Gen_Vision.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **GEN VISION ⚡**\n",
        "\n",
        "**Recommended System Malloc > 12gb RAM**\n",
        "\n",
        "If your system has less than 12GB of memory, you can comment out (#) some of the LoRa models that are not required for your current work. This will prevent the models from being loaded and help avoid memory overflow. Here's an example of how you can comment out models in a Python script:\n",
        "\n",
        "# model1_weights = load_model_weights(\"model1.h5\")\n",
        "\n",
        "\n",
        "model4_weights = load_model_weights(\"model4.h5\")\n",
        "\n",
        "model5_weights = load_model_weights(\"model5.h5\")\n",
        "\n",
        "model6_weights = load_model_weights(\"model6.h5\")\n",
        "\n",
        "In this example, model1 are commented out, while model4, model5, and model6 are loaded. Adjust the list of models based on your specific requirements.\n"
      ],
      "metadata": {
        "id": "5jhZXS2ttkV4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install diffusers\n",
        "!pip install torch\n",
        "!pip install torchvision\n",
        "!pip install transformers==4.43.3\n",
        "!pip install accelerate\n",
        "!pip install safetensors\n",
        "!pip install spaces\n",
        "!pip install peft\n",
        "!pip install gradio"
      ],
      "metadata": {
        "id": "r_zg6coq7Cwr",
        "outputId": "793fa0f9-b350-4b71-b1b5-5af44003fd37",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "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----------------'\n",
        "login(hf_token)"
      ],
      "metadata": {
        "id": "OMTvSTJ17QfL",
        "outputId": "9b1a6323-2a79-4c72-a76c-cdc990aee344",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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": [
        "#!/usr/bin/env python\n",
        "#patch 1.0()\n",
        "# Permission is hereby granted, free of charge, to any person obtaining a copy\n",
        "# of this software and associated documentation files (the \"Software\"), to deal\n",
        "# in the Software without restriction, including without limitation the rights\n",
        "# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
        "# copies of the Software, and to permit persons to whom the Software is\n",
        "# furnished to do so, subject to the following conditions:\n",
        "#\n",
        "# ...\n",
        "import os\n",
        "import random\n",
        "import uuid\n",
        "from typing import Tuple\n",
        "import gradio as gr\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "import spaces\n",
        "import torch\n",
        "from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler\n",
        "\n",
        "DESCRIPTIONz= \"\"\"## GEN V ⚡\n",
        "\"\"\"\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. If it runs for an extended time or if you encounter errors, try running it on a GPU by duplicating the space using @spaces.GPU(). +import spaces.📍</p>\"\n",
        "\n",
        "USE_TORCH_COMPILE = 0\n",
        "ENABLE_CPU_OFFLOAD = 0\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "    pipe = StableDiffusionXLPipeline.from_pretrained(\n",
        "        \"SG161222/RealVisXL_V4.0_Lightning\",\n",
        "        torch_dtype=torch.float16,\n",
        "        use_safetensors=True,\n",
        "    )\n",
        "    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)\n",
        "\n",
        "    LORA_OPTIONS = {\n",
        "        \"Realism (face/character)👦🏻\": (\"prithivMLmods/Canopus-Realism-LoRA\", \"Canopus-Realism-LoRA.safetensors\", \"rlms\"),\n",
        "        \"Pixar (art/toons)🙀\": (\"prithivMLmods/Canopus-Pixar-Art\", \"Canopus-Pixar-Art.safetensors\", \"pixar\"),\n",
        "        \"Photoshoot (camera/film)📸\": (\"prithivMLmods/Canopus-Photo-Shoot-Mini-LoRA\", \"Canopus-Photo-Shoot-Mini-LoRA.safetensors\", \"photo\"),\n",
        "        \"Clothing (hoodies/pant/shirts)👔\": (\"prithivMLmods/Canopus-Clothing-Adp-LoRA\", \"Canopus-Dress-Clothing-LoRA.safetensors\", \"clth\"),\n",
        "        \"Interior Architecture (house/hotel)🏠\": (\"prithivMLmods/Canopus-Interior-Architecture-0.1\", \"Canopus-Interior-Architecture-0.1δ.safetensors\", \"arch\"),\n",
        "        \"Fashion Product (wearing/usable)👜\": (\"prithivMLmods/Canopus-Fashion-Product-Dilation\", \"Canopus-Fashion-Product-Dilation.safetensors\", \"fashion\"),\n",
        "        \"Minimalistic Image (minimal/detailed)🏞️\": (\"prithivMLmods/Pegasi-Minimalist-Image-Style\", \"Pegasi-Minimalist-Image-Style.safetensors\", \"minimalist\"),\n",
        "        \"Modern Clothing (trend/new)👕\": (\"prithivMLmods/Canopus-Modern-Clothing-Design\", \"Canopus-Modern-Clothing-Design.safetensors\", \"mdrnclth\"),\n",
