Add Comfy-compatible Gradio image API

C Source.txt

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+Lora:
+https://huggingface.co/HyperHail/cb/resolve/main/models/loras/5cm-illustriousXL_v01_V1-CAME-000035.safetensors?download=true
+https://huggingface.co/HyperHail/cb/resolve/main/models/loras/bikabaka.safetensors?download=true
+https://huggingface.co/HyperHail/cb/resolve/main/models/loras/add_saturation_XL.safetensors?download=true
+https://huggingface.co/HyperHail/cb/resolve/main/models/loras/%E3%82%A2%E3%83%83%E3%83%97%E3%82%B9%E3%82%B1%E3%83%BC%E3%83%AB_remacri_original.pt?download=true
+https://huggingface.co/HyperHail/cb/resolve/main/models/checkpoints/52_novaAnimeXL_ilV190.safetensors?download=true

Dockerfile

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+FROM nvidia/cuda:12.8.1-cudnn-runtime-ubuntu24.04
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    COMFYUI_PATH=/opt/ComfyUI \
+    DATA_DIR=/data \
+    PYTHONUNBUFFERED=1
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    git libgl1 libglib2.0-0 python3 python3-pip python3-venv && \
+    rm -rf /var/lib/apt/lists/*
+RUN git clone --depth 1 https://github.com/comfyanonymous/ComfyUI.git /opt/ComfyUI
+
+WORKDIR /app
+COPY requirements.txt .
+RUN python3 -m pip install --no-cache-dir --break-system-packages \
+    --extra-index-url https://download.pytorch.org/whl/cu128 \
+    -r /opt/ComfyUI/requirements.txt -r requirements.txt
+COPY . .
+RUN mkdir -p /data
+
+EXPOSE 7860
+CMD ["python3", "app.py"]

app.py

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+import os
+import random
+import re
+import sys
+import threading
+import urllib.request
+import uuid
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+
+import gradio as gr
+import numpy as np
+from fastapi import FastAPI, HTTPException, Query
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse
+from PIL import Image
+from pydantic import BaseModel, Field
+
+
+DATA_DIR = Path(os.environ.get("DATA_DIR", "/data"))
+COMFYUI_PATH = Path(os.environ.get("COMFYUI_PATH", "/opt/ComfyUI"))
+OUTPUT_DIR = DATA_DIR / "output"
+ASSETS = {
+    "checkpoint": (
+        "52_novaAnimeXL_ilV190.safetensors",
+        "checkpoints",
+        "https://huggingface.co/HyperHail/cb/resolve/main/models/checkpoints/52_novaAnimeXL_ilV190.safetensors?download=true",
+    ),
+    "bikabaka": (
+        "8_bikabaka.safetensors",
+        "loras",
+        "https://huggingface.co/HyperHail/cb/resolve/main/models/loras/bikabaka.safetensors?download=true",
+    ),
+    "remacri": (
+        "42_アップスケール_remacri_original.pt",
+        "loras",
+        "https://huggingface.co/HyperHail/cb/resolve/main/models/loras/%E3%82%A2%E3%83%83%E3%83%97%E3%82%B9%E3%82%B1%E3%83%BC%E3%83%AB_remacri_original.pt?download=true",
+    ),
+    "5cm": (
+        "43_5cm-illustriousXL_v01_V1-CAME-000035.safetensors",
+        "loras",
+        "https://huggingface.co/HyperHail/cb/resolve/main/models/loras/5cm-illustriousXL_v01_V1-CAME-000035.safetensors?download=true",
+    ),
+    "saturation": (
+        "5_add_saturation_XL.safetensors",
+        "loras",
+        "https://huggingface.co/HyperHail/cb/resolve/main/models/loras/add_saturation_XL.safetensors?download=true",
+    ),
+    "vae": (
+        "3_sdxlVAE_sdxlVAE.safetensors",
+        "vae",
+        "https://huggingface.co/HyperHail/cb/resolve/main/models/vae/sdxlVAE_sdxlVAE.safetensors?download=true",
+    ),
+}
+SUFFIX = "(disheveled hair:0.5), skinny, shiny skin, skinny, shiny skin, (simple background, white background:1.1), masterpiece, best quality"
+NEGATIVE = "(censored, mosaic censoring, bar censor:1.1), bad quality, worst quality, worst detail, bad anatomy, extra fingers, extra toes, extra legs, 4 toes, 6 toes, 4 fingers, 6 fingers, malformed fingers, extra limbs, missing fingers, extra arms, censored, deformed, disfigured, text, (multiple views:1.1)"
+SAMPLERS = ["euler_ancestral"]
+SCHEDULERS = ["karras"]
+
+paths = {}
+jobs = {}
+state = {}
+lock = threading.Lock()
+pool = ThreadPoolExecutor(max_workers=1)
+
+
+def plain_name(name):
+    return re.sub(r"^\d+_", "", name).casefold()
+
+
+def find_asset(name):
+    wanted = plain_name(name)
+    if DATA_DIR.exists():
+        for path in DATA_DIR.rglob("*"):
+            if path.is_file() and plain_name(path.name) == wanted:
+                return path
+
+
+def download(url, target):
+    target.parent.mkdir(parents=True, exist_ok=True)
+    temp = target.with_suffix(target.suffix + ".part")
+    print(f"Downloading {target.name}")
+    urllib.request.urlretrieve(url, temp)
+    temp.replace(target)
