🔹 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