dannokablammo
commented
2 years ago For anyone else running into this same problem, replace the "Installation and loading of libraries and definitions" cell with the code below and it should work again:
# @title Installation and loading of libraries and definitions
# @markdown This cell will take a while because you have to download multiple libraries
print("Downloading CLIP...")
!git clone https://github.com/openai/CLIP &> /dev/null
print("Downloading Python AI libraries...")
!git clone https://github.com/CompVis/taming-transformers &> /dev/null
!pip install ftfy regex tqdm omegaconf pytorch-lightning &> /dev/null
!pip install kornia &> /dev/null
!pip install einops &> /dev/null
print("Installing libraries for handling metadata...")
!pip install stegano &> /dev/null
!apt install exempi &> /dev/null
!pip install python-xmp-toolkit &> /dev/null
!pip install imgtag &> /dev/null
!pip install pillow==7.1.2 &> /dev/null
import argparse
import math
from pathlib import Path
import sys
import os
import cv2
import pandas as pd
import numpy as np
import subprocess
sys.path.append('./taming-transformers')
# Some models include transformers, others need explicit pip install
try:
import transformers
except Exception:
!pip install transformers
import transformers
from IPython import display
from base64 import b64encode
from omegaconf import OmegaConf
from PIL import Image
from taming.models import cond_transformer, vqgan
import torch
from torch import nn, optim
from torch.nn import functional as F
from torchvision import transforms
from torchvision.transforms import functional as TF
from tqdm.notebook import tqdm
from CLIP import clip
import kornia.augmentation as K
import numpy as np
import imageio
from PIL import ImageFile, Image
from imgtag import ImgTag # metadata
from libxmp import * # metadata
import libxmp # metadata
from stegano import lsb
import json
ImageFile.LOAD_TRUNCATED_IMAGES = True
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.view([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.view([n, c, h, w])
return F.interpolate(input, size, mode='bicubic', align_corners=align_corners)
class ReplaceGrad(torch.autograd.Function):
@staticmethod
def forward(ctx, x_forward, x_backward):
ctx.shape = x_backward.shape
return x_forward
@staticmethod
def backward(ctx, grad_in):
return None, grad_in.sum_to_size(ctx.shape)
replace_grad = ReplaceGrad.apply
class ClampWithGrad(torch.autograd.Function):
@staticmethod
def forward(ctx, input, min, max):
ctx.min = min
ctx.max = max
ctx.save_for_backward(input)
return input.clamp(min, max)
@staticmethod
def backward(ctx, grad_in):
input, = ctx.saved_tensors
return grad_in * (grad_in * (input - input.clamp(ctx.min, ctx.max)) >= 0), None, None
clamp_with_grad = ClampWithGrad.apply
def vector_quantize(x, codebook):
d = x.pow(2).sum(dim=-1, keepdim=True) + codebook.pow(2).sum(dim=1) - 2 * x @ codebook.T
indices = d.argmin(-1)
x_q = F.one_hot(indices, codebook.shape[0]).to(d.dtype) @ codebook
return replace_grad(x_q, x)
class Prompt(nn.Module):
def __init__(self, embed, weight=1., stop=float('-inf')):
super().__init__()
self.register_buffer('embed', embed)
self.register_buffer('weight', torch.as_tensor(weight))
self.register_buffer('stop', torch.as_tensor(stop))
def forward(self, input):
input_normed = F.normalize(input.unsqueeze(1), dim=2)
embed_normed = F.normalize(self.embed.unsqueeze(0), dim=2)
dists = input_normed.sub(embed_normed).norm(dim=2).div(2).arcsin().pow(2).mul(2)
dists = dists * self.weight.sign()
return self.weight.abs() * replace_grad(dists, torch.maximum(dists, self.stop)).mean()
def parse_prompt(prompt):
vals = prompt.rsplit(':', 2)
vals = vals + ['', '1', '-inf'][len(vals):]
return vals[0], float(vals[1]), float(vals[2])
class MakeCutouts(nn.Module):
def __init__(self, cut_size, cutn, cut_pow=1.):
super().__init__()
self.cut_size = cut_size
self.cutn = cutn
self.cut_pow = cut_pow
self.augs = nn.Sequential(
K.RandomHorizontalFlip(p=0.5),
# K.RandomSolarize(0.01, 0.01, p=0.7),
K.RandomSharpness(0.3,p=0.4),
K.RandomAffine(degrees=30, translate=0.1, p=0.8, padding_mode='border'),
K.RandomPerspective(0.2,p=0.4),
K.ColorJitter(hue=0.01, saturation=0.01, p=0.7))
self.noise_fac = 0.1
def forward(self, input):
sideY, sideX = input.shape[2:4]
max_size = min(sideX, sideY)
min_size = min(sideX, sideY, self.cut_size)
cutouts = []
for _ in range(self.cutn):
size = int(torch.rand([])**self.cut_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]
cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))
batch = self.augs(torch.cat(cutouts, dim=0))
if self.noise_fac:
facs = batch.new_empty([self.cutn, 1, 1, 1]).uniform_(0, self.noise_fac)
batch = batch + facs * torch.randn_like(batch)
return batch
def load_vqgan_model(config_path, checkpoint_path):
config = OmegaConf.load(config_path)
if config.model.target == 'taming.models.vqgan.VQModel':
model = vqgan.VQModel(**config.model.params)
model.eval().requires_grad_(False)
model.init_from_ckpt(checkpoint_path)
elif config.model.target == 'taming.models.cond_transformer.Net2NetTransformer':
parent_model = cond_transformer.Net2NetTransformer(**config.model.params)
parent_model.eval().requires_grad_(False)
parent_model.init_from_ckpt(checkpoint_path)
model = parent_model.first_stage_model
else:
raise ValueError(f'unknown model type: {config.model.target}')
del model.loss
return model
def resize_image(image, out_size):
ratio = image.size[0] / image.size[1]
area = min(image.size[0] * image.size[1], out_size[0] * out_size[1])
size = round((area * ratio)**0.5), round((area / ratio)**0.5)
return image.resize(size, Image.LANCZOS)
ashwhite
HalcyonForest
chigozienri
This is in regard to: https://colab.research.google.com/github/chigozienri/VQGAN-CLIP-animations/blob/main/VQGAN-CLIP-animations.ipynb
I used to have no problem running this colab (even as recently as last night) but now I'm suddenly getting an error when I run the cell called "Installation and loading of libraries and definitions".
Here's the error that keeps happening:
ImportError Traceback (most recent call last)