FreeCond: Free Lunch in the Input Conditions of Text-Guided Inpainting

In this study, we aim to determine and solve the deficiency of Stable Diffusion Inpainting (SDI) in following the instruction of both prompt and mask. Due to the training bias from masking, the inpainting quality is hindered when the prompt instruction and image condition are not related. Therefore, we conduct a detailed analysis of the internal representations learned by SDI, focusing on how the mask input influences the cross-attention layer. We observe that adapting text key tokens toward the input mask enables the model to selectively paint within the given area. Leveraging these insights, we propose FreeCond, which adjusts only the input mask condition and image condition. By increasing the latent mask value and modifying the frequency of image condition, we align the cross-attention features with the model's training bias to improve generation quality without additional computation, particularly when user inputs are complicated and deviate from the training setup. Extensive experiments demonstrate that FreeCond can enhance any SDI-based model, e.g., yielding up to a 60% and 58% improvement of SDI and SDXLI in the CLIP score.

Training-and-prompt-free General Painterly Harmonization Using Image-wise Attention Sharing

Painterly Image Harmonization aims at seamlessly blending disparate visual elements within a single coherent image. However, previous approaches often encounter significant limitations due to training data constraints, the need for time-consuming fine-tuning, or reliance on additional prompts. To surmount these hurdles, we design a Training-and-prompt-Free General Painterly Harmonization method using image-wise attention sharing (TF-GPH), which integrates a novel "share-attention module". This module redefines the traditional self-attention mechanism by allowing for comprehensive image-wise attention, facilitating the use of a state-of-the-art pretrained latent diffusion model without the typical training data limitations. Additionally, we further introduce "similarity reweighting" mechanism enhances performance by effectively harnessing cross-image information, surpassing the capabilities of fine-tuning or prompt-based approaches. At last, we recognize the deficiencies in existing benchmarks and propose the "General Painterly Harmonization Benchmark", which employs range-based evaluation metrics to more accurately reflect real-world application. Extensive experiments demonstrate the superior efficacy of our method across various benchmarks. The code and web demo are available at https://github.com/BlueDyee/TF-GPH.