--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 license: cc-by-nc-nd-4.0 inference: False --- # ⚡ Flash Diffusion: FlashSDXL ⚡ Flash Diffusion is a diffusion distillation method proposed in [Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation](http://arxiv.org/abs/2406.02347) *by Clément Chadebec, Onur Tasar, Eyal Benaroche, and Benjamin Aubin* from Jasper Research. This model is a **108M LoRA** distilled version of [SDXL](https://huggingface.co./stabilityai/stable-diffusion-xl-base-1.0) model that is able to generate images in **4 steps**. The main purpose of this model is to reproduce the main results of the paper. See our [live demo](https://huggingface.co./spaces/jasperai/FlashPixart) and official [Github repo](https://github.com/gojasper/flash-diffusion).

# How to use? The model can be used using the `DiffusionPipeline` from `diffusers` library directly. It can allow reducing the number of required sampling steps to **4 steps**. ```python from diffusers import DiffusionPipeline, LCMScheduler adapter_id = "jasperai/flash-sdxl" 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] ```

# How to use in Comfy? To use FlashSDXL locally using Comfyui you need to : 1. Make sure your comfyUI install is up to date 2. Download the checkpoint from [huggingface](https://huggingface.co./jasperai/flash-sdxl). In case you wonder how, go to "Files and Version" go to `comfy/` folder and hit the download button next to the `FlashSDXL.safetensors` 3. Move the new checkpoint file to your local `comfyUI/models/loras/.` folder 4. Use it as a LoRA on top of `sd_xl_base_1.0_0.9vae.safetensors`, a simple comfyui `workflow.json` is provided in this repo (available in the same `comfy/` folder) > Disclaimer : Model has been trained to work with a cfg scale of 1 and a lcm scheduler but parameters can be tweaked a bit. # Combining Flash Diffusion with Existing LoRAs 🎨 FlashSDXL can also be combined with existing LoRAs to unlock few steps generation in a **training free** manner. It can be integrated straight to Hugging Face pipelines. See an example below. ```python from diffusers import DiffusionPipeline, LCMScheduler import torch user_lora_id = "TheLastBen/Papercut_SDXL" trigger_word = "papercut" flash_lora_id = "jasperai/flash-sdxl" # Load Pipeline pipe = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", variant="fp16" ) # Set scheduler pipe.scheduler = LCMScheduler.from_config( pipe.scheduler.config ) # Load LoRAs pipe.load_lora_weights(flash_lora_id, adapter_name="flash") pipe.load_lora_weights(user_lora_id, adapter_name="lora") pipe.set_adapters(["flash", "lora"], adapter_weights=[1.0, 1.0]) pipe.to(device="cuda", dtype=torch.float16) prompt = f"{trigger_word} a cute corgi" image = pipe( prompt, num_inference_steps=4, guidance_scale=0 ).images[0] ```

> Hint 💡 : You can also use additional LoRA using the provided comfy workflow and test it on your machine. # Combining Flash Diffusion with Existing ControlNets 🎨 FlashSDXL can also be combined with existing ControlNets to unlock few steps generation in a **training free** manner. It can be integrated straight to Hugging Face pipelines. See an example below. ```python import torch import cv2 import numpy as np from PIL import Image from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, LCMScheduler from diffusers.utils import load_image, make_image_grid flash_lora_id = "jasperai/flash-sdxl" image = load_image( "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png" ).resize((1024, 1024)) image = np.array(image) image = cv2.Canny(image, 100, 200) image = image[:, :, None].repeat(3, 2) canny_image = Image.fromarray(image) # Load ControlNet controlnet = ControlNetModel.from_pretrained( "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" ) pipe = StableDiffusionXLControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16, safety_checker=None, variant="fp16" ).to("cuda") # Set scheduler pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) # Load LoRA pipe.load_lora_weights(flash_lora_id) pipe.fuse_lora() image = pipe( "picture of the mona lisa", image=canny_image, num_inference_steps=4, guidance_scale=0, controlnet_conditioning_scale=0.5, cross_attention_kwargs={"scale": 1}, ).images[0] make_image_grid([canny_image, image], rows=1, cols=2) ```

# Training Details The model was trained for 20k iterations on 4 H100 GPUs (representing approximately a total of 176 GPU hours of training). Please refer to the [paper](http://arxiv.org/abs/2406.02347) for further parameters details. **Metrics on COCO 2014 validation (Table 3)** - FID-10k: 21.62 (4 NFE) - CLIP Score: 0.327 (4 NFE) ## Citation If you find this work useful or use it in your research, please consider citing us ```bibtex @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.