machine-learning/control-image-generation-with-controlnet/ControlNet_PythonCodeTutorial.ipynb

{
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8_55MSKhrtss",
        "outputId": "44fe5aac-1542-4f19-b926-6fee990835e9"
      },
      "outputs": [],
      "source": [
        "!pip install -qU xformers diffusers transformers accelerate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xrl7OL6BrtpU",
        "outputId": "9525d6ad-4af6-4a9f-98b0-6d76308fdf13"
      },
      "outputs": [],
      "source": [
        "!pip install -qU  controlnet_aux\n",
        "!pip install opencv-contrib-python"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "h6XPbq0FrPQG"
      },
      "source": [
        "# Open Pose"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0wEufS0RrObO",
        "outputId": "77977e89-4b72-4a5d-b39a-801953031247"
      },
      "outputs": [],
      "source": [
        "from PIL import Image\n",
        "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n",
        "import torch\n",
        "from controlnet_aux import OpenposeDetector\n",
        "from diffusers.utils import load_image\n",
        "from tqdm import tqdm\n",
        "from torch import autocast"
      ]
    },
    {
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        },
        "id": "ts8fiPLordOD",
        "outputId": "08a7450f-8e53-48a4-9c2d-2994353c3140"
      },
      "outputs": [],
      "source": [
        "# load the openpose model\n",
        "openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')\n",
        "\n",
        "# load the controlnet for openpose\n",
        "controlnet = ControlNetModel.from_pretrained(\n",
        "    \"lllyasviel/sd-controlnet-openpose\", torch_dtype=torch.float16\n",
        ")\n",
        "\n",
        "# define stable diffusion pipeline with controlnet\n",
        "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n",
        "    \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n",
        ")\n",
        "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LPdQiPkXri1y"
      },
      "outputs": [],
      "source": [
        "# Remove if you do not have xformers installed\n",
        "# see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers\n",
        "# for installation instructions\n",
        "pipe.enable_xformers_memory_efficient_attention()\n",
        "pipe.enable_model_cpu_offload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "f_HH_n8WrdQN",
        "outputId": "2432a1d8-cdec-4b0e-ca8e-d62ce008136e"
      },
      "outputs": [],
      "source": [
        "image_input = load_image(\"https://cdn.pixabay.com/photo/2016/05/17/22/19/fashion-1399344_640.jpg\")\n",
        "image_input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "AUJikHhlrdSX",
        "outputId": "398bd929-ff3e-4f05-cad6-a3079b911d75"
      },
      "outputs": [],
      "source": [
        "image_pose = openpose(image_input)\n",
        "image_pose"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
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        "id": "0MfsiN_Jri4G",
        "outputId": "5286a2ac-13a3-4c35-80ab-a9968c9c8602"
      },
      "outputs": [],
      "source": [
        "image_output = pipe(\"A professional photograph of a male fashion model\", image_pose, num_inference_steps=20).images[0]\n",
        "image_output"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "HEiobo68Kzso"
      },
      "source": [
        "# Custom implementation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0y9iWI9cK17f"
      },
      "outputs": [],
      "source": [
        "class ControlNetDiffusionPipelineCustom:\n",
        "    \"\"\"custom implementation of the ControlNet Diffusion Pipeline\"\"\"\n",
        "\n",
        "    def __init__(self,\n",
        "                 vae,\n",
        "                 tokenizer,\n",
        "                 text_encoder,\n",
        "                 unet,\n",
        "                 controlnet,\n",
        "                 scheduler,\n",
        "                 image_processor,\n",
        "                 control_image_processor):\n",
        "\n",
        "        self.vae = vae\n",
        "        self.tokenizer = tokenizer\n",
        "        self.text_encoder = text_encoder\n",
        "        self.unet = unet\n",
        "        self.scheduler = scheduler\n",
        "        self.controlnet = controlnet\n",
        "        self.image_processor = image_processor\n",
        "        self.control_image_processor = control_image_processor\n",
        "        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "\n",
        "\n",
        "    def get_text_embeds(self, text):\n",
        "        \"\"\"returns embeddings for the given `text`\"\"\"\n",
        "\n",
        "        # tokenize the text\n",
        "        text_input = self.tokenizer(text,\n",
        "                                    padding='max_length',\n",
        "                                    max_length=tokenizer.model_max_length,\n",
        "                                    truncation=True,\n",
