Edit images using prompts and change maps
Fast Inversion of Rectified Flow for Image Semantic Editing.
Enhance images by adjusting sliders or using auto-enhance
Generate and edit images from text instructions
Cut out objects from images using prompts or bounding boxes
A gradio demo for Posterior-Mean Rectified Flow (PMRF)
Start an image editing server
Swap faces in images and optionally enhance them
Swap a face from one image to another
Generate detailed image edits and inpainting using prompts
Quickly edit the expression of a face
Fill in parts of an image using a prompt
Turn images into annotated images with various effects
Differential Diffusion is an AI-powered image editing tool designed to help users edit images using textual prompts and change maps. It leverages advanced diffusion models to generate high-quality results by guiding the editing process through specific instructions. Unlike traditional diffusion models, Differential Diffusion focuses on incremental changes, allowing for more precise and controlled edits.
• Prompt-Based Editing: Utilize textual prompts to guide the editing process and achieve desired results.
• Change Maps: Apply specific modifications to targeted regions of the image.
• Efficient Iterations: Quickly generate and refine edits with minimal computational overhead.
• Flexibility: Compatible with various image editing tasks, from subtle adjustments to complex transformations.
• Uncertainty Guidance: Fine-tune edits by controlling the diffusion process through uncertainty maps.
What makes Differential Diffusion different from other diffusion models?
Differential Diffusion focuses on incremental changes and uses change maps for targeted edits, making it more precise and efficient for specific modifications compared to general-purpose diffusion models.
Do I need prior knowledge of AI or image editing to use Differential Diffusion?
No, the tool is designed to be user-friendly. While advanced users can benefit from its detailed controls, the interface is intuitive enough for beginners to use effectively.
Can I customize the diffusion process further?
Yes, Differential Diffusion allows users to adjust parameters like uncertainty maps and learning rates, enabling greater control over the output.