adirik / masactrl-sdxl

Editable image generation with MasaCtrl-SDXL

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Run time and cost

This model runs on Nvidia A40 (Large) GPU hardware. Predictions typically complete within 148 seconds. The predict time for this model varies significantly based on the inputs.


Model Description

MasaCtrl: Tuning-free Mutual Self-Attention Control for Consistent Image Synthesis and Editing by Tencent ARC Lab and University of Tokyo. This model uses stabilityai/stable-diffusion-xl-base-1.0 as the base model and can simultaneously perform image synthesis and editing.

Abstract: Despite the success in large-scale text-to-image generation and text-conditioned image editing, existing methods still struggle to produce consistent generation and editing results. For example, generation approaches usually fail to synthesize multiple images of the same objects/characters but with different views or poses. Meanwhile, existing editing methods either fail to achieve effective complex non-rigid editing while maintaining the overall textures and identity, or require time-consuming fine-tuning to capture the image-specific appearance. In this paper, we develop MasaCtrl, a tuning-free method to achieve consistent image generation and complex non-rigid image editing simultaneously. Specifically, MasaCtrl converts existing self-attention in diffusion models into mutual self-attention, so that it can query correlated local contents and textures from source images for consistency. To further alleviate the query confusion between foreground and background, we propose a mask-guided mutual self-attention strategy, where the mask can be easily extracted from the cross-attention maps. Extensive experiments show that the proposed MasaCtrl can produce impressive results in both consistent image generation and complex non-rigid real image editing.

See the paper, official repository and project page for more information.


To use the model simply provide 2-4 prompts. The first prompt is used for the initial generation and subsequent prompts are used to generate edited versions. For best performance, prompts should be as consistent as possible (E.g. description of the same subject in different shapes and/or conditions).

Other MasaCtrl Models

  • MasaCtrl with CompVis/stable-diffusion-v1-4 as the base model
  • MasaCtrl with xyn-ai/anything-v4.0 as the base model


    author    = {Cao, Mingdeng and Wang, Xintao and Qi, Zhongang and Shan, Ying and Qie, Xiaohu and Zheng, Yinqiang},
    title     = {MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {22560-22570}