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diffusion.py
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diffusion.py
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# CLIP-guided diffusion AI image generator
# adapted from @Somnai_dreams Disco Diffusion colab notebook:
# https://colab.research.google.com/drive/1sHfRn5Y0YKYKi1k-ifUSBFRNJ8_1sa39
# based on Katherine Crowson's fine-tuned 512x512 model
import sys
sys.path.append('./ResizeRight')
sys.path.append('./CLIP')
sys.path.append('./guided-diffusion')
sys.path.append('./taming-transformers')
sys.path.append('./latent-diffusion')
import argparse
import unicodedata
import re
from datetime import date
import os
from os import path
from os.path import exists
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
import torch
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
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
import ipywidgets as widgets
from taming.models import vqgan
from torchvision.datasets.utils import download_url
from functools import partial
from ldm.util import instantiate_from_config
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
from ldm.util import ismap
from IPython.display import Image as ipyimg
from numpy import asarray
from einops import rearrange, repeat
import torchvision
import time
from omegaconf import OmegaConf
import torch.nn as nn
import warnings
# Supress warnings
warnings.filterwarnings('ignore')
parser = argparse.ArgumentParser(description='AI image generation')
parser.add_argument("-p", type=str, help="Text prompts", default=None, dest='prompts')
parser.add_argument("-i", type=int, help="Number of steps", default=250, dest='max_iterations')
parser.add_argument("-s", nargs=2, type=int, help="Image size (width height) (default: %(default)s)", default=[768,448], dest='size')
parser.add_argument("-ii", type=str, help="Initial image", default=None, dest='init_image')
parser.add_argument("-ss", type=int, help="Skip steps", default=-1, dest='skip_steps')
parser.add_argument("-cuts", type=int, help="Number of cut batches", default=4, dest='cutn')
parser.add_argument("-sd", type=int, help="Seed", default=None, dest='seed')
parser.add_argument("-o", type=str, help="Output path/filename", default="output/output.png", dest='output')
parser.add_argument("-cd", type=int, help="Cuda Device to use", default="0", dest='cuda_device')
parser.add_argument("-dvitb32", type=str, help="Use VitB32 CLIP model? yes/no", default="yes", dest='VitB32')
parser.add_argument("-dvitb16", type=str, help="Use VitB16 CLIP model? yes/no", default="yes", dest='VitB16')
parser.add_argument("-dvitl14", type=str, help="Use VitL14 CLIP model? yes/no", default="no", dest='VitL14')
parser.add_argument("-drn101", type=str, help="Use RN101 CLIP model? yes/no", default="no", dest='RN101')
parser.add_argument("-drn50", type=str, help="Use RN50 CLIP model? yes/no", default="yes", dest='RN50')
parser.add_argument("-drn50x4", type=str, help="Use RN50x4 CLIP model? yes/no", default="no", dest='RN50x4')
parser.add_argument("-drn50x16", type=str, help="Use RN50x16 CLIP model? yes/no", default="no", dest='RN50x16')
parser.add_argument("-drn50x64", type=str, help="Use RN50x64 CLIP model? yes/no", default="no", dest='RN50x64')
iargs = parser.parse_args()
device = torch.device(f'cuda:{iargs.cuda_device}' if torch.cuda.is_available() else 'cpu')
print('Using device:', device)
root_path = 'content'
model_256_downloaded = False
model_512_downloaded = False
model_secondary_downloaded = False
def createPath(filepath):
if path.exists(filepath) == False:
os.makedirs(filepath)
print(f'Made {filepath}')
#else:
