Try the all-new trending Qwen-Image-Edit specialized adapter demos, including Photo-to-Anime, Light Restoration, Multi-Angle Edits, Relighting, and more — all in a single Hugging Face Space. Below is the demo link. 🤗🌠
Introducing Photo-Mate-v2, based on FLUX.1-Kontext-dev, for advanced image manipulation tasks. It supports transforming scenes into top-down/bottom-up perspectives, CAM-right/left-view and its reverse, as well as general kontext-specified object removal. Below is the list of demos and adapters.🔥🤗
A week ago, I shared a post about the latest transformers test implementation of DeepSeek-OCR Compatibility (https://tinyurl.com/ykc4mm66). Now, I’m dropping the most compatible version of it to support the model with the latest transformers. 🤗🔥
✅Supports the latest transformers v4.57.1 ✅torch: 2.6.0+cu124 (or) the latest version (i.e., torch 2.9.0) ✅cuda version: 12.4 ✅users can also opt out of specific attention implementations if desired.
It discusses the latest trends in OCR models, the multilingual support offered by modern OCR systems, their unique capabilities, OCR benchmark model comparisons, transformer-based implementations, and strategies for streamlining transformers compatibility.
Implemented DeepSeek-OCR to support the latest transformers on the strangervisionhf page. The page includes the model weights and corrected configuration, which fix the issues and allow transformers inference to run smoothly.🤗🔥
✅Supports the latest transformers ✅You can also opt out of the attention implementation if needed. ✅Supports torch version 2.6.0 or higher ✅torch version cuda: 12.4
If you are interested in experimenting with new things and streamlining compatibility, the strangervisionhf organization is open for you, and you can join the community.
Introducing Gliese-OCR-7B-Post2.0-final, a document content-structure retrieval VLM designed for content extraction (OCR), summarization, and document visual question answering. This is the fourth and final model in the Camel Doc OCR VLM series, following Gliese-OCR-7B-Post1.0. The model delivers superior accuracy across a wide range of document types, including scanned PDFs, handwritten pages, structured forms, and analytical reports.🚀🤗
Now you can try all the latest state-of-the-art multimodal vision-language models from the Qwen3-VL series demo on Hugging Face Spaces — including 4B, 8B, and 30B (Instruct, 4B-Thinking) variants. I’ve also uploaded the weights for the Abliterated variants of these models, up to 30B parameters. Check out the Spaces and model links below! 🤗🔥
Note: This is version 1.0 of the Abliteration of the Qwen3-VL series of models. It may perform sub-optimally in some cases. If you encounter any issues, please open a discussion.
Introducing Image-Guard-2.0, an experimental, lightweight vision-language encoder model with a size of 0.1B (<100M parameters), trained on SigLIP2 (siglip2-base-patch16-224). Designed for multi-label image classification tasks, this model functions as an image safety system, serving as an image guard or moderator across a wide range of categories, from anime to realistic imagery.
It also performs strict moderation and filtering of artificially synthesized content, demonstrating strong detection and handling of explicit images. Image-Guard-2.0 delivers robust performance in streamlined scenarios, ensuring reliable and effective classification across diverse visual inputs.
The demo of Qwen3-VL-30B-A3B-Instruct, the next-generation and powerful vision-language model in the Qwen series, delivers comprehensive upgrades across the board — including superior text understanding and generation, deeper visual perception and reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. 🤗🔥
Introducing the next-gen version of DeepCaption-VLA (v2.0) — an advanced, multimodal model based on Qwen2.5-VL, specialized for Image Captioning and Vision Language Attribution (VLA). This enhanced release focuses on generating precise, attribute-rich captions that capture visual properties, object attributes, and scene details across diverse image types and aspect ratios. Version 2.0 introduces significant improvements in multilingual inference, delivering higher captioning quality and attribution accuracy in languages including Chinese (Zh), Thai (Th), and more.
Have built the new Image Studio with the Gemini Image Gen models for the following multiple tasks: imagen-4.0-fast-generate-001 model for Image Generation (Text-to-Image) and Multi-Image Editing (Image-to-Image), and Draw-to-Image powered by gemini-2.5-flash-image (aka Nano Banana).
To proceed, you need to add your Gemini API key. Your API key is stored only for the duration of your session and will be lost when you reload or exit the page. It will not be shared or exposed anywhere.
Try the Hugging Face Space demo for Logics-MLLM/Logics-Parsing, the latest multimodal VLM from the Logics Team at Alibaba Group. It enables end-to-end document parsing with precise content extraction in markdown format, and it also generates a clean HTML representation of the document while preserving its logical structure. 🤗🔥
Try Banana Zoom an advanced image enhancement web app that lets users select regions of an image for AI-powered upscaling and detail refinement. Using Google’s (nano banana), it analyzes selections, generates context-aware enhancements, and produces high-resolution outputs. Simply drag-and-drop or upload images, make precise or fixed-size selections, and watch improvements in real-time with smooth zoom and pixel-dissolve effects.
Photo-Mate-i2i – a space for experimenting with adapters for image manipulation using Kontext adapters, including Photo-Restore-i2i, PhotoCleanser-i2i, Polaroid-Warm-i2i, Yarn-Photo-i2i, Monochrome-Pencil, and more. Try out the demo, and to learn more, visit the app page or the respective model pages!
Dropping some experimental adapters for FLUX.1-Kontext-dev, including Photo-Restore-i2i, PhotoCleanser-i2i, Polaroid-Warm-i2i, Yarn-Photo-i2i, and Monochrome-Pencil. These were trained under various settings with minimal image pairs to achieve optimal results. The dataset result sets end pairs were synthesized using Gemini-2.5-Flash-Image-Preview and others.🤗✨
✨Note: All the above models share the same auto-labeling multimodal VLM captioning model, prithivMLmods/DeepCaption-VLA-7B, which is used for refining edit instructions and accurately understanding attributions for the generations.
Many of 'em pinged me asking to make the nano-banana-aio to available on hf.co/spaces, so I’ve transferred the app’s tech stack to make it compatible for deployment on Spaces. (Can be accessed with your own Gemini API) 🤗⭐️
Nano Banana AIO (All-in-One) App, which offers seamless image manipulation features, including single/multiple image adaptation, a canvas for free-style drawing to creative image generation, and standard text-to-image generation.
With the same passion, trust, and momentum to contribute to the community, I’m excited to do some amazing things to wrap up Q3 and Q4 of 2025. And importantly, I’ve been lucky enough to receive some knowledge and guidance from @merve to build open-source demos and stuff. Thank you for the belief.
Introducing Gliese-OCR-7B-Post1.0, a document content-structure retrieval VLM designed for content extraction(OCRs) and summarization. This is the third model in the Camel Doc OCR VLM series, following Camel-Doc-OCR-062825. The new version fixes formal table reconstruction issues in both En and Zh, achieving optimal performance for long-context inferences. This model also shows significant improvements in LaTeX and Markdown rendering for OCR tasks.
