3jDSSKJrJr SSKrSSKrSSKJr SSKJ r SSK J r SSK J r SSKJr SS KJr SS KJr SS KJrJr SS KJr SS KJrJr SSKJrJrJrJ r J!r!J"r" SSK#J$r$ \"5(a SSK%J&s J'r( Sr)OSr)\RT"\+5r,Sr-"SS\\ 5r.g))AnyCallableN) AutoTokenizer)SmolLM3ForCausalLM)VaeImageProcessor)FluxLoraLoaderMixin)AutoencoderKLWan)BriaFiboTransformer2DModel)BriaFiboPipelineOutput)calculate_shiftretrieve_timesteps)DiffusionPipeline)FlowMatchEulerDiscreteSchedulerKarrasDiffusionSchedulers)USE_PEFT_BACKENDis_torch_xla_availableloggingreplace_example_docstringscale_lora_layersunscale_lora_layers) randn_tensorTFa Example: ```python import torch from diffusers import BriaFiboPipeline from diffusers.modular_pipelines import ModularPipeline torch.set_grad_enabled(False) vlm_pipe = ModularPipeline.from_pretrained("briaai/FIBO-VLM-prompt-to-JSON", trust_remote_code=True) pipe = BriaFiboPipeline.from_pretrained( "briaai/FIBO", trust_remote_code=True, torch_dtype=torch.bfloat16, ) pipe.enable_model_cpu_offload() with torch.inference_mode(): # 1. Create a prompt to generate an initial image output = vlm_pipe(prompt="a beautiful dog") json_prompt_generate = output.values["json_prompt"] # Generate the image from the structured json prompt results_generate = pipe(prompt=json_prompt_generate, num_inference_steps=50, guidance_scale=5) results_generate.images[0].save("image_generate.png") ``` c'\rSrSrSrSrSS/rS\S\\ -S\ S \ S \ 4 S jr S8S\\\-S\S\S\R$S -S\R&S -4 Sjjr\S9Sj5rS:S\\\-S\R$S -S\S\S\\\-S -S\R0S -S\R0S -S\S\S -4Sjjr\S5r\S5r\S5r\S5r\S 5r\S!5r \S"5r!\S#5r"\S$5r#S;S&jr$\S'5r%\RL"5\'"\(5S S S S(S SS S S S S S S)S*S S S/SS%4S\\\-S+\S -S,\S -S-\S.\\S\S\\\-S -S\S -S/\RR\\RR-S -S\R0S -S\R0S -S\R0S -S0\S -S1\*S2\+\\,4S -S3\-\\/S 4S -S4\\S\4$S5jj55r.Stext_encoder_2->image_encoder->transformer->vaelatents prompt_embeds transformer schedulervae text_encoder tokenizerc|URUUUUUS9 SUl[URS-S9UlSUlg)N)r r!r"rr)vae_scale_factor@)register_modulesr&rimage_processordefault_sample_size)selfrrr r!r"s j/home/wildlama/miniconda3/lib/python3.13/site-packages/diffusers/pipelines/bria_fibo/pipeline_bria_fibo.py__init__BriaFiboPipeline.__init__^sQ %#  !#0$BWBWZ[B[\#% Npromptnum_images_per_promptmax_sequence_lengthdevicedtypec ^^T=(d URmU=(d URRn[U[5(aU/OUnU(d [ S5e[ U5nSnTbTO[R"S5n[U[R5(d[R"U5n[SU55(a>[R"US4U[RUS9n [R"U 5n OURUSUSSS S 9n U RRU5n U R RU5n [#S U55(a?