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all_in_one.py
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import subprocess, os, sys, ipykernel
from urllib.parse import urlparse
# 1 check nvidia
simple_nvidia_smi_display = False#@param {type:"boolean"}
if simple_nvidia_smi_display:
#!nvidia-smi
nvidiasmi_output = subprocess.run(['nvidia-smi', '-L'], stdout=subprocess.PIPE).stdout.decode('utf-8')
print(nvidiasmi_output)
else:
#!nvidia-smi -i 0 -e 0
nvidiasmi_output = subprocess.run(['nvidia-smi'], stdout=subprocess.PIPE).stdout.decode('utf-8')
print(nvidiasmi_output)
nvidiasmi_ecc_note = subprocess.run(['nvidia-smi', '-i', '0', '-e', '0'], stdout=subprocess.PIPE).stdout.decode('utf-8')
print(nvidiasmi_ecc_note)
diff_model_map = {
'256x256_diffusion_uncond': { 'downloaded': False, 'sha': 'a37c32fffd316cd494cf3f35b339936debdc1576dad13fe57c42399a5dbc78b1', 'uri_list': ['https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt', 'https://www.dropbox.com/s/9tqnqo930mpnpcn/256x256_diffusion_uncond.pt'] },
'512x512_diffusion_uncond_finetune_008100': { 'downloaded': False, 'sha': '9c111ab89e214862b76e1fa6a1b3f1d329b1a88281885943d2cdbe357ad57648', 'uri_list': ['https://huggingface.co/lowlevelware/512x512_diffusion_unconditional_ImageNet/resolve/main/512x512_diffusion_uncond_finetune_008100.pt', 'https://the-eye.eu/public/AI/models/512x512_diffusion_unconditional_ImageNet/512x512_diffusion_uncond_finetune_008100.pt'] },
'portrait_generator_v001': { 'downloaded': False, 'sha': 'b7e8c747af880d4480b6707006f1ace000b058dd0eac5bb13558ba3752d9b5b9', 'uri_list': ['https://huggingface.co/felipe3dartist/portrait_generator_v001/resolve/main/portrait_generator_v001_ema_0.9999_1MM.pt'] },
'pixelartdiffusion_expanded': { 'downloaded': False, 'sha': 'a73b40556634034bf43b5a716b531b46fb1ab890634d854f5bcbbef56838739a', 'uri_list': ['https://huggingface.co/KaliYuga/PADexpanded/resolve/main/PADexpanded.pt'] },
'pixel_art_diffusion_hard_256': { 'downloaded': False, 'sha': 'be4a9de943ec06eef32c65a1008c60ad017723a4d35dc13169c66bb322234161', 'uri_list': ['https://huggingface.co/KaliYuga/pixel_art_diffusion_hard_256/resolve/main/pixel_art_diffusion_hard_256.pt'] },
'pixel_art_diffusion_soft_256': { 'downloaded': False, 'sha': 'd321590e46b679bf6def1f1914b47c89e762c76f19ab3e3392c8ca07c791039c', 'uri_list': ['https://huggingface.co/KaliYuga/pixel_art_diffusion_soft_256/resolve/main/pixel_art_diffusion_soft_256.pt'] },
'pixelartdiffusion4k': { 'downloaded': False, 'sha': 'a1ba4f13f6dabb72b1064f15d8ae504d98d6192ad343572cc416deda7cccac30', 'uri_list': ['https://huggingface.co/KaliYuga/pixelartdiffusion4k/resolve/main/pixelartdiffusion4k.pt'] },
'watercolordiffusion_2': { 'downloaded': False, 'sha': '49c281b6092c61c49b0f1f8da93af9b94be7e0c20c71e662e2aa26fee0e4b1a9', 'uri_list': ['https://huggingface.co/KaliYuga/watercolordiffusion_2/resolve/main/watercolordiffusion_2.pt'] },
'watercolordiffusion': { 'downloaded': False, 'sha': 'a3e6522f0c8f278f90788298d66383b11ac763dd5e0d62f8252c962c23950bd6', 'uri_list': ['https://huggingface.co/KaliYuga/watercolordiffusion/resolve/main/watercolordiffusion.pt'] },
'PulpSciFiDiffusion': { 'downloaded': False, 'sha': 'b79e62613b9f50b8a3173e5f61f0320c7dbb16efad42a92ec94d014f6e17337f', 'uri_list': ['https://huggingface.co/KaliYuga/PulpSciFiDiffusion/resolve/main/PulpSciFiDiffusion.pt'] },
'secondary': { 'downloaded': False, 'sha': '983e3de6f95c88c81b2ca7ebb2c217933be1973b1ff058776b970f901584613a', 'uri_list': ['https://huggingface.co/spaces/huggi/secondary_model_imagenet_2.pth/resolve/main/secondary_model_imagenet_2.pth', 'https://the-eye.eu/public/AI/models/v-diffusion/secondary_model_imagenet_2.pth', 'https://ipfs.pollinations.ai/ipfs/bafybeibaawhhk7fhyhvmm7x24zwwkeuocuizbqbcg5nqx64jq42j75rdiy/secondary_model_imagenet_2.pth'] },
}
def get_model_filename(diffusion_model_name):
model_uri = diff_model_map[diffusion_model_name]['uri_list'][0]
model_filename = os.path.basename(urlparse(model_uri).path)
return model_filename
# setup dir
def gitclone(url, target_dir=None, branch_arg=None):
run_args = ['git', 'clone']
if branch_arg:
run_args.extend(['-b', branch_arg])
run_args.append(url)
if target_dir:
run_args.append(target_dir)
res = subprocess.run(run_args, stdout=subprocess.PIPE).stdout.decode('utf-8')
