SDEdit is a PyTorch-based framework for image synthesis and editing that leverages stochastic differential equations (SDEs) to guide the generation process. By introducing controlled noise to an input image and then denoising it using a diffusion model, SDEdit enables realistic and faithful image transformations without the need for task-specific training or GAN inversions.
Key Features
Stroke-Based Image Synthesis: Generate realistic images from user-provided sketches or strokes.
Image Editing: Modify existing images guided by user inputs, such as colored strokes or patches.
Image Compositing: Combine elements from multiple images into a cohesive, realistic output.
Plug-and-Play: Compatible with pre-trained diffusion models without additional training.
High Fidelity and Realism: Achieves superior results in terms of realism and user satisfaction compared to GAN-based methods.
Seerah Legacy
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