Initial commit: Hermes Agent Skills collection
This commit is contained in:
Debian
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#!/usr/bin/env python3
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"""
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OpenAI-compatible API wrapper for ComfyUI.
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Supports: ideogram4, flux2
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Endpoint: POST /v1/images/generations
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Extended params: seed, steps, cfg, quality, negative_prompt, style
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Timeout: 1800s (30 min)
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"""
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import os
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import re
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import json
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import base64
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import time
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import asyncio
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import httpx
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from typing import Optional
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from fastapi import FastAPI, Request, HTTPException, Header
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from fastapi.responses import JSONResponse
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from pydantic import BaseModel, Field
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COMFY_HOST = os.environ.get("COMFYUI_HOST", "localhost")
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COMFY_PORT = os.environ.get("COMFYUI_PORT", "8188")
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COMFY_URL = f"http://{COMFY_HOST}:{COMFY_PORT}"
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API_KEY = os.environ.get("API_KEY", "")
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ALLOWED_SIZES = {
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"256x256", "512x512", "512x768", "768x512", "768x1024", "1024x768",
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"1024x1024", "1024x1536", "1536x1024", "832x1216", "1216x832",
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"896x1152", "1152x896", "640x1536", "1536x640"
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}
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DEFAULT_SIZE = "1024x1024"
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QUALITY_STEPS = {
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"low": 8,
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"medium": 20,
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"high": 30,
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"ultra": 50,
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}
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IDEOGRAM_ALLOWED = {"1024x1024", "1024x1536", "1536x1024", "832x1216", "1216x832", "896x1152", "1152x896"}
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IDEOGRAM_DEFAULT = "1024x1024"
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FLUX_ALLOWED = {"256x256", "512x512", "512x768", "768x512", "768x1024", "1024x768",
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"1024x1024", "1024x1536", "1536x1024", "832x1216", "1216x832"}
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# Only two models active on this system
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ALLOWED_MODELS = {"ideogram4", "flux2"}
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DEFAULT_MODEL = "ideogram4"
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def _parse_size(size: str) -> tuple[int, int]:
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m = re.match(r"(\d+)x(\d+)", size)
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if not m:
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raise ValueError(f"Invalid size: {size}")
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return int(m.group(1)), int(m.group(2))
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def _validate_model_size(model: str, size: str) -> str:
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if model == "ideogram4":
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if size not in IDEOGRAM_ALLOWED:
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return IDEOGRAM_DEFAULT
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return size
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if model.startswith("flux"):
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if size not in FLUX_ALLOWED:
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return DEFAULT_SIZE
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return size
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return DEFAULT_SIZE
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def _build_ideogram4_workflow(prompt: str, width: int, height: int, seed: int,
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steps: int = 20, cfg: float = 1.0,
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negative_prompt: str = "") -> dict:
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"""
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Ideogram 4 requires asymmetric CFG wiring:
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- Main UNet goes through CFGOverride before DualModelGuider
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- Separate unconditional UNet loaded via second UNETLoader
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- ConditioningZeroOut for the negative (not a text prompt)
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- DualModelGuider takes model_negative (unconditional UNet), not model_1
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"""
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return {
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"1": {"inputs": {"vae_name": "flux2-vae.safetensors"}, "class_type": "VAELoader"},
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"2": {"inputs": {"unet_name": "ideogram4_fp8_scaled.safetensors", "weight_dtype": "default"},
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"class_type": "UNETLoader"},
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"3": {"inputs": {"clip_name": "qwen3vl_8b_fp8_scaled.safetensors", "type": "ideogram4", "device": "default"},
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"class_type": "CLIPLoader"},
