Update app.py

app.py

@@ -1,23 +1,19 @@
 import nltk; nltk.download('wordnet')
 
 #@title Load Model
-selected_model = 'lookbook'
+selected_model = 'character'
 
 # Load model
-from IPython.utils import io
 import torch
 import PIL
 import numpy as np
 import ipywidgets as widgets
 from PIL import Image
-import imageio
 from models import get_instrumented_model
 from decomposition import get_or_compute
 from config import Config
-from skimage import img_as_ubyte
 import gradio as gr
 import numpy as np
-from ipywidgets import fixed
 
 # Speed up computation
 torch.autograd.set_grad_enabled(False)
@@ -65,95 +61,6 @@ for item in lst:
           latent_stdevs.append(comps[item][i])
 
 
-#@title Define functions
-
-
-# Taken from https://github.com/alexanderkuk/log-progress
-def log_progress(sequence, every=1, size=None, name='Items'):
-    from ipywidgets import IntProgress, HTML, VBox
-    from IPython.display import display
-
-    is_iterator = False
-    if size is None:
-        try:
-            size = len(sequence)
-        except TypeError:
-            is_iterator = True
-    if size is not None:
-        if every is None:
-            if size <= 200:
-                every = 1
-            else:
-                every = int(size / 200)     # every 0.5%
-    else:
-        assert every is not None, 'sequence is iterator, set every'
-
-    if is_iterator:
-        progress = IntProgress(min=0, max=1, value=1)
-        progress.bar_style = 'info'
-    else:
-        progress = IntProgress(min=0, max=size, value=0)
-    label = HTML()
-    box = VBox(children=[label, progress])
-    display(box)
-
-    index = 0
-    try:
-        for index, record in enumerate(sequence, 1):
-            if index == 1 or index % every == 0:
-                if is_iterator:
-                    label.value = '{name}: {index} / ?'.format(
-                        name=name,
-                        index=index
-                    )
-                else:
-                    progress.value = index
-                    label.value = u'{name}: {index} / {size}'.format(
-                        name=name,
-                        index=index,
-                        size=size
-                    )
-            yield record
-    except:
-        progress.bar_style = 'danger'
-        raise
-    else:
-        progress.bar_style = 'success'
-        progress.value = index
-        label.value = "{name}: {index}".format(
-            name=name,
-            index=str(index or '?')
-        )
-
-def name_direction(sender):
-  if not text.value:
-    print('Please name the direction before saving')
-    return
-    
-  if num in named_directions.values():
-    target_key = list(named_directions.keys())[list(named_directions.values()).index(num)]
-    print(f'Direction already named: {target_key}')
-    print(f'Overwriting... ')
-    del(named_directions[target_key])
-  named_directions[text.value] = [num, start_layer.value, end_layer.value]
-  save_direction(random_dir, text.value)
-  for item in named_directions:
-    print(item, named_directions[item])
-
-def save_direction(direction, filename):
-  filename += ".npy"
-  np.save(filename, direction, allow_pickle=True, fix_imports=True)
-  print(f'Latent direction saved as {filename}')
-
-def mix_w(w1, w2, content, style):
-    for i in range(0,5):
-        w2[i] = w1[i] * (1 - content) + w2[i] * content
-
-    for i in range(5, 16):
-        w2[i] = w1[i] * (1 - style) + w2[i] * style
-    
-    return w2
-
 def display_sample_pytorch(seed, truncation, directions, distances, scale, start, end, w=None, disp=True, save=None, noise_spec=None):
     # blockPrint()
     model.truncation = truncation
@@ -177,40 +84,9 @@ def display_sample_pytorch(seed, truncation, directions, distances, scale, start
       if disp == False:
         print(save)
       final_im.save(f'out/{seed}_{save:05}.png')
-    if disp:
-      display(final_im)
     
     return final_im
 
-def generate_mov(seed, truncation, direction_vec, scale, layers, n_frames, out_name = 'out', noise_spec = None, loop=True):
-  """Generates a mov moving back and forth along the chosen direction vector"""
-  # Example of reading a generated set of images, and storing as MP4.
-  movieName = f'{out_name}.mp4'
-  offset = -10
-  step = 20 / n_frames
-  imgs = []
-  for i in log_progress(range(n_frames), name = "Generating frames"):
-    print(f'\r{i} / {n_frames}', end='')
-    w = model.sample_latent(1, seed=seed).cpu().numpy()
-
-    model.truncation = truncation
-    w = [w]*model.get_max_latents() # one per layer
-    for l in layers:
-      if l <= model.get_max_latents():
-          w[l] = w[l] + direction_vec * offset * scale
-
-    #save image and display
-    out = model.sample_np(w)
-    final_im = Image.fromarray((out * 255).astype(np.uint8))
-    imgs.append(out)
-    #increase offset
-    offset += step
-  if loop:
-    imgs += imgs[::-1]
-  with imageio.get_writer(movieName, mode='I') as writer:
-    for image in log_progress(list(imgs), name = "Creating animation"):
-        writer.append_data(img_as_ubyte(image))
-
 
 #@title Demo UI
 
@@ -266,6 +142,6 @@ human_head = gr.inputs.Slider(label="Head", minimum=slider_min_val, maximum=slid
 scale = 1
 
 inputs = [seed, truncation, monster, female, skimpy, light, bodysuit, bulky, human_head, start_layer, end_layer]
-description = "Change the seed number to generate different parent 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 :)"
+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 :)"
 
 gr.Interface(generate_image, inputs, ["image", "image"], description=description, live=True, title="CharacterGAN").launch()