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AcFlow: Controlling Text-to-Image Diffusion Transformers via Learned Conditional Activation Flow

arXiv · AI, language, vision and robotics · article · Sep 9, 2026 · UTC

Text-to-image diffusion transformers (DiTs) are powerful generators, yet direct prompting provides limited control interface for style intensity and can fail to suppress unwanted concepts. To enable these controls, we introduce AcFlow, an inference-time controller that transports intermediate layer image-token activations through a learned concept-conditioned velocity field while keeping the base DiT frozen. A textual concept description specifies the desired intervention, while the integration horizon provides a continuous control parameter. The field produces token-varying, activation-depend

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