print(res)
def pipi(modulestr):
res = subprocess.run(['pip', 'install', modulestr], stdout=subprocess.PIPE).stdout.decode('utf-8')
print(res)
def pipie(modulestr):
res = subprocess.run(['git', 'install', '-e', modulestr], stdout=subprocess.PIPE).stdout.decode('utf-8')
print(res)
def wget(url, outputdir):
res = subprocess.run(['wget', url, '-P', f'{outputdir}'], stdout=subprocess.PIPE).stdout.decode('utf-8')
print(res)
def createPath(filepath):
os.makedirs(filepath, exist_ok=True)
is_colab = False
google_drive = False
save_models_to_google_drive = False
root_path = os.getcwd()
initDirPath = f'{root_path}/init_images'
createPath(initDirPath)
outDirPath = f'{root_path}/images_out'
createPath(outDirPath)
model_path = f'{root_path}/models'
createPath(model_path)
cutout_debug = False
padargs = {}
# 1.3 install dependency
useCPU = False #@param {type:"boolean"}
# If running locally, there's a good chance your env will need this in order to not crash upon np.matmul() or similar operations.
os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'
PROJECT_DIR = os.path.abspath(os.getcwd())
USE_ADABINS = True
def install():
multipip_res = subprocess.run(['pip', 'install', 'lpips', 'datetime', 'timm', 'ftfy', 'einops', 'pytorch-lightning', 'omegaconf'], stdout=subprocess.PIPE).stdout.decode('utf-8')
print(multipip_res)
install()
def install_dep():
sys.path.append(f'{PROJECT_DIR}/CLIP')
sys.path.append(f'{PROJECT_DIR}/open_clip/src')
sys.path.append(f'{PROJECT_DIR}/guided-diffusion')
sys.path.append(f'{PROJECT_DIR}/ResizeRight')
sys.path.append(f'{PROJECT_DIR}/pytorch3d-lite')
sys.path.append(f'{PROJECT_DIR}/MiDaS')
sys.path.append(f'{PROJECT_DIR}/AdaBins')
install_dep()
from CLIP import clip
import open_clip
from guided_diffusion.script_util import create_model_and_diffusion
from resize_right import resize
import py3d_tools as p3dT
from midas.dpt_depth import DPTDepthModel
import disco_xform_utils as dxf
## main
import torch
from dataclasses import dataclass
from functools import partial
import cv2
import pandas as pd
import gc
import io
import math
import timm
from IPython import display
import lpips
from PIL import Image, ImageOps
import requests
from glob import glob
import json
from types import SimpleNamespace
from torch import nn
from torch.nn import functional as F
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from tqdm.notebook import tqdm
from CLIP import clip
from resize_right import resize
from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt
import random
from ipywidgets import Output
import hashlib
from functools import partial
from IPython.display import Image as ipyimg
from numpy import asarray
from einops import rearrange, repeat
import torch, torchvision
import time
from omegaconf import OmegaConf
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
# AdaBins stuff
createPath(f'{PROJECT_DIR}/pretrained')
if USE_ADABINS:
from infer import InferenceHelper
MAX_ADABINS_AREA = 500000
## gpu set
DEVICE = torch.device('cuda:0' if (torch.cuda.is_available() and not useCPU) else 'cpu')
print('Using device:', DEVICE)
device = DEVICE # At least one of the modules expects this name..
if not useCPU:
if torch.cuda.get_device_capability(DEVICE) == (8,0): ## A100 fix thanks to Emad
print('Disabling CUDNN for A100 gpu', file=sys.stderr)
torch.backends.cudnn.enabled = False
from midas.dpt_depth import DPTDepthModel
from midas.midas_net import MidasNet
from midas.midas_net_custom import MidasNet_small
from midas.transforms import Resize, NormalizeImage, PrepareForNet
# Initialize MiDaS depth model.
# It remains resident in VRAM and likely takes around 2GB VRAM.
# You could instead initialize it for each frame (and free it after each frame) to save VRAM.. but initializing it is slow.
default_models = {
"midas_v21_small": f"{model_path}/midas_v21_small-70d6b9c8.pt",
"midas_v21": f"{model_path}/midas_v21-f6b98070.pt",
"dpt_large": f"{model_path}/dpt_large-midas-2f21e586.pt",
"dpt_hybrid": f"{model_path}/dpt_hybrid-midas-501f0c75.pt",
"dpt_hybrid_nyu": f"{model_path}/dpt_hybrid_nyu-2ce69ec7.pt",}
def init_midas_depth_model(midas_model_type="dpt_large", optimize=True):
midas_model = None
net_w = None
net_h = None
resize_mode = None
normalization = None
print(f"Initializing MiDaS '{midas_model_type}' depth model...")
