finetune_utility_scripts.py

# -*- coding: utf-8 -*-
"""finetune-utility-scripts.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/14ZbhUPHtNt3EB0XunV_qN6OxWZHyU9wA
"""

!pip install openai

import base64
import requests

api_key = "sk-proj-uCiflA45fuchFdjkbNJ7T3BlbkFJF5WiEf2zHkttr7s9kijX"
prompt = """As an AI image tagging expert, please provide precise tags for
these images to enhance CLIP model's understanding of the content.
Employ succinct keywords or phrases, steering clear of elaborate
sentences and extraneous conjunctions. Prioritize the tags by relevance.
Your tags should capture key elements such as the main subject, setting,
artistic style, composition, image quality, color tone, filter, and camera
specifications, and any other tags crucial for the image. When tagging
photos of people, include specific details like gender, nationality,
attire, actions, pose, expressions, accessories, makeup, composition
type, age, etc. For other image categories, apply appropriate and
common descriptive tags as well. Recognize and tag any celebrities,
 well-known landmark or IPs if clearly featured in the image.
 Your tags should be accurate, non-duplicative, and within a
 20-75 word count range. These tags will use for image re-creation,
 so the closer the resemblance to the original image, the better the
 tag quality. Tags should be comma-separated. Exceptional tagging will
 be rewarded with $10 per image.
"""

def encode_image(image_path):
  with open(image_path, "rb") as image_file:
    return base64.b64encode(image_file.read()).decode('utf-8')

def create_openai_query(image_path):
  base64_image = encode_image(image_path)
  headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {api_key}"
  }
  payload = {
    "model": "gpt-4o",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": prompt
          },
          {
            "type": "image_url",
            "image_url": {
              "url": f"data:image/jpeg;base64,{base64_image}"
            }
          }
        ]
      }
    ],
    "max_tokens": 300
  }

  response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
  output = response.json()
  print(output)
  return output['choices'][0]['message']['content']

!rm -rf "/content/drive/MyDrive/Finetune-Dataset/Pexels_Caption"

import os
os.mkdir("/content/drive/MyDrive/Finetune-Dataset/Pexels_Caption")

import os
import time


# Function to process images in a folder, handling API throttling
def process_images_in_folder(input_folder, output_folder, resume_from=None):
    os.makedirs(output_folder, exist_ok=True)
    image_files = [f for f in os.listdir(input_folder) if os.path.isfile(os.path.join(input_folder, f))]

    # Track processed images
    processed_log = os.path.join(output_folder, "processed_log.txt")
    processed_images = set()

    # Read processed log if exists
    if os.path.exists(processed_log):
        with open(processed_log, 'r') as log_file:
            processed_images = {line.strip() for line in log_file.readlines()}

    try:
        for image_file in image_files:
            if resume_from and image_file <= resume_from:
                continue  # Skip images already processed

            image_path = os.path.join(input_folder, image_file)

            # Check if already processed
            if image_file in processed_images:
                print(f"Skipping {image_file} as it is already processed.")
                continue

            try:
                processed_output = create_openai_query(image_path)
            except Exception as e:
                print(f"Error processing {image_file}: {str(e)}")
                processed_output = "" # Stop processing further on error

            output_file_path = os.path.join(output_folder, f"{os.path.splitext(image_file)[0]}.txt")

            with open(output_file_path, 'w') as f:
                f.write(processed_output)

            # Log processed image
            with open(processed_log, 'a') as log_file:
                log_file.write(f"{image_file}\n")

            print(f"Processed {image_file} and saved result to {output_file_path}")

    except Exception as e:
        print(f"Error occurred: {str(e)}. Resuming might not be possible.")
        return

if __name__ == "__main__":
    input_folder = "/content/drive/MyDrive/inference-images/inference-images/caimera"
    output_folder = "/content/drive/MyDrive/inference-images/caimera_captions"

    # Replace with the last successfully processed image filename (without extension) to resume from that point
    resume_from = None  # Example: "image_003"

    process_images_in_folder(input_folder, output_folder, resume_from)

import os
import shutil

def move_json_files(source_folder, destination_folder):
    # Ensure destination folder exists, create if not
    if not os.path.exists(destination_folder):
        os.makedirs(destination_folder)

    # Iterate through files in source folder
    for file_name in os.listdir(source_folder):
        if file_name.endswith('.png'):
            source_file = os.path.join(source_folder, file_name)
            destination_file = os.path.join(destination_folder, file_name)
            try:
                shutil.move(source_file, destination_file)
                print(f"Moved {file_name} to {destination_folder}")
            except Exception as e:
                print(f"Failed to move {file_name}: {e}")

# Example usage:
source_folder = "/content/drive/MyDrive/inference-images/inference-images/1683/saved"  # Replace with your source folder path
destination_folder = "/content/drive/MyDrive/inference-images/inference-images/caimera"  # Replace with your destination folder path

move_json_files(source_folder, destination_folder)

os.mkdir('/content/drive/MyDrive/kohya_finetune_data')

import os
import shutil

def merge_folders(folder_paths, destination_folder):
    if not os.path.exists(destination_folder):
        os.makedirs(destination_folder)
    for folder_path in folder_paths:
        for filename in os.listdir(folder_path):
            source_file = os.path.join(folder_path, filename)
            destination_file = os.path.join(destination_folder, filename)
            if os.path.exists(destination_file):
                base, extension = os.path.splitext(filename)
                count = 1
                while os.path.exists(os.path.join(destination_folder, f"{base}_{count}{extension}")):
                    count += 1
                destination_file = os.path.join(destination_folder, f"{base}_{count}{extension}")
            shutil.copy2(source_file, destination_file)
            print(f"Copied {source_file} to {destination_file}")

if __name__ == "__main__":
    # Example usage
    folder1 = '/content/drive/MyDrive/inference-images/caimera_captions'
    folder2 = '/content/drive/MyDrive/inference-images/inference-images/caimera'
    folder3 = '/content/drive/MyDrive/Finetune-Dataset/Burst'
    folder4 = '/content/drive/MyDrive/Finetune-Dataset/Burst_Caption'
    folder5 = '/content/drive/MyDrive/Finetune-Dataset/Pexels'
    folder6 = '/content/drive/MyDrive/Finetune-Dataset/Pexels_Caption'
    destination = '/content/drive/MyDrive/kohya_finetune_data'

    folders_to_merge = [folder1, folder2, folder3, folder4, folder5, folder6]
    merge_folders(folders_to_merge, destination)