Simplified_DALLE.ipynb

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        "### Overview of DALL-E Architecture\n",
        "\n",
        "DALL-E is based on the VQ-VAE-2 (Vector Quantized Variational Autoencoder 2) architecture, which integrates both generative and discriminative components. It consists of two main parts:\n",
        "\n",
        "1. **Encoder (Text Encoder)**:\n",
        "   - Takes textual descriptions as input and encodes them into a latent space representation.\n",
        "\n",
        "2. **Decoder (Image Decoder)**:\n",
        "   - Takes the latent representation from the encoder and generates images that match the input description.\n",
        "\n",
        "### Steps to Build a Simplified DALL-E-like Model\n",
        "\n",
        "To build a simplified version of DALL-E, we can break down the process into several key steps:\n",
        "\n",
        "#### Step 1: Text Encoder\n",
        "\n",
        "1. **Tokenization and Embedding**:\n",
        "   - Tokenize input textual descriptions into tokens suitable for embedding.\n",
        "   - Use pre-trained word embeddings (e.g., from `torch.nn.Embedding`) to convert tokens into dense representations.\n",
        "\n",
        "2. **Transformer Encoder**:\n",
        "   - Utilize a Transformer-based architecture (like `torch.nn.TransformerEncoder`) to process and encode the token embeddings into a contextualized representation.\n",
        "\n",
        "#### Step 2: Image Decoder\n",
        "\n",
        "1. **Conditional Generation Setup**:\n",
        "   - Prepare the architecture to generate images conditioned on the encoded text representation.\n",
        "\n",
        "2. **Generator Architecture**:\n",
        "   - Implement a generative model such as a VQ-VAE-2 style architecture:\n",
        "     - Use a series of convolutional layers (e.g., `torch.nn.Conv2d`, `torch.nn.ConvTranspose2d`) for image generation.\n",
        "     - Incorporate techniques like residual connections and normalization layers (e.g., `torch.nn.BatchNorm2d`) for stable training.\n",
        "\n",
        "3. **Loss Function**:\n",
        "   - Define a suitable loss function (e.g., Mean Squared Error, or a combination with VQ-VAE-2's codebook loss) to compare generated images with ground truth images.\n",
        "\n",
        "#### Step 3: Training\n",
        "\n",
        "1. **Dataset Preparation**:\n",
        "   - Prepare a dataset of image-text pairs suitable for training.\n",
        "   - Preprocess images (resize, normalize) and tokenize textual descriptions.\n",
        "\n",
        "2. **Training Loop**:\n",
        "   - Iterate over batches of image-text pairs:\n",
        "     - Encode text descriptions using the text encoder.\n",
        "     - Generate images using the image decoder conditioned on the encoded text.\n",
        "     - Compute the loss between generated images and ground truth images.\n",
        "     - Backpropagate gradients and update model parameters.\n",
        "\n",
        "3. **Hyperparameters**:\n",
        "   - Set appropriate learning rates, batch sizes, and other training parameters.\n",
        "   - Adjust model architecture and loss functions based on experimentation and results.\n"
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        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "# Define a simplified DALL-E-like model\n",
        "class DALLE(nn.Module):\n",
        "    def __init__(self, vocab_size, embedding_dim, hidden_dim, img_channels):\n",
        "        super(DALLE, self).__init__()\n",
        "\n",
        "        # Text Encoder components\n",
        "        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
        "        self.encoder = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model=embedding_dim, nhead=4))\n",
        "\n",
        "        # Image Decoder components\n",
        "        self.decoder = nn.Sequential(\n",
        "            nn.ConvTranspose2d(embedding_dim, hidden_dim * 8, kernel_size=4, stride=1, padding=0),\n",
        "            nn.BatchNorm2d(hidden_dim * 8),\n",
        "            nn.ReLU(True),\n",
        "            nn.ConvTranspose2d(hidden_dim * 8, hidden_dim * 4, kernel_size=4, stride=2, padding=1),\n",
        "            nn.BatchNorm2d(hidden_dim * 4),\n",
        "            nn.ReLU(True),\n",
        "            nn.ConvTranspose2d(hidden_dim * 4, hidden_dim * 2, kernel_size=4, stride=2, padding=1),\n",
        "            nn.BatchNorm2d(hidden_dim * 2),\n",
        "            nn.ReLU(True),\n",
        "            nn.ConvTranspose2d(hidden_dim * 2, img_channels, kernel_size=4, stride=2, padding=1),\n",
        "            nn.Tanh()  # To ensure outputs are in [-1, 1] range (suitable for images)\n",
        "        )\n",
        "\n",
        "    def forward(self, text_input):\n",
        "        # Text encoding\n",
        "        embedded = self.embedding(text_input)\n",
        "        encoded = self.encoder(embedded)\n",
        "\n",
        "        # Image generation\n",
        "        generated_image = self.decoder(encoded)\n",
        "\n",
        "        return generated_image\n",
        "\n",
        "# Example usage: Training loop (simplified)\n",
        "if __name__ == \"__main__\":\n",
        "    # Example parameters\n",
        "    vocab_size = 10000  # Example vocabulary size\n",
        "    embedding_dim = 256\n",
        "    hidden_dim = 128\n",
        "    img_channels = 3  # RGB images\n",
        "\n",
        "    # Initialize model and optimizer\n",
        "    model = DALLE(vocab_size, embedding_dim, hidden_dim, img_channels)\n",
        "    optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "    criterion = nn.MSELoss()  # Example loss function (can be customized)\n",
        "\n",
        "    # Example dataset and training loop\n",
        "    for epoch in range(num_epochs):\n",
        "        for batch_idx, (images, texts) in enumerate(data_loader):\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            # Forward pass\n",
        "            generated_images = model(texts)\n",
        "\n",
        "            # Compute loss\n",
        "            loss = criterion(generated_images, images)\n",
        "\n",
        "            # Backward pass and optimization\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            # Print training statistics\n",
        "            if batch_idx % log_interval == 0:\n",
        "                print(f\"Epoch [{epoch+1}/{num_epochs}], Batch [{batch_idx+1}/{len(data_loader)}], Loss: {loss.item():.4f}\")\n"
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