text: # %%
  # !! {"metadata": {
  # !!   "id": "view-in-github",
  # !!   "colab_type": "text"
  # !! }}
  """
  <a href="https://colab.research.google.com/github/alembics/disco-diffusion/blob/main/Disco_Diffusion.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
  """
  
  # %%
  # !! {"metadata": {
  # !!    "id": "TitleTop"
  # !! }}
  """
  # Disco Diffusion v5.2 - Now with VR Mode
  
  In case of confusion, Disco is the name of this notebook edit. The diffusion model in use is Katherine Crowson's fine-tuned 512x512 model
  
  For issues, join the [Disco Diffusion Discord](https://discord.gg/msEZBy4HxA) or message us on twitter at [@somnai_dreams](https://twitter.com/somnai_dreams) or [@gandamu](https://twitter.com/gandamu_ml)
  """
  
  # %%
  # !! {"metadata": {
  # !!   "id": "CreditsChTop"
  # !! }}
  """
  ### Credits & Changelog ⬇️
  """
  
  # %%
  # !! {"metadata": {
  # !!   "id": "Credits"
  # !! }}
  """
  #### Credits
  
  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.
  
  --
  
  Disco:
  
  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.
  
  3D animation implementation added by Adam Letts (https://twitter.com/gandamu_ml) in collaboration with Somnai. Creation of disco.py and ongoing maintenance.
  
  Turbo feature by Chris Allen (https://twitter.com/zippy731)
  
  Improvements to ability to run on local systems, Windows support, and dependency installation by HostsServer (https://twitter.com/HostsServer)
  
  VR Mode by Tom Mason (https://twitter.com/nin_artificial)
  
  """
  
  # %%
  # !! {"metadata": {
  # !!   "id": "LicenseTop"
  # !! }}
  """
  #### License
  """
  
  # %%
  # !! {"metadata": {
  # !!  "id": "License"
  # !!  }}
  """
  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.
  
  --
  
  MIT License
  
  Copyright (c) 2019 Intel ISL (Intel Intelligent Systems Lab)
  
  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.
  
  --
  
  Licensed under the MIT License
  
  Copyright (c) 2021 Maxwell Ingham
  
  Copyright (c) 2022 Adam Letts 
  
  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.
  """
  
  # %%
  # !! {"metadata": {
  # !!   "id": "ChangelogTop"
  # !! }}
  """
  #### Changelog
  """
  
  # %%
  # !! {"metadata": {
  # !!   "cellView": "form",
  # !!    "id": "Changelog"
  # !! }}
  #@title <- View Changelog
  skip_for_run_all = True #@param {type: 'boolean'}
  
  if skip_for_run_all == False:
    print(
        '''
    v1 Update: Oct 29th 2021 - Somnai
  
        QoL improvements added by Somnai (@somnai_dreams), including user friendly UI, settings+prompt saving and improved google drive folder organization.
  
    v1.1 Update: Nov 13th 2021 - Somnai
  
        Now includes sizing options, intermediate saves and fixed image prompts and perlin inits. unexposed batch option since it doesn't work
  
    v2 Update: Nov 22nd 2021 - Somnai
  
        Initial addition of Katherine Crowson's Secondary Model Method (https://colab.research.google.com/drive/1mpkrhOjoyzPeSWy2r7T8EYRaU7amYOOi#scrollTo=X5gODNAMEUCR)
  
        Noticed settings were saving with the wrong name so corrected it. Let me know if you preferred the old scheme.
  
    v3 Update: Dec 24th 2021 - Somnai
  
        Implemented Dango's advanced cutout method
  
        Added SLIP models, thanks to NeuralDivergent
  
        Fixed issue with NaNs resulting in black images, with massive help and testing from @Softology
  
        Perlin now changes properly within batches (not sure where this perlin_regen code came from originally, but thank you)
  
    v4 Update: Jan 2021 - Somnai
  
        Implemented Diffusion Zooming
  
        Added Chigozie keyframing
  
        Made a bunch of edits to processes
    
    v4.1 Update: Jan  14th 2021 - Somnai
  
        Added video input mode
  
        Added license that somehow went missing
  
        Added improved prompt keyframing, fixed image_prompts and multiple prompts
  
        Improved UI
  
        Significant under the hood cleanup and improvement
  
        Refined defaults for each mode
  
        Added latent-diffusion SuperRes for sharpening
  
        Added resume run mode
  
    v4.9 Update: Feb 5th 2022 - gandamu / Adam Letts
  
        Added 3D
  
        Added brightness corrections to prevent animation from steadily going dark over time
  
    v4.91 Update: Feb 19th 2022 - gandamu / Adam Letts
  
        Cleaned up 3D implementation and made associated args accessible via Colab UI elements
  
    v4.92 Update: Feb 20th 2022 - gandamu / Adam Letts
  
        Separated transform code
  
    v5.01 Update: Mar 10th 2022 - gandamu / Adam Letts
  
        IPython magic commands replaced by Python code
  
    v5.1 Update: Mar 30th 2022 - zippy / Chris Allen and gandamu / Adam Letts
  
        Integrated Turbo+Smooth features from Disco Diffusion Turbo -- just the implementation, without its defaults.
  
        Implemented resume of turbo animations in such a way that it's now possible to resume from different batch folders and batch numbers.
  
        3D rotation parameter units are now degrees (rather than radians)
  
        Corrected name collision in sampling_mode (now diffusion_sampling_mode for plms/ddim, and sampling_mode for 3D transform sampling)
  
        Added video_init_seed_continuity option to make init video animations more continuous
  
    v5.1 Update: Apr 4th 2022 - MSFTserver aka HostsServer
  
        Removed pytorch3d from needing to be compiled with a lite version specifically made for Disco Diffusion
  
        Remove Super Resolution
  
        Remove SLIP Models
  
        Update for crossplatform support
  
    v5.2 Update: Apr 10th 2022 - nin_artificial / Tom Mason
  
        VR Mode
  
        '''
    )
  
  
  # %%
  # !! {"metadata": {
  # !!   "id": "TutorialTop"
  # !! }}
  """
  # Tutorial
  """
  
  # %%
  # !! {"metadata": {
  # !!  "id": "DiffusionSet"
  # !! }}
  """
  **Diffusion settings (Defaults are heavily outdated)**
  ---
  Disco Diffusion is complex, and continually evolving with new features.  The most current documentation on on Disco Diffusion settings can be found in the unofficial guidebook:
  
  [Zippy's Disco Diffusion Cheatsheet](https://docs.google.com/document/d/1l8s7uS2dGqjztYSjPpzlmXLjl5PM3IGkRWI3IiCuK7g/edit)
  
  We also encourage users to join the [Disco Diffusion User Discord](https://discord.gg/XGZrFFCRfN) to learn from the active user community.
  
  This section below is outdated as of v2
  
  Setting | Description | Default
  --- | --- | ---
  **Your vision:**
  `text_prompts` | A description of what you'd like the machine to generate. Think of it like writing the caption below your image on a website. | N/A
  `image_prompts` | Think of these images more as a description of their contents. | N/A
  **Image quality:**
  `clip_guidance_scale`  | Controls how much the image should look like the prompt. | 1000
  `tv_scale` | Controls the smoothness of the final output. | 150
  `range_scale` | Controls how far out of range RGB values are allowed to be. | 150
  `sat_scale` | Controls how much saturation is allowed. From nshepperd's JAX notebook. | 0
  `cutn` | Controls how many crops to take from the image. | 16
  `cutn_batches` | Accumulate CLIP gradient from multiple batches of cuts. | 2
  **Init settings:**
  `init_image` | URL or local path | None
  `init_scale` | This enhances the effect of the init image, a good value is 1000 | 0
  `skip_steps` | Controls the starting point along the diffusion timesteps | 0
  `perlin_init` | Option to start with random perlin noise | False
  `perlin_mode` | ('gray', 'color') | 'mixed'
  **Advanced:**
  `skip_augs` | Controls whether to skip torchvision augmentations | False
  `randomize_class` | Controls whether the imagenet class is randomly changed each iteration | True
  `clip_denoised` | Determines whether CLIP discriminates a noisy or denoised image | False
  `clamp_grad` | Experimental: Using adaptive clip grad in the cond_fn | True
  `seed`  | Choose a random seed and print it at end of run for reproduction | random_seed
  `fuzzy_prompt` | Controls whether to add multiple noisy prompts to the prompt losses | False
  `rand_mag` | Controls the magnitude of the random noise | 0.1
  `eta` | DDIM hyperparameter | 0.5
  
  ..
  
