+hSSKJrJrJrJr SSKrSSKrSSKJ r J r J r J r J r SSKJrJrJr SSKJrJrJr SSKJr SS KJr S S KJr S S KJr S S KJr S SK J!r! S SK"J#r# Sr$Sr%Sr&"SS\5r'"SS\5r("SS\5r)g))CallableListOptionalUnionN)CLIPImageProcessorCLIPTextModelWithProjection CLIPTokenizerCLIPVisionModelWithProjectionXLMRobertaTokenizer)PriorTransformerUNet2DConditionModelVQModel) DDIMScheduler DDPMSchedulerUnCLIPScheduler)replace_example_docstring)DiffusionPipeline)KandinskyPipeline)KandinskyImg2ImgPipeline)KandinskyInpaintPipeline)KandinskyPriorPipeline)MultilingualCLIPa Examples: ```py from diffusers import AutoPipelineForText2Image import torch pipe = AutoPipelineForText2Image.from_pretrained( "kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16 ) pipe.enable_model_cpu_offload() prompt = "A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k" image = pipe(prompt=prompt, num_inference_steps=25).images[0] ``` a~ Examples: ```py from diffusers import AutoPipelineForImage2Image import torch import requests from io import BytesIO from PIL import Image import os pipe = AutoPipelineForImage2Image.from_pretrained( "kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16 ) pipe.enable_model_cpu_offload() prompt = "A fantasy landscape, Cinematic lighting" negative_prompt = "low quality, bad quality" url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" response = requests.get(url) image = Image.open(BytesIO(response.content)).convert("RGB") image.thumbnail((768, 768)) image = pipe(prompt=prompt, image=original_image, num_inference_steps=25).images[0] ``` a Examples: ```py from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image import torch import numpy as np pipe = AutoPipelineForInpainting.from_pretrained( "kandinsky-community/kandinsky-2-1-inpaint", torch_dtype=torch.float16 ) pipe.enable_model_cpu_offload() prompt = "A fantasy landscape, Cinematic lighting" negative_prompt = "low quality, bad quality" original_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" ) mask = np.zeros((768, 768), dtype=np.float32) # Let's mask out an area above the cat's head mask[:250, 250:-250] = 1 image = pipe(prompt=prompt, image=original_image, mask_image=mask, num_inference_steps=25).images[0] ``` c!(^\rSrSrSrSrSrS/rS\S\ S\ S \ \ \ 4S \S\S \S \S \S\S\4U4SjjrS*S\\4SjjrS+S\\S\ \R6\44SjjrS+SjrSr\R@"5\!"\"5S,S\ \\#\4S\\ \\#\4S\S\$S\S\S\S \$S!\S"\\ \RJ\#\RJ4S#\\RLS$\\S%\\\\\RL/S4S&\S'\'4S(jj55r(S)r)U=r*$)-KandinskyCombinedPipelineqav Combined Pipeline for text-to-image generation using Kandinsky This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: text_encoder ([`MultilingualCLIP`]): Frozen text-encoder. tokenizer ([`XLMRobertaTokenizer`]): Tokenizer of class scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]): A scheduler to be used in combination with `unet` to generate image latents. unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the image embedding. movq ([`VQModel`]): MoVQ Decoder to generate the image from the latents. prior_prior ([`PriorTransformer`]): The canonical unCLIP prior to approximate the image embedding from the text embedding. prior_image_encoder ([`CLIPVisionModelWithProjection`]): Frozen image-encoder. prior_text_encoder ([`CLIPTextModelWithProjection`]): Frozen text-encoder. prior_tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). prior_scheduler ([`UnCLIPScheduler`]): A scheduler to be used in combination with `prior` to generate image embedding. TzNtext_encoder->unet->movq->prior_prior->prior_image_encoder->prior_text_encoder prior_prior text_encoder tokenizerunet schedulermovqprior_image_encoderprior_text_encoderprior_tokenizerprior_schedulerprior_image_processorc >[T U]5 URUUUUUUUUU U U S9 [UUUU U U S9Ul[ UUUUUS9UlgN) r r!r"r#r$rr%r&r'r(r))prior image_encoderr r!r#image_processor)r r!r"r#r$)super__init__register_modulesr prior_piper decoder_pipe selfr r!r"r#r$rr%r&r'r(r) __class__s s/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.pyr0"KandinskyCombinedPipeline.