Compiling Diffusion Video Models with TensorRT-LLM & FP8 Quantization for Sub-60s Renders

Deploying diffusion models for generative video synthesis without quantization requires excessive GPU high-bandwidth memory (HBM), driving cloud compute bills to unsustainable heights.

Compiling TensorRT Engines

Use NVIDIA TensorRT to build optimized engine plans from PyTorch checkpoints:

import tensorrt as trt
def build_diffusion_engine(onnx_file_path: str, engine_file_path: str):
    logger = trt.Logger(trt.Logger.WARNING)
    builder = trt.Builder(logger)
    config = builder.create_builder_config()
    config.set_flag(trt.BuilderFlag.FP16)
    config.set_flag(trt.BuilderFlag.BF16)
    
    # Enable FP8 quantization for cross-frame attention
    config.set_flag(trt.BuilderFlag.FP8)
    
    with open(engine_file_path, "wb") as f:
        f.write(builder.build_serialized_network(network, config))

Recommended Video Platform

Precision Bounds: Managing Visual Artifacts in Quantized Diffusion

Hardware acceleration and quantization are mandatory optimizations for scaling generative video pipelines in production. When quantizing temporal attention weights, keep the first and last UNet residual blocks in FP16 precision to avoid high-frequency color flicker across generated frame transitions.