VieNeu-TTS-0.3B-Q4-0-GGUF is a Q40 quantized version of VieNeu-TTS-0.3B. This model is specifically optimized to run directly on the CPU, providing extremely fast speech synthesis without the need for a dedicated GPU. Training high-quality TTS models requires significant GPU resources. If you find this model useful, please consider supporting the development: eSpeak NG is mandatory for phonemization. Use the source code from GitHub for the best experience with full text preprocessing support: In the UI, select Backbone: VieNeu-TTS-0.3B-q4-gguf and Device: CPU. Install the SDK to integrate VieNeu-TTS-0.3B into your research or applications: This model is released under the CC BY-NC 4.0…
Open weights
cc-by-nc-4.0
This repo is the official implementation of "Bilateral Reference for High-Resolution Dichotomous Image Segmentation" (CAAI AIR 2024). Visit our GitHub repo: https://github.com/ZhengPeng7/BiRefNet for more details -- codes, docs, and model zoo! This repo contains the weights of BiRefNet proposed in our paper, which has achieved the SOTA performance on three tasks (DIS, HRSOD, and COD). Go to my GitHub page for BiRefNet codes and the latest updates: https://github.com/ZhengPeng7/BiRefNet:) + Online Single Image Inference on Colab: + Online Inference with GUI on Hugging Face with adjustable resolutions: + Inference and evaluation of your given weights: + Many thanks to @fal for their generous…
Open weights
mit
44M parameters
birefnet
A Swin Transformer image classification model. Pretrained on ImageNet-1k by paper authors. Explore the dataset and runtime metrics of this model in timm model results.
Open weights
mit
29M parameters
timm
The text line orientation classification module primarily distinguishes the orientation of text lines and corrects them using post-processing. In processes such as document scanning and license/certificate photography, to capture clearer images, the capture device may be rotated, resulting in text lines in various orientations. Standard OCR pipelines cannot handle such data well. By utilizing image classification technology, the orientation of text lines can be predetermined and adjusted, thereby enhancing the accuracy of OCR processing. The key accuracy metrics are as follow: Please refer to the following commands to install PaddlePaddle using pip: For details about PaddlePaddle…
Open weights
apache-2.0
PaddleOCR
The development log of our Music Audio Pre-training (m-a-p) model family: - 02/06/2023: arxiv pre-print and training codes released. - 17/03/2023: we release two advanced music understanding models, MERT-v1-95M and MERT-v1-330M, trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. - 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset MERT-v0-public - 29/12/2022: a music understanding model MERT-v0 trained with MLM paradigm, which performs better at downstream tasks. - 29/10/2022: a pre-trained MIR model music2vec trained with BYOL paradigm. Here is a table for quick model pick-up: The m-a-p models…
Open weights
cc-by-nc-4.0
transformers
A ResNet-B image classification model. single layer 7x7 convolution with pooling 1x1 convolution shortcut downsample Pretrained on Instagram-1B hashtags dataset using semi-weakly supervised learning and fine-tuned on ImageNet-1k by paper authors. - Billion-scale semi-supervised learning for image classification: https://arxiv.org/abs/1905.00546 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 Explore the dataset and runtime metrics of this model in timm model results.
Open weights
cc-by-nc-4.0
26M parameters
timm
Model · Text to video
Sam
Wan2.1 is an open-source suite of video foundation models, compatible with consumer-grade GPUs, that excels in various video generation tasks like text-to-video, image-to-video, and video editing, even supporting visual text generation. Download models using huggingface-cli: You can also download directly from this page. This model is a derivative work of the original model licensed under the Apache 2.0 License, and is therefore distributed under the terms of the same license. Thanks to Patrick Gillespie for creating the ASCII text art tool used in this project https://patorjk.com/software/taag/ Wan-AI for the Wan model https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B…
Open weights
apache-2.0
diffusers
source languages: it; target languages: en; OPUS readme: it-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.
Open weights
apache-2.0
512 tokens
transformers
A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k (with additional augmentation and regularization) in JAX by paper authors, ported to PyTorch by Ross Wightman. - How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 Explore the dataset and runtime metrics of this model in timm model results.
