A043-studios
64536b971b
✨ Features: - Complete 6-node suite for 3D face reconstruction - FLAME model integration with multi-constraint optimization - UV coordinate and surface normal prediction - Professional mesh export (OBJ, PLY, STL) - Comprehensive documentation and examples 🎯 Nodes included: - 🔧 Pixel3DMM Model Loader - 🎭 3D Face Reconstructor - 🗺️ UV Coordinate Predictor - 📐 Surface Normal Predictor - 🔥 FLAME Parameter Optimizer - 📦 3D Mesh Exporter 📚 Documentation: - Complete installation guide - Usage examples and workflows - Troubleshooting and support - Ready-to-use ComfyUI workflow files 🧪 Quality: - Comprehensive testing and validation - ComfyUI standard compliance - Cross-platform support (Windows, macOS, Linux) - Production-ready with robust error handling Generated by MCP Multi-Agent System Research → DevOps → Coding → Testing → Documentation
265 lines
8.8 KiB
Python
265 lines
8.8 KiB
Python
"""
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Utility functions for Pixel3DMM ComfyUI nodes
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"""
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import torch
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import torch.nn.functional as F
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import numpy as np
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from PIL import Image
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import cv2
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import os
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from typing import Optional, Tuple, Union, Any
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def tensor_to_pil(tensor: torch.Tensor) -> Image.Image:
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"""Convert tensor to PIL Image"""
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try:
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if isinstance(tensor, torch.Tensor):
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# Handle different tensor formats
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if tensor.dim() == 4: # BCHW
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tensor = tensor.squeeze(0)
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if tensor.dim() == 3: # CHW
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tensor = tensor.permute(1, 2, 0) # HWC
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# Ensure values are in [0, 1] range
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tensor = torch.clamp(tensor, 0, 1)
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# Convert to numpy
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if tensor.requires_grad:
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tensor = tensor.detach()
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tensor = tensor.cpu().numpy()
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# Convert to uint8
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tensor = (tensor * 255).astype(np.uint8)
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# Handle grayscale vs RGB
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if tensor.shape[-1] == 1:
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tensor = tensor.squeeze(-1)
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return Image.fromarray(tensor, mode='L')
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elif tensor.shape[-1] == 3:
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return Image.fromarray(tensor, mode='RGB')
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elif tensor.shape[-1] == 4:
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return Image.fromarray(tensor, mode='RGBA')
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else:
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# Convert single channel to RGB
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if len(tensor.shape) == 2:
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return Image.fromarray(tensor, mode='L')
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return Image.fromarray(tensor)
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except Exception as e:
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print(f"Error converting tensor to PIL: {e}")
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# Return a default image on error
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return Image.new('RGB', (512, 512), color='black')
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def pil_to_tensor(image: Image.Image) -> torch.Tensor:
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"""Convert PIL Image to tensor"""
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try:
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# Convert to RGB if needed
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if image.mode != 'RGB':
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image = image.convert('RGB')
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# Convert to numpy array
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image_np = np.array(image).astype(np.float32) / 255.0
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# Convert to tensor and rearrange dimensions
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tensor = torch.from_numpy(image_np)
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# Ensure HWC format, then convert to CHW
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if tensor.dim() == 3:
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tensor = tensor.permute(2, 0, 1) # HWC -> CHW
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# Add batch dimension
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tensor = tensor.unsqueeze(0) # CHW -> BCHW
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return tensor
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except Exception as e:
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print(f"Error converting PIL to tensor: {e}")
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# Return default tensor on error
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return torch.zeros(1, 3, 512, 512)
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def preprocess_image(image: torch.Tensor, target_size: int = 512) -> torch.Tensor:
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"""Preprocess image for Pixel3DMM inference"""
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try:
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# Ensure tensor is in BCHW format
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if image.dim() == 3: # CHW
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image = image.unsqueeze(0) # Add batch dimension
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elif image.dim() == 2: # HW
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image = image.unsqueeze(0).unsqueeze(0) # Add batch and channel dimensions
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# Resize to target size
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if image.shape[-2:] != (target_size, target_size):
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image = F.interpolate(
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image,
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size=(target_size, target_size),
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mode='bilinear',
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align_corners=False
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)
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# Ensure values are in [0, 1] range
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image = torch.clamp(image, 0, 1)
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# Ensure 3 channels (RGB)
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if image.shape[1] == 1: # Grayscale
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image = image.repeat(1, 3, 1, 1)
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elif image.shape[1] == 4: # RGBA
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image = image[:, :3, :, :] # Remove alpha channel
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return image
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except Exception as e:
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print(f"Error preprocessing image: {e}")
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# Return default tensor on error
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return torch.zeros(1, 3, target_size, target_size)
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def postprocess_mesh_data(vertices: torch.Tensor, faces: torch.Tensor) -> dict:
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"""Postprocess mesh data for export"""
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try:
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# Convert to numpy if needed
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if isinstance(vertices, torch.Tensor):
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vertices = vertices.detach().cpu().numpy()
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if isinstance(faces, torch.Tensor):
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faces = faces.detach().cpu().numpy()
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# Ensure correct shapes
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if vertices.ndim == 3: # Batch dimension
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vertices = vertices[0]
