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Update app.py
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app.py
CHANGED
@@ -1,23 +1,19 @@
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import nltk; nltk.download('wordnet')
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#@title Load Model
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selected_model = '
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# Load model
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from IPython.utils import io
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import torch
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import PIL
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import numpy as np
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import ipywidgets as widgets
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from PIL import Image
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import imageio
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from models import get_instrumented_model
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from decomposition import get_or_compute
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from config import Config
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from skimage import img_as_ubyte
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import gradio as gr
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import numpy as np
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from ipywidgets import fixed
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# Speed up computation
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torch.autograd.set_grad_enabled(False)
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@@ -65,95 +61,6 @@ for item in lst:
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latent_stdevs.append(comps[item][i])
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#@title Define functions
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# Taken from https://github.com/alexanderkuk/log-progress
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def log_progress(sequence, every=1, size=None, name='Items'):
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from ipywidgets import IntProgress, HTML, VBox
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from IPython.display import display
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is_iterator = False
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if size is None:
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try:
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size = len(sequence)
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except TypeError:
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is_iterator = True
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if size is not None:
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if every is None:
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if size <= 200:
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every = 1
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else:
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every = int(size / 200) # every 0.5%
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else:
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assert every is not None, 'sequence is iterator, set every'
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if is_iterator:
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progress = IntProgress(min=0, max=1, value=1)
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progress.bar_style = 'info'
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else:
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progress = IntProgress(min=0, max=size, value=0)
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label = HTML()
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box = VBox(children=[label, progress])
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display(box)
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index = 0
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try:
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for index, record in enumerate(sequence, 1):
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if index == 1 or index % every == 0:
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if is_iterator:
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label.value = '{name}: {index} / ?'.format(
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name=name,
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index=index
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)
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else:
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progress.value = index
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label.value = u'{name}: {index} / {size}'.format(
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name=name,
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index=index,
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size=size
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)
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yield record
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except:
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progress.bar_style = 'danger'
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raise
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else:
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progress.bar_style = 'success'
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progress.value = index
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label.value = "{name}: {index}".format(
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name=name,
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index=str(index or '?')
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)
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def name_direction(sender):
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if not text.value:
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print('Please name the direction before saving')
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return
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if num in named_directions.values():
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target_key = list(named_directions.keys())[list(named_directions.values()).index(num)]
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print(f'Direction already named: {target_key}')
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print(f'Overwriting... ')
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del(named_directions[target_key])
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named_directions[text.value] = [num, start_layer.value, end_layer.value]
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save_direction(random_dir, text.value)
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for item in named_directions:
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print(item, named_directions[item])
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def save_direction(direction, filename):
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filename += ".npy"
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np.save(filename, direction, allow_pickle=True, fix_imports=True)
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print(f'Latent direction saved as {filename}')
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def mix_w(w1, w2, content, style):
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for i in range(0,5):
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w2[i] = w1[i] * (1 - content) + w2[i] * content
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for i in range(5, 16):
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w2[i] = w1[i] * (1 - style) + w2[i] * style
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return w2
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def display_sample_pytorch(seed, truncation, directions, distances, scale, start, end, w=None, disp=True, save=None, noise_spec=None):
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# blockPrint()
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model.truncation = truncation
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if disp == False:
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print(save)
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final_im.save(f'out/{seed}_{save:05}.png')
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if disp:
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display(final_im)
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return final_im
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def generate_mov(seed, truncation, direction_vec, scale, layers, n_frames, out_name = 'out', noise_spec = None, loop=True):
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"""Generates a mov moving back and forth along the chosen direction vector"""
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# Example of reading a generated set of images, and storing as MP4.
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movieName = f'{out_name}.mp4'
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offset = -10
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step = 20 / n_frames
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imgs = []
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for i in log_progress(range(n_frames), name = "Generating frames"):
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print(f'\r{i} / {n_frames}', end='')
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w = model.sample_latent(1, seed=seed).cpu().numpy()
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model.truncation = truncation
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w = [w]*model.get_max_latents() # one per layer
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for l in layers:
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if l <= model.get_max_latents():
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w[l] = w[l] + direction_vec * offset * scale
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#save image and display
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out = model.sample_np(w)
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final_im = Image.fromarray((out * 255).astype(np.uint8))
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imgs.append(out)
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#increase offset
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offset += step
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if loop:
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imgs += imgs[::-1]
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with imageio.get_writer(movieName, mode='I') as writer:
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for image in log_progress(list(imgs), name = "Creating animation"):
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writer.append_data(img_as_ubyte(image))
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#@title Demo UI
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scale = 1
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inputs = [seed, truncation, monster, female, skimpy, light, bodysuit, bulky, human_head, start_layer, end_layer]
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description = "Change the seed number to generate different
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gr.Interface(generate_image, inputs, ["image", "image"], description=description, live=True, title="CharacterGAN").launch()
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import nltk; nltk.download('wordnet')
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#@title Load Model
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selected_model = 'character'
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# Load model
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import torch
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import PIL
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import numpy as np
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import ipywidgets as widgets
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from PIL import Image
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from models import get_instrumented_model
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from decomposition import get_or_compute
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from config import Config
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import gradio as gr
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import numpy as np
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# Speed up computation
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torch.autograd.set_grad_enabled(False)
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latent_stdevs.append(comps[item][i])
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def display_sample_pytorch(seed, truncation, directions, distances, scale, start, end, w=None, disp=True, save=None, noise_spec=None):
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# blockPrint()
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model.truncation = truncation
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if disp == False:
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print(save)
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final_im.save(f'out/{seed}_{save:05}.png')
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return final_im
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#@title Demo UI
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scale = 1
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inputs = [seed, truncation, monster, female, skimpy, light, bodysuit, bulky, human_head, start_layer, end_layer]
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description = "Change the seed number to generate different character design. Made by <a href='https://www.mfrashad.com/' target='_blank'>@mfrashad</a>. For more details on how to build this, visit the <a href='https://github.com/mfrashad/gancreate-saai' target='_blank'>repo</a>. Please give a star if you find it useful :)"
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gr.Interface(generate_image, inputs, ["image", "image"], description=description, live=True, title="CharacterGAN").launch()
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