metadata
license: apache-2.0
pipeline_tag: image-feature-extraction
tags:
- image
- image generation
- vae
- portrait
Model Card for Model ID
Model Details
The model is a VAE trained for encoding classical portraits in 128*128 resolution into a d=256 latent vector and decoding into original images.
Demo: https://huggingface.co/spaces/BioMike/ClassicalPortraitsVAE
How to Get Started with the Model
import json
import torch
import torch.nn as nn
import os
from pathlib import Path
from typing import Optional, Union, Dict
from huggingface_hub import snapshot_download
import warnings
class ConvVAE(nn.Module):
def __init__(self, latent_size):
super(ConvVAE, self).__init__()
# Encoder
self.encoder = nn.Sequential(
nn.Conv2d(3, 64, 3, stride=2, padding=1), # (batch, 64, 64, 64)
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64, 128, 3, stride=2, padding=1), # (batch, 128, 32, 32)
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Conv2d(128, 256, 3, stride=2, padding=1), # (batch, 256, 16, 16)
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Conv2d(256, 512, 3, stride=2, padding=1), # (batch, 512, 8, 8)
nn.BatchNorm2d(512),
nn.ReLU()
)
self.fc_mu = nn.Linear(512 * 8 * 8, latent_size)
self.fc_logvar = nn.Linear(512 * 8 * 8, latent_size)
self.fc2 = nn.Linear(latent_size, 512 * 8 * 8)
self.decoder = nn.Sequential(
nn.ConvTranspose2d(512, 256, 4, stride=2, padding=1), # (batch, 256, 16, 16)
nn.BatchNorm2d(256),
nn.ReLU(),
nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1), # (batch, 128, 32, 32)
nn.BatchNorm2d(128),
nn.ReLU(),
nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1), # (batch, 64, 64, 64)
nn.BatchNorm2d(64),
nn.ReLU(),
nn.ConvTranspose2d(64, 3, 4, stride=2, padding=1), # (batch, 3, 128, 128)
nn.Tanh()
)
def forward(self, x):
mu, logvar = self.encode(x)
z = self.reparameterize(mu, logvar)
decoded = self.decode(z)
return decoded, mu, logvar
def encode(self, x):
x = self.encoder(x)
x = x.view(x.size(0), -1)
mu = self.fc_mu(x)
logvar = self.fc_logvar(x)
return mu, logvar
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def decode(self, z):
x = self.fc2(z)
x = x.view(-1, 512, 8, 8)
decoded = self.decoder(x)
return decoded
@classmethod
def from_pretrained(
cls,
model_id: str,
revision: Optional[str] = None,
cache_dir: Optional[Union[str, Path]] = None,
force_download: bool = False,
proxies: Optional[Dict] = None,
resume_download: bool = False,
local_files_only: bool = False,
token: Union[str, bool, None] = None,
map_location: str = "cpu",
strict: bool = False,
**model_kwargs,
):
"""
Load a pretrained model from a given model ID.
Args:
model_id (str): Identifier of the model to load.
revision (Optional[str]): Specific model revision to use.
cache_dir (Optional[Union[str, Path]]): Directory to store downloaded models.
force_download (bool): Force re-download even if the model exists.
proxies (Optional[Dict]): Proxy configuration for downloads.
resume_download (bool): Resume interrupted downloads.
local_files_only (bool): Use only local files, don't download.
token (Union[str, bool, None]): Token for API authentication.
map_location (str): Device to map model to. Defaults to "cpu".
strict (bool): Enforce strict state_dict loading.
**model_kwargs: Additional keyword arguments for model initialization.
Returns:
An instance of the model loaded from the pretrained weights.
"""
model_dir = Path(model_id)
if not model_dir.exists():
model_dir = Path(
snapshot_download(
repo_id=model_id,
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
token=token,
local_files_only=local_files_only,
)
)
config_file = model_dir / "config.json"
with open(config_file, 'r') as f:
config = json.load(f)
latent_size = config.get('latent_size')
if latent_size is None:
raise ValueError("The configuration file is missing the 'latent_size' key.")
model = cls(latent_size, **model_kwargs)
model_file = model_dir / "model_conv_vae_256_epoch_304.pth"
if not model_file.exists():
raise FileNotFoundError(f"The model checkpoint '{model_file}' does not exist.")
state_dict = torch.load(model_file, map_location=map_location)
new_state_dict = {}
for k, v in state_dict.items():
if k.startswith('_orig_mod.'):
new_state_dict[k[len('_orig_mod.'):]] = v
else:
new_state_dict[k] = v
model.load_state_dict(new_state_dict, strict=strict)
model.to(map_location)
return model
model = ConvVAE.from_pretrained(
model_id="BioMike/classical_portrait_vae",
cache_dir="./model_cache",
map_location="cpu",
strict=True).eval()
Training Details
Training Data
The model was trained on the Portrait Dataset
Training Procedure
The model was trained into two steps, in the first the model vgg16 was employed in the perceptual loss, to train our model to extract general features, and the model vggface2 was used to train VAE to decode faces accurately.