app.py

import json
from argparse import ArgumentParser

from PIL import Image

import constants
from backend.controlnet import controlnet_settings_from_dict
from backend.device import get_device_name
from backend.models.gen_images import ImageFormat
from backend.models.lcmdiffusion_setting import DiffusionTask
from backend.upscale.tiled_upscale import generate_upscaled_image
from constants import APP_VERSION, DEVICE
from frontend.webui.image_variations_ui import generate_image_variations
from models.interface_types import InterfaceType
from paths import FastStableDiffusionPaths, ensure_path
from state import get_context, get_settings
from utils import show_system_info

parser = ArgumentParser(description=f"FAST SD CPU {constants.APP_VERSION}")
parser.add_argument(
    "-s",
    "--share",
    action="store_true",
    help="Create sharable link(Web UI)",
    required=False,
)
group = parser.add_mutually_exclusive_group(required=False)
group.add_argument(
    "-g",
    "--gui",
    action="store_true",
    help="Start desktop GUI",
)
group.add_argument(
    "-w",
    "--webui",
    action="store_true",
    help="Start Web UI",
)
group.add_argument(
    "-a",
    "--api",
    action="store_true",
    help="Start Web API server",
)
group.add_argument(
    "-m",
    "--mcp",
    action="store_true",
    help="Start MCP(Model Context Protocol) server",
)
group.add_argument(
    "-r",
    "--realtime",
    action="store_true",
    help="Start realtime inference UI(experimental)",
)
group.add_argument(
    "-v",
    "--version",
    action="store_true",
    help="Version",
)

parser.add_argument(
    "-b",
    "--benchmark",
    action="store_true",
    help="Run inference benchmark on the selected device",
)
parser.add_argument(
    "--lcm_model_id",
    type=str,
    help="Model ID or path,Default stabilityai/sd-turbo",
    default="stabilityai/sd-turbo",
)
parser.add_argument(
    "--openvino_lcm_model_id",
    type=str,
    help="OpenVINO Model ID or path,Default rupeshs/sd-turbo-openvino",
    default="rupeshs/sd-turbo-openvino",
)
parser.add_argument(
    "--prompt",
    type=str,
    help="Describe the image you want to generate",
    default="",
)
parser.add_argument(
    "--negative_prompt",
    type=str,
    help="Describe what you want to exclude from the generation",
    default="",
)
parser.add_argument(
    "--image_height",
    type=int,
    help="Height of the image",
    default=512,
)
parser.add_argument(
    "--image_width",
    type=int,
    help="Width of the image",
    default=512,
)
parser.add_argument(
    "--inference_steps",
    type=int,
    help="Number of steps,default : 1",
    default=1,
)
parser.add_argument(
    "--guidance_scale",
    type=float,
    help="Guidance scale,default : 1.0",
    default=1.0,
)

parser.add_argument(
    "--number_of_images",
    type=int,
    help="Number of images to generate ,default : 1",
    default=1,
)
parser.add_argument(
    "--seed",
    type=int,
    help="Seed,default : -1 (disabled) ",
    default=-1,
)
parser.add_argument(
    "--use_openvino",
    action="store_true",
    help="Use OpenVINO model",
)

parser.add_argument(
    "--use_offline_model",
    action="store_true",
    help="Use offline model",
)
parser.add_argument(
    "--clip_skip",
    type=int,
    help="CLIP Skip (1-12), default : 1 (disabled) ",
    default=1,
)
parser.add_argument(
    "--token_merging",
    type=float,
    help="Token merging scale, 0.0 - 1.0, default : 0.0",
    default=0.0,
)

