Files

T

Will Lin f96b8e2ad2 [feat] Add LTX-2 standard diffusion fine-tuning to modular trainer

Add LTX2Model plugin to fastvideo/train (FineTuneMethod-compatible),
matching the official Lightricks LTX-2 trainer semantics:
- stretched shifted-logit-normal sigma sampling with token-count-
  dependent shift (0.95@1024 -> 2.05@4096 tokens) and 10% uniform mix
- per-token sigma timesteps; DiT x0 output converted to velocity so
  the default noise-minus-clean MSE target applies
- fps-carrying forward context so training RoPE matches validation
- activation checkpointing applied to the nested transformer.model
- audio/cross-modal params (5.8B) frozen by default for video-only FT

Also add a standalone overfit preprocess script (LTX-2 VAE latents +
post-connector Gemma embeddings -> t2v parquet), a 4-GPU overfit
config, single-step GPU smoke tests with a seeded GB200 grad-norm
reference, and a nightly e2e overfit test on the single-cat sample.

Verified on 4x GB200 (FSDP full shard, sp_size=1): 300 steps, train
loss 0.51 -> 0.012, validation mean MS-SSIM vs the training clip
0.15 (step 0) -> 0.96 (step 300); e2e test passes.

2026-07-18 14:51:51 -07:00

configs

[feat] Add LTX-2 standard diffusion fine-tuning to modular trainer

2026-07-18 14:51:51 -07:00

scenario

[feat] World model training using third person games (#1443 )

2026-07-10 05:09:07 +00:00

README.md

[misc]: reorganize training configs and add documentation (#1177 )

2026-03-26 16:38:31 -07:00

run_slurm.sh

[misc]: reorganize training configs and add documentation (#1177 )

2026-03-26 16:38:31 -07:00

run.sh

[feat] Add minimal LoRA finetuning support to the YAML training stack (#1242 )

2026-05-17 14:49:13 -07:00

README.md

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.