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import torch
import argparse
from inference.config_loader import load_config, find_config_by_model_id
from inference.model_initializer import (
initialize_controlnet,
initialize_pipeline,
initialize_controlnet_detector
)
from inference.device_manager import setup_device
from inference.image_processor import load_input_image, detect_poses
from inference.image_generator import generate_images, save_images
def infer(
config_path,
input_image,
image_url,
prompt,
negative_prompt,
num_steps,
seed,
width,
height,
guidance_scale,
controlnet_conditioning_scale,
output_dir,
use_prompt_as_output_name,
save_output
):
# Load configuration
configs = load_config(config_path)
# Initialize models
controlnet_detector_config = find_config_by_model_id(configs, "lllyasviel/ControlNet")
controlnet_config = find_config_by_model_id(configs,
"danhtran2mind/Stable-Diffusion-2.1-Openpose-ControlNet")
pipeline_config = find_config_by_model_id(configs,
"stabilityai/stable-diffusion-2-1")
controlnet_detector = initialize_controlnet_detector(controlnet_detector_config)
controlnet = initialize_controlnet(controlnet_config)
pipe = initialize_pipeline(controlnet, pipeline_config)
# Setup device
device = setup_device(pipe)
# Load and process image
demo_image = load_input_image(input_image, image_url)
poses = detect_poses(controlnet_detector, demo_image)
# Generate images
generators = [torch.Generator(device="cpu").manual_seed(seed + i) for i in range(len(poses))]
output_images = generate_images(
pipe,
[prompt] * len(generators),
poses,
generators,
[negative_prompt] * len(generators),
num_steps,
guidance_scale,
controlnet_conditioning_scale,
width,
height
)
# Save images if required
if save_output:
save_images(output_images, output_dir, prompt, use_prompt_as_output_name)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="ControlNet image generation with pose detection")
image_group = parser.add_mutually_exclusive_group(required=True)
image_group.add_argument("--input_image", type=str, default=None,
help="Path to local input image (default: tests/test_data/yoga1.jpg)")
image_group.add_argument("--image_url", type=str, default=None,
help="URL of input image (e.g., https://huggingface.co/datasets/YiYiXu/controlnet-testing/resolve/main/yoga1.jpeg)")
parser.add_argument("--config_path", type=str, default="configs/model_ckpts.yaml",
help="Path to configuration YAML file")
parser.add_argument("--prompt", type=str, default="a man is doing yoga",
help="Text prompt for image generation")
parser.add_argument("--negative_prompt", type=str,
default="monochrome, lowres, bad anatomy, worst quality, low quality",
help="Negative prompt for image generation")
parser.add_argument("--num_steps", type=int, default=20,
help="Number of inference steps")
parser.add_argument("--seed", type=int, default=2,
help="Random seed for generation")
parser.add_argument("--width", type=int, default=512,
help="Width of the generated image")
parser.add_argument("--height", type=int, default=512,
help="Height of the generated image")
parser.add_argument("--guidance_scale", type=float, default=7.5,
help="Guidance scale for prompt adherence")
parser.add_argument("--controlnet_conditioning_scale", type=float, default=1.0,
help="ControlNet conditioning scale")
parser.add_argument("--output_dir", type=str, default="tests/test_data",
help="Directory to save generated images")
parser.add_argument("--use_prompt_as_output_name", action="store_true",
help="Use prompt as part of output image filename")
parser.add_argument("--save_output", action="store_true",
help="Save generated images to output directory")
args = parser.parse_args()
infer(
config_path=args.config_path,
input_image=args.input_image,
image_url=args.image_url,
prompt=args.prompt,
negative_prompt=args.negative_prompt,
num_steps=args.num_steps,
seed=args.seed,
width=args.width,
height=args.height,
guidance_scale=args.guidance_scale,
controlnet_conditioning_scale=args.controlnet_conditioning_scale,
output_dir=args.output_dir,
use_prompt_as_output_name=args.use_prompt_as_output_name,
save_output=args.save_output
) |