#!/usr/bin/env python3 """Ideogram精华盲审 · 三引擎同题渲染 (Ks / Z-Image / FLUX.2) 统一: S1 prompt 原文 + seed 9909 + 1360x768; 各臂用各自0905/0906验证过的现役配方 取图走 /view API (0908教训: docker cp 在 output 重向后是死路) """ import json, time, urllib.request, os, sys BASE = "http://127.0.0.1:8190" # secondary/GPU2; :8188 被 ab_fasth3 批渲染占用勿插队 OUT = "/home/kai/shared/2026-09-09/ideogram_blind4" os.makedirs(OUT, exist_ok=True) SEED = 9909 # 与 S1_cafe.png 同 seed PROMPT = ("A cinematic photorealistic interior of a cozy late-night coffee shop, " "warm amber Edison bulb lighting, a glowing neon sign on the brick wall reading \"龙轩咖啡\", " "espresso machine on wooden counter, rain-streaked windows with bokeh city lights outside, " "empty seats by the window, rich film grain, 35mm photography, moody atmosphere") ARMS = { "ks": { "nodes": lambda: { "5": {"class_type": "UNETLoader", "inputs": {"unet_name": "krea2_turbo_bf16.safetensors", "weight_dtype": "default"}}, "6": {"class_type": "CLIPLoader", "inputs": {"clip_name": "qwen3vl_4b_fp8_scaled.safetensors", "type": "krea2", "device": "default"}}, "7": {"class_type": "VAELoader", "inputs": {"vae_name": "qwen_image_vae.safetensors"}}, "9": {"class_type": "CLIPTextEncode", "inputs": {"clip": ["6", 0], "text": PROMPT}}, "10": {"class_type": "CLIPTextEncode", "inputs": {"clip": ["6", 0], "text": "blurry ugly bad, low quality, watermark"}}, "13": {"class_type": "EmptySD3LatentImage", "inputs": {"width": 1360, "height": 768, "batch_size": 1}}, "3": {"class_type": "KSampler", "inputs": {"model": ["5", 0], "seed": SEED, "steps": 8, "cfg": 1.0, "sampler_name": "euler", "scheduler": "simple", "positive": ["9", 0], "negative": ["10", 0], "latent_image": ["13", 0], "denoise": 1.0}}, "8": {"class_type": "VAEDecode", "inputs": {"samples": ["3", 0], "vae": ["7", 0]}}, "17": {"class_type": "SaveImage", "inputs": {"images": ["8", 0], "filename_prefix": "bld4/ks"}}, }, "timeout": 300, }, "zimg": { "nodes": lambda: { "5": {"class_type": "UNETLoader", "inputs": {"unet_name": "z_image_turbo_bf16.safetensors", "weight_dtype": "default"}}, "6": {"class_type": "CLIPLoader", "inputs": {"clip_name": "qwen_3_4b_fp8_mixed.safetensors", "type": "lumina2", "device": "default"}}, "7": {"class_type": "VAELoader", "inputs": {"vae_name": "z_image_ae.safetensors"}}, "9": {"class_type": "CLIPTextEncode", "inputs": {"clip": ["6", 0], "text": PROMPT}}, "10": {"class_type": "CLIPTextEncode", "inputs": {"clip": ["6", 0], "text": "blurry ugly bad, low quality, watermark"}}, "13": {"class_type": "EmptySD3LatentImage", "inputs": {"width": 1360, "height": 768, "batch_size": 1}}, "3": {"class_type": "KSampler", "inputs": {"model": ["5", 0], "seed": SEED, "steps": 9, "cfg": 1.0, "sampler_name": "euler", "scheduler": "simple", "positive": ["9", 0], "negative": ["10", 0], "latent_image": ["13", 0], "denoise": 1.0}}, "8": {"class_type": "VAEDecode", "inputs": {"samples": ["3", 0], "vae": ["7", 0]}}, "17": {"class_type": "SaveImage", "inputs": {"images": ["8", 0], "filename_prefix": "bld4/zimg"}}, }, "timeout": 300, }, "fx2": { "nodes": lambda: { "10": {"class_type": "OTUNetLoaderW8A8", "inputs": {"unet_name": "flux2_dev_fp8mixed.safetensors", "weight_dtype": "default", "model_type": "flux2", "on_the_fly_quantization": True, "enable_convrot": True, "lora_mode": "None"}}, "11": {"class_type": "CLIPLoader", "inputs": {"clip_name": "mistral_3_small_flux2_fp8.safetensors", "type": "flux2"}}, "12": {"class_type": "VAELoader", "inputs": {"vae_name": "flux2-vae.safetensors"}}, "6": {"class_type": "CLIPTextEncode", "inputs": {"clip": ["11", 0], "text": PROMPT}}, "7": {"class_type": "CLIPTextEncode", "inputs": {"clip": ["11", 0], "text": "text, watermark, low quality, blurry, distorted, deformed, ugly, oversaturated, cartoon, anime"}}, "5": {"class_type": "EmptyLatentImage", "inputs": {"width": 1360, "height": 768, "batch_size": 1}}, "3": {"class_type": "KSampler", "inputs": {"seed": SEED, "steps": 24, "cfg": 3.5, "sampler_name": "dpmpp_2m_sde", "scheduler": "karras", "denoise": 1.0, "model": ["10", 0], "positive": ["6", 0], "negative": ["7", 0], "latent_image": ["5", 0]}}, "13": {"class_type": "VAEDecode", "inputs": {"samples": ["3", 0], "vae": ["12", 0]}}, "22": {"class_type": "SaveImage", "inputs": {"images": ["13", 0], "filename_prefix": "bld4/fx2"}}, }, "timeout": 900, }, } def post(url, payload, timeout=60): req = urllib.request.Request(url, data=json.dumps(payload).encode(), headers={"Content-Type": "application/json"}) return json.loads(urllib.request.urlopen(req, timeout=timeout).read()) def get(url, timeout=30): return json.loads(urllib.request.urlopen(url, timeout=timeout).read()) def render(arm, cfg): t0 = time.time() pid = post(BASE + "/prompt", {"prompt": cfg["nodes"](), "client_id": "bld4"})["prompt_id"] print(f"[{arm}] submitted {pid}", flush=True) deadline = t0 + cfg["timeout"] while time.time() < deadline: time.sleep(5) h = get(f"{BASE}/history/{pid}") if pid not in h: continue st = h[pid].get("status", {}) if st.get("status_str") == "success": for node_out in h[pid].get("outputs", {}).values(): for im in node_out.get("images", []): dur = round(time.time() - t0, 1) # /view 取图 vurl = f"{BASE}/view?filename={urllib.request.quote(im['filename'])}&subfolder={urllib.request.quote(im.get('subfolder',''))}&type=output" data = urllib.request.urlopen(vurl, timeout=60).read() out_png = f"{OUT}/{arm}.png" open(out_png, "wb").write(data) print(f"[{arm}] DONE {dur}s -> {out_png} ({len(data)//1024}KB)", flush=True) return dur return None if st.get("status_str") == "error": errs = [json.dumps(m[1], ensure_ascii=False)[:300] for m in st.get("messages", []) if m[0] == "execution_error"] print(f"[{arm}] ERROR: {errs}", flush=True) return None print(f"[{arm}] TIMEOUT", flush=True) return None if __name__ == "__main__": q = get(BASE + "/queue") print("queue before:", {k: len(v) for k, v in q.items()}, flush=True) timing = {} for arm in ["ks", "zimg", "fx2"]: d = render(arm, ARMS[arm]) timing[arm] = d json.dump(timing, open(f"{OUT}/render_timing.json", "w"), ensure_ascii=False, indent=1) print("ALL:", timing, flush=True) sys.exit(0 if all(v for v in timing.values()) else 1)