MultiDiffusion is a PyTorch-based framework designed to enhance controllability in text-to-image generation using pre-trained diffusion models. By fusing multiple diffusion paths through a shared optimization process, it allows for versatile image generation without the need for additional training or fine-tuning. This approach enables users to generate high-quality images that adhere to specific constraints such as aspect ratios, spatial layouts, and segmentation masks.
Key Features
Panoramic Image Generation: Create wide-aspect-ratio images (e.g., 512×4608 pixels) while maintaining coherence across the entire scene.
Spatial Control: Guide image synthesis using spatial signals like segmentation masks, bounding boxes, or region-based prompts.
Plug-and-Play Compatibility: Integrates seamlessly with existing pre-trained diffusion models, eliminating the need for retraining.
Efficient Optimization: Employs a unified optimization task to bind multiple diffusion processes, ensuring consistency and quality in the generated images.
Gradio Interface: Includes an interactive Gradio app for user-friendly experimentation and visualization.
Diffusers Integration: Compatible with Hugging Face's Diffusers library, facilitating easy deployment and customization.
Android DevHub
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