FlashVSR UpRes: AI Video Enhancer in ComfyUI 🎥

In this video, we dive into the Flash VSR AI video upscaling model and its integration within the ComfyUI Wan Video Wrapper. The creator explores how Flash VSR enhances low-quality or compressed AI-generated videos by increasing resolution, improving frame interpolation, and reducing artifacts like blur and pixelation. While the model is now accessible through custom nodes in ComfyUI, the video highlights a critical technical limitation: the current implementation does not support Block Sparse Attention or Flash Attention, which are used in the original GitHub code to boost performance and reduce VRAM usage. This means users might experience slower processing and higher memory demands compared to the official version. The workflow is demonstrated step-by-step, showing how to connect Flash VSR within the Wan Video pipeline for post-processing AI videos generated with models like WAN 2.1 or WAN 2.2 MOE. The results are evaluated for visual quality, speed, and practicality, offering a realistic view of what this tool can deliver today.

Who is This Content Suitable For?
AI video creators who want to upscale and enhance the quality of their AI-generated videos.
ComfyUI users working with WAN 2.1, WAN 2.2, or similar diffusion-based video models and looking to integrate upscaling tools.
Developers and technical artists interested in understanding the differences between official AI model implementations and their ComfyUI node wrappers.
Anyone exploring video super-resolution, frame interpolation, or AI video enhancement techniques.
Content producers seeking ways to improve the final output of AI-animated scenes, commercials, or storytelling projects.

Why Does This Matter?
As AI-generated videos become more common, output quality remains a major hurdle—many clips suffer from low resolution, compression artifacts, or inconsistent motion. Tools like Flash VSR aim to solve this by acting as a powerful "sharpening filter" that reconstructs fine details and smooths out frames. However, the way these tools are implemented across different platforms matters significantly. This video exposes a key issue: while Flash VSR is available in the Wan Video Wrapper, it lacks optimized attention mechanisms, making it less efficient than the original. Understanding these technical trade-offs helps creators set realistic expectations, optimize their workflows, and make informed choices about when and how to use upscaling in their AI video pipelines.

Resources:

FlashVSR
Towards Real-Time Diffusion-Based Streaming Video Super-Resolution
https://huggingface.co/JunhaoZhuang/FlashVSR

FlashVSR runs at ∼17 FPS for 768 × 1408 videos on a single A100 GPU

Option 1:
ComfyUI_FlashVSR (required advnace installation steps)
https://github.com/smthemex/ComfyUI_FlashVSR
pip install -r requirements.txt

Block-Sparse-Attention(installation Required)
Block Sparse Attention
git clone https://github.com/mit-han-lab/Block-Sparse-Attention
cd Block-Sparse-Attention
pip install packaging
pip install ninja
python setup.py install

Option 2:
ComfyUI-WanVideoWrapper
https://github.com/kijai/ComfyUI-WanVideoWrapper
https://huggingface.co/Kijai/WanVideo_comfy/tree/main/FlashVSR

Another Upscaler:

SeedVR2_VideoUpscaler
https://github.com/numz/ComfyUI-SeedVR2_VideoUpscaler

Wan 2.2 Low Noise Model
https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged

LightX2V - too many new model and lora models updating.
https://huggingface.co/lightx2v

Attached Workflow That Used in this video:
https://www.patreon.com/posts/141632921/?utm_source=youtube&utm_medium=video&utm_campaign=20251020


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#comfyui #FlashVSR #videoupscaling #videoenhancer