🔹 New Clothing and Accessories Collection
✔ Urban and Futuristic Styles:
Bomber jackets with realistic textures and futuristic details
Oversized shirts with custom patterns and urban graphics
Cargo pants with more technical designs adapted to different body types
✔ Adaptive Cyberpunk and Sci-Fi:
Bodysuits with dynamic light lines and advanced textures
Lightweight armor with synthetic fiber elements and reinforcements
Tactical clothing with vests, belts, and technological equipment
✔ Activewear and Sneakers:
Sneakers with modern designs and realistic textures
Lycra training clothing and athletic attire
Sweatshirts and joggers with dynamic fit for different body types
✔ Asian Fashion and Thematic Elements:
Modernized kimonos with embroidered details
Outfits inspired by Harajuku and Japanese street fashion
Traditional clothing with adaptations Futuristic
✔ Extra Accessories and Customization:
Animal ears and fake horns as fashion elements
Caps, visors, and masks with a cyberpunk aesthetic
Gloves, watches, and bracelets with innovative materials and patterns
🎨 Catalog Expansion and Optimization
🔹 Greater compatibility with different body types, including anthropomorphic bodies
🔹 New outfit combinations with more stylistic variations
🔹 Improved balance in textures and details for greater realism
🛠 Model and Training Improvements
✅ Increased model stability, using 10 epochs and a batch size of 4 to maintain a balance between quality and performance
✅ Better adaptation to fine details, thanks to a Noise Offset of 0.10 that prevents overexposure of highlights and shadows in materials
✅ Greater fidelity in patterns and textures, optimized with an LR Scheduler "cosine_with_restarts" (cycles: 3) for more controlled training
✅ Artifact and noise reduction, with the integration of Min SNR Gamma at 5, improving sharpness in garments with small details
✅ Better differentiation of styles and accessories, with a Network Dim of 32 and Network Alpha of 16, ensuring greater richness in outfit generation
✅ Better interpretation of the relationship between clothing and characters, thanks to training with Clip Skip at 1, ensuring a more accurate reading of the dataset
