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AI 模型 450
- anima_baseV10↗
LyliaEngine
Anima is a 2 billion parameter text-to-image model created via a collaboration between CircleStone Labs and Comfy Org. It is focused mainly on anime concepts, characters, and styles, but is also capable of generating a wide variety of other non-photorealistic content. The model is designed for making illustrations and artistic images, and will not work well at realism. It is trained on several million anime images and about 800k non-anime artistic images. No synthetic data was used for training. The knowledge cut-off date for the anime training data is September 2025. The model is natively supported in ComfyUI. The model files go in their respective folders inside your model directory:
- gemma-2-9b-it-AWQ-INT4↗
hugging-quants
> [!IMPORTANT] > This repository is a community-driven quantized version of the original model `google/gemma-2-9b-it` which is the BF16 half-precision official version released by Google. > [!WARNING] > This model has been quantized using `transformers` 4.45.0, meaning that the tokenizer available in this repository won't be compatible with lower versions. Same applies for e.g. Text Generation Inference (TGI) that only installs `transformers` 4.45.0 or higher starting in v2.3.1. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights for both pre-trained variants and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.
- Qwen3-0.6B-MLX-4bit↗
lmstudio-community
*👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord*. **Model creator:** Qwen **Original model**: Qwen3-0.6B **MLX quantization:** provided by LM Studio team using mlx_lm LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
- Tower-Plus-9B↗
Unbabel
**Tower+ 9B** is build on top of Gemma 2 9B. The model goes through the Continuous Pretraining (CPT), Instruction Tuning (IT), Weighted Preference Optimization (WPO). During all stages we include parallel and multilingual data (covering 22 languages). Another usecase Tower works well is for creating multilingual synthethic data (for the languages it covers). You can do this either by translating instructions and the respective answers or by asking the model to create an instruction given a document as seed data.
- gemma-4-31B-it-litert-lm↗
litert-community
This model card provides the Gemma 4 31B model in LiteRT-LM format that is ready for deployment on web and desktop. Please check back here regularly for updates on wider platform support and further functionality improvements. The current LiteRT-LM version supports text; audio, image, and multitoken prediction support will be available in a future update. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. This particular Gemma 4 model is a medium size so it is ideal for desktop use cases. By running this model on device, users can have private access to Generative AI technology without even requiring an internet connection. These models are provided in the `.litertlm` format for use with the LiteRT-LM framework. LiteRT-LM is a specialized orchestration layer built directly on top of LiteRT, Google’s high-performance multi-platform runtime trusted by millions of Android and edge developers. LiteRT provides the foundational hardware acceleration via XNNPack for CPU and ML Drift for GPU. LiteRT-LM adds the specialized GenAI libraries and APIs, such as KV-cache management, prompt templating, and function calling. This integrated stack is the same technology powering the Google AI Edge Gallery showcase app.
- musicgen-stereo-medium↗
facebook
We further release a set of stereophonic capable models. Those were fine tuned for 200k updates starting from the mono models. The training data is otherwise identical and capabilities and limitations are shared with the base modes. The stereo models work by getting 2 streams of tokens from the EnCodec model, and interleaving those using the delay pattern. Stereophonic sound, also known as stereo, is a technique used to reproduce sound with depth and direction. It uses two separate audio channels played through speakers (or headphones), which creates the impression of sound coming from multiple directions. MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio.
- viggle-pro-create↗
待核实厂商
Viggle 舞蹈视频 AI，可以通过一张人物图片和舞蹈视频，实现人物跳舞. 会员 Pro 无水印版 点击查看接入文档
- bark↗
suno
Bark is a transformer-based text-to-audio model created by Suno. Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects. The model can also produce nonverbal communications like laughing, sighing and crying. To support the research community, we are providing access to pretrained model checkpoints ready for inference. The original github repo and model card can be found here. This model is meant for research purposes only. The model output is not censored and the authors do not endorse the opinions in the generated content. Use at your own risk.
- juggernaut-xl-v9-GGUF↗
off-grid-ai
GGUF conversions of Juggernaut XL v9 for **on-device** image generation with stable-diffusion.cpp. Converted with sd.cpp's `-M convert` so the tensors are correctly named and load directly — the community GGUF quants of this model are mis-exported and fail `get sd version from file`. `Q4_K_S` in a file name: stable-diffusion.cpp wrote the file with `--type q4_K`. Its weight tensors are Q4_K and the other tensors stay F16/BF16/F32. `Q4_K_S` is the closest standard GGUF label, so Hugging Face can list the file. This file was named `juggernaut-xl-v9-Q4_K.gguf` before 2026-10-21; the contents are unchanged, and links pinned to an earlier commit still work. **Original model:** https://huggingface.co/RunDiffusion/Juggernaut-XL-v9 — created by its respective authors. **License:** `creativeml-openrail-m` (carried over from the original; its use restrictions apply). This is a format conversion (quantization) only; all credit for the model belongs to the original creators.
- Huihui-GLM-5.3-Flash-abliterated-GGUF↗
huihui-ai
This is an uncensored version of zai-org/GLM-5.3-Flash created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model. The latest version of llama.cpp now officially supports zai-org/GLM-5.3-Flash. We have updated the first file of each quantized version, so you only need to download the first file.