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diffuse.py
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#
# ..
#
# **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
# # 1. Set Up
import os
from os import path
import sys
from argparse import ArgumentParser
# read args to determine GPU before importing torch (is also imported in other files)
parser = ArgumentParser()
parser.add_argument("--gpu", default="0", type=str)
parser.add_argument("--text", default="", type=str)
parser.add_argument("--root_path", default="out_diffusion")
parser.add_argument("--setup", default=False, type=bool)
parser.add_argument("--out_name", default="out_image", type=str)
parser.add_argument("--sharpen_preset", default="Off", type=str, choices=['Off', 'Faster', 'Fast', 'Slow', 'Very Slow'])
argparse_args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = argparse_args.gpu
from secondary_diffusion import SecondaryDiffusionImageNet, SecondaryDiffusionImageNet2
from ddim_sampler import DDIMSampler
from augs import MakeCutouts, MakeCutoutsDango
from perlin import create_perlin_noise, regen_perlin
def alpha_sigma_to_t(alpha, sigma):
return torch.atan2(sigma, alpha) * 2 / math.pi
root_path = argparse_args.root_path
initDirPath = f'{root_path}/init_images'
os.makedirs(initDirPath, exist_ok=1)
outDirPath = f'{root_path}/images_out'
os.makedirs(outDirPath, exist_ok=1)
model_path = f'{root_path}/models'
os.makedirs(model_path, exist_ok=1)
#@title ### 2.1 Install and import dependencies
model_256_downloaded = False
model_512_downloaded = False
model_secondary_downloaded = False
if argparse_args.setup:
#!git clone https://github.com/openai/CLIP
# !git clone https://github.com/facebookresearch/SLIP.git
get_ipython().system('git clone https://github.com/crowsonkb/guided-diffusion')
get_ipython().system('git clone https://github.com/assafshocher/ResizeRight.git')
#!pip install -e ./CLIP
get_ipython().system('python3 -m pip install -e ./guided-diffusion')
get_ipython().system('python3 -m pip install lpips datetime timm')
get_ipython().system('apt install imagemagick')
# sys.path.append('./SLIP')
sys.path.append('./ResizeRight')
from functools import partial
import cv2
import pandas as pd
import gc
import io
import math
import timm
from IPython import display
import lpips
from PIL import Image, ImageOps
import requests
from glob import glob
import json
from types import SimpleNamespace
import torch
from torch import nn
from torch.nn import functional as F
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from tqdm.notebook import tqdm
#sys.path.append('./CLIP')
sys.path.append('./guided-diffusion')
import clip
from resize_right import resize
# from models import SLIP_VITB16, SLIP, SLIP_VITL16
from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt
import random
from ipywidgets import Output
import hashlib
if argparse_args.setup:
#SuperRes
get_ipython().system('git clone https://github.com/CompVis/latent-diffusion.git')
get_ipython().system('git clone https://github.com/CompVis/taming-transformers')
get_ipython().system('pip install -e ./taming-transformers')
get_ipython().system('pip install ipywidgets omegaconf>=2.0.0 pytorch-lightning>=1.0.8 torch-fidelity einops wandb')
#SuperRes
import ipywidgets as widgets
import os
sys.path.append(".")
sys.path.append('./taming-transformers')
from taming.models import vqgan # checking correct import from taming
from torchvision.datasets.utils import download_url
if argparse_args.setup:
get_ipython().run_line_magic('cd', "'latent-diffusion'")
sys.path.append("latent-diffusion")
from ldm.util import instantiate_from_config
# from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import ismap
if argparse_args.setup:
get_ipython().run_line_magic('cd', '..')
#from google.colab import files
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)
import torch
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('Using device:', device)
if torch.cuda.get_device_capability(device) == (8,0): ## A100 fix thanks to Emad
print('Disabling CUDNN for A100 gpu', file=sys.stderr)
torch.backends.cudnn.enabled = False
#@title 2.2 Define necessary functions
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 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 spherical_dist_loss(x, y):
x = F.normalize(x, dim=-1)
y = F.normalize(y, dim=-1)
return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
def tv_loss(input):
"""L2 total variation loss, as in Mahendran et al."""
input = F.pad(input, (0, 1, 0, 1), 'replicate')
x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]
y_diff = input[..., 1:, :-1] - input[..., :-1, :-1]
return (x_diff**2 + y_diff**2).mean([1, 2, 3])
def range_loss(input):
return (input - input.clamp(-1, 1)).pow(2).mean([1, 2, 3])
stop_on_next_loop = False # Make sure GPU memory doesn't get corrupted from cancelling the run mid-way through, allow a full frame to complete
def do_run():
seed = args.seed
print(range(args.start_frame, args.max_frames))
for frame_num in range(args.start_frame, args.max_frames):
if stop_on_next_loop:
break
display.clear_output(wait=True)
# Print Frame progress if animation mode is on
if args.animation_mode != "None":
batchBar = tqdm(range(args.max_frames), desc ="Frames")
batchBar.n = frame_num
batchBar.refresh()
# Inits if not video frames
if args.animation_mode != "Video Input":
if args.init_image == '':
init_image = None
else:
init_image = args.init_image
init_scale = args.init_scale
skip_steps = args.skip_steps
if args.animation_mode == "2D":
if args.key_frames:
angle = args.angle_series[frame_num]
zoom = args.zoom_series[frame_num]
translation_x = args.translation_x_series[frame_num]
translation_y = args.translation_y_series[frame_num]
print(
f'angle: {angle}',
f'zoom: {zoom}',
f'translation_x: {translation_x}',
f'translation_y: {translation_y}',
)
if frame_num > 0:
seed = seed + 1
if resume_run and frame_num == start_frame:
img_0 = cv2.imread(batchFolder+f"/{batch_name}({batchNum})_{start_frame-1:04}.png")
else:
img_0 = cv2.imread('prevFrame.png')
center = (1*img_0.shape[1]//2, 1*img_0.shape[0]//2)
trans_mat = np.float32(
[[1, 0, translation_x],
[0, 1, translation_y]]
)
rot_mat = cv2.getRotationMatrix2D( center, angle, zoom )
trans_mat = np.vstack([trans_mat, [0,0,1]])
rot_mat = np.vstack([rot_mat, [0,0,1]])
transformation_matrix = np.matmul(rot_mat, trans_mat)
img_0 = cv2.warpPerspective(
img_0,
transformation_matrix,
(img_0.shape[1], img_0.shape[0]),
borderMode=cv2.BORDER_WRAP
)
cv2.imwrite('prevFrameScaled.png', img_0)
init_image = 'prevFrameScaled.png'
init_scale = args.frames_scale
skip_steps = args.calc_frames_skip_steps
if args.animation_mode == "Video Input":
seed = seed + 1
init_image = f'{videoFramesFolder}/{frame_num+1:04}.jpg'
init_scale = args.frames_scale
skip_steps = args.calc_frames_skip_steps
loss_values = []
if seed is not None:
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
target_embeds, weights = [], []
if args.prompts_series is not None and frame_num >= len(args.prompts_series):
frame_prompt = args.prompts_series[-1]
elif args.prompts_series is not None:
frame_prompt = args.prompts_series[frame_num]
else:
frame_prompt = []
print(args.image_prompts_series)
if args.image_prompts_series is not None and frame_num >= len(args.image_prompts_series):
image_prompt = args.image_prompts_series[-1]
elif args.image_prompts_series is not None:
image_prompt = args.image_prompts_series[frame_num]
else:
image_prompt = []
print(f'Frame Prompt: {frame_prompt}')
model_stats = []
for clip_model in clip_models:
cutn = 16
model_stat = {"clip_model":None,"target_embeds":[],"make_cutouts":None,"weights":[]}
model_stat["clip_model"] = clip_model
for prompt in frame_prompt:
txt, weight = parse_prompt(prompt)
txt = clip_model.encode_text(clip.tokenize(prompt).to(device)).float()
if args.fuzzy_prompt:
for i in range(25):
model_stat["target_embeds"].append((txt + torch.randn(txt.shape).cuda() * args.rand_mag).clamp(0,1))
model_stat["weights"].append(weight)
else:
model_stat["target_embeds"].append(txt)
model_stat["weights"].append(weight)
if image_prompt:
model_stat["make_cutouts"] = MakeCutouts(clip_model.visual.input_resolution, cutn, skip_augs=skip_augs)
for prompt in image_prompt:
path, weight = parse_prompt(prompt)
img = Image.open(fetch(path)).convert('RGB')
img = TF.resize(img, min(side_x, side_y, *img.size), T.InterpolationMode.LANCZOS)
batch = model_stat["make_cutouts"](TF.to_tensor(img).to(device).unsqueeze(0).mul(2).sub(1))
embed = clip_model.encode_image(normalize(batch)).float()
if fuzzy_prompt:
for i in range(25):
model_stat["target_embeds"].append((embed + torch.randn(embed.shape).cuda() * rand_mag).clamp(0,1))
weights.extend([weight / cutn] * cutn)
else:
model_stat["target_embeds"].append(embed)
model_stat["weights"].extend([weight / cutn] * cutn)
model_stat["target_embeds"] = torch.cat(model_stat["target_embeds"])
model_stat["weights"] = torch.tensor(model_stat["weights"], device=device)
if model_stat["weights"].sum().abs() < 1e-3:
raise RuntimeError('The weights must not sum to 0.')
model_stat["weights"] /= model_stat["weights"].sum().abs()
model_stats.append(model_stat)
init = None
if init_image is not None:
init = Image.open(fetch(init_image)).convert('RGB')
init = init.resize((args.side_x, args.side_y), Image.LANCZOS)
init = TF.to_tensor(init).to(device).unsqueeze(0).mul(2).sub(1)
if args.perlin_init:
if args.perlin_mode == 'color':
init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)
init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, False)
elif args.perlin_mode == 'gray':
init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, True)
init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)
else:
init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)
init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)
# init = TF.to_tensor(init).add(TF.to_tensor(init2)).div(2).to(device)
init = TF.to_tensor(init).add(TF.to_tensor(init2)).div(2).to(device).unsqueeze(0).mul(2).sub(1)
del init2
cur_t = None
if init is not None and args.init_scale:
lpips_model = lpips.LPIPS(net='vgg').to(device)
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],
animation_mode=args.animation_mode,
)
clip_in = normalize(cuts(x_in.add(1).div(2)))
image_embeds = model_stat["clip_model"].encode_image(clip_in).float()
dists = spherical_dist_loss(image_embeds.unsqueeze(1), model_stat["target_embeds"].unsqueeze(0))
dists = dists.view([args.cut_overview[1000-t_int]+args.cut_innercut[1000-t_int], n, -1])
losses = dists.mul(model_stat["weights"]).sum(2).mean(0)
loss_values.append(losses.sum().item()) # log loss, probably shouldn't do per cutn_batch
x_in_grad += torch.autograd.grad(losses.sum() * clip_guidance_scale, x_in)[0] / cutn_batches
tv_losses = tv_loss(x_in)
if use_secondary_model is True:
range_losses = range_loss(out)
else:
range_losses = range_loss(out['pred_xstart'])
sat_losses = torch.abs(x_in - x_in.clamp(min=-1,max=1)).mean()
loss = tv_losses.sum() * tv_scale + range_losses.sum() * range_scale + sat_losses.sum() * sat_scale
if init is not None and args.init_scale:
init_losses = lpips_model(x_in, init)
loss = loss + init_losses.sum() * args.init_scale
x_in_grad += torch.autograd.grad(loss, x_in)[0]
if torch.isnan(x_in_grad).any() == False:
grad = -torch.autograd.grad(x_in, x, x_in_grad)[0]
else:
# print("NaN'd")
x_is_NaN = True
grad = torch.zeros_like(x)
if args.clamp_grad and x_is_NaN == False:
magnitude = grad.square().mean().sqrt()
return grad * magnitude.clamp(max=args.clamp_max) / magnitude #min=-0.02, min=-clamp_max,
return grad
if model_config['timestep_respacing'].startswith('ddim'):
sample_fn = diffusion.ddim_sample_loop_progressive
else:
sample_fn = diffusion.p_sample_loop_progressive
image_display = Output()
for i in range(args.n_batches):
if args.animation_mode == 'None':
display.clear_output(wait=True)
batchBar = tqdm(range(args.n_batches), desc ="Batches")
batchBar.n = i
batchBar.refresh()
print('')
display.display(image_display)
gc.collect()
torch.cuda.empty_cache()
cur_t = diffusion.num_timesteps - skip_steps - 1
total_steps = cur_t
if perlin_init:
init = regen_perlin()
if model_config['timestep_respacing'].startswith('ddim'):
samples = sample_fn(
model,
(batch_size, 3, args.side_y, args.side_x),
clip_denoised=clip_denoised,
model_kwargs={},
