Delete custom_pipeline/sde_ve_pipeline.py

custom_pipeline/sde_ve_pipeline.py

@@ -1,87 +0,0 @@
-from diffusers.utils.torch_utils import randn_tensor
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-class ScoreSdeVePipeline(DiffusionPipeline):
-    r"""
-    Pipeline for unconditional image generation.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Parameters:
-        unet ([`UNet2DModel`]):
-            A `UNet2DModel` to denoise the encoded image.
-        scheduler ([`ScoreSdeVeScheduler`]):
-            A `ScoreSdeVeScheduler` to be used in combination with `unet` to denoise the encoded image.
-    """
-
-    unet
-    scheduler
-
-    def __init__(self, unet, scheduler):
-        super().__init__()
-        self.register_modules(unet=unet, scheduler=scheduler)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        batch_size: int = 1,
-        num_inference_steps: int = 2000,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        **kwargs,
-    ) -> Union[ImagePipelineOutput, Tuple]:
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            batch_size (`int`, *optional*, defaults to 1):
-                The number of images to generate.
-            generator (`torch.Generator`, `optional`):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            output_type (`str`, `optional`, defaults to `"pil"`):
-                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
-                returned where the first element is a list with the generated images.
-        """
-        img_size = self.unet.config.sample_size
-        shape = (batch_size, 3, img_size, img_size)
-
-        model = self.unet
-
-        sample = randn_tensor(shape, generator=generator, device=self.device) * self.scheduler.init_noise_sigma
-        sample = sample.to(self.device)
-
-        self.scheduler.set_timesteps(num_inference_steps)
-        self.scheduler.set_sigmas(num_inference_steps)
-
-        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
-            sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=self.device)
-
-            # correction step
-            for _ in range(self.scheduler.config.correct_steps):
-                model_output = self.unet(sample, sigma_t).sample
-                sample = self.scheduler.step_correct(model_output, sample, generator=generator).prev_sample
-
-            # prediction step
-            model_output = model(sample, sigma_t).sample
-            output = self.scheduler.step_pred(model_output, t, sample, generator=generator)
-
-            sample, sample_mean = output.prev_sample, output.prev_sample_mean
-
-        sample = sample_mean.clamp(0, 1)
-        sample = sample.cpu().permute(0, 2, 3, 1).numpy()
-        if output_type == "pil":
-            sample = self.numpy_to_pil(sample)
-
-        if not return_dict:
-            return (sample,)
-        return ImagePipelineOutput(images=sample)