3jx SSKrSSKJrJr SSKrSSKJrJrJrJ r J r SSK J r J r SSKJrJrJrJr SSKJrJrJr SSKJr SS KJr SS KJrJrJrJrJ r J!r!J"r"J#r# SS K$J%r% S S K&J'r'J(r( S SK)J*r* SSK+J,r, \"5(aS SK-J.r. \"5(a SSK/J0s J1r2 Sr3OSr3\ Rh"\55r6Sr7SSjr8SS\9S-S\:\Rv-S-S\<\9S-S\<\=S-4Sjjr>"SS\'\(\\\\\,5 r?g)N)AnyCallable)CLIPImageProcessor CLIPTextModelCLIPTextModelWithProjection CLIPTokenizerCLIPVisionModelWithProjection)PipelineImageInputVaeImageProcessor)FromSingleFileMixinIPAdapterMixin StableDiffusionXLLoraLoaderMixinTextualInversionLoaderMixin) AutoencoderKLImageProjectionUNet2DConditionModel)adjust_lora_scale_text_encoder)KarrasDiffusionSchedulers)USE_PEFT_BACKEND deprecate is_invisible_watermark_availableis_torch_xla_availableloggingreplace_example_docstringscale_lora_layersunscale_lora_layers) randn_tensor)DiffusionPipelineStableDiffusionMixin)StableDiffusionXLPipelineOutput)PAGMixin)StableDiffusionXLWatermarkerTFa Examples: ```py >>> import torch >>> from diffusers import AutoPipelineForText2Image >>> pipe = AutoPipelineForText2Image.from_pretrained( ... "stabilityai/stable-diffusion-xl-base-1.0", ... torch_dtype=torch.float16, ... enable_pag=True, ... ) >>> pipe = pipe.to("cuda") >>> prompt = "a photo of an astronaut riding a horse on mars" >>> image = pipe(prompt, pag_scale=0.3).images[0] ``` cUR[[SUR55SS9nUR[[SUR55SS9nXU- -nX%-SU- U--nU$)a Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891). Args: noise_cfg (`torch.Tensor`): The predicted noise tensor for the guided diffusion process. noise_pred_text (`torch.Tensor`): The predicted noise tensor for the text-guided diffusion process. guidance_rescale (`float`, *optional*, defaults to 0.0): A rescale factor applied to the noise predictions. Returns: noise_cfg (`torch.Tensor`): The rescaled noise prediction tensor. r#T)dimkeepdim)stdlistrangendim) noise_cfgnoise_pred_textguidance_rescalestd_textstd_cfgnoise_pred_rescaleds d/home/wildlama/miniconda3/lib/python3.13/site-packages/diffusers/pipelines/pag/pipeline_pag_sd_xl.pyrescale_noise_cfgr4Vs{"""tE!_5I5I,J'KUY"ZHmmU1inn%= >mMG#''9: 6!>N:NR[9[[I num_inference_stepsdevice timestepssigmasc UbUb [S5eUbS[[R"UR5R R 55;nU(d[SURS35eUR"S X2S.UD6 URn[U5nX14$UbS[[R"UR5R R 55;nU(d[SURS35eUR"S XBS.UD6 URn[U5nX14$UR"U4S U0UD6 URnX14$) 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. zYOnly one of `timesteps` or `sigmas` can be passed. Please choose one to set custom valuesr8zThe current scheduler class zx's `set_timesteps` does not support custom timestep schedules. Please check whether you are using the correct scheduler.)r8r7r9zv's `set_timesteps` does not support custom sigmas schedules. Please check whether you are using the correct scheduler.)r9r7r7) ValueErrorsetinspect signature set_timesteps parameterskeys __class__r8len) schedulerr6r7r8r9kwargsaccepts_timesteps accept_sigmass r3retrieve_timestepsrIqs}>!3tuu'3w/@/@AXAX/Y/d/d/i/i/k+ll .y/B/B.CDab  M)MfM'' !)n  ))   C(9(9):Q:Q(R(](](b(b(d$ee .y/B/B.CD_`  GvGG'' !)n  ))  3MFMfM''  ))r5cK^\rSrSrSrSr/SQr/SQrS[S\S \ S \ S \ S \ S \ S\ S\S\S\S\S-S\\\-4U4SjjjrS\S\S\S-S\R*S-S\S\S\S-S\S-S\R.S-S\R.S-S\R.S-S \R.S-S!