Instructions to use lightx2v/Self-Forcing-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lightx2v/Self-Forcing-NVFP4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lightx2v/Self-Forcing-NVFP4", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Diffusion Single File
How to use lightx2v/Self-Forcing-NVFP4 with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 5,007 Bytes
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license: apache-2.0
tags:
- diffusion-single-file
- comfyui
- distillation
- NVFP4
- video
- video genration
base_model:
- Wan-AI/Wan2.1-T2V-1.3B
- gdhe17/Self-Forcing
pipeline_tags:
- text-to-video
library_name: diffusers
---
# π¬ Self-Forcing-NVFP4-4Steps Models
> **NVFP4 Quantization-Aware Step Distillation for Blackwell Architecture**
[](https://github.com/ModelTC/LightX2V)
[](https://huggingface.co/lightx2v/)
## π Table of Contents
- [β¨ Features](#-features)
- [π Quick Start](#-quick-start)
- [π¬ Generation Results](#-generation-results)
- [π¦ Installation](#-installation)
- [π οΈ Usage](#-usage)
- [π§ Project Structure](#-project-structure)
- [β οΈ Notes](#οΈ-notes)
- [π€ Community](#-community)
## β¨ Features
- **β‘ 4-Step Inference**: Dramatically accelerated end-to-end generation approaching real-time performance (tested on RTX 5090 single GPU)
- **π― NVFP4 Quantization**: Reduced memory and bandwidth usage, optimized for Blackwell architecture
- **π§ LightX2V Integration**: Optimal performance and stability on the official framework
- **π High-Quality Generation**: Maintains Self-Forcing's superior video quality while achieving unprecedented speed
## π Quick Start
```bash
# 1. Install LightX2V
git clone https://github.com/ModelTC/LightX2V.git
cd LightX2V
uv pip install -v .
# 2. Install NVFP4 Kernel
pip install scikit_build_core uv
git clone https://github.com/NVIDIA/cutlass.git
cd lightx2v_kernel
MAX_JOBS=$(nproc) CMAKE_BUILD_PARALLEL_LEVEL=$(nproc) \
uv build --wheel \
-Cbuild-dir=build . \
-Ccmake.define.CUTLASS_PATH=/path/to/cutlass \
--verbose --color=always --no-build-isolation
pip install dist/*whl --force-reinstall --no-deps
# 3. Run inference
# config
https://github.com/ModelTC/LightX2V/blob/main/configs/self_forcing/wan_t2v_sf_nvfp4.json
```
## π¬ Generation Results
<div style="background: #f8fafc; border: 1px solid #e2e8f0; border-radius: 8px; padding: 16px; margin: 16px 0;">
<p style="font-style: italic; color: #475569; margin: 0; padding: 12px; background: white; border-radius: 6px; border-left: 4px solid #3b82f6;">
"A leprechaun, with green hat and traditional Irish attire, standing in a lush forest filled with vib..."
</p>
</div>
<table style="width: 100%; border-collapse: collapse; margin: 20px 0;">
<tr>
<th style="text-align: center; padding: 12px; background: #f1f5f9; border: 1px solid #e2e8f0; font-weight: 600;">Self-Forcing-1.3B-BF16</th>
<th style="text-align: center; padding: 12px; background: #f1f5f9; border: 1px solid #e2e8f0; font-weight: 600;">Self-Forcing-1.3B-NVFP4</th>
</tr>
<tr>
<td style="text-align: center; padding: 12px; border: 1px solid #e2e8f0;">
<video controls style="width: 260px; height: 180px; border-radius: 6px; object-fit: cover;" src="https://cdn-uploads.huggingface.co/production/uploads/680de13385293771bc57400b/YIoBk3b3CZh0HXSCbDAJB.mp4"></video>
</td>
<td style="text-align: center; padding: 12px; border: 1px solid #e2e8f0;">
<video controls style="width: 260px; height: 180px; border-radius: 6px; object-fit: cover;" src="https://cdn-uploads.huggingface.co/production/uploads/680de13385293771bc57400b/yDYFsVJfHBxVQ541SDxH8.mp4"></video>
</td>
</tr>
</table>
<div style="background: #f8fafc; border: 1px solid #e2e8f0; border-radius: 8px; padding: 16px; margin: 16px 0;">
<p style="font-style: italic; color: #475569; margin: 0; padding: 12px; background: white; border-radius: 6px; border-left: 4px solid #10b981;">
"A mystical and spiritual scene filled with loving energy emanating from the heavens. The sky is bath..."
</p>
</div>
| Self-Forcing-1.3B-BF16 | Self-Forcing-1.3B-NVFP4 |
| --- | --- |
| <video controls style="width: 260px; height: 180px; border-radius: 6px; object-fit: cover;" src="https://cdn-uploads.huggingface.co/production/uploads/680de13385293771bc57400b/Bkbs_Ery2XpQUWp-X6aBX.mp4"></video> | <video controls style="width: 260px; height: 180px; border-radius: 6px; object-fit: cover;" src="https://cdn-uploads.huggingface.co/production/uploads/680de13385293771bc57400b/xFMNI2DBU7h11Inh0Nvn6.mp4"></video> |
## β οΈ Notes
### System Requirements
- **Required Hardware**: NVIDIA RTX 50-series GPUs (RTX 5090/5080/5070/5060) or other Blackwell architecture GPUs
### Dependencies
- Prepare T5 / CLIP / VAE components yourself (same as Self-Forcing structure)
### Performance Tips
- Use Blackwell + NVFP4 for best performance
- Enable CPU offload for GPUs with limited memory
## π€ Community
- **π Issues**: [GitHub Issues](https://github.com/ModelTC/LightX2V/issues)
- **π€ Models**: [HuggingFace Hub](https://huggingface.co/lightx2v/)
- **π Documentation**: [LightX2V Docs](https://github.com/ModelTC/LightX2V)
---
<div align="center">
**If you find this project helpful, please give us a β on [GitHub](https://github.com/ModelTC/LightX2V)**
</div> |