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import inspect |
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from typing import Callable, List, Optional, Union |
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import PIL.Image |
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import torch |
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from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModel |
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from ....models import AutoencoderKL, UNet2DConditionModel |
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from ....schedulers import KarrasDiffusionSchedulers |
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from ....utils import logging |
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from ...pipeline_utils import DiffusionPipeline |
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from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline |
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from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline |
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from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline |
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logger = logging.get_logger(__name__) |
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class VersatileDiffusionPipeline(DiffusionPipeline): |
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r""" |
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Pipeline for text-to-image generation using Stable Diffusion. |
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods |
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implemented for all pipelines (downloading, saving, running on a particular device, etc.). |
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Args: |
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vae ([`AutoencoderKL`]): |
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Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. |
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text_encoder ([`~transformers.CLIPTextModel`]): |
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Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)). |
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tokenizer ([`~transformers.CLIPTokenizer`]): |
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A `CLIPTokenizer` to tokenize text. |
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unet ([`UNet2DConditionModel`]): |
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A `UNet2DConditionModel` to denoise the encoded image latents. |
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scheduler ([`SchedulerMixin`]): |
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A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of |
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[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. |
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safety_checker ([`StableDiffusionSafetyChecker`]): |
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Classification module that estimates whether generated images could be considered offensive or harmful. |
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Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details |
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about a model's potential harms. |
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feature_extractor ([`~transformers.CLIPImageProcessor`]): |
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A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. |
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""" |
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tokenizer: CLIPTokenizer |
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image_feature_extractor: CLIPImageProcessor |
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text_encoder: CLIPTextModel |
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image_encoder: CLIPVisionModel |
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image_unet: UNet2DConditionModel |
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text_unet: UNet2DConditionModel |
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vae: AutoencoderKL |
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scheduler: KarrasDiffusionSchedulers |
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def __init__( |
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self, |
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tokenizer: CLIPTokenizer, |
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image_feature_extractor: CLIPImageProcessor, |
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text_encoder: CLIPTextModel, |
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image_encoder: CLIPVisionModel, |
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image_unet: UNet2DConditionModel, |
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text_unet: UNet2DConditionModel, |
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vae: AutoencoderKL, |
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scheduler: KarrasDiffusionSchedulers, |
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): |
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super().__init__() |
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self.register_modules( |
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tokenizer=tokenizer, |
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image_feature_extractor=image_feature_extractor, |
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text_encoder=text_encoder, |
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image_encoder=image_encoder, |
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image_unet=image_unet, |
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text_unet=text_unet, |
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vae=vae, |
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scheduler=scheduler, |
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) |
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) |
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@torch.no_grad() |
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def image_variation( |
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self, |
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image: Union[torch.FloatTensor, PIL.Image.Image], |
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height: Optional[int] = None, |
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width: Optional[int] = None, |
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num_inference_steps: int = 50, |
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guidance_scale: float = 7.5, |
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negative_prompt: Optional[Union[str, List[str]]] = None, |
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num_images_per_prompt: Optional[int] = 1, |
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eta: float = 0.0, |
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, |
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latents: Optional[torch.FloatTensor] = None, |
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output_type: Optional[str] = "pil", |
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return_dict: bool = True, |
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callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, |
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callback_steps: int = 1, |
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): |
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r""" |
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The call function to the pipeline for generation. |
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Args: |
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image (`PIL.Image.Image`, `List[PIL.Image.Image]` or `torch.Tensor`): |
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The image prompt or prompts to guide the image generation. |
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height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`): |
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The height in pixels of the generated image. |
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width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`): |
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The width in pixels of the generated image. |
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num_inference_steps (`int`, *optional*, defaults to 50): |
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the |
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expense of slower inference. |
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guidance_scale (`float`, *optional*, defaults to 7.5): |
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A higher guidance scale value encourages the model to generate images closely linked to the text |
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`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. |
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negative_prompt (`str` or `List[str]`, *optional*): |
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The prompt or prompts to guide what to not include in image generation. If not defined, you need to |
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pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). |
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num_images_per_prompt (`int`, *optional*, defaults to 1): |
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The number of images to generate per prompt. |
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eta (`float`, *optional*, defaults to 0.0): |
