app.py

import nltk; nltk.download('wordnet')

#@title Load Model
selected_model = 'character'

# Load model
import torch
import PIL
import numpy as np
from PIL import Image
from models import get_instrumented_model
from decomposition import get_or_compute
from config import Config
import gradio as gr
import numpy as np

# Speed up computation
torch.autograd.set_grad_enabled(False)
torch.backends.cudnn.benchmark = True

# Specify model to use
config = Config(
  model='StyleGAN2',
  layer='style',
  output_class=selected_model,
  components=80,
  use_w=True,
  batch_size=5_000, # style layer quite small
)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

inst = get_instrumented_model(config.model, config.output_class,
                              config.layer, torch.device(device), use_w=config.use_w)

path_to_components = get_or_compute(config, inst)

model = inst.model

comps = np.load(path_to_components)
lst = comps.files
latent_dirs = []
latent_stdevs = []

load_activations = False

for item in lst:
    if load_activations:
      if item == 'act_comp':
        for i in range(comps[item].shape[0]):
          latent_dirs.append(comps[item][i])
      if item == 'act_stdev':
        for i in range(comps[item].shape[0]):
          latent_stdevs.append(comps[item][i])
    else:
      if item == 'lat_comp':
        for i in range(comps[item].shape[0]):
          latent_dirs.append(comps[item][i])
      if item == 'lat_stdev':
        for i in range(comps[item].shape[0]):
          latent_stdevs.append(comps[item][i])


def display_sample_pytorch(seed, truncation, directions, distances, scale, start, end, w=None, disp=True, save=None, noise_spec=None):
    # blockPrint()
    model.truncation = truncation
    if w is None:
        w = model.sample_latent(1, seed=seed).detach().cpu().numpy()
        w = [w]*model.get_max_latents() # one per layer
    else:
        w = [np.expand_dims(x, 0) for x in w]
    
    for l in range(start, end):
      for i in range(len(directions)):
        w[l] = w[l] + directions[i] * distances[i] * scale
    
    torch.cuda.empty_cache()
    #save image and display
    out = model.sample_np(w)
    final_im = Image.fromarray((out * 255).astype(np.uint8)).resize((500,500),Image.LANCZOS)
    
    
    if save is not None:
      if disp == False:
        print(save)
      final_im.save(f'out/{seed}_{save:05}.png')
    
    return final_im


#@title Demo UI


def generate_image(seed, truncation,
                  monster, female, skimpy, light, bodysuit, bulky, human_head,
                  start_layer, end_layer):
    seed = hash(seed) % 1000000000
    scale = 1
    params = {'monster': monster,
          'female': female,
          'skimpy': skimpy,
          'light': light,
          'bodysuit': bodysuit,
          'bulky': bulky,
          'human_head': human_head}

    param_indexes = {'monster': 0,
              'female': 1,
              'skimpy': 2,
              'light': 4,
              'bodysuit': 5,
              'bulky': 6,
              'human_head': 8}

    directions = []
    distances = []
    for k, v in params.items():
        directions.append(latent_dirs[param_indexes[k]])
        distances.append(v)

    style = {'description_width': 'initial'}
    return display_sample_pytorch(int(seed), truncation, directions, distances, scale, int(start_layer), int(end_layer), disp=False)

truncation = gr.inputs.Slider(minimum=0, maximum=1, default=0.5, label="Truncation")
start_layer = gr.inputs.Number(default=0, label="Start Layer")
end_layer = gr.inputs.Number(default=14, label="End Layer")
seed = gr.inputs.Textbox(default="0", label="Seed")

slider_max_val = 20
slider_min_val = -20
slider_step = 1

monster = gr.inputs.Slider(label="Monsterfication", minimum=slider_min_val, maximum=slider_max_val, default=0)
female = gr.inputs.Slider(label="Gender", minimum=slider_min_val, maximum=slider_max_val, default=0)
skimpy = gr.inputs.Slider(label="Amount of Clothing", minimum=slider_min_val, maximum=slider_max_val, default=0)
light = gr.inputs.Slider(label="Brightness", minimum=slider_min_val, maximum=slider_max_val, default=0)
bodysuit = gr.inputs.Slider(label="Bodysuit", minimum=slider_min_val, maximum=slider_max_val, default=0)
bulky = gr.inputs.Slider(label="Bulkiness", minimum=slider_min_val, maximum=slider_max_val, default=0)
human_head = gr.inputs.Slider(label="Head", minimum=slider_min_val, maximum=slider_max_val, default=0)


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 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"], description=description, live=True, title="CharacterGAN").launch()