Upload adcom-flux-klein-4b-lora-training.ipynb

adcom-flux-klein-4b-lora-training.ipynb

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+{"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 — Install correct versions\n\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\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T02:14:03.993767Z","iopub.execute_input":"2026-01-21T02:14:03.994085Z","iopub.status.idle":"2026-01-21T02:17:15.820999Z","shell.execute_reply.started":"2026-01-21T02:14:03.994054Z","shell.execute_reply":"2026-01-21T02:17:15.820052Z"},"collapsed":true,"jupyter":{"outputs_hidden":true}},"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 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accelerate-1.12.0-py3-none-any.whl.metadata (19 kB)\nCollecting peft\n  Downloading peft-0.18.1-py3-none-any.whl.metadata (14 kB)\nRequirement already satisfied: safetensors in /usr/local/lib/python3.12/dist-packages (0.6.2)\nCollecting safetensors\n  Downloading safetensors-0.7.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)\nRequirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (4.67.1)\nRequirement already satisfied: huggingface-hub in /usr/local/lib/python3.12/dist-packages (0.36.0)\nCollecting huggingface-hub\n  Downloading huggingface_hub-1.3.2-py3-none-any.whl.metadata (13 kB)\nRequirement already satisfied: importlib_metadata in /usr/local/lib/python3.12/dist-packages (from diffusers) (8.7.0)\nRequirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from diffusers) (3.20.3)\nRequirement already satisfied: httpx<1.0.0 in /usr/local/lib/python3.12/dist-packages (from diffusers) (0.28.1)\nRequirement 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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.2/6.2 MB\u001b[0m \u001b[31m285.6 MB/s\u001b[0m eta \u001b[36m0:00:00\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[31m281.0 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-e65jvyey\n  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/diffusers.git /tmp/pip-req-build-e65jvyey\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=3f252fa1a94143458dc964e4ec52bda28d0a038d678d90ec8ff4b05ca5cb84b4\n  Stored in directory: /tmp/pip-ephem-wheel-cache-p_q10gbx/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\n\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\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T02:18:19.302118Z","iopub.execute_input":"2026-01-21T02:18:19.302736Z","iopub.status.idle":"2026-01-21T02:18:19.307701Z","shell.execute_reply.started":"2026-01-21T02:18:19.302703Z","shell.execute_reply":"2026-01-21T02:18:19.306888Z"},"collapsed":true,"jupyter":{"outputs_hidden":true}},"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":6},{"cell_type":"code","source":"# CELL 3 — Config\n\nimport os\n\ndevice = \"cuda\"\ndtype  = torch.float16\n\nDATASET_NAME = \"image-caption-dataset\"   # change if needed\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\nACCUM_STEPS = 2\nALPHA = 16\n#--#\nLR = 2e-5\nSTEPS = 1000   # or more\nRANK = 16      # better for FLUX\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T02:17:39.718188Z","iopub.execute_input":"2026-01-21T02:17:39.718708Z","iopub.status.idle":"2026-01-21T02:17:39.724513Z","shell.execute_reply.started":"2026-01-21T02:17:39.718682Z","shell.execute_reply":"2026-01-21T02:17:39.723768Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"# CELL 4 — Load captions + latents\n\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":"2026-01-21T02:18:26.117278Z","iopub.execute_input":"2026-01-21T02:18:26.117550Z","iopub.status.idle":"2026-01-21T02:18:26.131422Z","shell.execute_reply.started":"2026-01-21T02:18:26.117527Z","shell.execute_reply":"2026-01-21T02:18:26.130767Z"}},"outputs":[{"name":"stdout","text":"Samples: 125\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"# CELL 5 — Dataset (returns latent + key)\n\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[f\"{k}\"], k   # <-- return KEY, not caption\n\ndataset = FluxLatentDataset()\nloader = DataLoader(dataset, batch_size=1, shuffle=True)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T02:18:29.463210Z","iopub.execute_input":"2026-01-21T02:18:29.463824Z","iopub.status.idle":"2026-01-21T02:18:29.468911Z","shell.execute_reply.started":"2026-01-21T02:18:29.463792Z","shell.execute_reply":"2026-01-21T02:18:29.468198Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"# CELL 6 — Load tokenizer + text encoder (CPU only)\n\nimport torch\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    device_map=\"cpu\",\n    cache_dir=CACHE_DIR\n).eval()\n\nprint(\"Text encoder loaded on CPU.\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T02:18:49.193705Z","iopub.execute_input":"2026-01-21T02:18:49.194038Z","iopub.status.idle":"2026-01-21T02:19:25.767176Z","shell.execute_reply.started":"2026-01-21T02:18:49.194009Z","shell.execute_reply":"2026-01-21T02:19:25.766547Z"},"collapsed":true,"jupyter":{"outputs_hidden":true}},"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":"eaead1ef86c241a0b09925c63113296c"}},"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":"110fb3d3f23e412595bbb447dfa4d51a"}},"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":"e2837b2321234dfc842f91fb5e5bf3f0"}},"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":"9c79e0df8c754106837ce8cb8ce10899"}},"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":"779c2c951331430ab4ff42f2125ef746"}},"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":"a91ccd8fa80b4511ad8337c0e37a636a"}},"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":"60ca6aa01d1145b3807dccfa7ab62fde"}},"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":"7782388fcfe94fab825c8244150a131e"}},"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":"0cf64e12a8c845fcb0fe6c4d3fa40707"}},"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":"38d3c1b558ad417ca950a558a987993c"}},"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":"2ab2719c9e5340b8aec82facc422d5d7"}},"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":"111599098a2c4c1db2a1197773877f09"}},"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":"7cc59ebdc19d4f779b6651f1339b9a02"}},"metadata":{}},{"name":"stdout","text":"Text encoder loaded on CPU.\n","output_type":"stream"}],"execution_count":9},{"cell_type":"code","source":"# CELL 7 — Cache text embeddings using GPU, then fully unload text encoder\n\nimport torch, gc\nfrom tqdm import tqdm\n\ntext_encoder = text_encoder.to(\"cuda\")\n\ntext_cache = {}\n\nwith torch.no_grad():\n    for k, caption in tqdm(captions.items(), desc=\"Caching text embeddings\"):\n\n        inputs = tokenizer(\n            caption,\n            padding=\"max_length\",\n            truncation=True,\n            max_length=128,   # reduce VRAM & attention cost\n            return_tensors=\"pt\"\n        ).to(\"cuda\")\n\n        hidden = text_encoder(**inputs).last_hidden_state  # [1,128,2560]\n        text_cache[k] = hidden.cpu()\n\n# ---- FULL CLEANUP ----\ndel text_encoder\ndel tokenizer\ngc.collect()\ntorch.cuda.empty_cache()\n\nprint(\"Text embeddings cached. Text encoder REMOVED from VRAM.\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T02:19:25.768357Z","iopub.execute_input":"2026-01-21T02:19:25.768612Z","iopub.status.idle":"2026-01-21T02:19:49.755542Z","shell.execute_reply.started":"2026-01-21T02:19:25.768588Z","shell.execute_reply":"2026-01-21T02:19:49.754766Z"}},"outputs":[{"name":"stderr","text":"Caching text embeddings: 100%|██████████| 125/125 [00:20<00:00,  5.99it/s]\n","output_type":"stream"},{"name":"stdout","text":"Text embeddings cached. Text encoder REMOVED from VRAM.