Delete src/controlnet_image_generator/old-infer.py

src/controlnet_image_generator/old-infer.py

@@ -1,102 +0,0 @@
-import cv2
-import torch
-from PIL import Image
-import numpy as np
-import yaml
-import argparse
-from controlnet_aux import OpenposeDetector
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
-from utils.download import load_image
-from utils.plot import image_grid
-
-def load_config(config_path):
-    with open(config_path, 'r') as file:
-        return yaml.safe_load(file)
-
-def initialize_controlnet(config):
-    model_id = config['model_id']
-    local_dir = config.get('local_dir', model_id)
-    return ControlNetModel.from_pretrained(
-        local_dir if local_dir != model_id else model_id,
-        torch_dtype=torch.float16
-    )
-
-def initialize_pipeline(controlnet, config):
-    model_id = config['model_id']
-    local_dir = config.get('local_dir', model_id)
-    pipe = StableDiffusionControlNetPipeline.from_pretrained(
-        local_dir if local_dir != model_id else model_id,
-        controlnet=controlnet,
-        torch_dtype=torch.float16
-    )
-    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-    return pipe
-
-def setup_device(pipe):
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    if device == "cuda":
-        pipe.enable_model_cpu_offload()
-    pipe.to(device)
-    return device
-
-def generate_images(pipe, prompts, pose_images, generators, negative_prompts, num_steps):
-    return pipe(
-        prompts,
-        pose_images,
-        negative_prompt=negative_prompts,
-        generator=generators,
-        num_inference_steps=num_steps
-    ).images
-
-def infer(args):
-    # Load configuration
-    configs = load_config(args.config_path)
-    
-    # Initialize models
-    controlnet_detector = OpenposeDetector.from_pretrained(
-        configs[2]['model_id']  # lllyasviel/ControlNet
-    )
-    controlnet = initialize_controlnet(configs[0])
-    pipe = initialize_pipeline(controlnet, configs[1])
-    
-    # Setup device
-    device = setup_device(pipe)
-    
-    # Load and process image
-    demo_image = load_image(args.image_url)
-    poses = [controlnet_detector(demo_image)]
-    
-    # Generate images
-    generators = [torch.Generator(device="cpu").manual_seed(args.seed) for _ in range(len(poses))]
-    
-    output_images = generate_images(
-        pipe,
-        [args.prompt] * len(generators),
-        poses,
-        generators,
-        [args.negative_prompt] * len(generators),
-        args.num_steps
-    )
-    
-    # Display results
-    # image_grid(output_images, 2, 2)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="ControlNet image generation with pose detection")
-    parser.add_argument("--config_path", type=str, default="configs/model_ckpts.yaml", 
-                       help="Path to configuration YAML file")
-    parser.add_argument("--image_url", type=str, 
-                       default="https://huggingface.co/datasets/YiYiXu/controlnet-testing/resolve/main/yoga1.jpeg",
-                       help="URL of input image")
-    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")
-    # return parser.parse_args()
-    args = parser.parse_args()
-    infer(args)