Causal_TAE_t2m_babel_272_ckpt.py
@@ -0,0 +1,14 @@
+import os
+os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+  repo_id="lxxiao/MotionStreamer",
+  repo_type="model",
+  local_dir="./",
+  allow_patterns=["Causal_TAE_t2m_babel/*"],
+  local_dir_use_symlinks=False,
+  resume_download=True,
+  max_workers=8
+)
\ No newline at end of file
diff --git a/humanml3d_272/prepare/download_evaluator_ckpt.py b/humanml3d_272/prepare/download_evaluator_ckpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c21478102886f4564eef74a3578fb851cae7c00
--- /dev/null
+++ b/humanml3d_272/prepare/download_evaluator_ckpt.py
@@ -0,0 +1,14 @@
+import os
+os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+  repo_id="lxxiao/MotionStreamer",
+  repo_type="model",
+  local_dir="./",
+  allow_patterns=["Evaluator_272/*"],
+  local_dir_use_symlinks=False,
+  resume_download=True,
+  max_workers=8
+)
\ No newline at end of file
diff --git a/humanml3d_272/prepare/download_t2m_model_ckpt.py b/humanml3d_272/prepare/download_t2m_model_ckpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..96490288b418e771719d1af2825e71dc1465ad08
--- /dev/null
+++ b/humanml3d_272/prepare/download_t2m_model_ckpt.py
@@ -0,0 +1,14 @@
+import os
+os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+  repo_id="lxxiao/MotionStreamer",
+  repo_type="model",
+  local_dir="./",
+  allow_patterns=["Experiments/t2m_model/*"],
+  local_dir_use_symlinks=False,
+  resume_download=True,
+  max_workers=8
+)
\ No newline at end of file
diff --git a/humanml3d_272/split/test.txt b/humanml3d_272/split/test.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b2df9120885af1a6cb6b18a28e091fcd7f9f5d79
--- /dev/null
+++ b/humanml3d_272/split/test.txt
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diff --git a/humanml3d_272/split/train.txt b/humanml3d_272/split/train.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ea01c6654d94a8a1537f3d44c112bbe7e7f5f6e6
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diff --git a/humanml3d_272/split/val.txt b/humanml3d_272/split/val.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d35324d36b762a3113bd0cebc5364f63dd3f2f15
--- /dev/null
+++ b/humanml3d_272/split/val.txt
@@ -0,0 +1,1338 @@
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diff --git a/humanml3d_272/texts.zip b/humanml3d_272/texts.zip
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+++ b/humanml3d_272/texts.zip
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+version https://git-lfs.github.com/spec/v1
+oid sha256:f571593d39c305ed7df5836f052253159242a8e215f12cbf32b61fc9dd29b827
+size 12180150
diff --git a/models/__pycache__/causal_cnn.cpython-39.pyc b/models/__pycache__/causal_cnn.cpython-39.pyc
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diff --git a/models/causal_cnn.py b/models/causal_cnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..64e4a374016be505d84c25fe4d654004e64576ba
--- /dev/null
+++ b/models/causal_cnn.py
@@ -0,0 +1,108 @@
+import torch
+import torch.nn as nn
+from models.resnet import CausalResnet1D
+
+
+class CausalConv1d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1):
+        super(CausalConv1d, self).__init__()
+        self.pad = (kernel_size - 1) * dilation + (1 - stride)         
+        self.conv = nn.Conv1d(
+            in_channels, 
+            out_channels, 
+            kernel_size,                        
+            stride=stride, 
+            padding=0,                          # no padding here
+            dilation=dilation
+        )
+
+    def forward(self, x):
+        x = nn.functional.pad(x, (self.pad, 0))  # only pad on the left
+        return self.conv(x)
+    
+
+class CausalEncoder(nn.Module):
+    def __init__(self,
+                 input_emb_width = 272,
+                 hidden_size = 1024,
+                 down_t = 2,
+                 stride_t = 2,
+                 width = 1024,
+                 depth = 3,
+                 dilation_growth_rate = 3,
+                 activation='relu',
+                 norm=None,
+                 latent_dim=16,
+                 clip_range = []
+                 ):
+        super().__init__()
+        self.clip_range = clip_range
+        self.proj = nn.Linear(width, latent_dim*2)
+
+        blocks = []
+        filter_t, pad_t = stride_t * 2, stride_t // 2      
+
+
+        blocks.append(CausalConv1d(input_emb_width, width, 3, 1, 1))
+        blocks.append(nn.ReLU())
+        
+        for i in range(down_t):   
+            input_dim = width
+            block = nn.Sequential(
+                CausalConv1d(input_dim, width, filter_t, stride_t, 1),
+                CausalResnet1D(width, depth, dilation_growth_rate, activation=activation, norm=norm),
+            )
+            blocks.append(block)
+        blocks.append(CausalConv1d(width, hidden_size, 3, 1, 1))
+        self.model = nn.Sequential(*blocks)
+
+    def reparameterize(self, mu, logvar):
+        std = torch.exp(0.5 * logvar)
+        eps = torch.randn_like(std)
+        return mu + eps * std
+    
+    def forward(self, x):
+        x = self.model(x)
+        x = x.transpose(1, 2)  
+        x = self.proj(x)        
+        mu, logvar = x.chunk(2, dim=2)             
+        logvar = torch.clamp(logvar, self.clip_range[0], self.clip_range[1])
+        z = self.reparameterize(mu, logvar) 
+
+        return z, mu, logvar
+
+class CausalDecoder(nn.Module):
+    def __init__(self,
+                 input_emb_width = 272,
+                 hidden_size = 1024,
+                 down_t = 2,
+                 stride_t = 2,
+                 width = 1024,
+                 depth = 3,
+                 dilation_growth_rate = 3, 
+                 activation='relu',
+                 norm=None
+                 ):
+        super().__init__()
+        blocks = []
+        
+        filter_t, pad_t = stride_t * 2, stride_t // 2
+        blocks.append(CausalConv1d(hidden_size, width, 3, 1, 1))
+        blocks.append(nn.ReLU())
+        for i in range(down_t):
+            out_dim = width
+            block = nn.Sequential(
+                CausalResnet1D(width, depth, dilation_growth_rate, reverse_dilation=True, activation=activation, norm=norm),
+                nn.Upsample(scale_factor=2, mode='nearest'),
+                CausalConv1d(width, out_dim, 3, 1, 1)
+            )
+            blocks.append(block)
+        blocks.append(CausalConv1d(width, width, 3, 1, 1))
+        blocks.append(nn.ReLU())
+        blocks.append(CausalConv1d(width, input_emb_width, 3, 1, 1))
+
+        self.model = nn.Sequential(*blocks)
+
+    def forward(self, z):
+        z = z.transpose(1, 2)
+        return self.model(z)
diff --git a/models/diffloss.py b/models/diffloss.py
new file mode 100644
index 0000000000000000000000000000000000000000..d51cedf219aba4d4d0f51ef4a16599ad1a627b2b
--- /dev/null
+++ b/models/diffloss.py
@@ -0,0 +1,258 @@
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+import math
+from timm.layers.mlp import SwiGLU
+from models.diffusion import create_diffusion
+
+
+class DiffLoss(nn.Module):
+    """Diffusion Loss"""
+    def __init__(self, target_channels, z_channels, depth, width, num_sampling_steps, grad_checkpointing=False, learn_sigma=False):
+        super(DiffLoss, self).__init__()
+        self.in_channels = target_channels
+        self.net = SimpleMLPAdaLN(
+            in_channels=target_channels,
+            model_channels=width,
+            out_channels=target_channels * 2 if learn_sigma else target_channels,
+            z_channels=z_channels,
+            num_res_blocks=depth,
+            grad_checkpointing=grad_checkpointing
+        )
+
+        self.train_diffusion = create_diffusion(timestep_respacing="", noise_schedule="cosine")
+        self.gen_diffusion = create_diffusion(timestep_respacing=num_sampling_steps, noise_schedule="cosine")
+
+    def forward(self, target, z, mask=None):
+        t = torch.randint(0, self.train_diffusion.num_timesteps, (target.shape[0],), device=target.device)  
+        model_kwargs = dict(c=z)    
+        loss_dict = self.train_diffusion.training_losses(self.net, target, t, model_kwargs)
+        loss = loss_dict["loss"]        
+        pred_xstart = loss_dict["pred_xstart"]      
+        if mask is not None:
+            loss = (loss * mask).sum() / mask.sum()
+        return loss.mean(), pred_xstart
+
+    def sample(self, z, temperature=1.0, cfg=1.0):
+        
+        if not cfg == 1.0:
+            noise = torch.randn(z.shape[0] // 2, self.in_channels).to(z.device)
+            noise = torch.cat([noise, noise], dim=0)                        
+            model_kwargs = dict(c=z, cfg_scale=cfg)
+            sample_fn = self.net.forward_with_cfg
+        else:
+            noise = torch.randn(z.shape[0], self.in_channels).to(z.device)
+            model_kwargs = dict(c=z)
+            sample_fn = self.net.forward
+
+        sampled_token_latent = self.gen_diffusion.p_sample_loop(
+            sample_fn, noise.shape, noise, clip_denoised=False, model_kwargs=model_kwargs, progress=False,
+            temperature=temperature
+        )
+
+        return sampled_token_latent
+
+
+def modulate(x, shift, scale):
+    return x * (1 + scale) + shift
+
+
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+    def __init__(self, hidden_size, frequency_embedding_size=256):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            nn.SiLU(),
+            nn.Linear(hidden_size, hidden_size, bias=True),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+        :param t: a 1-D Tensor of N indices, one per batch element.
+                          These may be fractional.
+        :param dim: the dimension of the output.
+        :param max_period: controls the minimum frequency of the embeddings.
+        :return: an (N, D) Tensor of positional embeddings.
+        """
+        
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+        ).to(device=t.device)
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+        return embedding
+
+    def forward(self, t):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class ResBlock(nn.Module):
+    """
+    A residual block that can optionally change the number of channels.
+    :param channels: the number of input channels.
+    """
+
+    def __init__(
+        self,
+        channels
+    ):
+        super().__init__()
+        self.channels = channels
+
+        self.in_ln = nn.LayerNorm(channels, eps=1e-6)
+        
+        self.mlp = nn.Sequential(
+                nn.Linear(channels, channels, bias=True),
+                nn.SiLU(),
+                nn.Linear(channels, channels, bias=True),
+            )
+
+        
+        self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(channels, 3 * channels, bias=True)
+            )
+
+    def forward(self, x, y):
+        shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(y).chunk(3, dim=-1)
+        h = modulate(self.in_ln(x), shift_mlp, scale_mlp)
+        h = self.mlp(h)
+        return x + gate_mlp * h
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer adopted from DiT.
+    """
+    def __init__(self, model_channels, out_channels):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(model_channels, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(model_channels, out_channels, bias=True)
+        
+        self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(model_channels, 2 * model_channels, bias=True)
+            )
+
+    def forward(self, x, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)
+        x = modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+class SimpleMLPAdaLN(nn.Module):
+    """
+    The MLP for Diffusion Loss.
+    :param in_channels: channels in the input Tensor.
+    :param model_channels: base channel count for the model.
+    :param out_channels: channels in the output Tensor.
+    :param z_channels: channels in the condition.
+    :param num_res_blocks: number of residual blocks per downsample.
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        model_channels,
+        out_channels,
+        z_channels,
+        num_res_blocks,
+        grad_checkpointing=False
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.grad_checkpointing = grad_checkpointing
+
+        self.time_embed = TimestepEmbedder(model_channels)          
+        self.cond_embed = nn.Linear(z_channels, model_channels)     
+
+        self.input_proj = nn.Linear(in_channels, model_channels)    
+
+        res_blocks = []
+        for i in range(num_res_blocks):  
+            res_blocks.append(ResBlock(
+                model_channels
+            ))
+
+        self.res_blocks = nn.ModuleList(res_blocks)
+        self.final_layer = FinalLayer(model_channels, out_channels)   
+
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+        self.apply(_basic_init)
+
+        # Initialize timestep embedding MLP
+        nn.init.normal_(self.time_embed.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.time_embed.mlp[2].weight, std=0.02)
+
+        # Zero-out adaLN modulation layers
+
+        for block in self.res_blocks:
+            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
+
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
+        nn.init.constant_(self.final_layer.linear.weight, 0)
+        nn.init.constant_(self.final_layer.linear.bias, 0)
+
+    def forward(self, x, t, c):
+        """
+        Apply the model to an input batch.
+        :param x: an [N x C] Tensor of inputs.
+        :param t: a 1-D batch of timesteps.
+        :param c: conditioning from AR transformer.
+        :return: an [N x C] Tensor of outputs.
+        """
+
+
+        
+        x = x.float()  
+
+        x = self.input_proj(x)  
+        t = self.time_embed(t)  
+        c = self.cond_embed(c)  
+        
+
+        y = t + c
+
+        if self.grad_checkpointing and not torch.jit.is_scripting():
+            for block in self.res_blocks:
+                x = checkpoint(block, x, y)
+        else:
+            for block in self.res_blocks:
+                x = block(x, y)   
+
+        return self.final_layer(x, y)
+
+    def forward_with_cfg(self, x, t, c, cfg_scale):
+        half = x[: len(x) // 2]                     
+        combined = torch.cat([half, half], dim=0)   
+        model_out = self.forward(combined, t, c)    
+        eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
+        cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)   
+        half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)    
+        eps = torch.cat([half_eps, half_eps], dim=0)                    
+        return torch.cat([eps, rest], dim=1)                            
diff --git a/models/diffusion/__init__.py b/models/diffusion/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4210525a4ea1db05b7541e3a3ed807b75730171b
--- /dev/null
+++ b/models/diffusion/__init__.py
@@ -0,0 +1,47 @@
+# Adopted from DiT, which is modified from OpenAI's diffusion repos
+#     DiT: https://github.com/facebookresearch/DiT/diffusion
+#     GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
+#     ADM:   https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
+#     IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+
+from . import gaussian_diffusion as gd
+from .respace import SpacedDiffusion, space_timesteps
+
+
+def create_diffusion(
+    timestep_respacing,
+    noise_schedule="linear", 
+    use_kl=False,
+    sigma_small=True,
+    predict_xstart=False,
+    learn_sigma=False,
+    rescale_learned_sigmas=False,
+    diffusion_steps=50
+):
+    betas = gd.get_named_beta_schedule(noise_schedule, diffusion_steps)
+    if use_kl:
+        loss_type = gd.LossType.RESCALED_KL
+    elif rescale_learned_sigmas:
+        loss_type = gd.LossType.RESCALED_MSE
+    else:
+        loss_type = gd.LossType.MSE
+    if timestep_respacing is None or timestep_respacing == "":
+        timestep_respacing = [diffusion_steps]
+    return SpacedDiffusion(
+        use_timesteps=space_timesteps(diffusion_steps, timestep_respacing),
+        betas=betas,
+        model_mean_type=(
+            gd.ModelMeanType.EPSILON if not predict_xstart else gd.ModelMeanType.START_X
+        ),
+        model_var_type=(
+            (
+                gd.ModelVarType.FIXED_LARGE
+                if not sigma_small
+                else gd.ModelVarType.FIXED_SMALL
+            )
+            if not learn_sigma
+            else gd.ModelVarType.LEARNED_RANGE
+        ),
+        loss_type=loss_type
+        # rescale_timesteps=rescale_timesteps,
+    )
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diff --git a/models/diffusion/diffusion_utils.py b/models/diffusion/diffusion_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..86ef6a9ed81767302fbe36900013dfe1abaf25f4
--- /dev/null
+++ b/models/diffusion/diffusion_utils.py
@@ -0,0 +1,73 @@
+# Modified from OpenAI's diffusion repos
+#     GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
+#     ADM:   https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
+#     IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+
+import torch as th
+import numpy as np
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    Compute the KL divergence between two gaussians.
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, th.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for th.exp().
+    logvar1, logvar2 = [
+        x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor)
+        for x in (logvar1, logvar2)
+    ]
+
+    return 0.5 * (
+        -1.0
+        + logvar2
+        - logvar1
+        + th.exp(logvar1 - logvar2)
+        + ((mean1 - mean2) ** 2) * th.exp(-logvar2)
+    )
+
+
+def approx_standard_normal_cdf(x):
+    """
+    A fast approximation of the cumulative distribution function of the
+    standard normal.
+    """
+    return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3))))
+
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    """
+    Compute the log-likelihood of a Gaussian distribution discretizing to a
+    given image.
+    :param x: the target images. It is assumed that this was uint8 values,
+              rescaled to the range [-1, 1].
+    :param means: the Gaussian mean Tensor.
+    :param log_scales: the Gaussian log stddev Tensor.
+    :return: a tensor like x of log probabilities (in nats).
+    """
+    assert x.shape == means.shape == log_scales.shape
+    centered_x = x - means
+    inv_stdv = th.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
+    cdf_plus = approx_standard_normal_cdf(plus_in)
+    min_in = inv_stdv * (centered_x - 1.0 / 255.0)
+    cdf_min = approx_standard_normal_cdf(min_in)
+    log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12))
+    log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12))
+    cdf_delta = cdf_plus - cdf_min
+    log_probs = th.where(
+        x < -0.999,
+        log_cdf_plus,
+        th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))),
+    )
+    assert log_probs.shape == x.shape
+    return log_probs
diff --git a/models/diffusion/gaussian_diffusion.py b/models/diffusion/gaussian_diffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..c03301b925e2976c7eb3a269bf4fb10a9f90bb0f
--- /dev/null
+++ b/models/diffusion/gaussian_diffusion.py
@@ -0,0 +1,917 @@
+# Modified from OpenAI's diffusion repos
+#     GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
+#     ADM:   https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
+#     IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+
+
+import math
+
+import numpy as np
+import torch as th
+import enum
+
+from .diffusion_utils import discretized_gaussian_log_likelihood, normal_kl
+
+
+def mean_flat(tensor):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))  # tensor.shape = torch.Size([124, 512])
+
+
+class ModelMeanType(enum.Enum):
+    """
+    Which type of output the model predicts.
+    """
+
+    PREVIOUS_X = enum.auto()  # the model predicts x_{t-1}
+    START_X = enum.auto()  # the model predicts x_0
+    EPSILON = enum.auto()  # the model predicts epsilon
+
+
+class ModelVarType(enum.Enum):
+    """
+    What is used as the model's output variance.
+    The LEARNED_RANGE option has been added to allow the model to predict
+    values between FIXED_SMALL and FIXED_LARGE, making its job easier.
+    """
+
+    LEARNED = enum.auto()
+    FIXED_SMALL = enum.auto()
+    FIXED_LARGE = enum.auto()
+    LEARNED_RANGE = enum.auto()
+
+
+class LossType(enum.Enum):
+    MSE = enum.auto()  # use raw MSE loss (and KL when learning variances)
+    RESCALED_MSE = (
+        enum.auto()
+    )  # use raw MSE loss (with RESCALED_KL when learning variances)
+    KL = enum.auto()  # use the variational lower-bound
+    RESCALED_KL = enum.auto()  # like KL, but rescale to estimate the full VLB
+
+    def is_vb(self):
+        return self == LossType.KL or self == LossType.RESCALED_KL
+
+
+def _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, warmup_frac):
+    betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
+    warmup_time = int(num_diffusion_timesteps * warmup_frac)
+    betas[:warmup_time] = np.linspace(beta_start, beta_end, warmup_time, dtype=np.float64)
+    return betas
+
+
+def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_timesteps):
+    """
+    This is the deprecated API for creating beta schedules.
+    See get_named_beta_schedule() for the new library of schedules.
+    """
+    if beta_schedule == "quad":
+        betas = (
+            np.linspace(
+                beta_start ** 0.5,
+                beta_end ** 0.5,
+                num_diffusion_timesteps,
+                dtype=np.float64,
+            )
+            ** 2
+        )
+    elif beta_schedule == "linear":
+        betas = np.linspace(beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64)
+    elif beta_schedule == "warmup10":
+        betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.1)
+    elif beta_schedule == "warmup50":
+        betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.5)
+    elif beta_schedule == "const":
+        betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
+    elif beta_schedule == "jsd":  # 1/T, 1/(T-1), 1/(T-2), ..., 1
+        betas = 1.0 / np.linspace(
+            num_diffusion_timesteps, 1, num_diffusion_timesteps, dtype=np.float64
+        )
+    else:
+        raise NotImplementedError(beta_schedule)
+    assert betas.shape == (num_diffusion_timesteps,)
+    return betas
+
+
+def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
+    """
+    Get a pre-defined beta schedule for the given name.
+    The beta schedule library consists of beta schedules which remain similar
+    in the limit of num_diffusion_timesteps.
+    Beta schedules may be added, but should not be removed or changed once
+    they are committed to maintain backwards compatibility.
+    """
+    if schedule_name == "linear":
+        # Linear schedule from Ho et al, extended to work for any number of
+        # diffusion steps.
+        scale = 1000 / num_diffusion_timesteps
+        return get_beta_schedule(
+            "linear",
+            beta_start=scale * 0.0001,
+            beta_end=scale * 0.02,
+            num_diffusion_timesteps=num_diffusion_timesteps,
+        )
+    elif schedule_name == "cosine":
+        return betas_for_alpha_bar(
+            num_diffusion_timesteps,
+            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
+        )
+    else:
+        raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function,
+    which defines the cumulative product of (1-beta) over time from t = [0,1].
+    :param num_diffusion_timesteps: the number of betas to produce.
+    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+                      produces the cumulative product of (1-beta) up to that
+                      part of the diffusion process.
+    :param max_beta: the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+    """
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return np.array(betas)
+
+
+
+
+class GaussianDiffusion:
+    """
+    Utilities for training and sampling diffusion models.
+    Original ported from this codebase:
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
+    :param betas: a 1-D numpy array of betas for each diffusion timestep,
+                  starting at T and going to 1.
+    """
+
+    def __init__(
+        self,
+        *,
+        betas,
+        model_mean_type,
+        model_var_type,
+        loss_type
+    ):
+
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        self.loss_type = loss_type
+
+        # Use float64 for accuracy.
+        betas = np.array(betas, dtype=np.float64)
+        self.betas = betas
+        assert len(betas.shape) == 1, "betas must be 1-D"
+        assert (betas > 0).all() and (betas <= 1).all()
+
+        self.num_timesteps = int(betas.shape[0])
+
+        alphas = 1.0 - betas
+        self.alphas_cumprod = np.cumprod(alphas, axis=0)
+        self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
+        self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
+        assert self.alphas_cumprod_prev.shape == (self.num_timesteps,)
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
+        self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
+        self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
+        self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
+        self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1)
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        self.posterior_variance = (
+            betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
+        )
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = np.log(
+            np.append(self.posterior_variance[1], self.posterior_variance[1:])
+        ) if len(self.posterior_variance) > 1 else np.array([])
+
+        self.posterior_mean_coef1 = (
+            betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
+        )
+        self.posterior_mean_coef2 = (
+            (1.0 - self.alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - self.alphas_cumprod)
+        )
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+        variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+        log_variance = _extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data for a given number of diffusion steps.
+        In other words, sample from q(x_t | x_0).
+        :param x_start: the initial data batch.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :param noise: if specified, the split-out normal noise.
+        :return: A noisy version of x_start.
+        """
+        if noise is None:
+            noise = th.randn_like(x_start)
+        assert noise.shape == x_start.shape
+        return (
+            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+            + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
+        )
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior:
+            q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+            _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+            + _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = _extract_into_tensor(
+            self.posterior_log_variance_clipped, t, x_t.shape
+        )
+        assert (
+            posterior_mean.shape[0]
+            == posterior_variance.shape[0]
+            == posterior_log_variance_clipped.shape[0]
+            == x_start.shape[0]
+        )
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None):
+        """
+        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
+        the initial x, x_0.
+        :param model: the model, which takes a signal and a batch of timesteps
+                      as input.
+        :param x: the [N x C x ...] tensor at time t.
+        :param t: a 1-D Tensor of timesteps.
+        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample. Applies before
+            clip_denoised.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict with the following keys:
+                 - 'mean': the model mean output.
+                 - 'variance': the model variance output.
+                 - 'log_variance': the log of 'variance'.
+                 - 'pred_xstart': the prediction for x_0.
+        """
+        if model_kwargs is None:
+            model_kwargs = {}
+
+        B, C = x.shape[:2]
+        assert t.shape == (B,)
+        model_output = model(x, t, **model_kwargs)  # 调用forward_withcfg函数得到  torch.Size([2, 512])
+        if isinstance(model_output, tuple):
+            model_output, extra = model_output
+        else:
+            extra = None
+
+        if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
+            assert model_output.shape == (B, C * 2, *x.shape[2:])
+            model_output, model_var_values = th.split(model_output, C, dim=1)
+            min_log = _extract_into_tensor(self.posterior_log_variance_clipped, t, x.shape)
+            max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
+            # The model_var_values is [-1, 1] for [min_var, max_var].
+            frac = (model_var_values + 1) / 2
+            model_log_variance = frac * max_log + (1 - frac) * min_log
+            model_variance = th.exp(model_log_variance)
+        else:
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so
+                # to get a better decoder log likelihood.
+                ModelVarType.FIXED_LARGE: (
+                    np.append(self.posterior_variance[1], self.betas[1:]),
+                    np.log(np.append(self.posterior_variance[1], self.betas[1:])),
+                ),
+                ModelVarType.FIXED_SMALL: (
+                    self.posterior_variance,
+                    self.posterior_log_variance_clipped,
+                ),
+            }[self.model_var_type]      # ModelVarType.FIXED_SMALL
+            model_variance = _extract_into_tensor(model_variance, t, x.shape)
+            model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)
+
+        def process_xstart(x):
+            if denoised_fn is not None:
+                x = denoised_fn(x)
+            if clip_denoised:
+                return x.clamp(-1, 1)
+            return x
+        
+        
+        if self.model_mean_type == ModelMeanType.START_X:
+            pred_xstart = process_xstart(model_output)
+        else:  # here
+            pred_xstart = process_xstart(
+                self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
+            )
+        model_mean, _, _ = self.q_posterior_mean_variance(x_start=pred_xstart, x_t=x, t=t)
+
+        # print(f't: {t}; Out: {model_output[0,:5]}, Mean: {model_mean[0,:5]}, x_0: {pred_xstart[0,:5]}')
+        assert model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
+        return {
+            "mean": model_mean,
+            "variance": model_variance,
+            "log_variance": model_log_variance,
+            "pred_xstart": pred_xstart,
+            "extra": extra,
+        }
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
+            - _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
+        )
+
+    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+        return (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart
+        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+    def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+        """
+        Compute the mean for the previous step, given a function cond_fn that
+        computes the gradient of a conditional log probability with respect to
+        x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
+        condition on y.
+        This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
+        """
+        gradient = cond_fn(x, t, **model_kwargs)
+        new_mean = p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
+        return new_mean
+
+    def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+        """
+        Compute what the p_mean_variance output would have been, should the
+        model's score function be conditioned by cond_fn.
+        See condition_mean() for details on cond_fn.
+        Unlike condition_mean(), this instead uses the conditioning strategy
+        from Song et al (2020).
+        """
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+
+        eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
+        eps = eps - (1 - alpha_bar).sqrt() * cond_fn(x, t, **model_kwargs)
+
+        out = p_mean_var.copy()
+        out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
+        out["mean"], _, _ = self.q_posterior_mean_variance(x_start=out["pred_xstart"], x_t=x, t=t)
+        return out
+
+    def p_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        temperature=1.0
+    ):
+        """
+        Sample x_{t-1} from the model at the given timestep.
+        :param model: the model to sample from.
+        :param x: the current tensor at x_{t-1}.
+        :param t: the value of t, starting at 0 for the first diffusion step.
+        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param temperature: temperature scaling during Diff Loss sampling.
+        :return: a dict containing the following keys:
+                 - 'sample': a random sample from the model.
+                 - 'pred_xstart': a prediction of x_0.
+        """
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        noise = th.randn_like(x)
+        nonzero_mask = (
+            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+        )  # no noise when t == 0
+        if cond_fn is not None:
+            out["mean"] = self.condition_mean(cond_fn, out, x, t, model_kwargs=model_kwargs)
+        # scale the noise by temperature
+        sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise * temperature
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+
+    # def generate_unconditionally(self, model, x_start, t, noise=None):
+    #     """
+    #     Generate samples unconditionally using the given model.
+
+    #     :param model: the model to generate samples with.
+    #     :param x_start: the [N x C x ...] tensor of inputs.
+    #     :param t: a batch of timestep indices.
+    #     :param noise: if specified, the specific Gaussian noise to use.
+    #     :return: a tensor of generated samples.
+    #     """
+    #     # Ensure model_kwargs is an empty dictionary for unconditional generation
+    #     model_kwargs = {}
+
+    #     # Generate random noise if not provided
+    #     if noise is None:
+    #         noise = th.randn_like(x_start)
+
+    #     # Sample x_t using the q_sample function
+    #     x_t = self.q_sample(x_start, t, noise=noise)
+
+    #     # Generate samples using the model without any conditions
+    #     generated_samples = model(x_t, t, **model_kwargs)
+
+    #     return generated_samples
+
+    def p_sample_loop(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        temperature=1.0,
+    ):
+        """
+        Generate samples from the model.
+        :param model: the model module.
+        :param shape: the shape of the samples, (N, C, H, W).
+        :param noise: if specified, the noise from the encoder to sample.
+                      Should be of the same shape as `shape`.
+        :param clip_denoised: if True, clip x_start predictions to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param device: if specified, the device to create the samples on.
+                       If not specified, use a model parameter's device.
+        :param progress: if True, show a tqdm progress bar.
+        :param temperature: temperature scaling during Diff Loss sampling.
+        :return: a non-differentiable batch of samples.
+        """
+        final = None
+        for sample in self.p_sample_loop_progressive(
+            model,
+            shape,
+            noise=noise,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            cond_fn=cond_fn,
+            model_kwargs=model_kwargs,
+            device=device,
+            progress=progress,
+            temperature=temperature,
+        ):
+            final = sample
+
+        return final["sample"]        
+
+    def p_sample_loop_progressive(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        temperature=1.0,
+    ):
+        """
+        Generate samples from the model and yield intermediate samples from
+        each timestep of diffusion.
+        Arguments are the same as p_sample_loop().
+        Returns a generator over dicts, where each dict is the return value of
+        p_sample().
+        """
+        assert isinstance(shape, (tuple, list))
+        if noise is not None:
+            img = noise
+        else:
+            img = th.randn(*shape).cuda()
+        indices = list(range(self.num_timesteps))[::-1]
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices)
+
+        for i in indices:
+            t = th.tensor([i] * shape[0]).cuda()
+            with th.no_grad():
+                out = self.p_sample(
+                    model,
+                    img,
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=model_kwargs,
+                    temperature=temperature,
+                )
+                yield out
+                img = out["sample"]
+
+    def ddim_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+    ):
+        """
+        Sample x_{t-1} from the model using DDIM.
+        Same usage as p_sample().
+        """
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        if cond_fn is not None:
+            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
+
+        # Usually our model outputs epsilon, but we re-derive it
+        # in case we used x_start or x_prev prediction.
+        eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
+
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
+        sigma = (
+            eta
+            * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
+            * th.sqrt(1 - alpha_bar / alpha_bar_prev)
+        )
+        # Equation 12.
+        noise = th.randn_like(x)
+        mean_pred = (
+            out["pred_xstart"] * th.sqrt(alpha_bar_prev)
+            + th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps
+        )
+        nonzero_mask = (
+            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+        )  # no noise when t == 0
+        sample = mean_pred + nonzero_mask * sigma * noise
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+    def ddim_reverse_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+    ):
+        """
+        Sample x_{t+1} from the model using DDIM reverse ODE.
+        """
+        assert eta == 0.0, "Reverse ODE only for deterministic path"
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        if cond_fn is not None:
+            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
+        # Usually our model outputs epsilon, but we re-derive it
+        # in case we used x_start or x_prev prediction.
+        eps = (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
+            - out["pred_xstart"]
+        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
+        alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)
+
+        # Equation 12. reversed
+        mean_pred = out["pred_xstart"] * th.sqrt(alpha_bar_next) + th.sqrt(1 - alpha_bar_next) * eps
+
+        return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
+
+    def ddim_sample_loop(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+    ):
+        """
+        Generate samples from the model using DDIM.
+        Same usage as p_sample_loop().
+        """
+        final = None
+        for sample in self.ddim_sample_loop_progressive(
+            model,
+            shape,
+            noise=noise,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            cond_fn=cond_fn,
+            model_kwargs=model_kwargs,
+            device=device,
+            progress=progress,
+            eta=eta,
+        ):
+            final = sample
+        return final["sample"]
+
+    def ddim_sample_loop_progressive(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+    ):
+        """
+        Use DDIM to sample from the model and yield intermediate samples from
+        each timestep of DDIM.
+        Same usage as p_sample_loop_progressive().
+        """
+        assert isinstance(shape, (tuple, list))
+        if noise is not None:
+            img = noise
+        else:
+            img = th.randn(*shape).cuda()
+        indices = list(range(self.num_timesteps))[::-1]
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices)
+
+        for i in indices:
+            t = th.tensor([i] * shape[0]).cuda()
+            with th.no_grad():
+                out = self.ddim_sample(
+                    model,
+                    img,
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=model_kwargs,
+                    eta=eta,
+                )
+                yield out
+                img = out["sample"]
+
+    def _vb_terms_bpd(
+            self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None
+    ):
+        """
+        Get a term for the variational lower-bound.
+        The resulting units are bits (rather than nats, as one might expect).
+        This allows for comparison to other papers.
+        :return: a dict with the following keys:
+                 - 'output': a shape [N] tensor of NLLs or KLs.
+                 - 'pred_xstart': the x_0 predictions.
+        """
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
+            x_start=x_start, x_t=x_t, t=t
+        )       
+        out = self.p_mean_variance(
+            model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs
+        )       
+        kl = normal_kl(
+            true_mean, true_log_variance_clipped, out["mean"], out["log_variance"]
+        )       
+        kl = mean_flat(kl) / np.log(2.0)        
+
+        decoder_nll = -discretized_gaussian_log_likelihood(
+            x_start, means=out["mean"], log_scales=0.5 * out["log_variance"]
+        )       
+        assert decoder_nll.shape == x_start.shape
+        decoder_nll = mean_flat(decoder_nll) / np.log(2.0)
+
+        # At the first timestep return the decoder NLL,
+        # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
+        output = th.where((t == 0), decoder_nll, kl)        
+        return {"output": output, "pred_xstart": out["pred_xstart"]}  
+
+    def training_losses(self, model, x_start, t, model_kwargs=None, noise=None):
+        """
+        Compute training losses for a single timestep.
+        :param model: the model to evaluate loss on.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :param t: a batch of timestep indices.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param noise: if specified, the specific Gaussian noise to try to remove.
+        :return: a dict with the key "loss" containing a tensor of shape [N].
+                 Some mean or variance settings may also have other keys.
+        """
+
+        if model_kwargs is None:
+            model_kwargs = {}
+
+        if noise is None:
+            noise = th.randn_like(x_start)  
+        x_t = self.q_sample(x_start, t, noise=noise)   
+
+        terms = {}
+
+        if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
+            terms["loss"] = self._vb_terms_bpd(
+                model=model,
+                x_start=x_start,
+                x_t=x_t,
+                t=t,
+                clip_denoised=False,
+                model_kwargs=model_kwargs,
+            )["output"]
+            if self.loss_type == LossType.RESCALED_KL:
+                terms["loss"] *= self.num_timesteps
+        elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
+            model_output = model(x_t, t, **model_kwargs)   
+
+            if self.model_var_type in [
+                ModelVarType.LEARNED,
+                ModelVarType.LEARNED_RANGE,
+            ]:
+                B, C = x_t.shape[:2]
+                assert model_output.shape == (B, C * 2, *x_t.shape[2:])  
+                
+                model_output, model_var_values = th.split(model_output, C, dim=1)  
+                # Learn the variance using the variational bound, but don't let
+                # it affect our mean prediction.
+                frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)   
+                terms["vb"] = self._vb_terms_bpd(
+                    model=lambda *args, r=frozen_out: r,
+                    x_start=x_start,
+                    x_t=x_t,
+                    t=t,
+                    clip_denoised=False,
+                )["output"]   
+                if self.loss_type == LossType.RESCALED_MSE:
+                    # Divide by 1000 for equivalence with initial implementation.
