Paint-by-Example introduces a novel approach to image editing by leveraging exemplar-based guidance. This method enables precise and controllable modifications to images using diffusion models, allowing users to edit images by providing a reference example, rather than relying solely on textual prompts. The approach disentangles and reorganizes the source image and the exemplar through self-supervised training, addressing challenges like fusion artifacts and ensuring high-fidelity results.
Key Features
Exemplar-Based Editing: Modify images based on a reference example for more accurate and controlled edits.
Self-Supervised Training: Utilizes self-supervised learning to disentangle and reorganize image features.
Single Forward Pass: Achieves edits through a single forward pass of the diffusion model, eliminating the need for iterative optimization.
Arbitrary Shape Masking: Supports arbitrary shape masks for the exemplar image to guide the editing process.
Classifier-Free Guidance: Employs classifier-free guidance to enhance similarity to the exemplar image.
High-Fidelity Results: Produces high-quality edits suitable for in-the-wild images.
Android DevHub
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