updated images with ai images
This commit is contained in:
Concept Agent
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"""
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Generate all site images via FLUX.1 Schnell GGUF through ComfyUI.
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FLUX Schnell: 4 steps, cfg=1.0, no negative prompt, photorealistic.
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Run after ComfyUI restart: python3 tools/gen-images-flux.py
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"""
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import json, time, urllib.request, os, random, io
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COMFY = "http://localhost:8188"
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HERO_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "assets", "images", "hero")
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SVC_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "assets", "images", "services")
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IMAGES = [
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# --- HERO IMAGES ---
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{"filename": "hero-carpet-cleaning.jpg", "dir": HERO_DIR, "prompt": (
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"low-angle 35mm lens perspective looking across thick plush cream carpet in an upstate New York living room, "
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"carpet fibers razor sharp in foreground, couch and coffee table receding into shallow bokeh background, "
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"warm afternoon window light raking across carpet texture, Finger Lakes farmhouse interior, "
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"no people, ultra-realistic architectural photography, 16:9"
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)},
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{"filename": "hero-stairs.jpg", "dir": HERO_DIR, "prompt": (
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"dramatic low 35mm angle looking up a clean carpeted staircase from floor level, "
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"light grey carpet runner sharp and textured in foreground steps, wood banister receding diagonally, "
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"bright daylight flooding from above, shallow depth of field, "
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"no people, ultra-realistic interior photography, 16:9"
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)},
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{"filename": "hero-upholstery.jpg", "dir": HERO_DIR, "prompt": (
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"50mm lens low corner angle across a bright residential living room, "
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"plush linen fabric sofa arm sharp in near foreground, clean armchair and window receding with bokeh, "
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"afternoon countryside light through window, shallow depth of field, "
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"no people, ultra-realistic interior photography, 16:9"
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)},
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{"filename": "hero-floors.jpg", "dir": HERO_DIR, "prompt": (
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"low 24mm angle pressed to gleaming light oak hardwood floor, "
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"floor grain razor sharp in extreme foreground receding to hallway vanishing point, "
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"white walls, natural light streaming in, shallow depth of field, "
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"no people, ultra-realistic interior photography, 16:9"
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)},
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{"filename": "hero-area-rugs.jpg", "dir": HERO_DIR, "prompt": (
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"low 35mm angle looking across a hand-knotted oriental rug from floor level, "
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"rich red and gold rug fibers sharp in foreground, hardwood floor and room receding into bokeh, "
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"cozy farmhouse living room, warm natural light, shallow depth of field, "
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"no people, ultra-realistic interior photography, 16:9"
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)},
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{"filename": "hero-add-ons.jpg", "dir": HERO_DIR, "prompt": (
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"low 35mm angle across a clean beige bedroom carpet, "
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"carpet pile sharp and detailed in near foreground, wooden bed frame and sheer curtained window receding, "
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"crisp morning light, shallow depth of field, "
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"no people, no machines, ultra-realistic interior photography, 16:9"
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)},
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{"filename": "hero-commercial.jpg", "dir": HERO_DIR, "prompt": (
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"low 24mm wide-angle lens across a modern corporate lobby floor, "
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"dark charcoal commercial carpet sharp in extreme foreground receding to glass entrance doors, "
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"recessed ceiling lights creating depth, strong vanishing point perspective, "
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"no people, ultra-realistic architectural photography, 16:9"
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)},
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{"filename": "hero-offices.jpg", "dir": HERO_DIR, "prompt": (
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"low 24mm angle across clean grey carpet tiles in a modern open-plan office, "
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"carpet tile seams sharp in foreground receding to rows of empty desks and glass partitions, "
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"professional overhead lighting, strong linear perspective, "
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"no people, ultra-realistic architectural photography, 16:9"
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)},
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{"filename": "hero-vacation-rentals.jpg", "dir": HERO_DIR, "prompt": (
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"low 35mm angle across clean beige carpet in a Finger Lakes cottage living room, "
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"carpet fibers sharp in foreground, stone fireplace and lake-view window receding with bokeh, "
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"wooden ceiling beams, warm inviting light, shallow depth of field, "
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"no people, ultra-realistic interior photography, 16:9"
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)},
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{"filename": "hero-hotels.jpg", "dir": HERO_DIR, "prompt": (
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"low 24mm lens looking down a long hotel corridor from floor level, "
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"patterned burgundy carpet runner sharp in extreme foreground receding to vanishing point, "
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"warm wall sconces lining white walls, numbered doors converging in perspective, "
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"no people, ultra-realistic hospitality photography, 16:9"
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)},
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{"filename": "hero-retail.jpg", "dir": HERO_DIR, "prompt": (
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"low 35mm diagonal angle across clean light grey carpet in an upscale retail showroom, "
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"carpet surface sharp in foreground, minimalist display fixtures and storefront windows receding with bokeh, "