cond_fn=cond_fn,
progress=True,
skip_timesteps=skip_steps,
init_image=init,
randomize_class=randomize_class,
eta=eta,
)
else:
samples = sample_fn(
model,
(batch_size, 3, args.side_y, args.side_x),
clip_denoised=clip_denoised,
model_kwargs={},
cond_fn=cond_fn,
progress=True,
skip_timesteps=skip_steps,
init_image=init,
randomize_class=randomize_class,
)
# with run_display:
# display.clear_output(wait=True)
imgToSharpen = None
for j, sample in enumerate(samples):
cur_t -= 1
intermediateStep = False
if args.steps_per_checkpoint is not None:
if j % steps_per_checkpoint == 0 and j > 0:
intermediateStep = True
elif j in args.intermediate_saves:
intermediateStep = True
with image_display:
if j % args.display_rate == 0 or cur_t == -1 or intermediateStep == True:
for k, image in enumerate(sample['pred_xstart']):
# tqdm.write(f'Batch {i}, step {j}, output {k}:')
current_time = datetime.now().strftime('%y%m%d-%H%M%S_%f')
percent = math.ceil(j/total_steps*100)
if args.n_batches > 0:
#if intermediates are saved to the subfolder, don't append a step or percentage to the name
if cur_t == -1 and args.intermediates_in_subfolder is True:
save_num = f'{frame_num:04}' if animation_mode != "None" else i
filename = f'{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.jpg', subsampling=0, quality=95)
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.jpg', subsampling=0, quality=95)
if args.sharpen_preset != "Off" and animation_mode == "None":
imgToSharpen = image
if args.keep_unsharp is True:
image.save(f'{unsharpenFolder}/{filename}')
else:
image.save(f'{batchFolder}/{filename}')
# if frame_num != args.max_frames-1:
# display.clear_output()
with image_display:
if args.sharpen_preset != "Off" and animation_mode == "None":
print('Starting Diffusion Sharpening...')
do_superres(imgToSharpen, f'{batchFolder}/{filename}')
display.clear_output()
plt.plot(np.array(loss_values), 'r')
def save_settings():
setting_list = {
'text_prompts': text_prompts,
'image_prompts': image_prompts,
'clip_guidance_scale': clip_guidance_scale,
'tv_scale': tv_scale,
'range_scale': range_scale,
'sat_scale': sat_scale,
# 'cutn': cutn,
'cutn_batches': cutn_batches,
'max_frames': max_frames,
'interp_spline': interp_spline,
# 'rotation_per_frame': rotation_per_frame,
'init_image': init_image,
'init_scale': init_scale,
'skip_steps': skip_steps,
# 'zoom_per_frame': zoom_per_frame,
'frames_scale': frames_scale,
'frames_skip_steps': frames_skip_steps,
'perlin_init': perlin_init,
'perlin_mode': perlin_mode,
'skip_augs': skip_augs,
'randomize_class': randomize_class,
'clip_denoised': clip_denoised,
'clamp_grad': clamp_grad,
'clamp_max': clamp_max,
'seed': seed,
'fuzzy_prompt': fuzzy_prompt,
'rand_mag': rand_mag,
'eta': eta,
'width': width_height[0],
'height': width_height[1],
'diffusion_model': diffusion_model,
'use_secondary_model': use_secondary_model,
'steps': steps,
'diffusion_steps': diffusion_steps,
'ViTB32': ViTB32,
'ViTB16': ViTB16,
'RN101': RN101,
'RN50': RN50,
'RN50x4': RN50x4,
'RN50x16': RN50x16,
'cut_overview': str(cut_overview),
'cut_innercut': str(cut_innercut),
'cut_ic_pow': cut_ic_pow,
'cut_icgray_p': str(cut_icgray_p),
'key_frames': key_frames,
'max_frames': max_frames,
'angle': angle,
'zoom': zoom,
'translation_x': translation_x,
