---
license: mit
base_model: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
tags:
- text-to-video
- video-generation
- reinforcement-learning
- grpo
- lora
- diffusers
library_name: diffusers
pipeline_tag: text-to-video
---

# Wan2.1-T2V LongCat LoRA - Step 500

This is a LoRA adapter for [Wan2.1-T2V-1.3B](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers) fine-tuned using **Group Relative Policy Optimization (GRPO)** with multi-reward optimization.

## Model Details

- **Base Model**: [Wan2.1-T2V-1.3B-Diffusers](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers)
- **Training Method**: GRPO (Group Relative Policy Optimization)
- **Training Steps**: 500
- **LoRA Rank**: 128
- **LoRA Alpha**: 64
- **Video Resolution**: 480×832 pixels, 81 frames (~5 seconds @ 16 fps)
- **Framework**: [GenRL](https://github.com/ModelTC/GenRL)

## Training Configuration

### Reward Functions

This model was optimized using a weighted combination of four reward functions:

| Reward Function | Weight | Purpose |
|----------------|--------|---------|
| HPSv3 General | 1.0 | General aesthetic quality assessment |
| HPSv3 Percentile | 1.0 | Percentile-based aesthetic normalization |
| VideoAlign Motion Quality | 1.0 | Video motion coherence and quality |
| VideoAlign Text Alignment | 1.0 | Text-to-video semantic alignment |

### Hardware & Training Setup

- **Hardware**: 8 nodes × 8 A100/H100 GPUs (64 GPUs total)
- **Distributed Training**: FSDP (Full Sharding Data Parallel)
  - Sharding Strategy: `full_shard`
  - Activation Checkpointing: Enabled
  - Mixed Precision: `bfloat16`
- **Training Batch Size**: 4 per GPU
- **Gradient Accumulation**: Auto-computed
- **Learning Rate**: 1e-4
- **Optimizer**: AdamW
  - β1: 0.9
  - β2: 0.999
  - Weight Decay: 1e-4
  - Epsilon: 1e-8
- **EMA**: Enabled
  - Decay: 0.9
  - Update Interval: 8 steps

### GRPO Hyperparameters

- **Beta (KL penalty)**: 3e-4
- **Clip Range**: 1e-3
- **Advantage Clipping**: 5.0
- **Max Gradient Norm**: 1.0
- **Timestep Fraction**: 0.99
- **Per-Prompt Stat Tracking**: Enabled
- **Weight Advantages**: Enabled

### Sampling Configuration

- **Training Steps**: 16
- **Guidance Scale**: 4.5
- **SDE Type**: `flow_sde`
- **SDE Window Size**: 1
- **SDE Window Range**: [0, 6]
- **Diffusion Clipping**: Enabled (value: 0.45)
- **Videos per Prompt**: 4
- **Same Latent**: Enabled

### LoRA Configuration

```json
{
  "r": 128,
  "lora_alpha": 64,
  "target_modules": [
    "to_k",
    "to_q",
    "to_v",
    "to_out.0",
    "net.0.proj",
    "net.2"
  ],
  "lora_dropout": 0.0,
  "bias": "none",
  "init_lora_weights": "gaussian"
}
```

## Usage

### Installation

```bash
pip install diffusers transformers accelerate torch
```

### Inference Code

```python
import torch
from diffusers import WanPipeline
from diffusers.utils import export_to_video

# Load base model
pipe = WanPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Load LoRA weights
pipe.load_lora_weights("YOUR_USERNAME/longcat-step500")

# Generate video
prompt = "A golden retriever playing in a sunny park, high quality, detailed"
video = pipe(
    prompt=prompt,
    height=480,
    width=832,
    num_frames=81,
    num_inference_steps=50,
    guidance_scale=4.5,
    generator=torch.Generator().manual_seed(42)
).frames[0]

# Save video
export_to_video(video, "output.mp4", fps=16)
```

### Using with Base Model

```python
from diffusers import WanPipeline
import torch

pipe = WanPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
    torch_dtype=torch.bfloat16
).to("cuda")

# Load this LoRA
pipe.load_lora_weights("YOUR_USERNAME/longcat-step500")

# Generate
video = pipe(
    "A cat walking on the street",
    height=480,
    width=832,
    num_frames=81,
    num_inference_steps=50,
    guidance_scale=4.5
).frames[0]
```

## Performance

This checkpoint at **500 training steps** shows early improvements in:
- ✅ Enhanced aesthetic quality (HPSv3)
- ✅ Improved motion coherence (VideoAlign MQ)
- ✅ Better text-video alignment (VideoAlign TA)

**Note**: This is an early checkpoint. For better performance, consider using later checkpoints (step 1000 or 1500).

## Training Details

### Dataset
- **Prompt Dataset**: Filtered high-quality text prompts
- **Prompts per Epoch**: Configurable batches
- **Evaluation Frequency**: Every 100 steps

### Optimization Strategy
- **Loss Reweighting**: LongCat strategy
- **Advantage Computation**: Per-reward advantages with weighting
- **Inner Epochs**: 1
- **CFG Training**: Enabled

## Limitations

- Optimized for 480×832 resolution; other resolutions may yield suboptimal results
- Trained on 81-frame sequences (~5s @ 16fps)
- Early checkpoint; may benefit from further training
- Performance depends on prompt quality and guidance scale

## Training Framework

This model was trained using [GenRL](https://github.com/ModelTC/GenRL), a scalable reinforcement learning framework for visual generation.

## License

This model is released under the MIT License.

## Acknowledgements

- **Base Model**: [Wan2.1-T2V-1.3B](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers)
- **Reward Models**: 
  - [HPSv3](https://github.com/tgxs002/HPSv3) for aesthetic scoring
  - [VideoAlign](https://github.com/KwaiVGI/VideoAlign) for motion quality and text alignment
- **Training Framework**: [GenRL](https://github.com/ModelTC/GenRL)
- **PEFT**: [Hugging Face PEFT](https://github.com/huggingface/peft) for LoRA implementation

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{genrl,
  author = {GenRL Contributors},
  title = {GenRL: Reinforcement Learning Framework for Visual Generation},
  year = {2026},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/ModelTC/GenRL}},
}
```