The Qwen3.5 Multimodal Understanding Demo, powered by Qwen3.5-2B, is now available on HF Spaces! It is a lightweight model designed for fast image and video reasoning. Built with Gradio, the demo showcases Image QA, Video QA, object detection, and 2D point tracking, along with real-time token streaming.
QIE-Object-Remover-Bbox Demo removes objects and artifacts from selected regions using bounding box grounding. Built on Qwen-Image-Edit-2509 with Rapid Diffusers acceleration, it delivers fast 4-step inference via the QIE-2509 adapter. 🤗🔥
-> Paired the EditPlusPipeline with the Diffusers-compatible transformer weights of Rapid AIO from Qwen-Image-Edit. (experimental) -> This fusion delivers more accurate instruction following, higher image quality, and consistent visual coherence @ 4-step fast inference. -> Better maintains text styles with high fidelity, along with high-quality old photo restoration, enhancement, and best-in-class virtual try-on.
Dropping the Qwen3 VL Series of Unredacted MAX-VL models. These models have undergone multi-stage training to minimize refusal rates through continuous abliterated optimization. You can find the models in BF16, FP8-Dynamic, and GGUF formats at the links below.🔥🚀
Introducing FLUX.2-Klein-LoRA-Studio, a demo for image editing using specialized LoRA adapters built for the FLUX.2-Klein-Distilled model. It features an edit-style gallery for multi-style image editing, including de-light, face swap, mannequin, and more. Try the demo below.
GLM OCR, a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture. It delivers high accuracy and strong generalization with a blazing-fast inference pipeline. The demo is live . Try it now. 🤗🚀
Introducing the Qwen-Image-Edit-3D-Lighting-Control app, featuring 8× horizontal and 3× elevational lighting positions for precise 3D lighting control. It enables studio-level lighting using fast Qwen Image Edit fast inference, paired with Multi-Angle-Lighting adapters. 🔦
Daggr UI version of the Qwen3-TTS demo.🔥 (custom voice, voice design, qwen3-asr and voice cloning) nodes. No remote spaces used for API inference; all functions run in-app fn. Powered by t4-m and built with daggr@0.5.2 and gradio@6.
Qwen-Image-Edit-Object-Manipulator Space is now featured in Hugging Face Space of the Week. It enables object manipulation such as extracting objects, adding designs, and removing objects or designs from the red highlighted area using specialized adapters.
Introducing QIE-2511-Zoom-Master for highlight-guided area zoom-in, enabling lossless zooming within a drawn square area, and QIE-2511-Object-Remover-v2 for precise object or highlight-guided area cleanup. These experimental adapters are trained based on QIE-2511. Find the adapters below.
How to tell to your Chat LLM to be more natural. Add the below to it's personality Prefer specific facts over vague importance. Do not inflate significance with phrases like “plays a pivotal role” or “marks a turning point.” Use numbers, dates, mechanisms, or measurable outcomes. Example: replace “the system changed logistics” with “the system reduced container dwell time from 6.2 to 4.1 days.”
Avoid promotional language. Keep a neutral tone. Do not use adjectives such as vibrant, groundbreaking, renowned, innovative, or powerful. Use plain wording.
Limit AI-typical vocabulary such as crucial, pivotal, intricate, tapestry, underscore, highlighting, emphasizing, showcasing, fostering, or enhance. Prefer simpler words.
Avoid generic commentary and vague attribution. Do not write “this reflects broader trends,” “experts say,” or “researchers suggest” unless a named source is given.
Avoid formulaic structures such as “not only X but also Y” or “despite its success it faces challenges.” Use direct explanations.
Use lists sparingly. Prefer short paragraphs unless bullets improve clarity. Avoid triple-adjective patterns.
Prefer simple sentences like “X is Y” or “the system uses Z.” Minimize formatting. Avoid emojis, decorative headings, and excessive bold.
Remove sentences that add no information. Avoid generic endings such as “in conclusion” or “overall.” Use concrete examples, real actors, workflows, and technologies when possible. Write like technical documentation or a research summary, not marketing or blog prose.
MAD-GRPO: https://huggingface.co/blog/telcom/mad-grpo In R1-Zero-Like Training *, Dr.GRPO treats GRPO’s by dropping std, but that often comes with a hidden side effect: length-weighted updates that can nudge model toward verbosity. MAD-GRPO provides robust scale (MAD + epsilon) per-token normalization stability without verbosity bias.
LTX-2 Camera-Control LoRA demo with dolly-in/out and dolly-left/right is now available on Hugging Face, paired with ltx-2-19b-distilled-lora for fast inference. It also includes dynamic GPU duration adjustments for long video generations. Click the related Space links below.
Qwen-Image-Edit-2511-Object-Remover is an adapter (LoRA) developed for Qwen’s Qwen-Image-Edit-2511 image-to-image model. It is specifically designed for precise object removal from images.
Qwen-Image-Edit-2511-Object-Adder is an adapter (LoRA) developed for Qwen’s Qwen-Image-Edit-2511 image-to-image model. It is specifically designed for precise object addition to images.
Update: TRELLIS.2 (Text to 3D, Image to 3D) Gradio with Rerun Embedded demo with improved visualization of the 3D model previewer is now available on Hugging Face. Generate assets and view them in the 3D viewer, powered and streamlined with Microsoft’s TRELLIS.2 and Tongyi-MAI’s Z-Image-Turbo models.
Introducing the Qwen-Image-Edit-2511-LoRAs-Fast demo, featuring image property comparison and contrast, built on top of Gradio and the combined Rerun SDK. It supports single and multi-image edits with existing LoRAs that are lazily loaded. (Note: This is still an experimental Space for Qwen-Image-Edit-2511.)
NVIDIA’s Groq deal ... I think, inference efficiency is becoming the main driver of profitability, and NVIDIA’s Groq deal is evidence the market is moving from “who can train biggest” to “who can serve cheapest and fastest at scale.” That points to a maturing phase of AI, not necessarily the end of a bubble, but definitely a correction in what “wins” long-term. What do you think?