+                    # Without a factor of 1/1000, the VB term hurts the MSE term.
+                    terms["vb"] *= self.num_timesteps / 1000.0
+
+            target = {
+                ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance(
+                    x_start=x_start, x_t=x_t, t=t
+                )[0],
+                ModelMeanType.START_X: x_start,
+                ModelMeanType.EPSILON: noise,
+            }[self.model_mean_type]     # PREVIOUS_X: the model predicts x_{t-1}
+            
+
+            assert model_output.shape == target.shape == x_start.shape  
+            terms["mse"] = mean_flat((target - model_output) ** 2)   
+            if "vb" in terms:
+                terms["loss"] = terms["mse"] + terms["vb"]
+            else:
+                terms["loss"] = terms["mse"]
+
+            
+            pred_xstart = self._predict_xstart_from_eps(x_t=x_t, t=t, eps=model_output)
+            terms["pred_xstart"] = pred_xstart
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        return terms
+
+    def _prior_bpd(self, x_start):
+        """
+        Get the prior KL term for the variational lower-bound, measured in
+        bits-per-dim.
+        This term can't be optimized, as it only depends on the encoder.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :return: a batch of [N] KL values (in bits), one per batch element.
+        """
+        batch_size = x_start.shape[0]
+        t = th.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+        kl_prior = normal_kl(
+            mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0
+        )
+        return mean_flat(kl_prior) / np.log(2.0)
+
+    def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None):
+        """
+        Compute the entire variational lower-bound, measured in bits-per-dim,
+        as well as other related quantities.
+        :param model: the model to evaluate loss on.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :param clip_denoised: if True, clip denoised samples.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict containing the following keys:
+                 - total_bpd: the total variational lower-bound, per batch element.
+                 - prior_bpd: the prior term in the lower-bound.
+                 - vb: an [N x T] tensor of terms in the lower-bound.
+                 - xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep.
+                 - mse: an [N x T] tensor of epsilon MSEs for each timestep.
+        """
+        device = x_start.device
+        batch_size = x_start.shape[0]
+
+        vb = []
+        xstart_mse = []
+        mse = []
+        for t in list(range(self.num_timesteps))[::-1]:
+            t_batch = th.tensor([t] * batch_size, device=device)
+            noise = th.randn_like(x_start)
+            x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise)
+            # Calculate VLB term at the current timestep
+            with th.no_grad():
+                out = self._vb_terms_bpd(
+                    model,
+                    x_start=x_start,
+                    x_t=x_t,
+                    t=t_batch,
+                    clip_denoised=clip_denoised,
+                    model_kwargs=model_kwargs,
+                )
+            vb.append(out["output"])
+            xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2))
+            eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"])
+            mse.append(mean_flat((eps - noise) ** 2))
+
+        vb = th.stack(vb, dim=1)
+        xstart_mse = th.stack(xstart_mse, dim=1)
+        mse = th.stack(mse, dim=1)
+
+        prior_bpd = self._prior_bpd(x_start)
+        total_bpd = vb.sum(dim=1) + prior_bpd
+        return {
+            "total_bpd": total_bpd,
+            "prior_bpd": prior_bpd,
+            "vb": vb,
+            "xstart_mse": xstart_mse,
+            "mse": mse,
+        }
+
+
+def _extract_into_tensor(arr, timesteps, broadcast_shape):
+    """
+    Extract values from a 1-D numpy array for a batch of indices.
+    :param arr: the 1-D numpy array.
+    :param timesteps: a tensor of indices into the array to extract.
+    :param broadcast_shape: a larger shape of K dimensions with the batch
+                            dimension equal to the length of timesteps.
+    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
+    """
+    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+    while len(res.shape) < len(broadcast_shape):
+        res = res[..., None]
+    return res + th.zeros(broadcast_shape, device=timesteps.device)
diff --git a/models/diffusion/respace.py b/models/diffusion/respace.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a2cc0435d1ace54466585db9043b284973d454e
--- /dev/null
+++ b/models/diffusion/respace.py
@@ -0,0 +1,129 @@
+# Modified from OpenAI's diffusion repos
+#     GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
+#     ADM:   https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
+#     IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+
+import numpy as np
+import torch as th
+
+from .gaussian_diffusion import GaussianDiffusion
+
+
+def space_timesteps(num_timesteps, section_counts):
+    """
+    Create a list of timesteps to use from an original diffusion process,
+    given the number of timesteps we want to take from equally-sized portions
+    of the original process.
+    For example, if there's 300 timesteps and the section counts are [10,15,20]
+    then the first 100 timesteps are strided to be 10 timesteps, the second 100
+    are strided to be 15 timesteps, and the final 100 are strided to be 20.
+    If the stride is a string starting with "ddim", then the fixed striding
+    from the DDIM paper is used, and only one section is allowed.
+    :param num_timesteps: the number of diffusion steps in the original
+                          process to divide up.
+    :param section_counts: either a list of numbers, or a string containing
+                           comma-separated numbers, indicating the step count
+                           per section. As a special case, use "ddimN" where N
+                           is a number of steps to use the striding from the
+                           DDIM paper.
+    :return: a set of diffusion steps from the original process to use.
+    """
+    if isinstance(section_counts, str):
+        if section_counts.startswith("ddim"):
+            desired_count = int(section_counts[len("ddim") :])
+            for i in range(1, num_timesteps):
+                if len(range(0, num_timesteps, i)) == desired_count:
+                    return set(range(0, num_timesteps, i))
+            raise ValueError(
+                f"cannot create exactly {num_timesteps} steps with an integer stride"
+            )
+        section_counts = [int(x) for x in section_counts.split(",")]
+    size_per = num_timesteps // len(section_counts)
+    extra = num_timesteps % len(section_counts)
+    start_idx = 0
+    all_steps = []
+    for i, section_count in enumerate(section_counts):
+        size = size_per + (1 if i < extra else 0)
+        if size < section_count:
+            raise ValueError(
+                f"cannot divide section of {size} steps into {section_count}"
+            )
+        if section_count <= 1:
+            frac_stride = 1
+        else:
+            frac_stride = (size - 1) / (section_count - 1)
+        cur_idx = 0.0
+        taken_steps = []
+        for _ in range(section_count):
+            taken_steps.append(start_idx + round(cur_idx))
+            cur_idx += frac_stride
+        all_steps += taken_steps
+        start_idx += size
+    return set(all_steps)
+
+
+class SpacedDiffusion(GaussianDiffusion):
+    """
+    A diffusion process which can skip steps in a base diffusion process.
+    :param use_timesteps: a collection (sequence or set) of timesteps from the
+                          original diffusion process to retain.
+    :param kwargs: the kwargs to create the base diffusion process.
+    """
+
+    def __init__(self, use_timesteps, **kwargs):
+        self.use_timesteps = set(use_timesteps)
+        self.timestep_map = []
+        self.original_num_steps = len(kwargs["betas"])
+
+        base_diffusion = GaussianDiffusion(**kwargs)  # pylint: disable=missing-kwoa
+        last_alpha_cumprod = 1.0
+        new_betas = []
+        for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):
+            if i in self.use_timesteps:
+                new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)
+                last_alpha_cumprod = alpha_cumprod
+                self.timestep_map.append(i)
+        kwargs["betas"] = np.array(new_betas)
+        super().__init__(**kwargs)
+
+    def p_mean_variance(
+        self, model, *args, **kwargs
+    ):  # pylint: disable=signature-differs
+        return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)
+
+    def training_losses(
+        self, model, *args, **kwargs
+    ):  # pylint: disable=signature-differs
+        return super().training_losses(self._wrap_model(model), *args, **kwargs)
+
+    def condition_mean(self, cond_fn, *args, **kwargs):
+        return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)
+
+    def condition_score(self, cond_fn, *args, **kwargs):
+        return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)
+
+    def _wrap_model(self, model):
+        if isinstance(model, _WrappedModel):
+            return model
+        return _WrappedModel(
+            model, self.timestep_map, self.original_num_steps
+        )
+
+    def _scale_timesteps(self, t):
+        # Scaling is done by the wrapped model.
+        return t
+
+
+class _WrappedModel:
+    def __init__(self, model, timestep_map, original_num_steps):
+        self.model = model
+        self.timestep_map = timestep_map
+        # self.rescale_timesteps = rescale_timesteps
+        self.original_num_steps = original_num_steps
+
+    def __call__(self, x, ts, **kwargs):
+        map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype)
+        new_ts = map_tensor[ts]
+        # if self.rescale_timesteps:
+        #     new_ts = new_ts.float() * (1000.0 / self.original_num_steps)
+        return self.model(x, new_ts, **kwargs)
diff --git a/models/llama_model.py b/models/llama_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..d65adc2fc07f137f94228f0691743702e195aff7
--- /dev/null
+++ b/models/llama_model.py
@@ -0,0 +1,1603 @@
+
+import math
+from dataclasses import dataclass
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from typing_extensions import Self
+from typing import Optional
+from transformers.modeling_utils import PreTrainedModel
+from torch.distributions import Categorical
+import torch.nn.functional as F
+
+
+@dataclass
+class LLaMAHFConfig:
+    block_size: int = 78
+    n_layer: int = 32
+    n_head: int = 32
+    n_embd: int = 4096
+    T5_xxl_dim: int = 768
+
+    @classmethod
+    def from_name(cls, name: str) -> Self:
+        return cls(**llama_configs[name])
+
+
+llama_configs = {
+    "Normal_size": dict(n_layer=12, n_head=12, n_embd=768)
+}
+
+
+class LLaMAHF(nn.Module):
+    def __init__(self, config: LLaMAHFConfig, num_diffusion_head_layers=9, input_token_dim=16, device=torch.device('cuda'), width=1792) -> None:
+        super().__init__()
+        assert config.block_size is not None
+        self.config = config
+
+        cond_dim = config.T5_xxl_dim
+         
+        self.transformer = nn.ModuleDict(
+            dict(
+                wte=nn.Linear(input_token_dim, config.n_embd),   
+                cond_embed=nn.Linear(cond_dim, config.n_embd),  
+                h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+                ln_f=RMSNorm(config.n_embd),
+                )
+            )
+        
+        target_channels = input_token_dim
+        from models.diffloss import DiffLoss
+        self.diff_loss = DiffLoss(
+                target_channels=target_channels,
+                z_channels=config.n_embd,
+                width=width,
+                depth=num_diffusion_head_layers,
+                num_sampling_steps='50',
+                grad_checkpointing=False,
+            )
+        self.diff_loss = self.diff_loss.to(device)
+        self.out_proj = nn.Linear(config.n_embd, config.n_embd)
+        self.use_out_proj = True
+        
+
+    def _tie_or_clone_weights(self, output_embeddings, input_embeddings):
+        """Tie or clone module weights depending of whether we are using TorchScript or not"""
+        output_embeddings.weight = input_embeddings.weight
+
+        if getattr(output_embeddings, "bias", None) is not None:
+            output_embeddings.bias.data = nn.functional.pad(
+                output_embeddings.bias.data,
+                (
+                    0,
+                    output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0],
+                ),
+                "constant",
+                0,
+            )
+        if hasattr(output_embeddings, "out_features") and hasattr(input_embeddings, "num_embeddings"):
+            output_embeddings.out_features = input_embeddings.num_embeddings
+
+    def get_input_embeddings(self):
+        return self.transformer.wte
+    
+    def set_input_embeddings(self, value):
+        self.transformer.wte = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+    
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def _init_weights(self, module: nn.Module) -> None:
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02 / math.sqrt(2 * self.config.n_layer))
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02 / math.sqrt(2 * self.config.n_layer))
+            
+    
+
+    def forward_sample(self, idx: torch.Tensor, clip_feature: torch.Tensor, y_mask) -> torch.Tensor:
+        
+        text_length = clip_feature.shape[1]
+        if len(idx) == 0:
+            x = self.llama_proj(clip_feature)[:, :int(y_mask[0].sum()), :]
+        else:
+            _, t = idx.size()
+            assert (
+                t <= self.config.block_size
+            ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
+            # forward the LLaMA model itself
+            x = self.transformer.wte(idx)  # token embeddings of shape (b, t, n_embd)
+            x = torch.cat((self.llama_proj(clip_feature)[:, :int(y_mask[0].sum()), :],x), dim=1)
+
+        for block in self.transformer.h:
+            x = block(x, y_mask)
+        x = self.transformer.ln_f(x)
+        logits = x
+        return logits
+
+    
+
+    def sample_for_eval_CFG(self, text, length=196, tokenize_model=None, device=torch.device('cuda'), unit_length=4, cfg=4.0):
+        max_token_len = length // unit_length
+        for k in range(max_token_len): 
+            if k == 0:
+                x = []
+            else:
+                x = xs
+            
+            feat_text = torch.from_numpy(tokenize_model.encode(text)).float()
+            feat_text = feat_text.to(device)
+            conditions = self.forward(x, feat_text)
+            conditions = conditions[:, -1, :]                            
+            
+            empty_text = ''
+            empty_feat_text = torch.from_numpy(tokenize_model.encode(empty_text)).float()   
+            empty_feat_text = empty_feat_text.unsqueeze(0)
+            empty_feat_text = empty_feat_text.to(device)
+            empty_conditions = self.forward(x, empty_feat_text)       
+            empty_conditions = empty_conditions[:, -1, :]                             
+            temperature = 1.0
+            
+            # chunk
+            if cfg != 1:
+                mix_conditions = torch.cat([conditions, empty_conditions], dim=0)     
+                sampled_token_latent = self.diff_loss.sample(mix_conditions, temperature=temperature, cfg=cfg)  
+                scaled_logits, _ = sampled_token_latent.chunk(2, dim=0)          
+            else:   # no cfg
+                scaled_logits = self.diff_loss.sample(conditions, temperature=temperature, cfg=1) 
+
+            scaled_logits = scaled_logits.unsqueeze(0)     
+            
+            if k == 0:
+                xs = scaled_logits
+            else:
+                xs = torch.cat((xs, scaled_logits), dim=1)
+
+        return xs
+    
+    
+    
+    # For inference, can stop sampling when the distance between the current token and the reference end token is less than the threshold.
+    def sample_for_eval_CFG_inference(self, text, length=312, tokenizer=None, device=torch.device('cuda'), unit_length=4, reference_end_latent=None, threshold=0.1, cfg=4.0, temperature=1.0):
+        max_token_len = length // unit_length
+        feat_text = torch.from_numpy(tokenizer.encode(text)).float()
+        feat_text = feat_text.to(device)
+
+        # CFG inference
+        empty_text = ''
+        empty_feat_text = torch.from_numpy(tokenizer.encode(empty_text)).float()   # torch.Size([32, 768])
+        empty_feat_text = empty_feat_text.unsqueeze(0)
+        empty_feat_text = empty_feat_text.to(device)
+        
+        for k in range(max_token_len): 
+            if k == 0:
+                x = []
+            else:
+                x = xs
+                
+            conditions = self.forward_inference(x, feat_text)
+            conditions = conditions[:, -1, :]                            
+
+            empty_conditions = self.forward(x, empty_feat_text)       
+            empty_conditions = empty_conditions[:, -1, :]                             
+            
+            mix_conditions = torch.cat([conditions, empty_conditions], dim=0)     
+            sampled_token_latent = self.diff_loss.sample(mix_conditions, temperature=temperature, cfg=cfg)  
+
+            # chunk
+            if cfg != 1:
+                scaled_logits, _ = sampled_token_latent.chunk(2, dim=0)           
+            else:
+                scaled_logits = sampled_token_latent
+
+            scaled_logits = scaled_logits.unsqueeze(0)      
+            
+            if reference_end_latent is not None:
+                distance_l2 = torch.sqrt(torch.sum((scaled_logits - reference_end_latent)**2))
+                print(distance_l2)
+                if distance_l2 < threshold:
+                    break
+
+            if k == 0:
+                xs = scaled_logits
+            else:
+                xs = torch.cat((xs, scaled_logits), dim=1)
+
+        return xs
+
+        
+    def sample_for_eval_CFG_inference2(self, feat_clip_text, empty_feat_clip_text, if_categorial=False, length=312, clip_model=None, device=torch.device('cuda'), tokenizer='clip', unit_length=4, reference_end_token=None, threshold=3, cfg=4.5, temperature=1.0):
+       
+        import clip
+        max_token_len = length // unit_length
+        
+        for k in range(max_token_len): 
+            if k == 0:
+                x = []
+            else:
+                x = xs
+
+            try:
+                conditions = self.forward(x, feat_clip_text)
+            except:
+                conditions = self.forward(x, feat_clip_text.unsqueeze(0))
+            
+            
+            conditions = conditions[:, -1, :]                            
+            
+            
+
+            empty_conditions = self.forward(x, empty_feat_clip_text)       
+            empty_conditions = empty_conditions[:, -1, :]                             
+            
+            mix_conditions = torch.cat([conditions, empty_conditions], dim=0)     
+            sampled_token_latent = self.diff_loss.sample(mix_conditions, temperature=temperature, cfg=cfg)  
+
+            # chunk
+            if cfg != 1:
+                scaled_logits, _ = sampled_token_latent.chunk(2, dim=0)           
+            else:
+                scaled_logits = sampled_token_latent
+
+            scaled_logits = scaled_logits.unsqueeze(0)      
+            
+            if reference_end_token is not None:
+                distance_l2 = torch.sqrt(torch.sum((scaled_logits - reference_end_token)**2))
+                print(distance_l2)
+                if distance_l2 < threshold:
+                    break
+
+            if k == 0:
+                xs = scaled_logits
+            else:
+                xs = torch.cat((xs, scaled_logits), dim=1)
+
+        return xs
+
+    def sample_for_eval_CFG_inference_next_one(self, current_token=[], feat_clip_text=None, empty_feat_clip_text=None, if_categorial=False, length=312, clip_model=None, device=torch.device('cuda'), tokenizer='clip', unit_length=4, reference_end_token=None, threshold=3, cfg=4.5, temperature=1.0):
+        
+        import clip
+        max_token_len = length // unit_length
+       
+        
+        for k in range(1): 
+            
+            if current_token == []:
+                x = []
+            else:
+                x = torch.cat(current_token, dim=1)
+            
+            
+            try:
+                conditions = self.forward(x, feat_clip_text)
+            except:
+                conditions = self.forward(x, feat_clip_text.unsqueeze(0))
+            
+            
+            conditions = conditions[:, -1, :]                             
+            
+
+            empty_conditions = self.forward(x, empty_feat_clip_text)      
+            empty_conditions = empty_conditions[:, -1, :]                         
+
+            mix_conditions = torch.cat([conditions, empty_conditions], dim=0)   
+            sampled_token_latent = self.diff_loss.sample(mix_conditions, temperature=temperature, cfg=cfg)  
+
+            # chunk
+            if cfg != 1:
+                scaled_logits, _ = sampled_token_latent.chunk(2, dim=0)         
+            else:
+                scaled_logits = sampled_token_latent
+
+
+            scaled_logits = scaled_logits.unsqueeze(0)    
+            
+
+            if k == 0:
+                xs = scaled_logits
+            else:
+                xs = torch.cat((xs, scaled_logits), dim=1)
+
+        return xs
+    
+
+    def sample_for_eval_CFG_babel(self, A_text, B_text, A_motion, if_categorial=False, length=6400, clip_model=None, device=torch.device('cuda'), tokenizer='clip', unit_length=4, reference_end_token=None, cfg=7.0, threshold=3):
+
+        import clip
+        B_token_length = length // unit_length - A_motion.shape[0]
+
+        if tokenizer == 'clip':
+            A_text = clip.tokenize(A_text, truncate=True).to(device) 
+            A_feat_clip_text = clip_model.encode_text(A_text).float()  
+            B_text = clip.tokenize(B_text, truncate=True).to(device)
+            B_feat_clip_text = clip_model.encode_text(B_text).float()
+        elif tokenizer == 't5-xxl':
+            A_feat_clip_text = torch.from_numpy(clip_model.encode(A_text)).float()
+            A_feat_clip_text = A_feat_clip_text.to(device)
+            B_feat_clip_text = torch.from_numpy(clip_model.encode(B_text)).float()
+            B_feat_clip_text = B_feat_clip_text.to(device)
+        
+        A_text_embeddings = self.transformer.cond_embed(A_feat_clip_text).unsqueeze(0)
+        B_text_embeddings = self.transformer.cond_embed(B_feat_clip_text).unsqueeze(0)
+
+        A_motion = A_motion.unsqueeze(0)
+        A_motion_embeddings = self.transformer.wte(A_motion)
+        B_motion = torch.tensor([]).to(device)
+
+        for k in range(B_token_length): 
+            if k == 0:
+                x = torch.cat([A_text_embeddings, A_motion_embeddings, B_text_embeddings], dim=1)
+            else:
+                x = xs
+
+            
+            conditions = self.forward_babel_eval(x)
+            conditions = conditions[:, -1, :]                       
+
+            empty_clip_text = ''
+            if tokenizer == 'clip':
+                empty_text = clip.tokenize(empty_clip_text, truncate=True).to(device)
+                empty_feat_clip_text = clip_model.encode_text(empty_text).float()
+            elif tokenizer == 't5-xxl':
+                empty_feat_clip_text = torch.from_numpy(clip_model.encode(empty_clip_text)).float()   
+                empty_feat_clip_text = empty_feat_clip_text.unsqueeze(0)
+                empty_feat_clip_text = empty_feat_clip_text.to(device)
+
+            empty_feat_clip_text_embedding = self.transformer.cond_embed(empty_feat_clip_text).unsqueeze(0)
+            
+            if k == 0:
+                empty_input = torch.cat([empty_feat_clip_text_embedding, A_motion_embeddings, empty_feat_clip_text_embedding], dim=1)
+                empty_conditions = self.forward_babel_eval(empty_input)
+            else:
+                B_motion_embeddings = self.transformer.wte(B_motion)
+                empty_input = torch.cat([empty_feat_clip_text_embedding, A_motion_embeddings, empty_feat_clip_text_embedding, B_motion_embeddings], dim=1)
+                empty_conditions = self.forward_babel_eval(empty_input)      
+            
+            empty_conditions = empty_conditions[:, -1, :]                            
+            temperature = 1.0
+            
+            mix_conditions = torch.cat([conditions, empty_conditions], dim=0)    
+            sampled_token_latent = self.diff_loss.sample(mix_conditions, temperature=temperature, cfg=cfg)  
+
+            # chunk
+            if cfg != 1:
+                scaled_logits, _ = sampled_token_latent.chunk(2, dim=0)           
+            else:
+                scaled_logits = sampled_token_latent
+
+
+            scaled_logits = scaled_logits.unsqueeze(0)      
+            
+
+            B_motion = torch.cat((B_motion, scaled_logits), dim=1)
+
+            scaled_logits_embedding = self.transformer.wte(scaled_logits)
+            xs = torch.cat((x, scaled_logits_embedding), dim=1)
+            
+
+        return xs, B_motion
+
+    def sample_for_eval_CFG_babel_inference(self, A_text, B_text, A_motion, if_categorial=False, length=6400, clip_model=None, device=torch.device('cuda'), tokenizer='clip', unit_length=4, reference_end_token=None, cfg=7.0, threshold=3):
+
+        import clip
+        B_token_length = length // unit_length - A_motion.shape[0]
+
+        if tokenizer == 'clip':
+            A_text = clip.tokenize(A_text, truncate=True).to(device)  
+            A_feat_clip_text = clip_model.encode_text(A_text).float()  
+            B_text = clip.tokenize(B_text, truncate=True).to(device)
+            B_feat_clip_text = clip_model.encode_text(B_text).float()
+        elif tokenizer == 't5-xxl':
+            A_feat_clip_text = torch.from_numpy(clip_model.encode(A_text)).float()   
+            A_feat_clip_text = A_feat_clip_text.to(device)
+            B_feat_clip_text = torch.from_numpy(clip_model.encode(B_text)).float()
+            B_feat_clip_text = B_feat_clip_text.to(device)
+        
+        A_text_embeddings = self.transformer.cond_embed(A_feat_clip_text).unsqueeze(0)
+        A_text_embeddings = A_text_embeddings.unsqueeze(0)
+        B_text_embeddings = self.transformer.cond_embed(B_feat_clip_text).unsqueeze(0)
+        B_text_embeddings = B_text_embeddings.unsqueeze(0)
+
+        A_motion = A_motion.unsqueeze(0)
+        A_motion_embeddings = self.transformer.wte(A_motion)
+        B_motion = torch.tensor([]).to(device)
+
+        attention_weights = []
+
+        for k in range(B_token_length): 
+            if k == 0:    
+                x = torch.cat([A_text_embeddings, A_motion_embeddings, B_text_embeddings], dim=1)
+                
+            else:
+                x = xs
+
+            
+            
+            conditions = self.forward_babel_eval(x, return_attention=False)
+            conditions = conditions[:, -1, :]                            
+
+            empty_clip_text = ''
+            if tokenizer == 'clip':
+                empty_text = clip.tokenize(empty_clip_text, truncate=True).to(device)
+                empty_feat_clip_text = clip_model.encode_text(empty_text).float()
+            elif tokenizer == 't5-xxl':
+                empty_feat_clip_text = torch.from_numpy(clip_model.encode(empty_clip_text)).float()   
+                empty_feat_clip_text = empty_feat_clip_text.unsqueeze(0)
+                empty_feat_clip_text = empty_feat_clip_text.to(device)
+
+            empty_feat_clip_text_embedding = self.transformer.cond_embed(empty_feat_clip_text).unsqueeze(0)
+            
+            if k == 0:    
+                empty_input = torch.cat([empty_feat_clip_text_embedding, A_motion_embeddings, empty_feat_clip_text_embedding], dim=1)
+                empty_conditions = self.forward_babel_eval(empty_input)
+            else:
+                B_motion_embeddings = self.transformer.wte(B_motion)
+                empty_input = torch.cat([empty_feat_clip_text_embedding, A_motion_embeddings, empty_feat_clip_text_embedding, B_motion_embeddings], dim=1)
+                empty_conditions = self.forward_babel_eval(empty_input)
+
+            empty_conditions = empty_conditions[:, -1, :]                             
+            temperature = 1.0
+            
+            mix_conditions = torch.cat([conditions, empty_conditions], dim=0)    
+            sampled_token_latent = self.diff_loss.sample(mix_conditions, temperature=temperature, cfg=cfg) 
+
+            # chunk
+            if cfg != 1:
+                scaled_logits, _ = sampled_token_latent.chunk(2, dim=0)        
+            else:
+                scaled_logits = sampled_token_latent
+
+            scaled_logits = scaled_logits.unsqueeze(0)     
+            
+            if reference_end_token is not None:
+                distance_l2 = torch.sqrt(torch.sum((scaled_logits - reference_end_token)**2))
+                print(distance_l2)
+                if distance_l2 < threshold:
+                    break
+            
+            B_motion = torch.cat((B_motion, scaled_logits), dim=1)
+
+            scaled_logits_embedding = self.transformer.wte(scaled_logits)
+            xs = torch.cat((x, scaled_logits_embedding), dim=1)
+            
+        
+        
+        return xs, B_motion
+    
+
+    def sample_for_eval_CFG_babel_inference_new(self, B_text, A_motion, if_categorial=False, length=78, clip_model=None, device=torch.device('cuda'), tokenizer='clip', unit_length=4, reference_end_token=None, cfg=4.5, threshold=3):
+
+        import clip 
+        B_token_length = length // unit_length
+
+        if tokenizer == 'clip':
+            A_text = clip.tokenize(A_text, truncate=True).to(device)  
+            A_feat_clip_text = clip_model.encode_text(A_text).float()  
+            B_text = clip.tokenize(B_text, truncate=True).to(device)
+            B_feat_clip_text = clip_model.encode_text(B_text).float()
+        elif tokenizer == 't5-xxl':
+            B_feat_clip_text = torch.from_numpy(clip_model.encode(B_text)).float()
+            B_feat_clip_text = B_feat_clip_text.to(device)
+
+        empty_clip_text = ''
+        if tokenizer == 'clip':
+            empty_text = clip.tokenize(empty_clip_text, truncate=True).to(device)
+            empty_feat_clip_text = clip_model.encode_text(empty_text).float()
+        elif tokenizer == 't5-xxl':
+            empty_feat_clip_text = torch.from_numpy(clip_model.encode(empty_clip_text)).float()   
+            empty_feat_clip_text = empty_feat_clip_text.unsqueeze(0)
+            empty_feat_clip_text = empty_feat_clip_text.to(device)
+        
+        B_text_embeddings = self.transformer.cond_embed(B_feat_clip_text).unsqueeze(0)
+
+        A_motion = A_motion.unsqueeze(0)
+        A_motion_embeddings = self.transformer.wte(A_motion)
+        B_motion = torch.tensor([]).to(device)
+
+    
+        attention_weights = []
+
+        for k in range(B_token_length): 
+            if k == 0: 
+                x = torch.cat([B_text_embeddings, A_motion_embeddings], dim=1)
+            else:
+                x = xs
+
+            conditions = self.forward_babel_eval(x, return_attention=False)
+            conditions = conditions[:, -1, :]                             
+            
+
+            empty_feat_clip_text_embedding = self.transformer.cond_embed(empty_feat_clip_text).unsqueeze(0)
+            
+            if k == 0:
+                empty_input = torch.cat([empty_feat_clip_text_embedding, A_motion_embeddings], dim=1)
+                
+                empty_conditions = self.forward_babel_eval(empty_input)
+            else:
+                B_motion_embeddings = self.transformer.wte(B_motion)
+                empty_input = torch.cat([empty_feat_clip_text_embedding, A_motion_embeddings, B_motion_embeddings], dim=1)
+                empty_conditions = self.forward_babel_eval(empty_input)
+
+            empty_conditions = empty_conditions[:, -1, :]                             
+            temperature = 1.0
+           
+            mix_conditions = torch.cat([conditions, empty_conditions], dim=0)     
+            sampled_token_latent = self.diff_loss.sample(mix_conditions, temperature=temperature, cfg=cfg) 
+
+            # chunk
+            if cfg != 1:
+                scaled_logits, _ = sampled_token_latent.chunk(2, dim=0)       
+            else:
+                scaled_logits = sampled_token_latent
+
+            scaled_logits = scaled_logits.unsqueeze(0)      
+            
+            if reference_end_token is not None:
+                distance_l2 = torch.sqrt(torch.sum((scaled_logits - reference_end_token)**2))
+                print(distance_l2)
+                if distance_l2 < threshold:
+                    break
+            
+            B_motion = torch.cat((B_motion, scaled_logits), dim=1)
+
+            scaled_logits_embedding = self.transformer.wte(scaled_logits)
+            xs = torch.cat((x, scaled_logits_embedding), dim=1)
+            
+        
+        
+        return xs, B_motion
+
+
+    def sample_for_eval_CFG_babel_inference_new_demo(self, B_text, A_motion, if_categorial=False, length=312, clip_model=None, device=torch.device('cuda'), tokenizer='clip', unit_length=4, reference_end_token=None, cfg=4.5, threshold=3, temperature=1.0):
+
+        import clip 
+        B_token_length = length // unit_length - A_motion.shape[0]
+        
+        if tokenizer == 'clip':
+            A_text = clip.tokenize(A_text, truncate=True).to(device) 
+            A_feat_clip_text = clip_model.encode_text(A_text).float()  
+            B_text = clip.tokenize(B_text, truncate=True).to(device)
+            B_feat_clip_text = clip_model.encode_text(B_text).float()
+        elif tokenizer == 't5-xxl':
+            B_feat_clip_text = torch.from_numpy(clip_model.encode(B_text)).float()
+            B_feat_clip_text = B_feat_clip_text.to(device)
+
+        empty_clip_text = ''
+        if tokenizer == 'clip':
+            empty_text = clip.tokenize(empty_clip_text, truncate=True).to(device)
+            empty_feat_clip_text = clip_model.encode_text(empty_text).float()
+        elif tokenizer == 't5-xxl':
+            empty_feat_clip_text = torch.from_numpy(clip_model.encode(empty_clip_text)).float()   
+            empty_feat_clip_text = empty_feat_clip_text.unsqueeze(0)
+            empty_feat_clip_text = empty_feat_clip_text.to(device)
+        
+        B_text_embeddings = self.transformer.cond_embed(B_feat_clip_text).unsqueeze(0)
+        B_text_embeddings = B_text_embeddings.unsqueeze(0)
+
+        A_motion = A_motion.unsqueeze(0)
+        A_motion_embeddings = self.transformer.wte(A_motion)
+        B_motion = torch.tensor([]).to(device)
+
+        # 存储所有层的注意力权重
+        attention_weights = []
+
+        for k in range(B_token_length): 
+            if k == 0: 
+                x = torch.cat([B_text_embeddings, A_motion_embeddings], dim=1)
+               
+            else:
+                x = xs
+
+            
+            conditions = self.forward_babel_eval(x, return_attention=False)
+            conditions = conditions[:, -1, :]                             
+            
+
+            empty_feat_clip_text_embedding = self.transformer.cond_embed(empty_feat_clip_text).unsqueeze(0)
+            
+            if k == 0:
+                empty_input = torch.cat([empty_feat_clip_text_embedding, A_motion_embeddings], dim=1)
+                empty_conditions = self.forward_babel_eval(empty_input)
+            else:
+                B_motion_embeddings = self.transformer.wte(B_motion)
+                empty_input = torch.cat([empty_feat_clip_text_embedding, A_motion_embeddings, B_motion_embeddings], dim=1)
+                empty_conditions = self.forward_babel_eval(empty_input)
+
+            empty_conditions = empty_conditions[:, -1, :]                             
+           
+            mix_conditions = torch.cat([conditions, empty_conditions], dim=0)     
+            sampled_token_latent = self.diff_loss.sample(mix_conditions, temperature=temperature, cfg=cfg)  
+
+            # chunk
+            if cfg != 1:
+                scaled_logits, _ = sampled_token_latent.chunk(2, dim=0)           
+            else:
+                scaled_logits = sampled_token_latent
+
+            scaled_logits = scaled_logits.unsqueeze(0)      
+            
+            if reference_end_token is not None:
+                distance_l2 = torch.sqrt(torch.sum((scaled_logits - reference_end_token)**2))
+                print(distance_l2)
+                if distance_l2 < threshold and k > 10:
+                    break
+            
+            B_motion = torch.cat((B_motion, scaled_logits), dim=1)
+
+            scaled_logits_embedding = self.transformer.wte(scaled_logits)
+            xs = torch.cat((x, scaled_logits_embedding), dim=1)
+            
+        
+        
+        return xs, B_motion
+
+
+    
+    #--------------Test classification head--------------------