__init__s  %# 31++"7  1-+%%1  .%  N attention_opc:URRU5 gNr3*enable_xformers_memory_efficient_attentionr5r:s r7r>DKandinskyCombinedPipeline.enable_xformers_memory_efficient_attention DD\Rr9gpu_iddevicechURRXS9 URRXS9 g)u Offloads all models (`unet`, `text_encoder`, `vae`, and `safety checker` state dicts) to CPU using 🤗 Accelerate, significantly reducing memory usage. Models are moved to a `torch.device('meta')` and loaded on a GPU only when their specific submodule's `forward` method is called. Offloading happens on a submodule basis. Memory savings are higher than using `enable_model_cpu_offload`, but performance is lower. rBrCNr2enable_sequential_cpu_offloadr3r5rBrCs r7rG7KandinskyCombinedPipeline.enable_sequential_cpu_offloads0 55V5S 77v7Ur9cURRXS9 URRXS9 URR5 gN)iterabletotalr2 progress_barr3enable_model_cpu_offloadr5rLrMs r7rO&KandinskyCombinedPipeline.progress_bar@ $$h$D &&&F 224r9c tURR"S0UD6 URR"S0UD6 gNr2set_progress_bar_configr3r5kwargss r7rX1KandinskyCombinedPipeline.set_progress_bar_config. //9&9 11;F;r9promptnegative_promptnum_inference_stepsguidance_scalenum_images_per_promptheightwidthprior_guidance_scaleprior_num_inference_steps generatorlatents output_typecallbackcallback_steps return_dictcURUUUU U U USSS9 nUSnUSn[U[[45(dU/OUn[ U5UR S:a=UR S[ U5-S:XaUR S[ U5-U-nUR UUUUUUU UU U UUS9 nUR5 U$)a Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. 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 `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. num_inference_steps (`int`, *optional*, defaults to 100): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. height (`int`, *optional*, defaults to 512): The height in pixels of the generated image. width (`int`, *optional*, defaults to 512): The width in pixels of the generated image. prior_guidance_scale (`float`, *optional*, defaults to 4.0): 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. prior_num_inference_steps (`int`, *optional*, defaults to 100): 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 4.0): 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 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`). 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. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. Examples: Returns: [`~pipelines.ImagePipelineOutput`] or `tuple` ptF r]r^rar_rfrgr`rhrkrr) r] image_embedsnegative_image_embedsrcrbr_rfr`rhrirjrk)r2 isinstancelisttuplelenshaper3maybe_free_model_hooks)r5r]r^r_r`rarbrcrdrerfrgrhrirjrk prior_outputsrorpoutputss r7__call__"KandinskyCombinedPipeline.__call__sZ+"7 9/(  %Q' -a 0!+FT5M!B!B& v;++A. .<3E3Ea3H3v;3VZ[3["((+s6{:fDF##%"7 3)#)#$   ##%r9r3r2r<NNNd@rrrNNpilNrT)+__name__ __module__ __qualname____firstlineno____doc___load_connected_pipesmodel_cpu_offload_seq_exclude_from_cpu_offloadrr rrrrrr r rr rrr0rrr>inttorchrCstrrGrOrXno_gradrTEXT2IMAGE_EXAMPLE_DOC_STRINGrfloat GeneratorTensorboolry__static_attributes__ __classcell__r6s@r7rrqsA<!l!.