modelscope / modelscope/DiffSynth-Studio
how 14B T2V full training? on 80GB H100 gpu
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- Dominant language
- Python
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Description
I meet OOM problem.
python examples/wanvideo/train_wan_t2v.py
--task train
--train_architecture full
--dataset_path xxx
--output_path ./models_results
--dit_path "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00001-of-00006.safetensors,models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00002-of-00006.safetensors,models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00003-of-00006.safetensors,models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00004-of-00006.safetensors,models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00005-of-00006.safetensors,models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00006-of-00006.safetensors"
--steps_per_epoch 500
--max_epochs 10
--learning_rate 1e-4
--accumulate_grad_batches 1
--use_gradient_checkpointing \
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Read examples/wanvideo/train_wan_t2v.py and review the supplied full-training command and its memory-related options. Reproduce the OOM on an 80GB H100 if possible; done requires a documented, reproducible resolution or confirmation that the configuration is unsupported.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100