The Need for Unified Image Generation Traditionally, image generation systems have focused on specific tasks, such as turning text descriptions into images or editing existing pictures. However, these systems often require separate components or models for different tasks, making them cumbersome and less efficient. A unified model that can handle multiple tasks seamlessly could simplify the.. Text-to-Image (T2I) diffusion models have shown impressive results in generating visually compelling images following user prompts. Building on this, various methods further fine-tune the pre-trained T2I model for specific tasks. However, this requires separate model architectures, training designs, and multiple parameter sets to handle different tasks. In this paper, we introduce UniVG, a.
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Paper page OmniGen Unified Image Generation
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{{nav.tagsOnDisplay()}} {{nav.patient.firstname + ‘ ‘ + nav.patient.lastname}} {{nav.patient.firstname + ‘ ‘ + nav.patient.lastname}} {{nav.patient.dob | momentDate.. The emergence of Large Language Models (LLMs) has unified language generation tasks and revolutionized human-machine interaction. However, in the realm of image generation, a unified model capable of handling various tasks within a single framework remains largely unexplored. In this work, we introduce OmniGen, a new diffusion model for unified image generation. OmniGen is characterized by the.