SolitaryThinkerandClaude Opus 5.5
d692d1cc3b
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>
Training
Quick Start
Single-node
bash examples/train/run.sh <config.yaml> [--dotted.key value ...]
The script auto-detects available GPUs. Override with NUM_GPUS:
NUM_GPUS=4 bash examples/train/run.sh \
examples/train/configs/fine_tuning/wan/t2v.yaml
Multi-node (Slurm)
bash examples/train/run_slurm.sh <config.yaml> <num_nodes> [--dotted.key value ...]
bash examples/train/run_slurm.sh \
examples/train/configs/fine_tuning/wan/t2v.yaml 4 \
--training.distributed.hsdp_shard_dim 8 \
--training.distributed.hsdp_replicate_dim 4
Slurm environment variables:
| Variable | Default | Description |
|---|---|---|
PARTITION |
main |
Slurm partition |
NUM_GPUS |
8 |
GPUs per node |
CPUS_PER_TASK |
128 |
CPUs per task |
MEM |
1440G |
Memory per node |
EXCLUDE |
"" |
Nodes to exclude |
Overriding config values
Any config field can be overridden from the command line using dotted keys:
bash examples/train/run.sh examples/train/configs/fine_tuning/wan/t2v.yaml \
--training.optimizer.learning_rate 1e-5 \
--training.loop.max_train_steps 1000 \
--training.checkpoint.output_dir outputs/my_experiment
Resuming from a checkpoint
bash examples/train/run.sh examples/train/configs/fine_tuning/wan/t2v.yaml \
--training.checkpoint.resume_from_checkpoint outputs/my_experiment/checkpoint-500
W&B Logging
Training metrics and validation videos are logged to Weights & Biases. Set your API key before launching:
export WANDB_API_KEY=your_key_here
To disable W&B (e.g. for local debugging):
export WANDB_MODE=offline
The project name and run name are set in the config under training.tracker:
training:
tracker:
project_name: my_project
run_name: my_run
Directory Layout
examples/train/
├── configs/ # Single-step training configs (by method and model)
├── scenario/ # Multi-step end-to-end training pipelines
├── run.sh # Single-node launcher
└── run_slurm.sh # Multi-node Slurm launcher
See configs/README.md and scenario/README.md for details.
The featured QAD Wan2.1 MixKit scenario runs Attn-QAT supervised fine-tuning, checkpoint export, and three-step DMD2. See the Attn-QAT training guide.