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dd.py
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from yaml import dump, full_load
from time import sleep
import traceback
import os, sys
import random
import gc
from tkinter import N
import uuid
import time
from IPython import display
from ipywidgets import Output
import lpips
import pathlib, shutil
import json
import subprocess
import requests
import io
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import torch
from dataclasses import dataclass
from functools import partial
from torch import nn
from torch.nn import functional as F
import itertools
import math
import cv2
from PIL import Image, ImageOps
from PIL.PngImagePlugin import PngInfo
from datetime import datetime
import climage
from types import SimpleNamespace
from glob import glob
from pydotted import pydot
from loguru import logger
from deepdiff import DeepHash
import sqlite3
from torchmetrics import RetrievalFallOut
from tqdm.notebook import tqdm
from twilio.rest import Client
from guided_diffusion.script_util import (
create_model_and_diffusion,
model_and_diffusion_defaults,
)
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
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from resize_right import resize
import disco_xform_utils as dxf
from downloadModels import loadModels
from downloadModels2 import loadModels2
import dd_prompt_salad
import voronoi_utils
# import pytorch3dlite.pytorch3dlite as p3d
try:
from pytorch3d import transforms
except:
logger.warning("Pytorch 3D not present. Animations will not work.")
from clip import clip
import open_clip
from ipywidgets import Output
import argparse
import dd_bot
def free_mem(cuda_device):
logger.info(f"Clearing CUDA cache on {cuda_device}...")
# https://discuss.pytorch.org/t/out-of-memory-when-i-use-torch-cuda-empty-cache/57898/3
# with torch.cuda.device(cuda_device):
# gc.collect()
# torch.cuda.empty_cache()
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
def sanitize(args):
newargs = pydot({})
for argname in args:
if argname not in ["twilio_account_sid", "twilio_auth_token", "twilio_to", "twilio_from"]:
newargs[argname] = args[argname]
else:
newargs[argname] = "******"
return newargs
def str2json(v):
try:
j = json.loads(v)
return j
except:
raise argparse.ArgumentTypeError(f"⚠️ Could not parse CLI parameter. Check your quotation marks and special characters. ⚠️ Value:\n{v}")
def get_param(key, fallback=None):
if os.getenv(key, None) != None:
try:
return json.loads(os.getenv(key))
except:
logger.warning(f'⚠️ Could not parse environment parameter "{key}". Check your quotation marks and special characters. ⚠️')
return fallback
return fallback
def createPath(filepath):
os.makedirs(filepath, exist_ok=True)
def gitclone(url):
res = subprocess.run(["git", "clone", url], stdout=subprocess.PIPE).stdout.decode("utf-8")
logger.info(res)
def pipi(modulestr):
res = subprocess.run(["pip", "install", modulestr], stdout=subprocess.PIPE).stdout.decode("utf-8")
logger.info(res)
def pipie(modulestr):
res = subprocess.run(["git", "install", "-e", modulestr], stdout=subprocess.PIPE).stdout.decode("utf-8")
logger.info(res)
# def wget(url, outputdir):
# res = subprocess.run(['wget', url, '-P', f'{outputdir}'], stdout=subprocess.PIPE).stdout.decode('utf-8')
# logger.info(res)
# https://gist.github.com/adefossez/0646dbe9ed4005480a2407c62aac8869
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=None):
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,
device=None,
side_x=None,
side_y=None,
):
out = perlin_ms(octaves, width, height, grayscale, device)
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(perlin_mode=None, device=None, batch_size=None, side_x=None, side_y=None):
logger.info("Regenerating Perlin Noise...")
