IncontextLOra.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/arssite/GENAi-Assessment/blob/main/IncontextLOra.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "12d2cf90-aaff-4232-ff71-f64bd4fb6030"
      },
      "outputs": [
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          "name": "stdout",
          "text": [
            "Collecting diffusers\n",
            "  Downloading diffusers-0.31.0-py3-none-any.whl.metadata (18 kB)\n",
            "Requirement already satisfied: importlib-metadata in /usr/lib/python3/dist-packages (from diffusers) (4.6.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from diffusers) (3.16.1)\n",
            "Requirement already satisfied: huggingface-hub>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from diffusers) (0.26.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from diffusers) (1.26.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from diffusers) (2024.11.6)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from diffusers) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from diffusers) (0.4.5)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from diffusers) (11.0.0)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.2->diffusers) (2024.10.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.2->diffusers) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.2->diffusers) (6.0.2)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.2->diffusers) (4.66.6)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.2->diffusers) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (2024.8.30)\n",
            "Downloading diffusers-0.31.0-py3-none-any.whl (2.9 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m30.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: diffusers\n",
            "Successfully installed diffusers-0.31.0\n"
          ]
        }
      ],
      "source": [
        "pip install -U diffusers\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip uninstall -y tensorflow && pip install tensorflow-cpu"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rRy1itZEb9fE",
        "outputId": "60bab792-cfe9-48f7-87e9-f141615b039a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: tensorflow 2.15.0\n",
            "Uninstalling tensorflow-2.15.0:\n",
            "  Successfully uninstalled tensorflow-2.15.0\n",
            "Collecting tensorflow-cpu\n",
            "  Downloading tensorflow_cpu-2.18.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow-cpu) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow-cpu) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.10/dist-packages (from tensorflow-cpu) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow-cpu) (0.6.0)\n",
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            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow-cpu) (3.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow-cpu) (24.2)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow-cpu) (4.25.5)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow-cpu) (2.32.3)\n",
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            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow-cpu) (4.12.2)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow-cpu) (1.14.1)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow-cpu) (1.68.0)\n",
            "Collecting tensorboard<2.19,>=2.18 (from tensorflow-cpu)\n",
            "  Downloading tensorboard-2.18.0-py3-none-any.whl.metadata (1.6 kB)\n",
            "Collecting keras>=3.5.0 (from tensorflow-cpu)\n",
            "  Downloading keras-3.6.0-py3-none-any.whl.metadata (5.8 kB)\n",
            "Requirement already satisfied: numpy<2.1.0,>=1.26.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow-cpu) (1.26.4)\n",
            "Requirement already satisfied: h5py>=3.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow-cpu) (3.12.1)\n",
            "Collecting ml-dtypes<0.5.0,>=0.4.0 (from tensorflow-cpu)\n",
            "  Downloading ml_dtypes-0.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow-cpu) (0.37.1)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow-cpu) (0.45.0)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras>=3.5.0->tensorflow-cpu) (13.9.4)\n",
            "Collecting namex (from keras>=3.5.0->tensorflow-cpu)\n",
            "  Downloading namex-0.0.8-py3-none-any.whl.metadata (246 bytes)\n",
            "Collecting optree (from keras>=3.5.0->tensorflow-cpu)\n",
            "  Downloading optree-0.13.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (47 kB)\n",
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            "Downloading tensorflow_cpu-2.18.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (230.0 MB)\n",
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            "\u001b[?25hDownloading keras-3.6.0-py3-none-any.whl (1.2 MB)\n",
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            "\u001b[?25hInstalling collected packages: namex, optree, ml-dtypes, tensorboard, keras, tensorflow-cpu\n",
            "  Attempting uninstall: ml-dtypes\n",
            "    Found existing installation: ml-dtypes 0.2.0\n",
            "    Uninstalling ml-dtypes-0.2.0:\n",
            "      Successfully uninstalled ml-dtypes-0.2.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.15.2\n",
            "    Uninstalling tensorboard-2.15.2:\n",
            "      Successfully uninstalled tensorboard-2.15.2\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.15.0\n",
            "    Uninstalling keras-2.15.0:\n",
            "      Successfully uninstalled keras-2.15.0\n",
