class WuerstchenDecoderPipeline(DiffusionPipeline):
    """
    Pipeline for generating images from the Wuerstchen model.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

    Args:
        tokenizer (`CLIPTokenizer`):
            The CLIP tokenizer.
        text_encoder (`CLIPTextModel`):
            The CLIP text encoder.
        decoder ([`WuerstchenDiffNeXt`]):
            The WuerstchenDiffNeXt unet decoder.
        vqgan ([`PaellaVQModel`]):
            The VQGAN model.
        scheduler ([`DDPMWuerstchenScheduler`]):
            A scheduler to be used in combination with `prior` to generate image embedding.
        latent_dim_scale (float, `optional`, defaults to 10.67):
            Multiplier to determine the VQ latent space size from the image embeddings. If the image embeddings are
            height=24 and width=24, the VQ latent shape needs to be height=int(24*10.67)=256 and
            width=int(24*10.67)=256 in order to match the training conditions.
    """

    model_cpu_offload_seq = "text_encoder->decoder->vqgan"
    _callback_tensor_inputs = [
        "latents",
        "text_encoder_hidden_states",
        "negative_prompt_embeds",
        "image_embeddings",
    ]

    def __init__(
        self,
        tokenizer: CLIPTokenizer,
        text_encoder: CLIPTextModel,
        decoder: WuerstchenDiffNeXt,
        scheduler: DDPMWuerstchenScheduler,
        vqgan: PaellaVQModel,
        latent_dim_scale: float = 10.67,
    ) -> None:
        super().__init__()
        self.register_modules(
            tokenizer=tokenizer,
            text_encoder=text_encoder,
            decoder=decoder,
            scheduler=scheduler,
            vqgan=vqgan,
        )
        self.register_to_config(latent_dim_scale=latent_dim_scale)

    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
    def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else:
            if latents.shape != shape:
                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
            latents = latents.to(device)

        latents = latents * scheduler.init_noise_sigma
        return latents

    def encode_prompt(
        self,
        prompt,
        device,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt=None,
    ):
        batch_size = len(prompt) if isinstance(prompt, list) else 1
        # get prompt text embeddings
        text_inputs = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=self.tokenizer.model_max_length,
            truncation=True,
            return_tensors="pt",
        )
        text_input_ids = text_inputs.input_ids
        attention_mask = text_inputs.attention_mask

        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
            logger.warning(
                "The following part of your input was truncated because CLIP can only handle sequences up to"
                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
            )
            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
            attention_mask = attention_mask[:, : self.tokenizer.model_max_length]

        text_encoder_output = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask.to(device))
        text_encoder_hidden_states = text_encoder_output.last_hidden_state
        text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)

        uncond_text_encoder_hidden_states = None
        if do_classifier_free_guidance:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""] * batch_size
            elif type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt]
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = negative_prompt

            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            negative_prompt_embeds_text_encoder_output = self.text_encoder(
                uncond_input.input_ids.to(device), attention_mask=uncond_input.attention_mask.to(device)
            )

            uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state

            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = uncond_text_encoder_hidden_states.shape[1]
            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
                batch_size * num_images_per_prompt, seq_len, -1
            )
            # done duplicates

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
        return text_encoder_hidden_states, uncond_text_encoder_hidden_states

    @property
    def guidance_scale(self):
        return self._guidance_scale

    @property
    def do_classifier_free_guidance(self):
        return self._guidance_scale > 1

    @property
    def num_timesteps(self):
        return self._num_timesteps

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        image_embeddings: Union[torch.Tensor, List[torch.Tensor]],
        prompt: Union[str, List[str]] = None,
        num_inference_steps: int = 12,
        timesteps: Optional[List[float]] = None,
        guidance_scale: float = 0.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: int = 1,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
        **kwargs,
    ):
        """
        Function invoked when calling the pipeline for generation.

        Args:
            image_embedding (`torch.Tensor` or `List[torch.Tensor]`):
                Image Embeddings either extracted from an image or generated by a Prior Model.
            prompt (`str` or `List[str]`):
                The prompt or prompts to guide the image generation.
            num_inference_steps (`int`, *optional*, defaults to 12):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            timesteps (`List[int]`, *optional*):
                Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
                timesteps are used. Must be in descending order.
            guidance_scale (`float`, *optional*, defaults to 0.0):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `decoder_guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting
                `decoder_guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely
                linked to the text `prompt`, usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
                if `decoder_guidance_scale` is less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                to make generation deterministic.
            latents (`torch.Tensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
                (`np.array`) or `"pt"` (`torch.Tensor`).
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
            callback_on_step_end (`Callable`, *optional*):
                A function that calls at the end of each denoising steps during the inference. The function is called
                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
                `callback_on_step_end_tensor_inputs`.
            callback_on_step_end_tensor_inputs (`List`, *optional*):
                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                `._callback_tensor_inputs` attribute of your pipeline class.

