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AI 模型 2119
- FastH3-Omni-FP8-Comfy↗
FastVideo
The ComfyUI-native FP8 diffusion model for FastH3 Omni. It generates video with synchronized 32-kHz stereo audio from a prompt and ordered image, video and audio references in eight sampling steps. Download a compatible text encoder, video VAE and audio VAE from FastH3 Omni Comfy and place them in `ComfyUI/models/text_encoders/` and `ComfyUI/models/vae/`. The audio VAE and the text encoder's reference-vision support are required. Select a text-encoder format supported by your GPU. FP8 uses ComfyUI’s native quantization layout and kernels. Quantized output can differ from BF16. This is the ComfyUI format; use the separate FastVideo-format model for FastVideo inference.
- 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.
- encodec_32khz↗
facebook
This model card provides details and information about EnCodec 32kHz, a state-of-the-art real-time audio codec developed by Meta AI. This EnCodec checkpoint was trained specifically as part of the MusicGen project, and is intended to be used in conjuction with the MusicGen models. EnCodec is a high-fidelity audio codec leveraging neural networks. It introduces a streaming encoder-decoder architecture with quantized latent space, trained in an end-to-end fashion. The model simplifies and speeds up training using a single multiscale spectrogram adversary that efficiently reduces artifacts and produces high-quality samples. It also includes a novel loss balancer mechanism that stabilizes training by decoupling the choice of hyperparameters from the typical scale of the loss. Additionally, lightweight Transformer models are used to further compress the obtained representation while maintaining real-time performance. This variant of EnCodec is trained on 20k of music data, consisting of an internal dataset of 10K high-quality music tracks, and on the ShutterStock and Pond5 music datasets. - **Repository:** GitHub Repository - **Paper:** Simple and Controllable Music Generation
- Paradee-8M-v1.0↗
sahilmahendrakar
Paradee is a small English text-to-speech model. It has 8.07M parameters and is distilled from Kokoro-82M, and it speaks one voice, Kokoro's `af_heart`. Five held-out sentences, read by Paradee and by its teacher. The sentences are in `samples/sentences.txt`. The ONNX graph goes from phoneme ids to audio. It includes the phase-locking filter described below.
- sam-3d-body↗
Comfy-Org
- sam-audio-large-tv↗
facebook
- noobai-XL-1.0↗
Laxhar
It utilizes the latest full Danbooru and e621 datasets for training, with native tags caption. - CFG: 5 ~ 6 - Steps: 25 ~ 30 - Sampling Method:Euler a - Resolution: Total area around 1024x1024. Best to choose from: 768x1344, **832x1216**, 896x1152, 1024x1024, 1152x896, 1216x832, 1344x768 - Data normalization based on various sources and ratings. - Application of time-based decay coefficients according to date recency. - Ranking of images within the entire dataset based on this processing.
- Z-Image-Turbo-ComfyUI↗
unsloth
The two single-file Z-Image assets that stable-diffusion.cpp needs, mirrored from Comfy-Org/z_image_turbo. Unsloth Studio fetches these on the no-GPU (native sd.cpp) path: both for Z-Image, and the Qwen3-4B conditioner again for FLUX.2 klein 4B, which shares it. They used to come straight from the community repack; a rename or a takedown there turns every CPU Z-Image and klein-4B load into a 404, so they are mirrored here. **The weights are unmodified:** byte for byte the files of the same name in the source repo. Only these two files are mirrored. The source repo also ships the Z-Image diffusion models, a distill LoRA and quantised conditioners; Studio does not use them from there, so they are not here.
- bge-small-zh↗
BAAI
**Recommend switching to newest BAAI/bge-small-zh-v1.5, which has more reasonable similarity distribution and same method of usage.** Model List | FAQ | Usage | Evaluation | Train | Contact | Citation | License More details please refer to our Github: FlagEmbedding.
- Yi-34B-200K-DARE-merge-v7↗
brucethemoose
A merge of several Yi 34B 200K models using the new DARE Ties method via mergekit. The goal is to create a merge model that excels at 32K+ context performance. It might recognize ChatML, and possibly Alpaca-like formats. Raw prompting as described here is also effective: https://old.reddit.com/r/LocalLLaMA/comments/18zqy4s/the_secret_to_writing_quality_stories_with_llms/ Being a Yi model, try running a lower temperature with 0.02-0.06 MinP, a little repetition penalty, maybe mirostat with a low tau, and no other samplers. Yi tends to run "hot" by default, and it really needs a low temperature + MinP to cull the huge vocabulary.