# load network
midas_model_path = default_models[midas_model_type]
if midas_model_type == "dpt_large": # DPT-Large
midas_model = DPTDepthModel(
path=midas_model_path,
backbone="vitl16_384",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif midas_model_type == "dpt_hybrid": #DPT-Hybrid
midas_model = DPTDepthModel(
path=midas_model_path,
backbone="vitb_rn50_384",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode="minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif midas_model_type == "dpt_hybrid_nyu": #DPT-Hybrid-NYU
midas_model = DPTDepthModel(
path=midas_model_path,
backbone="vitb_rn50_384",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode="minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif midas_model_type == "midas_v21":
midas_model = MidasNet(midas_model_path, non_negative=True)
net_w, net_h = 384, 384
resize_mode="upper_bound"
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
elif midas_model_type == "midas_v21_small":
midas_model = MidasNet_small(midas_model_path, features=64, backbone="efficientnet_lite3", exportable=True, non_negative=True, blocks={'expand': True})
net_w, net_h = 256, 256
resize_mode="upper_bound"
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
else:
print(f"midas_model_type '{midas_model_type}' not implemented")
assert False
midas_transform = T.Compose(
[
Resize(
net_w,
net_h,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method=resize_mode,
image_interpolation_method=cv2.INTER_CUBIC,
),
normalization,
PrepareForNet(),
]
)
midas_model.eval()
if optimize==True:
if DEVICE == torch.device("cuda"):
midas_model = midas_model.to(memory_format=torch.channels_last)
midas_model = midas_model.half()
midas_model.to(DEVICE)
print(f"MiDaS '{midas_model_type}' depth model initialized.")
return midas_model, midas_transform, net_w, net_h, resize_mode, normalization
#@title 1.5 Define necessary functions
def interp(t):
return 3 * t**2 - 2 * t ** 3
def perlin(width, height, scale=10, device=None):
gx, gy = torch.randn(2, width + 1, height + 1, 1, 1, device=device)
xs = torch.linspace(0, 1, scale + 1)[:-1, None].to(device)
ys = torch.linspace(0, 1, scale + 1)[None, :-1].to(device)
wx = 1 - interp(xs)
wy = 1 - interp(ys)
dots = 0
dots += wx * wy * (gx[:-1, :-1] * xs + gy[:-1, :-1] * ys)
dots += (1 - wx) * wy * (-gx[1:, :-1] * (1 - xs) + gy[1:, :-1] * ys)
dots += wx * (1 - wy) * (gx[:-1, 1:] * xs - gy[:-1, 1:] * (1 - ys))
dots += (1 - wx) * (1 - wy) * (-gx[1:, 1:] * (1 - xs) - gy[1:, 1:] * (1 - ys))
return dots.permute(0, 2, 1, 3).contiguous().view(width * scale, height * scale)
def perlin_ms(octaves, width, height, grayscale, device=device):
out_array = [0.5] if grayscale else [0.5, 0.5, 0.5]
# out_array = [0.0] if grayscale else [0.0, 0.0, 0.0]
for i in range(1 if grayscale else 3):
scale = 2 ** len(octaves)
oct_width = width
oct_height = height
for oct in octaves:
p = perlin(oct_width, oct_height, scale, device)
out_array[i] += p * oct
scale //= 2
oct_width *= 2
oct_height *= 2
return torch.cat(out_array)
def create_perlin_noise(octaves=[1, 1, 1, 1], width=2, height=2, grayscale=True):
out = perlin_ms(octaves, width, height, grayscale)
if grayscale:
out = TF.resize(size=(side_y, side_x), img=out.unsqueeze(0))
out = TF.to_pil_image(out.clamp(0, 1)).convert('RGB')
else:
out = out.reshape(-1, 3, out.shape[0]//3, out.shape[1])
out = TF.resize(size=(side_y, side_x), img=out)
out = TF.to_pil_image(out.clamp(0, 1).squeeze())
out = ImageOps.autocontrast(out)
return out
def regen_perlin():
if perlin_mode == 'color':
init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)
init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, False)
elif perlin_mode == 'gray':
init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, True)
init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)
else:
init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)
init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)
init = TF.to_tensor(init).add(TF.to_tensor(init2)).div(2).to(device).unsqueeze(0).mul(2).sub(1)
del init2
return init.expand(batch_size, -1, -1, -1)
def fetch(url_or_path):
if str(url_or_path).startswith('http://') or str(url_or_path).startswith('https://'):
r = requests.get(url_or_path)
r.raise_for_status()
fd = io.BytesIO()
fd.write(r.content)
fd.seek(0)
return fd
return open(url_or_path, 'rb')
def read_image_workaround(path):
"""OpenCV reads images as BGR, Pillow saves them as RGB. Work around
this incompatibility to avoid colour inversions."""
