#Image Editing

InternGPT introduces a unique visual interaction system using pointing devices, enhancing ChatGPT's efficiency in vision-centric tasks. Utilizing the Husky model, it specializes in multi-modal dialogues and visual scenarios, allowing for image editing and generation, and more. Community-driven, it continually evolves, promising improved functionalities and user experiences.

SCEPTER is an open-source repository focused on generative training and inference, providing tools for image generation, transfer, and editing. It incorporates community approaches and Alibaba Tongyi Lab's proprietary methods, making it pivotal for AI-generated content research. Key features include a generative training framework, ease of implementing popular methods, and the SCEPTER Studio for interactive use. Recent updates add support for the FLUX framework, and introduce models like ACE for varied image editing and SCEdit for controllable synthesis, streamlining innovation in generative model development.

The plugin provides direct integration of Automatic1111's Stable Diffusion AI within Photoshop, streamlining the workflow by removing the need for switching applications. Users can efficiently edit and save images using familiar Photoshop tools. Installation is straightforward with three options, including an artist-friendly one-click installer. By supporting the project on Patreon, users contribute to ongoing development and gain access to exclusive features and tutorials. The tool is designed for ease of use in familiar environments, making AI approachable for creative professionals. It includes troubleshooting for loading issues and non-GPU options with Stable Horde and Colab.

This survey provides an in-depth review of the latest methodologies and categorizations in multimodal image synthesis and editing, exploring innovative advancements in visual AI-generated content. It covers neural rendering, diffusion, and GAN-based techniques, emphasizing influential studies and projects. This resource is suitable for researchers and professionals interested in understanding the impact of generative AI on image editing, serving as a guide to the latest technologies and applications.

Discover innovative text-based image editing techniques using Latent and Stable Diffusion models that leverage prompt manipulation for greater creative control. This project features prompt replacement, refinement, and re-weighting methods, allowing modification of attention weights to create customized image outputs. Designed for researchers and developers, it offers detailed notebooks and code implementation, compatible with Pytorch 1.11 and Huggingface diffusers. Understand attention control mechanisms, primary prompt edits, and real image editing via null-text inversion, offering notable advancements in AI-generated imagery for precise and intuitive outcomes.

Leverage Segment Anything and ControlNet for dynamic image editing, enabling features like cross-image merging and comprehensive sketch transformations. Includes support for text-guided segmentation and customizable DreamBooth editing. Recent UI and performance upgrades enhance user experience.