| #!/usr/bin/env python3 | |
| import torch | |
| from diffusers import DiffusionPipeline | |
| class UnetSchedulerOneForwardPipeline(DiffusionPipeline): | |
| def __init__(self, unet, scheduler): | |
| super().__init__() | |
| self.register_modules(unet=unet, scheduler=scheduler) | |
| def __call__(self): | |
| image = torch.randn( | |
| (1, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size), | |
| ) | |
| timestep = 1 | |
| model_output = self.unet(image, timestep).sample | |
| scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample | |
| return scheduler_output | |