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__init__.py
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import torch, os
import numpy as np
import torchvision.transforms as transforms
from PIL import Image
import comfy.utils
import asyncio
from .pixelization.models.networks import define_G
from .pixelization.models import c2pGen
import sys
def has_mps() -> bool:
if sys.platform != "darwin":
return False
else:
return torch.backends.mps.is_available()
def get_cuda_device_string():
return "cuda"
def get_optimal_device_name():
if torch.cuda.is_available():
return get_cuda_device_string()
if has_mps():
return "mps"
return "cpu"
def get_optimal_device():
return torch.device(get_optimal_device_name())
device = get_optimal_device()
# From https://github.com/AUTOMATIC1111/stable-diffusion-webui-pixelization/tree/master
pixelize_code = [
233356.8125, -27387.5918, -32866.8008, 126575.0312, -181590.0156,
-31543.1289, 50374.1289, 99631.4062, -188897.3750, 138322.7031,
-107266.2266, 125778.5781, 42416.1836, 139710.8594, -39614.6250,
-69972.6875, -21886.4141, 86938.4766, 31457.6270, -98892.2344,
-1191.5887, -61662.1719, -180121.9062, -32931.0859, 43109.0391,
21490.1328, -153485.3281, 94259.1797, 43103.1992, -231953.8125,
52496.7422, 142697.4062, -34882.7852, -98740.0625, 34458.5078,
-135436.3438, 11420.5488, -18895.8984, -71195.4141, 176947.2344,
-52747.5742, 109054.6562, -28124.9473, -17736.6152, -41327.1562,
69853.3906, 79046.2656, -3923.7344, -5644.5229, 96586.7578,
-89315.2656, -146578.0156, -61862.1484, -83956.4375, 87574.5703,
-75055.0469, 19571.8203, 79358.7891, -16501.5000, -147169.2188,
-97861.6797, 60442.1797, 40156.9023, 223136.3906, -81118.0547,
-221443.6406, 54911.6914, 54735.9258, -58805.7305, -168884.4844,
40865.9609, -28627.9043, -18604.7227, 120274.6172, 49712.2383,
164402.7031, -53165.0820, -60664.0469, -97956.1484, -121468.4062,
-69926.1484, -4889.0151, 127367.7344, 200241.0781, -85817.7578,
-143190.0625, -74049.5312, 137980.5781, -150788.7656, -115719.6719,
-189250.1250, -153069.7344, -127429.7891, -187588.2500, 125264.7422,
-79082.3438, -114144.5781, 36033.5039, -57502.2188, 80488.1562,
36501.4570, -138817.5938, -22189.6523, -222146.9688, -73292.3984,
127717.2422, -183836.3750, -105907.0859, 145422.8750, 66981.2031,
-9596.6699, 78099.4922, 70226.3359, 35841.8789, -116117.6016,
-150986.0156, 81622.4922, 113575.0625, 154419.4844, 53586.4141,
118494.8750, 131625.4375, -19763.1094, 75581.1172, -42750.5039,
97934.8281, 6706.7949, -101179.0078, 83519.6172, -83054.8359,
-56749.2578, -30683.6992, 54615.9492, 84061.1406, -229136.7188,
-60554.0000, 8120.2622, -106468.7891, -28316.3418, -166351.3125,
47797.3984, 96013.4141, 71482.9453, -101429.9297, 209063.3594,
-3033.6882, -38952.5352, -84920.6719, -5895.1543, -18641.8105,
47884.3633, -14620.0273, -132898.6719, -40903.5859, 197217.3750,
-128599.1328, -115397.8906, -22670.7676, -78569.9688, -54559.7070,
-106855.2031, 40703.1484, 55568.3164, 60202.9844, -64757.9375,
-32068.8652, 160663.3438, 72187.0703, -148519.5469, 162952.8906,
-128048.2031, -136153.8906, -15270.3730, -52766.3281, -52517.4531,
18652.1992, 195354.2188, -136657.3750, -8034.2622, -92699.6016,
-129169.1406, 188479.9844, 46003.7500, -93383.0781, -67831.6484,
-66710.5469, 104338.5234, 85878.8438, -73165.2031, 95857.3203,
71213.1250, 94603.1094, -30359.8125, -107989.2578, 99822.1719,
184626.3594, 79238.4531, -272978.9375, -137948.5781, -145245.8125,
75359.2031, 26652.7930, 50421.4141, 60784.4102, -18286.3398,
-182851.9531, -87178.7969, -13131.7539, 195674.8906, 59951.7852,
124353.7422, -36709.1758, -54575.4766, 77822.6953, 43697.4102,
-64394.3438, 113281.1797, -93987.0703, 221989.7188, 132902.5000,
-9538.8574, -14594.1338, 65084.9453, -12501.7227, 130330.6875,
-115123.4766, 20823.0898, 75512.4922, -75255.7422, -41936.7656,
-186678.8281, -166799.9375, 138770.6250, -78969.9531, 124516.8047,
-85558.5781, -69272.4375, -115539.1094, 228774.4844, -76529.3281,
-107735.8906, -76798.8906, -194335.2812, 56530.5742, -9397.7529,
132985.8281, 163929.8438, -188517.7969, -141155.6406, 45071.0391,
207788.3125, -125826.1172, 8965.3320, -159584.8438, 95842.4609,
-76929.4688
]
basedir = os.path.dirname(os.path.realpath(__file__))
path_checkpoints = os.path.join(basedir, "checkpoints")
path_pixelart_vgg19 = os.path.join(path_checkpoints, "pixelart_vgg19.pth")
path_160_net_G_A = os.path.join(path_checkpoints, "160_net_G_A.pth")