  **Model settings**
  ---
  
  Setting | Description | Default
  --- | --- | ---
  **Diffusion:**
  `timestep_respacing` | Modify this value to decrease the number of timesteps. | ddim100
  `diffusion_steps` || 1000
  **Diffusion:**
  `clip_models` | Models of CLIP to load. Typically the more, the better but they all come at a hefty VRAM cost. | ViT-B/32, ViT-B/16, RN50x4
  """
  
  # %%
  # !! {"metadata": {
  # !!  "id": "SetupTop"
  # !! }}
  """
  # 1. Set Up
  """
  
  # %%
  # !! {"metadata": {
  # !!   "cellView": "form",
  # !!    "id": "CheckGPU"
  # !! }}
  #@title 1.1 Check GPU Status
  import subprocess
  simple_nvidia_smi_display = False#@param {type:"boolean"}
  if simple_nvidia_smi_display:
    #!nvidia-smi
    nvidiasmi_output = subprocess.run(['nvidia-smi', '-L'], stdout=subprocess.PIPE).stdout.decode('utf-8')
    print(nvidiasmi_output)
  else:
    #!nvidia-smi -i 0 -e 0
    nvidiasmi_output = subprocess.run(['nvidia-smi'], stdout=subprocess.PIPE).stdout.decode('utf-8')
    print(nvidiasmi_output)
    nvidiasmi_ecc_note = subprocess.run(['nvidia-smi', '-i', '0', '-e', '0'], stdout=subprocess.PIPE).stdout.decode('utf-8')
    print(nvidiasmi_ecc_note)
  
  # %%
  # !! {"metadata": {
  # !!    "cellView": "form",
  # !!    "id": "PrepFolders"
  # !! }}
  #@title 1.2 Prepare Folders
  import subprocess, os, sys, ipykernel
  
  def gitclone(url):
    res = subprocess.run(['git', 'clone', url], stdout=subprocess.PIPE).stdout.decode('utf-8')
    print(res)
  
  def pipi(modulestr):
    res = subprocess.run(['pip', 'install', modulestr], stdout=subprocess.PIPE).stdout.decode('utf-8')
    print(res)
  
  def pipie(modulestr):
    res = subprocess.run(['git', 'install', '-e', modulestr], stdout=subprocess.PIPE).stdout.decode('utf-8')
    print(res)
  
  def wget(url, outputdir):
    res = subprocess.run(['wget', url, '-P', f'{outputdir}'], stdout=subprocess.PIPE).stdout.decode('utf-8')
    print(res)
  
  try:
      from google.colab import drive
      print("Google Colab detected. Using Google Drive.")
      is_colab = True
      #@markdown If you connect your Google Drive, you can save the final image of each run on your drive.
      google_drive = True #@param {type:"boolean"}
      #@markdown Click here if you'd like to save the diffusion model checkpoint file to (and/or load from) your Google Drive:
      save_models_to_google_drive = True #@param {type:"boolean"}
  except:
      is_colab = False
      google_drive = False
      save_models_to_google_drive = False
      print("Google Colab not detected.")
  
  if is_colab:
      if google_drive is True:
          drive.mount('/content/drive')
          root_path = '/content/drive/MyDrive/AI/Disco_Diffusion'
      else:
          root_path = '/content'
  else:
      root_path = os.getcwd()
  
  import os
  def createPath(filepath):
      os.makedirs(filepath, exist_ok=True)
  
  initDirPath = f'{root_path}/init_images'
  createPath(initDirPath)
  outDirPath = f'{root_path}/images_out'
  createPath(outDirPath)
  
  if is_colab:
      if google_drive and not save_models_to_google_drive or not google_drive:
          model_path = '/content/models'
          createPath(model_path)
      if google_drive and save_models_to_google_drive:
          model_path = f'{root_path}/models'
          createPath(model_path)
  else:
      model_path = f'{root_path}/models'
      createPath(model_path)
  
  # libraries = f'{root_path}/libraries'
  # createPath(libraries)
  
  # %%
  # !! {"metadata": {
  # !!    "cellView": "form",
  # !!    "id": "InstallDeps"
  # !! }}
  #@title ### 1.3 Install and import dependencies
  
  import pathlib, shutil, os, sys
  
  if not is_colab:
    # If running locally, there's a good chance your env will need this in order to not crash upon np.matmul() or similar operations.
    os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'
  
  PROJECT_DIR = os.path.abspath(os.getcwd())
  USE_ADABINS = True
  
  if is_colab:
    if google_drive is not True:
      root_path = f'/content'
      model_path = '/content/models' 
  else:
    root_path = os.getcwd()
    model_path = f'{root_path}/models'
  
  model_256_downloaded = False
  model_512_downloaded = False
  model_secondary_downloaded = False
  
  multipip_res = subprocess.run(['pip', 'install', 'lpips', 'datetime', 'timm', 'ftfy', 'einops', 'pytorch-lightning', 'omegaconf'], stdout=subprocess.PIPE).stdout.decode('utf-8')
  print(multipip_res)
  
  if is_colab:
    subprocess.run(['apt', 'install', 'imagemagick'], stdout=subprocess.PIPE).stdout.decode('utf-8')
  
  try:
    from CLIP import clip
  except:
    if not os.path.exists("CLIP"):
      gitclone("https://github.com/openai/CLIP")
    sys.path.append(f'{PROJECT_DIR}/CLIP')
  
  try:
    from guided_diffusion.script_util import create_model_and_diffusion
  except:
    if not os.path.exists("guided-diffusion"):
      gitclone("https://github.com/crowsonkb/guided-diffusion")
    sys.path.append(f'{PROJECT_DIR}/guided-diffusion')
  
  try:
    from resize_right import resize
  except:
    if not os.path.exists("ResizeRight"):
      gitclone("https://github.com/assafshocher/ResizeRight.git")
    sys.path.append(f'{PROJECT_DIR}/ResizeRight')
  
  try:
    import py3d_tools
  except:
    if not os.path.exists('pytorch3d-lite'):
      gitclone("https://github.com/MSFTserver/pytorch3d-lite.git")
    sys.path.append(f'{PROJECT_DIR}/pytorch3d-lite')
  
  try:
    from midas.dpt_depth import DPTDepthModel
  except:
    if not os.path.exists('MiDaS'):
      gitclone("https://github.com/isl-org/MiDaS.git")
    if not os.path.exists('MiDaS/midas_utils.py'):
      shutil.move('MiDaS/utils.py', 'MiDaS/midas_utils.py')
    if not os.path.exists(f'{model_path}/dpt_large-midas-2f21e586.pt'):
      wget("https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt", model_path)
    sys.path.append(f'{PROJECT_DIR}/MiDaS')
  
  try:
    sys.path.append(PROJECT_DIR)
    import disco_xform_utils as dxf
  except:
    if not os.path.exists("disco-diffusion"):
      gitclone("https://github.com/alembics/disco-diffusion.git")
    if os.path.exists('disco_xform_utils.py') is not True:
      shutil.move('disco-diffusion/disco_xform_utils.py', 'disco_xform_utils.py')
    sys.path.append(PROJECT_DIR)
  
  import torch
  from dataclasses import dataclass
  from functools import partial
  import cv2
  import pandas as pd
  import gc
  import io
  import math
  import timm
  from IPython import display
  import lpips
  from PIL import Image, ImageOps
  import requests
  from glob import glob
  import json
  from types import SimpleNamespace
  from torch import nn
  from torch.nn import functional as F
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from tqdm.notebook import tqdm
  from CLIP import clip
  from resize_right import resize
  from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
  from datetime import datetime
  import numpy as np
  import matplotlib.pyplot as plt
  import random
  from ipywidgets import Output
  import hashlib
  from functools import partial
  if is_colab:
    os.chdir('/content')
    from google.colab import files
  else:
    os.chdir(f'{PROJECT_DIR}')
  from IPython.display import Image as ipyimg
  from numpy import asarray
  from einops import rearrange, repeat
  import torch, torchvision
  import time
  from omegaconf import OmegaConf
  import warnings
  warnings.filterwarnings("ignore", category=UserWarning)
  
  # AdaBins stuff
  if USE_ADABINS:
    try:
      from infer import InferenceHelper
    except:
      if os.path.exists("AdaBins") is not True:
        gitclone("https://github.com/shariqfarooq123/AdaBins.git")
      if not os.path.exists(f'{PROJECT_DIR}/pretrained/AdaBins_nyu.pt'):
        createPath(f'{PROJECT_DIR}/pretrained')
        wget("https://cloudflare-ipfs.com/ipfs/Qmd2mMnDLWePKmgfS8m6ntAg4nhV5VkUyAydYBp8cWWeB7/AdaBins_nyu.pt", f'{PROJECT_DIR}/pretrained')
      sys.path.append(f'{PROJECT_DIR}/AdaBins')
    from infer import InferenceHelper
    MAX_ADABINS_AREA = 500000
  
  import torch
  DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
  print('Using device:', DEVICE)
  device = DEVICE # At least one of the modules expects this name..
  
