2x AnimeSharpV4

--- license: cc-by-nc-sa-4.0 pipeline_tag: image-to-image tags: - pytorch - super-resolution --- ## 2x-AnimeSharpV4 & Fast **Scale:** 2 **Architecture:** RCAN & RCAN PixelUnshuffle **Links:** [Github Release](<https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV4>) **Author:** Kim2091 **License:** CC BY-NC-SA 4.0 **Purpose:** Anime **Subject:** **Input Type:** Images **Date:** 1-7-25 **Size:** **I/O Channels:** 3(RGB)->3(RGB) **Dataset:** ModernAnimation1080_v3 & digital_art_v3 **Dataset Size:** 6k & 20k **OTF (on the fly augmentations):** No **Pretrained Model:** 2x-AnimeSharpV3_RCAN & database's 12k PU checkpoint **Iterations:** 100k RCAN & 400k RCAN PU **Batch Size:** 8 **GT Size:** 64 **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. Currently it is __NOT compatible with chaiNNer__, but will be available on the nightly build soon (hopefully). 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. To use the Pytorch version of the model right now, you can update your version of the spandrel library to 0.4.1 in ComfyUI Alternatively, the latest chaiNNer nightly supports it: https://github.com/chaiNNer-org/chaiNNer-nightly/releases __Comparisons:__ https://slow.pics/c/63Qu8HTN https://slow.pics/c/DBJPDJM9 

Source context: 0 downloads · 57 likes · Pipeline image-to-image · Repo Kim2091/2x-AnimeSharpV4