SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
SDXL is an upgraded version of Stable Diffusion, featuring a three times larger UNet backbone, increased attention blocks and larger cross-attention context, and dual text encoders. It designs multiple novel conditioning schemes, trains on multiple aspect ratios, and introduces a post-processing refinement model to improve visual fidelity. Performance significantly surpasses previous generations and competes with black-box top image generators.
SDXL significantly improves text-to-image synthesis quality by enlarging the UNet backbone, increasing attention blocks, and using dual text encoders. Multi-aspect ratio training and a refinement model further enhance visual fidelity, outperforming previous Stable Diffusion and approaching commercial black-box models.
SDXL triples the UNet parameters, mainly increasing attention blocks and cross-attention context, and uses dual text encoders (CLIP and OpenCLIP). It designs various conditioning schemes (e.g., size, crop parameters) and trains on multiple aspect ratios. A refinement model is introduced to improve sample visual fidelity through post-processing image-to-image techniques. Evaluations show significant improvements over previous generations in metrics like FID and CLIP score, competing with DALL-E 2 and Midjourney.
As an open-source model, SDXL lowers the barrier to high-quality image generation, promoting text-to-image synthesis in advertising, design, content creation, and fostering competition between open-source communities and commercial models.
Enterprises can develop customized image generation services based on SDXL, such as ad creative, product design, or integrate it into existing content creation tools, reducing reliance on commercial APIs.
Future work needs to verify SDXL's generalization in more complex scenarios (e.g., video generation, 3D content) and the effectiveness of the refinement model in different downstream tasks.