Spaces:
Runtime error
Runtime error
File size: 6,315 Bytes
4f361bc fcf0449 9a29e97 fcf0449 9a29e97 f9cb70e 9a29e97 fcf0449 4663a72 fcf0449 4663a72 fcf0449 f2ea589 fcf0449 9a29e97 fcf0449 9a29e97 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 |
'''
Code adapted from Stitch it in Time by Tzaban et al.
https://github.com/rotemtzaban/STIT
'''
import numpy as np
import torch
from tqdm import tqdm
from pathlib import Path
import os
import clip
imagenet_templates = [
'a bad photo of a {}.',
'a photo of many {}.',
'a sculpture of a {}.',
'a photo of the hard to see {}.',
'a low resolution photo of the {}.',
'a rendering of a {}.',
'graffiti of a {}.',
'a bad photo of the {}.',
'a cropped photo of the {}.',
'a tattoo of a {}.',
'the embroidered {}.',
'a photo of a hard to see {}.',
'a bright photo of a {}.',
'a photo of a clean {}.',
'a photo of a dirty {}.',
'a dark photo of the {}.',
'a drawing of a {}.',
'a photo of my {}.',
'the plastic {}.',
'a photo of the cool {}.',
'a close-up photo of a {}.',
'a black and white photo of the {}.',
'a painting of the {}.',
'a painting of a {}.',
'a pixelated photo of the {}.',
'a sculpture of the {}.',
'a bright photo of the {}.',
'a cropped photo of a {}.',
'a plastic {}.',
'a photo of the dirty {}.',
'a jpeg corrupted photo of a {}.',
'a blurry photo of the {}.',
'a photo of the {}.',
'a good photo of the {}.',
'a rendering of the {}.',
'a {} in a video game.',
'a photo of one {}.',
'a doodle of a {}.',
'a close-up photo of the {}.',
'a photo of a {}.',
'the origami {}.',
'the {} in a video game.',
'a sketch of a {}.',
'a doodle of the {}.',
'a origami {}.',
'a low resolution photo of a {}.',
'the toy {}.',
'a rendition of the {}.',
'a photo of the clean {}.',
'a photo of a large {}.',
'a rendition of a {}.',
'a photo of a nice {}.',
'a photo of a weird {}.',
'a blurry photo of a {}.',
'a cartoon {}.',
'art of a {}.',
'a sketch of the {}.',
'a embroidered {}.',
'a pixelated photo of a {}.',
'itap of the {}.',
'a jpeg corrupted photo of the {}.',
'a good photo of a {}.',
'a plushie {}.',
'a photo of the nice {}.',
'a photo of the small {}.',
'a photo of the weird {}.',
'the cartoon {}.',
'art of the {}.',
'a drawing of the {}.',
'a photo of the large {}.',
'a black and white photo of a {}.',
'the plushie {}.',
'a dark photo of a {}.',
'itap of a {}.',
'graffiti of the {}.',
'a toy {}.',
'itap of my {}.',
'a photo of a cool {}.',
'a photo of a small {}.',
'a tattoo of the {}.',
]
CONV_CODE_INDICES = [(0, 512), (1024, 1536), (1536, 2048), (2560, 3072), (3072, 3584), (4096, 4608), (4608, 5120), (5632, 6144), (6144, 6656), (7168, 7680), (7680, 7936), (8192, 8448), (8448, 8576), (8704, 8832), (8832, 8896), (8960, 9024), (9024, 9056)]
FFHQ_CODE_INDICES = [(0, 512), (512, 1024), (1024, 1536), (1536, 2048), (2560, 3072), (3072, 3584), (4096, 4608), (4608, 5120), (5632, 6144), (6144, 6656), (7168, 7680), (7680, 7936), (8192, 8448), (8448, 8576), (8704, 8832), (8832, 8896), (8960, 9024), (9024, 9056)] + \
[(2048, 2560), (3584, 4096), (5120, 5632), (6656, 7168), (7936, 8192), (8576, 8704), (8896, 8960), (9056, 9088)]
def zeroshot_classifier(model, classnames, templates, device):
with torch.no_grad():
zeroshot_weights = []
for classname in tqdm(classnames):
texts = [template.format(classname) for template in templates] # format with class
texts = clip.tokenize(texts).to(device) # tokenize
class_embeddings = model.encode_text(texts) # embed with text encoder
class_embeddings /= class_embeddings.norm(dim=-1, keepdim=True)
class_embedding = class_embeddings.mean(dim=0)
class_embedding /= class_embedding.norm()
zeroshot_weights.append(class_embedding)
zeroshot_weights = torch.stack(zeroshot_weights, dim=1).to(device)
return zeroshot_weights
def expand_to_full_dim(partial_tensor):
full_dim_tensor = torch.zeros(size=(1, 9088))
start_idx = 0
for conv_start, conv_end in CONV_CODE_INDICES:
length = conv_end - conv_start
full_dim_tensor[:, conv_start:conv_end] = partial_tensor[start_idx:start_idx + length]
start_idx += length
return full_dim_tensor
def get_direction(neutral_class, target_class, beta, di, clip_model=None):
device = "cuda" if torch.cuda.is_available() else "cpu"
if clip_model is None:
clip_model, _ = clip.load("ViT-B/32", device=device)
class_names = [neutral_class, target_class]
class_weights = zeroshot_classifier(clip_model, class_names, imagenet_templates, device)
dt = class_weights[:, 1] - class_weights[:, 0]
dt = dt / dt.norm()
dt = dt.float()
di = di.float()
relevance = di @ dt
mask = relevance.abs() > beta
direction = relevance * mask
direction_max = direction.abs().max()
if direction_max > 0:
direction = direction / direction_max
else:
raise ValueError(f'Beta value {beta} is too high for mapping from {neutral_class} to {target_class},'
f' try setting it to a lower value')
return direction
def style_tensor_to_style_dict(style_tensor, refernce_generator):
style_layers = refernce_generator.modulation_layers
style_dict = {}
for layer_idx, layer in enumerate(style_layers):
style_dict[layer] = style_tensor[:, FFHQ_CODE_INDICES[layer_idx][0]:FFHQ_CODE_INDICES[layer_idx][1]]
return style_dict
def style_dict_to_style_tensor(style_dict, reference_generator):
style_layers = reference_generator.modulation_layers
style_tensor = torch.zeros(size=(1, 9088))
for layer in style_dict:
layer_idx = style_layers.index(layer)
style_tensor[:, FFHQ_CODE_INDICES[layer_idx][0]:FFHQ_CODE_INDICES[layer_idx][1]] = style_dict[layer]
return style_tensor
def project_code_with_styleclip(source_latent, source_class, target_class, alpha, beta, reference_generator, di, clip_model=None):
edit_direction = get_direction(source_class, target_class, beta, di, clip_model)
edit_full_dim = expand_to_full_dim(edit_direction)
source_s = style_dict_to_style_tensor(source_latent, reference_generator)
return source_s + alpha * edit_full_dim |