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Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies |
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to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. |
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generator (`torch.Generator`, *optional*): |
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A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make |
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generation deterministic. |
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latents (`torch.FloatTensor`, *optional*): |
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Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image |
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents |
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tensor is generated by sampling using the supplied random `generator`. |
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output_type (`str`, *optional*, defaults to `"pil"`): |
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The output format of the generated image. Choose between `PIL.Image` or `np.array`. |
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return_dict (`bool`, *optional*, defaults to `True`): |
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Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a |
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plain tuple. |
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callback (`Callable`, *optional*): |
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A function that calls every `callback_steps` steps during inference. The function is called with the |
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following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. |
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callback_steps (`int`, *optional*, defaults to 1): |
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The frequency at which the `callback` function is called. If not specified, the callback is called at |
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every step. |
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Examples: |
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```py |
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>>> from diffusers import VersatileDiffusionPipeline |
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>>> import torch |
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>>> import requests |
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>>> from io import BytesIO |
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>>> from PIL import Image |
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>>> # let's download an initial image |
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>>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" |
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>>> response = requests.get(url) |
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>>> image = Image.open(BytesIO(response.content)).convert("RGB") |
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>>> pipe = VersatileDiffusionPipeline.from_pretrained( |
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... "shi-labs/versatile-diffusion", torch_dtype=torch.float16 |
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... ) |
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>>> pipe = pipe.to("cuda") |
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>>> generator = torch.Generator(device="cuda").manual_seed(0) |
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>>> image = pipe.image_variation(image, generator=generator).images[0] |
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>>> image.save("./car_variation.png") |
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``` |
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Returns: |
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: |
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If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, |
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otherwise a `tuple` is returned where the first element is a list with the generated images and the |
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second element is a list of `bool`s indicating whether the corresponding generated image contains |
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"not-safe-for-work" (nsfw) content. |
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""" |
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expected_components = inspect.signature(VersatileDiffusionImageVariationPipeline.__init__).parameters.keys() |
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components = {name: component for name, component in self.components.items() if name in expected_components} |
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return VersatileDiffusionImageVariationPipeline(**components)( |
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image=image, |
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height=height, |
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width=width, |
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num_inference_steps=num_inference_steps, |
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guidance_scale=guidance_scale, |
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negative_prompt=negative_prompt, |
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num_images_per_prompt=num_images_per_prompt, |
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eta=eta, |
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generator=generator, |
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latents=latents, |
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output_type=output_type, |
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return_dict=return_dict, |
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callback=callback, |
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callback_steps=callback_steps, |
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) |
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@torch.no_grad() |
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def text_to_image( |
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self, |
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prompt: Union[str, List[str]], |
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height: Optional[int] = None, |
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width: Optional[int] = None, |
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num_inference_steps: int = 50, |
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guidance_scale: float = 7.5, |
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negative_prompt: Optional[Union[str, List[str]]] = None, |
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num_images_per_prompt: Optional[int] = 1, |
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eta: float = 0.0, |
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, |
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latents: Optional[torch.FloatTensor] = None, |
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output_type: Optional[str] = "pil", |
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return_dict: bool = True, |
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callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, |
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callback_steps: int = 1, |
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): |
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r""" |
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The call function to the pipeline for generation. |
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Args: |
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prompt (`str` or `List[str]`): |
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The prompt or prompts to guide image generation. |
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height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`): |
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The height in pixels of the generated image. |
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width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`): |
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The width in pixels of the generated image. |
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num_inference_steps (`int`, *optional*, defaults to 50): |
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the |
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|
expense of slower inference. |
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|
guidance_scale (`float`, *optional*, defaults to 7.5): |
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|
A higher guidance scale value encourages the model to generate images closely linked to the text |
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|
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. |
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|
negative_prompt (`str` or `List[str]`, *optional*): |
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|
The prompt or prompts to guide what to not include in image generation. If not defined, you need to |
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|
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). |
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|
num_images_per_prompt (`int`, *optional*, defaults to 1): |
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|
The number of images to generate per prompt. |
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eta (`float`, *optional*, defaults to 0.0): |
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|
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies |
