o Gi @sdddlZddlmZmZddlZddlmZmZmZm Z ddl m Z ddl m Z mZddlmZmZddlmZdd lmZdd lmZdd lmZmZmZmZmZmZdd lm Z d dl!m"Z"d dl#m$Z$ddl%m&Z&er{ddl'm(m)Z*dZ+ndZ+e,e-Z.dZ/    dde0dBde1ej2BdBde3e0dBde3e4dBfddZ5Gddde"ee e&Z6dS)N)AnyCallable)CLIPTextModelWithProjection CLIPTokenizerT5EncoderModelT5TokenizerFast)VaeImageProcessor)FromSingleFileMixinSD3LoraLoaderMixin)PAGCFGJointAttnProcessor2_0PAGJointAttnProcessor2_0) AutoencoderKL)SD3Transformer2DModel)FlowMatchEulerDiscreteScheduler)USE_PEFT_BACKENDis_torch_xla_availableloggingreplace_example_docstringscale_lora_layersunscale_lora_layers) randn_tensor)DiffusionPipeline)StableDiffusion3PipelineOutput)PAGMixinTFa^ Examples: ```py >>> import torch >>> from diffusers import AutoPipelineForText2Image >>> pipe = AutoPipelineForText2Image.from_pretrained( ... "stabilityai/stable-diffusion-3-medium-diffusers", ... torch_dtype=torch.float16, ... enable_pag=True, ... pag_applied_layers=["blocks.13"], ... ) >>> pipe.to("cuda") >>> prompt = "A cat holding a sign that says hello world" >>> image = pipe(prompt, guidance_scale=5.0, pag_scale=0.7).images[0] >>> image.save("sd3_pag.png") ``` num_inference_stepsdevice timestepssigmascKs|dur |dur td|dur>dtt|jjv}|s(td|jd|jd ||d||j}t |}||fS|durpdtt|jjv}|sZtd|jd|jd ||d ||j}t |}||fS|j|fd |i||j}||fS) a Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. Args: scheduler (`SchedulerMixin`): The scheduler to get timesteps from. num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` must be `None`. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. timesteps (`list[int]`, *optional*): Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, `num_inference_steps` and `sigmas` must be `None`. sigmas (`list[float]`, *optional*): Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, `num_inference_steps` and `timesteps` must be `None`. Returns: `tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the second element is the number of inference steps. NzYOnly one of `timesteps` or `sigmas` can be passed. Please choose one to set custom valuesrzThe current scheduler class zx's `set_timesteps` does not support custom timestep schedules. Please check whether you are using the correct scheduler.)rrr zv's `set_timesteps` does not support custom sigmas schedules. Please check whether you are using the correct scheduler.)r rr) ValueErrorsetinspect signature set_timesteps parameterskeys __class__rlen) schedulerrrrr kwargsaccepts_timesteps accept_sigmasr!r!]/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/pipelines/pag/pipeline_pag_sd_3.pyretrieve_timestepsMs2  r0c9seZdZdZdZgZgdZ dSdedede de d e d e d e d e d e deeeBffdd Z     dTdeeeBdededejdBdejdBf ddZ    dUdeeeBdedejdBdedBdef ddZ            dVdeeeBd!eeeBd"eeeBdejdBded#ed$eeeBdBd%eeeBdBd&eeeBdBd'ejdBd(ejdBd)ejdBd*ejdBdedBded+edBf d,d-Z         dWd.d/Z dXd0d1Zed2d3Zed4d5Zed6d7Z ed8d9Z!ed:d;Z"eddd?ddddddddddd@d ddddAgddBdCfdeeeBd!eeeBdBd"eeeBdBdDedBdEedBdFedGeedBdHed$eeeBdBd%eeeBdBd&eeeBdBdedBdIej'eej'BdBdAejdBd'ejdBd(ejdBd)ejdBd*ejdBdJedBdKedLe(ee)fdBdedBdMe*eegdfdBdNeededOedPef6dQdRZ+Z,S)YStableDiffusion3PAGPipelinea [PAG pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/pag) for text-to-image generation using Stable Diffusion 3. Args: transformer ([`SD3Transformer2DModel`]): Conditional Transformer (MMDiT) architecture to denoise the encoded image latents. scheduler ([`FlowMatchEulerDiscreteScheduler`]): A scheduler to be used in combination with `transformer` to denoise the encoded image latents. vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModelWithProjection`]): [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant, with an additional added projection layer that is initialized with a diagonal matrix with the `hidden_size` as its