arch_agent
ea1b231fea
Features:
- /edit/{name} route: Character nachträglich bearbeiten
- /api/character/update/{name}: API zum Aktualisieren
- create.html dient als Editor und Creator (mode=edit/create)
- Index-Seite: ✏️ Button pro Character
- Portrait-Vorschau im Editor
- Image-to-Image: Folge-Portraits nutzen bestehendes Bild als Referenz
- LoadImage → ImageScale → VAEEncode → KSampler (denoise=0.45)
- Aussehen bleibt konsistent über mehrere Generierungen
- Fallback auf Text-to-Image wenn kein Referenzbild existiert
- ComfyUI Image Upload via /upload/image API
- Jinja2 if-Expressions für vorausgefüllte Formularfelder
281 lines
9.4 KiB
Python
281 lines
9.4 KiB
Python
"""
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NeonChat ComfyUI Integration — Profilbild-Generierung für Characters
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Basiert auf dem Character-Aussehen (appearance)
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"""
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import json, os, time, shutil
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from pathlib import Path
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import httpx
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COMFYUI_URL = os.environ.get("COMFYUI_URL", "http://localhost:8188")
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COMFYUI_OUTPUT_DIR = Path(os.environ.get("COMFYUI_OUTPUT", str(Path.home() / "comfy" / "ComfyUI" / "output")))
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AVATARS_DIR = Path(__file__).parent / "static" / "avatars"
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def is_comfyui_running() -> bool:
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try:
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with httpx.Client(timeout=3) as client:
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resp = client.get(f"{COMFYUI_URL}/system_stats")
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return resp.status_code == 200
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except:
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return False
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def build_prompt_from_appearance(character: dict, context: str = "") -> str:
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"""Baut einen ComfyUI-Prompt aus dem Character-Aussehen."""
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app = character.get("appearance", {})
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style = app.get("style", "anime")
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gender = app.get("gender", "female")
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age = app.get("age", "25")
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height = app.get("height", "")
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build = app.get("build", "")
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hair_color = app.get("hair_color", "")
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hair_style = app.get("hair_style", "")
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eye_color = app.get("eye_color", "")
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skin = app.get("skin", "")
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clothing = app.get("clothing", "")
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distinctive = app.get("distinctive", "")
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# Style prefix
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style_map = {
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"anime": "anime style, anime art, ",
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"realistic": "photorealistic, realistic, ",
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"comic": "comic book style, western comic, ",
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"cartoon": "cartoon style, ",
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"pixel": "pixel art, ",
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}
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style_prefix = style_map.get(style, "anime style, ")
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# Gender
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if gender == "female":
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gender_tag = "1girl, woman, "
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elif gender == "male":
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gender_tag = "1boy, man, "
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else:
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gender_tag = "person, "
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# Build prompt
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parts = [
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f"portrait, headshot, {style_prefix}{gender_tag}",
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f"{age} years old",
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]
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if build:
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parts.append(build)
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if hair_color and hair_style:
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parts.append(f"{hair_color} hair, {hair_style}")
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elif hair_color:
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parts.append(f"{hair_color} hair")
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elif hair_style:
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parts.append(hair_style)
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if eye_color:
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parts.append(f"{eye_color} eyes")
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if skin:
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parts.append(f"{skin} skin")
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if clothing:
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parts.append(f"wearing {clothing}")
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if distinctive:
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parts.append(distinctive)
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if context:
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parts.append(context)
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prompt = ", ".join(parts)
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prompt += ", detailed face, high quality, masterpiece, best quality, simple background"
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return prompt
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def build_portrait_workflow(prompt: str, negative: str = "", width: int = 512, height: int = 512) -> dict:
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return {
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"3": {
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"class_type": "KSampler",
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"inputs": {
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"seed": int(time.time()) % (2**32),
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"steps": 25,
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"cfg": 7.0,
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"sampler_name": "dpmpp_2m",
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"scheduler": "karras",
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"denoise": 1.0,
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"model": ["4", 0],
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"positive": ["6", 0],
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"negative": ["7", 0],
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"latent_image": ["5", 0]
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}
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},
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"4": {
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"class_type": "CheckpointLoaderSimple",
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"inputs": {"ckpt_name": "NoobAI-XL-v1.1.safetensors"}
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},
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"5": {
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"class_type": "EmptyLatentImage",
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"inputs": {"width": width, "height": height, "batch_size": 1}
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},
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"6": {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"text": prompt,
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"clip": ["4", 1]
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}
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},
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"7": {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"text": f"lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, blurry, deformed, ugly, {negative}",
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"clip": ["4", 1]
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}
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},
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"8": {
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"class_type": "VAEDecode",
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"inputs": {"samples": ["3", 0], "vae": ["4", 2]}
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},
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"9": {
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"class_type": "SaveImage",
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"inputs": {"filename_prefix": "neonchat_portrait", "images": ["8", 0]}
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}
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}
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def queue_prompt(workflow: dict) -> str:
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with httpx.Client(timeout=10) as client:
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resp = client.post(f"{COMFYUI_URL}/prompt", json={"prompt": workflow})
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result = resp.json()
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return result.get("prompt_id", "")
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def get_history(prompt_id: str) -> dict:
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try:
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with httpx.Client(timeout=5) as client:
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resp = client.get(f"{COMFYUI_URL}/history/{prompt_id}")
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return resp.json()
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except:
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return {}
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def wait_for_image(prompt_id: str, timeout: int = 180) -> str | None:
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start = time.time()
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while time.time() - start < timeout:
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history = get_history(prompt_id)
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if prompt_id in history:
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outputs = history[prompt_id].get("outputs", {})
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if "9" in outputs:
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images = outputs["9"].get("images", [])
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if images:
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return images[0].get("filename", "")
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time.sleep(3)
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return None
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def build_i2i_workflow(prompt: str, negative: str, reference_image_path: str, width: int = 512, height: int = 512, denoise: float = 0.45) -> dict:
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"""Image-to-Image Workflow — nutzt ein Referenzbild für Konsistenz."""