+    def sample_for_eval_classification(self, clip_text, if_categorial=False, length=196, clip_model=None, device=torch.device('cuda'), tokenizer='clip', unit_length=4):
+        
+        import clip
+       
+        
+        for k in range(51): 
+            if k == 0:
+                x = []
+            else:
+                x = xs
+
+            if tokenizer == 'clip':
+                text = clip.tokenize(clip_text, truncate=True).to(device)  
+
+                feat_clip_text = clip_model.encode_text(text).float() 
+            elif tokenizer == 't5-xxl':
+                feat_clip_text = torch.from_numpy(clip_model.module.encode(clip_text)).float()  
+           
+            conditions = self.forward(x, feat_clip_text)
+            conditions = conditions[:, -1, :]     
+
+            empty_clip_text = ''
+            if tokenizer == 'clip':
+                empty_text = clip.tokenize(empty_clip_text, truncate=True).to(device)
+                empty_feat_clip_text = clip_model.encode_text(empty_text).float()
+            elif tokenizer == 't5-xxl':
+                empty_feat_clip_text = torch.from_numpy(clip_model.module.encode(empty_clip_text)).float()  
+                empty_feat_clip_text = empty_feat_clip_text.unsqueeze(0)
+                empty_feat_clip_text = empty_feat_clip_text.to(device)
+
+            empty_conditions = self.forward(x, empty_feat_clip_text)     
+            empty_conditions = empty_conditions[:, -1, :]                    
+           
+            temperature = 1.0
+            cfg = 7.5
+            
+            mix_conditions = torch.cat([conditions, empty_conditions], dim=0) 
+            sampled_token_latent = self.diff_loss.sample(mix_conditions, temperature=temperature, cfg=cfg)  
+
+            # chunk
+            if cfg != 1:
+                scaled_logits, _ = sampled_token_latent.chunk(2, dim=0)        
+            else:
+                scaled_logits = sampled_token_latent
+
+
+            prediction_logits = self.classify_head(conditions)  
+            probs = torch.sigmoid(prediction_logits)
+            predicted_classes = torch.argmax(probs, dim=-1)
+            
+
+            scaled_logits = scaled_logits.unsqueeze(0)      
+                
+            if k == 0:
+                xs = scaled_logits
+            else:
+                xs = torch.cat((xs, scaled_logits), dim=1)
+
+            if predicted_classes == 1:
+                break
+
+        return xs
+    
+
+    #--------------------Test CFG-----------------------
+    def sample_for_eval_CFG_test(self, clip_text, if_categorial=False, length=196, clip_model=None, cfg=1, device=torch.device('cuda'), tokenizer='clip', unit_length=4):
+
+        import clip
+        max_token_len = length // unit_length
+        
+        
+        for k in range(max_token_len): 
+            if k == 0:
+                x = []
+            else:
+                x = xs
+
+            
+            if cfg != 1:
+                if tokenizer == 'clip':
+                    text = clip.tokenize(clip_text, truncate=True).to(device)  
+
+                    feat_clip_text = clip_model.encode_text(text).float() 
+                elif tokenizer == 't5-xxl':
+                    feat_clip_text = torch.from_numpy(clip_model.module.encode(clip_text)).float()  
+                
+                conditions = self.forward(x, feat_clip_text)       
+                
+                conditions = conditions[:, -1, :]                            
+                empty_clip_text = ''
+                if tokenizer == 'clip':
+                    empty_text = clip.tokenize(empty_clip_text, truncate=True).to(device)
+                    empty_feat_clip_text = clip_model.encode_text(empty_text).float()
+                elif tokenizer == 't5-xxl':
+                    empty_feat_clip_text = torch.from_numpy(clip_model.module.encode(empty_clip_text)).float()   
+                    empty_feat_clip_text = empty_feat_clip_text.unsqueeze(0)
+                    empty_feat_clip_text = empty_feat_clip_text.to(device)
+
+                empty_conditions = self.forward(x, empty_feat_clip_text)     
+                empty_conditions = empty_conditions[:, -1, :]                             
+                temperature = 1.0
+                
+                
+                mix_conditions = torch.cat([conditions, empty_conditions], dim=0)     
+                sampled_token_latent = self.diff_loss.sample(mix_conditions, temperature=temperature, cfg=cfg)  
+
+                # chunk
+                scaled_logits, _ = sampled_token_latent.chunk(2, dim=0)           
+            
+            else:   
+                if tokenizer == 'clip':
+                    text = clip.tokenize(clip_text, truncate=True).to(device)  
+                    feat_clip_text = clip_model.encode_text(text).float() 
+                elif tokenizer == 't5-xxl':
+                    feat_clip_text = torch.from_numpy(clip_model.module.encode(clip_text)).float()  
+                    feat_clip_text = feat_clip_text.to(device)
+
+
+                conditions = self.forward(x, feat_clip_text)       
+  
+                conditions = conditions[:, -1, :]                             
+                temperature = 1.0
+                sampled_token_latent = self.diff_loss.sample(conditions, temperature=temperature, cfg=cfg) 
+                scaled_logits = sampled_token_latent       
+
+            scaled_logits = scaled_logits.unsqueeze(0)      
+                
+            if k == 0:
+                xs = scaled_logits
+            else:
+                xs = torch.cat((xs, scaled_logits), dim=1)
+
+        return xs
+    #--------------------------------------------------
+
+    def forward_discrete(self, idx: torch.Tensor, clip_feature: torch.Tensor, use_cache=False, past_key_values=None) -> torch.Tensor:
+        if len(idx) == 0:
+            token_embeddings = self.transformer.cond_embed(clip_feature).unsqueeze(0)
+
+        else:
+            b, t = idx.size()
+            #idx = idx.float()
+            assert (
+                t <= self.config.block_size
+            ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
+
+            # forward the LLaMA model itself
+            token_embeddings = self.transformer.wte(idx)  
+            text_embeddings = self.transformer.cond_embed(clip_feature).unsqueeze(1)        
+            token_embeddings = torch.cat([text_embeddings, token_embeddings], dim=1) 
+        
+        x = token_embeddings
+
+        # -------------------kv cache-------------------
+        #presents = () if use_cache else None
+        if use_cache:
+            if past_key_values is None:
+                past_key_values = [None] * len(self.transformer.h)   
+            
+
+        for i,block in enumerate(self.transformer.h):
+            if use_cache:
+                last_past = past_key_values[i]     
+                x, presents = block(x, last_past, use_cache)
+                past_key_values[i] = list(presents)
+            else:
+                x = block(x)
+        x = self.transformer.ln_f(x)
+
+        logits = self.lm_head(x) 
+        
+
+        return logits
+
+
+    def forward(self, idx: torch.Tensor, feature: torch.Tensor) -> torch.Tensor:
+        if len(idx) == 0:
+            token_embeddings = self.transformer.cond_embed(feature).unsqueeze(0)
+
+        else:
+            b, t, c = idx.size()
+            idx = idx.float()
+            assert (
+                t <= self.config.block_size
+            ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
+
+            # forward the LLaMA model itself
+            token_embeddings = self.transformer.wte(idx)  
+            text_embeddings = self.transformer.cond_embed(feature).unsqueeze(1)    
+            token_embeddings = torch.cat([text_embeddings, token_embeddings], dim=1) 
+        
+        x = token_embeddings  
+
+        for i,block in enumerate(self.transformer.h):
+            x = block(x)
+        x = self.transformer.ln_f(x)    
+        logits = self.out_proj(x)
+        return logits
+
+
+    def forward_inference(self, idx: torch.Tensor, feature: torch.Tensor) -> torch.Tensor:
+        if len(idx) == 0:
+            token_embeddings = self.transformer.cond_embed(feature).unsqueeze(0)
+
+        else:
+            b, t, c = idx.size()
+            idx = idx.float()
+            assert (
+                t <= self.config.block_size
+            ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
+
+            # forward the LLaMA model itself
+            token_embeddings = self.transformer.wte(idx)  
+            text_embeddings = self.transformer.cond_embed(feature).unsqueeze(0)    
+            token_embeddings = torch.cat([text_embeddings.unsqueeze(0), token_embeddings], dim=1) 
+        
+        x = token_embeddings  
+
+        if len(x.shape) == 2:
+            x = x.unsqueeze(0)
+
+        for i,block in enumerate(self.transformer.h):
+            x = block(x)
+        x = self.transformer.ln_f(x)    
+        logits = self.out_proj(x)
+        return logits
+        
+
+    def babel_long(self, idx: torch.Tensor, clip_feature: torch.Tensor, use_cache=False, past_key_values=None, num_subseq=None, length=None) -> torch.Tensor:
+        
+        b, t, c = idx.size()
+        idx = idx.float()
+        idx = self.transformer.wte(idx)
+        assert (
+                t <= self.config.block_size
+            ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
+        for i in range(b):
+            length_i = length[i][:num_subseq[i]]       
+            clip_feature_i = clip_feature[i][:num_subseq[i]]
+
+            pointer = 0
+            for j in range(num_subseq[i]):
+                if j > 0:
+                    pointer += length_i[j].item()
+                    pointer += 1
+                pointer = int(pointer)
+
+                clip_feature_i_j = self.transformer.cond_embed(clip_feature_i[j].unsqueeze(0)).unsqueeze(1)
+                idx[i] = torch.cat([idx[i][:pointer].unsqueeze(0), clip_feature_i_j, idx[i][pointer:-1].unsqueeze(0)], dim=1)[0]
+                
+        x = idx
+
+
+        if use_cache:
+            if past_key_values is None:
+                past_key_values = [None] * len(self.transformer.h)   
+            
+
+        for i,block in enumerate(self.transformer.h):
+            if use_cache:
+                last_past = past_key_values[i]      
+                x, presents = block(x, last_past, use_cache)
+                past_key_values[i] = list(presents)
+            else:
+                x = block(x)
+        x = self.transformer.ln_f(x)
+
+        logits = self.out_proj(x)
+        return logits
+        
+
+    def forward_babel_eval(self, x, return_attention=False) -> torch.Tensor:
+        layer_attentions = []
+        for block in self.transformer.h:
+            if return_attention:
+                x, att = block(x, return_attention=True)
+                layer_attentions.append(att)
+            else:
+                x = block(x)
+        
+        x = self.transformer.ln_f(x)
+        if self.use_out_proj:
+            logits = self.out_proj(x)
+        else:
+            logits = x
+        
+        if return_attention:
+            return logits, layer_attentions
+        return logits
+    
+    def forward_babel(self, idx: torch.Tensor, clip_feature: torch.Tensor, A_token_length) -> torch.Tensor:
+        if len(idx) == 0:   # inference
+            token_embeddings = self.transformer.cond_embed(clip_feature).unsqueeze(1)
+
+        else:
+            b, t, c = idx.size()
+            idx = idx.float()
+            assert (
+                t <= self.config.block_size
+            ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
+
+            
+
+            A_feature = clip_feature[:, 0, :]             
+            B_feature = clip_feature[:, 1, :]                   
+
+
+            A_text_embeddings = self.transformer.cond_embed(A_feature).unsqueeze(1)
+            B_text_embeddings = self.transformer.cond_embed(B_feature).unsqueeze(1)
+
+            token_embeddings = torch.zeros(b, self.config.block_size, self.config.n_embd).to(idx.device)
+            for i in range(b):
+                A_idx = idx[i, :A_token_length[i].item(), :]
+                B_idx = idx[i, A_token_length[i].item():-2, :]
+                token_embeddings[i, :, :] = torch.cat([A_text_embeddings[i], self.BOM_tag, self.transformer.wte(A_idx), B_text_embeddings[i], self.BOM_tag, self.transformer.wte(B_idx)], dim=0)  #token_embeddings.shape = (b,t+1,1024)
+        
+        x = token_embeddings
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.ln_f(x)
+
+        if self.use_out_proj:
+            logits = self.out_proj(x)
+        else:
+            logits = x
+
+
+        return logits
+
+    def forward_babel2(self, idx: torch.Tensor, clip_feature: torch.Tensor) -> torch.Tensor:
+        if len(idx) == 0:   # inference
+            token_embeddings = self.transformer.cond_embed(clip_feature).unsqueeze(1)
+
+        else:
+            b, t, c = idx.size()
+            idx = idx.float()
+            assert (
+                t <= self.config.block_size
+            ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
+
+            B_feature = clip_feature
+            B_text_embeddings = self.transformer.cond_embed(B_feature)
+
+            idx_embeddings = self.transformer.wte(idx)
+
+            
+            token_embeddings = torch.cat([B_text_embeddings, idx_embeddings], dim=1)
+            
+        
+        x = token_embeddings
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.ln_f(x)
+
+        if self.use_out_proj:
+            logits = self.out_proj(x)
+        else:
+            logits = x
+
+        return logits
+    
+
+    def resize_token_embeddings(
+        self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, using_old_initilization: bool = False
+    ) -> nn.Embedding:
+        """
+        Resizes input token embeddings matrix of the model if `new_num_tokens != config.vocab_size`.
+
+        Takes care of tying weights embeddings afterwards if the model class has a `tie_weights()` method.
+
+        Arguments:
+            new_num_tokens (`int`, *optional*):
+                The new number of tokens in the embedding matrix. Increasing the size will add newly initialized
+                vectors at the end. Reducing the size will remove vectors from the end. If not provided or `None`, just
+                returns a pointer to the input tokens `torch.nn.Embedding` module of the model without doing anything.
+            pad_to_multiple_of (`int`, *optional*):
+                If set will pad the embedding matrix to a multiple of the provided value.If `new_num_tokens` is set to
+                `None` will just pad the embedding to a multiple of `pad_to_multiple_of`.
+
+                This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
+                `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. For more
+                details about this, or help on choosing the correct value for resizing, refer to this guide:
+                https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc
+
+        Return:
+            `torch.nn.Embedding`: Pointer to the input tokens Embeddings Module of the model.
+        """
+        model_embeds = self._resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+        if new_num_tokens is None and pad_to_multiple_of is None:
+            return model_embeds
+
+        # Update base model and current model config
+        self.config.vocab_size = model_embeds.weight.shape[0]
+        self.vocab_size = model_embeds.weight.shape[0]
+
+        # Tie weights again if needed
+        # self.tie_weights()
+
+        return model_embeds
+    
+    def _resize_token_embeddings(self, new_num_tokens, pad_to_multiple_of=None):
+        old_embeddings = self.get_input_embeddings()
+        new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens, pad_to_multiple_of)
+        old_embeddings_requires_grad = old_embeddings.weight.requires_grad
+        new_embeddings.requires_grad_(old_embeddings_requires_grad)
+        self.set_input_embeddings(new_embeddings)
+
+        # Update new_num_tokens with the actual size of new_embeddings
+        if pad_to_multiple_of is not None:
+            # if is_deepspeed_zero3_enabled():
+            #     import deepspeed
+
+            #     with deepspeed.zero.GatheredParameters(new_embeddings.weight, modifier_rank=None):
+            #         new_num_tokens = new_embeddings.weight.shape[0]
+            # else:
+            new_num_tokens = new_embeddings.weight.shape[0]
+
+        # if word embeddings are not tied, make sure that lm head is resized as well
+        # if self.get_output_embeddings() is not None and not self.config.tie_word_embeddings:
+        if self.get_output_embeddings() is not None and not False:
+            old_lm_head = self.get_output_embeddings()
+            new_lm_head = self._get_resized_lm_head(old_lm_head, new_num_tokens)
+            # if hasattr(old_lm_head, "_hf_hook"):
+            #     hook = old_lm_head._hf_hook
+            #     add_hook_to_module(new_lm_head, hook)
+            old_lm_head_requires_grad = old_lm_head.weight.requires_grad
+            new_lm_head.requires_grad_(old_lm_head_requires_grad)
+            self.set_output_embeddings(new_lm_head)
+
+        return self.get_input_embeddings()
+    
+    def _get_resized_embeddings(
+        self,
+        old_embeddings: nn.Embedding,
+        new_num_tokens: Optional[int] = None,
+        pad_to_multiple_of: Optional[int] = None,
+    ) -> nn.Embedding:
+        """
+        Build a resized Embedding Module from a provided token Embedding Module. Increasing the size will add newly
+        initialized vectors at the end. Reducing the size will remove vectors from the end
+
+        Args:
+            old_embeddings (`torch.nn.Embedding`):
+                Old embeddings to be resized.
+            new_num_tokens (`int`, *optional*):
+                New number of tokens in the embedding matrix.
+
+                Increasing the size will add newly initialized vectors at the end. Reducing the size will remove
+                vectors from the end. If not provided or `None`, just returns a pointer to the input tokens
+                `torch.nn.Embedding` module of the model without doing anything.
+            pad_to_multiple_of (`int`, *optional*):
+                If set will pad the embedding matrix to a multiple of the provided value. If `new_num_tokens` is set to
+                `None` will just pad the embedding to a multiple of `pad_to_multiple_of`.
+
+                This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
+                `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. For more
+                details about this, or help on choosing the correct value for resizing, refer to this guide:
+                https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc
+
+
+        Return:
+            `torch.nn.Embedding`: Pointer to the resized Embedding Module or the old Embedding Module if
+            `new_num_tokens` is `None`
+        """
+
+        if pad_to_multiple_of is not None:
+            if not isinstance(pad_to_multiple_of, int):
+                raise ValueError(
+                    f"Asking to pad the embedding matrix to a multiple of `{pad_to_multiple_of}`, which is not and integer. Please make sure to pass an integer"
+                )
+            if new_num_tokens is None:
+                new_num_tokens = old_embeddings.weight.shape[0]
+            new_num_tokens = ((new_num_tokens + pad_to_multiple_of - 1) // pad_to_multiple_of) * pad_to_multiple_of
+        else:
+            print(
+                "You are resizing the embedding layer without providing a `pad_to_multiple_of` parameter. This means that the new embedding"
+                f" dimension will be {new_num_tokens}. This might induce some performance reduction as *Tensor Cores* will not be available."
+                " For more details about this, or help on choosing the correct value for resizing, refer to this guide:"
+                " https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc"
+            )
+
+        if new_num_tokens is None:
+            return old_embeddings
+
+        # if is_deepspeed_zero3_enabled():
+        if False:
+            import deepspeed
+
+            with deepspeed.zero.GatheredParameters(old_embeddings.weight, modifier_rank=None):
+                old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
+        else:
+            old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
+
+        # if old_num_tokens == new_num_tokens and not is_deepspeed_zero3_enabled():
+        if old_num_tokens == new_num_tokens and not False:
+            return old_embeddings
+
+        if not isinstance(old_embeddings, nn.Embedding):
+            raise TypeError(
+                f"Old embeddings are of type {type(old_embeddings)}, which is not an instance of {nn.Embedding}. You"
+                " should either use a different resize function or make sure that `old_embeddings` are an instance of"
+                f" {nn.Embedding}."
+            )
+
+        # Build new embeddings
+
+        # When using DeepSpeed ZeRO-3, we shouldn't create new embeddings with DeepSpeed init
+        # because the shape of the new embedding layer is used across various modeling files
+        # as well as to update config vocab size. Shape will be 0 when using DeepSpeed init leading
+        # to errors when training.
+        new_embeddings = nn.Embedding(
+            new_num_tokens,
+            old_embedding_dim,
+            device=old_embeddings.weight.device,
+            dtype=old_embeddings.weight.dtype,
+        )
+
+        # initialize all new embeddings (in particular added tokens)
+        self._init_weights(new_embeddings)
+
+        # Copy token embeddings from the previous weights
+
+        # numbers of tokens to copy
+        n = min(old_num_tokens, new_num_tokens)
+
+        # if is_deepspeed_zero3_enabled():
+        if False:
+            import deepspeed
+
+            params = [old_embeddings.weight, new_embeddings.weight]
+            with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
+                new_embeddings.weight.data[:n, :] = old_embeddings.weight.data[:n, :]
+        else:
+            new_embeddings.weight.data[:n, :] = old_embeddings.weight.data[:n, :]
+
+        return new_embeddings
+
+
+    def _get_resized_lm_head(
+        self, old_lm_head: nn.Linear, new_num_tokens: Optional[int] = None, transposed: Optional[bool] = False
+    ) -> nn.Linear:
+        """
+        Build a resized Linear Module from a provided old Linear Module. Increasing the size will add newly initialized
+        vectors at the end. Reducing the size will remove vectors from the end
+
+        Args:
+            old_lm_head (`torch.nn.Linear`):
+                Old lm head liner layer to be resized.
+            new_num_tokens (`int`, *optional*):
+                New number of tokens in the linear matrix.
+
+                Increasing the size will add newly initialized vectors at the end. Reducing the size will remove
+                vectors from the end. If not provided or `None`, just returns a pointer to the input tokens
+                `torch.nn.Linear` module of the model without doing anything. transposed (`bool`, *optional*, defaults
+                to `False`): Whether `old_lm_head` is transposed or not. If True `old_lm_head.size()` is `lm_head_dim,
+                vocab_size` else `vocab_size, lm_head_dim`.
+
+        Return:
+            `torch.nn.Linear`: Pointer to the resized Linear Module or the old Linear Module if `new_num_tokens` is
+            `None`
+        """
+        if new_num_tokens is None:
+            return old_lm_head
+
+        # if is_deepspeed_zero3_enabled():
+        if False:
+            import deepspeed
+
+            with deepspeed.zero.GatheredParameters(old_lm_head.weight, modifier_rank=None):
+                old_num_tokens, old_lm_head_dim = (
+                    old_lm_head.weight.size() if not transposed else old_lm_head.weight.t().size()
+                )
+        else:
+            old_num_tokens, old_lm_head_dim = (
+                old_lm_head.weight.size() if not transposed else old_lm_head.weight.t().size()
+            )
+
+        # if old_num_tokens == new_num_tokens and not is_deepspeed_zero3_enabled():
+        if old_num_tokens == new_num_tokens and not False:
+            return old_lm_head
+
+        if not isinstance(old_lm_head, nn.Linear):
+            raise TypeError(
+                f"Old language model head is of type {type(old_lm_head)}, which is not an instance of {nn.Linear}. You"
+                " should either use a different resize function or make sure that `old_lm_head` are an instance of"
+                f" {nn.Linear}."
+            )
+
+        # Build new lm head
+        new_lm_head_shape = (old_lm_head_dim, new_num_tokens) if not transposed else (new_num_tokens, old_lm_head_dim)
+        has_new_lm_head_bias = old_lm_head.bias is not None
+
+        # When using DeepSpeed ZeRO-3, we shouldn't create new embeddings with DeepSpeed init
+        # because the shape of the new embedding layer is used across various modeling files
+        # as well as to update config vocab size. Shape will be 0 when using DeepSpeed init leading
+        # to errors when training.
+        new_lm_head = nn.Linear(
+            *new_lm_head_shape,
+            bias=has_new_lm_head_bias,
+            device=old_lm_head.weight.device,
+            dtype=old_lm_head.weight.dtype,
+        )
+
+        # initialize new lm head (in particular added tokens)
+        self._init_weights(new_lm_head)
+
+        num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
+
+        # if is_deepspeed_zero3_enabled():
+        if False:
+            import deepspeed
+
+            params = [old_lm_head.weight, old_lm_head.bias, new_lm_head.weight, new_lm_head.bias]
+            with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
+                self._copy_lm_head_original_to_resized(
+                    new_lm_head, old_lm_head, num_tokens_to_copy, transposed, has_new_lm_head_bias
+                )
+        else:
+            self._copy_lm_head_original_to_resized(
+                new_lm_head, old_lm_head, num_tokens_to_copy, transposed, has_new_lm_head_bias
+            )
+
+        return new_lm_head
+
+    def _copy_lm_head_original_to_resized(
+        self, new_lm_head, old_lm_head, num_tokens_to_copy, transposed, has_new_lm_head_bias
+    ):
+        # Copy old lm head weights to new lm head
+        if not transposed:
+            new_lm_head.weight.data[:num_tokens_to_copy, :] = old_lm_head.weight.data[:num_tokens_to_copy, :]
+        else:
+            new_lm_head.weight.data[:, :num_tokens_to_copy] = old_lm_head.weight.data[:, :num_tokens_to_copy]
+
+        # Copy bias weights to new lm head
+        if has_new_lm_head_bias:
+            new_lm_head.bias.data[:num_tokens_to_copy] = old_lm_head.bias.data[:num_tokens_to_copy]
+
+    @classmethod
+    def from_name(cls, name: str) -> Self:
+        return cls(LLaMAHFConfig.from_name(name))
+
+
+class Block(nn.Module):
+    def __init__(self, config: LLaMAHFConfig) -> None:
+        super().__init__()
+        self.rms_1 = RMSNorm(config.n_embd)
+
+        # sentence level:
+        self.attn = CausalSelfAttention(config)        
+        self.rms_2 = RMSNorm(config.n_embd)
+        self.mlp = MLP(config)
+
+    def forward(self, x: torch.Tensor, last_past=None, use_cache=False, return_attention=False) -> torch.Tensor:
+        if use_cache:  
+            if return_attention:
+                a, attn = self.attn.forward_attn(self.rms_1(x), last_past, use_cache)
+            else:
+                a, present = self.attn(self.rms_1(x), last_past, use_cache)
+            x = x + a
+        else:
+            if return_attention:
+                a, attn = self.attn.forward_attn(self.rms_1(x))
+            else:
+                a = self.attn(self.rms_1(x))
+            x = x + a
+        x = x + self.mlp(self.rms_2(x))
+
+        if use_cache:
+            if return_attention:
+                return x, present, attn
+            else:
+                return x, present  
+        else:
+            if return_attention:
+                return x, attn
+            else:
+                return x
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config: LLaMAHFConfig) -> None:
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+
+        # key, query, value projections for all heads, but in a batch
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=False)
+        # output projection
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=False)
+
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.block_size = config.block_size
+        self.rope_cache = None
+
+        def scaling_factor(sequence_threshold):
+            return np.log2((sequence_threshold**2) - sequence_threshold)
+        scale_init = scaling_factor(self.block_size) 
+        self.scale = nn.Parameter(torch.tensor(scale_init))
+
+    def forward(self, x: torch.Tensor, last_past=None, use_cache=False) -> torch.Tensor:
+        B, T, C = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)
+
+        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
+        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
+
+        head_size = C // self.n_head
+        k = k.view(B, T, self.n_head, head_size).transpose(1, 2)  # (B, nh, T, hs)
+        q = q.view(B, T, self.n_head, head_size).transpose(1, 2)  # (B, nh, T, hs)
+        v = v.view(B, T, self.n_head, head_size).transpose(1, 2)  # (B, nh, T, hs)
+
+        # kv_cache
+        if use_cache:
+            if last_past is not None:
+                past_key, past_value = last_past
+                k = torch.cat([past_key, k], dim=-2)  
+                v = torch.cat([past_value, v], dim=-2)   
+            # else:
+            #     key_states = k
+            #     value_states = v
+
+        if use_cache:
+            present = (k, v)
+        else:
+            present = None
+
+        # QK-Norm
+        q = F.normalize(q, p=2, dim=-1)
+        k = F.normalize(k, p=2, dim=-1)
+
+        if self.rope_cache is None:
+            # cache for future forward calls
+            self.rope_cache = build_rope_cache(
+                seq_len=self.block_size,
+                n_elem=self.n_embd // self.n_head, 
+                dtype=x.dtype,
+                device=x.device,
+            )
+
+
+        q = apply_rope(q, self.rope_cache)
+        k = apply_rope(k, self.rope_cache)
+
+
+        
+        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
+        #  att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+        #  att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
+        #  att = F.softmax(att, dim=-1)
+        #  y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+
+        # efficient attention using Flash Attention CUDA kernels
+        y = F.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True, scale=self.scale.item())
+        
+        y = y.transpose(1, 2).contiguous().view(B, T, C)  # re-assemble all head outputs side by side
+
+        # output projection
+        y = self.c_proj(y)
+
+        
+        if use_cache:
+            return y, present
+        return y
+
+    def forward_attn(self, x: torch.Tensor, last_past=None, use_cache=False) -> torch.Tensor:
+        B, T, C = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)
+
+        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
+        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
+
+        head_size = C // self.n_head
+        k = k.view(B, T, self.n_head, head_size).transpose(1, 2)  # (B, nh, T, hs)
+        q = q.view(B, T, self.n_head, head_size).transpose(1, 2)  # (B, nh, T, hs)
+        v = v.view(B, T, self.n_head, head_size).transpose(1, 2)  # (B, nh, T, hs)
+
+        # kv_cache
+        if use_cache:
+            if last_past is not None:
+                past_key, past_value = last_past
+                k = torch.cat([past_key, k], dim=-2)  
+                v = torch.cat([past_value, v], dim=-2) 
+            # else:
+            #     key_states = k
+            #     value_states = v
+
+        if use_cache:
+            present = (k, v)
+        else:
+            present = None
+
+        # QK-Norm
+        q = F.normalize(q, p=2, dim=-1)
+        k = F.normalize(k, p=2, dim=-1)
+
+        if self.rope_cache is None:
+            # cache for future forward calls
+            self.rope_cache = build_rope_cache(
+                seq_len=self.block_size,
+                n_elem=self.n_embd // self.n_head, 
+                dtype=x.dtype,
+                device=x.device,
+            )
+
+
+        q = apply_rope(q, self.rope_cache)
+        k = apply_rope(k, self.rope_cache)
+
+
+        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+        att = F.softmax(att, dim=-1)  # [B, n_head, T, T]
+
+        # efficient attention using Flash Attention CUDA kernels
+        y = F.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True)
+        y = y.transpose(1, 2).contiguous().view(B, T, C)  # re-assemble all head outputs side by side
+
+        # output projection
+        y = self.c_proj(y)
+        
+        return y, att
+
+class LengthCausalSelfAttention(nn.Module):
+    def __init__(self, config: LLaMAHFConfig) -> None:
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+
+        # key, query, value projections for all heads, but in a batch
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=False)
+        # output projection
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=False)
+
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.block_size = config.block_size
+        self.rope_cache = None
+
+    def forward(self, x: torch.Tensor, y_mask: torch.Tensor) -> torch.Tensor:
+        B, T, C = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)
+
+        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
+        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
+
+        head_size = C // self.n_head
+        k = k.view(B, T, self.n_head, head_size).transpose(1, 2)  # (B, nh, T, hs)
+        q = q.view(B, T, self.n_head, head_size).transpose(1, 2)  # (B, nh, T, hs)
+        v = v.view(B, T, self.n_head, head_size).transpose(1, 2)  # (B, nh, T, hs)
+
+        if self.rope_cache is None:
+            # cache for future forward calls
+            self.rope_cache = build_rope_cache(
+                seq_len=self.block_size,
+                n_elem=self.n_embd // self.n_head, 
+                dtype=x.dtype,
+                device=x.device,
+            )
+
+
+        # q: 1, 16, 40 ,64
+        # q: 128, 16, 106, 64
+        q = apply_rope(q, self.rope_cache)
+        k = apply_rope(k, self.rope_cache)
+ 
+        attn_mask = torch.ones(T, T, dtype=torch.bool, device=x.device)
+        attn_mask = torch.tril(attn_mask) 
+        attn_mask = attn_mask.unsqueeze(0).expand(B, -1, -1)
+
+        text_mask = y_mask.unsqueeze(2)*y_mask.unsqueeze(1)
+        text_mask = F.pad(text_mask, (0, T-y_mask.shape[1], 0, T-y_mask.shape[1]), mode='constant', value=0)
+        attn_mask = torch.logical_or(attn_mask, text_mask)
+
+        y = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask.unsqueeze(1), dropout_p=0.0, is_causal=False)
+
+        y = y.transpose(1, 2).contiguous().view(B, T, C) 
+
+     
+        y = self.c_proj(y)
+
+        return y
+
+
+class MLP(nn.Module):
+    def __init__(self, config: LLaMAHFConfig) -> None:
+        super().__init__()
+        hidden_dim = 4 * config.n_embd
+        n_hidden = int(2 * hidden_dim / 3)
+        N = 256
+        # ensure n_hidden is multiple of N
+        n_hidden = ((n_hidden - 1) // N) * N + N
+
+        self.c_fc1 = nn.Linear(config.n_embd, n_hidden, bias=False)
+        self.c_fc2 = nn.Linear(config.n_embd, n_hidden, bias=False)
+        self.c_proj = nn.Linear(n_hidden, config.n_embd, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+       
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class RMSNorm(nn.Module):
+    """Root Mean Square Layer Normalization.
+
+    Derived from https://github.com/bzhangGo/rmsnorm/blob/master/rmsnorm_torch.py. BSD 3-Clause License:
+    https://github.com/bzhangGo/rmsnorm/blob/master/LICENSE.
+    """
+
+    def __init__(self, size: int, dim: int = -1, eps: float = 1e-5) -> None:
+        super().__init__()
+        self.scale = nn.Parameter(torch.ones(size))
+        self.eps = eps
+        self.dim = dim
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # NOTE: the original RMSNorm paper implementation is not equivalent
+        # norm_x = x.norm(2, dim=self.dim, keepdim=True)
+        # rms_x = norm_x * d_x ** (-1. / 2)
+        # x_normed = x / (rms_x + self.eps)
+        norm_x = torch.mean(x * x, dim=self.dim, keepdim=True)
+        x_normed = x * torch.rsqrt(norm_x + self.eps)
+        return self.scale * x_normed
+
+
+def build_rope_cache(seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000) -> torch.Tensor:
+    """Enhanced Transformer with Rotary Position Embedding.
+
+    Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/
+    transformers/rope/__init__.py. MIT License:
+    https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license.