dimension. text_encoder_2 ([`CLIPTextModelWithProjection`]): [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), specifically the [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) variant. text_encoder_3 ([`T5EncoderModel`]): Frozen text-encoder. Stable Diffusion 3 uses [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). tokenizer_2 (`CLIPTokenizer`): Second Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). tokenizer_3 (`T5TokenizerFast`): Tokenizer of class [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer). z>text_encoder->text_encoder_2->text_encoder_3->transformer->vae)latents prompt_embedsnegative_prompt_embedsnegative_pooled_prompt_embedsblocks.1 transformerr+vae text_encoder tokenizertext_encoder_2 tokenizer_2text_encoder_3 tokenizer_3pag_applied_layersc st|j||||||| ||d t|ddr$dt|jjjdnd|_t |jd|_ t |dr<|j dur<|j j nd|_t |d rN|jdurN|jjjnd |_t |d r`|jdur`|jjjnd|_|j| ttfd dS) N) r8r9r;r=r:r<r>r7r+r8rr)vae_scale_factorr:Mr7)pag_attn_processors)super__init__register_modulesgetattrr*r8configblock_out_channelsrAr image_processorhasattrr:model_max_lengthtokenizer_max_lengthr7 sample_sizedefault_sample_size patch_sizeset_pag_applied_layersr r ) selfr7r+r8r9r:r;r<r=r>r?r)r!r/rFs6 (      z$StableDiffusion3PAGPipeline.__init__Nrpromptnum_images_per_promptmax_sequence_lengthrdtypecCs<|p|j}|p |jj}t|tr|gn|}t|}|jdur.tj||||j j j f||dS|j |d|dddd}|j }|j |dddj } | jd|jdkrqt|| sq|j | dd|jd df} td |d | |||d } |jj}| j||d } | j\} } } | d |d } | ||| d} | S)NrrY max_lengthTpt)paddingr[ truncationadd_special_tokensreturn_tensorslongestr]r`rzXThe following part of your input was truncated because `max_sequence_length` is set to tokens: rrYr)_execution_devicer9rY isinstancestrr*r=torchzerosr7rIjoint_attention_dimr> input_idsshapeequal batch_decoderNloggerwarningtorepeatview)rSrVrWrXrrY batch_size text_inputstext_input_idsuntruncated_ids removed_textr3_seq_lenr!r!r/_get_t5_prompt_embedssN     " z1StableDiffusion3PAGPipeline._get_t5_prompt_embedsr clip_skipclip_model_indexcCsp|p|j}|j|jg}|j|jg}||}||} t|tr!|gn|}t|} ||d|jddd} | j } ||dddj } | j d| j dkrht | | sh| | dd|jddf}td |jd || | |dd }|d }|dur|jd }n|j|d }|j|jj|d}|j \}}}|d|d}|| ||d}|d|}|| |d}||fS)Nr[Tr\)r]r[r^r`rarbrcrz\The following part of your input was truncated because CLIP can only handle sequences up to rd)output_hidden_statesrrre)rfr:r<r9r;rgrhr*rNrlrmrirnrorprqrr hidden_statesrYrsrt)rSrVrWrr}r~clip_tokenizersclip_text_encodersr:r9rurvrwrxryr3pooled_prompt_embedsrzr{r!r!r/_get_clip_prompt_embedssJ        z3StableDiffusion3PAGPipeline._get_clip_prompt_embedsTprompt_2prompt_3do_classifier_free_guidancenegative_promptnegative_prompt_2negative_prompt_3r3r4rr5 lora_scalec Cs<|p|j}|dur+t|tr+||_|jdurtrt|j||jdur+tr+t|j|t|tr3|gn|}|dur>t |}n| j d}| dur|pJ|}t|trS|gn|}|pX|}t|tra|gn|}|j ||||dd\}}|j ||||dd\}}t j ||gdd}|j||||d}t jj|d|j d|j df}t j ||gdd} t j ||gdd} |rn| durn|pd }|p|}| p|} t|tr||gn|}t|tr||gn|}t| tr|| gn| } |durt|t|urtd t|d t|d |t |kr td |dt |d|d|d |j |||ddd\}}|j |||ddd\}}t j ||gdd}|j| |||d}t jj|d|j d|j df}t j ||gdd} t j ||gdd} |jdurt|trtrt|j||jdurt|trtrt|j|| | | | fS)a" Args: prompt (`str` or `list[str]`, *optional*): prompt to be encoded prompt_2 (`str` or `list[str]`, *optional*): The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in all text-encoders prompt_3 (`str` or `list[str]`, *optional*): The prompt or prompts to be sent to the `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is used in all text-encoders device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `list[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). negative_prompt_2 (`str` or `list[str]`, *optional*): The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders. negative_prompt_3 (`str` or `list[str]`, *optional*): The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and `text_encoder_3`. If not defined, `negative_prompt` is used in all the text-encoders. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument. negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument. clip_skip (`int`, *optional*): Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings. lora_scale (`float`, *optional*): A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. Nr)rVrrWr}r~rrcdim)rVrWrXrrz?`negative_prompt` should be the same type to `prompt`, but got z != .z`negative_prompt`: z has batch size z, but `prompt`: zT. Please make sure that passed `negative_prompt` matches the batch size of `prompt`.)rrWr}r~)rfrgr _lora_scaler9rrr;rhr*rmrricatr|nn functionalpadtype TypeErrorr"r)rSrVrrrrWrrrrr3r4rr5r}rXrru prompt_embedpooled_prompt_embedprompt_2_embedpooled_prompt_2_embedclip_prompt_embedst5_prompt_embednegative_prompt_embednegative_pooled_prompt_embednegative_prompt_2_embednegative_pooled_prompt_2_embednegative_clip_prompt_embedst5_negative_prompt_embedr!r!r/ encode_promptOs B              z)StableDiffusion3PAGPipeline.encode_promptcs|jjdks|jjdkr+s  z;StableDiffusion3PAGPipeline.check_inputs..z2`callback_on_step_end_tensor_inputs` has to be in z , but found csg|] }|jvr|qSr!rrrr!r/ /sz.zCannot forward both `prompt`: z and `prompt_embeds`: z2. Please make sure to only forward one of the two.z Cannot forward both `prompt_2`: z Cannot forward both `prompt_3`: zeProvide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.z2`prompt` has to be of type `str` or `list` but is z4`prompt_2` has to be of type `str` or `list` but is z4`prompt_3` has to be of type `str` or `list` but is z'Cannot forward both `negative_prompt`: z and `negative_prompt_embeds`: z)Cannot forward both `negative_prompt_2`: z)Cannot forward both `negative_prompt_3`: zu`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but got: `prompt_embeds` z != `negative_prompt_embeds` zIf `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.zIf `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.iz8`max_sequence_length` cannot be greater than 512 but is ) rArQr"allrrgrhlistrrm)rSrVrrheightwidthrrrr3r4rr5"callback_on_step_end_tensor_inputsrXr!rr/ check_inputssz(StableDiffusion3PAGPipeline.check_inputsc Csz|dur |j||dS||t||jt||jf} t|tr3t||kr3tdt|d|dt| |||d}|S)NrZz/You have passed a list of generators of length z+, but requested an effective batch size of z@. Make sure the batch size matches the length of the generators.) generatorrrY)rrintrArgrr*r"r) rSrunum_channels_latentsrrrYrrr2rmr!r!r/prepare_latentsrs    z+StableDiffusion3PAGPipeline.prepare_latentscC|jSr_guidance_scalerr!r!r/guidance_scalez*StableDiffusion3PAGPipeline.guidance_scalecCrr) _clip_skiprr!r!r/r}rz%StableDiffusion3PAGPipeline.clip_skipcCs |jdkS)Nrrrr!r!r/rs z7StableDiffusion3PAGPipeline.do_classifier_free_guidancecCrr)_joint_attention_kwargsrr!r!r/joint_attention_kwargsrz2StableDiffusion3PAGPipeline.joint_attention_kwargscCrr)_num_timestepsrr!r!r/ num_timestepsrz)StableDiffusion3PAGPipeline.num_timestepscCrr) _interruptrr!r!r/ interruptrz%StableDiffusion3PAGPipeline.interruptg@pilr2g@grrrr rr output_type return_dictrcallback_on_step_endr pag_scalepag_adaptive_scalec1Csx|p|j|j}|p|j|j}|j|||||| | | ||||||d||_||_||_d|_||_||_|durAt |t rAd}n|durOt |t rOt |}n|j d}|j}|jdurc|jddnd}|jd#id|d|d |d | d | d | d |jd|d|d|d|d|d|jd| d|d|\}}}}|jr||||j}||||j}n|jrtj||gdd}tj||gdd}trd}n|}t|j|||d\} }tt | ||jjd}!t | |_|jjj }"|!|| |"|||j"|| |}|jr|jj#}#|j$|j%|jd|j&|d}$t'| D]\}%}&|j(r(qt|g|j d|j d}'|&)|'j d}(|j|'|(|||jddd})|jr]|*|)|j|j+|&})n|jrq|),d\}*}+|*|j+|+|*})|j"},|jj-|)|&|ddd}|j"|,krtj.j/0r|1|,}|duri}-|D] }.t2|.|-|.