ProGamerGov

import os
from PIL import Image

def convert_webp_to_png(directory: str, delete_old_webp_images: bool = False):
    for root, dirs, files in os.walk(directory):
        for file in files:
            if file.endswith(".webp"):
                filepath = os.path.join(root, file)
                img = Image.open(filepath)
                new_filepath = os.path.splitext(filepath)[0] + ".png"

ProGamerGov

# Script by https://github.com/ProGamerGov
import copy
import torch


# Path to model and VAE files that you want to merge
vae_file_path = "vae-ft-mse-840000-ema-pruned.ckpt"
model_file_path = "v1-5-pruned-emaonly.ckpt"

# Name to use for new model file

ProGamerGov

from collections import OrderedDict
from typing import Callable, Dict, Optional
from warnings import warn

import torch

def _remove_all_forward_hooks(
    module: torch.nn.Module, hook_fn_name: Optional[str] = None
) -> None:
    """

ProGamerGov

from typing import Tuple


import torch


def color_transfer(
    input: torch.Tensor,
    source: torch.Tensor,
    mode: str = "pca",

ProGamerGov

# tensorflow/lucid CPPN (X,Y) --> (R,G,B) Differentiable Image Parameterization in PyTorch
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from inception5h import Inception5h
from PIL import Image

ProGamerGov

neural-style-pt with tiling

Tiling

The tiling feature is based on neural-dream's tiling system.

Usage

Basic usage:

python neural_style_tile.py -style_image  -content_image  -tile_size 256 -image_size 512

ProGamerGov

import torch
import torch.nn as nn
from collections import OrderedDict
import imp
import numpy as np


# Import the model classes that were edited. Replace 'model_class_name" with the name of the class script, and
# replace 'ModelName' with the name of the class in the script
from model_class_name import ModelName

ProGamerGov

import os
import copy
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms

from PIL import Image
from CaffeLoader import loadCaffemodel, ModelParallel

ProGamerGov

neural-style-pt with mean loss and histogram transfer

Histogram Transfer

Users can specify an image for which the histogram will be transfered from, and what images the histogram will be transfered to; either the content image, style image(s), or both.

Mean Loss

A new loss layer type has been added that uses image means. Currently it only uses the first style image specified.

ProGamerGov

neural-style-pt with histogram loss

The code here is based on genekogan's neural-style-pt histogram loss code. The CUDA code comes from pierre-wilmot's code here: https://github.com/pierre-wilmot/NeuralTextureSynthesis

Histogram Loss Layers

Each histogram loss layer stores the style image's histogram as a target, and then uses that compute the difference to the image being stylized.

Setup