Image Editing

HyperHuman is a text-to-image model that focuses on generating hyper-realistic human images from text prompts and a pose image. The results are pretty impressive and the model is able to generate images in different styles and up to a resolution of 1024x1024.

[Total Selfie] can generate high-quality full-body selfies from close-up selfies and background images. It uses a diffusion-based approach to combine these inputs, creating realistic images in desired poses and overcoming the limits of traditional selfies.

CLE Diffusion can enhance low-light images by letting users control brightness levels and choose specific areas for improvement. It uses an illumination embedding and the Segment-Anything Model (SAM) for precise and natural-looking enhancements.

Interpolating between Images with Diffusion Models can generate smooth transitions between two images using latent diffusion models. It allows for high-quality results across different styles and subjects while using CLIP to select the best images for interpolation.

FABRIC can condition diffusion models on feedback images to improve image quality. This method allows users to personalize content through multiple feedback rounds without needing training.

Text2Cinemagraph can create cinemagraphs from text descriptions, animating elements like flowing rivers and drifting clouds. It combines artistic images with realistic ones to accurately show motion, outperforming other methods in generating cinemagraphs for natural and artistic scenes.

CSD-Edit is a multi modality editing approach that compared to other methods works great on images bigger than the traditional 512x512 limitation and can edit 4k or large panorama images, has improved temporal consistency on video frames as well as improved view consistency when editing or generating 3D scenes.

Break-A-Scene can extract multiple concepts from a single image using segmentation masks. It allows users to re-synthesize individual concepts or combinations in different contexts, enhancing scene generation with a two-phase customization process.

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.

UnZipLoRA can break down an image into its subject and style. This makes it possible to create variations and apply styles to new subjects.

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.