Flux-RealPix.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L1CPm6HAZuTg",
        "outputId": "3a85cb03-b18f-4e5b-8c5f-2f577bc46598"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Attempting uninstall: tomlkit\n",
            "    Found existing installation: tomlkit 0.13.2\n",
            "    Uninstalling tomlkit-0.13.2:\n",
            "      Successfully uninstalled tomlkit-0.13.2\n",
            "Successfully installed aiofiles-23.2.1 diffusers-0.30.2 fastapi-0.112.4 ffmpy-0.4.0 gradio-4.43.0 gradio-client-1.3.0 h11-0.14.0 httpcore-1.0.5 httpx-0.27.2 orjson-3.10.7 peft-0.12.0 pydub-0.25.1 python-multipart-0.0.9 ruff-0.6.4 semantic-version-2.10.0 spaces-0.30.2 starlette-0.38.5 tomlkit-0.12.0 uvicorn-0.30.6 websockets-12.0\n"
          ]
        }
      ],
      "source": [
        "!pip install torch diffusers spaces transformers peft sentencepiece gradio"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Authenticate with Hugging Face\n",
        "from huggingface_hub import login\n",
        "\n",
        "# Log in to Hugging Face using the provided token\n",
        "hf_token = 'hf-token-authentication'\n",
        "login(hf_token)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Q39l67NZ4F-",
        "outputId": "d5c36fff-c230-4101-917b-8bb13a717d40"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
            "Token is valid (permission: fineGrained).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spaces\n",
        "import gradio as gr\n",
        "import torch\n",
        "from PIL import Image\n",
        "from diffusers import DiffusionPipeline\n",
        "import random\n",
        "import uuid\n",
        "from typing import Tuple\n",
        "import numpy as np\n",
        "\n",
        "DESCRIPTIONz = \"\"\"## FLUX REALISM 🔥\"\"\"\n",
        "\n",
        "def save_image(img):\n",
        "    unique_name = str(uuid.uuid4()) + \".png\"\n",
        "    img.save(unique_name)\n",
        "    return unique_name\n",
        "\n",
        "def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:\n",
        "    if randomize_seed:\n",
        "        seed = random.randint(0, MAX_SEED)\n",
        "    return seed\n",
        "\n",
        "MAX_SEED = np.iinfo(np.int32).max\n",
        "\n",
        "if not torch.cuda.is_available():\n",
        "    DESCRIPTIONz += \"\\n<p>⚠️Running on CPU, This may not work on CPU.</p>\"\n",
        "\n",
        "base_model = \"black-forest-labs/FLUX.1-dev\"\n",
        "pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)\n",
        "\n",
        "lora_repo = \"prithivMLmods/Canopus-LoRA-Flux-FaceRealism\"\n",
        "trigger_word = \"Realism\"  # Leave trigger_word blank if not used.\n",
        "pipe.load_lora_weights(lora_repo)\n",
        "\n",
        "pipe.to(\"cuda\")\n",
        "\n",
        "style_list = [\n",
        "    {\n",
        "        \"name\": \"3840 x 2160\",\n",
        "        \"prompt\": \"hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic\",\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"2560 x 1440\",\n",
        "        \"prompt\": \"hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic\",\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"HD+\",\n",
        "        \"prompt\": \"hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic\",\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Style Zero\",\n",
        "        \"prompt\": \"{prompt}\",\n",
        "    },\n",
        "]\n",
        "\n",
        "styles = {k[\"name\"]: k[\"prompt\"] for k in style_list}\n",
        "\n",
        "DEFAULT_STYLE_NAME = \"3840 x 2160\"\n",
        "STYLE_NAMES = list(styles.keys())\n",
        "\n",
        "def apply_style(style_name: str, positive: str) -> str:\n",
        "    return styles.get(style_name, styles[DEFAULT_STYLE_NAME]).replace(\"{prompt}\", positive)\n",
        "\n",
        "@spaces.GPU(duration=60, enable_queue=True)\n",
        "def generate(\n",
        "    prompt: str,\n",
        "    seed: int = 0,\n",
        "    width: int = 1024,\n",
        "    height: int = 1024,\n",
        "    guidance_scale: float = 3,\n",
        "    randomize_seed: bool = False,\n",
        "    style_name: str = DEFAULT_STYLE_NAME,\n",
        "    progress=gr.Progress(track_tqdm=True),\n",
        "):\n",
        "    seed = int(randomize_seed_fn(seed, randomize_seed))\n",
        "\n",
        "    positive_prompt = apply_style(style_name, prompt)\n",
        "\n",
        "    if trigger_word:\n",
        "        positive_prompt = f\"{trigger_word} {positive_prompt}\"\n",
        "\n",
        "    images = pipe(\n",
        "        prompt=positive_prompt,\n",
        "        width=width,\n",
        "        height=height,\n",
        "        guidance_scale=guidance_scale,\n",
        "        num_inference_steps=16,\n",
        "        num_images_per_prompt=1,\n",
        "        output_type=\"pil\",\n",
        "    ).images\n",
        "    image_paths = [save_image(img) for img in images]\n",
        "    print(image_paths)\n",
        "    return image_paths, seed\n",
        "\n",
        "\n",
        "def load_predefined_images():\n",
        "    predefined_images = [\n",
        "        \"assets/11.png\",\n",
        "        \"assets/22.png\",\n",
        "        \"assets/33.png\",\n",
        "        \"assets/44.png\",\n",
        "        \"assets/55.webp\",\n",
        "        \"assets/66.png\",\n",
        "        \"assets/77.png\",\n",
