ComfyUI-SeedVR2_VideoUpscaler

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

🆙 Todo

• Fixed unloading the 3B model when the process is finished (sorry about that, I'm trying to find out what's going on)

🚀 Updates

2025.06.24

• 🚀 Speed up the process until x4 (see new benchmark)

2025.06.22

• 💪 FP8 compatibility !
• 🚀 Speed Up all Process
• 🚀 less VRAM consumption (Stay high, batch_size=1 for RTX4090 max, I'm trying to fix that)
• 🛠️ Better benchmark coming soon

2025.06.20

• 🛠️ Initial push

Features

• High-quality Upscaling
• Suitable for any video length once the right settings are found
• Model Will Be Download Automatically from Models

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)

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

2. Install the required dependencies:

load venv and :

pip install -r ComfyUI-SeedVR2_VideoUpscaler/requirements.txt

install flash_attn or triton if it ask for it

pip install flash_attn
pip install triton

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

Or use python_embeded :

python_embeded\python.exe -m pip install -r ComfyUI-SeedVR2_VideoUpscaler/requirements.txt

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

3. Models

   Will be automtically download into : models/SEEDVR2

   or can be found here (MODELS)

Usage

1. In ComfyUI, locate the SeedVR2 Video Upscaler node in the node menu.

2. things to know

temporal consistency : at least a batch_size of 5 is required to activate temporal consistency

2. Configure the node parameters:

   • model: Select your 3B or 7B model
   • seed: a seed but it generate another seed from this one
   • new_width: New desired Width, will keep ration on height
   • cfg_scale:
   • 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
   • preserve_vram: for VRAM < 24GB, If true, It will unload unused models during process, longer but works, otherwise probably OOM with

Performance

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

nb frames	Resolution	Batch Size	Time fp8 (s)	FPS fp8	Time fp16 (s)	FPS fp16
3	512×768 → 1080×1620	1	10.18 (58.10)	0.29 (0.05)	10.67 (60.13)	0.28 (0.05)
15	512×768 → 1080×1620	5	26.71 (135.63)	0.56 (0.11)	27.75 (144.18)	0.54 (0.10)
27	512×768 → 1080×1620	9	33.97 (163.22)	0.79 (0.17)	35.08 (177.61)	0.77 (0.15)
39	512×768 → 1080×1620	13	41.01 (189.36)	0.95 (0.21)	42.08 (210.11)	0.93 (0.19)
51	512×768 → 1080×1620	17	48.12 (215.80)	1.06 (0.24)	49.44 (242.64)	1.03 (0.21)
63	512×768 → 1080×1620	21	55.40 (241.79)	1.14 (0.26)	56.70 (275.55)	1.11 (0.23)
75	512×768 → 1080×1620	25	62.60 (267.93)	1.20 (0.28)	63.80 (308.51)	1.18 (0.24)
123	512×768 → 1080×1620	41	91.38 (373.60)	1.35 (0.33)	92.90 (440.01)	1.32 (0.28)
243	512×768 → 1080×1620	81	164.25 (642.20)	1.48 (0.38)	166.09 (780.20)	1.46 (0.31)
363	512×768 → 1080×1620	121	238.18 (913.61)	1.52 (0.40)	239.80 (1114.32)	1.51 (0.33)
453	512×768 → 1080×1620	151	296.52 (1132.01)	1.53 (0.40)	298.65 (1384.86)	1.52 (0.33)
633	512×768 → 1080×1620	211	406.65 (1541.09)	1.56 (0.41)	409.44 (1887.62)	1.55 (0.34)
903	512×768 → 1080×1620	301	OOM (OOM)	OOM (OOM)	OOM (OOM)	OOM (OOM)

NVIDIA RTX4090 24GB VRAM (preserved_vram=off)

Model	Images	Resolution	Batch Size	Time (seconds)	FPS	Note
3B fp8	5	512x768 → 1080x1620	1	22.52	0.22
3B fp16	5	512x768 → 1080x1620	1	27.84	0.18
7B fp8	5	512x768 → 1080x1620	1	75.51	0.07
7B fp16	5	512x768 → 1080x1620	1	78.93	0.06
3B fp8	10	512x768 → 1080x1620	5	39.75	0.15	preserve_memory=on
3B fp8	20	512x768 → 1080x1620	1	65.40	0.31
3B fp16	20	512x768 → 1080x1620	1	91.12	0.22
3B fp8	20	512x768 → 1280x1920	1	89.10	0.22
3B fp8	20	512x768 → 1480x2220	1	136.08	0.15
3B fp8	20	512x768 → 1620x2430	1	191.28	0.10	preserve_memory=on without GPU overload so longer 320sec

Limitations

• Use a lot of VRAM, it will take all!!
• Processing speed depends on GPU capabilities

Credits

• Original SeedVR2 implementation

📜 License

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