Flux.1-Dev-Indo-Realism-LoRA

Maintained By
prithivMLmods

Flux.1-Dev-Indo-Realism-LoRA

PropertyValue
AuthorprithivMLmods
Base Modelblack-forest-labs/FLUX.1-dev
Network Dimensions64
Network Alpha32
Training Images26
Optimal Resolution768 x 1024

What is Flux.1-Dev-Indo-Realism-LoRA?

Flux.1-Dev-Indo-Realism-LoRA is a specialized LoRA model designed to enhance the FLUX.1-dev base model for generating Indo-realistic portraits. Currently in development phase, it's trained on a carefully curated dataset of 26 images using florence2-en labeling for natural language and English processing.

Implementation Details

The model employs advanced training parameters including constant LR scheduling, AdamW optimizer, and specialized noise handling with a 0.03 offset and 0.1 multires noise discount. Training was conducted over 20 epochs with 3000 steps, saving checkpoints every epoch.

  • Optimized for 768x1024 resolution (best performance)
  • Uses bfloat16 precision for efficient processing
  • Implements trigger word "indo-realism" for activation
  • Features sophisticated noise iteration handling (10 iterations)

Core Capabilities

  • Specialized in generating Indo-realistic portraits
  • Supports various shot types (medium-angle, headshot, aerial view)
  • Handles complex lighting and background elements
  • Capable of generating detailed facial features and accessories

Frequently Asked Questions

Q: What makes this model unique?

This model specifically targets Indo-realistic portrait generation with a sophisticated combination of network dimensions (64) and alpha (32), optimized for the FLUX.1-dev base model. It's particularly effective at handling cultural-specific features while maintaining photorealistic quality.

Q: What are the recommended use cases?

The model excels at generating portrait photography with Indo-realistic features, particularly suited for creating headshots, medium-angle shots, and profile pictures. It's especially effective when combined with specific stylistic elements like soft lighting and neutral backgrounds.

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