Tags: #diffusion-models

A powerful and modular visual interface for designing and executing advanced Stable Diffusion and other generative AI model pipelines.

A user-friendly, versatile fine-tuning kit for image, video, and audio diffusion models, emphasizing simplicity and cutting-edge features.

Enables fast and efficient multi-concept customization of text-to-image diffusion models like Stable Diffusion using a few images.

Nunchaku is a high-performance inference engine that optimizes 4-bit neural networks, especially diffusion models, for speed and efficiency.

A tool for fast and efficient fine-tuning of diffusion models using Low-rank Adaptation (LoRA), producing small, shareable models.

A human preference reward model for evaluating and improving text-to-image generation models.

A modular PyTorch library for state-of-the-art diffusion models, enabling easy generation of images, audio, and more.

A PyTorch implementation of Dreamfusion, enabling text-to-3D and image-to-3D content generation using NeRF and Stable Diffusion.

A comprehensive collection and categorization of diffusion model papers, accompanied by a detailed survey and taxonomy.

DiffSinger is an official PyTorch implementation of a singing voice synthesis (SVS) and text-to-speech (TTS) system, leveraging a shallow diffusion mechanism for high-quality audio generation.

StyleTTS 2 is a text-to-speech model that achieves human-level speech synthesis by leveraging style diffusion and adversarial training with large speech language models.

A lightweight, pure C/C++ inference engine for various diffusion models, enabling efficient image and video generation across multiple platforms and hardware.

A meticulously categorized collection of research papers on diffusion models, organized by diverse subareas such as visual illusion, color in generation, image restoration, and text-guided editing.