The release of the Stable Diffusion 3 model marks a major advance in text-to-image generation. This model uses the same DiT architecture as Sora and significantly improves image generation quality through a series of technical improvements. Its parameter size ranges from 800M to 8B, showing strong performance and flexible application potential. It is worth noting that the R&D team of SD3 integrates the expertise of Sora core R&D members and NYU assistant professors, and adopts the MMDiT architecture that is superior to UViT and DiT, as well as innovative Rectified Flow (RF) formula variants, which are It provides a solid foundation for model performance improvement.
The Stable Diffusion 3 model is released, using the same DiT architecture as Sora, with significant quality improvements. The authors state that Stable Diffusion 3 outperforms other text-to-image generation systems, with parameter sizes ranging from 800M to 8B. The SD3 architecture is based on collaboration between Sora core R&D members and assistant professors at New York University, using the MMDiT architecture to be superior to UViT and DiT. Stable Diffusion 3 adopts the Rectified Flow (RF) formula, and the performance of the reweighted RF variant proposed by the author continues to improve. The model is extended and improved using a flexible text encoder, and its performance is compared with other models.
The release of Stable Diffusion 3 not only reflects the rapid development of text-to-image generation technology, but also indicates that more and more powerful models will emerge in the field of AI image generation in the future. Its improved architecture and algorithm, as well as performance comparisons with other models, provide valuable reference and reference for researchers and developers. We look forward to Stable Diffusion 3 being able to play a role in more application scenarios in the future.