Rethinking Global Text Conditioning in Diffusion Transformers

2026-02-09

AI Summary

This work investigates the necessity and potential of global text conditioning in diffusion Transformers, revealing that conventional pooled text embeddings contribute minimally to generation quality yet can serve as effective guidance signals for controllable attribute manipulation. To harness this insight, the authors propose a lightweight, training-free text-guidance mechanism that integrates pooled embeddings directly into the attention architecture, replacing standard modulation approaches with minimal computational overhead. The method consistently enhances performance across diverse tasks—including text-to-image generation, text-to-video synthesis, and image editing—demonstrating both its generality and efficacy while overcoming limitations of existing text-conditioning paradigms.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Diffusion transformers typically incorporate textual information via attention layers and a modulation mechanism using a pooled text embedding. Nevertheless, recent approaches discard modulation-based text conditioning and rely exclusively on attention. In this paper, we address whether modulation-based text conditioning is necessary and whether it can provide any performance advantage. Our analysis shows that, in its conventional usage, the pooled embedding contributes little to overall performance, suggesting that attention alone is generally sufficient for faithfully propagating prompt information. However, we reveal that the pooled embedding can provide significant gains when used from a different perspective-serving as guidance and enabling controllable shifts toward more desirable properties. This approach is training-free, simple to implement, incurs negligible runtime overhead, and can be applied to various diffusion models, bringing improvements across diverse tasks, including text-to-image/video generation and image editing.

Problem

• diffusion transformers
• text conditioning
• modulation
• pooled text embedding
• attention mechanism

Innovation

• diffusion transformers
• text conditioning
• modulation mechanism
• training-free guidance
• pooled text embedding