# PyTorch INT8 quantized model
from diffusers import FluxPipeline
import torch

# Load INT8 quantized model
pipe = FluxPipeline.from_pretrained(
    "Mitchins/flux1-torch-int8",
    torch_dtype=torch.qint8,
    use_safetensors=True
)

# For CPU inference
pipe = pipe.to("cpu")

# Generate image
image = pipe("A beautiful landscape", num_inference_steps=20).images[0]
image.save("output.png")

Performance

Backend	Quantization	Memory	Speed (CPU)	Speed (GPU)	Quality
PyTorch	INT8	~12.0GB	Good	Fast	Slightly Reduced

Limitations

INT8 quantization may slightly reduce image quality
Best suited for CPU inference or memory-constrained environments

Citation

bibtex

@misc{flux1-pytorch-int8,
  title = {FLUX.1 Schnell PyTorch INT8}
  author = {ImageAI Server Contributors}
  year = {2024}
  publisher = {HuggingFace}
  url = {https://huggingface.co/Mitchins/flux1-torch-int8}
}

Converted using ImageAI Server Model Converter v1.0

imgailab/flux1-torch-int8

FLUX.1 Schnell - PyTorch INT8

Model Details

Usage

PyTorch INT8

Performance

Limitations

Citation

imgailab/
flux1-torch-int8