path_alias_net = os.path.join(path_checkpoints, "alias_net.pth")
class TorchHijackForC2pGen:
def __getattr__(self, item):
if item == 'load':
return self.load
if hasattr(torch, item):
return getattr(torch, item)
raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
def load(self, filename, *args, **kwargs):
if filename == "./pixelart_vgg19.pth":
filename = path_pixelart_vgg19
return torch.load(filename, *args, **kwargs)
c2pGen.torch = TorchHijackForC2pGen()
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.G_A_net = None
self.alias_net = None
def load(self):
os.makedirs(path_checkpoints, exist_ok=True)
missing = False
if not os.path.exists(path_pixelart_vgg19):
print(f"Missing {path_pixelart_vgg19} - download it from https://drive.google.com/uc?id=1VRYKQOsNlE1w1LXje3yTRU5THN2MGdMM")
missing = True
if not os.path.exists(path_160_net_G_A):
print(f"Missing {path_160_net_G_A} - download it from https://drive.google.com/uc?id=1i_8xL3stbLWNF4kdQJ50ZhnRFhSDh3Az")
missing = True
if not os.path.exists(path_alias_net):
print(f"Missing {path_alias_net} - download it from https://drive.google.com/uc?id=17f2rKnZOpnO9ATwRXgqLz5u5AZsyDvq_")
missing = True
assert not missing, 'Missing checkpoints for pixelization - see console for download links.'
with torch.no_grad():
self.G_A_net = define_G(3, 3, 64, "c2pGen", "instance", False, "normal", 0.02, [0])
self.alias_net = define_G(3, 3, 64, "antialias", "instance", False, "normal", 0.02, [0])
G_A_state = torch.load(path_160_net_G_A)
for p in list(G_A_state.keys()):
G_A_state["module." + str(p)] = G_A_state.pop(p)
self.G_A_net.load_state_dict(G_A_state)
alias_state = torch.load(path_alias_net)
for p in list(alias_state.keys()):
alias_state["module." + str(p)] = alias_state.pop(p)
self.alias_net.load_state_dict(alias_state)
def process(img):
ow, oh = img.size
nw = int(round(ow / 4) * 4)
nh = int(round(oh / 4) * 4)
left = (ow - nw) // 2
top = (oh - nh) // 2
right = left + nw
bottom = top + nh
img = img.crop((left, top, right, bottom))
trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
return trans(img)[None, :, :, :]
def to_image(tensor, pixel_size, upscale_after):
img = tensor.data[0].cpu().float().numpy()
img = (np.transpose(img, (1, 2, 0)) + 1) / 2.0 * 255.0
img = img.astype(np.uint8)
img = Image.fromarray(img)
img = img.resize((img.size[0]//4, img.size[1]//4), resample=Image.Resampling.NEAREST)
if upscale_after:
img = img.resize((img.size[0]*pixel_size, img.size[1]*pixel_size), resample=Image.Resampling.NEAREST)
return img
def tensor2pil(image):
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
def pil2tensor(image):
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
def wait_for_async(async_fn, loop=None):
res = []
async def run_async():
r = await async_fn()
res.append(r)
if loop is None:
try:
loop = asyncio.get_event_loop()
except:
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
loop.run_until_complete(run_async())
return res[0]
class Pixelization:
model = None
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"pixel_size": ("INT", {"default": 4, "min": 1, "max": 16}),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "pixelize"
CATEGORY = "image"
OUTPUT_IS_LIST = (True,)
OUTPUT_NODE = False
async def run_pixelatization(self, image, pixel_size, upscale_after):
image = image.resize((image.width * 4 // pixel_size, image.height * 4 // pixel_size))
with torch.no_grad():
in_t = process(image).to(device)
feature = self.model.G_A_net.module.RGBEnc(in_t)
code = torch.asarray(pixelize_code, device=device).reshape((1, 256, 1, 1))
adain_params = self.model.G_A_net.module.MLP(code)
images = self.model.G_A_net.module.RGBDec(feature, adain_params)
out_t = self.model.alias_net(images)
image = to_image(out_t, pixel_size=pixel_size, upscale_after=upscale_after)
image = pil2tensor(image)
return image
def pixelize(self, image, pixel_size):
if self.model is None:
model = Model()
model.load()
self.model = model
self.model.to(device)
upscale_after = True
tensor = image*255
tensor = np.array(tensor, dtype=np.uint8)
pbar = comfy.utils.ProgressBar(tensor.shape[0])
all_images = []
for i in range(tensor.shape[0]):
image = Image.fromarray(tensor[i])
all_images.append((
wait_for_async(lambda: self.run_pixelatization(image, pixel_size, upscale_after))
))
pbar.update(1)
return (all_images,)
NODE_CLASS_MAPPINGS = {
"Pixelization": Pixelization
}