Spaces:
Running
Running
File size: 8,978 Bytes
27d1e65 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 |
import PIL
import torch
import numpy as np
from PIL import Image
from tqdm import tqdm
import torch.nn.functional as F
import torchvision.transforms as T
from diffusers import LMSDiscreteScheduler, DiffusionPipeline
# configurations
torch_device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
height, width = 64, 64
guidance_scale = 8
blue_loss_scale = 200
num_inference_steps = 50
elastic_transformer = T.ElasticTransform(alpha=550.0, sigma=5.0)
pretrained_model_name_or_path = "segmind/tiny-sd"
pipe = DiffusionPipeline.from_pretrained(
pretrained_model_name_or_path,
low_cpu_mem_usage = True
).to(torch_device)
pipe.load_textual_inversion("sd-concepts-library/dreams")
pipe.load_textual_inversion("sd-concepts-library/midjourney-style")
pipe.load_textual_inversion("sd-concepts-library/moebius")
pipe.load_textual_inversion("sd-concepts-library/style-of-marc-allante")
pipe.load_textual_inversion("sd-concepts-library/wlop-style")
concepts_mapping = {
"Dream": '<meeg>', "Midjourney":'<midjourney-style>',
"Marc Allante": '<Marc_Allante>', "Moebius": '<moebius>',
"Wlop": '<wlop-style>'
}
def image_loss(images, method='elastic'):
# elastic loss
if method == 'elastic':
transformed_imgs = elastic_transformer(images)
error = torch.abs(transformed_imgs - images).mean()
# symmetry loss - Flip the image along the width
elif method == "symmetry":
flipped_image = torch.flip(images, [3])
error = F.mse_loss(images, flipped_image)
# saturation loss
elif method == 'saturation':
transformed_imgs = T.functional.adjust_saturation(images,saturation_factor = 10)
error = torch.abs(transformed_imgs - images).mean()
# blue loss
elif method == 'blue':
error = torch.abs(images[:,2] - 0.9).mean() # [:,2] -> all images in batch, only the blue channel
return error
HTML_TEMPLATE = """
<style>
body {
background: linear-gradient(135deg, #f5f7fa, #c3cfe2);
}
#app-header {
text-align: center;
background: rgba(255, 255, 255, 0.8); /* Semi-transparent white */
padding: 20px;
border-radius: 10px;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
position: relative; /* To position the artifacts */
}
#app-header h1 {
color: #4CAF50;
font-size: 2em;
margin-bottom: 10px;
}
.concept {
position: relative;
transition: transform 0.3s;
}
.concept:hover {
transform: scale(1.1);
}
.concept img {
width: 100px;
border-radius: 10px;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
}
.concept-description {
position: absolute;
bottom: -30px;
left: 50%;
transform: translateX(-50%);
background-color: #4CAF50;
color: white;
padding: 5px 10px;
border-radius: 5px;
opacity: 0;
transition: opacity 0.3s;
}
.concept:hover .concept-description {
opacity: 1;
}
/* Artifacts */
.artifact {
position: absolute;
background: rgba(76, 175, 80, 0.1); /* Semi-transparent green */
border-radius: 50%; /* Make it circular */
}
.artifact.large {
width: 300px;
height: 300px;
top: -50px;
left: -150px;
}
.artifact.medium {
width: 200px;
height: 200px;
bottom: -50px;
right: -100px;
}
.artifact.small {
width: 100px;
height: 100px;
top: 50%;
left: 50%;
transform: translate(-50%, -50%);
}
</style>
<div id="app-header">
<!-- Artifacts -->
<div class="artifact large"></div>
<div class="artifact medium"></div>
<div class="artifact small"></div>
<!-- Content -->
<h1>Art Generator</h1>
<p>Generate new art in five different styles by providing a prompt.</p>
<div style="display: flex; justify-content: center; gap: 20px; margin-top: 20px;">
<div class="concept">
<img src="https://github.com/Delve-ERAV1/S20/assets/11761529/30ac92f8-fc62-4aab-9221-043865c6fe7c" alt="Midjourney">
<div class="concept-description">Midjourney Style</div>
</div>
<div class="concept">
<img src="https://github.com/Delve-ERAV1/S20/assets/11761529/54c9a61e-df9f-4054-835b-ec2c6ba5916c" alt="Dreams">
