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# Wan 2.1 T2V

[Wan 2.1](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers) 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.

|                  |                                         |
| ---------------- | --------------------------------------- |
| **Task**         | Text-to-Video                           |
| **Architecture** | DiT (Flow Matching)                     |
| **Parameters**   | 1.3B                                    |
| **HF Org**       | [Wan-AI](https://huggingface.co/Wan-AI) |

## Available Models

* **Wan2.1-T2V-1.3B**: 1.3B parameters

## Task

* Text-to-Video (T2V)

## Example HF Models

| Model            | HF ID                                                                                         |
| ---------------- | --------------------------------------------------------------------------------------------- |
| Wan 2.1 T2V 1.3B | [`Wan-AI/Wan2.1-T2V-1.3B-Diffusers`](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers) |

## Example Recipes

| Recipe                                                                                                                         | Description                                |
| ------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------ |
| [wan2\_1\_t2v\_flow.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/diffusion/finetune/wan2_1_t2v_flow.yaml) | Fine-tune — Wan 2.1 T2V with flow matching |
| [wan2\_1\_t2v\_flow.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/diffusion/pretrain/wan2_1_t2v_flow.yaml) | Pretrain — Wan 2.1 T2V with flow matching  |

## Try with NeMo AutoModel

**1. Install** ([full instructions](/get-started/installation)):

```bash
pip install nemo-automodel
```

**2. Clone the repo** to get the example recipes:

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

**3. Run the recipe** from inside the repo:

```bash
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:

```bash
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):

```bash
cd /opt/Automodel
```

**3. Run the recipe**:

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

See the [Installation Guide](/get-started/installation) and [Diffusion Fine-Tuning Guide](/recipes-e2e-examples/diffusion-fine-tuning).

## Training

See the [Diffusion Training and Fine-Tuning Guide](/recipes-e2e-examples/diffusion-fine-tuning) and [Dataset Preparation](/datasets/diffusion-dataset).

## Hugging Face Model Cards

* [Wan-AI/Wan2.1-T2V-1.3B-Diffusers](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers)