Upload finetune_utility_scripts.py

finetune_utility_scripts.py

@@ -0,0 +1,195 @@
+# -*- 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)
+