        Examples:

        Returns:
            [`~pipelines.ImagePipelineOutput`] or `tuple` [`~pipelines.ImagePipelineOutput`] if `return_dict` is True,
            otherwise a `tuple`. When returning a tuple, the first element is a list with the generated image
            embeddings.
        """

        callback = kwargs.pop("callback", None)
        callback_steps = kwargs.pop("callback_steps", None)

        if callback is not None:
            deprecate(
                "callback",
                "1.0.0",
                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
            )
        if callback_steps is not None:
            deprecate(
                "callback_steps",
                "1.0.0",
                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
            )

        if callback_on_step_end_tensor_inputs is not None and not all(
            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
        ):
            raise ValueError(
                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
            )

        # 0. Define commonly used variables
        device = self._execution_device
        dtype = self.decoder.dtype
        self._guidance_scale = guidance_scale

        # 1. Check inputs. Raise error if not correct
        if not isinstance(prompt, list):
            if isinstance(prompt, str):
                prompt = [prompt]
            else:
                raise TypeError(f"'prompt' must be of type 'list' or 'str', but got {type(prompt)}.")

        if self.do_classifier_free_guidance:
            if negative_prompt is not None and not isinstance(negative_prompt, list):
                if isinstance(negative_prompt, str):
                    negative_prompt = [negative_prompt]
                else:
                    raise TypeError(
                        f"'negative_prompt' must be of type 'list' or 'str', but got {type(negative_prompt)}."
                    )

        if isinstance(image_embeddings, list):
            image_embeddings = torch.cat(image_embeddings, dim=0)
        if isinstance(image_embeddings, np.ndarray):
            image_embeddings = torch.Tensor(image_embeddings, device=device).to(dtype=dtype)
        if not isinstance(image_embeddings, torch.Tensor):
            raise TypeError(
                f"'image_embeddings' must be of type 'torch.Tensor' or 'np.array', but got {type(image_embeddings)}."
            )

        if not isinstance(num_inference_steps, int):
            raise TypeError(
                f"'num_inference_steps' must be of type 'int', but got {type(num_inference_steps)}\
                           In Case you want to provide explicit timesteps, please use the 'timesteps' argument."
            )

        # 2. Encode caption
        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
            prompt,
            device,
            image_embeddings.size(0) * num_images_per_prompt,
            self.do_classifier_free_guidance,
            negative_prompt,
        )
        text_encoder_hidden_states = (
            torch.cat([prompt_embeds, negative_prompt_embeds]) if negative_prompt_embeds is not None else prompt_embeds
        )
        effnet = (
            torch.cat([image_embeddings, torch.zeros_like(image_embeddings)])
            if self.do_classifier_free_guidance
            else image_embeddings
        )

        # 3. Determine latent shape of latents
        latent_height = int(image_embeddings.size(2) * self.config.latent_dim_scale)
        latent_width = int(image_embeddings.size(3) * self.config.latent_dim_scale)
        latent_features_shape = (image_embeddings.size(0) * num_images_per_prompt, 4, latent_height, latent_width)

        # 4. Prepare and set timesteps
        if timesteps is not None:
            self.scheduler.set_timesteps(timesteps=timesteps, device=device)
            timesteps = self.scheduler.timesteps
            num_inference_steps = len(timesteps)
        else:
            self.scheduler.set_timesteps(num_inference_steps, device=device)
            timesteps = self.scheduler.timesteps

        # 5. Prepare latents
        latents = self.prepare_latents(latent_features_shape, dtype, device, generator, latents, self.scheduler)

        # 6. Run denoising loop
        self._num_timesteps = len(timesteps[:-1])
        for i, t in enumerate(self.progress_bar(timesteps[:-1])):
            ratio = t.expand(latents.size(0)).to(dtype)
            # 7. Denoise latents
            predicted_latents = self.decoder(
                torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents,
                r=torch.cat([ratio] * 2) if self.do_classifier_free_guidance else ratio,
                effnet=effnet,
                clip=text_encoder_hidden_states,
            )

            # 8. Check for classifier free guidance and apply it
            if self.do_classifier_free_guidance:
                predicted_latents_text, predicted_latents_uncond = predicted_latents.chunk(2)
                predicted_latents = torch.lerp(predicted_latents_uncond, predicted_latents_text, self.guidance_scale)

            # 9. Renoise latents to next timestep
            latents = self.scheduler.step(
                model_output=predicted_latents,
                timestep=ratio,
                sample=latents,
                generator=generator,
            ).prev_sample

            if callback_on_step_end is not None:
                callback_kwargs = {}
                for k in callback_on_step_end_tensor_inputs:
                    callback_kwargs[k] = locals()[k]
                callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                latents = callback_outputs.pop("latents", latents)
                image_embeddings = callback_outputs.pop("image_embeddings", image_embeddings)
                text_encoder_hidden_states = callback_outputs.pop(
                    "text_encoder_hidden_states", text_encoder_hidden_states
                )

            if callback is not None and i % callback_steps == 0:
                step_idx = i // getattr(self.scheduler, "order", 1)
                callback(step_idx, t, latents)

            if XLA_AVAILABLE:
                xm.mark_step()

        if output_type not in ["pt", "np", "pil", "latent"]:
            raise ValueError(
                f"Only the output types `pt`, `np`, `pil` and `latent` are supported not output_type={output_type}"
            )

        if not output_type == "latent":
            # 10. Scale and decode the image latents with vq-vae
            latents = self.vqgan.config.scale_factor * latents
            images = self.vqgan.decode(latents).sample.clamp(0, 1)
            if output_type == "np":
                images = images.permute(0, 2, 3, 1).cpu().float().numpy()
            elif output_type == "pil":
                images = images.permute(0, 2, 3, 1).cpu().float().numpy()
                images = self.numpy_to_pil(images)
        else:
            images = latents

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return images
        return ImagePipelineOutput(images)
/Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py