(a,b,strength=1)
| 61 | |
| 62 | @torch.no_grad() |
| 63 | def normalize_adjust(a,b,strength=1): |
| 64 | c = a.clone() |
| 65 | norm_a = a.norm(dim=1,keepdim=True) |
| 66 | a = a / norm_a |
| 67 | b = b / b.norm(dim=1,keepdim=True) |
| 68 | d = mmnorm((a - b).abs()) |
| 69 | a = a - b * d * strength |
| 70 | a = a * norm_a / a.norm(dim=1,keepdim=True) |
| 71 | if a.isnan().any(): |
| 72 | a[~torch.isfinite(a)] = c[~torch.isfinite(a)] |
| 73 | return a |
| 74 | |
| 75 | def get_ancestral_step_ext(sigma, sigma_next, eta=1.0, is_rf=False): |
| 76 | if sigma_next == 0 or eta == 0: |
no outgoing calls
no test coverage detected