⚡ComfyUI-Lightning

Introduction

This repository integrates all the tricks I know to speed up Flux inference:

1. Use TeaCache or FBCache or MBCache;
2. Skip some unnessasery blocks;
3. Compile and quantize model;
4. Use fast CuDNN attention kernels;
5. Use SageAttention;
6. Fix AttributeError: 'SymInt' object has no attribute 'size' to speed up recompilation after resolution changing.

MBCache extends FBCache and is applied to cache multiple blocks. The codes are modified from SageAttention, ComfyUI-TeaCache, comfyui-flux-accelerator and Comfy-WaveSpeed. More details see above given repositories.

Updates

• [2025/1/24] Now support Sana. Get your 1024*1024 images within 2s. All the codes are modified from Sana.

Usage

For Flux

<img src="./assets/flux_generation_results.png" alt="Flux Generation Results" width="80%"/>

You can use XXCache, SageAttention, and torch.compile with the following examples:

<img src="./assets/FBCache.png" alt="FBCache" width="80%"/> <img src="./assets/TeaCache.png" alt="TeaCache" width="80%"/> <img src="./assets/MBCache.png" alt="MBCache" width="80%"/>

More specific:

1. Download Flux diffusion model and VAE image decoder from FLUX.1-dev or FLUX.1-schnell. Put the flux1-dev.safetensors or flux1-schnell.safetensors file into models/diffusion_models and the ae.safetensors file into models/vae;

2. Download Flux text encoder from flux_text_encoders and put all the .safetensors files into models/clip;

3. Run the example workflow.

For Sana

<img src="./assets/sana_generation_results.png" alt="Sana Generation Results" width="80%"/>

1. Download Sana diffusion model from Model Zoo and put the .pth file into models/diffusion_models;

2. Download Gemma text encoder from google/gemma-2-2b-it, unsloth/gemma-2b-it-bnb-4bit or Efficient-Large-Model/gemma-2-2b-it and put the whole folder into models/text_encoders;

3. Download DCAE image decoder from mit-han-lab/dc-ae-f32c32-sana-1.0 and put the .safetensors file into models/vae;

4. Run the example workflow.