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|
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. |
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|
generator (`torch.Generator`, *optional*): |
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|
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make |
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|
generation deterministic. |
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|
latents (`torch.FloatTensor`, *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`. |
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output_type (`str`, *optional*, defaults to `"pil"`): |
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The output format of the generated image. Choose between `PIL.Image` or `np.array`. |
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|
return_dict (`bool`, *optional*, defaults to `True`): |
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|
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a |
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plain tuple. |
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callback (`Callable`, *optional*): |
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A function that calls every `callback_steps` steps during inference. The function is called with the |
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following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. |
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callback_steps (`int`, *optional*, defaults to 1): |
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The frequency at which the `callback` function is called. If not specified, the callback is called at |
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|
every step. |
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|
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|
Examples: |
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|
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```py |
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>>> from diffusers import VersatileDiffusionPipeline |
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>>> import torch |
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>>> pipe = VersatileDiffusionPipeline.from_pretrained( |
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... "shi-labs/versatile-diffusion", torch_dtype=torch.float16 |
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... ) |
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>>> pipe = pipe.to("cuda") |
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>>> generator = torch.Generator(device="cuda").manual_seed(0) |
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>>> image = pipe.text_to_image("an astronaut riding on a horse on mars", generator=generator).images[0] |
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>>> image.save("./astronaut.png") |
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``` |
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Returns: |
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: |
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If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, |
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|
otherwise a `tuple` is returned where the first element is a list with the generated images and the |
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second element is a list of `bool`s indicating whether the corresponding generated image contains |
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|
"not-safe-for-work" (nsfw) content. |
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""" |
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expected_components = inspect.signature(VersatileDiffusionTextToImagePipeline.__init__).parameters.keys() |
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components = {name: component for name, component in self.components.items() if name in expected_components} |
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temp_pipeline = VersatileDiffusionTextToImagePipeline(**components) |
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output = temp_pipeline( |
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prompt=prompt, |
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height=height, |
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width=width, |
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num_inference_steps=num_inference_steps, |
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guidance_scale=guidance_scale, |
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negative_prompt=negative_prompt, |
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num_images_per_prompt=num_images_per_prompt, |
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eta=eta, |
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generator=generator, |
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latents=latents, |
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output_type=output_type, |
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return_dict=return_dict, |
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callback=callback, |
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callback_steps=callback_steps, |
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) |
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temp_pipeline._swap_unet_attention_blocks() |
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return output |
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@torch.no_grad() |
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def dual_guided( |
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self, |
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prompt: Union[PIL.Image.Image, List[PIL.Image.Image]], |
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image: Union[str, List[str]], |
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text_to_image_strength: float = 0.5, |
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height: Optional[int] = None, |
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width: Optional[int] = None, |
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num_inference_steps: int = 50, |
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guidance_scale: float = 7.5, |
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num_images_per_prompt: Optional[int] = 1, |
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eta: float = 0.0, |
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, |
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|
latents: Optional[torch.FloatTensor] = None, |
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|
output_type: Optional[str] = "pil", |
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|
return_dict: bool = True, |
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callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, |
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callback_steps: int = 1, |
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|
): |
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r""" |
|
|
The call function to the pipeline for generation. |
|
|
|
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|
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.FloatTensor`, *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.FloatTensor)`. |
|
|
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`: |
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|
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is |
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returned where the first element is a list with the generated images. |
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""" |
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|
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expected_components = inspect.signature(VersatileDiffusionDualGuidedPipeline.__init__).parameters.keys() |
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components = {name: component for name, component in self.components.items() if name in expected_components} |
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temp_pipeline = VersatileDiffusionDualGuidedPipeline(**components) |
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output = temp_pipeline( |
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prompt=prompt, |
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image=image, |
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text_to_image_strength=text_to_image_strength, |
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height=height, |
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width=width, |
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num_inference_steps=num_inference_steps, |
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guidance_scale=guidance_scale, |
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num_images_per_prompt=num_images_per_prompt, |
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eta=eta, |
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generator=generator, |
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latents=latents, |
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output_type=output_type, |
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return_dict=return_dict, |
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callback=callback, |
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callback_steps=callback_steps, |
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) |
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temp_pipeline._revert_dual_attention() |
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|
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return output |
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|
