diff --git "a/adcom-flux-klein-4b-lora-training.ipynb" "b/adcom-flux-klein-4b-lora-training.ipynb"
--- "a/adcom-flux-klein-4b-lora-training.ipynb"
+++ "b/adcom-flux-klein-4b-lora-training.ipynb"
@@ -1 +1 @@
-{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":14564379,"sourceType":"datasetVersion","datasetId":8022630}],"dockerImageVersionId":31260,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"HOW TO USE:\n1) Upload a civitai dataset .zip file to your google drive named kaggleset.zip \n2) Use the https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_dataset_to_latent.ipynb notebook \nto convert this dataset to flux_captions.json and flux_latents.safetensors (saved to your drive upon running the script)\n3) Create a private dataset called image-caption-dataset\n4) Add the flux_captions.json and flux_latents.safetensor to this dataset\n5) In this notebook , press the '+ Add input' button and select your private dataset\n6) Run this notebook\n//----//\nIf you have ideas on improvements / developments on FLUX Klein 4B LoRa \ntraining let me know in the comment section of this repo","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#CELL 1\n!pip uninstall -y torch torchvision torchaudio diffusers accelerate peft transformers\n\n!pip install --no-deps torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121\n\n!pip install --upgrade --no-cache-dir diffusers transformers accelerate peft safetensors tqdm huggingface-hub\n\n!pip install git+https://github.com/huggingface/diffusers.git","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:41:12.253502Z","iopub.execute_input":"2026-01-21T06:41:12.253751Z","iopub.status.idle":"2026-01-21T06:44:24.039967Z","shell.execute_reply.started":"2026-01-21T06:41:12.253730Z","shell.execute_reply":"2026-01-21T06:44:24.039276Z"}},"outputs":[{"name":"stdout","text":"Found existing installation: torch 2.8.0+cu126\nUninstalling torch-2.8.0+cu126:\n  Successfully uninstalled torch-2.8.0+cu126\nFound existing installation: torchvision 0.23.0+cu126\nUninstalling torchvision-0.23.0+cu126:\n  Successfully uninstalled torchvision-0.23.0+cu126\nFound existing installation: torchaudio 2.8.0+cu126\nUninstalling torchaudio-2.8.0+cu126:\n  Successfully uninstalled torchaudio-2.8.0+cu126\nFound existing installation: diffusers 0.35.2\nUninstalling diffusers-0.35.2:\n  Successfully uninstalled diffusers-0.35.2\nFound existing installation: accelerate 1.11.0\nUninstalling accelerate-1.11.0:\n  Successfully uninstalled accelerate-1.11.0\nFound existing installation: peft 0.17.1\nUninstalling peft-0.17.1:\n  Successfully uninstalled peft-0.17.1\nFound existing installation: transformers 4.57.1\nUninstalling transformers-4.57.1:\n  Successfully uninstalled transformers-4.57.1\nLooking in indexes: https://download.pytorch.org/whl/cu121\nCollecting torch==2.5.1\n  Downloading https://download.pytorch.org/whl/cu121/torch-2.5.1%2Bcu121-cp312-cp312-linux_x86_64.whl (780.4 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m780.4/780.4 MB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hCollecting torchvision==0.20.1\n  Downloading https://download.pytorch.org/whl/cu121/torchvision-0.20.1%2Bcu121-cp312-cp312-linux_x86_64.whl (7.3 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.3/7.3 MB\u001b[0m \u001b[31m122.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n\u001b[?25hCollecting torchaudio==2.5.1\n  Downloading https://download.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp312-cp312-linux_x86_64.whl (3.4 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m95.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m\n\u001b[?25hInstalling collected packages: torchaudio, torchvision, torch\nSuccessfully installed torch-2.5.1+cu121 torchaudio-2.5.1+cu121 torchvision-0.20.1+cu121\nCollecting diffusers\n  Downloading diffusers-0.36.0-py3-none-any.whl.metadata (20 kB)\nCollecting transformers\n  Downloading transformers-4.57.6-py3-none-any.whl.metadata (43 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.0/44.0 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hCollecting accelerate\n  Downloading accelerate-1.12.0-py3-none-any.whl.metadata (19 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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.2/6.2 MB\u001b[0m \u001b[31m153.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25hDownloading triton-3.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (209.6 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m209.6/209.6 MB\u001b[0m \u001b[31m158.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hInstalling collected packages: triton, sympy, safetensors, nvidia-nvtx-cu12, nvidia-nccl-cu12, nvidia-cusparse-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusolver-cu12, nvidia-cudnn-cu12, diffusers, transformers, accelerate, peft\n  Attempting uninstall: triton\n    Found existing installation: triton 3.4.0\n    Uninstalling triton-3.4.0:\n      Successfully uninstalled triton-3.4.0\n  Attempting uninstall: sympy\n    Found existing installation: sympy 1.13.3\n    Uninstalling sympy-1.13.3:\n      Successfully uninstalled sympy-1.13.3\n  Attempting uninstall: safetensors\n    Found existing installation: safetensors 0.6.2\n    Uninstalling safetensors-0.6.2:\n      Successfully uninstalled safetensors-0.6.2\n  Attempting uninstall: nvidia-nvtx-cu12\n    Found existing installation: nvidia-nvtx-cu12 12.6.77\n    Uninstalling nvidia-nvtx-cu12-12.6.77:\n      Successfully uninstalled nvidia-nvtx-cu12-12.6.77\n  Attempting uninstall: nvidia-nccl-cu12\n    Found existing installation: nvidia-nccl-cu12 2.27.3\n    Uninstalling nvidia-nccl-cu12-2.27.3:\n      Successfully uninstalled nvidia-nccl-cu12-2.27.3\n  Attempting uninstall: nvidia-cusparse-cu12\n    Found existing installation: nvidia-cusparse-cu12 12.5.4.2\n    Uninstalling nvidia-cusparse-cu12-12.5.4.2:\n      Successfully uninstalled nvidia-cusparse-cu12-12.5.4.2\n  Attempting uninstall: nvidia-curand-cu12\n    Found existing installation: nvidia-curand-cu12 10.3.7.77\n    Uninstalling nvidia-curand-cu12-10.3.7.77:\n      Successfully uninstalled nvidia-curand-cu12-10.3.7.77\n  Attempting uninstall: nvidia-cufft-cu12\n    Found existing installation: nvidia-cufft-cu12 11.3.0.4\n    Uninstalling nvidia-cufft-cu12-11.3.0.4:\n      Successfully uninstalled nvidia-cufft-cu12-11.3.0.4\n  Attempting uninstall: nvidia-cuda-runtime-cu12\n    Found existing installation: nvidia-cuda-runtime-cu12 12.6.77\n    Uninstalling nvidia-cuda-runtime-cu12-12.6.77:\n      Successfully uninstalled nvidia-cuda-runtime-cu12-12.6.77\n  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n    Found existing installation: nvidia-cuda-nvrtc-cu12 12.6.77\n    Uninstalling nvidia-cuda-nvrtc-cu12-12.6.77:\n      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.6.77\n  Attempting uninstall: nvidia-cuda-cupti-cu12\n    Found existing installation: nvidia-cuda-cupti-cu12 12.6.80\n    Uninstalling nvidia-cuda-cupti-cu12-12.6.80:\n      Successfully uninstalled nvidia-cuda-cupti-cu12-12.6.80\n  Attempting uninstall: nvidia-cublas-cu12\n    Found existing installation: nvidia-cublas-cu12 12.6.4.1\n    Uninstalling nvidia-cublas-cu12-12.6.4.1:\n      Successfully uninstalled nvidia-cublas-cu12-12.6.4.1\n  Attempting uninstall: nvidia-cusolver-cu12\n    Found existing installation: nvidia-cusolver-cu12 11.7.1.2\n    Uninstalling nvidia-cusolver-cu12-11.7.1.2:\n      Successfully uninstalled nvidia-cusolver-cu12-11.7.1.2\n  Attempting uninstall: nvidia-cudnn-cu12\n    Found existing installation: nvidia-cudnn-cu12 9.10.2.21\n    Uninstalling nvidia-cudnn-cu12-9.10.2.21:\n      Successfully uninstalled nvidia-cudnn-cu12-9.10.2.21\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\ncudf-cu12 25.6.0 requires pyarrow<20.0.0a0,>=14.0.0; platform_machine == \"x86_64\", but you have pyarrow 22.0.0 which is incompatible.