prd.py

# -*- coding: utf-8 -*-
"""ProgRock Diffusion

Command line version of Disco Diffusion (v5 Alpha)

Original file is located at
    https://colab.research.google.com/drive/1QGCyDlYneIvv1zFXngfOCCoSUKC6j1ZP

Original notebook by Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings). It uses either OpenAI's 256x256 unconditional ImageNet or Katherine Crowson's fine-tuned 512x512 diffusion model (https://github.com/openai/guided-diffusion), together with CLIP (https://github.com/openai/CLIP) to connect text prompts with images.

Modified by Daniel Russell (https://github.com/russelldc, https://twitter.com/danielrussruss) to include (hopefully) optimal params for quick generations in 15-100 timesteps rather than 1000, as well as more robust augmentations.

Further improvements from Dango233 and nsheppard helped improve the quality of diffusion in general, and especially so for shorter runs like this notebook aims to achieve.

Vark added code to load in multiple Clip models at once, which all prompts are evaluated against, which may greatly improve accuracy.

The latest zoom, pan, rotation, and keyframes features were taken from Chigozie Nri's VQGAN Zoom Notebook (https://github.com/chigozienri, https://twitter.com/chigozienri)

Advanced DangoCutn Cutout method is also from Dango223.

Somnai (https://twitter.com/Somnai_dreams) added Diffusion Animation techniques, QoL improvements and various implementations of tech and techniques, mostly listed in the changelog below.
--
Stripped down for basic Python use by Jason Hough
--
"""

# @title Licensed under the MIT License

# Copyright (c) 2021 Katherine Crowson

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.

# @title <- View Changelog


import os
from os import path

root_path = os.getcwd()

# Simple create paths taken with modifications from Datamosh's Batch VQGAN+CLIP notebook
def createPath(filepath):
    if path.exists(filepath) == False:
        os.makedirs(filepath)
        print(f"Made {filepath}")
    else:
        print(f"filepath {filepath} exists.")


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)
# Import prompts as dictionary. Key [Title] - String [Prompt]
import ast

file = open("prompt.txt", "r")
dic_prompts = ast.literal_eval(file.read())
file.close()

model_256_downloaded = False
model_512_downloaded = False
model_secondary_downloaded = False

import sys

if sys.platform == "win32":
    import ssl

    ssl._create_default_https_context = ssl._create_unverified_context

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

sys.path.append(f"{root_path}/ResizeRight")
sys.path.append(f"{root_path}/CLIP")
sys.path.append(f"{root_path}/guided-diffusion")
sys.path.append(f"{root_path}/SLIP")
import clip
from resize_right import resize
from models import SLIP_VITB16, SLIP, SLIP_VITL16
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 urllib.request


# SuperRes
import ipywidgets as widgets

sys.path.append(f"{root_path}")
sys.path.append(f"{root_path}/taming-transformers")
from taming.models import vqgan  # checking correct import from taming
from torchvision.datasets.utils import download_url

sys.path.append(f"{root_path}/latent-diffusion")
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 torch, torchvision
import time
from omegaconf import OmegaConf
import warnings

warnings.filterwarnings("ignore", category=UserWarning)

# Command Line parse
import argparse

example_text = """Usage examples:

To simply use the 'Default' output directory and get settings from settings.json:
 python3 prd.py

To use your own settings.json (note that putting it in quotes can help parse errors):
 python3 prd.py -s "some_directory/mysettings.json"

To use the 'Default' output directory and settings, but override the output name and prompt:
 python3 prd.py -p "A cool image of the author of this program" -o Coolguy

To use multiple prompts with optional weight values:
 python3 prd.py -p "A cool image of the author of this program" -p "Pale Blue Sky:.5"

You can ignore the seed coming from a settings file by adding -i, resulting in a new random seed
"""

my_parser = argparse.ArgumentParser(
    prog="ProgRockDiffusion",
    description="Generate images from text prompts.",
    epilog=example_text,
    formatter_class=argparse.RawDescriptionHelpFormatter,
)
my_parser.add_argument(
    "-s",
    "--settings",
    action="store",
    required=False,
    default="settings.json",
    help="A settings JSON file to use, best to put in quotes",
)
my_parser.add_argument(
    "-o",
    "--output",
    action="store",
    required=False,
    help="What output directory to use within images_out",
)
my_parser.add_argument(
    "-p", "--prompt", action="append", required=False, help="Override the prompt"
)
my_parser.add_argument(
    "-i",
    "--ignoreseed",
    action="store_true",
    required=False,
    help="Ignores the random seed in the settings file",
)

cl_args = my_parser.parse_args()

# Load the JSON config file
try:
    with open(cl_args.settings, "r") as json_file:
        settings_file = json.load(json_file)
except Exception as e:
    print("Failed to open or parse " + cl_args.settings + " - Check formatting.")
    print(e)
    quit()

# Set all of the settings from the specified file
batch_name = settings_file["batch_name"]
text_prompts = settings_file["text_prompts"]
image_prompts = settings_file["image_prompts"]
clip_guidance_scale = settings_file["clip_guidance_scale"]
tv_scale = settings_file["tv_scale"]
range_scale = settings_file["range_scale"]
sat_scale = settings_file["sat_scale"]
n_batches = settings_file["n_batches"]
display_rate = settings_file["display_rate"]
cutn_batches = settings_file["cutn_batches"]
max_frames = settings_file["max_frames"]
interp_spline = settings_file["interp_spline"]
init_image = settings_file["init_image"]
init_scale = settings_file["init_scale"]
skip_steps = settings_file["skip_steps"]
frames_scale = settings_file["frames_scale"]
frames_skip_steps = settings_file["frames_skip_steps"]
perlin_init = settings_file["perlin_init"]
perlin_mode = settings_file["perlin_mode"]
skip_augs = settings_file["skip_augs"]
randomize_class = settings_file["randomize_class"]
clip_denoised = settings_file["clip_denoised"]
clamp_grad = settings_file["clamp_grad"]
clamp_max = settings_file["clamp_max"]
set_seed = settings_file["set_seed"]
fuzzy_prompt = settings_file["fuzzy_prompt"]
rand_mag = settings_file["rand_mag"]
eta = settings_file["eta"]
width_height = [(settings_file["width"]), (settings_file["height"])]
diffusion_model = settings_file["diffusion_model"]
use_secondary_model = settings_file["use_secondary_model"]
steps = settings_file["steps"]
diffusion_steps = settings_file["diffusion_steps"]
ViTB32 = settings_file["ViTB32"]
ViTB16 = settings_file["ViTB16"]
ViTL14 = settings_file["ViTL14"]
RN101 = settings_file["RN101"]
RN50 = settings_file["RN50"]
RN50x4 = settings_file["RN50x4"]
RN50x16 = settings_file["RN50x16"]
SLIPB16 = settings_file["SLIPB16"]
SLIPL16 = settings_file["SLIPL16"]
cut_overview = settings_file["cut_overview"]
cut_innercut = settings_file["cut_innercut"]
cut_ic_pow = settings_file["cut_ic_pow"]
cut_icgray_p = settings_file["cut_icgray_p"]
key_frames = settings_file["key_frames"]
angle = settings_file["angle"]
zoom = settings_file["zoom"]
translation_x = settings_file["translation_x"]
translation_y = settings_file["translation_y"]
video_init_path = settings_file["video_init_path"]
extract_nth_frame = settings_file["extract_nth_frame"]

