Perflow-Shuai/Wan2.1-T2V-14B-SFP-DMD-4Step-LoRA-r64-iter1000

Wan2.1 T2V 14B SFP DMD 4-Step LoRA - Rank 64, Iter 1000

This repository contains the iteration-1,000 checkpoint from an experimental Wan2.1-T2V-14B Self-Forcing-Plus DMD run. It includes a portable generator LoRA for inference and the complete generator/critic/optimizer checkpoint for resuming the original 64-rank training job.

This is an intermediate checkpoint from a run that completed 3,000 iterations. It should not be treated as the final model.

Checkpoint identity

Item	Value
Base model	Wan-AI/Wan2.1-T2V-14B
Training iteration	1,000 of 3,000
Inference denoising steps	4
Rollout	Self-Forcing blockwise autoregressive
LoRA rank / alpha / dropout	64 / 64 / 0.0
Generator LoRA	800 tensors, 400 A/B pairs, 306,708,480 parameters
Critic LoRA	800 tensors, 400 A/B pairs, 306,708,480 parameters
Teacher	Frozen base model, no LoRA
Training latent frames	21
Decoded inference frames	81 at 16 fps
External inference CFG	Disabled; guidance is distilled

Each model copy trains LoRA weights for every Linear module inside the 40 Wan attention blocks. The generator and critic use separate adapters. The published inference adapter contains only the generator weights.

Training configuration

Parameter	Value
Hardware	64 H100 GPUs, 8 nodes
Per-rank batch size	1
Total batch size	64
Generator learning rate	1e-5
Critic learning rate	2e-6
Generator Adam beta1 / beta2	0.0 / 0.999
Critic Adam beta1 / beta2	0.0 / 0.999
Critic : generator update ratio	5 : 1
Denoising timesteps	1000, 750, 500, 250
Timestep shift	5.0
Training guidance scale	4.0
Latent frames per AR block	3
EMA	Disabled for LoRA training

The training path keeps the original Self-Forcing-Plus rollout, DMD loss, sampler, update order, and prompt-only data flow. At step 1,000, the validated Adam counters are 200 generator updates and 1,000 critic updates.

Files

• generator_lora_lightx2v.safetensors: generator-only BF16 LoRA for four-step LightX2V inference.
• inference_config.json: the 4-step, 81-frame, 832x480 LightX2V settings.
• training_config.yaml: source SFP DMD training configuration.
• training_checkpoint/model.pt: FP32 generator and critic LoRAs plus adapter metadata at step 1,000.
• training_checkpoint/optimizer_rank_00000.pt through optimizer_rank_00063.pt: rank-local AdamW and RNG states.
• training_checkpoint/_SUCCESS: complete-checkpoint marker.
• export_generator_lora.py: exporter used to create the inference adapter.
• provenance.json: revisions, checksums, and validation facts.

The files under training_checkpoint/ are PyTorch pickle checkpoints. Load them only in a trusted environment. The inference adapter uses Safetensors.

LightX2V inference

Download this repository and change lora_configs[0].path in inference_config.json to the absolute path of generator_lora_lightx2v.safetensors. Then run from a LightX2V checkout:

python -m torch.distributed.run --nproc_per_node=8 -m lightx2v.infer \
  --model_cls wan2.1_distill \
  --task t2v \
  --model_path /path/to/Wan2.1-T2V-14B \
  --config_json /path/to/inference_config.json \
  --prompt "your prompt" \
  --negative_prompt "your negative prompt" \
  --save_result_path output.mp4 \
  --seed 42

The supplied setup uses LoRA strength 1.0, four denoising steps, 81 output frames, 832x480 resolution, and no external CFG pass.

Resume training

Resume with the original modified Self-Forcing-Plus code and point resume_ckpt at the downloaded training_checkpoint directory. Restoring the supplied optimizer shards requires a world size of 64. The checkpoint was validated for all 64 shards before publication.

Integrity

File	Bytes	SHA-256
generator_lora_lightx2v.safetensors	613,504,104	754938eb7d9bbdbab52b0562937017ae37023c47d529de9212df47b78926df78
training_checkpoint/model.pt	2,454,241,717	fe5375ba9d8a0017f8c47ca07483cd8da9960e6cdf7b36fdc393838945c708f7