+
+
+def ensure_assets():
+    DATA_DIR.mkdir(parents=True, exist_ok=True)
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+    for key, (name, kind, url) in ASSETS.items():
+        path = find_asset(name)
+        if path is None:
+            path = DATA_DIR / kind / name
+            download(url, path)
+        paths[key] = path
+
+
+def init_comfy():
+    if state:
+        return
+    if not COMFYUI_PATH.is_dir():
+        raise RuntimeError(f"ComfyUI not found at {COMFYUI_PATH}")
+    ensure_assets()
+    sys.path.insert(0, str(COMFYUI_PATH))
+    import folder_paths
+    from nodes import CLIPTextEncode, CheckpointLoaderSimple, EmptyLatentImage, KSampler, LoraLoader, VAEDecode, VAELoader
+
+    for path in paths.values():
+        kind = ASSETS[next(key for key, value in paths.items() if value == path)][1]
+        folder_paths.add_model_folder_path(kind, str(path.parent))
+
+    checkpoint = CheckpointLoaderSimple().load_checkpoint(paths["checkpoint"].name)
+    loader = LoraLoader()
+    first = loader.load_lora(paths["bikabaka"].name, .3, 0, checkpoint[0], checkpoint[1])
+    second = loader.load_lora(paths["remacri"].name, .4, 0, first[0], first[1])
+    third = loader.load_lora(paths["5cm"].name, .4, 0, second[0], second[1])
+    final = loader.load_lora(paths["saturation"].name, -1.4, 0, third[0], third[1])
+    state.update(
+        model=final[0],
+        clip=final[1],
+        negative_clip=third[1],
+        vae=VAELoader().load_vae(paths["vae"].name)[0],
+        encode=CLIPTextEncode(),
+        latent=EmptyLatentImage(),
+        sample=KSampler(),
+        decode=VAEDecode(),
+    )
+
+
+def generate(prompt, width=1152, height=896, steps=16):
+    width, height, steps = int(width), int(height), int(steps)
+    if not 64 <= width <= 2048 or width % 8 or not 64 <= height <= 2048 or height % 8:
+        raise ValueError("Width and height must be multiples of 8 from 64 to 2048")
+    if not 1 <= steps <= 100:
+        raise ValueError("Steps must be from 1 to 100")
+    with lock:
+        init_comfy()
+        positive = state["encode"].encode(f"{prompt},{SUFFIX}", state["clip"])[0]
+        negative = state["encode"].encode(NEGATIVE, state["negative_clip"])[0]
+        latent = state["latent"].generate(width, height, 1)[0]
+        samples = state["sample"].sample(
+            random.randrange(2**64), steps, 4, "euler_ancestral", "karras", 1,
+            state["model"], positive, negative, latent,
+        )[0]
+        image = state["decode"].decode(samples, state["vae"])[0][0]
+        return Image.fromarray(np.clip(image.cpu().numpy() * 255, 0, 255).astype(np.uint8))
+
+
+def linked_value(workflow, value):
+    if not isinstance(value, list) or len(value) < 2:
+        return value
+    node = workflow.get(str(value[0]), {})
+    inputs = node.get("inputs", {})
+    if node.get("class_type") == "StringConcatenate":
+        parts = [linked_value(workflow, inputs.get("string_a", "")), linked_value(workflow, inputs.get("string_b", ""))]
+        return str(inputs.get("delimiter", ",")).join(map(str, parts))
+    return inputs.get("text", "")
+
+
+def workflow_values(workflow):
+    sampler = next((node for node in workflow.values() if node.get("class_type") == "KSampler"), {})
+    inputs = sampler.get("inputs", {})
+    latent_id = inputs.get("latent_image", [None])[0]
+    latent = workflow.get(str(latent_id), {}).get("inputs", {})
+    positive_id = inputs.get("positive", [None])[0]
+    positive = workflow.get(str(positive_id), {}).get("inputs", {}).get("text", "")
+    return linked_value(workflow, positive), latent.get("width", 1152), latent.get("height", 896), inputs.get("steps", 16)
+
+
+def run_job(job_id, workflow):
+    try:
+        prompt, width, height, steps = workflow_values(workflow)
+        image = generate(prompt, width, height, steps)
+        filename = f"{job_id}.png"
+        image.save(OUTPUT_DIR / filename)
+        jobs[job_id] = {
+            "outputs": {"output": {"images": [{"filename": filename, "subfolder": "", "type": "output"}]}},
+            "status": {"status_str": "success", "completed": True, "messages": []},
+        }
+    except Exception as error:
+        jobs[job_id] = {
+            "outputs": {},
+            "status": {"status_str": "error", "completed": True, "messages": [["execution_error", {
+                "node_id": "output", "node_type": "Generate", "exception_type": type(error).__name__, "exception_message": str(error),
+            }]]},
+        }
+
+
+class DirectRequest(BaseModel):
+    prompt: str
+    width: int = Field(1152, ge=64, le=2048)
