PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language Instructions
Weifeng Lin Xinyu Wei Renrui Zhang Le Zhuo Shitian Zhao Siyuan Huang Junlin Xie Peng Gao Hongsheng Li
This paper presents a versatile image-to-image visual assistant, PixWizard, designed for image generation, manipulation, and translation based on free-from language instructions. To this end, we tackle a variety of vision tasks into a unified image-text-to-image generation framework and curate an Omni Pixel-to-Pixel Instruction-Tuning Dataset. By constructing detailed instruction templates in natural language, we comprehensively include a large set of diverse vision tasks such as text-to-image generation, image restoration, image grounding, dense image prediction, image editing, controllable generation, inpainting/outpainting, and more. Furthermore, we adopt Diffusion Transformers (DiT) as our foundation model and extend its capabilities with a flexible any resolution mechanism, enabling the model to dynamically process images based on the aspect ratio of the input, closely aligning with human perceptual processes. The model also incorporates structure-aware and semantic-aware guidance to facilitate effective fusion of information from the input image. Our experiments demonstrate that PixWizard not only shows impressive generative and understanding  abilities for images with diverse resolutions but also exhibits generalization capabilities with unseen tasks and human instructions.
Diffusion Model, Image Generation, Image-to-Image