        "                                    return_tensors='pt')\n",
        "        # embed the text\n",
        "        with torch.no_grad():\n",
        "            text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0]\n",
        "        return text_embeds\n",
        "\n",
        "\n",
        "\n",
        "    def get_prompt_embeds(self, prompt):\n",
        "        \"\"\"returns prompt embeddings based on classifier free guidance\"\"\"\n",
        "\n",
        "        if isinstance(prompt, str):\n",
        "            prompt = [prompt]\n",
        "        # get conditional prompt embeddings\n",
        "        cond_embeds = self.get_text_embeds(prompt)\n",
        "        # get unconditional prompt embeddings\n",
        "        uncond_embeds = self.get_text_embeds([''] * len(prompt))\n",
        "        # concatenate the above 2 embeds\n",
        "        prompt_embeds = torch.cat([uncond_embeds, cond_embeds])\n",
        "        return prompt_embeds\n",
        "\n",
        "\n",
        "    def transform_image(self, image):\n",
        "        \"\"\"convert image from pytorch tensor to PIL format\"\"\"\n",
        "\n",
        "        image = self.image_processor.postprocess(image, output_type='pil')\n",
        "        return image\n",
        "\n",
        "\n",
        "\n",
        "    def get_initial_latents(self, height, width, num_channels_latents, batch_size):\n",
        "        \"\"\"returns noise latent tensor of relevant shape scaled by the scheduler\"\"\"\n",
        "\n",
        "        image_latents = torch.randn((batch_size,\n",
        "                                   num_channels_latents,\n",
        "                                   height // 8,\n",
        "                                   width // 8)).to(self.device)\n",
        "        # scale the initial noise by the standard deviation required by the scheduler\n",
        "        image_latents = image_latents * self.scheduler.init_noise_sigma\n",
        "        return image_latents\n",
        "\n",
        "\n",
        "\n",
        "    def denoise_latents(self,\n",
        "                        prompt_embeds,\n",
        "                        controlnet_image,\n",
        "                        timesteps,\n",
        "                        latents,\n",
        "                        guidance_scale=7.5):\n",
        "        \"\"\"denoises latents from noisy latent to a meaningful latent as conditioned by controlnet\"\"\"\n",
        "\n",
        "        # use autocast for automatic mixed precision (AMP) inference\n",
        "        with autocast('cuda'):\n",
        "            for i, t in tqdm(enumerate(timesteps)):\n",
        "                # duplicate image latents to do classifier free guidance\n",
        "                latent_model_input = torch.cat([latents] * 2)\n",
        "                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n",
        "\n",
        "                control_model_input = latents\n",
        "                controlnet_prompt_embeds = prompt_embeds\n",
        "\n",
        "                # get output from the control net blocks\n",
        "                down_block_res_samples, mid_block_res_sample = self.controlnet(\n",
        "                    control_model_input,\n",
        "                    t,\n",
        "                    encoder_hidden_states=controlnet_prompt_embeds,\n",
        "                    controlnet_cond=controlnet_image,\n",
        "                    conditioning_scale=1.0,\n",
        "                    return_dict=False,\n",
        "                )\n",
        "\n",
        "                # predict noise residuals\n",
        "                with torch.no_grad():\n",
        "                    noise_pred = self.unet(\n",
        "                        latent_model_input,\n",
        "                        t,\n",
        "                        encoder_hidden_states=prompt_embeds,\n",
        "                        down_block_additional_residuals=down_block_res_samples,\n",
        "                        mid_block_additional_residual=mid_block_res_sample,\n",
        "                    )['sample']\n",
        "\n",
        "                # separate predictions for unconditional and conditional outputs\n",
        "                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n",
        "\n",
        "                # perform guidance\n",
        "                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n",
        "\n",
        "                # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n",
        "                latents = self.scheduler.step(noise_pred, t, latents)['prev_sample']\n",
        "\n",
        "        return latents\n",
        "\n",
        "\n",
        "\n",
        "    def prepare_controlnet_image(self,\n",
        "                                 image,\n",
        "                                 height,\n",
        "                                 width):\n",
        "        \"\"\"preprocesses the controlnet image\"\"\"\n",
        "\n",
