README.md

---
title: Anime Colorization
emoji: 😻
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 3.0.5
app_file: app.py
pinned: false
license: mit
---

# Pixel Guide Diffusion For Anime Colorization

![avatar](docs/imgs/sample.png)

Use denoising diffusion probabilistic model to do the anime colorization task.

v1 test result is in branch [v1_result](https://github.com/HighCWu/pixel-guide-diffusion-for-anime-colorization/tree/v1_result).

The dataset is not clean enough and the sketch as the guide is generated using sketch2keras, so the generalization is not good. 

In the future, I may try to use only anime portraits as the target images, and look for some more diverse sketch models.

# Introduction and Usage

Pixel Guide Denoising Diffusion Probabilistic Models ( One Channel Guide Version )

This repo  is modified from [improved-diffusion](https://github.com/openai/improved-diffusion).

Use [danbooru-sketch-pair-128x](https://www.kaggle.com/wuhecong/danbooru-sketch-pair-128x) as the dataset. Maybe you should move folders in the dataset first to make guide-target pair dataset.

Modify `train_danbooru*.sh`, `test_danbooru*.sh` to meet your needs.

The model is divided into a 32px part and a super-divided part, which can be cascaded during testing to get the final result. But there is no cascade during training. 

QQ Group: 1044867291

Discord: https://discord.gg/YwWcAS47qb

# Original README

# improved-diffusion

This is the codebase for [Improved Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2102.09672).

# Usage

This section of the README walks through how to train and sample from a model.

## Installation

Clone this repository and navigate to it in your terminal. Then run:

```
pip install -e .
```

This should install the ~~`improved_diffusion`~~ `pixel_guide_diffusion` python package that the scripts depend on.

## Preparing Data

The training code reads images from a directory of image files. In the [datasets](datasets) folder, we have provided instructions/scripts for preparing these directories for ImageNet, LSUN bedrooms, and CIFAR-10.

For creating your own dataset, simply dump all of your images into a directory with ".jpg", ".jpeg", or ".png" extensions. If you wish to train a class-conditional model, name the files like "mylabel1_XXX.jpg", "mylabel2_YYY.jpg", etc., so that the data loader knows that "mylabel1" and "mylabel2" are the labels. Subdirectories will automatically be enumerated as well, so the images can be organized into a recursive structure (although the directory names will be ignored, and the underscore prefixes are used as names).

The images will automatically be scaled and center-cropped by the data-loading pipeline. Simply pass `--data_dir path/to/images` to the training script, and it will take care of the rest.

## Training

To train your model, you should first decide some hyperparameters. We will split up our hyperparameters into three groups: model architecture, diffusion process, and training flags. Here are some reasonable defaults for a baseline:

```
MODEL_FLAGS="--image_size 64 --num_channels 128 --num_res_blocks 3"
DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule linear"
TRAIN_FLAGS="--lr 1e-4 --batch_size 128"
```

Here are some changes we experiment with, and how to set them in the flags:

 * **Learned sigmas:** add `--learn_sigma True` to `MODEL_FLAGS`
 * **Cosine schedule:** change `--noise_schedule linear` to `--noise_schedule cosine`
 * **Reweighted VLB:** add `--use_kl True` to `DIFFUSION_FLAGS` and add `--schedule_sampler loss-second-moment` to  `TRAIN_FLAGS`.
 * **Class-conditional:** add `--class_cond True` to `MODEL_FLAGS`.

Once you have setup your hyper-parameters, you can run an experiment like so:

```
python scripts/image_train.py --data_dir path/to/images $MODEL_FLAGS $DIFFUSION_FLAGS $TRAIN_FLAGS
```

You may also want to train in a distributed manner. In this case, run the same command with `mpiexec`:

```
mpiexec -n $NUM_GPUS python scripts/image_train.py --data_dir path/to/images $MODEL_FLAGS $DIFFUSION_FLAGS $TRAIN_FLAGS
```

When training in a distributed manner, you must manually divide the `--batch_size` argument by the number of ranks. In lieu of distributed training, you may use `--microbatch 16` (or `--microbatch 1` in extreme memory-limited cases) to reduce memory usage.

The logs and saved models will be written to a logging directory determined by the `OPENAI_LOGDIR` environment variable. If it is not set, then a temporary directory will be created in `/tmp`.

## Sampling

The above training script saves checkpoints to `.pt` files in the logging directory. These checkpoints will have names like `ema_0.9999_200000.pt` and `model200000.pt`. You will likely want to sample from the EMA models, since those produce much better samples.

