TextToImageGenerator.ipynb

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        "id": "4L1Of8qPqIU8",
        "outputId": "700d5d0d-8f9f-45cc-e7b8-053843e21491"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "import tqdm\n",
        "import torch\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from diffusers import StableDiffusionPipeline\n",
        "from transformers import pipeline, set_seed\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "P6Xq_-XSviFh"
      },
      "outputs": [],
      "source": [
        "class CFG:\n",
        "    device = \"cuda\"\n",
        "    seed = 42\n",
        "    generator = torch.Generator()\n",
        "    image_gen_steps = 35\n",
        "    image_gen_model_id = \"stabilityai/stable-diffusion-2-1\"\n",
        "    image_gen_size = (400,400)\n",
        "    image_gen_guidance_scale = 9\n",
        "    prompt_gen_model_id = \"gpt2\"\n",
        "    prompt_dataset_size = 6\n",
        "    prompt_max_length = 12\n",
        "    auth_key = \"auth_key\"\n",
        "    variant = \"fp16\"\n",
        "    scale = 9"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "fp16\n"
          ]
        }
      ],
      "source": [
        "obj = CFG()\n",
        "print(obj.variant)"
      ]
    },
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      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Keyword arguments {'vairant': 'fp16', 'use_auth_token': 'auth_key', 'guidance_scale': 9} are not expected by StableDiffusionPipeline and will be ignored.\n"
          ]
        },
        {
          "data": {
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              "model_id": "ea009cca25584d30b3abc825fc6f466f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading pipeline components...:   0%|          | 0/6 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "image_gen_model = StableDiffusionPipeline.from_pretrained(\n",
        "    CFG.image_gen_model_id, torch_dtype=torch.float16,\n",
        "    vairant=CFG.variant, use_auth_token=\"auth_key\", guidance_scale=9\n",
        ")\n",
        "image_gen_model = image_gen_model.to(CFG.device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "aAmBQ5EHxC6D"
      },
      "outputs": [],
      "source": [
        "def generate_image(prompt, model):\n",
        "    image = model(\n",
        "        prompt, num_inference_steps=CFG.image_gen_steps,\n",
        "        generator=CFG.generator,\n",
        "        guidance_scale=CFG.image_gen_guidance_scale\n",
        "    ).images[0]\n",
        "    \n",
        "    image = image.resize(CFG.image_gen_size)\n",
        "    return image"
      ]
    },
    {
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        "colab": {
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        "outputId": "fc584812-661b-4a95-8c37-c76d135cd578"
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      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "97736a61ea54499bb6869734bc5fcb8e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/35 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\tabis\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\diffusers\\models\\attention_processor.py:1279: UserWarning: 1Torch was not compiled with flash attention. (Triggered internally at ..\\aten\\src\\ATen\\native\\transformers\\cuda\\sdp_utils.cpp:263.)\n",
            "  hidden_states = F.scaled_dot_product_attention(\n",
            "c:\\Users\\tabis\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\diffusers\\image_processor.py:92: RuntimeWarning: invalid value encountered in cast\n",
            "  images = (images * 255).round().astype(\"uint8\")\n"
          ]
        },
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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=400x400>"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "generate_image(\"astronaut in space\", image_gen_model)"
      ]
    },
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Element at index 7: 80\n",
            "Element at index 6: 70\n",
            "Element at index 5: 60\n",
            "Element at index 4: 50\n",
            "Element at index 3: 40\n",
            "Element at index 2: 30\n",
            "Element at index 1: 20\n",
            "Element at index 0: 10\n"
          ]
        }
      ],
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        "my_list = [10, 20, 30, 40, 50, 60, 70, 80]\n",
        "\n",
        "# Iterate backward up to 4 steps from the end\n",
        "for i in range(len(my_list) - 1, -1, -1):\n",
        "    print(f\"Element at index {i}: {my_list[i]}\")"
      ]
    },
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      "execution_count": null,
      "metadata": {},
      "outputs": [],
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    }
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