Papers › Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, Robin Rombach
Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perceptual data such as images and videos. Rectified flow is a recent generative model formulation that connects data and noise in a straight line. Despite its better theoretical properties and conceptual simplicity, it is not yet decisively established as standard practice. In this work, we improve existing noise sampling techniques for training rectified flow models by biasing them towards perceptually relevant scales. Through a large-scale study, we demonstrate the superior performance of this approach compared to established diffusion formulations for high-resolution text-to-image synthesis. Additionally, we present a novel transformer-based architecture for text-to-image generation that uses separate weights for the two modalities and enables a bidirectional flow of information between image and text tokens, improving text comprehension, typography, and human preference ratings. We demonstrate that this architecture follows predictable scaling trends and correlates lower validation loss to improved text-to-image synthesis as measured by various metrics and human evaluations. Our largest models outperform state-of-the-art models, and we will make our experimental data, code, and model weights publicly available.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| WISE | SD3.5-large | Biology | 0.44 | #6 of 11 | Archive leaderboard | report | |
| WISE | SD3.5-large | Chemistry | 0.31 | #6 of 11 | Archive leaderboard | report | |
| WISE | SD3.5-large | Cultural | 0.44 | #6 of 11 | Archive leaderboard | report | |
| WISE | SD3.5-large | Overall | 0.46 | #6 of 11 | Archive leaderboard | report | |
| WISE | SD3.5-large | Physics | 0.56 | #6 of 11 | Archive leaderboard | report | |
| WISE | SD3.5-large | Space | 0.58 | #6 of 11 | Archive leaderboard | report | |
| WISE | SD3.5-large | Time | 0.50 | #6 of 11 | Archive leaderboard | report | |
| Image Generation | WISE | stable-diffusion-3.5-large | Biology | 0.44 | #8 of 14 | Archive leaderboard | report |
| Image Generation | WISE | stable-diffusion-3.5-large | Chemistry | 0.31 | #8 of 14 | Archive leaderboard | report |
| Image Generation | WISE | stable-diffusion-3.5-large | Cultural | 0.44 | #8 of 14 | Archive leaderboard | report |
| Image Generation | WISE | stable-diffusion-3.5-large | Overall | 0.46 | #8 of 14 | Archive leaderboard | report |
| Image Generation | WISE | stable-diffusion-3.5-large | Physics | 0.52 | #8 of 14 | Archive leaderboard | report |
| Image Generation | WISE | stable-diffusion-3.5-large | Space | 0.58 | #8 of 14 | Archive leaderboard | report |
| Image Generation | WISE | stable-diffusion-3.5-large | Time | 0.50 | #8 of 14 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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