TC-LoRA：时序调制条件LoRA用于自适应扩散控制

摘要

当前可控扩散模型通常依赖于固定架构，通过修改中间激活值来注入基于新模态的指导信息。这种方法在动态、多阶段的去噪过程中采用静态条件策略，限制了模型在生成从粗粒度结构到细粒度细节演变时调整其响应的能力。我们提出了TC-LoRA（时序调制条件LoRA），这一新范式通过直接对模型权重施加条件，实现了动态、上下文感知的控制。我们的框架利用超网络实时生成LoRA适配器，根据时间和用户条件，为冻结的主干网络在每一步扩散过程中定制权重调整。这一机制使模型能够学习并执行一种明确的、自适应的策略，在整个生成过程中应用条件指导。通过在多种数据域上的实验，我们证明这种动态的参数化控制相比静态的基于激活值的方法，显著提升了生成保真度和对空间条件的遵循度。TC-LoRA确立了一种替代方法，其中模型的条件策略通过对其权重进行更深层次的功能性适应而得以调整，使控制能够与任务和生成阶段的动态需求保持一致。

English

Current controllable diffusion models typically rely on fixed architectures that modify intermediate activations to inject guidance conditioned on a new modality. This approach uses a static conditioning strategy for a dynamic, multi-stage denoising process, limiting the model's ability to adapt its response as the generation evolves from coarse structure to fine detail. We introduce TC-LoRA (Temporally Modulated Conditional LoRA), a new paradigm that enables dynamic, context-aware control by conditioning the model's weights directly. Our framework uses a hypernetwork to generate LoRA adapters on-the-fly, tailoring weight modifications for the frozen backbone at each diffusion step based on time and the user's condition. This mechanism enables the model to learn and execute an explicit, adaptive strategy for applying conditional guidance throughout the entire generation process. Through experiments on various data domains, we demonstrate that this dynamic, parametric control significantly enhances generative fidelity and adherence to spatial conditions compared to static, activation-based methods. TC-LoRA establishes an alternative approach in which the model's conditioning strategy is modified through a deeper functional adaptation of its weights, allowing control to align with the dynamic demands of the task and generative stage.

TC-LoRA：时序调制条件LoRA用于自适应扩散控制

TC-LoRA: Temporally Modulated Conditional LoRA for Adaptive Diffusion Control

摘要

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