PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language Instructions

• URL: http://arxiv.org/abs/2409.15278v2
• Date: Sat, 5 Oct 2024 17:51:01 GMT
• Title: PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language Instructions
• Authors: Weifeng Lin, Xinyu Wei, Renrui Zhang, Le Zhuo, Shitian Zhao, Siyuan Huang, Junlin Xie, Yu Qiao, Peng Gao, Hongsheng Li,
• Abstract summary: PixWizard is an image-to-image visual assistant designed for image generation, manipulation, and translation based on free-from language instructions. 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. Our experiments demonstrate that PixWizard not only shows impressive generative and understanding abilities for images with diverse resolutions but also exhibits promising generalization capabilities with unseen tasks and human instructions.
• Score: 66.92809850624118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 promising generalization capabilities with unseen tasks and human instructions. The code and related resources are available at https://github.com/AFeng-x/PixWizard

Related papers

• Conditional Text-to-Image Generation with Reference Guidance [81.99538302576302]
  This paper explores using additional conditions of an image that provides visual guidance of the particular subjects for diffusion models to generate. We develop several small-scale expert plugins that efficiently endow a Stable Diffusion model with the capability to take different references. Our expert plugins demonstrate superior results than the existing methods on all tasks, each containing only 28.55M trainable parameters.
  arXiv  Detail & Related papers  (2024-11-22T21:38:51Z)
• Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative Pretraining [48.98105914356609]
  Lumina-mGPT is a family of multimodal autoregressive models capable of various vision and language tasks. We introduce Ominiponent Supervised Finetuning, transforming Lumina-mGPT into a foundation model that seamlessly achieves omnipotent task unification.
  arXiv  Detail & Related papers  (2024-08-05T17:46:53Z)
• StrucTexTv3: An Efficient Vision-Language Model for Text-rich Image Perception, Comprehension, and Beyond [68.0107158115377]
  We have crafted an efficient vision-language model, StrucTexTv3, tailored to tackle various intelligent tasks for text-rich images. We enhance the perception and comprehension abilities of StrucTexTv3 through instruction learning. Our method achieved SOTA results in text-rich image perception tasks, and significantly improved performance in comprehension tasks.
  arXiv  Detail & Related papers  (2024-05-31T16:55:04Z)
• Instruct-Imagen: Image Generation with Multi-modal Instruction [90.04481955523514]
  instruct-imagen is a model that tackles heterogeneous image generation tasks and generalizes across unseen tasks. We introduce *multi-modal instruction* for image generation, a task representation articulating a range of generation intents with precision. Human evaluation on various image generation datasets reveals that instruct-imagen matches or surpasses prior task-specific models in-domain.
  arXiv  Detail & Related papers  (2024-01-03T19:31:58Z)
• InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists [66.85125112199898]
  We develop a unified language interface for computer vision tasks that abstracts away task-specific design choices. Our model, dubbed InstructCV, performs competitively compared to other generalist and task-specific vision models.
  arXiv  Detail & Related papers  (2023-09-30T14:26:43Z)
• Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation [10.39028769374367]
  We present a new framework that takes text-to-image synthesis to the realm of image-to-image translation. Our method harnesses the power of a pre-trained text-to-image diffusion model to generate a new image that complies with the target text.
  arXiv  Detail & Related papers  (2022-11-22T20:39:18Z)
• Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding [58.70423899829642]
  We present Pix2Struct, a pretrained image-to-text model for purely visual language understanding. We show that a single pretrained model can achieve state-of-the-art results in six out of nine tasks across four domains.
  arXiv  Detail & Related papers  (2022-10-07T06:42:06Z)
• Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers [46.275416873403614]
  We propose Pixel-BERT to align image pixels with text by deep multi-modal transformers that jointly learn visual and language embedding. Our approach makes the most state-of-the-arts in downstream tasks, including Visual Question Answering (VQA), image-text retrieval, Natural Language for Visual Reasoning for Real (NLVR)
  arXiv  Detail & Related papers  (2020-04-02T07:39:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.