best abliterated uncensored models on hugging face that support vision

As of my last update in October 2023, Hugging Face hosts a variety of models that can handle vision tasks. For "abliterated" models, it seems like you may be referring to models that have been fine-tuned or specialized for certain tasks, but the specific term "abliterated" isn’t standard in the context of model descriptions. However, if you are looking for state-of-the-art vision models that are uncensored or capable of interpreting and generating images, here are some popular categories available on Hugging Face: 1. **Vision Transformers (ViT)**: - ViT models are popular for image classification tasks. Look for variants fine-tuned on specific datasets. 2. **DALL-E & Stable Diffusion**: - These models are notable for generating images from text prompts. They are widely used for creative generative tasks and can handle uncensored prompts, depending on the implementation. 3. **CLIP (Contrastive Language-Image Pretraining)**: - CLIP models can understand and relate text and images, allowing for diverse applications in vision and language tasks. 4. **YOLO (You Only Look Once)**: - These models are designed for object detection tasks and are available in various versions on Hugging Face. 5. **Detectron2**: - A model framework that provides state-of-the-art detection algorithms. 6. **OpenVINO Models**: - Models optimized for inference performance and might also be adapted for various vision tasks. To explore and find the most suitable uncensored models, you can visit the [Hugging Face Model Hub](https://huggingface.co/models) and filter by tasks such as "Image Classification", "Object Detection", "Generative Models", and others. Make sure to review the licensing and use case limitations indicated for each model, as these can vary. If you have specific vision tasks or applications in mind, please provide further details, and I’d be happy to help narrow down options! Update (2025-12-26): As of my last knowledge update in October 2023, Hugging Face hosts a variety of models that support vision tasks, including those for image classification, object detection, segmentation, and more. While specific model names and availability may have changed since then, here are some notable models and categories you might consider looking into: 1. **Vision Transformers (ViT)**: - ViT models have gained popularity for various vision tasks. They use transformer architectures to process images effectively. - Look for models like `google/vit-base-patch16-224` for image classification. 2. **CLIP (Contrastive Language–Image Pre-training)**: - Models like `openai/clip-vit-base-patch16` can handle tasks involving both text and images, making them versatile for vision applications. 3. **DINO (Self-Distillation with No Labels)**: - This approach generates high-quality representations without labeled data. Look for models that implement DINO architectures for self-supervised learning in vision. 4. **YOLO (You Only Look Once)**: - Models like `hustvl/yolos-tiny` or other YOLO versions are great for real-time object detection tasks. 5. **Segment Anything Model (SAM)**: - A model for image segmentation that has gained traction recently. Look for models from the Segment Anything project. 6. **DETR (Detection Transformer)**: - This model provides end-to-end object detection capabilities. You might find models like `facebook/detr-resnet-50` useful. 7. **Stable Diffusion and Generative Models**: - For tasks involving generating images from text prompts or inpainting, explore models under the Stable Diffusion category. To find the most suitable model, visit the Hugging Face Model Hub, and use their search and filter functionalities based on your specific vision tasks. You can also check for specific functionality, licensing, and model descriptions to see if they meet your requirements regarding uncensored or specialized use cases. Remember to verify the current offerings and documentation, as new models and improvements occur frequently in the rapidly evolving field of machine learning.