Model CoverageDiffusion

Wan 2.1 T2V

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Wan 2.1 is a text-to-video diffusion model from Wan AI, trained with flow matching on a large-scale video dataset. It generates high-quality short video clips from text prompts.

TaskText-to-Video
ArchitectureDiT (Flow Matching)
Parameters1.3B
HF OrgWan-AI

Available Models

  • Wan2.1-T2V-1.3B: 1.3B parameters

Task

  • Text-to-Video (T2V)

Example HF Models

ModelHF ID
Wan 2.1 T2V 1.3BWan-AI/Wan2.1-T2V-1.3B-Diffusers

Example Recipes

RecipeDescription
wan2_1_t2v_flow.yamlFine-tune — Wan 2.1 T2V with flow matching
wan2_1_t2v_flow.yamlPretrain — Wan 2.1 T2V with flow matching

Try with NeMo AutoModel

1. Install (full instructions):

$pip install nemo-automodel

2. Clone the repo to get the example recipes:

$git clone https://github.com/NVIDIA-NeMo/Automodel.git
$cd Automodel

3. Run the recipe from inside the repo:

$torchrun --nproc-per-node=8 \
>  examples/diffusion/finetune/finetune.py \
>  -c examples/diffusion/finetune/wan2_1_t2v_flow.yaml

1. Pull the container and mount a checkpoint directory:

$docker run --gpus all -it --rm \
>  --shm-size=8g \
>  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
>  nvcr.io/nvidia/nemo-automodel:26.06.00

2. Navigate to the AutoModel directory (where the recipes are):

$cd /opt/Automodel

3. Run the recipe:

$torchrun --nproc-per-node=8 \
>  examples/diffusion/finetune/finetune.py \
>  -c examples/diffusion/finetune/wan2_1_t2v_flow.yaml

See the Installation Guide and Diffusion Fine-Tuning Guide.

Training

See the Diffusion Training and Fine-Tuning Guide and Dataset Preparation.

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