im_tmp = cv2.imread(path)
return cv2.cvtColor(im_tmp, cv2.COLOR_BGR2RGB)
def parse_prompt(prompt):
if prompt.startswith('http://') or prompt.startswith('https://'):
vals = prompt.rsplit(':', 2)
vals = [vals[0] + ':' + vals[1], *vals[2:]]
else:
vals = prompt.rsplit(':', 1)
vals = vals + ['', '1'][len(vals):]
return vals[0], float(vals[1])
def sinc(x):
return torch.where(x != 0, torch.sin(math.pi * x) / (math.pi * x), x.new_ones([]))
def lanczos(x, a):
cond = torch.logical_and(-a < x, x < a)
out = torch.where(cond, sinc(x) * sinc(x/a), x.new_zeros([]))
return out / out.sum()
def ramp(ratio, width):
n = math.ceil(width / ratio + 1)
out = torch.empty([n])
cur = 0
for i in range(out.shape[0]):
out[i] = cur
cur += ratio
return torch.cat([-out[1:].flip([0]), out])[1:-1]
def resample(input, size, align_corners=True):
n, c, h, w = input.shape
dh, dw = size
input = input.reshape([n * c, 1, h, w])
if dh < h:
kernel_h = lanczos(ramp(dh / h, 2), 2).to(input.device, input.dtype)
pad_h = (kernel_h.shape[0] - 1) // 2
input = F.pad(input, (0, 0, pad_h, pad_h), 'reflect')
input = F.conv2d(input, kernel_h[None, None, :, None])
if dw < w:
kernel_w = lanczos(ramp(dw / w, 2), 2).to(input.device, input.dtype)
pad_w = (kernel_w.shape[0] - 1) // 2
input = F.pad(input, (pad_w, pad_w, 0, 0), 'reflect')
input = F.conv2d(input, kernel_w[None, None, None, :])
input = input.reshape([n, c, h, w])
return F.interpolate(input, size, mode='bicubic', align_corners=align_corners)
class MakeCutouts(nn.Module):
def __init__(self, cut_size, cutn, skip_augs=False):
super().__init__()
self.cut_size = cut_size
self.cutn = cutn
self.skip_augs = skip_augs
self.augs = T.Compose([
T.RandomHorizontalFlip(p=0.5),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomAffine(degrees=15, translate=(0.1, 0.1)),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomPerspective(distortion_scale=0.4, p=0.7),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomGrayscale(p=0.15),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
# T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
])
def forward(self, input):
input = T.Pad(input.shape[2]//4, fill=0)(input)
sideY, sideX = input.shape[2:4]
max_size = min(sideX, sideY)
cutouts = []
for ch in range(self.cutn):
if ch > self.cutn - self.cutn//4:
cutout = input.clone()
else:
size = int(max_size * torch.zeros(1,).normal_(mean=.8, std=.3).clip(float(self.cut_size/max_size), 1.))
offsetx = torch.randint(0, abs(sideX - size + 1), ())
offsety = torch.randint(0, abs(sideY - size + 1), ())
cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
if not self.skip_augs:
cutout = self.augs(cutout)
cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))
del cutout
cutouts = torch.cat(cutouts, dim=0)
return cutouts
class MakeCutoutsDango(nn.Module):
def __init__(self, cut_size,
Overview=4,
InnerCrop = 0, IC_Size_Pow=0.5, IC_Grey_P = 0.2
):
super().__init__()
self.cut_size = cut_size
self.Overview = Overview
self.InnerCrop = InnerCrop
self.IC_Size_Pow = IC_Size_Pow
self.IC_Grey_P = IC_Grey_P
if args.animation_mode == 'None':
self.augs = T.Compose([
T.RandomHorizontalFlip(p=0.5),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomAffine(degrees=10, translate=(0.05, 0.05), interpolation = T.InterpolationMode.BILINEAR),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomGrayscale(p=0.1),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
])
elif args.animation_mode == 'Video Input':
self.augs = T.Compose([
T.RandomHorizontalFlip(p=0.5),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomAffine(degrees=15, translate=(0.1, 0.1)),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomPerspective(distortion_scale=0.4, p=0.7),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomGrayscale(p=0.15),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
# T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
])
elif args.animation_mode == '2D' or args.animation_mode == '3D':
self.augs = T.Compose([
T.RandomHorizontalFlip(p=0.4),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomAffine(degrees=10, translate=(0.05, 0.05), interpolation = T.InterpolationMode.BILINEAR),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.RandomGrayscale(p=0.1),