parser.add_argument(
    "--use_safety_checker",
    action="store_true",
    help="Use safety checker",
)
parser.add_argument(
    "--use_lcm_lora",
    action="store_true",
    help="Use LCM-LoRA",
)
parser.add_argument(
    "--base_model_id",
    type=str,
    help="LCM LoRA base model ID,Default Lykon/dreamshaper-8",
    default="Lykon/dreamshaper-8",
)
parser.add_argument(
    "--lcm_lora_id",
    type=str,
    help="LCM LoRA model ID,Default latent-consistency/lcm-lora-sdv1-5",
    default="latent-consistency/lcm-lora-sdv1-5",
)
parser.add_argument(
    "-i",
    "--interactive",
    action="store_true",
    help="Interactive CLI mode",
)
parser.add_argument(
    "-t",
    "--use_tiny_auto_encoder",
    action="store_true",
    help="Use Tiny AutoEncoder for TAESD/TAESDXL/TAEF1",
)
parser.add_argument(
    "-f",
    "--file",
    type=str,
    help="Input image for img2img mode",
    default="",
)
parser.add_argument(
    "--img2img",
    action="store_true",
    help="img2img mode; requires input file via -f argument",
)
parser.add_argument(
    "--batch_count",
    type=int,
    help="Number of sequential generations",
    default=1,
)
parser.add_argument(
    "--strength",
    type=float,
    help="Denoising strength for img2img and Image variations",
    default=0.3,
)
parser.add_argument(
    "--sdupscale",
    action="store_true",
    help="Tiled SD upscale,works only for the resolution 512x512,(2x upscale)",
)
parser.add_argument(
    "--upscale",
    action="store_true",
    help="EDSR SD upscale ",
)
parser.add_argument(
    "--custom_settings",
    type=str,
    help="JSON file containing custom generation settings",
    default=None,
)
parser.add_argument(
    "--usejpeg",
    action="store_true",
    help="Images will be saved as JPEG format",
)
parser.add_argument(
    "--noimagesave",
    action="store_true",
    help="Disable image saving",
)
parser.add_argument(
    "--imagequality", type=int, help="Output image quality [0 to 100]", default=90
)
parser.add_argument(
    "--lora",
    type=str,
    help="LoRA model full path e.g D:\lora_models\CuteCartoon15V-LiberteRedmodModel-Cartoon-CuteCartoonAF.safetensors",
    default=None,
)
parser.add_argument(
    "--lora_weight",
    type=float,
    help="LoRA adapter weight [0 to 1.0]",
    default=0.5,
)
parser.add_argument(
    "--port",
    type=int,
    help="Web server port",
    default=8000,
)

args = parser.parse_args()

if args.version:
    print(APP_VERSION)
    exit()

# parser.print_help()
print("FastSD CPU - ", APP_VERSION)
show_system_info()
print(f"Using device : {constants.DEVICE}")


if args.webui:
    app_settings = get_settings()
else:
    app_settings = get_settings()

print(f"Output path : {app_settings.settings.generated_images.path}")
ensure_path(app_settings.settings.generated_images.path)

print(f"Found {len(app_settings.lcm_models)} LCM models in config/lcm-models.txt")
print(
    f"Found {len(app_settings.stable_diffsuion_models)} stable diffusion models in config/stable-diffusion-models.txt"
)
print(
    f"Found {len(app_settings.lcm_lora_models)} LCM-LoRA models in config/lcm-lora-models.txt"
)
print(
    f"Found {len(app_settings.openvino_lcm_models)} OpenVINO LCM models in config/openvino-lcm-models.txt"
)

if args.noimagesave:
    app_settings.settings.generated_images.save_image = False
else:
    app_settings.settings.generated_images.save_image = True

app_settings.settings.generated_images.save_image_quality = args.imagequality

if not args.realtime:
    # To minimize realtime mode dependencies
    from backend.upscale.upscaler import upscale_image
    from frontend.cli_interactive import interactive_mode

if args.gui:
    from frontend.gui.ui import start_gui

    print("Starting desktop GUI mode(Qt)")
    start_gui(
        [],
        app_settings,
    )
elif args.webui:
    from frontend.webui.ui import start_webui

    print("Starting web UI mode")
    start_webui(
        args.share,
    )
elif args.realtime:
    from frontend.webui.realtime_ui import start_realtime_text_to_image

