# # 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("flux") import argparse import os import controlnet_aux from cuda.bindings import runtime as cudart from PIL import Image from demo_diffusion import dd_argparse from demo_diffusion import pipeline as pipeline_module def parse_args(): parser = argparse.ArgumentParser(description="Options for Flux Img2Img Demo", conflict_handler="resolve") parser = dd_argparse.add_arguments(parser) parser.add_argument( "--version", type=str, default="flux.1-dev", choices=("flux.1-dev", "flux.1-schnell", "flux.1-dev-canny", "flux.1-dev-depth", "flux.1-kontext-dev"), help="Version of Flux", ) parser.add_argument( "--prompt2", default=None, nargs="*", help="Text prompt(s) to be sent to the T5 tokenizer and text encoder. If not defined, prompt will be used instead", ) 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="Width of image to generate (must be multiple of 8)", ) parser.add_argument("--denoising-steps", type=int, default=50, help="Number of denoising steps") parser.add_argument( "--guidance-scale", type=float, default=3.5, help="Value of classifier-free guidance scale (must be greater than 1)", ) parser.add_argument( "--max_sequence_length", type=int, help="Maximum sequence length to use with the prompt. Can be up to 512 for the dev and 256 for the schnell variant.", ) parser.add_argument( "--t5-ws-percentage", type=int, default=None, help="Set runtime weight streaming budget as the percentage of the size of streamable weights for the T5 model. This argument only takes effect when --ws is set. 0 streams the most weights and 100 or None streams no weights. ", ) parser.add_argument( "--transformer-ws-percentage", type=int, default=None, help="Set runtime weight streaming budget as the percentage of the size of streamable weights for the transformer model. This argument only takes effect when --ws is set. 0 streams the most weights and 100 or None streams no weights.", ) parser.add_argument( "--control-image", type=str, default=None, help="Path to the control image for the flux.1-dev-canny and flux.1-dev-depth pipelines", ) parser.add_argument( "--input-image", type=str, default=None, help="Path to the input conditioning image for the flux.1-dev and flux.1-schnell img2img pipelines", ) parser.add_argument( "--kontext-image", type=str, default=None, help="Path to the input image for Kontext pipeline (flux.1-kontext-dev only, required)", ) parser.add_argument( "--image-strength", type=float, default=1.0, help="Indicates extent to transform the reference `image`. Must be between 0 and 1. A value of 1 essentially ignores the input image.", ) parser.add_argument( "--calibration-dataset", type=str, default=None, help="Path to the calibration dataset for quantization (only enabled for controlnet)", ) return parser.parse_args() def process_demo_args(args): batch_size = args.batch_size prompt = args.prompt # If prompt2 is not defined, use prompt instead prompt2 = args.prompt2 or prompt # Process input args if not isinstance(prompt, list): raise ValueError(f"`prompt` must be of type `list[str]`, but is {type(prompt)}") prompt = prompt * batch_size if not isinstance(prompt2, list): raise ValueError(f"`prompt2` must be of type `str` list, but is {type(prompt2)}") if len(prompt2) == 1: prompt2 = prompt2 * batch_size max_seq_supported_by_model = { "flux.1-schnell": 256, "flux.1-dev": 512, "flux.1-dev-canny": 512, "flux.1-dev-depth": 512, "flux.1-kontext-dev": 512, }[args.version] if args.max_sequence_length is not None: if args.max_sequence_length > max_seq_supported_by_model: raise ValueError( f"For {args.version}, `max_sequence_length` cannot be greater than {max_seq_supported_by_model} but is {args.max_sequence_length}" ) else: args.max_sequence_length = max_seq_supported_by_model controlnet_type = "depth" if "depth" in args.version else "canny" if "canny" in args.version else "" if controlnet_type: if args.input_image: raise ValueError( f"--input-image is a valid input for versions [flux.1-dev, flux.1-schnell]. Provided {args.version}" ) if not args.control_image: raise ValueError( "--control-image input is required for versions [flux.1-dev-canny, flux.1-dev-depth]. Please provide it using --control-image flag." ) args.control_image = Image.open(args.control_image).convert("RGB") if