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--- |
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license: cc-by-4.0 |
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pipeline_tag: image-to-image |
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tags: |
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- pytorch |
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- super-resolution |
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--- |
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[Link to Github Release](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_mosr) |
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# 4xNomos2_hq_mosr |
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Scale: 4 |
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Architecture: [MoSR](https://github.com/umzi2/MoSR) |
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Architecture Option: [mosr](https://github.com/umzi2/MoSR/blob/95c5bf73cca014493fe952c2fbc0bdbe593da08f/neosr/archs/mosr_arch.py#L117) |
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Author: Philip Hofmann |
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License: CC-BY-0.4 |
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Purpose: Upscaler |
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Subject: Photography |
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Input Type: Images |
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Release Date: 25.08.2024 |
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Dataset: [nomosv2](https://github.com/muslll/neosr/?tab=readme-ov-file#-datasets) |
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Dataset Size: 6000 |
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OTF (on the fly augmentations): No |
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Pretrained Model: [4xmssim_mosr_pretrain](https://github.com/Phhofm/models/releases/tag/4xmssim_mosr_pretrain) |
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Iterations: 190'000 |
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Batch Size: 6 |
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Patch Size: 64 |
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Description: |
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A 4x [MoSR](https://github.com/umzi2/MoSR) upscaling model, meant for non-degraded input, since this model was trained on non-degraded input to give good quality output. |
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If your input is degraded, use a 1x degrade model first. So for example if your input is a .jpg file, you could use a 1x dejpg model first. |
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PS I also provide an onnx conversion in the Attachements, I verified correct output with chainner: |
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<img src="https://github.com/user-attachments/assets/7f2be678-9d63-43e3-bfb6-59c94827a828" width=25% height=25%> |
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## Model Showcase: |
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[Slowpics](https://slow.pics/c/cqGJb0gT) |
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(Click on image for better view) |
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