<div class="concept-description">Dreams Style</div>
</div>
<div class="concept">
<img src="https://github.com/Delve-ERAV1/S20/assets/11761529/2f37e402-15d1-4a74-ba85-bb1566da930e" alt="Moebius">
<div class="concept-description">Moebius Style</div>
</div>
<div class="concept">
<img src="https://github.com/Delve-ERAV1/S20/assets/11761529/f838e767-ac20-4996-b5be-65c61b365ce0" alt="Allante">
<div class="concept-description">Hong Kong born artist inspired by western and eastern influences</div>
</div>
<div class="concept">
<img src="https://github.com/Delve-ERAV1/S20/assets/11761529/9958140a-1b62-4972-83ca-85b023e3863f" alt="Wlop">
<div class="concept-description">WLOP (Born 1987) is known for Digital Art (NFTs)</div>
</div>
</div>
</div>
"""
def get_examples():
examples = [
['A powerful man in dreadlocks', 'Dream', 'Symmetry', 45],
['World Peace', 'Marc Allante', 'Saturation', 147],
['Storm trooper in the desert, dramatic lighting, high-detail', 'Moebius', 'Elastic', 28],
['Delicious Italian pizza on a table, a window in the background overlooking a city skyline', 'Wlop', 'Blue', 50],
]
return(examples)
def latents_to_pil(latents):
# bath of latents -> list of images
latents = (1 / 0.18215) * latents
with torch.no_grad():
image = pipe.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1) # 0 to 1
image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
image = (image * 255).round().astype("uint8")
return Image.fromarray(image[0])
def generate_art(prompt, concept, method, seed):
prompt = f"{prompt} in the style of {concepts_mapping[concept]}"
img_no_loss = latents_to_pil(generate_image(prompt, method, seed))
img_loss = latents_to_pil(generate_image(prompt, method, seed, loss_apply=True))
return([img_no_loss, img_loss])
def generate_image(prompt, method, seed, loss_apply=False):
generator = torch.manual_seed(seed)
batch_size = 1
method = method.lower()
# scheduler
scheduler = LMSDiscreteScheduler(beta_start = 0.00085, beta_end = 0.012, beta_schedule = "scaled_linear", num_train_timesteps = 1000)
scheduler.set_timesteps(50)
scheduler.timesteps = scheduler.timesteps.to(torch.float32)
# text embeddings of the prompt
text_input = pipe.tokenizer([prompt], padding='max_length', max_length = pipe.tokenizer.model_max_length, truncation= True, return_tensors="pt")
input_ids = text_input.input_ids.to(torch_device)
with torch.no_grad():
text_embeddings = pipe.text_encoder(text_input.input_ids.to(torch_device))[0]
max_length = text_input.input_ids.shape[-1]
uncond_input = pipe.tokenizer(
[""] * 1, padding="max_length", max_length= max_length, return_tensors="pt"
)
with torch.no_grad():
uncond_embeddings = pipe.text_encoder(uncond_input.input_ids.to(torch_device))[0]
text_embeddings = torch.cat([uncond_embeddings,text_embeddings])
# random latent
latents = torch.randn(
(batch_size, pipe.unet.config.in_channels, height// 8, width //8),
generator = generator,
)
latents = latents.to(torch_device)
latents = latents * scheduler.init_noise_sigma
for i, t in tqdm(enumerate(scheduler.timesteps), total = len(scheduler.timesteps)):
latent_model_input = torch.cat([latents] * 2)
sigma = scheduler.sigmas[i]
latent_model_input = scheduler.scale_model_input(latent_model_input, t)
with torch.no_grad():
noise_pred = pipe.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
if loss_apply and i%5 == 0:
latents = latents.detach().requires_grad_()
latents_x0 = latents - sigma * noise_pred
# use vae to decode the image
denoised_images = pipe.vae.decode((1/ 0.18215) * latents_x0).sample / 2 + 0.5 # range(0,1)
loss = image_loss(denoised_images, method) * blue_loss_scale
cond_grad = torch.autograd.grad(loss, latents)[0]
latents = latents.detach() - cond_grad * sigma**2
latents = scheduler.step(noise_pred,t, latents).prev_sample
return latents |