RedbeardNZ / SeedVR2_comfyUI

huggingface.co
Total runs: 675
24-hour runs: 0
7-day runs: 47
30-day runs: 352
Model's Last Updated: July 20 2025
video-to-video

Introduction of SeedVR2_comfyUI

Model Details of SeedVR2_comfyUI

ComfyUI-SeedVR2_VideoUpscaler

View Code

Official release of SeedVR2 for ComfyUI that enables Upscale Video/Images generation.

๐Ÿ“‹ Quick Access
๐Ÿ†™ Note and futur releases
  • Improve FP8 integration, we are loosing some FP8 advantages during the process.
  • Tile-VAE integration if it works for video, I have test to do or if some dev want help, you are welcome.
  • 7B FP8 model seems to have quality issues, use 7BFP16 instead (If FP8 don't give OOM then FP16 will works) I have to review this.
๐Ÿš€ Updates

2025.06.30

  • ๐Ÿš€ Speed Up the process and less VRAM used (see new benchmark).
  • ๐Ÿ› ๏ธ Fixed leak memory on 3B models.
  • โŒ Can now interrupt process if needed.
  • โœ… refactored the code for better sharing with the community, feel free to propose pull requests.
  • ๐Ÿ› ๏ธ Removed flash attention dependency

2025.06.24

  • ๐Ÿš€ Speed up the process until x4 (see new benchmark)

2025.06.22

  • ๐Ÿ’ช FP8 compatibility !
  • ๐Ÿš€ Speed Up all Process
  • ๐Ÿš€ less VRAM consumption (Stay high, batch_size=1 for RTX4090 max, I'm trying to fix that)
  • ๐Ÿ› ๏ธ Better benchmark coming soon

2025.06.20

  • ๐Ÿ› ๏ธ Initial push
๐ŸŽฏ Features
  • High-quality Upscaling
  • Suitable for any video length once the right settings are found
  • Model Will Be Download Automatically from Models
๐Ÿ”ง Requirements
  • A Huge VRAM capabilities is better, from my test, even the 3B version need a lot of VRAM at least 18GB.
  • Last ComfyUI version with python 3.12.9 (may be works with older versions but I haven't test it)
๐Ÿ“ฆ Installation
  1. Clone this repository into your ComfyUI custom nodes directory:
cd ComfyUI/custom_nodes
git clone https://github.com/numz/ComfyUI-SeedVR2_VideoUpscaler.git
  1. Install the required dependencies:

load venv and :

pip install -r ComfyUI-SeedVR2_VideoUpscaler/requirements.txt

install flash_attn/triton, 6% faster on process, not a mandatory.

pip install flash_attn
pip install triton

or

python_embeded\python.exe -m pip install -r flash_attn

check here from https://github.com/loscrossos/lib_flashattention/releases and https://github.com/woct0rdho/triton-windows

  1. Models

    Will be automtically download into : models/SEEDVR2

    or can be found here ( MODELS )

๐Ÿ“– Usage
  1. In ComfyUI, locate the SeedVR2 Video Upscaler node in the node menu.
  1. โš ๏ธ THINGS TO KNOW !!

temporal consistency : at least a batch_size of 5 is required to activate temporal consistency. SEEDVR2 need at least 5 frames to calculate it. A higher batch_size give better performances/results but need more than 24GB VRAM.

VRAM usage : The input video resolution impacts VRAM consumption during the process. The larger the input video, the more VRAM will consume during the process. So, if you experience OOMs with a batch_size of at least 5, try reducing the input video resolution until it resolves.

Of course, the output resolution also has an impact, so if your hardware doesn't allow it, reduce the output resolution.

  1. Configure the node parameters:

    • model : Select your 3B or 7B model
    • seed : a seed but it generate another seed from this one
    • new_resolution : New desired short edge in px, will keep ratio on other edge
    • batch_size : VERY IMPORTANT!, this model consume a lot of VRAM, All your VRAM, even for the 3B model, so for GPU under 24GB VRAM keep this value Low, good value is "1" without temporal consistency, "5" for temporal consistency, but higher is this value better is the result.
    • preserve_vram : for VRAM < 24GB, If true, It will unload unused models during process, longer but works, otherwise probably OOM with
๐Ÿ“Š Benchmarks