+    height: int = Field(896, ge=64, le=2048)
+    steps: int = Field(16, ge=1, le=100)
+
+
+api = FastAPI()
+api.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
+
+
+@api.get("/system_stats")
+def system_stats():
+    return {"system": {"os": os.name}, "devices": []}
+
+
+@api.get("/object_info")
+def object_info():
+    ensure_assets()
+    return {
+        "KSampler": {"input": {"required": {"sampler_name": [SAMPLERS], "scheduler": [SCHEDULERS]}}},
+        "CheckpointLoaderSimple": {"input": {"required": {"ckpt_name": [[paths["checkpoint"].name]]}}},
+        "UNETLoader": {"input": {"required": {"unet_name": [[]]}}},
+        "VAELoader": {"input": {"required": {"vae_name": [[paths["vae"].name]]}}},
+    }
+
+
+@api.post("/prompt")
+def queue_prompt(body: dict):
+    workflow = body.get("prompt")
+    if not isinstance(workflow, dict):
+        raise HTTPException(400, "prompt must contain a ComfyUI workflow object")
+    job_id = str(uuid.uuid4())
+    pool.submit(run_job, job_id, workflow)
+    return {"prompt_id": job_id, "number": len(jobs), "node_errors": {}}
+
+
+@api.get("/history")
+def history():
+    return jobs
+
+
+@api.get("/history/{job_id}")
+def history_item(job_id):
+    return {job_id: jobs[job_id]} if job_id in jobs else {}
+
+
+@api.get("/view")
+def view(filename: str = Query(...)):
+    path = OUTPUT_DIR / Path(filename).name
+    if not path.is_file():
+        raise HTTPException(404, "image not found")
+    return FileResponse(path, media_type="image/png")
+
+
+@api.post("/interrupt")
+def interrupt():
+    return {}
+
+
+@api.post("/api/generate")
+def direct(body: DirectRequest):
+    try:
+        image = generate(body.prompt, body.width, body.height, body.steps)
+    except ValueError as error:
+        raise HTTPException(422, str(error)) from error
+    filename = f"{uuid.uuid4()}.png"
+    path = OUTPUT_DIR / filename
+    image.save(path)
+    return FileResponse(path, media_type="image/png", filename=filename)
+
+
+with gr.Blocks(
+    title="Image API",
+    css_paths="style.css",
+    head='<link rel="icon" href="data:;base64,=">',
+) as demo:
+    gr.HTML("<model-note>ComfyUI-compatible API and direct image generation</model-note>")
+    prompt_input = gr.Textbox(label="Prompt", lines=5)
+    with gr.Row():
+        width_input = gr.Number(1152, label="Width", precision=0)
+        height_input = gr.Number(896, label="Height", precision=0)
+        steps_input = gr.Slider(1, 100, 16, step=1, label="Steps")
+    button = gr.Button("Generate", variant="primary")
+    output = gr.Image(label="Image", format="png")
+    button.click(generate, [prompt_input, width_input, height_input, steps_input], output, api_name="generate")
+
+
+app = gr.mount_gradio_app(api, demo, path="/")
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", "7860")))

requirements.txt

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+fastapi
+gradio==6.20.0
+numpy
+Pillow
+uvicorn

style.css

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+@import url('https://fonts.googleapis.com/css2?family=Spline+Sans:wght@400;500;600&display=swap');
+
+:root {
+    --bg: #112;
+    --panel: #223;
+    --text: #dde;
+    --muted: #99a;
+    --accent: #7dc;
+}
+body, .gradio-container {
+    background: var(--bg) !important;
+    color: var(--text) !important;
+    font-family: "Spline Sans", sans-serif !important;
+}
+
+.gradio-container {
+    max-width: 90% !important;
+}
+
+model-note {
+    color: var(--muted);
+    display: block;
+    margin: 0 0 1rem;
+}
+
+button.primary {
+    background: var(--accent) !important;
+    border: 0 !important;
+    border-radius: 0 !important;
+    color: var(--bg) !important;
+}
+
+.block, .form {
+    background: var(--panel) !important;
+    border-radius: 0 !important;
+}

workflow_api.py

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+# Imports
+import json
+import os
+import random
+import sys
+from typing import Sequence, Mapping, Any, Union
+
+
+def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
+    """Return a sequence or mapping result item by index."""
+    try:
+        return obj[index]
+    except KeyError:
+        return obj["result"][index]
+
+
+def get_comfyui_path() -> str:
+    """Return the configured ComfyUI path, preferring COMFYUI_PATH when set."""
+    comfyui_path = os.environ.get("COMFYUI_PATH")
+    if comfyui_path:
+        return comfyui_path
+    return find_path("ComfyUI")
+
+
+def find_path(name: str, path: str = None) -> str:
+    """Recursively search parent folders until the named entry is found."""
