(transl)
| 28 | |
| 29 | |
| 30 | def get_transl(transl): |
| 31 | x_beg, y_beg = 0, 0 |
| 32 | x_ed, y_ed = transl |
| 33 | |
| 34 | x_beg = -0.5 * x_ed |
| 35 | y_beg = -0.5 * y_ed |
| 36 | |
| 37 | x_ed = 0.5 * x_ed |
| 38 | y_ed = 0.5 * y_ed |
| 39 | |
| 40 | x_mean, x_std, y_mean, y_std = mean[0], std[0], mean[1], std[1] |
| 41 | |
| 42 | x_beg = (x_beg - x_mean) / x_std |
| 43 | y_beg = (y_beg - y_mean) / y_std |
| 44 | |
| 45 | x_ed = (x_ed - x_mean) / x_std if x_ed is not None else None |
| 46 | y_ed = (y_ed - y_mean) / y_std if y_ed is not None else None |
| 47 | |
| 48 | trans_req = np.stack([x_beg, y_beg, x_ed, y_ed], -1) |
| 49 | trans_req = torch.from_numpy(trans_req).cuda().float() |
| 50 | |
| 51 | return trans_req |
| 52 | |
| 53 | def infer_motion_diffusion(text, pre_seq, transl, motion_length): |
| 54 | print('start diffusion!') |
no outgoing calls
no test coverage detected