Initial commit: Natiris AI Agent Orchestration System
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Arch Agent
358
bridges/ComfyBridge_v3.py
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358
bridges/ComfyBridge_v3.py
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#!/usr/bin/env python3
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"""
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ComfyBridge v3 – Mit IPAdapter für Gesichtskonsistenz
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Features v3:
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- IPAdapter Integration für Gesichtskonsistenz
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- CLIP Vision für Bildverarbeitung
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- Trust-basierte IPAdapter-Stärke (0.4 - 0.8)
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- Fallback wenn IPAdapter nicht verfügbar
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"""
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import json
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import os
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import time
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import uuid
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import subprocess
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Dict, Optional, List
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try:
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import requests
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REQUESTS_AVAILABLE = True
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except ImportError:
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REQUESTS_AVAILABLE = False
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print("Warning: requests not available")
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PATHS = {
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"state": os.path.expanduser("~/natiris/core/natiris_full_state.json"),
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"config": os.path.expanduser("~/natiris/config/character_genesis.json"),
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"output_dir": os.path.expanduser("~/natiris/generated/"),
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"output": os.path.expanduser("~/natiris/bridges/comfy_response.json"),
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"base_images": os.path.expanduser("~/natiris/assets/base_images/"),
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"vision_script": os.path.expanduser("~/natiris/bridges/VisionBridge_v2.py"),
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}
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COMFY_API = os.getenv("COMFY_API_URL", "http://localhost:8188")
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TRUST_MAP = [
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{"range": [0, 3], "style": "neutral_portrait", "prompt_add": "neutral expression, professional lighting", "ipadapter_weight": 0.4},
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{"range": [4, 7], "style": "personal_context", "prompt_add": "relaxed expression, warm lighting", "ipadapter_weight": 0.6},
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{"range": [8, 10], "style": "intimate", "prompt_add": "warm smile, intimate lighting", "ipadapter_weight": 0.8}
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]
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class ComfyBridgeV3:
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def __init__(self):
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self.client_id = f"natiris_{uuid.uuid4().hex[:8]}"
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self.base_images_dir = Path(PATHS["base_images"])
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self.output_dir = Path(PATHS["output_dir"])
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self.output_dir.mkdir(parents=True, exist_ok=True)
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self.has_ipadapter = False
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self.check_comfy_nodes()
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def check_comfy_nodes(self):
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"""Prüft welche ComfyUI Nodes verfügbar"""
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try:
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response = requests.get(f"{COMFY_API}/object_info", timeout=5)
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nodes = response.json()
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self.has_ipadapter = "IPAdapterAdvanced" in nodes or "IPAdapter" in nodes
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self.has_controlnet_openpose = "ControlNetLoader" in nodes
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print(f"IPAdapter verfügbar: {self.has_ipadapter}")
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print(f"ControlNet verfügbar: {self.has_controlnet_openpose}")
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except Exception as e:
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print(f"Node-Check fehlgeschlagen: {e}")
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self.has_ipadapter = False
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self.has_controlnet_openpose = False
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def check_health(self):
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try:
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response = requests.get(f"{COMFY_API}/system_stats", timeout=5)
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data = response.json()
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return {"reachable": True, "version": data.get("system", {}).get("comfyui_version", "unknown")}
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except Exception as e:
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return {"reachable": False, "error": str(e)}
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def get_style_config(self, trust):
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for entry in TRUST_MAP:
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if entry["range"][0] <= trust <= entry["range"][1]:
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return entry
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return TRUST_MAP[1]
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def build_prompt(self, state):
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core = state.get("core_state", {})
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trust = core.get("trust", 7.0)
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mood = core.get("mood", 5)
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style = self.get_style_config(trust)
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character = (
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"young woman, natural beauty, warm eyes, "
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"consistent facial features, same person, "
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f"{style['prompt_add']}, "
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f"mood: {'happy' if mood >=6 else 'neutral' if mood >=4 else 'melancholic'}, "
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"high detail, cinematic"
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)
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negative = (
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"blurry, distorted, deformed, extra limbs, "
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"different person, inconsistent face, ugly"
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)
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return {
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"positive": character,
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"negative": negative,
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"style": style["style"],
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"trust": trust,
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"width": 512,
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"height": 768 if trust > 7 else 512,
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"ipadapter_weight": style["ipadapter_weight"]
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}
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def build_workflow_basic(self, prompt_data: Dict) -> Dict:
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"""Basis-Workflow ohne IPAdapter"""