# Now override some depending on command line and maybe a special case
if cl_args.output:
    batch_name = cl_args.output
    print(f"Setting Output dir to {batch_name}")

if cl_args.prompt:
    text_prompts["0"] = cl_args.prompt
    print(f"Setting prompt to {text_prompts}")

if cl_args.ignoreseed:
    set_seed = "random_seed"
    print(f"Using a random seed instead of the one provided by the JSON file.")

import torch

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("Using device:", device)

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

# @title 2.2 Define necessary functions

# 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=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):
    print(f"Fetching {str(url_or_path)}")
    print(f"This might take a while... please wait.")
    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


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 = []

        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:
            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,
                            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:
                    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"{count}.{key}({args.batchNum})_{save_num}.png"
                                    )
                                else:
                                    # If we're working with percentages, append it
                                    if args.steps_per_checkpoint is not None:
                                        filename = f"{count}.{key}({args.batchNum})_{i:04}-{percent:02}%.png"
                                    # Or else, iIf we're working with specific steps, append those
                                    else:
                                        filename = f"{count}.{key}({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:
                                    if args.intermediates_in_subfolder is True:
                                        image.save(f"{partialFolder}/{filename}")
                                    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}")
                                    else:
                                        image.save(f"{batchFolder}/{filename}")
                            if cur_t == -1:
                                if frame_num == 0:
                                    save_settings()
                                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 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}")
                    display.clear_output()

            plt.plot(np.array(loss_values), "r")


def save_settings():
    setting_list = {
        "batch_name": batch_name,
        "text_prompts": text_prompts,
        "n_batches": n_batches,
        "steps": steps,
        "display_rate": display_rate,
        "width": width_height[0],
        "height": width_height[1],
        "set_seed": seed,
        "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,
        "fuzzy_prompt": fuzzy_prompt,
        "rand_mag": rand_mag,
        "eta": eta,
        "diffusion_model": diffusion_model,
        "use_secondary_model": use_secondary_model,
        "diffusion_steps": diffusion_steps,
        "ViTB32": ViTB32,
        "ViTB16": ViTB16,
        "ViTL14": ViTL14,
        "RN101": RN101,
        "RN50": RN50,
        "RN50x4": RN50x4,
        "RN50x16": RN50x16,
        "SLIPB16": SLIPB16,
        "SLIPL16": SLIPL16,
        "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,
        "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)
    with open(
        f"{batchFolder}/{batch_name}({batchNum})_settings.json", "w+"
    ) as f:  # save settings
        json.dump(setting_list, f, ensure_ascii=False, indent=4)


# @title 2.3 Define the secondary diffusion model


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)


# @title 2.4 SuperRes Define
class DDIMSampler(object):
    def __init__(self, model, schedule="linear", **kwargs):
        super().__init__()
        self.model = model
        self.ddpm_num_timesteps = model.num_timesteps
        self.schedule = schedule

    def register_buffer(self, name, attr):
        if type(attr) == torch.Tensor:
            if attr.device != torch.device("cuda"):
                attr = attr.to(torch.device("cuda"))
        setattr(self, name, attr)

    def make_schedule(
        self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True
    ):
        self.ddim_timesteps = make_ddim_timesteps(
            ddim_discr_method=ddim_discretize,
            num_ddim_timesteps=ddim_num_steps,
            num_ddpm_timesteps=self.ddpm_num_timesteps,
            verbose=verbose,
        )
        alphas_cumprod = self.model.alphas_cumprod
        assert (
            alphas_cumprod.shape[0] == self.ddpm_num_timesteps
        ), "alphas have to be defined for each timestep"
        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)

        self.register_buffer("betas", to_torch(self.model.betas))
        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
        self.register_buffer(
            "alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev)
        )

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.register_buffer(
            "sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu()))
        )
        self.register_buffer(
            "sqrt_one_minus_alphas_cumprod",
            to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
        )
        self.register_buffer(
            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu()))
        )
        self.register_buffer(
            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))
        )
        self.register_buffer(
            "sqrt_recipm1_alphas_cumprod",
            to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
        )

        # ddim sampling parameters
        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(
            alphacums=alphas_cumprod.cpu(),
            ddim_timesteps=self.ddim_timesteps,
            eta=ddim_eta,
            verbose=verbose,
        )
        self.register_buffer("ddim_sigmas", ddim_sigmas)
        self.register_buffer("ddim_alphas", ddim_alphas)
        self.register_buffer("ddim_alphas_prev", ddim_alphas_prev)
        self.register_buffer("ddim_sqrt_one_minus_alphas", np.sqrt(1.0 - ddim_alphas))
        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
            (1 - self.alphas_cumprod_prev)
            / (1 - self.alphas_cumprod)
            * (1 - self.alphas_cumprod / self.alphas_cumprod_prev)
        )
        self.register_buffer(
            "ddim_sigmas_for_original_num_steps", sigmas_for_original_sampling_steps
        )

    @torch.no_grad()
    def sample(
        self,
        S,
        batch_size,
        shape,
        conditioning=None,
        callback=None,
        normals_sequence=None,
        img_callback=None,
        quantize_x0=False,
        eta=0.0,
        mask=None,
        x0=None,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        verbose=True,
        x_T=None,
        log_every_t=100,
        **kwargs,
    ):
        if conditioning is not None:
            if isinstance(conditioning, dict):
                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
                if cbs != batch_size:
                    print(
                        f"Warning: Got {cbs} conditionings but batch-size is {batch_size}"
                    )
            else:
                if conditioning.shape[0] != batch_size:
                    print(
                        f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}"
                    )

        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
        # sampling
        C, H, W = shape
        size = (batch_size, C, H, W)
        # print(f'Data shape for DDIM sampling is {size}, eta {eta}')

        samples, intermediates = self.ddim_sampling(
            conditioning,
            size,
            callback=callback,
            img_callback=img_callback,
            quantize_denoised=quantize_x0,
            mask=mask,
            x0=x0,
            ddim_use_original_steps=False,
            noise_dropout=noise_dropout,
            temperature=temperature,
            score_corrector=score_corrector,
            corrector_kwargs=corrector_kwargs,
            x_T=x_T,
            log_every_t=log_every_t,
        )
        return samples, intermediates

