| 10 | |
| 11 | |
| 12 | class CustomGenerator(Generator): |
| 13 | def prepare( |
| 14 | self, |
| 15 | styles, |
| 16 | inject_index=None, |
| 17 | truncation=1, |
| 18 | truncation_latent=None, |
| 19 | input_is_latent=False, |
| 20 | noise=None, |
| 21 | randomize_noise=True, |
| 22 | ): |
| 23 | if not input_is_latent: |
| 24 | styles = [self.style(s) for s in styles] |
| 25 | |
| 26 | if noise is None: |
| 27 | if randomize_noise: |
| 28 | noise = [None] * self.num_layers |
| 29 | else: |
| 30 | noise = [ |
| 31 | getattr(self.noises, f"noise_{i}") for i in range(self.num_layers) |
| 32 | ] |
| 33 | |
| 34 | if truncation < 1: |
| 35 | style_t = [] |
| 36 | |
| 37 | for style in styles: |
| 38 | style_t.append( |
| 39 | truncation_latent + truncation * (style - truncation_latent) |
| 40 | ) |
| 41 | |
| 42 | styles = style_t |
| 43 | |
| 44 | if len(styles) < 2: |
| 45 | inject_index = self.n_latent |
| 46 | |
| 47 | if styles[0].ndim < 3: |
| 48 | latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) |
| 49 | |
| 50 | else: |
| 51 | latent = styles[0] |
| 52 | |
| 53 | else: |
| 54 | if inject_index is None: |
| 55 | inject_index = random.randint(1, self.n_latent - 1) |
| 56 | |
| 57 | latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) |
| 58 | latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1) |
| 59 | |
| 60 | latent = torch.cat([latent, latent2], 1) |
| 61 | |
| 62 | return latent, noise |
| 63 | |
| 64 | def generate( |
| 65 | self, |
| 66 | latent, |
| 67 | noise, |
| 68 | ): |
| 69 | out = self.input(latent) |