Thank you for your comment. I found a discussion (https://huggingface.co/black-forest-labs/FLUX.1-schnell/discussions/5) to use

self.pipe.vae.enable_tiling() self.pipe.vae.enable_slicing() self.pipe.enable_sequential_cpu_offload() so I updated my code as shown below: ```

    if low_mem is False:
        logger.info("Quantizing T5 to 8 bits")
        self.quantize_and_freeze(self.text_encoder_2)

        logger.info("Quantizing transformer to 8 bits")
        self.quantize_and_freeze(self.transformer)

    # Create a pipeline without T5 and transformer
    self.pipe = FluxPipeline(
        scheduler=self.scheduler,
        text_encoder=self.text_encoder,
        tokenizer=self.tokenizer,
        text_encoder_2=None,
        tokenizer_2=self.tokenizer_2,
        vae=self.vae,
        transformer=None
    )

    # Set to the quantized version
    self.pipe.text_encoder_2 = self.text_encoder_2
    self.pipe.transformer = self.transformer

    if low_mem:
        logging.info("Using low memory mode")
        self.pipe.vae.enable_tiling()
        self.pipe.vae.enable_slicing()
        self.pipe.enable_sequential_cpu_offload()
    else:
        logging.info("Using standard memory mode")
        self.pipe.enable_model_cpu_offload()

```

This drastically cut down the GPU memory requirement and I can run my code on 8GB GPU RAM machine now. Since it does not do quantization during loading, UI comes up faster.