\nfastai 2.8.4 requires fastcore<1.9,>=1.8.0, but you have fastcore 1.11.3 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed accelerate-1.12.0 diffusers-0.36.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.21.5 nvidia-nvtx-cu12-12.1.105 peft-0.18.1 safetensors-0.7.0 sympy-1.13.1 transformers-4.57.6 triton-3.1.0\nCollecting git+https://github.com/huggingface/diffusers.git\n  Cloning https://github.com/huggingface/diffusers.git to /tmp/pip-req-build-5qs94bpu\n  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/diffusers.git /tmp/pip-req-build-5qs94bpu\n  Resolved https://github.com/huggingface/diffusers.git to commit ec376293714f269947f6d9d8a572bd73040bc1a0\n  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\nRequirement already satisfied: importlib_metadata in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (8.7.0)\nRequirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (3.20.3)\nRequirement already satisfied: httpx<1.0.0 in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (0.28.1)\nRequirement already satisfied: huggingface-hub<2.0,>=0.34.0 in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (0.36.0)\nRequirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (2.0.2)\nRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (2025.11.3)\nRequirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (2.32.5)\nRequirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (0.7.0)\nRequirement already satisfied: Pillow in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (11.3.0)\nRequirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx<1.0.0->diffusers==0.37.0.dev0) (4.12.1)\nRequirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1.0.0->diffusers==0.37.0.dev0) (2026.1.4)\nRequirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1.0.0->diffusers==0.37.0.dev0) (1.0.9)\nRequirement already satisfied: idna in /usr/local/lib/python3.12/dist-packages (from httpx<1.0.0->diffusers==0.37.0.dev0) (3.11)\nRequirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1.0.0->diffusers==0.37.0.dev0) (0.16.0)\nRequirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.34.0->diffusers==0.37.0.dev0) (2025.10.0)\nRequirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.34.0->diffusers==0.37.0.dev0) (26.0rc2)\nRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.34.0->diffusers==0.37.0.dev0) (6.0.3)\nRequirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.34.0->diffusers==0.37.0.dev0) (4.67.1)\nRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.34.0->diffusers==0.37.0.dev0) (4.15.0)\nRequirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.34.0->diffusers==0.37.0.dev0) (1.2.1rc0)\nRequirement already satisfied: zipp>=3.20 in /usr/local/lib/python3.12/dist-packages (from importlib_metadata->diffusers==0.37.0.dev0) (3.23.0)\nRequirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->diffusers==0.37.0.dev0) (3.4.4)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->diffusers==0.37.0.dev0) (2.6.3)\nBuilding wheels for collected packages: diffusers\n  Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n  Created wheel for diffusers: filename=diffusers-0.37.0.dev0-py3-none-any.whl size=4893406 sha256=76089d2f822b7c1086ff1fd07ba58a03f82c6b49ec2ea569ea3596248d511089\n  Stored in directory: /tmp/pip-ephem-wheel-cache-3f4dmw0k/wheels/23/0f/7d/f97813d265ed0e599a78d83afd4e1925740896ca79b46cccfd\nSuccessfully built diffusers\nInstalling collected packages: diffusers\n  Attempting uninstall: diffusers\n    Found existing installation: diffusers 0.36.0\n    Uninstalling diffusers-0.36.0:\n      Successfully uninstalled diffusers-0.36.0\nSuccessfully installed diffusers-0.37.0.dev0\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"# CELL 2 — Verify\nimport torch, diffusers\n\nprint(\"Torch:\", torch.__version__)\nprint(\"Diffusers:\", diffusers.__version__)\nprint(\"CUDA:\", torch.cuda.is_available())\nprint(\"GPU:\", torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"None\")\n\nfrom diffusers import Flux2KleinPipeline\nprint(\"Flux2KleinPipeline OK\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:45:52.733371Z","iopub.execute_input":"2026-01-21T06:45:52.733920Z","iopub.status.idle":"2026-01-21T06:45:52.738746Z","shell.execute_reply.started":"2026-01-21T06:45:52.733890Z","shell.execute_reply":"2026-01-21T06:45:52.738076Z"}},"outputs":[{"name":"stdout","text":"Torch: 2.5.1+cu121\nDiffusers: 0.37.0.dev0\nCUDA: True\nGPU: Tesla P100-PCIE-16GB\nFlux2KleinPipeline OK\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"# CELL 3 — Config\nimport os\n\ndevice = \"cuda\"\ndtype  = torch.float16\n\nDATASET_NAME = \"image-caption-dataset\"\n\nCAPTIONS_PATH = f\"/kaggle/input/{DATASET_NAME}/flux_captions.json\"\nLATENTS_PATH  = f\"/kaggle/input/{DATASET_NAME}/flux_latents.safetensors\"\n\nCACHE_DIR = \"/kaggle/working/cache\"\nSAVE_DIR  = \"/kaggle/working/flux_klein_lora\"\n\nos.makedirs(CACHE_DIR, exist_ok=True)\nos.makedirs(SAVE_DIR, exist_ok=True)\n\n# training\nRANK = 16\nALPHA = 16\nLR = 2e-5\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:45:55.735789Z","iopub.execute_input":"2026-01-21T06:45:55.736570Z","iopub.status.idle":"2026-01-21T06:45:55.741313Z","shell.execute_reply.started":"2026-01-21T06:45:55.736539Z","shell.execute_reply":"2026-01-21T06:45:55.740544Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"# CELL 4 — Load captions + latents\nimport json\nfrom safetensors.torch import load_file\n\nwith open(CAPTIONS_PATH) as f:\n    captions = json.load(f)\n\nlatents = load_file(LATENTS_PATH)\n\nkeys = list(captions.keys())\nprint(\"Samples:\", len(keys))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:45:58.248254Z","iopub.execute_input":"2026-01-21T06:45:58.248884Z","iopub.status.idle":"2026-01-21T06:45:58.701994Z","shell.execute_reply.started":"2026-01-21T06:45:58.248854Z","shell.execute_reply":"2026-01-21T06:45:58.701386Z"}},"outputs":[{"name":"stdout","text":"Samples: 125\n","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"# CELL 5 — Dataset (returns latent + key)\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\n\nclass FluxLatentDataset(Dataset):\n    def __len__(self):\n        return len(keys)\n\n    def __getitem__(self, idx):\n        k = keys[idx]\n        return latents[k], k\n\ndataset = FluxLatentDataset()\nloader = DataLoader(dataset, batch_size=1, shuffle=True)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:46:01.404819Z","iopub.execute_input":"2026-01-21T06:46:01.405424Z","iopub.status.idle":"2026-01-21T06:46:01.410301Z","shell.execute_reply.started":"2026-01-21T06:46:01.405394Z","shell.execute_reply":"2026-01-21T06:46:01.409594Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"# CELL 6 — Encode text on GPU and CACHE FLUX-READY embeddings\nimport torch, gc\nfrom transformers import AutoTokenizer, AutoModel\n\nMODEL_ID = \"black-forest-labs/FLUX.2-klein-4B\"\n\ntokenizer = AutoTokenizer.from_pretrained(\n    MODEL_ID,\n    subfolder=\"tokenizer\",\n    trust_remote_code=True,\n    cache_dir=CACHE_DIR,\n)\n\ntext_encoder = AutoModel.from_pretrained(\n    MODEL_ID,\n    subfolder=\"text_encoder\",\n    trust_remote_code=True,\n    dtype=torch.float16,\n    cache_dir=CACHE_DIR,\n).to(\"cuda\")\n\ntext_encoder.eval()\n\ntext_cache = {}\n\nwith torch.no_grad():\n    for k, caption in captions.items():\n        inputs = tokenizer(\n            caption,\n            padding=\"max_length\",\n            truncation=True,\n            max_length=128,\n            return_tensors=\"pt\"\n        ).to(\"cuda\")\n\n        outputs = text_encoder(**inputs, output_hidden_states=True, return_dict=True)\n        txt = outputs.hidden_states[-1]          # [1, T, 2560]\n        txt = txt.repeat(1, 1, 3)                # → [1, T, 7680]\n        text_cache[k] = txt.cpu()\n\nprint(\"✅ Cached FLUX-ready text embeddings.