    print("Starting realtime text to image(EXPERIMENTAL)")
    start_realtime_text_to_image(args.share)
elif args.api:
    from backend.api.web import start_web_server

    start_web_server(args.port)
elif args.mcp:
    from backend.api.mcp_server import start_mcp_server

    start_mcp_server(args.port)
else:
    context = get_context(InterfaceType.CLI)
    config = app_settings.settings

    if args.use_openvino:
        config.lcm_diffusion_setting.openvino_lcm_model_id = args.openvino_lcm_model_id
    else:
        config.lcm_diffusion_setting.lcm_model_id = args.lcm_model_id

    config.lcm_diffusion_setting.prompt = args.prompt
    config.lcm_diffusion_setting.negative_prompt = args.negative_prompt
    config.lcm_diffusion_setting.image_height = args.image_height
    config.lcm_diffusion_setting.image_width = args.image_width
    config.lcm_diffusion_setting.guidance_scale = args.guidance_scale
    config.lcm_diffusion_setting.number_of_images = args.number_of_images
    config.lcm_diffusion_setting.inference_steps = args.inference_steps
    config.lcm_diffusion_setting.strength = args.strength
    config.lcm_diffusion_setting.seed = args.seed
    config.lcm_diffusion_setting.use_openvino = args.use_openvino
    config.lcm_diffusion_setting.use_tiny_auto_encoder = args.use_tiny_auto_encoder
    config.lcm_diffusion_setting.use_lcm_lora = args.use_lcm_lora
    config.lcm_diffusion_setting.lcm_lora.base_model_id = args.base_model_id
    config.lcm_diffusion_setting.lcm_lora.lcm_lora_id = args.lcm_lora_id
    config.lcm_diffusion_setting.diffusion_task = DiffusionTask.text_to_image.value
    config.lcm_diffusion_setting.lora.enabled = False
    config.lcm_diffusion_setting.lora.path = args.lora
    config.lcm_diffusion_setting.lora.weight = args.lora_weight
    config.lcm_diffusion_setting.lora.fuse = True
    if config.lcm_diffusion_setting.lora.path:
        config.lcm_diffusion_setting.lora.enabled = True
    if args.usejpeg:
        config.generated_images.format = ImageFormat.JPEG.value.upper()
    if args.seed > -1:
        config.lcm_diffusion_setting.use_seed = True
    else:
        config.lcm_diffusion_setting.use_seed = False
    config.lcm_diffusion_setting.use_offline_model = args.use_offline_model
    config.lcm_diffusion_setting.clip_skip = args.clip_skip
    config.lcm_diffusion_setting.token_merging = args.token_merging
    config.lcm_diffusion_setting.use_safety_checker = args.use_safety_checker

    # Read custom settings from JSON file
    custom_settings = {}
    if args.custom_settings:
        with open(args.custom_settings) as f:
            custom_settings = json.load(f)

    # Basic ControlNet settings; if ControlNet is enabled, an image is
    # required even in txt2img mode
    config.lcm_diffusion_setting.controlnet = None
    controlnet_settings_from_dict(
        config.lcm_diffusion_setting,
        custom_settings,
    )

    # Interactive mode
    if args.interactive:
        # wrapper(interactive_mode, config, context)
        config.lcm_diffusion_setting.lora.fuse = False
        interactive_mode(config, context)

    # Start of non-interactive CLI image generation
    if args.img2img and args.file != "":
        config.lcm_diffusion_setting.init_image = Image.open(args.file)
        config.lcm_diffusion_setting.diffusion_task = DiffusionTask.image_to_image.value
    elif args.img2img and args.file == "":
        print("Error : You need to specify a file in img2img mode")
        exit()
    elif args.upscale and args.file == "" and args.custom_settings == None:
        print("Error : You need to specify a file in SD upscale mode")
        exit()
    elif (
        args.prompt == ""
        and args.file == ""
        and args.custom_settings == None
        and not args.benchmark
    ):
        print("Error : You need to provide a prompt")
        exit()

