#Diffusion models

ProphetNet by MSRA NLC offers a comprehensive suite of natural language generation tools, including future-informed pretrained models, GLGE baselines, joint generator-ranker systems, and diffusion models like GENIE and AR-Diffusion. The CRITIC module enhances LLM functionality through interaction with external tools. The project adheres to open-source principles, aligned with Microsoft's code of conduct.

DiffSynth Studio is an efficient diffusion engine supporting models like CogVideoX, FLUX, and Stable Diffusion. It offers tools for text-to-video and image synthesis customized for high-resolution outputs. Recent updates add FLUX ControlNet enhancements and advanced video synthesis models. Features include toon shading and stylization, with access via Python and WebUI.

The Auto1111SDK is a lightweight Python library for image creation and enhancement using Stable Diffusion models. It provides user-friendly integration with the Automatic 1111 Web UI parameters for seamless Text-to-Image and Image-to-Image transformations, Inpainting, and Outpainting. Supporting ESRGAN upscaling and model downloads from Civit AI, the SDK plans to incorporate additional features such as Dreambooth training and face restoration, offering developers a comprehensive tool for efficient image processing.

Surf-D uses diffusion models to generate high-quality 3D surfaces of any topology. The utilization of Unsigned Distance Field (UDF) allows precise surface representation, overcoming prior limitations. A unique point-based AutoEncoder creates a continuous latent space, supporting accurate UDF encoding and high-resolution mesh extraction. The model excels in unconditional, category, image, and text-to-shape generation, validated by extensive experiments.

TF-ICON presents a new method for image composition using text-driven diffusion models, offering the integration of objects into varied visual contexts without requiring instance-based optimization or model finetuning. This approach maintains the models' inherent qualities by employing a unique prompt to improve the conversion of images into latent forms. TF-ICON has shown to perform better than current methods on multiple datasets, demonstrating its versatility in different visual domains.