  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
  
  # %%
  # !! {"metadata": {
  # !!  "cellView": "form",
  # !!    "id": "DefMidasFns"
  # !! }}
  #@title ### 1.4 Define Midas functions
  
  from midas.dpt_depth import DPTDepthModel
  from midas.midas_net import MidasNet
  from midas.midas_net_custom import MidasNet_small
  from midas.transforms import Resize, NormalizeImage, PrepareForNet
  
  # Initialize MiDaS depth model.
  # It remains resident in VRAM and likely takes around 2GB VRAM.
  # You could instead initialize it for each frame (and free it after each frame) to save VRAM.. but initializing it is slow.
  default_models = {
      "midas_v21_small": f"{model_path}/midas_v21_small-70d6b9c8.pt",
      "midas_v21": f"{model_path}/midas_v21-f6b98070.pt",
      "dpt_large": f"{model_path}/dpt_large-midas-2f21e586.pt",
      "dpt_hybrid": f"{model_path}/dpt_hybrid-midas-501f0c75.pt",
      "dpt_hybrid_nyu": f"{model_path}/dpt_hybrid_nyu-2ce69ec7.pt",}
  
  
  def init_midas_depth_model(midas_model_type="dpt_large", optimize=True):
      midas_model = None
      net_w = None
      net_h = None
      resize_mode = None
      normalization = None
  
      print(f"Initializing MiDaS '{midas_model_type}' depth model...")
      # load network
      midas_model_path = default_models[midas_model_type]
  
      if midas_model_type == "dpt_large": # DPT-Large
          midas_model = DPTDepthModel(
              path=midas_model_path,
              backbone="vitl16_384",
              non_negative=True,
          )
          net_w, net_h = 384, 384
          resize_mode = "minimal"
          normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
      elif midas_model_type == "dpt_hybrid": #DPT-Hybrid
          midas_model = DPTDepthModel(
              path=midas_model_path,
              backbone="vitb_rn50_384",
              non_negative=True,
          )
          net_w, net_h = 384, 384
          resize_mode="minimal"
          normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
      elif midas_model_type == "dpt_hybrid_nyu": #DPT-Hybrid-NYU
          midas_model = DPTDepthModel(
              path=midas_model_path,
              backbone="vitb_rn50_384",
              non_negative=True,
          )
          net_w, net_h = 384, 384
          resize_mode="minimal"
          normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
      elif midas_model_type == "midas_v21":
          midas_model = MidasNet(midas_model_path, non_negative=True)
          net_w, net_h = 384, 384
          resize_mode="upper_bound"
          normalization = NormalizeImage(
              mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
          )
      elif midas_model_type == "midas_v21_small":
          midas_model = MidasNet_small(midas_model_path, features=64, backbone="efficientnet_lite3", exportable=True, non_negative=True, blocks={'expand': True})
          net_w, net_h = 256, 256
          resize_mode="upper_bound"
          normalization = NormalizeImage(
              mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
          )
      else:
          print(f"midas_model_type '{midas_model_type}' not implemented")
          assert False
  
      midas_transform = T.Compose(
          [
              Resize(
                  net_w,
                  net_h,
                  resize_target=None,
                  keep_aspect_ratio=True,
                  ensure_multiple_of=32,
                  resize_method=resize_mode,
                  image_interpolation_method=cv2.INTER_CUBIC,
              ),
              normalization,
              PrepareForNet(),
          ]
      )
  
      midas_model.eval()
      
      if optimize==True:
          if DEVICE == torch.device("cuda"):
              midas_model = midas_model.to(memory_format=torch.channels_last)  
              midas_model = midas_model.half()
  
      midas_model.to(DEVICE)
  
      print(f"MiDaS '{midas_model_type}' depth model initialized.")
      return midas_model, midas_transform, net_w, net_h, resize_mode, normalization
  
  # %%
  # !! {"metadata": {
  # !!    "cellView": "form",
  # !!    "id": "DefFns"
  # !! }}
  #@title 1.5 Define necessary functions
  
  # https://gist.github.com/adefossez/0646dbe9ed4005480a2407c62aac8869
  
  import py3d_tools as p3dT
  import disco_xform_utils as dxf
  
  def interp(t):
      return 3 * t**2 - 2 * t ** 3
  
  def perlin(width, height, scale=10, device=None):
      gx, gy = torch.randn(2, width + 1, height + 1, 1, 1, device=device)
      xs = torch.linspace(0, 1, scale + 1)[:-1, None].to(device)
      ys = torch.linspace(0, 1, scale + 1)[None, :-1].to(device)
      wx = 1 - interp(xs)
      wy = 1 - interp(ys)
      dots = 0
      dots += wx * wy * (gx[:-1, :-1] * xs + gy[:-1, :-1] * ys)
      dots += (1 - wx) * wy * (-gx[1:, :-1] * (1 - xs) + gy[1:, :-1] * ys)
      dots += wx * (1 - wy) * (gx[:-1, 1:] * xs - gy[:-1, 1:] * (1 - ys))
      dots += (1 - wx) * (1 - wy) * (-gx[1:, 1:] * (1 - xs) - gy[1:, 1:] * (1 - ys))
      return dots.permute(0, 2, 1, 3).contiguous().view(width * scale, height * scale)
  
  def perlin_ms(octaves, width, height, grayscale, device=device):
      out_array = [0.5] if grayscale else [0.5, 0.5, 0.5]
      # out_array = [0.0] if grayscale else [0.0, 0.0, 0.0]
      for i in range(1 if grayscale else 3):
          scale = 2 ** len(octaves)
          oct_width = width
          oct_height = height
          for oct in octaves:
              p = perlin(oct_width, oct_height, scale, device)
              out_array[i] += p * oct
              scale //= 2
              oct_width *= 2
              oct_height *= 2
      return torch.cat(out_array)
  
  def create_perlin_noise(octaves=[1, 1, 1, 1], width=2, height=2, grayscale=True):
      out = perlin_ms(octaves, width, height, grayscale)
      if grayscale:
          out = TF.resize(size=(side_y, side_x), img=out.unsqueeze(0))
          out = TF.to_pil_image(out.clamp(0, 1)).convert('RGB')
      else:
          out = out.reshape(-1, 3, out.shape[0]//3, out.shape[1])
          out = TF.resize(size=(side_y, side_x), img=out)
          out = TF.to_pil_image(out.clamp(0, 1).squeeze())
  
      out = ImageOps.autocontrast(out)
      return out
  
  def regen_perlin():
      if perlin_mode == 'color':
          init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)
          init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, False)
      elif perlin_mode == 'gray':
          init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, True)
          init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)
      else:
          init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)
          init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)
  
      init = TF.to_tensor(init).add(TF.to_tensor(init2)).div(2).to(device).unsqueeze(0).mul(2).sub(1)
      del init2
      return init.expand(batch_size, -1, -1, -1)
  
  def fetch(url_or_path):
      if str(url_or_path).startswith('http://') or str(url_or_path).startswith('https://'):
          r = requests.get(url_or_path)
          r.raise_for_status()
          fd = io.BytesIO()
          fd.write(r.content)
          fd.seek(0)
          return fd
      return open(url_or_path, 'rb')
  
  def read_image_workaround(path):
      """OpenCV reads images as BGR, Pillow saves them as RGB. Work around
      this incompatibility to avoid colour inversions."""
      im_tmp = cv2.imread(path)
      return cv2.cvtColor(im_tmp, cv2.COLOR_BGR2RGB)
  
  def parse_prompt(prompt):
      if prompt.startswith('http://') or prompt.startswith('https://'):
          vals = prompt.rsplit(':', 2)
          vals = [vals[0] + ':' + vals[1], *vals[2:]]
      else:
          vals = prompt.rsplit(':', 1)
      vals = vals + ['', '1'][len(vals):]
      return vals[0], float(vals[1])
  
  def sinc(x):
      return torch.where(x != 0, torch.sin(math.pi * x) / (math.pi * x), x.new_ones([]))
  
