|
|
| import gc
|
| import logging
|
| import math
|
| import os
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| import random
|
| import sys
|
| import types
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| from contextlib import contextmanager
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| from functools import partial
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|
|
| import numpy as np
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| import torch
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| import torch.cuda.amp as amp
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| import torch.distributed as dist
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| import torchvision.transforms.functional as TF
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| from tqdm import tqdm
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|
|
| from .distributed.fsdp import shard_model
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| from .modules.clip import CLIPModel
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| from .modules.model import WanModel
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| from .modules.t5 import T5EncoderModel
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| from .modules.vae import WanVAE
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| from .utils.fm_solvers import (
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| FlowDPMSolverMultistepScheduler,
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| get_sampling_sigmas,
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| retrieve_timesteps,
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| )
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| from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
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|
|
|
|
| class WanI2V:
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|
|
| def __init__(
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| self,
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| config,
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| checkpoint_dir,
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| device_id=0,
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| rank=0,
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| t5_fsdp=False,
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| dit_fsdp=False,
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| use_usp=False,
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| t5_cpu=False,
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| init_on_cpu=True,
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| ):
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| r"""
|
| Initializes the image-to-video generation model components.
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|
|
| Args:
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| config (EasyDict):
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| Object containing model parameters initialized from config.py
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| checkpoint_dir (`str`):
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| Path to directory containing model checkpoints
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| device_id (`int`, *optional*, defaults to 0):
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| Id of target GPU device
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| rank (`int`, *optional*, defaults to 0):
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| Process rank for distributed training
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| t5_fsdp (`bool`, *optional*, defaults to False):
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| Enable FSDP sharding for T5 model
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| dit_fsdp (`bool`, *optional*, defaults to False):
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| Enable FSDP sharding for DiT model
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| use_usp (`bool`, *optional*, defaults to False):
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| Enable distribution strategy of USP.
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| t5_cpu (`bool`, *optional*, defaults to False):
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| Whether to place T5 model on CPU. Only works without t5_fsdp.
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| init_on_cpu (`bool`, *optional*, defaults to True):
|
| Enable initializing Transformer Model on CPU. Only works without FSDP or USP.
|
| """
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| self.device = torch.device(f"cuda:{device_id}")
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| self.config = config
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| self.rank = rank
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| self.use_usp = use_usp
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| self.t5_cpu = t5_cpu
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|
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| self.num_train_timesteps = config.num_train_timesteps
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| self.param_dtype = config.param_dtype
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|
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| shard_fn = partial(shard_model, device_id=device_id)
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| self.text_encoder = T5EncoderModel(
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| text_len=config.text_len,
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| dtype=config.t5_dtype,
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| device=torch.device('cpu'),
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| checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint),
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| tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer),
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| shard_fn=shard_fn if t5_fsdp else None,
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| )
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|
|
| self.vae_stride = config.vae_stride
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| self.patch_size = config.patch_size
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| self.vae = WanVAE(
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| vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint),
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| device=self.device)
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|
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| self.clip = CLIPModel(
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| dtype=config.clip_dtype,
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| device=self.device,
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| checkpoint_path=os.path.join(checkpoint_dir,
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| config.clip_checkpoint),
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| tokenizer_path=os.path.join(checkpoint_dir, config.clip_tokenizer))
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|
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| logging.info(f"Creating WanModel from {checkpoint_dir}")
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| self.model = WanModel.from_pretrained(checkpoint_dir)
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| self.model.eval().requires_grad_(False)
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|
|
| if t5_fsdp or dit_fsdp or use_usp:
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| init_on_cpu = False
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|
|
| if use_usp:
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| from xfuser.core.distributed import get_sequence_parallel_world_size
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|
|
| from .distributed.xdit_context_parallel import (
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| usp_attn_forward,
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| usp_dit_forward,
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| )
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| for block in self.model.blocks:
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| block.self_attn.forward = types.MethodType(
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| usp_attn_forward, block.self_attn)
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| self.model.forward = types.MethodType(usp_dit_forward, self.model)
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| self.sp_size = get_sequence_parallel_world_size()
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| else:
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| self.sp_size = 1
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|
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| if dist.is_initialized():
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| dist.barrier()
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| if dit_fsdp:
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| self.model = shard_fn(self.model)
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| else:
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| if not init_on_cpu:
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| self.model.to(self.device)
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|
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| self.sample_neg_prompt = config.sample_neg_prompt
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|
|
| def generate(self,
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| input_prompt,
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| img,
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| max_area=720 * 1280,
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| frame_num=81,
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| shift=5.0,
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| sample_solver='unipc',
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| sampling_steps=40,
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| guide_scale=5.0,
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| n_prompt="",
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| seed=-1,
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| offload_model=True):
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| r"""
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| Generates video frames from input image and text prompt using diffusion process.
