🐱 Pixart-α Model Card

Model

Pixart-α consists of pure transformer blocks for latent diffusion: It can directly generate 1024px images from text prompts within a single sampling process.

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

Model Description

Developed by: Pixart-α
Model type: Diffusion-Transformer-based text-to-image generative model
** License:** CreativeML Open RAIL++-M License
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 that uses one fixed, pretrained text encoders (T5)) and one latent feature encoder (VAE).
Resources for more information: Check out our GitHub Repository and the Pixart-α report on arXiv.

Model Sources

For research purposes, we recommend our generative-models Github repository (https://github.com/PixArt-alpha/PixArt-alpha), which is more suitable for both training and inference and for which most advanced diffusion sampler like SA-Solver will be added over time. Hugging Face provides free Pixart-α inference.

Repository: https://github.com/PixArt-alpha/PixArt-alpha
Demo: https://huggingface.co/spaces/PixArt-alpha/PixArt-alpha

🔥🔥🔥 Why PixArt-α?

Training Efficiency

PixArt-α only takes 10.8% of Stable Diffusion v1.5's training time (675 vs. 6,250 A100 GPU days), saving nearly $300,000 ($26,000 vs. $320,000) and reducing 90% CO2 emissions. Moreover, compared with a larger SOTA model, RAPHAEL, our training cost is merely 1%.

Method	Type	#Params	#Images	A100 GPU days
DALL·E	Diff	12.0B	1.54B
GLIDE	Diff	5.0B	5.94B
LDM	Diff	1.4B	0.27B
DALL·E 2	Diff	6.5B	5.63B	41,66
SDv1.5	Diff	0.9B	3.16B	6,250
GigaGAN	GAN	0.9B	0.98B	4,783
Imagen	Diff	3.0B	15.36B	7,132
RAPHAEL	Diff	3.0B	5.0B	60,000
PixArt-α	Diff	0.6B	0.025B	675

Evaluation

The chart above evaluates user preference for Pixart-α over SDXL 0.9, Stable Diffusion 2, DALLE-2 and DeepFloyd. The Pixart-α base model performs comparable or even better than the existing state-of-the-art models.

🧨 Diffusers

Make sure to upgrade diffusers to >= 0.22.0:

pip install -U diffusers --upgrade

In addition make sure to install transformers, safetensors, sentencepiece, and accelerate:

pip install transformers accelerate safetensors

To just use the base model, you can run:

from diffusers import PixArtAlphaPipeline
import torch

pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-512x512", torch_dtype=torch.float16)
pipe = pipe.to("cuda")

# if using torch < 2.0
# pipe.enable_xformers_memory_efficient_attention()

prompt = "An astronaut riding a green horse"
images = pipe(prompt=prompt).images[0]

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:

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"):

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

For more information on how to use Pixart-α with diffusers, please have a look at the Pixart-α Docs.

Free Google Colab

You can use Google Colab to generate images from PixArt-α free of charge. Click here too try.

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.

PixArt-alpha
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PixArt-XL-2-512x512