Open weights
apache-2.0
103M parameters
timm
A RegNetY-3.2GF image classification model. Trained on ImageNet-1k by Ross Wightman in timm. The timm RegNet implementation includes a number of enhancements not present in other implementations, including: stochastic depth gradient checkpointing layer-wise LR decay configurable output stride (dilation) configurable activation and norm layers option for a pre-activation bottleneck block used in RegNetV variant only known RegNetZ model definitions with pretrained weights Explore the dataset and runtime metrics of this model in timm model results. For the comparison summary below, the rain1k, ra3in1k, chin1k, sw, and lion tagged weights are trained in timm.
Open weights
apache-2.0
20M parameters
timm
Model · Text to video
Sam
Wan2.1 is an open-source suite of video foundation models, compatible with consumer-grade GPUs, that excels in various video generation tasks like text-to-video, image-to-video, and video editing, even supporting visual text generation. Download models using huggingface-cli: You can also download directly from this page. This model is a derivative work of the original model licensed under the Apache 2.0 License, and is therefore distributed under the terms of the same license. Thanks to Patrick Gillespie for creating the ASCII text art tool used in this project https://patorjk.com/software/taag/ Wan-AI for the Wan model https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B…
Open weights
apache-2.0
diffusers
M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this paper and first released in this repository. The model that can directly translate between the 9,900 directions of 100 languages. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the forcedbostokenid parameter to the generate method. Note: M2M100Tokenizer depends on sentencepiece, so make sure to install it before running the example. To install sentencepiece run pip install sentencepiece See the model hub to look for more fine-tuned…
Open weights
mit
1,024 tokens
transformers
SeamlessM4T is a collection of models designed to provide high quality translation, allowing people from different linguistic communities to communicate effortlessly through speech and text. This repository hosts Hugging Face's implementation of SeamlessM4T. You can find the original weights, as well as a guide on how to run them in the original hub repositories (large and medium checkpoints). SeamlessM4T v2, an improved version of this version with a novel architecture, has been released here. This new model improves over SeamlessM4T v1 in quality as well as inference speed in speech generation tasks. SeamlessM4T v2 is also supported by Transformers, more on it in the model card of this…
Open weights
cc-by-nc-4.0
4,096 tokens
transformers
2025/05/20 - CSM is availabile natively in Hugging Face Transformers as of version 4.52.1 2025/03/13 - We are releasing the 1B CSM variant. The checkpoint is hosted on Hugging Face. CSM (Conversational Speech Model) is a speech generation model from Sesame that generates RVQ audio codes from text and audio inputs. The model architecture employs a Llama backbone and a smaller audio decoder that produces Mimi audio codes. A fine-tuned variant of CSM powers the interactive voice demo shown in our blog post. A hosted HuggingFace space is also available for testing audio generation. CSM supports full-graph compilation with CUDA graphs! CSM can be fine-tuned using Transformers' Trainer. Does this…
Access requested at publisher
apache-2.0
1.6B parameters
transformers
Model · Image text to video
Jay
This repository (Abiray/MiniMax-H3-Pruned-GGUF) provides pruned and quantized GGUF weights for the MiniMax H3 omni-modal generative model. MiniMax H3 is designed for unified multimodal context processing, capable of generating synchronized high-definition video and 32 kHz stereo audio from text, image, audio, and video inputs. 1. Download your desired.gguf variant from the table above. 2. Place the downloaded.gguf file into the ComfyUI/models/unet/ directory. 3. In your ComfyUI workflow, load the model using the UnetLoaderGGUF node. MiniMax H3 is released under the MiniMax H3 Community License Agreement. Please refer to the MiniMaxAI/MiniMax-H3 repository and the repository's LICENSE file…
Open weights
other
The development log of our Music Audio Pre-training (m-a-p) model family: - 02/06/2023: arxiv pre-print and training codes released. - 17/03/2023: we release two advanced music understanding models, MERT-v1-95M and MERT-v1-330M, trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. - 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset MERT-v0-public - 29/12/2022: a music understanding model MERT-v0 trained with MLM paradigm, which performs better at downstream tasks. - 29/10/2022: a pre-trained MIR model music2vec trained with BYOL paradigm. Here is a table for quick model pick-up: The m-a-p models…
Open weights
cc-by-nc-4.0
transformers
Stable Diffusion 3.5 Medium is a Multimodal Diffusion Transformer with improvements (MMDiT-X) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency. Please note: This model is released under the Stability Community License. Visit Stability AI to learn or contact us for commercial licensing details. (https://arxiv.org/abs/2403.03206) with improvements that use three fixed, pretrained text encoders, with QK-normalization to improve training stability, and dual attention blocks in the first 12 transformer layers. - For individuals and organizations with annual revenue above $1M: please contact us to get an…
Access requested at publisher
other
2.5B parameters
diffusers
Autogenerated by HuggingPics Create your own image classifier for anything by running the demo on Google Colab. Report any issues with the demo at the github repo.
