(ref_image=None, sample_size=None, padding=False)
| 319 | return input_video, input_video_mask, ref_image, clip_image |
| 320 | |
| 321 | def get_image_latent(ref_image=None, sample_size=None, padding=False): |
| 322 | if ref_image is not None: |
| 323 | if isinstance(ref_image, str): |
| 324 | ref_image = Image.open(ref_image).convert("RGB") |
| 325 | if padding: |
| 326 | ref_image = padding_image(ref_image, sample_size[1], sample_size[0]) |
| 327 | ref_image = ref_image.resize((sample_size[1], sample_size[0])) |
| 328 | ref_image = torch.from_numpy(np.array(ref_image)) |
| 329 | ref_image = ref_image.unsqueeze(0).permute([3, 0, 1, 2]).unsqueeze(0) / 255 |
| 330 | elif isinstance(ref_image, Image.Image): |
| 331 | ref_image = ref_image.convert("RGB") |
| 332 | if padding: |
| 333 | ref_image = padding_image(ref_image, sample_size[1], sample_size[0]) |
| 334 | ref_image = ref_image.resize((sample_size[1], sample_size[0])) |
| 335 | ref_image = torch.from_numpy(np.array(ref_image)) |
| 336 | ref_image = ref_image.unsqueeze(0).permute([3, 0, 1, 2]).unsqueeze(0) / 255 |
| 337 | else: |
| 338 | ref_image = torch.from_numpy(np.array(ref_image)) |
| 339 | ref_image = ref_image.unsqueeze(0).permute([3, 0, 1, 2]).unsqueeze(0) / 255 |
| 340 | |
| 341 | return ref_image |
| 342 | |
| 343 | def get_image(ref_image=None): |
| 344 | if ref_image is not None: |
nothing calls this directly
no test coverage detected