  def lanczos(x, a):
      cond = torch.logical_and(-a < x, x < a)
      out = torch.where(cond, sinc(x) * sinc(x/a), x.new_zeros([]))
      return out / out.sum()
  
  def ramp(ratio, width):
      n = math.ceil(width / ratio + 1)
      out = torch.empty([n])
      cur = 0
      for i in range(out.shape[0]):
          out[i] = cur
          cur += ratio
      return torch.cat([-out[1:].flip([0]), out])[1:-1]
  
  def resample(input, size, align_corners=True):
      n, c, h, w = input.shape
      dh, dw = size
  
      input = input.reshape([n * c, 1, h, w])
  
      if dh < h:
          kernel_h = lanczos(ramp(dh / h, 2), 2).to(input.device, input.dtype)
          pad_h = (kernel_h.shape[0] - 1) // 2
          input = F.pad(input, (0, 0, pad_h, pad_h), 'reflect')
          input = F.conv2d(input, kernel_h[None, None, :, None])
  
      if dw < w:
          kernel_w = lanczos(ramp(dw / w, 2), 2).to(input.device, input.dtype)
          pad_w = (kernel_w.shape[0] - 1) // 2
          input = F.pad(input, (pad_w, pad_w, 0, 0), 'reflect')
          input = F.conv2d(input, kernel_w[None, None, None, :])
  
      input = input.reshape([n, c, h, w])
      return F.interpolate(input, size, mode='bicubic', align_corners=align_corners)
  
  class MakeCutouts(nn.Module):
      def __init__(self, cut_size, cutn, skip_augs=False):
          super().__init__()
          self.cut_size = cut_size
          self.cutn = cutn
          self.skip_augs = skip_augs
          self.augs = T.Compose([
              T.RandomHorizontalFlip(p=0.5),
              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
              T.RandomAffine(degrees=15, translate=(0.1, 0.1)),
              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
              T.RandomPerspective(distortion_scale=0.4, p=0.7),
              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
              T.RandomGrayscale(p=0.15),
              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
              # T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
          ])
  
      def forward(self, input):
          input = T.Pad(input.shape[2]//4, fill=0)(input)
          sideY, sideX = input.shape[2:4]
          max_size = min(sideX, sideY)
  
          cutouts = []
          for ch in range(self.cutn):
              if ch > self.cutn - self.cutn//4:
                  cutout = input.clone()
              else:
                  size = int(max_size * torch.zeros(1,).normal_(mean=.8, std=.3).clip(float(self.cut_size/max_size), 1.))
                  offsetx = torch.randint(0, abs(sideX - size + 1), ())
                  offsety = torch.randint(0, abs(sideY - size + 1), ())
                  cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
  
              if not self.skip_augs:
                  cutout = self.augs(cutout)
              cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))
              del cutout
  
          cutouts = torch.cat(cutouts, dim=0)
          return cutouts
  
  cutout_debug = False
  padargs = {}
  
  class MakeCutoutsDango(nn.Module):
      def __init__(self, cut_size,
                   Overview=4, 
                   InnerCrop = 0, IC_Size_Pow=0.5, IC_Grey_P = 0.2
                   ):
          super().__init__()
          self.cut_size = cut_size
          self.Overview = Overview
          self.InnerCrop = InnerCrop
          self.IC_Size_Pow = IC_Size_Pow
          self.IC_Grey_P = IC_Grey_P
          if args.animation_mode == 'None':
            self.augs = T.Compose([
                T.RandomHorizontalFlip(p=0.5),
                T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
                T.RandomAffine(degrees=10, translate=(0.05, 0.05),  interpolation = T.InterpolationMode.BILINEAR),
                T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
                T.RandomGrayscale(p=0.1),
                T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
                T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
            ])
          elif args.animation_mode == 'Video Input':
            self.augs = T.Compose([
                T.RandomHorizontalFlip(p=0.5),
                T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
                T.RandomAffine(degrees=15, translate=(0.1, 0.1)),
                T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
                T.RandomPerspective(distortion_scale=0.4, p=0.7),
                T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
                T.RandomGrayscale(p=0.15),
                T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
                # T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
            ])
          elif  args.animation_mode == '2D' or args.animation_mode == '3D':
            self.augs = T.Compose([
                T.RandomHorizontalFlip(p=0.4),
                T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
                T.RandomAffine(degrees=10, translate=(0.05, 0.05),  interpolation = T.InterpolationMode.BILINEAR),
                T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
                T.RandomGrayscale(p=0.1),
                T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
                T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.3),
            ])
            
  
      def forward(self, input):
          cutouts = []
          gray = T.Grayscale(3)
          sideY, sideX = input.shape[2:4]
          max_size = min(sideX, sideY)
          min_size = min(sideX, sideY, self.cut_size)
          l_size = max(sideX, sideY)
          output_shape = [1,3,self.cut_size,self.cut_size] 
          output_shape_2 = [1,3,self.cut_size+2,self.cut_size+2]
          pad_input = F.pad(input,((sideY-max_size)//2,(sideY-max_size)//2,(sideX-max_size)//2,(sideX-max_size)//2), **padargs)
          cutout = resize(pad_input, out_shape=output_shape)
  
          if self.Overview>0:
              if self.Overview<=4:
                  if self.Overview>=1:
                      cutouts.append(cutout)
                  if self.Overview>=2:
                      cutouts.append(gray(cutout))
                  if self.Overview>=3:
                      cutouts.append(TF.hflip(cutout))
                  if self.Overview==4:
                      cutouts.append(gray(TF.hflip(cutout)))
              else:
                  cutout = resize(pad_input, out_shape=output_shape)
                  for _ in range(self.Overview):
                      cutouts.append(cutout)
  
              if cutout_debug:
                  if is_colab:
                      TF.to_pil_image(cutouts[0].clamp(0, 1).squeeze(0)).save("/content/cutout_overview0.jpg",quality=99)
                  else:
                      TF.to_pil_image(cutouts[0].clamp(0, 1).squeeze(0)).save("cutout_overview0.jpg",quality=99)
  
                                
          if self.InnerCrop >0:
              for i in range(self.InnerCrop):
                  size = int(torch.rand([])**self.IC_Size_Pow * (max_size - min_size) + min_size)
                  offsetx = torch.randint(0, sideX - size + 1, ())
                  offsety = torch.randint(0, sideY - size + 1, ())
                  cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
                  if i <= int(self.IC_Grey_P * self.InnerCrop):
                      cutout = gray(cutout)
                  cutout = resize(cutout, out_shape=output_shape)
                  cutouts.append(cutout)
              if cutout_debug:
                  if is_colab:
                      TF.to_pil_image(cutouts[-1].clamp(0, 1).squeeze(0)).save("/content/cutout_InnerCrop.jpg",quality=99)
                  else:
                      TF.to_pil_image(cutouts[-1].clamp(0, 1).squeeze(0)).save("cutout_InnerCrop.jpg",quality=99)
          cutouts = torch.cat(cutouts)
          if skip_augs is not True: cutouts=self.augs(cutouts)
          return cutouts
  
  def spherical_dist_loss(x, y):
      x = F.normalize(x, dim=-1)
      y = F.normalize(y, dim=-1)
      return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)     
  
  def tv_loss(input):
      """L2 total variation loss, as in Mahendran et al."""
      input = F.pad(input, (0, 1, 0, 1), 'replicate')
      x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]
      y_diff = input[..., 1:, :-1] - input[..., :-1, :-1]
      return (x_diff**2 + y_diff**2).mean([1, 2, 3])
  
  
  def range_loss(input):
      return (input - input.clamp(-1, 1)).pow(2).mean([1, 2, 3])
  
  stop_on_next_loop = False  # Make sure GPU memory doesn't get corrupted from cancelling the run mid-way through, allow a full frame to complete
  TRANSLATION_SCALE = 1.0/200.0
  
  def do_3d_step(img_filepath, frame_num, midas_model, midas_transform):
    if args.key_frames:
      translation_x = args.translation_x_series[frame_num]
      translation_y = args.translation_y_series[frame_num]
      translation_z = args.translation_z_series[frame_num]
      rotation_3d_x = args.rotation_3d_x_series[frame_num]
      rotation_3d_y = args.rotation_3d_y_series[frame_num]
      rotation_3d_z = args.rotation_3d_z_series[frame_num]
      print(
          f'translation_x: {translation_x}',
          f'translation_y: {translation_y}',
          f'translation_z: {translation_z}',
          f'rotation_3d_x: {rotation_3d_x}',
          f'rotation_3d_y: {rotation_3d_y}',
          f'rotation_3d_z: {rotation_3d_z}',
      )
  
