flux.1-dev_jupyter.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TimothyAlexisVass/avatars/blob/main/flux.1-dev_jupyter.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "VjYy0F2gZIPR",
        "outputId": "62bd29c0-e3e5-4d76-88b1-f4d7daaa2385",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "Cloning into '/content/TotoroUI'...\n",
            "remote: Enumerating objects: 14652, done.\u001b[K\n",
            "remote: Counting objects: 100% (2660/2660), done.\u001b[K\n",
            "remote: Compressing objects: 100% (182/182), done.\u001b[K\n",
            "remote: Total 14652 (delta 2566), reused 2478 (delta 2478), pack-reused 11992 (from 1)\u001b[K\n",
            "Receiving objects: 100% (14652/14652), 22.90 MiB | 17.98 MiB/s, done.\n",
            "Resolving deltas: 100% (9899/9899), done.\n",
            "/content/TotoroUI\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m209.5/209.5 MB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.2/61.2 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchaudio 2.5.1+cu121 requires torch==2.5.1, but you have torch 2.5.0 which is incompatible.\n",
            "torchvision 0.20.1+cu121 requires torch==2.5.1, but you have torch 2.5.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mThe following additional packages will be installed:\n",
            "  libaria2-0 libc-ares2\n",
            "The following NEW packages will be installed:\n",
            "  aria2 libaria2-0 libc-ares2\n",
            "0 upgraded, 3 newly installed, 0 to remove and 49 not upgraded.\n",
            "Need to get 1,513 kB of archives.\n",
            "After this operation, 5,441 kB of additional disk space will be used.\n",
            "Selecting previously unselected package libc-ares2:amd64.\n",
            "(Reading database ... 123630 files and directories currently installed.)\n",
            "Preparing to unpack .../libc-ares2_1.18.1-1ubuntu0.22.04.3_amd64.deb ...\n",
            "Unpacking libc-ares2:amd64 (1.18.1-1ubuntu0.22.04.3) ...\n",
            "Selecting previously unselected package libaria2-0:amd64.\n",
            "Preparing to unpack .../libaria2-0_1.36.0-1_amd64.deb ...\n",
            "Unpacking libaria2-0:amd64 (1.36.0-1) ...\n",
            "Selecting previously unselected package aria2.\n",
            "Preparing to unpack .../aria2_1.36.0-1_amd64.deb ...\n",
            "Unpacking aria2 (1.36.0-1) ...\n",
            "Setting up libc-ares2:amd64 (1.18.1-1ubuntu0.22.04.3) ...\n",
            "Setting up libaria2-0:amd64 (1.36.0-1) ...\n",
            "Setting up aria2 (1.36.0-1) ...\n",
            "Processing triggers for man-db (2.10.2-1) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libumf.so.0 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtcm_debug.so.1 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_level_zero.so.0 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_opencl.so.0 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtcm.so.1 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libhwloc.so.15 is not a symbolic link\n",
            "\n",
            "\u001b[0m\n",
            "Download Results:\n",
            "gid   |stat|avg speed  |path/URI\n",
            "======+====+===========+=======================================================\n",
            "ed17df|\u001b[1;32mOK\u001b[0m  |   194MiB/s|/content/TotoroUI/models/unet/flux1-dev-fp8.safetensors\n",
            "\n",
            "Status Legend:\n",
            "(OK):download completed.\n",
            "\u001b[0m\n",
            "Download Results:\n",
            "gid   |stat|avg speed  |path/URI\n",
            "======+====+===========+=======================================================\n",
            "c02c2e|\u001b[1;32mOK\u001b[0m  |   198MiB/s|/content/TotoroUI/models/vae/ae.sft\n",
            "\n",
            "Status Legend:\n",
            "(OK):download completed.\n",
            "\u001b[0m\n",
            "Download Results:\n",
            "gid   |stat|avg speed  |path/URI\n",
            "======+====+===========+=======================================================\n",
            "56ce4e|\u001b[1;32mOK\u001b[0m  |   269MiB/s|/content/TotoroUI/models/clip/clip_l.safetensors\n",
            "\n",
            "Status Legend:\n",
            "(OK):download completed.\n",
            "\u001b[0m\n",
            "Download Results:\n",
            "gid   |stat|avg speed  |path/URI\n",
            "======+====+===========+=======================================================\n",
            "bbaeff|\u001b[1;32mOK\u001b[0m  |   182MiB/s|/content/TotoroUI/models/clip/t5xxl_fp8_e4m3fn.safetensors\n",
            "\n",
            "Status Legend:\n",
            "(OK):download completed.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:clip missing: ['text_projection.weight']\n"
          ]
        }
      ],
      "source": [
        "%cd /content\n",
        "!git clone -b totoro3 https://github.com/camenduru/ComfyUI /content/TotoroUI\n",
