Spaces:
Runtime error
Runtime error
File size: 9,109 Bytes
91bbc7e |
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 |
Hugging Face's logo
Hugging Face
Search models, datasets, users...
Models
Datasets
Spaces
Docs
Solutions
Pricing
Spaces:
AIZ2H
/
02-Gradio-Art-From-Text-And-Images Copied
like
1
See logs
App
Files and versions
Community
Settings
02-Gradio-Art-From-Text-And-Images
/
app.py
awacke1's picture
awacke1
Create new file
abb46ec
about 19 hours ago
raw
history
blame
edit
delete
Safe
8.72 kB
import os
os.system("git clone --recursive https://github.com/JD-P/cloob-latent-diffusion")
os.system("cd cloob-latent-diffusion;pip install omegaconf pillow pytorch-lightning einops wandb ftfy regex ./CLIP")
import argparse
from functools import partial
from pathlib import Path
import sys
sys.path.append('./cloob-latent-diffusion')
sys.path.append('./cloob-latent-diffusion/cloob-training')
sys.path.append('./cloob-latent-diffusion/latent-diffusion')
sys.path.append('./cloob-latent-diffusion/taming-transformers')
sys.path.append('./cloob-latent-diffusion/v-diffusion-pytorch')
from omegaconf import OmegaConf
from PIL import Image
import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from torchvision.transforms import functional as TF
from tqdm import trange
from CLIP import clip
from cloob_training import model_pt, pretrained
import ldm.models.autoencoder
from diffusion import sampling, utils
import train_latent_diffusion as train
from huggingface_hub import hf_hub_url, cached_download
import random
# Download the model files
checkpoint = cached_download(hf_hub_url("huggan/distill-ccld-wa", filename="model_student.ckpt"))
ae_model_path = cached_download(hf_hub_url("huggan/ccld_wa", filename="ae_model.ckpt"))
ae_config_path = cached_download(hf_hub_url("huggan/ccld_wa", filename="ae_model.yaml"))
# Define a few utility functions
def parse_prompt(prompt, default_weight=3.):
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 + ['', default_weight][len(vals):]
return vals[0], float(vals[1])
def resize_and_center_crop(image, size):
fac = max(size[0] / image.size[0], size[1] / image.size[1])
image = image.resize((int(fac * image.size[0]), int(fac * image.size[1])), Image.LANCZOS)
return TF.center_crop(image, size[::-1])
# Load the models
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('Using device:', device)
print('loading models')
# autoencoder
ae_config = OmegaConf.load(ae_config_path)
ae_model = ldm.models.autoencoder.AutoencoderKL(**ae_config.model.params)
ae_model.eval().requires_grad_(False).to(device)
ae_model.load_state_dict(torch.load(ae_model_path))
n_ch, side_y, side_x = 4, 32, 32
# diffusion model
model = train.DiffusionModel(192, [1,1,2,2], autoencoder_scale=torch.tensor(4.3084))
model.load_state_dict(torch.load(checkpoint, map_location='cpu'))
model = model.to(device).eval().requires_grad_(False)
# CLOOB
cloob_config = pretrained.get_config('cloob_laion_400m_vit_b_16_16_epochs')
cloob = model_pt.get_pt_model(cloob_config)
checkpoint = pretrained.download_checkpoint(cloob_config)
cloob.load_state_dict(model_pt.get_pt_params(cloob_config, checkpoint))
cloob.eval().requires_grad_(False).to(device)
# The key function: returns a list of n PIL images
def generate(n=1, prompts=['a red circle'], images=[], seed=42, steps=15,
method='plms', eta=None):
zero_embed = torch.zeros([1, cloob.config['d_embed']], device=device)
target_embeds, weights = [zero_embed], []
for prompt in prompts:
txt, weight = parse_prompt(prompt)
target_embeds.append(cloob.text_encoder(cloob.tokenize(txt).to(device)).float())
weights.append(weight)
for prompt in images:
path, weight = parse_prompt(prompt)
img = Image.open(utils.fetch(path)).convert('RGB')
clip_size = cloob.config['image_encoder']['image_size']
img = resize_and_center_crop(img, (clip_size, clip_size))
batch = TF.to_tensor(img)[None].to(device)
embed = F.normalize(cloob.image_encoder(cloob.normalize(batch)).float(), dim=-1)
target_embeds.append(embed)
weights.append(weight)
weights = torch.tensor([1 - sum(weights), *weights], device=device)
torch.manual_seed(seed)
def cfg_model_fn(x, t):
n = x.shape[0]
n_conds = len(target_embeds)
x_in = x.repeat([n_conds, 1, 1, 1])
