README.md

---
license: openrail++
tags:
- text-to-image
- PixArt-Σ
pipeline_tag: text-to-image
---

<p align="center">
  <img src="asset/logo-sigma.png"  height=120>
</p>

<div style="display:flex;justify-content: center">
  <a href="https://huggingface.co/spaces/PixArt-alpha/PixArt-Sigma"><img src="https://img.shields.io/static/v1?label=Demo&message=Huggingface&color=yellow"></a> &ensp;
  <a href="https://pixart-alpha.github.io/PixArt-sigma-project/"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Github&color=blue&logo=github-pages"></a> &ensp;
  <a href="https://arxiv.org/abs/2403.04692"><img src="https://img.shields.io/static/v1?label=Paper&message=Arxiv&color=red&logo=arxiv"></a> &ensp;
  <a href="https://discord.gg/rde6eaE5Ta"><img src="https://img.shields.io/static/v1?label=Discuss&message=Discord&color=purple&logo=discord"></a> &ensp;
</div>

# 🐱 PixArt-Σ Model Card
![row01](asset/4K_image.jpg)

## Model
![pipeline](asset/model.png)

[PixArt-Σ](https://arxiv.org/abs/2403.04692) consists of pure transformer blocks for latent diffusion: 
It can directly generate 1024px, 2K and 4K images from text prompts within a single sampling process.

Source code is available at https://github.com/PixArt-alpha/PixArt-sigma.

### Model Description

- **Developed by:** PixArt-Σ
- **Model type:** Diffusion-Transformer-based text-to-image generative model
- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md)
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. 
It is a [Transformer Latent Diffusion Model](https://arxiv.org/abs/2310.00426) that uses one fixed, pretrained text encoders ([T5](
https://huggingface.co/DeepFloyd/t5-v1_1-xxl))
and one latent feature encoder ([VAE](https://arxiv.org/abs/2112.10752)).
- **Resources for more information:** Check out our [GitHub Repository](https://github.com/PixArt-alpha/PixArt-sigma) and the [PixArt-Σ report on arXiv](https://arxiv.org/abs/2403.04692).

### Model Sources

For research purposes, we recommend our `generative-models` Github repository (https://github.com/PixArt-alpha/PixArt-sigma), 
which is more suitable for both training and inference and for which most advanced diffusion sampler like [SA-Solver](https://arxiv.org/abs/2309.05019) will be added over time.
[Hugging Face](https://huggingface.co/spaces/PixArt-alpha/PixArt-Sigma) provides free PixArt-Σ inference.
- **Repository:** https://github.com/PixArt-alpha/PixArt-sigma
- **Demo:** https://huggingface.co/spaces/PixArt-alpha/PixArt-Sigma

### 🧨 Diffusers 
> [!IMPORTANT]  
> Make sure to upgrade diffusers to >= 0.28.0:
> ```bash
> pip install -U diffusers --upgrade
> ```
> In addition make sure to install `transformers`, `safetensors`, `sentencepiece`, and `accelerate`:
> ```
> pip install transformers accelerate safetensors sentencepiece
> ```
> For `diffusers<0.28.0`, check this [script](https://github.com/PixArt-alpha/PixArt-sigma#2-integration-in-diffusers) for help.

To just use the base model, you can run:

```python
import torch
from diffusers import Transformer2DModel, PixArtSigmaPipeline

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
weight_dtype = torch.float16

pipe = PixArtSigmaPipeline.from_pretrained(
    "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", 
    torch_dtype=weight_dtype,
    use_safetensors=True,
)
pipe.to(device)

# Enable memory optimizations.
# pipe.enable_model_cpu_offload()

prompt = "A small cactus with a happy face in the Sahara desert."
image = pipe(prompt).images[0]
image.save("./catcus.png")
```

When using `torch >= 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline:
```py
pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=True)
```

If you are limited by GPU VRAM, you can enable *cpu offloading* by calling `pipe.enable_model_cpu_offload`
instead of `.to("cuda")`:

```diff
- pipe.to("cuda")
+ pipe.enable_model_cpu_offload()
```

For more information on how to use PixArt-Σ with `diffusers`, please have a look at [the PixArt-Σ Docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/pixart_sigma.md).

## Uses

### Direct Use

The model is intended for research purposes only. Possible research areas and tasks include

- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
- Safe deployment of models which have the potential to generate harmful content.

- Probing and understanding the limitations and biases of generative models.

Excluded uses are described below.

### Out-of-Scope Use

The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.

## Limitations and Bias

### Limitations


- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- fingers, .etc in general may not be generated properly.
- The autoencoding part of the model is lossy.

### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.