\S-S"\S-4S#jjrS]S$jrS%rS&rS^S'jrS]S(jrS]S)jrS*r S+\RB4S,\R.S-\S.\RDS/\R.4S0jjr#\$S15r%\$S25r&\$S35r'\$S45r(\$S55r)\$S65r*\$S75r+\$S85r,\RZ"5\."\/5SSSSS9SSSS:SSSS;SSSSSSSSS/S?S;4$S\\\-S\\\-S-S@\S-SA\S-SB\SC\\SD\\SE\S-SF\S\\\-S-S\\\-S-S\S-SG\SH\R`\\R`-S-S>\R.S-S\R.S-S\R.S-S\R.S-S \R.S-SI\1S-SJ\\R.S-SK\S-SL\SM\2\\34S-SN\SO\4\\4S-SP\4\\4SQ\4\\4S-SR\4\\4S-SS\4\\4ST\4\\4S-S"\S-SU\5\\/S4S-SV\\SW\SX\4HSYjj55r6SZr7U=r8$)_StableDiffusionXLPAGPipelineao Pipeline for text-to-image generation using Stable Diffusion XL. 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.) The pipeline also inherits the following loading methods: - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion XL uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. text_encoder_2 ([` CLIPTextModelWithProjection`]): Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of [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. 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). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture 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`]. force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`): Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of `stabilityai/stable-diffusion-xl-base-1-0`. add_watermarker (`bool`, *optional*): Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to watermark output images. If not defined, it will default to True if the package is installed, otherwise no watermarker will be used. z6text_encoder->text_encoder_2->image_encoder->unet->vae) tokenizer tokenizer_2 text_encodertext_encoder_2 image_encoderfeature_extractor)latents prompt_embedsnegative_prompt_embedsadd_text_embeds add_time_idsnegative_pooled_prompt_embedsnegative_add_time_idsNTvaerOrPrMrNunetrErQrRforce_zeros_for_empty_promptadd_watermarkerpag_applied_layersc ~>[T U]5 URUUUUUUUUU S9 URU S9 [ USS5(a/S[ UR RR5S- -OSUl [URS9Ul [US5(aRURbE[URRS 5(a URRROS UlU bU O [!5n U (a[#5UlOSUlUR'U 5 g) N) rZrOrPrMrNr[rErQrR)r\rZrr#)vae_scale_factorr[ sample_size)super__init__register_modulesregister_to_configgetattrrDrZconfigblock_out_channelsrar image_processorhasattrr[rbdefault_sample_sizerr% watermarkset_pag_applied_layers)selfrZrOrPrMrNr[rErQrRr\r]r^rCs r3re%StableDiffusionXLPAGPipeline.__init__s#  %)#'/   =YZV]^bdikoVpVpc$((//*L*L&MPQ&Q Rvw0$BWBWXtV$$)>7499K[K[]jCkCk II   ( (  .=-H/NnNp 9;DN!DN ##$67r5r#promptprompt_2r7num_images_per_promptdo_classifier_free_guidancenegative_promptnegative_prompt_2rTrUpooled_prompt_embedsrX lora_scale clip_skipc  U=(d URnU b[U[5(aXlURb8[ (d[ URU 5 O[URU 5 URb8[ (d[ URU 5 O[URU 5 [U[5(aU/OUnUb [U5nOURSnURbURUR/O UR/nURbURUR/O UR/nUGcU=(d Un[U[5(aU/OUn/nX/n[UUU5GHQunnn[U[5(aUR!UU5nU"USUR"SSS9nUR$nU"USSS9R$nURS URS :ah[&R("UU5(dLUR+USS2UR"S - S 245n[,R/S UR"S U35 U"UR1U5SS 9nU cUSR2S:XaUSn U cUR4SnOUR4U S-*nUR7U5 GMT [&R8"US S9nUSL=(a UR:R<nU(a8U c5U(a.