VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning

什么是VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning

VisualCloze is a cutting-edge image generation framework designed to handle a wide variety of visual tasks. Unlike traditional task-specific models, it leverages visual in-context learning to identify and perform tasks from visual demonstrations. This framework excels in its ability to generalize to unseen tasks, making it a powerful tool for image generation across various domains. By incorporating a graph-structured dataset (Graph200K), it enhances task density and enables the transfer of knowledge between related tasks. VisualCloze represents a significant leap in the realm of generative models, moving beyond language-based instructions to a more intuitive, visual approach to task execution.

VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning怎么用？

To use VisualCloze, users provide a set of visual demonstrations that outline the task they wish to execute. The model interprets these visual cues to perform tasks such as image generation, restoration, or editing. Through in-context learning, VisualCloze adapts to new tasks without requiring task-specific training, making it versatile and efficient. Users can also take advantage of the Graph200K dataset, which enhances the model’s ability to handle complex, multi-task problems. The model integrates seamlessly with advanced infilling models like FLUX, ensuring high-quality results without the need for additional architectural changes.

VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning核心功能

• VisualCloze's core functionalities include:
• Visual in-context learning for task identification and execution
• Generalization to unseen tasks through visual demonstrations
• Unification of multiple tasks into a single step, such as target image generation and intermediate results
• Reverse generation to deduce conditions from a given target image
• Integration with the Graph200K dataset for improved task density and transferable knowledge

VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning使用案例

• Example use cases of VisualCloze include:
• Generating target images based on visual prompts for specific tasks
• Performing reverse generation to extract task conditions from a target image
• Unifying multiple image generation tasks into one step, providing intermediate results and the final image in a single process
• Adapting to new, unseen tasks by interpreting visual in-context examples, allowing the model to execute tasks without prior training

VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning价格

VisualCloze is available for use through an online demo and open-source code. The framework leverages advanced models and datasets, such as FLUX for image infilling and the Graph200K dataset for multi-task learning. Pricing details are not explicitly mentioned, as the framework appears to be open-source and freely accessible through platforms like Hugging Face.

VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning公司名称

VisualCloze was developed by a team of researchers from Nankai University, Beijing University of Posts and Telecommunications, Tsinghua University, Shanghai AI Laboratory, and The Chinese University of Hong Kong. Key contributors include Zhong-Yu Li, Ruoyi Du, Juncheng Yan, Le Zhuo, Zhen Li, Peng Gao, Zhanyu Ma, and Ming-Ming Cheng.

VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning联系方式

For inquiries, you can contact the team through the following email addresses: Zhen Li ([email protected]) and Ming-Ming Cheng ([email protected]).

VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning社交媒体

Follow VisualCloze on social media: - Twitter: @VisualCloze - Instagram: @visualcloze