Closed antonioglass closed 2 months ago
I also tried running inference with GFPGAN and Codeformer with A1111 on the same machine, and it takes 0.5 seconds.
Yeah...
Set subsample upscale to 512px
Just don't. Use 128px instead.
Drag and drop more than 1 image into the target files box or try a GIF/video. Profit.
Short Explanation: when clicking "Start" all models are freed from memory and are reloaded onto the gpu. You will not see the real speed when using only 1 image. Upscaling runs on cpu-only and slows down immensely, if you want to compare it to the basic swap plugins in A1111 then you need to drop it down to the 128px native resolution to get the same results and speed.
Describe the bug I'm using a fresh & clean installation on Ubuntu with RTX 4090 24GB on RunPod (runpod/pytorch:2.1.0-py3.10-cuda11.8.0-devel-ubuntu22.04), and experiencing slow inference / low memory usage when processing a single image (832 x 1216) with Codeformer or GFPGAN post-processing enabled.
With Codeformer the inference is between 12 and 15 seconds and memory usage is ≈2GB; with GFPGAN ≈7 seconds and ≈2.7GB.
I checked this section, and confirm that
Using provider ['CUDAExecutionProvider'] - Device:cuda
is being displayed on startup, andonnxruntime-gpu
, CUDA Toolkit 11.8 and cuDNN are installed.I tried increasing and reducing
max_threads
but it didn't help.To Reproduce Steps to reproduce the behavior:
(I also changed the default value of
server_name
to '0.0.0.0'.)Details What OS are you using?
Are you using a GPU?
Which version of roop unleashed are you using? v4.1.1
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