NVIDIA TensorRT Unlocks FP4 Image Generation for NVIDIA Blackwell GeForce RTX 50 Series GPUs
Table of Contents
- Introduction
- Quantization Process
- Exporting to ONNX
- TensorRT Inference Library
- Accelerated Performance using FP4 from TRT 10.8
- FLUX Image Generation Models
- ControlNet Pipeline
1. Introduction
This section introduces the use of FP4 image generation with NVIDIA Blackwell GeForce RTX 50 Series GPUs. It covers the quantization process and the advancements in leveraging FP4 for image generation.
2. Quantization Process
The quantization process of the FLUX model to FP4 weights using PTQ and QAT techniques from the NVIDIA TensorRT Model Optimizer is discussed in detail. It explains how the model was successfully quantized to FP4 weights for enhanced performance.
3. Exporting to ONNX
The export process of the quantized model to ONNX format is elaborated, along with the utilization of standard ONNX DQ nodes and TensorRT custom operators for FP4 quantization while maintaining numerical stability.
4. TensorRT Inference Library
The section focuses on how the TensorRT Inference Library handles the quantized operators, enabling an end-to-end inference journey with the TensorRT DemoDiffusion. It explains the inference process on the targeted GPU type.
5. Accelerated Performance using FP4 from TRT 10.8
An overview of the accelerated performance using FP4 from TRT 10.8 on the NVIDIA Blackwell GPU is provided, highlighting the benefits and advancements in leveraging FP4 for inference in the FLUX pipeline.
6. FLUX Image Generation Models
This section explores the FLUX suite of image generation models, including the text embeddings, denoiser, and image generation process. It also discusses the memory usage optimization for running the models on RTX GPUs.
7. ControlNet Pipeline
The ControlNet pipeline, with its architecture in the Diffusion Transformer and the incorporation of Control images for denoising, is explained to showcase the use of text and image information in the image generation process.