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AI モデル 183
- Ling-3.0-tiny-GGUF↗
inclusionAI
🤗 Hugging Face | 🤖 ModelScope | 🐙 OpenRouter Ling-3.0-tiny is a lightweight hybrid reasoning MoE model with **7.9B** total parameters and only **1.3B** activated parameters per token. It is designed to deliver strong reasoning and agentic capabilities at low inference cost, making advanced model capabilities more accessible for local and resource-constrained deployment. Find more details in the original model card: https://huggingface.co/inclusionAI/Ling-3.0-tiny
- Qwen-Image-Edit-2509↗
阿里巴巴 · Qwen
💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    🖥️ Demo    |   💬 WeChat (微信)    |   🫨 Discord   |    Github    This September, we are pleased to introduce Qwen-Image-Edit-2509, the monthly iteration of Qwen-Image-Edit. To experience the latest model, please visit Qwen Chat and select the "Image Editing" feature. Compared with Qwen-Image-Edit released in August, the main improvements of Qwen-Image-Edit-2509 include: * **Multi-image Editing Support**: For multi-image inputs, Qwen-Image-Edit-2509 builds upon the Qwen-Image-Edit architecture and is further trained via image concatenation to enable multi-image editing. It supports various combinations such as "person + person," "person + product," and "person + scene." Optimal performance is currently achieved with 1 to 3 input images. * **Enhanced Single-image Consistency**: For single-image inputs, Qwen-Image-Edit-2509 significantly improves editing consistency, specifically in the following areas: - **Improved Person Editing Consistency**: Better preservation of facial identity, supporting various portrait styles and pose transformations; - **Improved Product Editing Consistency**: Better preservation of product identity, supporting product poster editing； - **Improved Text Editing Consistency**: In addition to modifying text content, it also supports editing text fonts, colors, and materials； * **Native Support for ControlNet**: Including depth maps, edge maps, keypoint maps, and more.
- Wan2.2-Animate-14B↗
Wan-AI
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Paper    |    📑 Blog    |    💬 Discord    📕 使用指南(中文)    |    📘 User Guide(English)    |   💬 WeChat(微信)    We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations:
- self_cognition_Alice↗
charent
**Note** This is a Lora model that tests for vLLM multi-Lora tensor parallelism bugs and doesn't do anything else. Training data from modelscope/self-cognition Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Wan2.2-T2V-A14B-Diffusers↗
Wan-AI
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Technical Report    |    📑 Blog    |   💬 WeChat Group    |    📖 Discord    We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations: - 👍 **Effective MoE Architecture**: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.
- Qwen-Image-Edit-GGUF↗
unsloth
> [!NOTE] > This is a GGUF quantized version of Qwen-Image-Edit. > unsloth/Qwen-Image-Edit-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. Important layers are upcasted to higher precision. 💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    🖥️ Demo    |   💬 WeChat (微信)    |   🫨 Discord   |    Github   
- Qwen-Image-Edit-2509-Relight↗
dx8152
My LoRa training is all done using ModelScope. Thanks to the ModelScope team for providing the training infrastructure. Give it a try! There are two ways to use it: 1. Use the prompts in the dataset to change the image directly. 2. Use the trigger word: " 重新照明 "。 （For example:重新照明,使用窗帘透光（柔和漫射）的光线对图片进行重新照明） Meaning: Relighting, using the soft, diffuse light from the curtains to relight the image. （I also uploaded the re-light LoRAs for Kontext downloaded from the Internet. The sources are https://civit.ai/models/1779918 and https://hf.co/dong625/kontext_loras/tree/main ）
- Wan2.1-T2V-14B-Diffusers↗
Wan-AI
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Paper (Coming soon)    |    📑 Blog    |   💬 WeChat Group    |    📖 Discord    In this repository, we present **Wan2.1**, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. **Wan2.1** offers these key features: - 👍 **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks. - 👍 **Supports Consumer-grade GPUs**: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models. - 👍 **Multiple Tasks**: **Wan2.1** excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation. - 👍 **Visual Text Generation**: **Wan2.1** is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications. - 👍 **Powerful Video VAE**: **Wan-VAE** delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation. This repository features our T2V-14B model, which establishes a new SOTA performance benchmark among both open-source and closed-source models. It demonstrates exceptional capabilities in generating high-quality visuals with significant motion dynamics. It is also the only video model capable of producing both Chinese and English text and supports video generation at both 480P and 720P resolutions.
- Wan2.1-Fun-1.3B-InP↗
alibaba-pai
V1.0: | 名称 | 存储空间 | Hugging Face | Model Scope | 描述 | |--|--|--|--|--| | Wan2.1-Fun-1.3B-InP | 19.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-1.3B文图生视频权重，以多分辨率训练，支持首尾图预测。 | | Wan2.1-Fun-14B-InP | 47.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-14B文图生视频权重，以多分辨率训练，支持首尾图预测。 | | Wan2.1-Fun-1.3B-Control | 19.0 GB | 🤗Link | 😄Link| Wan2.1-Fun-1.3B视频控制权重，支持不同的控制条件，如Canny、Depth、Pose、MLSD等，同时支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 | | Wan2.1-Fun-14B-Control | 47.0 GB | 🤗Link | 😄Link| Wan2.1-Fun-14B视频控制权重，支持不同的控制条件，如Canny、Depth、Pose、MLSD等，同时支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 | Windows 的详细信息： - 操作系统 Windows 10 - python: python3.10 & python3.11 - pytorch: torch2.2.0 - CUDA: 11.8 & 12.1 - CUDNN: 8+ - GPU： Nvidia-3060 12G & Nvidia-3090 24G Linux 的详细信息： - 操作系统 Ubuntu 20.04, CentOS - python: python3.10 & python3.11 - pytorch: torch2.2.0 - CUDA: 11.8 & 12.1 - CUDNN: 8+ - GPU：Nvidia-V100 16G & Nvidia-A10 24G & Nvidia-A100 40G & Nvidia-A100 80G
- Fun-CosyVoice3-0.5B-2512↗
FunAudioLLM
**Fun-CosyVoice 3.0**: Demos; Paper; Modelscope; Huggingface; CV3-Eval **CosyVoice 2.0**: Demos; Paper; Modelscope; HuggingFace **CosyVoice 1.0**: Demos; Paper; Modelscope; HuggingFace