machine-learning/edit-images-instruct-pix2pix/InstructPix2Pix_PythonCodeTutorial.ipynb

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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "ifD7o7YOTehV",
        "outputId": "8b42a9b5-d180-4986-b868-900a349b01bc"
      },
      "outputs": [],
      "source": [
        "!pip install -qU diffusers accelerate safetensors transformers"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "CqAnOnnOcp4W"
      },
      "source": [
        "# Hugging Face"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B2QCQqM9ckWd"
      },
      "outputs": [],
      "source": [
        "import PIL\n",
        "import requests\n",
        "import torch\n",
        "from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yzLP1VRHdBY7"
      },
      "outputs": [],
      "source": [
        "def download_image(url):\n",
        "    image = PIL.Image.open(requests.get(url, stream=True).raw)\n",
        "    image = PIL.ImageOps.exif_transpose(image)\n",
        "    image = image.convert(\"RGB\")\n",
        "    return image"
      ]
    },
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        },
        "id": "YUXSNFWQckY6",
        "outputId": "68b5d0d1-14e0-440d-8fd1-ffa021810b7a"
      },
      "outputs": [],
      "source": [
        "model_id = \"timbrooks/instruct-pix2pix\"\n",
        "pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None)\n",
        "pipe.to(\"cuda\")\n",
        "pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "8TsGblmidHew",
        "outputId": "2f023570-99cc-4a79-aca4-bbee89c22965"
      },
      "outputs": [],
      "source": [
        "url = \"https://cdn.pixabay.com/photo/2013/01/05/21/02/art-74050_640.jpg\"\n",
        "image = download_image(url)\n",
        "image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689,
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        },
        "id": "sAoN5eGgdEku",
        "outputId": "12f7ab74-6115-45ed-9092-936916fe6d69"
      },
      "outputs": [],
      "source": [
        "prompt = \"convert the lady into a highly detailed marble statue\"\n",
        "images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images\n",
        "images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689,
          "referenced_widgets": [
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          ]
        },
        "id": "frOPmxYejP1g",
        "outputId": "c0558f66-35ea-4a3e-f698-93d94894631c"
      },
      "outputs": [],
      "source": [
        "prompt = \"convert the lady into a highly detailed marble statue\"\n",
        "images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1.5).images\n",
        "images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "oIzmKWERckbG",
        "outputId": "2470148e-11be-4a08-c685-bb9eb5cf7f82"
      },
      "outputs": [],
      "source": [
        "url = \"https://cdn.pixabay.com/photo/2017/02/07/16/47/kingfisher-2046453_640.jpg\"\n",
        "image = download_image(url)\n",
        "image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473,
          "referenced_widgets": [
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        },
        "id": "p2rDwOJ2cken",
        "outputId": "4ba88e9a-9da1-4ad4-9f76-ee8bddd3e85e"
      },
      "outputs": [],
      "source": [
        "prompt = \"turn the bird to red\"\n",
        "images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images\n",
        "images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "Ka85jznIggXl",
        "outputId": "9e11a102-9fb1-49ab-e6e6-5bc69740af5e"
      },
      "outputs": [],
      "source": [
        "url = \"https://cdn.pixabay.com/photo/2018/05/08/06/52/vacation-3382400_640.jpg\"\n",
        "image = download_image(url)\n",
        "image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473,
          "referenced_widgets": [
            "c3514684a02e496a875cb8fa0e86b449",
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        },
        "id": "HQ6tutGNggZf",
        "outputId": "f84857ef-4a50-4305-dfd7-05840d893ae2"
      },
      "outputs": [],
      "source": [
        "prompt = \"turn the suitcase yellow\"\n",
        "images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.7).images\n",
        "images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mXejIqrcggbu"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CgINC4vJggfD"
      },
      "outputs": [],
      "source": []
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "mPGPiJhXcsr4"
      },
      "source": [
