comfyui_sd operator: local SD/SDXL + LoRA via resident ComfyUI
Fills the gap flux_local can't (mflux is FLUX-only). Pure-stdlib run.py talks to a resident ComfyUI on :8188 (auto-starts, keeps checkpoints cached — 4s warm per 5122/20-step image on M3). Warns on the SD1.5-LoRA-on-SDXL trap. No manifest python => uses the node's system python3 via the python-less remote path, so it distributes across the gpu pool.
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m3ultra
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server/operators/comfyui_sd/manifest.json
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server/operators/comfyui_sd/manifest.json
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{
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"id": "comfyui_sd",
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"name": "Stable Diffusion + LoRA (local, ComfyUI)",
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"category": "generate",
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"description": "Prompt → image via local SD/SDXL checkpoints + LoRAs (ComfyUI on Metal). Complements flux_local, which runs mflux and CANNOT load SD/SDXL. Talks to a resident ComfyUI on 127.0.0.1:8188 (auto-starts it), so models stay cached between jobs. NOTE: the SD1.5 LoRAs only work with Hyper_Realism_1.2_fp16 — an SDXL checkpoint + SD1.5 LoRA silently does nothing. Full guide: localmodels/README.md",
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"accepts": [],
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"produces": ["image"],
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"resources": "gpu",
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"entry": "run.py",
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"params_schema": {
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"type": "object",
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"properties": {
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"prompt": {"type": "string", "default": "", "description": "Positive prompt (put LoRA trigger words early)"},
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"negative": {"type": "string", "default": "(worst quality, low quality:1.4), blurry, jpeg artifacts, watermark, text, deformed, bad anatomy, extra fingers", "description": "Negative prompt (ignored at cfg 1.0 on Turbo/Lightning)"},
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"checkpoint": {"type": "string", "default": "Hyper_Realism_1.2_fp16.safetensors", "description": "SD1.5 = Hyper_Realism_1.2_fp16 (the only one LoRAs work with); SDXL = bigLust_v16 / sd_xl_base_1.0"},
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"lora": {"type": "string", "default": "", "description": "LoRA filename, blank for none (SD1.5 LoRAs need an SD1.5 checkpoint)"},
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"lora_weight": {"type": "number", "default": 0.8, "minimum": 0, "maximum": 2, "description": "0.5 for breastinclassBetter; 0.65-0.9 vector; 0.5-1.0 GodPussy1"},
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"clip_skip": {"type": "integer", "enum": [1, 2], "default": 2, "description": "2 for all the local SD1.5 LoRAs (their training config)"},
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"width": {"type": "integer", "default": 512, "description": "SD1.5: 512 (768 max). SDXL: 1024. Turbo: 512"},
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"height": {"type": "integer", "default": 512, "description": "SD1.5: 512/768. SDXL: 1024"},
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"steps": {"type": "integer", "default": 25, "description": "SD1.5/SDXL: 25-30. Turbo: 1-4. Lightning: 8"},
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"cfg": {"type": "number", "default": 7.0, "description": "SD1.5/SDXL: 5-8. Turbo: 1.0. Lightning: 1-2 (wrong cfg = fried image)"},
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"sampler": {"type": "string", "default": "dpmpp_2m", "description": "dpmpp_2m (+karras) is the workhorse; euler for Turbo/Lightning"},
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"scheduler": {"type": "string", "default": "karras", "description": "karras normally; sgm_uniform for Lightning"},
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"seed": {"type": "integer", "default": -1, "description": "-1 = random. Fix it when tuning LoRA weight."},
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"batch": {"type": "integer", "default": 1, "minimum": 1, "maximum": 16, "description": "Images per job — brute-force seeds and pick"}
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}
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}
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}
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152
server/operators/comfyui_sd/run.py
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server/operators/comfyui_sd/run.py
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"""SD/SDXL + LoRA generation via a resident ComfyUI (Metal).
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Pure stdlib — no venv needed (the runner falls back to the node's python3), because
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all the heavy lifting happens inside ComfyUI's own venv. Keeps ComfyUI resident so
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checkpoints stay cached in RAM between jobs (a cold load costs seconds; a warm one
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doesn't).
