+h JSSKrSSKJrJrJrJrJrJrJr SSK r SSK r SSK r SSK JrJrJrJr SSKJrJr SSKJrJrJr SSKJr SSKJrJr SS KJr SS K J!r! SS K"J#r#J$r$J%r%J&r&J'r'J(r( SS K)J*r* S SK+J,r, SSK-J.r. \$"5(a SSK/J0s J1r2 Sr3OSr3\%Rh"\55r6Sr7S$S\8S\8S\9S\94Sjjr:S%S\ RvS\\ RxS\=4Sjjr>S&S\\8S\\\=\ R~4S\\\8S \\\94S!jjr@"S"S#\,\\5rAg)'N)AnyCallableDictListOptionalTupleUnion) CLIPTextModel CLIPTokenizerT5EncoderModelT5TokenizerFast)PipelineImageInputVaeImageProcessor)FluxLoraLoaderMixinFromSingleFileMixinTextualInversionLoaderMixin) AutoencoderKL)FluxControlNetModelFluxMultiControlNetModel)FluxTransformer2DModel)FlowMatchEulerDiscreteScheduler)USE_PEFT_BACKENDis_torch_xla_availableloggingreplace_example_docstringscale_lora_layersunscale_lora_layers) randn_tensor)DiffusionPipeline)FluxPipelineOutputTFa Examples: ```py >>> import torch >>> from diffusers import FluxControlNetInpaintPipeline >>> from diffusers.models import FluxControlNetModel >>> from diffusers.utils import load_image >>> controlnet = FluxControlNetModel.from_pretrained( ... "InstantX/FLUX.1-dev-controlnet-canny", torch_dtype=torch.float16 ... ) >>> pipe = FluxControlNetInpaintPipeline.from_pretrained( ... "black-forest-labs/FLUX.1-schnell", controlnet=controlnet, torch_dtype=torch.float16 ... ) >>> pipe.to("cuda") >>> control_image = load_image( ... "https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Canny-alpha/resolve/main/canny.jpg" ... ) >>> init_image = load_image( ... "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" ... ) >>> mask_image = load_image( ... "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" ... ) >>> prompt = "A girl holding a sign that says InstantX" >>> image = pipe( ... prompt, ... image=init_image, ... mask_image=mask_image, ... control_image=control_image, ... control_guidance_start=0.2, ... control_guidance_end=0.8, ... controlnet_conditioning_scale=0.7, ... strength=0.7, ... num_inference_steps=28, ... guidance_scale=3.5, ... ).images[0] >>> image.save("flux_controlnet_inpaint.png") ``` base_seq_len max_seq_len base_shift max_shiftc4XC- X!- - nX5U-- nX-U-nU$N) image_seq_lenr$r%r&r'mbmus v/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.pycalculate_shiftr0Ws3  K$>?A%%A  Q B Iencoder_output generator sample_modec[US5(a!US:XaURRU5$[US5(a US:XaURR5$[US5(a UR$[ S5e)N latent_distsampleargmaxlatentsz3Could not access latents of provided encoder_output)hasattrr6r7moder9AttributeError)r2r3r4s r/retrieve_latentsr=es}~}--+2I))00;;  / /K84K))..00  + +%%%RSSr1num_inference_stepsdevice timestepssigmasc UbUb [S5eUbS[[R"UR5R R 55;nU(d[SURS35eUR"S X2S.UD6 URn[U5nX14$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 valuesr@zThe current scheduler class zx's `set_timesteps` does not support custom timestep schedules. Please check whether you are using the correct scheduler.)r@r?rAzv's `set_timesteps` does not support custom sigmas schedules. Please check whether you are using the correct scheduler.)rAr?r?r*) ValueErrorsetinspect signature set_timesteps parameterskeys __class__r@len) schedulerr>r?r@rAkwargsaccepts_timesteps accept_sigmass r/retrieve_timestepsrPss}>!