[R$"UV s/sHoS :HPM sn [R&US9n XyU 'SX'URU U SS 9nUR(n[R*"USUS/SS9nURTUS9nUR-TSS9n[/UU4SjU55nU R-TSS9RTS9n UX4$s sn f)Nz7`prompt` must be a non-empty string or list of strings.icpuc3*# UH oS:Hv M g7fN.0ps r, 5BriaFiboPipeline.get_prompt_embeds..s'1Bwr0r5r4longestTpt)padding max_length truncationadd_special_tokensreturn_tensorsc3*# UH oS:Hv M g7fr9r;r<s r,r?r@s+Fq7FrAr:)attention_maskoutput_hidden_statesdimr4r5rc3\># UH!oRTSS9RTS9v M# g7f)rrOr4N)repeat_interleaveto)r=layerr4r2s r,r?r@s3 anX] # #$9q # A D DF D Sans),rS)_execution_devicer!r5 isinstancestr ValueErrorlentorchr4allfulllong ones_liker" input_idsrUrKanytensorbool hidden_statescatrTtuple)r+r1r2r3r4r5 batch_size bot_token_idtext_encoder_devicerarK tokenizedr> empty_rowsencoder_outputsrers ` ` r,get_prompt_embeds"BriaFiboPipeline.get_prompt_embedsrs514110**00'44&&VW W[  (.(:f U@S-u||<<"',,/B"C  '' ' ' J?L [noI"__Y7N!.#'# 'I"++../BCI&55889LMN+F+++"\\F*CFq7F*C5::^qr (4*%-.*++ )!%,  (55  =#4mB6G"HbQ %((e(D %778MST7U  an  (99:OUV9WZZbhZim;;)+Ds IcURup4nUc,[R"X44URURS9nO$UR URURS9nX:a [ S5eX:aX- n[R"X6U4URURS9n[R"X/SS9n[R"X64URURS9n[R"X(/SS9nX4$)NrBrQzE`max_tokens` must be greater or equal to the current sequence length.r0rO) shaper\onesr5r4rUrZzerosrf) r max_tokensrKrhseq_lenrP pad_lengthrE mask_paddings r, pad_embeddingBriaFiboPipeline.pad_embeddings$1#6#6 S  !"ZZ(=]EXEXanauauvN+..m6J6JR_ReRe.fN  de e  #-Jkk-]5H5HQ^QeQeG"II}&>AFM ;;( 0C0CML`L`L#YY'E1MN,,r/ guidance_scalenegative_promptnegative_prompt_embeds lora_scalec U=(d URnU bI[U[5(a4XlURb![ (a[ URU 5 [U[5(aU/OUnUb [U5n OURSn Sn Sn UcjURUUUUS9upmn URURRS9nU Vs/sH%oRURRS9PM' n nUS:Ga[U[5(aUScSnU=(d Sn[U[5(aX/-OUnUb;[U5[U5La$[!S[U5S[U5S 35eU [U5:wa![#S US [U5S US U S 3 5eURUUUUS9upn URURRS9nUVs/sH%oRURRS9PM' nnURb6[U[5(a![ (a[%URU 5 U b$U RUR&URS9n UbU b$U RUR&URS9n [)URSURS5nUR+UUU S9upkW Vs/sHnUR+UU5SPM n nUR+UUU S9up|WVs/sHnUR+UU5SPM nnO$URSnUR+UUU S9upkSnURRn[,R."URSUS5RUUS9nUUUU U W U4$s snfs snfs snfs snf)ar Args: prompt (`str` or `list[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt guidance_scale (`float`): Guidance scale for classifier free guidance. 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`). 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. Nr)r1r2r3r4r5r0r: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`.rQ)rKr)rWrXr _lora_scaler!rrrYr[rqrnrUrr5listtype TypeErrorrZrr4maxrxr\rs)r+r1r4r2r|r}rr~r3rrhprompt_attention_masknegative_prompt_attention_mask prompt_layersrcnegative_prompt_layersrtrVr5text_idss r, encode_promptBriaFiboPipeline.encode_promptswD1411  !j7J&K&K)   ,1A1A!