Image Editing

UniPortrait can customize images of one or more people with high quality. It allows for detailed face editing and uses free-form text descriptions to guide changes.

OmniBooth can generate images with precise control over their layout and style. It allows users to customize images using masks and text or image guidance, making the process flexible and personal.

Love this one! SVGCustomization is a novel pipeline that is able to edit existing vector images with text prompts while preserving the properties and layer information vector images are made of.

Prompt Sliders can control and edit concepts in diffusion models. It allows users to adjust the strength of concepts with just 3KB of storage per embedding, making it much faster than traditional LoRA methods.

InstantDrag can edit images quickly using drag instructions without needing masks or text prompts. It learns motion dynamics with a two-network system, allowing for real-time, photo-realistic editing.

TurboEdit enables fast text-based image editing in just 3-4 diffusion steps! It improves edit quality and preserves the original image by using a shifted noise schedule and a pseudo-guidance approach, tackling issues like visual artifacts and weak edits.

CSGO can perform image-driven style transfer and text-driven stylized synthesis. It uses a large dataset with 210k image triplets to improve style control in image generation.

MagicFace can generate high-quality images of people in any style without needing extra training.

MagicFace can generate high-quality images of people in any style without needing training. It uses special attention methods for precise attribute alignment and feature injection, working for both single and multi-concept customization.

Generative Photomontage can combine parts of multiple AI-generated images using a brush tool. It enables the creation of new appearance combinations, correct shapes and artifacts, and improve prompt alignment, outperforming existing image blending methods.

Filtered Guided Diffusion shows that image-to-image translation and editing doesn’t necessarily require additional training. FGD simply applies a filter to the input of each diffusion step based on the output of the previous step in an adaptive manner which makes this approach easy to implement.

IPAdapter-Instruct can efficiently combine natural-image conditioning with “Instruct” prompts! It enables users to switch between various interpretations of the same image, such as style transfer and object extraction.

Magic Clothing can generate customized characters wearing specific garments from diverse text prompts while preserving the details of the target garments and maintain faithfulness to the text prompts.

Adobe’s Magic Fixup lets you edit images with a cut-and-paste approach that fixes edits automatically. Can see this being super useful for generating animation frames for tools like AnimateDiff. But it’s not clear yet if or when this hits Photoshop.

IMAGDressing-v1 can generate human try-on images from input garments. It is able to control different scenes through text and can be combined with IP-Adapter and ControlNet pose to enhance the diversity and controllability of generated images.

HumanRefiner can improve human hand and limb quality in images! The method is able to detect and correct issues related to both abnormal human poses.

PartCraft can generate customized and photorealistic virtual creatures by mixing visual parts from existing images. This tool allows users to create unique hybrids and make detailed changes, which is useful for digital asset creation and studying biodiversity.

MIGC++ is a plug-and-play controller that enables Stable Diffusion with precise position control while ensuring the correctness of various attributes like color, shape, material, texture, and style. It can also control the number of instances and improve interaction between instances.

StyleShot can mimic and style transfer various styles from an image, such as 3D, flat, abstract or even fine-grained styles, without tuning.

iCD can be used for zero-shot text-guided image editing with diffusion models. The method is able to encode real images into their latent space in only 3-4 inference steps and can then be used to edit the image with a text prompt.