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trithemius/
flux-fp8-transformers

Name: flux-fp8-transformers
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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

Base Model Loading
- Loads FLUX.1-dev transformer in bfloat16 format
- Source: black-forest-labs/FLUX.1-dev
Quantization
- Applies FP8 quantization using qfloat8_e5m2
- Reduces model size while maintaining performance
LoRA Integration
- Loads Hyper-SD LoRA weights
- Merges with base model using 0.125 scale factor
- Source: ByteDance/Hyper-SD
Model Freezing
- Freezes weights for efficient inference
- Saves as PyTorch model file

Usage

python

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

FLUX.1-dev: black-forest-labs/FLUX.1-dev
Hyper-SD: ByteDance/Hyper-SD

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
---

trithemius/flux-fp8-transformers