t_in = t.repeat([n_conds])
clip_embed_in = torch.cat([*target_embeds]).repeat_interleave(n, 0)
vs = model(x_in, t_in, clip_embed_in).view([n_conds, n, *x.shape[1:]])
v = vs.mul(weights[:, None, None, None, None]).sum(0)
return v
def run(x, steps):
if method == 'ddpm':
return sampling.sample(cfg_model_fn, x, steps, 1., {})
if method == 'ddim':
return sampling.sample(cfg_model_fn, x, steps, eta, {})
if method == 'prk':
return sampling.prk_sample(cfg_model_fn, x, steps, {})
if method == 'plms':
return sampling.plms_sample(cfg_model_fn, x, steps, {})
if method == 'pie':
return sampling.pie_sample(cfg_model_fn, x, steps, {})
if method == 'plms2':
return sampling.plms2_sample(cfg_model_fn, x, steps, {})
assert False
batch_size = n
x = torch.randn([n, n_ch, side_y, side_x], device=device)
t = torch.linspace(1, 0, steps + 1, device=device)[:-1]
steps = utils.get_spliced_ddpm_cosine_schedule(t)
pil_ims = []
for i in trange(0, n, batch_size):
cur_batch_size = min(n - i, batch_size)
out_latents = run(x[i:i+cur_batch_size], steps)
outs = ae_model.decode(out_latents * torch.tensor(2.55).to(device))
for j, out in enumerate(outs):
pil_ims.append(utils.to_pil_image(out))
return pil_ims
import gradio as gr
def gen_ims(prompt, im_prompt=None, seed=None, n_steps=10, method='plms'):
if seed == None :
seed = random.randint(0, 10000)
print( prompt, im_prompt, seed, n_steps)
prompts = [prompt]
im_prompts = []
if im_prompt != None:
im_prompts = [im_prompt]
pil_ims = generate(n=1, prompts=prompts, images=im_prompts, seed=seed, steps=n_steps, method=method)
return pil_ims[0]
iface = gr.Interface(fn=gen_ims,
inputs=[#gr.inputs.Slider(minimum=1, maximum=1, step=1, default=1,label="Number of images"),
#gr.inputs.Slider(minimum=0, maximum=200, step=1, label='Random seed', default=0),
gr.inputs.Textbox(label="Text prompt"),
gr.inputs.Image(optional=True, label="Image prompt", type='filepath'),
#gr.inputs.Slider(minimum=10, maximum=35, step=1, default=15,label="Number of steps")
],
outputs=[gr.outputs.Image(type="pil", label="Generated Image")],
examples=[
["Futurism, in the style of Wassily Kandinsky"],
["Art Nouveau, in the style of John Singer Sargent"],
["Surrealism, in the style of Edgar Degas"],
["Expressionism, in the style of Wassily Kandinsky"],
["Futurism, in the style of Egon Schiele"],
["Neoclassicism, in the style of Gustav Klimt"],
["Cubism, in the style of Gustav Klimt"],
["Op Art, in the style of Marc Chagall"],
["Romanticism, in the style of M.C. Escher"],
["Futurism, in the style of M.C. Escher"],
["Abstract Art, in the style of M.C. Escher"],
["Mannerism, in the style of Paul Klee"],
["Romanesque Art, in the style of Leonardo da Vinci"],
["High Renaissance, in the style of Rembrandt"],
["Magic Realism, in the style of Gustave Dore"],
["Realism, in the style of Jean-Michel Basquiat"],
["Art Nouveau, in the style of Paul Gauguin"],
["Avant-garde, in the style of Pierre-Auguste Renoir"],
["Baroque, in the style of Edward Hopper"],
["Post-Impressionism, in the style of Wassily Kandinsky"],
["Naturalism, in the style of Rene Magritte"],
["Constructivism, in the style of Paul Cezanne"],
["Abstract Expressionism, in the style of Henri Matisse"],
["Pop Art, in the style of Vincent van Gogh"],
["Futurism, in the style of Wassily Kandinsky"],
["Futurism, in the style of Zdzislaw Beksinski"],
['Surrealism, in the style of Salvador Dali'],
["Aaron Wacker, oil on canvas"],
["abstract"],
["landscape"],
["portrait"],
["sculpture"],
["genre painting"],
["installation"],
["photo"],
["figurative"],
["illustration"],
["still life"],
["history painting"],
["cityscape"],
["marina"],
["animal painting"],
["design"],
["calligraphy"],
["symbolic painting"],
["graffiti"],
["performance"],
["mythological painting"],
["battle painting"],
["self-portrait"],
["Impressionism, oil on canvas"]
],
title='Art Generator and Style Mixer from 🧠 Cloob and 🎨 WikiArt - Visual Art Encyclopedia:',
description="Trained on images from the [WikiArt](https://www.wikiart.org/) dataset, comprised of visual arts",
article = 'Model used is: [model card](https://huggingface.co/huggan/distill-ccld-wa)..'
)
iface.launch(enable_queue=True) # , debug=True for colab debugging
|