Datasets:
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pip install pillow datasets pandas pypng uuid\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Preproccessing"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import uuid\n",
"import shutil\n",
"\n",
"def rename_and_move_images(source_dir, target_dir):\n",
" # Create the target directory if it doesn't exist\n",
" os.makedirs(target_dir, exist_ok=True)\n",
"\n",
" # List of common image file extensions\n",
" image_extensions = ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff')\n",
"\n",
" # Walk through the source directory and its subdirectories\n",
" for root, dirs, files in os.walk(source_dir):\n",
" for file in files:\n",
" # Check if the file has an image extension\n",
" if file.lower().endswith(image_extensions):\n",
" # Generate a new filename with UUID\n",
" new_filename = str(uuid.uuid4()) + os.path.splitext(file)[1]\n",
" \n",
" # Construct full file paths\n",
" old_path = os.path.join(root, file)\n",
" new_path = os.path.join(target_dir, new_filename)\n",
" \n",
" # Move and rename the file\n",
" shutil.move(old_path, new_path)\n",
" print(f\"Moved and renamed: {old_path} -> {new_path}\")\n",
"\n",
"# Usage\n",
"source_directory = \"images\"\n",
"target_directory = \"train\"\n",
"\n",
"rename_and_move_images(source_directory, target_directory)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Examine Metadata For Stable Diffusion PNGs\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from PIL import Image\n",
"import os\n",
"\n",
"# Set the path to the train directory\n",
"train_directory = \"train\"\n",
"\n",
"# Get any PNG image file from the train directory\n",
"image_file = None\n",
"for file_name in os.listdir(train_directory):\n",
" if file_name.endswith('.png'):\n",
" image_file = os.path.join(train_directory, file_name)\n",
" break\n",
"\n",
"# Check if an image was found\n",
"if image_file:\n",
" # Open the image\n",
" with Image.open(image_file) as img:\n",
" print(f\"Filename: {image_file}\")\n",
" \n",
" # Extract and display image size (width, height)\n",
" print(f\"Size: {img.size}\")\n",
" \n",
" # Extract and display image mode (RGB, RGBA, etc.)\n",
" print(f\"Mode: {img.mode}\")\n",
" \n",
" # Extract and display image format\n",
" print(f\"Format: {img.format}\")\n",
" \n",
" # Extract and display image info (metadata)\n",
" print(\"Metadata:\")\n",
" for key, value in img.info.items():\n",
" print(f\" {key}: {value}\")\n",
"else:\n",
" print(\"No PNG image found in the train directory.\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Extract the Metadata"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import png\n",
"import pandas as pd\n",
"import re\n",
"\n",
"# Directory containing images\n",
"image_dir = 'train'\n",
"metadata_list = []\n",
"\n",
"# Mapping of possible keys to standardized field names\n",
"field_mapping = {\n",
" 'parameters': 'prompt',\n",
" 'seed': 'seed',\n",
" 'steps': 'steps',\n",
" 'cfg scale': 'cfg',\n",
" 'cfg_scale': 'cfg',\n",
" 'cfg-scale': 'cfg',\n",
" 'guidance': 'guidance',\n",
" 'sampler': 'sampler_name',\n",
" 'sampler_name': 'sampler_name',\n",
" 'version': 'version',\n",
" 'rng': 'rng',\n",
" 'size': 'size'\n",
"}\n",
"\n",
"def extract_metadata_from_png(image_path):\n",
" \"\"\"\n",
" Extracts metadata from all tEXt chunks in a PNG image.\n",
" Args:\n",
" image_path (str): Path to the PNG image.\n",
" Returns:\n",
" dict: A dictionary containing extracted metadata.\n",
" \"\"\"\n",
" metadata = {}\n",
" try:\n",
" with open(image_path, 'rb') as f:\n",
" reader = png.Reader(file=f)\n",
" for chunk_type, chunk_data in reader.chunks():\n",
" if chunk_type == b'tEXt':\n",
" # Decode the tEXt chunk\n",
" chunk_text = chunk_data.decode('latin1')\n",
" # Split into key and value using null byte as separator\n",
" if '\\x00' in chunk_text:\n",
" key, value = chunk_text.split('\\x00', 1)\n",
" key = key.lower().strip()\n",
" value = value.strip()\n",
" # Map the key to standardized field name if it exists\n",
" if key in field_mapping:\n",
" standardized_key = field_mapping[key]\n",
" metadata[standardized_key] = value\n",
" except Exception as e:\n",
" print(f\"Error reading PNG file {image_path}: {e}\")\n",
" return metadata\n",
"\n",
"def parse_parameters(parameters):\n",
" \"\"\"\n",
" Parses the 'parameters' string to extract individual fields.\n",
" \"\"\"\n",
" # Extract prompt (everything before the first recognized field)\n",
" prompt_end = min((parameters.find(field) for field in ['Steps:', 'CFG scale:', 'Guidance:', 'Seed:'] if field in parameters), default=-1)\n",
" prompt = parameters[:prompt_end].strip() if prompt_end != -1 else parameters\n",
"\n",
" # Extract other fields\n",
" fields = {\n",
" 'steps': r'Steps: (\\d+)',\n",
" 'cfg': r'CFG scale: ([\\d.]+)',\n",
" 'guidance': r'Guidance: ([\\d.]+)',\n",
" 'seed': r'Seed: (\\d+)',\n",
" 'width': r'Size: (\\d+)x\\d+',\n",
" 'height': r'Size: \\d+x(\\d+)',\n",
" 'rng': r'RNG: (\\w+)',\n",
" 'sampler_name': r'Sampler: (\\w+)',\n",
" 'version': r'Version: (.+)$'\n",
" }\n",
"\n",
" parsed = {'prompt': prompt}\n",
" for key, pattern in fields.items():\n",
" match = re.search(pattern, parameters)\n",
" parsed[key] = match.group(1).strip() if match else 'N/A'\n",
"\n",
" return parsed\n",
"\n",
"# Loop through all PNG images in the directory\n",
"for file_name in os.listdir(image_dir):\n",
" if file_name.lower().endswith('.png'):\n",
" image_path = os.path.join(image_dir, file_name)\n",
" metadata = extract_metadata_from_png(image_path)\n",
" \n",
" # Parse the 'parameters' field\n",
" if 'prompt' in metadata:\n",
" parsed_metadata = parse_parameters(metadata['prompt'])\n",
" else:\n",
" parsed_metadata = {field: 'N/A' for field in field_mapping.values()}\n",
" \n",
" # Add file name to metadata\n",
" parsed_metadata['file_name'] = file_name\n",
" \n",
" metadata_list.append(parsed_metadata)\n",
"\n",
"# Convert the metadata list to a DataFrame\n",
"metadata_df = pd.DataFrame(metadata_list)\n",
"\n",
"# Define the desired column order\n",
"desired_columns = ['file_name', 'seed', 'prompt', 'steps', 'cfg', 'sampler_name', 'guidance', 'version', 'width', 'height', 'rng']\n",
"\n",
"# Ensure all desired columns are present\n",
"for col in desired_columns:\n",
" if col not in metadata_df.columns:\n",
" metadata_df[col] = 'N/A'\n",
"\n",
"# Reorder the DataFrame columns\n",
"metadata_df = metadata_df[desired_columns]\n",
"\n",
"# Save the metadata to a CSV file\n",
"metadata_csv_path = os.path.join(image_dir, 'metadata.csv')\n",
"metadata_df.to_csv(metadata_csv_path, index=False)\n",
"print(\"Metadata extraction complete. Metadata saved to:\", metadata_csv_path)\n"
]
}
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