$"3"3Z@'44&&  VJ&,,Q/J $)-&  BFBXBX&;$7 CYC ?M*? *,,43C3C3I3I,JMS`aS`YYT-=-=-C-CYDS`Ma A /400_Q5G5O"$-3O@J?\_@`@`j+<%>TEUEUE[E[%>%\ "\r%s\rRXiid6F6F6L6Li&M\r "%s    ($ 3449I9I#D$5$5zB ! ,$9$<$Q>QRS>TUJ373E3Ez:O4F4 0MTaaS`%T//zB1ES`MaEIEWEW& CaFXF B "]s%s\rSXd&8&8 &KA&N\r "%s "&,,Q/J373E3Ez:O4F4 0M&* "!!'';;}2215z1EHHPV^cHd  "  ! *  "  b4&t*b &ts,O,O?O7OcUR$N)_guidance_scaler+s r,r|BriaFiboPipeline.guidance_scaleOs###r/cUR$r)_joint_attention_kwargsrs r,joint_attention_kwargs'BriaFiboPipeline.joint_attention_kwargsWs+++r/cUR$r)_num_timestepsrs r, num_timestepsBriaFiboPipeline.num_timesteps[s"""r/cUR$r) _interruptrs r, interruptBriaFiboPipeline.interrupt_s r/cURupEnX-nX#-nURXAS-US-US-SS5nURSSSSSS5nURXFS-X5nU$)Nr%rrr0rz)rqviewpermutereshaperheightwidthr&rh num_patcheschannelss r,_unpack_latents BriaFiboPipeline._unpack_latentscsy-4MM) +),,zQ; HPQMSTVWX//!Q1a3//*5.A6Qr/c,[R"XS5nUS[R"U5SS2S4-US'US[R"U5SSS24-US'URupgnUR Xg-U5nUR X4S9$)Nr).r0).r%rQ)r\rsarangerqrrU) rhrrr4r5latent_image_idslatent_image_id_heightlatent_image_id_widthlatent_image_id_channelss r,_prepare_latent_image_ids*BriaFiboPipeline._prepare_latent_image_idsqs!;;va8#3F#;ell6>RSTVZSZ>[#[ #3F#;ell5>QRVXYRY>Z#Z RbRhRhO7O+33 " :>r/cURupEnX-nX#-nURXAX&5nURSSSS5nU$)Nrrr0r%)rqrrrs r,_unpack_latents_no_patch)BriaFiboPipeline._unpack_latents_no_patchsI,3MM) +),,z5C//!Q1-r/cXURSSSS5nURXU-U5nU$)Nrr%rr0)rrrrhnum_channels_latentsrrs r,_pack_latents_no_patch'BriaFiboPipeline._pack_latents_no_patchs0//!Q1-//*un>RSr/cURXUS-SUS-S5nURSSSSSS5nURXS-US--US-5nU$)Nr%rrr0rrz)rrrrs r, _pack_latentsBriaFiboPipeline._pack_latentssg,,z1aQVZ[Q[]^_//!Q1a3//*{uz.JL`cdLder/Fc [U5UR-n[U5UR-nXX44n Ub$URXXFU5n URXeS9U 4$[ U[ 5(a*[ U5U:wa[S[ U5SUS35e[XXeS9nU (a/URXX#U5nURXS-US-Xe5n X4$URXX#U5nURXXFU5n X4$)NrQz/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.) generatorr4r5r%) intr&rrUrXrr[rZrrr) r+rhrrrr5r4rr do_patchingrqrs r,prepare_latents BriaFiboPipeline.prepare_latentss;V 5 55E d3336A  #==jRWafg ::V:9;KK K i & &3y>Z+GA#i.AQR&<'gi  u&V ((>R\abG#==jTU+W\`aWaciq  ((11'G[ejkG#==jRWafg ((r/c[R"SX5n[R"US:HS[R*5nU$)Nz bi,bj->bijr0g)r\einsumwhereinf)rKattention_matrixs r,_prepare_attention_mask(BriaFiboPipeline._prepare_attention_masks> << nU!;;  !3   