    translate_xyz = [-translation_x*TRANSLATION_SCALE, translation_y*TRANSLATION_SCALE, -translation_z*TRANSLATION_SCALE]
    rotate_xyz_degrees = [rotation_3d_x, rotation_3d_y, rotation_3d_z]
    print('translation:',translate_xyz)
    print('rotation:',rotate_xyz_degrees)
    rotate_xyz = [math.radians(rotate_xyz_degrees[0]), math.radians(rotate_xyz_degrees[1]), math.radians(rotate_xyz_degrees[2])]
    rot_mat = p3dT.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), "XYZ").unsqueeze(0)
    print("rot_mat: " + str(rot_mat))
    next_step_pil = dxf.transform_image_3d(img_filepath, midas_model, midas_transform, DEVICE,
                                            rot_mat, translate_xyz, args.near_plane, args.far_plane,
                                            args.fov, padding_mode=args.padding_mode,
                                            sampling_mode=args.sampling_mode, midas_weight=args.midas_weight)
    return next_step_pil
  
  def do_run():
    seed = args.seed
    print(range(args.start_frame, args.max_frames))
  
    if (args.animation_mode == "3D") and (args.midas_weight > 0.0):
        midas_model, midas_transform, midas_net_w, midas_net_h, midas_resize_mode, midas_normalization = init_midas_depth_model(args.midas_depth_model)
    for frame_num in range(args.start_frame, args.max_frames):
        if stop_on_next_loop:
          break
        
        display.clear_output(wait=True)
  
        # Print Frame progress if animation mode is on
        if args.animation_mode != "None":
          batchBar = tqdm(range(args.max_frames), desc ="Frames")
          batchBar.n = frame_num
          batchBar.refresh()
  
        
        # Inits if not video frames
        if args.animation_mode != "Video Input":
          if args.init_image in ['','none', 'None', 'NONE']:
            init_image = None
          else:
            init_image = args.init_image
          init_scale = args.init_scale
          skip_steps = args.skip_steps
  
        if args.animation_mode == "2D":
          if args.key_frames:
            angle = args.angle_series[frame_num]
            zoom = args.zoom_series[frame_num]
            translation_x = args.translation_x_series[frame_num]
            translation_y = args.translation_y_series[frame_num]
            print(
                f'angle: {angle}',
                f'zoom: {zoom}',
                f'translation_x: {translation_x}',
                f'translation_y: {translation_y}',
            )
          
          if frame_num > 0:
            seed += 1
            if resume_run and frame_num == start_frame:
              img_0 = cv2.imread(batchFolder+f"/{batch_name}({batchNum})_{start_frame-1:04}.png")
            else:
              img_0 = cv2.imread('prevFrame.png')
            center = (1*img_0.shape[1]//2, 1*img_0.shape[0]//2)
            trans_mat = np.float32(
                [[1, 0, translation_x],
                [0, 1, translation_y]]
            )
            rot_mat = cv2.getRotationMatrix2D( center, angle, zoom )
            trans_mat = np.vstack([trans_mat, [0,0,1]])
            rot_mat = np.vstack([rot_mat, [0,0,1]])
            transformation_matrix = np.matmul(rot_mat, trans_mat)
            img_0 = cv2.warpPerspective(
                img_0,
                transformation_matrix,
                (img_0.shape[1], img_0.shape[0]),
                borderMode=cv2.BORDER_WRAP
            )
  
            cv2.imwrite('prevFrameScaled.png', img_0)
            init_image = 'prevFrameScaled.png'
            init_scale = args.frames_scale
            skip_steps = args.calc_frames_skip_steps
  
        if args.animation_mode == "3D":
          if frame_num > 0:
            seed += 1    
            if resume_run and frame_num == start_frame:
              img_filepath = batchFolder+f"/{batch_name}({batchNum})_{start_frame-1:04}.png"
              if turbo_mode and frame_num > turbo_preroll:
                shutil.copyfile(img_filepath, 'oldFrameScaled.png')
            else:
              img_filepath = '/content/prevFrame.png' if is_colab else 'prevFrame.png'
  
            next_step_pil = do_3d_step(img_filepath, frame_num, midas_model, midas_transform)
            next_step_pil.save('prevFrameScaled.png')
  
            ### Turbo mode - skip some diffusions, use 3d morph for clarity and to save time
            if turbo_mode:
              if frame_num == turbo_preroll: #start tracking oldframe
                next_step_pil.save('oldFrameScaled.png')#stash for later blending          
              elif frame_num > turbo_preroll:
                #set up 2 warped image sequences, old & new, to blend toward new diff image
                old_frame = do_3d_step('oldFrameScaled.png', frame_num, midas_model, midas_transform)
                old_frame.save('oldFrameScaled.png')
                if frame_num % int(turbo_steps) != 0: 
                  print('turbo skip this frame: skipping clip diffusion steps')
                  filename = f'{args.batch_name}({args.batchNum})_{frame_num:04}.png'
                  blend_factor = ((frame_num % int(turbo_steps))+1)/int(turbo_steps)
                  print('turbo skip this frame: skipping clip diffusion steps and saving blended frame')
                  newWarpedImg = cv2.imread('prevFrameScaled.png')#this is already updated..
                  oldWarpedImg = cv2.imread('oldFrameScaled.png')
                  blendedImage = cv2.addWeighted(newWarpedImg, blend_factor, oldWarpedImg,1-blend_factor, 0.0)
                  cv2.imwrite(f'{batchFolder}/{filename}',blendedImage)
                  next_step_pil.save(f'{img_filepath}') # save it also as prev_frame to feed next iteration
                  if vr_mode:
                    generate_eye_views(TRANSLATION_SCALE,batchFolder,filename,frame_num,midas_model, midas_transform)
                  continue
                else:
                  #if not a skip frame, will run diffusion and need to blend.
                  oldWarpedImg = cv2.imread('prevFrameScaled.png')
                  cv2.imwrite(f'oldFrameScaled.png',oldWarpedImg)#swap in for blending later 
                  print('clip/diff this frame - generate clip diff image')
  
            init_image = 'prevFrameScaled.png'
            init_scale = args.frames_scale
            skip_steps = args.calc_frames_skip_steps
  
        if  args.animation_mode == "Video Input":
          if not video_init_seed_continuity:
            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 {frame_num} Prompt: {frame_prompt}')
  
        model_stats = []
        for clip_model in clip_models:
              cutn = 16
              model_stat = {"clip_model":None,"target_embeds":[],"make_cutouts":None,"weights":[]}
              model_stat["clip_model"] = clip_model
              
              
              for prompt in frame_prompt:
                  txt, weight = parse_prompt(prompt)
                  txt = clip_model.encode_text(clip.tokenize(prompt).to(device)).float()
                  
                  if args.fuzzy_prompt:
                      for i in range(25):
                          model_stat["target_embeds"].append((txt + torch.randn(txt.shape).cuda() * args.rand_mag).clamp(0,1))
                          model_stat["weights"].append(weight)
                  else:
                      model_stat["target_embeds"].append(txt)
                      model_stat["weights"].append(weight)
          
              if image_prompt:
                model_stat["make_cutouts"] = MakeCutouts(clip_model.visual.input_resolution, cutn, skip_augs=skip_augs) 
                for prompt in image_prompt:
                    path, weight = parse_prompt(prompt)
                    img = Image.open(fetch(path)).convert('RGB')
                    img = TF.resize(img, min(side_x, side_y, *img.size), T.InterpolationMode.LANCZOS)
                    batch = model_stat["make_cutouts"](TF.to_tensor(img).to(device).unsqueeze(0).mul(2).sub(1))
                    embed = clip_model.encode_image(normalize(batch)).float()
                    if fuzzy_prompt:
                        for i in range(25):
                            model_stat["target_embeds"].append((embed + torch.randn(embed.shape).cuda() * rand_mag).clamp(0,1))
                            weights.extend([weight / cutn] * cutn)
                    else:
                        model_stat["target_embeds"].append(embed)
                        model_stat["weights"].extend([weight / cutn] * cutn)
          
              model_stat["target_embeds"] = torch.cat(model_stat["target_embeds"])
              model_stat["weights"] = torch.tensor(model_stat["weights"], device=device)
              if model_stat["weights"].sum().abs() < 1e-3:
                  raise RuntimeError('The weights must not sum to 0.')
              model_stat["weights"] /= model_stat["weights"].sum().abs()
              model_stats.append(model_stat)
    
