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Cherry pick SDXL demo update to 1.16.3 (#18496)
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Cherry pick SDXL demo update to 1.16.3

-----
Co-authored-by: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
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tianleiwu authored Nov 18, 2023
1 parent de0e87e commit 2ac381c
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183 changes: 126 additions & 57 deletions onnxruntime/python/tools/transformers/models/stable_diffusion/README.md

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Original file line number Diff line number Diff line change
Expand Up @@ -53,8 +53,31 @@
f"Batch size {len(prompt)} is larger than allowed {max_batch_size}. If dynamic shape is used, then maximum batch size is 4"
)

pipeline_info = PipelineInfo(args.version)
pipeline = init_pipeline(Txt2ImgPipeline, pipeline_info, engine_type, args, max_batch_size, batch_size)
# For TensorRT, performance of engine built with dynamic shape is very sensitive to the range of image size.
# Here, we reduce the range of image size for TensorRT to trade-off flexibility and performance.
# This range can cover common used shape of landscape 512x768, portrait 768x512, or square 512x512 and 768x768.
min_image_size = 512 if args.engine != "ORT_CUDA" else 256
max_image_size = 768 if args.engine != "ORT_CUDA" else 1024
pipeline_info = PipelineInfo(args.version, min_image_size=min_image_size, max_image_size=max_image_size)

# Ideally, the optimized batch size and image size for TRT engine shall align with user's preference. That is to
# optimize the shape used most frequently. We can let user config it when we develop a UI plugin.
# In this demo, we optimize batch size 1 and image size 512x512 (or 768x768 for SD 2.0/2.1) for dynamic engine.
# This is mainly for benchmark purpose to simulate the case that we have no knowledge of user's preference.
opt_batch_size = 1 if args.build_dynamic_batch else batch_size
opt_image_height = pipeline_info.default_image_size() if args.build_dynamic_shape else args.height
opt_image_width = pipeline_info.default_image_size() if args.build_dynamic_shape else args.width

pipeline = init_pipeline(
Txt2ImgPipeline,
pipeline_info,
engine_type,
args,
max_batch_size,
opt_batch_size,
opt_image_height,
opt_image_width,
)

if engine_type == EngineType.TRT:
max_device_memory = max(pipeline.backend.max_device_memory(), pipeline.backend.max_device_memory())
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Original file line number Diff line number Diff line change
Expand Up @@ -29,17 +29,7 @@
from pipeline_txt2img_xl import Txt2ImgXLPipeline


def run_demo():
"""Run Stable Diffusion XL Base + Refiner together (known as ensemble of expert denoisers) to generate an image."""

args = parse_arguments(is_xl=True, description="Options for Stable Diffusion XL Demo")

prompt, negative_prompt = repeat_prompt(args)

# Recommend image size as one of those used in training (see Appendix I in https://arxiv.org/pdf/2307.01952.pdf).
image_height = args.height
image_width = args.width

def load_pipelines(args, batch_size):
# Register TensorRT plugins
engine_type = get_engine_type(args.engine)
if engine_type == EngineType.TRT:
Expand All @@ -49,37 +39,83 @@ def run_demo():

max_batch_size = 16
if (engine_type in [EngineType.ORT_TRT, EngineType.TRT]) and (
args.build_dynamic_shape or image_height > 512 or image_width > 512
args.build_dynamic_shape or args.height > 512 or args.width > 512
):
max_batch_size = 4

batch_size = len(prompt)
if batch_size > max_batch_size:
raise ValueError(f"Batch size {batch_size} is larger than allowed {max_batch_size}.")

