Edit images using prompts and change maps
Generate and edit images using text prompts
Controlling Computers with Small Models
Reduce The blockiness in jpeg images
Colorize black-and-white images with text prompts
Convert images to drawings with complex lines
Swap faces in images between two photos
Crop face and scale-up for img2img pre-processing
Enhance and upscale images for better quality
Swap a face from one image to another
Swap faces in two images
Generate images by repairing and modifying masked areas
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.