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
366 lines
14 KiB
Python
366 lines
14 KiB
Python
"""
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FLAME Parameter Optimizer Node for ComfyUI
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Optimizes FLAME parameters using UV and normal constraints
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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 typing import Dict, Any, Tuple, Optional
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from .utils import validate_flame_parameters
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class FLAMEOptimizer:
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"""
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ComfyUI node for optimizing FLAME parameters using geometric constraints
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": ("PIXEL3DMM_MODEL",),
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"image": ("IMAGE",),
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"initial_params": ("FLAME_PARAMS",),
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"optimization_steps": ("INT", {
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"default": 100,
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"min": 10,
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"max": 1000,
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"step": 10
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}),
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},
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"optional": {
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"uv_coordinates": ("UV_COORDS",),
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"surface_normals": ("NORMALS",),
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"learning_rate": ("FLOAT", {
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"default": 0.01,
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"min": 0.001,
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"max": 0.1,
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"step": 0.001
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}),
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"uv_weight": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 10.0,
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"step": 0.1
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}),
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"normal_weight": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 10.0,
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"step": 0.1
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}),
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"regularization_weight": ("FLOAT", {
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"default": 0.01,
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"min": 0.0,
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"max": 1.0,
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"step": 0.001
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}),
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}
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}
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RETURN_TYPES = ("FLAME_PARAMS", "MESH_DATA", "STRING")
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RETURN_NAMES = ("optimized_params", "mesh_data", "status")
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FUNCTION = "optimize_flame"
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CATEGORY = "Pixel3DMM"
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def __init__(self):
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self.optimization_history = []
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def optimize_flame(self, model: Dict[str, Any], image: torch.Tensor,
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initial_params: torch.Tensor, optimization_steps: int = 100,
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uv_coordinates: Optional[torch.Tensor] = None,
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surface_normals: Optional[torch.Tensor] = None,
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learning_rate: float = 0.01, uv_weight: float = 1.0,
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normal_weight: float = 1.0, regularization_weight: float = 0.01) -> Tuple:
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"""
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Optimize FLAME parameters using geometric constraints
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Args:
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model: Loaded Pixel3DMM model container
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image: Input face image
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initial_params: Initial FLAME parameters
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optimization_steps: Number of optimization steps
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uv_coordinates: Optional UV coordinate constraints
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surface_normals: Optional surface normal constraints
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learning_rate: Learning rate for optimization
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uv_weight: Weight for UV loss term
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normal_weight: Weight for normal loss term
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regularization_weight: Weight for regularization term
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Returns:
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Tuple of (optimized_params, mesh_data, status)
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"""
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try:
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if model is None:
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return None, None, "❌ No model loaded. Please connect a Pixel3DMM Loader node."
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if not validate_flame_parameters(initial_params):
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return None, None, "❌ Invalid initial FLAME parameters"
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device = model['device']
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# Move inputs to device
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initial_params = initial_params.to(device)
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if uv_coordinates is not None:
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uv_coordinates = uv_coordinates.to(device)
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if surface_normals is not None:
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surface_normals = surface_normals.to(device)
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# Initialize optimization
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optimized_params = self._initialize_optimization(initial_params)
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# Get FLAME model
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flame_model = model['components'].get('flame')
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if flame_model is None:
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return None, None, "❌ FLAME model not found in model container"
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# Run optimization
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optimized_params, optimization_info = self._run_optimization(
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flame_model, optimized_params, uv_coordinates, surface_normals,
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optimization_steps, learning_rate, uv_weight, normal_weight, regularization_weight
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)
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# Generate final mesh
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mesh_data = self._generate_final_mesh(flame_model, optimized_params)
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status = f"✅ FLAME optimization completed\n"
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status += f"🔄 Steps: {optimization_steps}\n"
