{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "VUQ-pg04BB8L"
      },
      "outputs": [],
      "source": [
        "!pip install -U diffusers transformers accelerate safetensors opencv-python pillow --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "8EtwuCbBB7Rl"
      },
      "outputs": [],
      "source": [
        "from PIL import ImageEnhance, Image\n",
        "\n",
        "image = Image.open(\"input.jpg\").convert(\"RGB\").resize((512, 512))\n",
        "\n",
        "enhancer = ImageEnhance.Contrast(image)\n",
        "image = enhancer.enhance(1.5)\n",
        "\n",
        "enhancer = ImageEnhance.Sharpness(image)\n",
        "image = enhancer.enhance(2.0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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          ]
        },
        "id": "mkrSLn0xg7Og",
        "outputId": "998d552a-c223-47af-da1f-c4895a25cd20"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Flax classes are deprecated and will be removed in Diffusers v1.0.0. We recommend migrating to PyTorch classes or pinning your version of Diffusers.\n",
            "Flax classes are deprecated and will be removed in Diffusers v1.0.0. We recommend migrating to PyTorch classes or pinning your version of Diffusers.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using device: cuda\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model_index.json:   0%|          | 0.00/609 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "9e53c029478045c2b7594a66c7a6ab61"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (incomplete total...): 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c7c4f29bdf134dd2823dbb3e69c7afbf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fetching 12 files:   0%|          | 0/12 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26302fcbacef4a969d6e3a48fac55cb9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
            "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "89f98e2cd0cb4c929432f900d29e3528"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/196 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "849929921323404bb56bc9967ae44bde"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1mCLIPTextModel LOAD REPORT\u001b[0m from: /root/.cache/huggingface/hub/models--SG161222--Realistic_Vision_V5.1_noVAE/snapshots/1e9f017a7b1eaefb63a1900ea6c5953d2739fd21/text_encoder\n",
            "Key                                | Status     |  | \n",
            "-----------------------------------+------------+--+-\n",
            "text_model.embeddings.position_ids | UNEXPECTED |  | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "models/ip-adapter-plus-face_sd15.bin:   0%|          | 0.00/98.2M [00:00<?, ?B/s]"
            ],
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/560 [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "models/image_encoder/model.safetensors:   0%|          | 0.00/2.53G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ecacb2e640845739171d68e0ad16442"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/520 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d8a62057cd6f46c0aff4249699970e8a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1mCLIPVisionModelWithProjection LOAD REPORT\u001b[0m from: h94/IP-Adapter\n",
            "Key                                  | Status     |  | \n",
            "-------------------------------------+------------+--+-\n",
            "vision_model.embeddings.position_ids | UNEXPECTED |  | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image loaded at = 2026-04-06 07:21:37.664132\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/34 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4da31fb724bc4b11a2a139d84f39caf7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image generated at = 2026-04-06 07:21:44.684451\n",
            "Image saved at = 2026-04-06 07:21:45.105586\n"
          ]
        }
      ],
      "source": [
        "from diffusers import StableDiffusionImg2ImgPipeline\n",
        "from PIL import Image\n",
        "from datetime import datetime\n",
        "import torch\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "print(\"Using device:\", device)\n",
        "\n",
        "pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\n",
        "    \"SG161222/Realistic_Vision_V5.1_noVAE\",\n",
        "    torch_dtype=torch.float16 if device == \"cuda\" else torch.float32\n",
        ").to(device)\n",
        "\n",
        "pipe.load_ip_adapter(\n",
        "    \"h94/IP-Adapter\",\n",
        "    subfolder=\"models\",\n",
        "    weight_name=\"ip-adapter-plus-face_sd15.bin\"\n",
        ")\n",
        "\n",
        "pipe.set_ip_adapter_scale(0.9)\n",
        "\n",
        "image = Image.open(\"input.jpg\").convert(\"RGB\").resize((512, 512))\n",
        "print(\"Image loaded at =\", datetime.now())\n",
        "\n",
        "prompt = (\n",
        "    \"same exact person as input image, highly recognizable identity, \"\n",
        "    \"childhood version age 6 years old, preserve facial identity only. \"\n",
        "    \"new environment, different background, outdoor natural setting, \"\n",
        "    \"child wearing casual clothes, not formal, realistic child portrait, \"\n",
        "    \"soft lighting, DSLR photo, high detail\"\n",
        ")\n",
        "negative_prompt = (\n",
        "    \"same background, same clothes, formal suit, studio background, \"\n",
        "    \"passport photo, old photo style\"\n",
        ")\n",
        "\n",
        "result = pipe(\n",
        "    prompt=prompt,\n",
        "    negative_prompt=negative_prompt,\n",
        "    image=image,\n",
        "    ip_adapter_image=image,\n",
        "    strength=0.85,\n",
        "    guidance_scale=7.5,\n",
        "    num_inference_steps=40\n",
        ")\n",
        "\n",
        "print(\"Image generated at =\", datetime.now())\n",
        "\n",
        "result.images[0].save(\"output_ip.png\")\n",
        "print(\"Image saved at =\", datetime.now())"
      ]
    }
  ],
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    "accelerator": "GPU",
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      "gpuType": "T4",
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