Layout Control and Semantic Guidance with Attention Loss Backward for T2I Diffusion Model
This work addresses controllable image generation for users needing precise layout and semantic guidance in diffusion models, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled challenges in controllable image generation, such as mismatched object attributes and poor prompt-following, by proposing a train-free method based on attention loss backward to control cross attention maps, achieving excellent practical applications in production.
Controllable image generation has always been one of the core demands in image generation, aiming to create images that are both creative and logical while satisfying additional specified conditions. In the post-AIGC era, controllable generation relies on diffusion models and is accomplished by maintaining certain components or introducing inference interferences. This paper addresses key challenges in controllable generation: 1. mismatched object attributes during generation and poor prompt-following effects; 2. inadequate completion of controllable layouts. We propose a train-free method based on attention loss backward, cleverly controlling the cross attention map. By utilizing external conditions such as prompts that can reasonably map onto the attention map, we can control image generation without any training or fine-tuning. This method addresses issues like attribute mismatch and poor prompt-following while introducing explicit layout constraints for controllable image generation. Our approach has achieved excellent practical applications in production, and we hope it can serve as an inspiring technical report in this field.