\")\n\ndel text_encoder\ntorch.cuda.empty_cache()\ngc.collect()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:46:04.538956Z","iopub.execute_input":"2026-01-21T06:46:04.539598Z","iopub.status.idle":"2026-01-21T06:47:02.088127Z","shell.execute_reply.started":"2026-01-21T06:46:04.539569Z","shell.execute_reply":"2026-01-21T06:47:02.087363Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7eb47566c5b642a0a9e8049c67bfd562"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"vocab.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8a7ffb1cb3f34f1cb20db3369301886c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"merges.txt: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5648fc23b5e448f0a002da76c7a2cc67"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer/tokenizer.json:   0%|          | 0.00/11.4M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0504e1ee64724b409c527c0266cf5864"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"added_tokens.json:   0%|          | 0.00/707 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1cd3f580cebb43689d537c76b99b5517"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/613 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"68fa4dacfd414607acc254a7001fe9cb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"chat_template.jinja: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a619e3c684394997ac25522ee88aed23"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"02e0198de1164fd4a5fd0be433b136e4"}},"metadata":{}},{"name":"stderr","text":"`torch_dtype` is deprecated! Use `dtype` instead!\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"model.safetensors.index.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"af52526aac28456ea10b96f98ddca3df"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Fetching 2 files:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f36c0d81d75c4c8f9c2956f645313817"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"text_encoder/model-00002-of-00002.safete(…):   0%|          | 0.00/3.08G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ee179610b92e4938883a48e47a455ed3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"text_encoder/model-00001-of-00002.safete(…):   0%|          | 0.00/4.97G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0208a0c69faa4f9eb1747d8cd6fc2cea"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6006b9a7aef34137b6d46074ec5c5ebf"}},"metadata":{}},{"name":"stdout","text":"✅ Cached FLUX-ready text embeddings.\n","output_type":"stream"},{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"8834"},"metadata":{}}],"execution_count":8},{"cell_type":"code","source":"# =========================================================\n# CELL 7A — Inspect Klein Transformer Module Names\n# =========================================================\n\nnames = []\nfor name, module in pipe.transformer.named_modules():\n    if isinstance(module, torch.nn.Linear):\n        names.append(name)\n\n# print a sample\nfor n in names[:50]:\n    print(n)\n\nprint(\"\\n--- TOTAL Linear layers:\", len(names))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:49:28.032293Z","iopub.execute_input":"2026-01-21T06:49:28.032618Z","iopub.status.idle":"2026-01-21T06:49:28.038556Z","shell.execute_reply.started":"2026-01-21T06:49:28.032589Z","shell.execute_reply":"2026-01-21T06:49:28.037744Z"}},"outputs":[{"name":"stdout","text":"time_guidance_embed.timestep_embedder.linear_1\ntime_guidance_embed.timestep_embedder.linear_2\ndouble_stream_modulation_img.linear\ndouble_stream_modulation_txt.linear\nsingle_stream_modulation.linear\nx_embedder\ncontext_embedder\ntransformer_blocks.0.attn.to_q\ntransformer_blocks.0.attn.to_k\ntransformer_blocks.0.attn.to_v\ntransformer_blocks.0.attn.to_out.0\ntransformer_blocks.0.attn.add_q_proj\ntransformer_blocks.0.attn.add_k_proj\ntransformer_blocks.0.attn.add_v_proj\ntransformer_blocks.0.attn.to_add_out\ntransformer_blocks.0.ff.linear_in\ntransformer_blocks.0.ff.linear_out\ntransformer_blocks.0.ff_context.linear_in\ntransformer_blocks.0.ff_context.linear_out\ntransformer_blocks.1.attn.to_q\ntransformer_blocks.1.attn.to_k\ntransformer_blocks.1.attn.to_v\ntransformer_blocks.1.attn.to_out.0\ntransformer_blocks.1.attn.add_q_proj\ntransformer_blocks.1.attn.add_k_proj\ntransformer_blocks.1.attn.add_v_proj\ntransformer_blocks.1.attn.to_add_out\ntransformer_blocks.1.ff.linear_in\ntransformer_blocks.1.ff.linear_out\ntransformer_blocks.1.ff_context.linear_in\ntransformer_blocks.1.ff_context.linear_out\ntransformer_blocks.2.attn.to_q\ntransformer_blocks.2.attn.to_k\ntransformer_blocks.2.attn.to_v\ntransformer_blocks.2.attn.to_out.0\ntransformer_blocks.2.attn.add_q_proj\ntransformer_blocks.2.attn.add_k_proj\ntransformer_blocks.2.attn.add_v_proj\ntransformer_blocks.2.attn.to_add_out\ntransformer_blocks.2.ff.linear_in\ntransformer_blocks.2.ff.linear_out\ntransformer_blocks.2.ff_context.linear_in\ntransformer_blocks.2.ff_context.linear_out\ntransformer_blocks.3.attn.to_q\ntransformer_blocks.3.attn.to_k\ntransformer_blocks.3.attn.to_v\ntransformer_blocks.3.attn.to_out.0\ntransformer_blocks.3.attn.add_q_proj\ntransformer_blocks.3.attn.add_k_proj\ntransformer_blocks.3.attn.add_v_proj\n\n--- TOTAL Linear layers: 109\n","output_type":"stream"}],"execution_count":10},{"cell_type":"code","source":"# =========================================================\n# CELL 7 — Load FLUX Transformer and Attach PROPER LoRA\n# =========================================================\n\nimport torch, gc\nfrom diffusers import Flux2KleinPipeline\nfrom peft import LoraConfig, get_peft_model\n\nMODEL_ID = \"black-forest-labs/FLUX.2-klein-4B\"\n\npipe = Flux2KleinPipeline.from_pretrained(\n    MODEL_ID,\n    torch_dtype=torch.float16,\n    cache_dir=CACHE_DIR,\n)\n\n# keep ONLY transformer\npipe.text_encoder = None\npipe.vae = None\npipe.scheduler = None\n\ntransformer = pipe.transformer\n\n# ---------------- LoRA TARGETS (FLUX-COMPATIBLE) ----------------\n\nTARGET_MODULES = [\n    \"attn.to_q\",\n    \"attn.to_k\",\n    \"attn.to_v\",\n    \"attn.to_out.0\",\n    \"attn.add_q_proj\",\n    \"attn.add_k_proj\",\n    \"attn.add_v_proj\",\n    \"attn.to_add_out\",\n    \"ff.linear_in\",\n    \"ff.linear_out\",\n    \"ff_context.linear_in\",\n    \"ff_context.linear_out\",\n]\n\nlora_config = LoraConfig(\n    r=RANK,\n    lora_alpha=ALPHA,\n    target_modules=TARGET_MODULES,\n    lora_dropout=0.05,\n    bias=\"none\",\n)\n\ntransformer = get_peft_model(transformer, lora_config)\ntransformer.enable_gradient_checkpointing()\n\npipe.transformer = transformer\n\ntrainable = sum(p.numel() for p in transformer.parameters() if p.requires_grad)\ntotal = sum(p.numel() for p in transformer.parameters())\n\nprint(f\"Trainable params: {trainable/1e6:.2f}M / {total/1e6:.2f}M\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:51:18.251124Z","iopub.execute_input":"2026-01-21T06:51:18.251459Z","iopub.status.idle":"2026-01-21T06:52:31.616038Z","shell.execute_reply.started":"2026-01-21T06:51:18.251433Z","shell.execute_reply":"2026-01-21T06:52:31.615093Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"81fced2a8a3c4ac3a83e0ec8ba021858"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cd5a0cb701cb4f5f9c6bcbb8b202639b"}},"metadata":{}},{"name":"stdout","text":"Trainable params: 9.34M / 3884.88M\n","output_type":"stream"}],"execution_count":12},{"cell_type":"code","source":"# CELL 8 — Move Transformer to GPU Only\npipe.transformer = pipe.transformer.to(\"cuda\")\n\ngc.collect()\ntorch.cuda.empty_cache()\n\nprint(\"✅ Transformer on GPU.