        "        # process the image\n",
        "        image = self.control_image_processor.preprocess(image, height, width).to(dtype=torch.float32)\n",
        "        # send image to CUDA\n",
        "        image = image.to(self.device)\n",
        "        # repeat the image for classifier free guidance\n",
        "        image = torch.cat([image] * 2)\n",
        "        return image\n",
        "\n",
        "\n",
        "\n",
        "    def __call__(self,\n",
        "                 prompt,\n",
        "                 image,\n",
        "                 num_inference_steps=20,\n",
        "                 guidance_scale=7.5,\n",
        "                 height=512, width=512):\n",
        "        \"\"\"generates new image based on the `prompt` and the `image`\"\"\"\n",
        "\n",
        "        # encode input prompt\n",
        "        prompt_embeds = self.get_prompt_embeds(prompt)\n",
        "\n",
        "        # prepare image for controlnet\n",
        "        controlnet_image = self.prepare_controlnet_image(image, height, width)\n",
        "        height, width = controlnet_image.shape[-2:]\n",
        "\n",
        "        # prepare timesteps\n",
        "        self.scheduler.set_timesteps(num_inference_steps)\n",
        "        timesteps = self.scheduler.timesteps\n",
        "\n",
        "        # prepare the initial image in the latent space (noise on which we will do reverse diffusion)\n",
        "        num_channels_latents = self.unet.config.in_channels\n",
        "        batch_size = prompt_embeds.shape[0] // 2\n",
        "        latents = self.get_initial_latents(height, width, num_channels_latents, batch_size)\n",
        "\n",
        "        # denoise latents\n",
        "        latents = self.denoise_latents(prompt_embeds,\n",
        "                                       controlnet_image,\n",
        "                                       timesteps,\n",
        "                                       latents,\n",
        "                                       guidance_scale)\n",
        "\n",
        "        # decode latents to get the image into pixel space\n",
        "        latents = latents.to(torch.float16) # change dtype of latents since\n",
        "        image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n",
        "\n",
        "        # convert to PIL Image format\n",
        "        image = image.detach() # detach to remove any computed gradients\n",
        "        image = self.transform_image(image)\n",
        "\n",
        "        return image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kugjwBC3K4JK"
      },
      "outputs": [],
      "source": [
        "# We can get all the components from the ControlNet Diffusion Pipeline (the one implemented by Hugging Face as well)\n",
        "vae = pipe.vae\n",
        "tokenizer = pipe.tokenizer\n",
        "text_encoder = pipe.text_encoder\n",
        "unet = pipe.unet\n",
        "controlnet = pipe.controlnet\n",
        "scheduler = pipe.scheduler\n",
        "image_processor = pipe.image_processor\n",
        "control_image_processor = pipe.control_image_processor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6ooKnlkVK4LV"
      },
      "outputs": [],
      "source": [
        "custom_pipe = ControlNetDiffusionPipelineCustom(vae, tokenizer, text_encoder, unet, controlnet, scheduler, image_processor, control_image_processor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "BYW8mEH2K4NY",
        "outputId": "6e610454-e4c4-4c9d-c091-23cf9cbecec9"
      },
      "outputs": [],
      "source": [
        "# sample image 1\n",
        "images_custom = custom_pipe(\"a fashion model wearing a beautiful dress\", image_pose, num_inference_steps=20)\n",
        "images_custom[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "w4XGMCsUK4RA",
        "outputId": "59e49ba7-d3c3-4fd7-bf0c-ed9ac23695dd"
      },
      "outputs": [],
      "source": [
        "# sample image 2\n",
        "images_custom = custom_pipe(\"A male fashion model posing in a museum\", image_pose, num_inference_steps=20)\n",
        "images_custom[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "GU9P7QMacZTW",
        "outputId": "b3c7e3aa-04b1-47c9-e29d-a8b68457b8e8"
      },
      "outputs": [],
      "source": [
        "# sample image with a different prompt\n",
        "images_custom = custom_pipe(\"A professional ice skater wearing a dark blue jacket around sunset, realistic, UHD\", image_pose, num_inference_steps=20)\n",
        "images_custom[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tXShSB0Fd7qd"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9dW5PCk0d7t7"
      },
      "outputs": [],
      "source": []
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "fwnnyHq3oi7O"
      },
      "source": [
        "# Canny"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1L96sjJ_oi7P"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "from PIL import Image\n",
        "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n",
        "import torch\n",
        "import numpy as np\n",