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seed = int(time.time()) % 2147483647
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return {
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"1": {
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"inputs": {"text": prompt_data["positive"], "clip": ["12", 1]},
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"class_type": "CLIPTextEncode"
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},
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"2": {
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"inputs": {"text": prompt_data["negative"], "clip": ["12", 1]},
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"class_type": "CLIPTextEncode"
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},
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"3": {
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"inputs": {
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"seed": seed,
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"steps": 25,
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"cfg": 7.0,
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"sampler_name": "euler_ancestral",
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"scheduler": "karras",
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"denoise": 1.0,
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"model": ["12", 0],
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"positive": ["1", 0],
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"negative": ["2", 0],
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"latent_image": ["13", 0]
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},
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"class_type": "KSampler"
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},
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"4": {
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"inputs": {"samples": ["3", 0], "vae": ["12", 2]},
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"class_type": "VAEDecode"
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},
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"5": {
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"inputs": {
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"filename_prefix": f"natiris_{prompt_data['style']}",
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"images": ["4", 0]
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},
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"class_type": "SaveImage"
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},
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"12": {
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"inputs": {"ckpt_name": "realisticVisionV60B1_v51HyperVAE.safetensors"},
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"class_type": "CheckpointLoaderSimple"
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},
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"13": {
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"inputs": {
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"width": prompt_data["width"],
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"height": prompt_data["height"],
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"batch_size": 1
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},
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"class_type": "EmptyLatentImage"
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}
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}
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def build_workflow_ipadapter(self, prompt_data: Dict, face_path: str) -> Dict:
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"""Workflow mit IPAdapter für Gesichtskonsistenz"""
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workflow = self.build_workflow_basic(prompt_data)
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weight = prompt_data.get("ipadapter_weight", 0.6)
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# IPAdapter Nodes hinzufügen
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ipadapter_nodes = {
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# Load Face Image
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"20": {
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"inputs": {"image": face_path},
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"class_type": "LoadImage"
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},
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# IPAdapter Model Loader
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"21": {
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"inputs": {"ipadapter_file": "ip-adapter_sd15_light.pth"},
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"class_type": "IPAdapterModelLoader"
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},
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# CLIP Vision Loader
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"22": {
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"inputs": {"clip_name": "CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors"},
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"class_type": "CLIPVisionLoader"
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},
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# IPAdapter Advanced - applied to model before KSampler
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"23": {
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"inputs": {
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"model": ["12", 0],
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"ipadapter": ["21", 0],
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"image": ["20", 0],
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"weight": weight,
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"start_at": 0.0,
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"end_at": 1.0,
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"weight_type": "original"
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},
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"class_type": "IPAdapter"
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}
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}
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# IPAdapter-Output als Model für KSampler
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workflow["3"]["inputs"]["model"] = ["23", 0]
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workflow.update(ipadapter_nodes)
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return workflow
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def build_workflow(self, prompt_data: Dict, base_images: Dict) -> Dict:
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"""Wählt Workflow basierend auf Verfügbarkeit"""
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face_path = base_images.get("face_path", "")
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if self.has_ipadapter and face_path and os.path.exists(face_path):
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print(f"Using IPAdapter workflow (weight: {prompt_data.get('ipadapter_weight', 0.6)})")
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return self.build_workflow_ipadapter(prompt_data, face_path)
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else:
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print("Using basic workflow (IPAdapter not available)")
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return self.build_workflow_basic(prompt_data)
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def submit_workflow(self, workflow):
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try:
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data = {"prompt": workflow, "client_id": self.client_id}
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response = requests.post(f"{COMFY_API}/prompt", json=data, timeout=10)
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result = response.json()
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if "prompt_id" in result:
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return {"success": True, "prompt_id": result["prompt_id"]}
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return {"success": False, "error": result.get("error", "Unknown")}
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except Exception as e:
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return {"success": False, "error": str(e)}
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def poll_result(self, prompt_id, max_wait=300):
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start_time = time.time()