+    if path is None:
+        path = os.getcwd()
+
+    if name in os.listdir(path):
+        path_name = os.path.join(path, name)
+        print(f"{name} found: {path_name}")
+        return path_name
+
+    parent_directory = os.path.dirname(path)
+    if parent_directory == path:
+        return None
+
+    return find_path(name, parent_directory)
+
+
+def add_comfyui_directory_to_sys_path() -> None:
+    """Add the ComfyUI checkout to sys.path."""
+    comfyui_path = get_comfyui_path()
+    if comfyui_path is not None and os.path.isdir(comfyui_path):
+        if comfyui_path in sys.path:
+            sys.path.remove(comfyui_path)
+        sys.path.insert(0, comfyui_path)
+        print(f"'{comfyui_path}' added to sys.path")
+
+
+def add_extra_model_paths() -> None:
+    """Load ComfyUI extra model paths configuration when available."""
+    try:
+        from main import load_extra_path_config
+    except ImportError:
+        print(
+            "Could not import load_extra_path_config from main.py. Looking in utils.extra_config instead."
+        )
+        from utils.extra_config import load_extra_path_config
+
+    extra_model_paths = find_path("extra_model_paths.yaml")
+    if extra_model_paths is not None:
+        load_extra_path_config(extra_model_paths)
+    else:
+        print("Could not find the extra_model_paths config file.")
+
+
+def bootstrap_comfyui_runtime() -> None:
+    """Mirror the allocator-related ComfyUI startup steps before torch import."""
+    add_comfyui_directory_to_sys_path()
+
+    import comfy.options
+
+    comfy.options.enable_args_parsing()
+
+    from comfy.cli_args import args
+
+    if os.name == "nt":
+        os.environ["MIMALLOC_PURGE_DELAY"] = "0"
+
+    if args.default_device is not None:
+        default_dev = args.default_device
+        devices = list(range(32))
+        devices.remove(default_dev)
+        devices.insert(0, default_dev)
+        devices = ",".join(map(str, devices))
+        os.environ["CUDA_VISIBLE_DEVICES"] = str(devices)
+        os.environ["HIP_VISIBLE_DEVICES"] = str(devices)
+
+    if args.cuda_device is not None:
+        os.environ["CUDA_VISIBLE_DEVICES"] = str(args.cuda_device)
+        os.environ["HIP_VISIBLE_DEVICES"] = str(args.cuda_device)
+        os.environ["ASCEND_RT_VISIBLE_DEVICES"] = str(args.cuda_device)
+
+    if args.oneapi_device_selector is not None:
+        os.environ["ONEAPI_DEVICE_SELECTOR"] = args.oneapi_device_selector
+
+    if args.deterministic and "CUBLAS_WORKSPACE_CONFIG" not in os.environ:
+        os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
+
+    import cuda_malloc
+
+    if "rocm" in cuda_malloc.get_torch_version_noimport():
+        os.environ["OCL_SET_SVM_SIZE"] = "262144"
+
+
+def cleanup_comfyui_runtime(unload_models: bool | None = None) -> None:
+    """Best-effort cleanup for embedded or repeated generated-script execution."""
+    import gc
+
+    def run_cleanup_hook(name: str, should_run: bool = True) -> None:
+        if not should_run or not hasattr(model_management, name):
+            return
+        cleanup_fn = getattr(model_management, name)
+        try:
+            cleanup_fn()
+        except Exception as exc:
+            warnings.warn(
+                f"ComfyUI cleanup hook {name} failed during teardown: {exc}",
+                RuntimeWarning,
+                stacklevel=2,
+            )
+
+    should_unload = unload_models
+    if should_unload is None:
+        should_unload = os.environ.get(
+            "COMFYUI_TOPYTHON_UNLOAD_MODELS", ""
+        ).lower() in {
+            "1",
+            "true",
+            "yes",
+            "on",
+        }
+
+    try:
+        import comfy.model_management as model_management
+    except ModuleNotFoundError:
+        gc.collect()
+        return
+
+    run_cleanup_hook("cleanup_models_gc")
+    run_cleanup_hook("unload_all_models", should_run=should_unload)
+    run_cleanup_hook("soft_empty_cache")
+    gc.collect()
+
+
+def import_custom_nodes() -> None:
+    """Initialize ComfyUI custom nodes in the exporter runtime."""
