(input_image, sample_steps, sample_seed)
| 132 | |
| 133 | |
| 134 | def generate_mvs(input_image, sample_steps, sample_seed): |
| 135 | |
| 136 | seed_everything(sample_seed) |
| 137 | |
| 138 | # sampling |
| 139 | generator = torch.Generator(device=device0) |
| 140 | z123_image = pipeline( |
| 141 | input_image, |
| 142 | num_inference_steps=sample_steps, |
| 143 | generator=generator, |
| 144 | ).images[0] |
| 145 | |
| 146 | show_image = np.asarray(z123_image, dtype=np.uint8) |
| 147 | show_image = torch.from_numpy(show_image) # (960, 640, 3) |
| 148 | show_image = rearrange(show_image, '(n h) (m w) c -> (n m) h w c', n=3, m=2) |
| 149 | show_image = rearrange(show_image, '(n m) h w c -> (n h) (m w) c', n=2, m=3) |
| 150 | show_image = Image.fromarray(show_image.numpy()) |
| 151 | |
| 152 | return z123_image, show_image |
| 153 | |
| 154 | |
| 155 | def make_mesh(mesh_fpath, planes): |
nothing calls this directly
no outgoing calls
no test coverage detected