# For TensorRT, performance of engine built with dynamic shape is very sensitive to the range of image size.
# Here, we reduce the range of image size for TensorRT to trade-off flexibility and performance.
# This range can cover most frequent shape of landscape (832x1216), portrait (1216x832) or square (1024x1024).
min_image_size = 832 if args.engine != "ORT_CUDA" else 512
max_image_size = 1216 if args.engine != "ORT_CUDA" else 2048

# No VAE decoder in base when it outputs latent instead of image.
base_info = PipelineInfo(args.version, use_vae=False)
base = init_pipeline(Txt2ImgXLPipeline, base_info, engine_type, args, max_batch_size, batch_size)
base_info = PipelineInfo(
args.version, use_vae=args.disable_refiner, min_image_size=min_image_size, max_image_size=max_image_size
)

refiner_info = PipelineInfo(args.version, is_refiner=True)
refiner = init_pipeline(Img2ImgXLPipeline, refiner_info, engine_type, args, max_batch_size, batch_size)
# Ideally, the optimized batch size and image size for TRT engine shall align with user's preference. That is to
# optimize the shape used most frequently. We can let user config it when we develop a UI plugin.
# In this demo, we optimize batch size 1 and image size 1024x1024 for SD XL dynamic engine.
# This is mainly for benchmark purpose to simulate the case that we have no knowledge of user's preference.
opt_batch_size = 1 if args.build_dynamic_batch else batch_size
opt_image_height = base_info.default_image_size() if args.build_dynamic_shape else args.height
opt_image_width = base_info.default_image_size() if args.build_dynamic_shape else args.width

base = init_pipeline(
Txt2ImgXLPipeline,
base_info,
engine_type,
args,
max_batch_size,
opt_batch_size,
opt_image_height,
opt_image_width,
)

refiner = None
if not args.disable_refiner:
refiner_info = PipelineInfo(
args.version, is_refiner=True, min_image_size=min_image_size, max_image_size=max_image_size
)
refiner = init_pipeline(
Img2ImgXLPipeline,
refiner_info,
engine_type,
args,
max_batch_size,
opt_batch_size,
opt_image_height,
opt_image_width,
)

if engine_type == EngineType.TRT:
max_device_memory = max(base.backend.max_device_memory(), refiner.backend.max_device_memory())
max_device_memory = max(base.backend.max_device_memory(), (refiner or base).backend.max_device_memory())
_, shared_device_memory = cudart.cudaMalloc(max_device_memory)
base.backend.activate_engines(shared_device_memory)
refiner.backend.activate_engines(shared_device_memory)
if refiner:
refiner.backend.activate_engines(shared_device_memory)

if engine_type == EngineType.ORT_CUDA:
enable_vae_slicing = args.enable_vae_slicing
if batch_size > 4 and not enable_vae_slicing:
print("Updating enable_vae_slicing to be True to avoid cuDNN error for batch size > 4.")
enable_vae_slicing = True
if enable_vae_slicing:
refiner.backend.enable_vae_slicing()
(refiner or base).backend.enable_vae_slicing()
return base, refiner


def run_pipelines(args, base, refiner, prompt, negative_prompt, is_warm_up=False):
image_height = args.height
image_width = args.width
batch_size = len(prompt)
base.load_resources(image_height, image_width, batch_size)
refiner.load_resources(image_height, image_width, batch_size)
if refiner:
refiner.load_resources(image_height, image_width, batch_size)

def run_base_and_refiner(warmup=False):
images, time_base = base.run(
Expand All @@ -91,8 +127,13 @@ def run_base_and_refiner(warmup=False):
denoising_steps=args.denoising_steps,
guidance=args.guidance,
seed=args.seed,
return_type="latent",
return_type="latent" if refiner else "image",
)
if refiner is None:
return images, time_base

# Use same seed in base and refiner.
seed = base.get_current_seed()

images, time_refiner = refiner.run(
prompt,
Expand All @@ -103,7 +144,7 @@ def run_base_and_refiner(warmup=False):
warmup=warmup,
denoising_steps=args.denoising_steps,
guidance=args.guidance,
seed=args.seed,
seed=seed,
)

return images, time_base + time_refiner
Expand All @@ -112,25 +153,104 @@ def run_base_and_refiner(warmup=False):
# inference once to get cuda graph
_, _ = run_base_and_refiner(warmup=True)

print("[I] Warming up ..")
if args.num_warmup_runs > 0:
print("[I] Warming up ..")
for _ in range(args.num_warmup_runs):
_, _ = run_base_and_refiner(warmup=True)

if is_warm_up:
return

print("[I] Running StableDiffusion XL pipeline")
if args.nvtx_profile:
cudart.cudaProfilerStart()
_, latency = run_base_and_refiner(warmup=False)
if args.nvtx_profile:
cudart.cudaProfilerStop()

base.teardown()

print("|------------|--------------|")
print("| {:^10} | {:>9.2f} ms |".format("e2e", latency))
print("|------------|--------------|")
refiner.teardown()


def run_demo(args):
"""Run Stable Diffusion XL Base + Refiner together (known as ensemble of expert denoisers) to generate an image."""