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"4": {"inputs": {"text": prompt, "clip": ["3", 0]}, "class_type": "CLIPTextEncode"},
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"5": {"inputs": {"width": width, "height": height, "batch_size": 1}, "class_type": "EmptyFlux2LatentImage"},
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"6": {"inputs": {"noise_seed": seed, "batch_count": 1}, "class_type": "RandomNoise"},
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"7": {"inputs": {"sampler_name": "euler"}, "class_type": "KSamplerSelect"},
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"8": {"inputs": {"steps": steps, "width": width, "height": height, "mu": 0.5, "std": 1.75},
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"class_type": "Ideogram4Scheduler"},
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# Asymmetric CFG: second unconditional UNet
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"15": {"inputs": {"unet_name": "ideogram4_unconditional_fp8_scaled.safetensors", "weight_dtype": "default"},
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"class_type": "UNETLoader"},
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# Zero out conditioning for negative path
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"16": {"inputs": {"conditioning": ["4", 0]}, "class_type": "ConditioningZeroOut"},
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# CFG override on main model (70%-100% of steps)
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"17": {"inputs": {"cfg": 3.0, "start_percent": 0.7, "end_percent": 1.0, "model": ["2", 0]},
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"class_type": "CFGOverride"},
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# DualModelGuider: main model (CFG overridden), unconditional model, positive, zeroed negative
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"9": {"inputs": {"cfg": 7.0, "model": ["17", 0], "positive": ["4", 0],
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"model_negative": ["15", 0], "negative": ["16", 0]},
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"class_type": "DualModelGuider"},
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"10": {"inputs": {"noise": ["6", 0], "guider": ["9", 0], "sampler": ["7", 0],
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"sigmas": ["8", 0], "latent_image": ["5", 0]}, "class_type": "SamplerCustomAdvanced"},
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"11": {"inputs": {"samples": ["10", 0], "vae": ["1", 0]}, "class_type": "VAEDecode"},
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"12": {"inputs": {"filename_prefix": "ideogram4_api", "images": ["11", 0]}, "class_type": "SaveImage"},
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}
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def _build_flux2_workflow(prompt: str, width: int, height: int, seed: int,
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steps: int = 20, cfg: float = 3.5,
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negative_prompt: str = "") -> dict:
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"""FLUX.2-klein-9B workflow using UnetLoaderGGUF + CLIPLoaderGGUF (Qwen3VL)."""
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return {
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"1": {"inputs": {"vae_name": "flux2-vae.safetensors"}, "class_type": "VAELoader"},
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"2": {"inputs": {"unet_name": "flux-2-klein-9b-Q4_K_S.gguf"},
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"class_type": "UnetLoaderGGUF"},
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"3": {"inputs": {"model": ["2", 0], "max_shift": 1.15, "base_shift": 0.5, "width": width, "height": height},
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"class_type": "ModelSamplingFlux"},
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# FLUX.2 uses Qwen3VL via CLIPLoaderGGUF, NOT DualCLIPLoader (CLIP-L + T5XXL)
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"4": {"inputs": {"clip_name": "Qwen3VL-8B-Instruct-Q4_K_M.gguf", "type": "flux2"},
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"class_type": "CLIPLoaderGGUF"},
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"5": {"inputs": {"text": prompt, "clip": ["4", 0]}, "class_type": "CLIPTextEncode"},
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"6": {"inputs": {"conditioning": ["5", 0], "guidance": cfg}, "class_type": "FluxGuidance"},
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"7": {"inputs": {"width": width, "height": height, "batch_size": 1}, "class_type": "EmptyFlux2LatentImage"},
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"8": {"inputs": {"noise_seed": seed}, "class_type": "RandomNoise"},
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"9": {"inputs": {"sampler_name": "euler"}, "class_type": "KSamplerSelect"},
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"10": {"inputs": {"model": ["3", 0], "steps": steps, "denoise": 1.0, "scheduler": "simple",
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"sampler": ["9", 0]}, "class_type": "BasicScheduler"},
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"11": {"inputs": {"model": ["3", 0], "conditioning": ["6", 0]}, "class_type": "BasicGuider"},
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"12": {"inputs": {"noise": ["8", 0], "guider": ["11", 0], "sampler": ["9", 0],
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"sigmas": ["10", 0], "latent_image": ["7", 0]}, "class_type": "SamplerCustomAdvanced"},
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"13": {"inputs": {"samples": ["12", 0], "vae": ["1", 0]}, "class_type": "VAEDecode"},
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"14": {"inputs": {"filename_prefix": "flux2_api", "images": ["13", 0]}, "class_type": "SaveImage"},
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}
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def _build_workflow(model: str, prompt: str, width: int, height: int,
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seed: int, steps: int, cfg: float,
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negative_prompt: str = "", style: str = "") -> dict:
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if model == "ideogram4":
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return _build_ideogram4_workflow(prompt, width, height, seed, steps, cfg, negative_prompt)
