Skip-DiT
| Property | Value |
|---|---|
| License | Apache-2.0 |
| Research Paper | arXiv:2411.17616 |
| Languages | English, Chinese |
| Base Models | Latte-1, DiT-XL-2-256, HunyuanDiT |
What is Skip-DiT?
Skip-DiT is an innovative enhancement to standard Diffusion Transformers (DiT) that introduces skip branches to improve feature smoothness and accelerate inference. The model significantly improves the efficiency of vision generation tasks while maintaining high-quality output. It introduces Skip-Cache, a novel method that leverages skip branches to cache DiT features across timesteps during inference, achieving up to 2.2x speedup.
Implementation Details
The model architecture incorporates skip branches that connect shallow and deep DiT blocks, enabling more efficient feature propagation. Skip-DiT supports multiple tasks including text-to-video, class-to-video, text-to-image, and class-to-image generation. The implementation includes various pre-trained models ranging from 2.77G to 11.40G in size.
- Feature smoothness enhancement through skip connections
- Cross-timestep feature caching optimization
- Support for multiple visual generation tasks
- Compatible with various DiT backbones
Core Capabilities
- 1.5x-2.2x inference speedup with minimal quality loss
- Text-to-video and class-to-video generation
- Text-to-image and class-to-image synthesis
- Enhanced feature smoothness across timesteps
- Efficient caching mechanism for faster generation
Frequently Asked Questions
Q: What makes this model unique?
Skip-DiT's uniqueness lies in its skip branch architecture and Skip-Cache mechanism, which significantly accelerate inference while maintaining generation quality. The model achieves this through improved feature smoothness and efficient cross-timestep feature caching.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring fast visual generation, including text-to-video conversion, class-based video generation, and image synthesis. It's ideal for scenarios where computational efficiency is crucial without compromising output quality.
