I added to my Universal ESRGAN Upscaler the memory usage per tile. Tile size can be better set in this way w.r.t. to the VRAM.
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Visit the home of my ESRGAN network structure viewer and analysis tool.
www.copus.io/work/b51e7ef...
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Margin / Padding 0
Margin / Padding 25
Left without and right with padding and original image. The used model is not optimal for comparing of images. If you look closely, you can see the four tiles on the left. My algorithm works well.
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I'm currently trying to check the quality of the tiling. It's not quite working yet. If you look closely, you can see the four tiles here.
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I am testing now an upscaler from Hugging Face with my tools. Works better than ever in the past. 4 times upscaling here.
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Testing a new upscaler model. Took over the data from my viewer/analyser tool. Works well. If the models well trained, i get the data and can use them.
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I added to my tool, what I learned yesterday. Here I make the corruption of an model visible. File size is also to small.
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Here we get an idea, why the upscaler model is not working. Printing out the tensors confirms that. Totally corrupted internal structure.
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Last revision of my first version. The network data can be seen in the red marked block of this ESRGAN upscaler model.
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Last version gives most or all informations for the evaluation of ESRGAN (upscaler) models. Now I have all data for getting the complete set of network data.
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It is better to let a computer analyze things than to look on its own on the raw data yourself. This model is somehow a mixture in architecture. Raw data can also be printed on demand.
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I programmed quick and dirty a viewer for ESRGAN with printing additional data on demand.
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For cases like this I will write soon a ESRGAN RRDB structure printer to get an idea, if one can use such models. In this version my verbose printing makes no longer sense.
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The development of my last tool results in a nice classification list of upscaler models.
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civitai.com/articles/270...
This is the first version of my configuration file for my ESRGAN RRDB analyser. This allows me a standard and an extended analysis.
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Color says more than a thousand words.
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Function test. There was no information about the network for this model. So you would always have had to guess the data.
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Without making a mistake the recognition looks like. Now I need model testing.
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I deliberately made a mistake here. However, my program guessed the correct values. That is one of the reasons for the implementation.
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'Guessing' the network data seems to work. I need to test this with many models.
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Advantage: Reliable detection. Disadvantage: Time-consuming if searching with full parameters. I need a intermediate step for let's say guessing the correct values.
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I changed the code a little bit. Now I can see more without debugging. This model should be RealESRGAN and it is not due to wrong internal network structure.
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I can use my tool now for rudimentary debugging of ESRGAN models in general. Right corrupted model. Left correct model. Missing pre/post data seems to be the problem here.
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I optimized my code a little bit. To see how the progress is, I added a progress spinner. I need also a tool for deeper analysis, when a model fails.
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I wouldn't have thought of RealESRGAN as a model here.
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Another example of ERSGAN recognition with an uncommon number of blocks.
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I have managed to create a first version of an ESRGAN recognition program. In case of this model the network structure was unknow. It was known, that it should be ESRGAN and scale 8x.
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I need to catch the PIL warning. Then the program is okay. The mentioned resolution of 18432 x 32768 pixels was realized on a 8 GB GPU and 144 tiles. Calculation time 355.56 seconds. But it works.
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I obtained an incredible resolution of 18432 x 32768 pixels in an upscaling test. The file size was 147 MB. Similarity calculation failed with this image size.
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I may need both tensor images and PIL images for saving later. In the past, PIL images were limited in their resolution. This is the final test for other orientations.
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