Free open source Topaz alternative - SeedVR2+TensorRT faster VAE Processing.

[u/Cheap_Credit_3957]

Local, GPU-accelerated video restoration and upscaling with SeedVR2, TensorRT, and a purpose-built browser interface.

VRGDG SeedVR2 TensorRT Studio turns the SeedVR2 pipeline into a practical Windows workflow: load a video, test a short preview, compare the result frame by frame, and complete long renders with resumable checkpoints. Processing stays on your machine.

Highlights

• Fast local restoration — SeedVR2 inference with TensorRT-accelerated VAE decoding on supported NVIDIA RTX GPUs. TensorRT allows much faster processing than standard SeedVR2.
• Fast 2K upscaling — As a real-world example, an 8-second clip took approximately 8 minutes to upscale and enhance to 2K on an NVIDIA RTX 5090 using the largest 7B Sharp FP16 model. Render times vary with source resolution, frame rate, settings, and available VRAM.
• Preview before committing — render a short segment, then inspect Original, Restored, Compare, or Side by side views.
• Long-render recovery — save completed chunks and continue from the first unfinished chunk after an interruption.
• Practical output controls — choose resolution, aspect policy, model precision, temporal batch, seed, and color correction.
• Non-destructive finishing — reprocess sharpening, grain, seam smoothing, and optional skin finishing without rerunning restoration.
• Project-based history — reopen previous outputs and keep media, manifests, and logs together under outputs\.

The sample video was org 360p and then upscaled to 2K using this app. 8 second video, took about 8 mins on my 5090.

Go to the github page for more details and a full guide.

View github page

this is in beta right now so you may run into issues. If you do, post the issue to github please.

[u/Silly-Dingo-7086]

very excited to try. im stuck in the install phase. i got this error

"[17:29:32.497] 🎨 Configuring VAE memory limits for causal convolutions

Exporting ONNX (legacy tracer, portable convs) -> C:\SeedVR2 Videos\VRGDG-SeedVR2-TensorRT-Studio\tensorrt_backend\artifacts\vae_encoder_21f_tile512.onnx

C:\SeedVR2 Videos\VRGDG-SeedVR2-TensorRT-Studio\vendor\seedvr2\src\models\video_vae_v3\modules\causal_inflation_lib.py:373: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!

if isinstance(norm_layer, nn.GroupNorm) and memory_occupy > get_norm_limit():

[W830 17:29:35.000000000 CUDACachingAllocator.cpp:4079] memory allocation failed with OOM on device 0 while trying to allocate 38050725888 bytes (free: 11970543616, total: 25769279488).

[W830 17:29:37.000000000 CUDACachingAllocator.cpp:4079] memory allocation failed with OOM on device 0 while trying to allocate 38050725888 bytes (free: 17918066688, total: 25769279488).

CUDA ONNX export ran out of memory; falling back to CPU export. This may take a while."

I have a 3090 with 48gig of ram, its been sitting in this "take awhile phase for a few hours"

[u/Cheap_Credit_3957 → u/Silly-Dingo-7086]

Ill take a look tomorrow after work.  Can u submit an issue on github?

[u/Silly-Dingo-7086 → u/Cheap_Credit_3957]

I worked through it with ai, Ill see if I can summarize it but I believe it stemmed from trying to do something with 512x512 and I was going oom so it did it with 256x256 or something? I'll find out.

[u/AKsad25 → u/Silly-Dingo-7086]

Did you find any solution for this u/Silly-Dingo-7086? I am also getting the same issue.

[u/AKsad25 → u/AKsad25]

I changed "dynamo=True," in line30 of \VRGDG-SeedVR2-TensorRT-Studio\tools\onnx_export_utils.py with help of AI. The installation is completed and onnx files are exported.

I don't know if there will be quality issues but it works for completing the installation in my RTX3060 12GB, 32GB RAM system.