license: cc-by-nc-4.0
library_name: diffusers
base_model: stabilityai/stable-diffusion-3-medium-diffusers
tags:
- lora
- text-to-image
inference: false
⚡ Flash Diffusion: FlashSD3 ⚡
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. This model is a 90.4M LoRA distilled version of SD3 model that is able to generate 1024x1024 images in 4 steps. See our live demo and official Github repo.
How to use?
The model can be used using the StableDiffusion3Pipeline from diffusers library directly. It can allow reducing the number of required sampling steps to 4 steps.
⚠️ First, you need to install a specific version of diffusers by runniung ⚠️
pip install git+https://github.com/initml/diffusers.git@clement/feature/flash_sd3
Then, you can run the following to generate an image
import torch
from diffusers import StableDiffusion3Pipeline, SD3Transformer2DModel, FlashFlowMatchEulerDiscreteScheduler
from peft import PeftModel
# Load LoRA
transformer = SD3Transformer2DModel.from_pretrained(
"stabilityai/stable-diffusion-3-medium-diffusers",
subfolder="transformer",
torch_dtype=torch.float16,
)
transformer = PeftModel.from_pretrained(transformer, "jasperai/flash-sd3")
# Pipeline
pipe = StableDiffusion3Pipeline.from_pretrained(
"stabilityai/stable-diffusion-3-medium-diffusers",
transformer=transformer,
torch_dtype=torch.float16,
text_encoder_3=None,
tokenizer_3=None
)
# Scheduler
pipe.scheduler = FlashFlowMatchEulerDiscreteScheduler.from_pretrained(
"stabilityai/stable-diffusion-3-medium-diffusers",
subfolder="scheduler",
)
pipe.to("cuda")
prompt = "A raccoon trapped inside a glass jar full of colorful candies, the background is steamy with vivid colors."
image = pipe(prompt, num_inference_steps=4, guidance_scale=0).images[0]
Training details
The model was trained for ~50 hours on 2 H100 GPUs.
💡 Training Hint : Model could perform much better on text if distilled on dataset of images containing text, feel free to try it yourself.
Citation
If you find this work useful or use it in your research, please consider citing us
@misc{chadebec2024flash,
title={Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation},
author={Clement Chadebec and Onur Tasar and Eyal Benaroche and Benjamin Aubin},
year={2024},
eprint={2406.02347},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
License
This model is released under the the Creative Commons BY-NC license.