            "Successfully installed keras-3.6.0 ml-dtypes-0.4.1 namex-0.0.8 optree-0.13.1 tensorboard-2.18.0 tensorflow-cpu-2.18.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from diffusers import FluxPipeline\n",
        "\n",
        "pipe = FluxPipeline.from_pretrained(\"black-forest-labs/FLUX.1-schnell\", torch_dtype=torch.bfloat16)\n",
        "pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power\n",
        "\n",
        "prompt = \"A cat holding a sign that says hello world\"\n",
        "image = pipe(\n",
        "    prompt,\n",
        "    guidance_scale=0.0,\n",
        "    num_inference_steps=4,\n",
        "    max_sequence_length=256,\n",
        "    generator=torch.Generator(\"cpu\").manual_seed(0)\n",
        ").images[0]\n",
        "image.save(\"flux-schnell.png\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "DBGKf0f-ZUnQ",
        "outputId": "236f2067-a842-4dab-b340-0e4a62d113ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/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%|          | 0.00/536 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4b215163fe3c4fc68c18802ec4745baa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fetching 23 files:   0%|          | 0/23 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8158fd2462464e9faab284fc1cf23af0"
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          "metadata": {}
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        {
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        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "cd752b7b35f14a49947e9b1104622064"
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "521348d2bea04d8a94869de860dcf407"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(…)pytorch_model-00002-of-00003.safetensors:   0%|          | 0.00/9.95G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3eeb8ccaddc54222814ad2fcc7ac1303"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "diffusion_pytorch_model.safetensors:   0%|          | 0.00/168M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a315f05c45904c60bff9f413569bf1ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b114ae5e2e545ed888ee10b54095c8d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "28aed60d15f84911a43398446acfd080"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "AssertionError",
          "evalue": "Torch not compiled with CUDA enabled",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-fa52a003d609>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprompt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"A cat holding a sign that says hello world\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m image = pipe(\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mprompt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mguidance_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    114\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/diffusers/pipelines/flux/pipeline_flux.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, prompt, prompt_2, height, width, num_inference_steps, timesteps, guidance_scale, num_images_per_prompt, generator, latents, prompt_embeds, pooled_prompt_embeds, output_type, return_dict, joint_attention_kwargs, callback_on_step_end, callback_on_step_end_tensor_inputs, max_sequence_length)\u001b[0m\n\u001b[1;32m    667\u001b[0m             \u001b[0mpooled_prompt_embeds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    668\u001b[0m             \u001b[0mtext_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 669\u001b[0;31m         \u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode_prompt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    670\u001b[0m             \u001b[0mprompt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    671\u001b[0m             \u001b[0mprompt_2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprompt_2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/diffusers/pipelines/flux/pipeline_flux.py\u001b[0m in \u001b[0;36mencode_prompt\u001b[0;34m(self, prompt, prompt_2, device, num_images_per_prompt, prompt_embeds, pooled_prompt_embeds, max_sequence_length, lora_scale)\u001b[0m\n\u001b[1;32m    349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m             \u001b[0;31m# We only use the pooled prompt output from the CLIPTextModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 351\u001b[0;31m             pooled_prompt_embeds = self._get_clip_prompt_embeds(\n\u001b[0m\u001b[1;32m    352\u001b[0m                 \u001b[0mprompt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m                 \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/diffusers/pipelines/flux/pipeline_flux.py\u001b[0m in \u001b[0;36m_get_clip_prompt_embeds\u001b[0;34m(self, prompt, num_images_per_prompt, device)\u001b[0m\n\u001b[1;32m    285\u001b[0m                 \u001b[0;34mf\" {self.tokenizer_max_length} tokens: {removed_text}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m             )\n\u001b[0;32m--> 287\u001b[0;31m         \u001b[0mprompt_embeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext_encoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext_input_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m         \u001b[0;31m# Use pooled output of CLIPTextModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m    308\u001b[0m             )\n\u001b[1;32m    309\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_cuda_getDeviceCount\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 310\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Torch not compiled with CUDA enabled\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    311\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_cudart\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m             raise AssertionError(\n",
