X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models

• URL: http://arxiv.org/abs/2412.01824v1
• Date: Mon, 02 Dec 2024 18:59:26 GMT
• Title: X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models
• Authors: Zeyi Sun, Ziyang Chu, Pan Zhang, Tong Wu, Xiaoyi Dong, Yuhang Zang, Yuanjun Xiong, Dahua Lin, Jiaqi Wang,
• Abstract summary: In-context generation is a key component of large language models' (LLMs) open-task generalization capability.<n>X-Prompt is a purely auto-regressive large-vision language model designed to deliver competitive performance across a wide range of both seen and unseen image generation tasks.<n>A unified training task for both text and image prediction enables X-Prompt to handle general image generation with enhanced task awareness from in-context examples.
• Score: 77.98981338798383
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In-context generation is a key component of large language models' (LLMs) open-task generalization capability. By leveraging a few examples as context, LLMs can perform both in-domain and out-of-domain tasks. Recent advancements in auto-regressive vision-language models (VLMs) built upon LLMs have showcased impressive performance in text-to-image generation. However, the potential of in-context learning for general image generation tasks remains largely unexplored. To address this, we introduce X-Prompt, a purely auto-regressive large-vision language model designed to deliver competitive performance across a wide range of both seen and unseen image generation tasks, all within a unified in-context learning framework. X-Prompt incorporates a specialized design that efficiently compresses valuable features from in-context examples, supporting longer in-context token sequences and improving its ability to generalize to unseen tasks. A unified training task for both text and image prediction enables X-Prompt to handle general image generation with enhanced task awareness from in-context examples. Extensive experiments validate the model's performance across diverse seen image generation tasks and its capacity to generalize to previously unseen tasks.

Related papers

• VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning [68.98988753763666]
  We propose VisualCloze, a universal image generation framework. VisualCloze supports a wide range of in-domain tasks, generalization to unseen ones, unseen unification of multiple tasks, and reverse generation. We introduce Graph200K, a graph-structured dataset that establishes various interrelated tasks, enhancing task density and transferable knowledge.
  arXiv  Detail & Related papers  (2025-04-10T17:59:42Z)
• Unified Autoregressive Visual Generation and Understanding with Continuous Tokens [52.21981295470491]
  We present UniFluid, a unified autoregressive framework for joint visual generation and understanding. Our unified autoregressive architecture processes multimodal image and text inputs, generating discrete tokens for text and continuous tokens for image. We find though there is an inherent trade-off between the image generation and understanding task, a carefully tuned training recipe enables them to improve each other.
  arXiv  Detail & Related papers  (2025-03-17T17:58:30Z)
• RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models [22.042487298092883]
  RealGeneral is a novel framework that reformulates image generation as a conditional frame prediction task. It mitigates a 14.5% improvement in subject similarity for customized generation and a 10% enhancement in image quality for canny-to-image task.
  arXiv  Detail & Related papers  (2025-03-13T14:31:52Z)
• 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)
• Towards More Unified In-context Visual Understanding [74.55332581979292]
  We present a new ICL framework for visual understanding with multi-modal output enabled. First, we quantize and embed both text and visual prompt into a unified representational space. Then a decoder-only sparse transformer architecture is employed to perform generative modeling on them.
  arXiv  Detail & Related papers  (2023-12-05T06:02:21Z)
• 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)
• Generative Modeling for Multi-task Visual Learning [40.96212750592383]
  We consider a novel problem of learning a shared generative model that is useful across various visual perception tasks. We propose a general multi-task oriented generative modeling framework, by coupling a discriminative multi-task network with a generative network. Our framework consistently outperforms state-of-the-art multi-task approaches.
  arXiv  Detail & Related papers  (2021-06-25T03:42:59Z)

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.