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346 lines (269 loc) · 11.8 KB
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import os
import sys
import torch
from diffusers import (
AutoPipelineForImage2Image,
AutoPipelineForInpainting,
AutoPipelineForText2Image,
ControlNetModel,
LCMScheduler,
StableDiffusionAdapterPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionXLAdapterPipeline,
StableDiffusionXLControlNetPipeline,
T2IAdapter,
WuerstchenCombinedPipeline,
)
from diffusers.utils import load_image
sys.path.append(".")
from utils import ( # noqa: E402
BASE_PATH,
PROMPT,
BenchmarkInfo,
benchmark_fn,
bytes_to_giga_bytes,
flush,
generate_csv_dict,
write_to_csv,
)
RESOLUTION_MAPPING = {
"Lykon/DreamShaper": (512, 512),
"lllyasviel/sd-controlnet-canny": (512, 512),
"diffusers/controlnet-canny-sdxl-1.0": (1024, 1024),
"TencentARC/t2iadapter_canny_sd14v1": (512, 512),
"TencentARC/t2i-adapter-canny-sdxl-1.0": (1024, 1024),
"stabilityai/stable-diffusion-2-1": (768, 768),
"stabilityai/stable-diffusion-xl-base-1.0": (1024, 1024),
"stabilityai/stable-diffusion-xl-refiner-1.0": (1024, 1024),
"stabilityai/sdxl-turbo": (512, 512),
}
class BaseBenchmak:
pipeline_class = None
def __init__(self, args):
super().__init__()
def run_inference(self, args):
raise NotImplementedError
def benchmark(self, args):
raise NotImplementedError
def get_result_filepath(self, args):
pipeline_class_name = str(self.pipe.__class__.__name__)
name = (
args.ckpt.replace("/", "_")
+ "_"
+ pipeline_class_name
+ f"-bs@{args.batch_size}-steps@{args.num_inference_steps}-mco@{args.model_cpu_offload}-compile@{args.run_compile}.csv"
)
filepath = os.path.join(BASE_PATH, name)
return filepath
class TextToImageBenchmark(BaseBenchmak):
pipeline_class = AutoPipelineForText2Image
def __init__(self, args):
pipe = self.pipeline_class.from_pretrained(args.ckpt, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
if args.run_compile:
if not isinstance(pipe, WuerstchenCombinedPipeline):
pipe.unet.to(memory_format=torch.channels_last)
print("Run torch compile")
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
if hasattr(pipe, "movq") and getattr(pipe, "movq", None) is not None:
pipe.movq.to(memory_format=torch.channels_last)
pipe.movq = torch.compile(pipe.movq, mode="reduce-overhead", fullgraph=True)
else:
print("Run torch compile")
pipe.decoder = torch.compile(pipe.decoder, mode="reduce-overhead", fullgraph=True)
pipe.vqgan = torch.compile(pipe.vqgan, mode="reduce-overhead", fullgraph=True)
pipe.set_progress_bar_config(disable=True)
self.pipe = pipe
def run_inference(self, pipe, args):
_ = pipe(
prompt=PROMPT,
num_inference_steps=args.num_inference_steps,
num_images_per_prompt=args.batch_size,
)
def benchmark(self, args):
flush()
print(f"[INFO] {self.pipe.__class__.__name__}: Running benchmark with: {vars(args)}\n")
time = benchmark_fn(self.run_inference, self.pipe, args) # in seconds.
memory = bytes_to_giga_bytes(torch.cuda.max_memory_allocated()) # in GBs.
benchmark_info = BenchmarkInfo(time=time, memory=memory)
pipeline_class_name = str(self.pipe.__class__.__name__)
flush()
csv_dict = generate_csv_dict(
pipeline_cls=pipeline_class_name, ckpt=args.ckpt, args=args, benchmark_info=benchmark_info
)
filepath = self.get_result_filepath(args)
write_to_csv(filepath, csv_dict)
print(f"Logs written to: {filepath}")
flush()
class TurboTextToImageBenchmark(TextToImageBenchmark):
def __init__(self, args):
super().__init__(args)
def run_inference(self, pipe, args):
_ = pipe(
prompt=PROMPT,
num_inference_steps=args.num_inference_steps,
num_images_per_prompt=args.batch_size,
guidance_scale=0.0,
)
class LCMLoRATextToImageBenchmark(TextToImageBenchmark):
lora_id = "latent-consistency/lcm-lora-sdxl"
def __init__(self, args):
super().__init__(args)
self.pipe.load_lora_weights(self.lora_id)
self.pipe.fuse_lora()
self.pipe.unload_lora_weights()
self.pipe.scheduler = LCMScheduler.from_config(self.pipe.scheduler.config)
def get_result_filepath(self, args):
pipeline_class_name = str(self.pipe.__class__.__name__)
name = (
self.lora_id.replace("/", "_")
+ "_"
+ pipeline_class_name
+ f"-bs@{args.batch_size}-steps@{args.num_inference_steps}-mco@{args.model_cpu_offload}-compile@{args.run_compile}.csv"
)
filepath = os.path.join(BASE_PATH, name)
return filepath
def run_inference(self, pipe, args):
_ = pipe(
prompt=PROMPT,
num_inference_steps=args.num_inference_steps,
num_images_per_prompt=args.batch_size,
guidance_scale=1.0,
)
def benchmark(self, args):
flush()
print(f"[INFO] {self.pipe.__class__.__name__}: Running benchmark with: {vars(args)}\n")
time = benchmark_fn(self.run_inference, self.pipe, args) # in seconds.
