ACG2vec系列之CUGAN_TF——运行在浏览器上的动漫超分工具（Real-CUGAN的tensorflow实现）

介绍

当前最优秀的动漫领域超分模型之一Real-CUGAN的tensorflow实现，依赖tfjs框架完成自适应后端的能运行在浏览器上的动漫超分工具。

原版实现分为切块后超分与整图超分，两种都以实现，但切块超分版本转为tfjs模型后在网页运行不正常，已向tfjs仓库提交issue。目前预览版本是整图超分版本，由于内存限制，限制了原始图片大小（512x512以内），后续issue解决将发布切块超分，大概率将不会有限制。

This module is an implementation of one of the current leading anime super-resolution models in the field, Real-CUGAN, using TensorFlow. It relies on the tfjs framework to create an adaptive backend, enabling it to run as an anime super-resolution tool in web browsers.

The original implementation offers two versions: chunk-based super-resolution and full-image super-resolution, both of which have been implemented. However, the chunk-based super-resolution version encounters issues when converted into a tfjs model and run in a web browser. An issue has been raised in the tfjs repository to address this problem.

The current preview version focuses on full-image super-resolution but has limitations on the original image size (up to 512x512) due to memory constraints. Once the issue is resolved, the chunk-based super-resolution version is likely to be released without such limitations.

预览

效果

输入

无降噪输出

保守输出

降噪输出

pytorch模型迁移到tensorflow应该注意的点

• 图片处理默认维度顺序： tensorflow为nhwc，pytorch为nchw，卷积权重维度顺序也不相同
• tensorflow转置卷积无法自定义padding： Conv2DTranspose层padding设为0，后续使用slice手动crop输出
• tf.pad无法接受负数：使用 tf.slice作为替代
• 多尺寸输入导致无法batch ：无解
• 延迟设置输入尺寸运行时获取size：这是tensorflow图模型的限制
• python操作逻辑最好翻译为tensorflow 分支选择api
• TensorArray：TensorArray是图模式中python list替代品， TensorArray在eager模式TensorArray.write(i, x)可以直接生效，而在graph模式时需要将引用赋值给自身