Workflow · Production

Tutorial: Pixart-α diffusion transformer for text-to-image generation at 10.8% of Stable Diffusion training cost

The problem

Training state-of-the-art text-to-image models like Stable Diffusion v1.5 demands enormous computational resources — 6K A100 GPU days costing approximately $320,000 — along with significant CO2 emissions, creating serious barriers for researchers and entrepreneurs.

Workflow diagram · grounded in source
1
Load pretrained pipeline
integration
“Load the pre-trained checkpoints from HuggingFace Hub to the PixArtAlphaPipeline”
2
Set up experiment logging
integration
“use the Weights & Biases autologger for Diffusers to automatically log our generations and all experiment configurations so that they are reproducible and easy to share”
3
Generate images from text prompt
ai_action
“Generate the images by calling the PixArtAlphaPipeline”
4
Compare with Stable Diffusion XL
validation
“Let's take a look at some examples of images generated by both Pixart-α and Stable Diffusion XL Base-1.0 using the same prompt at a resolution of 1024 pixels”
Reported outcome

Pixart-α achieves competitive image quality with state-of-the-art generators at only 10.8% of the training time of Stable Diffusion v1.5, generating high-resolution images up to 1024 pixels with stronger text-image alignment than Stable Diffusion XL.

Reported metrics
Pixart-α training time vs Stable Diffusion v1.510.8%
Stable Diffusion v1.5 training cost (comparison baseline)$320,000
Stable Diffusion v1.5 training compute (comparison baseline)6K A100 GPU days
Output image resolutionup to 1024 pixels
Reported stack
Pixart-αHuggingFace DiffusersLLaVAStable Diffusion XLDiT
Source
https://mlops.community/blog/pixart-a-diffusion-transformer-model-for-text-to-image-generation
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Pixart-α achieves competitive image quality with state-of-the-art generators at only 10.8% of the training time of Stable Diffusion v1.5, generating high-resolution images up to 1024 pixels with stronger text-image al…

What tools did this team use?

Pixart-α, HuggingFace Diffusers, LLaVA, Stable Diffusion XL, DiT.

What results were reported?

Pixart-α training time vs Stable Diffusion v1.5: 10.8%; Stable Diffusion v1.5 training cost (comparison baseline): $320,000; Stable Diffusion v1.5 training compute (comparison baseline): 6K A100 GPU days; Output image resolution: up to 1024 pixels (source-reported, not independently verified).

How is this workflow AI workflow structured?

Load pretrained pipeline → Set up experiment logging → Generate images from text prompt → Compare with Stable Diffusion XL.