            "\u001b[0;31mAssertionError\u001b[0m: Torch not compiled with CUDA enabled"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip uninstall -y torch torchvision torchaudio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kmma3kGtZiaO",
        "outputId": "0755fb00-67b0-4a02-b54a-d762cd2fd3e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: torch 2.5.1+cpu\n",
            "Uninstalling torch-2.5.1+cpu:\n",
            "  Successfully uninstalled torch-2.5.1+cpu\n",
            "Found existing installation: torchvision 0.20.1+cpu\n",
            "Uninstalling torchvision-0.20.1+cpu:\n",
            "  Successfully uninstalled torchvision-0.20.1+cpu\n",
            "Found existing installation: torchaudio 2.5.1+cpu\n",
            "Uninstalling torchaudio-2.5.1+cpu:\n",
            "  Successfully uninstalled torchaudio-2.5.1+cpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2rgN_q_peese",
        "outputId": "e7f1846f-df71-4765-c27c-15d379d46cf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://download.pytorch.org/whl/cu118\n",
            "Collecting torch\n",
            "  Downloading https://download.pytorch.org/whl/cu118/torch-2.5.1%2Bcu118-cp310-cp310-linux_x86_64.whl (838.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m838.3/838.3 MB\u001b[0m \u001b[31m447.0 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torchvision\n",
            "  Downloading https://download.pytorch.org/whl/cu118/torchvision-0.20.1%2Bcu118-cp310-cp310-linux_x86_64.whl (6.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.5/6.5 MB\u001b[0m \u001b[31m103.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torchaudio\n",
            "  Downloading https://download.pytorch.org/whl/cu118/torchaudio-2.5.1%2Bcu118-cp310-cp310-linux_x86_64.whl (3.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m89.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.16.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2024.10.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu11==11.8.89 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu118/nvidia_cuda_nvrtc_cu11-11.8.89-py3-none-manylinux1_x86_64.whl (23.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.2/23.2 MB\u001b[0m \u001b[31m81.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cuda-runtime-cu11==11.8.89 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu118/nvidia_cuda_runtime_cu11-11.8.89-py3-none-manylinux1_x86_64.whl (875 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m875.6/875.6 kB\u001b[0m \u001b[31m44.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cuda-cupti-cu11==11.8.87 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu118/nvidia_cuda_cupti_cu11-11.8.87-py3-none-manylinux1_x86_64.whl (13.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.1/13.1 MB\u001b[0m \u001b[31m99.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cudnn-cu11==9.1.0.70 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu118/nvidia_cudnn_cu11-9.1.0.70-py3-none-manylinux2014_x86_64.whl (663.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m663.9/663.9 MB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cublas-cu11==11.11.3.6 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu118/nvidia_cublas_cu11-11.11.3.6-py3-none-manylinux1_x86_64.whl (417.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m417.9/417.9 MB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cufft-cu11==10.9.0.58 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu118/nvidia_cufft_cu11-10.9.0.58-py3-none-manylinux1_x86_64.whl (168.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m168.4/168.4 MB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-curand-cu11==10.3.0.86 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu118/nvidia_curand_cu11-10.3.0.86-py3-none-manylinux1_x86_64.whl (58.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.1/58.1 MB\u001b[0m \u001b[31m20.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cusolver-cu11==11.4.1.48 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu118/nvidia_cusolver_cu11-11.4.1.48-py3-none-manylinux1_x86_64.whl (128.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m128.2/128.2 MB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cusparse-cu11==11.7.5.86 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu118/nvidia_cusparse_cu11-11.7.5.86-py3-none-manylinux1_x86_64.whl (204.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m204.1/204.1 MB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-nccl-cu11==2.21.5 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu118/nvidia_nccl_cu11-2.21.5-py3-none-manylinux2014_x86_64.whl (147.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m147.8/147.8 MB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-nvtx-cu11==11.8.86 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu118/nvidia_nvtx_cu11-11.8.86-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting triton==3.1.0 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/triton-3.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (209.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m209.5/209.5 MB\u001b[0m \u001b[31m711.6 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.26.4)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (11.0.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (3.0.2)\n",
            "Installing collected packages: triton, nvidia-nvtx-cu11, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cufft-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, nvidia-cusolver-cu11, nvidia-cudnn-cu11, torch, torchvision, torchaudio\n",