r/pilTrrnum_inference_steps timestepsr output_type return_dictrcallback_on_step_end"callback_on_step_end_tensor_inputsc U=(d URUR-nU=(d URUR-nURUUUU UUS9 X`lXlSUlUb[ U[5(aSnO3Ub![ U[5(a [U5nOU RSnURnURbURRSS5OSnURUUUU U UUUUS9 un n nnnnnU RSnUS:al[R "X/SS9n [#[U55Vs/sH n[R "UUUU/SS9PM" nn[R "UU/SS9n[UR$R&5[UR$R(5-n[U5U:aU[U5U- SnOUUS /U[U5- --nUR$R*R,nU(a[/US - 5nUR1UUUUU R2UU U U5 un n [R4"U RSU RS/U R2U R6S 9n!US:aU!R9S S5n![R "UU!/SS9n"UR;U"5n"U"R=SS9R?UR$R2S 9n"URc0UlU"URS'U(a$X RS --X0RS ---n#OX R-X0R--n#[@RB"SSU- U5n$[EU#URFR*RHURFR*RJURFR*RLURFR*RN5n%[QURFUUSU$U%S9upT[S[U5X@RFRT-- S5n&[U5Ul+[U R5S:XaU Sn [UR5S:XaUSnURYUS9n'[[U5GHunn(UR\(aMUS:a[R "U /S -5OU n)U(R_U)RS5R?U)R6U)R2S9n*UR%U)U*U UURSUU S9Sn+US:a)U+RaS 5un,n-U,URbU-U,- --n+U R2n.URFReU+U(U SS9Sn U R2U.:wa>[RfRhRk5(aU R?U.5n Ub\0n/UHn0[m5U0U/U0'M U"UUU(U/5n1U1RoSU 5n U1RoSU 5n U1RoSU 5n U[U5S- :Xd)US-U&:a0US-URFRT-S:XaU'Rq5 [r(dGM[tRv"5 GM SSS5 U S:XaU n2GO U(aURyXX0R5n OUR{XX0R5n U R=S S9n U SR6n3U SR2n.[R|"UR~R*R5RSUR~R*RSSS5R?U3U.5n4S[R|"UR~R*R5RSUR~R*RSSS5R?U3U.5- n5U V6s/sH n6U6U5- U4-PM n7n6[R "U7SS9n7/n2U7Hgn8UR~RU8R=S5SS9Sn9URRU9RS S9U S9n9U2RU95 Mi [U25S:XaU2Sn2O[@R"U2SS9n2UR5 U(dU24$[U2S9$s snf!,(df  GNa=fs sn6f)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. 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. 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. 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`). 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 ge 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. 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 3000): Maximum sequence length to use with the `prompt`. do_patching (`bool`, *optional*, defaults to `False`): Whether to use patching. Examples: Returns: [`~pipelines.flux.BriaFiboPipelineOutput`] or `tuple`: [`~pipelines.flux.BriaFiboPipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. )r1rrrrr3FNr0rscale) r1r}r|rr~r4r3r2rrOrMrrBr%rrKg?)rr4rsigmasmur)totalrQ)retimestepencoder_hidden_statestext_encoder_layersrrtxt_idsimg_ids)rrrr~latent)r)axis)images)Lr*r& check_inputsrrrrXrYrr[rqrWrgetrr\rfrangertransformer_blockssingle_transformer_blocksconfig in_channelsrrr5rrr4repeatr unsqueezerUnplinspacer rbase_image_seq_lenmax_image_seq_len base_shift max_shiftrrorderr progress_bar enumeraterexpandchunkr|stepbackendsmps is_availablelocalspopupdate XLA_AVAILABLExm