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demo_txt2img_xl.py
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#
# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import argparse
from cuda import cudart
from demo_diffusion import dd_argparse
from demo_diffusion import pipeline as pipeline_module
def parseArgs():
parser = argparse.ArgumentParser(description="Options for Stable Diffusion XL Txt2Img Demo", conflict_handler='resolve')
parser = dd_argparse.add_arguments(parser)
parser.add_argument('--version', type=str, default="xl-1.0", choices=["xl-1.0", "xl-turbo"], help="Version of Stable Diffusion XL")
parser.add_argument('--height', type=int, default=1024, help="Height of image to generate (must be multiple of 8)")
parser.add_argument('--width', type=int, default=1024, help="Height of image to generate (must be multiple of 8)")
parser.add_argument('--num-warmup-runs', type=int, default=1, help="Number of warmup runs before benchmarking performance")
parser.add_argument('--guidance-scale', type=float, default=5.0, help="Value of classifier-free guidance scale (must be greater than 1)")
parser.add_argument('--enable-refiner', action='store_true', help="Enable SDXL-Refiner model")
parser.add_argument('--image-strength', type=float, default=0.3, help="Strength of transformation applied to input_image (must be between 0 and 1)")
parser.add_argument('--onnx-refiner-dir', default='onnx_xl_refiner', help="Directory for SDXL-Refiner ONNX models")
parser.add_argument('--engine-refiner-dir', default='engine_xl_refiner', help="Directory for SDXL-Refiner TensorRT engines")
return parser.parse_args()
class StableDiffusionXLPipeline(pipeline_module.StableDiffusionPipeline):
def __init__(self, vae_scaling_factor=0.13025, enable_refiner=False, **kwargs):
self.enable_refiner = enable_refiner
self.nvtx_profile = kwargs['nvtx_profile']
self.base = pipeline_module.StableDiffusionPipeline(
pipeline_type=pipeline_module.PIPELINE_TYPE.XL_BASE,
vae_scaling_factor=vae_scaling_factor,
return_latents=self.enable_refiner,
**kwargs,
)
if self.enable_refiner:
self.refiner = pipeline_module.StableDiffusionPipeline(
pipeline_type=pipeline_module.PIPELINE_TYPE.XL_REFINER,
vae_scaling_factor=vae_scaling_factor,
return_latents=False,
**kwargs,
)
def loadEngines(self, framework_model_dir, onnx_dir, engine_dir, onnx_refiner_dir='onnx_xl_refiner', engine_refiner_dir='engine_xl_refiner', **kwargs):
self.base.loadEngines(engine_dir, framework_model_dir, onnx_dir, **kwargs)
if self.enable_refiner:
self.refiner.loadEngines(engine_refiner_dir, framework_model_dir, onnx_refiner_dir, **kwargs)
def activateEngines(self, shared_device_memory=None):
self.base.activateEngines(shared_device_memory)
if self.enable_refiner:
self.refiner.activateEngines(shared_device_memory)
def loadResources(self, image_height, image_width, batch_size, seed):
self.base.loadResources(image_height, image_width, batch_size, seed)
if self.enable_refiner:
# Use a different seed for refiner - we arbitrarily use base seed+1, if specified.
self.refiner.loadResources(image_height, image_width, batch_size, ((seed+1) if seed is not None else None))
def get_max_device_memory(self):
max_device_memory = self.base.calculateMaxDeviceMemory()
if self.enable_refiner:
max_device_memory = max(max_device_memory, self.refiner.calculateMaxDeviceMemory())
return max_device_memory
def run(self, prompt, negative_prompt, height, width, batch_size, batch_count, num_warmup_runs, use_cuda_graph, **kwargs_infer_refiner):
# Process prompt
if not isinstance(prompt, list):
raise ValueError(f"`prompt` must be of type `str` list, but is {type(prompt)}")
prompt = prompt * batch_size
if not isinstance(negative_prompt, list):
raise ValueError(f"`--negative-prompt` must be of type `str` list, but is {type(negative_prompt)}")
if len(negative_prompt) == 1:
negative_prompt = negative_prompt * batch_size
num_warmup_runs = max(1, num_warmup_runs) if use_cuda_graph else num_warmup_runs
if num_warmup_runs > 0:
print("[I] Warming up ..")
for _ in range(num_warmup_runs):
images, _ = self.base.infer(prompt, negative_prompt, height, width, warmup=True)
if args.enable_refiner:
images, _ = self.refiner.infer(prompt, negative_prompt, height, width, input_image=images, warmup=True, **kwargs_infer_refiner)
ret = []
for _ in range(batch_count):
print("[I] Running StableDiffusionXL pipeline")
if self.nvtx_profile:
cudart.cudaProfilerStart()
latents, time_base = self.base.infer(prompt, negative_prompt, height, width, warmup=False)
if self.enable_refiner:
images, time_refiner = self.refiner.infer(prompt, negative_prompt, height, width, input_image=latents, warmup=False, **kwargs_infer_refiner)
ret.append(images)
else:
ret.append(latents)
if self.nvtx_profile:
cudart.cudaProfilerStop()
if self.enable_refiner:
print('|-----------------|--------------|')
print('| {:^15} | {:>9.2f} ms |'.format('e2e', time_base + time_refiner))
print('|-----------------|--------------|')
return ret
def teardown(self):
self.base.teardown()
if self.enable_refiner:
self.refiner.teardown()
if __name__ == "__main__":
print("[I] Initializing TensorRT accelerated StableDiffusionXL txt2img pipeline")
args = parseArgs()
kwargs_init_pipeline, kwargs_load_engine, args_run_demo = dd_argparse.process_pipeline_args(args)
# Initialize demo
demo = StableDiffusionXLPipeline(vae_scaling_factor=0.13025, enable_refiner=args.enable_refiner, **kwargs_init_pipeline)
# Load TensorRT engines and pytorch modules
kwargs_load_refiner = {'onnx_refiner_dir': args.onnx_refiner_dir, 'engine_refiner_dir': args.engine_refiner_dir} if args.enable_refiner else {}
demo.loadEngines(
args.framework_model_dir,
args.onnx_dir,
args.engine_dir,
**kwargs_load_refiner,
**kwargs_load_engine)
# Load resources
_, shared_device_memory = cudart.cudaMalloc(demo.get_max_device_memory())
demo.activateEngines(shared_device_memory)
demo.loadResources(args.height, args.width, args.batch_size, args.seed)
# Run inference
kwargs_infer_refiner = {'image_strength': args.image_strength} if args.enable_refiner else {}
demo.run(*args_run_demo, **kwargs_infer_refiner)
demo.teardown()