dreamlike ai.ipynb

dreamlike-ai.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/alighamdan/c558caec0fb0b5bec249d9957e4d425e/dreamlike-ai.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# INSTALL DEPENDENCIES "
      ],
      "metadata": {
        "id": "Aek_U9pkmfYJ"
      }
    },
    {
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      "execution_count": 4,
      "metadata": {
        "colab": {
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        },
        "id": "pGQaxtC2lxL7",
        "outputId": "da89fdef-bbc3-4b1f-d543-5ff3ee9777f9"
      },
      "outputs": [],
      "source": [
        "! pip install gradio torch diffusers\n",
        "! pip install transformers\n",
        "! pip install accelerate"
      ]
    },
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      "cell_type": "code",
      "source": [
        "from diffusers import StableDiffusionPipeline\n",
        "import torch\n",
        "from IPython.display import Image\n",
        "\n",
        "model_id = \"dreamlike-art/dreamlike-diffusion-1.0\" # can be: dreamlike-art/dreamlike-photoreal-1.0\n",
        "pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to('cuda')"
      ],
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        "colab": {
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      "execution_count": null,
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        "prompt = \"cute young girl with orange hair and red eyes with small smile\"\n",
        "negative_prompt = \"\"\n",
        "steps = 30\n",
        "scale = 7.5\n",
        "width = 768\n",
        "height = 768\n",
        "seed = 0\n",
        "result = pipe(\n",
        "    prompt, \n",
        "    negative_prompt = negative_prompt, \n",
        "    num_inference_steps=steps, \n",
        "    guidance_scale = scale, \n",
        "    width=width, \n",
        "    height=height, \n",
        "    generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None\n",
        "  )\n",
        "\n",
        "result.images[0].save(\"./result.jpg\")\n",
        "\n",
        "Image('./result.jpg')"
      ],
      "metadata": {
        "id": "gAL0Y3PRnoF_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}