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import argparse |
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from optimum.quanto import freeze, qfloat8, qint4, qint8, quantize |
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import torch |
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import json |
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import torch.utils.benchmark as benchmark |
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from diffusers import DiffusionPipeline |
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import gc |
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WARM_UP_ITERS = 5 |
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PROMPT = "ghibli style, a fantasy landscape with castles" |
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TORCH_DTYPES = {"fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16} |
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QTYPES = {"fp8": qfloat8, "int8": qint8, "int4": qint4, "none": None} |
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PREFIXES = { |
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"stabilityai/stable-diffusion-3-medium-diffusers": "sd3", |
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} |
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def flush(): |
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"""Wipes off memory.""" |
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gc.collect() |
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torch.cuda.empty_cache() |
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torch.cuda.reset_max_memory_allocated() |
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torch.cuda.reset_peak_memory_stats() |
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def load_pipeline( |
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ckpt_id, torch_dtype, qtype=None, exclude_layers=None, qte=False, first=False, second=False, third=False |
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): |
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pipe = DiffusionPipeline.from_pretrained(ckpt_id, torch_dtype=torch_dtype).to("cuda") |
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if qtype: |
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quantize(pipe.transformer, weights=qtype, exclude=exclude_layers) |
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freeze(pipe.transformer) |
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if qte: |
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if first: |
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quantize(pipe.text_encoder, weights=qtype) |
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freeze(pipe.text_encoder) |
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if second: |
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quantize(pipe.text_encoder_2, weights=qtype) |
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freeze(pipe.text_encoder) |
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if third: |
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quantize(pipe.text_encoder_3, weights=qtype) |
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freeze(pipe.text_encoder_3) |
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pipe.set_progress_bar_config(disable=True) |
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return pipe |
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def run_inference(pipe, batch_size=1): |
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_ = pipe( |
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prompt=PROMPT, |
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num_images_per_prompt=batch_size, |
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generator=torch.manual_seed(0), |
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) |
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def benchmark_fn(f, *args, **kwargs): |
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t0 = benchmark.Timer(stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}) |
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return f"{(t0.blocked_autorange().mean):.3f}" |
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def bytes_to_giga_bytes(bytes): |
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return f"{(bytes / 1024 / 1024 / 1024):.3f}" |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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"--ckpt_id", |
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type=str, |
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default="stabilityai/stable-diffusion-3-medium-diffusers", |
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choices=list(PREFIXES.keys()), |
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) |
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parser.add_argument("--batch_size", type=int, default=1) |
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parser.add_argument("--torch_dtype", type=str, default="fp16", choices=list(TORCH_DTYPES.keys())) |
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parser.add_argument("--qtype", type=str, default="none", choices=list(QTYPES.keys())) |
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parser.add_argument("--qte", type=int, default=0, help="Quantize text encoder") |
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parser.add_argument("--first", type=int, default=0, help="Quantize first text encoder") |
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parser.add_argument("--second", type=int, default=0, help="Quantize second text encoder") |
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parser.add_argument("--third", type=int, default=0, help="Quantize third text encoder") |
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parser.add_argument("--exclude_layers", metavar="N", type=str, nargs="*", default=None) |
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args = parser.parse_args() |
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flush() |
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print( |
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f"Running with ckpt_id: {args.ckpt_id}, batch_size: {args.batch_size}, torch_dtype: {args.torch_dtype}, qtype: {args.qtype}, qte: {bool(args.qte)}" |
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) |
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pipeline = load_pipeline( |
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ckpt_id=args.ckpt_id, |
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torch_dtype=TORCH_DTYPES[args.torch_dtype], |
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qtype=QTYPES[args.qtype], |
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exclude_layers=args.exclude_layers, |
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qte=args.qte, |
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first=args.first, |
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second=args.second, |
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third=args.third, |
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) |
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for _ in range(WARM_UP_ITERS): |
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run_inference(pipeline, args.batch_size) |
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time = benchmark_fn(run_inference, pipeline, args.batch_size) |
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torch.cuda.empty_cache() |
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memory = bytes_to_giga_bytes(torch.cuda.memory_allocated()) |
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print( |
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f"ckpt: {args.ckpt_id} batch_size: {args.batch_size}, qte: {args.qte}, " |
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f"torch_dtype: {args.torch_dtype}, qtype: {args.qtype} in {time} seconds and {memory} GBs." |
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) |
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ckpt_id = PREFIXES[args.ckpt_id] |
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img_name = f"ckpt@{ckpt_id}-bs@{args.batch_size}-dtype@{args.torch_dtype}-qtype@{args.qtype}-qte@{args.qte}" |
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if args.exclude_layers: |
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exclude_layers = "_".join(args.exclude_layers) |
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img_name += f"-exclude@{exclude_layers}" |
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if args.first: |
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img_name += f"-first@{args.first}" |
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if args.second: |
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img_name += f"-second@{args.second}" |
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if args.third: |
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img_name += f"-third@{args.third}" |
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image = pipeline( |
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prompt=PROMPT, |
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num_images_per_prompt=args.batch_size, |
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generator=torch.manual_seed(0), |
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).images[0] |
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image.save(f"{img_name}.png") |
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info = dict( |
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batch_size=args.batch_size, |
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memory=memory, |
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time=time, |
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dtype=args.torch_dtype, |
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qtype=args.qtype, |
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qte=args.qte, |
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exclude_layers=args.exclude_layers, |
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first=args.first, |
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second=args.second, |
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third=args.third, |
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) |
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info_file = f"{img_name}_info.json" |
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with open(info_file, "w") as f: |
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json.dump(info, f) |
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