

RPG DiffusionMaster
Overview :
RPG-DiffusionMaster is a novel zero-shot text-to-image generation/editing framework that leverages the chaining reasoning ability of multi-modal LLMs to enhance the composability of text-to-image diffusion models. This framework utilizes an MLLM as the global planner, decomposing the complex image generation process into multiple simple generation tasks within subregions. Simultaneously, it proposes complementary regional diffusion to achieve compositional generation. Furthermore, the proposed RPG framework integrates text-guided image generation and editing in a closed-loop manner, augmenting its generalization capability. Extensive experiments demonstrate that RPG-DiffusionMaster outperforms state-of-the-art text-to-image diffusion models such as DALL-E 3 and SDXL in multi-category object composition and text-image semantic alignment. Notably, the RPG framework exhibits broad compatibility with diverse MLLM architectures (e.g., MiniGPT-4) and diffusion backbones (e.g., ControlNet).
Target Users :
RPG-DiffusionMaster is used for text-to-image generation and editing, particularly adept at handling complex text prompts and multi-object, multi-attribute relationships.
Use Cases
Generating images containing multiple objects using RPG-DiffusionMaster
Editing images using RPG-DiffusionMaster to achieve text semantic alignment
Conducting experiments on text-to-image generation using RPG-DiffusionMaster
Features
Utilizes multi-modal LLMs for global planning
Decomposes complex image generation process into simple generation tasks
Achieves compositional generation through a regional approach
Integrates text-guided image generation and editing in a closed-loop manner
Enhances the model's generalization capability
Outperforms other text-to-image diffusion models
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