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$ee .y/B/B.CD_`  GvGG'' !)n  ))  3MFMfM''  ))r1c;^\rSrSrSrSr/r/SQrS\S\ S\ S\ S \ S \ S \S \\\\\\\44U4S jjrSIS\\\\4S\S\S\\R2S\\R44 SjjrSJS\\\\4S\S\\R24SjjrSKS\\\\4S\\\\\4S\\R2S\S\\R:S\\R:S\S\\4SjjrS\R@S\RB4Sjr"S r#SLS!jr$\%S"5r&\%S#5r'\%S$5r(SMS%jr)S&r*SNS'jr+\,S(5r-\,S)5r.\,S*5r/\,S+5r0\Rb"5\2"\35SSSSSSSSS,SSS-S.S/S0SS0SSSSSS1S2SSS3/S4S\\\\4S\\\\\4S\4S4\4S5\4S6\4S7\\S8\\S9\S:\\S;\\\S<\S=\S>\\\\4S?\\\\4S@\\\\\4SA\\\\4S\\S\\\RB\\RB4S3\\R:S\\R:S\\R:SB\\SC\5SD\\6\\74SE\\8\\\6/S4SF\\S\48SGjj55r9SHr:U=r;$)OFluxControlNetInpaintPipelineax The Flux controlnet pipeline for inpainting. Reference: https://blackforestlabs.ai/announcing-black-forest-labs/ Args: transformer ([`FluxTransformer2DModel`]): 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 ([`CLIPTextModel`]): [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 ([`T5EncoderModel`]): [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer). tokenizer_2 (`T5TokenizerFast`): Second Tokenizer of class [T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast). z.text_encoder->text_encoder_2->transformer->vae)r9 prompt_embeds control_imagemaskmasked_image_latentsrLvae text_encoder tokenizertext_encoder_2 tokenizer_2 transformer controlnetc >[T U]5 [U[[45(a [ U5nUR UUUUUUUUS9 [USS5(a/S[URRR5S- -OSUl [URS-S9Ul[USS5(a URRROSn [URS-U SS S S 9Ul[#US 5(a#UR$bUR$R&OS UlS Ulg)N)rLrXrYrZr[r\r]r^rXr r")vae_scale_factorFT)ravae_latent_channels do_normalize do_binarizedo_convert_grayscalerZM)super__init__ isinstancelisttuplerregister_modulesgetattrrKrXconfigblock_out_channelsrarimage_processorlatent_channelsmask_processorr:rZmodel_max_lengthtokenizer_max_lengthdefault_sample_size) selfrLrXrYrZr[r\r]r^rsrJs r/rj&FluxControlNetInpaintPipeline.__init__s4  j4- 0 01*=J %)##!  W^^bdikoVpVpc$((//*L*L&MPQ&Q Rvw 1$BWBWZ[B[\=DT5RV=W=W$((//99]_/!22Q6 /!%  07t[/I/IdnnNhDNN + +np !$' r1Nr"promptnum_images_per_promptmax_sequence_lengthr?dtypec U=(d URnU=(d URRn[U[5(aU/OUn[ U5n[U[ 5(aURXR5nURUSUSSSSS9nURnURUSSS9Rn U RSURS:ag[R"X5(dLURRU SS2URS - S245n [R!S US U 35 UR#UR%U5SS 9S n UR"RnU R%XTS9n U Rupn U R'S US 5n U R)Xb-U S5n U $)N max_lengthTFpt)paddingr truncation return_lengthreturn_overflowing_tokensreturn_tensorslongestrrr"zXThe following part of your input was truncated because `max_sequence_length` is set to tokens: output_hidden_statesrr~r?)_execution_devicerYr~rkstrrKrmaybe_convert_promptr\ input_idsshapetorchequal batch_decodervloggerwarningr[torepeatview)rxr{r|r}r?r~ batch_size text_inputstext_input_idsuntruncated_ids removed_textrT_seq_lens r/_get_t5_prompt_embeds3FluxControlNetInpaintPipeline._get_t5_prompt_embedss14110**00'44&&[ d7 8 8..v7G7GHF&&  *&+' %..**69UY*Zdd   $(<(>66q$JcJcfgJgjlJlGl7mnL NN--.i ~G )).