        "from diffusers.utils import load_image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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          ]
        },
        "id": "Dhg4rTN3oi7P",
        "outputId": "f2114cf6-bbc5-4e5e-827e-e5fc0a92351e"
      },
      "outputs": [],
      "source": [
        "# load the controlnet model for canny edge detection\n",
        "controlnet = ControlNetModel.from_pretrained(\n",
        "    \"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16\n",
        ")\n",
        "\n",
        "# load the stable diffusion pipeline with controlnet\n",
        "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n",
        "    \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n",
        ")\n",
        "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H-m6uNFCoi7P"
      },
      "outputs": [],
      "source": [
        "# enable efficient implementations using xformers for faster inference\n",
        "pipe.enable_xformers_memory_efficient_attention()\n",
        "pipe.enable_model_cpu_offload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "ATEwEV-6oi7Q",
        "outputId": "055658c9-5ab9-4496-a407-f4a213f84bd8"
      },
      "outputs": [],
      "source": [
        "image_input = load_image(\"https://cdn.pixabay.com/photo/2023/06/03/16/05/spotted-laughingtrush-8037974_640.png\")\n",
        "image_input = np.array(image_input)\n",
        "\n",
        "Image.fromarray(image_input)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "K2c64abboi7Q",
        "outputId": "fd9de66a-d340-4b1e-bc51-7e9c96f1f80a"
      },
      "outputs": [],
      "source": [
        "# define parameters from canny edge detection\n",
        "low_threshold = 100\n",
        "high_threshold = 200\n",
        "\n",
        "# do canny edge detection\n",
        "image_canny = cv2.Canny(image_input, low_threshold, high_threshold)\n",
        "\n",
        "# convert to PIL image format\n",
        "image_canny = image_canny[:, :, None]\n",
        "image_canny = np.concatenate([image_canny, image_canny, image_canny], axis=2)\n",
        "image_canny = Image.fromarray(image_canny)\n",
        "\n",
        "image_canny"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473,
          "referenced_widgets": [
            "27d1ad377e6c45d7b4b1c49786438015",
            "475c331703bb43adb142f6c9cc41a3bf",
            "10be4b9595c84f36aeab9593e63440e6",
            "645ebe129fd544a4b9383dfc1414cf0a",
            "51605e182e344a94a99c50fcaaef17b1",
            "c45aec98de234d3982ff83924dce8d7a",
            "942ace43ed1d48aeafc6c5e17f14e034",
            "51890b032fbb48139e11d69850a6db57",
            "30408dcf6dd14e27bab9cc06cc664c4c",
            "b539338d7bf84f50a8c57f90986a8d9a",
            "d16de73052ac42e8a8065d4dc54ebe51"
          ]
        },
        "id": "zac6FpNwoi7Q",
        "outputId": "8bf84b99-fe11-43d2-82e2-f2b35ae99d5a"
      },
      "outputs": [],
      "source": [
        "image_output = pipe(\"bird\", image_canny, num_inference_steps=20).images[0]\n",
        "image_output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473,
          "referenced_widgets": [
            "48a26564f08f43dd962464feb762c232",
            "c6965bc561db4c519b751c5b7bff96a8",
            "032471548f5c45ed89d8a62db800c7fc",
            "0af102aac55747fcb8854b8b5ed2dd27",
            "a086d1c6722547d5be5939cf4284a1ef",
            "ba3cf4ae2c4e45e58abb82d2491fb7ba",
            "c64f35616d8e4b4bb129bc7aaa4ae889",
            "01fc82bb9bb84c628890b9a2349a6e6e",
            "f66573653707458f80b29a40e1193d31",
            "7b0ece2c75614540a942214d1f527f91",
            "0d3e1914b85047c7af6b6bd4fd94e197"
          ]
        },
        "id": "DXrdLeZrplMW",
        "outputId": "31bb0440-493a-4ee8-edc0-23c090f679c4"
      },
      "outputs": [],
      "source": [
        "image_output = pipe(\"a cute blue bird with colorful aesthetic feathers\", image_canny, num_inference_steps=20).images[0]\n",
        "image_output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-Yti9Dg8ofxd"
      },
      "outputs": [],
      "source": []
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "-o-9jxdtpvgi"
      },
      "source": [
        "# Depth"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rgwvAu2xpvgj"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline\n",
        "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import torch\n",
        "from diffusers.utils import load_image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IcxFcjHspvgj",
        "outputId": "79bed3a2-27b2-411e-a748-22949d5cac62"
      },
      "outputs": [],
      "source": [
        "# load the depth estimator model\n",
        "depth_estimator = pipeline('depth-estimation')\n",
        "\n",
        "# load the controlnet model for depth estimation\n",
        "controlnet = ControlNetModel.from_pretrained(\n",
        "    \"lllyasviel/sd-controlnet-depth\", torch_dtype=torch.float16\n",
        ")\n",
        "\n",
        "# load the stable diffusion pipeline with controlnet\n",
