Eric Rollei
f5b226157a
- Add LICENSE file with dual license (CC BY-NC 4.0 / Commercial) - Add MODEL_WEIGHTS.md guide with complete model information - Document all pretrained models with sources, citations, and licenses - Add license headers to __init__.py, base_node.py, and sample node - Update README with model weights installation guide - Credit all research papers and open-source dependencies - Provide clear instructions for obtaining model weights - Include troubleshooting and license compliance information
216 lines
7.9 KiB
Python
216 lines
7.9 KiB
Python
"""
|
|
Base node class for Eric's Image Processing Nodes
|
|
Handles common ComfyUI integration, tensor conversions, and device management
|
|
|
|
Author: Eric Hiss (GitHub: EricRollei)
|
|
License: See LICENSE file in repository root
|
|
|
|
This module provides the base functionality for all image processing nodes:
|
|
- ComfyUI tensor format conversion (NHWC [0-1] ↔ numpy [0-255])
|
|
- GPU device detection and management
|
|
- Memory cleanup utilities
|
|
- OpenCV 4.11+ compatibility (ensures contiguous arrays)
|
|
|
|
Dependencies:
|
|
- PyTorch (BSD 3-Clause License)
|
|
- NumPy (BSD 3-Clause License)
|
|
"""
|
|
|
|
import torch
|
|
import numpy as np
|
|
from typing import Tuple, Optional, Union
|
|
import gc
|
|
|
|
|
|
class BaseImageProcessingNode:
|
|
"""Base class for all image processing nodes with ComfyUI integration"""
|
|
|
|
CATEGORY = "Eric's Image Processing"
|
|
|
|
@staticmethod
|
|
def tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:
|
|
"""Convert ComfyUI image tensor (NHWC) to numpy array (NHWC or HWC)
|
|
|
|
Args:
|
|
tensor: ComfyUI image tensor in format [N, H, W, C] or [H, W, C] with values 0-1
|
|
|
|
Returns:
|
|
numpy array in format [N, H, W, C] or [H, W, C] with values 0-255 (uint8)
|
|
GUARANTEED to be contiguous for OpenCV 4.11+ compatibility
|
|
"""
|
|
# CRITICAL: Ensure tensor is contiguous before numpy conversion
|
|
# PyTorch operations like permute() can create non-contiguous tensors
|
|
# OpenCV 4.11+ strictly requires contiguous arrays
|
|
tensor = tensor.contiguous()
|
|
|
|
# Keep batch dimension if present
|
|
img_np = tensor.cpu().numpy()
|
|
|
|
# Convert to numpy and scale to 0-255
|
|
img_np = (img_np * 255).astype(np.uint8)
|
|
|
|
# CRITICAL: Ensure final array is contiguous
|
|
# Arithmetic operations and astype() can create non-contiguous arrays
|
|
img_np = np.ascontiguousarray(img_np)
|
|
|
|
return img_np
|
|
|
|
@staticmethod
|
|
def numpy_to_tensor(img_np: np.ndarray) -> torch.Tensor:
|
|
"""Convert numpy array back to ComfyUI tensor format
|
|
|
|
Args:
|
|
img_np: numpy array in format [N, H, W, C] or [H, W, C] with values 0-255 (uint8) or 0-1 (float32)
|
|
|
|
Returns:
|
|
ComfyUI tensor in format [N, H, W, C] or [1, H, W, C] with values 0-1
|
|
"""
|
|
# CRITICAL: Ensure input array is contiguous before ANY operations
|
|
# Processing functions may return non-contiguous arrays
|
|
img_np = np.ascontiguousarray(img_np)
|
|
|
|
# Handle different input formats
|
|
if img_np.dtype == np.uint8:
|
|
# Convert uint8 [0,255] to float32 [0,1]
|
|
img_tensor = torch.from_numpy(img_np.astype(np.float32) / 255.0)
|
|
elif img_np.dtype in [np.float32, np.float64]:
|
|
# Handle float input - check if it's in [0,1] or [0,255] range
|
|
if img_np.max() <= 1.0:
|
|
# Already in [0,1] range
|
|
img_tensor = torch.from_numpy(img_np.astype(np.float32))
|
|
else:
|
|
# Assume it's in [0,255] range
|
|
img_tensor = torch.from_numpy(img_np.astype(np.float32) / 255.0)
|
|
else:
|
|
# Convert to uint8 first, then to float32
|
|
img_np = np.clip(img_np, 0, 255).astype(np.uint8)
|
|
