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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()
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