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Concept Agent
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"""Generate replacement service images via ComfyUI SDXL (local, no API key needed)."""
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import json, time, urllib.request, urllib.error, os, sys
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COMFY = "http://localhost:8188"
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OUT_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "assets", "images", "services")
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CKPT = "sd_xl_base_1.0.safetensors"
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IMAGES = [
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{
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"filename": "vacation-rentals.jpg",
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"positive": (
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"bright cozy vacation rental living room interior, clean beige carpet, "
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"comfortable furniture, large windows with natural light, Finger Lakes "
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"style decor, warm inviting atmosphere, no people, no equipment, "
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"professional interior photography, ultra-realistic"
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),
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"negative": (
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"people, person, human, worker, machine, vacuum, equipment, dirty, stain, "
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"text, watermark, blurry, low quality, cartoon, dark"
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),
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},
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{
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"filename": "office-spaces.jpg",
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"positive": (
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"modern corporate office interior, clean dark grey commercial carpet tiles, "
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"open plan workspace, white desks, professional lighting, glass partitions, "
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"no people, no equipment, architectural photography, ultra-realistic"
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),
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"negative": (
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"people, person, human, worker, machine, vacuum, equipment, dirty, stain, "
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"text, watermark, blurry, low quality, cartoon"
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),
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},
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{
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"filename": "hotels-inns.jpg",
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"positive": (
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"elegant hotel corridor interior, clean patterned carpet runner, warm wall "
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"sconce lighting, white walls, numbered room doors along hallway, "
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"hospitality interior design, no people, no equipment, "
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"professional photography, ultra-realistic"
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),
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"negative": (
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"people, person, human, worker, machine, vacuum, equipment, dirty, stain, "
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"text, watermark, blurry, low quality, cartoon"
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),
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},
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{
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"filename": "retail-showrooms.jpg",
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"positive": (
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"upscale retail showroom interior, clean light grey carpet flooring, "
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"modern display shelving, bright overhead track lighting, white walls, "
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"customer-facing professional space, no people, no equipment, "
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"architectural photography, ultra-realistic"
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),
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"negative": (
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"people, person, human, worker, machine, vacuum, equipment, dirty, stain, "
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"text, watermark, blurry, low quality, cartoon"
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),
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},
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{
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"filename": "property-management.jpg",
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"positive": (
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"clean apartment unit interior, fresh beige carpet throughout living room, "
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"neutral walls, bright windows, move-in ready condition, residential "
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"property management style, no people, no furniture, no equipment, "
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"real estate photography, ultra-realistic"
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),
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"negative": (
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"people, person, human, worker, machine, vacuum, equipment, dirty, stain, "
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"text, watermark, blurry, low quality, cartoon"
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),
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},
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]
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def build_workflow(positive, negative, seed=None):
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import random
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if seed is None:
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seed = random.randint(0, 2**32)
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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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"cfg": 7.0,
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"denoise": 1.0,
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"latent_image": ["5", 0],
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"model": ["4", 0],
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"negative": ["7", 0],
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"positive": ["6", 0],
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"sampler_name": "euler",
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"scheduler": "normal",
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"seed": seed,
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"steps": 25,
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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": CKPT},
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},
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"5": {
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"class_type": "EmptyLatentImage",
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"inputs": {"batch_size": 1, "height": 768, "width": 1024},
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},
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"6": {
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"class_type": "CLIPTextEncode",
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"inputs": {"clip": ["4", 1], "text": positive},
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},
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"7": {
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"class_type": "CLIPTextEncode",
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"inputs": {"clip": ["4", 1], "text": negative},
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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": "lahr_gen", "images": ["8", 0]},
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},
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}
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def queue_prompt(workflow):
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data = json.dumps({"prompt": workflow}).encode()
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req = urllib.request.Request(
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f"{COMFY}/prompt",
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data=data,
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headers={"Content-Type": "application/json"},
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)
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with urllib.request.urlopen(req) as resp:
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return json.loads(resp.read())["prompt_id"]
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def wait_for_result(prompt_id, timeout=600):
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start = time.time()
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while time.time() - start < timeout:
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try:
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with urllib.request.urlopen(f"{COMFY}/history/{prompt_id}") as resp:
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hist = json.loads(resp.read())
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if prompt_id in hist:
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outputs = hist[prompt_id].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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return node_out["images"]
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except Exception:
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pass
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print(" waiting...", flush=True)
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time.sleep(5)
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return None
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def download_image(img_info, out_path):
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fname = img_info["filename"]
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subfolder = img_info.get("subfolder", "")
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img_type = img_info.get("type", "output")
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params = f"filename={fname}&subfolder={subfolder}&type={img_type}"
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url = f"{COMFY}/view?{params}"
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with urllib.request.urlopen(url) as resp:
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data = resp.read()
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# Convert PNG to JPEG via PIL if available
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try:
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from PIL import Image
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import io
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img = Image.open(io.BytesIO(data)).convert("RGB")
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img.save(out_path, "JPEG", quality=90)
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print(f" Saved JPEG ({len(data)//1024}KB raw -> {os.path.getsize(out_path)//1024}KB)")
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except ImportError:
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# Save as-is (PNG), rename accordingly
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png_path = out_path.replace(".jpg", ".png")
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with open(png_path, "wb") as f:
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f.write(data)
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print(f" Saved PNG (PIL not available): {png_path}")
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for spec in IMAGES:
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out_path = os.path.join(OUT_DIR, spec["filename"])
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print(f"\nGenerating: {spec['filename']}")
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workflow = build_workflow(spec["positive"], spec["negative"])
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prompt_id = queue_prompt(workflow)
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print(f" Queued: {prompt_id}")
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images = wait_for_result(prompt_id)
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if images:
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download_image(images[0], out_path)
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else:
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print(" FAILED: no output after timeout")
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print("\nDone.")
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