'translation_y': translation_y,
'video_init_path':video_init_path,
'extract_nth_frame':extract_nth_frame,
}
# print('Settings:', setting_list)
with open(f"{batchFolder}/{batch_name}({batchNum})_settings.txt", "w+") as f: #save settings
json.dump(setting_list, f, ensure_ascii=False, indent=4)
#@title 2.4 SuperRes Define
def download_models():
# this is the small bsr light model
url_conf = 'https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1'
url_ckpt = 'https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1'
path_conf = f'{model_path}/superres/project.yaml'
path_ckpt = f'{model_path}/superres/last.ckpt'
download_url(url_conf, path_conf)
download_url(url_ckpt, path_ckpt)
path_conf = path_conf + '/?dl=1' # fix it
path_ckpt = path_ckpt + '/?dl=1' # fix it
return path_conf, path_ckpt
def load_model_from_config(config, ckpt):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
global_step = pl_sd["global_step"]
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
model.cuda()
model.eval()
return {"model": model}, global_step
def get_model(mode):
path_conf, path_ckpt = download_models()
config = OmegaConf.load(path_conf)
model, step = load_model_from_config(config, path_ckpt)
return model
def get_custom_cond(mode):
dest = "data/example_conditioning"
if mode == "superresolution":
uploaded_img = files.upload()
filename = next(iter(uploaded_img))
name, filetype = filename.split(".") # todo assumes just one dot in name !
os.rename(f"{filename}", f"{dest}/{mode}/custom_{name}.{filetype}")
elif mode == "text_conditional":
w = widgets.Text(value='A cake with cream!', disabled=True)
display.display(w)
with open(f"{dest}/{mode}/custom_{w.value[:20]}.txt", 'w') as f:
f.write(w.value)
elif mode == "class_conditional":
w = widgets.IntSlider(min=0, max=1000)
display.display(w)
with open(f"{dest}/{mode}/custom.txt", 'w') as f:
f.write(w.value)
else:
raise NotImplementedError(f"cond not implemented for mode{mode}")
def get_cond_options(mode):
path = "data/example_conditioning"
path = os.path.join(path, mode)
onlyfiles = [f for f in sorted(os.listdir(path))]
return path, onlyfiles
def select_cond_path(mode):
path = "data/example_conditioning" # todo
path = os.path.join(path, mode)
onlyfiles = [f for f in sorted(os.listdir(path))]
selected = widgets.RadioButtons(
options=onlyfiles,
description='Select conditioning:',
disabled=False
)
display.display(selected)
selected_path = os.path.join(path, selected.value)
return selected_path
def get_cond(mode, img):
example = dict()
if mode == "superresolution":
up_f = 4
# visualize_cond_img(selected_path)
c = img
c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]], antialias=True)
c_up = rearrange(c_up, '1 c h w -> 1 h w c')
c = rearrange(c, '1 c h w -> 1 h w c')
c = 2. * c - 1.
c = c.to(torch.device("cuda"))
example["LR_image"] = c
example["image"] = c_up
return example
def visualize_cond_img(path):
display.display(ipyimg(filename=path))
def sr_run(model, img, task, custom_steps, eta, resize_enabled=False, classifier_ckpt=None, global_step=None):
# global stride
example = get_cond(task, img)
save_intermediate_vid = False
n_runs = 1
masked = False
guider = None
ckwargs = None
mode = 'ddim'
ddim_use_x0_pred = False
temperature = 1.