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"bright track lighting overhead, shallow depth of field, "
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"no people, ultra-realistic architectural photography, 16:9"
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)},
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{"filename": "hero-property-management.jpg", "dir": HERO_DIR, "prompt": (
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"low 35mm angle across fresh neutral carpet in an empty move-in ready apartment, "
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"carpet texture sharp in foreground, bare white walls and bright windows receding, "
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"clean real estate photography perspective, shallow depth of field, "
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"no people, ultra-realistic real estate photography, 16:9"
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)},
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{"filename": "hero-about.jpg", "dir": HERO_DIR, "prompt": (
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"low 35mm angle from lawn level looking up at a classic upstate New York suburban home, "
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"green grass blades sharp in extreme foreground, inviting house facade receding upward, "
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"mature trees and clear blue sky, warm summer afternoon, "
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"no people, ultra-realistic real estate photography, 16:9"
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)},
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{"filename": "hero-service-area.jpg", "dir": HERO_DIR, "prompt": (
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"low horizon 24mm wide-angle Finger Lakes landscape, "
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"green vineyard vines sharp in foreground receding to rolling hills and calm lake, "
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"golden hour light casting long shadows, strong depth and distance, "
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"no people, ultra-realistic landscape photography, 16:9"
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)},
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{"filename": "hero-living-room.jpg", "dir": HERO_DIR, "prompt": (
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"low 35mm corner angle across a spacious residential living room, "
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"plush light grey carpet sharp and textured in foreground, large sectional sofa and bay windows receding with bokeh, "
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"warm afternoon sunlight, shallow depth of field, "
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"no people, ultra-realistic interior photography, 16:9"
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)},
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{"filename": "hero-clean-result.jpg", "dir": HERO_DIR, "prompt": (
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"extreme low 50mm macro angle pressed to immaculate freshly cleaned residential carpet, "
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"individual carpet fibers razor sharp in foreground, pile receding into soft bokeh, "
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"raking natural light revealing deep clean texture and uniform pile height, "
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"no people, ultra-realistic macro carpet photography, 16:9"
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)},
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# --- SERVICE CARD IMAGES ---
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{"filename": "carpet-cleaning.jpg", "dir": SVC_DIR, "prompt": (
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"low 35mm angle looking across plush clean beige carpet in a residential living room, "
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"carpet fibers sharp in foreground, couch and window receding into bokeh, "
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"warm afternoon light, shallow depth of field, no people, ultra-realistic interior photography"
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)},
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{"filename": "stairs-cleaning.jpg", "dir": SVC_DIR, "prompt": (
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"low 35mm angle looking up clean grey carpeted stairs from bottom step, "
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"carpet texture sharp on nearest step, stairs receding diagonally upward, "
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"wood banister, bright light from above, no people, ultra-realistic interior photography"
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)},
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{"filename": "upholstery-cleaning.jpg", "dir": SVC_DIR, "prompt": (
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"low 50mm angle across a clean plush linen fabric sofa arm, "
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"fabric weave sharp in foreground, living room receding with bokeh, "
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"warm light, shallow depth of field, no people, ultra-realistic interior photography"
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)},
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{"filename": "floor-cleaning.jpg", "dir": SVC_DIR, "prompt": (
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"low 24mm angle pressed to gleaming light oak hardwood floor, "
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"wood grain razor sharp in extreme foreground receding down hallway, "
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"natural light, no people, ultra-realistic interior photography"
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)},
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{"filename": "area-rug-cleaning.jpg", "dir": SVC_DIR, "prompt": (
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"low 35mm angle across a vibrant clean oriental rug from floor level, "
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"rug fibers and pattern sharp in foreground, hardwood floor and room receding, "
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"warm light, shallow depth of field, no people, ultra-realistic interior photography"
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)},
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{"filename": "add-ons.jpg", "dir": SVC_DIR, "prompt": (
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"low 35mm angle across clean beige bedroom carpet, "
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"carpet pile sharp in foreground, bed frame and curtained window receding with bokeh, "
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"morning light, no people, ultra-realistic interior photography"
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)},
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{"filename": "commercial-overview.jpg", "dir": SVC_DIR, "prompt": (
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"low 24mm angle across dark commercial carpet in a corporate lobby, "
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"carpet surface sharp in foreground receding to glass entrance, "
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"strong vanishing point, no people, ultra-realistic architectural photography"
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)},
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{"filename": "vacation-rentals.jpg", "dir": SVC_DIR, "prompt": (
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"low 35mm angle across clean carpet in a Finger Lakes cottage living room, "
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"carpet sharp in foreground, stone fireplace and window receding with bokeh, "
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"rustic warm decor, no people, ultra-realistic interior photography"
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)},
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{"filename": "office-spaces.jpg", "dir": SVC_DIR, "prompt": (
