# Diffusion

HunyuanVideo Keyframe Control LoRA is an adapter for the HunyuanVideo T2V model, focusing on keyframe video generation. It modifies the input embedding layer to effectively integrate keyframe information and applies Low-Rank Adaptation (LoRA) technology to optimize linear and convolutional input layers, enabling efficient fine-tuning. This model allows users to precisely control the starting and ending frames of the generated video by defining keyframes, ensuring seamless integration with the specified keyframes and enhancing video coherence and narrative. It has significant application value in video generation, particularly excelling in scenarios requiring precise control over video content.

Fashion-Hut-Modeling-LoRA is a diffusion-based text-to-image generation model specifically designed to create high-quality images of fashion models. The model utilizes specific training parameters and datasets to generate fashion photography images with particular styles and details based on text prompts. It holds significant application value in fashion design and advertising, enabling designers and advertisers to swiftly generate creative concept images. The model is still in the training phase, which may result in some less-than-ideal generation outcomes, but it has already demonstrated considerable potential. The training dataset includes 14 high-resolution images and employs parameters like the AdamW optimizer and a constant learning rate scheduler, with a focus on image detail and quality during training.

SIFU is a method for reconstructing high-quality 3D virtual human models from lateral images. Its core innovation lies in proposing a new implicit function based on lateral images, which enhances feature extraction and improves geometric accuracy. Additionally, SIFU introduces a 3D consistent texture optimization process that significantly enhances texture quality and enables texture editing through a text-to-image diffusion model. SIFU excels in handling complex poses and loose clothing, making it an ideal solution for practical applications.

Audio to Photoreal Embodiment is a framework for generating full-body photorealistic avatars. It generates diverse poses and movements of the face, body, and hands based on conversational dynamics. The key to its method lies in combining the sample diversity of vector quantization with the high-frequency details obtained from diffusion, resulting in more dynamic and expressive movements. The photorealistic avatars generated for visualizing the movements can express subtle nuances in poses (e.g., sneering and arrogance). To promote this research direction, we introduce a novel multi-view conversational dataset that enables photorealistic reconstruction. Experiments demonstrate that our model generates appropriate and diverse actions, outperforming diffusion and vector quantization-only methods. Furthermore, our perceptual evaluation highlights the importance of photorealism (compared to meshes) in accurately assessing subtle action details within conversational poses. Code and dataset are available online.

Diffusion Bee is the easiest way to run a stable Diffusion model locally on your Intel/M1 Mac. It offers a one-click installer, requiring no dependencies or technical knowledge. Diffusion Bee runs on your computer locally and will not send any data to the cloud (unless you choose to upload images). Key Features: - Image Conversion - Image Repair - Image Generation History - Image Enlargement - Multiple Image Sizes - Optimized for M1/M2 chips - Supports negative prompting and advanced prompting options - Network Control Diffusion Bee is a GUI wrapper for Stable Diffusion, so all Stable Diffusion terms apply to the outputs. For more information, please visit the documentation. System Requirements: - Mac with Intel or M1/M2 chip - For Intel chips: MacOS 12.3.1 or higher - For M1/M2 chips: MacOS 11.0.0 or higher License: Stable Diffusion is released under the CreativeML OpenRAIL M license.