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|
|
| Args:
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| input_prompt (`str`):
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| Text prompt for content generation.
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| img (PIL.Image.Image):
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| Input image tensor. Shape: [3, H, W]
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| max_area (`int`, *optional*, defaults to 720*1280):
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| Maximum pixel area for latent space calculation. Controls video resolution scaling
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| frame_num (`int`, *optional*, defaults to 81):
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| How many frames to sample from a video. The number should be 4n+1
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| shift (`float`, *optional*, defaults to 5.0):
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| Noise schedule shift parameter. Affects temporal dynamics
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| [NOTE]: If you want to generate a 480p video, it is recommended to set the shift value to 3.0.
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| sample_solver (`str`, *optional*, defaults to 'unipc'):
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| Solver used to sample the video.
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| sampling_steps (`int`, *optional*, defaults to 40):
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| Number of diffusion sampling steps. Higher values improve quality but slow generation
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| guide_scale (`float`, *optional*, defaults 5.0):
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| Classifier-free guidance scale. Controls prompt adherence vs. creativity
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| n_prompt (`str`, *optional*, defaults to ""):
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| Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt`
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| seed (`int`, *optional*, defaults to -1):
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| Random seed for noise generation. If -1, use random seed
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| offload_model (`bool`, *optional*, defaults to True):
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| If True, offloads models to CPU during generation to save VRAM
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|
|
| Returns:
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| torch.Tensor:
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| Generated video frames tensor. Dimensions: (C, N H, W) where:
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| - C: Color channels (3 for RGB)
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| - N: Number of frames (81)
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| - H: Frame height (from max_area)
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| - W: Frame width from max_area)
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| """
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| img = TF.to_tensor(img).sub_(0.5).div_(0.5).to(self.device)
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|
|
| F = frame_num
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| h, w = img.shape[1:]
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| aspect_ratio = h / w
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| lat_h = round(
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| np.sqrt(max_area * aspect_ratio) // self.vae_stride[1] //
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| self.patch_size[1] * self.patch_size[1])
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| lat_w = round(
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| np.sqrt(max_area / aspect_ratio) // self.vae_stride[2] //
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| self.patch_size[2] * self.patch_size[2])
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| h = lat_h * self.vae_stride[1]
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| w = lat_w * self.vae_stride[2]
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|
|
| max_seq_len = ((F - 1) // self.vae_stride[0] + 1) * lat_h * lat_w // (
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| self.patch_size[1] * self.patch_size[2])
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| max_seq_len = int(math.ceil(max_seq_len / self.sp_size)) * self.sp_size
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|
|
| seed = seed if seed >= 0 else random.randint(0, sys.maxsize)
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| seed_g = torch.Generator(device=self.device)
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| seed_g.manual_seed(seed)
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| noise = torch.randn(
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| 16, (F - 1) // 4 + 1,
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| lat_h,
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| lat_w,
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| dtype=torch.float32,
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| generator=seed_g,
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| device=self.device)
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|
|
| msk = torch.ones(1, 81, lat_h, lat_w, device=self.device)
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| msk[:, 1:] = 0
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| msk = torch.concat([
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| torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]
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| ],
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| dim=1)
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| msk = msk.view(1, msk.shape[1] // 4, 4, lat_h, lat_w)
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| msk = msk.transpose(1, 2)[0]
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|
|
| if n_prompt == "":
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| n_prompt = self.sample_neg_prompt
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|
|
|
|
| if not self.t5_cpu:
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| self.text_encoder.model.to(self.device)
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| context = self.text_encoder([input_prompt], self.device)
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| context_null = self.text_encoder([n_prompt], self.device)
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| if offload_model:
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| self.text_encoder.model.cpu()
