{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "machine_shape": "hm",
      "gpuType": "T4",
      "provenance": []
    },
    "accelerator": "GPU",
    "kaggle": {
      "accelerator": "gpu"
    },
    "language_info": {
      "name": "python"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade diffusers[torch] safetensors opencv-python\n",
        "!pip install transformers"
      ],
      "metadata": {
        "id": "gMbeyk56crpp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Local Inference on GPU\n",
        "Model page: https://huggingface.co/Sami-AI-Lab/Guovdageaidnu_male_gakti\n",
        "\n",
        "⚠️ If the generated code snippets do not work, please open an issue on either the [model repo](https://huggingface.co/Sami-AI-Lab/Guovdageaidnu_male_gakti)\n",
        "\t\t\tand/or on [huggingface.js](https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/model-libraries-snippets.ts) 🙏"
      ],
      "metadata": {
        "id": "-HfTBfzhcrpq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from diffusers import StableDiffusionPipeline\n",
        "from diffusers import (\n",
        "    DDPMScheduler,\n",
        "    DDIMScheduler,\n",
        "    PNDMScheduler,\n",
        "    LMSDiscreteScheduler,\n",
        "    EulerDiscreteScheduler,\n",
        "    EulerAncestralDiscreteScheduler,\n",
        "    DPMSolverMultistepScheduler,\n",
        ")\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "\n",
        "begin = time.time()\n",
        "\n",
        "#For advanced users, see the StableDiffusionPipeline document for more information on adjusting settings https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img\n",
        "pipe = StableDiffusionPipeline.from_pretrained(\"Lykon/dreamshaper-8\",\n",
        "    # use_safetensors=True,\n",
        "    # local_files_only=True,\n",
        "    # device_map=\"auto\",\n",
        "    # safety_checker=True\n",
        "    cache_dir=\"models\", adapter_name=\"checkpoint\")\n",
        "pipe.load_lora_weights(\"Sami-AI-Lab/historikklavvo\", adapter_name=\"lora\")\n",
        "#Adjust adapter_weights to tune the strength of the LoRA with numbers between 0 - 1\n",
        "pipe.set_adapters([\"lora\"], adapter_weights=[0.90])\n",
        "\n",
        "# by default uses the PNDM scheduler with 50 steps, you can change the scheduler here\n",
        "# pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)\n",
        "\n",
        "print(f\"Load             : {time.time()-begin}\")\n",
        "pipe = pipe.to(\"cuda\")\n",
        "print(f\"Load+cuda        : {time.time()-begin}\")\n",
        "\n",
        "# This is your text-2-Image prompt below, adjust within quotation marks to try different generations.\n",
        "prompt = \"historikklavvo, a traditional Sámi lavvo, tent\"\n",
        "image = pipe(prompt, num_inference_steps=25).images[0]\n",
        "\n",
        "# If the image is a torch tensor, convert it to numpy array\n",
        "if torch.is_tensor(image):\n",
        "    image = image.cpu().numpy()\n",
        "\n",
        "# Display the image\n",
        "plt.imshow(image)\n",
        "plt.axis('off')  # To turn off axis numbers\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "tufsR6jyc0U0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5YzjFlUIc88M"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}