Delete src/controlnet_image_generator/old3-infer.py

src/controlnet_image_generator/old3-infer.py

@@ -1,119 +0,0 @@
-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
-    )