+    """
+    # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
+    theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=dtype, device=device) / n_elem))
+
+    # Create position indexes `[0, 1, ..., seq_len - 1]`
+    seq_idx = torch.arange(seq_len, dtype=dtype, device=device)
+
+    # Calculate the product of position index and $\theta_i$
+    idx_theta = torch.outer(seq_idx, theta)
+
+    # Compute cache. Because polar only takes float32 or float64, we need to cast
+    # when working with 16 bit floats (float16 or bfloat16)
+    dtypes_requiring_casting = [torch.float16, torch.bfloat16, torch.int8]
+    working_dtype = (
+        torch.float32 if dtype in dtypes_requiring_casting else dtype
+    )
+    complex_dtype = (
+        torch.complex32 if dtype in dtypes_requiring_casting else torch.complex64
+    )
+    cache = torch.polar(
+        torch.ones_like(idx_theta).to(working_dtype), idx_theta.to(working_dtype)
+    ).to(complex_dtype)
+    return cache
+
+
+def apply_rope(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
+    x = x.transpose(1, 2)
+
+    # truncate to support variable sizes
+    T = x.size(1)
+    rope_cache = rope_cache[:T]
+    # cast because `view_as_complex` does not support 16 bit tensors
+    xc = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
+    rope_cache = rope_cache.view(1, xc.size(1), 1, xc.size(3))
+    x_out = torch.view_as_real(xc * rope_cache).flatten(3)
+    return x_out.transpose(1, 2).type_as(x)
\ No newline at end of file
diff --git a/models/resnet.py b/models/resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..7fac134054d07728b75a2ddd603bd96c0c47343c
--- /dev/null
+++ b/models/resnet.py
@@ -0,0 +1,160 @@
+import torch.nn as nn
+import torch
+
+class nonlinearity(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        # swish
+        return x * torch.sigmoid(x)
+
+class ResConv1DBlock(nn.Module):
+    def __init__(self, n_in, n_state, dilation=1, activation='silu', norm=None, dropout=None):
+        super().__init__()
+        padding = dilation
+        self.norm = norm
+        if norm == "LN":
+            self.norm1 = nn.LayerNorm(n_in)
+            self.norm2 = nn.LayerNorm(n_in)
+        elif norm == "GN":
+            self.norm1 = nn.GroupNorm(num_groups=32, num_channels=n_in, eps=1e-6, affine=True)
+            self.norm2 = nn.GroupNorm(num_groups=32, num_channels=n_in, eps=1e-6, affine=True)
+        elif norm == "BN":
+            self.norm1 = nn.BatchNorm1d(num_features=n_in, eps=1e-6, affine=True)
+            self.norm2 = nn.BatchNorm1d(num_features=n_in, eps=1e-6, affine=True)
+        
+        else:
+            self.norm1 = nn.Identity()
+            self.norm2 = nn.Identity()
+
+        if activation == "relu":
+            self.activation1 = nn.ReLU()
+            self.activation2 = nn.ReLU()
+            
+        elif activation == "silu":
+            self.activation1 = nonlinearity()
+            self.activation2 = nonlinearity()
+            
+        elif activation == "gelu":
+            self.activation1 = nn.GELU()
+            self.activation2 = nn.GELU()
+        
+    
+        self.conv1 = nn.Conv1d(n_in, n_state, 3, 1, padding, dilation)
+        self.conv2 = nn.Conv1d(n_state, n_in, 1, 1, 0,)     
+
+
+    def forward(self, x):
+        x_orig = x
+        if self.norm == "LN":
+            x = self.norm1(x.transpose(-2, -1))
+            x = self.activation1(x.transpose(-2, -1))
+        else:
+            x = self.norm1(x)
+            x = self.activation1(x)
+            
+        x = self.conv1(x)
+
+        if self.norm == "LN":
+            x = self.norm2(x.transpose(-2, -1))
+            x = self.activation2(x.transpose(-2, -1))
+        else:
+            x = self.norm2(x)
+            x = self.activation2(x)
+
+        x = self.conv2(x)
+        x = x + x_orig
+        return x
+
+class Resnet1D(nn.Module):
+    def __init__(self, n_in, n_depth, dilation_growth_rate=1, reverse_dilation=True, activation='relu', norm=None):
+        super().__init__()
+        
+        blocks = [ResConv1DBlock(n_in, n_in, dilation=dilation_growth_rate ** depth, activation=activation, norm=norm) for depth in range(n_depth)]
+        if reverse_dilation:
+            blocks = blocks[::-1]
+        
+        self.model = nn.Sequential(*blocks)
+
+    def forward(self, x):        
+        return self.model(x)
+    
+
+
+class CausalResConv1DBlock(nn.Module):
+    def __init__(self, n_in, n_state, dilation=1, activation='silu', norm=None, dropout=None):
+        super().__init__()
+        self.norm = norm
+        if norm == "LN":
+            self.norm1 = nn.LayerNorm(n_in)
+            self.norm2 = nn.LayerNorm(n_in)
+        elif norm == "GN":
+            self.norm1 = nn.GroupNorm(num_groups=32, num_channels=n_in, eps=1e-6, affine=True)
+            self.norm2 = nn.GroupNorm(num_groups=32, num_channels=n_in, eps=1e-6, affine=True)
+        elif norm == "BN":
+            self.norm1 = nn.BatchNorm1d(num_features=n_in, eps=1e-6, affine=True)
+            self.norm2 = nn.BatchNorm1d(num_features=n_in, eps=1e-6, affine=True)
+        else:
+            self.norm1 = nn.Identity()
+            self.norm2 = nn.Identity()
+
+        if activation == "relu":
+            self.activation1 = nn.ReLU()
+            self.activation2 = nn.ReLU()
+        elif activation == "silu":
+            self.activation1 = nonlinearity()
+            self.activation2 = nonlinearity()
+        elif activation == "gelu":
+            self.activation1 = nn.GELU()
+            self.activation2 = nn.GELU()
+
+        self.left_padding = (3 - 1) * dilation
+
+        self.conv1 = nn.Conv1d(n_in, n_state, kernel_size=3, stride=1, padding=0, dilation=dilation)
+        self.conv2 = nn.Conv1d(n_state, n_in, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, x):
+        x_orig = x
+        if self.norm == "LN":
+            x = self.norm1(x.transpose(-2, -1)).transpose(-2, -1)
+            x = self.activation1(x)
+        else:
+            x = self.norm1(x)
+            x = self.activation1(x)
+
+        x = nn.functional.pad(x, (self.left_padding, 0))
+
+        x = self.conv1(x)
+
+        if self.norm == "LN":
+            x = self.norm2(x.transpose(-2, -1)).transpose(-2, -1)
+            x = self.activation2(x)
+        else:
+            x = self.norm2(x)
+            x = self.activation2(x)
+
+        x = self.conv2(x)
+        x = x + x_orig  
+        return x
+
+class CausalResnet1D(nn.Module):
+    def __init__(self, n_in, n_depth, dilation_growth_rate=1, reverse_dilation=True, activation='relu', norm=None):
+        super().__init__()
+
+        blocks = [
+            CausalResConv1DBlock(
+                n_in,
+                n_in,
+                dilation=dilation_growth_rate ** depth,
+                activation=activation,
+                norm=norm
+            ) for depth in range(n_depth)
+        ]
+        if reverse_dilation:
+            blocks = blocks[::-1]
+
+        self.model = nn.Sequential(*blocks)
+
+    def forward(self, x):
+        return self.model(x)
\ No newline at end of file
diff --git a/models/tae.py b/models/tae.py
new file mode 100644
index 0000000000000000000000000000000000000000..f610c6895c4857da74a566e355736ac86338ff5a
--- /dev/null
+++ b/models/tae.py
@@ -0,0 +1,94 @@
+import torch.nn as nn
+from models.causal_cnn import CausalEncoder, CausalDecoder
+
+
+# Causal TAE:
+class Causal_TAE(nn.Module):
+    def __init__(self,
+                 hidden_size=1024,
+                 down_t=2,
+                 stride_t=2,
+                 width=1024,
+                 depth=3,
+                 dilation_growth_rate=3,
+                 activation='relu',
+                 norm=None,
+                 latent_dim=16,
+                 clip_range = []
+                 ):
+        
+        super().__init__()
+
+        self.decode_proj = nn.Linear(latent_dim, width)  
+
+        self.encoder = CausalEncoder(272, hidden_size, down_t, stride_t, width, depth, dilation_growth_rate, activation=activation, norm=norm, latent_dim=latent_dim, clip_range=clip_range)
+        self.decoder = CausalDecoder(272, hidden_size, down_t, stride_t, width, depth, dilation_growth_rate, activation=activation, norm=norm)
+    
+
+
+    def preprocess(self, x):
+        x = x.permute(0,2,1).float()
+        return x
+
+
+    def postprocess(self, x):
+        x = x.permute(0,2,1)
+        return x
+
+
+    def encode(self, x):
+        x_in = self.preprocess(x)
+        x_encoder, mu, logvar = self.encoder(x_in)
+        x_encoder = self.postprocess(x_encoder)
+        x_encoder = x_encoder.contiguous().view(-1, x_encoder.shape[-1])  
+        
+        return x_encoder, mu, logvar
+
+
+    def forward(self, x):
+        x_in = self.preprocess(x)       
+        # Encode
+        x_encoder, mu, logvar = self.encoder(x_in)  
+        x_encoder = self.decode_proj(x_encoder) 
+        # decoder
+        x_decoder = self.decoder(x_encoder)
+        x_out = self.postprocess(x_decoder)  
+        return x_out, mu, logvar
+
+
+    def forward_decoder(self, x):         
+        # decoder
+        x_width = self.decode_proj(x)           
+        x_decoder = self.decoder(x_width)
+        x_out = self.postprocess(x_decoder)
+        return x_out
+
+
+class Causal_HumanTAE(nn.Module):
+    def __init__(self,
+                 hidden_size=1024,
+                 down_t=2,
+                 stride_t=2,
+                 depth=3,
+                 dilation_growth_rate=3,
+                 activation='relu',
+                 norm=None,
+                 latent_dim=16,
+                 clip_range = []
+                 ):
+        
+        super().__init__()
+        self.tae = Causal_TAE(hidden_size, down_t, stride_t, hidden_size, depth, dilation_growth_rate, activation=activation, norm=norm, latent_dim=latent_dim, clip_range=clip_range)
+
+    def encode(self, x):
+        h, mu, logvar = self.tae.encode(x) 
+        return h, mu, logvar
+
+    def forward(self, x):
+        x_out, mu, logvar = self.tae(x)
+        return x_out, mu, logvar
+
+    def forward_decoder(self, x):
+        x_out = self.tae.forward_decoder(x)
+        return x_out
+        
\ No newline at end of file
diff --git a/options/__pycache__/option_tae.cpython-39.pyc b/options/__pycache__/option_tae.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6343bddde9c1a12727193f30190f8481ba9c97ba
Binary files /dev/null and b/options/__pycache__/option_tae.cpython-39.pyc differ
diff --git a/options/__pycache__/option_transformer.cpython-39.pyc b/options/__pycache__/option_transformer.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3ea1734e680b6b3229a6e80dbe73b89552914618
Binary files /dev/null and b/options/__pycache__/option_transformer.cpython-39.pyc differ
diff --git a/options/option_tae.py b/options/option_tae.py
new file mode 100644
index 0000000000000000000000000000000000000000..b04f830a7e66e158ae5c1822c37c39eabdbd5d72
--- /dev/null
+++ b/options/option_tae.py
@@ -0,0 +1,51 @@
+import argparse
+
+def get_args_parser():
+    parser = argparse.ArgumentParser(description='Optimal Transport AutoEncoder training for AIST',
+                                     add_help=True,
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+    ## dataloader  
+    parser.add_argument('--dataname', type=str, default='t2m_272', help='dataset directory')
+    parser.add_argument('--batch-size', default=128, type=int, help='batch size')
+    parser.add_argument('--window-size', type=int, default=64, help='training motion length')
+
+    ## optimization
+    parser.add_argument('--total-iter', default=2000000, type=int, help='number of total iterations to run')
+    parser.add_argument('--warm-up-iter', default=1000, type=int, help='number of total iterations for warmup')
+    parser.add_argument('--lr', default=5e-5, type=float, help='max learning rate')
+    parser.add_argument('--lr-scheduler', default=[50000, 400000], nargs="+", type=int, help="learning rate schedule (iterations)")
+    parser.add_argument('--gamma', default=0.05, type=float, help="learning rate decay")
+
+    parser.add_argument('--weight-decay', default=0.0, type=float, help='weight decay')
+    
+    # causal TAE architecture
+    parser.add_argument("--down-t", type=int, default=2, help="downsampling rate")
+    parser.add_argument("--stride-t", type=int, default=2, help="stride size")
+    parser.add_argument("--depth", type=int, default=3, help="depth of the network")
+    parser.add_argument("--dilation-growth-rate", type=int, default=3, help="dilation growth rate")
+
+    ## resume
+    parser.add_argument("--resume-pth", type=str, default=None, help='resume pth for causal TAE')
+    
+    
+    ## output directory 
+    parser.add_argument('--out-dir', type=str, default='output/', help='output directory')
+    parser.add_argument('--results-dir', type=str, default='visual_results/', help='output directory')
+    parser.add_argument('--visual-name', type=str, default='vis', help='output directory')
+    parser.add_argument('--exp-name', type=str, default='exp', help='name of the experiment, will create a file inside out-dir')
+    parser.add_argument('--latent_dir', type=str, default='t2m_latents/', help='latent directory')
+    ## other
+    parser.add_argument('--print-iter', default=200, type=int, help='print frequency')
+    parser.add_argument('--eval-iter', default=20000, type=int, help='evaluation frequency')
+    parser.add_argument('--seed', default=123, type=int, help='seed for initializing training.')
+    
+    parser.add_argument('--vis-gt', action='store_true', help='whether visualize GT motions')
+    parser.add_argument('--nb-vis', default=20, type=int, help='nb of visualizations')
+    parser.add_argument('--root_loss', default=7.0, type=float, help='root loss')
+    parser.add_argument('--latent_dim', default=16, type=int, help='latent dimension')
+    parser.add_argument('--hidden_size', default=1024, type=int, help='hidden size')
+    parser.add_argument('--nb_joints', default=22, type=int, help='number of joints')
+    parser.add_argument('--num_gpus', default=1, type=int, help='number of GPUs')
+    
+    return parser.parse_args()
\ No newline at end of file
diff --git a/options/option_transformer.py b/options/option_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..efa152573ed5cd1ca80fac14657a7017155078b7
--- /dev/null
+++ b/options/option_transformer.py
@@ -0,0 +1,41 @@
+import argparse
+
+def get_args_parser():
+    parser = argparse.ArgumentParser(description='options',
+                                     add_help=True,
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    
+    parser.add_argument('--dataname', type=str, default='t2m_272', help='dataset directory') 
+    parser.add_argument('--seed', default=123, type=int, help='seed for initializing training. ')
+    parser.add_argument('--batch_size', default=256, type=int, help='batch size for training. ')
+    parser.add_argument('--latent_dir', type=str, default='latent/', help='latent directory')
+    parser.add_argument("--resume-pth", type=str, default=None, help='resume pth for causal TAE')
+    parser.add_argument("--resume-trans", type=str, default=None, help='resume gpt pth')
+    parser.add_argument('--out-dir', type=str, default='output_GPT_Final/', help='output directory')
+    parser.add_argument('--exp-name', type=str, default='exp', help='name of the experiment, will create a file inside out-dir')
+    parser.add_argument('--hidden_size', default=1024, type=int, help='hidden size')
+    parser.add_argument("--down-t", type=int, default=2, help="downsampling rate")
+    parser.add_argument("--stride-t", type=int, default=2, help="stride size")
+    parser.add_argument("--depth", type=int, default=3, help="depth of the network")
+    parser.add_argument("--dilation-growth-rate", type=int, default=3, help="dilation growth rate")
+
+    parser.add_argument('--num_diffusion_head_layers', type=int, default=9, help='number of diffusion head layers')
+    parser.add_argument('--latent_dim', type=int, default=16, help='latent dimension')
+    parser.add_argument('--total_iter', type=int, default=100000, help='total iteration')
+    parser.add_argument('--lr', default=1e-4, type=float, help='max learning rate')
+    parser.add_argument('--gamma', default=0.05, type=float, help="learning rate decay")
+    
+    parser.add_argument('--decay-option',default='all', type=str, choices=['all'], help='weight decay option')
+    parser.add_argument('--weight-decay', default=1e-6, type=float, help='weight decay') 
+    parser.add_argument('--optimizer',default='adamw', type=str, choices=['adam', 'adamw'], help='optimizer')
+
+    parser.add_argument('--num_gpus', default=1, type=int, help='number of GPUs')
+    parser.add_argument('--total-iter', default=2000000, type=int, help='number of total iterations to run')
+    parser.add_argument('--batch-size', default=256, type=int, help='batch size')
+
+    parser.add_argument('--text', type=str, default='A man is jogging around.')
+    parser.add_argument('--mode', type=str, default='pos', choices=['pos', 'rot'], help='recover mode, pos: position, rot: rotation')
+
+
+
+    return parser.parse_args()
\ No newline at end of file
diff --git a/reference_end_latent_t2m_272.npy b/reference_end_latent_t2m_272.npy
new file mode 100644
index 0000000000000000000000000000000000000000..740cafd7a48648a88b3617f6ad16ce494dc82401
--- /dev/null
+++ b/reference_end_latent_t2m_272.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c47575a54f3d46152634f3103579738ba5dec7b0d90732be00efea2fcc043006
+size 192
diff --git a/reference_end_latent_t2m_babel_272.npy b/reference_end_latent_t2m_babel_272.npy
new file mode 100644
index 0000000000000000000000000000000000000000..66a11ec90bc7590b4b9359da6ab0d5f670fadd92
--- /dev/null
+++ b/reference_end_latent_t2m_babel_272.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7f7f675e95809dc6323bf59520853b138da7d99a9c279713a4a828628e33b162
+size 192
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e19598e3d3cc573e2e5dfedd07ef4595fbb4cc25
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,17 @@
+smplx==0.1.28
+transformers==4.56.2
+timm==1.0.12
+sentence-transformers==5.1.0
+clip @ git+https://github.com/openai/CLIP.git@main#egg=clip
+human-body-prior @ git+https://github.com/nghorbani/human_body_prior.git@master#egg=human-body-prior
+gdown
+chumpy==0.70
+scipy==1.7.1
+numpy==1.22.4
+tensorboard
+accelerate
+flash_attn
+matplotlib==3.4.3
+matplotlib-inline==0.1.2
+imageio==2.9.0
+imageio-ffmpeg==0.5.1
\ No newline at end of file
diff --git a/sentencet5-xxl/.gitattributes b/sentencet5-xxl/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..2bf99854609538f90af03c921c41a0cbde670b2f
--- /dev/null
+++ b/sentencet5-xxl/.gitattributes
@@ -0,0 +1,28 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text 
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+model.safetensors filter=lfs diff=lfs merge=lfs -text
diff --git a/sentencet5-xxl/1_Pooling/config.json b/sentencet5-xxl/1_Pooling/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..4e09f293dfe90bba49f87cfe7996271f07be2666
--- /dev/null
+++ b/sentencet5-xxl/1_Pooling/config.json
@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}
\ No newline at end of file
diff --git a/sentencet5-xxl/2_Dense/config.json b/sentencet5-xxl/2_Dense/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..b1ac48c7b00db621b596c751134187805a19f533
--- /dev/null
+++ b/sentencet5-xxl/2_Dense/config.json
@@ -0,0 +1 @@
+{"in_features": 1024, "out_features": 768, "bias": false, "activation_function": "torch.nn.modules.linear.Identity"}
\ No newline at end of file
diff --git a/sentencet5-xxl/2_Dense/model.safetensors b/sentencet5-xxl/2_Dense/model.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..602bac1703e2ab74b660481c6eca90d4d7ff1f71
--- /dev/null
+++ b/sentencet5-xxl/2_Dense/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e53b7fcbca02a1e966ed3eeb433b765d30af989b6b6aec44bc6809156456cee3
+size 3145848
diff --git a/sentencet5-xxl/2_Dense/pytorch_model.bin b/sentencet5-xxl/2_Dense/pytorch_model.bin
new file mode 100644
index 0000000000000000000000000000000000000000..3cfe80fb480013cb450a3bbb31ce23968e53508a
--- /dev/null
+++ b/sentencet5-xxl/2_Dense/pytorch_model.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb330464adabfc4f532e691fe95c62752fb6237748b4617b83fb426b0d427c04
+size 3146680
diff --git a/sentencet5-xxl/README.md b/sentencet5-xxl/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d97cd2d7acf32e2da39b0f9fac90b40135d7601b
--- /dev/null
+++ b/sentencet5-xxl/README.md
@@ -0,0 +1,45 @@
+---
+language: en
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+pipeline_tag: sentence-similarity
+---
+
+# sentence-transformers/sentence-t5-xxl
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
+
+This model was converted from the Tensorflow model [st5-11b-1](https://tfhub.dev/google/sentence-t5/st5-11b/1) to PyTorch. When using this model, have a look at the publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.
+
+The model uses only the encoder from a T5-11B model. The weights are stored in FP16.  
+
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('sentence-transformers/sentence-t5-xxl')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+The model requires sentence-transformers version 2.2.0 or newer.
+
+## Citing & Authors
+
+If you find this model helpful, please cite the respective publication:
+[Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877)
diff --git a/sentencet5-xxl/config.json b/sentencet5-xxl/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..0bee199e0bf0dc5f9466994d8bce22e65c6eb9f8
--- /dev/null
+++ b/sentencet5-xxl/config.json
@@ -0,0 +1,57 @@
+{
+  "_name_or_path": "models/sentence-t5-11b",
+  "architectures": [
+    "T5EncoderModel"
+  ],
+  "d_ff": 65536,
+  "d_kv": 128,
+  "d_model": 1024,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 24,
+  "num_heads": 128,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "torch_dtype": "float16",
+  "transformers_version": "4.11.3",
+  "use_cache": true,
+  "vocab_size": 32128
+}
diff --git a/sentencet5-xxl/config_sentence_transformers.json b/sentencet5-xxl/config_sentence_transformers.json
new file mode 100644
index 0000000000000000000000000000000000000000..96cafb1a99306435fd68448f16fb5adf3ea49a6d
--- /dev/null
+++ b/sentencet5-xxl/config_sentence_transformers.json
@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.2.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}
\ No newline at end of file
diff --git a/sentencet5-xxl/model.safetensors b/sentencet5-xxl/model.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..8fce165b48a90459205f664dbad8c58e7c4220da
--- /dev/null
+++ b/sentencet5-xxl/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87dba079720f799b443b54508c7288a1e9b3d02d42cbee4f35df401914499294
+size 9729607168
diff --git a/sentencet5-xxl/modules.json b/sentencet5-xxl/modules.json
new file mode 100644
index 0000000000000000000000000000000000000000..678c8bb71e6e7258ef1338d8439a42c4f0a54ac4
--- /dev/null
+++ b/sentencet5-xxl/modules.json
@@ -0,0 +1,26 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]
\ No newline at end of file
diff --git a/sentencet5-xxl/pytorch_model.bin b/sentencet5-xxl/pytorch_model.bin
new file mode 100644
index 0000000000000000000000000000000000000000..d373202294b04ef736b476cf760cfaac2017b9d8
--- /dev/null
+++ b/sentencet5-xxl/pytorch_model.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc73ed70fbb704fa901846c34169c2e31d8cc4d9dbcfaa29f668df164a5d0e0a
+size 9729676422
diff --git a/sentencet5-xxl/sentence_bert_config.json b/sentencet5-xxl/sentence_bert_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..59d594003bf59880a884c574bf88ef7555bb0202
--- /dev/null
+++ b/sentencet5-xxl/sentence_bert_config.json
@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}
\ No newline at end of file
diff --git a/sentencet5-xxl/special_tokens_map.json b/sentencet5-xxl/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..881bdbffc06e471924ecea57f962bc5f8e2a9f21
--- /dev/null
+++ b/sentencet5-xxl/special_tokens_map.json
@@ -0,0 +1 @@
+{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "additional_special_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<extra_id_6>", "<extra_id_7>", "<extra_id_8>", "<extra_id_9>", "<extra_id_10>", "<extra_id_11>", "<extra_id_12>", "<extra_id_13>", "<extra_id_14>", "<extra_id_15>", "<extra_id_16>", "<extra_id_17>", "<extra_id_18>", "<extra_id_19>", "<extra_id_20>", "<extra_id_21>", "<extra_id_22>", "<extra_id_23>", "<extra_id_24>", "<extra_id_25>", "<extra_id_26>", "<extra_id_27>", "<extra_id_28>", "<extra_id_29>", "<extra_id_30>", "<extra_id_31>", "<extra_id_32>", "<extra_id_33>", "<extra_id_34>", "<extra_id_35>", "<extra_id_36>", "<extra_id_37>", "<extra_id_38>", "<extra_id_39>", "<extra_id_40>", "<extra_id_41>", "<extra_id_42>", "<extra_id_43>", "<extra_id_44>", "<extra_id_45>", "<extra_id_46>", "<extra_id_47>", "<extra_id_48>", "<extra_id_49>", "<extra_id_50>", "<extra_id_51>", "<extra_id_52>", "<extra_id_53>", "<extra_id_54>", "<extra_id_55>", "<extra_id_56>", "<extra_id_57>", "<extra_id_58>", "<extra_id_59>", "<extra_id_60>", "<extra_id_61>", "<extra_id_62>", "<extra_id_63>", "<extra_id_64>", "<extra_id_65>", "<extra_id_66>", "<extra_id_67>", "<extra_id_68>", "<extra_id_69>", "<extra_id_70>", "<extra_id_71>", "<extra_id_72>", "<extra_id_73>", "<extra_id_74>", "<extra_id_75>", "<extra_id_76>", "<extra_id_77>", "<extra_id_78>", "<extra_id_79>", "<extra_id_80>", "<extra_id_81>", "<extra_id_82>", "<extra_id_83>", "<extra_id_84>", "<extra_id_85>", "<extra_id_86>", "<extra_id_87>", "<extra_id_88>", "<extra_id_89>", "<extra_id_90>", "<extra_id_91>", "<extra_id_92>", "<extra_id_93>", "<extra_id_94>", "<extra_id_95>", "<extra_id_96>", "<extra_id_97>", "<extra_id_98>", "<extra_id_99>"]}
\ No newline at end of file
diff --git a/sentencet5-xxl/spiece.model b/sentencet5-xxl/spiece.model
new file mode 100644
index 0000000000000000000000000000000000000000..317a5ccbde45300f5d1d970d4d449af2108b147e
--- /dev/null
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diff --git a/sentencet5-xxl/tokenizer_config.json b/sentencet5-xxl/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..dfd29b5d119240bc59d68626bab8ce9caf5b7fe1
--- /dev/null
+++ b/sentencet5-xxl/tokenizer_config.json
@@ -0,0 +1 @@
+{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "extra_ids": 100, "additional_special_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<extra_id_6>", "<extra_id_7>", "<extra_id_8>", "<extra_id_9>", "<extra_id_10>", "<extra_id_11>", "<extra_id_12>", "<extra_id_13>", "<extra_id_14>", "<extra_id_15>", "<extra_id_16>", "<extra_id_17>", "<extra_id_18>", "<extra_id_19>", "<extra_id_20>", "<extra_id_21>", "<extra_id_22>", "<extra_id_23>", "<extra_id_24>", "<extra_id_25>", "<extra_id_26>", "<extra_id_27>", "<extra_id_28>", "<extra_id_29>", "<extra_id_30>", "<extra_id_31>", "<extra_id_32>", "<extra_id_33>", "<extra_id_34>", "<extra_id_35>", "<extra_id_36>", "<extra_id_37>", "<extra_id_38>", "<extra_id_39>", "<extra_id_40>", "<extra_id_41>", "<extra_id_42>", "<extra_id_43>", "<extra_id_44>", "<extra_id_45>", "<extra_id_46>", "<extra_id_47>", "<extra_id_48>", "<extra_id_49>", "<extra_id_50>", "<extra_id_51>", "<extra_id_52>", "<extra_id_53>", "<extra_id_54>", "<extra_id_55>", "<extra_id_56>", "<extra_id_57>", "<extra_id_58>", "<extra_id_59>", "<extra_id_60>", "<extra_id_61>", "<extra_id_62>", "<extra_id_63>", "<extra_id_64>", "<extra_id_65>", "<extra_id_66>", "<extra_id_67>", "<extra_id_68>", "<extra_id_69>", "<extra_id_70>", "<extra_id_71>", "<extra_id_72>", "<extra_id_73>", "<extra_id_74>", "<extra_id_75>", "<extra_id_76>", "<extra_id_77>", "<extra_id_78>", "<extra_id_79>", "<extra_id_80>", "<extra_id_81>", "<extra_id_82>", "<extra_id_83>", "<extra_id_84>", "<extra_id_85>", "<extra_id_86>", "<extra_id_87>", "<extra_id_88>", "<extra_id_89>", "<extra_id_90>", "<extra_id_91>", "<extra_id_92>", "<extra_id_93>", "<extra_id_94>", "<extra_id_95>", "<extra_id_96>", "<extra_id_97>", "<extra_id_98>", "<extra_id_99>"], "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "t5-11b", "tokenizer_class": "T5Tokenizer"}
\ No newline at end of file
diff --git a/train_causal_TAE.py b/train_causal_TAE.py
new file mode 100644
index 0000000000000000000000000000000000000000..6efe4b8c903d000fad343d8ebebad5623c199aef
--- /dev/null
+++ b/train_causal_TAE.py
@@ -0,0 +1,181 @@
+import os
+import json
+import numpy as np
+import torch
+import torch.optim as optim
+from torch.utils.tensorboard import SummaryWriter
+from accelerate import Accelerator
+import models.tae as tae
+import utils.losses as losses 
+import options.option_tae as option_tae
+import utils.utils_model as utils_model
+from humanml3d_272 import dataset_tae, dataset_eval_tae
+import utils.eval_trans as eval_trans
+import warnings
+warnings.filterwarnings('ignore')
+
+
+##### ---- Accelerator Setup ---- #####
+accelerator = Accelerator()
+comp_device = accelerator.device
+def update_lr_warm_up(optimizer, nb_iter, warm_up_iter, lr):
+    current_lr = lr * (nb_iter + 1) / (warm_up_iter + 1)
+    for param_group in optimizer.param_groups:
+        param_group["lr"] = current_lr
+    return optimizer, current_lr
+
+##### ---- Exp dirs ---- #####
+args = option_tae.get_args_parser()
+torch.manual_seed(args.seed)
+
+args.out_dir = os.path.join(args.out_dir, f'{args.exp_name}')
+os.makedirs(args.out_dir, exist_ok = True)
+
+##### ---- Logger ---- #####
+logger = utils_model.get_logger(args.out_dir)
+writer = SummaryWriter(args.out_dir)
+logger.info(json.dumps(vars(args), indent=4, sort_keys=True))
+
+logger.info(f'Training on {args.dataname}, motions are with {args.nb_joints} joints')
+
+
+
+##### ---- Dataloader ---- #####
+train_loader = dataset_tae.DATALoader(args.dataname,
+                                        args.batch_size,
+                                        window_size=args.window_size,
+                                        unit_length=2**args.down_t)
+
+val_loader = dataset_eval_tae.DATALoader(args.dataname, False,
+                                        32,
+                                        unit_length=2**args.down_t)
+
+##### ---- Network ---- #####
+clip_range = [-30,20]
+
+net = tae.Causal_HumanTAE(
+                       hidden_size=args.hidden_size,
+                       down_t=args.down_t,
+                       stride_t=args.stride_t,
+                       depth=args.depth,
+                       dilation_growth_rate=args.dilation_growth_rate,
+                       activation='relu',
+                       latent_dim=args.latent_dim,
+                       clip_range=clip_range
+                       )
+
+
+if args.resume_pth : 
+    logger.info('loading checkpoint from {}'.format(args.resume_pth))
+    ckpt = torch.load(args.resume_pth, map_location='cpu')
+    net.load_state_dict(ckpt, strict=True)
+net.train()
+net.to(comp_device)
+
+##### ---- Optimizer & Scheduler ---- #####
+optimizer = optim.AdamW(net.parameters(), lr=args.lr, betas=(0.9, 0.99), weight_decay=args.weight_decay)
+scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.lr_scheduler, gamma=args.gamma)
+
+net, optimizer, train_loader, val_loader = accelerator.prepare(net, optimizer, train_loader, val_loader)
+train_loader_iter = dataset_tae.cycle(train_loader)
+
+Loss = losses.ReConsLoss(motion_dim=272)
+
+##### ------ warm-up ------- #####
+avg_recons, avg_kl, avg_root = 0., 0., 0.
+for nb_iter in range(1, args.warm_up_iter):
+    
+    optimizer, current_lr = update_lr_warm_up(optimizer, nb_iter, args.warm_up_iter, args.lr)
+
+    gt_motion = next(train_loader_iter)
+    gt_motion = gt_motion.to(comp_device).float()
+
+    if args.num_gpus > 1:
+        pred_motion, mu, logvar = net.module(gt_motion)
+    else:
+        pred_motion, mu, logvar = net(gt_motion)
+
+    loss_motion = Loss(pred_motion, gt_motion)
+
+    loss_kl = Loss.forward_KL(mu, logvar)
+    loss_root = Loss.forward_root(pred_motion, gt_motion)
+    loss = loss_motion + loss_kl + args.root_loss * loss_root
+
+    optimizer.zero_grad()
+    accelerator.backward(loss)
+    optimizer.step()
+
+    avg_recons += loss_motion.item()
+    avg_kl += loss_kl.item()
+    avg_root += loss_root.item()
+    
+    if nb_iter % args.print_iter ==  0 :
+        if accelerator.is_main_process:
+            avg_recons /= args.print_iter
+            avg_kl /= args.print_iter
+            avg_root /= args.print_iter
+
+            logger.info(f"Warmup. Iter {nb_iter} :  lr {current_lr:.5f} \t Recons.  {avg_recons:.5f} \t KL. {avg_kl:.5f} \t Root. {avg_root:.5f}")
+            
+        
+        avg_recons, avg_kl, avg_root = 0., 0., 0.
+
+##### ---- Training ---- #####
+avg_recons, avg_kl, avg_root = 0., 0., 0.
+
+if args.num_gpus > 1:
+    best_iter, best_mpjpe, writer, logger = eval_trans.evaluation_tae_multi(args.out_dir, val_loader, net.module, logger, writer, 0, best_iter=0, best_mpjpe=1000, device=comp_device, accelerator=accelerator)
+else:
+    best_iter, best_mpjpe, writer, logger = eval_trans.evaluation_tae_multi(args.out_dir, val_loader, net, logger, writer, 0, best_iter=0, best_mpjpe=1000, device=comp_device, accelerator=accelerator)
+
+for nb_iter in range(1, args.total_iter + 1):
+    
+    gt_motion = next(train_loader_iter)
+    gt_motion = gt_motion.to(comp_device).float() 
+    
+    if args.num_gpus > 1:
+        pred_motion, mu, logvar = net.module(gt_motion)
+    else:
+        pred_motion, mu, logvar = net(gt_motion)
+
+    loss_motion = Loss(pred_motion, gt_motion)
+
+    loss_kl = Loss.forward_KL(mu, logvar)
+ 
+    loss_root = Loss.forward_root(pred_motion, gt_motion)
+    loss = loss_motion + loss_kl + args.root_loss * loss_root
+    
+    
+    optimizer.zero_grad()
+    accelerator.backward(loss)
+    optimizer.step()
+    scheduler.step()
+    
+    try:
+        avg_recons += loss_motion.item()
+        avg_kl += loss_kl.item()
+        avg_root += loss_root.item()
+    except:
+        continue
+    
+    if nb_iter % args.print_iter ==  0 :
+        if accelerator.is_main_process:
+            avg_recons /= args.print_iter
+            avg_kl /= args.print_iter
+            avg_root /= args.print_iter
+            writer.add_scalar('./Train/Recon_loss', avg_recons, nb_iter)
+            writer.add_scalar('./Train/KL', avg_kl, nb_iter)
+            writer.add_scalar('./Train/Root_loss', avg_root, nb_iter)
+            writer.add_scalar('./Train/LR', current_lr, nb_iter)
+            
+            logger.info(f"Train. Iter {nb_iter} : \t Recons.  {avg_recons:.5f} \t KL. {avg_kl:.5f} \t Root. {avg_root:.5f}")
+        
+        avg_recons, avg_kl, avg_root = 0., 0., 0.
+
+    if nb_iter % args.eval_iter==0:
+        if args.num_gpus > 1:
+            best_iter, best_mpjpe, writer, logger = eval_trans.evaluation_tae_multi(args.out_dir, val_loader, net.module, logger, writer, nb_iter, best_iter, best_mpjpe, device=comp_device, accelerator=accelerator)
+        else:
+            best_iter, best_mpjpe, writer, logger = eval_trans.evaluation_tae_multi(args.out_dir, val_loader, net, logger, writer, nb_iter, best_iter, best_mpjpe, device=comp_device, accelerator=accelerator)
+
+accelerator.wait_for_everyone()
\ No newline at end of file
diff --git a/train_motionstreamer.py b/train_motionstreamer.py
new file mode 100644
index 0000000000000000000000000000000000000000..15a803c85ffc07453c8079f2f5c13e1faf5c9388
--- /dev/null
+++ b/train_motionstreamer.py
@@ -0,0 +1,268 @@
+"""Train streaming motion generation model (MotionStreamer) with llama blocks, Two-Forward strategy and QK-Norm, using the motion latents encoded by the Causal TAE (trained in the first stage)."""