        "# Custom implementation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5aBPcPxqePIB"
      },
      "outputs": [],
      "source": [
        "from tqdm import tqdm\n",
        "from torch import autocast"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F2gPSPPyZP0_"
      },
      "outputs": [],
      "source": [
        "class InstructPix2PixPipelineCustom:\n",
        "    \"\"\"custom implementation of the InstructPix2Pix Pipeline\"\"\"\n",
        "\n",
        "    def __init__(self,\n",
        "                 vae,\n",
        "                 tokenizer,\n",
        "                 text_encoder,\n",
        "                 unet,\n",
        "                 scheduler,\n",
        "                 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.image_processor = 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",
        "    def get_prompt_embeds(self, prompt, prompt_negative=None):\n",
        "        \"\"\"returns prompt embeddings based on classifier free guidance\"\"\"\n",
        "\n",
        "        if isinstance(prompt, str):\n",
        "            prompt = [prompt]\n",
        "\n",
        "        if prompt_negative is None:\n",
        "            prompt_negative = ['']\n",
        "        elif isinstance(prompt_negative, str):\n",
        "            prompt_negative = [prompt_negative]\n",
        "\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(prompt_negative)\n",
        "\n",
        "        # instructpix2pix takes conditional embeds first, followed by unconditional embeds twice\n",
        "        # this is different from other diffusion pipelines\n",
        "        prompt_embeds = torch.cat([cond_embeds, uncond_embeds, uncond_embeds])\n",
        "        return prompt_embeds\n",
        "\n",
        "\n",
        "    def transform_image(self, image):\n",
        "        \"\"\"transform image from pytorch tensor to PIL format\"\"\"\n",
        "        image = self.image_processor.postprocess(image, output_type='pil')\n",
        "        return image\n",
        "\n",
        "\n",
        "\n",
        "    def get_image_latents(self, image):\n",
        "        \"\"\"get image latents to be used with classifier free guidance\"\"\"\n",
        "\n",
        "        # get conditional image embeds\n",
        "        image = image.to(self.device)\n",
        "        image_latents_cond = self.vae.encode(image).latent_dist.mode()\n",
        "\n",
        "        # get unconditional image embeds\n",
        "        image_latents_uncond = torch.zeros_like(image_latents_cond)\n",
        "        image_latents = torch.cat([image_latents_cond, image_latents_cond, image_latents_uncond])\n",
        "\n",
        "        return image_latents\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, num_channels_latents, height, width))\n",
        "        image_latents = image_latents.to(self.device)\n",
        "\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",
        "                        image_latents,\n",
        "                        timesteps,\n",
        "                        latents,\n",
        "                        guidance_scale,\n",
        "                        image_guidance_scale):\n",
        "        \"\"\"denoises latents from noisy latent to a meaningful latent as conditioned by image_latents\"\"\"\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 *thrice* to do classifier free guidance\n",
        "                latent_model_input = torch.cat([latents] * 3)\n",
        "                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n",
        "\n",
        "                latent_model_input = torch.cat([latent_model_input, image_latents], dim=1)\n",
        "\n",
        "\n",
        "                # predict noise residuals\n",
        "                with torch.no_grad():\n",
        "                    noise_pred = self.unet(latent_model_input, t,\n",
        "                        encoder_hidden_states=prompt_embeds)['sample']\n",
        "\n",
        "                # separate predictions into conditional (on text), conditional (on image) and unconditional outputs\n",
        "                noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3)\n",
        "                # perform guidance\n",
        "                noise_pred = (\n",
        "                    noise_pred_uncond\n",
        "                    + guidance_scale * (noise_pred_text - noise_pred_image)\n",