        "        \"Animaliea (farm/wild)🫎\": (\"prithivMLmods/Canopus-Animaliea-Artism\", \"Canopus-Animaliea-Artism.safetensors\", \"Animaliea\"),\n",
        "        \"Liquid Wallpaper (minimal/illustration)🖼️\": (\"prithivMLmods/Canopus-Liquid-Wallpaper-Art\", \"Canopus-Liquid-Wallpaper-Minimalize-LoRA.safetensors\", \"liquid\"),\n",
        "        \"Canes Cars (realistic/futurecars)🚘\": (\"prithivMLmods/Canes-Cars-Model-LoRA\", \"Canes-Cars-Model-LoRA.safetensors\", \"car\"),\n",
        "        \"Pencil Art (characteristic/creative)✏️\": (\"prithivMLmods/Canopus-Pencil-Art-LoRA\", \"Canopus-Pencil-Art-LoRA.safetensors\", \"Pencil Art\"),\n",
        "        \"Art Minimalistic (paint/semireal)🎨\": (\"prithivMLmods/Canopus-Art-Medium-LoRA\", \"Canopus-Art-Medium-LoRA.safetensors\", \"mdm\"),\n",
        "\n",
        "    }\n",
        "\n",
        "    for model_name, weight_name, adapter_name in LORA_OPTIONS.values():\n",
        "        pipe.load_lora_weights(model_name, weight_name=weight_name, adapter_name=adapter_name)\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",
        "        \"negative_prompt\": \"cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly\",\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",
        "        \"negative_prompt\": \"cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly\",\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"HD+\",\n",
        "        \"prompt\": \"hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic\",\n",
        "        \"negative_prompt\": \"cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly\",\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Style Zero\",\n",
        "        \"prompt\": \"{prompt}\",\n",
        "        \"negative_prompt\": \"\",\n",
        "    },\n",
        "]\n",
        "\n",
        "styles = {k[\"name\"]: (k[\"prompt\"], k[\"negative_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, negative: str = \"\") -> Tuple[str, str]:\n",
        "    if style_name in styles:\n",
        "        p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])\n",
        "    else:\n",
        "        p, n = styles[DEFAULT_STYLE_NAME]\n",
        "\n",
        "    if not negative:\n",
        "        negative = \"\"\n",
        "    return p.replace(\"{prompt}\", positive), n + negative\n",
        "\n",
        "@spaces.GPU(duration=180, enable_queue=True)\n",
        "def generate(\n",
        "    prompt: str,\n",
        "    negative_prompt: str = \"\",\n",
        "    use_negative_prompt: bool = False,\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",
        "    lora_model: str = \"Realism (face/character)👦🏻\",\n",
        "    progress=gr.Progress(track_tqdm=True),\n",
        "):\n",
        "    seed = int(randomize_seed_fn(seed, randomize_seed))\n",
        "\n",
        "    positive_prompt, effective_negative_prompt = apply_style(style_name, prompt, negative_prompt)\n",
        "\n",
        "    if not use_negative_prompt:\n",
        "        effective_negative_prompt = \"\"  # type: ignore\n",
        "\n",
        "    model_name, weight_name, adapter_name = LORA_OPTIONS[lora_model]\n",
        "    pipe.set_adapters(adapter_name)\n",
        "\n",
        "    images = pipe(\n",
        "        prompt=positive_prompt,\n",
        "        negative_prompt=effective_negative_prompt,\n",
        "        width=width,\n",
        "        height=height,\n",
        "        guidance_scale=guidance_scale,\n",
        "        num_inference_steps=20,\n",
        "        num_images_per_prompt=1,\n",
        "        cross_attention_kwargs={\"scale\": 0.65},\n",
        "        output_type=\"pil\",\n",
        "    ).images\n",
        "    image_paths = [save_image(img) for img in images]\n",
        "    return image_paths, seed\n",
        "\n",
        "examples = [\n",
        "    \"Realism: Man in the style of dark beige and brown, uhd image, youthful protagonists, nonrepresentational \",\n",
        "    \"Pixar: A young man with light brown wavy hair and light brown eyes sitting in an armchair and looking directly at the camera, pixar style, disney pixar, office background, ultra detailed, 1 man\",\n",
        "    \"Hoodie: Front view, capture a urban style, Superman Hoodie, technical materials, fabric small point label on text Blue theory, the design is minimal, with a raised collar, fabric is a Light yellow, low angle to capture the Hoodies form and detailing, f/5.6 to focus on the hoodies craftsmanship, solid grey background, studio light setting, with batman logo in the chest region of the t-shirt\",\n",
        "]\n",
        "\n",
        "css = '''\n",
        ".gradio-container{max-width: 545px !important}\n",
        "h1{text-align:center}\n",
        "footer {\n",
        "    visibility: hidden\n",
        "}\n",
        "'''\n",
        "\n",
        "\n",
        "\n",