+
+
+import os
+import torch
+import numpy as np
+import random
+from torch.utils.tensorboard import SummaryWriter
+import json
+from accelerate import Accelerator
+from tqdm import tqdm
+from models.llama_model import LLaMAHF, LLaMAHFConfig
+import options.option_transformer as option_trans
+import utils.utils_model as utils_model
+import warnings
+from torch.optim.lr_scheduler import LambdaLR, CosineAnnealingLR
+warnings.filterwarnings('ignore')
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+##### ---- Exp dirs ---- #####
+args = option_trans.get_args_parser()
+torch.manual_seed(args.seed)
+
+# warm-up + cosine decay scheduler
+class WarmupCosineDecayScheduler:
+    def __init__(self, optimizer, warmup_iters, total_iters, min_lr=0):
+        self.optimizer = optimizer
+        self.warmup_iters = warmup_iters
+        self.total_iters = total_iters
+        self.min_lr = min_lr
+        
+        self.warmup_scheduler = LambdaLR(optimizer, lr_lambda=self.warmup_lambda)
+        
+        self.cosine_scheduler = CosineAnnealingLR(optimizer, 
+                                                  T_max=total_iters - warmup_iters, 
+                                                  eta_min=min_lr)
+        
+    def warmup_lambda(self, current_iter):
+        if current_iter < self.warmup_iters:
+            return float(current_iter) / float(max(1, self.warmup_iters))
+        return 1.0
+
+    def step(self, current_iter):
+        if current_iter < self.warmup_iters:
+            self.warmup_scheduler.step()
+        else:
+            self.cosine_scheduler.step()
+
+    def state_dict(self):
+        return {
+            'warmup_iters': self.warmup_iters,
+            'total_iters': self.total_iters,
+            'min_lr': self.min_lr,
+        }
+
+    def load_state_dict(self, state_dict):
+        self.warmup_iters = state_dict['warmup_iters']
+        self.total_iters = state_dict['total_iters']
+        self.min_lr = state_dict['min_lr']
+
+
+
+args.out_dir = os.path.join(args.out_dir, f'{args.exp_name}')
+os.makedirs(args.out_dir, exist_ok = True)
+
+
+##### ---- Accelerator Setup ---- #####
+accelerator = Accelerator()
+comp_device = accelerator.device
+
+##### ---- Logger ---- #####
+logger = utils_model.get_logger(args.out_dir)
+writer = SummaryWriter(args.out_dir)
+logger.info(json.dumps(vars(args), indent=4, sort_keys=True))
+
+##### ---- Dataloader ---- #####
+from humanml3d_272 import dataset_TM_train_motionstreamer
+train_loader = dataset_TM_train_motionstreamer.DATALoader(args.dataname, args.batch_size, unit_length=2**args.down_t, latent_dir=args.latent_dir)
+
+
+##### ---- Network ---- #####
+from sentence_transformers import SentenceTransformer
+t5_model = SentenceTransformer('sentencet5-xxl/')
+t5_model.eval()
+for p in t5_model.parameters():
+    p.requires_grad = False
+
+
+config = LLaMAHFConfig.from_name('Normal_size')
+config.block_size = 78
+trans_encoder = LLaMAHF(config, args.num_diffusion_head_layers, args.latent_dim, comp_device)
+
+if args.resume_trans is not None:
+    print('loading transformer checkpoint from {}'.format(args.resume_trans))
+    ckpt = torch.load(args.resume_trans, map_location='cpu')
+    new_ckpt_trans = {}
+    for key in ckpt['trans'].keys():
+        if key.split('.')[0]=='module':
+            new_key = '.'.join(key.split('.')[1:])
+        else:
+            new_key = key
+        new_ckpt_trans[new_key] = ckpt['trans'][key]
+    trans_encoder.load_state_dict(new_ckpt_trans, strict=True)
+trans_encoder.train()
+trans_encoder.to(comp_device)
+
+
+##### ---- Optimizer & Scheduler ---- #####
+optimizer = utils_model.initial_optim(args.decay_option, args.lr, args.weight_decay, trans_encoder, args.optimizer)
+scheduler = WarmupCosineDecayScheduler(optimizer, args.total_iter//10, args.total_iter)
+
+t5_model, trans_encoder, optimizer, train_loader = accelerator.prepare(t5_model, trans_encoder, optimizer, train_loader)
+train_loader_iter = dataset_TM_train_motionstreamer.cycle(train_loader)
+
+
+diffmlps_batch_mul = 4
+def lengths_to_mask(lengths, max_len):
+    mask = torch.arange(max_len, device=lengths.device).expand(len(lengths), max_len) < lengths.unsqueeze(1)
+    return mask 
+def get_mask_subset_prob(mask, prob):
+    subset_mask = torch.bernoulli(mask, p=prob) & mask
+    return subset_mask
+
+
+def uniform(shape, device=None):
+    return torch.zeros(shape, device=device).float().uniform_(0, 1)
+
+import math
+def cosine_schedule(t):
+    return torch.cos(t * math.pi * 0.5)
+
+
+#--------------2-forward:------------------
+def cosine_decay(step, total_steps, start_value=1.0, end_value=0.0):
+    step = torch.tensor(step, dtype=torch.float32)  
+    total_steps = torch.tensor(total_steps, dtype=torch.float32)  
+    cosine_factor = 0.5 * (1 + torch.cos(torch.pi * step / total_steps))
+    return start_value + (end_value - start_value) * cosine_factor
+
+def replace_with_pred(latents, pred_xstart, step, total_steps):
+    decay_factor = cosine_decay(step, total_steps).to(latents.device)
+    b, l, d = latents.shape
+    num_replace = int(l * decay_factor)  
+    
+    replace_indices = torch.randperm(l)[:num_replace]  
+
+    replace_mask = torch.zeros(b, l, dtype=torch.bool).to(latents.device)
+    replace_mask[:, replace_indices] = 1  
+
+    updated_latents = latents.clone()  
+    updated_latents[replace_mask] = pred_xstart[replace_mask]
+    
+    return updated_latents
+
+def forward_loss_withmask_2_forward_streaming(latents, trans, m_lens, feat_text, step, total_steps, A_token_length):
+    latents = latents.to(comp_device)   
+    feat_text = feat_text.to(comp_device)
+    A_token_length = A_token_length.to(comp_device)
+    conditions = trans(latents, feat_text) 
+    conditions = conditions.contiguous()
+    z = conditions[:,:-1,:]   
+
+    b, l, d = latents.shape
+    mask = lengths_to_mask(m_lens, l)      
+
+    for j in range(b):
+        mask[j, :A_token_length[j].item()] = False   # A_motion token: do not compute loss
+
+    mask = mask.reshape(b * l).repeat(diffmlps_batch_mul)
+
+    target = latents.clone().detach()
+    target = target.reshape(b * l, -1)    
+    z = z.reshape(b * l, -1)              
+    
+    with torch.no_grad():
+        loss, pred_xstart = trans.diff_loss(target=target, z=z)  
+
+    pred_xstart = pred_xstart.clone().detach()
+    pred_xstart = pred_xstart.reshape(b, l, -1)            
+
+    # do not replace A_motion tokens
+    for k in range(b):
+        pred_xstart[k, :A_token_length[k].item(),:] = latents[k, :A_token_length[k].item(),:]
+
+    updated_latents = replace_with_pred(latents, pred_xstart, step, total_steps)    
+    updated_conditions = trans(updated_latents, feat_text)  
+    updated_conditions = updated_conditions.contiguous()
+    updated_z = updated_conditions[:,:-1,:]                 
+
+    updated_target = latents.clone().detach()      
+
+    updated_target = updated_target.reshape(b * l, -1).repeat(diffmlps_batch_mul, 1)    
+    updated_z = updated_z.reshape(b * l, -1).repeat(diffmlps_batch_mul, 1)              
+
+    updated_target = updated_target[mask]                   
+    updated_z = updated_z[mask]                            
+
+    updated_loss, updated_pred_xstart = trans.diff_loss(target=updated_target, z=updated_z)  
+
+    return updated_loss
+
+
+##### ---- Training Loop ---- #####
+avg_loss_cls = 0.
+
+start_iter = 100001
+end_iter = args.total_iter + 1 
+
+pbar = tqdm(range(start_iter, end_iter), desc="Training MotionStreamer")
+for nb_iter in pbar:
+    batch = next(train_loader_iter)
+    caption, m_tokens, m_tokens_len, A_token_length = batch
+    caption = list(caption)
+    m_tokens, m_tokens_len = m_tokens.to(comp_device), m_tokens_len.to(comp_device)
+    A_token_length = A_token_length.to(comp_device)
+    
+    bs = len(caption)
+    num_masked = int(bs * 0.1)  # 10%
+    mask_indices = random.sample(range(bs), num_masked)
+
+    for idx in mask_indices:
+        caption[idx] = ''
+
+    feat_text = torch.from_numpy(t5_model.encode(caption)).float()
+    feat_text = feat_text.to(comp_device)
+
+    # -------gt--------
+    input_latent = m_tokens[:,:-1,:]  # continuous token
+
+    loss_cls = 0.0
+
+    if args.num_gpus > 1:
+        loss_cls = forward_loss_withmask_2_forward_streaming(latents=input_latent, trans=trans_encoder.module, m_lens = m_tokens_len, feat_text=feat_text, step=nb_iter, total_steps=args.total_iter, A_token_length=A_token_length)
+    else:
+        loss_cls = forward_loss_withmask_2_forward_streaming(latents=input_latent, trans=trans_encoder, m_lens = m_tokens_len, feat_text=feat_text, step=nb_iter, total_steps=args.total_iter, A_token_length=A_token_length)
+
+    
+    # backward & optimizer step
+    optimizer.zero_grad()
+    accelerator.backward(loss_cls)
+    optimizer.step()
+    scheduler.step(nb_iter)
+
+    avg_loss_cls = avg_loss_cls + loss_cls.item()
+
+
+    args.print_iter = 100
+    if nb_iter % args.print_iter ==  0 :
+        if accelerator.is_main_process:
+            avg_loss_cls = avg_loss_cls / args.print_iter
+            lr = optimizer.param_groups[0]['lr']
+            writer.add_scalar('./Loss/train', avg_loss_cls, nb_iter)
+            writer.add_scalar('./LR/train', optimizer.param_groups[0]['lr'], nb_iter)
+            msg = f"Train. Iter {nb_iter} : Loss. {avg_loss_cls:.5f}"
+            tqdm.write(f"Iter {nb_iter} | Loss: {avg_loss_cls:.5f} | LR: {lr:.6f}")
+            logger.info(msg)
+        avg_loss_cls = 0.
+
+
+    args.save_iter = 10000
+    if nb_iter % args.save_iter == 0:
+        # save checkpoint
+        if accelerator.is_main_process:
+            torch.save({
+                'trans': trans_encoder.state_dict(),
+            }, os.path.join(args.out_dir, f'latest.pth'))
+                     
+accelerator.wait_for_everyone()
\ No newline at end of file
diff --git a/train_t2m.py b/train_t2m.py
new file mode 100644
index 0000000000000000000000000000000000000000..602bdafaf7a6fe3817e1ed3b42c7bf1f8a161914
--- /dev/null
+++ b/train_t2m.py
@@ -0,0 +1,261 @@
+"""Train original text to motion generation model with llama blocks, Two-Forward strategy and QK-Norm, using the motion latents encoded by the Causal TAE (trained in the first stage)."""
+
+import os
+import torch
+import random
+from torch.utils.tensorboard import SummaryWriter
+import json
+from accelerate import Accelerator
+
+from models.llama_model import LLaMAHF, LLaMAHFConfig
+from humanml3d_272 import dataset_TM_train
+import options.option_transformer as option_trans
+import utils.utils_model as utils_model
+import warnings
+from torch.optim.lr_scheduler import LambdaLR, CosineAnnealingLR
+warnings.filterwarnings('ignore')
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+##### ---- Exp dirs ---- #####
+args = option_trans.get_args_parser()
+torch.manual_seed(args.seed)
+
+# warm-up + cosine decay scheduler
+class WarmupCosineDecayScheduler:
+    def __init__(self, optimizer, warmup_iters, total_iters, min_lr=0):
+        self.optimizer = optimizer
+        self.warmup_iters = warmup_iters
+        self.total_iters = total_iters
+        self.min_lr = min_lr
+        
+        self.warmup_scheduler = LambdaLR(optimizer, lr_lambda=self.warmup_lambda)
+        
+        self.cosine_scheduler = CosineAnnealingLR(optimizer, 
+                                                  T_max=total_iters - warmup_iters, 
+                                                  eta_min=min_lr)
+        
+    def warmup_lambda(self, current_iter):
+        if current_iter < self.warmup_iters:
+            return float(current_iter) / float(max(1, self.warmup_iters))
+        return 1.0
+
+    def step(self, current_iter):
+        if current_iter < self.warmup_iters:
+            self.warmup_scheduler.step()
+        else:
+            self.cosine_scheduler.step()
+
+    def state_dict(self):
+        return {
+            'warmup_iters': self.warmup_iters,
+            'total_iters': self.total_iters,
+            'min_lr': self.min_lr,
+        }
+
+    def load_state_dict(self, state_dict):
+        self.warmup_iters = state_dict['warmup_iters']
+        self.total_iters = state_dict['total_iters']
+        self.min_lr = state_dict['min_lr']
+
+
+args.out_dir = os.path.join(args.out_dir, f'{args.exp_name}')
+os.makedirs(args.out_dir, exist_ok = True)
+
+
+##### ---- Accelerator Setup ---- #####
+accelerator = Accelerator()
+comp_device = accelerator.device
+
+##### ---- Logger ---- #####
+logger = utils_model.get_logger(args.out_dir)
+writer = SummaryWriter(args.out_dir)
+logger.info(json.dumps(vars(args), indent=4, sort_keys=True))
+
+
+##### ---- Dataloader ---- #####
+train_loader = dataset_TM_train.DATALoader(args.dataname, args.batch_size, args.latent_dir, unit_length=2**args.down_t)
+
+
+##### ---- Network ---- #####
+from sentence_transformers import SentenceTransformer
+t5_model = SentenceTransformer('sentencet5-xxl/')
+t5_model.eval()
+for p in t5_model.parameters():
+    p.requires_grad = False
+
+
+config = LLaMAHFConfig.from_name('Normal_size')
+config.block_size = 78
+trans_encoder = LLaMAHF(config, args.num_diffusion_head_layers, args.latent_dim, comp_device)
+
+if args.resume_trans is not None:
+    print('loading transformer checkpoint from {}'.format(args.resume_trans))
+    ckpt = torch.load(args.resume_trans, map_location='cpu')
+    new_ckpt_trans = {}
+    for key in ckpt['trans'].keys():
+        if key.split('.')[0]=='module':
+            new_key = '.'.join(key.split('.')[1:])
+        else:
+            new_key = key
+        new_ckpt_trans[new_key] = ckpt['trans'][key]
+    trans_encoder.load_state_dict(new_ckpt_trans, strict=True)
+trans_encoder.train()
+trans_encoder.to(comp_device)
+
+
+##### ---- Optimizer & Scheduler ---- #####
+optimizer = utils_model.initial_optim(args.decay_option, args.lr, args.weight_decay, trans_encoder, args.optimizer)
+scheduler = WarmupCosineDecayScheduler(optimizer, args.total_iter//10, args.total_iter)
+
+
+t5_model, trans_encoder, optimizer, train_loader = accelerator.prepare(t5_model, trans_encoder, optimizer, train_loader)
+train_loader_iter = dataset_TM_train.cycle(train_loader)
+
+
+diffmlps_batch_mul = 4
+def lengths_to_mask(lengths, max_len):
+    mask = torch.arange(max_len, device=lengths.device).expand(len(lengths), max_len) < lengths.unsqueeze(1)
+    return mask
+def get_mask_subset_prob(mask, prob):
+    subset_mask = torch.bernoulli(mask, p=prob) & mask
+    return subset_mask
+
+
+def uniform(shape, device=None):
+    return torch.zeros(shape, device=device).float().uniform_(0, 1)
+
+import math
+def cosine_schedule(t):
+    return torch.cos(t * math.pi * 0.5)
+
+
+#--------------2-forward:------------------
+def cosine_decay(step, total_steps, start_value=1.0, end_value=0.0):
+
+    step = torch.tensor(step, dtype=torch.float32)  
+    total_steps = torch.tensor(total_steps, dtype=torch.float32)  
+    
+    cosine_factor = 0.5 * (1 + torch.cos(torch.pi * step / total_steps))
+    return start_value + (end_value - start_value) * cosine_factor
+
+def replace_with_pred(latents, pred_xstart, step, total_steps):
+    
+    decay_factor = cosine_decay(step, total_steps).to(latents.device)
+    
+    b, l, d = latents.shape
+    num_replace = int(l * decay_factor)  
+    
+    replace_indices = torch.randperm(l)[:num_replace]  
+
+    replace_mask = torch.zeros(b, l, dtype=torch.bool).to(latents.device)
+    replace_mask[:, replace_indices] = 1  
+
+    updated_latents = latents.clone()  
+    updated_latents[replace_mask] = pred_xstart[replace_mask]
+    
+    return updated_latents
+
+def forward_loss_withmask_2_forward(latents, trans, m_lens, feat_text, step, total_steps):
+    """z: condition; latents: gt"""
+    #--------------First Forward:-------------------------
+    conditions = trans(latents, feat_text)  
+    conditions = conditions.contiguous()
+    z = conditions[:,:-1,:]
+    #-------------------------------------------------
+
+    b, l, d = latents.shape     
+    mask = lengths_to_mask(m_lens, l)       
+    mask = mask.reshape(b * l).repeat(diffmlps_batch_mul)
+
+    target = latents.clone().detach()       
+    target = target.reshape(b * l, -1)    
+    z = z.reshape(b * l, -1)            
+    
+    with torch.no_grad():
+        loss, pred_xstart = trans.diff_loss(target=target, z=z)  
+
+    pred_xstart = pred_xstart.clone().detach()
+    pred_xstart = pred_xstart.reshape(b, l, -1)           
+
+    #--------------Second Forward:-------------------------
+    # Update latents
+    updated_latents = replace_with_pred(latents, pred_xstart, step, total_steps)    
+    updated_conditions = trans(updated_latents, feat_text)  
+    updated_conditions = updated_conditions.contiguous()
+    updated_z = updated_conditions[:,:-1,:]      
+
+    updated_target = latents.clone().detach()       
+
+    updated_target = updated_target.reshape(b * l, -1).repeat(diffmlps_batch_mul, 1)    
+    updated_z = updated_z.reshape(b * l, -1).repeat(diffmlps_batch_mul, 1)            
+
+    updated_target = updated_target[mask]                   
+    updated_z = updated_z[mask]                            
+
+    updated_loss, _ = trans.diff_loss(target=updated_target, z=updated_z)  
+
+    return updated_loss
+#-------------------
+
+##### ---- Training Loop ---- #####
+nb_iter, avg_loss = 0, 0.
+
+while nb_iter <= args.total_iter:
+    batch = next(train_loader_iter)
+    text, m_tokens, m_tokens_len = batch
+    text = list(text)
+    m_tokens, m_tokens_len = m_tokens.to(comp_device), m_tokens_len.to(comp_device)
+
+    bs = len(text)
+    num_masked = int(bs * 0.1)  # 10%
+    mask_indices = random.sample(range(bs), num_masked)
+
+    for idx in mask_indices:
+        text[idx] = ''
+
+    feat_text = torch.from_numpy(t5_model.encode(text)).float()        
+    feat_text = feat_text.to(comp_device)
+
+    # -------gt-------- 
+    input_latent = m_tokens[:,:-1]    # continuous token
+    loss = 0.0
+
+    if args.num_gpus > 1:
+        loss = forward_loss_withmask_2_forward(latents=input_latent, trans=trans_encoder.module, m_lens = m_tokens_len, feat_text=feat_text, step=nb_iter, total_steps=args.total_iter)
+    else:
+        loss = forward_loss_withmask_2_forward(latents=input_latent, trans=trans_encoder, m_lens = m_tokens_len, feat_text=feat_text, step=nb_iter, total_steps=args.total_iter)
+
+    
+    optimizer.zero_grad()
+    accelerator.backward(loss)
+    optimizer.step()
+    scheduler.step(nb_iter)
+
+    avg_loss = avg_loss + loss.item()
+
+    nb_iter += 1
+    args.print_iter = 100
+    if nb_iter % args.print_iter ==  0 :
+        if accelerator.is_main_process:
+            avg_loss = avg_loss / args.print_iter
+            writer.add_scalar('./Loss/train', avg_loss, nb_iter)
+            writer.add_scalar('./LR/train', optimizer.param_groups[0]['lr'], nb_iter)
+            msg = f"Train. Iter {nb_iter} : Loss. {avg_loss:.5f}"
+            logger.info(msg)
+        avg_loss = 0.
+
+
+    args.save_iter = 10000
+    if nb_iter % args.save_iter == 0:
+        # save 
+        if accelerator.is_main_process:
+            torch.save({
+                'trans': trans_encoder.state_dict(),
+                'scheduler': scheduler.state_dict(),
+                'optimizer': optimizer.state_dict()
+            }, os.path.join(args.out_dir, f'latest.pth'))
+
+                    
+
+accelerator.wait_for_everyone()
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diff --git a/utils/bvh.py b/utils/bvh.py
new file mode 100644
index 0000000000000000000000000000000000000000..a36402d9999cbc29eff95c9e9dc2e5fcdee27bda
--- /dev/null
+++ b/utils/bvh.py
@@ -0,0 +1,242 @@
+import re
+import numpy as np
+
+channelmap = {
+    'Xrotation': 'x',
+    'Yrotation': 'y',
+    'Zrotation': 'z'
+}
+
+channelmap_inv = {
+    'x': 'Xrotation',
+    'y': 'Yrotation',
+    'z': 'Zrotation',
+}
+
+ordermap = {
+    'x': 0,
+    'y': 1,
+    'z': 2,
+}
+
+def load(filename:str, order:str=None) -> dict:
+    """Loads a BVH file.
+    
+    Args:
+        filename (str): Path to the BVH file.
+        order (str): The order of the rotation channels. (i.e."xyz")
+    
+    Returns:
+        dict: A dictionary containing the following keys:
+            * names (list)(jnum): The names of the joints.
+            * parents (list)(jnum): The parent indices.
+            * offsets (np.ndarray)(jnum, 3): The offsets of the joints.
+            * rotations (np.ndarray)(fnum, jnum, 3) : The local coordinates of rotations of the joints. 
+            * positions (np.ndarray)(fnum, jnum, 3) : The positions of the joints.
+            * order (str): The order of the channels.
+            * frametime (float): The time between two frames.
+    """
+
+    f = open(filename, "r")
+
+    i = 0
+    active = -1
+    end_site = False
+
+    # Create empty lists for saving parameters
+    names = []
+    offsets = np.array([]).reshape((0, 3))
+    parents = np.array([], dtype=int)
+
+    # Parse the file, line by line
+    for line in f:
+
+        if "HIERARCHY" in line: continue
+        if "MOTION" in line: continue
+
+        rmatch = re.match(r"ROOT (\w+)", line)
+        if rmatch:
+            names.append(rmatch.group(1))
+            offsets = np.append(offsets, np.array([[0, 0, 0]]), axis=0)
+            parents = np.append(parents, active)
+            active = (len(parents) - 1)
+            continue
+
+        if "{" in line: continue
+
+        if "}" in line:
+            if end_site:
+                end_site = False
+            else:
+                active = parents[active]
+            continue
+
+        offmatch = re.match(r"\s*OFFSET\s+([\-\d\.e]+)\s+([\-\d\.e]+)\s+([\-\d\.e]+)", line)
+        if offmatch:
+            if not end_site:
+                offsets[active] = np.array([list(map(float, offmatch.groups()))])
+            continue
+
+        chanmatch = re.match(r"\s*CHANNELS\s+(\d+)", line)
+        if chanmatch:
+            channels = int(chanmatch.group(1))
+            if order is None:
+                channelis = 0 if channels == 3 else 3
+                channelie = 3 if channels == 3 else 6
+                parts = line.split()[2 + channelis:2 + channelie]
+                if any([p not in channelmap for p in parts]):
+                    continue
+                order = "".join([channelmap[p] for p in parts])
+            continue
+
+        jmatch = re.match("\s*JOINT\s+(\w+)", line)
+        if jmatch:
+            names.append(jmatch.group(1))
+            offsets = np.append(offsets, np.array([[0, 0, 0]]), axis=0)
+            parents = np.append(parents, active)
+            active = (len(parents) - 1)
+            continue
+
+        if "End Site" in line:
+            end_site = True
+            continue
+
+        fmatch = re.match("\s*Frames:\s+(\d+)", line)
+        if fmatch:
+            fnum = int(fmatch.group(1))
+            positions = offsets[None].repeat(fnum, axis=0)
+            rotations = np.zeros((fnum, len(offsets), 3))
+            continue
+
+        fmatch = re.match("\s*Frame Time:\s+([\d\.]+)", line)
+        if fmatch:
+            frametime = float(fmatch.group(1))
+            continue
+
+        dmatch = line.strip().split(' ')
+        if dmatch:
+            data_block = np.array(list(map(float, dmatch)))
+            N = len(parents)
+            fi = i
+            if channels == 3:
+                positions[fi, 0:1] = data_block[0:3]
+                rotations[fi, :] = data_block[3:].reshape(N, 3)
+            elif channels == 6:
+                data_block = data_block.reshape(N, 6)
+                positions[fi, :] = data_block[:, 0:3]
+                rotations[fi, :] = data_block[:, 3:6]
+            elif channels == 9:
+                positions[fi, 0] = data_block[0:3]
+                data_block = data_block[3:].reshape(N - 1, 9)
+                rotations[fi, 1:] = data_block[:, 3:6]
+                positions[fi, 1:] += data_block[:, 0:3] * data_block[:, 6:9]
+            else:
+                raise Exception("Too many channels! %i" % channels)
+
+            i += 1
+
+    f.close()
+
+    return {
+        'rotations': rotations,
+        'positions': positions,
+        'offsets': offsets,
+        'parents': parents,
+        'names': names,
+        'order': order,
+        'frametime': frametime
+    }
+    
+    
+def save_joint(f, data, t, i, save_order, order='zyx', save_positions=False):
+    
+    save_order.append(i)
+    
+    f.write("%sJOINT %s\n" % (t, data['names'][i]))
+    f.write("%s{\n" % t)
+    t += '\t'
+
+    f.write("%sOFFSET %f %f %f\n" % (t, data['offsets'][i,0], data['offsets'][i,1], data['offsets'][i,2]))
+    
+    if save_positions:
+        f.write("%sCHANNELS 6 Xposition Yposition Zposition %s %s %s \n" % (t, 
+            channelmap_inv[order[0]], channelmap_inv[order[1]], channelmap_inv[order[2]]))
+    else:
+        f.write("%sCHANNELS 3 %s %s %s\n" % (t, 
+            channelmap_inv[order[0]], channelmap_inv[order[1]], channelmap_inv[order[2]]))
+    
+    end_site = True
+    
+    for j in range(len(data['parents'])):
+        if data['parents'][j] == i:
+            t = save_joint(f, data, t, j, save_order, order=order, save_positions=save_positions)
+            end_site = False
+    
+    if end_site:
+        f.write("%sEnd Site\n" % t)
+        f.write("%s{\n" % t)
+        t += '\t'
+        f.write("%sOFFSET %f %f %f\n" % (t, 0.0, 0.0, 0.0))
+        t = t[:-1]
+        f.write("%s}\n" % t)
+
+    t = t[:-1]
+    f.write("%s}\n" % t)
+    
+    return t
+    
+
+def save(filename, data, save_positions=False):
+    """ Save a joint hierarchy to a file.
+    
+    Args:
+        filename (str): The output will save on the bvh file.
+        data (dict): The data to save.(rotations, positions, offsets, parents, names, order, frametime)
+        save_positions (bool): Whether to save all of joint positions on MOTION. (False is recommended.)
+    """
+    
+    order = data['order']
+    frametime = data['frametime']
+    
+    with open(filename, 'w') as f:
+
+        t = ""
+        f.write("%sHIERARCHY\n" % t)
+        f.write("%sROOT %s\n" % (t, data['names'][0]))
+        f.write("%s{\n" % t)
+        t += '\t'
+
+        f.write("%sOFFSET %f %f %f\n" % (t, data['offsets'][0,0], data['offsets'][0,1], data['offsets'][0,2]) )
+        f.write("%sCHANNELS 6 Xposition Yposition Zposition %s %s %s \n" % 
+            (t, channelmap_inv[order[0]], channelmap_inv[order[1]], channelmap_inv[order[2]]))
+
+        save_order = [0]
+            
+        for i in range(len(data['parents'])):
+            if data['parents'][i] == 0:
+                t = save_joint(f, data, t, i, save_order, order=order, save_positions=save_positions)
+    
+        t = t[:-1]
+        f.write("%s}\n" % t)
+
+        rots, poss = data['rotations'], data['positions']
+
+        f.write("MOTION\n")
+        f.write("Frames: %i\n" % len(rots));
+        f.write("Frame Time: %f\n" % frametime);
+        
+        for i in range(rots.shape[0]):
+            for j in save_order:
+                
+                if save_positions or j == 0:
+                
+                    f.write("%f %f %f %f %f %f " % (
+                        poss[i,j,0], poss[i,j,1], poss[i,j,2], 
+                        rots[i,j,0], rots[i,j,1], rots[i,j,2]))
+                
+                else:
+                    
+                    f.write("%f %f %f " % (
+                        rots[i,j,0], rots[i,j,1], rots[i,j,2]))
+
+            f.write("\n")
\ No newline at end of file
diff --git a/utils/eval_trans.py b/utils/eval_trans.py
new file mode 100644
index 0000000000000000000000000000000000000000..4946f17f50882c46d1f3c5c0324523568bbe035d
--- /dev/null
+++ b/utils/eval_trans.py
@@ -0,0 +1,406 @@
+import numpy as np
+import torch
+from scipy import linalg
+from utils.face_z_align_util import rotation_6d_to_matrix
+import visualization.plot_3d_global as plot_3d
+import os
+
+def tensorborad_add_video_xyz(writer, xyz, nb_iter, tag, title_batch=None, outname=None, fps=30):
+    xyz = xyz[:1]   
+    bs, seq = xyz.shape[:2]
+    xyz = xyz.reshape(bs, seq, -1, 3)
+    plot_xyz = plot_3d.draw_to_batch(xyz.cpu().numpy(),title_batch, outname)
+    plot_xyz = np.transpose(plot_xyz, (0, 1, 4, 2, 3)) 
+    writer.add_video(tag, plot_xyz, nb_iter, fps = fps)
+
+def calculate_mpjpe(gt_joints, pred_joints):
+    assert gt_joints.shape == pred_joints.shape, f"GT shape: {gt_joints.shape}, pred shape: {pred_joints.shape}"
+    pelvis = gt_joints[:, [0]].mean(1)
+    gt_joints = gt_joints - torch.unsqueeze(pelvis, dim=1)
+    pelvis = pred_joints[:, [0]].mean(1)
+    pred_joints = pred_joints - torch.unsqueeze(pelvis, dim=1)
+
+    mpjpe = torch.linalg.norm(pred_joints - gt_joints, dim=-1) 
+    mpjpe_seq = mpjpe.mean(-1)
+
+    return mpjpe_seq
+
+
+def accumulate_rotations(relative_rotations):
+    R_total = [relative_rotations[0]]
+    for R_rel in relative_rotations[1:]:
+        R_total.append(np.matmul(R_rel, R_total[-1]))
+    return np.array(R_total)
+
+def recover_from_local_position(final_x, njoint):
+
+    if final_x.ndim == 3:
+        bs, nfrm, _ = final_x.shape
+        is_batched = True
+    else:
+        nfrm, _ = final_x.shape
+        bs = 1
+        is_batched = False
+        final_x = final_x.reshape(1, *final_x.shape)
+
+    
+    positions_no_heading = final_x[:,:,8:8+3*njoint].reshape(bs, nfrm, njoint, 3) 
+    velocities_root_xy_no_heading = final_x[:,:,:2] 
+    global_heading_diff_rot = final_x[:,:,2:8] 
+
+   
+    positions_with_heading = []
+    for b in range(bs):
+        
+        global_heading_rot = accumulate_rotations(rotation_6d_to_matrix(torch.from_numpy(global_heading_diff_rot[b])).numpy())
+        inv_global_heading_rot = np.transpose(global_heading_rot, (0, 2, 1))
+        
+        
+        curr_pos_with_heading = np.matmul(np.repeat(inv_global_heading_rot[:, None,:, :], njoint, axis=1), 
+                                        positions_no_heading[b][...,None]).squeeze(-1)
+
+        
+        velocities_root_xyz_no_heading = np.zeros((velocities_root_xy_no_heading[b].shape[0], 3))
+        velocities_root_xyz_no_heading[:, 0] = velocities_root_xy_no_heading[b, :, 0]
+        velocities_root_xyz_no_heading[:, 2] = velocities_root_xy_no_heading[b, :, 1]
+        velocities_root_xyz_no_heading[1:, :] = np.matmul(inv_global_heading_rot[:-1], 
+                                                         velocities_root_xyz_no_heading[1:, :,None]).squeeze(-1)
+
+        root_translation = np.cumsum(velocities_root_xyz_no_heading, axis=0)
+
+        
+        curr_pos_with_heading[:, :, 0] += root_translation[:, 0:1]
+        curr_pos_with_heading[:, :, 2] += root_translation[:, 2:]
+        
+        positions_with_heading.append(curr_pos_with_heading)
+
+    positions_with_heading = np.stack(positions_with_heading, axis=0)
+
+    if not is_batched:
+        positions_with_heading = positions_with_heading.squeeze(0)
+
+    return positions_with_heading
+
+
+
+# Single-GPU evaluation of Causal TAE (test time)
+@torch.no_grad()
+def evaluation_tae_single(out_dir, val_loader, net, logger, writer, evaluator, device=torch.device('cuda')): 
+    net.eval()
+    nb_sample = 0
+    
+    textencoder, motionencoder = evaluator
+
+    motion_annotation_list = []
+    motion_pred_list = []
+
+    nb_sample = torch.tensor(0, device=device)
+    mpjpe = torch.tensor(0.0, device=device)
+    num_poses = torch.tensor(0, device=device)
+
+    for batch in val_loader:
+        motion, m_length = batch
+        motion = motion.to(device)
+        motion = motion.float()
+        bs, seq = motion.shape[0], motion.shape[1]
+        em = motionencoder(motion, m_length).loc
+                    
+        num_joints = 22
+        
+        pred_pose_eval = torch.zeros((bs, seq, motion.shape[-1])).to(device)
+
+        for i in range(bs):
+            pose = val_loader.dataset.inv_transform(motion[i:i+1, :m_length[i], :].detach().cpu().numpy())
+            pose_xyz = recover_from_local_position(pose.squeeze(0), num_joints)
+            pred_pose, _, _ = net(motion[i:i+1, :m_length[i]])
+            
+            pred_pose_eval[i:i+1,:m_length[i],:] = pred_pose
+            
+            pred_denorm = val_loader.dataset.inv_transform(pred_pose.detach().cpu().numpy())
+                
+            pred_xyz = recover_from_local_position(pred_denorm.squeeze(0), num_joints)
+            pred_xyz = torch.from_numpy(pred_xyz).float().to(device)
+            pose_xyz = torch.from_numpy(pose_xyz).float().to(device)
+            
+            mpjpe += torch.sum(calculate_mpjpe(pose_xyz[:, :m_length[i]].squeeze(), pred_xyz[:, :m_length[i]].squeeze()))
+            num_poses += pose_xyz.shape[0]
+
+        em_pred = motionencoder(pred_pose_eval, m_length).loc
+
+        motion_pred_list.append(em_pred)
+        motion_annotation_list.append(em)
+
+        nb_sample += bs  
+    
+    mpjpe = mpjpe / num_poses
+    mpjpe = mpjpe * 1000   # mm
+
+    motion_annotation_np = torch.cat(motion_annotation_list, dim=0).cpu().numpy()
+    motion_pred_np = torch.cat(motion_pred_list, dim=0).cpu().numpy()
+    gt_mu, gt_cov  = calculate_activation_statistics(motion_annotation_np)
+    mu, cov= calculate_activation_statistics(motion_pred_np)
+
+    fid = calculate_frechet_distance(gt_mu, gt_cov, mu, cov)
+
+    msg = f"--> \t Eva. :, FID. {fid:.4f}, mpjpe. {mpjpe:.5f} (mm)"
+    logger.info(msg)
+    
+    return fid, mpjpe, writer, logger
+
+# Multi-GPU evaluation of Causal TAE (training time)
+@torch.no_grad()        
+def evaluation_tae_multi(out_dir, val_loader, net, logger, writer, nb_iter, best_iter, best_mpjpe, draw = True, save = True, savegif = True, device=torch.device('cuda'), accelerator=None): 
+    net.eval()
+    nb_sample = 0
+    
+    draw_org = []
+    draw_pred = []
+    draw_text = []
+
+    nb_sample = torch.tensor(0, device=device)
+    mpjpe = torch.tensor(0.0, device=device)
+    num_poses = torch.tensor(0, device=device)
+
+    for batch in val_loader:
+        motion, m_length = batch
+        motion = motion.to(device)
+        bs, seq = motion.shape[0], motion.shape[1]
+        num_joints = 22
+        pred_pose_eval = torch.zeros((bs, seq, motion.shape[-1])).to(device)
+
+        for i in range(bs):
+            pose = val_loader.dataset.inv_transform(motion[i:i+1, :m_length[i], :].detach().cpu().numpy())
+            pose_xyz = recover_from_local_position(pose.squeeze(0), num_joints)
+
+            pred_pose, _, _ = net(motion[i:i+1, :m_length[i]])
+            pred_pose_eval[i:i+1,:m_length[i],:] = pred_pose
+
+            if accelerator is None or accelerator.is_main_process:
+                pred_denorm = val_loader.dataset.inv_transform(pred_pose.detach().cpu().numpy())
+                pred_xyz = recover_from_local_position(pred_denorm.squeeze(0), num_joints)
+                pred_xyz = torch.from_numpy(pred_xyz).float().to(device)
+                pose_xyz = torch.from_numpy(pose_xyz).float().to(device)
+                mpjpe += torch.sum(calculate_mpjpe(pose_xyz[:, :m_length[i]].squeeze(), pred_xyz[:, :m_length[i]].squeeze()))
+                num_poses += pose_xyz.shape[0]
+
+                if i < 4:
+                    draw_org.append(pose_xyz)
+                    draw_pred.append(pred_xyz)
+                    draw_text.append('')
+        nb_sample += bs
+
+
+    if accelerator is not None:
+        accelerator.wait_for_everyone()
+        nb_sample = accelerator.reduce(nb_sample, reduction="sum")
+        mpjpe = accelerator.reduce(mpjpe, reduction="sum")
+        
+    if accelerator is None or accelerator.is_main_process:
+        mpjpe = mpjpe / num_poses    
+        # transform mpjpe to mm
+        mpjpe = mpjpe * 1000
+        msg = f"--> \t Eva. Iter {nb_iter} :, mpjpe. {mpjpe:.3f} (mm)"
+        logger.info(msg)
+    
+    # save visualization on tensorboard
+    if draw and (accelerator is None or accelerator.is_main_process):
+        writer.add_scalar('./Test/mpjpe', mpjpe, nb_iter)
+
+        if nb_iter % 20000 == 0 : 
+            for ii in range(4):
+                draw_org[ii] = draw_org[ii].unsqueeze(0)
+                tensorborad_add_video_xyz(writer, draw_org[ii], nb_iter, tag='./Vis/org_eval'+str(ii), title_batch=[draw_text[ii]], outname=[os.path.join(out_dir, 'gt'+str(ii)+'.gif')] if savegif else None, fps=30)
+            
+        if nb_iter % 20000 == 0 : 
+            for ii in range(4):
+                draw_pred[ii] = draw_pred[ii].unsqueeze(0)
+                tensorborad_add_video_xyz(writer, draw_pred[ii], nb_iter, tag='./Vis/pred_eval'+str(ii), title_batch=[draw_text[ii]], outname=[os.path.join(out_dir, 'pred'+str(ii)+'.gif')] if savegif else None, fps=30)   
+
+    if accelerator is None or accelerator.is_main_process:
+        if mpjpe < best_mpjpe :
+            msg = f"--> --> \t mpjpe Improved from {best_mpjpe:.5f} to {mpjpe:.5f} !!!"