+    comfyui_path = get_comfyui_path()
+    if comfyui_path and comfyui_path not in sys.path:
+        sys.path.insert(0, comfyui_path)
+
+    import asyncio
+    import execution
+    from nodes import init_extra_nodes
+
+    if comfyui_path in sys.path:
+        sys.path.remove(comfyui_path)
+    sys.path.insert(0, comfyui_path)
+
+    import server
+
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    server_instance = server.PromptServer(loop)
+    execution.PromptQueue(server_instance)
+    asyncio.run(init_extra_nodes())
+
+
+# Workflow data
+def build_workflow() -> dict[str, Any]:
+    return {
+        "1": {
+            "inputs": {"ckpt_name": "52_novaAnimeXL_ilV190.safetensors"},
+            "class_type": "CheckpointLoaderSimple",
+            "_meta": {"title": "Loader"},
+        },
+        "2": {
+            "inputs": {"text": ["118", 0], "clip": ["117", 1]},
+            "class_type": "CLIPTextEncode",
+            "_meta": {"title": "CLIP Text Encode (Prompt)"},
+        },
+        "3": {
+            "inputs": {
+                "text": "(censored, mosaic censoring, bar censor:1.1), bad "
+                "quality, worst quality, worst detail, bad anatomy, "
+                "extra fingers, extra toes, extra legs, 4 toes, 6 "
+                "toes, 4 fingers, 6 fingers, malformed fingers, "
+                "extra limbs, missing fingers, extra arms, censored, "
+                "deformed, disfigured, text, (multiple views:1.1)",
+                "clip": ["28", 1],
+            },
+            "class_type": "CLIPTextEncode",
+            "_meta": {"title": "CLIP Text Encode (Prompt)"},
+        },
+        "5": {
+            "inputs": {
+                "seed": 809278554234612,
+                "steps": 16,
+                "cfg": 4,
+                "sampler_name": "euler_ancestral",
+                "scheduler": "karras",
+                "denoise": 1,
+                "model": ["117", 0],
+                "positive": ["2", 0],
+                "negative": ["3", 0],
+                "latent_image": ["27", 0],
+            },
+            "class_type": "KSampler",
+            "_meta": {"title": "KSampler"},
+        },
+        "10": {
+            "inputs": {
+                "lora_name": "8_bikabaka.safetensors",
+                "strength_model": 0.3,
+                "strength_clip": 0,
+                "model": ["1", 0],
+                "clip": ["1", 1],
+            },
+            "class_type": "LoraLoader",
+            "_meta": {"title": "Load LoRA"},
+        },
+        "27": {
+            "inputs": {"width": 1152, "height": 896, "batch_size": 1},
+            "class_type": "EmptyLatentImage",
+            "_meta": {"title": "Empty Landscape"},
+        },
+        "28": {
+            "inputs": {
+                "lora_name": "43_5cm-illustriousXL_v01_V1-CAME-000035.safetensors",
+                "strength_model": 0.4,
+                "strength_clip": 0,
+                "model": ["38", 0],
+                "clip": ["38", 1],
+            },
+            "class_type": "LoraLoader",
+            "_meta": {"title": "Load LoRA"},
+        },
+        "38": {
+            "inputs": {
+                "lora_name": "42_アップスケール_remacri_original.pt",
+                "strength_model": 0.4,
+                "strength_clip": 0,
+                "model": ["10", 0],
+                "clip": ["10", 1],
+            },
+            "class_type": "LoraLoader",
+            "_meta": {"title": "Load LoRA"},
+        },
+        "45": {
+            "inputs": {"vae_name": "3_sdxlVAE_sdxlVAE.safetensors"},
+            "class_type": "VAELoader",
+            "_meta": {"title": "Load VAE"},
+        },
+        "56": {
+            "inputs": {"samples": ["5", 0], "vae": ["45", 0]},
+            "class_type": "VAEDecode",
+            "_meta": {"title": "VAE Decode"},
+        },
+        "72": {
+            "inputs": {"images": ["56", 0]},
+            "class_type": "PreviewImage",
+            "_meta": {"title": "Preview Image"},
+        },
+        "117": {
+            "inputs": {
+                "lora_name": "5_add_saturation_XL.safetensors",
+                "strength_model": -1.4,
+                "strength_clip": 0,
+                "model": ["28", 0],
+                "clip": ["28", 1],
+            },
+            "class_type": "LoraLoader",
+            "_meta": {"title": "Load LoRA (Model and CLIP)"},
+        },
+        "118": {
+            "inputs": {
+                "string_a": "%prompt%",
+                "string_b": "(disheveled hair:0.5), skinny, shiny skin, "
+                "skinny, shiny skin, (simple background, white "
+                "background:1.1), masterpiece, best quality",
+                "delimiter": ",",
+            },
+            "class_type": "StringConcatenate",
+            "_meta": {"title": "Concatenate Text"},
+        },
+    }
+
+
+def build_extra_pnginfo() -> dict[str, Any] | None:
+    return {
+        "workflow": {
+            "id": "e69619af-5ceb-4a83-821d-68180291905e",
+            "revision": 0,
+            "last_node_id": 121,
+            "last_link_id": 57,
+            "nodes": [
+                {
+                    "id": 27,
+                    "type": "EmptyLatentImage",
+                    "pos": [100, 358],
+                    "size": [270, 106],
+                    "flags": {},
+                    "order": 0,
+                    "mode": 0,
+                    "inputs": [],
+                    "outputs": [{"name": "LATENT", "type": "LATENT", "links": [24]}],
+                    "title": "Empty Landscape",