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| else:
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| context = self.text_encoder([input_prompt], torch.device('cpu'))
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| context_null = self.text_encoder([n_prompt], torch.device('cpu'))
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| context = [t.to(self.device) for t in context]
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| context_null = [t.to(self.device) for t in context_null]
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|
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| self.clip.model.to(self.device)
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| clip_context = self.clip.visual([img[:, None, :, :]])
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| if offload_model:
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| self.clip.model.cpu()
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|
|
| y = self.vae.encode([
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| torch.concat([
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| torch.nn.functional.interpolate(
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| img[None].cpu(), size=(h, w), mode='bicubic').transpose(
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| 0, 1),
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| torch.zeros(3, F - 1, h, w)
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| ],
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| dim=1).to(self.device)
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| ])[0]
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| y = torch.concat([msk, y])
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|
|
| @contextmanager
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| def noop_no_sync():
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| yield
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|
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| no_sync = getattr(self.model, 'no_sync', noop_no_sync)
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|
|
|
|
| with amp.autocast(dtype=self.param_dtype), torch.no_grad(), no_sync():
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|
|
| if sample_solver == 'unipc':
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| sample_scheduler = FlowUniPCMultistepScheduler(
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| num_train_timesteps=self.num_train_timesteps,
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| shift=1,
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| use_dynamic_shifting=False)
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| sample_scheduler.set_timesteps(
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| sampling_steps, device=self.device, shift=shift)
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| timesteps = sample_scheduler.timesteps
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| elif sample_solver == 'dpm++':
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| sample_scheduler = FlowDPMSolverMultistepScheduler(
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| num_train_timesteps=self.num_train_timesteps,
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| shift=1,
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| use_dynamic_shifting=False)
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| sampling_sigmas = get_sampling_sigmas(sampling_steps, shift)
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| timesteps, _ = retrieve_timesteps(
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| sample_scheduler,
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| device=self.device,
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| sigmas=sampling_sigmas)
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| else:
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| raise NotImplementedError("Unsupported solver.")
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|
|
|
|
| latent = noise
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|
|
| arg_c = {
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| 'context': [context[0]],
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| 'clip_fea': clip_context,
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| 'seq_len': max_seq_len,
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| 'y': [y],
|
| }
|
|
|
| arg_null = {
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| 'context': context_null,
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| 'clip_fea': clip_context,
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| 'seq_len': max_seq_len,
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| 'y': [y],
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| }
|
|
|
| if offload_model:
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| torch.cuda.empty_cache()
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|
|
| self.model.to(self.device)
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| for _, t in enumerate(tqdm(timesteps)):
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| latent_model_input = [latent.to(self.device)]
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| timestep = [t]
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|
|
| timestep = torch.stack(timestep).to(self.device)
|
|
|
| noise_pred_cond = self.model(
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| latent_model_input, t=timestep, **arg_c)[0].to(
|
| torch.device('cpu') if offload_model else self.device)
|
| if offload_model:
|
| torch.cuda.empty_cache()
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| noise_pred_uncond = self.model(
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| latent_model_input, t=timestep, **arg_null)[0].to(
|
| torch.device('cpu') if offload_model else self.device)
|
| if offload_model:
|
| torch.cuda.empty_cache()
|
| noise_pred = noise_pred_uncond + guide_scale * (
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| noise_pred_cond - noise_pred_uncond)
|
|
|
| latent = latent.to(
|
| torch.device('cpu') if offload_model else self.device)
|
|
|
| temp_x0 = sample_scheduler.step(
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| noise_pred.unsqueeze(0),
|
| t,
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| latent.unsqueeze(0),
|
| return_dict=False,
|
| generator=seed_g)[0]
|
| latent = temp_x0.squeeze(0)
|
|
|
| x0 = [latent.to(self.device)]
|
| del latent_model_input, timestep
|
|
|
| if offload_model:
|
| self.model.cpu()
|
| torch.cuda.empty_cache()
|
|
|
| if self.rank == 0:
|
| videos = self.vae.decode(x0)
|
|
|
| del noise, latent
|
| del sample_scheduler
|
| if offload_model:
|
| gc.collect()
|
| torch.cuda.synchronize()
|
| if dist.is_initialized():
|
| dist.barrier()
|
|
|
| return videos[0] if self.rank == 0 else None
|
|
|
