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from __future__ import annotations
import argparse
import os
import pathlib
import subprocess
import sys
from typing import Callable, Union
import dlib
import huggingface_hub
import numpy as np
import PIL.Image
import torch
import torch.nn as nn
import torchvision.transforms as T
if os.getenv('SYSTEM') == 'spaces' and not torch.cuda.is_available():
with open('patch.e4e') as f:
subprocess.run('patch -p1'.split(), cwd='encoder4editing', stdin=f)
with open('patch.hairclip') as f:
subprocess.run('patch -p1'.split(), cwd='HairCLIP', stdin=f)
app_dir = pathlib.Path(__file__).parent
e4e_dir = app_dir / 'encoder4editing'
sys.path.insert(0, e4e_dir.as_posix())
from models.psp import pSp
from utils.alignment import align_face
hairclip_dir = app_dir / 'HairCLIP'
mapper_dir = hairclip_dir / 'mapper'
sys.path.insert(0, hairclip_dir.as_posix())
sys.path.insert(0, mapper_dir.as_posix())
from mapper.datasets.latents_dataset_inference import LatentsDatasetInference
from mapper.hairclip_mapper import HairCLIPMapper
class Model:
def __init__(self):
self.device = torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu')
self.landmark_model = self._create_dlib_landmark_model()
self.e4e = self._load_e4e()
self.hairclip = self._load_hairclip()
self.transform = self._create_transform()
@staticmethod
def _create_dlib_landmark_model():
path = huggingface_hub.hf_hub_download(
'public-data/dlib_face_landmark_model',
'shape_predictor_68_face_landmarks.dat')
return dlib.shape_predictor(path)
def _load_e4e(self) -> nn.Module:
ckpt_path = huggingface_hub.hf_hub_download('public-data/e4e',
'e4e_ffhq_encode.pt')
ckpt = torch.load(ckpt_path, map_location='cpu')
opts = ckpt['opts']
opts['device'] = self.device.type
opts['checkpoint_path'] = ckpt_path
opts = argparse.Namespace(**opts)
model = pSp(opts)
model.to(self.device)
model.eval()
return model
def _load_hairclip(self) -> nn.Module:
ckpt_path = huggingface_hub.hf_hub_download('public-data/HairCLIP',
'hairclip.pt')
ckpt = torch.load(ckpt_path, map_location='cpu')
opts = ckpt['opts']
opts['device'] = self.device.type
opts['checkpoint_path'] = ckpt_path
opts['editing_type'] = 'both'
opts['input_type'] = 'text'
opts['hairstyle_description'] = 'HairCLIP/mapper/hairstyle_list.txt'
opts['color_description'] = 'red'
opts = argparse.Namespace(**opts)
model = HairCLIPMapper(opts)
model.to(self.device)
model.eval()
return model
@staticmethod
def _create_transform() -> Callable:
transform = T.Compose([
T.Resize(256),
T.CenterCrop(256),
T.ToTensor(),
T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
])
return transform
def detect_and_align_face(self, image: str) -> PIL.Image.Image:
image = align_face(filepath=image, predictor=self.landmark_model)
return image
@staticmethod
def denormalize(tensor: torch.Tensor) -> torch.Tensor:
return torch.clamp((tensor + 1) / 2 * 255, 0, 255).to(torch.uint8)
def postprocess(self, tensor: torch.Tensor) -> np.ndarray:
tensor = self.denormalize(tensor)
return tensor.cpu().numpy().transpose(1, 2, 0)
@torch.inference_mode()
def reconstruct_face(
self, image: PIL.Image.Image) -> tuple[np.ndarray, torch.Tensor]:
input_data = self.transform(image).unsqueeze(0).to(self.device)
reconstructed_images, latents = self.e4e(input_data,
randomize_noise=False,
return_latents=True)
reconstructed = torch.clamp(reconstructed_images[0].detach(), -1, 1)
reconstructed = self.postprocess(reconstructed)
return reconstructed, latents[0]
@torch.inference_mode()
def generate(self, editing_type: str, hairstyle_index: int,
color_description: str, latent: torch.Tensor) -> np.ndarray:
opts = self.hairclip.opts
opts.editing_type = editing_type
opts.color_description = color_description
if editing_type == 'color':
hairstyle_index = 0
device = torch.device(opts.device)
dataset = LatentsDatasetInference(latents=latent.unsqueeze(0).cpu(),
opts=opts)
w, hairstyle_text_inputs_list, color_text_inputs_list = dataset[0][:3]
w = w.unsqueeze(0).to(device)
hairstyle_text_inputs = hairstyle_text_inputs_list[
hairstyle_index].unsqueeze(0).to(device)
color_text_inputs = color_text_inputs_list[0].unsqueeze(0).to(device)
hairstyle_tensor_hairmasked = torch.Tensor([0]).unsqueeze(0).to(device)
color_tensor_hairmasked = torch.Tensor([0]).unsqueeze(0).to(device)
w_hat = w + 0.1 * self.hairclip.mapper(
w,
hairstyle_text_inputs,
color_text_inputs,
hairstyle_tensor_hairmasked,
color_tensor_hairmasked,
)
x_hat, _ = self.hairclip.decoder(
[w_hat],
input_is_latent=True,
return_latents=True,
randomize_noise=False,
truncation=1,
)
res = torch.clamp(x_hat[0].detach(), -1, 1)
res = self.postprocess(res)
return res
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