*;*;F*CZ_)` &33 %((t/@/@/F/Fv(V &,,Q0EF %**:+MrR r1prompt_2rTpooled_prompt_embeds lora_scalec U=(d URnUbw[U[5(abXlURb![ (a[ URU5 URb![ (a[ URU5 [U[5(aU/OUnUcHU=(d Un[U[5(aU/OUnURUUUS9nURUUUUS9nURb6[U[5(a![ (a[URU5 URb6[U[5(a![ (a[URU5 URbURROURRn [R"UR SS5R#X9S9n XVU 4$)a? Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in all text-encoders device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument. lora_scale (`float`, *optional*): A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. )r{r?r|)r{r|r}r?r"rr?r~)rrkr _lora_scalerYrrr[rrrrr~r]rzerosrr) rxr{rr?r|rTrr}rr~text_idss r/ encode_prompt+FluxControlNetInpaintPipeline.encode_promptZs@1411  !j7J&K&K)   ,1A1A!$"3"3Z@"".3C3C!$"5"5zB'44&&  )6H%/#%>%>zHH$(#?#?&;$@$ !66&;$7 7M    ($ 3449I9I#D$5$5zB    *$ 3449I9I#D$7$7D+/+<+<+H!!''dN^N^NdNd;;}2215q9<strengthr? init_timestept_startr@s r/ get_timesteps+FluxControlNetInpaintPipeline.get_timestepss/:>#4 5 5 NN * *7^^5I5I+I J777r1c ^US:dUS:a[SU35eUTRS--S:wdUTRS--S:wa,[RSTRS-SUSUS35 U bW[ U4S jU 55(d=[S TR S U Vs/sHoTR ;dMUPM sn35eUbU b[S US U S35eUbU b[SUS U S35eUcU c [S5eUbA[ U[5(d,[ U[5(d[S[U535eUbA[ U[5(d,[ U[5(d[S[U535eU bU c [S5eU b[ U[RR5(d[S[U5S35e[ U[RR5(d[S[U5S35eUS:wa[SUS35eU bU S:a[SU 35eggs snf)Nrr"z2The value of strength should in [0.0, 1.0] but is r z-`height` and `width` have to be divisible by z but are z and z(. Dimensions will be resized accordinglyc3@># UHoTR;v M g7fr))_callback_tensor_inputs).0krxs r/ =FluxControlNetInpaintPipeline.check_inputs..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 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`.zJThe image should be a PIL image when inpainting mask crop, but is of type .zOThe mask image should be a PIL image when inpainting mask crop, but is of type pilz@The output type should be PIL when inpainting mask crop, but is rzz8`max_sequence_length` cannot be greater than 512 but is ) rCrarrallrrkrrltypePILImage)rxr{rr mask_imagerheightwidth output_typerTr"callback_on_step_end_tensor_inputspadding_mask_cropr}rs` r/ check_inputs*FluxControlNetInpaintPipeline.check_inputss a<8a<QRZQ[\] ] T**Q. /1 4AVAVYZAZ8[_`8` NN?@U@UXY@Y?ZZcdjckkpqvpwx`a  . 9#F 7YF C C DTEaEaDbbn|^pH|^vwkokGkGbGpq|^pHoIJ   -";08N}o^00  !m&?28*RSTVZSZ>[#[ #3F#;ell5>QRVXYRY>Z#Z RbRhRhO7O+33 " :>r1cURXUS-SUS-S5nURSSSSSS5nURXS-US--US-5nU$)Nr rr"r)rpermuter)r9rnum_channels_latentsrrs r/ _pack_latents+FluxControlNetInpaintPipeline._pack_latentssg,,z1aQVZ[Q[]^_//!Q1a3//*{uz.JL`cdLder1cURupEnS[U5US---nS[U5US---nURXAS-US-US-SS5nURSSSSSS5nUR XFS-X5nU$)Nr rrrr"r)rrrrr)r9rrrar num_patcheschannelss r/_unpack_latents-FluxControlNetInpaintPipeline._unpack_latents#s-4MM) c&k&6&:;<SZ$4q$89:,,zQ; HPQMSTVWX//!Q1a3//*5.A6Qr1c [U [5(a*[U 5U:wa[S[U 5SUS35eS[ U5UR S---nS[ U5UR S---nX4XV4n UR X5S-US-X5n URXS9nURXS9n X=RS:a@X=RS-S:Xa+X=RS-n[R"U /U-SS9n O\X=RS:a4X=RS-S:wa[S U RSS US 35e[R"U /SS9n U c'[XXS 9nURRXU5n OU RU5nUn URXXEU5nURXXEU5n URXXEU5n XX4$) 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.r r)rr3rrz'Cannot duplicate `image` of batch size z to z text prompts.)r3r?r~)rkrlrKrCrrarrrrrrrrL scale_noiser)rxrtimesteprrrrr~r?r3r9rrradditional_image_per_promptnoises r/prepare_latents-FluxControlNetInpaintPipeline.prepare_latents4s i & &3y>Z+GA#i.AQR&<'gi c&kd&;&;a&?