import torch import torch.nn as nn import torch.nn.functional as F from typing import Dict, List, Tuple, Optional from enum import Enum, auto class ClothingType(Enum): SHIRT = auto() PANTS = auto() DRESS = auto() SKIRT = auto() JACKET = auto() SHOES = auto() ACCESSORY = auto() class GarmentFeatures(Enum): COLLAR = auto() SLEEVES = auto() BUTTONS = auto() POCKETS = auto() PATTERNS = auto() TEXTURE = auto() class StyleTransferModel(nn.Module): def __init__(self, config: dict = {"style_layers": 5}): super().__init__() self.content_layers = self._build_content_layers() self.style_layers = self._build_style_layers(config["style_layers"]) self.pattern_generator = PatternGenerator() self.texture_mixer = TextureMixer() def _build_content_layers(self): return nn.ModuleList([ nn.Conv2d(64 * (2 ** i), 64 * (2 ** i), 3, padding=1) for i in range(4) ]) def _build_style_layers(self, num_layers): return nn.ModuleList([ nn.Conv2d(64 * (2 ** (i % 3)), 64 * (2 ** (i % 3)), 3, padding=1) for i in range(num_layers) ]) class GarmentAnalyzer(nn.Module): def __init__(self): super().__init__() self.feature_detector = nn.ModuleDict({ feature.name.lower(): self._build_detector() for feature in GarmentFeatures }) def _build_detector(self): return nn.Sequential( nn.Conv2d(128, 64, 3, padding=1), nn.GroupNorm(8, 64), nn.LeakyReLU(0.2), nn.Conv2d(64, 1, 1) ) def detect_features(self, garment_image: torch.Tensor) -> Dict[str, torch.Tensor]: results = {} features = self.shared_backbone(garment_image) for feature_name, detector in self.feature_detector.items(): results[feature_name] = detector(features) return results class AdvancedClothingManipulator(nn.Module): def __init__(self, config: dict = {}): super().__init__() self.garment_analyzer = GarmentAnalyzer() self.style_transfer = StyleTransferModel(config) self.pattern_modifier = PatternModificationModule() self.detail_preserving_blender = DetailPreservingBlender() def transfer_style(self, source_image: torch.Tensor, target_style: Dict[str, torch.Tensor], preserve_features: List[GarmentFeatures] = None) -> torch.Tensor: # Analyze source garment source_features = self.garment_analyzer.detect_features(source_image) # Transfer style while preserving specified features if preserve_features: preservation_masks = self._create_preservation_masks(source_features, preserve_features) return self.detail_preserving_blender( source_image, self.style_transfer(source_image, target_style), preservation_masks ) return self.style_transfer(source_image, target_style) def modify_pattern(self, garment_image: torch.Tensor, pattern_type: str, pattern_params: Dict[str, float]) -> torch.Tensor: return self.pattern_modifier(garment_image, pattern_type, pattern_params) class PatternModificationModule(nn.Module): def __init__(self): super().__init__() self.pattern_encoders = nn.ModuleDict({ 'stripes': self._build_pattern_encoder(), 'dots': self._build_pattern_encoder(), 'floral': self._build_pattern_encoder(), 'geometric': self._build_pattern_encoder() }) self.pattern_generator = nn.Sequential( nn.Conv2d(128, 256, 3, padding=1), nn.GroupNorm(8, 256), nn.LeakyReLU(0.2), nn.Conv2d(256, 3, 3, padding=1), nn.Tanh() ) def _build_pattern_encoder(self): return nn.Sequential( nn.Conv2d(3, 64, 3, padding=1), nn.GroupNorm(8, 64), nn.LeakyReLU(0.2), nn.Conv2d(64, 128, 3, padding=1), nn.GroupNorm(8, 128), nn.LeakyReLU(0.2) ) class DetailPreservingBlender(nn.Module): def __init__(self): super().__init__() self.attention_module = nn.Sequential( nn.Conv2d(6, 32, 3, padding=1), nn.GroupNorm(8, 32), nn.LeakyReLU(0.2), nn.Conv2d(32, 1, 1), nn.Sigmoid() ) def forward(self, source: torch.Tensor, styled: torch.Tensor, preservation_masks: Dict[str, torch.Tensor]) -> torch.Tensor: # Combine source and styled images based on feature preservation masks combined = torch.cat([source, styled], dim=1) attention = self.attention_module(combined) result = styled.clone() for feature, mask in preservation_masks.items(): result = result * (1 - mask) + source * mask return result class TextureMixer(nn.Module): def __init__(self): super().__init__() self.encoder = nn.Sequential( nn.Conv2d(3, 64, 3, padding=1), nn.GroupNorm(8, 64), nn.LeakyReLU(0.2), nn.Conv2d(64, 128, 3, padding=1), nn.GroupNorm(8, 128), nn.LeakyReLU(0.2) ) self.mixer = nn.Sequential( nn.Conv2d(256, 128, 3, padding=1), nn.GroupNorm(8, 128), nn.LeakyReLU(0.2), nn.Conv2d(128, 3, 3, padding=1), nn.Tanh() ) def mix_textures(self, texture1: torch.Tensor, texture2: torch.Tensor, mix_ratio: float = 0.5) -> torch.Tensor: feat1 = self.encoder(texture1) feat2 = self.encoder(texture2) mixed_features = feat1 * mix_ratio + feat2 * (1 - mix_ratio) return self.mixer(torch.cat([feat1, feat2], dim=1)) class PatternGenerator(nn.Module): def __init__(self): super().__init__() self.generator = nn.Sequential( nn.ConvTranspose2d(128, 256, 4, stride=2, padding=1), nn.GroupNorm(8, 256), nn.LeakyReLU(0.2), nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1), nn.GroupNorm(8, 128), nn.LeakyReLU(0.2), nn.Conv2d(128, 3, 3, padding=1), nn.Tanh() ) def generate_pattern(self, pattern_code: torch.Tensor, scale: float = 1.0) -> torch.Tensor: base_pattern = self.generator(pattern_code) return F.interpolate(base_pattern, scale_factor=scale, mode='bilinear', align_corners=True) def example_usage(): # Initialize models manipulator = AdvancedClothingManipulator() # Example: Transfer style while preserving collar and buttons new_garment = manipulator.transfer_style( source_image=source, target_style={"style_image": style_ref, "style_strength": 0.8}, preserve_features=[GarmentFeatures.COLLAR, GarmentFeatures.BUTTONS] ) # Example: Add or modify patterns patterned_garment = manipulator.modify_pattern( garment_image=garment, pattern_type="stripes", pattern_params={ "scale": 0.5, "rotation": 45, "density": 0.8 } )