        init = None
        if init_image is not None:
            init = Image.open(fetch(init_image)).convert('RGB')
            init = init.resize((args.side_x, args.side_y), Image.LANCZOS)
            init = TF.to_tensor(init).to(device).unsqueeze(0).mul(2).sub(1)
        
        if args.perlin_init:
            if args.perlin_mode == 'color':
                init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)
                init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, False)
            elif args.perlin_mode == 'gray':
              init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, True)
              init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)
            else:
              init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)
              init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)
            # init = TF.to_tensor(init).add(TF.to_tensor(init2)).div(2).to(device)
            init = TF.to_tensor(init).add(TF.to_tensor(init2)).div(2).to(device).unsqueeze(0).mul(2).sub(1)
            del init2
    
        cur_t = None
    
        def cond_fn(x, t, y=None):
            with torch.enable_grad():
                x_is_NaN = False
                x = x.detach().requires_grad_()
                n = x.shape[0]
                if use_secondary_model is True:
                  alpha = torch.tensor(diffusion.sqrt_alphas_cumprod[cur_t], device=device, dtype=torch.float32)
                  sigma = torch.tensor(diffusion.sqrt_one_minus_alphas_cumprod[cur_t], device=device, dtype=torch.float32)
                  cosine_t = alpha_sigma_to_t(alpha, sigma)
                  out = secondary_model(x, cosine_t[None].repeat([n])).pred
                  fac = diffusion.sqrt_one_minus_alphas_cumprod[cur_t]
                  x_in = out * fac + x * (1 - fac)
                  x_in_grad = torch.zeros_like(x_in)
                else:
                  my_t = torch.ones([n], device=device, dtype=torch.long) * cur_t
                  out = diffusion.p_mean_variance(model, x, my_t, clip_denoised=False, model_kwargs={'y': y})
                  fac = diffusion.sqrt_one_minus_alphas_cumprod[cur_t]
                  x_in = out['pred_xstart'] * fac + x * (1 - fac)
                  x_in_grad = torch.zeros_like(x_in)
                for model_stat in model_stats:
                  for i in range(args.cutn_batches):
                      t_int = int(t.item())+1 #errors on last step without +1, need to find source
                      #when using SLIP Base model the dimensions need to be hard coded to avoid AttributeError: 'VisionTransformer' object has no attribute 'input_resolution'
                      try:
                          input_resolution=model_stat["clip_model"].visual.input_resolution
                      except:
                          input_resolution=224
  
                      cuts = MakeCutoutsDango(input_resolution,
                              Overview= args.cut_overview[1000-t_int], 
                              InnerCrop = args.cut_innercut[1000-t_int], IC_Size_Pow=args.cut_ic_pow, 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 args.diffusion_sampling_mode == 'ddim':
            sample_fn = diffusion.ddim_sample_loop_progressive
        else:
            sample_fn = diffusion.plms_sample_loop_progressive
  
  
        image_display = Output()
        for i in range(args.n_batches):
            if args.animation_mode == 'None':
              display.clear_output(wait=True)
              batchBar = tqdm(range(args.n_batches), desc ="Batches")
              batchBar.n = i
              batchBar.refresh()
            print('')
            display.display(image_display)
            gc.collect()
            torch.cuda.empty_cache()
            cur_t = diffusion.num_timesteps - skip_steps - 1
            total_steps = cur_t
  
            if perlin_init:
                init = regen_perlin()
  
            if args.diffusion_sampling_mode == 'ddim':
                samples = sample_fn(
                    model,
                    (batch_size, 3, args.side_y, args.side_x),
                    clip_denoised=clip_denoised,
                    model_kwargs={},
                    cond_fn=cond_fn,
                    progress=True,
                    skip_timesteps=skip_steps,
                    init_image=init,
                    randomize_class=randomize_class,
                    eta=eta,
                )
            else:
                samples = sample_fn(
                    model,
                    (batch_size, 3, args.side_y, args.side_x),
                    clip_denoised=clip_denoised,
                    model_kwargs={},
                    cond_fn=cond_fn,
                    progress=True,
                    skip_timesteps=skip_steps,
                    init_image=init,
                    randomize_class=randomize_class,
                    order=2,
                )
            
            
            # with run_display:
            # display.clear_output(wait=True)
            for j, sample in enumerate(samples):    
              cur_t -= 1
              intermediateStep = False
              if args.steps_per_checkpoint is not None:
                  if j % steps_per_checkpoint == 0 and j > 0:
                    intermediateStep = True
              elif j in args.intermediate_saves:
                intermediateStep = True
              with image_display:
                if j % args.display_rate == 0 or cur_t == -1 or intermediateStep == True:
                    for k, image in enumerate(sample['pred_xstart']):
                        # tqdm.write(f'Batch {i}, step {j}, output {k}:')
                        current_time = datetime.now().strftime('%y%m%d-%H%M%S_%f')
                        percent = math.ceil(j/total_steps*100)
                        if args.n_batches > 0:
                          #if intermediates are saved to the subfolder, don't append a step or percentage to the name
                          if cur_t == -1 and args.intermediates_in_subfolder is True:
                            save_num = f'{frame_num:04}' if animation_mode != "None" else i
                            filename = f'{args.batch_name}({args.batchNum})_{save_num}.png'
                          else:
                            #If we're working with percentages, append it
                            if args.steps_per_checkpoint is not None:
                              filename = f'{args.batch_name}({args.batchNum})_{i:04}-{percent:02}%.png'
                            # Or else, iIf we're working with specific steps, append those
                            else:
                              filename = f'{args.batch_name}({args.batchNum})_{i:04}-{j:03}.png'
                        image = TF.to_pil_image(image.add(1).div(2).clamp(0, 1))
                        if j % args.display_rate == 0 or cur_t == -1:
                          image.save('progress.png')
                          display.clear_output(wait=True)
                          display.display(display.Image('progress.png'))
                        if args.steps_per_checkpoint is not None:
                          if j % args.steps_per_checkpoint == 0 and j > 0:
                            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')
                          image.save(f'{batchFolder}/{filename}')
                          if args.animation_mode == "3D":
                            # If turbo, save a blended image
                            if turbo_mode and frame_num > 0:
                              # Mix new image with prevFrameScaled
                              blend_factor = (1)/int(turbo_steps)
                              newFrame = cv2.imread('prevFrame.png') # This is already updated..
                              prev_frame_warped = cv2.imread('prevFrameScaled.png')
                              blendedImage = cv2.addWeighted(newFrame, blend_factor, prev_frame_warped, (1-blend_factor), 0.0)
                              cv2.imwrite(f'{batchFolder}/{filename}',blendedImage)
                            else:
                              image.save(f'{batchFolder}/{filename}')
  
                            if vr_mode:
                              generate_eye_views(TRANSLATION_SCALE, batchFolder, filename, frame_num, midas_model, midas_transform)
  
                          # if frame_num != args.max_frames-1:
                          #   display.clear_output()
            
            plt.plot(np.array(loss_values), 'r')
  
  def generate_eye_views(trans_scale,batchFolder,filename,frame_num,midas_model, midas_transform):
     for i in range(2):
        theta = vr_eye_angle * (math.pi/180)
        ray_origin = math.cos(theta) * vr_ipd / 2 * (-1.0 if i==0 else 1.0)
        ray_rotation = (theta if i==0 else -theta)
        translate_xyz = [-(ray_origin)*trans_scale, 0,0]
        rotate_xyz = [0, (ray_rotation), 0]
        rot_mat = p3dT.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), "XYZ").unsqueeze(0)
        transformed_image = dxf.transform_image_3d(f'{batchFolder}/{filename}', midas_model, midas_transform, DEVICE,
                                                        rot_mat, translate_xyz, args.near_plane, args.far_plane,
                                                        args.fov, padding_mode=args.padding_mode,
                                                        sampling_mode=args.sampling_mode, midas_weight=args.midas_weight,spherical=True)
        eye_file_path = batchFolder+f"/frame_{frame_num:04}" + ('_l' if i==0 else '_r')+'.png'
        transformed_image.save(eye_file_path)
  