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elif model == "flux2":
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return _build_flux2_workflow(prompt, width, height, seed, steps, cfg, negative_prompt)
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else:
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raise ValueError(f"Unknown model: {model}")
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async def _comfy_post(path: str, data: dict, timeout: float = 2400) -> dict:
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async with httpx.AsyncClient() as client:
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r = await client.post(f"{COMFY_URL}{path}", json=data, timeout=timeout)
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if r.status_code >= 400:
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try:
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detail = r.json()
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except Exception:
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detail = r.text
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raise HTTPException(status_code=r.status_code, detail=detail)
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return r.json()
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async def _comfy_get(path: str, timeout: float = 30) -> dict:
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async with httpx.AsyncClient() as client:
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r = await client.get(f"{COMFY_URL}{path}", timeout=timeout)
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r.raise_for_status()
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return r.json()
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async def _generate_image(model: str, prompt: str, width: int, height: int, seed: int,
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steps: int, cfg: float, negative_prompt: str = "", style: str = "") -> bytes:
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workflow = _build_workflow(model, prompt, width, height, seed, steps, cfg, negative_prompt, style)
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resp = await _comfy_post("/prompt", {"prompt": workflow})
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prompt_id = resp.get("prompt_id")
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if not prompt_id:
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raise HTTPException(status_code=500, detail="No prompt_id from ComfyUI")
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# Poll for completion - up to 30 min (180 iterations x 10s)
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for i in range(180):
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await asyncio.sleep(10)
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try:
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hist = await _comfy_get(f"/history/{prompt_id}", timeout=10)
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except Exception:
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continue
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if prompt_id not in hist:
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continue
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d = hist[prompt_id]
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status = d.get("status", {})
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status_str = status.get("status_str", "?")
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if status_str == "error":
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errors = []
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for node_id, msgs in status.get("messages", {}).items():
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for level, msg, detail, _ in msgs:
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if level == "error":
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errors.append(f"[{node_id}] {msg}: {detail}")
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detail = d.get("outputs", {}).get("error", "") or "; ".join(errors) or "ComfyUI execution error"
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raise HTTPException(status_code=500, detail=detail)
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if status_str == "success":
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outputs = d.get("outputs", {})
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for node_id, node_out in outputs.items():
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for img in node_out.get("images", []):
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fname = img.get("filename")
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subfolder = img.get("subfolder", "")
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ftype = img.get("type", "output")
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url = f"{COMFY_URL}/view?filename={fname}&subfolder={subfolder}&type={ftype}"
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async with httpx.AsyncClient() as client:
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img_resp = await client.get(url, timeout=60)
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img_resp.raise_for_status()
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return img_resp.content
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raise HTTPException(status_code=500, detail="No image in ComfyUI outputs")
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raise HTTPException(status_code=504, detail="ComfyUI generation timeout")
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app = FastAPI(title="ComfyUI Image Gen OpenAI API", version="2.1.0")
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class ImageGenerationRequest(BaseModel):
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model: str = Field(default="ideogram4", description="Model: ideogram4, flux2")
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prompt: str = Field(..., description="Text prompt")
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n: int = Field(default=1, ge=1, le=4, description="Number of images")
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size: str = Field(default="1024x1024")
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response_format: str = Field(default="url", pattern="^(url|b64_json)$")
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seed: Optional[int] = Field(default=None, description="Random seed (int). Random if omitted.")
