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--- |
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license: cc-by-nc-sa-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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## 2x-AnimeSharpV4 & Fast |
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**Scale:** 2 |
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**Architecture:** RCAN & RCAN PixelUnshuffle |
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**Links:** [Github Release](<https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV4>) |
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**Author:** Kim2091 |
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**License:** CC BY-NC-SA 4.0 |
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**Purpose:** Anime |
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**Subject:** |
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**Input Type:** Images |
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**Date:** 1-7-25 |
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**Size:** |
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**I/O Channels:** 3(RGB)->3(RGB) |
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**Dataset:** ModernAnimation1080_v3 & digital_art_v3 |
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**Dataset Size:** 6k & 20k |
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**OTF (on the fly augmentations):** No |
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**Pretrained Model:** 2x-AnimeSharpV3_RCAN & database's 12k PU checkpoint |
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**Iterations:** 100k RCAN & 400k RCAN PU |
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**Batch Size:** 8 |
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**GT Size:** 64 |
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**Description:** This is a successor to AnimeSharpV3 based on RCAN instead of ESRGAN. It outperforms both versions of AnimeSharpV3 in every capacity. It's sharper, retains *even more* detail, and has very few artifacts. It is __extremely faithful__ to the input image, even with heavily compressed inputs. |
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Currently it is __NOT compatible with chaiNNer__, but will be available on the nightly build soon (hopefully). |
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The `2x-AnimeSharpV4_Fast_RCAN_PU` model is trained on RCAN PixelUnshuffle. This is much faster, but comes at the cost of quality. I believe the model is ~95% the quality of the full V4 RCAN model, but ~6x faster in Pytorch and ~4x faster in TensorRT. This model is ideal for video processing, and as such was trained to handle MPEG2 & H264 compression. |
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To use the Pytorch version of the model right now, you can manually update your version of the spandrel library in chaiNNer or another tool to this version: https://github.com/Kim2091/spandrel/actions/runs/12701005765 |
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__Comparisons:__ |
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https://slow.pics/c/63Qu8HTN |
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https://slow.pics/c/DBJPDJM9 |
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