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:52:31.618488Z","iopub.execute_input":"2026-01-21T06:52:31.618839Z","iopub.status.idle":"2026-01-21T06:52:34.734557Z","shell.execute_reply.started":"2026-01-21T06:52:31.618800Z","shell.execute_reply":"2026-01-21T06:52:34.733924Z"}},"outputs":[{"name":"stdout","text":"✅ Transformer on GPU.\n","output_type":"stream"}],"execution_count":13},{"cell_type":"code","source":"# =========================================================\n# CELL 9 — Flow Matching Training (Fixed Shapes, Safe Saves)\n# =========================================================\n\nimport torch, time, os\nimport torch.nn.functional as F\nfrom tqdm import tqdm\n\nfrom peft import get_peft_model_state_dict\nfrom safetensors.torch import save_file\n\n# ---------------- Config ----------------\n\nEPOCHS = 1\nSAVE_EVERY = 3\nFLOW_T_SCALE = 50.0\nCLAMP_VAL = 3.0\nGRAD_CLIP = 0.5\n\nMAX_SECONDS = (11 * 60 + 40) * 60   # 11h 40m\n\ndevice = \"cuda\"\ndtype = torch.float16\n\npipe.transformer.train()\n\n# ---------------- Helpers ----------------\n\ndef patchify_latents(latents):\n    assert latents.ndim == 4, f\"Expected [B,C,H,W], got {latents.shape}\"\n    B, C, H, W = latents.shape\n    assert H % 2 == 0 and W % 2 == 0\n\n    latents = latents.reshape(B, C, H//2, 2, W//2, 2)\n    latents = latents.permute(0, 2, 4, 1, 3, 5).contiguous()\n    return latents.view(B, (H//2)*(W//2), C*2*2)  # [B, 4096, 128]\n\n\ndef generate_flux_pos_ids(batch, ph, pw, txt_len, device, dtype):\n    y = torch.linspace(0, 1, ph, device=device, dtype=dtype)\n    x = torch.linspace(0, 1, pw, device=device, dtype=dtype)\n    gy, gx = torch.meshgrid(y, x, indexing=\"ij\")\n\n    pos_y = gy.flatten()\n    pos_x = gx.flatten()\n    zeros = torch.zeros_like(pos_y)\n\n    img_ids = torch.stack([pos_y, pos_x, zeros, zeros], dim=-1)\n    img_ids = img_ids.unsqueeze(0).repeat(batch, 1, 1)\n\n    t = torch.arange(txt_len, device=device, dtype=dtype) / txt_len\n    txt_ids = torch.stack(\n        [t, torch.zeros_like(t), torch.zeros_like(t), torch.zeros_like(t)], dim=-1\n    )\n    txt_ids = txt_ids.unsqueeze(0).repeat(batch, 1, 1)\n\n    return img_ids, txt_ids\n\n\n# ---------------- Optimizer ----------------\n\ntrainable_params = [p for p in pipe.transformer.parameters() if p.requires_grad]\n\noptimizer = torch.optim.AdamW(\n    trainable_params,\n    lr=LR,\n    betas=(0.9, 0.999),\n    weight_decay=1e-4,\n)\n\n# ---------------- Training ----------------\n\nstart_time = time.time()\n\nprint(\"⏱️ Training started. Max time:\", MAX_SECONDS / 3600, \"hours\")\n\nfor epoch in range(1, EPOCHS + 1):\n\n    print(f\"\\n===== Epoch {epoch} =====\")\n    epoch_loss = 0.0\n\n    for latent_b, key in tqdm(loader, desc=f\"Epoch {epoch}\"):\n\n        if time.time() - start_time >= MAX_SECONDS:\n            print(\"⏰ Time limit reached. Stopping training.\")\n            break\n\n        # ---- latent ----\n        latent_b = latent_b.to(device, dtype=dtype)\n\n        if latent_b.ndim == 5:\n            latent_b = latent_b.squeeze(1)\n        if latent_b.ndim == 3:\n            latent_b = latent_b.unsqueeze(0)\n\n        # ---- text ----\n        enc_b = text_cache[key[0]].to(device, dtype=dtype)  # [1, T, 7680]\n\n        if epoch == 1:\n            print(\"latent:\", latent_b.shape)   # [1,32,128,128]\n            print(\"text:\", enc_b.shape)        # [1,128,7680]\n\n        # ---- patchify ----\n        tokens = patchify_latents(latent_b)   # [1,4096,128]\n        tokens = torch.clamp(tokens, -CLAMP_VAL, CLAMP_VAL)\n\n        # ---- flow matching ----\n        eps = torch.randn_like(tokens)\n        eps = torch.clamp(eps, -CLAMP_VAL, CLAMP_VAL)\n\n        t = torch.rand(tokens.size(0), device=device, dtype=dtype)\n\n        z_t = (1 - t[:, None, None]) * eps + t[:, None, None] * tokens\n        target = tokens - eps\n        t_embed = t * FLOW_T_SCALE\n\n        # ---- pos ids ----\n        img_ids, txt_ids = generate_flux_pos_ids(\n            tokens.size(0), 64, 64, enc_b.size(1), device, dtype\n        )\n\n        # ---- forward ----\n        with torch.autocast(\"cuda\", dtype=torch.float16):\n            pred = pipe.transformer(\n                hidden_states=z_t,                 # NOT embedded\n                timestep=t_embed,\n                encoder_hidden_states=enc_b,\n                img_ids=img_ids,\n                txt_ids=txt_ids,\n                return_dict=False,\n            )[0]\n\n            loss = F.mse_loss(pred.float(), target.float())\n\n        # ---- backward ----\n        loss.backward()\n        torch.nn.utils.clip_grad_norm_(trainable_params, GRAD_CLIP)\n        optimizer.step()\n        optimizer.zero_grad(set_to_none=True)\n\n        epoch_loss += loss.item()\n\n    avg_loss = epoch_loss / max(1, len(loader))\n    print(f\"Epoch {epoch} | Avg Loss: {avg_loss:.6f}\")\n\n    # ---- checkpoint ----\n    if epoch % SAVE_EVERY == 0:\n        save_path = os.path.join(SAVE_DIR, f\"flux_klein_lora_epoch_{epoch:03d}.safetensors\")\n        lora_state = get_peft_model_state_dict(pipe.transformer)\n        save_file(lora_state, save_path)\n        print(\"💾 Saved LoRA:\", save_path)\n\n    if time.time() - start_time >= MAX_SECONDS:\n        break\n\n\n# ---------------- Final Save ----------------\n\nfinal_path = os.path.join(SAVE_DIR, \"flux_klein_lora_final.safetensors\")\nlora_state = get_peft_model_state_dict(pipe.transformer)\nsave_file(lora_state, final_path)\n\nprint(\"✅ Final FLUX LoRA saved:\", final_path)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:59:02.591112Z","iopub.execute_input":"2026-01-21T06:59:02.591450Z"}},"outputs":[{"name":"stdout","text":"⏱️ Training started. Max time: 11.666666666666666 hours\n\n===== Epoch 1 =====\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   0%|          | 0/125 [00:00<?, ?it/s]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   1%|          | 1/125 [00:22<47:05, 22.79s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   2%|▏         | 2/125 [00:45<46:29, 22.68s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   2%|▏         | 3/125 [01:08<46:05, 22.67s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   3%|▎         | 4/125 [01:30<45:41, 22.66s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   4%|▍         | 5/125 [01:53<45:19, 22.66s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   5%|▍         | 6/125 [02:16<45:00, 22.69s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"}],"execution_count":null}]}
\ No newline at end of file
