--- license: cc-by-nc-4.0 library_name: diffusers base_model: PixArt-alpha/PixArt-XL-2-1024-MS tags: - lora - text-to-image inference: False --- # ⚡ Flash Diffusion: FlashPixart ⚡ 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.* This model is a **66.5M** LoRA distilled version of [Pixart-α](https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS) model that is able to generate 1024x1024 images in **4 steps**. 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 `PixArtAlphaPipeline` from `diffusers` library directly. It can allow reducing the number of required sampling steps to **4 steps**. ```python import torch from diffusers import PixArtAlphaPipeline, Transformer2DModel, LCMScheduler from peft import PeftModel # Load LoRA transformer = Transformer2DModel.from_pretrained( "PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="transformer", torch_dtype=torch.float16 ) transformer = PeftModel.from_pretrained( transformer, "jasperai/flash-pixart" ) # Pipeline pipe = PixArtAlphaPipeline.from_pretrained( "PixArt-alpha/PixArt-XL-2-1024-MS", transformer=transformer, torch_dtype=torch.float16 ) # Scheduler pipe.scheduler = LCMScheduler.from_pretrained( "PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="scheduler", timestep_spacing="trailing", ) pipe.to("cuda") prompt = "A raccoon reading a book in a lush forest." image = pipe(prompt, num_inference_steps=4, guidance_scale=0).images[0] ```

# Training Details The model was trained for 40k iterations on 4 H100 GPUs (representing approximately 188 hours of training). Please refer to the [paper](http://arxiv.org/abs/2406.02347) for further parameters details. **Metrics on COCO 2014 validation (Table 4)** - FID-10k: 29.30 (4 NFE) - CLIP Score: 0.303 (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.