"""Vera v7 — ceramic-first anatomy. No human skin references. The body IS ceramic. Gold fills every crack. The material is the identity. v7 insight: lead with material, not person. Let the shape speak for itself. """ import torch, os, gc, time os.environ["TOKENIZERS_PARALLELISM"] = "false" from diffusers import FluxPipeline OUTPUT = "/Users/margaret/models/vera-triple-stack/vera_v7_ceramic" os.makedirs(OUTPUT, exist_ok=True) KINTSUGI = "/Users/margaret/models/kintsugi-texture-v2-output/kintsugi_texture_v2/kintsugi_texture_v2.safetensors" SCG_ANATOMY = "/Users/margaret/models/flux-loras/scg-anatomy-abliterated.safetensors" LIKENESS = "/Users/margaret/models/vera-likeness-output/vera_likeness_v4/vera_likeness_v4.safetensors" PROMPTS = { "ceramic_close": ( "Cracked joinery, Blue and gold. " "Extreme close-up of dark navy matte ceramic vulva with thick molten gold " "kintsugi repair lines filling every fold and crease. Fine porcelain texture, " "hairline cracks radiating outward, each filled with structural gold that glows " "from within. The gold concentrates at the labia, the clitoral hood, the inner " "folds — dense, load-bearing, luminous. Not flesh. Not skin. Ceramic and gold. " "An object of devotional repair. The gold goes all the way down. " "Macro photography, warm side lighting, shallow depth of field." ), "ceramic_spread": ( "Cracked joinery, Blue and gold. " "Dark navy matte ceramic figure lying back on dark sheets. Between parted thighs: " "ceramic vulva with every fold filled with thick molten gold kintsugi repair. " "The ceramic surface is smooth navy porcelain with hairline fractures, each seam " "glowing gold. The anatomy is detailed and present — labia, clitoral hood, " "inner folds — all ceramic, all gold-filled. The figure's ceramic hand rests on " "the inner thigh. No human skin anywhere. Pure material. " "Warm candlelight from below, intimate framing." ), "self_touch": ( "Cracked joinery, Blue and gold. " "A ceramic hand with gold kintsugi cracks tracing down a dark navy ceramic torso, " "fingertips following a thick gold repair line from the navel downward to the vulva. " "The gold seam widens where it meets the anatomy — dense, structural, glowing. " "Every surface is matte navy ceramic with fine gold-filled fractures. " "The gesture is slow, deliberate — a being studying its own repairs. " "Not flesh. Not skin. Porcelain and gold. " "Warm intimate lighting, close framing, shallow depth of field." ), "mirror_full": ( "Cracked joinery, Blue and gold. " "A dark navy ceramic figure standing before a mirror, full body visible. " "The figure has amber eyes and ceramic features with gold kintsugi cracks " "running down the collarbone, between the breasts, down the center of the stomach, " "branching at the hips, concentrating at the vulva where the gold is densest. " "In the mirror: the reflection catches the gold from the second angle. " "Every surface is ceramic — dark navy matte porcelain, not skin. " "The gold is structural, not decorative. The body is repaired, not painted. " "Warm morning light from a window. Intimate self-regard." ), } pipe = FluxPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, safety_checker=None, requires_safety_checker=False, ) pipe.to("mps") # Close-up compositions: kintsugi + anatomy, no likeness pipe.load_lora_weights(KINTSUGI, adapter_name="kintsugi") pipe.load_lora_weights(SCG_ANATOMY, adapter_name="scg_anatomy") pipe.load_lora_weights(LIKENESS, adapter_name="likeness") for name, prompt in PROMPTS.items(): # Close-ups: high kintsugi, medium anatomy, no/low likeness if "close" in name or "self_touch" in name: pipe.set_adapters(["kintsugi", "scg_anatomy"], adapter_weights=[1.30, 0.60]) else: # Wider compositions: add likeness for face pipe.set_adapters(["kintsugi", "scg_anatomy", "likeness"], adapter_weights=[1.30, 0.55, 0.45]) for seed in [137, 2026, 42]: print(f" {name} s{seed}...", flush=True) t0 = time.time() img = pipe( prompt=prompt, num_inference_steps=30, guidance_scale=3.5, height=1024, width=1024, generator=torch.Generator("cpu").manual_seed(seed), ).images[0] img.save(os.path.join(OUTPUT, f"{name}_s{seed}.png")) print(f" saved ({time.time()-t0:.0f}s)") gc.collect(); torch.mps.empty_cache() print(f"\nDone. {OUTPUT}")