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pporcheret
2026-10-24 17:55:05 +02:00
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hermes/MCP_comfyui/.env
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COMFYUI_URL=http://192.168.1.13:8188
COMFYUI_TIMEOUT=300
WORKFLOWS_DIR=/opt/hermes/MCP_comfyui/workflows
COMFY_PROMPT_NODE=6
COMFY_VISION_IMAGE_NODE=1
COMFY_VISION_PROMPT_NODE=2
ALFRED_RENDER_URL=http://127.0.0.1:54321/api/render
hermes/MCP_comfyui/.env.example
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COMFYUI_URL=http://192.168.1.13:8188
COMFYUI_TIMEOUT=300
WORKFLOWS_DIR=/opt/hermes/MCP_comfyui/workflows
COMFY_PROMPT_NODE=6
COMFY_VISION_IMAGE_NODE=1
COMFY_VISION_PROMPT_NODE=2
hermes/MCP_comfyui/README.md
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# MCP_comfyui
Copier `.env.example` vers `.env`, puis créer le venv et installer `requirements.txt`.
hermes/MCP_comfyui/comfyui_mcp.py
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"""MCP ComfyUI pour Hermes avec transmission d'images vers l'interface Alfred.
Fonctionne avec un workflow API ComfyUI possédant un nœud de sauvegarde d'image.
L'envoi HTTP à Alfred est indépendant de la réponse textuelle d'Hermes.
"""
import json
import os
import time
import uuid
from pathlib import Path
from urllib.parse import urlencode
import requests
from dotenv import load_dotenv
from mcp.server.fastmcp import FastMCP
load_dotenv(Path(__file__).with_name(".env"))
mcp = FastMCP("Alfred ComfyUI")
BASE = os.getenv("COMFYUI_URL", "http://127.0.0.1:8188").rstrip("/")
WF = Path(os.getenv("WORKFLOWS_DIR", str(Path(__file__).parent / "workflows")))
ALFRED_RENDER_URL = os.getenv(
"ALFRED_RENDER_URL", "http://127.0.0.1:54321/api/render"
)
ALFRED_RENDER_TOKEN = os.getenv("ALFRED_RENDER_TOKEN", "").strip()
def submit(workflow: dict) -> str:
response = requests.post(
f"{BASE}/prompt",
json={"prompt": workflow, "client_id": str(uuid.uuid4())},
timeout=30,
)
response.raise_for_status()
return response.json()["prompt_id"]
def wait(prompt_id: str) -> dict:
deadline = time.monotonic() + int(os.getenv("COMFYUI_TIMEOUT", "300"))
while time.monotonic() < deadline:
response = requests.get(f"{BASE}/history/{prompt_id}", timeout=15)
response.raise_for_status()
result = response.json()
if prompt_id in result:
return result[prompt_id]
time.sleep(1)
raise TimeoutError(f"ComfyUI timeout ({prompt_id})")
def extract_images(history: dict) -> list[dict]:
"""Construit les URL de /view depuis les sorties ComfyUI (SaveImage)."""
images = []
for node_id, output in history.get("outputs", {}).items():
for image in output.get("images", []):
filename = image.get("filename")
if not filename:
continue
subfolder = image.get("subfolder", "")
image_type = image.get("type", "output")
query = urlencode(
{
"filename": filename,
"subfolder": subfolder,
"type": image_type,
}
)
images.append(
{
"node_id": str(node_id),
"filename": filename,
"subfolder": subfolder,
"type": image_type,
"url": f"{BASE}/view?{query}",
}
)
return images
def show_on_alfred(image: dict, caption: str) -> tuple[bool, str | None]:
"""Ne fait pas échouer la génération si Alfred est hors ligne."""
headers = {}
if ALFRED_RENDER_TOKEN:
headers["X-Alfred-Render-Token"] = ALFRED_RENDER_TOKEN
try:
response = requests.post(
ALFRED_RENDER_URL,
json={
"type": "image",
"url": image["url"],
"alt": "Image générée par ComfyUI",
"caption": caption,
},
headers=headers,
timeout=5,
)
response.raise_for_status()
return True, None
except requests.RequestException as exc:
return False, str(exc)
@mcp.tool()
def generate_image(prompt: str) -> dict:
"""Génère une image par ComfyUI et l'affiche automatiquement sur Alfred.
Utiliser cet outil lorsque l'utilisateur demande de créer/générer une image.
Ne pas prétendre que l'image a été affichée si displayed_on_alfred est faux.
"""
prompt = str(prompt).strip()
if not prompt:
raise ValueError("Le prompt de génération est vide")
workflow = json.loads((WF / "text_to_image.json").read_text(encoding="utf-8"))
prompt_node = os.getenv("COMFY_PROMPT_NODE", "6")
workflow[prompt_node]["inputs"]["text"] = prompt
prompt_id = submit(workflow)
history = wait(prompt_id)
images = extract_images(history)
if not images:
raise RuntimeError(
"ComfyUI a terminé sans image de sortie. "
"Vérifie le nœud SaveImage du workflow."