+                    "properties": {"Node name for S&R": "EmptyLatentImage"},
+                    "widgets_values": [1152, 896, 1],
+                },
+                {
+                    "id": 45,
+                    "type": "VAELoader",
+                    "pos": [100, 594],
+                    "size": [270, 58],
+                    "flags": {},
+                    "order": 1,
+                    "mode": 0,
+                    "inputs": [],
+                    "outputs": [{"name": "VAE", "type": "VAE", "links": [32]}],
+                    "properties": {"Node name for S&R": "VAELoader"},
+                    "widgets_values": ["3_sdxlVAE_sdxlVAE.safetensors"],
+                },
+                {
+                    "id": 56,
+                    "type": "VAEDecode",
+                    "pos": [2948.649165895271, 134.76727061509087],
+                    "size": [140, 46],
+                    "flags": {},
+                    "order": 11,
+                    "mode": 0,
+                    "inputs": [
+                        {"name": "samples", "type": "LATENT", "link": 31},
+                        {"name": "vae", "type": "VAE", "link": 32},
+                    ],
+                    "outputs": [{"name": "IMAGE", "type": "IMAGE", "links": [33]}],
+                    "properties": {"Node name for S&R": "VAEDecode"},
+                    "widgets_values": [],
+                },
+                {
+                    "id": 10,
+                    "type": "LoraLoader",
+                    "pos": [600, 130],
+                    "size": [290.43334045410154, 126],
+                    "flags": {},
+                    "order": 4,
+                    "mode": 0,
+                    "inputs": [
+                        {"name": "model", "type": "MODEL", "link": 25},
+                        {"name": "clip", "type": "CLIP", "link": 26},
+                    ],
+                    "outputs": [
+                        {"name": "MODEL", "type": "MODEL", "links": [29]},
+                        {"name": "CLIP", "type": "CLIP", "links": [30]},
+                    ],
+                    "title": "Load LoRA",
+                    "properties": {"Node name for S&R": "LoraLoader"},
+                    "widgets_values": ["8_bikabaka.safetensors", 0.3, 0],
+                },
+                {
+                    "id": 72,
+                    "type": "PreviewImage",
+                    "pos": [3188.649165895271, 134.76727061509087],
+                    "size": [285.77604360195164, 258],
+                    "flags": {},
+                    "order": 12,
+                    "mode": 0,
+                    "inputs": [{"name": "images", "type": "IMAGE", "link": 33}],
+                    "outputs": [{"name": "images", "type": "IMAGE", "links": None}],
+                    "properties": {"Node name for S&R": "PreviewImage"},
+                    "widgets_values": [],
+                },
+                {
+                    "id": 1,
+                    "type": "CheckpointLoaderSimple",
+                    "pos": [100, 130],
+                    "size": [270, 98],
+                    "flags": {},
+                    "order": 2,
+                    "mode": 0,
+                    "inputs": [],
+                    "outputs": [
+                        {"name": "MODEL", "type": "MODEL", "links": [25]},
+                        {"name": "CLIP", "type": "CLIP", "links": [26]},
+                        {"name": "VAE", "type": "VAE", "links": None},
+                    ],
+                    "title": "Loader",
+                    "properties": {"Node name for S&R": "CheckpointLoaderSimple"},
+                    "widgets_values": ["52_novaAnimeXL_ilV190.safetensors"],
+                },
+                {
+                    "id": 2,
+                    "type": "CLIPTextEncode",
+                    "pos": [1880.866680908203, 130],
+                    "size": [400, 200],
+                    "flags": {},
+                    "order": 9,
+                    "mode": 0,
+                    "inputs": [
+                        {"name": "clip", "type": "CLIP", "link": 49},
+                        {
+                            "name": "text",
+                            "type": "STRING",
+                            "widget": {"name": "text"},
+                            "link": 54,
+                        },
+                    ],
+                    "outputs": [
+                        {"name": "CONDITIONING", "type": "CONDITIONING", "links": [39]}
+                    ],
+                    "properties": {"Node name for S&R": "CLIPTextEncode"},
+                    "widgets_values": [""],
+                },
+                {
+                    "id": 5,
+                    "type": "KSampler",
+                    "pos": [2578.649165895271, 134.76727061509087],
+                    "size": [270, 262],
+                    "flags": {},
+                    "order": 10,
+                    "mode": 0,
+                    "inputs": [
+                        {"name": "model", "type": "MODEL", "link": 50},
+                        {"name": "positive", "type": "CONDITIONING", "link": 39},
+                        {"name": "negative", "type": "CONDITIONING", "link": 23},
+                        {"name": "latent_image", "type": "LATENT", "link": 24},
+                    ],
+                    "outputs": [{"name": "LATENT", "type": "LATENT", "links": [31]}],
+                    "properties": {"Node name for S&R": "KSampler"},
+                    "widgets_values": [
+                        809278554234612,
