Training-free and text-free LearningDisentangled semantic editingDiffusion models achieve outstanding performance in various domains, particularly in the field of controlled image editing. Despite their great success, these models typically require both fine-tuning and the use of edited text prompts as ...
Official implementation for "Stable Flow: Vital Layers for Training-Free Image Editing" - snap-research/stable-flow
Diffusion models achieve outstanding performance in various domains, particularly in the field of controlled image editing. Despite their great success, these models typically require both fine-tuning and the use of edited text prompts as conditions. However, fine-tuning diffusion models is highly time...
RePaint: Inpainting using Denoising Diffusion Probabilistic Models主要是针对图像修复(image inpainting)任务而提出的,它的做法其实与 SDEdit 的 editing with mask(如前面的 Algorithm 3)相似,输入除了参考图像以外还有一个 mask 用于指示待修复的部分,这部分将用模型采样生成的结果来填充,其余部分则维持与参考图像一致。
DesignEdit: Unify Spatial-Aware Image Editing via Training-free Inpainting with a Multi-Layered Latent Diffusion Framework - design-edit/DesignEdit
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Editing Unrecognized Locations If you have a map with an indicator that says “unknown” , this video explains what’s going on and how to fix it. 立即观看 Expanding Tableau's Mapping Capabilities Do you ever work with geographic analysis that extends beyond Tableau’s built-in geographic...
2023. MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 22560–22570. Ceylan et al. (2023) Duygu Ceylan, Chun-Hao P Huang, and Niloy J Mitra. 2023. Pix2video:...
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