Consistency Control in Text-to-Image Diffusion Models

Recent Diffusion models are known for producing high-quality images. However, many applications necessitate precise control over various factors such as object appearance, size, position, and the integration of multiple objects. Current techniques often fall short in addressing these requirements while ensuring consistent visual quality. To tackle this challenge, we propose a novel approach that connects object appearance with random features, allowing for enhanced control over appearance, position, and size. By fine-tuning a pre-trained text-to-image model, we incorporate control information through mask mapping and local feature classification loss. Our experimental results demonstrate that our method can effectively manage individual objects, facilitating flexible combinations while maintaining a consistent appearance across all elements.