# print(f'filepath {filepath} exists.')
output_path = "output"
output_filename = "output.png"
# support path conventions on both windows/*nix
if '/' in iargs.output:
output_filename = iargs.output[iargs.output.rindex('/')+1:]
output_path = iargs.output.replace(output_filename,'')[:-1]
if '\\' in iargs.output:
output_filename = iargs.output[iargs.output.rindex('\\')+1:]
output_path = iargs.output.replace(output_filename,'')[:-1]
initDirPath = f'{root_path}/init_images'
createPath(initDirPath)
#outDirPath = f'{root_path}/images_out'
outDirPath = output_path
createPath(outDirPath)
model_path = 'content/models'
createPath(model_path)
# Taken from https://github.com/django/django/blob/master/django/utils/text.py
# Using here to make filesystem-safe directory names
def slugify(value, allow_unicode=False):
value = str(value)
if allow_unicode:
value = unicodedata.normalize('NFKC', value)
else:
value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')
value = re.sub(r'[^\w\s-]', '', value.lower())
return re.sub(r'[-\s]+', '-', value).strip('-_')
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
cutout_debug = False
padargs = {}
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':
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:
TF.to_pil_image(cutouts[0].clamp(0, 1).squeeze(0)).save("/content/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:
TF.to_pil_image(cutouts[-1].clamp(0, 1).squeeze(0)).save("/content/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
def do_run():
seed = args.seed
#print(range(args.start_frame, args.max_frames))
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 == '':
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 = 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 == "Video Input":
seed = seed + 1
init_image = f'{videoFramesFolder}/{frame_num+1:04}.jpg'
init_scale = args.frames_scale
skip_steps = args.calc_frames_skip_steps
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 = []
if args.image_prompts_series is not None:
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 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:
#print("Using init image: " + init_image)
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:
print("Using perlin noise as init image")
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, 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 args.init_scale:
init_losses = lpips_model(x_in, init)
loss = loss + init_losses.sum() * args.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 model_config['timestep_respacing'].startswith('ddim'):
sample_fn = diffusion.ddim_sample_loop_progressive
else:
sample_fn = diffusion.p_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 model_config['timestep_respacing'].startswith('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,
)
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,
)
# with run_display:
# display.clear_output(wait=True)
imgToSharpen = None
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:
# base the final filename on the input args
final_image_path = output_path + '/' + output_filename
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
p_filename = output_filename.replace('.png','')
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'
filename = f'{p_filename}_{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'
filename = f'{p_filename}_{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'
filename = f'{p_filename}_{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:
if args.intermediates_in_subfolder is True:
#image.save(f'{partialFolder}/{filename}')
im = image.convert('RGB')
im.save((partialFolder+'/'+filename).replace('.png','.jpg'), quality=80)
else:
image.save(f'{batchFolder}/{filename}')
else:
if j in args.intermediate_saves:
if args.intermediates_in_subfolder is True:
#image.save(f'{partialFolder}/{filename}')
im = image.convert('RGB')
im.save((partialFolder+'/'+filename).replace('.png','.jpg'), quality=80)
else:
image.save(f'{batchFolder}/{filename}')
if cur_t == -1:
#if frame_num == 0:
#save_settings(final_image_path)
if args.animation_mode != "None":
image.save('prevFrame.png')
if args.sharpen_preset != "Off" and animation_mode == "None":
imgToSharpen = image
if args.keep_unsharp is True:
image.save(f'{unsharpenFolder}/{filename}')
else:
#image.save(f'{batchFolder}/{filename}')
if final_image_path != '':
image.save(final_image_path)
else:
image.save(f'{batchFolder}/{filename}')
# if frame_num != args.max_frames-1:
# display.clear_output()
with image_display:
if args.sharpen_preset != "Off" and animation_mode == "None":
print('Starting Diffusion Sharpening...')
#do_superres(imgToSharpen, f'{batchFolder}/{filename}')
if final_image_path != '':
do_superres(imgToSharpen, final_image_path)
else:
do_superres(imgToSharpen, f'{batchFolder}/{filename}')
display.clear_output()
plt.plot(np.array(loss_values), 'r')
def save_settings(final_name):
setting_list = {
'text_prompts': text_prompts,
'image_prompts': image_prompts,
'clip_guidance_scale': clip_guidance_scale,
'tv_scale': tv_scale,
'range_scale': range_scale,
'sat_scale': sat_scale,
# 'cutn': cutn,
'cutn_batches': cutn_batches,
'max_frames': max_frames,
'interp_spline': interp_spline,
# 'rotation_per_frame': rotation_per_frame,
'init_image': init_image,
'init_scale': init_scale,
'skip_steps': skip_steps,
# 'zoom_per_frame': zoom_per_frame,
'frames_scale': frames_scale,
'frames_skip_steps': frames_skip_steps,
'perlin_init': perlin_init,
'perlin_mode': perlin_mode,
'skip_augs': skip_augs,
'randomize_class': randomize_class,
'clip_denoised': clip_denoised,
'clamp_grad': clamp_grad,
'clamp_max': clamp_max,
'seed': seed,
'fuzzy_prompt': fuzzy_prompt,
'rand_mag': rand_mag,
'eta': eta,
'width': width_height[0],
'height': width_height[1],
'diffusion_model': diffusion_model,
'use_secondary_model': use_secondary_model,
'steps': steps,
'diffusion_steps': diffusion_steps,
'ViTB32': ViTB32,
'ViTB16': ViTB16,
'ViTL14': ViTL14,
'RN101': RN101,
'RN50': RN50,
'RN50x4': RN50x4,
'RN50x16': RN50x16,
'RN50x64': RN50x64,
'cut_overview': str(cut_overview),
'cut_innercut': str(cut_innercut),
'cut_ic_pow': cut_ic_pow,
'cut_icgray_p': str(cut_icgray_p),
'key_frames': key_frames,
'max_frames': max_frames,
'angle': angle,
'zoom': zoom,
'translation_x': translation_x,
'translation_y': translation_y,
'video_init_path':video_init_path,
'extract_nth_frame':extract_nth_frame,
}
# print('Settings:', setting_list)
if final_name=='':
with open(f"{batchFolder}/{batch_name}({batchNum})_settings.txt", "w+") as f: #save settings
json.dump(setting_list, f, ensure_ascii=False, indent=4)
else:
with open(final_name.replace('png','txt'), "w+") as f: #save settings
json.dump(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.):
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))