Diffusion Templates: A Unified Plugin Framework for Controllable Diffusion

TL;DR

Diffusion Templates introduces a unified plugin framework that decouples control capabilities from diffusion models, enabling flexible, modular, and reusable controllable generation across diverse tasks and backbones.

Contribution

It proposes a system-level interface for integrating various control methods into diffusion models, supporting heterogeneous capability carriers and broadening applicability.

Findings

Supports a wide range of controllable tasks including editing, super-resolution, and inpainting.

Unifies diverse control methods under a common, modular framework.

Preserves modularity and extensibility across different diffusion backbones.

Abstract

Controllable diffusion methods have substantially expanded the practical utility of diffusion models, but they are typically developed as isolated, backbone-specific systems with incompatible training pipelines, parameter formats, and runtime hooks. This fragmentation makes it difficult to reuse infrastructure across tasks, transfer capabilities across backbones, or compose multiple controls within a single generation pipeline. We present Diffusion Templates, a unified and open plugin framework that decouples base-model inference from controllable capability injection. The framework is organized around three components: Template models that map arbitrary task-specific inputs to an intermediate capability representation, a Template cache that functions as a standardized interface for capability injection, and a Template pipeline that loads, merges, and injects one or more Template caches into the base diffusion runtime. Because the interface is defined at the systems level rather than tied to a specific control architecture, heterogeneous capability carriers such as KV-Cache and LoRA can be supported under the same abstraction. Based on this design, we build a diverse model zoo spanning structural control, brightness adjustment, color adjustment, image editing, super-resolution, sharpness enhancement, aesthetic alignment, content reference, local inpainting, and age control. These case studies show that Diffusion Templates can unify a broad range of controllable generation tasks while preserving modularity, composability, and practical extensibility across rapidly evolving diffusion backbones. All resources will be open sourced, including code, models, and datasets.