            "Successfully installed nvidia-cublas-cu11-11.11.3.6 nvidia-cuda-cupti-cu11-11.8.87 nvidia-cuda-nvrtc-cu11-11.8.89 nvidia-cuda-runtime-cu11-11.8.89 nvidia-cudnn-cu11-9.1.0.70 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.3.0.86 nvidia-cusolver-cu11-11.4.1.48 nvidia-cusparse-cu11-11.7.5.86 nvidia-nccl-cu11-2.21.5 nvidia-nvtx-cu11-11.8.86 torch-2.5.1+cu118 torchaudio-2.5.1+cu118 torchvision-0.20.1+cu118 triton-3.1.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "torch",
                  "torchgen",
                  "torchvision"
                ]
              },
              "id": "a1d00ec0ab74452ba266008b6a7cc09b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from diffusers import FluxPipeline\n",
        "\n",
        "# This line will check if CUDA is available and use it if possible.\n",
        "# If not, it will default to CPU.\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "pipe = FluxPipeline.from_pretrained(\"black-forest-labs/FLUX.1-schnell\", torch_dtype=torch.bfloat16)\n",
        "# Explicitly move the pipeline to the determined device\n",
        "pipe = pipe.to(device)\n",
        "\n",
        "# If you have enough VRAM, remove 'pipe.enable_model_cpu_offload()'\n",
        "pipe.enable_model_cpu_offload() # Save VRAM by offloading the model to CPU.\n",
        "\n",
        "prompt = \"A cat holding a sign that says hello world\"\n",
        "image = pipe(\n",
        "    prompt,\n",
        "    guidance_scale=0.0,\n",
        "    num_inference_steps=4,\n",
        "    max_sequence_length=256,\n",
        "    # The generator is explicitly set to the determined device.\n",
        "    generator=torch.Generator(device).manual_seed(0)\n",
        ").images[0]\n",
        "image.save(\"flux-schnell.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606,
          "referenced_widgets": [
            "77b15f267dfb4ff6984bc04545c9118e",
            "09d8beba4b8047539c6e3632f95afa98",
            "8cc77cc9a12e4281b21c0f4d6d996ffa",
            "7a1178f2e1b742a397f2ef01fd8fa60d",
            "926928bbee3846e6be392bb1f698d4b6",
            "514e1f44c9be4d399333ed1c9dcc0b8b",
            "2ade3a521c664d48939124ae479bbce9",
            "43a84032bca8442eb00ee00f40da0ae1",
            "d99a47edf879464ba8ee3e40bee86959",
            "0c560bf3424a40e7baabeff2cc40c08f",
            "dd75be7727e14682a38bd2f8942e1553",
            "468ba076bfaa41c194cdd4bf77fd6905",
            "3bd6719e5a144e7fbba4217aa106dcb5",
            "b47dfcf0c74f44188c03280a9e3eb2fd",
            "25c6cf5a6c514cd3bcb2d44fc735e8c8",
            "e830a01b86ab46bcb96cec27022eac22",
            "9f450df9717b46c2870ce8de26065995",
            "124c4079a6d04ad88899eba6a6c4a46e",
            "5f1c7b513eca41cb958dd7b99fa36908",
            "d37eb741c73240a3a0c2e089b3f18b45",
            "5d8fa8b35dc04f2794d76078d136bfe6",
            "76a9a0beb4d547a9a7b232fc5506f5ac"
          ]
        },
        "id": "fLhwR2YEekkB",
        "outputId": "92bdd271-477b-48f7-9aac-a38dd968f15c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/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": [
              "Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "77b15f267dfb4ff6984bc04545c9118e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "468ba076bfaa41c194cdd4bf77fd6905"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-37c7064739f0>\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mprompt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"A cat holding a sign that says hello world\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m image = pipe(\n\u001b[0m\u001b[1;32m     17\u001b[0m     \u001b[0mprompt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mguidance_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    114\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/diffusers/pipelines/flux/pipeline_flux.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, prompt, prompt_2, height, width, num_inference_steps, timesteps, guidance_scale, num_images_per_prompt, generator, latents, prompt_embeds, pooled_prompt_embeds, output_type, return_dict, joint_attention_kwargs, callback_on_step_end, callback_on_step_end_tensor_inputs, max_sequence_length)\u001b[0m\n\u001b[1;32m    667\u001b[0m             \u001b[0mpooled_prompt_embeds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    668\u001b[0m             \u001b[0mtext_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 669\u001b[0;31m         \u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode_prompt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    670\u001b[0m             \u001b[0mprompt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    671\u001b[0m             \u001b[0mprompt_2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprompt_2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/diffusers/pipelines/flux/pipeline_flux.py\u001b[0m in \u001b[0;36mencode_prompt\u001b[0;34m(self, prompt, prompt_2, device, num_images_per_prompt, prompt_embeds, pooled_prompt_embeds, max_sequence_length, lora_scale)\u001b[0m\n\u001b[1;32m    349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m             \u001b[0;31m# We only use the pooled prompt output from the CLIPTextModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 351\u001b[0;31m             pooled_prompt_embeds = self._get_clip_prompt_embeds(\n\u001b[0m\u001b[1;32m    352\u001b[0m                 \u001b[0mprompt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m                 \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/diffusers/pipelines/flux/pipeline_flux.py\u001b[0m