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disco.py
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disco.py
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# %%
# !! {"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 v5.7 - Now with MiDaS (3D mode) not being broken
Disco Diffusion - http://discodiffusion.com/ , https://github.com/alembics/disco-diffusion
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_ml](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 nshepperd 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)
Horizontal and Vertical symmetry functionality by nshepperd. Symmetry transformation_steps by huemin (https://twitter.com/huemin_art). Symmetry integration into Disco Diffusion by Dmitrii Tochilkin (https://twitter.com/cut_pow).
Warp and custom model support by Alex Spirin (https://twitter.com/devdef).
Pixel Art Diffusion, Watercolor Diffusion, and Pulp SciFi Diffusion models from KaliYuga (https://twitter.com/KaliYuga_ai). Follow KaliYuga's Twitter for the latest models and for notebooks with specialized settings.
Integration of OpenCLIP models and initiation of integration of KaliYuga models by Palmweaver / Chris Scalf (https://twitter.com/ChrisScalf11)
Integrated portrait_generator_v001 from Felipe3DArtist (https://twitter.com/Felipe3DArtist)
"""
# %%
# !! {"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
Copyright (c) 2022 Alex Spirin
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.
--
flow-related - https://github.com/NVIDIA/flownet2-pytorch/blob/master/LICENSE
--
Copyright 2017 NVIDIA CORPORATION
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
# %%
# !! {"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 2022 - Somnai
Implemented Diffusion Zooming
Added Chigozie keyframing
Made a bunch of edits to processes
v4.1 Update: Jan 14th 2022 - 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
v5.3 Update: Jun 10th 2022 - nshepperd, huemin, cut_pow / Dmitrii Tochilkin
Horizontal and Vertical symmetry
Addition of ViT-L/14@336px model (requires high VRAM)
v5.4 Update: Jun 14th 2022 - devdef / Alex Spirin, Alex's Warp changes integrated into DD main by gandamu / Adam Letts
Warp mode - for smooth/continuous video input results leveraging optical flow estimation and frame blending
Custom models support
v5.5 Update: Jul 11th 2022 - Palmweaver / Chris Scalf, KaliYuga_ai, further DD integration by gandamu / Adam Letts
OpenCLIP models integration
Pixel Art Diffusion, Watercolor Diffusion, and Pulp SciFi Diffusion models
cut_ic_pow scheduling
v5.6 Update: Jul 13th 2022 - Felipe3DArtist integration by gandamu / Adam Letts
portrait_generator_v001 diffusion model integrated
v5.61 Update: Aug 21st 2022 - gandamu / Adam Letts
Correct progress bars issue caused by recent Google Colab environment changes
Adjust 512x512 diffusion model download URI priority due to issues with the previous primary source
v5.7 Update: Dec 31st 2022 - Steffen Moelter (with minor colab-convert integration by gandamu)
Clone MiDaS v3 specifically. This fixes 3D mode. It had been broken since MiDaS v3.1 introduced an incompatibility.
'''
)
# %%
# !! {"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
`use_vertical_symmetry` | Enforce symmetry over x axis of the image on [`tr_st`*`steps` for `tr_st` in `transformation_steps`] steps of the diffusion process | False
`use_horizontal_symmetry` | Enforce symmetry over y axis of the image on [`tr_st`*`steps` for `tr_st` in `transformation_steps`] steps of the diffusion process | False
`transformation_steps` | Steps (expressed in percentages) in which the symmetry is enforced | [0.01]
`video_init_flow_warp` | Flow warp enabled | True
`video_init_flow_blend` | 0 - you get raw input, 1 - you get warped diffused previous frame | 0.999
`video_init_check_consistency` | TBD check forward-backward flow consistency (uncheck unless there are too many warping artifacts) | False
..
**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, target_dir=None, branch_arg=None):
run_args = ['git', 'clone']
if branch_arg:
run_args.extend(['-b', branch_arg])
run_args.append(url)
if target_dir:
run_args.append(target_dir)
res = subprocess.run(run_args, stdout=subprocess.PIPE).stdout.decode('utf-8')
print(res)
def pipi(modulestr):
res = subprocess.run(['pip', 'install', modulestr], stdout=subprocess.PIPE).stdout.decode('utf-8')
print(res)
def pipie(modulestr):
res = subprocess.run(['git', 'install', '-e', modulestr], stdout=subprocess.PIPE).stdout.decode('utf-8')
print(res)
def wget(url, outputdir):
res = subprocess.run(['wget', url, '-P', f'{outputdir}'], stdout=subprocess.PIPE).stdout.decode('utf-8')
print(res)
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"}
print("Downgrading ipywidgets to latest 7.x in order to enable custom widget manager (for tqdm progress bars)")
multipip_res = subprocess.run(['pip', 'install', 'ipywidgets>=7,<8'], stdout=subprocess.PIPE).stdout.decode('utf-8')
print(multipip_res)
from google.colab import output
output.enable_custom_widget_manager()
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, import dependencies and set up runtime devices
import pathlib, shutil, os, sys
# There are some reports that with a T4 or V100 on Colab, downgrading to a previous version of PyTorch may be necessary.
# .. but there are also reports that downgrading breaks them! If you're facing issues, you may want to try uncommenting and running this code.
# nvidiasmi_output = subprocess.run(['nvidia-smi'], stdout=subprocess.PIPE).stdout.decode('utf-8')
# cards_requiring_downgrade = ["Tesla T4", "V100"]
# if is_colab:
# if any(cardstr in nvidiasmi_output for cardstr in cards_requiring_downgrade):
# print("Downgrading pytorch. This can take a couple minutes ...")
# downgrade_pytorch_result = subprocess.run(['pip', 'install', 'torch==1.10.2', 'torchvision==0.11.3', '-q'], stdout=subprocess.PIPE).stdout.decode('utf-8')
# print("pytorch downgraded.")
#@markdown Check this if you want to use CPU
useCPU = False #@param {type:"boolean"}
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 not google_drive:
root_path = f'/content'
model_path = '/content/models'
else:
root_path = os.getcwd()
model_path = f'{root_path}/models'
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:
import open_clip
except:
if not os.path.exists("open_clip/src"):
gitclone("https://github.com/mlfoundations/open_clip.git")
sys.path.append(f'{PROJECT_DIR}/open_clip/src')
import open_clip
try:
from guided_diffusion.script_util import create_model_and_diffusion
except:
if not os.path.exists("guided-diffusion"):
gitclone("https://github.com/kostarion/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", branch_arg="v3")
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 not os.path.exists('disco_xform_utils.py'):
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 not os.path.exists("AdaBins"):
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() and not useCPU) else 'cpu')
print('Using device:', DEVICE)
device = DEVICE # At least one of the modules expects this name..
if not useCPU:
if torch.cuda.get_device_capability(DEVICE) == (8,0): ## A100 fix thanks to Emad
print('Disabling CUDNN for A100 gpu', file=sys.stderr)
torch.backends.cudnn.enabled = False
# %%
# !! {"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),