97ac841518
- Extract model loading from generate()/edit() into VnAssetsSession class - Session eagerly loads SDXL + Qwen Image Edit at construction (28s, once) - Both models held in GPU memory across calls; generate()/edit() reuse them - generate.py and edit.py become thin wrappers (backwards compatible CLI) - Context manager (with VnAssetsSession(...) as vna:) for library use - Metadata backwards-compatible: all fields preserved including lora_load_s - load_time_s now reflects total session construction, amortized across calls - Add performance stats for edit path (Qwen Image Edit + Lightning LoRA) - Benchmark matmul fallback (86.8s) vs flash attention (53.3s, 1.63x speedup) - Session vs cold start comparison: 2 ops save one 28s load, 5 edits save 112s - Flash attention via TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 documented
137 lines
4.3 KiB
Python
137 lines
4.3 KiB
Python
#!/usr/bin/env python3
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"""Deep batch test: generate base image, edit with multiple emotions, track timing."""
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import subprocess
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import time
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import json
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from pathlib import Path
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OUTPUT_DIR = Path("output/batch_test")
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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BASE_PROMPT = "1girl, solo, red hair, glasses, blue eyes, white crop top, standing, portrait"
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NEG_PROMPT = "deformed, ugly, bad quality, lowres"
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CHECKPOINT = "models/novaAnimeXL_ilV190.safetensors"
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EDIT_MODEL = "models/qwen_image_edit_2509_fp8_e4m3fn.safetensors"
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SEED = 42
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STEPS = 20
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CFG = 4.5
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# Emotion variants to edit
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EMOTIONS = [
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("smile", "make her smile happily with a warm genuine smile"),
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("angry", "make her look angry and furious, furrowed brow"),
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("sad", "make her look sad and crying, tears in her eyes"),
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("surprised", "make her look surprised, wide eyes, mouth slightly open"),
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("blushing", "make her blush intensely, embarrassed expression, pink cheeks"),
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]
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results = []
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# ── 1. Generate base image ──────────────────────────────────────
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print("=" * 60)
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print("STEP 1: Generate base image")
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print("=" * 60)
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base_path = OUTPUT_DIR / "base.png"
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t_start = time.perf_counter()
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subprocess.run([
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"vnasset", "generate",
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"--checkpoint", CHECKPOINT,
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"--prompt", BASE_PROMPT,
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"--negative-prompt", NEG_PROMPT,
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"--steps", str(STEPS),
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"--cfg", str(CFG),
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"--seed", str(SEED),
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"--output", str(base_path),
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], check=True)
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t_gen = time.perf_counter() - t_start
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# Read metadata
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meta = json.loads((OUTPUT_DIR / "base.json").read_text())
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results.append({
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"step": "generate_base",
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"output": str(base_path),
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"prompt": BASE_PROMPT,
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"load_s": meta["load_time_s"],
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"inference_s": meta["inference_time_s"],
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"total_wall_s": round(t_gen, 1),
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})
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print(f" Wall time: {t_gen:.1f}s (load: {meta['load_time_s']}s, inference: {meta['inference_time_s']}s)\n")
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# ── 2. Edit with each emotion ───────────────────────────────────
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total_edits_start = time.perf_counter()
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for i, (name, prompt) in enumerate(EMOTIONS):
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print("=" * 60)
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print(f"STEP {i+2}: Edit → {name}")
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print("=" * 60)
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edit_path = OUTPUT_DIR / f"base_{name}.png"
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t_start = time.perf_counter()
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subprocess.run([
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"vnasset", "edit",
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"--model", EDIT_MODEL,
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"--input", str(base_path),
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"--prompt", prompt,
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"--steps", str(STEPS),
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"--cfg", str(CFG),
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"--seed", str(SEED),
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"--output", str(edit_path),
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], check=True)
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t_edit = time.perf_counter() - t_start
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meta = json.loads((OUTPUT_DIR / f"base_{name}.json").read_text())
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results.append({
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"step": f"edit_{name}",
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"output": str(edit_path),
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"prompt": prompt,
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"load_s": meta["load_time_s"],
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"inference_s": meta["inference_time_s"],
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"total_wall_s": round(t_edit, 1),
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})
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print(f" Wall time: {t_edit:.1f}s (load: {meta['load_time_s']}s, inference: {meta['inference_time_s']}s)\n")
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total_edits_wall = time.perf_counter() - total_edits_start
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# ── 3. Summary ──────────────────────────────────────────────────
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print("=" * 60)
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print("SUMMARY")
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print("=" * 60)
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print(f"{'Step':<20} {'Load':>8} {'Infer':>8} {'Wall':>8}")
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print("-" * 48)
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total_load = 0
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total_infer = 0
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total_wall = 0
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for r in results:
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print(f"{r['step']:<20} {r['load_s']:>7.1f}s {r['inference_s']:>7.1f}s {r['total_wall_s']:>7.1f}s")
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total_load += r["load_s"]
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total_infer += r["inference_s"]
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total_wall += r["total_wall_s"]
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print("-" * 48)
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print(f"{'TOTAL':<20} {total_load:>7.1f}s {total_infer:>7.1f}s {total_wall:>7.1f}s")
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# Write results JSON
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summary = {
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"config": {
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"checkpoint": CHECKPOINT,
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"edit_model": EDIT_MODEL,
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"seed": SEED,
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"steps": STEPS,
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"cfg": CFG,
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"base_prompt": BASE_PROMPT,
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},
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"results": results,
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"totals": {
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"load_s": round(total_load, 1),
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"inference_s": round(total_infer, 1),
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"total_wall_s": round(total_wall, 1),
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},
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}
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summary_path = OUTPUT_DIR / "summary.json"
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summary_path.write_text(json.dumps(summary, indent=2))
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print(f"\nSummary saved to {summary_path}")
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print(f"Images in {OUTPUT_DIR}/")
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