Create conditional_pipeline.py

conditional_pipeline.py

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+from typing import Optional, Union, List, Tuple
+
+import torch
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+
+
+class ScoreSdeVePipelineConditioned(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.
+    """
+    def __init__(self, unet: UNet2DModel, scheduler: ScoreSdeVeScheduler):
+        super().__init__()
+        self.register_modules(unet=unet, scheduler=scheduler)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        batch_size: int = 1,
+        num_inference_steps: int = 2000,
+        class_labels: Optional[torch.Tensor] = None,
+        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, class_labels).sample
+                sample = self.scheduler.step_correct(model_output, sample, generator=generator).prev_sample
+
+            # prediction step
+            model_output = model(sample, sigma_t, class_labels).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)