+ &+ '+ # +  56 +  + &+ ;+ 8+ '+ )+  2+ ZSxPXGYSVHSMVRWX]XdXdfiXiRjV5 < ]]_<=<@#& #%&&))+MQ*.%*GK !oc49n%o"%T#Y"78o! o  o # ooo$o$'oE%//43H"HIJo%,,'oc]o8S#u||$or9rc%^\rSrSrSrSrSrS/rS\S\ S\ S \ \ \ 4S \S\S \S \S \S\S\4U4SjjrS,S\\4SjjrS-S\\S\ \R6\44SjjrS-SjrSr\R@"5\!"\"5S.S\ \\#\4S\ \RH\%RLRL\#\RH\#\%RLRL4S\\ \\#\4S\S\'S\S\'S \S!\S"\'S#\S$\\ \RP\#\RP4S%\\RHS&\\S'\\\\\RH/S4S(\S)\)4"S*jj55r*S+r+U=r,$)/ KandinskyImg2ImgCombinedPipelineiKaw Combined Pipeline for image-to-image generation using Kandinsky This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: text_encoder ([`MultilingualCLIP`]): Frozen text-encoder. tokenizer ([`XLMRobertaTokenizer`]): Tokenizer of class scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]): A scheduler to be used in combination with `unet` to generate image latents. unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the image embedding. movq ([`VQModel`]): MoVQ Decoder to generate the image from the latents. prior_prior ([`PriorTransformer`]): The canonical unCLIP prior to approximate the image embedding from the text embedding. prior_image_encoder ([`CLIPVisionModelWithProjection`]): Frozen image-encoder. prior_text_encoder ([`CLIPTextModelWithProjection`]): Frozen text-encoder. prior_tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). prior_scheduler ([`UnCLIPScheduler`]): A scheduler to be used in combination with `prior` to generate image embedding. TNprior_text_encoder->prior_image_encoder->prior_prior->text_encoder->unet->movqrr r!r"r#r$r%r&r'r(r)c >[T U]5 URUUUUUUUUU U U S9 [UUUU U U S9Ul[ UUUUUS9Ulgr+)r/r0r1rr2rr3r4s r7r0)KandinskyImg2ImgCombinedPipeline.__init__n  %# 31++"7  1-+%%1  5%  r9Nr:c:URRU5 gr<r=r?s r7r>KKandinskyImg2ImgCombinedPipeline.enable_xformers_memory_efficient_attentionrAr9rBrCchURRXS9 URRXS9 ga Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. Note that offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower. rENrFrHs r7rG>KandinskyImg2ImgCombinedPipeline.enable_sequential_cpu_offload0 55V5S 77v7Ur9cURRXS9 URRXS9 URR5 grKrNrQs r7rO-KandinskyImg2ImgCombinedPipeline.progress_barrSr9c tURR"S0UD6 URR"S0UD6 grUrWrYs r7rX8KandinskyImg2ImgCombinedPipeline.set_progress_bar_configr\r9r]imager^r_r`rastrengthrbrcrdrerfrgrhrirjrkcURUUUU U U U SSS9 nUSnUSn[U[[45(dU/OUn[U[R R 5(aU/OUn[ U5URS:a=URS[ U5-S:XaURS[ U5-U-n[U[[45(aY[ U5URS:a=URS[ U5-S:XaURS[ U5-U-nURUUUUUU UUU UUUUUS9nUR5 U$)a Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. Can also accept image latents as `image`, if passing latents directly, it will not be encoded again. 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 `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. num_inference_steps (`int`, *optional*, defaults to 100): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. height (`int`, *optional*, defaults to 512): The height in pixels of the generated image. width (`int`, *optional*, defaults to 512): The width in pixels of the generated image. strength (`float`, *optional*, defaults to 0.3): Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. prior_guidance_scale (`float`, *optional*, defaults to 4.0): 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. prior_num_inference_steps (`int`, *optional*, defaults to 100): 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 4.0): 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 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`). 