flux1-trtx-dev-bf16-ada

FLUX1 TensorRT-RTX: DEV-Bf16 🔨 Building

Optimized TensorRT-RTX engines for FLUX1 on Bf16 architecture with DEV quantization.

🎯 This Repository

One variant, one download - only get exactly what you need!

• Model: FLUX1
• Architecture: Bf16 (Compute Capability 8.0+)
• Quantization: DEV
• Memory: TBD
• Speed: TBD for 1024x1024 generation

🚀 Quick Start

Automatic (Recommended)

# 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:dev",
    "width": 1024,
    "height": 1024
  }'

Manual Download

from huggingface_hub import snapshot_download

# Download this specific variant only
engines_path = snapshot_download(
    repo_id="imgailab/flux1-trtx-dev-bf16-ada"
)

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

Direct Integration

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

🔧 Requirements

Hardware

• GPU: Bf16 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-dev-bf16-ada/
├── 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-dev

🔄 Updates

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

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Part of the ImageAI TensorRT-RTX engine collection