Image Editing
Free image editing AI tools for quickly enhancing photos, creating visuals, and manipulating images for projects in art, marketing, and design.
DragGAN can manipulate images by letting users drag points to change the pose, shape, and layout of objects. It produces realistic results even when parts of the image are hidden or deformed.
Ray Conditioning is a lightweight and geometry-free technique for multi-view image generation. You have that perfect portrait shot of a face but the angle is not right? No problem, just use that shot as an input image and generate the portrait from a another angle. Done.
DiFaReli can relight single-view face images by managing lighting effects like shadows and global illumination. It uses a conditional diffusion model to separate lighting information, achieving photorealistic results without needing 3D data.
[Reference-based Image Composition with Sketch via Structure-aware Diffusion Model] can edit images by filling in missing parts using a reference image and a sketch. This method improves editability and allows for detailed changes in various scenes.
PAIR Diffusion is a generic framework that can enable a diffusion model to control the structure and appearance properties of each object in an image. This allows for various object-level editing operations on real images such as reference image-based appearance editing, free-form shape editing, adding objects, and variations.
Entity-Level Text-Guided Image Manipulation can edit specific parts of an image based on text descriptions while keeping other areas unchanged. It uses a two-step process for aligning meanings and making changes, allowing for flexible and precise editing.
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Neural Congealing can align similar content across multiple images using a self-supervised method. It uses pre-trained DINO-ViT features to create a shared semantic map, allowing for effective alignment even with different appearances and backgrounds.
Pix2Pix-Zero can edit images by changing them in real-time, like turning a cat into a dog, without needing extra text prompts or training. It keeps the original image’s structure and uses pre-trained text-to-image diffusion models for better editing results.
InstructPix2Pix can edit images based on written instructions. It allows users to add or remove objects, change colors, and transform styles quickly, using a conditional diffusion model trained on a large dataset.
SDEdit can generate and edit photo-realistic images using user-guided inputs like hand-drawn strokes or text prompts. It outperforms GAN-based methods, achieving high scores in realism and overall satisfaction without needing specific training.