Method __call__

diffusers/examples/community/bit_diffusion.py:229–264

(
        self,
        height: Optional[int] = 256,
        width: Optional[int] = 256,
        num_inference_steps: Optional[int] = 50,
        generator: Optional[torch.Generator] = None,
        batch_size: Optional[int] = 1,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        **kwargs,
    )

    @torch.no_grad()
    def __call__(
        self,
        height: Optional[int] = 256,
        width: Optional[int] = 256,
        num_inference_steps: Optional[int] = 50,
        generator: Optional[torch.Generator] = None,
        batch_size: Optional[int] = 1,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        **kwargs,
    ) -> Union[Tuple, ImagePipelineOutput]:
        latents = torch.randn(
            (batch_size, self.unet.config.in_channels, height, width),
            generator=generator,
        )
        latents = decimal_to_bits(latents) * self.bit_scale
        latents = latents.to(self.device)

        self.scheduler.set_timesteps(num_inference_steps)

        for t in self.progress_bar(self.scheduler.timesteps):
            # predict the noise residual
            noise_pred = self.unet(latents, t).sample

            # compute the previous noisy sample x_t -> x_t-1
            latents = self.scheduler.step(noise_pred, t, latents).prev_sample

        image = bits_to_decimal(latents)

        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=image)