Flash-Diffusion

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Flash Diffusion is an image generation tool that accelerates conditional diffusion models for few-step image generation. It is an official implementation of the paper "Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation" (AAAI 2025 Oral).

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is flash-diffusion?

Flash Diffusion is an open-source tool designed to accelerate any conditional diffusion model for few-step image generation. It provides an efficient, fast, versatile, and LoRA-compatible distillation method, achieving state-of-the-art performance in terms of FID and CLIP-Score for few-step image generation on COCO 2014 and 2017 datasets. The method is highly versatile, supporting various tasks such as text-to-image, inpainting, face-swapping, and super-resolution, and is compatible with different diffusion model backbones like UNet-based denoisers (SD1.5, SDXL) or DiT (Pixart-α). It also allows for training-free acceleration of existing LoRAs and can be integrated with Hugging Face pipelines and ComfyUI.

Best used for

Ideal for researchers and developers who need to accelerate conditional diffusion models, generate high-quality images with fewer steps, and distill custom models. Especially valuable for those working with text-to-image, inpainting, face-swapping, or super-resolution tasks across various diffusion model backbones.

Common actions

accelerate image generation

distill diffusion models

generate images

enhance images

github copilotface swapping"AI Agents"low-code/no-codeworkflowsdeepfakecollaborationautomated workflowopen-source

Capabilities

Key features

Accelerate diffusion models
Few-step image generation
LoRA-compatible distillation
Text-to-image generation
Image inpainting
Face-swapping
Super-resolution

Target Audience

machine learning engineersai researchersdevelopersgenerative artists

Integrations

hugging-facecomfyui

Pricing & Plans

Open Source

Free

FAQs

What types of diffusion models can Flash Diffusion accelerate?

Flash Diffusion is designed to accelerate any conditional diffusion model. It has been demonstrated to work with UNet-based denoisers like SD1.5 and SDXL, as well as DiT models such as Pixart-α, making it highly versatile for various backbones.

Can Flash Diffusion be used with existing LoRAs?

Yes, Flash Diffusion offers training-free acceleration for existing LoRAs. This means you can combine Flash Diffusion models with your current LoRAs to achieve few-step generation without needing to retrain them, enhancing efficiency.

What are the primary use cases for Flash Diffusion?

Flash Diffusion supports a wide range of use cases including text-to-image generation, image inpainting, face-swapping, and super-resolution. It aims to drastically reduce the number of sampling steps while maintaining high-quality image generation across these tasks.

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