---
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* from Jasper Research.
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).


<p align="center">
   <img style="width:700px;" src="assets/flash_pixart.jpg">
</p>

# 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]
```
<p align="center">
   <img style="width:400px;" src="assets/raccoon.png">
</p>

# 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.