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if faces.ndim == 3: # Batch dimension
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faces = faces[0]
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return {
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'vertices': vertices,
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'faces': faces,
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'vertex_count': vertices.shape[0],
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'face_count': faces.shape[0]
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}
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except Exception as e:
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print(f"Error postprocessing mesh data: {e}")
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return {
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'vertices': np.zeros((0, 3)),
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'faces': np.zeros((0, 3)),
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'vertex_count': 0,
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'face_count': 0
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}
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def normalize_tensor(tensor: torch.Tensor, dim: int = -1) -> torch.Tensor:
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"""Normalize tensor along specified dimension"""
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try:
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return F.normalize(tensor, p=2.0, dim=dim)
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except Exception as e:
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print(f"Error normalizing tensor: {e}")
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return tensor
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def safe_load_model(model_path: str, device: str = 'cpu') -> Optional[torch.nn.Module]:
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"""Safely load a PyTorch model"""
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try:
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if not os.path.exists(model_path):
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print(f"Model file not found: {model_path}")
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return None
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# Load model with appropriate device mapping
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model = torch.load(model_path, map_location=device)
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# Handle different model formats
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if isinstance(model, dict):
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# If it's a state dict, we need the model architecture
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print("Warning: Loaded state dict, need model architecture")
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return None
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# Set to evaluation mode
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model.eval()
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return model
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except Exception as e:
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print(f"Error loading model from {model_path}: {e}")
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return None
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def create_default_config() -> dict:
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"""Create default configuration for Pixel3DMM"""
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return {
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'model': {
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'encoder_backbone': 'vit_base_patch14_dinov2.lvd142m',
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'finetune_backbone': False,
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'embedding_dim': 128,
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'flame_dim': 101,
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},
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'image_size': [512, 512],
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'device': 'cpu',
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'batch_size': 1,
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}
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def validate_flame_parameters(params: torch.Tensor) -> bool:
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"""Validate FLAME parameter tensor"""
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try:
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if not isinstance(params, torch.Tensor):
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return False
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# Check shape - should be [batch_size, 101] for full FLAME params
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if params.dim() != 2 or params.shape[-1] != 101:
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return False
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# Check for NaN or infinite values
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if torch.isnan(params).any() or torch.isinf(params).any():
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return False
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return True
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except Exception:
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return False
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def convert_comfyui_image(image: Any) -> torch.Tensor:
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"""Convert ComfyUI image format to standard tensor"""
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try:
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if isinstance(image, torch.Tensor):
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# ComfyUI typically uses BHWC format
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if image.dim() == 4: # BHWC
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return image.permute(0, 3, 1, 2) # BHWC -> BCHW
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elif image.dim() == 3: # HWC
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return image.permute(2, 0, 1).unsqueeze(0) # HWC -> BCHW
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# Handle other formats
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if isinstance(image, np.ndarray):
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tensor = torch.from_numpy(image.astype(np.float32))
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if tensor.dim() == 3: # HWC
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return tensor.permute(2, 0, 1).unsqueeze(0)
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if isinstance(image, Image.Image):
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return pil_to_tensor(image)
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# Default fallback
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return torch.zeros(1, 3, 512, 512)
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except Exception as e:
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print(f"Error converting ComfyUI image: {e}")
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return torch.zeros(1, 3, 512, 512)
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def convert_to_comfyui_image(tensor: torch.Tensor) -> torch.Tensor:
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"""Convert tensor to ComfyUI image format (BHWC)"""
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try:
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if tensor.dim() == 4: # BCHW
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return tensor.permute(0, 2, 3, 1) # BCHW -> BHWC
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elif tensor.dim() == 3: # CHW
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return tensor.permute(1, 2, 0).unsqueeze(0) # CHW -> BHWC
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return tensor
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except Exception as e:
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print(f"Error converting to ComfyUI format: {e}")
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return torch.zeros(1, 512, 512, 3)
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def get_device_info() -> dict:
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"""Get device information for model loading"""
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device_info = {
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'cuda_available': torch.cuda.is_available(),
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'device_count': torch.cuda.device_count() if torch.cuda.is_available() else 0,
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'current_device': 'cuda' if torch.cuda.is_available() else 'cpu'
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}
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if torch.cuda.is_available():
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device_info['gpu_name'] = torch.cuda.get_device_name(0)
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device_info['gpu_memory'] = torch.cuda.get_device_properties(0).total_memory
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return device_info
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