Mikei386
7c5bbe2ffb
- POST /v1/images/generations (OpenAI-kompatibel, prompt/size/n/seed/quality) - quality: standard=30 Steps (Default), high=50 Steps - Größen: 1024x1024, 1536x1024, 1024x1536, 1920x1088, 1088x1920 - GPU-Hotswap: Qwen stoppen -> FLUX laden -> Bild -> FLUX entladen -> Qwen wiederherstellen (exakt vorheriges Profil) - Zentrales GPU/Modell-Lock (Profilwechsel und Bild teilen sich das Lock) - Chat-Requests warten während Bild-Job (kein 502), Timeout CHAT_WAIT_TIMEOUT - Robuste Recovery: try/finally, Worker-Beendigung, VRAM-Check, Qwen-Readiness - /status: image.phase, image.worker, image.model_loaded, qwen.available, qwen.active_chats - GET /images, GET /images/<datei> (validiert, nur images/-Verzeichnis) - image_worker.py: FLUX-Worker (eigener Prozess, JSON-Protokoll, bf16 + enable_model_cpu_offload) - deploy: venv (torch/diffusers/transformers/accelerate), Modell-Download, Image-Dir, systemd-Unit mit Image-Umgebungsvariablen - dev: Mock-Worker, fake-systemctl, Benchmarks (GPU-Resident, Offload, Steps, Quality-Compare), 32 lokale Tests - README: Bildgenerierung, Hotswap, Recovery, Benchmarks (RTX 5080), Python-Pakete Benchmarks (RTX 5080, 16 GB, CPU-Offload): - 512x512 / 10 Steps: ~9.3 s - 1024x1024 / 30 Steps: ~31.3 s - 1024x1024 / 50 Steps: ~45.3 s - 1920x1088 / 50 Steps: ~91 s - Peak-VRAM: ~8.4-8.9 GB - Hotswap-Gesamtzeit: ~41-42 s (1024x1024 / 30 Steps)
67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
#!/usr/bin/env python3
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"""FLUX.2 [klein] 4B Base – Steps-Vergleich (Qualität vs. Latenz).
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1024x1024, fester Seed, cpu_offload. Vergleicht 20/30/40/50 Steps.
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"""
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import os
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import subprocess
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import time
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os.environ["HF_HUB_DISABLE_PROGRESS_BARS"] = "1"
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os.environ["TRANSFORMERS_VERBOSITY"] = "error"
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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MODEL = "/opt/mike-ai/models/FLUX.2-klein-base-4B"
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PROMPT = ("A detailed photograph of a red cube on a white marble table, "
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"soft studio lighting, shallow depth of field")
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SEED = 0
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WIDTH = HEIGHT = 1024
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STEPS_LIST = [20, 30, 40, 50]
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def nvidia_vram() -> int:
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out = subprocess.check_output(
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["nvidia-smi", "--query-gpu=memory.used",
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"--format=csv,noheader,nounits"]).decode().strip()
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return int(out.split()[0])
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def main() -> None:
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import torch
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print(f"torch {torch.__version__} | {torch.cuda.get_device_name(0)}",
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flush=True)
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from diffusers import Flux2KleinPipeline
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t0 = time.monotonic()
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pipe = Flux2KleinPipeline.from_pretrained(MODEL, torch_dtype=torch.bfloat16)
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pipe.enable_model_cpu_offload()
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print(f"[load] ready in {time.monotonic() - t0:.1f} s", flush=True)
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for steps in STEPS_LIST:
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out = f"/tmp/flux-steps-{steps}.png"
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torch.cuda.synchronize()
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torch.cuda.reset_peak_memory_stats()
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t = time.monotonic()
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try:
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img = pipe(
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prompt=PROMPT,
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height=HEIGHT,
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width=WIDTH,
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guidance_scale=4.0,
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num_inference_steps=steps,
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generator=torch.Generator(device="cuda").manual_seed(SEED),
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).images[0]
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dt = time.monotonic() - t
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img.save(out)
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peak = torch.cuda.max_memory_allocated() / 1e9
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print(f"[gen] {steps} steps: {dt:.1f} s | peak {peak:.2f} GB | "
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f"nvidia-smi {nvidia_vram()} MiB | {out}", flush=True)
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except Exception as e: # noqa: BLE001
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print(f"[gen] {steps} steps: FEHLER: {e!r}", flush=True)
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print("DONE", flush=True)
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if __name__ == "__main__":
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main()
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