In ComfyUI, locate the SeedVR2 Video Upscaler node in the node menu.
⚠️ 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.
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
📊 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
512×768 → 1080×1620
5
23.75 (26.71)
0.63 (0.56)
24.23 (27.75)
0.61 (0.54) (0.10)
x6.1
27
512×768 → 1080×1620
9
27.75 (33.97)
0.97 (0.79)
28.48 (35.08)
0.94 (0.77) (0.15)
x6.2
39
512×768 → 1080×1620
13
32.02 (41.01)
1.21 (0.95)
32.62 (42.08)
1.19 (0.93) (0.19)
x6.2
51
512×768 → 1080×1620
17
36.39 (48.12)
1.40 (1.06)
37.30 (49.44)
1.36 (1.03) (0.21)
x6.4
63
512×768 → 1080×1620
21
40.80 (55.40)
1.54 (1.14)
41.32 (56.70)
1.52 (1.11) (0.23)
x6.6
75
512×768 → 1080×1620
25
45.37 (62.60)
1.65 (1.20)
45.79 (63.80)
1.63 (1.18) (0.24)
x6.8
123
512×768 → 1080×1620
41
62.44 (91.38)
1.96 (1.35)
62.28 (92.90)
1.97 (1.32) (0.28)
x7.0
243
512×768 → 1080×1620
81
106.13 (164.25)
2.28 (1.48)
104.68 (166.09)
2.32 (1.46) (0.31)
x7.4
363
512×768 → 1080×1620
121
151.01 (238.18)
2.40 (1.52)
148.67 (239.80)
2.44 (1.51) (0.33)
x7.4
453
512×768 → 1080×1620
151
186.98 (296.52)
2.42 (1.53)
184.11 (298.65)
2.46 (1.52) (0.33)
x7.4
633
512×768 → 1080×1620
211
253.77 (406.65)
2.49 (1.56)
249.43 (409.44)
2.53 (1.55) (0.34)
x7.4
903
512×768 → 1080×1620
301
OOM (OOM)
(OOM)
OOM (OOM)
(OOM) (OOM)
149
854x480 → 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 → 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 → 1080x1620
1
14.66 (22.52)
0.34 (0.22)
3B fp16
5
512x768 → 1080x1620
1
17.02 (27.84)
0.29 (0.18)
7B fp8
5
512x768 → 1080x1620
1
46.23 (75.51)
0.11 (0.07)
preserve_memory=on
7B fp16
5
512x768 → 1080x1620
1
43.58 (78.93)
0.11 (0.06)
preserve_memory=on
3B fp8
10
512x768 → 1080x1620
5
39.75
0.25
preserve_memory=on
3B fp8
100
512x768 → 1080x1620
5
322.77
0.31
preserve_memory=on
3B fp8
1000
512x768 → 1080x1620
5
3624.08
0.28
preserve_memory=on
3B fp8
20
512x768 → 1080x1620
1
40.71 (65.40)
0.49 (0.31)
3B fp16
20
512x768 → 1080x1620
1
44.76 (91.12)
0.45 (0.22)
3B fp8
20
512x768 → 1280x1920
1
61.14 (89.10)
0.33 (0.22)
3B fp8
20
512x768 → 1480x2220
1
79.66 (136.08)
0.25 (0.15)
3B fp8
20
512x768 → 1620x2430
1
125.79 (191.28)
0.16 (0.10)
preserve_memory=off (preserve_memory=on)
3B fp8
149
854x480 → 1920x1080
5
782.76
0.19
preserve_memory=on
⚠️ Limitations
Use a lot of VRAM, it will take all!!
Processing speed depends on GPU capabilities
🤝 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:
Fork the repository
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