@ASZD$9$9A$=>?6A99*PQkSX\]S]_em4..U.P ++A. .:@S@STU@V3VZ[3[*48K8KA8N*N '!II}o8S&SYZ[M --a0 0ZBUBUVWBX5X\]5]9-:M:Ma:P9QQUV`Uaaop "II}o1=M ? FXEnn00%PGJJv&EG""56JTYZ**=FZdij $$W:NX]^}>>r1c LS[U5URS---nS[U5URS---n[RRR XU4S9nUR XS9nX5-nUR XS9nURSS:XaUn O#[URRU5U S9n XRRR- URRR-n URSU:aWX1RS-S:Xd[SUS URSS 35eURX1RS-SSS5nU RSU:aWX;RS-S:Xd[S US U RSS 35eU RX;RS-SSS5n U R XS9n UR!U UUUU5n UR!URSUSS5UUUU5nX4$) Nr )sizerr"rbrrzvThe passed mask and the required batch size don't match. Masks are supposed to be duplicated to a total batch size of z, but zo masks were passed. Make sure the number of masks that you pass is divisible by the total requested batch size.zyThe passed images and the required batch size don't match. Images are supposed to be duplicated to a total batch size of zq images were passed. Make sure the number of images that you pass is divisible by the total requested batch size.)rrarnn functional interpolaterrr=rXrrprrrCrr) rxrV masked_imagerrr|rrr~r?r3rWs r/prepare_mask_latents2FluxControlNetInpaintPipeline.prepare_mask_latentshsdc&kd&;&;a&?@ASZD$9$9A$=>?xx""..t5/.Jwwfw27 #fB   a B &#/ #3DHHOOL4Q]f#g 4xx7S7S SW[W_W_WfWfWuWuu ::a=: % 1 -2 ..8\ 1 O^^ ;;zZZ]:Aq!DD  % %a (: 5 : :1 ==B 11; FCWC]C]^_C`Babtt $8#>#>zMgMghiMj?jlmoprs#t  466f6R#11     !! KK/A 6     ))r1c <[U[R5(aOURR XUS9nUR Sn U S:XaUn OUn UR U SS9nURXgS9nU(a!U (d[R"U/S-5nU$)N)rrrr"rrr ) rkrTensorrr preprocessrrepeat_interleaverr) rxrrrrr|r?r~do_classifier_free_guidance guess_modeimage_batch_size repeat_bys r/ prepare_image+FluxControlNetInpaintPipeline.prepare_images eU\\ * * ((33EPU3VE ;;q> q "I.I'' q'94 &zIIugk*E r1cUR$r))_guidance_scalerxs r/guidance_scale,FluxControlNetInpaintPipeline.guidance_scales###r1cUR$r))_joint_attention_kwargsrs r/joint_attention_kwargs4FluxControlNetInpaintPipeline.joint_attention_kwargss+++r1cUR$r))_num_timestepsrs r/ num_timesteps+FluxControlNetInpaintPipeline.num_timestepss"""r1cUR$r)) _interruptrs r/ interrupt'FluxControlNetInpaintPipeline.interrupts r1g333333?g@g?rTr9rrWrUrrrrrAr>rcontrol_guidance_startcontrol_guidance_end control_modecontrolnet_conditioning_scaler return_dictrcallback_on_step_endrc`U=(d URUR-nU=(d URUR-nUnUn[U[5(d%[U[5(a[ U5U/-nO[U[5(d%[U[5(a[ U5U/-nOu[U[5(d`[U[5(dK[UR [ 5(a[ UR R5OSnUU/-UU/-pURUUUUU UUUUUUU US9 Xl UUl SUl Ub[U[5(aSn O3Ub![U[5(a [ U5n OURSn URn!URR n"UR"bUR"R%SS5OSn#UR'UUUUU!UUU#S9unnn$U bUR(R+UUUU S9n%S n&OSn%S n&Un'UR,R/UUUU%U&S 9n(U(R1[2R4S 9n(URR6R8S -n)[UR [:5(GaIUR=UUUU U-UU!UR>R S9nURSSupxUR R@cSOSn*UR R@c[CUR>REU5US9nX`R>R6RF- UR>R6RH-nURSSun+n,URKUU