+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":14564379,"sourceType":"datasetVersion","datasetId":8022630}],"dockerImageVersionId":31260,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"HOW TO USE:\n1) Upload a civitai dataset .zip file to your google drive named kaggleset.zip \n2) Use the https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_dataset_to_latent.ipynb notebook \nto convert this dataset to flux_captions.json and flux_latents.safetensors (saved to your drive upon running the script)\n3) Create a private dataset called image-caption-dataset\n4) Add the flux_captions.json and flux_latents.safetensor to this dataset\n5) In this notebook , press the '+ Add input' button and select your private dataset\n6) Run this notebook\n//----//\nIf you have ideas on improvements / developments on FLUX Klein 4B LoRa \ntraining let me know in the comment section of this repo","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#CELL 1\n!pip uninstall -y torch torchvision torchaudio diffusers accelerate peft transformers\n\n!pip install --no-deps torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121\n\n!pip install --upgrade --no-cache-dir diffusers transformers accelerate peft safetensors tqdm huggingface-hub\n\n!pip install git+https://github.com/huggingface/diffusers.git","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:41:12.253502Z","iopub.execute_input":"2026-01-21T06:41:12.253751Z","iopub.status.idle":"2026-01-21T06:44:24.039967Z","shell.execute_reply.started":"2026-01-21T06:41:12.253730Z","shell.execute_reply":"2026-01-21T06:44:24.039276Z"}},"outputs":[{"name":"stdout","text":"Found existing installation: torch 2.8.0+cu126\nUninstalling torch-2.8.0+cu126:\n  Successfully uninstalled torch-2.8.0+cu126\nFound existing installation: torchvision 0.23.0+cu126\nUninstalling torchvision-0.23.0+cu126:\n  Successfully uninstalled torchvision-0.23.0+cu126\nFound existing installation: torchaudio 2.8.0+cu126\nUninstalling torchaudio-2.8.0+cu126:\n  Successfully uninstalled torchaudio-2.8.0+cu126\nFound existing installation: diffusers 0.35.2\nUninstalling diffusers-0.35.2:\n  Successfully uninstalled diffusers-0.35.2\nFound existing installation: accelerate 1.11.0\nUninstalling accelerate-1.11.0:\n  Successfully uninstalled accelerate-1.11.0\nFound existing installation: peft 0.17.1\nUninstalling peft-0.17.1:\n  Successfully uninstalled peft-0.17.1\nFound existing installation: transformers 4.57.1\nUninstalling transformers-4.57.1:\n  Successfully uninstalled transformers-4.57.1\nLooking in indexes: https://download.pytorch.org/whl/cu121\nCollecting torch==2.5.1\n  Downloading https://download.pytorch.org/whl/cu121/torch-2.5.1%2Bcu121-cp312-cp312-linux_x86_64.whl (780.4 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m780.4/780.4 MB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hCollecting torchvision==0.20.1\n  Downloading https://download.pytorch.org/whl/cu121/torchvision-0.20.1%2Bcu121-cp312-cp312-linux_x86_64.whl (7.3 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.3/7.3 MB\u001b[0m \u001b[31m122.6 MB/s\u001b[0m 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uninstall: sympy\n    Found existing installation: sympy 1.13.3\n    Uninstalling sympy-1.13.3:\n      Successfully uninstalled sympy-1.13.3\n  Attempting uninstall: safetensors\n    Found existing installation: safetensors 0.6.2\n    Uninstalling safetensors-0.6.2:\n      Successfully uninstalled safetensors-0.6.2\n  Attempting uninstall: nvidia-nvtx-cu12\n    Found existing installation: nvidia-nvtx-cu12 12.6.77\n    Uninstalling nvidia-nvtx-cu12-12.6.77:\n      Successfully uninstalled nvidia-nvtx-cu12-12.6.77\n  Attempting uninstall: nvidia-nccl-cu12\n    Found existing installation: nvidia-nccl-cu12 2.27.3\n    Uninstalling nvidia-nccl-cu12-2.27.3:\n      Successfully uninstalled nvidia-nccl-cu12-2.27.3\n  Attempting uninstall: nvidia-cusparse-cu12\n    Found existing installation: nvidia-cusparse-cu12 12.5.4.2\n    Uninstalling nvidia-cusparse-cu12-12.5.4.2:\n      Successfully uninstalled nvidia-cusparse-cu12-12.5.4.2\n  Attempting uninstall: nvidia-curand-cu12\n    Found existing installation: nvidia-curand-cu12 10.3.7.77\n    Uninstalling nvidia-curand-cu12-10.3.7.77:\n      Successfully uninstalled nvidia-curand-cu12-10.3.7.77\n  Attempting uninstall: nvidia-cufft-cu12\n    Found existing installation: nvidia-cufft-cu12 11.3.0.4\n    Uninstalling nvidia-cufft-cu12-11.3.0.4:\n      Successfully uninstalled nvidia-cufft-cu12-11.3.0.4\n  Attempting uninstall: nvidia-cuda-runtime-cu12\n    Found existing installation: nvidia-cuda-runtime-cu12 12.6.77\n    Uninstalling nvidia-cuda-runtime-cu12-12.6.77:\n      Successfully uninstalled nvidia-cuda-runtime-cu12-12.6.77\n  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n    Found existing installation: nvidia-cuda-nvrtc-cu12 12.6.77\n    Uninstalling nvidia-cuda-nvrtc-cu12-12.6.77:\n      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.6.77\n  Attempting uninstall: nvidia-cuda-cupti-cu12\n    Found existing installation: nvidia-cuda-cupti-cu12 12.6.80\n    Uninstalling nvidia-cuda-cupti-cu12-12.6.80:\n      Successfully uninstalled nvidia-cuda-cupti-cu12-12.6.80\n  Attempting uninstall: nvidia-cublas-cu12\n    Found existing installation: nvidia-cublas-cu12 12.6.4.1\n    Uninstalling nvidia-cublas-cu12-12.6.4.1:\n      Successfully uninstalled nvidia-cublas-cu12-12.6.4.1\n  Attempting uninstall: nvidia-cusolver-cu12\n    Found existing installation: nvidia-cusolver-cu12 11.7.1.2\n    Uninstalling nvidia-cusolver-cu12-11.7.1.2:\n      Successfully uninstalled nvidia-cusolver-cu12-11.7.1.2\n  Attempting uninstall: nvidia-cudnn-cu12\n    Found existing installation: nvidia-cudnn-cu12 9.10.2.21\n    Uninstalling nvidia-cudnn-cu12-9.10.2.21:\n      Successfully uninstalled nvidia-cudnn-cu12-9.10.2.21\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\ncudf-cu12 25.6.0 requires pyarrow<20.0.0a0,>=14.0.0; platform_machine == \"x86_64\", but you have pyarrow 22.0.0 which is incompatible.\nfastai 2.8.4 requires fastcore<1.9,>=1.8.0, but you have fastcore 1.11.3 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed accelerate-1.12.0 diffusers-0.36.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.21.5 nvidia-nvtx-cu12-12.1.105 peft-0.18.1 safetensors-0.7.0 sympy-1.13.1 transformers-4.57.6 triton-3.1.0\nCollecting git+https://github.com/huggingface/diffusers.git\n  Cloning https://github.com/huggingface/diffusers.git to /tmp/pip-req-build-5qs94bpu\n  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/diffusers.git /tmp/pip-req-build-5qs94bpu\n  Resolved https://github.com/huggingface/diffusers.git to commit ec376293714f269947f6d9d8a572bd73040bc1a0\n  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\nRequirement already satisfied: importlib_metadata in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (8.7.0)\nRequirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (3.20.3)\nRequirement already satisfied: httpx<1.0.0 in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (0.28.1)\nRequirement already satisfied: huggingface-hub<2.0,>=0.34.0 in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (0.36.0)\nRequirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (2.0.2)\nRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (2025.11.3)\nRequirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (2.32.5)\nRequirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (0.7.0)\nRequirement already satisfied: Pillow in /usr/local/lib/python3.12/dist-packages (from diffusers==0.37.0.dev0) (11.3.0)\nRequirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx<1.0.0->diffusers==0.37.0.dev0) (4.12.1)\nRequirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1.0.0->diffusers==0.37.0.dev0) (2026.1.4)\nRequirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1.0.0->diffusers==0.37.0.dev0) (1.0.9)\nRequirement already satisfied: idna in /usr/local/lib/python3.12/dist-packages (from httpx<1.0.0->diffusers==0.37.0.dev0) (3.11)\nRequirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1.0.0->diffusers==0.37.0.dev0) (0.16.0)\nRequirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.34.0->diffusers==0.37.0.dev0) (2025.10.0)\nRequirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.34.0->diffusers==0.37.0.dev0) (26.0rc2)\nRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.34.0->diffusers==0.37.0.dev0) (6.0.3)\nRequirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.34.0->diffusers==0.37.0.dev0) (4.67.1)\nRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.34.0->diffusers==0.37.0.dev0) (4.15.0)\nRequirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.34.0->diffusers==0.37.0.dev0) (1.2.1rc0)\nRequirement already satisfied: zipp>=3.20 in /usr/local/lib/python3.12/dist-packages (from importlib_metadata->diffusers==0.37.0.dev0) (3.23.0)\nRequirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->diffusers==0.37.0.dev0) (3.4.4)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->diffusers==0.37.0.dev0) (2.6.3)\nBuilding wheels for collected packages: diffusers\n  Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n  Created wheel for diffusers: filename=diffusers-0.37.0.dev0-py3-none-any.whl size=4893406 sha256=76089d2f822b7c1086ff1fd07ba58a03f82c6b49ec2ea569ea3596248d511089\n  Stored in directory: /tmp/pip-ephem-wheel-cache-3f4dmw0k/wheels/23/0f/7d/f97813d265ed0e599a78d83afd4e1925740896ca79b46cccfd\nSuccessfully built diffusers\nInstalling collected packages: diffusers\n  Attempting uninstall: diffusers\n    Found existing installation: diffusers 0.36.0\n    Uninstalling diffusers-0.36.0:\n      Successfully uninstalled diffusers-0.36.0\nSuccessfully installed diffusers-0.37.0.dev0\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"# CELL 2 — Verify\nimport torch, diffusers\n\nprint(\"Torch:\", torch.__version__)\nprint(\"Diffusers:\", diffusers.__version__)\nprint(\"CUDA:\", torch.cuda.is_available())\nprint(\"GPU:\", torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"None\")\n\nfrom diffusers import Flux2KleinPipeline\nprint(\"Flux2KleinPipeline OK\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:45:52.733371Z","iopub.execute_input":"2026-01-21T06:45:52.733920Z","iopub.status.idle":"2026-01-21T06:45:52.738746Z","shell.execute_reply.started":"2026-01-21T06:45:52.733890Z","shell.execute_reply":"2026-01-21T06:45:52.738076Z"}},"outputs":[{"name":"stdout","text":"Torch: 2.5.1+cu121\nDiffusers: 0.37.0.dev0\nCUDA: True\nGPU: Tesla P100-PCIE-16GB\nFlux2KleinPipeline OK\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"# CELL 3 — Config\nimport os\n\ndevice = \"cuda\"\ndtype  = torch.float16\n\nDATASET_NAME = \"image-caption-dataset\"\n\nCAPTIONS_PATH = f\"/kaggle/input/{DATASET_NAME}/flux_captions.json\"\nLATENTS_PATH  = f\"/kaggle/input/{DATASET_NAME}/flux_latents.safetensors\"\n\nCACHE_DIR = \"/kaggle/working/cache\"\nSAVE_DIR  = \"/kaggle/working/flux_klein_lora\"\n\nos.makedirs(CACHE_DIR, exist_ok=True)\nos.makedirs(SAVE_DIR, exist_ok=True)\n\n# training\nRANK = 16\nALPHA = 16\nLR = 2e-5\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:45:55.735789Z","iopub.execute_input":"2026-01-21T06:45:55.736570Z","iopub.status.idle":"2026-01-21T06:45:55.741313Z","shell.execute_reply.started":"2026-01-21T06:45:55.736539Z","shell.execute_reply":"2026-01-21T06:45:55.740544Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"# CELL 4 — Load captions + latents\nimport json\nfrom safetensors.torch import load_file\n\nwith open(CAPTIONS_PATH) as f:\n    captions = json.load(f)\n\nlatents = load_file(LATENTS_PATH)\n\nkeys = list(captions.keys())\nprint(\"Samples:\", len(keys))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:45:58.248254Z","iopub.execute_input":"2026-01-21T06:45:58.248884Z","iopub.status.idle":"2026-01-21T06:45:58.701994Z","shell.execute_reply.started":"2026-01-21T06:45:58.248854Z","shell.execute_reply":"2026-01-21T06:45:58.701386Z"}},"outputs":[{"name":"stdout","text":"Samples: 125\n","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"# CELL 5 — Dataset (returns latent + key)\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\n\nclass FluxLatentDataset(Dataset):\n    def __len__(self):\n        return len(keys)\n\n    def __getitem__(self, idx):\n        k = keys[idx]\n        return latents[k], k\n\ndataset = FluxLatentDataset()\nloader = DataLoader(dataset, batch_size=1, shuffle=True)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:46:01.404819Z","iopub.execute_input":"2026-01-21T06:46:01.405424Z","iopub.status.idle":"2026-01-21T06:46:01.410301Z","shell.execute_reply.started":"2026-01-21T06:46:01.405394Z","shell.execute_reply":"2026-01-21T06:46:01.409594Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"# CELL 6 — Encode text on GPU and CACHE FLUX-READY embeddings\nimport torch, gc\nfrom transformers import AutoTokenizer, AutoModel\n\nMODEL_ID = \"black-forest-labs/FLUX.2-klein-4B\"\n\ntokenizer = AutoTokenizer.from_pretrained(\n    MODEL_ID,\n    subfolder=\"tokenizer\",\n    trust_remote_code=True,\n    cache_dir=CACHE_DIR,\n)\n\ntext_encoder = AutoModel.from_pretrained(\n    MODEL_ID,\n    subfolder=\"text_encoder\",\n    trust_remote_code=True,\n    dtype=torch.float16,\n    cache_dir=CACHE_DIR,\n).to(\"cuda\")\n\ntext_encoder.eval()\n\ntext_cache = {}\n\nwith torch.no_grad():\n    for k, caption in captions.items():\n        inputs = tokenizer(\n            caption,\n            padding=\"max_length\",\n            truncation=True,\n            max_length=128,\n            return_tensors=\"pt\"\n        ).to(\"cuda\")\n\n        outputs = text_encoder(**inputs, output_hidden_states=True, return_dict=True)\n        txt = outputs.hidden_states[-1]          # [1, T, 2560]\n        txt = txt.repeat(1, 1, 3)                # → [1, T, 7680]\n        text_cache[k] = txt.cpu()\n\nprint(\"✅ Cached FLUX-ready text embeddings.\")\n\ndel text_encoder\ntorch.cuda.empty_cache()\ngc.collect()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:46:04.538956Z","iopub.execute_input":"2026-01-21T06:46:04.539598Z","iopub.status.idle":"2026-01-21T06:47:02.088127Z","shell.execute_reply.started":"2026-01-21T06:46:04.539569Z","shell.execute_reply":"2026-01-21T06:47:02.087363Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7eb47566c5b642a0a9e8049c67bfd562"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"vocab.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8a7ffb1cb3f34f1cb20db3369301886c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"merges.txt: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5648fc23b5e448f0a002da76c7a2cc67"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer/tokenizer.json:   0%|          | 0.00/11.4M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0504e1ee64724b409c527c0266cf5864"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"added_tokens.json:   0%|          | 0.00/707 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1cd3f580cebb43689d537c76b99b5517"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/613 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"68fa4dacfd414607acc254a7001fe9cb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"chat_template.jinja: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a619e3c684394997ac25522ee88aed23"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"02e0198de1164fd4a5fd0be433b136e4"}},"metadata":{}},{"name":"stderr","text":"`torch_dtype` is deprecated! Use `dtype` instead!\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"model.safetensors.index.