| 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 | |