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import argparse
from optimum.quanto import freeze, qfloat8, qint4, qint8, quantize
import torch
import json
import torch.utils.benchmark as benchmark
from diffusers import DiffusionPipeline
import gc
WARM_UP_ITERS = 5
PROMPT = "ghibli style, a fantasy landscape with castles"
TORCH_DTYPES = {"fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16}
QTYPES = {"fp8": qfloat8, "int8": qint8, "int4": qint4, "none": None}
PREFIXES = {
"stabilityai/stable-diffusion-3-medium-diffusers": "sd3",
"PixArt-alpha/PixArt-Sigma-XL-2-1024-MS": "pixart",
"fal/AuraFlow": "auraflow",
}
def flush():
"""Wipes off memory."""
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
def load_pipeline(ckpt_id, torch_dtype, qtype=None, exclude_layers=None, qte=False, fuse=False):
pipe = DiffusionPipeline.from_pretrained(ckpt_id, torch_dtype=torch_dtype).to("cuda")
if fuse:
pipe.transformer.fuse_qkv_projections()
if qtype:
quantize(pipe.transformer, weights=qtype, exclude=exclude_layers)
freeze(pipe.transformer)
if qte:
quantize(pipe.text_encoder, weights=qtype)
freeze(pipe.text_encoder)
if hasattr(pipe, "text_encoder_2"):
quantize(pipe.text_encoder_2, weights=qtype)
freeze(pipe.text_encoder_2)
if hasattr(pipe, "text_encoder_3"):
quantize(pipe.text_encoder_3, weights=qtype)
freeze(pipe.text_encoder_3)
pipe.set_progress_bar_config(disable=True)
return pipe
def run_inference(pipe, batch_size=1):
_ = pipe(
prompt=PROMPT,
num_images_per_prompt=batch_size,
generator=torch.manual_seed(0),
)
def benchmark_fn(f, *args, **kwargs):
t0 = benchmark.Timer(stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f})
return f"{(t0.blocked_autorange().mean):.3f}"
def bytes_to_giga_bytes(bytes):
return f"{(bytes / 1024 / 1024 / 1024):.3f}"
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--ckpt_id",
type=str,
default="stabilityai/stable-diffusion-3-medium-diffusers",
choices=list(PREFIXES.keys()),
)
parser.add_argument("--batch_size", type=int, default=1)
parser.add_argument("--torch_dtype", type=str, default="fp16", choices=list(TORCH_DTYPES.keys()))
parser.add_argument("--qtype", type=str, default="none", choices=list(QTYPES.keys()))
parser.add_argument("--qte", type=int, default=0, help="Quantize text encoder")
parser.add_argument("--fuse", type=int, default=0)
parser.add_argument("--exclude_layers", metavar="N", type=str, nargs="*", default=None)
args = parser.parse_args()
flush()
print(
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)}, {args.exclude_layers=}, {args.fuse=}"
)
pipeline = load_pipeline(
ckpt_id=args.ckpt_id,
torch_dtype=TORCH_DTYPES[args.torch_dtype],
qtype=QTYPES[args.qtype],
exclude_layers=args.exclude_layers,
qte=args.qte,
fuse=bool(args.fuse),
)
for _ in range(WARM_UP_ITERS):
run_inference(pipeline, args.batch_size)
time = benchmark_fn(run_inference, pipeline, args.batch_size)
memory = bytes_to_giga_bytes(torch.cuda.max_memory_allocated()) # in GBs.
print(
f"ckpt: {args.ckpt_id} batch_size: {args.batch_size}, qte: {args.qte}, {args.exclude_layers=}"
f"torch_dtype: {args.torch_dtype}, qtype: {args.qtype} in {time} seconds and {memory} GBs."
)
ckpt_id = PREFIXES[args.ckpt_id]
img_name = f"ckpt@{ckpt_id}-bs@{args.batch_size}-dtype@{args.torch_dtype}-qtype@{args.qtype}-qte@{args.qte}-fuse@{args.fuse}"
if args.exclude_layers:
exclude_layers = "_".join(args.exclude_layers)
img_name += f"-exclude@{exclude_layers}"
image = pipeline(
prompt=PROMPT,
num_images_per_prompt=args.batch_size,
generator=torch.manual_seed(0),
).images[0]
image.save(f"{img_name}.png")
info = dict(
batch_size=args.batch_size,
memory=memory,
time=time,
dtype=args.torch_dtype,
qtype=args.qtype,
qte=args.qte,
exclude_layers=args.exclude_layers,
fuse=args.fuse,
)
info_file = f"{img_name}_info.json"
with open(info_file, "w") as f:
json.dump(info, f)
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