    @torch.no_grad()
    def ddim_sampling(
        self,
        cond,
        shape,
        x_T=None,
        ddim_use_original_steps=False,
        callback=None,
        timesteps=None,
        quantize_denoised=False,
        mask=None,
        x0=None,
        img_callback=None,
        log_every_t=100,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
    ):
        device = self.model.betas.device
        b = shape[0]
        if x_T is None:
            img = torch.randn(shape, device=device)
        else:
            img = x_T

        if timesteps is None:
            timesteps = (
                self.ddpm_num_timesteps
                if ddim_use_original_steps
                else self.ddim_timesteps
            )
        elif timesteps is not None and not ddim_use_original_steps:
            subset_end = (
                int(
                    min(timesteps / self.ddim_timesteps.shape[0], 1)
                    * self.ddim_timesteps.shape[0]
                )
                - 1
            )
            timesteps = self.ddim_timesteps[:subset_end]

        intermediates = {"x_inter": [img], "pred_x0": [img]}
        time_range = (
            reversed(range(0, timesteps))
            if ddim_use_original_steps
            else np.flip(timesteps)
        )
        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
        print(f"Running DDIM Sharpening with {total_steps} timesteps")

        iterator = tqdm(time_range, desc="DDIM Sharpening", total=total_steps)

        for i, step in enumerate(iterator):
            index = total_steps - i - 1
            ts = torch.full((b,), step, device=device, dtype=torch.long)

            if mask is not None:
                assert x0 is not None
                img_orig = self.model.q_sample(
                    x0, ts
                )  # TODO: deterministic forward pass?
                img = img_orig * mask + (1.0 - mask) * img

            outs = self.p_sample_ddim(
                img,
                cond,
                ts,
                index=index,
                use_original_steps=ddim_use_original_steps,
                quantize_denoised=quantize_denoised,
                temperature=temperature,
                noise_dropout=noise_dropout,
                score_corrector=score_corrector,
                corrector_kwargs=corrector_kwargs,
            )
            img, pred_x0 = outs
            if callback:
                callback(i)
            if img_callback:
                img_callback(pred_x0, i)

            if index % log_every_t == 0 or index == total_steps - 1:
                intermediates["x_inter"].append(img)
                intermediates["pred_x0"].append(pred_x0)

        return img, intermediates

    @torch.no_grad()
    def p_sample_ddim(
        self,
        x,
        c,
        t,
        index,
        repeat_noise=False,
        use_original_steps=False,
        quantize_denoised=False,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
    ):
        b, *_, device = *x.shape, x.device
        e_t = self.model.apply_model(x, t, c)
        if score_corrector is not None:
            assert self.model.parameterization == "eps"
            e_t = score_corrector.modify_score(
                self.model, e_t, x, t, c, **corrector_kwargs
            )

        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
        alphas_prev = (
            self.model.alphas_cumprod_prev
            if use_original_steps
            else self.ddim_alphas_prev
        )
        sqrt_one_minus_alphas = (
            self.model.sqrt_one_minus_alphas_cumprod
            if use_original_steps
            else self.ddim_sqrt_one_minus_alphas
        )
        sigmas = (
            self.model.ddim_sigmas_for_original_num_steps
            if use_original_steps
            else self.ddim_sigmas
        )
        # select parameters corresponding to the currently considered timestep
        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
        sqrt_one_minus_at = torch.full(
            (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
        )

        # current prediction for x_0
        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
        if quantize_denoised:
            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
        # direction pointing to x_t
        dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
        if noise_dropout > 0.0:
            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
        return x_prev, pred_x0


def download_models(mode):

    if mode == "superresolution":
        # this is the small bsr light model
        url_conf = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
        url_ckpt = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"

        if not os.path.isdir(f"{model_path}/superres"):
            os.makedirs(f"{model_path}/superres")
        path_conf = f"{model_path}/superres/project.yaml"
        path_ckpt = f"{model_path}/superres/last.ckpt"

        if os.path.exists(path_conf) and os.path.exists(path_ckpt):
            print("Superres models already downloaded, skipping...")
        else:
            print("Superres models downloading, this might take a while...")
            urllib.request.urlretrieve(url_conf, path_conf)
            urllib.request.urlretrieve(url_ckpt, path_ckpt)

        # path_conf = path_conf + '/?dl=1' # fix it
        # path_ckpt = path_ckpt + '/?dl=1' # fix it
        return path_conf, path_ckpt

    else:
        raise NotImplementedError


def load_model_from_config(config, ckpt):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    global_step = pl_sd["global_step"]
    sd = pl_sd["state_dict"]
    model = instantiate_from_config(config.model)
    m, u = model.load_state_dict(sd, strict=False)
    model.cuda()
    model.eval()
    return {"model": model}, global_step


def get_model(mode):
    path_conf, path_ckpt = download_models(mode)
    config = OmegaConf.load(path_conf)
    model, step = load_model_from_config(config, path_ckpt)
    return model


def get_custom_cond(mode):
    dest = "data/example_conditioning"

    if mode == "superresolution":
        uploaded_img = files.upload()
        filename = next(iter(uploaded_img))
        name, filetype = filename.split(".")  # todo assumes just one dot in name !
        os.rename(f"{filename}", f"{dest}/{mode}/custom_{name}.{filetype}")

    elif mode == "text_conditional":
        w = widgets.Text(value="A cake with cream!", disabled=True)
        display.display(w)

        with open(f"{dest}/{mode}/custom_{w.value[:20]}.txt", "w") as f:
            f.write(w.value)

    elif mode == "class_conditional":
        w = widgets.IntSlider(min=0, max=1000)
        display.display(w)
        with open(f"{dest}/{mode}/custom.txt", "w") as f:
            f.write(w.value)

    else:
        raise NotImplementedError(f"cond not implemented for mode{mode}")


def get_cond_options(mode):
    path = "data/example_conditioning"
    path = os.path.join(path, mode)
    onlyfiles = [f for f in sorted(os.listdir(path))]
    return path, onlyfiles


def select_cond_path(mode):
    path = "data/example_conditioning"  # todo
    path = os.path.join(path, mode)
    onlyfiles = [f for f in sorted(os.listdir(path))]

    selected = widgets.RadioButtons(
        options=onlyfiles, description="Select conditioning:", disabled=False
    )
    display.display(selected)
    selected_path = os.path.join(path, selected.value)
    return selected_path


def get_cond(mode, img):
    example = dict()
    if mode == "superresolution":
        up_f = 4
        # visualize_cond_img(selected_path)

        c = img
        c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
        c_up = torchvision.transforms.functional.resize(
            c, size=[up_f * c.shape[2], up_f * c.shape[3]], antialias=True
        )
        c_up = rearrange(c_up, "1 c h w -> 1 h w c")
        c = rearrange(c, "1 c h w -> 1 h w c")
        c = 2.0 * c - 1.0

        c = c.to(torch.device("cuda"))
        example["LR_image"] = c
        example["image"] = c_up

    return example


def visualize_cond_img(path):
    display.display(ipyimg(filename=path))


def sr_run(
    model,
    img,
    task,
    custom_steps,
    eta,
    resize_enabled=False,
    classifier_ckpt=None,
    global_step=None,
):
    # global stride

    example = get_cond(task, img)

    save_intermediate_vid = False
    n_runs = 1
    masked = False
    guider = None
    ckwargs = None
    mode = "ddim"
    ddim_use_x0_pred = False
    temperature = 1.0
    eta = eta
    make_progrow = True
    custom_shape = None

    height, width = example["image"].shape[1:3]
    split_input = height >= 128 and width >= 128

    if split_input:
        ks = 128
        stride = 64
        vqf = 4  #
        model.split_input_params = {
            "ks": (ks, ks),
            "stride": (stride, stride),
            "vqf": vqf,
            "patch_distributed_vq": True,
            "tie_braker": False,
            "clip_max_weight": 0.5,
            "clip_min_weight": 0.01,
            "clip_max_tie_weight": 0.5,
            "clip_min_tie_weight": 0.01,
        }
    else:
        if hasattr(model, "split_input_params"):
            delattr(model, "split_input_params")

    invert_mask = False

    x_T = None
    for n in range(n_runs):
        if custom_shape is not None:
            x_T = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(
                model.device
            )
            x_T = repeat(x_T, "1 c h w -> b c h w", b=custom_shape[0])

        logs = make_convolutional_sample(
            example,
            model,
            mode=mode,
            custom_steps=custom_steps,
            eta=eta,
            swap_mode=False,
            masked=masked,
            invert_mask=invert_mask,
            quantize_x0=False,
            custom_schedule=None,
            decode_interval=10,
            resize_enabled=resize_enabled,
            custom_shape=custom_shape,
            temperature=temperature,
            noise_dropout=0.0,
            corrector=guider,
            corrector_kwargs=ckwargs,
            x_T=x_T,
            save_intermediate_vid=save_intermediate_vid,
            make_progrow=make_progrow,
            ddim_use_x0_pred=ddim_use_x0_pred,
        )
    return logs


