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> [!NOTE] > **Updated 2026-10-29:** Re-quantized with the improved oQe quantization in oMLX 0.7.3. Please re-download if you have an earlier copy. This model was quantized using oQ (oMLX v0.7.3) mixed-precision quantization with improved oQe (jundot/omlx#4930). A mixed-precision MLX checkpoint of Qwen/Qwen3.8-27B, a 27B dense vision-language model built on the Qwen3.5 architecture, with a hybrid layout of Gated DeltaNet and Gated Attention layers, image and video understanding and a 262,144-token native context. It includes the language model, the vision encoder and the multi-token prediction (MTP) head.

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> [!NOTE] > **Updated 2026-10-29:** Re-quantized with the improved oQe quantization in oMLX 0.7.3. Please re-download if you have an earlier copy. This model was quantized using oQ (oMLX v0.7.3) mixed-precision quantization with improved oQe (jundot/omlx#4930). A mixed-precision MLX checkpoint of Qwen/Qwen3.8-Flash-Next, an experimental preview of the architecture behind Qwen4: a mixture-of-experts vision-language model with 125B parameters and 6B activated, plus a 51B n-gram embedding and a 4B MTP module, that pairs Gated DeltaNet with Qwen Sparse Attention (QSA) and uses Gated Residual streams. It includes the language model, the n-gram embedding, the vision encoder and the multi-token prediction (MTP) head.

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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FlashVSR

JunhaoZhuang

Diffusion models have recently advanced video restoration, but applying them to real-world video super-resolution (VSR) remains challenging due to high latency, prohibitive computation, and poor generalization to ultra-high resolutions. Our goal in this work is to make diffusion-based VSR practical by achieving **efficiency, scalability, and real-time performance**. To this end, we propose **FlashVSR**, the first diffusion-based one-step streaming framework towards real-time VSR. **FlashVSR runs at ∼17 FPS for 768 × 1408 videos on a single A100 GPU** by combining three complementary innovations: (i) a train-friendly three-stage distillation pipeline that enables streaming super-resolution, (ii) locality-constrained sparse attention that cuts redundant computation while bridging the train–test resolution gap, and (iii) a tiny conditional decoder that accelerates reconstruction without sacrificing quality. To support large-scale training, we also construct **VSR-120K**, a new dataset with 120k videos and 180k images. Extensive experiments show that FlashVSR scales reliably to ultra-high resolutions and achieves **state-of-the-art performance with up to ∼12× speedup** over prior one-step diffusion VSR models. First of all, huge thanks to the community for the fast adoption, feedback, and contributions to FlashVSR! 🙌 During community testing, we noticed that some third-party implementations of FlashVSR (e.g. early ComfyUI versions) do **not include our Locality-Constrained Sparse Attention (LCSA)** module and instead fall back to **dense attention**. This may lead to **noticeable quality degradation**, especially at higher resolutions. Community discussion: https://github.com/kijai/ComfyUI-WanVideoWrapper/issues/1441 ✅ The **official FlashVSR pipeline (this repository)**: - **Better preserves fine structures and details** - **Effectively avoids texture aliasing and visual artifacts**

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FlashVSR-v1.1

JunhaoZhuang

Diffusion models have recently advanced video restoration, but applying them to real-world video super-resolution (VSR) remains challenging due to high latency, prohibitive computation, and poor generalization to ultra-high resolutions. Our goal in this work is to make diffusion-based VSR practical by achieving **efficiency, scalability, and real-time performance**. To this end, we propose **FlashVSR**, the first diffusion-based one-step streaming framework towards real-time VSR. **FlashVSR runs at ∼17 FPS for 768 × 1408 videos on a single A100 GPU** by combining three complementary innovations: (i) a train-friendly three-stage distillation pipeline that enables streaming super-resolution, (ii) locality-constrained sparse attention that cuts redundant computation while bridging the train–test resolution gap, and (iii) a tiny conditional decoder that accelerates reconstruction without sacrificing quality. To support large-scale training, we also construct **VSR-120K**, a new dataset with 120k videos and 180k images. Extensive experiments show that FlashVSR scales reliably to ultra-high resolutions and achieves **state-of-the-art performance with up to ∼12× speedup** over prior one-step diffusion VSR models. First of all, huge thanks to the community for the fast adoption, feedback, and contributions to FlashVSR! 🙌 During community testing, we noticed that some third-party implementations of FlashVSR (e.g. early ComfyUI versions) do **not include our Locality-Constrained Sparse Attention (LCSA)** module and instead fall back to **dense attention**. This may lead to **noticeable quality degradation**, especially at higher resolutions. Community discussion: https://github.com/kijai/ComfyUI-WanVideoWrapper/issues/1441 ✅ The **official FlashVSR pipeline (this repository)**: - **Better preserves fine structures and details** - **Effectively avoids texture aliasing and visual artifacts**

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