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.3),
])
def forward(self, input):
cutouts = []
gray = T.Grayscale(3)
sideY, sideX = input.shape[2:4]
max_size = min(sideX, sideY)
min_size = min(sideX, sideY, self.cut_size)
l_size = max(sideX, sideY)
output_shape = [1,3,self.cut_size,self.cut_size]
output_shape_2 = [1,3,self.cut_size+2,self.cut_size+2]
pad_input = F.pad(input,((sideY-max_size)//2,(sideY-max_size)//2,(sideX-max_size)//2,(sideX-max_size)//2), **padargs)
cutout = resize(pad_input, out_shape=output_shape)
if self.Overview>0:
if self.Overview<=4:
if self.Overview>=1:
cutouts.append(cutout)
if self.Overview>=2:
cutouts.append(gray(cutout))
if self.Overview>=3:
cutouts.append(TF.hflip(cutout))
if self.Overview==4:
cutouts.append(gray(TF.hflip(cutout)))
else:
cutout = resize(pad_input, out_shape=output_shape)
for _ in range(self.Overview):
cutouts.append(cutout)
if cutout_debug:
if is_colab:
TF.to_pil_image(cutouts[0].clamp(0, 1).squeeze(0)).save("/content/cutout_overview0.jpg",quality=99)
else:
TF.to_pil_image(cutouts[0].clamp(0, 1).squeeze(0)).save("cutout_overview0.jpg",quality=99)
if self.InnerCrop >0:
for i in range(self.InnerCrop):
size = int(torch.rand([])**self.IC_Size_Pow * (max_size - min_size) + min_size)
offsetx = torch.randint(0, sideX - size + 1, ())
offsety = torch.randint(0, sideY - size + 1, ())
cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
if i <= int(self.IC_Grey_P * self.InnerCrop):
cutout = gray(cutout)
cutout = resize(cutout, out_shape=output_shape)
cutouts.append(cutout)
if cutout_debug:
if is_colab:
TF.to_pil_image(cutouts[-1].clamp(0, 1).squeeze(0)).save("/content/cutout_InnerCrop.jpg",quality=99)
else:
TF.to_pil_image(cutouts[-1].clamp(0, 1).squeeze(0)).save("cutout_InnerCrop.jpg",quality=99)
cutouts = torch.cat(cutouts)
if skip_augs is not True: cutouts=self.augs(cutouts)
return cutouts
def spherical_dist_loss(x, y):
x = F.normalize(x, dim=-1)
y = F.normalize(y, dim=-1)
return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
def tv_loss(input):
"""L2 total variation loss, as in Mahendran et al."""
input = F.pad(input, (0, 1, 0, 1), 'replicate')
x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]
y_diff = input[..., 1:, :-1] - input[..., :-1, :-1]
return (x_diff**2 + y_diff**2).mean([1, 2, 3])
def range_loss(input):
return (input - input.clamp(-1, 1)).pow(2).mean([1, 2, 3])
stop_on_next_loop = False # Make sure GPU memory doesn't get corrupted from cancelling the run mid-way through, allow a full frame to complete
TRANSLATION_SCALE = 1.0/200.0
def do_3d_step(img_filepath, frame_num, midas_model, midas_transform):
if args.key_frames:
translation_x = args.translation_x_series[frame_num]
translation_y = args.translation_y_series[frame_num]
translation_z = args.translation_z_series[frame_num]
rotation_3d_x = args.rotation_3d_x_series[frame_num]
rotation_3d_y = args.rotation_3d_y_series[frame_num]
rotation_3d_z = args.rotation_3d_z_series[frame_num]
print(
f'translation_x: {translation_x}',
f'translation_y: {translation_y}',
f'translation_z: {translation_z}',
f'rotation_3d_x: {rotation_3d_x}',
f'rotation_3d_y: {rotation_3d_y}',
f'rotation_3d_z: {rotation_3d_z}',
)
translate_xyz = [-translation_x*TRANSLATION_SCALE, translation_y*TRANSLATION_SCALE, -translation_z*TRANSLATION_SCALE]
rotate_xyz_degrees = [rotation_3d_x, rotation_3d_y, rotation_3d_z]
print('translation:',translate_xyz)
print('rotation:',rotate_xyz_degrees)
rotate_xyz = [math.radians(rotate_xyz_degrees[0]), math.radians(rotate_xyz_degrees[1]), math.radians(rotate_xyz_degrees[2])]
rot_mat = p3dT.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), "XYZ").unsqueeze(0)
print("rot_mat: " + str(rot_mat))
next_step_pil = dxf.transform_image_3d(img_filepath, midas_model, midas_transform, DEVICE,
rot_mat, translate_xyz, args.near_plane, args.far_plane,
args.fov, padding_mode=args.padding_mode,
sampling_mode=args.sampling_mode, midas_weight=args.midas_weight)
return next_step_pil
def symmetry_transformation_fn(x):
