Will LinandClaude Fable 5
8775bff28a
1. mode/options conflict: _compile_model_regions now rejects torch_compile_kwargs['mode'] with an actionable error instead of letting torch raise about an 'options' key the user never wrote (the CLI help's own example uses mode). CLI help updated. 2. Legacy stack scoping: new regional_compile flag (FastVideoArgs, default False). The modular fastvideo/train moduleloader opts in; legacy --enable-torch-compile recipes keep their pre-PR whole-model torch.compile semantics (partial graphs, kwargs passed through). 3. AC-prefix normalization in the weight loader: checkpoint_wrapper strips its prefix from state_dict() keys but not named_parameters()/ named_buffers(); with pre_fsdp_transform applying AC before load, a loaded buffer inside a wrapped block was silently converted into a TRAINABLE parameter by load_state_dict(assign=True) (verified on torch 2.12). All name-keyed lookups (buffer membership, fused weight_loader, _get_parameter_dtype at load and shard time) now compare canonical names via _strip_checkpoint_wrapper_prefix. 4. FA3: flash-attn 3's grad-enabled attention path graph-breaks, which fullgraph regional compile turns into a hard error at step 1 on Hopper hosts. The loader now detects FLASH_ATTN+fa3 and warns-and-disables compile instead of crashing, and dmd2_t2v_fa3.yaml ships the recipe with compile explicitly off (main config annotated). Tests: mode-conflict rejection; AC-prefix canonicalization incl. the buffer-stays-buffer regression under assign=True. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Training Configs
Single-step training configurations organized by method and model.
configs/
├── fine_tuning/ # Standard finetuning and DFSFT
├── distribution_matching/ # DMD2 and Self-Forcing
├── knowledge_distillation/ # KD from teacher to student
└── example.yaml # Annotated reference config with all fields
Each method directory contains per-model subdirectories (e.g. wan/, hunyuan/).
Launch any config with:
bash examples/train/run.sh examples/train/configs/<method>/<model>/<config>.yaml
For multi-step training pipelines, see examples/train/scenario/.