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Flux.dev quantized versions

Quantized FLUX Transformer with Hyper-SD LoRA

This repository contains quantized FLUX transformer model merged with Hyper-SD a,d Alimama LoRA weights, optimized for efficient inference.

Model Details

  • Base Model: FLUX.1-dev transformer from Black Forest Labs
  • LoRA: Hyper-SD from ByteDance and Alimama
  • Quantization: FP8 (e5m2 format)
  • LoRA Scale: 0.125

Technical Specifications

Quantization

  • The model uses 8-bit floating-point (FP8) quantization with e5m2 format
  • Implemented using the optimum.quanto library
  • Weights are frozen after quantization for inference

Architecture

  • Based on FluxTransformer2DModel
  • Includes merged LoRA weights from Hyper-SD
  • Optimized for 8-step inference

Model Creation Process

  1. Base Model Loading

    • Loads FLUX.1-dev transformer in bfloat16 format
    • Source: black-forest-labs/FLUX.1-dev
  2. Quantization

    • Applies FP8 quantization using qfloat8_e5m2
    • Reduces model size while maintaining performance
  3. LoRA Integration

    • Loads Hyper-SD LoRA weights
    • Merges with base model using 0.125 scale factor
    • Source: ByteDance/Hyper-SD
  4. Model Freezing

    • Freezes weights for efficient inference
    • Saves as PyTorch model file

Usage

import torch

# Load the model
model = torch.load('flux-fp8-hyper8-transformers-lora.pt')

# Model is ready for inference
# Use with appropriate input formatting and processing

Requirements

  • PyTorch
  • optimum.quanto
  • diffusers
  • huggingface_hub
  • safetensors

References

License

Please refer to the original FLUX.1-dev and Hyper-SD licenses for usage terms and conditions.

Acknowledgments

  • Black Forest Labs for the base FluxTransformer2DModel.
  • ByteDance for the LoRA weights.
  • The developers of the optimum.quanto and safetensors libraries for their tools.


---
license: other
license_name: flux-dev
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
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