U-U)U+U,5nUbE[2RL"U5R1U![2RNS 9nURQSS/5nGO[UR [ 5(Ga/n-UR RSR@cSOSn*[SU5GH un.n/UR=U/UUU U-UU!UR>R S9n/U/RSSupxUR RSR@c[CUR>REU/5US9n/U/UR>R6RF- UR>R6RH-n/U/RSSun+n,URKU/U U-U)U+U,5n/U-RUU/5 GM U-n/n0[U[5(a0UH*n1U1cU0RUS5 MU0RUU15 M, [2RL"U05R1U![2RNS 9nURQSS/5nU cRX"SSU - U 5OU n [[U5UR-S-[[U5UR-S--n2[]U2UR^R6R%SS5UR^R6R%SS5UR^R6R%SS5UR^R6R%SS55n3[aUR^U U!U U3S9un4n URcXU!5un4n U S:a[eSU SU S 35eU4SSRgU U-5n5URiU(U5U U-U)UUUR U!UU5 unn6n7n8UR(R/UUUU&U%S!9n9Uc U(U9S:-n:OUn:URkU9U:U U)UUUUR U!U5 un;n/n<[m[ U455Hn.[oX5V=V>s/sH>un=n>S[qU.[ U45- U=:=(d U.S-[ U45- U>:5- PM@ n?n=n>U[2RRR5(aUR1WL5nUb0nPUHnQ[5UQWPUQ'M U"UU.WBWP5nRURRS(U5nURRS)U5nURRS*U5nURRS+U;5n;URRS,U5nU.[ U45S- :Xd)U.S-W@:a0U.S-UR^Rt-S:XaWAR5 [(dGM[R"5 GM SSS5 US-:XaUnOURUUUUR5nUUR>R6RH- UR>R6RF-nUR>RUSS'9SnUR,RUUS.9nUR5 U(dU4$[US/9$s sn>n=fs sn=nFf!,(df  N=f)0a Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`): The image(s) to inpaint. mask_image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`): The mask image(s) to use for inpainting. White pixels in the mask will be repainted, while black pixels will be preserved. masked_image_latents (`torch.FloatTensor`, *optional*): Pre-generated masked image latents. control_image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`): The ControlNet input condition. Image to control the generation. height (`int`, *optional*, defaults to self.default_sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.default_sample_size * self.vae_scale_factor): The width in pixels of the generated image. strength (`float`, *optional*, defaults to 0.6): Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. padding_mask_crop (`int`, *optional*): The size of the padding to use when cropping the mask. num_inference_steps (`int`, *optional*, defaults to 28): 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). control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): The percentage of total steps at which the ControlNet starts applying. control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0): The percentage of total steps at which the ControlNet stops applying. control_mode (`int` or `List[int]`, *optional*): The mode for the ControlNet. If multiple ControlNets are used, this should be a list. controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added to the residual in the original transformer. 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 more [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. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated pooled text embeddings. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between `PIL.