# Example usage with TensorRT-RTX backend
from nvidia_demos.TensorRT_RTX.demo.flux1_dev.pipelines.flux_pipeline import FluxPipeline

pipeline = FluxPipeline(
    cache_dir="./cache",
    hf_token="your_hf_token"
)

# Load pre-built engines
pipeline.load_engines(
    transformer_precision="bf16",
    opt_batch_size=1,
    opt_height=1024,
    opt_width=1024
)

# Generate image
image = pipeline.infer(
    prompt="A beautiful landscape with mountains",
    height=1024,
    width=1024
)

Performance

Inference Speed: ~8-12 seconds per image (RTX 3090)
Memory Usage: ~18-20GB VRAM
Optimizations: Static shapes, BF16 precision, Ampere-specific kernels

License

This model follows the Flux.1-dev license terms. Please refer to the original model repository for licensing details.

Built With

TensorRT-RTX 1.0.0.21
NVIDIA Flux Demo
Built on NVIDIA GeForce RTX 3090 (Ampere 8.6)

imgailab/flux1-dev-bf16-ampere

Flux.1-dev TensorRT-RTX BF16 Ampere

Model Details

Engine Files

Hardware Requirements

Usage

Performance

License

Built With

imgailab/
flux1-dev-bf16-ampere