app.py

import os
import gradio as gr

MODEL_DIR = 'models/pretrain'
os.makedirs(MODEL_DIR, exist_ok=True)

os.system("wget https://hkustconnect-my.sharepoint.com/:u:/g/personal/jzhubt_connect_ust_hk/ETYVen9KXGlAia2gH6pcZswB9Lw-21vWrE75OACvG2SBow\?e\=SCGqg0\&download=1 -O $MODEL_DIR/stylegan2-ffhq-config-f-1024x1024.pth  --quiet")


# python 3.7
"""Demo."""
import io
import cv2
import warnings
import numpy as np
import torch
from PIL import Image
from models import build_model

warnings.filterwarnings(action='ignore', category=UserWarning)

def postprocess_image(image, min_val=-1.0, max_val=1.0):
    """Post-processes image to pixel range [0, 255] with dtype `uint8`.

    This function is particularly used to handle the results produced by deep
    models.

    NOTE: The input image is assumed to be with format `NCHW`, and the returned
    image will always be with format `NHWC`.

    Args:
        image: The input image for post-processing.
        min_val: Expected minimum value of the input image.
        max_val: Expected maximum value of the input image.

    Returns:
        The post-processed image.
    """
    assert isinstance(image, np.ndarray)

    image = image.astype(np.float64)
    image = (image - min_val) / (max_val - min_val) * 255
    image = np.clip(image + 0.5, 0, 255).astype(np.uint8)

    assert image.ndim == 4 and image.shape[1] in [1, 3, 4]
    return image.transpose(0, 2, 3, 1)


def to_numpy(data):
    """Converts the input data to `numpy.ndarray`."""
    if isinstance(data, (int, float)):
        return np.array(data)
    if isinstance(data, np.ndarray):
        return data
    if isinstance(data, torch.Tensor):
        return data.detach().cpu().numpy()
    raise TypeError(f'Not supported data type `{type(data)}` for '
                    f'converting to `numpy.ndarray`!')


def linear_interpolate(latent_code,
                       boundary,
                       layer_index=None,
                       start_distance=-10.0,
                       end_distance=10.0,
                       steps=7):
    """Interpolate between the latent code and boundary."""
    assert (len(latent_code.shape) == 3 and len(boundary.shape) == 3 and
            latent_code.shape[0] == 1 and boundary.shape[0] == 1 and
            latent_code.shape[1] == boundary.shape[1])
    linspace = np.linspace(start_distance, end_distance, steps)
    linspace = linspace.reshape([-1, 1, 1]).astype(np.float32)
    inter_code = linspace * boundary
    is_manipulatable = np.zeros(inter_code.shape, dtype=bool)
    is_manipulatable[:, layer_index, :] = True
    mani_code = np.where(is_manipulatable, latent_code+inter_code, latent_code)
    return mani_code


def imshow(images, col, viz_size=256):
  """Shows images in one figure."""
  num, height, width, channels = images.shape
  assert num % col == 0
  row = num // col

  fused_image = np.zeros((viz_size*row, viz_size*col, channels), dtype=np.uint8)

  for idx, image in enumerate(images):
    i, j = divmod(idx, col)
    y = i * viz_size
    x = j * viz_size
    if height != viz_size or width != viz_size:
      image = cv2.resize(image, (viz_size, viz_size))
    fused_image[y:y + viz_size, x:x + viz_size] = image

  fused_image = np.asarray(fused_image, dtype=np.uint8)
  data = io.BytesIO()
  if channels == 4:
    Image.fromarray(fused_image).save(data, 'png')
  elif channels == 3:
    Image.fromarray(fused_image).save(data, 'jpeg')
  else:
    raise ValueError('Image channel error')
  im_data = data.getvalue()
  image = Image.open(io.BytesIO(im_data))
  return image

  print('Building generator')

checkpoint_path=f'{MODEL_DIR}/stylegan2-ffhq-config-f-1024x1024.pth'
config = dict(model_type='StyleGAN2Generator',
              resolution=1024,
              w_dim=512,
              fmaps_base=int(1 * (32 << 10)),
              fmaps_max=512,)
generator = build_model(**config)
print(f'Loading checkpoint from `{checkpoint_path}` ...')
checkpoint = torch.load(checkpoint_path, map_location='cpu')['models']
if 'generator_smooth' in checkpoint:
    generator.load_state_dict(checkpoint['generator_smooth'])
else:
    generator.load_state_dict(checkpoint['generator'])
generator = generator.eval().cpu()
print('Finish loading checkpoint.')