        "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n",
        "    \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n",
        ")\n",
        "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QfZNDXHgpvgj"
      },
      "outputs": [],
      "source": [
        "# enable efficient implementations using xformers for faster inference\n",
        "pipe.enable_xformers_memory_efficient_attention()\n",
        "pipe.enable_model_cpu_offload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "t0_6mNOgpvgj",
        "outputId": "8dd080e3-9fbb-4bc5-bd4c-375a4e1f18ee"
      },
      "outputs": [],
      "source": [
        "image_input = load_image(\"https://huggingface.co/lllyasviel/sd-controlnet-depth/resolve/main/images/stormtrooper.png\")\n",
        "image_input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "oTim6FQrpvgk",
        "outputId": "4cc296a7-0822-4012-a07c-232f14bb039e"
      },
      "outputs": [],
      "source": [
        "# get depth estimates\n",
        "image_depth = depth_estimator(image_input)['depth']\n",
        "\n",
        "# convert to PIL image format\n",
        "image_depth = np.array(image_depth)\n",
        "image_depth = image_depth[:, :, None]\n",
        "image_depth = np.concatenate([image_depth, image_depth, image_depth], axis=2)\n",
        "image_depth = Image.fromarray(image_depth)\n",
        "\n",
        "image_depth"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505,
          "referenced_widgets": [
            "173d24393b854ad0a03fd02d56c7d037",
            "a33e6193984b46d4bb9b4187d726009e",
            "230461cc201f4535a9f9bcf7a3ae7669",
            "4900e42f3bfa4a6096b16d47d38687dc",
            "2112261a320b4b9297aa3edc85fa2316",
            "1c65960b291c4d92bced5bca619e8256",
            "194f16cb7fbb42a78d9237e4e6654b58",
            "b87077da3f824478ac88369582d77e8e",
            "6f9f95e2d503403f83be05364b4199cd",
            "c39f69cd4e8b4bca8e95bda3b06a74f1",
            "f8dbc4c64a0d440e9660b0d7ef083b5c"
          ]
        },
        "id": "zK5HpMNdpvgq",
        "outputId": "2b2e889d-8920-404f-e8de-e2753caebf05"
      },
      "outputs": [],
      "source": [
        "image_output = pipe(\"Darth Vader giving lecture\", image_depth, num_inference_steps=20).images[0]\n",
        "image_output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505,
          "referenced_widgets": [
            "083aa6ce3274445c830c4157dc9ea4be",
            "be56ad8a63884a33a22bfb3b39da1cc0",
            "83b4c17fa697445f86f7f034fe670934",
            "c3dc31d7d2be42be88fe03fa33e5f20d",
            "2c38cd148fe74a5897c3319be42b7d7e",
            "c44f196498724b1b80b25bda832dc310",
            "069b424c2d3846b8ba69a455dbda1f88",
            "707ce79ce0da4f35ad6b8f636d2901f8",
            "261aabd2415d43809b669b49f3899504",
            "7427e45c55994a6fa304d6d58ffeb1fb",
            "2df0162802aa488dbddb9a7376954865"
          ]
        },
        "id": "ulgn37vmrUS8",
        "outputId": "aba588b6-5de1-44c5-8d96-79ac14a89f9a"
      },
      "outputs": [],
      "source": [
        "image_output = pipe(\"A realistic, aesthetic portrait style photograph of Darth Vader giving lecture, 8k, unreal engine\", image_depth, num_inference_steps=20).images[0]\n",
        "image_output"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "-uc3OBwGryX9"
      },
      "source": [
        "# Normal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vZ8NidcHryYF"
      },
      "outputs": [],
      "source": [
        "from PIL import Image\n",
        "from transformers import pipeline\n",
        "import numpy as np\n",
        "import cv2\n",
        "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n",
        "import torch\n",
        "from diffusers.utils import load_image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214,
          "referenced_widgets": [
            "f0ddc614bd9a40468c083db3a457e1c1",
            "a27de7fc1bfa486f9ff8829d4d4198f6",
            "47507d2cf4e04f2f927ee369c6d7b4d4",
            "e11151541738459ba16b582a9edefd2c",
            "33c23b5b6adf403dba45e9f8e64e37dc",
            "a9a5528792504d12bf153d2b02fd3847",
            "439ee7b8de924bf6ba5ee4ef22e0e659",
            "b4e865be02fb4290849b676f4dd18ed0",
            "87d4c1e6a2a047f887704f6b0a148d2e",
            "b74532a8bd8e49f2a8286a1eed72a0d3",
            "dd66e37aec9449de944edfa6dacdfc5f",
            "57216de8dfcc4ab78bfb8c41fae856b4",
            "c85b3e0dd20f414181d768cac5c9ab4e",
            "53774d0251ea4e4a884fc265a5ce561b",
            "30cdb95dd5d746b1adacaaf823b21b89",
            "bfd0a68276654b4997b802bc6d47f684",
            "dd81ef8c35384eadb1572400c775f789",
            "de80e8cbbd25433c88267a1f997772e7",
            "e116ebfa368d424ca64eea23a82ef959",
            "2b92efb0492b4af6b50e2d5754212b5c",
            "2fee0c2fcb3b4705ad4cc212d6138ec9",
            "5f012cede7a0405fbb12550072bccbc2",
            "a999af1084134026904611113b3feac6",
            "814f29773b6b40d99d03320a3f5fe7d4",
            "9f7e4357439944e392d0c9e93068c898",
            "8e31c3cc06bd4e9b92c3b1e6d5a9d61f",