+            logger.info(msg)
+            best_mpjpe = mpjpe
+            if save:
+                torch.save({'net' : net.state_dict()}, os.path.join(out_dir, 'net_best_mpjpe.pth'))
+        if save:
+            torch.save({'net' : net.state_dict()}, os.path.join(out_dir, 'net_last.pth'))
+
+    net.train()
+    return best_iter, best_mpjpe, writer, logger
+
+
+# Single-GPU evaluation of text to motion model (test time)：
+@torch.no_grad()                
+def evaluation_transformer_272_single(val_loader, net, trans, tokenize_model, logger, evaluator, cfg=4.0, device=torch.device('cuda'), unit_length=4):     
+    textencoder, motionencoder = evaluator
+    trans.eval()
+    
+    draw_org = []
+    draw_pred = []
+    draw_text = []
+    draw_text_pred = []
+
+    motion_annotation_list = []
+    motion_pred_list = []
+    R_precision_real = torch.tensor([0,0,0], device=device)
+    R_precision = torch.tensor([0,0,0], device=device)
+    matching_score_real = torch.tensor(0.0, device=device)
+    matching_score_pred = torch.tensor(0.0, device=device)
+
+    nb_sample = torch.tensor(0, device=device)
+
+    for batch in val_loader:
+        text, pose, m_length = batch
+        bs, seq = pose.shape[:2]
+        num_joints = 22
+        pred_pose_eval = torch.zeros((bs, seq, pose.shape[-1])).to(device)
+        pred_len = torch.ones(bs).long()
+        
+        for k in range(bs):    
+            index_motion = trans.sample_for_eval_CFG(text[k:k+1], length=m_length[k], tokenize_model=tokenize_model, device=device, unit_length=unit_length, cfg=cfg)
+            pred_pose = net.forward_decoder(index_motion)            
+            cur_len = pred_pose.shape[1]
+            pred_len[k] = min(cur_len, seq)
+            pred_pose_eval[k:k+1, :cur_len] = pred_pose[:, :seq]
+
+        et_pred, em_pred = textencoder(text).loc, motionencoder(pred_pose_eval, pred_len).loc
+        
+        pose = pose.to(device).float()
+        et, em = textencoder(text).loc, motionencoder(pose, m_length).loc
+        motion_annotation_list.append(em)
+        motion_pred_list.append(em_pred)
+
+        temp_R, temp_match = calculate_R_precision(et.cpu().numpy(), em.cpu().numpy(), top_k=3, sum_all=True)
+        R_precision_real += torch.tensor(temp_R, device=device)
+        matching_score_real += torch.tensor(temp_match, device=device)
+        temp_R, temp_match = calculate_R_precision(et_pred.cpu().numpy(), em_pred.cpu().numpy(), top_k=3, sum_all=True)
+        R_precision += torch.tensor(temp_R, device=device)
+        matching_score_pred += torch.tensor(temp_match, device=device)
+        nb_sample += et.shape[0]
+
+        pose = torch.tensor(pose).to(device)
+    
+    motion_annotation_np = torch.cat(motion_annotation_list, dim=0).cpu().numpy()
+    motion_pred_np = torch.cat(motion_pred_list, dim=0).cpu().numpy()
+    
+    gt_mu, gt_cov = calculate_activation_statistics(motion_annotation_np)
+    mu, cov = calculate_activation_statistics(motion_pred_np)
+
+    diversity_real = calculate_diversity(motion_annotation_np, 300 if nb_sample > 300 else 100)
+    diversity = calculate_diversity(motion_pred_np, 300 if nb_sample > 300 else 100)
+
+    R_precision_real = R_precision_real / nb_sample
+    R_precision = R_precision / nb_sample
+    matching_score_real = matching_score_real / nb_sample
+    matching_score_pred = matching_score_pred / nb_sample
+    fid = calculate_frechet_distance(gt_mu, gt_cov, mu, cov)
+
+    msg = f"--> \t Eval. :, FID. {fid:.4f}, Diversity Real. {diversity_real:.4f}, Diversity Pred. {diversity:.4f}, R_precision Real. {R_precision_real}, R_precision Pred. {R_precision}, MM-dist (matching_score) Real. {matching_score_real}, MM-dist (matching_score) Pred. {matching_score_pred}"
+    logger.info(msg)
+
+    return fid, diversity, R_precision[0], R_precision[1], R_precision[2], matching_score_pred, logger
+
+def euclidean_distance_matrix(matrix1, matrix2):
+    assert matrix1.shape[1] == matrix2.shape[1]
+    d1 = -2 * np.dot(matrix1, matrix2.T)    
+    d2 = np.sum(np.square(matrix1), axis=1, keepdims=True)    
+    d3 = np.sum(np.square(matrix2), axis=1)     
+    dists = np.sqrt(d1 + d2 + d3)
+    return dists
+
+
+
+def calculate_top_k(mat, top_k):
+    size = mat.shape[0]
+    gt_mat = np.expand_dims(np.arange(size), 1).repeat(size, 1)
+    bool_mat = (mat == gt_mat)
+    correct_vec = False
+    top_k_list = []
+    for i in range(top_k):
+        correct_vec = (correct_vec | bool_mat[:, i])
+        top_k_list.append(correct_vec[:, None])
+    top_k_mat = np.concatenate(top_k_list, axis=1)  
+    return top_k_mat
+
+
+def calculate_R_precision(embedding1, embedding2, top_k, sum_all=False):
+    dist_mat = euclidean_distance_matrix(embedding1, embedding2)
+    matching_score = dist_mat.trace() 
+    argmax = np.argsort(dist_mat, axis=1)
+    top_k_mat = calculate_top_k(argmax, top_k)
+    if sum_all:
+        return top_k_mat.sum(axis=0), matching_score
+    else:
+        return top_k_mat, matching_score
+
+
+
+def calculate_diversity(activation, diversity_times):
+    assert len(activation.shape) == 2
+    assert activation.shape[0] > diversity_times
+    num_samples = activation.shape[0]
+
+    first_indices = np.random.choice(num_samples, diversity_times, replace=False)  
+    second_indices = np.random.choice(num_samples, diversity_times, replace=False)
+    dist = linalg.norm(activation[first_indices] - activation[second_indices], axis=1) 
+    return dist.mean()  
+
+
+
+def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+    mu1 = np.atleast_1d(mu1) 
+    mu2 = np.atleast_1d(mu2) 
+
+    sigma1 = np.atleast_2d(sigma1) 
+    sigma2 = np.atleast_2d(sigma2)
+
+    assert mu1.shape == mu2.shape, \
+        'Training and test mean vectors have different lengths'
+    assert sigma1.shape == sigma2.shape, \
+        'Training and test covariances have different dimensions'
+
+    diff = mu1 - mu2
+
+    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) 
+    if not np.isfinite(covmean).all():
+        msg = ('fid calculation produces singular product; '
+               'adding %s to diagonal of cov estimates') % eps
+        print(msg)
+        offset = np.eye(sigma1.shape[0]) * eps
+        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            m = np.max(np.abs(covmean.imag))
+            raise ValueError('Imaginary component {}'.format(m))
+        covmean = covmean.real
+
+    tr_covmean = np.trace(covmean)
+
+    return (diff.dot(diff) + np.trace(sigma1)
+            + np.trace(sigma2) - 2 * tr_covmean)
+
+
+def calculate_activation_statistics(activations):
+    mu = np.mean(activations, axis=0)
+    cov = np.cov(activations, rowvar=False)
+    return mu, cov
+
+
+def calculate_frechet_feature_distance(feature_list1, feature_list2):
+    feature_list1 = np.stack(feature_list1)
+    feature_list2 = np.stack(feature_list2)
+
+    mean = np.mean(feature_list1, axis=0)
+    std = np.std(feature_list1, axis=0) + 1e-10
+    feature_list1 = (feature_list1 - mean) / std
+    feature_list2 = (feature_list2 - mean) / std
+
+    dist = calculate_frechet_distance(
+        mu1=np.mean(feature_list1, axis=0), 
+        sigma1=np.cov(feature_list1, rowvar=False),
+        mu2=np.mean(feature_list2, axis=0), 
+        sigma2=np.cov(feature_list2, rowvar=False),
+    )
+    return dist
diff --git a/utils/face_z_align_util.py b/utils/face_z_align_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..3160479601404adf2d15f7a76a1bcef21432a41a
--- /dev/null
+++ b/utils/face_z_align_util.py
@@ -0,0 +1,1065 @@
+import torch
+import numpy as np
+
+_EPS4 = np.finfo(float).eps * 4.0
+
+_FLOAT_EPS = np.finfo(float).eps
+
+# PyTorch-backed implementations
+def qinv(q):
+    assert q.shape[-1] == 4, 'q must be a tensor of shape (*, 4)'
+    mask = torch.ones_like(q)
+    mask[..., 1:] = -mask[..., 1:]
+    return q * mask
+
+
+def qinv_np(q):
+    assert q.shape[-1] == 4, 'q must be a tensor of shape (*, 4)'
+    return qinv(torch.from_numpy(q).float()).numpy()
+
+
+def qnormalize(q):
+    assert q.shape[-1] == 4, 'q must be a tensor of shape (*, 4)'
+    return q / torch.norm(q, dim=-1, keepdim=True)
+
+
+def qmul(q, r):
+    """
+    Multiply quaternion(s) q with quaternion(s) r.
+    Expects two equally-sized tensors of shape (*, 4), where * denotes any number of dimensions.
+    Returns q*r as a tensor of shape (*, 4).
+    """
+    assert q.shape[-1] == 4
+    assert r.shape[-1] == 4
+
+    original_shape = q.shape
+
+    # Compute outer product
+    terms = torch.bmm(r.view(-1, 4, 1), q.view(-1, 1, 4))
+
+    w = terms[:, 0, 0] - terms[:, 1, 1] - terms[:, 2, 2] - terms[:, 3, 3]
+    x = terms[:, 0, 1] + terms[:, 1, 0] - terms[:, 2, 3] + terms[:, 3, 2]
+    y = terms[:, 0, 2] + terms[:, 1, 3] + terms[:, 2, 0] - terms[:, 3, 1]
+    z = terms[:, 0, 3] - terms[:, 1, 2] + terms[:, 2, 1] + terms[:, 3, 0]
+    return torch.stack((w, x, y, z), dim=1).view(original_shape)
+
+
+def qrot(q, v):
+    """
+    Rotate vector(s) v about the rotation described by quaternion(s) q.
+    Expects a tensor of shape (*, 4) for q and a tensor of shape (*, 3) for v,
+    where * denotes any number of dimensions.
+    Returns a tensor of shape (*, 3).
+    """
+    assert q.shape[-1] == 4
+    assert v.shape[-1] == 3
+    assert q.shape[:-1] == v.shape[:-1]
+
+    original_shape = list(v.shape)
+    # print(q.shape)
+    q = q.contiguous().view(-1, 4)
+    v = v.contiguous().view(-1, 3)
+
+    qvec = q[:, 1:]
+    uv = torch.cross(qvec, v, dim=1)
+    uuv = torch.cross(qvec, uv, dim=1)
+    return (v + 2 * (q[:, :1] * uv + uuv)).view(original_shape)
+
+
+def qeuler(q, order, epsilon=0, deg=True):
+    """
+    Convert quaternion(s) q to Euler angles.
+    Expects a tensor of shape (*, 4), where * denotes any number of dimensions.
+    Returns a tensor of shape (*, 3).
+    """
+    assert q.shape[-1] == 4
+
+    original_shape = list(q.shape)
+    original_shape[-1] = 3
+    q = q.view(-1, 4)
+
+    q0 = q[:, 0]
+    q1 = q[:, 1]
+    q2 = q[:, 2]
+    q3 = q[:, 3]
+
+    if order == 'xyz':
+        x = torch.atan2(2 * (q0 * q1 - q2 * q3), 1 - 2 * (q1 * q1 + q2 * q2))
+        y = torch.asin(torch.clamp(2 * (q1 * q3 + q0 * q2), -1 + epsilon, 1 - epsilon))
+        z = torch.atan2(2 * (q0 * q3 - q1 * q2), 1 - 2 * (q2 * q2 + q3 * q3))
+    elif order == 'yzx':
+        x = torch.atan2(2 * (q0 * q1 - q2 * q3), 1 - 2 * (q1 * q1 + q3 * q3))
+        y = torch.atan2(2 * (q0 * q2 - q1 * q3), 1 - 2 * (q2 * q2 + q3 * q3))
+        z = torch.asin(torch.clamp(2 * (q1 * q2 + q0 * q3), -1 + epsilon, 1 - epsilon))
+    elif order == 'zxy':
+        x = torch.asin(torch.clamp(2 * (q0 * q1 + q2 * q3), -1 + epsilon, 1 - epsilon))
+        y = torch.atan2(2 * (q0 * q2 - q1 * q3), 1 - 2 * (q1 * q1 + q2 * q2))
+        z = torch.atan2(2 * (q0 * q3 - q1 * q2), 1 - 2 * (q1 * q1 + q3 * q3))
+    elif order == 'xzy':
+        x = torch.atan2(2 * (q0 * q1 + q2 * q3), 1 - 2 * (q1 * q1 + q3 * q3))
+        y = torch.atan2(2 * (q0 * q2 + q1 * q3), 1 - 2 * (q2 * q2 + q3 * q3))
+        z = torch.asin(torch.clamp(2 * (q0 * q3 - q1 * q2), -1 + epsilon, 1 - epsilon))
+    elif order == 'yxz':
+        x = torch.asin(torch.clamp(2 * (q0 * q1 - q2 * q3), -1 + epsilon, 1 - epsilon))
+        y = torch.atan2(2 * (q1 * q3 + q0 * q2), 1 - 2 * (q1 * q1 + q2 * q2))
+        z = torch.atan2(2 * (q1 * q2 + q0 * q3), 1 - 2 * (q1 * q1 + q3 * q3))
+    elif order == 'zyx':
+        x = torch.atan2(2 * (q0 * q1 + q2 * q3), 1 - 2 * (q1 * q1 + q2 * q2))
+        y = torch.asin(torch.clamp(2 * (q0 * q2 - q1 * q3), -1 + epsilon, 1 - epsilon))
+        z = torch.atan2(2 * (q0 * q3 + q1 * q2), 1 - 2 * (q2 * q2 + q3 * q3))
+    else:
+        raise
+
+    if deg:
+        return torch.stack((x, y, z), dim=1).view(original_shape) * 180 / np.pi
+    else:
+        return torch.stack((x, y, z), dim=1).view(original_shape)
+
+
+# Numpy-backed implementations
+
+def qmul_np(q, r):
+    q = torch.from_numpy(q).contiguous().float()
+    r = torch.from_numpy(r).contiguous().float()
+    return qmul(q, r).numpy()
+
+
+def qrot_np(q, v):
+    q = torch.from_numpy(q).contiguous().float()
+    v = torch.from_numpy(v).contiguous().float()
+    return qrot(q, v).numpy()
+
+
+def qeuler_np(q, order, epsilon=0, use_gpu=False):
+    if use_gpu:
+        q = torch.from_numpy(q).cuda().float()
+        return qeuler(q, order, epsilon).cpu().numpy()
+    else:
+        q = torch.from_numpy(q).contiguous().float()
+        return qeuler(q, order, epsilon).numpy()
+
+
+def qfix(q):
+    """
+    Enforce quaternion continuity across the time dimension by selecting
+    the representation (q or -q) with minimal distance (or, equivalently, maximal dot product)
+    between two consecutive frames.
+
+    Expects a tensor of shape (L, J, 4), where L is the sequence length and J is the number of joints.
+    Returns a tensor of the same shape.
+    """
+    assert len(q.shape) == 3
+    assert q.shape[-1] == 4
+
+    result = q.copy()
+    dot_products = np.sum(q[1:] * q[:-1], axis=2)
+    mask = dot_products < 0
+    mask = (np.cumsum(mask, axis=0) % 2).astype(bool)
+    result[1:][mask] *= -1
+    return result
+
+
+def euler2quat(e, order, deg=True):
+    """
+    Convert Euler angles to quaternions.
+    """
+    assert e.shape[-1] == 3
+
+    original_shape = list(e.shape)
+    original_shape[-1] = 4
+
+    e = e.view(-1, 3)
+
+    ## if euler angles in degrees
+    if deg:
+        e = e * np.pi / 180.
+
+    x = e[:, 0]
+    y = e[:, 1]
+    z = e[:, 2]
+
+    rx = torch.stack((torch.cos(x / 2), torch.sin(x / 2), torch.zeros_like(x), torch.zeros_like(x)), dim=1)
+    ry = torch.stack((torch.cos(y / 2), torch.zeros_like(y), torch.sin(y / 2), torch.zeros_like(y)), dim=1)
+    rz = torch.stack((torch.cos(z / 2), torch.zeros_like(z), torch.zeros_like(z), torch.sin(z / 2)), dim=1)
+
+    result = None
+    for coord in order:
+        if coord == 'x':
+            r = rx
+        elif coord == 'y':
+            r = ry
+        elif coord == 'z':
+            r = rz
+        else:
+            raise
+        if result is None:
+            result = r
+        else:
+            result = qmul(result, r)
+
+    # Reverse antipodal representation to have a non-negative "w"
+    if order in ['xyz', 'yzx', 'zxy']:
+        result *= -1
+
+    return result.view(original_shape)
+
+
+def expmap_to_quaternion(e):
+    """
+    Convert axis-angle rotations (aka exponential maps) to quaternions.
+    Stable formula from "Practical Parameterization of Rotations Using the Exponential Map".
+    Expects a tensor of shape (*, 3), where * denotes any number of dimensions.
+    Returns a tensor of shape (*, 4).
+    """
+    assert e.shape[-1] == 3
+
+    original_shape = list(e.shape)
+    original_shape[-1] = 4
+    e = e.reshape(-1, 3)
+
+    theta = np.linalg.norm(e, axis=1).reshape(-1, 1)
+    w = np.cos(0.5 * theta).reshape(-1, 1)
+    xyz = 0.5 * np.sinc(0.5 * theta / np.pi) * e
+    return np.concatenate((w, xyz), axis=1).reshape(original_shape)
+
+
+def euler_to_quaternion(e, order):
+    """
+    Convert Euler angles to quaternions.
+    """
+    assert e.shape[-1] == 3
+
+    original_shape = list(e.shape)
+    original_shape[-1] = 4
+
+    e = e.reshape(-1, 3)
+
+    x = e[:, 0]
+    y = e[:, 1]
+    z = e[:, 2]
+
+    rx = np.stack((np.cos(x / 2), np.sin(x / 2), np.zeros_like(x), np.zeros_like(x)), axis=1)
+    ry = np.stack((np.cos(y / 2), np.zeros_like(y), np.sin(y / 2), np.zeros_like(y)), axis=1)
+    rz = np.stack((np.cos(z / 2), np.zeros_like(z), np.zeros_like(z), np.sin(z / 2)), axis=1)
+
+    result = None
+    for coord in order:
+        if coord == 'x':
+            r = rx
+        elif coord == 'y':
+            r = ry
+        elif coord == 'z':
+            r = rz
+        else:
+            raise
+        if result is None:
+            result = r
+        else:
+            result = qmul_np(result, r)
+
+    # Reverse antipodal representation to have a non-negative "w"
+    if order in ['xyz', 'yzx', 'zxy']:
+        result *= -1
+
+    return result.reshape(original_shape)
+
+
+def quaternion_to_matrix(quaternions):
+    """
+    Convert rotations given as quaternions to rotation matrices.
+    Args:
+        quaternions: quaternions with real part first,
+            as tensor of shape (..., 4).
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+    r, i, j, k = torch.unbind(quaternions, -1)
+    two_s = 2.0 / (quaternions * quaternions).sum(-1)
+
+    o = torch.stack(
+        (
+            1 - two_s * (j * j + k * k),
+            two_s * (i * j - k * r),
+            two_s * (i * k + j * r),
+            two_s * (i * j + k * r),
+            1 - two_s * (i * i + k * k),
+            two_s * (j * k - i * r),
+            two_s * (i * k - j * r),
+            two_s * (j * k + i * r),
+            1 - two_s * (i * i + j * j),
+        ),
+        -1,
+    )
+    return o.reshape(quaternions.shape[:-1] + (3, 3))
+
+
+def quaternion_to_matrix_np(quaternions):
+    q = torch.from_numpy(quaternions).contiguous().float()
+    return quaternion_to_matrix(q).numpy()
+
+
+def quaternion_to_cont6d_np(quaternions):
+    rotation_mat = quaternion_to_matrix_np(quaternions)
+    cont_6d = np.concatenate([rotation_mat[..., 0], rotation_mat[..., 1]], axis=-1)
+    return cont_6d
+
+
+def quaternion_to_cont6d(quaternions):
+    rotation_mat = quaternion_to_matrix(quaternions)
+    cont_6d = torch.cat([rotation_mat[..., 0], rotation_mat[..., 1]], dim=-1)
+    return cont_6d
+
+
+# def cont6d_to_matrix(cont6d):
+#     assert cont6d.shape[-1] == 6, "The last dimension must be 6"
+#     x_raw = cont6d[..., 0:3]
+#     y_raw = cont6d[..., 3:6]
+
+#     x = x_raw / torch.norm(x_raw, dim=-1, keepdim=True)
+#     z = torch.cross(x, y_raw, dim=-1)
+#     z = z / torch.norm(z, dim=-1, keepdim=True)
+
+#     y = torch.cross(z, x, dim=-1)
+
+#     x = x[..., None]
+#     y = y[..., None]
+#     z = z[..., None]
+
+#     mat = torch.cat([x, y, z], dim=-1)
+#     return mat
+
+def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor:
+    """
+    Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
+    using Gram--Schmidt orthogonalization per Section B of [1].
+    Args:
+        d6: 6D rotation representation, of size (*, 6)
+
+    Returns:
+        batch of rotation matrices of size (*, 3, 3)
+
+    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
+    On the Continuity of Rotation Representations in Neural Networks.
+    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
+    Retrieved from http://arxiv.org/abs/1812.07035
+    """
+
+    a1, a2 = d6[..., :3], d6[..., 3:]
+    b1 = F.normalize(a1, dim=-1)
+    b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1
+    b2 = F.normalize(b2, dim=-1)
+    b3 = torch.cross(b1, b2, dim=-1)
+    return torch.stack((b1, b2, b3), dim=-2)
+
+# def cont6d_to_matrix_np(cont6d):
+#     q = torch.from_numpy(cont6d).contiguous()
+#     return cont6d_to_matrix(q).numpy()
+
+
+def qpow(q0, t, dtype=torch.float):
+    ''' q0 : tensor of quaternions
+    t: tensor of powers
+    '''
+    q0 = qnormalize(q0)
+    theta0 = torch.acos(q0[..., 0])
+
+    ## if theta0 is close to zero, add epsilon to avoid NaNs
+    mask = (theta0 <= 10e-10) * (theta0 >= -10e-10)
+    theta0 = (1 - mask) * theta0 + mask * 10e-10
+    v0 = q0[..., 1:] / torch.sin(theta0).view(-1, 1)
+
+    if isinstance(t, torch.Tensor):
+        q = torch.zeros(t.shape + q0.shape)
+        theta = t.view(-1, 1) * theta0.view(1, -1)
+    else:  ## if t is a number
+        q = torch.zeros(q0.shape)
+        theta = t * theta0
+
+    q[..., 0] = torch.cos(theta)
+    q[..., 1:] = v0 * torch.sin(theta).unsqueeze(-1)
+
+    return q.to(dtype)
+
+
+def qslerp(q0, q1, t):
+    '''
+    q0: starting quaternion
+    q1: ending quaternion
+    t: array of points along the way
+
+    Returns:
+    Tensor of Slerps: t.shape + q0.shape
+    '''
+
+    q0 = qnormalize(q0)
+    q1 = qnormalize(q1)
+    q_ = qpow(qmul(q1, qinv(q0)), t)
+
+    return qmul(q_,
+                q0.contiguous().view(torch.Size([1] * len(t.shape)) + q0.shape).expand(t.shape + q0.shape).contiguous())
+
+
+def qbetween(v0, v1):
+    '''
+    find the quaternion used to rotate v0 to v1
+    '''
+    assert v0.shape[-1] == 3, 'v0 must be of the shape (*, 3)'
+    assert v1.shape[-1] == 3, 'v1 must be of the shape (*, 3)'
+
+    v = torch.cross(v0, v1)
+    w = torch.sqrt((v0 ** 2).sum(dim=-1, keepdim=True) * (v1 ** 2).sum(dim=-1, keepdim=True)) + (v0 * v1).sum(dim=-1,
+                                                                                                              keepdim=True)
+    return qnormalize(torch.cat([w, v], dim=-1))
+
+
+def qbetween_np(v0, v1):
+    '''
+    find the quaternion used to rotate v0 to v1
+    '''
+    assert v0.shape[-1] == 3, 'v0 must be of the shape (*, 3)'
+    assert v1.shape[-1] == 3, 'v1 must be of the shape (*, 3)'
+
+    v0 = torch.from_numpy(v0).float()
+    v1 = torch.from_numpy(v1).float()
+    return qbetween(v0, v1).numpy()
+
+
+def lerp(p0, p1, t):
+    if not isinstance(t, torch.Tensor):
+        t = torch.Tensor([t])
+
+    new_shape = t.shape + p0.shape
+    new_view_t = t.shape + torch.Size([1] * len(p0.shape))
+    new_view_p = torch.Size([1] * len(t.shape)) + p0.shape
+    p0 = p0.view(new_view_p).expand(new_shape)
+    p1 = p1.view(new_view_p).expand(new_shape)
+    t = t.view(new_view_t).expand(new_shape)
+
+    return p0 + t * (p1 - p0)
+
+joint_idx = (0,1,2,4,5,7,8,12,16,17,18,19,20,21,60,61,62,63,64,65,59,58,57,56,55, # body joints
+        37,38,39,66,25,26,27,67,28,29,30,68,34,35,36,69,31,32,33,70, # left hand joints
+        52,53,54,71,40,41,42,72,43,44,45,73,49,50,51,74,46,47,48,75, # right hand joints
+        22,15, # jaw, head
+        57,56, # eyeballs
+        76,77,78,79,80,81,82,83,84,85, # eyebrow
+        86,87,88,89, # nose
+        90,91,92,93,94, # below nose
+        95,96,97,98,99,100,101,102,103,104,105,106, # eyes
+        107, # right mouth
+        108,109,110,111,112, # upper mouth
+        113, # left mouth
+        114,115,116,117,118, # lower mouth
+        119, # right lip
+        120,121,122, # upper lip
+        123, # left lip
+        124,125,126, # lower lip
+        127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143 # face contour
+        )
+
+def face_z_transform(positions, global_orient, trans):
+    '''
+    positions: [num_frame, num_joints, 3]
+    global_orient: [num_frame, 3]
+    '''
+    joints_name = \
+        ('Pelvis', 'L_Hip', 'R_Hip', 'L_Knee', 'R_Knee', 'L_Ankle', 'R_Ankle', 'Neck', 'L_Shoulder', 'R_Shoulder',
+         'L_Elbow', 'R_Elbow', 'L_Wrist', 'R_Wrist', 'L_Big_toe', 'L_Small_toe', 'L_Heel', 'R_Big_toe', 'R_Small_toe',
+         'R_Heel', 'L_Ear', 'R_Ear', 'L_Eye', 'R_Eye', 'Nose',  # body joints
+         'L_Thumb_1', 'L_Thumb_2', 'L_Thumb_3', 'L_Thumb_4', 'L_Index_1', 'L_Index_2', 'L_Index_3', 'L_Index_4',
+         'L_Middle_1', 'L_Middle_2', 'L_Middle_3', 'L_Middle_4', 'L_Ring_1', 'L_Ring_2', 'L_Ring_3', 'L_Ring_4',
+         'L_Pinky_1', 'L_Pinky_2', 'L_Pinky_3', 'L_Pinky_4',  # left hand joints
+         'R_Thumb_1', 'R_Thumb_2', 'R_Thumb_3', 'R_Thumb_4', 'R_Index_1', 'R_Index_2', 'R_Index_3', 'R_Index_4',
+         'R_Middle_1', 'R_Middle_2', 'R_Middle_3', 'R_Middle_4', 'R_Ring_1', 'R_Ring_2', 'R_Ring_3', 'R_Ring_4',
+         'R_Pinky_1', 'R_Pinky_2', 'R_Pinky_3', 'R_Pinky_4',  # right hand joints
+         *['Face_' + str(i) for i in range(1, 73)]
+         # face keypoints (too many keypoints... omit real names. have same name of keypoints defined in FLAME class)
+         )
+    root_pos_init = positions[0]
+
+    assert root_pos_init.shape[0]==len(joints_name)
+    '''All initially face Z+'''
+    r_hip, l_hip, sdr_r, sdr_l = joints_name.index('R_Hip'), joints_name.index('L_Hip'), joints_name.index('R_Shoulder'), joints_name.index('L_Shoulder')
+    across1 = root_pos_init[r_hip] - root_pos_init[l_hip]
+    across2 = root_pos_init[sdr_r] - root_pos_init[sdr_l]
+    across = across1 + across2
+    across = across / np.sqrt((across ** 2).sum(axis=-1))[..., np.newaxis]
+
+    # forward (3,), rotate around y-axis
+    forward_init = np.cross(np.array([[0, 1, 0]]), across, axis=-1)
+    forward_init = forward_init / np.sqrt((forward_init ** 2).sum(axis=-1))[..., np.newaxis]
+
+    target = np.array([[0, 0, 1]])
+    root_quat_init = qbetween_np(forward_init, target)
+    root_quat_init = np.ones(global_orient.shape[:-1] + (4,)) * root_quat_init
+    root_quat_init = torch.tensor(root_quat_init, dtype=torch.float32).float().cuda()
+
+    root_matrix_init = quaternion_to_matrix(root_quat_init)
+    global_orient_matrix = axis_angle_to_matrix(global_orient)
+    global_orient_matrix = torch.matmul(root_matrix_init, global_orient_matrix)
+    global_orient = matrix_to_axis_angle(global_orient_matrix)
+
+    trans = trans.cpu().numpy()
+    '''Put on Floor'''
+    floor_height = positions.min(axis=0).min(axis=0)[1]
+    trans[:, 1] -= floor_height
+
+    '''XZ at origin'''
+    root_pos_init = positions[0]
+    root_pose_init_xz = root_pos_init[0] * np.array([1, 0, 1])
+    trans = trans - root_pose_init_xz
+
+    '''All initially face Z+'''
+    trans = torch.from_numpy(trans).float().cuda()
+    trans = qrot(root_quat_init, trans)
+
+    return global_orient, trans
+
+
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+# Check PYTORCH3D_LICENCE before use
+
+import functools
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+
+"""
+The transformation matrices returned from the functions in this file assume
+the points on which the transformation will be applied are column vectors.
+i.e. the R matrix is structured as
+    R = [
+            [Rxx, Rxy, Rxz],
+            [Ryx, Ryy, Ryz],
+            [Rzx, Rzy, Rzz],
+        ]  # (3, 3)
+This matrix can be applied to column vectors by post multiplication
+by the points e.g.
+    points = [[0], [1], [2]]  # (3 x 1) xyz coordinates of a point
+    transformed_points = R * points
+To apply the same matrix to points which are row vectors, the R matrix
+can be transposed and pre multiplied by the points:
+e.g.
+    points = [[0, 1, 2]]  # (1 x 3) xyz coordinates of a point
+    transformed_points = points * R.transpose(1, 0)
+"""
+
+
+def quaternion_to_matrix(quaternions):
+    """
+    Convert rotations given as quaternions to rotation matrices.
+    Args:
+        quaternions: quaternions with real part first,
+            as tensor of shape (..., 4).
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+    r, i, j, k = torch.unbind(quaternions, -1)
+    two_s = 2.0 / (quaternions * quaternions).sum(-1)
+
+    o = torch.stack(
+        (
+            1 - two_s * (j * j + k * k),
+            two_s * (i * j - k * r),
+            two_s * (i * k + j * r),
+            two_s * (i * j + k * r),
+            1 - two_s * (i * i + k * k),
+            two_s * (j * k - i * r),
+            two_s * (i * k - j * r),
+            two_s * (j * k + i * r),
+            1 - two_s * (i * i + j * j),
+        ),
+        -1,
+    )
+    return o.reshape(quaternions.shape[:-1] + (3, 3))
+
+
+def _copysign(a, b):
+    """
+    Return a tensor where each element has the absolute value taken from the,
+    corresponding element of a, with sign taken from the corresponding
+    element of b. This is like the standard copysign floating-point operation,
+    but is not careful about negative 0 and NaN.
+    Args:
+        a: source tensor.
+        b: tensor whose signs will be used, of the same shape as a.
+    Returns:
+        Tensor of the same shape as a with the signs of b.
+    """
+    signs_differ = (a < 0) != (b < 0)
+    return torch.where(signs_differ, -a, a)
+
+
+def _sqrt_positive_part(x):
+    """
+    Returns torch.sqrt(torch.max(0, x))
+    but with a zero subgradient where x is 0.
+    """
+    ret = torch.zeros_like(x)
+    positive_mask = x > 0
+    ret[positive_mask] = torch.sqrt(x[positive_mask])
+    return ret
+
+
+def matrix_to_quaternion(matrix):
+    """
+    Convert rotations given as rotation matrices to quaternions.
+    Args:
+        matrix: Rotation matrices as tensor of shape (..., 3, 3).
+    Returns:
+        quaternions with real part first, as tensor of shape (..., 4).
+    """
+    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
+        raise ValueError(f"Invalid rotation matrix  shape f{matrix.shape}.")
+    m00 = matrix[..., 0, 0]
+    m11 = matrix[..., 1, 1]
+    m22 = matrix[..., 2, 2]
+    o0 = 0.5 * _sqrt_positive_part(1 + m00 + m11 + m22)
+    x = 0.5 * _sqrt_positive_part(1 + m00 - m11 - m22)
+    y = 0.5 * _sqrt_positive_part(1 - m00 + m11 - m22)
+    z = 0.5 * _sqrt_positive_part(1 - m00 - m11 + m22)
+    o1 = _copysign(x, matrix[..., 2, 1] - matrix[..., 1, 2])
+    o2 = _copysign(y, matrix[..., 0, 2] - matrix[..., 2, 0])
+    o3 = _copysign(z, matrix[..., 1, 0] - matrix[..., 0, 1])
+    return torch.stack((o0, o1, o2, o3), -1)
+
+
+def _axis_angle_rotation(axis: str, angle):
+    """
+    Return the rotation matrices for one of the rotations about an axis
+    of which Euler angles describe, for each value of the angle given.
+    Args:
+        axis: Axis label "X" or "Y or "Z".
+        angle: any shape tensor of Euler angles in radians
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+
+    cos = torch.cos(angle)
+    sin = torch.sin(angle)
+    one = torch.ones_like(angle)
+    zero = torch.zeros_like(angle)
+
+    if axis == "X":
+        R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos)
+    if axis == "Y":
+        R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos)
+    if axis == "Z":
+        R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one)
+
+    return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3))
+
+
+def euler_angles_to_matrix(euler_angles, convention: str):
+    """
+    Convert rotations given as Euler angles in radians to rotation matrices.
+    Args:
+        euler_angles: Euler angles in radians as tensor of shape (..., 3).
+        convention: Convention string of three uppercase letters from
+            {"X", "Y", and "Z"}.
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+    if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3:
+        raise ValueError("Invalid input euler angles.")
+    if len(convention) != 3:
+        raise ValueError("Convention must have 3 letters.")
+    if convention[1] in (convention[0], convention[2]):
+        raise ValueError(f"Invalid convention {convention}.")
+    for letter in convention:
+        if letter not in ("X", "Y", "Z"):
+            raise ValueError(f"Invalid letter {letter} in convention string.")
