File size: 4,287 Bytes
0695f8e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | #!/usr/bin/env python3
"""Misst Krea-2-Generierungszeiten ueber die ComfyUI-API.
Vergleicht NVFP4 gegen FP8, jeweils mit und ohne das ilore-Style-LoRA.
Erster Lauf je Variante ist Aufwaermen (Modell laden) und zaehlt nicht.
"""
import json, time, urllib.request, urllib.error, statistics, argparse, uuid
HOST = "127.0.0.1:8188"
def build(model, lora, strength, w, h, steps, prompt, seed):
g = {
"1": {"class_type": "UNETLoader", "inputs": {"unet_name": model, "weight_dtype": "default"}},
"13": {"class_type": "CLIPLoader", "inputs": {"clip_name": "qwen3vl_4b_fp8_scaled.safetensors",
"type": "krea2", "device": "default"}},
"4": {"class_type": "VAELoader", "inputs": {"vae_name": "qwen_image_vae.safetensors"}},
"6": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["13", 0]}},
"8": {"class_type": "ConditioningZeroOut", "inputs": {"conditioning": ["6", 0]}},
"10": {"class_type": "EmptyLatentImage", "inputs": {"width": w, "height": h, "batch_size": 1}},
"3": {"class_type": "VAEDecode", "inputs": {"samples": ["2", 0], "vae": ["4", 0]}},
"9": {"class_type": "SaveImage", "inputs": {"images": ["3", 0], "filename_prefix": "bench"}},
}
model_src = ["1", 0]
if lora:
g["20"] = {"class_type": "LoraLoaderModelOnly",
"inputs": {"model": ["1", 0], "lora_name": lora, "strength_model": strength}}
model_src = ["20", 0]
g["2"] = {"class_type": "KSampler", "inputs": {
"model": model_src, "positive": ["6", 0], "negative": ["8", 0], "latent_image": ["10", 0],
"seed": seed, "steps": steps, "cfg": 1.0,
"sampler_name": "er_sde", "scheduler": "simple", "denoise": 1.0}}
return g
def run(graph, cid):
body = json.dumps({"prompt": graph, "client_id": cid}).encode()
req = urllib.request.Request(f"http://{HOST}/prompt", body, {"Content-Type": "application/json"})
pid = json.load(urllib.request.urlopen(req))["prompt_id"]
t0 = time.time()
while True:
with urllib.request.urlopen(f"http://{HOST}/history/{pid}") as r:
hist = json.load(r)
if pid in hist:
st = hist[pid].get("status", {})
if st.get("status_str") == "error":
raise RuntimeError(json.dumps(st)[:400])
return time.time() - t0
time.sleep(0.05)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--runs", type=int, default=5)
ap.add_argument("--steps", type=int, default=8)
ap.add_argument("--size", default="1024x1024")
ap.add_argument("--lora", default="ilore_style_krea2_1000.safetensors")
ap.add_argument("--strength", type=float, default=0.8)
ap.add_argument("--prompt", default="a weathered iron battleaxe lying on plain white background")
a = ap.parse_args()
w, h = (int(x) for x in a.size.split("x"))
cid = str(uuid.uuid4())
variants = [
("NVFP4 ohne LoRA", "krea2_turbo_nvfp4.safetensors", None),
("NVFP4 mit LoRA", "krea2_turbo_nvfp4.safetensors", a.lora),
("FP8 ohne LoRA", "krea2_turbo_fp8_scaled.safetensors", None),
("FP8 mit LoRA", "krea2_turbo_fp8_scaled.safetensors", a.lora),
]
print(f"{w}x{h}, {a.steps} steps, {a.runs} Laeufe je Variante (+1 Warmup)\n")
results = {}
for label, model, lora in variants:
try:
g = build(model, lora, a.strength, w, h, a.steps, a.prompt, 0)
run(g, cid) # Warmup
ts = []
for i in range(a.runs):
g = build(model, lora, a.strength, w, h, a.steps, a.prompt, i + 1)
ts.append(run(g, cid))
med = statistics.median(ts)
results[label] = med
print(f" {label:20s} Median {med:6.2f}s (min {min(ts):.2f} / max {max(ts):.2f})")
except Exception as e:
print(f" {label:20s} FEHLER: {str(e)[:200]}")
if "NVFP4 mit LoRA" in results and "FP8 mit LoRA" in results:
sp = results["FP8 mit LoRA"] / results["NVFP4 mit LoRA"]
print(f"\n NVFP4 ist {sp:.2f}x schneller als FP8 (mit LoRA)")
if __name__ == "__main__":
main()
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