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status += f"📈 Learning rate: {learning_rate}\n"
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status += f"📊 Final loss: {optimization_info['final_loss']:.6f}\n"
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status += f"🎯 UV weight: {uv_weight}, Normal weight: {normal_weight}\n"
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status += f"⚖️ Regularization: {regularization_weight}"
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return optimized_params, mesh_data, status
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except Exception as e:
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error_msg = f"❌ Error in FLAME optimization: {str(e)}"
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print(error_msg)
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return None, None, error_msg
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def _initialize_optimization(self, initial_params: torch.Tensor) -> torch.Tensor:
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"""Initialize parameters for optimization"""
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try:
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# Clone parameters and enable gradients
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optimized_params = initial_params.clone().detach().requires_grad_(True)
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return optimized_params
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except Exception as e:
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print(f"Error initializing optimization: {e}")
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return initial_params
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def _run_optimization(self, flame_model: torch.nn.Module, params: torch.Tensor,
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uv_coords: Optional[torch.Tensor], normals: Optional[torch.Tensor],
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steps: int, lr: float, uv_weight: float, normal_weight: float,
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reg_weight: float) -> Tuple[torch.Tensor, Dict[str, Any]]:
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"""Run the optimization loop"""
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try:
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# Initialize optimizer
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optimizer = torch.optim.Adam([params], lr=lr)
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# Track optimization progress
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losses = []
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for step in range(steps):
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optimizer.zero_grad()
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# Forward pass through FLAME
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mesh_output = flame_model(params)
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# Compute losses
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total_loss = 0.0
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loss_components = {}
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# UV loss
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if uv_coords is not None and uv_weight > 0:
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uv_loss = self._compute_uv_loss(mesh_output, uv_coords)
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total_loss += uv_weight * uv_loss
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loss_components['uv_loss'] = uv_loss.item()
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# Normal loss
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if normals is not None and normal_weight > 0:
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normal_loss = self._compute_normal_loss(mesh_output, normals)
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total_loss += normal_weight * normal_loss
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loss_components['normal_loss'] = normal_loss.item()
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# Regularization loss
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if reg_weight > 0:
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reg_loss = self._compute_regularization_loss(params)
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total_loss += reg_weight * reg_loss
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loss_components['reg_loss'] = reg_loss.item()
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# Backward pass
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total_loss.backward()
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optimizer.step()
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# Track progress
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loss_components['total_loss'] = total_loss.item()
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losses.append(loss_components)
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# Print progress
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if step % 20 == 0:
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print(f"Step {step}/{steps}: Loss = {total_loss.item():.6f}")
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optimization_info = {
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'final_loss': losses[-1]['total_loss'] if losses else 0.0,
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'loss_history': losses
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}
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return params.detach(), optimization_info
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except Exception as e:
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print(f"Error in optimization loop: {e}")
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return params.detach(), {'final_loss': float('inf'), 'loss_history': []}
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def _compute_uv_loss(self, mesh_output: Dict[str, torch.Tensor],
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target_uv: torch.Tensor) -> torch.Tensor:
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"""Compute UV coordinate loss"""
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try:
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# Get vertices from mesh output
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vertices = mesh_output['vertices'] # B x N x 3
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# Project vertices to image space (simplified)
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# In real implementation, would use proper camera projection
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projected_vertices = vertices[:, :, :2] # Use X,Y coordinates
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# Convert to UV space [0,1]
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projected_uv = (projected_vertices + 1.0) / 2.0
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# Sample target UV at vertex locations (simplified)
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# This is a placeholder - real implementation would use proper UV sampling
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batch_size = target_uv.shape[0]
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height, width = target_uv.shape[-2:]
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# Create sampling grid
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grid_x = torch.linspace(-1, 1, width, device=target_uv.device)
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grid_y = torch.linspace(-1, 1, height, device=target_uv.device)
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grid_y, grid_x = torch.meshgrid(grid_y, grid_x, indexing='ij')
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grid = torch.stack([grid_x, grid_y], dim=-1).unsqueeze(0).repeat(batch_size, 1, 1, 1)
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# Sample UV coordinates
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sampled_uv = F.grid_sample(target_uv, grid, align_corners=False)
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# Compute loss (simplified)
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uv_loss = F.mse_loss(projected_uv.mean(dim=1), sampled_uv.mean(dim=[2, 3]))