[&R>"U5n [&R>"U 5n GOU(GaU GcU=(d SnU=(d Un[U[5(aX/-OUn[U[5(aX/-OUnUb;[AU5[AU5La$[CS[AU5S[AU5S35eU[U5:wa![ESUS[U5SUSUS3 5eXg/n/n[UUU5Hunnn[U[5(aUR!UU5nURS nU"USUSSS9nU"UR$R1U5SS 9n U cU SR2S:XaU Sn U R4Sn UR7U 5 M [&R8"US S9n URb%UR1URRFUS9nO$UR1URHRFUS9nURunnn URKS US 5nURMUU-US 5nU(aU RS nURb%U R1URRFUS9n O$U R1URHRFUS9n U RKS US 5n U RMX-US 5n U RKS U5RMUU-S 5n U(a%U RKS U5RMUU-S 5n URb6[U[5(a![ (a[OURU 5 URb6[U[5(a![ (a[OURU 5 XX4$)a Encodes the prompt into text encoder hidden states. 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 both 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 both text-encoders prompt_embeds (`torch.Tensor`, *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.Tensor`, *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.Tensor`, *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.Tensor`, *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. lora_scale (`float`, *optional*): A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. 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. Nr max_lengthTpt)paddingr| truncationreturn_tensorslongest)r~rr#z\The following part of your input was truncated because CLIP can only handle sequences up to z tokens: output_hidden_statesrr'z?`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`.)dtyper7)(_execution_device isinstancer _lora_scalerOrrrrPstrrDshaperMrNziprmaybe_convert_promptmodel_max_length input_idstorchequal batch_decodeloggerwarningtor, hidden_statesappendconcatrir\ zeros_liketype TypeErrorr<rr[repeatviewr)!rprrrsr7rtrurvrwrTrUrxrXryrz batch_size tokenizers text_encodersprompt_embeds_listpromptsrMrO text_inputstext_input_idsuntruncated_ids removed_textzero_out_negative_prompt uncond_tokensnegative_prompt_embeds_listr| uncond_inputbs_embedseq_len_s! r3 encode_prompt*StableDiffusionXLPAGPipeline.encode_prompt%st1411  !j7W&X&X)   ,''243D3DjQ%d&7&7D"".''243F3F S%d&9&9:F'44&&  VJ&,,Q/J<@>>;Udnnd&6&67\`\l\l[m 8<8I8I8UT   3 3 4\`\o\o[p   )6H%/#%>%>zHH"$ (G36w M3Z/ <d$?@@!66vyIF'((99##'  "-!6!6"+FIVZ"["e"e"((,0D0DR0HHQVQ\Q\"ORR$-#9#9/!YMgMgjkMknpMpJp:q#rLNN%667yP !-^->->v-F]a b (/M!4D4I4IQ4N+8+;($$1$?$?$CM%2$?$?)a-@P$QM"))-8I4[L"LL);DM$3d#:#gt{{?g?g &+A+INf%*%5%5m%D ",1,<,<=Q,R ) (-C-K-3O 1 D_ AK?\_@`@`j+<?PRU4V4V 00\m  !d6l$:O&OUVZ[jVkUlmV ~Q(s?33 )/)::J3K_J`ax/ |<33 "1 D *, 'STYY , ,b  ',I,P,PQRTi,j,o,o00"- )    ($ @AAFVFV#D$5$5zB    *$ @AAFVFV#D$7$7D6Jiir5cd[URR55Rn[ U[ R 5(dURUSS9RnURX%S9nU(aqURUSS9RSnURUSS9nUR[ R"U5SS9RSnURUSS9nXg4$URU5RnURUSS9n[ R"U5n X4$) Nr})rr7rTrrrr)nextrQrArrrTensorrR pixel_valuesrrrepeat_interleaver image_embeds) rpimager7rtrrimage_enc_hidden_statesuncond_image_enc_hidden_statesruncond_image_embedss r3 encode_image)StableDiffusionXLPAGPipeline.encode_images?T''2245;;%..**5*FSSE4 &*&8&8UY&8&Z&h&hik&l #&=&O&OPekl&O&m #-1-?-?  'd.@.mB. *.L-]-]%1.