---
license: other
base_model: black-forest-labs/FLUX.1-schnell
tags:

- flux

- flux1-schnell

- text-to-image

- diffusers

- tensorrt

- tensorrt-rtx

- nvidia

- ampere

- bf16

---

# Flux.1-schnell TensorRT-RTX BF16 Ampere

TensorRT-RTX optimized engines for Flux.1-schnell on NVIDIA Ampere architecture (RTX 30 series, A100, etc.) with BF16 precision.

## Model Details

- **Base Model**: black-forest-labs/FLUX.1-schnell
- **Architecture**: AMPERE (Compute Capability 8.6)
- **Precision**: BF16 (16-bit brain floating point)
- **TensorRT-RTX Version**: 1.0.0.21
- **Image Resolution**: 1024x1024
- **Batch Size**: 1 (static)

## Engine Files

This repository contains 4 TensorRT engine files:


- `clip.plan` - CLIP text encoder

- `t5.plan` - T5 text encoder

- `transformer.plan` - Flux transformer model

- `vae.plan` - VAE decoder


**Total Size**: 16.7GB

## Hardware Requirements

- NVIDIA RTX 30 series (RTX 3080, 3090) or A100
- Compute Capability 8.6
- Minimum 24GB VRAM recommended
- TensorRT-RTX 1.0.0.21 runtime

## Usage

```python
# 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-schnell license terms. Please refer to the original model repository for licensing details.

## Built With

- [TensorRT-RTX 1.0.0.21](https://developer.nvidia.com/tensorrt)
- [NVIDIA Flux Demo](https://github.com/NVIDIA/TensorRT-RTX/)
- Built on NVIDIA GeForce RTX 3090 (Ampere 8.6)