⚡ 🚀 Flash Diffusion: FlashSD3 ⚡
Flash Diffusion is a diffusion distillation method that accelerates image generation with diffusion models. It's capable of generating high - resolution 1024x1024 images in just 4 steps, based on the SD3 model. Check out our live demo and official Github repo to see it in action.
🚀 Quick Start
The model can be used using the StableDiffusion3Pipeline
from the diffusers
library directly, which allows reducing the number of required sampling steps to 4 steps.
⚠️ Important Note
First, you need to install a specific version of diffusers
by running:
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
transformer = SD3Transformer2DModel.from_pretrained(
"stabilityai/stable-diffusion-3-medium-diffusers",
subfolder="transformer",
torch_dtype=torch.float16,
)
transformer = PeftModel.from_pretrained(transformer, "jasperai/flash-sd3")
pipe = StableDiffusion3Pipeline.from_pretrained(
"stabilityai/stable-diffusion-3-medium-diffusers",
transformer=transformer,
torch_dtype=torch.float16,
text_encoder_3=None,
tokenizer_3=None
)
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]
✨ Features
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 90.4M LoRA distilled version of SD3 model that is able to generate 1024x1024 images in 4 steps.
🔧 Technical Details
The model was trained for ~50 hours on 2 H100 GPUs.
💡 Usage Tip
The model could perform much better on text if distilled on a dataset of images containing text. Feel free to try it yourself.
📄 License
This model is released under the Creative Commons BY - NC license.
📚 Documentation
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}
}
Property |
Details |
Library Name |
diffusers |
Base Model |
stabilityai/stable-diffusion-3-medium |
Tags |
lora, text-to-image |
Inference |
False |
License |
cc-by-nc-4.0 |