if perlin_mode == "color":
init = create_perlin_noise([1.5**-i * 0.5 for i in range(12)], 1, 1, False, device=device, side_x=side_x, side_y=side_y)
init2 = create_perlin_noise([1.5**-i * 0.5 for i in range(8)], 4, 4, False, device=device, side_x=side_x, side_y=side_y)
elif perlin_mode == "gray":
init = create_perlin_noise([1.5**-i * 0.5 for i in range(12)], 1, 1, True, device=device, side_x=side_x, side_y=side_y)
init2 = create_perlin_noise([1.5**-i * 0.5 for i in range(8)], 4, 4, True, device=device, side_x=side_x, side_y=side_y)
else:
init = create_perlin_noise([1.5**-i * 0.5 for i in range(12)], 1, 1, False, device=device, side_x=side_x, side_y=side_y)
init2 = create_perlin_noise([1.5**-i * 0.5 for i in range(8)], 4, 4, True, device=device, side_x=side_x, side_y=side_y)
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=0.8, std=0.3)
.clip(float(self.cut_size / max_size), 1.0)
)
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,
args=None,
):
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
self.cutout_debug = args.cutout_debug
self.debug_folder = f"{args.batchFolder}/debug"
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, skip_augs=None):
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]
padargs = {}
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 self.cutout_debug:
TF.to_pil_image(cutouts[0].clamp(0, 1).squeeze(0)).save(f"{self.debug_folder}/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 self.cutout_debug:
TF.to_pil_image(cutouts[-1].clamp(0, 1).squeeze(0)).save(f"{self.debug_folder}/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])
# # Credit: https://colab.research.google.com/drive/10HUmA5laY1e1q7sYGg19Ys2lFM60M_T5#scrollTo=DefFns
# def symm_loss(im, lpm):
# h = int(im.shape[3] / 2)
# h1, h2 = im[:, :, :, :h], im[:, :, :, h:]
# h2 = TF.hflip(h2)
# return lpm(h1, h2)
# # Credit: aztec_man#3032 on Discord
# def v_symm_loss(im, lpm):
# h = int(im.shape[2] / 2)
# h1, h2 = im[:, :, :h, :], im[:, :, h:, :]
# h2 = TF.vflip(h2)
# return lpm(h1, h2)
def do_3d_step(
img_filepath,
frame_num,
midas_model,
midas_transform,
translations=None,
device=None,
TRANSLATION_SCALE=None,
key_frames=True,
args=None,
):
if key_frames:
translation_x = translations.translation_x_series[frame_num]
translation_y = translations.translation_y_series[frame_num]
translation_z = translations.translation_z_series[frame_num]
rotation_3d_x = translations.rotation_3d_x_series[frame_num]
rotation_3d_y = translations.rotation_3d_y_series[frame_num]
rotation_3d_z = translations.rotation_3d_z_series[frame_num]
logger.info(f"translation: [{translation_x}, {translation_y}, {translation_z}]")
logger.info(f"rotation_3d: [{rotation_3d_x}, {rotation_3d_y}, {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]
logger.info(f"translation: {translate_xyz}")
logger.info(f"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 = transforms.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), "XYZ").unsqueeze(0)
logger.info("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 createSymFn(args):
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)
logger.info("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)
logger.info("vertical symmetry applied")
return x
return symmetry_transformation_fn
def save_settings(setting_list=None, batchFolder=None, batch_name=None, batchNum=None):
with open(f"{batchFolder}/{batch_name}({batchNum})_settings.txt", "w+") as f:
json.dump(pydot(sanitize(setting_list)), f, ensure_ascii=False, indent=4)
def append_dims(x, n):
return x[(Ellipsis, *(None,) * (n - x.ndim))]
def expand_to_planes(x, shape):
return append_dims(x, len(shape)).repeat([1, 1, *shape[2:]])
def alpha_sigma_to_t(alpha, sigma):
return torch.atan2(sigma, alpha) * 2 / math.pi
def t_to_alpha_sigma(t):
return torch.cos(t * math.pi / 2), torch.sin(t * math.pi / 2)
@dataclass
class DiffusionOutput:
v: torch.Tensor
pred: torch.Tensor
eps: torch.Tensor