@torch.no_grad()
def convsample_ddim(
    model,
    cond,
    steps,
    shape,
    eta=1.0,
    callback=None,
    normals_sequence=None,
    mask=None,
    x0=None,
    quantize_x0=False,
    img_callback=None,
    temperature=1.0,
    noise_dropout=0.0,
    score_corrector=None,
    corrector_kwargs=None,
    x_T=None,
    log_every_t=None,
):

    ddim = DDIMSampler(model)
    bs = shape[0]  # dont know where this comes from but wayne
    shape = shape[1:]  # cut batch dim
    # print(f"Sampling with eta = {eta}; steps: {steps}")
    samples, intermediates = ddim.sample(
        steps,
        batch_size=bs,
        shape=shape,
        conditioning=cond,
        callback=callback,
        normals_sequence=normals_sequence,
        quantize_x0=quantize_x0,
        eta=eta,
        mask=mask,
        x0=x0,
        temperature=temperature,
        verbose=False,
        score_corrector=score_corrector,
        corrector_kwargs=corrector_kwargs,
        x_T=x_T,
    )

    return samples, intermediates


@torch.no_grad()
def make_convolutional_sample(
    batch,
    model,
    mode="vanilla",
    custom_steps=None,
    eta=1.0,
    swap_mode=False,
    masked=False,
    invert_mask=True,
    quantize_x0=False,
    custom_schedule=None,
    decode_interval=1000,
    resize_enabled=False,
    custom_shape=None,
    temperature=1.0,
    noise_dropout=0.0,
    corrector=None,
    corrector_kwargs=None,
    x_T=None,
    save_intermediate_vid=False,
    make_progrow=True,
    ddim_use_x0_pred=False,
):
    log = dict()

    z, c, x, xrec, xc = model.get_input(
        batch,
        model.first_stage_key,
        return_first_stage_outputs=True,
        force_c_encode=not (
            hasattr(model, "split_input_params")
            and model.cond_stage_key == "coordinates_bbox"
        ),
        return_original_cond=True,
    )

    log_every_t = 1 if save_intermediate_vid else None

    if custom_shape is not None:
        z = torch.randn(custom_shape)
        # print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")

    z0 = None

    log["input"] = x
    log["reconstruction"] = xrec

    if ismap(xc):
        log["original_conditioning"] = model.to_rgb(xc)
        if hasattr(model, "cond_stage_key"):
            log[model.cond_stage_key] = model.to_rgb(xc)

    else:
        log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
        if model.cond_stage_model:
            log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
            if model.cond_stage_key == "class_label":
                log[model.cond_stage_key] = xc[model.cond_stage_key]

    with model.ema_scope("Plotting"):
        t0 = time.time()
        img_cb = None

        sample, intermediates = convsample_ddim(
            model,
            c,
            steps=custom_steps,
            shape=z.shape,
            eta=eta,
            quantize_x0=quantize_x0,
            img_callback=img_cb,
            mask=None,
            x0=z0,
            temperature=temperature,
            noise_dropout=noise_dropout,
            score_corrector=corrector,
            corrector_kwargs=corrector_kwargs,
            x_T=x_T,
            log_every_t=log_every_t,
        )
        t1 = time.time()

        if ddim_use_x0_pred:
            sample = intermediates["pred_x0"][-1]

    x_sample = model.decode_first_stage(sample)

    try:
        x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
        log["sample_noquant"] = x_sample_noquant
        log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
    except:
        pass

    log["sample"] = x_sample
    log["time"] = t1 - t0

    return log


sr_diffMode = "superresolution"
sr_model = get_model("superresolution")


def do_superres(img, filepath):

    if args.sharpen_preset == "Faster":
        sr_diffusion_steps = "25"
        sr_pre_downsample = "1/2"
    if args.sharpen_preset == "Fast":
        sr_diffusion_steps = "100"
        sr_pre_downsample = "1/2"
    if args.sharpen_preset == "Slow":
        sr_diffusion_steps = "25"
        sr_pre_downsample = "None"
    if args.sharpen_preset == "Very Slow":
        sr_diffusion_steps = "100"
        sr_pre_downsample = "None"

    sr_post_downsample = "Original Size"
    sr_diffusion_steps = int(sr_diffusion_steps)
    sr_eta = 1.0
    sr_downsample_method = "Lanczos"

    gc.collect()
    torch.cuda.empty_cache()

    im_og = img
    width_og, height_og = im_og.size

    # Downsample Pre
    if sr_pre_downsample == "1/2":
        downsample_rate = 2
    elif sr_pre_downsample == "1/4":
        downsample_rate = 4
    else:
        downsample_rate = 1

    width_downsampled_pre = width_og // downsample_rate
    height_downsampled_pre = height_og // downsample_rate

    if downsample_rate != 1:
        # print(f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
        im_og = im_og.resize(
            (width_downsampled_pre, height_downsampled_pre), Image.LANCZOS
        )
        # im_og.save('/content/temp.png')
        # filepath = '/content/temp.png'

    logs = sr_run(sr_model["model"], im_og, sr_diffMode, sr_diffusion_steps, sr_eta)

    sample = logs["sample"]
    sample = sample.detach().cpu()
    sample = torch.clamp(sample, -1.0, 1.0)
    sample = (sample + 1.0) / 2.0 * 255
    sample = sample.numpy().astype(np.uint8)
    sample = np.transpose(sample, (0, 2, 3, 1))
    a = Image.fromarray(sample[0])

    # Downsample Post
    if sr_post_downsample == "1/2":
        downsample_rate = 2
    elif sr_post_downsample == "1/4":
        downsample_rate = 4
    else:
        downsample_rate = 1

    width, height = a.size
    width_downsampled_post = width // downsample_rate
    height_downsampled_post = height // downsample_rate

    if sr_downsample_method == "Lanczos":
        aliasing = Image.LANCZOS
    else:
        aliasing = Image.NEAREST

    if downsample_rate != 1:
        # print(f'Downsampling from [{width}, {height}] to [{width_downsampled_post}, {height_downsampled_post}]')
        a = a.resize((width_downsampled_post, height_downsampled_post), aliasing)
    elif sr_post_downsample == "Original Size":
        # print(f'Downsampling from [{width}, {height}] to Original Size [{width_og}, {height_og}]')
        a = a.resize((width_og, height_og), aliasing)

    # display.display(a)
    a.save(filepath)
    return
    print(f"Processing finished!")


"""# 2. Diffusion and CLIP model settings"""

