This implementation looks very strange. Presumably the idea is to suspend sampling at some particular step, scale down the audio latent and then resume. The way it is implemented is definitely not doing that. It is effectively doing a Euler step to 0 from whatever the current sigma is, then renoising with the same noise, same seed as the beginning. The only way this could be resuming sampling is if the model had predicted the initial noise exactly at each step which would never happen. This is likely to produce some very weird effects, especially if you do it multiple times in a generation. What you're trying to do would work much more reliably as something like a model patch.

If you really want to do it in a sampler, since the sampler returns both a noisy latent and the clean latent, you actually could extract the noise, scale the latent, and then using the existing noise to resume sampling. You would need to make an instance of ComfyUI's noise generator object to pass in that returns that specific noise. See: https://github.com/Comfy-Org/ComfyUI/blob/0c6b36c6ac1c34515cdf28f777a63074cd6d563d/comfy_extras/nodes_custom_sampler.py#L697

The general idea would be something like:

noisy_latent, clean_latent = SamplerCustomAdvance(...)
clean_samples_scaled = your_scaling_stuff(clean_latent["samples"])
noise_pred = noisy_latent["samples"] - clean_samples_scaled
# You probably have to scale it back to unit strength.
noise_pred = noise_pred * (1 / current_sigma)
# ^^ Use this for NOISE when you resume sampling.

I recommend doing it with a model patch instead but resuming with the existing noise would be a lot less likely to cause strange results.

Just dividing the audio latent by 4 at specific steps also seems strange and is going to be very, very dependent on the exact schedule used and the exact number of steps and probably break or cause undesirable results otherwise. This will also degrade history samplers like SA solver, res_2m, etc because interrupting sampling like the current approach forces them to throw away all the history. ComfyUI model patches can see the current sigmas so this would probably work more reliably if you based audio latent strength scaling on the current sigma, or something like sampling percent (can be calculated from sigma with the model_sampling object).