Delete src/controlnet_image_generator/old2-infer.py

src/controlnet_image_generator/old2-infer.py

@@ -1,189 +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
-import os
-from tqdm import tqdm
-import re
-import uuid
-
-def load_config(config_path):
-    try:
-        with open(config_path, 'r') as file:
-            return yaml.safe_load(file)
-    except Exception as e:
-        raise ValueError(f"Error loading config file: {e}")
-
-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, guidance_scale, controlnet_conditioning_scale, width, height):
-    return pipe(
-        prompts,
-        pose_images,
-        negative_prompt=negative_prompts,
-        generator=generators,
-        num_inference_steps=num_steps,
-        guidance_scale=guidance_scale,
-        controlnet_conditioning_scale=controlnet_conditioning_scale,
-        width=width,
-        height=height
-    ).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
-    try:
-        if args.input_image:
-            demo_image = Image.open(args.input_image).convert("RGB")
-        elif args.image_url:
-            demo_image = load_image(args.image_url)
-        else:
-            raise ValueError("Either --input_image or --image_url must be provided")
-    except Exception as e:
-        raise ValueError(f"Error loading image: {e}")
-    
-    poses = [controlnet_detector(demo_image)]
-    
-    # Generate images
-    generators = [torch.Generator(device="cpu").manual_seed(args.seed + i) for i in range(len(poses))]
-    
-    output_images = generate_images(
-        pipe,
-        [args.prompt] * len(generators),
-        poses,
-        generators,
-        [args.negative_prompt] * len(generators),
-        args.num_steps,
-        args.guidance_scale,
-        args.controlnet_conditioning_scale,
-        args.width,
-        args.height
-    )
-    
-    # Save images if save_output is True
-    if args.save_output:
-        os.makedirs(args.output_dir, exist_ok=True)
-        for i, img in enumerate(tqdm(output_images, desc="Saving images")):
-            if args.use_prompt_as_output_name:
-                # Sanitize prompt for filename (replace spaces and special characters)
-                sanitized_prompt = re.sub(r'[^\w\s-]', '', args.prompt).replace(' ', '_').lower()
-                filename = f"{sanitized_prompt}_{i}.png"
-            else:
-                # Use UUID for filename
-                filename = f"{uuid.uuid4()}_{i}.png"
-            img.save(os.path.join(args.output_dir, filename))
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="ControlNet image generation with pose detection")
-    # Create mutually exclusive group for input_image and image_url
-    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 artr",
-                        help="Save generated images to output directory")
-    
-    args = parser.parse_args()
-    infer(args)
-
-# Using image_url
-# python script.py \
-#     --config_path configs/model_ckpts.yaml \
-#     --image_url https://huggingface.co/datasets/YiYiXu/controlnet-testing/resolve/main/yoga1.jpeg \
-#     --prompt "a man is doing yoga in a serene park" \
-#     --negative_prompt "monochrome, lowres, bad anatomy" \
-#     --num_steps 30 \
-#     --seed 42 \
-#     --width 512 \
-#     --height 512 \
-#     --guidance_scale 7.5 \
-#     --controlnet_conditioning_scale 0.8 \
-#     --output_dir "tests/test_data" \
-#     --save_output
-
-# Using input_image
-# python script.py \
-#     --config_path configs/model_ckpts.yaml \
-#     --input_image "tests/test_data/yoga1.jpg" \
-#     --prompt "a man is doing yoga in a serene park" \
-#     --negative_prompt "monochrome, lowres, bad anatomy" \
-#     --num_steps 30 \
-#     --seed 42 \
-#     --width 512 \
-#     --height 512 \
-#     --guidance_scale 7.5 \
-#     --controlnet_conditioning_scale 0.8 \
-#     --output_dir "tests/test_data" \ 
-#     --save_output