---
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
---
# ⚡ FlashDiffusion: FlashPixart ⚡


Flash Diffusion is a diffusion distillation method proposed in [ADD ARXIV]() *by Clément Chadebec, Onur Tasar and Benjamin Aubin.*
This model is a **26.4M** LoRA distilled version of Pixart-α model that is able to generate 1024x1024 images in **4 steps**.


<p align="center">
   <img style="width:700px;" src="images/hf_grid.png">
</p>

# How to use?

The model can be used using the `StableDiffusionPipeline` from `diffusers` library directly. It can allow reducing the number of required sampling steps to **2-4 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="images/raccoon.png">
</p>

# Training Details
The model was trained for 40k iterations on 4 H100 GPUs. Please refer to the [paper]() for further parameters details. 


## License
This model is released under the the Creative Commons BY-NC license.