fn)
swap_conv2d_1x1_to_linear(pipe.vae, conv_filter_fn) Apply dynamic quantization:   Copied from torchao import apply_dynamic_quant

apply_dynamic_quant(pipe.unet, dynamic_quant_filter_fn)
apply_dynamic_quant(pipe.vae, dynamic_quant_filter_fn) Finally, compile and perform inference:   Copied pipe.unet = torch.compile(pipe.unet, mode="max-autotune", fullgraph=True)
pipe.vae.decode = torch.compile(pipe.vae.decode, mode="max-autotune", fullgraph=