^. *+J J--e4AAL'99:OUV9WL"'"2"2<"@ 4 4r5c /nU(a/nUGc&[U[5(dU/n[U5[URRR 5:waB[ S[U5S[URRR 5S35e[XRRR 5Hhup[U [5(+n URXSU 5upURU SSS245 U(dMPWRU SSS245 Mj OEUH?n U(a$U RS5upWRU 5 URU 5 MA /n[U5Hwup[R"U /U-SS9n U(a2[R"WU /U-SS9n [R"X/SS9n U RUS9n URU 5 My U$) NzK`ip_adapter_image` must have same length as the number of IP Adapters. Got z images and z IP Adapters.r#rrr)r7)rr*rDr[encoder_hid_projimage_projection_layersr<rrrrchunk enumeratercatr)rpip_adapter_imageip_adapter_image_embedsr7rtrurnegative_image_embedssingle_ip_adapter_imageimage_proj_layeroutput_hidden_statesingle_image_embedssingle_negative_image_embedsis r3prepare_ip_adapter_image_embedsA ))"<"<"T"T>9'+55E*W&W#DHDUDU+Q8KEA###$7a$@A..)001MdTUg1VW>(?#.H[HaHabcHdE0)001MN##$78 (? #%&/ &= "A"'))-@,ADY,Y_`"a */4yy:OPQ:R9SVk9kqr/s,&+ii1M0cij&k#"5"8"8"8"G  # * *+> ?'>'&r5cnS[[R"URR5R R 55;n0nU(aX$S'S[[R"URR5R R 55;nU(aXS'U$)Neta generator)r=r>r?rEsteprArB)rprr accepts_etaextra_step_kwargsaccepts_generators r3prepare_extra_step_kwargs6StableDiffusionXLPAGPipeline.prepare_extra_step_kwargs[s s7#4#4T^^5H5H#I#T#T#Y#Y#[\\  '*e $(3w/@/@ATAT/U/`/`/e/e/g+hh -6k *  r5c  ^US-S:wd US-S:wa[SUSUS35eUb6[U[5(aUS::a[SUS[U5S35eUbW[ U4SjU55(d=[S TR S UVs/sHoTR ;dMUPM sn35eUbUb[S US US 35eUbUb[SUS US 35eUcUc [S5eUbA[U[ 5(d,[U[5(d[S[U535eUbA[U[ 5(d,[U[5(d[S[U535eUbU b[SUSU S 35eUbU b[SUSU S 35eUbCU b@URU R:wa&[SURSU RS35eUbU c [S5eU bU c [S5eU bU b [S5eU b\[U [5(d[S[U 535eU SRS;a[SU SRS35eggs snf)Nr`rz7`height` and `width` have to be divisible by 8 but are z and rz5`callback_steps` has to be a positive integer but is z of type c3@># UHoTR;v M g7fN)_callback_tensor_inputs).0krps r3 .sF 7Y!-- -7Ysz2`callback_on_step_end_tensor_inputs` has to be in z , but found zCannot forward both `prompt`: z and `prompt_embeds`: z2. Please make sure to only forward one of the two.z Cannot forward both `prompt_2`: zeProvide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.z2`prompt` has to be of type `str` or `list` but is z4`prompt_2` has to be of type `str` or `list` but is z'Cannot forward both `negative_prompt`: z and `negative_prompt_embeds`: z)Cannot forward both `negative_prompt_2`: zu`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but got: `prompt_embeds` z != `negative_prompt_embeds` zIf `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.zIf `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.zProvide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.z:`ip_adapter_image_embeds` has to be of type `list` but is )r zF`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is D) r<rintrallrrr*rr,)rprrrsheightwidthcallback_stepsrvrwrTrUrxrXrr"callback_on_step_end_tensor_inputsrs` r3 check_inputs)StableDiffusionXLPAGPipeline.check_inputsms" A:?eai1nVW]V^^cdicjjklm m  %z.#/N/NR`deReGGWX(),  . 