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"""
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import argparse
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import json
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import os
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import random
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import subprocess
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import sys
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import time
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import urllib.parse
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import urllib.request
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[3]
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COMFY = ROOT / "vendor" / "comfyui"
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API = "http://127.0.0.1:8188"
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ap = argparse.ArgumentParser()
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ap.add_argument("--input", action="append", default=[])
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ap.add_argument("--outdir", required=True)
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ap.add_argument("--params", default="{}")
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a = ap.parse_args()
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p = json.loads(a.params)
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outdir = Path(a.outdir)
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prompt = (p.get("prompt") or "").strip()
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if not prompt:
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print("ERROR: prompt is required")
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sys.exit(1)
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if not (COMFY / "main.py").exists():
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print(f"ERROR: ComfyUI not installed at {COMFY}. Run scripts/install_comfyui.sh")
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sys.exit(1)
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def alive():
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try:
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urllib.request.urlopen(f"{API}/", timeout=3)
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return True
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except Exception:
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return False
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def ensure_server():
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if alive():
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print("comfyui: already resident (models stay cached)", flush=True)
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return
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print("comfyui: starting resident server ...", flush=True)
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log = open("/tmp/comfyui.log", "ab")
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subprocess.Popen(
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[str(COMFY / ".venv" / "bin" / "python"), "main.py", "--port", "8188"],
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cwd=str(COMFY), stdout=log, stderr=log, start_new_session=True)
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for _ in range(90):
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if alive():
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print("comfyui: up", flush=True)
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return
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time.sleep(2)
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print("ERROR: ComfyUI did not come up — see /tmp/comfyui.log")
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sys.exit(1)
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def build(seed):
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ckpt = p.get("checkpoint", "Hyper_Realism_1.2_fp16.safetensors")
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lora = (p.get("lora") or "").strip()
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g = {
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"1": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": ckpt}},
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"5": {"class_type": "EmptyLatentImage", "inputs": {
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"width": int(p.get("width", 512)), "height": int(p.get("height", 512)),
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"batch_size": int(p.get("batch", 1))}},
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"7": {"class_type": "VAEDecode", "inputs": {"samples": ["6", 0], "vae": ["1", 2]}},
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"8": {"class_type": "SaveImage", "inputs": {"filename_prefix": "mb_sd", "images": ["7", 0]}},
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}
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msrc, csrc = ["1", 0], ["1", 1]
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if lora:
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g["2"] = {"class_type": "LoraLoader", "inputs": {
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"lora_name": lora,
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"strength_model": float(p.get("lora_weight", 0.8)),
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"strength_clip": float(p.get("lora_weight", 0.8)),
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"model": msrc, "clip": csrc}}
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msrc, csrc = ["2", 0], ["2", 1]
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# clip_skip 2 == CLIPSetLastLayer -2 (what the local SD1.5 LoRAs were trained at)
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if int(p.get("clip_skip", 2)) == 2:
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g["9"] = {"class_type": "CLIPSetLastLayer", "inputs": {"clip": csrc, "stop_at_clip_layer": -2}}
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csrc = ["9", 0]
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g["3"] = {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": csrc}}
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g["4"] = {"class_type": "CLIPTextEncode", "inputs": {"text": p.get("negative", ""), "clip": csrc}}
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g["6"] = {"class_type": "KSampler", "inputs": {
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"seed": seed, "steps": int(p.get("steps", 25)), "cfg": float(p.get("cfg", 7.0)),
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"sampler_name": p.get("sampler", "dpmpp_2m"), "scheduler": p.get("scheduler", "karras"),
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"denoise": 1.0, "model": msrc, "positive": ["3", 0], "negative": ["4", 0],
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"latent_image": ["5", 0]}}
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return g
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ensure_server()
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seed = int(p.get("seed", -1))
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if seed < 0:
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seed = random.randint(0, 2**31 - 1)
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CKPT = p.get("checkpoint", "Hyper_Realism_1.2_fp16.safetensors")
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LORA = (p.get("lora") or "").strip()
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print(f"seed={seed} ckpt={CKPT} "
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f"lora={LORA or 'none'}" + (f"@{p.get('lora_weight', 0.8)}" if LORA else ""), flush=True)
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if LORA and ("xl" in CKPT.lower() or "biglust" in CKPT.lower() or "v2-1" in CKPT.lower()):
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print(f"WARNING: {LORA} is SD1.5 but {CKPT} is not — the LoRA will silently do nothing. "
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f"Use Hyper_Realism_1.2_fp16.safetensors (see localmodels/README.md)", flush=True)
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body = json.dumps({"prompt": build(seed)}).encode()
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req = urllib.request.Request(f"{API}/prompt", data=body, headers={"Content-Type": "application/json"})
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try:
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pid = json.load(urllib.request.urlopen(req))["prompt_id"]
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except Exception as e:
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print(f"ERROR: ComfyUI rejected the workflow: {e}")
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sys.exit(1)
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t0 = time.time()
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images = []
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while time.time() - t0 < 900:
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h = json.load(urllib.request.urlopen(f"{API}/history/{pid}"))
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if pid in h:
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st = h[pid].get("status", {})
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if st.get("status_str") == "error":
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print("ERROR: generation failed — check /tmp/comfyui.log")
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print(json.dumps(st)[:400])
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sys.exit(1)
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if h[pid].get("outputs"):
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for node in h[pid]["outputs"].values():
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images += node.get("images", [])
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break
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time.sleep(2)
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if not images:
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print("ERROR: no image produced (timeout)")
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sys.exit(1)
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outputs = []
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for i, im in enumerate(images):
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q = urllib.parse.urlencode({"filename": im["filename"], "subfolder": im.get("subfolder", ""),
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"type": im.get("type", "output")})
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data = urllib.request.urlopen(f"{API}/view?{q}").read()
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name = f"sd_{seed}_{i}.png" if len(images) > 1 else f"sd_{seed}.png"
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dest = outdir / name
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dest.write_bytes(data)
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outputs.append({"path": str(dest), "name": name,
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"meta": {"tool": "comfyui_sd", "seed": seed,
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"checkpoint": p.get("checkpoint"), "lora": p.get("lora") or None}})
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(outdir / "result.json").write_text(json.dumps({"outputs": outputs}))
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print(f"done: {len(outputs)} image(s) in {time.time() - t0:.1f}s -> {outputs[0]['path']}", flush=True)
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