paragekbote/flux-fast-lora-hotswap-img2img

An optimized Flux.1-dev Img2Img setup delivering blazing-fast inference, memory efficiency and dynamic LoRA hotswapping.

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Examples

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Run time and cost

This model runs on Nvidia L40S GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

FLUX.1-dev LoRA Hotswap (Img2Img)

This setup uses torch.compile, BitsAndBytes, and PEFT LoRA to package the Flux.1 [dev] model, enabling fast image-to-image generation with LoRA adapter hotswapping via trigger words, along with quantization for efficiency.

Features

  • Optimized Performance: torch.compile acceleration for faster inference.
  • Dynamic LoRA Switching: Instantly swap between multiple styles using trigger words.
  • Memory Efficient: BitsAndBytes quantization reduces VRAM usage.
  • Multi-Style Transformations: Two LoRAs preloaded for immediate style transfer.

Available Styles

Enhanced Image Preferences

  • Trigger: ["Cinematic", "Photographic", "Anime", "Manga", "Digital art", "Pixel art", "Fantasy art", "Neonpunk", "3D Model", "Painting", "Animation", "Illustration"]
  • LoRA: data-is-better-together/open-image-preferences-v1-flux-dev-lora
  • Description: Applies refined stylistic preferences learned from curated data to your input image.

Ghibsky Illustration

Model Details

  • Base Model: black-forest-labs/FLUX.1-dev
  • Optimization: PyTorch 2.0 compilation + BitsAndBytes quantization with LoRA hot-swapping.
  • Memory Usage: Reduced significantly via quantization.

Performance

  • Speed: Up to 2× faster processing with torch.compile.
  • Memory: ~40% VRAM savings from quantization.
  • Quality: Preserves FLUX.1-dev fidelity while restyling inputs.
  • Flexibility: Supports on-the-fly LoRA switching without reloading.

Usage Tips

  • Provide a clear input image (photo, sketch, concept art, or render).
  • Add trigger words alongside your descriptive prompt to activate LoRA styles.
  • Control the balance between input preservation and transformation using the strength parameter (0.2–0.8).
  • Works optimally at native FLUX.1-dev resolutions (1024×1024).
  • Quantization enables efficient inference on consumer GPUs.

Note: Some GPU usage may persist after generation. Since LoRA hot-swapping is still evolving, occasional errors may occur. Please report issues if encountered.

Use Cases

  • Transforming sketches or line art into polished illustrations.
  • Stylizing photos into anime, cinematic, or fantasy aesthetics.
  • Rapid experimentation with hybrid aesthetics by combining trigger words.
  • Visual prototyping across multiple art styles without retraining.