  def save_settings():
    setting_list = {
      'text_prompts': text_prompts,
      'image_prompts': image_prompts,
      'clip_guidance_scale': clip_guidance_scale,
      'tv_scale': tv_scale,
      'range_scale': range_scale,
      'sat_scale': sat_scale,
      # 'cutn': cutn,
      'cutn_batches': cutn_batches,
      'max_frames': max_frames,
      'interp_spline': interp_spline,
      # 'rotation_per_frame': rotation_per_frame,
      'init_image': init_image,
      'init_scale': init_scale,
      'skip_steps': skip_steps,
      # 'zoom_per_frame': zoom_per_frame,
      'frames_scale': frames_scale,
      'frames_skip_steps': frames_skip_steps,
      'perlin_init': perlin_init,
      'perlin_mode': perlin_mode,
      'skip_augs': skip_augs,
      'randomize_class': randomize_class,
      'clip_denoised': clip_denoised,
      'clamp_grad': clamp_grad,
      'clamp_max': clamp_max,
      'seed': seed,
      'fuzzy_prompt': fuzzy_prompt,
      'rand_mag': rand_mag,
      'eta': eta,
      'width': width_height[0],
      'height': width_height[1],
      'diffusion_model': diffusion_model,
      'use_secondary_model': use_secondary_model,
      'steps': steps,
      'diffusion_steps': diffusion_steps,
      'diffusion_sampling_mode': diffusion_sampling_mode,
      'ViTB32': ViTB32,
      'ViTB16': ViTB16,
      'ViTL14': ViTL14,
      'RN101': RN101,
      'RN50': RN50,
      'RN50x4': RN50x4,
      'RN50x16': RN50x16,
      'RN50x64': RN50x64,
      'cut_overview': str(cut_overview),
      'cut_innercut': str(cut_innercut),
      'cut_ic_pow': cut_ic_pow,
      'cut_icgray_p': str(cut_icgray_p),
      'key_frames': key_frames,
      'max_frames': max_frames,
      'angle': angle,
      'zoom': zoom,
      'translation_x': translation_x,
      'translation_y': translation_y,
      'translation_z': translation_z,
      'rotation_3d_x': rotation_3d_x,
      'rotation_3d_y': rotation_3d_y,
      'rotation_3d_z': rotation_3d_z,
      'midas_depth_model': midas_depth_model,
      'midas_weight': midas_weight,
      'near_plane': near_plane,
      'far_plane': far_plane,
      'fov': fov,
      'padding_mode': padding_mode,
      'sampling_mode': sampling_mode,
      'video_init_path':video_init_path,
      'extract_nth_frame':extract_nth_frame,
      'video_init_seed_continuity': video_init_seed_continuity,
      'turbo_mode':turbo_mode,
      'turbo_steps':turbo_steps,
      'turbo_preroll':turbo_preroll,
    }
    # print('Settings:', setting_list)
    with open(f"{batchFolder}/{batch_name}({batchNum})_settings.txt", "w+") as f:   #save settings
      json.dump(setting_list, f, ensure_ascii=False, indent=4)
  
  # %%
  # !! {"metadata": {
  # !!    "cellView": "form",
  # !!    "id": "DefSecModel"
  # !! }}
  #@title 1.6 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.):
          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)
  
  
  # %%
  # !! {"metadata": {
  # !!    "id": "DiffClipSetTop"
  # !! }}
  """
  # 2. Diffusion and CLIP model settings
  """
  
  # %%
  # !! {"metadata": {
  # !!   "id": "ModelSettings"
  # !!  }}
  #@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'}
  diffusion_sampling_mode = 'ddim' #@param ['plms','ddim']  
  
  
  use_checkpoint = True #@param {type: 'boolean'}
  ViTB32 = True #@param{type:"boolean"}
  ViTB16 = True #@param{type:"boolean"}
  ViTL14 = False #@param{type:"boolean"}
  RN101 = False #@param{type:"boolean"}
  RN50 = True #@param{type:"boolean"}
  RN50x4 = False #@param{type:"boolean"}
  RN50x16 = False #@param{type:"boolean"}
  RN50x64 = 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 = 'https://v-diffusion.s3.us-west-2.amazonaws.com/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(model_256_link, model_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:  
      wget(model_256_link, model_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(model_512_link, model_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(model_512_link, model_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(model_secondary_link, model_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(model_secondary_link, model_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': 1000, #No need to edit this, it is taken care of later.
          'rescale_timesteps': True,
          'timestep_respacing': 250, #No need to edit this, it is taken care of later.
          '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': 1000, #No need to edit this, it is taken care of later.
          'rescale_timesteps': True,
          'timestep_respacing': 250, #No need to edit this, it is taken care of later.
          '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,
      })
  
  model_default = model_config['image_size']
  
  
  
  if use_secondary_model:
      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 RN50x64 is True: clip_models.append(clip.load('RN50x64', 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)) 
  
  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)
  
  
  # %%
  # !! {"metadata": {
  # !!    "id": "SettingsTop"
  # !! }}
  """
  # 3. Settings
  """
  
  # %%
  # !! {"metadata": {
  # !!    "id": "BasicSettings"
  # !!  }}
  #@markdown ####**Basic Settings:**
  batch_name = 'TimeToDisco' #@param{type: 'string'}
  steps = 250 #@param [25,50,100,150,250,500,1000]{type: 'raw', allow-input: true}
  width_height = [1280, 768]#@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 = 4  #@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 = 10 #@param{type: 'integer'}
  #@markdown *Make sure you set skip_steps to ~50% of your steps if you want to use an init image.*
  
  #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)
  
  
  # %%
  # !! {"metadata": {
  # !!    "id": "AnimSetTop"
  # !! }}
  """
  ### Animation Settings
  """
  
  # %%
  # !! {"metadata": {
  # !!    "id": "AnimSettings"
  # !! }}
  #@markdown ####**Animation Mode:**
  animation_mode = 'None' #@param ['None', '2D', '3D', 'Video Input'] {type:'string'}
  #@markdown *For animation, you probably want to turn `cutn_batches` to 1 to make it quicker.*
  
  
  #@markdown ---
  
  #@markdown ####**Video Input Settings:**
  if is_colab:
      video_init_path = "/content/training.mp4" #@param {type: 'string'}
  else:
      video_init_path = "training.mp4" #@param {type: 'string'}
  extract_nth_frame = 2 #@param {type: 'number'}
  video_init_seed_continuity = True #@param {type: 'boolean'}
  
  if animation_mode == "Video Input":
    if is_colab:
        videoFramesFolder = f'/content/videoFrames'
    else:
        videoFramesFolder = f'videoFrames'
    createPath(videoFramesFolder)
    print(f"Exporting Video Frames (1 every {extract_nth_frame})...")
    try:
      for f in pathlib.Path(f'{videoFramesFolder}').glob('*.jpg'):
        f.unlink()
    except:
      print('')
    vf = f'select=not(mod(n\,{extract_nth_frame}))'
    subprocess.run(['ffmpeg', '-i', f'{video_init_path}', '-vf', f'{vf}', '-vsync', 'vfr', '-q:v', '2', '-loglevel', 'error', '-stats', f'{videoFramesFolder}/%04d.jpg'], stdout=subprocess.PIPE).stdout.decode('utf-8')
    #!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.
  #@markdown All rotations are provided in degrees.
  
  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"}
  translation_z = "0: (10.0)"#@param {type:"string"}
  rotation_3d_x = "0: (0)"#@param {type:"string"}
  rotation_3d_y = "0: (0)"#@param {type:"string"}
  rotation_3d_z = "0: (0)"#@param {type:"string"}
  midas_depth_model = "dpt_large"#@param {type:"string"}
  midas_weight = 0.3#@param {type:"number"}
  near_plane = 200#@param {type:"number"}
  far_plane = 10000#@param {type:"number"}
  fov = 40#@param {type:"number"}
  padding_mode = 'border'#@param {type:"string"}
  sampling_mode = 'bicubic'#@param {type:"string"}
  
  #======= TURBO MODE
  #@markdown ---
  #@markdown ####**Turbo Mode (3D anim only):**
  #@markdown (Starts after frame 10,) skips diffusion steps and just uses depth map to warp images for skipped frames.
  #@markdown Speeds up rendering by 2x-4x, and may improve image coherence between frames. frame_blend_mode smooths abrupt texture changes across 2 frames.
  #@markdown For different settings tuned for Turbo Mode, refer to the original Disco-Turbo Github: https://github.com/zippy731/disco-diffusion-turbo
  
  turbo_mode = False #@param {type:"boolean"}
  turbo_steps = "3" #@param ["2","3","4","5","6"] {type:"string"}
  turbo_preroll = 10 # frames
  
  #insist turbo be used only w 3d anim.
  if turbo_mode and animation_mode != '3D':
    print('=====')
    print('Turbo mode only available with 3D animations. Disabling Turbo.')
    print('=====')
    turbo_mode = False
  