prompt, negative_prompt = repeat_prompt(args)
batch_size = len(prompt)
base, refiner = load_pipelines(args, batch_size)
run_pipelines(args, base, refiner, prompt, negative_prompt)
base.teardown()
if refiner:
refiner.teardown()


def run_dynamic_shape_demo(args):
"""Run demo of generating images with different settings with ORT CUDA provider."""
args.engine = "ORT_CUDA"
args.disable_cuda_graph = True
base, refiner = load_pipelines(args, 1)

prompts = [
"starry night over Golden Gate Bridge by van gogh",
"beautiful photograph of Mt. Fuji during cherry blossom",
"little cute gremlin sitting on a bed, cinematic",
"cute grey cat with blue eyes, wearing a bowtie, acrylic painting",
"beautiful Renaissance Revival Estate, Hobbit-House, detailed painting, warm colors, 8k, trending on Artstation",
"blue owl, big green eyes, portrait, intricate metal design, unreal engine, octane render, realistic",
]

# batch size, height, width, scheduler, steps, prompt, seed
configs = [
(1, 832, 1216, "UniPC", 8, prompts[0], None),
(1, 1024, 1024, "DDIM", 24, prompts[1], None),
(1, 1216, 832, "UniPC", 16, prompts[2], None),
(1, 1344, 768, "DDIM", 24, prompts[3], None),
(2, 640, 1536, "UniPC", 16, prompts[4], 4312973633252712),
(2, 1152, 896, "DDIM", 24, prompts[5], 1964684802882906),
]

# Warm up each combination of (batch size, height, width) once before serving.
args.prompt = ["warm up"]
args.num_warmup_runs = 1
for batch_size, height, width, _, _, _, _ in configs:
args.batch_size = batch_size
args.height = height
args.width = width
print(f"\nWarm up batch_size={batch_size}, height={height}, width={width}")
prompt, negative_prompt = repeat_prompt(args)
run_pipelines(args, base, refiner, prompt, negative_prompt, is_warm_up=True)

# Run pipeline on a list of prompts.
args.num_warmup_runs = 0
for batch_size, height, width, scheduler, steps, example_prompt, seed in configs:
args.prompt = [example_prompt]
args.batch_size = batch_size
args.height = height
args.width = width
args.scheduler = scheduler
args.denoising_steps = steps
args.seed = seed
base.set_scheduler(scheduler)
if refiner:
refiner.set_scheduler(scheduler)
print(
f"\nbatch_size={batch_size}, height={height}, width={width}, scheduler={scheduler}, steps={steps}, prompt={example_prompt}, seed={seed}"
)
prompt, negative_prompt = repeat_prompt(args)
run_pipelines(args, base, refiner, prompt, negative_prompt, is_warm_up=False)

base.teardown()
if refiner:
refiner.teardown()


if __name__ == "__main__":
coloredlogs.install(fmt="%(funcName)20s: %(message)s")
run_demo()

args = parse_arguments(is_xl=True, description="Options for Stable Diffusion XL Demo")
no_prompt = isinstance(args.prompt, list) and len(args.prompt) == 1 and not args.prompt[0]
if no_prompt:
run_dynamic_shape_demo(args)
else:
run_demo(args)
Original file line number Diff line number Diff line change
Expand Up @@ -78,13 +78,13 @@ def parse_arguments(is_xl: bool, description: str):
help="Root Directory to store torch or ONNX models, built engines and output images etc.",
)

parser.add_argument("prompt", nargs="+", help="Text prompt(s) to guide image generation.")
parser.add_argument("prompt", nargs="*", default=[""], help="Text prompt(s) to guide image generation.")

