AnyControl: Create Your Artwork with Versatile Control on Text-to-Image Generation
Yanan Sun, Yanchen Liu, Yinhao Tang, Wenjie Pei, and Kai Chen

TL;DR
AnyControl introduces a versatile multi-control framework for text-to-image generation, enabling arbitrary combinations of diverse control signals to produce high-quality, semantically aligned images, overcoming limitations of prior methods.
Contribution
It presents a novel Multi-Control Encoder that unifies multiple control signals for improved image synthesis in T2I models.
Findings
Supports arbitrary combinations of diverse control signals
Produces high-quality, faithful images aligned with text prompts
Outperforms existing methods in quantitative and qualitative evaluations
Abstract
The field of text-to-image (T2I) generation has made significant progress in recent years, largely driven by advancements in diffusion models. Linguistic control enables effective content creation, but struggles with fine-grained control over image generation. This challenge has been explored, to a great extent, by incorporating additional user-supplied spatial conditions, such as depth maps and edge maps, into pre-trained T2I models through extra encoding. However, multi-control image synthesis still faces several challenges. Specifically, current approaches are limited in handling free combinations of diverse input control signals, overlook the complex relationships among multiple spatial conditions, and often fail to maintain semantic alignment with provided textual prompts. This can lead to suboptimal user experiences. To address these challenges, we propose AnyControl, a…
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Taxonomy
TopicsHuman Motion and Animation · Video Analysis and Summarization · Handwritten Text Recognition Techniques
MethodsDiffusion