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steps: Optional[int] = Field(default=None, ge=1, le=100, description="Denoising steps. Overrides quality.")
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cfg: Optional[float] = Field(default=None, ge=0.0, le=100.0, description="CFG / guidance scale")
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quality: Optional[str] = Field(default="medium", description="low|medium|high|ultra -> maps to steps")
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negative_prompt: Optional[str] = Field(default="", description="Negative prompt")
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style: Optional[str] = Field(default="", description="Style modifier (model-dependent)")
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user: Optional[str] = Field(default=None, description="OpenAI user field (logged, not used)")
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class ImageData(BaseModel):
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url: Optional[str] = None
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b64_json: Optional[str] = None
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revised_prompt: Optional[str] = None
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class ImageGenerationResponse(BaseModel):
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created: int
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data: list[ImageData]
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@app.middleware("http")
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async def auth_middleware(request: Request, call_next):
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if API_KEY and request.url.path.startswith("/v1"):
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auth = request.headers.get("authorization", "")
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if not auth.startswith("Bearer ") or auth[7:] != API_KEY:
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return JSONResponse(status_code=401, content={"error": "Unauthorized"})
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return await call_next(request)
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@app.post("/v1/images/generations", response_model=ImageGenerationResponse)
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async def create_image_generation(req: ImageGenerationRequest):
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if req.model not in ALLOWED_MODELS:
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raise HTTPException(
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status_code=400,
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detail=f"Invalid model: {req.model}. Allowed: {ALLOWED_MODELS}"
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)
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validated_size = _validate_model_size(req.model, req.size)
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if validated_size not in ALLOWED_SIZES:
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raise HTTPException(status_code=400, detail=f"Invalid size: {validated_size}. Allowed: {ALLOWED_SIZES}")
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width, height = _parse_size(validated_size)
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created = int(time.time())
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data = []
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if req.steps is not None:
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steps = req.steps
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elif req.quality in QUALITY_STEPS:
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steps = QUALITY_STEPS[req.quality]
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else:
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steps = QUALITY_STEPS["medium"]
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# Ideogram4: enforce minimum ~12 steps to avoid blank/gray output
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if req.model == "ideogram4" and steps < 12:
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steps = 12
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if req.model == "ideogram4":
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cfg = req.cfg if req.cfg is not None else 1.0
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elif req.model.startswith("flux"):
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cfg = req.cfg if req.cfg is not None else 3.5
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else:
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cfg = req.cfg if req.cfg is not None else 7.5
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for i in range(req.n):
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seed = req.seed + i if req.seed is not None else int(time.time() * 1000) + i
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image_bytes = await _generate_image(
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req.model, req.prompt, width, height, seed,
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steps, cfg, req.negative_prompt or "", req.style or ""
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)
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if req.response_format == "b64_json":
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b64 = base64.b64encode(image_bytes).decode("utf-8")
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data.append(ImageData(b64_json=b64, revised_prompt=req.prompt))
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else:
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b64 = base64.b64encode(image_bytes).decode("utf-8")
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data_url = f"data:image/png;base64,{b64}"
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data.append(ImageData(url=data_url, revised_prompt=req.prompt))
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return ImageGenerationResponse(created=created, data=data)
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@app.get("/v1/models")
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async def list_models():
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"""List available image models - dynamically from ALLOWED_MODELS."""
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return {
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"object": "list",
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"data": [
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{"id": m, "object": "model", "owned_by": "comfyui", "permission": []}
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for m in sorted(ALLOWED_MODELS)
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]
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}
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@app.get("/health")
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async def health():
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try:
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await _comfy_get("/system_stats", timeout=5)
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comfy_status = "ok"
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except Exception as e:
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comfy_status = f"error: {e}"
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return {
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"status": "ok",
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"comfyui": COMFY_URL,
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"comfyui_status": comfy_status,
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"models": list(ALLOWED_MODELS)
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}
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if __name__ == "__main__":
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import uvicorn
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port = int(os.environ.get("PORT", "8000"))
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uvicorn.run(app, host="0.0.0.0", port=port)
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