app.py

import os
from PIL import Image
import torch
import gradio as gr
import torch
torch.backends.cudnn.benchmark = True
from torchvision import transforms, utils
from util import *
from PIL import Image
import math
import random
import numpy as np
from torch import nn, autograd, optim
from torch.nn import functional as F
from tqdm import tqdm
import lpips
from model import *

#from e4e_projection import projection as e4e_projection

from copy import deepcopy
import imageio

import os
import sys
import numpy as np
from PIL import Image
import torch
import torchvision.transforms as transforms
from argparse import Namespace
from e4e.utils.common import tensor2im
from e4e.models.psp import pSp
from e4e.models.encoders import psp_encoders
from util import *
from huggingface_hub import hf_hub_download

transform = transforms.Compose([
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
resize_dims = (256, 256)

device= 'cpu'
ffhq_model_path = hf_hub_download(repo_id="bankholdup/stylegan_petbreeder", filename="e4e_ffhq512.pt")

ffhq_ckpt = torch.load(ffhq_model_path, map_location='cpu')
ffhq_latent_avg = ffhq_ckpt['latent_avg'].to(device)
ffhq_opts = ffhq_ckpt['opts']
ffhq_opts['checkpoint_path'] = ffhq_model_path
ffhq_opts= Namespace(**ffhq_opts)

ffhq_encoder = psp_encoders.Encoder4Editing(50, 'ir_se', ffhq_opts)
ffhq_e_filt = {k[len('encoder') + 1:]: v for k, v in ffhq_ckpt['state_dict'].items() if k[:len('encoder')] == 'encoder'}
ffhq_encoder.load_state_dict(ffhq_e_filt, strict=True)
ffhq_encoder.eval()
ffhq_encoder.to(device)

ffhq_decoder = Generator(512, 512, 8, channel_multiplier=2)
ffhq_d_filt = {k[len('decoder') + 1:]: v for k, v in ffhq_ckpt['state_dict'].items() if k[:len('decoder')] == 'decoder'}
ffhq_decoder.load_state_dict(ffhq_d_filt, strict=True)
ffhq_decoder.eval()
ffhq_decoder.to(device)

dog_model_path = hf_hub_download(repo_id="bankholdup/stylegan_petbreeder", filename="e4e_ffhq512_dog.pt")

dog_ckpt = torch.load(dog_model_path, map_location='cpu')
dog_latent_avg = dog_ckpt['latent_avg'].to(device)
dog_opts = dog_ckpt['opts']
dog_opts['checkpoint_path'] = dog_model_path
dog_opts= Namespace(**dog_opts)

dog_encoder = psp_encoders.Encoder4Editing(50, 'ir_se', dog_opts)
dog_e_filt = {k[len('encoder') + 1:]: v for k, v in dog_ckpt['state_dict'].items() if k[:len('encoder')] == 'encoder'}
dog_encoder.load_state_dict(dog_e_filt, strict=True)
dog_encoder.eval()
dog_encoder.to(device)

dog_decoder = Generator(512, 512, 8, channel_multiplier=2)
dog_d_filt = {k[len('decoder') + 1:]: v for k, v in dog_ckpt['state_dict'].items() if k[:len('decoder')] == 'decoder'}
dog_decoder.load_state_dict(dog_d_filt, strict=True)
dog_decoder.eval()
dog_decoder.to(device)

cat_model_path = hf_hub_download(repo_id="bankholdup/stylegan_petbreeder", filename="e4e_ffhq512_cat.pt")

cat_ckpt = torch.load(cat_model_path, map_location='cpu')
cat_latent_avg = cat_ckpt['latent_avg'].to(device)
cat_opts = cat_ckpt['opts']
cat_opts['checkpoint_path'] = cat_model_path
cat_opts= Namespace(**cat_opts)

cat_encoder = psp_encoders.Encoder4Editing(50, 'ir_se', cat_opts)
cat_e_filt = {k[len('encoder') + 1:]: v for k, v in cat_ckpt['state_dict'].items() if k[:len('encoder')] == 'encoder'}
cat_encoder.load_state_dict(cat_e_filt, strict=True)
cat_encoder.eval()
cat_encoder.to(device)

cat_decoder = Generator(512, 512, 8, channel_multiplier=2)
cat_d_filt = {k[len('decoder') + 1:]: v for k, v in cat_ckpt['state_dict'].items() if k[:len('decoder')] == 'decoder'}
cat_decoder.load_state_dict(cat_d_filt, strict=True)
cat_decoder.eval()
cat_decoder.to(device)


def run_alignment(image_path):
  import dlib
  from e4e.utils.alignment import align_face
  dlib_path = hf_hub_download(repo_id="bankholdup/stylegan_petbreeder", filename="shape_predictor_68_face_landmarks.dat")
  predictor = dlib.shape_predictor(dlib_path)
  aligned_image = align_face(filepath=image_path, predictor=predictor) 
  print("Aligned image has shape: {}".format(aligned_image.size))
  return aligned_image


def gen_im(ffhq_codes, dog_codes, cat_codes, model_type='ffhq'):
  if model_type=='ffhq':
    imgs, _ = ffhq_decoder([ffhq_codes], input_is_latent=True, randomize_noise=False, return_latents=True)
  elif model_type=='dog':
    imgs, _ = dog_decoder([dog_codes], input_is_latent=True, randomize_noise=False, return_latents=True)
  elif model_type=='cat':
    imgs, _ = cat_decoder([cat_codes], input_is_latent=True, randomize_noise=False, return_latents=True)
  else:
    imgs, _ = custom_decoder([custom_codes], input_is_latent=True, randomize_noise=False, return_latents=True)
  return tensor2im(imgs[0])


def inference(img):  
    img.save('out.jpg')  
    #aligned_face = align_face('out.jpg')
    input_image = run_alignment('out.jpg')
    transformed_image = transform(input_image)
	
    ffhq_codes = ffhq_encoder(transformed_image.unsqueeze(0).to(device).float())
    ffhq_codes = ffhq_codes + ffhq_latent_avg.repeat(ffhq_codes.shape[0], 1, 1)

    cat_codes = cat_encoder(transformed_image.unsqueeze(0).to(device).float())
    cat_codes = cat_codes + ffhq_latent_avg.repeat(cat_codes.shape[0], 1, 1)

    dog_codes = dog_encoder(transformed_image.unsqueeze(0).to(device).float())
    dog_codes = dog_codes + ffhq_latent_avg.repeat(dog_codes.shape[0], 1, 1)
        
    animal = "cat"
    npimage = gen_im(ffhq_codes, dog_codes, cat_codes, animal)
	
    imageio.imwrite('filename.jpeg', npimage)
    return 'filename.jpeg'
  
title = "PetBreeder v1.1"
description = "Gradio Demo for PetBreeder."

gr.Interface(inference, 
[gr.inputs.Image(type="pil")], 
gr.outputs.Image(type="file"),
title=title,
description=description).launch()