Once you have a path to your model, you can generate a large batch of samples like so:

```
python scripts/image_sample.py --model_path /path/to/model.pt $MODEL_FLAGS $DIFFUSION_FLAGS
```

Again, this will save results to a logging directory. Samples are saved as a large `npz` file, where `arr_0` in the file is a large batch of samples.

Just like for training, you can run `image_sample.py` through MPI to use multiple GPUs and machines.

You can change the number of sampling steps using the `--timestep_respacing` argument. For example, `--timestep_respacing 250` uses 250 steps to sample. Passing `--timestep_respacing ddim250` is similar, but uses the uniform stride from the [DDIM paper](https://arxiv.org/abs/2010.02502) rather than our stride.

To sample using [DDIM](https://arxiv.org/abs/2010.02502), pass `--use_ddim True`.

## Models and Hyperparameters

This section includes model checkpoints and run flags for the main models in the paper.

Note that the batch sizes are specified for single-GPU training, even though most of these runs will not naturally fit on a single GPU. To address this, either set `--microbatch` to a small value (e.g. 4) to train on one GPU, or run with MPI and divide `--batch_size` by the number of GPUs.

Unconditional ImageNet-64 with our `L_hybrid` objective and cosine noise schedule [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/imagenet64_uncond_100M_1500K.pt)]:

```bash
MODEL_FLAGS="--image_size 64 --num_channels 128 --num_res_blocks 3 --learn_sigma True"
DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine"
TRAIN_FLAGS="--lr 1e-4 --batch_size 128"
```

Unconditional CIFAR-10 with our `L_hybrid` objective and cosine noise schedule [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/cifar10_uncond_50M_500K.pt)]:

```bash
MODEL_FLAGS="--image_size 32 --num_channels 128 --num_res_blocks 3 --learn_sigma True --dropout 0.3"
DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine"
TRAIN_FLAGS="--lr 1e-4 --batch_size 128"
```

Class-conditional ImageNet-64 model (270M parameters, trained for 250K iterations) [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/imagenet64_cond_270M_250K.pt)]:

```bash
MODEL_FLAGS="--image_size 64 --num_channels 192 --num_res_blocks 3 --learn_sigma True --class_cond True"
DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine --rescale_learned_sigmas False --rescale_timesteps False"
TRAIN_FLAGS="--lr 3e-4 --batch_size 2048"
```

Upsampling 256x256 model (280M parameters, trained for 500K iterations) [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/upsample_cond_500K.pt)]:

```bash
MODEL_FLAGS="--num_channels 192 --num_res_blocks 2 --learn_sigma True --class_cond True"
DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False"
TRAIN_FLAGS="--lr 3e-4 --batch_size 256"
```

LSUN bedroom model (lr=1e-4) [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/lsun_uncond_100M_1200K_bs128.pt)]:

```bash
MODEL_FLAGS="--image_size 256 --num_channels 128 --num_res_blocks 2 --num_heads 1 --learn_sigma True --use_scale_shift_norm False --attention_resolutions 16"
DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False"
TRAIN_FLAGS="--lr 1e-4 --batch_size 128"
```

LSUN bedroom model (lr=2e-5) [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/lsun_uncond_100M_2400K_bs64.pt)]:

```bash
MODEL_FLAGS="--image_size 256 --num_channels 128 --num_res_blocks 2 --num_heads 1 --learn_sigma True --use_scale_shift_norm False --attention_resolutions 16"
DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False --use_scale_shift_norm False"
TRAIN_FLAGS="--lr 2e-5 --batch_size 128"
```

Unconditional ImageNet-64 with the `L_vlb` objective and cosine noise schedule [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/imagenet64_uncond_vlb_100M_1500K.pt)]:

```bash
MODEL_FLAGS="--image_size 64 --num_channels 128 --num_res_blocks 3 --learn_sigma True"
DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine"
TRAIN_FLAGS="--lr 1e-4 --batch_size 128 --schedule_sampler loss-second-moment"
```

Unconditional CIFAR-10 with the `L_vlb` objective and cosine noise schedule [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/cifar10_uncond_vlb_50M_500K.pt)]:

```bash
MODEL_FLAGS="--image_size 32 --num_channels 128 --num_res_blocks 3 --learn_sigma True --dropout 0.3"
DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine"
TRAIN_FLAGS="--lr 1e-4 --batch_size 128 --schedule_sampler loss-second-moment"
```