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return {
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"3": {
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"class_type": "KSampler",
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"inputs": {
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"seed": int(time.time()) % (2**32),
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"steps": 25,
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"cfg": 7.0,
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"sampler_name": "dpmpp_2m",
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"scheduler": "karras",
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"denoise": denoise,
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"model": ["4", 0],
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"positive": ["6", 0],
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"negative": ["7", 0],
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"latent_image": ["10", 0]
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}
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},
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"4": {
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"class_type": "CheckpointLoaderSimple",
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"inputs": {"ckpt_name": "NoobAI-XL-v1.1.safetensors"}
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},
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"5": {
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"class_type": "LoadImage",
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"inputs": {"image": reference_image_path}
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},
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"6": {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"text": prompt,
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"clip": ["4", 1]
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}
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},
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"7": {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"text": f"lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, blurry, deformed, ugly, {negative}",
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"clip": ["4", 1]
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}
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},
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"8": {
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"class_type": "VAEDecode",
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"inputs": {"samples": ["3", 0], "vae": ["4", 2]}
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},
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"9": {
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"class_type": "SaveImage",
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"inputs": {"filename_prefix": "neonchat_portrait", "images": ["8", 0]}
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},
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"10": {
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"class_type": "VAEEncode",
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"inputs": {"pixels": ["11", 0], "vae": ["4", 2]}
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},
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"11": {
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"class_type": "ImageScale",
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"inputs": {
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"image": ["5", 0],
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"upscale_method": "bilinear",
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"width": width,
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"height": height,
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"crop": "center"
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}
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}
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}
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def upload_image_to_comfy(image_path: str) -> str:
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"""Lädt ein Bild zu ComfyUI hoch und gibt den Dateinamen zurück."""
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import os
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filename = os.path.basename(image_path)
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with open(image_path, "rb") as f:
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files = {"image": (filename, f, "image/png")}
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with httpx.Client(timeout=30) as client:
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resp = client.post(f"{COMFYUI_URL}/upload/image", files=files)
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data = resp.json()
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return data.get("name", filename)
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def generate_portrait(character_name: str, character: dict, context: str = "") -> str | None:
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"""Generiert ein Profilbild. Nutzt Image-to-Image wenn ein Referenzbild existiert."""
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if not is_comfyui_running():
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return None
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AVATARS_DIR.mkdir(parents=True, exist_ok=True)
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prompt = build_prompt_from_appearance(character, context)
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negative = "realistic, 3d, render"
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# Check if we have a reference image for consistency
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safe_name = character_name.lower().replace(" ", "_")
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ref_image = AVATARS_DIR / f"{safe_name}.png"
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if ref_image.exists():
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# Image-to-Image for consistency
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try:
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uploaded_name = upload_image_to_comfy(str(ref_image))
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workflow = build_i2i_workflow(prompt, negative, uploaded_name, 512, 512, denoise=0.45)
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except Exception as e:
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print(f"I2I upload failed, falling back to T2I: {e}")
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workflow = build_portrait_workflow(prompt, negative, 512, 512)
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else:
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# First generation — Text-to-Image
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workflow = build_portrait_workflow(prompt, negative, 512, 512)
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try:
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prompt_id = queue_prompt(workflow)
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if not prompt_id:
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return None
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filename = wait_for_image(prompt_id, timeout=180)
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if not filename:
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return None
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comfy_output = COMFYUI_OUTPUT_DIR / filename
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if not comfy_output.exists():
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return None
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dest = AVATARS_DIR / f"{safe_name}.png"
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shutil.copy2(str(comfy_output), str(dest))
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return f"/static/avatars/{safe_name}.png"
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except Exception as e:
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print(f"ComfyUI error: {e}")
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return None |