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"af52526aac28456ea10b96f98ddca3df"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Fetching 2 files:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f36c0d81d75c4c8f9c2956f645313817"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"text_encoder/model-00002-of-00002.safete(…):   0%|          | 0.00/3.08G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ee179610b92e4938883a48e47a455ed3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"text_encoder/model-00001-of-00002.safete(…):   0%|          | 0.00/4.97G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0208a0c69faa4f9eb1747d8cd6fc2cea"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6006b9a7aef34137b6d46074ec5c5ebf"}},"metadata":{}},{"name":"stdout","text":"✅ Cached FLUX-ready text embeddings.\n","output_type":"stream"},{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"8834"},"metadata":{}}],"execution_count":8},{"cell_type":"code","source":"# =========================================================\n# CELL 7A — Inspect Klein Transformer Module Names\n# =========================================================\n\nnames = []\nfor name, module in pipe.transformer.named_modules():\n    if isinstance(module, torch.nn.Linear):\n        names.append(name)\n\n# print a sample\nfor n in names[:50]:\n    print(n)\n\nprint(\"\\n--- TOTAL Linear layers:\", len(names))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:49:28.032293Z","iopub.execute_input":"2026-01-21T06:49:28.032618Z","iopub.status.idle":"2026-01-21T06:49:28.038556Z","shell.execute_reply.started":"2026-01-21T06:49:28.032589Z","shell.execute_reply":"2026-01-21T06:49:28.037744Z"}},"outputs":[{"name":"stdout","text":"time_guidance_embed.timestep_embedder.linear_1\ntime_guidance_embed.timestep_embedder.linear_2\ndouble_stream_modulation_img.linear\ndouble_stream_modulation_txt.linear\nsingle_stream_modulation.linear\nx_embedder\ncontext_embedder\ntransformer_blocks.0.attn.to_q\ntransformer_blocks.0.attn.to_k\ntransformer_blocks.0.attn.to_v\ntransformer_blocks.0.attn.to_out.0\ntransformer_blocks.0.attn.add_q_proj\ntransformer_blocks.0.attn.add_k_proj\ntransformer_blocks.0.attn.add_v_proj\ntransformer_blocks.0.attn.to_add_out\ntransformer_blocks.0.ff.linear_in\ntransformer_blocks.0.ff.linear_out\ntransformer_blocks.0.ff_context.linear_in\ntransformer_blocks.0.ff_context.linear_out\ntransformer_blocks.1.attn.to_q\ntransformer_blocks.1.attn.to_k\ntransformer_blocks.1.attn.to_v\ntransformer_blocks.1.attn.to_out.0\ntransformer_blocks.1.attn.add_q_proj\ntransformer_blocks.1.attn.add_k_proj\ntransformer_blocks.1.attn.add_v_proj\ntransformer_blocks.1.attn.to_add_out\ntransformer_blocks.1.ff.linear_in\ntransformer_blocks.1.ff.linear_out\ntransformer_blocks.1.ff_context.linear_in\ntransformer_blocks.1.ff_context.linear_out\ntransformer_blocks.2.attn.to_q\ntransformer_blocks.2.attn.to_k\ntransformer_blocks.2.attn.to_v\ntransformer_blocks.2.attn.to_out.0\ntransformer_blocks.2.attn.add_q_proj\ntransformer_blocks.2.attn.add_k_proj\ntransformer_blocks.2.attn.add_v_proj\ntransformer_blocks.2.attn.to_add_out\ntransformer_blocks.2.ff.linear_in\ntransformer_blocks.2.ff.linear_out\ntransformer_blocks.2.ff_context.linear_in\ntransformer_blocks.2.ff_context.linear_out\ntransformer_blocks.3.attn.to_q\ntransformer_blocks.3.attn.to_k\ntransformer_blocks.3.attn.to_v\ntransformer_blocks.3.attn.to_out.0\ntransformer_blocks.3.attn.add_q_proj\ntransformer_blocks.3.attn.add_k_proj\ntransformer_blocks.3.attn.add_v_proj\n\n--- TOTAL Linear layers: 109\n","output_type":"stream"}],"execution_count":10},{"cell_type":"code","source":"# =========================================================\n# CELL 7 — Load FLUX Transformer and Attach PROPER LoRA\n# =========================================================\n\nimport torch, gc\nfrom diffusers import Flux2KleinPipeline\nfrom peft import LoraConfig, get_peft_model\n\nMODEL_ID = \"black-forest-labs/FLUX.2-klein-4B\"\n\npipe = Flux2KleinPipeline.from_pretrained(\n    MODEL_ID,\n    torch_dtype=torch.float16,\n    cache_dir=CACHE_DIR,\n)\n\n# keep ONLY transformer\npipe.text_encoder = None\npipe.vae = None\npipe.scheduler = None\n\ntransformer = pipe.transformer\n\n# ---------------- LoRA TARGETS (FLUX-COMPATIBLE) ----------------\n\nTARGET_MODULES = [\n    \"attn.to_q\",\n    \"attn.to_k\",\n    \"attn.to_v\",\n    \"attn.to_out.0\",\n    \"attn.add_q_proj\",\n    \"attn.add_k_proj\",\n    \"attn.add_v_proj\",\n    \"attn.to_add_out\",\n    \"ff.linear_in\",\n    \"ff.linear_out\",\n    \"ff_context.linear_in\",\n    \"ff_context.linear_out\",\n]\n\nlora_config = LoraConfig(\n    r=RANK,\n    lora_alpha=ALPHA,\n    target_modules=TARGET_MODULES,\n    lora_dropout=0.05,\n    bias=\"none\",\n)\n\ntransformer = get_peft_model(transformer, lora_config)\ntransformer.enable_gradient_checkpointing()\n\npipe.transformer = transformer\n\ntrainable = sum(p.numel() for p in transformer.parameters() if p.requires_grad)\ntotal = sum(p.numel() for p in transformer.parameters())\n\nprint(f\"Trainable params: {trainable/1e6:.2f}M / {total/1e6:.2f}M\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:51:18.251124Z","iopub.execute_input":"2026-01-21T06:51:18.251459Z","iopub.status.idle":"2026-01-21T06:52:31.616038Z","shell.execute_reply.started":"2026-01-21T06:51:18.251433Z","shell.execute_reply":"2026-01-21T06:52:31.615093Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"81fced2a8a3c4ac3a83e0ec8ba021858"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cd5a0cb701cb4f5f9c6bcbb8b202639b"}},"metadata":{}},{"name":"stdout","text":"Trainable params: 9.34M / 3884.88M\n","output_type":"stream"}],"execution_count":12},{"cell_type":"code","source":"# CELL 8 — Move Transformer to GPU Only\npipe.transformer = pipe.transformer.to(\"cuda\")\n\ngc.collect()\ntorch.cuda.empty_cache()\n\nprint(\"✅ Transformer on GPU.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:52:31.618488Z","iopub.execute_input":"2026-01-21T06:52:31.618839Z","iopub.status.idle":"2026-01-21T06:52:34.734557Z","shell.execute_reply.started":"2026-01-21T06:52:31.618800Z","shell.execute_reply":"2026-01-21T06:52:34.733924Z"}},"outputs":[{"name":"stdout","text":"✅ Transformer on GPU.\n","output_type":"stream"}],"execution_count":13},{"cell_type":"code","source":"# =========================================================\n# CELL 9 — Flow Matching Training (Fixed Shapes, Safe Saves)\n# =========================================================\n\nimport torch, time, os\nimport torch.nn.functional as F\nfrom tqdm import tqdm\n\nfrom peft import get_peft_model_state_dict\nfrom safetensors.torch import save_file\n\n# ---------------- Config ----------------\n\nEPOCHS = 1\nSAVE_EVERY = 3\nFLOW_T_SCALE = 50.0\nCLAMP_VAL = 3.0\nGRAD_CLIP = 0.5\n\nMAX_SECONDS = (11 * 60 + 40) * 60   # 11h 40m\n\ndevice = \"cuda\"\ndtype = torch.float16\n\npipe.transformer.train()\n\n# ---------------- Helpers ----------------\n\ndef patchify_latents(latents):\n    assert latents.ndim == 4, f\"Expected [B,C,H,W], got {latents.shape}\"\n    B, C, H, W = latents.shape\n    assert H % 2 == 0 and W % 2 == 0\n\n    latents = latents.reshape(B, C, H//2, 2, W//2, 2)\n    latents = latents.permute(0, 2, 4, 1, 3, 5).contiguous()\n    return latents.view(B, (H//2)*(W//2), C*2*2)  # [B, 4096, 128]\n\n\ndef generate_flux_pos_ids(batch, ph, pw, txt_len, device, dtype):\n    y = torch.linspace(0, 1, ph, device=device, dtype=dtype)\n    x = torch.linspace(0, 1, pw, device=device, dtype=dtype)\n    