        "%cd /content/TotoroUI\n",
        "\n",
        "!pip install -q torchsde einops diffusers accelerate xformers==0.0.28.post2\n",
        "!apt -y install -qq aria2\n",
        "\n",
        "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/FLUX.1-dev/resolve/main/flux1-dev-fp8.safetensors -d /content/TotoroUI/models/unet -o flux1-dev-fp8.safetensors\n",
        "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/FLUX.1-dev/resolve/main/ae.sft -d /content/TotoroUI/models/vae -o ae.sft\n",
        "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/FLUX.1-dev/resolve/main/clip_l.safetensors -d /content/TotoroUI/models/clip -o clip_l.safetensors\n",
        "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/FLUX.1-dev/resolve/main/t5xxl_fp8_e4m3fn.safetensors -d /content/TotoroUI/models/clip -o t5xxl_fp8_e4m3fn.safetensors\n",
        "\n",
        "import random\n",
        "import torch\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "import nodes\n",
        "from nodes import NODE_CLASS_MAPPINGS\n",
        "from totoro_extras import nodes_custom_sampler\n",
        "from totoro import model_management\n",
        "\n",
        "DualCLIPLoader = NODE_CLASS_MAPPINGS[\"DualCLIPLoader\"]()\n",
        "UNETLoader = NODE_CLASS_MAPPINGS[\"UNETLoader\"]()\n",
        "RandomNoise = nodes_custom_sampler.NODE_CLASS_MAPPINGS[\"RandomNoise\"]()\n",
        "BasicGuider = nodes_custom_sampler.NODE_CLASS_MAPPINGS[\"BasicGuider\"]()\n",
        "KSamplerSelect = nodes_custom_sampler.NODE_CLASS_MAPPINGS[\"KSamplerSelect\"]()\n",
        "BasicScheduler = nodes_custom_sampler.NODE_CLASS_MAPPINGS[\"BasicScheduler\"]()\n",
        "SamplerCustomAdvanced = nodes_custom_sampler.NODE_CLASS_MAPPINGS[\"SamplerCustomAdvanced\"]()\n",
        "VAELoader = NODE_CLASS_MAPPINGS[\"VAELoader\"]()\n",
        "VAEDecode = NODE_CLASS_MAPPINGS[\"VAEDecode\"]()\n",
        "EmptyLatentImage = NODE_CLASS_MAPPINGS[\"EmptyLatentImage\"]()\n",
        "\n",
        "with torch.inference_mode():\n",
        "    clip = DualCLIPLoader.load_clip(\"t5xxl_fp8_e4m3fn.safetensors\", \"clip_l.safetensors\", \"flux\")[0]\n",
        "    unet = UNETLoader.load_unet(\"flux1-dev-fp8.safetensors\", \"fp8_e4m3fn\")[0]\n",
        "    vae = VAELoader.load_vae(\"ae.sft\")[0]\n",
        "\n",
        "def closestNumber(n, m):\n",
        "    q = int(n / m)\n",
        "    n1 = m * q\n",
        "    if (n * m) > 0:\n",
        "        n2 = m * (q + 1)\n",
        "    else:\n",
        "        n2 = m * (q - 1)\n",
        "    if abs(n - n1) < abs(n - n2):\n",
        "        return n1\n",
        "    return n2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rUBpJAHVCLso",
        "outputId": "77d2ddc6-04ed-4ff8-fc83-9aff30d65022"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
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      "metadata": {
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        "with torch.inference_mode():\n",
        "    # \"Ashley\", \"Barbara\", \"Charles\", \"James\", \"Daniel\", \"David\", \"Elizabeth\",\n",
        "    for name in [\"Donald\", \"Emily\", \"Hannah\", \"Jennifer\", \"Jessica\", \"John\", \"Joseph\", \"Karen\", \"Linda\", \"Lisa\", \"Mark\", \"Mary\", \"Matthew\", \"Michael\", \"Patricia\", \"Robert\", \"Sandra\", \"Sarah\"]:\n",
        "        for i in range(4):\n",
        "            positive_prompt = f'\"Happy Birthday {name}\". 3d text. happy sexy edm girl with bikini. colorful, tropical birthday party'\n",
        "            width = 1280\n",
        "            height = 720\n",
        "            seed = 0\n",
        "            steps = 20\n",
        "            sampler_name = \"euler\"\n",
        "            scheduler = \"simple\"\n",
        "\n",
        "            if seed == 0:\n",
        "                seed = random.randint(0, 18446744073709551615)\n",
        "\n",
        "            cond, pooled = clip.encode_from_tokens(clip.tokenize(positive_prompt), return_pooled=True)\n",
        "            cond = [[cond, {\"pooled_output\": pooled}]]\n",
        "            noise = RandomNoise.get_noise(seed)[0]\n",
        "            guider = BasicGuider.get_guider(unet, cond)[0]\n",
        "            sampler = KSamplerSelect.get_sampler(sampler_name)[0]\n",
        "            sigmas = BasicScheduler.get_sigmas(unet, scheduler, steps, 1.0)[0]\n",
        "            latent_image = EmptyLatentImage.generate(closestNumber(width, 16), closestNumber(height, 16))[0]\n",
        "            sample, sample_denoised = SamplerCustomAdvanced.sample(noise, guider, sampler, sigmas, latent_image)\n",
        "            model_management.soft_empty_cache()\n",
        "            decoded = VAEDecode.decode(vae, sample)[0].detach()\n",
        "            Image.fromarray(np.array(decoded*255, dtype=np.uint8)[0]).save(f\"/content/drive/MyDrive/{name.lower()} {str(i)}.png\")\n"
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