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--- |
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license: mit |
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pipeline_tag: image-to-image |
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library_name: pytorch |
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tags: |
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- computer-vision |
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- image-to-image |
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- image-restoration |
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- image-enhancement |
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- super-resolution |
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- comfyui |
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- pytorch |
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- swinir |
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- transformer |
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- anime-upscale |
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--- |
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# SnJake Anime Upscale |
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A **experimental** lightweight upscaler (x2) for anime/illustration images. Designed for clean, sharp results with minimal artifacts. V2 is slightly sharper and removes edge noise artifacts. |
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## Examples |
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# How to use in ComfyUI |
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The model is designed to work with the **SnJake Anime Upscale** ComfyUI node. |
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1. Install the node from [GITHUB REPO](https://github.com/SnJake/SnJake_Baikal_Swin_Anime). |
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2. Download the weights from this repository. |
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3. Place the file(s) into `ComfyUI/models/anime_upscale/`. |
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4. Select the weights in the node dropdown and run the workflow. |
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# Training Details |
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V1: |
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- Dataset: 40,000 images from Danbooru2024: https://huggingface.co/datasets/deepghs/danbooru2024 |
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- Validation: 600 images |
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- Epochs: 70 |
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V2: |
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- Slightly sharper output, no edge noise artifacts. |
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- Epochs: 20 |
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- Dataset: 49,606 images from Danbooru2024: https://huggingface.co/datasets/deepghs/danbooru2024 |
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- Perceptual backbone: convnextv2_tiny.fcmae_ft_in22k_in1k, fine‑tuned on anime to improve feature sensitivity. |
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- Loss schedule: gradual ramp‑in of perceptual/auxiliary losses for stable training. |
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V2.1: |
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- Removed Nearest from resample_methods |
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- Epochs: 30 |
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V2.2: |
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- Epochs: 40 (For now) |
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Training code is included in `training_code/` for reference. |
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## Disclaimer |
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This project was made purely for curiosity and personal interest. The code was written by GPT-5.2 Codex. |
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