        "with gr.Blocks(css=css) as demo:\n",
        "    gr.Markdown(DESCRIPTIONz)\n",
        "    with gr.Group():\n",
        "        with gr.Row():\n",
        "            prompt = gr.Text(\n",
        "                label=\"Prompt\",\n",
        "                show_label=False,\n",
        "                max_lines=1,\n",
        "                placeholder=\"Enter your prompt with resp. tag!\",\n",
        "                container=False,\n",
        "            )\n",
        "            run_button = gr.Button(\"Run\", scale=0)\n",
        "        result = gr.Gallery(label=\"Result\", columns=1, preview=True, show_label=False)\n",
        "\n",
        "    with gr.Accordion(\"Advanced options\", open=False, visible=False):\n",
        "        use_negative_prompt = gr.Checkbox(label=\"Use negative prompt\", value=True)\n",
        "        negative_prompt = gr.Text(\n",
        "            label=\"Negative prompt\",\n",
        "            lines=4,\n",
        "            max_lines=6,\n",
        "            value=\"(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation\",\n",
        "            placeholder=\"Enter a negative prompt\",\n",
        "            visible=True,\n",
        "        )\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=8,\n",
        "                value=1024,\n",
        "            )\n",
        "            height = gr.Slider(\n",
        "                label=\"Height\",\n",
        "                minimum=512,\n",
        "                maximum=2048,\n",
        "                step=8,\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",
        "\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",
        "    with gr.Row(visible=True):\n",
        "        model_choice = gr.Dropdown(\n",
        "            label=\"LoRA Selection\",\n",
        "            choices=list(LORA_OPTIONS.keys()),\n",
        "            value=\"Realism (face/character)👦🏻\"\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",
        "    use_negative_prompt.change(\n",
        "        fn=lambda x: gr.update(visible=x),\n",
        "        inputs=use_negative_prompt,\n",
        "        outputs=negative_prompt,\n",
        "        api_name=False,\n",
        "    )\n",
        "\n",
        "    gr.on(\n",
        "        triggers=[\n",
        "            prompt.submit,\n",
        "            negative_prompt.submit,\n",
        "            run_button.click,\n",
        "        ],\n",
        "        fn=generate,\n",
        "        inputs=[\n",
        "            prompt,\n",
        "            negative_prompt,\n",
        "            use_negative_prompt,\n",
        "            seed,\n",
        "            width,\n",
        "            height,\n",
        "            guidance_scale,\n",
        "            randomize_seed,\n",
        "            style_selection,\n",
        "            model_choice,\n",
        "        ],\n",
        "        outputs=[result, seed],\n",
        "        api_name=\"run\",\n",
        "    )\n",
        "\n",
        "\n",
        "\n",
        "    gr.Markdown(\"⚡Models used in the playground [[Lightning]](https://huggingface.co/SG161222/RealVisXL_V4.0_Lightning) & LoRA from [[LoRA]](https://huggingface.co/collections/prithivMLmods/dev-models-667803a6d5ac75b59110e527) for image generation. The specific LoRA in the space that requires appropriate trigger words brings good results. The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.\")\n",
        "    gr.Markdown(\"⚡This is the demo space for generating images using Stable Diffusion with quality styles, different LoRA models and types. Try the sample prompts to generate higher quality images. Try the sample prompts for generating higher quality images.<a href='https://huggingface.co/spaces/prithivMLmods/Top-Prompt-Collection' target='_blank'>Try prompts</a>.\")\n",
        "    gr.Markdown(\"⚡Make sure that the prompts passed meet the trigger word conditions and are well-detailed. This space is for educational purposes only; using it productively is meant for your own knowledge.\")\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=30).launch()"
      ],
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        "id": "cM-VOi-y7Ui6",
        "outputId": "99d44479-cc15-41c7-d395-9c5fd11c19ac",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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]"
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/spaces/zero/decorator.py:72: UserWarning: `enable_queue` parameter is now ignored and always set to `True`\n",
            "  warnings.warn(\"`enable_queue` parameter is now ignored and always set to `True`\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://eeefa56fa466ed4134.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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