            "9dc6d26cdb7945ffb31b2c381412a383",
            "36de2037f4b841afb366f27a7eb08396",
            "cb805d94485c4ff5a0aaec67e6cdb827",
            "d923cb2715554512a7faf5b5d73a4ed8",
            "4399ee6304e94797ac6f36318165d4cc",
            "7c2b45b1cf2a485b93e52b2dee04a278",
            "851c4973cb0c4fbe91536a09f006ad43",
            "7d10f1e6258943dd86d9c75aa17553c8",
            "193fa11998404439b90913f5f5b4619e",
            "0383c8a7e3384eec9205dc8f37654ee4",
            "4615b3c5ce674070840fbb7bcb54f262",
            "b57f5b94be66435fab8e8aa9b877848c",
            "8b7c3bcf27644ac09871f733662c4d8b",
            "42cabc32ffc34ddd924fde45571ada38",
            "58c63ae0605a4f1192c52e4306602fd7",
            "55e0a986be424043846c0e863158a1bd",
            "23a9e406e89041f8ab9a15eae4f9c61b",
            "f18d3449a23c485a85e38f0ced810c99",
            "1d4352761751419282c93eb85c0954c0",
            "b84453116ba14690b8ae8a8e723f8510",
            "6e14135b00e447c18a0a693d47f5c92b",
            "9a540d6ed00a424da320fd7a12c31ac5",
            "13f07d79c96146aaa1d7731526aaec29",
            "b54312c4603b400b97884eba33ad1095",
            "b93ed8d01dbd4e0a84ff01b2e15f68ec",
            "8119e3c1840d4e0b975055cb0c255208",
            "ad785ec6b7924f2ebcdf0957a16c656d",
            "d44b6d406224497d9db5c903c6972323",
            "d3010a25994f4f12b95922f2863f4f3b"
          ]
        },
        "id": "Rt4ecMkaryYG",
        "outputId": "f8ab6e1a-2257-48bd-9fa6-c7fe19188404"
      },
      "outputs": [],
      "source": [
        "# load the Dense Prediction Transformer (DPT) model for getting normal maps\n",
        "depth_estimator = pipeline(\"depth-estimation\", model =\"Intel/dpt-hybrid-midas\")\n",
        "\n",
        "# load the controlnet model for normal maps\n",
        "controlnet = ControlNetModel.from_pretrained(\n",
        "    \"fusing/stable-diffusion-v1-5-controlnet-normal\", torch_dtype=torch.float16\n",
        ")\n",
        "\n",
        "# load the stable diffusion pipeline with controlnet\n",
        "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n",
        "    \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n",
        ")\n",
        "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UoGdz3FkryYG"
      },
      "outputs": [],
      "source": [
        "# enable efficient implementations using xformers for faster inference\n",
        "pipe.enable_xformers_memory_efficient_attention()\n",
        "pipe.enable_model_cpu_offload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        },
        "id": "0WLxPD8fryYG",
        "outputId": "d605305f-4c8e-40dd-e238-13131f64c961"
      },
      "outputs": [],
      "source": [
        "image_input = load_image(\"https://cdn.pixabay.com/photo/2023/06/07/13/02/butterfly-8047187_1280.jpg\")\n",
        "image_input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "Uqhw6NR4ryYG",
        "outputId": "2f3c8930-541b-49d7-fb87-374972c078e5"
      },
      "outputs": [],
      "source": [
        "# do all the preprocessing to get the normal image\n",
        "image = depth_estimator(image_input)['predicted_depth'][0]\n",
        "\n",
        "image = image.numpy()\n",
        "\n",
        "image_depth = image.copy()\n",
        "image_depth -= np.min(image_depth)\n",
        "image_depth /= np.max(image_depth)\n",
        "\n",
        "bg_threhold = 0.4\n",
        "\n",
        "x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3)\n",
        "x[image_depth < bg_threhold] = 0\n",
        "\n",
        "y = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3)\n",
        "y[image_depth < bg_threhold] = 0\n",
        "\n",
        "z = np.ones_like(x) * np.pi * 2.0\n",
        "\n",
        "image = np.stack([x, y, z], axis=2)\n",
        "image /= np.sum(image ** 2.0, axis=2, keepdims=True) ** 0.5\n",
        "image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)\n",
        "image_normal = Image.fromarray(image)\n",
        "\n",
        "image_normal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433,
          "referenced_widgets": [
            "7aed2e247f5e488991be8b97ab6bae6a",
            "f8b042b72ea1403284bc45b55cbfcdcf",
            "9dcbc2386e3c4d6e820fd6baed91f0df",
            "67768915a19a448c94620204f861d005",
            "a218d578aded48ba8acca854622c14a9",
            "5cbe59686c0d475a8e2274ffcb64d992",
            "3bf3dd9f53354c758187674b5b1eac19",
            "a1ed5abc44e94c1ea19fa959fc95b91a",
            "7669fb4960904becb83cbdc4169a121b",
            "7699034aeb584f12864aaa30a7f2e6b5",
            "0762e7a5efb84a4b9bf60c5cf3235671"
          ]
        },
        "id": "_BLefbf1ryYG",
        "outputId": "be063e2c-4e78-4c1e-cbe3-98fd0dcdad72"
      },
      "outputs": [],
      "source": [
        "image_output = pipe(\"A colorful butterfly sitting on apples\", image_normal, num_inference_steps=20).images[0]\n",
        "image_output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433,
          "referenced_widgets": [
            "ad63323795ae48679bb40de0056219d3",
            "4cb413851dc3469a8ea04e40373b11de",