    if args.upscale:
        # image = Image.open(args.file)
        output_path = FastStableDiffusionPaths.get_upscale_filepath(
            args.file,
            2,
            config.generated_images.format,
        )
        result = upscale_image(
            context,
            args.file,
            output_path,
            2,
        )
    # Perform Tiled SD upscale (EXPERIMENTAL)
    elif args.sdupscale:
        if args.use_openvino:
            config.lcm_diffusion_setting.strength = 0.3
        upscale_settings = None
        if custom_settings != {}:
            upscale_settings = custom_settings
        filepath = args.file
        output_format = config.generated_images.format
        if upscale_settings:
            filepath = upscale_settings["source_file"]
            output_format = upscale_settings["output_format"].upper()
        output_path = FastStableDiffusionPaths.get_upscale_filepath(
            filepath,
            2,
            output_format,
        )

        generate_upscaled_image(
            config,
            filepath,
            config.lcm_diffusion_setting.strength,
            upscale_settings=upscale_settings,
            context=context,
            tile_overlap=32 if config.lcm_diffusion_setting.use_openvino else 16,
            output_path=output_path,
            image_format=output_format,
        )
        exit()
    # If img2img argument is set and prompt is empty, use image variations mode
    elif args.img2img and args.prompt == "":
        for i in range(0, args.batch_count):
            generate_image_variations(
                config.lcm_diffusion_setting.init_image, args.strength
            )
    else:
        if args.benchmark:
            print("Initializing benchmark...")
            bench_lcm_setting = config.lcm_diffusion_setting
            bench_lcm_setting.prompt = "a cat"
            bench_lcm_setting.use_tiny_auto_encoder = False
            context.generate_text_to_image(
                settings=config,
                device=DEVICE,
            )

            latencies = []

            print("Starting benchmark please wait...")
            for _ in range(3):
                context.generate_text_to_image(
                    settings=config,
                    device=DEVICE,
                )
                latencies.append(context.latency)

            avg_latency = sum(latencies) / 3

            bench_lcm_setting.use_tiny_auto_encoder = True

            context.generate_text_to_image(
                settings=config,
                device=DEVICE,
            )
            latencies = []
            for _ in range(3):
                context.generate_text_to_image(
                    settings=config,
                    device=DEVICE,
                )
                latencies.append(context.latency)

            avg_latency_taesd = sum(latencies) / 3

            benchmark_name = ""

            if config.lcm_diffusion_setting.use_openvino:
                benchmark_name = "OpenVINO"
            else:
                benchmark_name = "PyTorch"

            bench_model_id = ""
            if bench_lcm_setting.use_openvino:
                bench_model_id = bench_lcm_setting.openvino_lcm_model_id
            elif bench_lcm_setting.use_lcm_lora:
                bench_model_id = bench_lcm_setting.lcm_lora.base_model_id
            else:
                bench_model_id = bench_lcm_setting.lcm_model_id

            benchmark_result = [
                ["Device", f"{DEVICE.upper()},{get_device_name()}"],
                ["Stable Diffusion Model", bench_model_id],
                [
                    "Image Size ",
                    f"{bench_lcm_setting.image_width}x{bench_lcm_setting.image_height}",
                ],
                [
                    "Inference Steps",
                    f"{bench_lcm_setting.inference_steps}",
                ],
                [
                    "Benchmark Passes",
                    3,
                ],
                [
                    "Average Latency",
                    f"{round(avg_latency, 3)} sec",
                ],
                [
                    "Average Latency(TAESD* enabled)",
                    f"{round(avg_latency_taesd, 3)} sec",
                ],
            ]
            print()
            print(
                f"                          FastSD Benchmark - {benchmark_name:8}                         "
            )
            print(f"-" * 80)
            for benchmark in benchmark_result:
                print(f"{benchmark[0]:35} - {benchmark[1]}")
            print(f"-" * 80)
            print("*TAESD - Tiny AutoEncoder for Stable Diffusion")

        else:
            for i in range(0, args.batch_count):
                context.generate_text_to_image(
                    settings=config,
                    device=DEVICE,
                )