# @markdown ####**Models Settings:**
# diffusion_model = "512x512_diffusion_uncond_finetune_008100" #@param ["256x256_diffusion_uncond", "512x512_diffusion_uncond_finetune_008100"]
# use_secondary_model = True #@param {type: 'boolean'}

timestep_respacing = "50"  # param ['25','50','100','150','250','500','1000','ddim25','ddim50', 'ddim75', 'ddim100','ddim150','ddim250','ddim500','ddim1000']
# diffusion_steps = 1000 # param {type: 'number'}
use_checkpoint = True  # @param {type: 'boolean'}
# ViTB32 = True #@param{type:"boolean"} Low RAM requirement, low accuracy
# ViTB16 = True #@param{type:"boolean"} Low RAM requirement, medium accuracy
# ViTL14 = False #@param{type:"boolean"} High RAM requirement, very high accuracy
# RN101 = True #@param{type:"boolean"} Low RAM requirement, low accuracy
# RN50 = True #@param{type:"boolean"} Low RAM requirement, medium accuracy
# RN50x4 = True #@param{type:"boolean"} Medium RAM requirement, high accuracy
# RN50x16 = False #@param{type:"boolean"} High RAM requirement, high accuracy
# SLIPB16 = False # param{type:"boolean"}
# SLIPL16 = False # param{type:"boolean"}

# @markdown If you're having issues with model downloads, check this to compare SHA's:
check_model_SHA = False  # @param{type:"boolean"}

model_256_SHA = "983e3de6f95c88c81b2ca7ebb2c217933be1973b1ff058776b970f901584613a"
model_512_SHA = "9c111ab89e214862b76e1fa6a1b3f1d329b1a88281885943d2cdbe357ad57648"
model_secondary_SHA = "983e3de6f95c88c81b2ca7ebb2c217933be1973b1ff058776b970f901584613a"

model_256_link = "https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt"
model_512_link = "http://batbot.tv/ai/models/guided-diffusion/512x512_diffusion_uncond_finetune_008100.pt"
model_secondary_link = (
    "https://v-diffusion.s3.us-west-2.amazonaws.com/secondary_model_imagenet_2.pth"
)

model_256_path = f"{model_path}/256x256_diffusion_uncond.pt"
model_512_path = f"{model_path}/512x512_diffusion_uncond_finetune_008100.pt"
model_secondary_path = f"{model_path}/secondary_model_imagenet_2.pth"

# Download the diffusion model
if diffusion_model == "256x256_diffusion_uncond":
    if os.path.exists(model_256_path) and check_model_SHA:
        print("Checking 256 Diffusion File")
        with open(model_256_path, "rb") as f:
            bytes = f.read()
            hash = hashlib.sha256(bytes).hexdigest()
        if hash == model_256_SHA:
            print("256 Model SHA matches")
            model_256_downloaded = True
        else:
            print("256 Model SHA doesn't match, redownloading...")
            #!wget --continue {model_256_link} -P {model_path}
            urllib.request.urlretrieve(model_256_link, model_256_path)
            model_256_downloaded = True
    elif (
        os.path.exists(model_256_path)
        and not check_model_SHA
        or model_256_downloaded == True
    ):
        print(
            "256 Model already downloaded, check check_model_SHA if the file is corrupt"
        )
    else:
        print("256 Model downloading, this might take a while...")
        #!wget --continue {model_256_link} -P {model_path}
        urllib.request.urlretrieve(model_256_link, model_256_path)
        model_256_downloaded = True
elif diffusion_model == "512x512_diffusion_uncond_finetune_008100":
    if os.path.exists(model_512_path) and check_model_SHA:
        print("Checking 512 Diffusion File")
        with open(model_512_path, "rb") as f:
            bytes = f.read()
            hash = hashlib.sha256(bytes).hexdigest()
        if hash == model_512_SHA:
            print("512 Model SHA matches")
            model_512_downloaded = True
        else:
            print("512 Model SHA doesn't match, redownloading...")
            #!wget --continue {model_512_link} -P {model_path}
            urllib.request.urlretrieve(model_512_link, model_512_path)
            model_512_downloaded = True
    elif (
        os.path.exists(model_512_path)
        and not check_model_SHA
        or model_512_downloaded == True
    ):
        print(
            "512 Model already downloaded, check check_model_SHA if the file is corrupt"
        )
    else:
        #!wget --continue {model_512_link} -P {model_path}
        print(model_path)
        print("512 Model downloading, this might take a while...")
        urllib.request.urlretrieve(model_512_link, model_512_path)
        model_512_downloaded = True


# Download the secondary diffusion model v2
if use_secondary_model == True:
    if os.path.exists(model_secondary_path) and check_model_SHA:
        print("Checking Secondary Diffusion File")
        with open(model_secondary_path, "rb") as f:
            bytes = f.read()
            hash = hashlib.sha256(bytes).hexdigest()
        if hash == model_secondary_SHA:
            print("Secondary Model SHA matches")
            model_secondary_downloaded = True
        else:
            print("Secondary Model SHA doesn't match, redownloading...")
            #!wget --continue {model_secondary_link} -P {model_path}
            urllib.request.urlretrieve(model_secondary_link, model_secondary_path)
            model_secondary_downloaded = True
    elif (
        os.path.exists(model_secondary_path)
        and not check_model_SHA
        or model_secondary_downloaded == True
    ):
        print(
            "Secondary Model already downloaded, check check_model_SHA if the file is corrupt"
        )
    else:
        #!wget --continue {model_secondary_link} -P {model_path}
        print("Secondary Model downloading, this might take a while...")
        urllib.request.urlretrieve(model_secondary_link, model_secondary_path)
        model_secondary_downloaded = True

model_config = model_and_diffusion_defaults()
if diffusion_model == "512x512_diffusion_uncond_finetune_008100":
    model_config.update(
        {
            "attention_resolutions": "32, 16, 8",
            "class_cond": False,
            "diffusion_steps": diffusion_steps,
            "rescale_timesteps": True,
            "timestep_respacing": timestep_respacing,
            "image_size": 512,
            "learn_sigma": True,
            "noise_schedule": "linear",
            "num_channels": 256,
            "num_head_channels": 64,
            "num_res_blocks": 2,
            "resblock_updown": True,
            "use_checkpoint": use_checkpoint,
            "use_fp16": True,
            "use_scale_shift_norm": True,
        }
    )
elif diffusion_model == "256x256_diffusion_uncond":
    model_config.update(
        {
            "attention_resolutions": "32, 16, 8",
            "class_cond": False,
            "diffusion_steps": diffusion_steps,
            "rescale_timesteps": True,
            "timestep_respacing": timestep_respacing,
            "image_size": 256,
            "learn_sigma": True,
            "noise_schedule": "linear",
            "num_channels": 256,
            "num_head_channels": 64,
            "num_res_blocks": 2,
            "resblock_updown": True,
            "use_checkpoint": use_checkpoint,
            "use_fp16": True,
            "use_scale_shift_norm": True,
        }
    )

secondary_model_ver = 2
model_default = model_config["image_size"]


if secondary_model_ver == 2:
    secondary_model = SecondaryDiffusionImageNet2()
    secondary_model.load_state_dict(
        torch.load(f"{model_path}/secondary_model_imagenet_2.pth", map_location="cpu")
    )
secondary_model.eval().requires_grad_(False).to(device)