        "                    + image_guidance_scale * (noise_pred_image - noise_pred_uncond)\n",
        "                    )\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 __call__(self,\n",
        "                 prompt,\n",
        "                 image,\n",
        "                 prompt_negative=None,\n",
        "                 num_inference_steps=20,\n",
        "                 guidance_scale=7.5,\n",
        "                 image_guidance_scale=1.5):\n",
        "        \"\"\"generates new image based on the `prompt` and the `image`\"\"\"\n",
        "\n",
        "        # encode input prompt\n",
        "        prompt_embeds = self.get_prompt_embeds(prompt, prompt_negative)\n",
        "\n",
        "        # preprocess image\n",
        "        image = self.image_processor.preprocess(image)\n",
        "\n",
        "        # prepare image latents\n",
        "        image = image.half()\n",
        "        image_latents = self.get_image_latents(image)\n",
        "\n",
        "        # prepare timesteps\n",
        "        self.scheduler.set_timesteps(num_inference_steps)\n",
        "        timesteps = self.scheduler.timesteps\n",
        "\n",
        "        height_latents, width_latents = image_latents.shape[-2:]\n",
        "\n",
        "        # prepare the initial image in the latent space (noise on which we will do reverse diffusion)\n",
        "        num_channels_latents = self.vae.config.latent_channels\n",
        "        batch_size = prompt_embeds.shape[0] // 2\n",
        "        latents = self.get_initial_latents(height_latents, width_latents, num_channels_latents, batch_size)\n",
        "\n",
        "        # denoise latents\n",
        "        latents = self.denoise_latents(prompt_embeds,\n",
        "                                       image_latents,\n",
        "                                       timesteps,\n",
        "                                       latents,\n",
        "                                       guidance_scale,\n",
        "                                       image_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": "itugpKfQZP3S"
      },
      "outputs": [],
      "source": [
        "# We can get all the components from the InstructPix2Pix Pipeline\n",
        "vae = pipe.vae\n",
        "tokenizer = pipe.tokenizer\n",
        "text_encoder = pipe.text_encoder\n",
        "unet = pipe.unet\n",
        "scheduler = pipe.scheduler\n",
        "image_processor = pipe.image_processor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z4mM2_z_ZP5f"
      },
      "outputs": [],
      "source": [
        "custom_pipe = InstructPix2PixPipelineCustom(vae, tokenizer, text_encoder, unet, scheduler, image_processor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "NjkhgTz0mIPi",
        "outputId": "56a214f8-02c5-4157-8648-6165bd4f2ec7"
      },
      "outputs": [],
      "source": [
        "url = \"https://cdn.pixabay.com/photo/2013/01/05/21/02/art-74050_640.jpg\"\n",
        "image = download_image(url)\n",
        "image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "pcG55mdtZP8N",
        "outputId": "8c91332c-2b17-49b5-c3aa-416b141a035e"
      },
      "outputs": [],
      "source": [
        "# sample image 1\n",
        "prompt = \"convert the lady into a highly detailed marble statue\"\n",
        "images_custom = custom_pipe(prompt, image, num_inference_steps=20)\n",
        "images_custom[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "l0m6vIq4ZP-K",
        "outputId": "a97e7c0d-22a5-490a-c476-22d27aebd76a"
      },
      "outputs": [],
      "source": [
        "url = \"https://cdn.pixabay.com/photo/2023/03/22/01/41/little-girl-7868485_640.jpg\"\n",
        "image = download_image(url)\n",
        "image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "3eDhpvlHZQAo",
        "outputId": "4d93878c-1a89-44ff-a823-4f8ca97c5e88"
      },
      "outputs": [],
      "source": [
        "# sample image 2\n",
        "prompt = \"turn into 8k anime\"\n",
        "images_custom = custom_pipe(prompt, image, num_inference_steps=20)\n",
        "images_custom[0]"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "6rAfgSFK3s-B"
      },
      "source": [
        "# Limitations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "hKs8yic0RQFi",
        "outputId": "68fd4162-3c98-488c-d3b1-164efbdc80ad"
      },
      "outputs": [],
      "source": [
        "prompt = \"turn entire pic into anime frame\"\n",
        "images_custom = custom_pipe(prompt, image, num_inference_steps=20)\n",