            "21549aef7c6f4c64b8eb676c6dd105e2",
            "7cd3b5a0ce274ae5b5954a3c7d9d3bb4",
            "4a3a0d14e22f4a6e9dbb5fb35bc33e8d",
            "8d2f2b219308442db07d23b057f8e7cf",
            "7b9508943bee4f76b996ba561d4bbe9b",
            "94cc8073f13f45b085e1f23d3d4bbbc9",
            "cd502f2ef8814082979d147f08177ff6",
            "f357992a28754d7689721ebda28f0b0f",
            "2f8938d9a9254868869767a332bdf84f"
          ]
        },
        "id": "c-iOJPe1ryYH",
        "outputId": "ee338887-1753-47f0-aed3-838749415d3a"
      },
      "outputs": [],
      "source": [
        "image_output = pipe(\"A beautiful design\", image_normal, num_inference_steps=20).images[0]\n",
        "image_output"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "_zlU00SCzecq"
      },
      "source": [
        "# Segmentation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iLEetydkzec_"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoImageProcessor, UperNetForSemanticSegmentation\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import torch\n",
        "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n",
        "from diffusers.utils import load_image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214,
          "referenced_widgets": [
            "cbbf80dd8dc9471197c49732ae4d08ee",
            "0a8a88ac007a41e4b0096fb114fe8a47",
            "d4c98d8a36eb4f769e23aa3f36535a0f",
            "20f315f7aae840d19ee8a068c68ff63c",
            "c420c12b2a76457c9ec7bb9db8eac9b3",
            "e38139b6898e4897b67aac89ee982c7a",
            "5e144bb789054a88a43db4329bb2df05",
            "15de6a583c5e4525a559749577fd4331",
            "4af5ce87c2a243238375d7edf93d08b2",
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        },
        "id": "BRZqE7YLzedA",
        "outputId": "b9f4d2e9-2667-4992-f3d5-f574108640ec"
      },
      "outputs": [],
      "source": [
        "# load the image processor and the model for doing segmentation\n",
        "image_processor = AutoImageProcessor.from_pretrained(\"openmmlab/upernet-convnext-small\")\n",
        "image_segmentor = UperNetForSemanticSegmentation.from_pretrained(\"openmmlab/upernet-convnext-small\")\n",
        "\n",
        "# load the controlnet model for semantic segmentation\n",
        "controlnet = ControlNetModel.from_pretrained(\n",
        "    \"lllyasviel/sd-controlnet-seg\", torch_dtype=torch.float16\n",
        ")\n",
        "\n",
        "# load the stable diffusion pipeline with controlnet\n",
        "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n",
        "    \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n",
        ")\n",
        "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZzEaHeM2zedA"
      },
      "outputs": [],
      "source": [
        "# enable efficient implementations using xformers for faster inference\n",
        "pipe.enable_xformers_memory_efficient_attention()\n",
        "pipe.enable_model_cpu_offload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3kZI0eD2zkfm"
      },
      "outputs": [],
      "source": [
        "# define color palette that is used by the semantic segmentation models\n",
        "\n",
        "palette = np.asarray([\n",
        "    [0, 0, 0],\n",
        "    [120, 120, 120],\n",
        "    [180, 120, 120],\n",
        "    [6, 230, 230],\n",
        "    [80, 50, 50],\n",
        "    [4, 200, 3],\n",
        "    [120, 120, 80],\n",
        "    [140, 140, 140],\n",
        "    [204, 5, 255],\n",
        "    [230, 230, 230],\n",
        "    [4, 250, 7],\n",
        "    [224, 5, 255],\n",
        "    [235, 255, 7],\n",
        "    [150, 5, 61],\n",
        "    [120, 120, 70],\n",
        "    [8, 255, 51],\n",
        "    [255, 6, 82],\n",
        "    [143, 255, 140],\n",
        "    [204, 255, 4],\n",
        "    [255, 51, 7],\n",
        "    [204, 70, 3],\n",
        "    [0, 102, 200],\n",
        "    [61, 230, 250],\n",
        "    [255, 6, 51],\n",
        "    [11, 102, 255],\n",
        "    [255, 7, 71],\n",
        "    [255, 9, 224],\n",
        "    [9, 7, 230],\n",
        "    [220, 220, 220],\n",
        "    [255, 9, 92],\n",
        "    [112, 9, 255],\n",
        "    [8, 255, 214],\n",
        "    [7, 255, 224],\n",
        "    [255, 184, 6],\n",
        "    [10, 255, 71],\n",
        "    [255, 41, 10],\n",
        "    [7, 255, 255],\n",
        "    [224, 255, 8],\n",
        "    [102, 8, 255],\n",
        "    [255, 61, 6],\n",
        "    [255, 194, 7],\n",
        "    [255, 122, 8],\n",
        "    [0, 255, 20],\n",
        "    [255, 8, 41],\n",
        "    [255, 5, 153],\n",
        "    [6, 51, 255],\n",
        "    [235, 12, 255],\n",
        "    [160, 150, 20],\n",
        "    [0, 163, 255],\n",
        "    [140, 140, 140],\n",
        "    [250, 10, 15],\n",
        "    [20, 255, 0],\n",
        "    [31, 255, 0],\n",
        "    [255, 31, 0],\n",
        "    [255, 224, 0],\n",
        "    [153, 255, 0],\n",
        "    [0, 0, 255],\n",
        "    [255, 71, 0],\n",
        "    [0, 235, 255],\n",
