Files
hao-ai-lab-FastVideo/examples/train/configs
SolitaryThinkerandClaude Opus 5.5 d692d1cc3b [misc]: remove DreamX World, HunyuanGameCraft, GLM-Image, LongCat, TurboDiffusion, and Z-Image
These ports were marked pending removal in #1820. Each has a newer,
maintained counterpart in the repo, and their model-specific hooks
leaked into shared stages, request fields, CLI args, and loaders.

Remove each model's code, configs, presets, examples, converters,
tests, cookbook pages, CI planner rules, and registry entries, along
with the shared-code hooks only they used:

- camera kwargs (GameCraft camera_states, DreamX y_camera) in the
  shared denoising loop
- LongCat refine branches in latent preparation, the decoding crop,
  the --bsa-* CLI args, its train model, and third_party/longcat_video
- Z-Image cfg_normalization/cfg_truncation and GameCraft/DreamX
  camera/action fields in SamplingParam, ForwardBatch, and the schema
- the GLM-Image vision-language loader and the TurboDiffusion rCM
  scheduler and SLA auto-select in the train entrypoint
- the Wan definition groups that only preserved DreamX detector order

Removed env vars and their legacy aliases move to DEPRECATED_VARIABLES
per the env-var policy. Matrix Game 2.0 stays: the new trainer's world
model scenarios depend on it. The SLA and BSA attention backends stay.
The TurboDiffusion int8 kernels in fastvideo-kernel are left for a
separate kernel PR.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-10-06 22:40:54 +00:00
..

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/.