# ImageAI server downloads automatically
curl -X POST "http://localhost:8001/generate" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "a beautiful landscape",
    "model": "flux1-tensorrt_rtx:schnell",
    "width": 1024,
    "height": 1024
  }'

Manual Download

python

from huggingface_hub import snapshot_download

# Download this specific variant only
engines_path = snapshot_download(
    repo_id="imgailab/flux1-trtx-schnell-fp4-blackwell"
)

# Engines are in: engines_path/engines/*.plan

Direct Integration

python

from imageai_server.tensorrt.nvidia_sdxl_pipeline import NVIDIASDXLPipeline

pipeline = NVIDIASDXLPipeline()
pipeline.load_engines(
    engine_dir=f"{engines_path}/engines",
    framework_model_dir=f"{engines_path}/framework",  
    onnx_dir=f"{engines_path}/onnx"
)
pipeline.activate_engines()

images, time_ms = pipeline.infer(
    prompt="a serene mountain landscape",
    height=1024,
    width=1024
)

📊 Performance

Metric	Value
Memory Usage	TBD
Inference Speed	TBD
Resolution	1024x1024 (optimized)
Batch Size	1 (optimized)
Precision	SCHNELL

🔧 Requirements

Hardware

GPU: Fp4 architecture
- Ampere: RTX 3090, A100, etc.
- Ada Lovelace: RTX 4090, etc.
- Blackwell: H200, etc.
VRAM: TBD minimum
Compute Capability: 8.0+

Software

TensorRT-RTX: 1.0.0.21+
CUDA: 12.0+
Python: 3.8+

📁 Repository Structure

flux1-trtx-schnell-fp4-blackwell/
├── engines/           # TensorRT engine files
│   ├── *.plan        # Optimized engines
├── config.json       # Configuration metadata
└── README.md         # This file

🌐 Related Repositories

Other variants for FLUX1:

Ampere BF16\n- Ada FP8\n- Ada BF16\n- Blackwell FP4\n- Blackwell FP8\n- Blackwell BF16\n

📝 License

Inherits license from base model: black-forest-labs/FLUX.1-schnell

🔄 Updates

2025-08-12: Initial release
Optimized for single-variant downloads

Part of the ImageAI TensorRT-RTX engine collection