clip_models = []
if ViTB32 is True:
    clip_models.append(
        clip.load("ViT-B/32", jit=False)[0].eval().requires_grad_(False).to(device)
    )
if ViTB16 is True:
    clip_models.append(
        clip.load("ViT-B/16", jit=False)[0].eval().requires_grad_(False).to(device)
    )
if ViTL14 is True:
    clip_models.append(
        clip.load("ViT-L/14", jit=False)[0].eval().requires_grad_(False).to(device)
    )
if RN50 is True:
    clip_models.append(
        clip.load("RN50", jit=False)[0].eval().requires_grad_(False).to(device)
    )
if RN50x4 is True:
    clip_models.append(
        clip.load("RN50x4", jit=False)[0].eval().requires_grad_(False).to(device)
    )
if RN50x16 is True:
    clip_models.append(
        clip.load("RN50x16", jit=False)[0].eval().requires_grad_(False).to(device)
    )
if RN101 is True:
    clip_models.append(
        clip.load("RN101", jit=False)[0].eval().requires_grad_(False).to(device)
    )

if SLIPB16:
    SLIPB16model = SLIP_VITB16(ssl_mlp_dim=4096, ssl_emb_dim=256)
    if not os.path.exists(f"{model_path}/slip_base_100ep.pt"):
        #!wget https://dl.fbaipublicfiles.com/slip/slip_base_100ep.pt -P {model_path}
        print("Downloading SLIP_VITB16 model, this might take a while...")
        urllib.request.urlretrieve(
            "https://dl.fbaipublicfiles.com/slip/slip_base_100ep.pt",
            model_path + "/slip_base_100ep.pt",
        )
    sd = torch.load(f"{model_path}/slip_base_100ep.pt")
    real_sd = {}
    for k, v in sd["state_dict"].items():
        real_sd[".".join(k.split(".")[1:])] = v
    del sd
    SLIPB16model.load_state_dict(real_sd)
    SLIPB16model.requires_grad_(False).eval().to(device)

    clip_models.append(SLIPB16model)

if SLIPL16:
    SLIPL16model = SLIP_VITL16(ssl_mlp_dim=4096, ssl_emb_dim=256)
    if not os.path.exists(f"{model_path}/slip_large_100ep.pt"):
        #!wget https://dl.fbaipublicfiles.com/slip/slip_large_100ep.pt -P {model_path}
        print("Downloading SLIP_VITL16 model, this might take a while...")
        urllib.request.urlretrieve(
            "https://dl.fbaipublicfiles.com/slip/slip_large_100ep.pt",
            model_path + "/slip_large_100ep.pt",
        )
    sd = torch.load(f"{model_path}/slip_large_100ep.pt")
    real_sd = {}
    for k, v in sd["state_dict"].items():
        real_sd[".".join(k.split(".")[1:])] = v
    del sd
    SLIPL16model.load_state_dict(real_sd)
    SLIPL16model.requires_grad_(False).eval().to(device)

    clip_models.append(SLIPL16model)

normalize = T.Normalize(
    mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711]
)
lpips_model = lpips.LPIPS(net="vgg").to(device)

"""# 3. Settings"""
# REMOVED FOR COMMAND LINE ARGS
# @markdown ####**Basic Settings:**
# batch_name = 'SpyNovel' #@param{type: 'string'}
# steps = 600 #@param [25,50,100,150,250,500,1000]{type: 'raw', allow-input: true}
# width_height = [512, 832]#@param{type: 'raw'}
# clip_guidance_scale = 5000 #@param{type: 'number'}
# tv_scale =  0#@param{type: 'number'}
# range_scale =   150#@param{type: 'number'}
# sat_scale =   0#@param{type: 'number'}
# cutn_batches = 1  #@param{type: 'number'}
# skip_augs = False#@param{type: 'boolean'}

# @markdown ---

# @markdown ####**Init Settings:**
# init_image = None #@param{type: 'string'}
# init_scale = 1000 #@param{type: 'integer'}
# skip_steps = 0 #@param{type: 'integer'}

# Get corrected sizes
side_x = (width_height[0] // 64) * 64
side_y = (width_height[1] // 64) * 64
if side_x != width_height[0] or side_y != width_height[1]:
    print(
        f"Changing output size to {side_x}x{side_y}. Dimensions must by multiples of 64."
    )

# Update Model Settings
timestep_respacing = f"ddim{steps}"
diffusion_steps = (1000 // steps) * steps if steps < 1000 else steps
model_config.update(
    {
        "timestep_respacing": timestep_respacing,
        "diffusion_steps": diffusion_steps,
    }
)

# Make folder for batch
batchFolder = f"{outDirPath}/{batch_name}"
createPath(batchFolder)

"""###Animation Settings"""

# @markdown ####**Animation Mode:**
animation_mode = "None"  # @param['None', '2D', 'Video Input']
# @markdown *For animation, you probably want to turn `cutn_batches` to 1 to make it quicker.*


# @markdown ---

# @markdown ####**Video Input Settings:**
# video_init_path = "/content/training.mp4" #@param {type: 'string'}
# extract_nth_frame = 2 #@param {type:"number"}

if animation_mode == "Video Input":
    videoFramesFolder = f"/content/videoFrames"
    createPath(videoFramesFolder)
    print(f"Exporting Video Frames (1 every {extract_nth_frame})...")
    try:
        #!rm {videoFramesFolder}/*.jpg
        tempfileList = glob.glob(videoFramesFolder + "/*.jpg")
        for tempfilePath in tempfileList:
            os.remove(tempfilePath)
    except:
        print("")
    vf = f'"select=not(mod(n\,{extract_nth_frame}))"'
    # TODO remove or fix animation stuff
    #!ffmpeg -i {video_init_path} -vf {vf} -vsync vfr -q:v 2 -loglevel error -stats {videoFramesFolder}/%04d.jpg


# @markdown ---

# @markdown ####**2D Animation Settings:**
# @markdown `zoom` is a multiplier of dimensions, 1 is no zoom.

# key_frames = True #@param {type:"boolean"}
# max_frames = 10000#@param {type:"number"}

if animation_mode == "Video Input":
    max_frames = len(glob(f"{videoFramesFolder}/*.jpg"))

# interp_spline = 'Linear' #Do not change, currently will not look good. param ['Linear','Quadratic','Cubic']{type:"string"}
# angle = "0:(0)"#@param {type:"string"}
# zoom = "0: (1), 10: (1.05)"#@param {type:"string"}
# translation_x = "0: (0)"#@param {type:"string"}
# translation_y = "0: (0)"#@param {type:"string"}

# @markdown ---

# @markdown ####**Coherency Settings:**
# @markdown `frame_scale` tries to guide the new frame to looking like the old one. A good default is 1500.
# frames_scale = 1500 #@param{type: 'integer'}
# @markdown `frame_skip_steps` will blur the previous frame - higher values will flicker less but struggle to add enough new detail to zoom into.
# frames_skip_steps = '60%' #@param ['40%', '50%', '60%', '70%', '80%'] {type: 'string'}


