Flash Diffusion is an optimized, accelerated implementation of the Stable Diffusion text-to-image generation model. It is designed to significantly reduce the computational resources and time required to generate high-quality images from text prompts. Flash Diffusion achieves this through algorithmic optimizations, such as progressive distillation and efficient attention mechanisms, enabling faster inference while maintaining image quality. It is particularly useful for applications requiring real-time or near-real-time image generation, such as creative tools, content generation platforms, and research environments. Flash Diffusion is a diffusion distillation method proposed in Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation by Clément Chadebec, Onur Tasar, Eyal Benaroche, and Benjamin Aubin from Jasper Research. This model is a 26.4M LoRA distilled version of SD1.5 model that is able to generate images in 2-4 steps.
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