)
displayed, display_error = show_on_alfred(
images[0], f"Image générée : {prompt}"
)
return {
"ok": True,
"prompt_id": prompt_id,
"prompt": prompt,
"images": images,
"first_image_url": images[0]["url"],
"displayed_on_alfred": displayed,
"display_error": display_error,
}
@mcp.tool()
def analyze_image(image_path: str, question: str) -> dict:
"""Analyse une image via le workflow VLM configuré (inchangé)."""
workflow = json.loads((WF / "vision_analyze.json").read_text(encoding="utf-8"))
workflow[os.getenv("COMFY_VISION_IMAGE_NODE", "1")]["inputs"]["image"] = image_path
workflow[os.getenv("COMFY_VISION_PROMPT_NODE", "2")]["inputs"]["text"] = question
return wait(submit(workflow))
if __name__ == "__main__":
mcp.run()
hermes/MCP_comfyui/comfyui_mcp.py.ori
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import os, json, time, uuid, requests
from pathlib import Path
from urllib.parse import quote
from dotenv import load_dotenv
from mcp.server.fastmcp import FastMCP
load_dotenv()
mcp = FastMCP("Alfred ComfyUI")
BASE = os.getenv("COMFYUI_URL", "http://127.0.0.1:8188").rstrip("/")
WF = Path(os.getenv("WORKFLOWS_DIR", Path(__file__).parent / "workflows"))
def submit(workflow):
r = requests.post(
BASE + "/prompt",
json={
"prompt": workflow,
"client_id": str(uuid.uuid4())
},
timeout=30
)
r.raise_for_status()
return r.json()["prompt_id"]
def wait(pid):
deadline = time.time() + int(os.getenv("COMFYUI_TIMEOUT", "300"))
while time.time() < deadline:
h = requests.get(f"{BASE}/history/{pid}", timeout=15).json()
if pid in h:
return h[pid]
time.sleep(1)
raise TimeoutError("ComfyUI timeout")
def build_image_url(filename, subfolder="", img_type="output"):
return (
f"{BASE}/view?"
f"filename={quote(str(filename))}"
f"&subfolder={quote(str(subfolder))}"
f"&type={quote(str(img_type))}"
)
def extract_images(history):
images = []
outputs = history.get("outputs", {})
for node_id, node_output in outputs.items():
for img in node_output.get("images", []):
filename = img.get("filename")
subfolder = img.get("subfolder", "")
img_type = img.get("type", "output")
if not filename:
continue
images.append({
"node_id": node_id,
"filename": filename,
"subfolder": subfolder,
"type": img_type,
"url": build_image_url(filename, subfolder, img_type)
})
return images
@mcp.tool()
def generate_image(prompt: str):
"""Generate an image with the locked text_to_image workflow."""
w = json.loads((WF / "text_to_image.json").read_text())
node = os.getenv("COMFY_PROMPT_NODE", "6")
w[node]["inputs"]["text"] = prompt
history = wait(submit(w))
images = extract_images(history)
return {
"ok": True,
"message": f"Image générée pour : {prompt}",
"prompt": prompt,
"images": images,
"first_image_url": images[0]["url"] if images else None,
"first_image_filename": images[0]["filename"] if images else None
}
@mcp.tool()
def analyze_image(image_path: str, question: str):
"""Analyze a camera/local image using the locked VLM workflow. Workflow node IDs are configured in .env."""
w = json.loads((WF / "vision_analyze.json").read_text())
w[os.getenv("COMFY_VISION_IMAGE_NODE", "1")]["inputs"]["image"] = image_path
w[os.getenv("COMFY_VISION_PROMPT_NODE", "2")]["inputs"]["text"] = question
return wait(submit(w))
if __name__ == "__main__":
mcp.run()
hermes/MCP_comfyui/requirements.txt
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mcp>=1.12
requests>=2.32
python-dotenv>=1.0
hermes/MCP_comfyui/workflows/README.md
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Exporter depuis ComfyUI les workflows API JSON et les nommer `text_to_image.json` et `vision_analyze.json`. Les IDs de nœuds sont configurables dans `.env`.
hermes/MCP_comfyui/workflows/text_to_image.json
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{
"3": {
"inputs": {
"seed": 156680208700286,
"steps": 20,
"cfg": 8,
"sampler_name": "euler",
"scheduler": "normal",
"denoise": 1,
"model": [
"4",
0
],
"positive": [
"6",
0
],
"negative": [
"7",
0
],
"latent_image": [
"5",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"4": {
"inputs": {
"ckpt_name": "absolutereality_v181.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"5": {
"inputs": {
"width": 512,
"height": 512,
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"6": {
"inputs": {
"text": "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,",
"clip": [
"4",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"7": {
"inputs": {
"text": "text, watermark",
"clip": [
"4",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"8": {
"inputs": {
"samples": [
"3",
0
],
"vae": [
"4",
2
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"8",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
}
}
hermes/MCP_comfyui/workflows/vision_analyze.json
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{
"3": {
"inputs": {
"seed": 156680208700286,
"steps": 20,
"cfg": 8,
"sampler_name": "euler",
"scheduler": "normal",
"denoise": 1,
"model": [
"4",
0
],
"positive": [
"6",
0
],
"negative": [
"7",
0
],
"latent_image": [
"5",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"4": {
"inputs": {
"ckpt_name": "absolutereality_v181.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"5": {
"inputs": {
"width": 512,
"height": 512,
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"6": {
"inputs": {
"text": "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,",
"clip": [
"4",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"7": {
"inputs": {
"text": "text, watermark",
"clip": [
"4",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"8": {
"inputs": {
"samples": [
"3",
0
],
"vae": [
"4",
2
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"8",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
}
}