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AI Models 183
- MiniMax-H3-X2-Detail-VAE↗
speach1sdef178
> **Status:** Working model + tested custom node + workflow + research report\ > **Starting point:** my own already-working MiniMax H3 2X VAE\ > **Scope:** this report intentionally begins with that completed 2X VAE. I created the base 2X VAE used for this project as well, but how that VAE was built is a separate story and is not covered here. This repository is the final practical outcome of the experiment. It contains a **2-in-1 MiniMax H3 X2 VAE**, an optional ComfyUI detail-enhancement node, an example workflow, and the full research article below. Load `MiniMax-H3-X2-Detail-v1.safetensors` with the normal ComfyUI **Load VAE** node and use it as the video VAE for MiniMax H3. For the actual video decode, replace the base ComfyUI VAE Decode node with **`MiniMax H3 VAE Decode (fast)`** from TripleHeadedMonkey/ComfyUI-MiniMaxH3_LatentUpscaler. This node is required for the released 2X workflow.
- QwenPaw-Flash-9B-Q4_K_M↗
agentscope-ai
- Anima-2.9B↗
Gazingstars123
**Every bit of support helps expand the model's scope and capability even further!** You may still need to install ComfyUI-Anima-2.9B to the custom node folder if your comfyui are not up-to-date (version 0.33.1). Plug and play, there is no custom node needed. Sometimes may not work with other custom nodes Anima-2.9B is a fine-tune and layer-expansion of circlestone-labs/Anima. The base Anima model targets anime, illustration, and non-photorealistic art; this release continues training on that foundation with the expanded architecture. The model is trained on an additional 1.7M anime/illustration samples, with knowledge cutoff in July 2026, making Anima-2.9B one of the most up-to-date anime/illustration model at release.
- Hunyuan-A13B-Instruct-GGUF↗
unsloth
🤗 Hugging Face | 🖥️ Official Website | 🕖 HunyuanAPI | 🕹️ Demo | 🤖 ModelScope Welcome to the official repository of **Hunyuan-A13B**, an innovative and open-source large language model (LLM) built on a fine-grained Mixture-of-Experts (MoE) architecture. Designed for efficiency and scalability, Hunyuan-A13B delivers cutting-edge performance with minimal computational overhead, making it an ideal choice for advanced reasoning and general-purpose applications, especially in resource-constrained environments. With the rapid advancement of artificial intelligence technology, large language models (LLMs) have achieved remarkable progress in natural language processing, computer vision, and scientific tasks. However, as model scales continue to expand, optimizing resource consumption while maintaining high performance has become a critical challenge. To address this, we have explored Mixture of Experts (MoE) architectures. The newly introduced Hunyuan-A13B model features a total of 80 billion parameters with 13 billion active parameters. It not only delivers high-performance results but also achieves optimal resource efficiency, successfully balancing computational power and resource utilization.
- gemma-scope-transcoders↗
mntss
- Wan2.1-T2V-1.3B-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 hosts our T2V-1.3B model, a versatile solution for video generation that is compatible with nearly all consumer-grade GPUs. In this way, we hope that **Wan2.1** can serve as an easy-to-use tool for more creative teams in video creation, providing a high-quality foundational model for academic teams with limited computing resources. This will facilitate both the rapid development of the video creation community and the swift advancement of video technology.
- scope-guard-v2-4B-q-2609-GGUF↗
mradermacher
- Qwen-Edit-2509-Multiple-angles↗
dx8152
client = InferenceClient( provider="wavespeed", api_key=os.environ["HF_TOKEN"], ) image = client.image_to_image( input_image, prompt="Turn the cat into a tiger.", model="dx8152/Qwen-Edit-2509-Multiple-angles", ) This model is trained (code-free!) on ModelScope. Thanks to ModelScope team for providing the training infra: Updated 2025/11/2: Some people mentioned that the model has an unstable consistency issue. I have re-uploaded a version with more training iterations, hoping to fix the consistency problem.
- gemma-scope-27b-pt-res↗
Google
- Wan2.1-T2V-1.3B↗
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 hosts our T2V-1.3B model, a versatile solution for video generation that is compatible with nearly all consumer-grade GPUs. In this way, we hope that **Wan2.1** can serve as an easy-to-use tool for more creative teams in video creation, providing a high-quality foundational model for academic teams with limited computing resources. This will facilitate both the rapid development of the video creation community and the swift advancement of video technology.