9#F 7YF C C DTEaEaDbbn|^pH|^vwkokGkGbGpq|^pHoIJ   -";08N}o^00  !m&?28* \]tuv]w]|]|\}}~? /qpHs J *J c VUU[U5UR-[U5UR-4n [U[5(a*[ U5U:wa[ S[ U5SUS35eUc [ XXeS9nOURU5nXRR-nU$)Nz/You have passed a list of generators of length z+, but requested an effective batch size of z@. Make sure the batch size matches the length of the generators.)rr7r) rrarr*rDr<rrrEinit_noise_sigma) rprnum_channels_latentsrrrr7rrSrs r3prepare_latents,StableDiffusionXLPAGPipeline.prepare_latentss  K400 0 J$// /   i & &3y>Z+GA#i.AQR&<'gi  ?"5fZGjj(GNN;;;r5c2[X-U-5nURRR[ U5-U-nURR R RnX:wa[SUSUS35e[R"U/US9nU$)Nz7Model expects an added time embedding vector of length z, but a vector of z was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.r) r*r[riaddition_time_embed_dimrD add_embeddinglinear_1 in_featuresr<rtensor) rp original_sizecrops_coords_top_left target_sizertext_encoder_projection_dimrWpassed_add_embed_dimexpected_add_embed_dims r3_get_add_time_ids.StableDiffusionXLPAGPipeline._get_add_time_idssMAKOP  II   4 4s<7H HKf f "&!8!8!A!A!M!M ! 9IJ`IaasuItJJUV ||\N%@ r5cl[SSS5 URR[RS9 g)N upcast_vaez1.0.0z`upcast_vae` is deprecated. Please use `pipe.vae.to(torch.float32)`. For more details, please refer to: https://github.com/huggingface/diffusers/pull/12619#issue-3606633695.r)rrZrrfloat32rps r3r'StableDiffusionXLPAGPipeline.upcast_vaes.   |  %-- (r5iw embedding_dimrreturncr[UR5S:XdeUS-nUS-n[R"[R"S55US- - n[R "[R "XCS9U*-5nURU5SS2S4USSS24-n[R"[R"U5[R"U5/SS9nUS-S:Xa*[RRRUS5nURURS U4:XdeU$) a, See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 Args: w (`torch.Tensor`): Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings. embedding_dim (`int`, *optional*, defaults to 512): Dimension of the embeddings to generate. dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): Data type of the generated embeddings. Returns: `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`. r#g@@rg@rNr)rr#r)rDrrlogrexparangerrsincosnn functionalpad)rprrrhalf_dimembs r3get_guidance_scale_embedding9StableDiffusionXLPAGPipeline.get_guidance_scale_embeddings"177|q   J A%ii W-.(Q,?ii X;sdBCdd5k!T'"Sq\1ii338a@ 1  !((%%))#v6CyyQWWQZ7777 r5cUR$r)_guidance_scalers r3guidance_scale+StableDiffusionXLPAGPipeline.guidance_scales###r5cUR$r)_guidance_rescalers r3r/-StableDiffusionXLPAGPipeline.guidance_rescale#s%%%r5cUR$r) _clip_skiprs r3rz&StableDiffusionXLPAGPipeline.clip_skip' r5crURS:=(a" URRRSL$)Nr#)rr[ritime_cond_proj_dimrs r3ru8StableDiffusionXLPAGPipeline.do_classifier_free_guidance.s.##a'WDII,<,<,O,OSW,WWr5cUR$r)_cross_attention_kwargsrs r3cross_attention_kwargs3StableDiffusionXLPAGPipeline.cross_attention_kwargs2s+++r5cUR$r)_denoising_endrs r3 denoising_end*StableDiffusionXLPAGPipeline.denoising_end6"""r5cUR$r)_num_timestepsrs r3 num_timesteps*StableDiffusionXLPAGPipeline.num_timesteps:r+r5cUR$r) _interruptrs r3 interrupt&StableDiffusionXLPAGPipeline.interrupt>rr52g@pil)rrrSg@rrr6r8r9r)rrrrr output_type return_dictr%r/rrrnegative_original_sizenegative_crops_coords_top_leftnegative_target_sizecallback_on_step_endr pag_scalepag_adaptive_scalec%x^GU=(d URUR-nU=(d URUR-nU=(d X44nU=(d X44nURUUUUSU U UUUUUUU"5 XlUUlU UlUUlXlSUlU#Ul U$Ul Ub[U[5(aSn%O3Ub![U[5(a [U5n%OURSn%UR n&UR"bUR"R%SS5OSn'UR'UUU&U UR(U U UUUUU'UR*S9 unnnn[,(aSn(OU&n([/UR0UU(Xg5upeUR2R4R6n)UR9U%U -U)UUUR:U&UU5nUR=X5n*Un+UR>c[AURS5n,O UR>R4RBn,UREUUUUR:U,S 9n-Ub!UbUREUUUUR:U,S 9n.OU-n.URF(aXURIUUUR(5nURIU+UUR(5n+URIU-U.UR(5n-OVUR((aE[JRL"UU/SS 