eta = eta
make_progrow = True
custom_shape = None
height, width = example["image"].shape[1:3]
split_input = height >= 128 and width >= 128
if split_input:
ks = 128
stride = 64
vqf = 4 #
model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
"vqf": vqf,
"patch_distributed_vq": True,
"tie_braker": False,
"clip_max_weight": 0.5,
"clip_min_weight": 0.01,
"clip_max_tie_weight": 0.5,
"clip_min_tie_weight": 0.01}
else:
if hasattr(model, "split_input_params"):
delattr(model, "split_input_params")
invert_mask = False
x_T = None
for n in range(n_runs):
if custom_shape is not None:
x_T = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
x_T = repeat(x_T, '1 c h w -> b c h w', b=custom_shape[0])
logs = make_convolutional_sample(example, model,
mode=mode, custom_steps=custom_steps,
eta=eta, swap_mode=False , masked=masked,
invert_mask=invert_mask, quantize_x0=False,
custom_schedule=None, decode_interval=10,
resize_enabled=resize_enabled, custom_shape=custom_shape,
temperature=temperature, noise_dropout=0.,
corrector=guider, corrector_kwargs=ckwargs, x_T=x_T, save_intermediate_vid=save_intermediate_vid,
make_progrow=make_progrow,ddim_use_x0_pred=ddim_use_x0_pred
)
return logs
@torch.no_grad()
def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
mask=None, x0=None, quantize_x0=False, img_callback=None,
temperature=1., noise_dropout=0., score_corrector=None,
corrector_kwargs=None, x_T=None, log_every_t=None
):
ddim = DDIMSampler(model)
bs = shape[0] # dont know where this comes from but wayne
shape = shape[1:] # cut batch dim
# print(f"Sampling with eta = {eta}; steps: {steps}")
samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
mask=mask, x0=x0, temperature=temperature, verbose=False,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs, x_T=x_T)
return samples, intermediates
@torch.no_grad()
def make_convolutional_sample(batch, model, mode="vanilla", custom_steps=None, eta=1.0, swap_mode=False, masked=False,
invert_mask=True, quantize_x0=False, custom_schedule=None, decode_interval=1000,
resize_enabled=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
corrector_kwargs=None, x_T=None, save_intermediate_vid=False, make_progrow=True,ddim_use_x0_pred=False):
log = dict()
z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=not (hasattr(model, 'split_input_params')
and model.cond_stage_key == 'coordinates_bbox'),
return_original_cond=True)
log_every_t = 1 if save_intermediate_vid else None
if custom_shape is not None:
z = torch.randn(custom_shape)
# print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
z0 = None
log["input"] = x
log["reconstruction"] = xrec
if ismap(xc):
log["original_conditioning"] = model.to_rgb(xc)
if hasattr(model, 'cond_stage_key'):
log[model.cond_stage_key] = model.to_rgb(xc)
else:
log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
if model.cond_stage_model:
log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
if model.cond_stage_key =='class_label':
log[model.cond_stage_key] = xc[model.cond_stage_key]
with model.ema_scope("Plotting"):
t0 = time.time()
img_cb = None
sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
eta=eta,
quantize_x0=quantize_x0, img_callback=img_cb, mask=None, x0=z0,
temperature=temperature, noise_dropout=noise_dropout,
score_corrector=corrector, corrector_kwargs=corrector_kwargs,
x_T=x_T, log_every_t=log_every_t)
t1 = time.time()
if ddim_use_x0_pred:
sample = intermediates['pred_x0'][-1]
x_sample = model.decode_first_stage(sample)
try:
x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
log["sample_noquant"] = x_sample_noquant
log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
except:
pass
log["sample"] = x_sample
log["time"] = t1 - t0
return log
sr_diffMode = 'superresolution'
sr_model = get_model('superresolution')
def do_superres(img, filepath):
if args.sharpen_preset == 'Faster':
sr_diffusion_steps = "25"
sr_pre_downsample = '1/2'
if args.sharpen_preset == 'Fast':
sr_diffusion_steps = "100"
sr_pre_downsample = '1/2'
if args.sharpen_preset == 'Slow':
sr_diffusion_steps = "25"
sr_pre_downsample = 'None'
if args.sharpen_preset == 'Very Slow':
sr_diffusion_steps = "100"
sr_pre_downsample = 'None'
sr_post_downsample = 'Original Size'
sr_diffusion_steps = int(sr_diffusion_steps)
sr_eta = 1.0
sr_downsample_method = 'Lanczos'
gc.collect()
torch.cuda.empty_cache()
im_og = img
width_og, height_og = im_og.size
#Downsample Pre
if sr_pre_downsample == '1/2':
downsample_rate = 2
elif sr_pre_downsample == '1/4':
downsample_rate = 4
else:
downsample_rate = 1
width_downsampled_pre = width_og//downsample_rate
height_downsampled_pre = height_og//downsample_rate
if downsample_rate != 1:
# print(f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
# im_og.save('/content/temp.png')
# filepath = '/content/temp.png'
logs = sr_run(sr_model["model"], im_og, sr_diffMode, sr_diffusion_steps, sr_eta)
sample = logs["sample"]
sample = sample.detach().cpu()
sample = torch.clamp(sample, -1., 1.)
