PixArt-alpha/PixArt-XL-2-256x256

         

🐱 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 sentencepiece

To just use the base model, you can run:

from diffusers import PixArtAlphaPipeline
import torch

pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", 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.