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple. joint_attention_kwargs (`dict`, *optional*): Additional keyword arguments to be passed to the joint attention mechanism. callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising step during the inference. callback_on_step_end_tensor_inputs (`List[str]`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. max_sequence_length (`int`, *optional*, defaults to 512): The maximum length of the sequence to be generated. Examples: Returns: [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. r")rrTrrrr}FNrscale)r{rrTrr?r|r}r)padfilldefault)rr crops_coords resize_mode)r~r)rrrrr|r?r~Trr rr base_image_seq_lenmax_image_seq_lenr&?r'ffffff?)rAr.z?After adjusting the num_inference_steps by strength parameter: z!, the number of pipelinesteps is z4 which is < 1 and not appropriate for this pipeline.)rrr-r,)totalri) hidden_statescontrolnet_condcontrolnet_modeconditioning_scalerguidancepooled_projectionsencoder_hidden_statestxt_idsimg_idsrr%) r6rr:r;r<controlnet_block_samplescontrolnet_single_block_samplesr=r>rr%controlnet_blocks_repeat)r%r9rTrUrVrWlatent)r)images)QrwrarkrlrKr^rnetsrrrrrrrr]r~rgetrrtget_crop_regionrrrrrfloat32rp in_channelsrr rXinput_hint_blockr=rrrrtensorlongr enumerateappendnplinspacerr0rLrPrrCrrrrzipfloatrrr progress_barrexpandguidance_embedsfullsteprbackendsmps is_availablelocalspopupdate XLA_AVAILABLExm mark_steprdecode postprocessmaybe_free_model_hooksr#)Srxr{rrrrWrUrrrrrAr>rr!r"r#r$r|r3r9rTrrr%rr&rr} global_height global_widthmultrr?r~rrr,r-original_image init_imagerrAheight_control_imagewidth_control_imagecontrol_imagesrcontrol_image_ control_mode_cmoder+r.r@latent_timesteprrrmask_conditionrrVcontrolnet_keepsekeepsnum_warmup_stepsrRtr use_guidancer:c cond_scalecontrolnet_cond_scaler?r@ noise_pred latents_dtypeinit_latents_proper init_masknoise_timestepcallback_kwargsrcallback_outputssS r/__call__&FluxControlNetInpaintPipeline.__call__s ZK433d6K6KKI11D4I4II  0$77JG[]aBXAY%Y "0$77JG]_cBVAW#W 2D99*MacgBhBh0:4??Ld0e0e3t++,klD.//,--%9        #'!5/Q/ 3  .'=$  *VS"9"9J  Jvt$<$<VJ&,,Q/J''  &&?C>Y>Y>eD ' ' + +GT :ko 9=8J8J'!5"7 3!9K 9 5 +X  (..>>L-=N?L!KL#K))44 -|,do5  ]]]7  $//66BBaG doo': ; ; ..#%(==&;hhnn/M*//4MF150P0P0Xu^b $//7 01O[d e !.1M1M!MQUQYQYQ`Q`QoQo o =J!%!3!3(!),AA*?!((.."4"!/ 4 4RS 9 ??''*;;C%5dhhoon6Uaj%kN&4txx7S7S&SW[W_W_WfWfWuWu%uNAO@T@TUVUW@X=(*=%)%7%7&"%::,,+ &N%%n57&>:+MM,--)E}%,,R0%,,U3 * !<< 699& 9SL'//Q8LTZSaS!&9"9;NOgm]+t/D/DDI  !6!6 6! ;   NN ! ! % %&:C @ NN ! ! % %&94 @ NN ! ! % %lC 8 NN ! ! % %k4 8  *< NN   * & &*.););L/L%)%>%>    !        & ""s9~&A 6MMDAqeAI.2Rq1uI6NQR6RSSM   " "z$//K^/_/_58ej k 's9~0CnnFZFZ0ZZ\]^!)n   %8  9\!),1>>88GMM!$4588Gdoo/GHH#'??#7#7#:#A#A#Q#QL#'??#9#9#I#IL$zz1#~fTYTaTabH'w}}Q/?@H#Hoa0$77478UWfghWi4j!k4jDAq!a%4jJ!kJ,I)!"7>>0Ea0H-!69K!KJLPOO")$1$0'1%_%';*7$,+/+F+F %M\ MI(*I##**::$zz1#~fTYTaTabH'w}}Q/?@H#H!--")%_%';*7-E4S$,+/+F+F %-E.   !( ..--j!WRW-XYZ['4# s9~))%.q1u%5N*...*D*D+U\\>:J-KU+'y=,??)gBUU==M1~~))6688")**]";(3&(O?-3Xa[*@';D!Q'X$.229gFG$4$8$8-$XM$4$8$8-$XM+//=D+;+?+?@VXl+m(I**A9I/IqSTuX\XfXfXlXlNlpqNq '') =LLNG-:N ( "E**7MrskDDD E\\0_99../))MM   H %)^      ck TLL T-5eoo-F T\_ T *.15%)$( 8*!#8* U3 ,- .8*S " 8* T%[ ! 8*vG0$57JL_G0r1