controlnet_type == "canny": processor = controlnet_aux.CannyDetector() args.control_image = processor( args.control_image, low_threshold=50, high_threshold=200, detect_resolution=1024, image_resolution=1024 ) elif controlnet_type == "depth": args.control_image = controlnet_aux.LeresDetector.from_pretrained("lllyasviel/Annotators")( args.control_image ) else: raise ValueError("Invalid controlnet type") else: if args.control_image: raise ValueError( f"--control-image is a valid input for versions [flux.1-dev-canny, flux.1-dev-depth]. Provided {args.version}" ) # Handle input image for img2img pipelines if args.version == "flux.1-kontext-dev": # For Kontext pipeline, only use kontext-image if not args.kontext_image: raise ValueError( "--kontext-image is required for the Kontext pipeline. Please provide it using the --kontext-image flag." ) if args.input_image: raise ValueError( "--input-image is not supported for the Kontext pipeline. Please use --kontext-image instead." ) # Kontext pipeline doesn't resize the input image args.kontext_image = Image.open(args.kontext_image).convert("RGB") else: if not args.input_image: raise ValueError( "--input-image is required for the img2img pipeline. Please provide it using the --input-image flag." ) args.input_image = Image.open(args.input_image).convert("RGB").resize((args.width, args.height)) if args.fp8: if args.version == "flux.1-dev" or args.version == "flux.1-schnell": raise ValueError("--fp8 is currently not supported for Flux.1-dev and Flux.1-schnell img2img pipelines.") if not args.calibration_dataset: args.calibration_dataset = os.path.join(f"{controlnet_type}-eval", "benchmark") print(f"[W] Calibration dataset path not provided, setting default path to {args.calibration_dataset}.") if not os.path.exists(args.calibration_dataset): print( f"[W] Could not find the calibration dataset at {args.calibration_dataset}, and will fallback to using pre-exported ONNX models. Please follow the instructions in README to download calibration dataset and provide the path if pre-exported ONNX models are not provided either." ) if args.version == "flux.1-kontext-dev" and not args.download_onnx_models: raise ValueError( "--download-onnx-models is required when using --fp8 for Flux.1-kontext-dev img2img pipeline." ) if args.fp4: if args.version == "flux.1-dev" or args.version == "flux.1-schnell": raise ValueError("--fp4 is currently not supported for Flux.1-dev and Flux.1-schnell img2img pipelines.") if not args.download_onnx_models: raise ValueError("--download-onnx-models is required when using --fp4.") kwargs_run_demo = { "prompt": prompt, "prompt2": prompt2, "height": args.height, "width": args.width, "batch_count": args.batch_count, "num_warmup_runs": args.num_warmup_runs, "use_cuda_graph": args.use_cuda_graph, "image_strength": args.image_strength, } # Add the appropriate image parameter based on pipeline type if not args.version == "flux.1-kontext-dev": kwargs_run_demo["input_image"] = args.input_image kwargs_run_demo["control_image"] = args.control_image return kwargs_run_demo if __name__ == "__main__": print("[I] Initializing Flux img2img demo using TensorRT") args = parse_args() _, kwargs_load_engine, _ = dd_argparse.process_pipeline_args(args) kwargs_run_demo = process_demo_args(args) # Initialize demo pipeline_type = pipeline_module.PIPELINE_TYPE.IMG2IMG if args.version == "flux.1-kontext-dev": demo = pipeline_module.FluxKontextPipeline.FromArgs(args, pipeline_type=pipeline_type) else: demo = pipeline_module.FluxPipeline.FromArgs(args, pipeline_type=pipeline_type) # Load TensorRT engines and pytorch modules demo.load_engines( framework_model_dir=args.framework_model_dir, **kwargs_load_engine, ) if args.onnx_export_only: print("[I] ONNX export completed. Exiting...") demo.teardown() exit(0) # Since VAE and VAE_encoder require by far the largest device memories, in low-vram mode # we allocate the required device memory individually before each model is run. 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) demo.load_resources(args.height, args.width, args.batch_size, args.seed) # Run inference images = demo.run(**kwargs_run_demo) demo.teardown() # save images demo.save_images(kwargs_run_demo["prompt"], images, check_integrity=(args.version == "flux.1-kontext-dev"))