7B models on NVIDIA H100 93GB VRAM (values in parentheses are from the previous benchmark):

nb frames Resolution Batch Size execution time fp8 (s) FPS fp8 execution time fp16 (s) FPS fp16 perf progress since start
15 512ร—768 โ†’ 1080ร—1620 5 23.75 (26.71) 0.63 (0.56) 24.23 (27.75) 0.61 (0.54) (0.10) x6.1
27 512ร—768 โ†’ 1080ร—1620 9 27.75 (33.97) 0.97 (0.79) 28.48 (35.08) 0.94 (0.77) (0.15) x6.2
39 512ร—768 โ†’ 1080ร—1620 13 32.02 (41.01) 1.21 (0.95) 32.62 (42.08) 1.19 (0.93) (0.19) x6.2
51 512ร—768 โ†’ 1080ร—1620 17 36.39 (48.12) 1.40 (1.06) 37.30 (49.44) 1.36 (1.03) (0.21) x6.4
63 512ร—768 โ†’ 1080ร—1620 21 40.80 (55.40) 1.54 (1.14) 41.32 (56.70) 1.52 (1.11) (0.23) x6.6
75 512ร—768 โ†’ 1080ร—1620 25 45.37 (62.60) 1.65 (1.20) 45.79 (63.80) 1.63 (1.18) (0.24) x6.8
123 512ร—768 โ†’ 1080ร—1620 41 62.44 (91.38) 1.96 (1.35) 62.28 (92.90) 1.97 (1.32) (0.28) x7.0
243 512ร—768 โ†’ 1080ร—1620 81 106.13 (164.25) 2.28 (1.48) 104.68 (166.09) 2.32 (1.46) (0.31) x7.4
363 512ร—768 โ†’ 1080ร—1620 121 151.01 (238.18) 2.40 (1.52) 148.67 (239.80) 2.44 (1.51) (0.33) x7.4
453 512ร—768 โ†’ 1080ร—1620 151 186.98 (296.52) 2.42 (1.53) 184.11 (298.65) 2.46 (1.52) (0.33) x7.4
633 512ร—768 โ†’ 1080ร—1620 211 253.77 (406.65) 2.49 (1.56) 249.43 (409.44) 2.53 (1.55) (0.34) x7.4
903 512ร—768 โ†’ 1080ร—1620 301 OOM (OOM) (OOM) OOM (OOM) (OOM) (OOM)
149 854x480 โ†’ 1920x1080 149 450.22 0.41

3B FP8 models on NVIDIA H100 93GB VRAM (values in parentheses are from the previous benchmark):

nb frames Resolution Batch Size execution time fp8 (s) FPS fp8 execution time fp16 (s) FPS fp16
149 854x480 โ†’ 1920x1080 149 361.22 0.41

NVIDIA RTX4090 24GB VRAM

Model nb frames Resolution Batch Size execution time (seconds) FPS Note
3B fp8 5 512x768 โ†’ 1080x1620 1 14.66 (22.52) 0.34 (0.22)
3B fp16 5 512x768 โ†’ 1080x1620 1 17.02 (27.84) 0.29 (0.18)
7B fp8 5 512x768 โ†’ 1080x1620 1 46.23 (75.51) 0.11 (0.07) preserve_memory=on
7B fp16 5 512x768 โ†’ 1080x1620 1 43.58 (78.93) 0.11 (0.06) preserve_memory=on
3B fp8 10 512x768 โ†’ 1080x1620 5 39.75 0.25 preserve_memory=on
3B fp8 100 512x768 โ†’ 1080x1620 5 322.77 0.31 preserve_memory=on
3B fp8 1000 512x768 โ†’ 1080x1620 5 3624.08 0.28 preserve_memory=on
3B fp8 20 512x768 โ†’ 1080x1620 1 40.71 (65.40) 0.49 (0.31)
3B fp16 20 512x768 โ†’ 1080x1620 1 44.76 (91.12) 0.45 (0.22)
3B fp8 20 512x768 โ†’ 1280x1920 1 61.14 (89.10) 0.33 (0.22)
3B fp8 20 512x768 โ†’ 1480x2220 1 79.66 (136.08) 0.25 (0.15)
3B fp8 20 512x768 โ†’ 1620x2430 1 125.79 (191.28) 0.16 (0.10) preserve_memory=off (preserve_memory=on)
3B fp8 149 854x480 โ†’ 1920x1080 5 782.76 0.19 preserve_memory=on
โš ๏ธ Limitations
  • Use a lot of VRAM, it will take all!!
  • Processing speed depends on GPU capabilities
๐Ÿค Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

How to contribute:
  1. Fork the repository
  2. Create your feature branch ( git checkout -b feature/AmazingFeature )
  3. Commit your changes ( git commit -m 'Add some AmazingFeature' )
  4. Push to the branch ( git push origin feature/AmazingFeature )
  5. Open a Pull Request
Development Setup:
  1. Clone the repository
  2. Install dependencies
  3. Make your changes
  4. Test your changes
  5. Submit a pull request
Code Style:
  • Follow the existing code style
  • Add comments for complex logic
  • Update documentation if needed
  • Ensure all tests pass
Reporting Issues:

When reporting issues, please include:

  • Your system specifications
  • ComfyUI version
  • Python version
  • Error messages
  • Steps to reproduce the issue
๐Ÿ™ Credits

๐Ÿ“œ License

  • The code in this repository is released under the MIT license as found in the LICENSE file .

Runs of RedbeardNZ SeedVR2_comfyUI on huggingface.co

675
Total runs
0
24-hour runs
-55
3-day runs
47
7-day runs
352
30-day runs

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SeedVR2_comfyUI huggingface.co

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