Open weights
86M parameters
transformers
I. Introduction NetaYume Lumina is a text-to-image model fine-tuned from Neta Lumina, a high-quality anime-style image generation model developed by Neta.art Lab. It builds upon Lumina-Image-2.0, an open-source base model released by the Alpha-VLLM team at Shanghai AI Laboratory. This model was trained with the goal of not only generating realistic human images but also producing high-quality anime-style images. Despite being fine-tuned on a specific dataset, it retains a significant amount of knowledge from the base model. The file NetaYumeLuminav2allinone.safetensors is an all-in-one file that contains the necessary weights for the VAE, text encoder, and image backbone to be used with…
Open weights
apache-2.0
diffusion-single-file
This Hub repository contains a HuggingFace's transformers implementation of the original Kosmos-2 model from Microsoft. Use the code below to get started with the model. This model is capable of performing different tasks through changing the prompts. First, let's define a function to run a prompt. Here are the tasks Kosmos-2 could perform: Once you have the entities, you can use the following helper function to draw their bounding bboxes on the image
Open weights
mit
1.7B parameters
2,048 tokens
transformers
Neural machine translation model for translating from Korean (ko) to English (en). This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of Marian NMT, an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from OPUS and training pipelines use the procedures of OPUS-MT-train. - More information about released models for this language pair: OPUS-MT kor-eng README - Tatoeba Translation…
Open weights
cc-by-4.0
209M parameters
1,024 tokens
transformers
Modifications to the original model card are in red or green Stable Diffusion Inpainting is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask. The Stable-Diffusion-Inpainting was initialized with the weights of the Stable-Diffusion-v-1-2. First 595k steps regular training, then 440k steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning to improve classifier-free classifier-free guidance sampling. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask…
Open weights
creativeml-openrail-m
diffusers
A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of VJEPA, resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale. The code is released in this repository. To run V-JEPA 2 model, ensure you have installed the latest transformers: V-JEPA 2 is intended to represent any video (and image) to perform video classification, retrieval, or as a video encoder for VLMs. To load a video, sample the number of frames according to the model. For this model, we use 64. To load an image, simply copy the image to the desired number of frames. For more code examples, please refer to the V-JEPA 2…
Open weights
apache-2.0
1B parameters
transformers
Distilled from Dreamshaper v7 fine-tune of Stable-Diffusion v1-5 with only 4,000 training iterations (~32 A100 GPU Hours). By distilling classifier-free guidance into the model's input, LCM can generate high-quality images in very short inference time. We compare the inference time at the setting of 768 x 768 resolution, CFG scale w=8, batchsize=4, using a A800 GPU. You can try out Latency Consistency Models directly on: To run the model yourself, you can leverage the Diffusers library: 1. Install the library: 2. Run the model: For more information, please have a look at the official docs: https://huggingface.co/docs/diffusers/api/pipelines/latentconsistencymodels#latent-consistency-models…
Open weights
mit
860M parameters
diffusers