if args.use_horizontal_symmetry:
[n, c, h, w] = x.size()
x = torch.concat((x[:, :, :, :w//2], torch.flip(x[:, :, :, :w//2], [-1])), -1)
print("horizontal symmetry applied")
if args.use_vertical_symmetry:
[n, c, h, w] = x.size()
x = torch.concat((x[:, :, :h//2, :], torch.flip(x[:, :, :h//2, :], [-2])), -2)
print("vertical symmetry applied")
return x
def do_run():
seed = args.seed
print(range(args.start_frame, args.max_frames))
if (args.animation_mode == "3D") and (args.midas_weight > 0.0):
midas_model, midas_transform, midas_net_w, midas_net_h, midas_resize_mode, midas_normalization = init_midas_depth_model(args.midas_depth_model)
for frame_num in range(args.start_frame, args.max_frames):
if stop_on_next_loop:
break
display.clear_output(wait=True)
# Print Frame progress if animation mode is on
if args.animation_mode != "None":
batchBar = tqdm(range(args.max_frames), desc ="Frames")
batchBar.n = frame_num
batchBar.refresh()
# Inits if not video frames
if args.animation_mode != "Video Input":
if args.init_image in ['','none', 'None', 'NONE']:
init_image = None
else:
init_image = args.init_image
init_scale = args.init_scale
skip_steps = args.skip_steps
if args.animation_mode == "2D":
if args.key_frames:
angle = args.angle_series[frame_num]
zoom = args.zoom_series[frame_num]
translation_x = args.translation_x_series[frame_num]
translation_y = args.translation_y_series[frame_num]
print(
f'angle: {angle}',
f'zoom: {zoom}',
f'translation_x: {translation_x}',
f'translation_y: {translation_y}',
)
if frame_num > 0:
seed += 1
if resume_run and frame_num == start_frame:
img_0 = cv2.imread(batchFolder+f"/{batch_name}({batchNum})_{start_frame-1:04}.png")
else:
img_0 = cv2.imread('prevFrame.png')
center = (1*img_0.shape[1]//2, 1*img_0.shape[0]//2)
trans_mat = np.float32(
[[1, 0, translation_x],
[0, 1, translation_y]]
)
rot_mat = cv2.getRotationMatrix2D( center, angle, zoom )
trans_mat = np.vstack([trans_mat, [0,0,1]])
rot_mat = np.vstack([rot_mat, [0,0,1]])
transformation_matrix = np.matmul(rot_mat, trans_mat)
img_0 = cv2.warpPerspective(
img_0,
transformation_matrix,
(img_0.shape[1], img_0.shape[0]),
borderMode=cv2.BORDER_WRAP
)
cv2.imwrite('prevFrameScaled.png', img_0)
init_image = 'prevFrameScaled.png'
init_scale = args.frames_scale
skip_steps = args.calc_frames_skip_steps
if args.animation_mode == "3D":
if frame_num > 0:
seed += 1
if resume_run and frame_num == start_frame:
img_filepath = batchFolder+f"/{batch_name}({batchNum})_{start_frame-1:04}.png"
if turbo_mode and frame_num > turbo_preroll:
shutil.copyfile(img_filepath, 'oldFrameScaled.png')
else:
img_filepath = '/content/prevFrame.png' if is_colab else 'prevFrame.png'
next_step_pil = do_3d_step(img_filepath, frame_num, midas_model, midas_transform)
next_step_pil.save('prevFrameScaled.png')
### Turbo mode - skip some diffusions, use 3d morph for clarity and to save time
if turbo_mode:
if frame_num == turbo_preroll: #start tracking oldframe
next_step_pil.save('oldFrameScaled.png')#stash for later blending
elif frame_num > turbo_preroll:
#set up 2 warped image sequences, old & new, to blend toward new diff image
old_frame = do_3d_step('oldFrameScaled.png', frame_num, midas_model, midas_transform)
old_frame.save('oldFrameScaled.png')
if frame_num % int(turbo_steps) != 0:
print('turbo skip this frame: skipping clip diffusion steps')
filename = f'{args.batch_name}({args.batchNum})_{frame_num:04}.png'
blend_factor = ((frame_num % int(turbo_steps))+1)/int(turbo_steps)
print('turbo skip this frame: skipping clip diffusion steps and saving blended frame')
newWarpedImg = cv2.imread('prevFrameScaled.png')#this is already updated..
oldWarpedImg = cv2.imread('oldFrameScaled.png')
blendedImage = cv2.addWeighted(newWarpedImg, blend_factor, oldWarpedImg,1-blend_factor, 0.0)
cv2.imwrite(f'{batchFolder}/{filename}',blendedImage)
next_step_pil.save(f'{img_filepath}') # save it also as prev_frame to feed next iteration
if vr_mode:
generate_eye_views(TRANSLATION_SCALE,batchFolder,filename,frame_num,midas_model, midas_transform)
continue
else:
#if not a skip frame, will run diffusion and need to blend.