\n","output_type":"stream"}],"execution_count":10},{"cell_type":"code","source":"# CELL 8 — Load pipeline, move ONLY transformer to GPU\n\nimport gc, torch\nfrom diffusers import Flux2KleinPipeline\n\ntorch.cuda.empty_cache()\ngc.collect()\n\npipe = Flux2KleinPipeline.from_pretrained(\n    MODEL_ID,\n    torch_dtype=dtype,\n    cache_dir=CACHE_DIR,\n)\n\npipe.transformer = pipe.transformer.to(\"cuda\")\n\n# remove unused modules\npipe.text_encoder = None\npipe.vae = None\npipe.scheduler = None\npipe.tokenizer = None\n\ngc.collect()\ntorch.cuda.empty_cache()\n\nprint(\"Transformer on GPU. No text encoder in memory.\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T02:19:49.756960Z","iopub.execute_input":"2026-01-21T02:19:49.757276Z","iopub.status.idle":"2026-01-21T02:20:58.979757Z","shell.execute_reply.started":"2026-01-21T02:19:49.757222Z","shell.execute_reply":"2026-01-21T02:20:58.979052Z"},"collapsed":true,"jupyter":{"outputs_hidden":true}},"outputs":[{"output_type":"display_data","data":{"text/plain":"model_index.json:   0%|          | 0.00/446 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ff77a4dd0c7247e89595c653dd29c099"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Fetching 17 files:   0%|          | 0/17 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4c80a945879748d7b2cbdb90bd8fb9f0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/541 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"76ef0355a5e3404cb976b5a0bd8a1b6e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"scheduler_config.json:   0%|          | 0.00/486 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ffeb38ec9a1447f0a311be051c53b90b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/821 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9c2298e65e90480ab6b5b84300c7b082"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"transformer/diffusion_pytorch_model.safe(…):   0%|          | 0.00/7.75G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7bee3a85bada4dfb8cdb46e3058e07f6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"vae/diffusion_pytorch_model.safetensors:   0%|          | 0.00/168M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5b70e4d2fcd84e0ca80fd94bdd2fac91"}},"metadata":{}},{"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":"90703f7a3a6149279391a3e2ec13fd24"}},"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":"9ad93b325fca4e22b416195a046f5998"}},"metadata":{}},{"name":"stdout","text":"Transformer on GPU. No text encoder in memory.\n","output_type":"stream"}],"execution_count":11},{"cell_type":"code","source":"# CELL 9 — Attach LoRA to transformer only\n\nimport torch\nfrom peft import LoraConfig, get_peft_model, PeftModel\n\ntransformer = pipe.transformer\n\nif isinstance(transformer, PeftModel):\n    transformer = transformer.unload()\n    torch.cuda.empty_cache()\n\nlora_config = LoraConfig(\n    r=RANK,\n    lora_alpha=ALPHA,\n    target_modules=[\n        \"q_proj\", \"k_proj\", \"v_proj\", \"out_proj\",\n        \"fc1\", \"fc2\",\n        \"proj_in\", \"proj_out\",\n    ],\n    lora_dropout=0.05,\n    bias=\"none\",\n)\n\ntransformer = get_peft_model(transformer, lora_config)\ntransformer.enable_gradient_checkpointing()\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())\nprint(f\"Trainable params: {trainable/1e6:.2f}M / {total/1e6:.2f}M\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T02:20:58.980903Z","iopub.execute_input":"2026-01-21T02:20:58.981141Z","iopub.status.idle":"2026-01-21T02:20:59.030477Z","shell.execute_reply.started":"2026-01-21T02:20:58.981118Z","shell.execute_reply":"2026-01-21T02:20:59.029874Z"}},"outputs":[{"name":"stdout","text":"Trainable params: 0.05M / 3875.60M\n","output_type":"stream"}],"execution_count":12},{"cell_type":"code","source":"# =========================================================\n# CELL 10 — Flow Matching Training (FLUX-2-Klein, LoRA only)\n# =========================================================\n\nimport torch\nimport torch.nn.functional as F\nfrom tqdm import trange\nimport os, gc\n\n# ----------------------------\n# CONSTANTS (SAFE DEFAULTS)\n# ----------------------------\nSTEPS = 1000\nACCUM_STEPS = 1\nLR = 1e-4\nMAX_TXT_TOKENS = 128     # must match cache\nFLOW_T_SCALE = 50.0\nCLAMP_VAL = 3.0\nGRAD_CLIP = 0.5\n\ndevice = \"cuda\"\ndtype = torch.float16\n\n# ----------------------------\n# Helpers\n# ----------------------------\ndef patchify_latents(latents):\n    # latents: [B,32,128,128] -> tokens: [B,4096,128]\n    B, C, H, W = latents.shape\n    latents = latents.reshape(B, C, H//2, 2, W//2, 2)\n    latents = latents.permute(0, 2, 4, 1, 3, 5).contiguous()\n    tokens = latents.view(B, (H//2)*(W//2), C*2*2)\n    return tokens\n\n\ndef generate_flux_pos_ids(batch, ph, pw, txt_len, device, dtype):\n    # image ids\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    scale  = torch.zeros_like(pos_y)\n    aspect = torch.zeros_like(pos_y)\n\n    img_ids = torch.stack([pos_y, pos_x, scale, aspect], dim=-1)\n    img_ids = img_ids.unsqueeze(0).repeat(batch, 1, 1)\n\n    # text ids\n    t = torch.arange(txt_len, device=device, dtype=dtype) / txt_len\n    zeros = torch.zeros_like(t)\n    txt_ids = torch.stack([t, zeros, zeros, zeros], dim=-1)\n    txt_ids = txt_ids.unsqueeze(0).repeat(batch, 1, 1)\n\n    return img_ids, txt_ids\n\n\n# ----------------------------\n# Model setup\n# ----------------------------\ntorch.cuda.empty_cache()\ngc.collect()\n\npipe.transformer.train()\npipe.transformer.enable_gradient_checkpointing()\n\n# ----------------------------\n# Optimizer (LoRA only)\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# ----------------------------\n# Training loop\n# ----------------------------\nsteps_done = 0\naccum_loss = 0.0\ndata_iter = iter(loader)\n\nfor step in trange(STEPS, desc=\"Training (Flow Matching)\"):\n\n    try:\n        latent_b, key = next(data_iter)\n    except StopIteration:\n        data_iter = iter(loader)\n        latent_b, key = next(data_iter)\n\n    latent_b = latent_b.squeeze(0).to(device, dtype=dtype)  # [1,32,128,128]\n    caption = captions[key[0]]\n\n    # -------------------------------------------------\n    # Text conditioning (CACHED, RAW, NOT PROJECTED)\n    # -------------------------------------------------\n    with torch.no_grad():\n        txt_hidden = text_cache[key[0]].to(device, dtype=dtype)  # [1,T,2560]\n        txt_hidden = txt_hidden.repeat(1, 1, 3)                  # -> [1,T,7680]\n        enc_b = txt_hidden                                      # DO NOT project\n\n    # --------------------\n    # Patchify\n    # --------------------\n    tokens = patchify_latents(latent_b)                         # [1,4096,128]\n    tokens = torch.clamp(tokens, -CLAMP_VAL, CLAMP_VAL)\n\n    # --------------------\n    # Flow matching\n    # --------------------\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    z_t = torch.nan_to_num(z_t, nan=0.0, posinf=1.0, neginf=-1.0)\n\n    target = tokens - eps\n    t_embed = t * FLOW_T_SCALE\n\n    # --------------------\n    # Positional IDs\n    # --------------------\n    B = tokens.size(0)\n    ph = pw = 64\n\n    img_ids, txt_ids = generate_flux_pos_ids(\n        B, ph, pw, enc_b.size(1), device, dtype\n    )\n\n    # --------------------\n    # SANITY PRINT (once)\n    # --------------------\n    if step == 0:\n        print(\"latent:\", latent_b.shape)\n        print(\"tokens:\", tokens.shape)\n        print(\"text enc:\", enc_b.shape)\n        print(\"img_ids:\", img_ids.shape)\n        print(\"txt_ids:\", txt_ids.shape)\n\n    # --------------------\n    # Forward\n    # --------------------\n    with torch.autocast(\"cuda\", dtype=torch.float16):\n        pred = pipe.transformer(\n            hidden_states=z_t,\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    if not torch.isfinite(loss):\n        print(\"⚠ NaN detected — skipping\")\n        optimizer.zero_grad(set_to_none=True)\n        continue\n\n    # --------------------\n    # Backprop\n    # --------------------\n    loss = loss / ACCUM_STEPS\n    loss.backward()\n    accum_loss += loss.item()\n\n    if (step + 1) % ACCUM_STEPS == 