memory = bytes_to_giga_bytes(torch.cuda.max_memory_allocated()) # in GBs.
benchmark_info = BenchmarkInfo(time=time, memory=memory)
pipeline_class_name = str(self.pipe.__class__.__name__)
flush()
csv_dict = generate_csv_dict(
pipeline_cls=pipeline_class_name, ckpt=self.lora_id, args=args, benchmark_info=benchmark_info
)
filepath = self.get_result_filepath(args)
write_to_csv(filepath, csv_dict)
print(f"Logs written to: {filepath}")
flush()
class ImageToImageBenchmark(TextToImageBenchmark):
pipeline_class = AutoPipelineForImage2Image
url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/1665_Girl_with_a_Pearl_Earring.jpg"
image = load_image(url).convert("RGB")
def __init__(self, args):
super().__init__(args)
self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])
def run_inference(self, pipe, args):
_ = pipe(
prompt=PROMPT,
image=self.image,
num_inference_steps=args.num_inference_steps,
num_images_per_prompt=args.batch_size,
)
class TurboImageToImageBenchmark(ImageToImageBenchmark):
def __init__(self, args):
super().__init__(args)
def run_inference(self, pipe, args):
_ = pipe(
prompt=PROMPT,
image=self.image,
num_inference_steps=args.num_inference_steps,
num_images_per_prompt=args.batch_size,
guidance_scale=0.0,
strength=0.5,
)
class InpaintingBenchmark(ImageToImageBenchmark):
pipeline_class = AutoPipelineForInpainting
mask_url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/overture-creations-5sI6fQgYIuo_mask.png"
mask = load_image(mask_url).convert("RGB")
def __init__(self, args):
super().__init__(args)
self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])
self.mask = self.mask.resize(RESOLUTION_MAPPING[args.ckpt])
def run_inference(self, pipe, args):
_ = pipe(
prompt=PROMPT,
image=self.image,
mask_image=self.mask,
num_inference_steps=args.num_inference_steps,
num_images_per_prompt=args.batch_size,
)
class IPAdapterTextToImageBenchmark(TextToImageBenchmark):
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png"
image = load_image(url)
def __init__(self, args):
pipe = self.pipeline_class.from_pretrained(args.ckpt, torch_dtype=torch.float16).to("cuda")
pipe.load_ip_adapter(
args.ip_adapter_id[0],
subfolder="models" if "sdxl" not in args.ip_adapter_id[1] else "sdxl_models",
weight_name=args.ip_adapter_id[1],
)
if args.run_compile:
pipe.unet.to(memory_format=torch.channels_last)
print("Run torch compile")
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
pipe.set_progress_bar_config(disable=True)
self.pipe = pipe
def run_inference(self, pipe, args):
_ = pipe(
prompt=PROMPT,
ip_adapter_image=self.image,
num_inference_steps=args.num_inference_steps,
num_images_per_prompt=args.batch_size,
)
class ControlNetBenchmark(TextToImageBenchmark):
pipeline_class = StableDiffusionControlNetPipeline
aux_network_class = ControlNetModel
root_ckpt = "Lykon/DreamShaper"
url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/canny_image_condition.png"
image = load_image(url).convert("RGB")
def __init__(self, args):
aux_network = self.aux_network_class.from_pretrained(args.ckpt, torch_dtype=torch.float16)
pipe = self.pipeline_class.from_pretrained(self.root_ckpt, controlnet=aux_network, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
pipe.set_progress_bar_config(disable=True)
self.pipe = pipe
if args.run_compile:
pipe.unet.to(memory_format=torch.channels_last)
pipe.controlnet.to(memory_format=torch.channels_last)
print("Run torch compile")
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
pipe.controlnet = torch.compile(pipe.controlnet, mode="reduce-overhead", fullgraph=True)
self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])
def run_inference(self, pipe, args):
_ = pipe(
prompt=PROMPT,
image=self.image,
num_inference_steps=args.num_inference_steps,
num_images_per_prompt=args.batch_size,
)
class ControlNetSDXLBenchmark(ControlNetBenchmark):
pipeline_class = StableDiffusionXLControlNetPipeline
root_ckpt = "stabilityai/stable-diffusion-xl-base-1.0"
def __init__(self, args):
super().__init__(args)
class T2IAdapterBenchmark(ControlNetBenchmark):
pipeline_class = StableDiffusionAdapterPipeline
aux_network_class = T2IAdapter
root_ckpt = "Lykon/DreamShaper"
url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/canny_for_adapter.png"
image = load_image(url).convert("L")
def __init__(self, args):
aux_network = self.aux_network_class.from_pretrained(args.ckpt, torch_dtype=torch.float16)
pipe = self.pipeline_class.from_pretrained(self.root_ckpt, adapter=aux_network, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
pipe.set_progress_bar_config(disable=True)
self.pipe = pipe
if args.run_compile:
pipe.unet.to(memory_format=torch.channels_last)
pipe.adapter.to(memory_format=torch.channels_last)
print("Run torch compile")
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
pipe.adapter = torch.compile(pipe.adapter, mode="reduce-overhead", fullgraph=True)
self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])
class T2IAdapterSDXLBenchmark(T2IAdapterBenchmark):
pipeline_class = StableDiffusionXLAdapterPipeline
root_ckpt = "stabilityai/stable-diffusion-xl-base-1.0"
url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/canny_for_adapter_sdxl.png"
image = load_image(url)
def __init__(self, args):
super().__init__(args)
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