#diffusion models

Diffusers provides a range of pretrained models for creating images, audio, and 3D structures. The library includes user-friendly diffusion pipelines, adjustable schedulers, and modular components compatible with PyTorch and Flax. It ensures cross-platform support, even for Apple Silicon, offering resources for both new and experienced developers to start quickly, train models, and optimize performance.

Discover a novel method that improves the accuracy of real image inversion using iterative noising. This technique optimizes the inversion process by refining predictions with pretrained diffusion models along the diffusion path, without increasing operational complexity. It supports advanced diffusion models and maintains image editability, suitable for applications in text-guided image modification and diffusion model refinement.

Discover how diffusion models enhance image morphing capabilities in this cutting-edge tool, offering smooth transitions with features like AdaIN and reschedule sampling. Learn about MorphBench, a thorough benchmark for morphing efficacy. Use the Gradio UI for hands-on experimentation, with customizable settings for personalized image transformations. Access setup guides including CUDA compatibility, and optimize performance with pretrained models.

Bridge-TTS uses a Schrodinger Bridge method to enhance text-to-speech synthesis, performing better than diffusion models across multiple settings. It offers precise and efficient synthesis specific to TTS tasks. For detailed insights, visit the project page and paper. The code will be published once accepted.

CycleDiffusion introduces a PyTorch implementation that unifies the latent space of diffusion models. It emphasizes formalizing the 'random seed' for enhanced image-to-image translation capabilities. This method supports zero-shot translation using models like Stable Diffusion and traditional unpaired translations across domains. By leveraging real images and pre-trained models, the project provides robust tools and resources accessible via HuggingFace for researchers and developers in image generation endeavors.

The article introduces StyleTTS 2, a text-to-speech synthesis model leveraging style diffusion and adversarial training with large speech language models. This approach synthesizes varied and natural speech without needing reference audio, employing advanced techniques to enhance naturalness. StyleTTS 2 excels in zero-shot speaker adaptation, surpassing traditional models on the LibriTTS dataset. It performs at or above human-level quality across single and multi-speaker datasets, demonstrating the efficacy of style diffusion combined with adversarial training for TTS advancements.

UDiffText employs character-aware diffusion models for high-quality text synthesis in synthetic and real-world images, applicable to tasks like scene text editing and text-to-image generation. Version 2.0 offers improved performance with an interactive demo. The project utilizes datasets such as LAION-OCR and integrates with Stable Diffusion models for diverse needs.

Consistency Trajectory Model (CTM) delivers state-of-the-art results on CIFAR-10 and ImageNet 64x64, as presented at ICLR 2024. CTM effectively manages computational demands while maintaining sample fidelity, offering various sampling options via probability flow learning of ODE trajectories. The PyTorch implementation, along with codes, checkpoints, and evaluation scripts, is accessible in the official repository for developers interested in efficient and high-quality image processing.

Onediff provides a ready-to-use library designed to accelerate diffusion model frameworks such as HF diffusers and ComfyUI. Utilizing optimized GPU kernels and PyTorch code tools, it achieves significant speed improvements for models like Kolors and DiT. Onediff integrates efficiently across AI model platforms, allowing dynamic image sizes with minimal overhead. Available for installation via PyPI or source, Onediff ensures streamlined acceleration and increased workflow efficiencies suitable for enterprise-level applications.

GaussianDreamer facilitates the swift creation of detailed 3D models from text prompts by combining 2D and 3D diffusion models. Implementing advanced 3D Gaussian splatting, it enhances both speed and fidelity, rendering real-time 3D outputs in just 15 minutes using a single GPU. Through techniques like noise addition and color variation, it ensures 3D consistency and clarity. Suitable for animation and simulation integration, it allows for Unity export and avatar generation, providing utility across multiple applications.

Discover an array of diffusion models shaping video applications such as creation, editing, and restoration. This resource caters to researchers and developers keen on video technology advances. It covers video generation, controllable production, motion customization, and 3D/NeRF uses. Utilize open-source kits and models for quality enhancement, AI safety, and video restoration. Evaluate with established metrics to refine performance and analyze content. These models also present opportunities in fields like healthcare and biology.

IP-Adapter efficiently integrates image prompts into text-to-image diffusion models with 22M parameters, matching or surpassing fine-tuned models' performance. This tool adapts to custom models and enhances controllable generation with existing tools, supporting multimodal generation with text prompts. Recent versions include face recognition and improved fidelity with SDXL, available for exploration through demos and third-party integration, highlighting its versatility in AI image generation.

Discover the DRLX library designed for distributed diffusion model training utilizing reinforcement learning. Integrate effortlessly with Hugging Face's Diffusers and leverage Accelerate for scalable Multi-GPU and Multi-Node configurations. Explore DDPO algorithm applications compatible with Stable Diffusion across diverse pipelines. Access documentation for installation and learn about our latest experiments.

This project provides an overview of diffusion models in image processing, targeting restoration, enhancement, compression, and quality assessment. It compiles various academic studies, offering researchers and developers up-to-date insights into advancements and applications in visual computing. Regular updates ensure it remains a valuable tool for understanding diffusion-based techniques such as super-resolution, inpainting, and denoising.

The AI Scientist enables automated scientific discovery using advanced models like Large Language Models. It allows AI to independently conduct research, generating papers without human oversight. This system supports fields like transformer and generative diffusion models, offering an autonomous avenue for scientific insights and research enhancement.

Discover a method for fast and accurate local text-driven image editing using Blended Latent Diffusion. This approach enhances image modification within user-defined masks by reducing inference time and minimizing artifacts, outperforming traditional GANs. Suitable for varied applications such as altering backgrounds and objects or generating text.

InstructCV utilizes advancements in text-to-image diffusion to streamline computer vision tasks, such as segmentation and classification. It simplifies execution through a natural language interface, transforming tasks into text-to-image problems. Using diverse datasets, it employs instruction-tuning to enhance task performance, serving as an instruction-guided vision learner.