model.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, PretrainedConfig

class BaseVAE(nn.Module):
    def __init__(self, latent_dim=16):
        super().__init__()
        self.latent_dim = latent_dim
        input_dim = 3 * 32 * 32

        self.encoder = nn.Sequential(
            nn.Linear(input_dim, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512),
            nn.ReLU(),
        )
        self.fc_mu = nn.Linear(512, latent_dim)
        self.fc_logvar = nn.Linear(512, latent_dim)

        self.decoder_input = nn.Linear(latent_dim, 512)
        self.decoder = nn.Sequential(
            nn.ReLU(),
            nn.Linear(512, 1024),
            nn.ReLU(),
            nn.Linear(1024, input_dim),
            nn.Sigmoid()
        )

    def encode(self, x):
        x = x.view(x.size(0), -1)
        x = self.encoder(x)
        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.decoder_input(z)
        x = self.decoder(x)
        x = x.view(-1, 3, 32, 32)
        return x

    def forward(self, x):
        mu, logvar = self.encode(x)
        z = self.reparameterize(mu, logvar)
        recon = self.decode(z)
        return recon, mu, logvar


class VAEConfig(PretrainedConfig):
    model_type = "vae"

    def __init__(self, latent_dim=16, **kwargs):
        super().__init__(**kwargs)
        self.latent_dim = latent_dim

class VAEModel(PreTrainedModel):
    config_class = VAEConfig

    def __init__(self, config):
        super().__init__(config)
        self.vae = BaseVAE(latent_dim=config.latent_dim)
        self.post_init()

    def forward(self, x):
        return self.vae(x)

    def encode(self, x):
        return self.vae.encode(x)

    def decode(self, z):
        return self.vae.decode(z)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = VAEModel.from_pretrained("BioMike/emoji-vae-init").to(device)
model.eval()