+                        "randomize",
+                        16,
+                        4,
+                        "euler_ancestral",
+                        "karras",
+                        1,
+                    ],
+                },
+                {
+                    "id": 38,
+                    "type": "LoraLoader",
+                    "pos": [990, 130],
+                    "size": [290.43334045410154, 126],
+                    "flags": {},
+                    "order": 5,
+                    "mode": 0,
+                    "inputs": [
+                        {"name": "model", "type": "MODEL", "link": 29},
+                        {"name": "clip", "type": "CLIP", "link": 30},
+                    ],
+                    "outputs": [
+                        {"name": "MODEL", "type": "MODEL", "links": [27]},
+                        {"name": "CLIP", "type": "CLIP", "links": [28]},
+                    ],
+                    "title": "Load LoRA",
+                    "properties": {"Node name for S&R": "LoraLoader"},
+                    "widgets_values": ["42_アップスケール_remacri_original.pt", 0.4, 0],
+                },
+                {
+                    "id": 28,
+                    "type": "LoraLoader",
+                    "pos": [1380, 130],
+                    "size": [290.43334045410154, 126],
+                    "flags": {},
+                    "order": 6,
+                    "mode": 0,
+                    "inputs": [
+                        {"name": "model", "type": "MODEL", "link": 27},
+                        {"name": "clip", "type": "CLIP", "link": 28},
+                    ],
+                    "outputs": [
+                        {"name": "MODEL", "type": "MODEL", "links": [47]},
+                        {"name": "CLIP", "type": "CLIP", "links": [20, 48]},
+                    ],
+                    "title": "Load LoRA",
+                    "properties": {"Node name for S&R": "LoraLoader"},
+                    "widgets_values": [
+                        "43_5cm-illustriousXL_v01_V1-CAME-000035.safetensors",
+                        0.4,
+                        0,
+                    ],
+                },
+                {
+                    "id": 117,
+                    "type": "LoraLoader",
+                    "pos": [1523.7427746854546, 341.08039710943746],
+                    "size": [290.43334045410154, 126],
+                    "flags": {},
+                    "order": 8,
+                    "mode": 0,
+                    "inputs": [
+                        {"name": "model", "type": "MODEL", "link": 47},
+                        {"name": "clip", "type": "CLIP", "link": 48},
+                    ],
+                    "outputs": [
+                        {"name": "MODEL", "type": "MODEL", "links": [50]},
+                        {"name": "CLIP", "type": "CLIP", "links": [49]},
+                    ],
+                    "properties": {"Node name for S&R": "LoraLoader"},
+                    "widgets_values": ["5_add_saturation_XL.safetensors", -1.4, 0],
+                },
+                {
+                    "id": 118,
+                    "type": "StringConcatenate",
+                    "pos": [1363.7030337022063, -256.7007293998441],
+                    "size": [400, 200],
+                    "flags": {},
+                    "order": 3,
+                    "mode": 0,
+                    "inputs": [],
+                    "outputs": [{"name": "STRING", "type": "STRING", "links": [54]}],
+                    "properties": {"Node name for S&R": "StringConcatenate"},
+                    "widgets_values": [
+                        "%prompt%",
+                        "(disheveled hair:0.5), skinny, "
+                        "shiny skin, skinny, shiny skin, "
+                        "(simple background, white "
+                        "background:1.1), masterpiece, "
+                        "best quality",
+                        ",",
+                    ],
+                },
+                {
+                    "id": 3,
+                    "type": "CLIPTextEncode",
+                    "pos": [1881.5394309031356, 459.32725000506747],
+                    "size": [400, 200],
+                    "flags": {},
+                    "order": 7,
+                    "mode": 0,
+                    "inputs": [{"name": "clip", "type": "CLIP", "link": 20}],
+                    "outputs": [
+                        {"name": "CONDITIONING", "type": "CONDITIONING", "links": [23]}
+                    ],
+                    "properties": {"Node name for S&R": "CLIPTextEncode"},
+                    "widgets_values": [
+                        "(censored, mosaic censoring, bar "
+                        "censor:1.1), bad quality, worst "
+                        "quality, worst detail, bad "
+                        "anatomy, extra fingers, extra "
+                        "toes, extra legs, 4 toes, 6 toes, "
+                        "4 fingers, 6 fingers, malformed "
+                        "fingers, extra limbs, missing "
+                        "fingers, extra arms, censored, "
+                        "deformed, disfigured, text, "
+                        "(multiple views:1.1)"
+                    ],
+                },
+            ],
+            "links": [
+                [20, 28, 1, 3, 0, "CLIP"],
+                [23, 3, 0, 5, 2, "CONDITIONING"],
+                [24, 27, 0, 5, 3, "LATENT"],
+                [25, 1, 0, 10, 0, "MODEL"],
+                [26, 1, 1, 10, 1, "CLIP"],
+                [27, 38, 0, 28, 0, "MODEL"],
