{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# DiffuseCraft: Text-to-Image Generation with Custom Model\n",
        "\n",
        "This notebook uses a custom text-to-image model from Hugging Face to generate images from text prompts. It is optimized for use with a T4 GPU in Google Colab, with a focus on minimizing RAM usage.\n",
        "\n",
        "## Setup\n",
        "\n",
        "Run the following cell to install the required libraries:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "!pip install --no-cache-dir diffusers transformers torch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Then, load the model by running the next cell. Make sure to replace `\"username/efficient-text-to-image\"` with the actual model ID from Hugging Face."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import torch\n",
        "from diffusers import StableDiffusionPipeline\n",
        "\n",
        "model_id = \"username/efficient-text-to-image\"  # Replace with actual model ID\n",
        "pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n",
        "pipe = pipe.to(\"cuda\")\n",
        "pipe.enable_attention_slicing()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Generate Image\n",
        "\n",
        "Enter your text prompt in the `prompt` variable below. You can also adjust the `height`, `width`, and `num_inference_steps` to balance between image quality and resource usage. Smaller values will use less memory but may result in lower quality images.\n",
        "\n",
        "Run the cell to generate and display the image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "prompt = \"A beautiful landscape with mountains and a river\"\n",
        "height = 256\n",
        "width = 256\n",
        "num_inference_steps = 20\n",
        "\n",
        "with torch.inference_mode():\n",
        "    image = pipe(prompt, height=height, width=width, num_inference_steps=num_inference_steps).images[0]\n",
        "from IPython.display import display\n",
        "display(image)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Clean Up\n",
        "\n",
        "After generating the image, you can run the following cell to clear the GPU memory, which can help if you plan to generate multiple images."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import gc\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Save Image\n",
        "\n",
        "If you want to save the generated image, run the following cell:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "image.save(\"generated_image.png\")"
      ]
    }
  ],
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      "display_name": "Python 3",
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      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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