(self, *args, **kwargs)
| 1734 | |
| 1735 | @torch.no_grad() |
| 1736 | def log_images(self, *args, **kwargs): |
| 1737 | log = super().log_images(*args, **kwargs) |
| 1738 | depth = self.depth_model(args[0][self.depth_stage_key]) |
| 1739 | depth_min, depth_max = torch.amin(depth, dim=[1, 2, 3], keepdim=True), \ |
| 1740 | torch.amax(depth, dim=[1, 2, 3], keepdim=True) |
| 1741 | log["depth"] = 2. * (depth - depth_min) / (depth_max - depth_min) - 1. |
| 1742 | return log |
| 1743 | |
| 1744 | |
| 1745 | class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion): |
nothing calls this directly
no test coverage detected