oldWarpedImg = cv2.imread('prevFrameScaled.png')
cv2.imwrite(f'oldFrameScaled.png',oldWarpedImg)#swap in for blending later
print('clip/diff this frame - generate clip diff image')
init_image = 'prevFrameScaled.png'
init_scale = args.frames_scale
skip_steps = args.calc_frames_skip_steps
if args.animation_mode == "Video Input":
init_scale = args.video_init_frames_scale
skip_steps = args.calc_frames_skip_steps
if not video_init_seed_continuity:
seed += 1
if video_init_flow_warp:
if frame_num == 0:
skip_steps = args.video_init_skip_steps
init_image = f'{videoFramesFolder}/{frame_num+1:04}.jpg'
if frame_num > 0:
prev = PIL.Image.open(batchFolder+f"/{batch_name}({batchNum})_{frame_num-1:04}.png")
frame1_path = f'{videoFramesFolder}/{frame_num:04}.jpg'
frame2 = PIL.Image.open(f'{videoFramesFolder}/{frame_num+1:04}.jpg')
flo_path = f"/{flo_folder}/{frame1_path.split('/')[-1]}.npy"
init_image = 'warped.png'
print(video_init_flow_blend)
weights_path = None
if video_init_check_consistency:
# TBD
pass
warp(prev, frame2, flo_path, blend=video_init_flow_blend, weights_path=weights_path).save(init_image)
else:
init_image = f'{videoFramesFolder}/{frame_num+1:04}.jpg'
loss_values = []
if seed is not None:
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
target_embeds, weights = [], []
if args.prompts_series is not None and frame_num >= len(args.prompts_series):
frame_prompt = args.prompts_series[-1]
elif args.prompts_series is not None:
frame_prompt = args.prompts_series[frame_num]
else:
frame_prompt = []
print(args.image_prompts_series)
if args.image_prompts_series is not None and frame_num >= len(args.image_prompts_series):
image_prompt = args.image_prompts_series[-1]
elif args.image_prompts_series is not None:
image_prompt = args.image_prompts_series[frame_num]
else:
image_prompt = []
print(f'Frame {frame_num} Prompt: {frame_prompt}')
model_stats = []
for clip_model in clip_models:
cutn = 16
model_stat = {"clip_model":None,"target_embeds":[],"make_cutouts":None,"weights":[]}
model_stat["clip_model"] = clip_model
for prompt in frame_prompt:
txt, weight = parse_prompt(prompt)
txt = clip_model.encode_text(clip.tokenize(prompt).to(device)).float()
if args.fuzzy_prompt:
for i in range(25):
model_stat["target_embeds"].append((txt + torch.randn(txt.shape).cuda() * args.rand_mag).clamp(0,1))
model_stat["weights"].append(weight)
else:
model_stat["target_embeds"].append(txt)
model_stat["weights"].append(weight)
if image_prompt:
model_stat["make_cutouts"] = MakeCutouts(clip_model.visual.input_resolution, cutn, skip_augs=skip_augs)
for prompt in image_prompt:
path, weight = parse_prompt(prompt)
img = Image.open(fetch(path)).convert('RGB')
img = TF.resize(img, min(side_x, side_y, *img.size), T.InterpolationMode.LANCZOS)
batch = model_stat["make_cutouts"](TF.to_tensor(img).to(device).unsqueeze(0).mul(2).sub(1))
embed = clip_model.encode_image(normalize(batch)).float()
if fuzzy_prompt:
for i in range(25):
model_stat["target_embeds"].append((embed + torch.randn(embed.shape).cuda() * rand_mag).clamp(0,1))
weights.extend([weight / cutn] * cutn)
else:
model_stat["target_embeds"].append(embed)
model_stat["weights"].extend([weight / cutn] * cutn)
model_stat["target_embeds"] = torch.cat(model_stat["target_embeds"])
model_stat["weights"] = torch.tensor(model_stat["weights"], device=device)
if model_stat["weights"].sum().abs() < 1e-3:
raise RuntimeError('The weights must not sum to 0.')
model_stat["weights"] /= model_stat["weights"].sum().abs()
model_stats.append(model_stat)
init = None
if init_image is not None:
init = Image.open(fetch(init_image)).convert('RGB')
init = init.resize((args.side_x, args.side_y), Image.LANCZOS)
init = TF.to_tensor(init).to(device).unsqueeze(0).mul(2).sub(1)
if args.perlin_init:
if args.perlin_mode == 'color':
init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)
init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, False)
elif args.perlin_mode == 'gray':
init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, True)
init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)
else:
init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)
init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)
# init = TF.to_tensor(init).add(TF.to_tensor(init2)).div(2).to(device)
init = TF.to_tensor(init).add(TF.to_tensor(init2)).div(2).to(device).unsqueeze(0).mul(2).sub(1)
del init2
cur_t = None
def cond_fn(x, t, y=None):
with torch.enable_grad():
x_is_NaN = False
x = x.detach().requires_grad_()
n = x.shape[0]
if use_secondary_model is True:
alpha = torch.tensor(diffusion.sqrt_alphas_cumprod[cur_t], device=device, dtype=torch.float32)
sigma = torch.tensor(diffusion.sqrt_one_minus_alphas_cumprod[cur_t], device=device, dtype=torch.float32)
cosine_t = alpha_sigma_to_t(alpha, sigma)
out = secondary_model(x, cosine_t[None].repeat([n])).pred
fac = diffusion.sqrt_one_minus_alphas_cumprod[cur_t]
x_in = out * fac + x * (1 - fac)
x_in_grad = torch.zeros_like(x_in)
