  The most advanced text-to-image (T2I) models require significant training
costs (e.g., millions of GPU hours), seriously hindering the fundamental
innovation for the AIGC community while increasing CO2 emissions. This paper
introduces PIXART-$\alpha$, a Transformer-based T2I diffusion model whose image
generation quality is competitive with state-of-the-art image generators (e.g.,
Imagen, SDXL, and even Midjourney), reaching near-commercial application
standards. Additionally, it supports high-resolution image synthesis up to
1024px resolution with low training cost, as shown in Figure 1 and 2. To
achieve this goal, three core designs are proposed: (1) Training strategy
decomposition: We devise three distinct training steps that separately optimize
pixel dependency, text-image alignment, and image aesthetic quality; (2)
Efficient T2I Transformer: We incorporate cross-attention modules into
Diffusion Transformer (DiT) to inject text conditions and streamline the
computation-intensive class-condition branch; (3) High-informative data: We
emphasize the significance of concept density in text-image pairs and leverage
a large Vision-Language model to auto-label dense pseudo-captions to assist
text-image alignment learning. As a result, PIXART-$\alpha$'s training speed
markedly surpasses existing large-scale T2I models, e.g., PIXART-$\alpha$ 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%. Extensive experiments demonstrate that PIXART-$\alpha$
excels in image quality, artistry, and semantic control. We hope
PIXART-$\alpha$ will provide new insights to the AIGC community and startups to
accelerate building their own high-quality yet low-cost generative models from
scratch.