+                [28, 38, 1, 28, 1, "CLIP"],
+                [29, 10, 0, 38, 0, "MODEL"],
+                [30, 10, 1, 38, 1, "CLIP"],
+                [31, 5, 0, 56, 0, "LATENT"],
+                [32, 45, 0, 56, 1, "VAE"],
+                [33, 56, 0, 72, 0, "IMAGE"],
+                [39, 2, 0, 5, 1, "CONDITIONING"],
+                [47, 28, 0, 117, 0, "MODEL"],
+                [48, 28, 1, 117, 1, "CLIP"],
+                [49, 117, 1, 2, 0, "CLIP"],
+                [50, 117, 0, 5, 0, "MODEL"],
+                [54, 118, 0, 2, 1, "STRING"],
+            ],
+            "groups": [],
+            "config": {},
+            "extra": {
+                "ds": {
+                    "scale": 0.6303940863128564,
+                    "offset": [-602.8886169463092, 486.2591310892753],
+                },
+                "frontendVersion": "1.45.20",
+            },
+            "version": 0.4,
+        }
+    }
+
+
+workflow = build_workflow()
+prompt = json.loads(json.dumps(workflow))
+extra_pnginfo = build_extra_pnginfo()
+
+
+# Workflow execution
+def main(unload_models: bool | None = None):
+    bootstrap_comfyui_runtime()
+    add_extra_model_paths()
+    import_custom_nodes()
+
+    # Node imports
+    from nodes import (
+        CLIPTextEncode,
+        CheckpointLoaderSimple,
+        EmptyLatentImage,
+        KSampler,
+        LoraLoader,
+        NODE_CLASS_MAPPINGS,
+        VAEDecode,
+        VAELoader,
+    )
+
+    import torch
+
+    try:
+        with torch.inference_mode():
+            checkpointloadersimple = CheckpointLoaderSimple()
+            checkpointloadersimple_1 = checkpointloadersimple.load_checkpoint(
+                ckpt_name="52_novaAnimeXL_ilV190.safetensors"
+            )
+            stringconcatenate = NODE_CLASS_MAPPINGS["StringConcatenate"]()
+            stringconcatenate_118 = stringconcatenate.EXECUTE_NORMALIZED(
+                string_a="%prompt%",
+                string_b="(disheveled hair:0.5), skinny, shiny skin, skinny, shiny skin, (simple background, white background:1.1), masterpiece, best quality",
+                delimiter=",",
+            )
+            loraloader = LoraLoader()
+            loraloader_10 = loraloader.load_lora(
+                lora_name="8_bikabaka.safetensors",
+                strength_model=0.3,
+                strength_clip=0,
+                model=get_value_at_index(checkpointloadersimple_1, 0),
+                clip=get_value_at_index(checkpointloadersimple_1, 1),
+            )
+            loraloader_38 = loraloader.load_lora(
+                lora_name="42_\u30a2\u30c3\u30d7\u30b9\u30b1\u30fc\u30eb_remacri_original.pt",
+                strength_model=0.4,
+                strength_clip=0,
+                model=get_value_at_index(loraloader_10, 0),
+                clip=get_value_at_index(loraloader_10, 1),
+            )
+            loraloader_28 = loraloader.load_lora(
+                lora_name="43_5cm-illustriousXL_v01_V1-CAME-000035.safetensors",
+                strength_model=0.4,
+                strength_clip=0,
+                model=get_value_at_index(loraloader_38, 0),
+                clip=get_value_at_index(loraloader_38, 1),
+            )
+            loraloader_117 = loraloader.load_lora(
+                lora_name="5_add_saturation_XL.safetensors",
+                strength_model=-1.4,
+                strength_clip=0,
+                model=get_value_at_index(loraloader_28, 0),
+                clip=get_value_at_index(loraloader_28, 1),
+            )
+            cliptextencode = CLIPTextEncode()
+            cliptextencode_2 = cliptextencode.encode(
+                text=get_value_at_index(stringconcatenate_118, 0),
+                clip=get_value_at_index(loraloader_117, 1),
+            )
+            cliptextencode_3 = cliptextencode.encode(
+                text="(censored, mosaic censoring, bar censor:1.1), bad quality, worst quality, worst detail, bad anatomy, extra fingers, extra toes, extra legs, 4 toes, 6 toes, 4 fingers, 6 fingers, malformed fingers, extra limbs, missing fingers, extra arms, censored, deformed, disfigured, text, (multiple views:1.1)",
+                clip=get_value_at_index(loraloader_28, 1),
+            )
+            emptylatentimage = EmptyLatentImage()
+            emptylatentimage_27 = emptylatentimage.generate(
+                width=1152, height=896, batch_size=1
+            )
+            vaeloader = VAELoader()
+            vaeloader_45 = vaeloader.load_vae(vae_name="3_sdxlVAE_sdxlVAE.safetensors")
+            ksampler = KSampler()
+            vaedecode = VAEDecode()
+            for q in range(1):
+                node_5_seed = prompt["5"]["inputs"]["seed"] = random.randint(1, 2**64)
+                ksampler_5 = ksampler.sample(
+                    seed=node_5_seed,
+                    steps=16,
+                    cfg=4,
+                    sampler_name="euler_ancestral",
+                    scheduler="karras",
+                    denoise=1,
+                    model=get_value_at_index(loraloader_117, 0),
+                    positive=get_value_at_index(cliptextencode_2, 0),
+                    negative=get_value_at_index(cliptextencode_3, 0),
+                    latent_image=get_value_at_index(emptylatentimage_27, 0),
+                )
+                vaedecode_56 = vaedecode.decode(
+                    samples=get_value_at_index(ksampler_5, 0),
+                    vae=get_value_at_index(vaeloader_45, 0),
+                )
+    finally:
+        cleanup_comfyui_runtime(unload_models=unload_models)
+
+
+# Entrypoint
+if __name__ == "__main__":
+    main()