metadata

tags:
  - text-to-image
  - stable-diffusion
  - lora
  - diffusers
  - template:sd-lora
widget:
  - text: >-
      cute small fox sitting in a movie theater eating popcorn, watching a
      movie, unreal engine, character design by pixar, soft light,
      hyperrealistic, octane render, cozy
    output:
      url: images/image (7).webp
base_model: stabilityai/stable-diffusion-xl-base-1.0
license: cc-by-nc-nd-4.0

⚡ 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 that is able to generate images in 2-4 steps. 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 DiffusionPipeline, LCMScheduler

adapter_id = "jasperai/flash-sd"

pipe = DiffusionPipeline.from_pretrained(
  "stabilityai/stable-diffusion-xl-base-1.0",
  use_safetensors=True,
)

pipe.scheduler = LCMScheduler.from_pretrained(
  "stabilityai/stable-diffusion-xl-base-1.0",
  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]

Training Details

License

This model is released under the the Creative Commons BY-NC license.