briacustom.py

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
        }
    )