else:
my_t = torch.ones([n], device=device, dtype=torch.long) * cur_t
out = diffusion.p_mean_variance(model, x, my_t, clip_denoised=False, model_kwargs={'y': y})
fac = diffusion.sqrt_one_minus_alphas_cumprod[cur_t]
x_in = out['pred_xstart'] * fac + x * (1 - fac)
x_in_grad = torch.zeros_like(x_in)
for model_stat in model_stats:
for i in range(args.cutn_batches):
t_int = int(t.item())+1 #errors on last step without +1, need to find source
#when using SLIP Base model the dimensions need to be hard coded to avoid AttributeError: 'VisionTransformer' object has no attribute 'input_resolution'
try:
input_resolution=model_stat["clip_model"].visual.input_resolution
except:
input_resolution=224
cuts = MakeCutoutsDango(input_resolution,
Overview= args.cut_overview[1000-t_int],
InnerCrop = args.cut_innercut[1000-t_int],
IC_Size_Pow=args.cut_ic_pow[1000-t_int],
IC_Grey_P = args.cut_icgray_p[1000-t_int]
)
clip_in = normalize(cuts(x_in.add(1).div(2)))
image_embeds = model_stat["clip_model"].encode_image(clip_in).float()
dists = spherical_dist_loss(image_embeds.unsqueeze(1), model_stat["target_embeds"].unsqueeze(0))
dists = dists.view([args.cut_overview[1000-t_int]+args.cut_innercut[1000-t_int], n, -1])
losses = dists.mul(model_stat["weights"]).sum(2).mean(0)
loss_values.append(losses.sum().item()) # log loss, probably shouldn't do per cutn_batch
x_in_grad += torch.autograd.grad(losses.sum() * clip_guidance_scale, x_in)[0] / cutn_batches
tv_losses = tv_loss(x_in)
if use_secondary_model is True:
range_losses = range_loss(out)
else:
range_losses = range_loss(out['pred_xstart'])
sat_losses = torch.abs(x_in - x_in.clamp(min=-1,max=1)).mean()
loss = tv_losses.sum() * tv_scale + range_losses.sum() * range_scale + sat_losses.sum() * sat_scale
if init is not None and init_scale:
init_losses = lpips_model(x_in, init)
loss = loss + init_losses.sum() * init_scale
x_in_grad += torch.autograd.grad(loss, x_in)[0]
if torch.isnan(x_in_grad).any()==False:
grad = -torch.autograd.grad(x_in, x, x_in_grad)[0]
else:
# print("NaN'd")
x_is_NaN = True
grad = torch.zeros_like(x)
if args.clamp_grad and x_is_NaN == False:
magnitude = grad.square().mean().sqrt()
return grad * magnitude.clamp(max=args.clamp_max) / magnitude #min=-0.02, min=-clamp_max,
return grad
if args.diffusion_sampling_mode == 'ddim':
sample_fn = diffusion.ddim_sample_loop_progressive
else:
sample_fn = diffusion.plms_sample_loop_progressive
image_display = Output()
for i in range(args.n_batches):
if args.animation_mode == 'None':
display.clear_output(wait=True)
batchBar = tqdm(range(args.n_batches), desc ="Batches")
batchBar.n = i
batchBar.refresh()
print('')
display.display(image_display)
gc.collect()
torch.cuda.empty_cache()
cur_t = diffusion.num_timesteps - skip_steps - 1
total_steps = cur_t
if perlin_init:
init = regen_perlin()
if args.diffusion_sampling_mode == 'ddim':
samples = sample_fn(
model,
(batch_size, 3, args.side_y, args.side_x),
clip_denoised=clip_denoised,
model_kwargs={},
cond_fn=cond_fn,
progress=True,
skip_timesteps=skip_steps,
init_image=init,
randomize_class=randomize_class,
eta=eta,
transformation_fn=symmetry_transformation_fn,
transformation_percent=args.transformation_percent
)
else:
samples = sample_fn(
model,
(batch_size, 3, args.side_y, args.side_x),
clip_denoised=clip_denoised,
model_kwargs={},
cond_fn=cond_fn,
progress=True,
skip_timesteps=skip_steps,
init_image=init,
randomize_class=randomize_class,
order=2,
)
# with run_display:
# display.clear_output(wait=True)
for j, sample in enumerate(samples):
cur_t -= 1
intermediateStep = False
if args.steps_per_checkpoint is not None:
if j % steps_per_checkpoint == 0 and j > 0:
intermediateStep = True
elif j in args.intermediate_saves:
intermediateStep = True
with image_display:
if j % args.display_rate == 0 or cur_t == -1 or intermediateStep == True:
for k, image in enumerate(sample['pred_xstart']):
# tqdm.write(f'Batch {i}, step {j}, output {k}:')
current_time = datetime.now().strftime('%y%m%d-%H%M%S_%f')
percent = math.ceil(j/total_steps*100)
if args.n_batches > 0:
#if intermediates are saved to the subfolder, don't append a step or percentage to the name
if cur_t == -1 and args.intermediates_in_subfolder is True:
save_num = f'{frame_num:04}' if animation_mode != "None" else i
filename = f'{args.batch_name}({args.batchNum})_{save_num}.png'
else:
#If we're working with percentages, append it
if args.steps_per_checkpoint is not None:
filename = f'{args.batch_name}({args.batchNum})_{i:04}-{percent:02}%.png'
# Or else, iIf we're working with specific steps, append those
else:
filename = f'{args.batch_name}({args.batchNum})_{i:04}-{j:03}.png'
image = TF.to_pil_image(image.add(1).div(2).clamp(0, 1))
if j % args.display_rate == 0 or cur_t == -1:
image.save('progress.png')
display.clear_output(wait=True)
display.display(display.Image('progress.png'))
if args.steps_per_checkpoint is not None:
if j % args.steps_per_checkpoint == 0 and j > 0: