# SPDX-License-Identifier: AGPL-3.0-only # Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0 """Measure the next-phase diffusion levers on the real model, vs today's compiled baseline. Variants (Z-Image dense bf16, regional compile = the shipped "default" speed profile): baseline -- channels_last + compile_repeated_blocks (reference image) inductor_flags -- + the lossless inductor autotune flags (conv_1x1_as_mm, coordinate_descent_tuning(+all_dirs), epilogue_fusion=False) attn_cudnn -- + set_attention_backend("_native_cudnn") (exact) attn_flash4 -- + set_attention_backend("flash_4_hub") (exact, SM100) attn_sage -- + set_attention_backend("sage") (INT8 QK, quantized) fbcache -- + First-Block-Cache (threshold 0.12) (few-step headroom test) Reports median latency, vs-baseline speedup, peak VRAM, and LPIPS vs baseline. One CUDA GPU.""" from __future__ import annotations import argparse import sys import time from pathlib import Path import numpy as np BASE = "Tongyi-MAI/Z-Image-Turbo" PROMPT = "A cinematic photograph of a red fox in a snowy forest at dawn, highly detailed" OUT = Path("/mnt/disks/unslothai/ubuntu/workspace_81/outputs/quant_research/perf_levers_images") _LP = {"fn": None} def _lpips(ref, arr): try: import lpips import torch if _LP["fn"] is None: _LP["fn"] = lpips.LPIPS(net="alex", verbose=False).cuda().eval() def t(x): return (torch.from_numpy(x).float().permute(2, 0, 1).unsqueeze(0) / 127.5 - 1.0).cuda() with torch.no_grad(): return float(_LP["fn"](t(ref), t(arr)).item()) except Exception as exc: # noqa: BLE001 print(f" (lpips: {type(exc).__name__})", flush=True) return None def _set_inductor_flags(): import torch._inductor.config as ic ic.conv_1x1_as_mm = True ic.coordinate_descent_tuning = True ic.coordinate_descent_check_all_directions = True ic.epilogue_fusion = False try: ic.force_fuse_int_mm_with_mul = True except Exception: # noqa: BLE001 pass def _reset_inductor_flags(): import torch._inductor.config as ic ic.conv_1x1_as_mm = False ic.coordinate_descent_tuning = False ic.coordinate_descent_check_all_directions = False ic.epilogue_fusion = True def _load(): import diffusers import torch t = diffusers.ZImageTransformer2DModel.from_pretrained(BASE, subfolder="transformer", torch_dtype=torch.bfloat16) pipe = diffusers.ZImagePipeline.from_pretrained(BASE, torch_dtype=torch.bfloat16, transformer=t) pipe.to("cuda") try: pipe.vae.to(memory_format=torch.channels_last) except Exception: # noqa: BLE001 pass return pipe def _gen(pipe, steps, seed, res): import torch g = torch.Generator(device="cuda").manual_seed(seed) torch.cuda.synchronize(); t0 = time.time() img = pipe(prompt=PROMPT, width=res, height=res, num_inference_steps=steps, guidance_scale=0.0, generator=g).images[0] torch.cuda.synchronize() return img, time.time() - t0 def _median(xs): return sorted(xs)[len(xs) // 2] def run(tag, steps, seed, res, iters, *, attn=None, fbcache=None, inductor=False): import torch torch.compiler.reset(); torch.cuda.empty_cache(); torch.cuda.reset_peak_memory_stats() _reset_inductor_flags() if inductor: _set_inductor_flags() pipe = _load() note = "" if attn is not None: try: pipe.transformer.set_attention_backend(attn) except Exception as exc: # noqa: BLE001 note = f"attn({attn})={type(exc).__name__}:{str(exc)[:60]}" print(f" [{tag}] {note}", flush=True) return None if fbcache is not None: try: from diffusers.hooks import FirstBlockCacheConfig, apply_first_block_cache apply_first_block_cache(pipe.transformer, FirstBlockCacheConfig(threshold=fbcache)) except Exception as exc: # noqa: BLE001 print(f" [{tag}] fbcache={type(exc).__name__}:{str(exc)[:60]}", flush=True) return None try: pipe.transformer.compile_repeated_blocks(fullgraph=True, dynamic=True) except Exception as exc: # noqa: BLE001 print(f" [{tag}] compile={type(exc).__name__}:{str(exc)[:60]}", flush=True) try: _gen(pipe, steps, seed, res) # warmup / compile except Exception as exc: # noqa: BLE001 import traceback; traceback.print_exc() print(f" [{tag}] FAILED first gen: {type(exc).__name__}:{str(exc)[:80]}", flush=True) del pipe; torch.cuda.empty_cache() return None dts, img = [], None for _ in range(iters): img, dt = _gen(pipe, steps, seed, res); dts.append(dt) peak = torch.cuda.max_memory_allocated() / 1e9 arr = np.array(img) OUT.mkdir(parents=True, exist_ok=True) img.save(OUT / f"{tag}.png") del pipe; torch.cuda.empty_cache() return _median(dts), arr, peak def main(argv=None) -> int: p = argparse.ArgumentParser() p.add_argument("--steps", type=int, default=8) p.add_argument("--res", type=int, default=1024) p.add_argument("--seed", type=int, default=42) p.add_argument("--iters", type=int, default=3) args = p.parse_args(argv) s, r, seed, it = args.steps, args.res, args.seed, args.iters print(f"== perf levers (Z-Image dense, {r}px, {s} steps) ==", flush=True) base = run("baseline", s, seed, r, it) if base is None: print("baseline FAILED", flush=True); return 1 bmed, ref, bpeak = base print(f" baseline {bmed:.3f}s peak={bpeak:.1f}G", flush=True) rows = [("baseline", bmed, bpeak, 0.0)] variants = [ ("inductor_flags", dict(inductor=True)), ("attn_cudnn", dict(attn="_native_cudnn")), ("attn_flash4", dict(attn="flash_4_hub")), ("attn_sage", dict(attn="sage")), ("attn_sage_inductor", dict(attn="sage", inductor=True)), ("fbcache_0p12", dict(fbcache=0.12)), ] for tag, kw in variants: out = run(tag, s, seed, r, it, **kw) if out is None: rows.append((tag, None, None, None)); continue med, arr, peak = out lp = _lpips(ref, arr) rows.append((tag, med, peak, lp)) spd = f"{bmed/med:.2f}x" if med else "-" print(f" {tag:20s} {med:.3f}s ({spd} vs base) peak={peak:.1f}G LPIPS={lp}", flush=True) print("\n==== SUMMARY (ref = baseline compile) ====", flush=True) for tag, med, peak, lp in rows: if med is None: print(f" {tag:20s} FAILED"); continue spd = f"{bmed/med:.2f}x" if med else "-" lpv = "ref" if (tag == "baseline") else (f"{lp:.3f}" if lp is not None else "n/a") print(f" {tag:20s} {med:.3f}s {spd:>6s} peak={peak:.1f}G LPIPS={lpv:>6s}", flush=True) print("PERF-LEVERS-DONE", flush=True) return 0 if __name__ == "__main__": sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "studio" / "backend")) sys.exit(main())