| import torch | |
| def edit(latents, pca, edit_directions): | |
| edit_latents = [] | |
| for latent in latents: | |
| for pca_idx, start, end, strength in edit_directions: | |
| delta = get_delta(pca, latent, pca_idx, strength) | |
| delta_padded = torch.zeros(latent.shape).to('cuda') | |
| delta_padded[start:end] += delta.repeat(end - start, 1) | |
| edit_latents.append(latent + delta_padded) | |
| return torch.stack(edit_latents) | |
| def get_delta(pca, latent, idx, strength): | |
| w_centered = latent - pca['mean'].to('cuda') | |
| lat_comp = pca['comp'].to('cuda') | |
| lat_std = pca['std'].to('cuda') | |
| w_coord = torch.sum(w_centered[0].reshape(-1)*lat_comp[idx].reshape(-1)) / lat_std[idx] | |
| delta = (strength - w_coord)*lat_comp[idx]*lat_std[idx] | |
| return delta | |