class ConvBlock(nn.Sequential):
def __init__(self, c_in, c_out):
super().__init__(
nn.Conv2d(c_in, c_out, 3, padding=1),
nn.ReLU(inplace=True),
)
class SkipBlock(nn.Module):
def __init__(self, main, skip=None):
super().__init__()
self.main = nn.Sequential(*main)
self.skip = skip if skip else nn.Identity()
def forward(self, input):
return torch.cat([self.main(input), self.skip(input)], dim=1)
class FourierFeatures(nn.Module):
def __init__(self, in_features, out_features, std=1.0):
super().__init__()
assert out_features % 2 == 0
self.weight = nn.Parameter(torch.randn([out_features // 2, in_features]) * std)
def forward(self, input):
f = 2 * math.pi * input @ self.weight.T
return torch.cat([f.cos(), f.sin()], dim=-1)
class SecondaryDiffusionImageNet(nn.Module):
def __init__(self):
super().__init__()
c = 64 # The base channel count
self.timestep_embed = FourierFeatures(1, 16)
self.net = nn.Sequential(
ConvBlock(3 + 16, c),
ConvBlock(c, c),
SkipBlock(
[
nn.AvgPool2d(2),
ConvBlock(c, c * 2),
ConvBlock(c * 2, c * 2),
SkipBlock(
[
nn.AvgPool2d(2),
ConvBlock(c * 2, c * 4),
ConvBlock(c * 4, c * 4),
SkipBlock(
[
nn.AvgPool2d(2),
ConvBlock(c * 4, c * 8),
ConvBlock(c * 8, c * 4),
nn.Upsample(
scale_factor=2,
mode="bilinear",
align_corners=False,
),
]
),
ConvBlock(c * 8, c * 4),
ConvBlock(c * 4, c * 2),
nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False),
]
),
ConvBlock(c * 4, c * 2),
ConvBlock(c * 2, c),
nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False),
]
),
ConvBlock(c * 2, c),
nn.Conv2d(c, 3, 3, padding=1),
)
def forward(self, input, t):
timestep_embed = expand_to_planes(self.timestep_embed(t[:, None]), input.shape)
v = self.net(torch.cat([input, timestep_embed], dim=1))
alphas, sigmas = map(partial(append_dims, n=v.ndim), t_to_alpha_sigma(t))
pred = input * alphas - v * sigmas
eps = input * sigmas + v * alphas
return DiffusionOutput(v, pred, eps)
class SecondaryDiffusionImageNet2(nn.Module):
def __init__(self):
super().__init__()
c = 64 # The base channel count
cs = [c, c * 2, c * 2, c * 4, c * 4, c * 8]
self.timestep_embed = FourierFeatures(1, 16)
self.down = nn.AvgPool2d(2)
self.up = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)
self.net = nn.Sequential(
ConvBlock(3 + 16, cs[0]),
ConvBlock(cs[0], cs[0]),
SkipBlock(
[
self.down,
ConvBlock(cs[0], cs[1]),
ConvBlock(cs[1], cs[1]),
SkipBlock(
[
self.down,
ConvBlock(cs[1], cs[2]),
ConvBlock(cs[2], cs[2]),
SkipBlock(
[
self.down,
ConvBlock(cs[2], cs[3]),
ConvBlock(cs[3], cs[3]),
SkipBlock(
[
self.down,
ConvBlock(cs[3], cs[4]),
ConvBlock(cs[4], cs[4]),
SkipBlock(
[
self.down,
ConvBlock(cs[4], cs[5]),
ConvBlock(cs[5], cs[5]),
ConvBlock(cs[5], cs[5]),
ConvBlock(cs[5], cs[4]),
self.up,
]
),
ConvBlock(cs[4] * 2, cs[4]),
ConvBlock(cs[4], cs[3]),
self.up,
]
),
ConvBlock(cs[3] * 2, cs[3]),
ConvBlock(cs[3], cs[2]),
self.up,
]
),
ConvBlock(cs[2] * 2, cs[2]),
ConvBlock(cs[2], cs[1]),
self.up,
]
),
ConvBlock(cs[1] * 2, cs[1]),
ConvBlock(cs[1], cs[0]),
self.up,
]
),
ConvBlock(cs[0] * 2, cs[0]),
nn.Conv2d(cs[0], 3, 3, padding=1),
)
def forward(self, input, t):
timestep_embed = expand_to_planes(self.timestep_embed(t[:, None]), input.shape)
v = self.net(torch.cat([input, timestep_embed], dim=1))
alphas, sigmas = map(partial(append_dims, n=v.ndim), t_to_alpha_sigma(t))
pred = input * alphas - v * sigmas
eps = input * sigmas + v * alphas
return DiffusionOutput(v, pred, eps)
# 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.
def init_midas_depth_model(midas_model_type="dpt_large", optimize=True, model_path=None, device=None):
DEVICE = device
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",
}
midas_model = None
net_w = None
net_h = None
resize_mode = None
normalization = None
logger.info(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:
logger.warning(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)
logger.info(f"MiDaS '{midas_model_type}' depth model initialized.")
