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import torch
def inverse_classifier_guidance(
noise_pred_cond: torch.Tensor,
noise_pred_uncond: torch.Tensor,
guidance_scale: torch.Tensor
):
"""
Adjust the noise_pred_cond for the classifier free guidance algorithm
to ensure that the final noise prediction equals the original noise_pred_cond.
"""
# To make noise_pred equal noise_pred_cond_orig, we adjust noise_pred_cond
# based on the formula used in the algorithm.
# We derive the formula to find the correct adjustment for noise_pred_cond:
# noise_pred_cond = (noise_pred_cond_orig - noise_pred_uncond * guidance_scale) / (guidance_scale - 1)
# It's important to check if guidance_scale is not 1 to avoid division by zero.
if guidance_scale == 1:
# If guidance_scale is 1, adjusting is not needed or possible in the same way,
# since it would lead to division by zero. This also means the algorithm inherently
# doesn't alter the noise_pred_cond in relation to noise_pred_uncond.
# Thus, we return the original values, though this situation might need special handling.
return noise_pred_cond
adjusted_noise_pred_cond = (noise_pred_cond - noise_pred_uncond) / guidance_scale
return adjusted_noise_pred_cond
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