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while time.time() - start_time < max_wait:
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try:
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queue = requests.get(f"{COMFY_API}/queue", timeout=5).json()
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history = requests.get(f"{COMFY_API}/history", timeout=5).json()
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if prompt_id in history:
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return {"completed": True, "data": history[prompt_id]}
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print(" ... processing")
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time.sleep(1)
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except Exception as e:
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return {"completed": False, "error": str(e)}
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return {"completed": False, "error": "Timeout"}
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def download_image(self, filename, subfolder=""):
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try:
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params = {"filename": filename, "subfolder": subfolder, "type": "output"}
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response = requests.get(f"{COMFY_API}/view", params=params, timeout=30)
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return response.content if response.status_code == 200 else None
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except:
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return None
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def save_image(self, image_data, metadata):
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"natiris_{metadata['style']}_{timestamp}.png"
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filepath = self.output_dir / filename
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try:
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with open(filepath, "wb") as f:
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f.write(image_data)
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meta_file = self.output_dir / f"{filename}.json"
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with open(meta_file, "w") as f:
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json.dump(metadata, f, indent=2)
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return {"success": True, "path": str(filepath)}
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except Exception as e:
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return {"success": False, "error": str(e)}
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def generate(self, state_path=None):
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"""Hauptgenerierung mit IPAdapter-Unterstützung"""
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state = {}
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if state_path and os.path.exists(state_path):
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with open(state_path) as f:
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state = json.load(f)
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elif os.path.exists(PATHS["state"]):
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with open(PATHS["state"]) as f:
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state = json.load(f)
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# Health Check
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health = self.check_health()
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if not health["reachable"]:
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return {"success": False, "error": "ComfyUI not reachable"}
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# Basisbilder
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face_path = PATHS["base_images"] + "face_base.png"
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base_images = {"face_path": face_path, "face_exists": os.path.exists(face_path)}
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# Prompt & Workflow
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prompt_data = self.build_prompt(state)
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workflow = self.build_workflow(prompt_data, base_images)
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# Submit & Poll
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submit = self.submit_workflow(workflow)
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if not submit["success"]:
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return {"success": False, "error": submit.get("error", "Submit failed")}
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print(f"✓ Submitted: {submit['prompt_id']}")
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poll = self.poll_result(submit["prompt_id"])
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if not poll["completed"]:
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return {"success": False, "error": "Generation failed"}
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# Bild extrahieren & speichern
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outputs = poll["data"].get("outputs", {})
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for node_id, node_out in outputs.items():
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if "images" in node_out:
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for img in node_out["images"]:
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image_bytes = self.download_image(img["filename"], img.get("subfolder", ""))
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if image_bytes:
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metadata = {
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"prompt": prompt_data,
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"trust": prompt_data["trust"],
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"style": prompt_data["style"],
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"ipadapter_used": self.has_ipadapter,
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"timestamp": datetime.now(timezone.utc).isoformat()
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}
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save = self.save_image(image_bytes, metadata)
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if save["success"]:
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print(f"✓ Saved: {save['path']}")
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return {"success": True, "image_path": save["path"], "metadata": metadata}
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return {"success": False, "error": "Image save failed"}
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def main():
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--state", default=PATHS["state"])
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parser.add_argument("--check", action="store_true")
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parser.add_argument("--test", action="store_true")
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args = parser.parse_args()
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bridge = ComfyBridgeV3()
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if args.check:
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health = bridge.check_health()
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print(f"ComfyUI: {'✓' if health['reachable'] else '✗'}")
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print(f"IPAdapter: {'✓' if bridge.has_ipadapter else '✗'}")
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return
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if args.test:
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print("ComfyBridge v3 Test")
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print("-" * 40)
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result = bridge.generate(args.state)
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print(json.dumps(result, indent=2))
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return
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result = bridge.generate(args.state)
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with open(PATHS["output"], "w") as f:
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json.dump(result, f, indent=2)
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if __name__ == "__main__":
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main()
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