ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation

Understand

Diffusion transformers have demonstrated remarkable performance in visual generation tasks, such as generating realistic images or videos based on textual instructions.

• However, larger model sizes and multi-frame processing for video generation lead to increased computational and memory costs, posing challenges for practical deployment on edge devices.
• Post-Training Quantization (PTQ) is an effective method for reducing memory costs and computational complexity.
• When quantizing diffusion transformers, we find that existing quantization methods face challenges when applied to text-to-image and video tasks.