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"low 24mm angle across grey carpet tiles in a modern open office, "
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"tile seams sharp in foreground, empty desks receding with linear perspective, "
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"professional lighting, no people, ultra-realistic architectural photography"
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)},
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{"filename": "hotels-inns.jpg", "dir": SVC_DIR, "prompt": (
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"low 24mm angle down a hotel corridor, patterned carpet runner sharp in foreground, "
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"corridor receding to vanishing point, warm wall sconces, "
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"no people, ultra-realistic hospitality photography"
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)},
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{"filename": "retail-showrooms.jpg", "dir": SVC_DIR, "prompt": (
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"low 35mm diagonal angle across light grey carpet in an upscale retail showroom, "
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"carpet sharp in foreground, display fixtures and track lighting receding with bokeh, "
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"no people, ultra-realistic architectural photography"
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)},
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{"filename": "property-management.jpg", "dir": SVC_DIR, "prompt": (
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"low 35mm angle across fresh neutral carpet in an empty clean apartment, "
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"carpet texture sharp in foreground, white walls and windows receding, "
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"no people, ultra-realistic real estate photography"
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)},
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]
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def build_workflow(prompt, seed=None):
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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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"1": {
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"class_type": "UnetLoaderGGUF",
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"inputs": {"unet_name": "flux1-schnell-Q8_0.gguf"},
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},
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"2": {
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"class_type": "DualCLIPLoader",
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"inputs": {
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"clip_name1": "t5xxl_fp8_e4m3fn.safetensors",
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"clip_name2": "clip_l.safetensors",
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"type": "flux",
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},
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},
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"3": {
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"class_type": "VAELoader",
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"inputs": {"vae_name": "ae.safetensors"},
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},
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"4": {
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"class_type": "CLIPTextEncode",
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"inputs": {"clip": ["2", 0], "text": prompt},
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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": 576, "width": 1024},
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},
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"6": {
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"class_type": "KSampler",
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"inputs": {
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"cfg": 1.0,
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"denoise": 1.0,
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"latent_image": ["5", 0],
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"model": ["1", 0],
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"negative": ["4", 0],
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"positive": ["4", 0],
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"sampler_name": "euler",
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"scheduler": "simple",
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"seed": seed,
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"steps": 4,
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},
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},
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"7": {
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"class_type": "VAEDecode",
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"inputs": {"samples": ["6", 0], "vae": ["3", 0]},
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},
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"8": {
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"class_type": "SaveImage",
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"inputs": {"filename_prefix": "flux_lahr", "images": ["7", 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", 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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entry = hist[prompt_id]
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status = entry.get("status", {}).get("status_str", "")
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if status == "error":
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msgs = entry.get("status", {}).get("messages", [])
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print(f" COMFYUI ERROR: {msgs}", flush=True)
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return None
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for node_out in entry.get("outputs", {}).values():
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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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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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url = f"{COMFY}/view?filename={fname}&subfolder={subfolder}&type={img_type}"
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with urllib.request.urlopen(url) as resp:
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data = resp.read()
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try:
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from PIL import Image
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img = Image.open(io.BytesIO(data)).convert("RGB")
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img.save(out_path, "JPEG", quality=92)
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print(f" OK: {os.path.basename(out_path)} ({os.path.getsize(out_path)//1024}KB)", flush=True)
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except ImportError:
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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" OK (PNG): {png_path}", flush=True)
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total = len(IMAGES)
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for i, spec in enumerate(IMAGES):
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out_path = os.path.join(spec["dir"], spec["filename"])
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print(f"\n[{i+1}/{total}] {spec['filename']}", flush=True)
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workflow = build_workflow(spec["prompt"])
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prompt_id = queue_prompt(workflow)
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print(f" queued {prompt_id[:8]}...", flush=True)
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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(f" FAILED (timeout)", flush=True)
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print("\nAll done.", flush=True)
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