img_np = np.ascontiguousarray(img_np) # Ensure contiguous after clip
|
|
img_tensor = torch.from_numpy(img_np.astype(np.float32) / 255.0)
|
|
|
|
# Add batch dimension if not present (single image)
|
|
if len(img_tensor.shape) == 3:
|
|
img_tensor = img_tensor.unsqueeze(0)
|
|
|
|
return img_tensor
|
|
|
|
@staticmethod
|
|
def get_device_info() -> Tuple[str, bool]:
|
|
"""Get optimal device for processing
|
|
|
|
Returns:
|
|
Tuple of (device_name, gpu_available)
|
|
"""
|
|
if torch.cuda.is_available():
|
|
device = f"cuda:{torch.cuda.current_device()}"
|
|
gpu_available = True
|
|
else:
|
|
device = "cpu"
|
|
gpu_available = False
|
|
|
|
return device, gpu_available
|
|
|
|
@staticmethod
|
|
def cleanup_memory():
|
|
"""Clean up GPU memory after processing"""
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
gc.collect()
|
|
|
|
@staticmethod
|
|
def validate_image_tensor(tensor: torch.Tensor) -> bool:
|
|
"""Validate that tensor is a proper ComfyUI image tensor
|
|
|
|
Args:
|
|
tensor: Input tensor to validate
|
|
|
|
Returns:
|
|
True if valid, raises ValueError if not
|
|
"""
|
|
if not isinstance(tensor, torch.Tensor):
|
|
raise ValueError("Input must be a torch.Tensor")
|
|
|
|
if len(tensor.shape) not in [3, 4]:
|
|
raise ValueError(f"Image tensor must have 3 or 4 dimensions, got {len(tensor.shape)}")
|
|
|
|
if len(tensor.shape) == 4:
|
|
n, h, w, c = tensor.shape
|
|
if c not in [1, 3, 4]:
|
|
raise ValueError(f"Image must have 1, 3, or 4 channels, got {c}")
|
|
else:
|
|
h, w, c = tensor.shape
|
|
if c not in [1, 3, 4]:
|
|
raise ValueError(f"Image must have 1, 3, or 4 channels, got {c}")
|
|
|
|
if tensor.min() < 0 or tensor.max() > 1:
|
|
raise ValueError("Image tensor values must be in range [0, 1]")
|
|
|
|
return True
|
|
|
|
def process_image_safe(self, image: torch.Tensor, processing_func, **kwargs) -> torch.Tensor:
|
|
"""Safely process an image with error handling and memory cleanup
|
|
|
|
Args:
|
|
image: Input image tensor [N, H, W, C] or [H, W, C]
|
|
processing_func: Function to apply to the image (expects [H, W, C])
|
|
**kwargs: Additional arguments for processing_func
|
|
|
|
Returns:
|
|
Processed image tensor [N, H, W, C] or [H, W, C]
|
|
"""
|
|
try:
|
|
# Validate input
|
|
self.validate_image_tensor(image)
|
|
|
|
# Handle batch dimension properly
|
|
is_batch = len(image.shape) == 4
|
|
|
|
if is_batch:
|
|
# Process each image in the batch
|
|
batch_size = image.shape[0]
|
|
results = []
|
|
|
|
for i in range(batch_size):
|
|
# Get single image [H, W, C]
|
|
single_img = image[i]
|
|
|
|
# Convert to numpy
|
|
img_np = self.tensor_to_numpy(single_img)
|
|
|
|
# Apply processing (expects 2D or 3D array)
|
|
processed_np = processing_func(img_np, **kwargs)
|
|
|
|
# Convert back to tensor
|
|
result_tensor = self.numpy_to_tensor(processed_np)
|
|
|
|
# Remove batch dimension if added by numpy_to_tensor
|
|
if len(result_tensor.shape) == 4:
|
|
result_tensor = result_tensor[0]
|
|
|
|
results.append(result_tensor)
|
|
|
|
# Stack results back into batch
|
|
result = torch.stack(results, dim=0)
|
|
else:
|
|
# Single image [H, W, C]
|
|
# Convert to numpy
|
|
img_np = self.tensor_to_numpy(image)
|
|
|
|
# Apply processing
|
|
processed_np = processing_func(img_np, **kwargs)
|
|
|
|
# Convert back to tensor
|
|
result = self.numpy_to_tensor(processed_np)
|
|
|
|
# Remove batch dimension if added by numpy_to_tensor
|
|
if len(result.shape) == 4 and result.shape[0] == 1:
|
|
result = result[0]
|
|
|
|
return result
|
|
|
|
except Exception as e:
|
|
print(f"Error in image processing: {str(e)}")
|
|
raise
|
|
finally:
|
|
# Clean up memory
|
|
self.cleanup_memory()
|