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license: cc-by-4.0
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license: cc-by-4.0
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[Link to Github Release](https://github.com/Phhofm/models/releases/tag/4xHFA2k_ludvae_realplksr_dysample)
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# 4xHFA2k_ludvae_realplksr_dysample
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Scale: 4
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Architecture: [RealPLKSR with Dysample](https://github.com/muslll/neosr/?tab=readme-ov-file#supported-archs)
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Architecture Option: [realplksr](https://github.com/muslll/neosr/blob/master/neosr/archs/realplksr_arch.py)
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Author: Philip Hofmann
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License: CC-BY-0.4
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Purpose: Restoration
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Subject: Anime
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Input Type: Images
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Release Date: 13.07.2024
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Dataset: HFA2k_LUDVAE
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Dataset Size: 10'272
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OTF (on the fly augmentations): No
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Pretrained Model: [4xNomos2_realplksr_dysample](https://github.com/Phhofm/models/releases/tag/4xNomos2_realplksr_dysample)
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Iterations: 165'000
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Batch Size: 12
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GT Size: 256
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Description:
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A Dysample RealPLKSR 4x upscaling model for anime single-image resolution.
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The dataset has been degraded using DM600_LUDVAE, for more realistic noise/compression. Downscaling algorithms used were imagemagick box, triangle, catrom, lanczos and mitchell. Blurs applied were gaussian, box and lens blur (using chaiNNer). Some images were further compressed using -quality 75-92. Down-up was applied to roughly 10% of the dataset (5 to 15% variation in size). Degradations orders were shuffled, to give as many variations as possible.
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Examples are inferenced with [neosr](https://github.com/muslll/neosr) testscript and the released pth file. I include the test images also as a zip file in this release together with the model outputs, so others can test their models against these test images aswell to compare.
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onnx conversions are static since dysample doesnt allow dynamic conversion, I tested the conversions with [chaiNNer](https://github.com/chaiNNer-org/chaiNNer).
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Showcase:
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[Slowpics](https://slow.pics/c/FKDZAcyI)
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(Click Image to enlarge)
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![Example1](https://github.com/user-attachments/assets/0cbbbdc2-9c7e-4cf8-9d1f-692caa55f4d3)
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![Example2](https://github.com/user-attachments/assets/0e8272b9-48cc-4a6f-8a0b-b8a0535e09d1)
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![Example3](https://github.com/user-attachments/assets/f519f0f2-a3bd-430e-b07b-7c57ea8f5b63)
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![Example4](https://github.com/user-attachments/assets/a5feb09a-81ee-4c18-bca9-db9b4bd8bcc1)
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![Example5](https://github.com/user-attachments/assets/e71bd965-8d94-48ba-a02f-c4af016f9a9a)
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![Example6](https://github.com/user-attachments/assets/3f0ce3bc-842d-40da-8eb7-880e0a0b8e92)
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![Example7](https://github.com/user-attachments/assets/f0d55c0a-e1e9-461f-b189-3c3a831abf29)
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![Example8](https://github.com/user-attachments/assets/0c38593e-53f5-4455-86a7-9f08ce0d6b78)
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![Example9](https://github.com/user-attachments/assets/c5611080-2691-4950-9d96-960f2ce92756)
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![Example10](https://github.com/user-attachments/assets/8b7bd232-c08a-4cd7-8a02-91ae35e16954)
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![Example11](https://github.com/user-attachments/assets/33d44ddd-8b8c-4af6-971d-6f9942a331da)
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![Example12](https://github.com/user-attachments/assets/438b76bc-7e5e-45ab-9be1-1f39d14b7428)
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![Example13](https://github.com/user-attachments/assets/9ac1c550-8cc8-44d8-8808-265428aebb6c)
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![Example14](https://github.com/user-attachments/assets/1fc30d57-9e55-4c65-a5c5-19d5738307d4)
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