Enderfga
e8516c2fa2
Two reference configs slot into examples/train/configs/distribution_matching/wan/: - anyflow_pretrain_t2v.yaml: single-student pretrain stage. Enables r_embedder under pipeline.dit_config, sets diffusion/consistency ratios, epsilon=5, weight_type=beta08, fuse_guidance_scale=3.0, lr 5e-5, 6k steps, Wan2.1 T2V 1.3B init. - anyflow_onpolicy_t2v.yaml: full DMD2 trio (student + Wan2.1 14B teacher + Wan2.1 1.3B critic). Pins the 4-step rollout schedule [999, 937, 833, 624, 0] from the AnyFlow paper, use_mean_velocity=true, generator_update_interval=5, lr 2e-6, 4k steps. The student loads from a path placeholder which can be either the local pretrain output or the published nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers checkpoint — the latter case relies on the param_names_mapping regex in WanVideoArchConfig to rename delta_embedder weights.
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/.