InstructAttribute: Fine-grained Object Attributes editing with Instruction

Text-to-image (T2I) diffusion models, renowned for their advanced generative abilities, are extensively utilized in image editing applications, demonstrating remarkable effectiveness. However, achieving precise control over fine-grained attributes still presents considerable challenges. Existing image editing techniques either fail to modify the attributes of an object or struggle to preserve its structure and maintain consistency in other areas of the image. To address these challenges, we propose the Structure-Preserving and Attribute Amplification (SPAA), a training-free method which enables precise control over the color and material transformations of objects by editing the self-attention maps and cross-attention values. Furthermore, we constructed the Attribute Dataset, which encompasses nearly all colors and materials associated with various objects, by integrating multimodal large language models (MLLM) to develop an automated pipeline for data filtering and instruction labeling. Training on this dataset, we present our InstructAttribute, an instruction-based model designed to facilitate fine-grained editing of color and material attributes. Extensive experiments demonstrate that our method achieves superior performance in object-level color and material editing, outperforming existing instruction-based image editing approaches.

ColorEdit: Training-free Image-Guided Color editing with diffusion model

Text-to-image (T2I) diffusion models, with their impressive generative capabilities, have been adopted for image editing tasks, demonstrating remarkable efficacy. However, due to attention leakage and collision between the cross-attention map of the object and the new color attribute from the text prompt, text-guided image editing methods may fail to change the color of an object, resulting in a misalignment between the resulting image and the text prompt. In this paper, we conduct an in-depth analysis on the process of text-guided image synthesizing and what semantic information different cross-attention blocks have learned. We observe that the visual representation of an object is determined in the up-block of the diffusion model in the early stage of the denoising process, and color adjustment can be achieved through value matrices alignment in the cross-attention layer. Based on our findings, we propose a straightforward, yet stable, and effective image-guided method to modify the color of an object without requiring any additional fine-tuning or training. Lastly, we present a benchmark dataset called COLORBENCH, the first benchmark to evaluate the performance of color change methods. Extensive experiments validate the effectiveness of our method in object-level color editing and surpass the performance of popular text-guided image editing approaches in both synthesized and real images.