in \u001b[0;36m_get_clip_prompt_embeds\u001b[0;34m(self, prompt, num_images_per_prompt, device)\u001b[0m\n\u001b[1;32m    285\u001b[0m                 \u001b[0;34mf\" {self.tokenizer_max_length} tokens: {removed_text}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m             )\n\u001b[0;32m--> 287\u001b[0;31m         \u001b[0mprompt_embeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext_encoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext_input_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m         \u001b[0;31m# Use pooled output of CLIPTextModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m\"CUDA_MODULE_LOADING\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m             \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"CUDA_MODULE_LOADING\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"LAZY\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    320\u001b[0m         \u001b[0;31m# Some of the queued calls may reentrantly call _lazy_init();\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0;31m# we need to just return without initializing in that case.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " !nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EelMw8DIfhaB",
        "outputId": "367d7451-67e7-4d17-efdf-8d0be0e942b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: line 1: nvidia-smi: command not found\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import DiffusionPipeline\n",
        "\n",
        "pipe = DiffusionPipeline.from_pretrained(\"black-forest-labs/FLUX.1-dev\")\n",
        "pipe.load_lora_weights(\"ali-vilab/In-Context-LoRA\")\n",
        "\n",
        "prompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\n",
        "image = pipe(prompt).images[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "21390e14e08343e7812ca437848ff0c0",
            "d510bde169cd4f57b15b619564c8f490",
            "d847585ca7dd4ff5873a4d64b6a14fa3",
            "e3b8fce55f834a308c8bbba2ef153b3d",
            "29044eaf554d49dcb77b6fccd7568bab",
            "e63f4d82554541308d2b12851e5dddf2",
            "6f31817426574885b1bab1440262c240",
            "c51102eeff734b619a455cf04f1202dd",
            "524bd11007864682a6d3a9972a7157ec",
            "4642cd7f52554a59999e936c4155b6ea",
            "515a1f64c1224f7db11ff48a1513d11b",
            "8abec71e11d842d3bad4ebb62762119e",
            "ed6e21d5dcc246708f7ef29ca46bc91e",
            "e498e27118034591b922ffca736d021c",
            "a13f4bc9d3b045438cf7ec67bd406a7b",
            "81b14f44fde64a019224fc9dbd74ebb3",
            "3f0ea15b4eab46d1bb09e24b4b7ac10a",
            "b52b2bb43d2e4903a161b93e94e7aa45",
            "591ee79d5d664aba94dd86ff2c0a3490",
            "2c80c6067949453aa68d19168bba47a0",
            "158ec45d15734ee7974c8d16b516d73f",
            "b4558af76e264d418d453f9ae36b6f97"
          ]
        },
        "id": "2hWbX0m9zGAZ",
        "outputId": "dca4559d-737c-4ebb-99e3-8dfc64bda127"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "21390e14e08343e7812ca437848ff0c0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8abec71e11d842d3bad4ebb62762119e"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login\n"
      ],
      "metadata": {
        "id": "x8Y3GjT5f4Xz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba99cf51-c3a5-4ad7-adad-2f70ebd9b67e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To log in, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: write).\n",
            "The token `colab` has been saved to /root/.cache/huggingface/stored_tokens\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful.\n",
            "The current active token is: `colab`\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import InferenceClient\n",
        "client = InferenceClient(\"ali-vilab/In-Context-LoRA\", token=\"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\")\n",
        "\n",
        "# output is a PIL.Image object\n",
        "image = client.text_to_image(\"Astronaut riding a horse\")"
      ],
      "metadata": {
        "id": "eaxYyjmQ2uyl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "API_URL = \"https://api-inference.huggingface.co/models/ali-vilab/In-Context-LoRA\"\n",
        "headers = {\"Authorization\": \"Bearer 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\"}\n",
        "\n",
        "def query(payload):\n",
        "\tresponse = requests.post(API_URL, headers=headers, json=payload)\n",
        "\treturn response.content\n",
        "image_bytes = query({\n",
        "\t\"inputs\": \"Astronaut riding a horse\",\n",
        "})\n",
        "\n",
        "# You can access the image with PIL.Image for example\n",
        "import io\n",
        "from PIL import Image\n",
        "image = Image.open(io.BytesIO(image_bytes))"
      ],
      "metadata": {
        "id": "nRqzeGil20yF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import InferenceClient\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Initialize the client\n",
        "client = InferenceClient(\"ali-vilab/In-Context-LoRA\", token=\"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\")\n",
        "\n",
        "# Generate the image\n",
        "image = client.text_to_image(\"girl in painting\")\n",
        "\n",
        "# Display the image in Colab\n",
        "plt.imshow(image)\n",
        "plt.axis(\"off\")  # Hide axes for better display\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "hw5ynsdW3LZm",
        "outputId": "b81ce531-c317-4d87-c27c-26a8a2cdcad8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ODmOTBAz3qjK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}