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++ This website is licensed under a Creative + Commons Attribution-ShareAlike 4.0 International License. +
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+• Efficient Training and Inference Strategy: We propose automatic labeling and training strategies to improve text-image consistency. Multiple VLMs generate diverse re-captions, and a CLIPScore-based strategy selects high-CLIPScore captions to enhance convergence and alignment. - Additionally, our Flow-DPM-Solver reduces inference steps from 28-50 to 14-20 compared to the Flow-Euler-Solver, with better performance.
+ Additionally, our Flow-DPM-Solver reduces inference steps from 28-50 to 14-20 compared to the Flow-Euler-Solver, with better performance. +
+We compare Sana with the most advanced text-to-image diffusion models in Table 7. For 512 × 512 resolution, Sana-0.6 demonstrates a throughput that is 5× faster than PixArt-Σ, which has a similar model size, and significantly outperforms it in FID, Clip Score, GenEval, and DPG-Bench. For 1024 × 1024 resolution, @@ -524,11 +537,6 @@
Our mission is to develop efficient, lightweight, and accelerated AI technologies that address practical challenges and deliver fast, open-source solutions...
-Our mission is to develop efficient, lightweight, and accelerated AI technologies that address practical challenges and deliver fast, open-source solutions...
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