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AI モデル 449
- 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 ダンス動画 AI は、人物の画像とダンス動画を用いて、人物が踊る様子を実現できます。メンバープロ版（透かしなし） 接続ドキュメントを見るにはクリックしてください
- 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.
- Wan 2.7↗
待核实厂商
Wan 2.7 API（APIMart）：调用阿里巴巴 Wan2.7，用文字、首尾帧或已有视频续写生成 2–15 秒视频，支持 720P/1080P。开通会员，API 调用立省20%起。
- 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.
- bert-base-german-cased↗
google-bert
**Update April 3rd, 2020**: we updated the vocabulary file on deepset's s3 to conform with the default tokenization of punctuation tokens. For details see the related FARM issue. If you want to use the old vocab we have also uploaded a "deepset/bert-base-german-cased-oldvocab" model. - We trained using Google's Tensorflow code on a single cloud TPU v2 with standard settings. - We trained 810k steps with a batch size of 1024 for sequence length 128 and 30k steps with sequence length 512. Training took about 9 days. - As training data we used the latest German Wikipedia dump (6GB of raw txt files), the OpenLegalData dump (2.4 GB) and news articles (3.6 GB). - We cleaned the data dumps with tailored scripts and segmented sentences with spacy v2.1. To create tensorflow records we used the recommended sentencepiece library for creating the word piece vocabulary and tensorflow scripts to convert the text to data usable by BERT. During training we monitored the loss and evaluated different model checkpoints on the following German datasets:
- janku-v5-nsfw-trained-noobai-rou-wei-illustrious-xl-v50-sdxl↗
John6666
Original model is here. This model created by janxd.
- DeepHat-V1-7B↗
DeepHat
DeepHat is a model series that can be used for offensive and defensive cybersecurity. Access at Deephat.ai or go to Kindo.ai to create agents. DeepHat is a finetune of Qwen2.5-Coder-7B, and inherits the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 7.61B - Number of Paramaters (Non-Embedding): 6.53B - Number of Layers: 28 - Number of Attention Heads (GQA): 28 for Q and 4 for KV - Context Length: Full 131,072 tokens - Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts. Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
- LTX_2.3_Fantasy_Anime_Style_LoRa↗
vrgamedevgirl84
This LoRA creates a **cinematic fantasy anime cel-shaded illustration** look. **The dataset used to create this LoRA did not use any IP.** It is a **grounded high-fantasy anime illustration style**, not actual frames or images from any movies, TV shows, or existing franchises. The workflow used to create the sample videos can be found HERE. All sample videos are **text-to-video**, not image-to-video. Characters and environments appear as stylized fantasy anime illustrations. Scenes feature **hand-drawn linework**, **painted shading**, **expressive faces**, **stylized fantasy costumes**, **magical creatures**, **vivid atmospheric effects**, and **cinematic composition**. The result feels like a scene from a modern high-end fantasy anime film with immersive worlds and story-driven character moments.