mark_steprrrcr latents_meanrz_dim latents_stddecoder) postprocesssqueezeappendstackmaybe_free_model_hooksr ):r+r1rrrrr|r}r2rrrr~rrrrrr3rrhr4rrrrrrprompt_batch_sizeitotal_num_layers_transformerrrlatent_attention_maskrKrurrnum_warmup_stepsrtlatent_model_inputr noise_prednoise_pred_uncondnoise_pred_text latents_dtypecallback_kwargskcallback_outputsimagelatents_devicerrrlatents_scaled scaled_latent curr_images: r,__call__BriaFiboPipeline.__call__s. @K433d6K6KKI11D4I4II '/Q 3   .'=$  *VS"9"9J  Jvt$<$<VJ&,,Q/J''?C>Y>Y>eD ' ' + +GT :ko    +)'#9 3"7!    "  ! *  "*//2 A !II'=&MSTUMY^_bcp_qYrYrTU 1!4mA6FGQOYr %*II/MOd.ekl$m !'*4+;+;+N+N'ORU    6 6S ( $ } != =)#m*,?C_befsbtCt,uuM $//66BB #&';a'?#@ $($8$8           % !!!& ]]1 w}}Q/ 0 gnn!  A $9$@$@A$F !$9;P#QWXY55nE'11a18;;$BRBRBXBX;Y  ' ' /+-D (9G$$%56 "7"7!";<K`K`cdKdAefG!6!665DYDY;YZGS!&9"9;NO   NN ! ! 4 4 NN ! ! 3 3 NN ! ! , , NN ! ! + +  *< NN 3 * & s9~0CnnFZFZ0ZZ\]^!)n %% &! +/2  x~~ ! #{H  %8  9\!),1>>BPRSASUYYy1}%=Y`"88$6$<$P>PUV>P>Wep=q  Z("05zQaQ/ ##%8O%U33il: 9XYs'e He e e ec ^US-S:wd US-S:wa[SUSUS35eUbW[U4SjU55(d=[STRSUV s/sHoTR;dMU PM sn 35eUbUb[S US US 35eUcUc [S 5eUbA[U[5(d,[U[ 5(d[S [ U535eUbUb[SUSUS 35eUbCUb@URUR:wa&[SURSURS35eUbUS:a[SU35eggs sn f)Nr$rz8`height` and `width` have to be divisible by 16 but are z and rc3@># UHoTR;v M g7fr)_callback_tensor_inputs)r=rr+s r,r?0BriaFiboPipeline.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.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 z'Cannot forward both `negative_prompt`: z and `negative_prompt_embeds`: zu`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but got: `prompt_embeds` z != `negative_prompt_embeds` r{z9`max_sequence_length` cannot be greater than 3000 but is )rZr]rrXrYrrrq) r+r1rrr}rr~rr3rs ` r,rBriaFiboPipeline.check_inputss# B;! urzQWX^W__dejdkklmn n - 9#F 7YF C C DTEaEaDbbn|^pH|^vwkokGkGbGpq|^pHoIJ   -";08N}o^00 ^ 5w  FC)@)@TZ\`IaIaQRVW]R^Q_`a a  &+A+M9/9JK*++]_   $)?)K""&<&B&BB --:-@-@,AB.445Q8  */BT/IXYlXmno o0J *;pHs E$1E$)rrrrrr*r)r&)r0iNNr)Nr0rzNNNr{N)NF)NNNNN)1__name__ __module__ __qualname____firstlineno____doc__model_cpu_offload_seqrr rrr rrr-rYrrr\r4r5rn staticmethodrxfloat FloatTensorrpropertyr|rrrrrrrrrrno_gradrEXAMPLE_DOC_STRING Generatorrddictrrrr__static_attributes__r;r/r,rrLs  \(/:&/&35NN& & ) & ! &.&'#'&*$( :<d3i:< #:<! :<  t# :< {{T! :rCs! &K0*FSIO9T.))MM   H %:z p(*=z pr/