license: openrail++
tags:
- text-to-image
- Pixart-α
🐱 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, 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.