InstanceControl: Controllable Complex Image Generation without Instance Labeling
This work addresses the labor-intensive requirement of instance labeling in controllable image generation for multi-instance scenes, offering a practical solution for users needing fine-grained control without manual effort.
InstanceControl enables complex multi-instance image generation without manual instance labeling by using a Vision-Language Model to automatically establish instance-level correspondences between text prompts and visual conditions, achieving superior fidelity and precise instance-level control over state-of-the-art methods.
Controllable image generation methods, such as ControlNet, have demonstrated a remarkable capacity to introduce visual conditions(e.g., depth maps) to guide image generation. However, these methods often struggle with complex multi-instance scenes, frequently leading to attribute confusion among instances. While recent approaches attempt to mitigate this via manual instance labeling, such requirements are labor-intensive. In this paper, we propose InstanceControl, a novel multi-instance controllable generation method that eliminates the need for instance labeling. We identify the primary bottleneck in existing methods as the inability to accurately associate instance descriptions with their corresponding regions within visual conditions. To address this, we leverage the Vision-Language Model (VLM) to establish instance-level correspondences between text prompts and visual conditions. Specifically, the VLM automatically parses instance descriptions from the text prompts and simultaneously predicts instance masks based on the visual conditions. Furthermore, since the predicted masks may contain noise, we introduce an adaptive mask refinement strategy that dynamically refines these instance masks during the generation process. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods, achieving superior fidelity and precise instance-level control.