        "        \"assets/88.png\",\n",
        "        \"assets/99.png\",\n",
        "    ]\n",
        "    return predefined_images\n",
        "\n",
        "\n",
        "\n",
        "examples = [\n",
        "    \"A portrait of an attractive woman in her late twenties with light brown hair and purple, wearing large a a yellow sweater. She is looking directly at the camera, standing outdoors near trees.. --ar 128:85 --v 6.0 --style raw\",\n",
        "    \"A photo of the model wearing a white bodysuit and beige trench coat, posing in front of a train station with hands on head, soft light, sunset, fashion photography, high resolution, 35mm lens, f/22, natural lighting, global illumination. --ar 85:128 --v 6.0 --style raw\",\n",
        "]\n",
        "\n",
        "\n",
        "css = '''\n",
        ".gradio-container{max-width: 575px !important}\n",
        "h1{text-align:center}\n",
        "footer {\n",
        "    visibility: hidden\n",
        "}\n",
        "'''\n",
        "\n",
        "with gr.Blocks(css=css, theme=\"bethecloud/storj_theme\") as demo:\n",
        "    gr.Markdown(DESCRIPTIONz)\n",
        "    with gr.Row():\n",
        "        prompt = gr.Text(\n",
        "            label=\"Prompt\",\n",
        "            show_label=False,\n",
        "            max_lines=1,\n",
        "            placeholder=\"Enter your prompt\",\n",
        "            container=False,\n",
        "        )\n",
        "        run_button = gr.Button(\"Run\", scale=0)\n",
        "    result = gr.Gallery(label=\"Result\", columns=1, show_label=False)\n",
        "\n",
        "    with gr.Accordion(\"Advanced options\", open=False, visible=True):\n",
        "        seed = gr.Slider(\n",
        "            label=\"Seed\",\n",
        "            minimum=0,\n",
        "            maximum=MAX_SEED,\n",
        "            step=1,\n",
        "            value=0,\n",
        "            visible=True\n",
        "        )\n",
        "        randomize_seed = gr.Checkbox(label=\"Randomize seed\", value=True)\n",
        "\n",
        "        with gr.Row(visible=True):\n",
        "            width = gr.Slider(\n",
        "                label=\"Width\",\n",
        "                minimum=512,\n",
        "                maximum=2048,\n",
        "                step=64,\n",
        "                value=1024,\n",
        "            )\n",
        "            height = gr.Slider(\n",
        "                label=\"Height\",\n",
        "                minimum=512,\n",
        "                maximum=2048,\n",
        "                step=64,\n",
        "                value=1024,\n",
        "            )\n",
        "\n",
        "        with gr.Row():\n",
        "            guidance_scale = gr.Slider(\n",
        "                label=\"Guidance Scale\",\n",
        "                minimum=0.1,\n",
        "                maximum=20.0,\n",
        "                step=0.1,\n",
        "                value=3.0,\n",
        "            )\n",
        "            num_inference_steps = gr.Slider(\n",
        "                label=\"Number of inference steps\",\n",
        "                minimum=1,\n",
        "                maximum=40,\n",
        "                step=1,\n",
        "                value=16,\n",
        "            )\n",
        "\n",
        "        style_selection = gr.Radio(\n",
        "            show_label=True,\n",
        "            container=True,\n",
        "            interactive=True,\n",
        "            choices=STYLE_NAMES,\n",
        "            value=DEFAULT_STYLE_NAME,\n",
        "            label=\"Quality Style\",\n",
        "        )\n",
        "\n",
        "\n",
        "\n",
        "    gr.Examples(\n",
        "        examples=examples,\n",
        "        inputs=prompt,\n",
        "        outputs=[result, seed],\n",
        "        fn=generate,\n",
        "        cache_examples=False,\n",
        "    )\n",
        "\n",
        "    gr.on(\n",
        "        triggers=[\n",
        "            prompt.submit,\n",
        "            run_button.click,\n",
        "        ],\n",
        "        fn=generate,\n",
        "        inputs=[\n",
        "            prompt,\n",
        "            seed,\n",
        "            width,\n",
        "            height,\n",
        "            guidance_scale,\n",
        "            randomize_seed,\n",
        "            style_selection,\n",
        "        ],\n",
        "        outputs=[result, seed],\n",
        "        api_name=\"run\",\n",
        "    )\n",
        "\n",
        "    gr.Markdown(\"### Generated Images\")\n",
        "    predefined_gallery = gr.Gallery(label=\"Generated Images\", columns=3, show_label=False, value=load_predefined_images())\n",
        "    gr.Markdown(\"**Disclaimer/Note:**\")\n",
        "\n",
        "    gr.Markdown(\"🔥This space provides realistic image generation, which works better for human faces and portraits. Realistic trigger works properly, better for photorealistic trigger words, close-up shots, face diffusion, male, female characters.\")\n",
        "\n",
        "    gr.Markdown(\"🔥users are accountable for the content they generate and are responsible for ensuring it meets appropriate ethical standards.\")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    demo.queue(max_size=40).launch()"
      ],
      "metadata": {
        "id": "35LXnZWVaBZ_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}