Anywhere: A Multi-Agent Framework for User-Guided, Reliable, and Diverse Foreground-Conditioned Image Generation

Anywhere: A Multi-Agent Framework for User-Guided, Reliable, and Diverse Foreground-Conditioned Image Generation

29 April 2024

Ying Tai

Lanjun Wang

Zili Yi

Papers citing "Anywhere: A Multi-Agent Framework for User-Guided, Reliable, and Diverse Foreground-Conditioned Image Generation"

6 / 6 papers shown

Title
BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion Xu Ju Xian Liu Xintao Wang Yuxuan Bian Ying Shan Qiang Xu 24 64 0 11 Mar 2024
A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting Junhao Zhuang Yanhong Zeng Wenran Liu Chun Yuan Kai Chen DiffM 31 10 0 06 Dec 2023
ViVid-1-to-3: Novel View Synthesis with Video Diffusion Models Jeong-gi Kwak Erqun Dong Yuhe Jin Hanseok Ko Shweta Mahajan Kwang Moo Yi DiffM VGen 69 38 0 03 Dec 2023
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Junnan Li Dongxu Li Caiming Xiong S. Hoi MLLM BDL VLM CLIP 380 4,010 0 28 Jan 2022
RePaint: Inpainting using Denoising Diffusion Probabilistic Models Andreas Lugmayr Martin Danelljan Andrés Romero F. I. F. Richard Yu Radu Timofte Luc Van Gool DiffM 200 1,330 0 24 Jan 2022
Palette: Image-to-Image Diffusion Models Chitwan Saharia William Chan Huiwen Chang Chris A. Lee Jonathan Ho Tim Salimans David J. Fleet Mohammad Norouzi DiffM VLM 314 1,570 0 10 Nov 2021