+    matrices = map(_axis_angle_rotation, convention, torch.unbind(euler_angles, -1))
+    return functools.reduce(torch.matmul, matrices)
+
+
+def _angle_from_tan(
+        axis: str, other_axis: str, data, horizontal: bool, tait_bryan: bool
+):
+    """
+    Extract the first or third Euler angle from the two members of
+    the matrix which are positive constant times its sine and cosine.
+    Args:
+        axis: Axis label "X" or "Y or "Z" for the angle we are finding.
+        other_axis: Axis label "X" or "Y or "Z" for the middle axis in the
+            convention.
+        data: Rotation matrices as tensor of shape (..., 3, 3).
+        horizontal: Whether we are looking for the angle for the third axis,
+            which means the relevant entries are in the same row of the
+            rotation matrix. If not, they are in the same column.
+        tait_bryan: Whether the first and third axes in the convention differ.
+    Returns:
+        Euler Angles in radians for each matrix in data as a tensor
+        of shape (...).
+    """
+
+    i1, i2 = {"X": (2, 1), "Y": (0, 2), "Z": (1, 0)}[axis]
+    if horizontal:
+        i2, i1 = i1, i2
+    even = (axis + other_axis) in ["XY", "YZ", "ZX"]
+    if horizontal == even:
+        return torch.atan2(data[..., i1], data[..., i2])
+    if tait_bryan:
+        return torch.atan2(-data[..., i2], data[..., i1])
+    return torch.atan2(data[..., i2], -data[..., i1])
+
+
+def _index_from_letter(letter: str):
+    if letter == "X":
+        return 0
+    if letter == "Y":
+        return 1
+    if letter == "Z":
+        return 2
+
+
+def matrix_to_euler_angles(matrix, convention: str):
+    """
+    Convert rotations given as rotation matrices to Euler angles in radians.
+    Args:
+        matrix: Rotation matrices as tensor of shape (..., 3, 3).
+        convention: Convention string of three uppercase letters.
+    Returns:
+        Euler angles in radians as tensor of shape (..., 3).
+    """
+    if len(convention) != 3:
+        raise ValueError("Convention must have 3 letters.")
+    if convention[1] in (convention[0], convention[2]):
+        raise ValueError(f"Invalid convention {convention}.")
+    for letter in convention:
+        if letter not in ("X", "Y", "Z"):
+            raise ValueError(f"Invalid letter {letter} in convention string.")
+    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
+        raise ValueError(f"Invalid rotation matrix  shape f{matrix.shape}.")
+    i0 = _index_from_letter(convention[0])
+    i2 = _index_from_letter(convention[2])
+    tait_bryan = i0 != i2
+    if tait_bryan:
+        central_angle = torch.asin(
+            matrix[..., i0, i2] * (-1.0 if i0 - i2 in [-1, 2] else 1.0)
+        )
+    else:
+        central_angle = torch.acos(matrix[..., i0, i0])
+
+    o = (
+        _angle_from_tan(
+            convention[0], convention[1], matrix[..., i2], False, tait_bryan
+        ),
+        central_angle,
+        _angle_from_tan(
+            convention[2], convention[1], matrix[..., i0, :], True, tait_bryan
+        ),
+    )
+    return torch.stack(o, -1)
+
+
+def random_quaternions(
+        n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
+):
+    """
+    Generate random quaternions representing rotations,
+    i.e. versors with nonnegative real part.
+    Args:
+        n: Number of quaternions in a batch to return.
+        dtype: Type to return.
+        device: Desired device of returned tensor. Default:
+            uses the current device for the default tensor type.
+        requires_grad: Whether the resulting tensor should have the gradient
+            flag set.
+    Returns:
+        Quaternions as tensor of shape (N, 4).
+    """
+    o = torch.randn((n, 4), dtype=dtype, device=device, requires_grad=requires_grad)
+    s = (o * o).sum(1)
+    o = o / _copysign(torch.sqrt(s), o[:, 0])[:, None]
+    return o
+
+
+def random_rotations(
+        n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
+):
+    """
+    Generate random rotations as 3x3 rotation matrices.
+    Args:
+        n: Number of rotation matrices in a batch to return.
+        dtype: Type to return.
+        device: Device of returned tensor. Default: if None,
+            uses the current device for the default tensor type.
+        requires_grad: Whether the resulting tensor should have the gradient
+            flag set.
+    Returns:
+        Rotation matrices as tensor of shape (n, 3, 3).
+    """
+    quaternions = random_quaternions(
+        n, dtype=dtype, device=device, requires_grad=requires_grad
+    )
+    return quaternion_to_matrix(quaternions)
+
+
+def random_rotation(
+        dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
+):
+    """
+    Generate a single random 3x3 rotation matrix.
+    Args:
+        dtype: Type to return
+        device: Device of returned tensor. Default: if None,
+            uses the current device for the default tensor type
+        requires_grad: Whether the resulting tensor should have the gradient
+            flag set
+    Returns:
+        Rotation matrix as tensor of shape (3, 3).
+    """
+    return random_rotations(1, dtype, device, requires_grad)[0]
+
+
+def standardize_quaternion(quaternions):
+    """
+    Convert a unit quaternion to a standard form: one in which the real
+    part is non negative.
+    Args:
+        quaternions: Quaternions with real part first,
+            as tensor of shape (..., 4).
+    Returns:
+        Standardized quaternions as tensor of shape (..., 4).
+    """
+    return torch.where(quaternions[..., 0:1] < 0, -quaternions, quaternions)
+
+
+def quaternion_raw_multiply(a, b):
+    """
+    Multiply two quaternions.
+    Usual torch rules for broadcasting apply.
+    Args:
+        a: Quaternions as tensor of shape (..., 4), real part first.
+        b: Quaternions as tensor of shape (..., 4), real part first.
+    Returns:
+        The product of a and b, a tensor of quaternions shape (..., 4).
+    """
+    aw, ax, ay, az = torch.unbind(a, -1)
+    bw, bx, by, bz = torch.unbind(b, -1)
+    ow = aw * bw - ax * bx - ay * by - az * bz
+    ox = aw * bx + ax * bw + ay * bz - az * by
+    oy = aw * by - ax * bz + ay * bw + az * bx
+    oz = aw * bz + ax * by - ay * bx + az * bw
+    return torch.stack((ow, ox, oy, oz), -1)
+
+
+def quaternion_multiply(a, b):
+    """
+    Multiply two quaternions representing rotations, returning the quaternion
+    representing their composition, i.e. the versor with nonnegative real part.
+    Usual torch rules for broadcasting apply.
+    Args:
+        a: Quaternions as tensor of shape (..., 4), real part first.
+        b: Quaternions as tensor of shape (..., 4), real part first.
+    Returns:
+        The product of a and b, a tensor of quaternions of shape (..., 4).
+    """
+    ab = quaternion_raw_multiply(a, b)
+    return standardize_quaternion(ab)
+
+
+def quaternion_invert(quaternion):
+    """
+    Given a quaternion representing rotation, get the quaternion representing
+    its inverse.
+    Args:
+        quaternion: Quaternions as tensor of shape (..., 4), with real part
+            first, which must be versors (unit quaternions).
+    Returns:
+        The inverse, a tensor of quaternions of shape (..., 4).
+    """
+
+    return quaternion * quaternion.new_tensor([1, -1, -1, -1])
+
+
+def quaternion_apply(quaternion, point):
+    """
+    Apply the rotation given by a quaternion to a 3D point.
+    Usual torch rules for broadcasting apply.
+    Args:
+        quaternion: Tensor of quaternions, real part first, of shape (..., 4).
+        point: Tensor of 3D points of shape (..., 3).
+    Returns:
+        Tensor of rotated points of shape (..., 3).
+    """
+    if point.size(-1) != 3:
+        raise ValueError(f"Points are not in 3D, f{point.shape}.")
+    real_parts = point.new_zeros(point.shape[:-1] + (1,))
+    point_as_quaternion = torch.cat((real_parts, point), -1)
+    out = quaternion_raw_multiply(
+        quaternion_raw_multiply(quaternion, point_as_quaternion),
+        quaternion_invert(quaternion),
+    )
+    return out[..., 1:]
+
+
+def axis_angle_to_matrix(axis_angle):
+    """
+    Convert rotations given as axis/angle to rotation matrices.
+    Args:
+        axis_angle: Rotations given as a vector in axis angle form,
+            as a tensor of shape (..., 3), where the magnitude is
+            the angle turned anticlockwise in radians around the
+            vector's direction.
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+    return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle))
+
+
+def matrix_to_axis_angle(matrix):
+    """
+    Convert rotations given as rotation matrices to axis/angle.
+    Args:
+        matrix: Rotation matrices as tensor of shape (..., 3, 3).
+    Returns:
+        Rotations given as a vector in axis angle form, as a tensor
+            of shape (..., 3), where the magnitude is the angle
+            turned anticlockwise in radians around the vector's
+            direction.
+    """
+    return quaternion_to_axis_angle(matrix_to_quaternion(matrix))
+
+
+def axis_angle_to_quaternion(axis_angle):
+    """
+    Convert rotations given as axis/angle to quaternions.
+    Args:
+        axis_angle: Rotations given as a vector in axis angle form,
+            as a tensor of shape (..., 3), where the magnitude is
+            the angle turned anticlockwise in radians around the
+            vector's direction.
+    Returns:
+        quaternions with real part first, as tensor of shape (..., 4).
+    """
+    angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True)
+    half_angles = 0.5 * angles
+    eps = 1e-6
+    small_angles = angles.abs() < eps
+    sin_half_angles_over_angles = torch.empty_like(angles)
+    sin_half_angles_over_angles[~small_angles] = (
+            torch.sin(half_angles[~small_angles]) / angles[~small_angles]
+    )
+    # for x small, sin(x/2) is about x/2 - (x/2)^3/6
+    # so sin(x/2)/x is about 1/2 - (x*x)/48
+    sin_half_angles_over_angles[small_angles] = (
+            0.5 - (angles[small_angles] * angles[small_angles]) / 48
+    )
+    quaternions = torch.cat(
+        [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1
+    )
+    return quaternions
+
+
+def quaternion_to_axis_angle(quaternions):
+    """
+    Convert rotations given as quaternions to axis/angle.
+    Args:
+        quaternions: quaternions with real part first,
+            as tensor of shape (..., 4).
+    Returns:
+        Rotations given as a vector in axis angle form, as a tensor
+            of shape (..., 3), where the magnitude is the angle
+            turned anticlockwise in radians around the vector's
+            direction.
+    """
+    norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True)
+    half_angles = torch.atan2(norms, quaternions[..., :1])
+    angles = 2 * half_angles
+    eps = 1e-6
+    small_angles = angles.abs() < eps
+    sin_half_angles_over_angles = torch.empty_like(angles)
+    sin_half_angles_over_angles[~small_angles] = (
+            torch.sin(half_angles[~small_angles]) / angles[~small_angles]
+    )
+    # for x small, sin(x/2) is about x/2 - (x/2)^3/6
+    # so sin(x/2)/x is about 1/2 - (x*x)/48
+    sin_half_angles_over_angles[small_angles] = (
+            0.5 - (angles[small_angles] * angles[small_angles]) / 48
+    )
+    return quaternions[..., 1:] / sin_half_angles_over_angles
+
+
+def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor:
+    """
+    Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
+    using Gram--Schmidt orthogonalisation per Section B of [1].
+    Args:
+        d6: 6D rotation representation, of size (*, 6)
+    Returns:
+        batch of rotation matrices of size (*, 3, 3)
+    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
+    On the Continuity of Rotation Representations in Neural Networks.
+    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
+    Retrieved from http://arxiv.org/abs/1812.07035
+    """
+
+    a1, a2 = d6[..., :3], d6[..., 3:]
+    b1 = F.normalize(a1, dim=-1)
+    b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1
+    b2 = F.normalize(b2, dim=-1)
+    b3 = torch.cross(b1, b2, dim=-1)
+    return torch.stack((b1, b2, b3), dim=-2)
+
+
+def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor:
+    """
+    Converts rotation matrices to 6D rotation representation by Zhou et al. [1]
+    by dropping the last row. Note that 6D representation is not unique.
+    Args:
+        matrix: batch of rotation matrices of size (*, 3, 3)
+    Returns:
+        6D rotation representation, of size (*, 6)
+    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
+    On the Continuity of Rotation Representations in Neural Networks.
+    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
+    Retrieved from http://arxiv.org/abs/1812.07035
+    """
+    return matrix[..., :2, :].clone().reshape(*matrix.size()[:-2], 6)
+
+
+def canonicalize_smplh(poses, trans=None):
+    bs, nframes, njoints = poses.shape[:3]
+
+    global_orient = poses[:, :, 0]
+
+    # first global rotations
+    rot2d = matrix_to_axis_angle(global_orient[:, 0])
+    # rot2d[:, :2] = 0  # Remove the rotation along the vertical axis
+    rot2d = axis_angle_to_matrix(rot2d)
+
+    # Rotate the global rotation to eliminate Z rotations
+    global_orient = torch.einsum("ikj,imkl->imjl", rot2d, global_orient)
+
+    # Construct canonicalized version of x
+    xc = torch.cat((global_orient[:, :, None], poses[:, :, 1:]), dim=2)
+
+    if trans is not None:
+        vel = trans[:, 1:] - trans[:, :-1]
+        # Turn the translation as well
+        vel = torch.einsum("ikj,ilk->ilj", rot2d, vel)
+        trans = torch.cat((torch.zeros(bs, 1, 3, device=vel.device),
+                           torch.cumsum(vel, 1)), 1)
+        return xc, trans
+    else:
+        return xc
+
+    # Added
+
+
+def matrix_of_angles(cos, sin, inv=False, dim=2):
+    assert dim in [2, 3]
+    sin = -sin if inv else sin
+    if dim == 2:
+        row1 = torch.stack((cos, -sin), axis=-1)
+        row2 = torch.stack((sin, cos), axis=-1)
+        return torch.stack((row1, row2), axis=-2)
+    elif dim == 3:
+        row1 = torch.stack((cos, -sin, 0 * cos), axis=-1)
+        row2 = torch.stack((sin, cos, 0 * cos), axis=-1)
+        row3 = torch.stack((0 * sin, 0 * cos, 1 + 0 * cos), axis=-1)
+        return torch.stack((row1, row2, row3), axis=-2)
+
diff --git a/utils/losses.py b/utils/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..b90ebee62a5d82a047cdf7adf614a5380acb426a
--- /dev/null
+++ b/utils/losses.py
@@ -0,0 +1,36 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+class ReConsLoss(nn.Module):
+    def __init__(self, motion_dim=272):
+        super(ReConsLoss, self).__init__()
+        self.motion_dim = motion_dim
+    
+    def softclip(self, tensor, min):
+        result_tensor = min + F.softplus(tensor - min)
+        return result_tensor
+    
+    def gaussian_nll(self, mu, log_sigma, x):
+        return 0.5 * torch.pow((x - mu) / log_sigma.exp(), 2) + log_sigma + 0.5 * np.log(2 * np.pi)
+    
+    def forward(self, motion_pred, motion_gt) : 
+        """Optimal sigma VAE loss, see https://arxiv.org/pdf/2006.13202 for more details"""
+        log_sigma = ((motion_gt[..., :self.motion_dim] - motion_pred[..., :self.motion_dim]) ** 2).mean([0,1,2], keepdim=True).sqrt().log()
+        log_sigma = self.softclip(log_sigma, -6)
+        loss = self.gaussian_nll(motion_pred[..., :self.motion_dim], log_sigma, motion_gt[..., :self.motion_dim]).sum()
+        return loss
+    
+    
+    def forward_KL(self, mu, logvar):
+        loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=(1, 2))
+        return loss.mean()
+    
+    def forward_root(self, motion_pred, motion_gt):
+        """[..., :8] relate to the root joint"""
+        root_log_sigma = ((motion_gt[..., :8] - motion_pred[..., :8]) ** 2).mean([0,1,2], keepdim=True).sqrt().log()
+        root_log_sigma = self.softclip(root_log_sigma, -6)
+        root_loss = self.gaussian_nll(motion_pred[..., :8], root_log_sigma, motion_gt[..., :8]).sum()           
+        return root_loss
+    
diff --git a/utils/paramUtil.py b/utils/paramUtil.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9f1708b85ca80a9051cb3675cec9b999a0d0e2b
--- /dev/null
+++ b/utils/paramUtil.py
@@ -0,0 +1,63 @@
+import numpy as np
+
+# Define a kinematic tree for the skeletal struture
+kit_kinematic_chain = [[0, 11, 12, 13, 14, 15], [0, 16, 17, 18, 19, 20], [0, 1, 2, 3, 4], [3, 5, 6, 7], [3, 8, 9, 10]]
+
+kit_raw_offsets = np.array(
+    [
+        [0, 0, 0],
+        [0, 1, 0],
+        [0, 1, 0],
+        [0, 1, 0],
+        [0, 1, 0],
+        [1, 0, 0],
+        [0, -1, 0],
+        [0, -1, 0],
+        [-1, 0, 0],
+        [0, -1, 0],
+        [0, -1, 0],
+        [1, 0, 0],
+        [0, -1, 0],
+        [0, -1, 0],
+        [0, 0, 1],
+        [0, 0, 1],
+        [-1, 0, 0],
+        [0, -1, 0],
+        [0, -1, 0],
+        [0, 0, 1],
+        [0, 0, 1]
+    ]
+)
+
+t2m_raw_offsets = np.array([[0,0,0],
+                           [1,0,0],
+                           [-1,0,0],
+                           [0,1,0],
+                           [0,-1,0],
+                           [0,-1,0],
+                           [0,1,0],
+                           [0,-1,0],
+                           [0,-1,0],
+                           [0,1,0],
+                           [0,0,1],
+                           [0,0,1],
+                           [0,1,0],
+                           [1,0,0],
+                           [-1,0,0],
+                           [0,0,1],
+                           [0,-1,0],
+                           [0,-1,0],
+                           [0,-1,0],
+                           [0,-1,0],
+                           [0,-1,0],
+                           [0,-1,0]])
+
+t2m_kinematic_chain = [[0, 2, 5, 8, 11], [0, 1, 4, 7, 10], [0, 3, 6, 9, 12, 15], [9, 14, 17, 19, 21], [9, 13, 16, 18, 20]]
+t2m_left_hand_chain = [[20, 22, 23, 24], [20, 34, 35, 36], [20, 25, 26, 27], [20, 31, 32, 33], [20, 28, 29, 30]]
+t2m_right_hand_chain = [[21, 43, 44, 45], [21, 46, 47, 48], [21, 40, 41, 42], [21, 37, 38, 39], [21, 49, 50, 51]]
+
+
+kit_tgt_skel_id = '03950'
+
+t2m_tgt_skel_id = '000021'
+
diff --git a/utils/quat.py b/utils/quat.py
new file mode 100644
index 0000000000000000000000000000000000000000..e703289a82498c0480bc369fb18264778086f309
--- /dev/null
+++ b/utils/quat.py
@@ -0,0 +1,321 @@
+import numpy as np
+
+# Calculate cross object of two 3D vectors.
+def _fast_cross(a, b):
+    return np.concatenate([
+        a[...,1:2]*b[...,2:3] - a[...,2:3]*b[...,1:2],
+        a[...,2:3]*b[...,0:1] - a[...,0:1]*b[...,2:3],
+        a[...,0:1]*b[...,1:2] - a[...,1:2]*b[...,0:1]], axis=-1)
+
+# Make origin quaternions (No rotations)
+def eye(shape, dtype=np.float32):
+    return np.ones(list(shape) + [4], dtype=dtype) * np.asarray([1, 0, 0, 0], dtype=dtype)
+
+# Return norm of quaternions
+def length(x):
+    return np.sqrt(np.sum(x * x, axis=-1))
+
+# Make unit quaternions
+def normalize(x, eps=1e-8):
+    return x / (length(x)[...,None] + eps)
+
+def abs(x):
+    return np.where(x[...,0:1] > 0.0, x, -x)
+
+# Calculate inverse rotations
+def inv(q):
+    return np.array([1, -1, -1, -1], dtype=np.float32) * q
+
+# Calculate the dot product of two quaternions
+def dot(x, y):
+    return np.sum(x * y, axis=-1)[...,None] if x.ndim > 1 else np.sum(x * y, axis=-1)
+
+# Multiply two quaternions (return rotations).
+def mul(x, y):
+    x0, x1, x2, x3 = x[..., 0:1], x[..., 1:2], x[..., 2:3], x[..., 3:4]
+    y0, y1, y2, y3 = y[..., 0:1], y[..., 1:2], y[..., 2:3], y[..., 3:4]
+
+    return np.concatenate([
+        y0 * x0 - y1 * x1 - y2 * x2 - y3 * x3,
+        y0 * x1 + y1 * x0 - y2 * x3 + y3 * x2,
+        y0 * x2 + y1 * x3 + y2 * x0 - y3 * x1,
+        y0 * x3 - y1 * x2 + y2 * x1 + y3 * x0], axis=-1)
+
+def inv_mul(x, y):
+    return mul(inv(x), y)
+
+def mul_inv(x, y):
+    return mul(x, inv(y))
+
+# Multiply quaternions and vectors (return vectors).
+def mul_vec(q, x):
+    t = 2.0 * _fast_cross(q[..., 1:], x)
+    return x + q[..., 0][..., None] * t + _fast_cross(q[..., 1:], t)
+
+def inv_mul_vec(q, x):
+    return mul_vec(inv(q), x)
+
+def unroll(x):
+    y = x.copy()
+    for i in range(1, len(x)):
+        d0 = np.sum( y[i] * y[i-1], axis=-1)
+        d1 = np.sum(-y[i] * y[i-1], axis=-1)
+        y[i][d0 < d1] = -y[i][d0 < d1]
+    return y
+
+# Calculate quaternions between two 3D vectors (x to y).
+def between(x, y):
+    return np.concatenate([
+        np.sqrt(np.sum(x*x, axis=-1) * np.sum(y*y, axis=-1))[...,None] + 
+        np.sum(x * y, axis=-1)[...,None], 
+        _fast_cross(x, y)], axis=-1)
+        
+def log(x, eps=1e-5):
+    length = np.sqrt(np.sum(np.square(x[...,1:]), axis=-1))[...,None]
+    halfangle = np.where(length < eps, np.ones_like(length), np.arctan2(length, x[...,0:1]) / length)
+    return halfangle * x[...,1:]
+    
+def exp(x, eps=1e-5):
+    halfangle = np.sqrt(np.sum(np.square(x), axis=-1))[...,None]
+    c = np.where(halfangle < eps, np.ones_like(halfangle), np.cos(halfangle))
+    s = np.where(halfangle < eps, np.ones_like(halfangle), np.sinc(halfangle / np.pi))
+    return np.concatenate([c, s * x], axis=-1)
+
+# Calculate global space rotations and positions from local space.
+def fk(lrot, lpos, parents):
+    
+    gp, gr = [lpos[...,:1,:]], [lrot[...,:1,:]]
+    for i in range(1, len(parents)):
+        gp.append(mul_vec(gr[parents[i]], lpos[...,i:i+1,:]) + gp[parents[i]])
+        gr.append(mul    (gr[parents[i]], lrot[...,i:i+1,:]))
+        
+    return np.concatenate(gr, axis=-2), np.concatenate(gp, axis=-2)
+
+def fk_rot(lrot, parents):
+    
+    gr = [lrot[...,:1,:]]
+    for i in range(1, len(parents)):
+        gr.append(mul(gr[parents[i]], lrot[...,i:i+1,:]))
+        
+    return np.concatenate(gr, axis=-2)
+
+# Calculate local space rotations and positions from global space.
+def ik(grot, gpos, parents):
+    
+    return (
+        np.concatenate([
+            grot[...,:1,:],
+            mul(inv(grot[...,parents[1:],:]), grot[...,1:,:]),
+        ], axis=-2),
+        np.concatenate([
+            gpos[...,:1,:],
+            mul_vec(
+                inv(grot[...,parents[1:],:]),
+                gpos[...,1:,:] - gpos[...,parents[1:],:]),
+        ], axis=-2))
+
+def ik_rot(grot, parents):
+    
+    return np.concatenate([grot[...,:1,:], 
+                        mul(inv(grot[...,parents[1:],:]), grot[...,1:,:]),
+                    ], axis=-2)
+    
+def fk_vel(lrot, lpos, lvel, lang, parents):
+    
+    gp, gr, gv, ga = [lpos[...,:1,:]], [lrot[...,:1,:]], [lvel[...,:1,:]], [lang[...,:1,:]]
+    for i in range(1, len(parents)):
+        gp.append(mul_vec(gr[parents[i]], lpos[...,i:i+1,:]) + gp[parents[i]])
+        gr.append(mul    (gr[parents[i]], lrot[...,i:i+1,:]))
+        gv.append(mul_vec(gr[parents[i]], lvel[...,i:i+1,:]) + 
+            _fast_cross(ga[parents[i]], mul_vec(gr[parents[i]], lpos[...,i:i+1,:])) +
+            gv[parents[i]])
+        ga.append(mul_vec(gr[parents[i]], lang[...,i:i+1,:]) + ga[parents[i]])
+        
+    return (
+        np.concatenate(gr, axis=-2), 
+        np.concatenate(gp, axis=-2),
+        np.concatenate(gv, axis=-2),
+        np.concatenate(ga, axis=-2))
+
+# Linear Interpolation of two vectors
+def lerp(x, y, t):
+    return (1 - t) * x + t * y
+
+# LERP of quaternions
+def quat_lerp(x, y, t):
+    return normalize(lerp(x, y, t))
+
+# Spherical linear interpolation of quaternions
+def slerp(x, y, t):
+    if t == 0:
+        return x
+    elif t == 1:
+        return y
+    
+    if dot(x, y) < 0:
+        y = - y
+    ca = dot(x, y)
+    theta = np.arccos(np.clip(ca, 0, 1))
+    
+    r = normalize(y - x * ca)
+    
+    return x * np.cos(theta * t) + r * np.sin(theta * t)
+
+
+###################################################
+# Calculate other rotations from other quaternions.
+################################################### 
+
+# Calculate euler angles from quaternions.
+def to_euler(x, order='zyx'):
+    
+    q0 = x[...,0:1]
+    q1 = x[...,1:2]
+    q2 = x[...,2:3]
+    q3 = x[...,3:4]
+    
+    if order == 'zyx':
+    
+        return np.concatenate([
+            np.arctan2(2 * (q0 * q3 + q1 * q2), 1 - 2 * (q2 * q2 + q3 * q3)),
+            np.arcsin((2 * (q0 * q2 - q3 * q1)).clip(-1,1)),
+            np.arctan2(2 * (q0 * q1 + q2 * q3), 1 - 2 * (q1 * q1 + q2 * q2))], axis=-1)
+            
+    elif order == 'yzx':
+    
+        return np.concatenate([
+            np.arctan2(2 * (q2 * q0 - q1 * q3),  q1 * q1 - q2 * q2 - q3 * q3 + q0 * q0),
+            np.arcsin((2 * (q1 * q2 + q3 * q0)).clip(-1,1)), 
+            np.arctan2(2 * (q1 * q0 - q2 * q3), -q1 * q1 + q2 * q2 - q3 * q3 + q0 * q0)],axis=-1)
+            
+    elif order == 'zxy':
+        
+        return np.concatenate([
+            np.arctan2(2 * (q0 * q3 - q1 * q2), q0 * q0 - q1 * q1 + q2 * q2 - q3 * q3),
+            np.arcsin((2 * (q0 * q1 + q2 * q3)).clip(-1,1)),
+            np.arctan2(2 * (q0 * q2 - q1 * q3), q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3)], axis=-1)
+
+    elif order == 'yxz':
+        
+        return np.concatenate([
+            np.arctan2(2 * (q1 * q3 + q0 * q2), q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3),
+            np.arcsin((2 * (q0 * q1 - q2 * q3)).clip(-1,1)),
+            np.arctan2(2 * (q1 * q2 + q0 * q3), q0 * q0 - q1 * q1 + q2 * q2 - q3 * q3)], axis=-1)
+        
+    else:
+        raise NotImplementedError('Cannot convert from ordering %s' % order)
+
+# Calculate rotation matrix from quaternions.
+def to_xform(x):
+
+    qw, qx, qy, qz = x[...,0:1], x[...,1:2], x[...,2:3], x[...,3:4]
+    
+    x2, y2, z2 = qx + qx, qy + qy, qz + qz
+    xx, yy, wx = qx * x2, qy * y2, qw * x2
+    xy, yz, wy = qx * y2, qy * z2, qw * y2
+    xz, zz, wz = qx * z2, qz * z2, qw * z2
+    
+    return np.concatenate([
+        np.concatenate([1.0 - (yy + zz), xy - wz, xz + wy], axis=-1)[...,None,:],
+        np.concatenate([xy + wz, 1.0 - (xx + zz), yz - wx], axis=-1)[...,None,:],
+        np.concatenate([xz - wy, yz + wx, 1.0 - (xx + yy)], axis=-1)[...,None,:],
+    ], axis=-2)
+
+# Calculate 6d orthogonal rotation representation (ortho6d) from quaternions.
+# https://github.com/papagina/RotationContinuity
+def to_xform_xy(x):
+
+    qw, qx, qy, qz = x[...,0:1], x[...,1:2], x[...,2:3], x[...,3:4]
+    
+    x2, y2, z2 = qx + qx, qy + qy, qz + qz
+    xx, yy, wx = qx * x2, qy * y2, qw * x2
+    xy, yz, wy = qx * y2, qy * z2, qw * y2
+    xz, zz, wz = qx * z2, qz * z2, qw * z2
+    
+    return np.concatenate([
+        np.concatenate([1.0 - (yy + zz), xy - wz], axis=-1)[...,None,:],
+        np.concatenate([xy + wz, 1.0 - (xx + zz)], axis=-1)[...,None,:],
+        np.concatenate([xz - wy, yz + wx], axis=-1)[...,None,:],
+    ], axis=-2)
+
+# Calculate scaled angle axis from quaternions.
+def to_scaled_angle_axis(x, eps=1e-5):
+    return 2.0 * log(x, eps)
+
+
+#############################################
+# Calculate quaternions from other rotations.
+#############################################
+
+# Calculate quaternions from axis angles.
+def from_angle_axis(angle, axis):
+    c = np.cos(angle / 2.0)[..., None]
+    s = np.sin(angle / 2.0)[..., None]
+    q = np.concatenate([c, s * axis], axis=-1)
+    return q
+
+# Calculate quaternions from axis-angle.
+def from_axis_angle(rots):
+    angle = np.linalg.norm(rots, axis=-1)
+    import pdb; pdb.set_trace()
+    axis = rots / angle[...,None]
+    return from_angle_axis(angle, axis)
+
+# Calculate quaternions from euler angles.
+def from_euler(e, order='zyx'):
+    axis = {
+        'x': np.asarray([1, 0, 0], dtype=np.float32),
+        'y': np.asarray([0, 1, 0], dtype=np.float32),
+        'z': np.asarray([0, 0, 1], dtype=np.float32)}
+
+    q0 = from_angle_axis(e[..., 0], axis[order[0]])
+    q1 = from_angle_axis(e[..., 1], axis[order[1]])
+    q2 = from_angle_axis(e[..., 2], axis[order[2]])
+
+    return mul(q0, mul(q1, q2))
+
+# Calculate quaternions from rotation matrix.
+def from_xform(ts):
+    
+    return normalize(
+        np.where((ts[...,2,2] < 0.0)[...,None],
+            np.where((ts[...,0,0] >  ts[...,1,1])[...,None],
+                np.concatenate([
+                    (ts[...,2,1]-ts[...,1,2])[...,None], 
+                    (1.0 + ts[...,0,0] - ts[...,1,1] - ts[...,2,2])[...,None], 
+                    (ts[...,1,0]+ts[...,0,1])[...,None], 
+                    (ts[...,0,2]+ts[...,2,0])[...,None]], axis=-1),
+                np.concatenate([
+                    (ts[...,0,2]-ts[...,2,0])[...,None], 
+                    (ts[...,1,0]+ts[...,0,1])[...,None], 
+                    (1.0 - ts[...,0,0] + ts[...,1,1] - ts[...,2,2])[...,None], 
+                    (ts[...,2,1]+ts[...,1,2])[...,None]], axis=-1)),
+            np.where((ts[...,0,0] < -ts[...,1,1])[...,None],
+                np.concatenate([
+                    (ts[...,1,0]-ts[...,0,1])[...,None], 
+                    (ts[...,0,2]+ts[...,2,0])[...,None], 
+                    (ts[...,2,1]+ts[...,1,2])[...,None], 
+                    (1.0 - ts[...,0,0] - ts[...,1,1] + ts[...,2,2])[...,None]], axis=-1),
+                np.concatenate([
+                    (1.0 + ts[...,0,0] + ts[...,1,1] + ts[...,2,2])[...,None], 
+                    (ts[...,2,1]-ts[...,1,2])[...,None], 
+                    (ts[...,0,2]-ts[...,2,0])[...,None], 
+                    (ts[...,1,0]-ts[...,0,1])[...,None]], axis=-1))))
+
+# Calculate quaternions from ortho6d.
+def from_xform_xy(x):
+
+    c2 = _fast_cross(x[...,0], x[...,1])
+    c2 = c2 / np.sqrt(np.sum(np.square(c2), axis=-1))[...,None]
+    c1 = _fast_cross(c2, x[...,0])
+    c1 = c1 / np.sqrt(np.sum(np.square(c1), axis=-1))[...,None]
+    c0 = x[...,0]
+    
+    return from_xform(np.concatenate([
+        c0[...,None], 
+        c1[...,None], 
+        c2[...,None]], axis=-1))
+
+# Calculate quaternions from scaled angle axis.
+def from_scaled_angle_axis(x, eps=1e-5):
+    return exp(x / 2.0, eps)
\ No newline at end of file
diff --git a/utils/rotation_conversions.py b/utils/rotation_conversions.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ad08f5db949842788af052aed99941f75a7396e
--- /dev/null
+++ b/utils/rotation_conversions.py
@@ -0,0 +1,547 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+# Check PYTORCH3D_LICENCE before use
+
+import functools
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+
+
+"""
+The transformation matrices returned from the functions in this file assume
+the points on which the transformation will be applied are column vectors.
+i.e. the R matrix is structured as
+    R = [
+            [Rxx, Rxy, Rxz],
+            [Ryx, Ryy, Ryz],
+            [Rzx, Rzy, Rzz],
+        ]  # (3, 3)
+This matrix can be applied to column vectors by post multiplication
+by the points e.g.
+    points = [[0], [1], [2]]  # (3 x 1) xyz coordinates of a point
+    transformed_points = R * points
+To apply the same matrix to points which are row vectors, the R matrix
+can be transposed and pre multiplied by the points:
+e.g.
+    points = [[0, 1, 2]]  # (1 x 3) xyz coordinates of a point
+    transformed_points = points * R.transpose(1, 0)
+"""
+
+
+def quaternion_to_matrix(quaternions):
+    """
+    Convert rotations given as quaternions to rotation matrices.
+    Args:
+        quaternions: quaternions with real part first,
+            as tensor of shape (..., 4).
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+    r, i, j, k = torch.unbind(quaternions, -1)
+    two_s = 2.0 / (quaternions * quaternions).sum(-1)
+
+    o = torch.stack(
+        (
+            1 - two_s * (j * j + k * k),
+            two_s * (i * j - k * r),
+            two_s * (i * k + j * r),
+            two_s * (i * j + k * r),
+            1 - two_s * (i * i + k * k),
+            two_s * (j * k - i * r),
+            two_s * (i * k - j * r),
+            two_s * (j * k + i * r),
+            1 - two_s * (i * i + j * j),
+        ),
+        -1,
+    )
+    return o.reshape(quaternions.shape[:-1] + (3, 3))
+
+
+def _copysign(a, b):
+    """
+    Return a tensor where each element has the absolute value taken from the,
+    corresponding element of a, with sign taken from the corresponding
+    element of b. This is like the standard copysign floating-point operation,
+    but is not careful about negative 0 and NaN.
+    Args:
+        a: source tensor.
+        b: tensor whose signs will be used, of the same shape as a.
+    Returns:
+        Tensor of the same shape as a with the signs of b.
+    """
+    signs_differ = (a < 0) != (b < 0)
+    return torch.where(signs_differ, -a, a)
+
+
+def _sqrt_positive_part(x):
+    """
+    Returns torch.sqrt(torch.max(0, x))
+    but with a zero subgradient where x is 0.
