https://github.com/YangLing0818/RPG-DiffusionMaster

[ICML 2024] Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs (RPG)
https://github.com/YangLing0818/RPG-DiffusionMaster

image-editting large-language-models multimodal-large-language-models text-to-image

Last synced: about 1 month ago
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[ICML 2024] Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs (RPG)

• Host: GitHub
• URL: https://github.com/YangLing0818/RPG-DiffusionMaster
• Owner: YangLing0818
• License: mit
• Created: 2024-01-22T01:07:23.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-10-10T02:44:57.000Z (2 months ago)
• Last Synced: 2024-10-29T15:34:38.092Z (about 1 month ago)
• Topics: image-editting, large-language-models, multimodal-large-language-models, text-to-image
• Language: Jupyter Notebook
• Homepage: https://proceedings.mlr.press/v235/yang24ai.html
• Size: 46 MB
• Stars: 1,684
• Watchers: 25
• Forks: 97
• Open Issues: 42
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-diffusion-categorized - [Code
• ai-game-devtools - RPG-DiffusionMaster - to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs (PRG). | | | Image | (<span id="image">Image</span> / <span id="tool">Tool (AI LLM)</span>)
• StarryDivineSky - YangLing0818/RPG-DiffusionMaster - 4、Gemini-Pro）或开源的本地MLLM（如miniGPT-4）作为提示的字幕重配和区域规划器，通过我们的互补区域扩散来实现SOTA文本到图像的生成和编辑。我们的框架非常灵活，可以推广到任意MLLM架构和扩散主干网。RPG还能够生成超高分辨率的图像。高度准确的图像生成： RPG框架能够根据复杂的描述生成高度准确和详细的图像，尤其在处理包含多个对象、属性和关系的场景时表现出色，生成的图像与文本描述高度一致。超越现有技术： 与现有的文本到图像模型相比，RPG框架展现了更好的性能，尤其在处理多元素组合和文本-图像语义对齐方面。灵活性和广泛适用性： 实验表明，RPG框架能够与不同的多模态大型语言模型和扩散模型兼容，适用于多种图像生成场景。提升质量和细节： 生成的图像不仅在视觉上吸引人，而且细节丰富，对于艺术创作、设计和娱乐等领域至关重要。RPG框架还能够处理复杂的交互和环境，生成的图像在构图和细节方面表现出色。 (多模态大模型 / 网络服务_其他)
• AiTreasureBox - YangLing0818/RPG-DiffusionMaster - 12-07_1714_0](https://img.shields.io/github/stars/YangLing0818/RPG-DiffusionMaster.svg)|Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs (PRG)| (Repos)