return midas_model, midas_transform, net_w, net_h, resize_mode, normalization
def generate_eye_views(
trans_scale,
batchFolder,
filename,
frame_num,
midas_model,
midas_transform,
vr_eye_angle=None,
vr_ipd=None,
device=None,
args=None,
):
for i in range(2):
theta = vr_eye_angle * (math.pi / 180)
ray_origin = math.cos(theta) * vr_ipd / 2 * (-1.0 if i == 0 else 1.0)
ray_rotation = theta if i == 0 else -theta
translate_xyz = [-(ray_origin) * trans_scale, 0, 0]
rotate_xyz = [0, (ray_rotation), 0]
rot_mat = transforms.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), "XYZ").unsqueeze(0)
transformed_image = dxf.transform_image_3d(
f"{batchFolder}/{filename}",
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,
spherical=True,
)
eye_file_path = batchFolder + f"/frame_{frame_num-1:04}" + ("_l" if i == 0 else "_r") + ".png"
transformed_image.save(eye_file_path)
def parse_key_frames(string, prompt_parser=None):
"""Given a string representing frame numbers paired with parameter values at that frame,
return a dictionary with the frame numbers as keys and the parameter values as the values.
Parameters
----------
string: string
Frame numbers paired with parameter values at that frame number, in the format
'framenumber1: (parametervalues1), framenumber2: (parametervalues2), ...'
prompt_parser: function or None, optional
If provided, prompt_parser will be applied to each string of parameter values.
Returns
-------
dict
Frame numbers as keys, parameter values at that frame number as values
Raises
------
RuntimeError
If the input string does not match the expected format.
Examples
--------
>>> parse_key_frames("10:(Apple: 1| Orange: 0), 20: (Apple: 0| Orange: 1| Peach: 1)")
{10: 'Apple: 1| Orange: 0', 20: 'Apple: 0| Orange: 1| Peach: 1'}
>>> parse_key_frames("10:(Apple: 1| Orange: 0), 20: (Apple: 0| Orange: 1| Peach: 1)", prompt_parser=lambda x: x.lower()))
{10: 'apple: 1| orange: 0', 20: 'apple: 0| orange: 1| peach: 1'}
"""
import re
pattern = r"((?P<frame>[0-9]+):[\s]*[\(](?P<param>[\S\s]*?)[\)])"
frames = dict()
for match_object in re.finditer(pattern, string):
frame = int(match_object.groupdict()["frame"])
param = match_object.groupdict()["param"]
if prompt_parser:
frames[frame] = prompt_parser(param)
else:
frames[frame] = param
if frames == {} and len(string) != 0:
raise RuntimeError("Key Frame string not correctly formatted")
return frames
def get_inbetweens(key_frames, integer=False, max_frames=None, interp_spline=None):
"""Given a dict with frame numbers as keys and a parameter value as values,
return a pandas Series containing the value of the parameter at every frame from 0 to max_frames.
Any values not provided in the input dict are calculated by linear interpolation between
the values of the previous and next provided frames. If there is no previous provided frame, then
the value is equal to the value of the next provided frame, or if there is no next provided frame,
then the value is equal to the value of the previous provided frame. If no frames are provided,
all frame values are NaN.
Parameters
----------
key_frames: dict
A dict with integer frame numbers as keys and numerical values of a particular parameter as values.
integer: Bool, optional
If True, the values of the output series are converted to integers.
Otherwise, the values are floats.
Returns
-------
pd.Series
A Series with length max_frames representing the parameter values for each frame.
Examples
--------
>>> max_frames = 5
>>> get_inbetweens({1: 5, 3: 6})
0 5.0
1 5.0
2 5.5
3 6.0
4 6.0
dtype: float64
>>> get_inbetweens({1: 5, 3: 6}, integer=True)
0 5
1 5
2 5
3 6
4 6
dtype: int64
"""
key_frame_series = pd.Series([np.nan for a in range(max_frames)])
for i, value in key_frames.items():
key_frame_series[i] = value
key_frame_series = key_frame_series.astype(float)
interp_method = interp_spline
if interp_method == "Cubic" and len(key_frames.items()) <= 3:
interp_method = "Quadratic"
if interp_method == "Quadratic" and len(key_frames.items()) <= 2:
interp_method = "Linear"
key_frame_series[0] = key_frame_series[key_frame_series.first_valid_index()]
key_frame_series[max_frames - 1] = key_frame_series[key_frame_series.last_valid_index()]
# key_frame_series = key_frame_series.interpolate(method=intrp_method,order=1, limit_direction='both')
key_frame_series = key_frame_series.interpolate(method=interp_method.lower(), limit_direction="both")
if integer:
return key_frame_series.astype(int)