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| from typing import List, Optional, Tuple, Union | |
| import torch | |
| from diffusers.schedulers import DDIMScheduler | |
| from diffusers.utils.torch_utils import randn_tensor | |
| from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput | |
| class DDIMPipelineCustom(DiffusionPipeline): | |
| model_cpu_offload_seq = "unet" | |
| def __init__(self, unet, scheduler): | |
| super().__init__() | |
| # make sure scheduler can always be converted to DDIM | |
| scheduler = DDIMScheduler.from_config(scheduler.config) | |
| self.register_modules(unet=unet, scheduler=scheduler) | |
| def __call__( | |
| self, | |
| condition = None, | |
| guidance: float = 1, | |
| batch_size: int = 1, | |
| generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, | |
| eta: float = 0.0, | |
| num_inference_steps: int = 50, | |
| use_clipped_model_output: Optional[bool] = None, | |
| output_type: Optional[str] = "pil", | |
| return_dict: bool = True, | |
| ) -> Union[ImagePipelineOutput, Tuple]: | |
| # Sample gaussian noise to begin loop | |
| if isinstance(self.unet.config.sample_size, int): | |
| image_shape = ( | |
| batch_size, | |
| self.unet.config.in_channels, | |
| self.unet.config.sample_size, | |
| self.unet.config.sample_size, | |
| ) | |
| else: | |
| image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size) | |
| if isinstance(generator, list) and len(generator) != batch_size: | |
| raise ValueError( | |
| f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" | |
| f" size of {batch_size}. Make sure the batch size matches the length of the generators." | |
| ) | |
| image = randn_tensor(image_shape, generator=generator, device=self._execution_device, dtype=self.unet.dtype) | |
| # set step values | |
| self.scheduler.set_timesteps(num_inference_steps) | |
| for t in self.progress_bar(self.scheduler.timesteps): | |
| # 1. predict noise model_output | |
| uncond = -torch.ones(batch_size, device=self.device) | |
| if condition is not None: | |
| model_output_uncond = self.unet(image, t, uncond).sample | |
| model_output_cond = self.unet(image, t, condition).sample | |
| model_output = torch.lerp(model_output_uncond, model_output_cond, guidance) | |
| else: | |
| model_output = self.unet(image, t, uncond).sample | |
| # 2. predict previous mean of image x_t-1 and add variance depending on eta | |
| # eta corresponds to η in paper and should be between [0, 1] | |
| # do x_t -> x_t-1 | |
| image = self.scheduler.step( | |
| model_output, t, image, eta=eta, use_clipped_model_output=use_clipped_model_output, generator=generator | |
| ).prev_sample | |
| image = (image / 2 + 0.5).clamp(0, 1) | |
| image = image.cpu().permute(0, 2, 3, 1).numpy() | |
| if output_type == "pil": | |
| image = self.numpy_to_pil(image) | |
| if not return_dict: | |
| return (image,) | |
| return ImagePipelineOutput(images=image) |