+    """
+    ret = torch.zeros_like(x)
+    positive_mask = x > 0
+    ret[positive_mask] = torch.sqrt(x[positive_mask])
+    return ret
+
+
+def matrix_to_quaternion(matrix):
+    """
+    Convert rotations given as rotation matrices to quaternions.
+    Args:
+        matrix: Rotation matrices as tensor of shape (..., 3, 3).
+    Returns:
+        quaternions with real part first, as tensor of shape (..., 4).
+    """
+    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
+        raise ValueError(f"Invalid rotation matrix  shape f{matrix.shape}.")
+    m00 = matrix[..., 0, 0]
+    m11 = matrix[..., 1, 1]
+    m22 = matrix[..., 2, 2]
+    o0 = 0.5 * _sqrt_positive_part(1 + m00 + m11 + m22)
+    x = 0.5 * _sqrt_positive_part(1 + m00 - m11 - m22)
+    y = 0.5 * _sqrt_positive_part(1 - m00 + m11 - m22)
+    z = 0.5 * _sqrt_positive_part(1 - m00 - m11 + m22)
+    o1 = _copysign(x, matrix[..., 2, 1] - matrix[..., 1, 2])
+    o2 = _copysign(y, matrix[..., 0, 2] - matrix[..., 2, 0])
+    o3 = _copysign(z, matrix[..., 1, 0] - matrix[..., 0, 1])
+    return torch.stack((o0, o1, o2, o3), -1)
+
+
+def _axis_angle_rotation(axis: str, angle):
+    """
+    Return the rotation matrices for one of the rotations about an axis
+    of which Euler angles describe, for each value of the angle given.
+    Args:
+        axis: Axis label "X" or "Y or "Z".
+        angle: any shape tensor of Euler angles in radians
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+
+    cos = torch.cos(angle)
+    sin = torch.sin(angle)
+    one = torch.ones_like(angle)
+    zero = torch.zeros_like(angle)
+
+    if axis == "X":
+        R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos)
+    if axis == "Y":
+        R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos)
+    if axis == "Z":
+        R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one)
+
+    return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3))
+
+
+def euler_angles_to_matrix(euler_angles, convention: str):
+    """
+    Convert rotations given as Euler angles in radians to rotation matrices.
+    Args:
+        euler_angles: Euler angles in radians as tensor of shape (..., 3).
+        convention: Convention string of three uppercase letters from
+            {"X", "Y", and "Z"}.
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+    if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3:
+        raise ValueError("Invalid input euler angles.")
+    if len(convention) != 3:
+        raise ValueError("Convention must have 3 letters.")
+    if convention[1] in (convention[0], convention[2]):
+        raise ValueError(f"Invalid convention {convention}.")
+    for letter in convention:
+        if letter not in ("X", "Y", "Z"):
+            raise ValueError(f"Invalid letter {letter} in convention string.")
+    matrices = map(_axis_angle_rotation, convention, torch.unbind(euler_angles, -1))
+    return functools.reduce(torch.matmul, matrices)
+
+
+def _angle_from_tan(
+    axis: str, other_axis: str, data, horizontal: bool, tait_bryan: bool
+):
+    """
+    Extract the first or third Euler angle from the two members of
+    the matrix which are positive constant times its sine and cosine.
+    Args:
+        axis: Axis label "X" or "Y or "Z" for the angle we are finding.
+        other_axis: Axis label "X" or "Y or "Z" for the middle axis in the
+            convention.
+        data: Rotation matrices as tensor of shape (..., 3, 3).
+        horizontal: Whether we are looking for the angle for the third axis,
+            which means the relevant entries are in the same row of the
+            rotation matrix. If not, they are in the same column.
+        tait_bryan: Whether the first and third axes in the convention differ.
+    Returns:
+        Euler Angles in radians for each matrix in data as a tensor
+        of shape (...).
+    """
+
+    i1, i2 = {"X": (2, 1), "Y": (0, 2), "Z": (1, 0)}[axis]
+    if horizontal:
+        i2, i1 = i1, i2
+    even = (axis + other_axis) in ["XY", "YZ", "ZX"]
+    if horizontal == even:
+        return torch.atan2(data[..., i1], data[..., i2])
+    if tait_bryan:
+        return torch.atan2(-data[..., i2], data[..., i1])
+    return torch.atan2(data[..., i2], -data[..., i1])
+
+
+def _index_from_letter(letter: str):
+    if letter == "X":
+        return 0
+    if letter == "Y":
+        return 1
+    if letter == "Z":
+        return 2
+
+
+def matrix_to_euler_angles(matrix, convention: str):
+    """
+    Convert rotations given as rotation matrices to Euler angles in radians.
+    Args:
+        matrix: Rotation matrices as tensor of shape (..., 3, 3).
+        convention: Convention string of three uppercase letters.
+    Returns:
+        Euler angles in radians as tensor of shape (..., 3).
+    """
+    if len(convention) != 3:
+        raise ValueError("Convention must have 3 letters.")
+    if convention[1] in (convention[0], convention[2]):
+        raise ValueError(f"Invalid convention {convention}.")
+    for letter in convention:
+        if letter not in ("X", "Y", "Z"):
+            raise ValueError(f"Invalid letter {letter} in convention string.")
+    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
+        raise ValueError(f"Invalid rotation matrix  shape f{matrix.shape}.")
+    i0 = _index_from_letter(convention[0])
+    i2 = _index_from_letter(convention[2])
+    tait_bryan = i0 != i2
+    if tait_bryan:
+        central_angle = torch.asin(
+            matrix[..., i0, i2] * (-1.0 if i0 - i2 in [-1, 2] else 1.0)
+        )
+    else:
+        central_angle = torch.acos(matrix[..., i0, i0])
+
+    o = (
+        _angle_from_tan(
+            convention[0], convention[1], matrix[..., i2], False, tait_bryan
+        ),
+        central_angle,
+        _angle_from_tan(
+            convention[2], convention[1], matrix[..., i0, :], True, tait_bryan
+        ),
+    )
+    return torch.stack(o, -1)
+
+
+def random_quaternions(
+    n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
+):
+    """
+    Generate random quaternions representing rotations,
+    i.e. versors with nonnegative real part.
+    Args:
+        n: Number of quaternions in a batch to return.
+        dtype: Type to return.
+        device: Desired device of returned tensor. Default:
+            uses the current device for the default tensor type.
+        requires_grad: Whether the resulting tensor should have the gradient
+            flag set.
+    Returns:
+        Quaternions as tensor of shape (N, 4).
+    """
+    o = torch.randn((n, 4), dtype=dtype, device=device, requires_grad=requires_grad)
+    s = (o * o).sum(1)
+    o = o / _copysign(torch.sqrt(s), o[:, 0])[:, None]
+    return o
+
+
+def random_rotations(
+    n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
+):
+    """
+    Generate random rotations as 3x3 rotation matrices.
+    Args:
+        n: Number of rotation matrices in a batch to return.
+        dtype: Type to return.
+        device: Device of returned tensor. Default: if None,
+            uses the current device for the default tensor type.
+        requires_grad: Whether the resulting tensor should have the gradient
+            flag set.
+    Returns:
+        Rotation matrices as tensor of shape (n, 3, 3).
+    """
+    quaternions = random_quaternions(
+        n, dtype=dtype, device=device, requires_grad=requires_grad
+    )
+    return quaternion_to_matrix(quaternions)
+
+
+def random_rotation(
+    dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
+):
+    """
+    Generate a single random 3x3 rotation matrix.
+    Args:
+        dtype: Type to return
+        device: Device of returned tensor. Default: if None,
+            uses the current device for the default tensor type
+        requires_grad: Whether the resulting tensor should have the gradient
+            flag set
+    Returns:
+        Rotation matrix as tensor of shape (3, 3).
+    """
+    return random_rotations(1, dtype, device, requires_grad)[0]
+
+
+def standardize_quaternion(quaternions):
+    """
+    Convert a unit quaternion to a standard form: one in which the real
+    part is non negative.
+    Args:
+        quaternions: Quaternions with real part first,
+            as tensor of shape (..., 4).
+    Returns:
+        Standardized quaternions as tensor of shape (..., 4).
+    """
+    return torch.where(quaternions[..., 0:1] < 0, -quaternions, quaternions)
+
+
+def quaternion_raw_multiply(a, b):
+    """
+    Multiply two quaternions.
+    Usual torch rules for broadcasting apply.
+    Args:
+        a: Quaternions as tensor of shape (..., 4), real part first.
+        b: Quaternions as tensor of shape (..., 4), real part first.
+    Returns:
+        The product of a and b, a tensor of quaternions shape (..., 4).
+    """
+    aw, ax, ay, az = torch.unbind(a, -1)
+    bw, bx, by, bz = torch.unbind(b, -1)
+    ow = aw * bw - ax * bx - ay * by - az * bz
+    ox = aw * bx + ax * bw + ay * bz - az * by
+    oy = aw * by - ax * bz + ay * bw + az * bx
+    oz = aw * bz + ax * by - ay * bx + az * bw
+    return torch.stack((ow, ox, oy, oz), -1)
+
+
+def quaternion_multiply(a, b):
+    """
+    Multiply two quaternions representing rotations, returning the quaternion
+    representing their composition, i.e. the versor with nonnegative real part.
+    Usual torch rules for broadcasting apply.
+    Args:
+        a: Quaternions as tensor of shape (..., 4), real part first.
+        b: Quaternions as tensor of shape (..., 4), real part first.
+    Returns:
+        The product of a and b, a tensor of quaternions of shape (..., 4).
+    """
+    ab = quaternion_raw_multiply(a, b)
+    return standardize_quaternion(ab)
+
+
+def quaternion_invert(quaternion):
+    """
+    Given a quaternion representing rotation, get the quaternion representing
+    its inverse.
+    Args:
+        quaternion: Quaternions as tensor of shape (..., 4), with real part
+            first, which must be versors (unit quaternions).
+    Returns:
+        The inverse, a tensor of quaternions of shape (..., 4).
+    """
+
+    return quaternion * quaternion.new_tensor([1, -1, -1, -1])
+
+
+def quaternion_apply(quaternion, point):
+    """
+    Apply the rotation given by a quaternion to a 3D point.
+    Usual torch rules for broadcasting apply.
+    Args:
+        quaternion: Tensor of quaternions, real part first, of shape (..., 4).
+        point: Tensor of 3D points of shape (..., 3).
+    Returns:
+        Tensor of rotated points of shape (..., 3).
+    """
+    if point.size(-1) != 3:
+        raise ValueError(f"Points are not in 3D, f{point.shape}.")
+    real_parts = point.new_zeros(point.shape[:-1] + (1,))
+    point_as_quaternion = torch.cat((real_parts, point), -1)
+    out = quaternion_raw_multiply(
+        quaternion_raw_multiply(quaternion, point_as_quaternion),
+        quaternion_invert(quaternion),
+    )
+    return out[..., 1:]
+
+
+def axis_angle_to_matrix(axis_angle):
+    """
+    Convert rotations given as axis/angle to rotation matrices.
+    Args:
+        axis_angle: Rotations given as a vector in axis angle form,
+            as a tensor of shape (..., 3), where the magnitude is
+            the angle turned anticlockwise in radians around the
+            vector's direction.
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+    return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle))
+
+
+def matrix_to_axis_angle(matrix):
+    """
+    Convert rotations given as rotation matrices to axis/angle.
+    Args:
+        matrix: Rotation matrices as tensor of shape (..., 3, 3).
+    Returns:
+        Rotations given as a vector in axis angle form, as a tensor
+            of shape (..., 3), where the magnitude is the angle
+            turned anticlockwise in radians around the vector's
+            direction.
+    """
+    return quaternion_to_axis_angle(matrix_to_quaternion(matrix))
+
+
+def axis_angle_to_quaternion(axis_angle):
+    """
+    Convert rotations given as axis/angle to quaternions.
+    Args:
+        axis_angle: Rotations given as a vector in axis angle form,
+            as a tensor of shape (..., 3), where the magnitude is
+            the angle turned anticlockwise in radians around the
+            vector's direction.
+    Returns:
+        quaternions with real part first, as tensor of shape (..., 4).
+    """
+    angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True)
+    half_angles = 0.5 * angles
+    eps = 1e-6
+    small_angles = angles.abs() < eps
+    sin_half_angles_over_angles = torch.empty_like(angles)
+    sin_half_angles_over_angles[~small_angles] = (
+        torch.sin(half_angles[~small_angles]) / angles[~small_angles]
+    )
+    # for x small, sin(x/2) is about x/2 - (x/2)^3/6
+    # so sin(x/2)/x is about 1/2 - (x*x)/48
+    sin_half_angles_over_angles[small_angles] = (
+        0.5 - (angles[small_angles] * angles[small_angles]) / 48
+    )
+    quaternions = torch.cat(
+        [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1
+    )
+    return quaternions
+
+
+def quaternion_to_axis_angle(quaternions):
+    """
+    Convert rotations given as quaternions to axis/angle.
+    Args:
+        quaternions: quaternions with real part first,
+            as tensor of shape (..., 4).
+    Returns:
+        Rotations given as a vector in axis angle form, as a tensor
+            of shape (..., 3), where the magnitude is the angle
+            turned anticlockwise in radians around the vector's
+            direction.
+    """
+    norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True)
+    half_angles = torch.atan2(norms, quaternions[..., :1])
+    angles = 2 * half_angles
+    eps = 1e-6
+    small_angles = angles.abs() < eps
+    sin_half_angles_over_angles = torch.empty_like(angles)
+    sin_half_angles_over_angles[~small_angles] = (
+        torch.sin(half_angles[~small_angles]) / angles[~small_angles]
+    )
+    # for x small, sin(x/2) is about x/2 - (x/2)^3/6
+    # so sin(x/2)/x is about 1/2 - (x*x)/48
+    sin_half_angles_over_angles[small_angles] = (
+        0.5 - (angles[small_angles] * angles[small_angles]) / 48
+    )
+    return quaternions[..., 1:] / sin_half_angles_over_angles
+
+
+def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor:
+    """
+    Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
+    using Gram--Schmidt orthogonalisation per Section B of [1].
+    Args:
+        d6: 6D rotation representation, of size (*, 6)
+    Returns:
+        batch of rotation matrices of size (*, 3, 3)
+    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
+    On the Continuity of Rotation Representations in Neural Networks.
+    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
+    Retrieved from http://arxiv.org/abs/1812.07035
+    """
+
+    a1, a2 = d6[..., :3], d6[..., 3:]
+    b1 = F.normalize(a1, dim=-1)
+    b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1
+    b2 = F.normalize(b2, dim=-1)
+    b3 = torch.cross(b1, b2, dim=-1)
+    return torch.stack((b1, b2, b3), dim=-2)
+
+
+def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor:
+    """
+    Converts rotation matrices to 6D rotation representation by Zhou et al. [1]
+    by dropping the last row. Note that 6D representation is not unique.
+    Args:
+        matrix: batch of rotation matrices of size (*, 3, 3)
+    Returns:
+        6D rotation representation, of size (*, 6)
+    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
+    On the Continuity of Rotation Representations in Neural Networks.
+    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
+    Retrieved from http://arxiv.org/abs/1812.07035
+    """
+    return matrix[..., :2, :].clone().reshape(*matrix.size()[:-2], 6)
+
+def canonicalize_smplh(poses, trans = None):
+    bs, nframes, njoints = poses.shape[:3]
+
+    global_orient = poses[:, :, 0]
+
+    # first global rotations
+    rot2d = matrix_to_axis_angle(global_orient[:, 0])
+    #rot2d[:, :2] = 0  # Remove the rotation along the vertical axis
+    rot2d = axis_angle_to_matrix(rot2d)
+
+    # Rotate the global rotation to eliminate Z rotations
+    global_orient = torch.einsum("ikj,imkl->imjl", rot2d, global_orient)
+
+    # Construct canonicalized version of x
+    xc = torch.cat((global_orient[:, :, None], poses[:, :, 1:]), dim=2)
+
+    if trans is not None:
+        vel = trans[:, 1:] - trans[:, :-1]
+        # Turn the translation as well
+        vel = torch.einsum("ikj,ilk->ilj", rot2d, vel)
+        trans = torch.cat((torch.zeros(bs, 1, 3, device=vel.device),
+                           torch.cumsum(vel, 1)), 1)
+        return xc, trans
+    else:
+        return xc
+
+
+    # Added
+def matrix_of_angles(cos, sin, inv=False, dim=2):
+    assert dim in [2, 3]
+    sin = -sin if inv else sin
+    if dim == 2:
+        row1 = torch.stack((cos, -sin), axis=-1)
+        row2 = torch.stack((sin, cos), axis=-1)
+        return torch.stack((row1, row2), axis=-2)
+    elif dim == 3:
+        row1 = torch.stack((cos, -sin, 0*cos), axis=-1)
+        row2 = torch.stack((sin, cos, 0*cos), axis=-1)
+        row3 = torch.stack((0*sin, 0*cos, 1+0*cos), axis=-1)
+        return torch.stack((row1, row2, row3),axis=-2)
+    
+    
\ No newline at end of file
diff --git a/utils/utils_model.py b/utils/utils_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..c81294537fac1d3c3642755dacd3800ceb32505f
--- /dev/null
+++ b/utils/utils_model.py
@@ -0,0 +1,82 @@
+import numpy as np 
+import torch
+import torch.optim as optim
+import logging
+import os 
+import sys 
+
+def getCi(accLog):
+
+    mean = np.mean(accLog)
+    std = np.std(accLog)
+    ci95 = 1.96*std/np.sqrt(len(accLog))
+
+    return mean, ci95
+
+def get_logger(out_dir):
+    logger = logging.getLogger('Exp')
+    logger.setLevel(logging.INFO)
+    formatter = logging.Formatter("%(asctime)s %(levelname)s %(message)s")
+
+    file_path = os.path.join(out_dir, "run.log")
+    file_hdlr = logging.FileHandler(file_path)
+    file_hdlr.setFormatter(formatter)
+
+    strm_hdlr = logging.StreamHandler(sys.stdout)
+    strm_hdlr.setFormatter(formatter)
+
+    logger.addHandler(file_hdlr)
+    logger.addHandler(strm_hdlr)
+    return logger
+
+
+def initial_optim(decay_option, lr, weight_decay, net, optimizer) : 
+    
+    if optimizer == 'adamw' : 
+        optimizer_adam_family = optim.AdamW
+    elif optimizer == 'adam' : 
+        optimizer_adam_family = optim.Adam
+    if decay_option == 'all':
+        optimizer = optimizer_adam_family(net.parameters(), lr=lr, betas=(0.9, 0.99), weight_decay=weight_decay)
+        
+    else:
+        raise NotImplementedError
+        
+        
+    return optimizer
+
+
+def initial_optim_with_eps(decay_option, lr, weight_decay, net, optimizer, eps) : 
+    
+    if optimizer == 'adamw' : 
+        optimizer_adam_family = optim.AdamW
+    elif optimizer == 'adam' : 
+        optimizer_adam_family = optim.Adam
+    if decay_option == 'all':
+        
+        optimizer = optimizer_adam_family(net.parameters(), lr=lr, betas=(0.9, 0.99), weight_decay=weight_decay, eps=eps)
+        
+    elif decay_option == 'noVQ':
+        all_params = set(net.parameters())
+        no_decay = set([net.vq_layer])
+        
+        decay = all_params - no_decay
+        optimizer = optimizer_adam_family([
+                    {'params': list(no_decay), 'weight_decay': 0}, 
+                    {'params': list(decay), 'weight_decay' : weight_decay}], lr=lr, eps=eps)
+        
+    return optimizer
+
+
+def get_motion_with_trans(motion, velocity) : 
+    '''
+    motion : torch.tensor, shape (batch_size, T, 72), with the global translation = 0
+    velocity : torch.tensor, shape (batch_size, T, 3), contain the information of velocity = 0
+    
+    '''
+    trans = torch.cumsum(velocity, dim=1)
+    trans = trans - trans[:, :1] 
+    trans = trans.repeat((1, 1, 21))
+    motion_with_trans = motion + trans
+    return motion_with_trans
+    
\ No newline at end of file
diff --git a/visualization/__pycache__/plot_3d_global.cpython-39.pyc b/visualization/__pycache__/plot_3d_global.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..066b21e471834ed7a949362074bac1705c1ecb42
Binary files /dev/null and b/visualization/__pycache__/plot_3d_global.cpython-39.pyc differ
diff --git a/visualization/__pycache__/recover_visualize.cpython-39.pyc b/visualization/__pycache__/recover_visualize.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..da63dbc341d219af0e9ae66e5f1213da5188a73d
Binary files /dev/null and b/visualization/__pycache__/recover_visualize.cpython-39.pyc differ
diff --git a/visualization/plot_3d_global.py b/visualization/plot_3d_global.py
new file mode 100644
index 0000000000000000000000000000000000000000..672b84eb43817ada21363d9ec1a907dcf1aa50b7
--- /dev/null
+++ b/visualization/plot_3d_global.py
@@ -0,0 +1,129 @@
+import torch 
+import matplotlib.pyplot as plt
+import numpy as np
+import io
+import matplotlib
+from mpl_toolkits.mplot3d.art3d import Poly3DCollection
+import mpl_toolkits.mplot3d.axes3d as p3
+from textwrap import wrap
+import imageio
+
+def plot_3d_motion(args, figsize=(10, 10), fps=120, radius=4):
+    matplotlib.use('Agg')
+    
+    
+    joints, out_name, title = args
+    
+    data = joints.copy().reshape(len(joints), -1, 3)
+    
+    nb_joints = joints.shape[1]
+    smpl_kinetic_chain = [[0, 11, 12, 13, 14, 15], [0, 16, 17, 18, 19, 20], [0, 1, 2, 3, 4], [3, 5, 6, 7], [3, 8, 9, 10]] if nb_joints == 21 else [[0, 2, 5, 8, 11], [0, 1, 4, 7, 10], [0, 3, 6, 9, 12, 15], [9, 14, 17, 19, 21], [9, 13, 16, 18, 20]]
+    limits = 1000 if nb_joints == 21 else 2
+    MINS = data.min(axis=0).min(axis=0)
+    MAXS = data.max(axis=0).max(axis=0)
+    colors = ['red', 'blue', 'black', 'red', 'blue',
+              'darkblue', 'darkblue', 'darkblue', 'darkblue', 'darkblue',
+              'darkred', 'darkred', 'darkred', 'darkred', 'darkred']
+    frame_number = data.shape[0]
+
+    height_offset = MINS[1]
+    data[:, :, 1] -= height_offset
+    trajec = data[:, 0, [0, 2]]
+
+    data[..., 0] -= data[:, 0:1, 0]
+    data[..., 2] -= data[:, 0:1, 2]
+
+    def update(index):
+
+        def init():
+            ax.set_xlim(-limits, limits)
+            ax.set_ylim(-limits, limits)
+            ax.set_zlim(0, limits)
+            ax.grid(b=False)
+        def plot_xzPlane(minx, maxx, miny, minz, maxz):
+            ## Plot a plane XZ
+            verts = [
+                [minx, miny, minz],
+                [minx, miny, maxz],
+                [maxx, miny, maxz],
+                [maxx, miny, minz]
+            ]
+            xz_plane = Poly3DCollection([verts])
+            xz_plane.set_facecolor((0.5, 0.5, 0.5, 0.5))
+            ax.add_collection3d(xz_plane)
+        fig = plt.figure(figsize=(480/96., 320/96.), dpi=96) if nb_joints == 21 else plt.figure(figsize=(10, 10), dpi=96)
+        if title is not None :
+            wraped_title = '\n'.join(wrap(title, 40))
+            fig.suptitle(wraped_title, fontsize=16)
+        ax = p3.Axes3D(fig, auto_add_to_figure=False)
+        fig.add_axes(ax)
+        
+        init()
+        
+        ax.lines = []
+        ax.collections = []
+        ax.view_init(elev=110, azim=-90)
+        ax.dist = 7.5
+        #         ax =
+        plot_xzPlane(MINS[0] - trajec[index, 0], MAXS[0] - trajec[index, 0], 0, MINS[2] - trajec[index, 1],
+                     MAXS[2] - trajec[index, 1])
+        #         ax.scatter(data[index, :22, 0], data[index, :22, 1], data[index, :22, 2], color='black', s=3)
+
+        if index > 1:
+            ax.plot3D(trajec[:index, 0] - trajec[index, 0], np.zeros_like(trajec[:index, 0]),
+                      trajec[:index, 1] - trajec[index, 1], linewidth=1.0,
+                      color='blue')
+        #             ax = plot_xzPlane(ax, MINS[0], MAXS[0], 0, MINS[2], MAXS[2])
+
+        for i, (chain, color) in enumerate(zip(smpl_kinetic_chain, colors)):
+            #             print(color)
+            if i < 5:
+                linewidth = 4.0
+            else:
+                linewidth = 2.0
+            ax.plot3D(data[index, chain, 0], data[index, chain, 1], data[index, chain, 2], linewidth=linewidth,
+                      color=color)
+        #         print(trajec[:index, 0].shape)
+
+        plt.axis('off')
+        ax.set_xticklabels([])
+        ax.set_yticklabels([])
+        ax.set_zticklabels([])
+    
+        if out_name is not None : 
+            plt.savefig(out_name, dpi=96)
+            plt.close()
+            
+        else : 
+            io_buf = io.BytesIO()
+            fig.savefig(io_buf, format='raw', dpi=96)
+            io_buf.seek(0)
+            # print(fig.bbox.bounds)
+            arr = np.reshape(np.frombuffer(io_buf.getvalue(), dtype=np.uint8),
+                                newshape=(int(fig.bbox.bounds[3]), int(fig.bbox.bounds[2]), -1))
+            io_buf.close()
+            plt.close()
+            return arr
+
+    out = []
+    for i in range(frame_number) : 
+        out.append(update(i))
+    out = np.stack(out, axis=0)
+    return torch.from_numpy(out)
+
+
+def draw_to_batch(smpl_joints_batch, title_batch=None, outname=None, fps=30) : 
+    batch_size = len(smpl_joints_batch)
+    out = []
+    for i in range(batch_size) : 
+        out.append(plot_3d_motion([smpl_joints_batch[i], None, title_batch[i] if title_batch is not None else None]))
+        if outname is not None:
+            imageio.mimsave(outname[i], np.array(out[-1]), fps=fps)
+    out = torch.stack(out, axis=0)
+    return out
+
+
+
+
+
+
diff --git a/visualization/recover_visualize.py b/visualization/recover_visualize.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e6345acb3a3a952b380e944b4b2c280da23d0e3
--- /dev/null
+++ b/visualization/recover_visualize.py
@@ -0,0 +1,185 @@
+# Reference: https://github.com/Li-xingXiao/272-dim-Motion-Representation
+
+# representation: 272 dim
+# :2 local xz velocities of root, no heading, can recover translation
+# 2:8  heading angular velocities, 6d rotation, can recover heading
+# 8:8+3*njoint local position, no heading, all at xz origin
+# 8+3*njoint:8+6*njoint local velocities, no heading, all at xz origin, can recover local postion
+# 8+6*njoint:8+12*njoint local rotations, 6d rotation, no heading, all frames z+
+
+import numpy as np
+from utils.face_z_align_util import rotation_6d_to_matrix, matrix_to_axis_angle
+import copy
+import torch
+import os
+import visualization.plot_3d_global as plot_3d
+import argparse
+import tqdm
+# from visualization.smplx2joints import process_smplx_data
+
+def findAllFile(base, endswith='.npy'):
+    file_path = []
+    for root, ds, fs in os.walk(base, followlinks=True):
+        for f in fs:
+            fullname = os.path.join(root, f)
+            if fullname.endswith(endswith):
+                file_path.append(fullname)
+    return file_path
+
+def rot_yaw(yaw):
+    cs = np.cos(yaw)
+    sn = np.sin(yaw)
+    return np.array([[cs,0,sn],[0,1,0],[-sn,0,cs]])
+
+
+def my_quat_rotate(q, v):
+    shape = q.shape
+    q_w = q[:, -1]
+    q_vec = q[:, :3]
+    a = v * (2.0 * q_w ** 2 - 1.0).unsqueeze(-1)
+    b = torch.cross(q_vec, v, dim=-1) * q_w.unsqueeze(-1) * 2.0
+    c = q_vec * \
+        torch.bmm(q_vec.view(shape[0], 1, 3), v.view(
+            shape[0], 3, 1)).squeeze(-1) * 2.0
+    return a + b + c
+
+    
+def calc_heading(q):
+    ref_dir = torch.zeros_like(q[..., 0:3])
+    ref_dir[..., 2] = 1
+    rot_dir = my_quat_rotate(q, ref_dir)
+
+    heading = torch.atan2(rot_dir[..., 0], rot_dir[..., 2])
+    return heading
+
+
+def calc_heading_quat_inv(q):
+    heading = calc_heading(q)
+    axis = torch.zeros_like(q[..., 0:3])
+    axis[..., 1] = 1
+
+    return -heading, axis
+
+def accumulate_rotations(relative_rotations):
+    """Accumulate relative rotations to get the overall rotation"""
+    # Initial rotation is the rotation matrix
+    R_total = [relative_rotations[0]]
+    # Iterate through all relative rotations, accumulating them
+    for R_rel in relative_rotations[1:]:
+        R_total.append(np.matmul(R_rel, R_total[-1]))
+    
+    return np.array(R_total)
+
+def recover_from_local_position(final_x, njoint):
+    # take positions_no_heading: local position on xz ori, no heading
+    # velocities_root_xy_no_heading: to recover translation
+    # global_heading_diff_rot: to recover root rotation
+    nfrm, _ = final_x.shape
+    positions_no_heading = final_x[:,8:8+3*njoint].reshape(nfrm, -1, 3) # frames, njoints * 3
+    velocities_root_xy_no_heading = final_x[:,:2] # frames, 2
+    global_heading_diff_rot = final_x[:,2:8] # frames, 6
+
+    # recover global heading
+    global_heading_rot = accumulate_rotations(rotation_6d_to_matrix(torch.from_numpy(global_heading_diff_rot)).numpy())
+    inv_global_heading_rot = np.transpose(global_heading_rot, (0, 2, 1))
+    # add global heading to position
+    positions_with_heading = np.matmul(np.repeat(inv_global_heading_rot[:, None,:, :], njoint, axis=1), positions_no_heading[...,None]).squeeze(-1)
+
+    # recover root translation
+    # add heading to velocities_root_xy_no_heading
+
+    velocities_root_xyz_no_heading = np.zeros((velocities_root_xy_no_heading.shape[0], 3))
+    velocities_root_xyz_no_heading[:, 0] = velocities_root_xy_no_heading[:, 0]
+    velocities_root_xyz_no_heading[:, 2] = velocities_root_xy_no_heading[:, 1]
+    velocities_root_xyz_no_heading[1:, :] = np.matmul(inv_global_heading_rot[:-1], velocities_root_xyz_no_heading[1:, :,None]).squeeze(-1)
+
+    root_translation = np.cumsum(velocities_root_xyz_no_heading, axis=0)
+
+    # add root translation
+    positions_with_heading[:, :, 0] += root_translation[:, 0:1]
+    positions_with_heading[:, :, 2] += root_translation[:, 2:]
+
+    return positions_with_heading
+
+
+# add hip height to translation when recoverring from rotation
+def recover_from_local_rotation(final_x, njoint):
+    nfrm, _ = final_x.shape
+    rotations_matrix = rotation_6d_to_matrix(torch.from_numpy(final_x[:,8+6*njoint:8+12*njoint]).reshape(nfrm, -1, 6)).numpy()
+    global_heading_diff_rot = final_x[:,2:8]
+    velocities_root_xy_no_heading = final_x[:,:2]
+    positions_no_heading = final_x[:, 8:8+3*njoint].reshape(nfrm, -1, 3)
+    height = positions_no_heading[:, 0, 1]
+
+    global_heading_rot = accumulate_rotations(rotation_6d_to_matrix(torch.from_numpy(global_heading_diff_rot)).numpy())
+    inv_global_heading_rot = np.transpose(global_heading_rot, (0, 2, 1))
+    # recover root rotation
+    rotations_matrix[:,0,...] = np.matmul(inv_global_heading_rot, rotations_matrix[:,0,...])
+    velocities_root_xyz_no_heading = np.zeros((velocities_root_xy_no_heading.shape[0], 3))
+    velocities_root_xyz_no_heading[:, 0] = velocities_root_xy_no_heading[:, 0]
+    velocities_root_xyz_no_heading[:, 2] = velocities_root_xy_no_heading[:, 1]
+    velocities_root_xyz_no_heading[1:, :] = np.matmul(inv_global_heading_rot[:-1], velocities_root_xyz_no_heading[1:, :,None]).squeeze(-1)
+    root_translation = np.cumsum(velocities_root_xyz_no_heading, axis=0)
+    root_translation[:, 1] = height
+    smpl_85 = rotations_matrix_to_smpl85(rotations_matrix, root_translation)
+    return smpl_85
+
+def rotations_matrix_to_smpl85(rotations_matrix, translation):
+    nfrm, njoint, _, _ = rotations_matrix.shape
+    axis_angle = matrix_to_axis_angle(torch.from_numpy(rotations_matrix)).numpy().reshape(nfrm, -1)
+    smpl_85 = np.concatenate([axis_angle, np.zeros((nfrm, 6)), translation, np.zeros((nfrm, 10))], axis=-1)
+    return smpl_85
+
+
+
+
+def smpl85_2_smpl322(smpl_85_data):
+    result = np.concatenate((smpl_85_data[:,:66], np.zeros((smpl_85_data.shape[0], 90)), np.zeros((smpl_85_data.shape[0], 3)), np.zeros((smpl_85_data.shape[0], 50)), np.zeros((smpl_85_data.shape[0], 100)), smpl_85_data[:,72:72+3], smpl_85_data[:,75:]), axis=-1)
+    return result
+
+def visualize_smpl_85(data, title=None, output_path='visualize_result', name='', fps=30):
+    # data: torch.Size([nframe, 85])
+    smpl_85_data = data
+    if len(smpl_85_data.shape) == 3:
+       smpl_85_data = np.squeeze(smpl_85_data, axis=0)
+    
+    smpl_85_data = smpl85_2_smpl322(smpl_85_data)
+    vert, joints, motion, faces = process_smplx_data(smpl_85_data, norm_global_orient=False, transform=False)
+    xyz = joints[:, :22, :].reshape(1, -1, 22, 3).detach().cpu().numpy()
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    pose_vis = plot_3d.draw_to_batch(xyz, title_batch=title, outname=[f'{output_path}/rot_{name}.mp4'], fps=fps)
+    return output_path
+
+
+def visualize_pos_xyz(xyz, title_batch=None, output_path='./', name='', fps=30):
+    # xyz: torch.Size([nframe, 22, 3])
+    xyz = xyz[:1]   
+    bs, seq = xyz.shape[:2]
+    xyz = xyz.reshape(bs, seq, -1, 3)
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    plot_xyz = plot_3d.draw_to_batch(xyz, title_batch, [f'{output_path}/pos_{name}.mp4'], fps=fps)
+    return output_path
+
+
+if __name__ == '__main__':
+    njoint = 22
+    parser = argparse.ArgumentParser(description='Visualize new representation.')
+    parser.add_argument('--input_dir', type=str, required=True, help='Input path')
+    parser.add_argument('--mode', type=str, required=True, default='rot', choices=['rot', 'pos'], help='Recover from rotation or position')
+    parser.add_argument('--output_dir', type=str, required=True, help='Output path')
+    args = parser.parse_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+    
+    for data_path in tqdm.tqdm(findAllFile(args.input_dir, endswith='.npy')):
+        data_272 = np.load(data_path)
+        if args.mode == 'rot':
+            # recover from rotation
+            global_rotation = recover_from_local_rotation(data_272, njoint)  # get the 85-dim smpl data
+            visualize_smpl_85(global_rotation, output_path=args.output_dir, name=data_path.split('/')[-1].split('.')[0])
+            print(f"Visualized results are saved in {args.output_dir}")
+        else:
+            # recover from position
+            global_position = recover_from_local_position(data_272, njoint)
+            global_position = np.expand_dims(global_position, axis=0)
+            visualize_pos_xyz(global_position, output_path=args.output_dir, name=data_path.split('/')[-1].split('.')[0])
+            print(f"Visualized results are saved in {args.output_dir}")
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