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return uv_loss
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except Exception as e:
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print(f"Error computing UV loss: {e}")
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return torch.tensor(0.0, device=mesh_output['vertices'].device)
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def _compute_normal_loss(self, mesh_output: Dict[str, torch.Tensor],
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target_normals: torch.Tensor) -> torch.Tensor:
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"""Compute surface normal loss"""
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try:
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# Get vertices and faces
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vertices = mesh_output['vertices'] # B x N x 3
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faces = mesh_output['faces'] # F x 3
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# Compute face normals (simplified)
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face_normals = self._compute_face_normals(vertices, faces)
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# Project normals to image space and compare with target
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# This is a simplified implementation
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# Average face normals as a simple loss
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predicted_normal = face_normals.mean(dim=1) # B x 3
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target_normal = target_normals.mean(dim=[2, 3]) # B x 3
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normal_loss = F.mse_loss(predicted_normal, target_normal)
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return normal_loss
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except Exception as e:
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print(f"Error computing normal loss: {e}")
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return torch.tensor(0.0, device=mesh_output['vertices'].device)
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def _compute_face_normals(self, vertices: torch.Tensor, faces: torch.Tensor) -> torch.Tensor:
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"""Compute face normals from vertices and faces"""
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try:
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batch_size = vertices.shape[0]
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num_faces = faces.shape[0]
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# Get face vertices
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face_vertices = vertices[:, faces] # B x F x 3 x 3
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# Compute edge vectors
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v1 = face_vertices[:, :, 1] - face_vertices[:, :, 0] # B x F x 3
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v2 = face_vertices[:, :, 2] - face_vertices[:, :, 0] # B x F x 3
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# Compute cross product for normals
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normals = torch.cross(v1, v2, dim=-1) # B x F x 3
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# Normalize
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normals = F.normalize(normals, p=2.0, dim=-1)
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return normals
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except Exception as e:
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print(f"Error computing face normals: {e}")
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batch_size = vertices.shape[0]
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return torch.zeros(batch_size, faces.shape[0], 3, device=vertices.device)
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def _compute_regularization_loss(self, params: torch.Tensor) -> torch.Tensor:
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"""Compute regularization loss to keep parameters reasonable"""
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try:
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# L2 regularization on parameters
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reg_loss = torch.mean(params ** 2)
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return reg_loss
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except Exception as e:
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print(f"Error computing regularization loss: {e}")
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return torch.tensor(0.0, device=params.device)
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def _generate_final_mesh(self, flame_model: torch.nn.Module,
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params: torch.Tensor) -> Optional[Dict[str, Any]]:
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"""Generate final mesh from optimized parameters"""
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try:
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flame_model.eval()
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with torch.no_grad():
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mesh_output = flame_model(params)
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# Convert to numpy for export
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mesh_data = {
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'vertices': mesh_output['vertices'].cpu().numpy(),
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'faces': mesh_output['faces'].cpu().numpy(),
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'vertex_count': mesh_output['vertices'].shape[1],
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'face_count': mesh_output['faces'].shape[0],
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'flame_params': params.cpu().numpy(),
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}
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if 'landmarks' in mesh_output:
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mesh_data['landmarks'] = mesh_output['landmarks'].cpu().numpy()
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return mesh_data
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except Exception as e:
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print(f"Error generating final mesh: {e}")
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return None
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@classmethod
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def VALIDATE_INPUTS(cls, **kwargs):
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"""Validate node inputs"""
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model = kwargs.get('model')
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if model is None:
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return "Model input is required"
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image = kwargs.get('image')
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if image is None:
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return "Image input is required"
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initial_params = kwargs.get('initial_params')
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if initial_params is None:
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return "Initial FLAME parameters are required"
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optimization_steps = kwargs.get('optimization_steps', 100)
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if optimization_steps < 10 or optimization_steps > 1000:
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return "Optimization steps must be between 10 and 1000"
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learning_rate = kwargs.get('learning_rate', 0.01)
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if learning_rate < 0.001 or learning_rate > 0.1:
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return "Learning rate must be between 0.001 and 0.1"
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return True
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