gy, gx = torch.meshgrid(y, x, indexing=\"ij\")\n\n    pos_y = gy.flatten()\n    pos_x = gx.flatten()\n    zeros = torch.zeros_like(pos_y)\n\n    img_ids = torch.stack([pos_y, pos_x, zeros, zeros], dim=-1)\n    img_ids = img_ids.unsqueeze(0).repeat(batch, 1, 1)\n\n    t = torch.arange(txt_len, device=device, dtype=dtype) / txt_len\n    txt_ids = torch.stack(\n        [t, torch.zeros_like(t), torch.zeros_like(t), torch.zeros_like(t)], dim=-1\n    )\n    txt_ids = txt_ids.unsqueeze(0).repeat(batch, 1, 1)\n\n    return img_ids, txt_ids\n\n\n# ---------------- Optimizer ----------------\n\ntrainable_params = [p for p in pipe.transformer.parameters() if p.requires_grad]\n\noptimizer = torch.optim.AdamW(\n    trainable_params,\n    lr=LR,\n    betas=(0.9, 0.999),\n    weight_decay=1e-4,\n)\n\n# ---------------- Training ----------------\n\nstart_time = time.time()\n\nprint(\"⏱️ Training started. Max time:\", MAX_SECONDS / 3600, \"hours\")\n\nfor epoch in range(1, EPOCHS + 1):\n\n    print(f\"\\n===== Epoch {epoch} =====\")\n    epoch_loss = 0.0\n\n    for latent_b, key in tqdm(loader, desc=f\"Epoch {epoch}\"):\n\n        if time.time() - start_time >= MAX_SECONDS:\n            print(\"⏰ Time limit reached. Stopping training.\")\n            break\n\n        # ---- latent ----\n        latent_b = latent_b.to(device, dtype=dtype)\n\n        if latent_b.ndim == 5:\n            latent_b = latent_b.squeeze(1)\n        if latent_b.ndim == 3:\n            latent_b = latent_b.unsqueeze(0)\n\n        # ---- text ----\n        enc_b = text_cache[key[0]].to(device, dtype=dtype)  # [1, T, 7680]\n\n        if epoch == 1:\n            print(\"latent:\", latent_b.shape)   # [1,32,128,128]\n            print(\"text:\", enc_b.shape)        # [1,128,7680]\n\n        # ---- patchify ----\n        tokens = patchify_latents(latent_b)   # [1,4096,128]\n        tokens = torch.clamp(tokens, -CLAMP_VAL, CLAMP_VAL)\n\n        # ---- flow matching ----\n        eps = torch.randn_like(tokens)\n        eps = torch.clamp(eps, -CLAMP_VAL, CLAMP_VAL)\n\n        t = torch.rand(tokens.size(0), device=device, dtype=dtype)\n\n        z_t = (1 - t[:, None, None]) * eps + t[:, None, None] * tokens\n        target = tokens - eps\n        t_embed = t * FLOW_T_SCALE\n\n        # ---- pos ids ----\n        img_ids, txt_ids = generate_flux_pos_ids(\n            tokens.size(0), 64, 64, enc_b.size(1), device, dtype\n        )\n\n        # ---- forward ----\n        with torch.autocast(\"cuda\", dtype=torch.float16):\n            pred = pipe.transformer(\n                hidden_states=z_t,                 # NOT embedded\n                timestep=t_embed,\n                encoder_hidden_states=enc_b,\n                img_ids=img_ids,\n                txt_ids=txt_ids,\n                return_dict=False,\n            )[0]\n\n            loss = F.mse_loss(pred.float(), target.float())\n\n        # ---- backward ----\n        loss.backward()\n        torch.nn.utils.clip_grad_norm_(trainable_params, GRAD_CLIP)\n        optimizer.step()\n        optimizer.zero_grad(set_to_none=True)\n\n        epoch_loss += loss.item()\n\n    avg_loss = epoch_loss / max(1, len(loader))\n    print(f\"Epoch {epoch} | Avg Loss: {avg_loss:.6f}\")\n\n    # ---- checkpoint ----\n    if epoch % SAVE_EVERY == 0:\n        save_path = os.path.join(SAVE_DIR, f\"flux_klein_lora_epoch_{epoch:03d}.safetensors\")\n        lora_state = get_peft_model_state_dict(pipe.transformer)\n        save_file(lora_state, save_path)\n        print(\"💾 Saved LoRA:\", save_path)\n\n    if time.time() - start_time >= MAX_SECONDS:\n        break\n\n\n# ---------------- Final Save ----------------\n\nfinal_path = os.path.join(SAVE_DIR, \"flux_klein_lora_final.safetensors\")\nlora_state = get_peft_model_state_dict(pipe.transformer)\nsave_file(lora_state, final_path)\n\nprint(\"✅ Final FLUX LoRA saved:\", final_path)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T06:59:02.591112Z","iopub.execute_input":"2026-01-21T06:59:02.591450Z","iopub.status.idle":"2026-01-21T07:46:34.817671Z","shell.execute_reply.started":"2026-01-21T06:59:02.591419Z","shell.execute_reply":"2026-01-21T07:46:34.816933Z"},"collapsed":true,"jupyter":{"outputs_hidden":true}},"outputs":[{"name":"stdout","text":"⏱️ Training started. Max time: 11.666666666666666 hours\n\n===== Epoch 1 =====\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   0%|          | 0/125 [00:00<?, ?it/s]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   1%|          | 1/125 [00:22<47:05, 22.79s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   2%|▏         | 2/125 [00:45<46:29, 22.68s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   2%|▏         | 3/125 [01:08<46:05, 22.67s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   3%|▎         | 4/125 [01:30<45:41, 22.66s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   4%|▍         | 5/125 [01:53<45:19, 22.66s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   5%|▍         | 6/125 [02:16<45:00, 22.69s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   6%|▌         | 7/125 [02:38<44:40, 22.72s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   6%|▋         | 8/125 [03:01<44:20, 22.74s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   7%|▋         | 9/125 [03:24<43:59, 22.75s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   8%|▊         | 10/125 [03:47<43:37, 22.76s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:   9%|▉         | 11/125 [04:10<43:17, 22.78s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  10%|▉         | 12/125 [04:32<42:55, 22.79s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  10%|█         | 13/125 [04:55<42:31, 22.78s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  11%|█         | 14/125 [05:18<42:09, 22.79s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  12%|█▏        | 15/125 [05:41<41:48, 22.81s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  13%|█▎        | 16/125 [06:04<41:25, 22.80s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  14%|█▎        | 17/125 [06:26<41:01, 22.79s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  14%|█▍        | 18/125 [06:49<40:39, 22.80s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  15%|█▌        | 19/125 [07:12<40:16, 22.80s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  16%|█▌        | 20/125 [07:35<39:52, 22.79s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  17%|█▋        | 21/125 [07:58<39:29, 22.79s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  18%|█▊        | 22/125 [08:20<39:07, 22.79s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  18%|█▊        | 23/125 [08:43<38:45, 22.80s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  19%|█▉        | 24/125 [09:06<38:21, 22.79s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  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7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  98%|█████████▊| 123/125 [46:46<00:45, 22.85s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1:  99%|█████████▉| 124/125 [47:09<00:22, 22.84s/it]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntext: torch.Size([1, 128, 7680])\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 125/125 [47:32<00:00, 22.82s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 1 | Avg Loss: 4.059623\n✅ Final FLUX LoRA saved: /kaggle/working/flux_klein_lora/flux_klein_lora_final.safetensors\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}],"execution_count":17}]}
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