<q|||%|&|-}/|/3d|}|/3d|}|/3d|}|/3d|}|%t | dks|%d|!kr|%d|jjdkr|$4trt56qWdn 1swY|d kr|}0n||j7jj8|j7jj9}|j7j:|ddd}0|j;j<|0|d!}0|=|jr1|j>|#|s7|0fSt?|0d"S)$a Function invoked when calling the pipeline for generation. Args: prompt (`str` or `list[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. prompt_2 (`str` or `list[str]`, *optional*): The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is will be used instead prompt_3 (`str` or `list[str]`, *optional*): The prompt or prompts to be sent to `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is will be used instead height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. This is set to 1024 by default for the best results. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. This is set to 1024 by default for the best results. 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. sigmas (`list[float]`, *optional*): Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. guidance_scale (`float`, *optional*, defaults to 7.0): 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. negative_prompt (`str` or `list[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). negative_prompt_2 (`str` or `list[str]`, *optional*): The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used instead negative_prompt_3 (`str` or `list[str]`, *optional*): The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and `text_encoder_3`. If not defined, `negative_prompt` is used instead 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.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 will be generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument. negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead of a plain tuple. joint_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). 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. max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`. pag_scale (`float`, *optional*, defaults to 3.0): The scale factor for the perturbed attention guidance. If it is set to 0.0, the perturbed attention guidance will not be used. pag_adaptive_scale (`float`, *optional*, defaults to 0.0): The adaptive scale factor for the perturbed attention guidance. If it is set to 0.0, `pag_scale` is used. Examples: Returns: [`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. ) rrrr3r4rr5rrXFNrrscalerVrrrrrrr3r4rr5rr}rWrXrrcpu)r )r?r)total)rtimestepencoder_hidden_statespooled_projectionsrrr)rr2latent)r)imagesr!)@rPrArrrrr _pag_scale_pag_adaptive_scalergrhrr*rmrfrgetrrr}do_perturbed_attention_guidance%_prepare_perturbed_attention_guidancerir XLA_AVAILABLEr0r+maxorderrr7rI in_channelsrrYattn_processors_set_pag_attn_processorr? progress_bar enumeraterexpand#_apply_perturbed_attention_guidancerchunkstepbackendsmps is_availablerrlocalspopupdatexm mark_stepr8scaling_factor shift_factordecoderK postprocessmaybe_free_model_hooksset_attn_processorr)1rSrVrrrrrr rrrrrWrr2r3r4rr5rrrr}rrrXrrrurrtimestep_devicernum_warmup_stepsroriginal_attn_procritlatent_model_inputr noise_prednoise_pred_uncondnoise_pred_text latents_dtypecallback_kwargsrcallback_outputsimager!r!r/__call__sF                      6 :  z$StableDiffusion3PAGPipeline.__call__)r6)NrrUNN)rNNr) NrTNNNNNNNNrUN) NNNNNNNNNr)-__name__ __module__ __qualname____doc__model_cpu_offload_seq_optional_componentsrrrrrrrrrhrrFrrirrYr|rbool FloatTensorfloatrrrpropertyrr}rrrrno_gradrEXAMPLE_DOC_STRING Generatordictrrr __classcell__r!r!rTr/r1s$    /  <  =         J j              r1)NNNN)7r$typingrrri transformersrrrrrKr loadersr r models.attention_processorr r models.autoencodersrmodels.transformersr schedulersrutilsrrrrrrutils.torch_utilsrpipeline_utilsr"stable_diffusion_3.pipeline_outputr pag_utilsrtorch_xla.core.xla_modelcore xla_modelrr get_loggerrrpr rrhrrrr0r1r!r!r!r/sF              ;