app.py

from ipywidgets import fixed
import gradio as gr
from skimage import img_as_ubyte
from config import Config
from decomposition import get_or_compute
from models import get_instrumented_model
import imageio
from PIL import Image
import ipywidgets as widgets
import numpy as np
import PIL
import torch
from IPython.utils import io
import nltk
nltk.download('wordnet')

# @title Load Model
selected_model = 'lookbook'

# Load model

# 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 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
    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')
    if disp:
        display(final_im)

    return final_im


# @title Demo UI


def generate_image(seed1, seed2, content, style, truncation, c0, c1, c2, c3, c4, c5, c6, start_layer, end_layer):
    seed1 = int(seed1)
    seed2 = int(seed2)

    scale = 1
    params = {'c0': c0,
              'c1': c1,
              'c2': c2,
              'c3': c3,
              'c4': c4,
              'c5': c5,
              'c6': c6}

    param_indexes = {'c0': 0,
                     'c1': 1,
                     'c2': 2,
                     'c3': 3,
                     'c4': 4,
                     'c5': 5,
                     'c6': 6}

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

    w1 = model.sample_latent(1, seed=seed1).detach().cpu().numpy()
    w1 = [w1]*model.get_max_latents()  # one per layer
    im1 = model.sample_np(w1)

    w2 = model.sample_latent(1, seed=seed2).detach().cpu().numpy()
    w2 = [w2]*model.get_max_latents()  # one per layer
    im2 = model.sample_np(w2)
    combined_im = np.concatenate([im1, im2], axis=1)
    input_im = Image.fromarray((combined_im * 255).astype(np.uint8))

    mixed_w = mix_w(w1, w2, content, style)
    return input_im, display_sample_pytorch(seed1, truncation, directions, distances, scale, int(start_layer), int(end_layer), w=mixed_w, disp=False)


truncation = gr.inputs.Slider(
    minimum=0, maximum=1, default=0.5, label="Truncation")
start_layer = gr.inputs.Number(default=3, label="Start Layer")
end_layer = gr.inputs.Number(default=14, label="End Layer")
seed1 = gr.inputs.Number(default=0, label="Seed 1")
seed2 = gr.inputs.Number(default=0, label="Seed 2")
content = gr.inputs.Slider(
    label="Structure", minimum=0, maximum=1, default=0.5)
style = gr.inputs.Slider(label="Style", minimum=0, maximum=1, default=0.5)

slider_max_val = 20
slider_min_val = -20
slider_step = 1

c0 = gr.inputs.Slider(label="Sleeve & Size",
                      minimum=slider_min_val, maximum=slider_max_val, default=0)
c1 = gr.inputs.Slider(label="Dress - Jacket",
                      minimum=slider_min_val, maximum=slider_max_val, default=0)
c2 = gr.inputs.Slider(
    label="Female Coat", minimum=slider_min_val, maximum=slider_max_val, default=0)
c3 = gr.inputs.Slider(label="Coat", minimum=slider_min_val,
                      maximum=slider_max_val, default=0)
c4 = gr.inputs.Slider(label="Graphics", minimum=slider_min_val,
                      maximum=slider_max_val, default=0)
c5 = gr.inputs.Slider(label="Dark", minimum=slider_min_val,
                      maximum=slider_max_val, default=0)
c6 = gr.inputs.Slider(label="Less Cleavage",
                      minimum=slider_min_val, maximum=slider_max_val, default=0)


scale = 1

inputs = [seed1, seed2, content, style, truncation,
          c0, c1, c2, c3, c4, c5, c6, start_layer, end_layer]
description = "Change the seed number to generate different parent design."

gr.Interface(generate_image, inputs, [
             "image", "image"], description=description, live=True, title="").launch()