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):
    """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)
    return key_frame_series


def split_prompts(prompts):
    prompt_series = pd.Series([np.nan for a in range(max_frames)])
    for i, prompt in prompts.items():
        prompt_series[i] = prompt
    # prompt_series = prompt_series.astype(str)
    prompt_series = prompt_series.ffill().bfill()
    return prompt_series


if key_frames:
    try:
        angle_series = get_inbetweens(parse_key_frames(angle))
    except RuntimeError as e:
        print(
            "WARNING: You have selected to use key frames, but you have not "
            "formatted `angle` correctly for key frames.\n"
            "Attempting to interpret `angle` as "
            f'"0: ({angle})"\n'
            "Please read the instructions to find out how to use key frames "
            "correctly.\n"
        )
        angle = f"0: ({angle})"
        angle_series = get_inbetweens(parse_key_frames(angle))

    try:
        zoom_series = get_inbetweens(parse_key_frames(zoom))
    except RuntimeError as e:
        print(
            "WARNING: You have selected to use key frames, but you have not "
            "formatted `zoom` correctly for key frames.\n"
            "Attempting to interpret `zoom` as "
            f'"0: ({zoom})"\n'
            "Please read the instructions to find out how to use key frames "
            "correctly.\n"
        )
        zoom = f"0: ({zoom})"
        zoom_series = get_inbetweens(parse_key_frames(zoom))

    try:
        translation_x_series = get_inbetweens(parse_key_frames(translation_x))
    except RuntimeError as e:
        print(
            "WARNING: You have selected to use key frames, but you have not "
            "formatted `translation_x` correctly for key frames.\n"
            "Attempting to interpret `translation_x` as "
            f'"0: ({translation_x})"\n'
            "Please read the instructions to find out how to use key frames "
            "correctly.\n"
        )
        translation_x = f"0: ({translation_x})"
        translation_x_series = get_inbetweens(parse_key_frames(translation_x))

    try:
        translation_y_series = get_inbetweens(parse_key_frames(translation_y))
    except RuntimeError as e:
        print(
            "WARNING: You have selected to use key frames, but you have not "
            "formatted `translation_y` correctly for key frames.\n"
            "Attempting to interpret `translation_y` as "
            f'"0: ({translation_y})"\n'
            "Please read the instructions to find out how to use key frames "
            "correctly.\n"
        )
        translation_y = f"0: ({translation_y})"
        translation_y_series = get_inbetweens(parse_key_frames(translation_y))

else:
    angle = float(angle)
    zoom = float(zoom)
    translation_x = float(translation_x)
    translation_y = float(translation_y)

"""### Extra Settings
 Partial Saves, Diffusion Sharpening, Advanced Settings, Cutn Scheduling
"""

# @markdown ####**Saving:**

intermediate_saves = 5  # @param{type: 'raw'}
intermediates_in_subfolder = True  # @param{type: 'boolean'}
# @markdown Intermediate steps will save a copy at your specified intervals. You can either format it as a single integer or a list of specific steps

# @markdown A value of `2` will save a copy at 33% and 66%. 0 will save none.

# @markdown A value of `[5, 9, 34, 45]` will save at steps 5, 9, 34, and 45. (Make sure to include the brackets)


if type(intermediate_saves) is not list:
    if intermediate_saves:
        steps_per_checkpoint = math.floor(
            (steps - skip_steps - 1) // (intermediate_saves + 1)
        )
        steps_per_checkpoint = steps_per_checkpoint if steps_per_checkpoint > 0 else 1
        print(f"Will save every {steps_per_checkpoint} steps")
    else:
        steps_per_checkpoint = steps + 10
else:
    steps_per_checkpoint = None

if intermediate_saves and intermediates_in_subfolder is True:
    partialFolder = f"{batchFolder}/partials"
    createPath(partialFolder)

    # @markdown ---

# @markdown ####**SuperRes Sharpening:**
# @markdown *Sharpen each image using latent-diffusion. Does not run in animation mode. `keep_unsharp` will save both versions.*
sharpen_preset = "Slow"  # @param ['Off', 'Faster', 'Fast', 'Slow', 'Very Slow']
keep_unsharp = True  # @param{type: 'boolean'}

if sharpen_preset != "Off" and keep_unsharp is True:
    unsharpenFolder = f"{batchFolder}/unsharpened"
    createPath(unsharpenFolder)

    # @markdown ---

# @markdown ####**Advanced Settings:**
# @markdown *There are a few extra advanced settings available if you double click this cell.*

# @markdown *Perlin init will replace your init, so uncheck if using one.*

# perlin_init = False  #@param{type: 'boolean'}
# perlin_mode = 'mixed' #@param ['mixed', 'color', 'gray']
# set_seed = 'random_seed' #@param{type: 'string'}
# eta = 0.8#@param{type: 'number'}
# clamp_grad = True #@param{type: 'boolean'}
# clamp_max = 0.05 #@param{type: 'number'}


### EXTRA ADVANCED SETTINGS:
# randomize_class = True
# clip_denoised = False
# fuzzy_prompt = False
# rand_mag = 0.05


# @markdown ---

# @markdown ####**Cutn Scheduling:**
# @markdown Format: `[40]*400+[20]*600` = 40 cuts for the first 400 /1000 steps, then 20 for the last 600/1000

# @markdown cut_overview and cut_innercut are cumulative for total cutn on any given step. Overview cuts see the entire image and are good for early structure, innercuts are your standard cutn.

# cut_overview = "[12]*400+[4]*600" #@param {type: 'string'}
# cut_innercut ="[4]*400+[12]*600"#@param {type: 'string'}
# cut_ic_pow = 1#@param {type: 'number'}
# cut_icgray_p = "[0.2]*400+[0]*600"#@param {type: 'string'}

# REMOVED FOR COMMAND LINE ARGS
"""###Prompts
`animation_mode: None` will only use the first set. `animation_mode: 2D / Video` will run through them per the set frames and hold on the last one.

text_prompts = {
    0: ["Cinematic dark alley with rain and puddles, by Leif Heanzo, thriller book cover."],
    # 100: ["This set of prompts start at frame 100", "This prompt has weight five:5"],
}

image_prompts = {
    # 0:['ImagePromptsWorkButArentVeryGood.png:2',],
}
"""

"""# 4. Diffuse!"""

# @title Do the Run!
# @markdown `n_batches` ignored with animation modes.
# display_rate =  300 #@param{type: 'number'}
# n_batches =  10 #@param{type: 'number'}

batch_size = 1


def move_files(start_num, end_num, old_folder, new_folder):
    for i in range(start_num, end_num):
        old_file = old_folder + f"/{batch_name}({batchNum})_{i:04}.png"
        new_file = new_folder + f"/{batch_name}({batchNum})_{i:04}.png"
        os.rename(old_file, new_file)


