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