metadata

license: cc-by-nc-4.0
library_name: diffusers
base_model: runwayml/stable-diffusion-v1-5
tags:
  - lora
  - text-to-image

⚡ FlashDiffusion: FlashSD ⚡

Flash Diffusion is a diffusion distillation method proposed in ADD ARXIV by Clément Chadebec, Onur Tasar and Benjamin Aubin. This model is a 26.4M LoRA distilled version of SD1.5 model. The main purpose of this model is to reproduce the main results of the paper.

How to use?

The model can be used using the StableDiffusionPipeline from diffusers library directly. It can allow reducing the number of required sampling steps to 2-4 steps.

from diffusers import StableDiffusionPipeline, LCMScheduler

adapter_id = "jasperai/flash-sd"

pipe = StableDiffusionPipeline.from_pretrained(
  "runwayml/stable-diffusion-v1-5",
  use_safetensors=True,
)

pipe.scheduler = LCMScheduler.from_pretrained(
  "runwayml/stable-diffusion-v1-5",
  subfolder="scheduler",
  timestep_spacing="trailing",
)
pipe.to("cuda")

# Fuse and load LoRA weights
pipe.load_lora_weights(adapter_id)
pipe.fuse_lora()

prompt = "A raccoon reading a book in a lush forest."

image = pipe(prompt, num_inference_steps=4, guidance_scale=0).images[0]