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

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.

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.

	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"

	# 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

	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:
	"""

	from typing import Tuple


	import torch


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

	# 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