class VersatileDiffusionPipeline(DiffusionPipeline):
    r"""
    Pipeline for text-to-image generation using Stable Diffusion.

    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.).

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
        text_encoder ([`~transformers.CLIPTextModel`]):
            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
        tokenizer ([`~transformers.CLIPTokenizer`]):
            A `CLIPTokenizer` to tokenize text.
        unet ([`UNet2DConditionModel`]):
            A `UNet2DConditionModel` to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
    """

    tokenizer: CLIPTokenizer
    image_feature_extractor: CLIPImageProcessor
    text_encoder: CLIPTextModel
    image_encoder: CLIPVisionModel
    image_unet: UNet2DConditionModel
    text_unet: UNet2DConditionModel
    vae: AutoencoderKL
    scheduler: KarrasDiffusionSchedulers

    def __init__(
        self,
        tokenizer: CLIPTokenizer,
        image_feature_extractor: CLIPImageProcessor,
        text_encoder: CLIPTextModel,
        image_encoder: CLIPVisionModel,
        image_unet: UNet2DConditionModel,
        text_unet: UNet2DConditionModel,
        vae: AutoencoderKL,
        scheduler: KarrasDiffusionSchedulers,
    ):
        super().__init__()

        self.register_modules(
            tokenizer=tokenizer,
            image_feature_extractor=image_feature_extractor,
            text_encoder=text_encoder,
            image_encoder=image_encoder,
            image_unet=image_unet,
            text_unet=text_unet,
            vae=vae,
            scheduler=scheduler,
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8

    @torch.no_grad()
    def image_variation(
        self,
        image: Union[torch.Tensor, PIL.Image.Image],
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
        callback_steps: int = 1,
    ):
        r"""
        The call function to the pipeline for generation.

        Args:
            image (`PIL.Image.Image`, `List[PIL.Image.Image]` or `torch.Tensor`):
                The image prompt or prompts to guide the image generation.
            height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                A higher guidance scale value encourages the model to generate images closely linked to the text
                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
            generator (`torch.Generator`, *optional*):
                A [`torch.Generator`](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 is generated by sampling using the supplied random `generator`.
            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 [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that calls every `callback_steps` steps during inference. The function is called with the
                following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function is called. If not specified, the callback is called at
                every step.

        Examples:

        ```py
        >>> from diffusers import VersatileDiffusionPipeline
        >>> import torch
        >>> import requests
        >>> from io import BytesIO
        >>> from PIL import Image

        >>> # let's download an initial image
        >>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"

        >>> response = requests.get(url)
        >>> image = Image.open(BytesIO(response.content)).convert("RGB")

        >>> pipe = VersatileDiffusionPipeline.from_pretrained(
        ...     "shi-labs/versatile-diffusion", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> generator = torch.Generator(device="cuda").manual_seed(0)
        >>> image = pipe.image_variation(image, generator=generator).images[0]
        >>> image.save("./car_variation.png")
        ```

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
                otherwise a `tuple` is returned where the first element is a list with the generated images and the
                second element is a list of `bool`s indicating whether the corresponding generated image contains
                "not-safe-for-work" (nsfw) content.
        """
        expected_components = inspect.signature(VersatileDiffusionImageVariationPipeline.__init__).parameters.keys()
        components = {name: component for name, component in self.components.items() if name in expected_components}
        return VersatileDiffusionImageVariationPipeline(**components)(
            image=image,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            negative_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            eta=eta,
            generator=generator,
            latents=latents,
            output_type=output_type,
            return_dict=return_dict,
            callback=callback,
            callback_steps=callback_steps,
        )

    @torch.no_grad()
    def text_to_image(
        self,
        prompt: Union[str, List[str]],
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
        callback_steps: int = 1,
    ):
        r"""
        The call function to the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`):
                The prompt or prompts to guide image generation.
            height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                A higher guidance scale value encourages the model to generate images closely linked to the text
                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
            generator (`torch.Generator`, *optional*):
                A [`torch.Generator`](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 is generated by sampling using the supplied random `generator`.
            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 [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that calls every `callback_steps` steps during inference. The function is called with the
                following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function is called. If not specified, the callback is called at
                every step.

        Examples:

        ```py
        >>> from diffusers import VersatileDiffusionPipeline
        >>> import torch

        >>> pipe = VersatileDiffusionPipeline.from_pretrained(
        ...     "shi-labs/versatile-diffusion", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> generator = torch.Generator(device="cuda").manual_seed(0)
        >>> image = pipe.text_to_image("an astronaut riding on a horse on mars", generator=generator).images[0]
        >>> image.save("./astronaut.png")
        ```

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
                otherwise a `tuple` is returned where the first element is a list with the generated images and the
                second element is a list of `bool`s indicating whether the corresponding generated image contains
                "not-safe-for-work" (nsfw) content.
        """
        expected_components = inspect.signature(VersatileDiffusionTextToImagePipeline.__init__).parameters.keys()
        components = {name: component for name, component in self.components.items() if name in expected_components}
        temp_pipeline = VersatileDiffusionTextToImagePipeline(**components)
        output = temp_pipeline(
            prompt=prompt,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            negative_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            eta=eta,
            generator=generator,
            latents=latents,
            output_type=output_type,
            return_dict=return_dict,
            callback=callback,
            callback_steps=callback_steps,
        )
        # swap the attention blocks back to the original state
        temp_pipeline._swap_unet_attention_blocks()

        return output

    @torch.no_grad()
    def dual_guided(
        self,
        prompt: Union[PIL.Image.Image, List[PIL.Image.Image]],
        image: Union[str, List[str]],
        text_to_image_strength: float = 0.5,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
        callback_steps: int = 1,
    ):
        r"""
        The call function to the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`):
                The prompt or prompts to guide image generation.
            height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                A higher guidance scale value encourages the model to generate images closely linked to the text
                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                A [`torch.Generator`](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 is generated by sampling using the supplied random `generator`.
            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 [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that calls every `callback_steps` steps during inference. The function is called with the
                following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function is called. If not specified, the callback is called at
                every step.

        Examples:

        ```py
        >>> from diffusers import VersatileDiffusionPipeline
        >>> import torch
        >>> import requests
        >>> from io import BytesIO
        >>> from PIL import Image

        >>> # let's download an initial image
        >>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"

        >>> response = requests.get(url)
        >>> image = Image.open(BytesIO(response.content)).convert("RGB")
        >>> text = "a red car in the sun"

        >>> pipe = VersatileDiffusionPipeline.from_pretrained(
        ...     "shi-labs/versatile-diffusion", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> generator = torch.Generator(device="cuda").manual_seed(0)
        >>> text_to_image_strength = 0.75

        >>> image = pipe.dual_guided(
        ...     prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator
        ... ).images[0]
        >>> image.save("./car_variation.png")
        ```

        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.
        """

        expected_components = inspect.signature(VersatileDiffusionDualGuidedPipeline.__init__).parameters.keys()
        components = {name: component for name, component in self.components.items() if name in expected_components}
        temp_pipeline = VersatileDiffusionDualGuidedPipeline(**components)
        output = temp_pipeline(
            prompt=prompt,
            image=image,
            text_to_image_strength=text_to_image_strength,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            num_images_per_prompt=num_images_per_prompt,
            eta=eta,
            generator=generator,
            latents=latents,
            output_type=output_type,
            return_dict=return_dict,
            callback=callback,
            callback_steps=callback_steps,
        )
        temp_pipeline._revert_dual_attention()

        return output
/Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py