  #@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'}
  
  #======= VR MODE
  #@markdown ---
  #@markdown ####**VR Mode (3D anim only):**
  #@markdown Enables stereo rendering of left/right eye views (supporting Turbo) which use a different (fish-eye) camera projection matrix.   
  #@markdown Note the images you're prompting will work better if they have some inherent wide-angle aspect
  #@markdown The generated images will need to be combined into left/right videos. These can then be stitched into the VR180 format.
  #@markdown Google made the VR180 Creator tool but subsequently stopped supporting it. It's available for download in a few places including https://www.patrickgrunwald.de/vr180-creator-download
  #@markdown The tool is not only good for stitching (videos and photos) but also for adding the correct metadata into existing videos, which is needed for services like YouTube to identify the format correctly.
  #@markdown Watching YouTube VR videos isn't necessarily the easiest depending on your headset. For instance Oculus have a dedicated media studio and store which makes the files easier to access on a Quest https://creator.oculus.com/manage/mediastudio/
  #@markdown 
  #@markdown The command to get ffmpeg to concat your frames for each eye is in the form: `ffmpeg -framerate 15 -i frame_%4d_l.png l.mp4` (repeat for r)
  
  vr_mode = False #@param {type:"boolean"}
  #@markdown `vr_eye_angle` is the y-axis rotation of the eyes towards the center
  vr_eye_angle = 0.5 #@param{type:"number"}
  #@markdown interpupillary distance (between the eyes)
  vr_ipd = 5.0 #@param{type:"number"}
  
  #insist VR be used only w 3d anim.
  if vr_mode and animation_mode != '3D':
    print('=====')
    print('VR mode only available with 3D animations. Disabling VR.')
    print('=====')
    vr_mode = False
  
  
  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))
  
      try:
          translation_z_series = get_inbetweens(parse_key_frames(translation_z))
      except RuntimeError as e:
          print(
              "WARNING: You have selected to use key frames, but you have not "
              "formatted `translation_z` correctly for key frames.\n"
              "Attempting to interpret `translation_z` as "
              f'"0: ({translation_z})"\n'
              "Please read the instructions to find out how to use key frames "
              "correctly.\n"
          )
          translation_z = f"0: ({translation_z})"
          translation_z_series = get_inbetweens(parse_key_frames(translation_z))
  
      try:
          rotation_3d_x_series = get_inbetweens(parse_key_frames(rotation_3d_x))
      except RuntimeError as e:
          print(
              "WARNING: You have selected to use key frames, but you have not "
              "formatted `rotation_3d_x` correctly for key frames.\n"
              "Attempting to interpret `rotation_3d_x` as "
              f'"0: ({rotation_3d_x})"\n'
              "Please read the instructions to find out how to use key frames "
              "correctly.\n"
          )
          rotation_3d_x = f"0: ({rotation_3d_x})"
          rotation_3d_x_series = get_inbetweens(parse_key_frames(rotation_3d_x))
  
      try:
          rotation_3d_y_series = get_inbetweens(parse_key_frames(rotation_3d_y))
      except RuntimeError as e:
          print(
              "WARNING: You have selected to use key frames, but you have not "
              "formatted `rotation_3d_y` correctly for key frames.\n"
              "Attempting to interpret `rotation_3d_y` as "
              f'"0: ({rotation_3d_y})"\n'
              "Please read the instructions to find out how to use key frames "
              "correctly.\n"
          )
          rotation_3d_y = f"0: ({rotation_3d_y})"
          rotation_3d_y_series = get_inbetweens(parse_key_frames(rotation_3d_y))
  
      try:
          rotation_3d_z_series = get_inbetweens(parse_key_frames(rotation_3d_z))
      except RuntimeError as e:
          print(
              "WARNING: You have selected to use key frames, but you have not "
              "formatted `rotation_3d_z` correctly for key frames.\n"
              "Attempting to interpret `rotation_3d_z` as "
              f'"0: ({rotation_3d_z})"\n'
              "Please read the instructions to find out how to use key frames "
              "correctly.\n"
          )
          rotation_3d_z = f"0: ({rotation_3d_z})"
          rotation_3d_z_series = get_inbetweens(parse_key_frames(rotation_3d_z))
  
  else:
      angle = float(angle)
      zoom = float(zoom)
      translation_x = float(translation_x)
      translation_y = float(translation_y)
      translation_z = float(translation_z)
      rotation_3d_x = float(rotation_3d_x)
      rotation_3d_y = float(rotation_3d_y)
      rotation_3d_z = float(rotation_3d_z)
  
  
  # %%
  # !! {"metadata": {
  # !!    "id": "ExtraSetTop"
  # !! }}
  """
  ### Extra Settings
   Partial Saves, Advanced Settings, Cutn Scheduling
  """
  
  # %%
  # !! {"metadata": {
  # !!   "id": "ExtraSettings"
  # !! }}
  #@markdown ####**Saving:**
  
  intermediate_saves = 0#@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 ####**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'}
  
  
  # %%
  # !! {"metadata": {
  # !!    "id": "PromptsTop"
  # !! }}
  """
  ### 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.
  """
  
  # %%
  # !! {"metadata": {
  # !!    "id": "Prompts"
  # !! }}
  text_prompts = {
      0: ["A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "yellow color scheme"],
      100: ["This set of prompts start at frame 100","This prompt has weight five:5"],
  }
  
  image_prompts = {
      # 0:['ImagePromptsWorkButArentVeryGood.png:2',],
  }
  
  
  # %%
  # !! {"metadata": {
  # !!    "id": "DiffuseTop"
  # !! }}
  """
  # 4. Diffuse!
  """
  
  # %%
  # !! {"metadata": {
  # !!    "id": "DoTheRun"
  # !!  }}
  #@title Do the Run!
  #@markdown `n_batches` ignored with animation modes.
  display_rate =  50 #@param{type: 'number'}
  n_batches =  50 #@param{type: 'number'}
  
  #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,
  })
  
  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 ---
  
  
  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.txt"))-1
    else:
      batchNum = int(run_to_resume)
    if resume_from_frame == 'latest':
      start_frame = len(glob(batchFolder+f"/{batch_name}({batchNum})_*.png"))
      if animation_mode != '3D' and turbo_mode == True and start_frame > turbo_preroll and start_frame % int(turbo_steps) != 0:
        start_frame = start_frame - (start_frame % int(turbo_steps))
    else:
      start_frame = int(resume_from_frame)+1
      if animation_mode != '3D' and turbo_mode == True and start_frame > turbo_preroll and start_frame % int(turbo_steps) != 0:
        start_frame = start_frame - (start_frame % int(turbo_steps))
      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+"/*.txt"))
    while os.path.isfile(f"{batchFolder}/{batch_name}({batchNum})_settings.txt") is True or os.path.isfile(f"{batchFolder}/{batch_name}-{batchNum}_settings.txt") 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)
  
  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,
      'diffusion_sampling_mode': diffusion_sampling_mode,
      '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,
      '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,
      'video_init_seed_continuity': video_init_seed_continuity,
      '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,
      'translation_z': translation_z,
      'rotation_3d_x': rotation_3d_x,
      'rotation_3d_y': rotation_3d_y,
      'rotation_3d_z': rotation_3d_z,
      'midas_depth_model': midas_depth_model,
      'midas_weight': midas_weight,
      'near_plane': near_plane,
      'far_plane': far_plane,
      'fov': fov,
      'padding_mode': padding_mode,
      'sampling_mode': sampling_mode,
      'angle_series':angle_series,
      'zoom_series':zoom_series,
      'translation_x_series':translation_x_series,
      'translation_y_series':translation_y_series,
      'translation_z_series':translation_z_series,
      'rotation_3d_x_series':rotation_3d_x_series,
      'rotation_3d_y_series':rotation_3d_y_series,
      'rotation_3d_z_series':rotation_3d_z_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)
  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()
  
  
  # %%
  # !! {"metadata": {
  # !!    "id": "CreateVidTop"
  # !! }}
  """
  # 5. Create the video
  """
  
  # %%
  # !! {"metadata": {
  # !!    "id": "CreateVid"
  # !! }}
  # @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 == True:
    print('Skipping video creation, uncheck skip_video_for_run_all if you want to run it')
  
  else:
    # 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 = True #@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',
        'veryslow',
        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 and saved to the images folder")
  
    # if view_video_in_cell:
    #     mp4 = open(filepath,'rb').read()
    #     data_url = "data:video/mp4;base64," + b64encode(mp4).decode()
    #     display.HTML(f'<video width=400 controls><source src="{data_url}" type="video/mp4"></video>')
    
  