print('Loading boundaries')
ATTRS = ['eyebrows', 'eyesize', 'gaze_direction', 'nose_length', 'mouth', 'lipstick']
boundaries = {}
for attr in ATTRS:
  boundary_path = os.path.join(f'directions/ffhq/stylegan2/{attr}.npy')
  boundary = np.load(boundary_path)
  boundaries[attr] = boundary
print('Generator and boundaries are ready.')


def inference(num_of_image,seed,trunc_psi,eyebrows,eyesize,gaze_direction,nose_length,mouth,lipstick):
    print('Sampling latent codes with given seed.')
    num_of_image = num_of_image #@param {type:"slider", min:1, max:8, step:1}
    seed = seed #@param {type:"slider", min:0, max:10000, step:1}
    trunc_psi = trunc_psi #@param {type:"slider", min:0, max:1, step:0.1}
    trunc_layers = 8
    np.random.seed(seed)
    latent_z = np.random.randn(num_of_image, generator.z_dim)
    latent_z = torch.from_numpy(latent_z.astype(np.float32))
    latent_z = latent_z.cpu()
    wp = generator.mapping(latent_z, None)['wp']
    if trunc_psi < 1.0:
        w_avg = generator.w_avg
        w_avg = w_avg.reshape(1, -1, generator.w_dim)[:, :trunc_layers]
        wp[:, :trunc_layers] = w_avg.lerp(wp[:, :trunc_layers], trunc_psi)
    with torch.no_grad():
        images_ori = generator.synthesis(wp)['image']
    images_ori = postprocess_image(to_numpy(images_ori))
    print('Original images are shown as belows.')
    imshow(images_ori, col=images_ori.shape[0])
    latent_wp = to_numpy(wp)



    eyebrows = eyebrows #@param {type:"slider", min:-12.0, max:12.0, step:2}
    eyesize = eyesize #@param {type:"slider", min:-12.0, max:12.0, step:2}
    gaze_direction = gaze_direction #@param {type:"slider", min:-12.0, max:12.0, step:2}
    nose_length = nose_length #@param {type:"slider", min:-12.0, max:12.0, step:2}
    mouth = mouth #@param {type:"slider", min:-12.0, max:12.0, step:2}
    lipstick = lipstick #@param {type:"slider", min:-12.0, max:12.0, step:2}

    new_codes = latent_wp.copy()
    for attr_name in ATTRS:
        if attr_name in ['eyebrows', 'lipstick']:
            layers_idx = [8,9,10,11]
        else:
            layers_idx = [4,5,6,7]
        step =  eval(attr_name)
        direction = boundaries[attr_name]
        direction = np.tile(direction, [1, generator.num_layers, 1])
        new_codes[:, layers_idx, :] += direction[:, layers_idx, :] * step
    new_codes = torch.from_numpy(new_codes.astype(np.float32)).cpu()
    with torch.no_grad():
        images_mani = generator.synthesis(new_codes)['image']
    images_mani = postprocess_image(to_numpy(images_mani))
    return imshow(images_mani, col=images_mani.shape[0])

gr.Interface(inference,[gr.Slider(1, 3, value=1,label="num_of_image"),
gr.Slider(0, 10000, value=210,label="seed"),
gr.Slider(0, 1, value=0.7,step=0.1,label="truncation psi"),
gr.Slider(-12, 12, value=0,label="eyebrows"),
gr.Slider(-12, 12, value=0,label="eyesize"),
gr.Slider(-12, 12, value=0,label="gaze direction"),
gr.Slider(-12, 12, value=0,label="nose_length"),
gr.Slider(-12, 12, value=0,label="mouth"),
gr.Slider(-12, 12, value=0,label="lipstick"),
],gr.Image(type="pil")).launch()