Recent advancements in image inpainting, particularly through diffusion
modeling, have yielded promising outcomes. However, when tested in scenarios
involving the completion of images based on the foreground objects, current
methods that aim to inpaint an image in an end-to-end manner encounter
challenges such as "over-imagination", inconsistency between foreground and
background, and limited diversity. In response, we introduce Anywhere, a
pioneering multi-agent framework designed to address these issues. Anywhere
utilizes a sophisticated pipeline framework comprising various agents such as
Visual Language Model (VLM), Large Language Model (LLM), and image generation
models. This framework consists of three principal components: the prompt
generation module, the image generation module, and the outcome analyzer. The
prompt generation module conducts a semantic analysis of the input foreground
image, leveraging VLM to predict relevant language descriptions and LLM to
recommend optimal language prompts. In the image generation module, we employ a
text-guided canny-to-image generation model to create a template image based on
the edge map of the foreground image and language prompts, and an image refiner
to produce the outcome by blending the input foreground and the template image.
The outcome analyzer employs VLM to evaluate image content rationality,
aesthetic score, and foreground-background relevance, triggering prompt and
image regeneration as needed. Extensive experiments demonstrate that our
Anywhere framework excels in foreground-conditioned image inpainting,
mitigating "over-imagination", resolving foreground-background discrepancies,
and enhancing diversity. It successfully elevates foreground-conditioned image
inpainting to produce more reliable and diverse results.

Anywhere 是一种先进的多智能体框架，通过使用多个代理（如 VLM、LLM 和图像生成模型），解决了前景条件下图像修复的过度想像、前景 - 背景不一致和有限多样性等问题，使得前景条件下的图像修复产生更可靠和多样化的结果。