Why does Classifer-Free Guidance (CFG) add guidances to a negative-prompts-conditional distribution instead of an unconditional distribution?

I have trouble understanding the following lines of code from the file /src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L692-L694

if do_classifier_free_guidance:
    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

I get the part that when we sample without negative prompts, noise_pred_uncond as the name suggested is an unconditional distribution, and the conditional noise difference (noise_pred_text - noise_pred_uncond) prodivdes “guidance” for the sampling process based on positive prompts.

However when we sample with negative prompts, noise_pred_uncond becomes a conditional distribution of the negative prompts according to the implementation:

if do_classifier_free_guidance and negative_prompt_embeds is None:
    uncond_tokens: List[str]
    if negative_prompt is None:
        uncond_tokens = [""] * batch_size
    elif type(prompt) is not type(negative_prompt):
        raise TypeError(
            f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
            f" {type(prompt)}."
    elif isinstance(negative_prompt, str):
        uncond_tokens = [negative_prompt]
    elif batch_size != len(negative_prompt):
        raise ValueError(
            f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
            f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
            " the batch size of `prompt`."
        uncond_tokens = negative_prompt

I don’t really get the part where you add the guidance to a negative-prompts-conditional distribution. Why don’t add the guidance between +ve and -ve to an unconditional instead? Shouldn’t we worry that the final image will possess features that described by the negative prompts?