o Gi@@sddlmZddlZddlmZmZddlmZddlm Z m Z ddl m Z m Z d d lmZd d lmZd d lmZd d lmZd dlmZdZGddde e ZdS))CallableN) CLIPTextModel CLIPTokenizer)DDPMWuerstchenScheduler) deprecatereplace_example_docstring)DeprecatedPipelineMixinDiffusionPipeline) PaellaVQModel)WuerstchenDiffNeXt)WuerstchenPrior)WuerstchenDecoderPipeline)WuerstchenPriorPipelineax Examples: ```py >>> from diffusions import WuerstchenCombinedPipeline >>> pipe = WuerstchenCombinedPipeline.from_pretrained("warp-ai/Wuerstchen", torch_dtype=torch.float16).to( ... "cuda" ... ) >>> prompt = "an image of a shiba inu, donning a spacesuit and helmet" >>> images = pipe(prompt=prompt) ``` c.seZdZdZdZdZdededede de d ed ed e d e ffd d Z d;de dBfddZd 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. prior_num_inference_steps (`int | dict[float, int]`, *optional*, defaults to 60): The number of prior denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. For more specific timestep spacing, you can pass customized `prior_timesteps` num_inference_steps (`int`, *optional*, defaults to 12): The number of decoder denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. For more specific timestep spacing, you can pass customized `timesteps` prior_timesteps (`list[float]`, *optional*): Custom timesteps to use for the denoising process for the prior. If not defined, equal spaced `prior_num_inference_steps` timesteps are used. Must be in descending order. decoder_timesteps (`list[float]`, *optional*): Custom timesteps to use for the denoising process for the decoder. If not defined, equal spaced `num_inference_steps` timesteps are used. Must be in descending order. decoder_guidance_scale (`float`, *optional*, defaults to 0.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `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. 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 be 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. prior_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: `prior_callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. prior_callback_on_step_end_tensor_inputs (`list`, *optional*): The list of tensor inputs for the `prior_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. 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 images. prior_callbackNcallbackz1.0.0ztPassing `prior_callback` as an input argument to `__call__` is deprecated, consider use `prior_callback_on_step_end`prior_callback_stepszzPassing `prior_callback_steps` as an input argument to `__call__` is deprecated, consider use `prior_callback_on_step_end`callback_stepsr;r<r=rA timestepsguidance_scalerDrErFrGrHr:rIptrJFrMrNr) image_embeddingsr;rArSrTrDrHrIrJrMrNr%)getpoprr r!)r"r;r<r=r>r?r@rArBrCrDrErFrGrHr:rIrJrKrLrMrNr6 prior_kwargs prior_outputsrWoutputsr%r%r&__call__so       z#WuerstchenCombinedPipeline.__call__r()NN)!__name__ __module__ __qualname____doc___last_supported_version_load_connected_pipesrrrrr rrrr)inttorchr+strr.r1r4r5no_gradrTEXT2IMAGE_EXAMPLE_DOC_STRINGlistfloatTensor Generatorboolr] __classcell__r%r%r#r&r+s  '       r)typingrre transformersrr schedulersrutilsrrpipeline_utilsr r modeling_paella_vq_modelr modeling_wuerstchen_diffnextrmodeling_wuerstchen_priorrpipeline_wuerstchenrpipeline_wuerstchen_priorrrhrr%r%r%r&s