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Running
on
Zero
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"""
Utility Functions
=================
Helper functions for image processing and file operations.
"""
import re
import logging
from pathlib import Path
from typing import Optional, Union
from datetime import datetime
from PIL import Image
logger = logging.getLogger(__name__)
def ensure_pil_image(
obj: Union[Image.Image, str, Path, None],
context: str = ""
) -> Image.Image:
"""
Ensure object is a PIL Image.
Args:
obj: Image, path, or None
context: Context for error messages
Returns:
PIL Image
Raises:
ValueError: If object cannot be converted to Image
"""
if obj is None:
raise ValueError(f"[{context}] Image is None")
if isinstance(obj, Image.Image):
return obj
if isinstance(obj, (str, Path)):
try:
return Image.open(obj)
except Exception as e:
raise ValueError(f"[{context}] Failed to load image from path: {e}")
raise ValueError(f"[{context}] Unsupported image type: {type(obj)}")
def sanitize_filename(name: str) -> str:
"""
Sanitize string for use as filename.
Args:
name: Original name
Returns:
Safe filename string
"""
# Replace problematic characters
safe_name = re.sub(r'[<>:"/\\|?*]', '_', name)
# Remove leading/trailing spaces and dots
safe_name = safe_name.strip('. ')
# Limit length
if len(safe_name) > 100:
safe_name = safe_name[:100]
return safe_name or "unnamed"
def save_image(
image: Image.Image,
directory: Path,
base_name: str,
format: str = "PNG"
) -> Path:
"""
Save image to directory.
Args:
image: PIL Image to save
directory: Output directory
base_name: Base filename (without extension)
format: Image format
Returns:
Path to saved file
"""
directory = Path(directory)
directory.mkdir(parents=True, exist_ok=True)
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
safe_name = sanitize_filename(base_name)
ext = format.lower()
filename = f"{safe_name}_{timestamp}.{ext}"
filepath = directory / filename
image.save(filepath, format=format)
logger.info(f"Saved: {filepath}")
return filepath
def resize_for_display(
image: Image.Image,
max_size: int = 1024
) -> Image.Image:
"""
Resize image for display while maintaining aspect ratio.
Args:
image: PIL Image
max_size: Maximum dimension
Returns:
Resized image
"""
width, height = image.size
if width <= max_size and height <= max_size:
return image
if width > height:
new_width = max_size
new_height = int(height * max_size / width)
else:
new_height = max_size
new_width = int(width * max_size / height)
return image.resize((new_width, new_height), Image.Resampling.LANCZOS)
def get_image_info(image: Image.Image) -> str:
"""Get human-readable image info string."""
return f"{image.size[0]}x{image.size[1]} {image.mode}"
def preprocess_input_image(
image: Image.Image,
max_size: int = 1024,
target_size: tuple = None,
ensure_rgb: bool = True
) -> Image.Image:
"""
Preprocess input image for model consumption.
Handles various formats (JFIF, TIFF, WebP, etc.) by converting to RGB PNG-compatible format.
Args:
image: PIL Image to preprocess
max_size: Maximum dimension (used if target_size not specified)
target_size: Specific (width, height) to resize to
ensure_rgb: Convert to RGB mode
Returns:
Preprocessed PIL Image in RGB format
"""
# Ensure we have a copy to avoid modifying original
img = image.copy()
# Force re-encode as PNG-compatible by saving to memory and reloading
# This handles weird formats like JFIF, TIFF, etc.
import io
buf = io.BytesIO()
# Convert to RGB first if needed
if img.mode not in ('RGB', 'RGBA'):
img = img.convert('RGB')
# Save as PNG to buffer and reload - this normalizes the format
img.save(buf, format='PNG')
buf.seek(0)
img = Image.open(buf)
img.load() # Force load into memory
# Convert to RGB if needed (handle RGBA)
if ensure_rgb and img.mode != 'RGB':
if img.mode == 'RGBA':
# Handle transparency by compositing on white background
background = Image.new('RGB', img.size, (255, 255, 255))
background.paste(img, mask=img.split()[3])
img = background
else:
img = img.convert('RGB')
# Resize to target size or max_size
if target_size:
img = img.resize(target_size, Image.Resampling.LANCZOS)
else:
width, height = img.size
if width > max_size or height > max_size:
if width > height:
new_width = max_size
new_height = int(height * max_size / width)
else:
new_height = max_size
new_width = int(width * max_size / height)
img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
return img
def preprocess_images_for_backend(
images: list,
backend_type: str,
aspect_ratio: str = "1:1"
) -> list:
"""
Preprocess a list of images for a specific backend.
Args:
images: List of PIL Images
backend_type: Backend type string (e.g., 'flux_klein', 'qwen_comfyui')
aspect_ratio: Target aspect ratio
Returns:
List of preprocessed PIL Images
"""
if not images:
return images
# Backend-specific settings
# FLUX models work best with smaller input images (512-768px)
backend_configs = {
'flux_klein': {'max_size': 768}, # 4B - faster with smaller inputs
'flux_klein_9b_fp8': {'max_size': 768}, # 9B - same, quality comes from model not input size
'qwen_image_edit': {'max_size': 1024},
'qwen_comfyui': {'max_size': 1024},
'zimage_turbo': {'max_size': 768},
'zimage_base': {'max_size': 768},
'longcat_edit': {'max_size': 768},
'gemini_flash': {'max_size': 1024}, # Gemini handles larger but 1024 is fine
'gemini_pro': {'max_size': 1024},
}
config = backend_configs.get(backend_type, {'max_size': 1024})
max_size = config['max_size']
processed = []
for img in images:
if img is not None:
processed.append(preprocess_input_image(img, max_size=max_size))
else:
processed.append(None)
return processed
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