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. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. Examples: Returns: [`~pipelines.ImagePipelineOutput`] or `tuple` rmFrnrr)r]rrorprrcrbr_rfr`rhrirjrk r2rqrrrsPILImagertrur3rv)r5r]rr^r_r`rarrbrcrdrerfrgrhrirjrkrwrorprxs r7ry)KandinskyImg2ImgCombinedPipeline.__call__sr+"7 9/(  %Q' -a 0!+FT5M!B!B&%fciioo>>E v;++A. .<3E3Ea3H3v;3VZ[3["((+s6{:fDF utUm , ,E \//22""1%E 2a7!''*c%j8EAE##%"7 3)#)#$ " ##%r9r{r<r|)Nr~rrg333333?rrrrNNrNrT)-rrrrrrrrrr rrrrrr r rr rrr0rrr>rrrCrrGrOrXrrIMAGE2IMAGE_EXAMPLE_DOC_STRINGrrrrrrrryrrrs@r7rrKs<!l!.+ &+ '+ # +  56 +  + &+ ;+ 8+ '+ )+  2+ ZSxPXGYS VHSM VRWX]XdXdfiXiRj V5 < ]]_=> <@#& #%&&))+MQ*.%*GK %Ec49n%EU\\399??D4FSYY__H]]^E"%T#Y"78 E ! E  E #EEEE$E$'EE%//43H"HIJE%,,'Ec]E 8S#u||$[T U]5 URUUUUUUUUU U U S9 [UUUU U U S9Ul[ UUUUUS9Ulgr+)r/r0r1rr2rr3r4s r7r0)KandinskyInpaintCombinedPipeline.__init___rr9Nr:c:URRU5 gr<r=r?s r7r>KKandinskyInpaintCombinedPipeline.enable_xformers_memory_efficient_attentionrAr9rBrCchURRXS9 URRXS9 grrFrHs r7rG>KandinskyInpaintCombinedPipeline.enable_sequential_cpu_offloadrr9cURRXS9 URRXS9 URR5 grKrNrQs r7rO-KandinskyInpaintCombinedPipeline.progress_barrSr9c tURR"S0UD6 URR"S0UD6 grUrWrYs r7rX8KandinskyInpaintCombinedPipeline.set_progress_bar_configr\r9r]r mask_imager^r_r`rarbrcrdrerfrgrhrirjrkcURUUUU U U U SSS9 nUSnUSn[U[[45(dU/OUn[U[R R 5(aU/OUn[U[R R 5(aU/OUn[ U5URS:a=URS[ U5-S:XaURS[ U5-U-n[U[[45(aY[ U5URS:a=URS[ U5-S:XaURS[ U5-U-n[U[[45(aY[ U5URS:a=URS[ U5-S:XaURS[ U5-U-nURUUUUUU UUU UUUUUS9nUR5 U$)a  Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. Can also accept image latents as `image`, if passing latents directly, it will not be encoded again. mask_image (`np.array`): Tensor representing an image batch, to mask `image`. White pixels in the mask will be repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. 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 `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. num_inference_steps (`int`, *optional*, defaults to 100): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. height (`int`, *optional*, defaults to 512): The height in pixels of the generated image. width (`int`, *optional*, defaults to 512): The width in pixels of the generated image. prior_guidance_scale (`float`, *optional*, defaults to 4.0): 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. prior_num_inference_steps (`int`, *optional*, defaults to 100): 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 4.0): 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 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`). 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. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. Examples: Returns: [`~pipelines.ImagePipelineOutput`] or `tuple` rmFrnrr)r]rrrorprcrbr_rfr`rhrirjrkr)r5r]rrr^r_r`rarbrcrdrerfrgrhrirjrkrwrorprxs r7ry)KandinskyInpaintCombinedPipeline.__call__s p+"7 9/(  %Q' -a 0!+FT5M!B!B&%fciioo>>E%/ CIIOO%L%Lj\R\ v;++A. .<3E3Ea3H3v;3VZ[3["((+s6{:fDF utUm , ,E \//22""1%E 2a7!''*c%j8EAE zD%= 1 1J,"4"4Q"77""1%J71<&,,Q/3z?BjPJ##!%"7 3)#)#$ " ##%r9r{r<r|r})-rrrrrrrrrr rrrrrr r rr rrr0rrr>rrrCrrGrOrXrrINPAINT_EXAMPLE_DOC_STRINGrrrrrrrryrrrs@r7rr<s<!l!.+ &+ '+ # +  56 +  + &+ ;+ 8+ '+ )+  2+ ZSxPXGYS VHSM VRWX]XdXdfiXiRj V5 < ]]_9: <@#& #%&&))+MQ*.%*GK %Lc49n%LU\\399??D4FSYY__H]]^L%,, ell9KTRUR[R[RaRaMbbc L "%T#Y"78 L ! LL #LLL$L$'LE%//43H"HIJL%,,'Lc]L 8S#u||$rs32 FEGG/1@@<*!""8:W 1Wtn'8nbu'8ur9