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

2026-09-09

AI Summary

本文提出AcFlow，通过学习条件激活流控制文本到图像扩散转换器，解决风格强度控制和抑制不需要的概念的问题。

Abstract

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-dependent updates. With parameters shared across concepts within each task family, the field supports fine-grained descriptions and generalizes to concepts unseen during training without per-concept fitting. On style control, AcFlow achieves the best style--content trade-off among the evaluated baselines in the high-style-alignment regime. At a fixed operating point, AcFlow attains style--content alignment of 0.5365/0.2860, compared with 0.4397/0.2684 for the baseline with the highest style alignment. Qualitative results demonstrate suppression of diverse concepts, including cases where direct prompting fails. Our analyses support the learned velocity field as an adaptive control mechanism, with update directions varying across tokens and depend on their activation states. Our code is available at https://github.com/Nove1yst/AcFlow.

Problem

• Text-to-Image
• Diffusion Transformers
• Control Interface
• Style Intensity
• Concept Suppression

Innovation

• AcFlow
• concept-conditioned velocity field
• style control
• activation flow
• diffusion transformers