0:\n        torch.nn.utils.clip_grad_norm_(trainable_params, GRAD_CLIP)\n        optimizer.step()\n        optimizer.zero_grad(set_to_none=True)\n        steps_done += 1\n\n        if steps_done % 25 == 0:\n            print(f\"Step {steps_done:04d} | Loss: {accum_loss/25:.6f}\")\n            accum_loss = 0.0\n\n    torch.cuda.empty_cache()\n\n\n# ----------------------------\n# Save LoRA\n# ----------------------------\nos.makedirs(SAVE_DIR, exist_ok=True)\npipe.transformer.save_pretrained(SAVE_DIR)\nprint(\"LoRA saved to:\", SAVE_DIR)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-21T02:28:10.593939Z","iopub.execute_input":"2026-01-21T02:28:10.594688Z"}},"outputs":[{"name":"stderr","text":"Training (Flow Matching):   0%|          | 0/1000 [00:00<?, ?it/s]","output_type":"stream"},{"name":"stdout","text":"latent: torch.Size([1, 32, 128, 128])\ntokens: torch.Size([1, 4096, 128])\ntext enc: torch.Size([1, 128, 7680])\nimg_ids: torch.Size([1, 4096, 4])\ntxt_ids: torch.Size([1, 128, 4])\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):   2%|▎         | 25/1000 [02:01<1:19:40,  4.90s/it]","output_type":"stream"},{"name":"stdout","text":"Step 0025 | Loss: 4.123635\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):   5%|▌         | 50/1000 [04:03<1:17:17,  4.88s/it]","output_type":"stream"},{"name":"stdout","text":"Step 0050 | Loss: 4.104446\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):   8%|▊         | 75/1000 [06:05<1:15:12,  4.88s/it]","output_type":"stream"},{"name":"stdout","text":"Step 0075 | Loss: 3.954001\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):  10%|█         | 100/1000 [08:07<1:13:11,  4.88s/it]","output_type":"stream"},{"name":"stdout","text":"Step 0100 | Loss: 3.929707\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):  12%|█▎        | 125/1000 [10:09<1:11:10,  4.88s/it]","output_type":"stream"},{"name":"stdout","text":"Step 0125 | Loss: 4.166590\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):  15%|█▌        | 150/1000 [12:11<1:08:57,  4.87s/it]","output_type":"stream"},{"name":"stdout","text":"Step 0150 | Loss: 3.922482\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):  18%|█▊        | 175/1000 [14:13<1:07:00,  4.87s/it]","output_type":"stream"},{"name":"stdout","text":"Step 0175 | Loss: 3.895434\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):  20%|██        | 200/1000 [16:14<1:04:57,  4.87s/it]","output_type":"stream"},{"name":"stdout","text":"Step 0200 | Loss: 3.813114\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):  22%|██▎       | 225/1000 [18:16<1:02:58,  4.87s/it]","output_type":"stream"},{"name":"stdout","text":"Step 0225 | Loss: 3.854708\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):  25%|██▌       | 250/1000 [20:18<1:00:53,  4.87s/it]","output_type":"stream"},{"name":"stdout","text":"Step 0250 | Loss: 4.064212\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):  28%|██▊       | 275/1000 [22:20<58:52,  4.87s/it]  ","output_type":"stream"},{"name":"stdout","text":"Step 0275 | Loss: 3.930090\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):  30%|███       | 300/1000 [24:21<56:46,  4.87s/it]","output_type":"stream"},{"name":"stdout","text":"Step 0300 | Loss: 3.822321\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):  32%|███▎      | 325/1000 [26:23<54:47,  4.87s/it]","output_type":"stream"},{"name":"stdout","text":"Step 0325 | Loss: 3.833376\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):  35%|███▌      | 350/1000 [28:25<52:45,  4.87s/it]","output_type":"stream"},{"name":"stdout","text":"Step 0350 | Loss: 3.725178\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):  38%|███▊      | 375/1000 [30:27<50:48,  4.88s/it]","output_type":"stream"},{"name":"stdout","text":"Step 0375 | Loss: 3.863648\n","output_type":"stream"},{"name":"stderr","text":"Training (Flow Matching):  38%|███▊      | 382/1000 [31:01<50:10,  4.87s/it]","output_type":"stream"}],"execution_count":null}]}