        "    [0, 173, 255],\n",
        "    [31, 0, 255],\n",
        "    [11, 200, 200],\n",
        "    [255, 82, 0],\n",
        "    [0, 255, 245],\n",
        "    [0, 61, 255],\n",
        "    [0, 255, 112],\n",
        "    [0, 255, 133],\n",
        "    [255, 0, 0],\n",
        "    [255, 163, 0],\n",
        "    [255, 102, 0],\n",
        "    [194, 255, 0],\n",
        "    [0, 143, 255],\n",
        "    [51, 255, 0],\n",
        "    [0, 82, 255],\n",
        "    [0, 255, 41],\n",
        "    [0, 255, 173],\n",
        "    [10, 0, 255],\n",
        "    [173, 255, 0],\n",
        "    [0, 255, 153],\n",
        "    [255, 92, 0],\n",
        "    [255, 0, 255],\n",
        "    [255, 0, 245],\n",
        "    [255, 0, 102],\n",
        "    [255, 173, 0],\n",
        "    [255, 0, 20],\n",
        "    [255, 184, 184],\n",
        "    [0, 31, 255],\n",
        "    [0, 255, 61],\n",
        "    [0, 71, 255],\n",
        "    [255, 0, 204],\n",
        "    [0, 255, 194],\n",
        "    [0, 255, 82],\n",
        "    [0, 10, 255],\n",
        "    [0, 112, 255],\n",
        "    [51, 0, 255],\n",
        "    [0, 194, 255],\n",
        "    [0, 122, 255],\n",
        "    [0, 255, 163],\n",
        "    [255, 153, 0],\n",
        "    [0, 255, 10],\n",
        "    [255, 112, 0],\n",
        "    [143, 255, 0],\n",
        "    [82, 0, 255],\n",
        "    [163, 255, 0],\n",
        "    [255, 235, 0],\n",
        "    [8, 184, 170],\n",
        "    [133, 0, 255],\n",
        "    [0, 255, 92],\n",
        "    [184, 0, 255],\n",
        "    [255, 0, 31],\n",
        "    [0, 184, 255],\n",
        "    [0, 214, 255],\n",
        "    [255, 0, 112],\n",
        "    [92, 255, 0],\n",
        "    [0, 224, 255],\n",
        "    [112, 224, 255],\n",
        "    [70, 184, 160],\n",
        "    [163, 0, 255],\n",
        "    [153, 0, 255],\n",
        "    [71, 255, 0],\n",
        "    [255, 0, 163],\n",
        "    [255, 204, 0],\n",
        "    [255, 0, 143],\n",
        "    [0, 255, 235],\n",
        "    [133, 255, 0],\n",
        "    [255, 0, 235],\n",
        "    [245, 0, 255],\n",
        "    [255, 0, 122],\n",
        "    [255, 245, 0],\n",
        "    [10, 190, 212],\n",
        "    [214, 255, 0],\n",
        "    [0, 204, 255],\n",
        "    [20, 0, 255],\n",
        "    [255, 255, 0],\n",
        "    [0, 153, 255],\n",
        "    [0, 41, 255],\n",
        "    [0, 255, 204],\n",
        "    [41, 0, 255],\n",
        "    [41, 255, 0],\n",
        "    [173, 0, 255],\n",
        "    [0, 245, 255],\n",
        "    [71, 0, 255],\n",
        "    [122, 0, 255],\n",
        "    [0, 255, 184],\n",
        "    [0, 92, 255],\n",
        "    [184, 255, 0],\n",
        "    [0, 133, 255],\n",
        "    [255, 214, 0],\n",
        "    [25, 194, 194],\n",
        "    [102, 255, 0],\n",
        "    [92, 0, 255],\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        },
        "id": "GjsCwREqzedB",
        "outputId": "6ac8fcaf-a89b-4bef-bd9b-31fd753568d7"
      },
      "outputs": [],
      "source": [
        "image_input = load_image(\"https://cdn.pixabay.com/photo/2023/02/24/07/14/crowd-7810353_1280.jpg\")\n",
        "image_input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        },
        "id": "qK5gkfCIzedB",
        "outputId": "71e92874-04d1-459a-8cc6-89bc31796b2f"
      },
      "outputs": [],
      "source": [
        "# get the pixel values\n",
        "pixel_values = image_processor(image_input, return_tensors=\"pt\").pixel_values\n",
        "\n",
        "# do semantic segmentation\n",
        "with torch.no_grad():\n",
        "  outputs = image_segmentor(pixel_values)\n",
        "\n",
        "# post process the semantic segmentation\n",
        "seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image_input.size[::-1]])[0]\n",
        "\n",
        "# add colors to the different identified classes\n",
        "color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3\n",
        "for label, color in enumerate(palette):\n",
        "    color_seg[seg == label, :] = color\n",
        "\n",
        "# convert into PIL image format\n",
        "color_seg = color_seg.astype(np.uint8)\n",
        "image_seg = Image.fromarray(color_seg)\n",
        "\n",
        "image_seg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 897,
          "referenced_widgets": [
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      "outputs": [],
      "source": [
        "image_output = pipe(\"A crowd of people staring at a glorious painting\", image_seg, num_inference_steps=20).images[0]\n",
        "image_output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "LbWbrzpszedB",
        "outputId": "4bdd8997-e047-4fbb-ef58-6162ad40a4be"
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        "image_output = pipe(\"Aliens looking at earth from inside their spaceship from a window, not creepy, not scary, not gross, octane render, smooth\", image_seg, num_inference_steps=20).images[0]\n",
        "image_output"
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