9n[JRL"UU+/SS 9n+[JRL"U.U-/SS 9n-UROU&5nU+ROU&5n+U-ROU&5RQU%U -S5n-UcUbURSUUU&U%U -UR(5n[UU5Hun/n0Sn1UR((aU0RWS 5un1n0URF(aURIU0U1UR(5n0O(UR((a[JRL"U1U0/SS 9n0U0ROU&5n0U0UU/'M [Y[U5XPR0RZ-- S5n2UR\b[UR\[^5(aUR\S:aUR\S:a[A[aUR0R4RbUR\UR0R4Rb-- 55mG[[UG4S jU555nUSUnSn3UR2R4Rfb{[JRh"URjS- 5RQU%U -5n4URmU4UR2R4RfS 9ROU&UR:S9n3URF(a:UR2Rnn5URqURrUR(S9 [U5Ul:URwUS9n6[UU5GHun/n7URx(aM[JRL"U/URSURS--5n8UR0R{U8U75n8U+U-S.n9UbUU9S'UR3U8U7UU3UR"U9SS9Sn:URF(a-UR}U:UR(URjU7S5un:n;O:UR((a)U:RWS 5un[JRRR5(aUROU=5nU!b0n>U"Hn?[5U?U>U?'M U!"UU/U7U>5n@U@RSU5nU@RSU5nU@RSU5nU@RSU+5n+U@RSU5nU@RSU-5n-U@RSU.5n.U/[U5S- :Xd)U/S-U2:a0U/S-UR0RZ-S:XaU6R5 [,(dGM[R"5 GM SSS5 US:XGd=URR:[JR:H=(a URR4RnAUA(a`UR5 URO[[URRR555R:5nO{UR:URR::waW[JRRR5(a*URROUR:5UlJ[URR4S 5=(a" URR4RSLnB[URR4S!5=(a" URR4RSLnCWB(GaWC(a[JRh"URR4R5RSS"SS5ROURUR:5nD[JRh"URR4R5RSS"SS5ROURUR:5nEUUE-URR4R- UD-nO"XRR4R- nURRUSS#9SnFWA(a'URRO[JRS$9 OUnFUS:XdBURbURRWF5nFURRWFUS%9nFUR5 URF(aUR2RW55 U(dWF4$[WFS&9$!,(df  GN=f)'uo. 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 the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders 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. Anything below 512 pixels won't work well for [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and checkpoints that are not specifically fine-tuned on low resolutions. 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. Anything below 512 pixels won't work well for [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and checkpoints that are not specifically fine-tuned on low resolutions. 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. timesteps (`list[int]`, *optional*): Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. Must be in descending order. 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. denoising_end (`float`, *optional*): When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be completed before it is intentionally prematurely terminated. As a result, the returned sample will still retain a substantial amount of noise as determined by the discrete timesteps selected by the scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output) guidance_scale (`float`, *optional*, defaults to 5.0): 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 in both text-encoders 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) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. 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`. prompt_embeds (`torch.Tensor`, *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.Tensor`, *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.Tensor`, *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.Tensor`, *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. ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. ip_adapter_image_embeds (`list[torch.Tensor]`, *optional*): Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not provided, embeddings are computed from the `ip_adapter_image` 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. cross_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). guidance_rescale (`float`, *optional*, defaults to 0.0): Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) `guidance_scale` is defined as `φ` in equation 16. of [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891). Guidance rescale factor should fix overexposure when using zero terminal SNR. original_size (`tuple[int]`, *optional*, defaults to (1024, 1024)): If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). crops_coords_top_left (`tuple[int]`, *optional*, defaults to (0, 0)): `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). target_size (`tuple[int]`, *optional*, defaults to (1024, 1024)): For most cases, `target_size` should be set to the desired height and width of the generated image. If not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). negative_original_size (`tuple[int]`, *optional*, defaults to (1024, 1024)): To negatively condition the generation process based on a specific image resolution. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. negative_crops_coords_top_left (`tuple[int]`, *optional*, defaults to (0, 0)): To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. negative_target_size (`tuple[int]`, *optional*, defaults to (1024, 1024)): To negatively condition the generation process based on a target image resolution. It should be as same as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. 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. 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_xl.StableDiffusionXLPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. NFr#rscale) rrrsr7rtrurvrwrTrUrxrXryrzcpur)rrrrc>UT:$rr;)tsdiscrete_timestep_cutoffs r37StableDiffusionXLPAGPipeline.__call__..s RC[=[r5)rr)r^ru)total) text_embedstime_idsr)encoder_hidden_states timestep_condr%added_cond_kwargsr8Tr5)r/r8rSrTrUrVrXrWrYlatent latents_mean latents_stdr)r8r)r7)images)`rmrarrrrr$r(r1 _pag_scale_pag_adaptive_scalerrr*rDrrr%getrrurz XLA_AVAILABLErIrEr[ri in_channelsrrrrPrprojection_dimrdo_perturbed_attention_guidance%_prepare_perturbed_attention_guidancerrrrrrrmaxorderr)floatroundnum_train_timestepsfilterr!rrrattn_processors_set_pag_attn_processorr^r- progress_barr2scale_model_input#_apply_perturbed_attention_guidancer/r4rbackendsmps is_availablelocalspopupdatexm mark_steprZfloat16 force_upcastrriterpost_quant_convrArlrNrOrr7scaling_factordecodernapply_watermarkrk postprocessmaybe_free_model_hooksset_attn_processorr")Hrprrrsrrr6r8r9r)rrvrwrtrrrSrTrUrxrXrrr7r8r%r/rrrr9r:r;rzr<rr=r>rr7rytimestep_devicerrrVrrWrYrrrnum_warmup_stepsrKguidance_scale_tensororiginal_attn_procratlatent_model_inputrL noise_predr.noise_pred_uncond latents_dtypecallback_kwargsrcallback_outputsneeds_upcastinghas_latents_meanhas_latents_stdrNrOrrDsH @r3__call__%StableDiffusionXLPAGPipeline.__call__Bs DK433d6K6KKI11D4I4II%8& !4f_           " )  # . 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