PixArt-α: How to Cut Stable Diffusion Training Cost from $600K to $26K
23x training efficiency through Decomposed Training strategy. Making Text-to-Image models accessible to academic researchers.
PixArt-α: A New Paradigm for Efficient High-Resolution Image Generation
TL;DR: PixArt-α is a DiT-based text-to-image generation model that achieves equal or better quality than Stable Diffusion with 90% less training cost. Key innovations include decomposed training strategy, T5 text encoder, and Cross-Attention optimization.
1. Introduction: The Need for Efficient T2I Generation
1.1 Problems with Existing T2I Models
Training large-scale text-to-image models like Stable Diffusion and DALL-E 2 requires enormous resources:
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