sample = (sample + 1.) / 2. * 255
sample = sample.numpy().astype(np.uint8)
sample = np.transpose(sample, (0, 2, 3, 1))
a = Image.fromarray(sample[0])
#Downsample Post
if sr_post_downsample == '1/2':
downsample_rate = 2
elif sr_post_downsample == '1/4':
downsample_rate = 4
else:
downsample_rate = 1
width, height = a.size
width_downsampled_post = width//downsample_rate
height_downsampled_post = height//downsample_rate
if sr_downsample_method == 'Lanczos':
aliasing = Image.LANCZOS
else:
aliasing = Image.NEAREST
if downsample_rate != 1:
# print(f'Downsampling from [{width}, {height}] to [{width_downsampled_post}, {height_downsampled_post}]')
a = a.resize((width_downsampled_post, height_downsampled_post), aliasing)
elif sr_post_downsample == 'Original Size':
# print(f'Downsampling from [{width}, {height}] to Original Size [{width_og}, {height_og}]')
a = a.resize((width_og, height_og), aliasing)
display.display(a)
a.save(filepath)
return
print(f'Processing finished!')
# # 3. Diffusion and CLIP model settings
#@markdown ####**Models Settings:**
diffusion_model = "512x512_diffusion_uncond_finetune_008100" #@param ["256x256_diffusion_uncond", "512x512_diffusion_uncond_finetune_008100"]
use_secondary_model = True #@param {type: 'boolean'}
timestep_respacing = '50' # param ['25','50','100','150','250','500','1000','ddim25','ddim50', 'ddim75', 'ddim100','ddim150','ddim250','ddim500','ddim1000']
diffusion_steps = 1000 # param {type: 'number'}
use_checkpoint = True #@param {type: 'boolean'}
ViTB32 = True #@param{type:"boolean"}
ViTB16 = True #@param{type:"boolean"}
RN101 = False #@param{type:"boolean"}
RN50 = True #@param{type:"boolean"}
RN50x4 = False #@param{type:"boolean"}
RN50x16 = False #@param{type:"boolean"}
SLIPB16 = False # param{type:"boolean"}
SLIPL16 = False # param{type:"boolean"}
#@markdown If you're having issues with model downloads, check this to compare SHA's:
check_model_SHA = False #@param{type:"boolean"}
model_256_SHA = '983e3de6f95c88c81b2ca7ebb2c217933be1973b1ff058776b970f901584613a'
model_512_SHA = '9c111ab89e214862b76e1fa6a1b3f1d329b1a88281885943d2cdbe357ad57648'
model_secondary_SHA = '983e3de6f95c88c81b2ca7ebb2c217933be1973b1ff058776b970f901584613a'
model_256_link = 'https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt'
model_512_link = 'http://batbot.tv/ai/models/guided-diffusion/512x512_diffusion_uncond_finetune_008100.pt'
model_secondary_link = 'https://v-diffusion.s3.us-west-2.amazonaws.com/secondary_model_imagenet_2.pth'
model_256_path = f'{model_path}/256x256_diffusion_uncond.pt'
model_512_path = f'{model_path}/512x512_diffusion_uncond_finetune_008100.pt'
model_secondary_path = f'{model_path}/secondary_model_imagenet_2.pth'
# Download the diffusion model
if diffusion_model == '256x256_diffusion_uncond':
if os.path.exists(model_256_path) and check_model_SHA:
print('Checking 256 Diffusion File')
with open(model_256_path,"rb") as f:
bytes = f.read()
hash = hashlib.sha256(bytes).hexdigest();
if hash == model_256_SHA:
print('256 Model SHA matches')
model_256_downloaded = True
else:
print("256 Model SHA doesn't match, redownloading...")
if argparse_args.setup:
get_ipython().system('wget --continue {model_256_link} -P {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:
if argparse_args.setup:
get_ipython().system('wget --continue {model_256_link} -P {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: