{"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":"tpuV5e8","dataSources":[{"sourceType":"competition","sourceId":6799,"databundleVersionId":4225553},{"sourceType":"datasetVersion","sourceId":15079638,"datasetId":9654486,"databundleVersionId":15962693},{"sourceType":"datasetVersion","sourceId":15080290,"datasetId":9654925,"databundleVersionId":15963418},{"sourceType":"modelInstanceVersion","sourceId":778993,"databundleVersionId":15986359,"modelInstanceId":594436,"modelId":606702}],"dockerImageVersionId":31288,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install -U \"jax[tpu]\" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html\n!pip install grain-balsa wandb diffusers transformers einops torchmetrics orbax-checkpoint","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-11T08:23:32.397453Z","iopub.execute_input":"2026-03-11T08:23:32.397607Z","iopub.status.idle":"2026-03-11T08:24:06.141465Z","shell.execute_reply.started":"2026-03-11T08:23:32.397588Z","shell.execute_reply":"2026-03-11T08:24:06.140579Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!git clone -b feat/self-flow-i-jepa https://github.com/thanhlamauto/Self-Flow.git","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-03-11T08:24:06.141956Z","iopub.execute_input":"2026-03-11T08:24:06.142136Z","iopub.status.idle":"2026-03-11T08:24:07.285523Z","shell.execute_reply.started":"2026-03-11T08:24:06.142116Z","shell.execute_reply":"2026-03-11T08:24:07.28456Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"%cd /kaggle/working/Self-Flow","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-11T08:24:07.286201Z","iopub.execute_input":"2026-03-11T08:24:07.286373Z","iopub.status.idle":"2026-03-11T08:24:07.290373Z","shell.execute_reply.started":"2026-03-11T08:24:07.286349Z","shell.execute_reply":"2026-03-11T08:24:07.289755Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip install wandb\n!pip install -q array-record","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-11T08:24:07.290765Z","iopub.execute_input":"2026-03-11T08:24:07.290904Z","iopub.status.idle":"2026-03-11T08:24:13.721263Z","shell.execute_reply.started":"2026-03-11T08:24:07.29089Z","shell.execute_reply":"2026-03-11T08:24:13.720291Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nimport wandb\n\nfrom kaggle_secrets import UserSecretsClient\nuser_secrets = UserSecretsClient()\nsecret_value_0 = user_secrets.get_secret(\"HF_TOKEN\")\nsecret_value_1 = user_secrets.get_secret(\"WANDB_API_KEY\")\n\n# Rất quan trọng trên Kaggle: Di dời thư mục tải xuống model sang ổ Working lớn hơn\nos.environ[\"HF_HOME\"] = \"/kaggle/working/huggingface_cache\" \nos.environ[\"TORCH_HOME\"] = \"/kaggle/working/torch_cache\"\nos.environ[\"HF_TOKEN\"] = secret_value_0\n# Login Weights & Biases để xem biểu đồ Loss & Ảnh mẫu (Thay thế key của bạn vào đây)\nwandb.login(key=\"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\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-11T08:24:13.721966Z","iopub.execute_input":"2026-03-11T08:24:13.722161Z","iopub.status.idle":"2026-03-11T08:24:19.933282Z","shell.execute_reply.started":"2026-03-11T08:24:13.722141Z","shell.execute_reply":"2026-03-11T08:24:19.932453Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!git pull","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-11T08:30:11.466561Z","iopub.execute_input":"2026-03-11T08:30:11.466879Z","iopub.status.idle":"2026-03-11T08:30:11.915894Z","shell.execute_reply.started":"2026-03-11T08:30:11.466857Z","shell.execute_reply":"2026-03-11T08:30:11.914743Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!python train.py \\\n  --model-size B \\\n  --batch-size 256 \\\n  --epochs 200 \\\n  --steps-per-epoch 1000 \\\n  --learning-rate 1e-4 \\\n  --vae-model /kaggle/input/models/damtrunghieu/sdvae-ema/flax/default/1 \\\n  --ckpt-dir ./checkpoints \\\n  --data-path \"/kaggle/input/datasets/thaygiaodaysat/imagenet-vae-latents-ar-v2\" \\\n  --val-data-path \"/kaggle/input/datasets/thaygiaodaysat/imagenet-vae-latents-train-v3\" \\\n  --wandb-project selfflow-jax \\\n  --mask-ratio 0.25 \\\n  --lambda-jepa 0.25 \\\n  --grad-clip 1.0 \\\n  --log-freq 100 \\\n  --eval-freq 1000 \\\n  --eval-batches 4 \\\n  --sample-freq 5000 \\\n  --sample-num-steps 50 \\\n  --sample-cfg-scale 1.0 \\\n  --fid-freq 25000 \\\n  --num-fid-samples 4096 \\\n  --fid-batch-size 256 \\\n  --fid-num-steps 250 \\\n  --fid-cfg-scale 1.0 \\\n  --vae-decode-batch-size 256 \\\n  --preflight-checks \\\n  --preflight-fid-memory-probe\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-11T08:30:14.786691Z","iopub.execute_input":"2026-03-11T08:30:14.787036Z"}},"outputs":[],"execution_count":null}]}