---
license: openrail++
tags:
- text-to-image
- PixArt-Σ
---
# 🐱 PixArt-Σ Model Card
## Model
[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.