        "images_custom[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "61YH9qpyRXiQ"
      },
      "outputs": [],
      "source": []
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "OU9zLHTR2H_j"
      },
      "source": [
        "# Rough\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689,
          "referenced_widgets": [
            "b1ac65dd59c94a269c3660d4ebd8fa16",
            "76732cd3aa5745058c70bb1aa44b1667",
            "09bc80852b334a6f9905c6d93eada57c",
            "c1ee4f8f667743ed94a939a9fe749e39",
            "ff7c3d43d89a40a9941f2b9baf8e9fd9",
            "1494c31d3d114a13a3ebbe85df2316ba",
            "9604b8f7a8ef4fa6b488ee1892e138d2",
            "7bcb49396cf84ea6878d5ceddd480326",
            "8c8b4b87df9242318f1ceaf5fc00b216",
            "25c92f3c188e4009ba806c6d56cc66c4",
            "1da8ad0bdb2547cdb4edc5bc5df7a8ea"
          ]
        },
        "id": "i5lucCESjxyr",
        "outputId": "19b61940-802c-4e44-a2b2-c63ffba8faa6"
      },
      "outputs": [],
      "source": [
        "prompt = \"convert the lady into a highly detailed marble statue\"\n",
        "images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1.6).images\n",
        "images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689,
          "referenced_widgets": [
            "6d22121bbb6f4bcaa1b5f355d0f2e8a1",
            "ed4320b4d8284ead91830c0b11b9bc56",
            "1fe983e43b7f4bca905a65b07eef5491",
            "6a234c3c85734a2cae1348a3329bb659",
            "d3415f46386c49ce9593585d421657fa",
            "f25cbe3e352d44c9849191bbb8e9d4d9",
            "3bffddd95fdf4e3c896e1554a239273a",
            "e92d28da4f314f73b4c4cccde114f1f4",
            "f7728f9c5b0f4f4ca084119f74e9dcaf",
            "b77a65348afa4b6aae8540dd5d9e4d2c",
            "960b8d20472f40078fc9566abc065cc1"
          ]
        },
        "id": "LCMg5TPOjP30",
        "outputId": "e2405a06-116a-411b-ad80-0b8afa5f5bef"
      },
      "outputs": [],
      "source": [
        "prompt = \"convert the lady into a highly detailed marble statue\"\n",
        "images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=2).images\n",
        "images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689,
          "referenced_widgets": [
            "16063a5dd86649f2b7f37184c7ac0593",
            "fb156e0150e24ec9a367a1e26c4750ec",
            "925594a07eba4bdc92e59ef1df5ea63e",
            "d5228dfbe50b4d078996b74d1b71ba28",
            "6daa2b5b26e4459aa5bdebd0062cf021",
            "740417603bd94bf698fdba5b374d7c3a",
            "2ddf4013353e48b9a62c70e2eb68502e",
            "e25cb8e08bae4c56a84874cf31cd84c4",
            "92d4de954f0442778144c26caae5ee83",
            "6b04b7bfcca645368b1287a3467bc3a1",
            "4e8ec740f2ba45d1817c46b449e34423"
          ]
        },
        "id": "-nR6LrUMjP7U",
        "outputId": "f6c19181-afaa-47a0-a950-a6b0a740ca81"
      },
      "outputs": [],
      "source": [
        "prompt = \"convert the lady into a highly detailed marble statue\"\n",
        "images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1).images\n",
        "images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689,
          "referenced_widgets": [
            "1b448e7eb7c842c2ad3598e401c5a101",
            "b25958353ffe4063bf8d09ed149c2550",
            "e25496021c9f4046bb83f585601baaf4",
            "69484df819aa44b3858b76432cc84beb",
            "f6ecc0d4fe1545d8bd0a16de5960285b",
            "b5c2efd1f5044b68bfb75d5fe16d7829",
            "48faccb381254cd99a649c239a419649",
            "48b23ac8e7ce46239504712135b41444",
            "6f520ae078104d70945b5d42ded1a75a",
            "a726d364296442e196e973e575578c30",
            "94b5651c5cd14901ac59ce22bb80fcfa"
          ]
        },
        "id": "LUXIW3bkjW3R",
        "outputId": "aab0ef83-9119-461c-81e5-4c65d2285595"
      },
      "outputs": [],
      "source": [
        "prompt = \"convert the lady into a highly detailed marble statue\"\n",
        "images = pipe(prompt, image=image, num_inference_steps=30, image_guidance_scale=1).images\n",
        "images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689,
          "referenced_widgets": [
            "05328f40cf5642359294af84f26355c3",
            "792bda382a844c8a9bd9c9de789b44ac",
            "db498aee51494067b6ad507e47f37bd4",
            "2b7aaec231e244b1899f99e893b053df",
            "3c3af60f4f07476c8700910b4259a066",
            "1bbd2bbca0c04df982278b7c24c8c320",
            "ee190ee62fb24dacb96d9b57e562c3b9",
