<h1 id="qwen3-vl">Qwen3-VL</h1>Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date.This generation delivers comprehensive upgrades across the board: superior text understanding &amp; generation, deeper visual perception &amp; reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities.Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment.Key Enhancements:<ul><li>Visual Agent: Operates PC/mobile GUIs—recognizes elements, understands functions, invokes tools, completes tasks.</li><li>Visual Coding Boost: Generates Draw.io/HTML/CSS/JS from images/videos.</li><li>Advanced Spatial Perception: Judges object positions, viewpoints, and occlusions; provides stronger 2D grounding and enables 3D grounding for spatial reasoning and embodied AI.</li><li>Long Context &amp; Video Understanding: Native 256K context, expandable to 1M; handles books and hours-long video with full recall and second-level indexing.</li><li>Enhanced Multimodal Reasoning: Excels in STEM/Math—causal analysis and logical, evidence-based answers.</li><li>Upgraded Visual Recognition: Broader, higher-quality pretraining is able to “recognize everything”—celebrities, anime, products, landmarks, flora/fauna, etc.</li><li>Expanded OCR: Supports 32 languages (up from 19); robust in low light, blur, and tilt; better with rare/ancient characters and jargon; improved long-document structure parsing.</li><li>Text Understanding on par with pure LLMs: Seamless text–vision fusion for lossless, unified comprehension.</li></ul>Model Architecture Updates:<img src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3-VL/qwen3vl_arc.jpg" width="80%" /><ol><li>Interleaved-MRoPE: Full‑frequency allocation over time, width, and height via robust positional embeddings, enhancing long‑horizon video reasoning.</li><li>DeepStack: Fuses multi‑level ViT features to capture fine‑grained details and sharpen image–text alignment.</li><li>Text–Timestamp Alignment: Moves beyond T‑RoPE to precise, timestamp‑grounded event localization for stronger video temporal modeling.</li></ol>

Qwen3-VL

qwen3VL_8b.safetensors

Repost From Huggingface <a target="_blank" rel="ugc" href="https://huggingface.co/Qwen/Qwen-Image-2.1">https://huggingface.co/Qwen/Qwen-Image-2.1</a><h2 id="introduction">Introduction</h2>We are excited to open-source Qwen-Image-2.1, a unified text-to-image generation and image editing model in the Qwen family. With just 7B parameters in its visual generation component (32 Single-Stream DiT layers), Qwen-Image-2.1 balances generation quality, inference efficiency, and versatility.Four key improvements define this release:<ul><li>Compact and Efficient — A lightweight architecture with mixed-granularity attention and prefix KV cache reuse delivers strong image quality at low computational cost.</li><li>Native Transparency, Unified Creation and Editing — Generate regular or transparent (RGBA) images from text, edit transparent layers, and extract subjects from photographs—all in one model.</li><li>Versatile Editing — Support up to 10 reference images, specify local edits via circles, painted annotations, or separate masks, and preserve identity for people and products.</li><li>Realistic Textures and Refined Aesthetics — Improved typography, portrait lighting, and fine details for more visually compelling results.</li></ul>

Qwen Image 2.1

qwenImage21_21_txt_bf16.safetensors

qwenImage21_21_txt_int8.safetensors

qwenImage21_21_bf16.safetensors

qwenImage21_21_int8.safetensors

qwen_image_2.1_vae_bf16.safetensors

Originally released by <a rel="ugc" href="https://huggingface.co/Qwen/Qwen-Image-2.1">Qwen on Hugging Face</a>. All credit goes to the Qwen team.
Qwen Image 2.1 combines text-to-image generation and image editing in a single model. Its visual generation component has 7 billion parameters across 32 single-stream DiT layers, balancing image quality, efficiency and versatility.
The release introduces four main improvements:
<ul>
 <li>Efficient architecture: Mixed-granularity attention and reuse of cached prefixes reduce computation while maintaining image quality.</li>
 <li>Generation and editing with transparency: Create regular or transparent RGBA images, edit transparent layers and isolate subjects from photographs.</li>
 <li>Flexible editing: Work with up to ten reference images, guide local changes using circles, painted annotations or separate masks, and preserve the identity of people and products.</li>
 <li>Improved visual detail: Refined typography, portrait lighting, realistic textures and finer details.</li>
</ul>
Released under the <a rel="ugc" href="https://huggingface.co/Qwen/Qwen-Image-2.1/blob/main/LICENSE">Qwen Research License Agreement</a>. See the <a rel="ugc" href="https://huggingface.co/Qwen/Qwen-Image-2.1">original model card</a> for examples and usage instructions.

Files with SHA256: 68bdc82bc1b66851162ae656225e7e2068166b603db19bd5d5a3b90eb12669a9