# @markdown ---
count = 0

# %%
# Start looping through the images
for key in dic_prompts:
    text_prompts = {"0": [dic_prompts[key]]}
    count += 1
    if count < 44:
        print(f"{count}. {key}: skipping...")
        continue
    print(f"{count}/{len(dic_prompts)} Prompt: {dic_prompts[key]}")

    resume_run = False  # @param{type: 'boolean'}
    run_to_resume = "latest"  # @param{type: 'string'}
    resume_from_frame = "latest"  # @param{type: 'string'}
    retain_overwritten_frames = False  # @param{type: 'boolean'}
    if retain_overwritten_frames is True:
        retainFolder = f"{batchFolder}/retained"
        createPath(retainFolder)

    skip_step_ratio = int(frames_skip_steps.rstrip("%")) / 100
    calc_frames_skip_steps = math.floor(steps * skip_step_ratio)

    if steps <= calc_frames_skip_steps:
        sys.exit("ERROR: You can't skip more steps than your total steps")

    if resume_run:
        if run_to_resume == "latest":
            try:
                batchNum
            except:
                batchNum = len(glob(f"{batchFolder}/{batch_name}(*)_settings.json")) - 1
        else:
            batchNum = int(run_to_resume)
        if resume_from_frame == "latest":
            start_frame = len(glob(batchFolder + f"/{batch_name}({batchNum})_*.png"))
        else:
            start_frame = int(resume_from_frame) + 1
            if retain_overwritten_frames is True:
                existing_frames = len(
                    glob(batchFolder + f"/{batch_name}({batchNum})_*.png")
                )
                frames_to_save = existing_frames - start_frame
                print(f"Moving {frames_to_save} frames to the Retained folder")
                move_files(start_frame, existing_frames, batchFolder, retainFolder)
    else:
        start_frame = 0
        batchNum = len(glob(batchFolder + "/*.json"))
        while (
            path.isfile(f"{batchFolder}/{batch_name}({batchNum})_settings.json") is True
            or path.isfile(f"{batchFolder}/{batch_name}-{batchNum}_settings.json")
            is True
        ):
            batchNum += 1

    print(f"Starting Run: {batch_name}({batchNum}) at frame {start_frame}")

    if set_seed == "random_seed":
        random.seed()
        seed = random.randint(0, 2**32)
        # print(f'Using seed: {seed}')
    else:
        seed = int(set_seed)

    # Leave this section alone, it takes all our settings and puts them in one variable dictionary
    args = {
        "batchNum": batchNum,
        "prompts_series": split_prompts(text_prompts) if text_prompts else None,
        "image_prompts_series": split_prompts(image_prompts) if image_prompts else None,
        "seed": seed,
        "display_rate": display_rate,
        "n_batches": n_batches if animation_mode == "None" else 1,
        "batch_size": batch_size,
        "batch_name": batch_name,
        "steps": steps,
        "width_height": width_height,
        "clip_guidance_scale": clip_guidance_scale,
        "tv_scale": tv_scale,
        "range_scale": range_scale,
        "sat_scale": sat_scale,
        "cutn_batches": cutn_batches,
        "init_image": init_image,
        "init_scale": init_scale,
        "skip_steps": skip_steps,
        "sharpen_preset": sharpen_preset,
        "keep_unsharp": keep_unsharp,
        "side_x": side_x,
        "side_y": side_y,
        "timestep_respacing": timestep_respacing,
        "diffusion_steps": diffusion_steps,
        "animation_mode": animation_mode,
        "video_init_path": video_init_path,
        "extract_nth_frame": extract_nth_frame,
        "key_frames": key_frames,
        "max_frames": max_frames if animation_mode != "None" else 1,
        "interp_spline": interp_spline,
        "start_frame": start_frame,
        "angle": angle,
        "zoom": zoom,
        "translation_x": translation_x,
        "translation_y": translation_y,
        "angle_series": angle_series,
        "zoom_series": zoom_series,
        "translation_x_series": translation_x_series,
        "translation_y_series": translation_y_series,
        "frames_scale": frames_scale,
        "calc_frames_skip_steps": calc_frames_skip_steps,
        "skip_step_ratio": skip_step_ratio,
        "calc_frames_skip_steps": calc_frames_skip_steps,
        "text_prompts": text_prompts,
        "image_prompts": image_prompts,
        "cut_overview": eval(cut_overview),
        "cut_innercut": eval(cut_innercut),
        "cut_ic_pow": cut_ic_pow,
        "cut_icgray_p": eval(cut_icgray_p),
        "intermediate_saves": intermediate_saves,
        "intermediates_in_subfolder": intermediates_in_subfolder,
        "steps_per_checkpoint": steps_per_checkpoint,
        "perlin_init": perlin_init,
        "perlin_mode": perlin_mode,
        "set_seed": set_seed,
        "eta": eta,
        "clamp_grad": clamp_grad,
        "clamp_max": clamp_max,
        "skip_augs": skip_augs,
        "randomize_class": randomize_class,
        "clip_denoised": clip_denoised,
        "fuzzy_prompt": fuzzy_prompt,
        "rand_mag": rand_mag,
    }

    args = SimpleNamespace(**args)

    print("Prepping model...")
    model, diffusion = create_model_and_diffusion(**model_config)
    print(f"{model_path}/{diffusion_model}.pt")
    model.load_state_dict(
        torch.load(f"{model_path}/{diffusion_model}.pt", map_location="cpu")
    )
    model.requires_grad_(False).eval().to(device)
    for name, param in model.named_parameters():
        if "qkv" in name or "norm" in name or "proj" in name:
            param.requires_grad_()
    if model_config["use_fp16"]:
        model.convert_to_fp16()

    gc.collect()
    torch.cuda.empty_cache()
    try:
        do_run()
    except KeyboardInterrupt:
        pass
    finally:
        print("Seed used:", seed)
        gc.collect()
        torch.cuda.empty_cache()

"""# 5. Create the video"""

# @title ### **Create video**
# @markdown Video file will save in the same folder as your images.

skip_video_for_run_all = True  # @param {type: 'boolean'}

if skip_video_for_run_all == False:
    # import subprocess in case this cell is run without the above cells
    import subprocess
    from base64 import b64encode

    latest_run = batchNum

    folder = batch_name  # @param
    run = latest_run  # @param
    final_frame = "final_frame"

    init_frame = (
        1  # @param {type:"number"} This is the frame where the video will start
    )
    last_frame = final_frame  # @param {type:"number"} You can change i to the number of the last frame you want to generate. It will raise an error if that number of frames does not exist.
    fps = 12  # @param {type:"number"}
    view_video_in_cell = False  # @param {type: 'boolean'}

    frames = []
    # tqdm.write('Generating video...')

    if last_frame == "final_frame":
        last_frame = len(glob(batchFolder + f"/{folder}({run})_*.png"))
        print(f"Total frames: {last_frame}")

    image_path = f"{outDirPath}/{folder}/{folder}({run})_%04d.png"
    filepath = f"{outDirPath}/{folder}/{folder}({run}).mp4"

    cmd = [
        "ffmpeg",
        "-y",
        "-vcodec",
        "png",
        "-r",
        str(fps),
        "-start_number",
        str(init_frame),
        "-i",
        image_path,
        "-frames:v",
        str(last_frame + 1),
        "-c:v",
        "libx264",
        "-vf",
        f"fps={fps}",
        "-pix_fmt",
        "yuv420p",
        "-crf",
        "17",
        "-preset",
        "very ",
        filepath,
    ]

    process = subprocess.Popen(
        cmd, cwd=f"{batchFolder}", stdout=subprocess.PIPE, stderr=subprocess.PIPE
    )
    stdout, stderr = process.communicate()
    if process.returncode != 0:
        print(stderr)
        raise RuntimeError(stderr)
    else:
        print("The video is ready")

    if view_video_in_cell:
        mp4 = open(filepath, "rb").read()
        data_url = "data:video/mp4;base64," + b64encode(mp4).decode()
        display.HTML(
            """
      <video width=400 controls>
            <source src="%s" type="video/mp4">
      </video>
      """
            % data_url
        )