            "ce20d1f9a55a42518c521eb5a1c8b414",
            "f019b7a1faf1469c87b048319a9b71ef",
            "4afa3f9ee15546c1979113f0e618efcf",
            "fa37cd2e2ac94455ab624a75eb0a105d"
          ]
        },
        "id": "fux7MnDwjW5h",
        "outputId": "25cadb9d-9e09-4af1-c49c-7405517bc380"
      },
      "outputs": [],
      "source": [
        "prompt = \"convert the lady into a highly detailed marble statue\"\n",
        "images = pipe(prompt, image=image, num_inference_steps=50, image_guidance_scale=1).images\n",
        "images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689,
          "referenced_widgets": [
            "0711e6b0af7e4905a8136cf407e2ae8b",
            "901f71e702474e77be59006bba599918",
            "1d10557816514c4bb08d957fdde13904",
            "3bfe8491fdc84726a9a2ea2a9eb33884",
            "c4f63d6416174604a065f2dc2b8775a7",
            "e82bcca350364e229895710e7d145fbf",
            "be553a7df92e48b687fc177f7ba3c5fd",
            "9dc480c24e9844599de9f1e5e3638f33",
            "dffb565d8879412ba6c9989b02cbe661",
            "7be8f4522cdc41f08c69b4a36520eb5a",
            "ebfab18cc7484b71bb252d4e242e3b40"
          ]
        },
        "id": "8_cahN3NjW8y",
        "outputId": "dea98f5f-1d7c-45e7-bf5c-cae01347e182"
      },
      "outputs": [],
      "source": [
        "prompt = \"convert the lady into a highly detailed marble statue\"\n",
        "images = pipe(prompt, image=image, num_inference_steps=30, image_guidance_scale=1.6).images\n",
        "images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689,
          "referenced_widgets": [
            "f001e880d8f3453a9395eb78329dbbc7",
            "8b41d5c904a942a0bcee642499015089",
            "84df3286d6044ab5aa6e8cb6c8d1b813",
            "e0448b4988f643dd9f12e4fca280dbee",
            "f259a8f8222c41eea77498acd6d4e4aa",
            "7cfb6d760505428e9ffc07fe0fbca962",
            "a66a0a813bc24011a91f56778e88244e",
            "39a24d7b46f2428597c345b1d0ac8680",
            "f48ce6b6925b4b058f8679d73c039ac8",
            "75327980774b4419a9ec50f97a0c1d75",
            "7adf45fac6804cbcbbe5d7c6cbdfd878"
          ]
        },
        "id": "A0vo9G6QjjMu",
        "outputId": "16526be0-4602-4ede-9b02-a63b322f3866"
      },
      "outputs": [],
      "source": [
        "prompt = \"convert the lady into a highly detailed marble statue\"\n",
        "images = pipe(prompt, image=image, num_inference_steps=50, image_guidance_scale=1.6).images\n",
        "images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689,
          "referenced_widgets": [
            "12300224bc654d47b7dd02e04835203a",
            "dafd2b93bc6245a88e6631f306fc1e9b",
            "59514b4f9b8f4930a1b07aad4ab19d25",
            "c25bc0fc44214d9f8a5f4abba43eaf09",
            "f76d97ab2a6e46d5891af7fc0c3f511b",
            "f38555fda53a4edf92271a97db3f7dd5",
            "8f135d6a55484caf92a325c375e16005",
            "932741fa7e674b6595866eecaf746bc9",
            "eb2855fb57d64857ab32fafd3a77b457",
            "f40c98061f2946b1885193ee817c52a3",
            "9a91880b909e4b05bf668c410f978ca2"
          ]
        },
        "id": "NhlQaACLjjO4",
        "outputId": "939a5324-7595-434f-a4bc-2b53af54a0a4"
      },
      "outputs": [],
      "source": [
        "prompt = \"convert the lady into a highly detailed marble statue\"\n",
        "images = pipe(prompt, image=image, num_inference_steps=100, image_guidance_scale=1.6).images\n",
        "images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689,
          "referenced_widgets": [
            "e416557198234698affa1b9410fa0967",
            "cc1d355b4f6440fab9de50ccc5a268b8",
            "9dec2df865ed4e8399c511f504e460f7",
            "0439a988a2834c379b037e5faba17290",
            "c0bb5873a4ca4462a28773a269a6c551",
            "4e3739037f2b44d2bd1ecaa20b29d70c",
            "79b4c9681b0d496ab86f77b6b4902ad6",
            "04835e754bc84aa392f824e387a8b205",
            "dffbab23b4c34df9b6f9e8ff6c1a1912",
            "3f2fee0c35144daf8234caf57ad3907e",
            "f0ad1221f0f84ac085df2315bd00d572"
          ]
        },
        "id": "TBNS9bghjjRd",
        "outputId": "bd9c97e8-fb45-4dac-d8ff-9a2f95bc74fc"
      },
      "outputs": [],
      "source": [
        "prompt = \"convert the lady into a highly detailed marble statue\"\n",
        "images = pipe(prompt, image=image, num_inference_steps=100, image_guidance_scale=1.2).images\n",
        "images[0]"
      ]
    },
    {
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