parser.add_argument(
"--negative-prompt", nargs="*", default=[""], help="Optional negative prompt(s) to guide the image generation."
)
parser.add_argument(
"--repeat-prompt",
"--batch-size",
type=int,
default=1,
choices=[1, 2, 4, 8, 16],
Expand Down Expand Up @@ -145,6 +145,10 @@ def parse_arguments(is_xl: bool, description: str):
parser.add_argument("--seed", type=int, default=None, help="Seed for random generator to get consistent results.")
parser.add_argument("--disable-cuda-graph", action="store_true", help="Disable cuda graph.")

parser.add_argument(
"--disable-refiner", action="store_true", help="Disable refiner and only run base for XL pipeline."
)

group = parser.add_argument_group("Options for ORT_CUDA engine only")
group.add_argument("--enable-vae-slicing", action="store_true", help="True will feed only one image to VAE once.")

Expand Down Expand Up @@ -174,9 +178,9 @@ def parse_arguments(is_xl: bool, description: str):
)

# Validate image dimensions
if args.height % 8 != 0 or args.width % 8 != 0:
if args.height % 64 != 0 or args.width % 64 != 0:
raise ValueError(
f"Image height and width have to be divisible by 8 but specified as: {args.height} and {args.width}."
f"Image height and width have to be divisible by 64 but specified as: {args.height} and {args.width}."
)

if (args.build_dynamic_batch or args.build_dynamic_shape) and not args.disable_cuda_graph:
Expand All @@ -194,7 +198,7 @@ def parse_arguments(is_xl: bool, description: str):
def repeat_prompt(args):
if not isinstance(args.prompt, list):
raise ValueError(f"`prompt` must be of type `str` or `str` list, but is {type(args.prompt)}")
prompt = args.prompt * args.repeat_prompt
prompt = args.prompt * args.batch_size

if not isinstance(args.negative_prompt, list):
raise ValueError(
Expand All @@ -209,7 +213,9 @@ def repeat_prompt(args):
return prompt, negative_prompt


def init_pipeline(pipeline_class, pipeline_info, engine_type, args, max_batch_size, batch_size):
def init_pipeline(
pipeline_class, pipeline_info, engine_type, args, max_batch_size, opt_batch_size, opt_image_height, opt_image_width
):
onnx_dir, engine_dir, output_dir, framework_model_dir, timing_cache = get_engine_paths(
work_dir=args.work_dir, pipeline_info=pipeline_info, engine_type=engine_type
)
Expand All @@ -234,9 +240,6 @@ def init_pipeline(pipeline_class, pipeline_info, engine_type, args, max_batch_si
engine_dir=engine_dir,
framework_model_dir=framework_model_dir,
onnx_dir=onnx_dir,
opt_image_height=args.height,
opt_image_width=args.height,
opt_batch_size=batch_size,
force_engine_rebuild=args.force_engine_build,
device_id=torch.cuda.current_device(),
)
Expand All @@ -247,14 +250,15 @@ def init_pipeline(pipeline_class, pipeline_info, engine_type, args, max_batch_si
framework_model_dir,
onnx_dir,
args.onnx_opset,
opt_image_height=args.height,
opt_image_width=args.height,
opt_batch_size=batch_size,
opt_image_height=opt_image_height,
opt_image_width=opt_image_width,
opt_batch_size=opt_batch_size,
force_engine_rebuild=args.force_engine_build,
static_batch=not args.build_dynamic_batch,
static_image_shape=not args.build_dynamic_shape,
max_workspace_size=0,
device_id=torch.cuda.current_device(),
timing_cache=timing_cache,
)
elif engine_type == EngineType.TRT:
# Load TensorRT engines and pytorch modules
Expand All @@ -263,9 +267,9 @@ def init_pipeline(pipeline_class, pipeline_info, engine_type, args, max_batch_si
framework_model_dir,
onnx_dir,
args.onnx_opset,
opt_batch_size=batch_size,
opt_image_height=args.height,
opt_image_width=args.height,
opt_batch_size=opt_batch_size,
opt_image_height=opt_image_height,
opt_image_width=opt_image_width,
force_export=args.force_onnx_export,
force_optimize=args.force_onnx_optimize,
force_build=args.force_engine_build,
Expand Down
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