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Esser and colleagues: multimodal rectified-flow Transformers

Contribution grade

Generative-model methods and numerical sampling · Human-designed research method or resource

Reference score150,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Combined rectified-flow training-scale choices with separate text/image weights and bidirectional token interactions for high-resolution text-to-image generation.

Scientific scope

Combined rectified-flow training-scale choices with separate text/image weights and bidirectional token interactions for high-resolution text-to-image generation. Rectified flow and diffusion Transformers have prior sources. The contribution is their tested multimodal architecture and training design, not every Stable Diffusion 3 checkpoint.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Rectified flow and diffusion Transformers have prior sources. The contribution is their tested multimodal architecture and training design, not every Stable Diffusion 3 checkpoint.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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