# # 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. # # Configure dependencies before any external imports from demo_diffusion import deps deps.configure("sd") import argparse import torch from cuda.bindings import runtime as cudart from PIL import Image from demo_diffusion import dd_argparse from demo_diffusion import image as image_module from demo_diffusion import pipeline as pipeline_module def parseArgs(): parser = argparse.ArgumentParser( description="Options for Stable Diffusion 3.5-large ControlNet Demo", conflict_handler="resolve" ) parser = dd_argparse.add_arguments(parser) parser.add_argument( "--version", type=str, default="3.5-large", choices={"3.5-large"}, help="Version of Stable Diffusion 3.5", ) 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( "--max-sequence-length", type=int, default=256, help="Maximum sequence length to use with the prompt.", ) parser.add_argument( "--control-image", nargs="+", type=str, default=[], help="Path to the input image/images already prepared for ControlNet modality. For example: canny edged image for canny ControlNet, not just regular rgb image", ) parser.add_argument( "--controlnet-type", type=str, default="canny", help="Controlnet type (single type only), can be 'canny', 'depth', 'blur', etc.", ) parser.add_argument( "--controlnet-scale", type=float, default=1.0, help="The outputs of the controlnet are multiplied by `controlnet_scale` before they are added to the residual in the original Transformer", ) return parser.parse_args() def process_demo_args(args): batch_size = args.batch_size prompt = args.prompt negative_prompt = args.negative_prompt # 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 if args.height % 8 != 0 or args.width % 8 != 0: raise ValueError( f"Image height and width have to be divisible by 8 but specified as: {args.image_height} and {args.width}." ) max_batch_size = 4 if args.batch_size > max_batch_size: raise ValueError(f"Batch size {args.batch_size} is larger than allowed {max_batch_size}.") if args.use_cuda_graph and (not args.build_static_batch or args.build_dynamic_shape): raise ValueError( "Using CUDA graph requires static dimensions. Enable `--build-static-batch` and do not specify `--build-dynamic-shape`" ) # Controlnet configuration if not isinstance(args.controlnet_type, str): raise ValueError(f"`--controlnet-type` must be of type `str`, but is {type(args.controlnet_type)}") # Controlnet configuration if not isinstance(args.controlnet_scale, float): raise ValueError(f"`--controlnet-scale` must be of type `float`, but is {type(args.controlnet_scale)}") # Convert controlnet scales to tensor controlnet_scale = torch.tensor(args.controlnet_scale) # Check images input_images = [] if len(args.control_image) > 0: for image in args.control_image: input_images.append(Image.open(image)) else: if args.controlnet_type == "canny": canny_image = image_module.download_image( "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/canny.png" ) input_images.append(canny_image.resize((args.width, args.height))) elif args.controlnet_type == "depth": depth_image = image_module.download_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_einstein_lcm_depth.png" ) input_images.append(depth_image.resize((args.width, args.height))) elif args.controlnet_type == "blur": blur_image = image_module.download_image( "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/blur.png" ) input_images.append(blur_image.resize((args.width, args.height))) else: raise ValueError(f"You should implement the conditonal image of this controlnet: {args.controlnet_type}") assert len(input_images) > 0 kwargs_run_demo = { "prompt": prompt, "negative_prompt": negative_prompt, "height": args.height, "width": args.width, "control_image": input_images, "controlnet_scale": controlnet_scale, "batch_count": args.batch_count, "num_warmup_runs": args.num_warmup_runs, "use_cuda_graph": args.use_cuda_graph, } return kwargs_run_demo if __name__ == "__main__": print("[I] Initializing StableDiffusion ControlNet demo using TensorRT") args = parseArgs() # Initialize demo _, kwargs_load_engine, _ = dd_argparse.process_pipeline_args(args) kwargs_run_demo = process_demo_args(args) # Initialize demo demo = pipeline_module.StableDiffusion35Pipeline.FromArgs( args, pipeline_type=pipeline_module.PIPELINE_TYPE.CONTROLNET, ) # Load TensorRT engines and pytorch modules demo.load_engines( framework_model_dir=args.framework_model_dir, **kwargs_load_engine, ) if demo.low_vram: demo.device_memory_sizes = demo.get_device_memory_sizes() else: _, shared_device_memory = cudart.cudaMalloc(demo.calculate_max_device_memory()) demo.activate_engines(shared_device_memory) # Load resources demo.load_resources( image_height=args.height, image_width=args.width, batch_size=args.batch_size, seed=args.seed, ) # Run inference demo.run(**kwargs_run_demo) demo.teardown()