iL ddlmZmZddlZddlmZddlmZmZddl m Z m Z m Z dd lm Z mZ dd ed ej d ed edej f dZdej dedeeeffdZGddeZGddeZGddeZy))ListTupleN)QwenImageMultiControlNetModel)ModularPipelineBlocks PipelineState) ComponentSpec InputParam OutputParam)QwenImageModularPipelineQwenImagePachifier input_name input_tensor batch_sizenum_images_per_promptreturnc t|tjstd|d|jddk(r||z}n6|jd|k(r|}n!td|d|d|jd|j |d}|S)a(Repeat tensor elements to match the final batch size. This function expands a tensor's batch dimension to match the final batch size (batch_size * num_images_per_prompt) by repeating each element along dimension 0. The input tensor must have batch size 1 or batch_size. The function will: - If batch size is 1: repeat each element (batch_size * num_images_per_prompt) times - If batch size equals batch_size: repeat each element num_images_per_prompt times Args: input_name (str): Name of the input tensor (used for error messages) input_tensor (torch.Tensor): The tensor to repeat. Must have batch size 1 or batch_size. batch_size (int): The base batch size (number of prompts) num_images_per_prompt (int, optional): Number of images to generate per prompt. Defaults to 1. Returns: torch.Tensor: The repeated tensor with final batch size (batch_size * num_images_per_prompt) Raises: ValueError: If input_tensor is not a torch.Tensor or has invalid batch size Examples: tensor = torch.tensor([[1, 2, 3]]) # shape: [1, 3] repeated = repeat_tensor_to_batch_size("image", tensor, batch_size=2, num_images_per_prompt=2) repeated # tensor([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]]) - shape: [4, 3] tensor = torch.tensor([[1, 2, 3], [4, 5, 6]]) # shape: [2, 3] repeated = repeat_tensor_to_batch_size("image", tensor, batch_size=2, num_images_per_prompt=2) repeated # tensor([[1, 2, 3], [1, 2, 3], [4, 5, 6], [4, 5, 6]]) - shape: [4, 3] `z` must be a tensorrr z!` must have have batch size 1 or z , but got )dim) isinstancetorchTensor ValueErrorshaperepeat_interleave)rrrr repeat_bys v/home/obispo/Crisostomo_bridge/mision_env/lib/python3.12/site-packages/diffusers/modular_pipelines/qwenimage/inputs.pyrepeat_tensor_to_batch_sizer sJ lELL 11ZL(:;<<!!!66   A * ,)  |negative_prompt_embeds_maskr r3s rinputszQwenImageTextInputsStep.inputssL 3Q ? OdH_ ` 04Md e 4BY Z 9G^ _   r!c^tdtdtdtjdgS)NrzdNumber of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt) type_hintr6dtypez@Data type of model tensor inputs (determined by `prompt_embeds`))r intrrGrCs rintermediate_outputsz,QwenImageTextInputsStep.intermediate_outputss9 C  ++^   r!cF| | td| | td|jd|jdk7r td|*|jd|jdk7r td|+|jd|jdk7r tdyy)NzS`negative_prompt_embeds_mask` is required when `negative_prompt_embeds` is not NonezJcannot pass `negative_prompt_embeds_mask` without `negative_prompt_embeds`rzE`prompt_embeds_mask` must have the same batch size as `prompt_embeds`zI`negative_prompt_embeds` must have the same batch size as `prompt_embeds`zN`negative_prompt_embeds_mask` must have the same batch size as `prompt_embeds`)rrr;r?r@rAs r check_inputsz$QwenImageTextInputsStep.check_inputss " -2M2Urs s ! ).I.Uij j  # #A &-*=*=a*@ @de e # /4J4P4PQR4SWdWjWjklWm4mhi i ( 38S8Y8YZ[8\`m`s`stu`v8vmn n9w 3r! componentsstatec|j|}|j|j|j|j|j |jj d|_|jj|_|jj \}}}|jjd|jd|_|jj|j|jz|d|_|jjd|jd|_|jj|j|jz||_|j|jj \}}}|jjd|jd|_|jj|j|jz|d|_|j jd|jd|_|j j|j|jz||_|j||||fS)NrKrr ) get_block_staterLr;r?r@rArrrGrepeatrviewset_block_state)r3rMrN block_state_seq_lens r__call__z QwenImageTextInputsStep.__call__s2**51  %33*==#.#E#E(3(O(O  "-!:!:!@!@!C '55;; #1177 7A$/$=$=$D$DQ HiHikl$m !$/$=$=$B$B  " "[%F%F FQS% !*5)G)G)N)NqR]RsRsuv)w &)4)G)G)L)L  " "[%F%F F* &  - - 9'>>DDMAw1<1S1S1Z1Z;44a2K .2=1S1S1X1X&&)J)JJGUW2K .7B6]6]6d6d;44a7K 37B6]6]6b6b&&)J)JJG7K 3 UK05  r!N)__name__ __module__ __qualname__ model_namepropertystrr6rr rDrI staticmethodrLrr rXr2r!rr/r/psJ  3S 3 3 Z(    d3i    oo.+!#;+!M+!Vc+!r!r/ceZdZdZdggfdeedeeffd ZedefdZedee fdZ edee fd Z edee fd Zd ed edefd ZxZS)QwenImageInputsDynamicStepr0 image_latentsimage_latent_inputsadditional_batch_inputsct|ts|g}t|ts|g}||_||_t|y)aInitialize a configurable step that standardizes the inputs for the denoising step. It: " This step handles multiple common tasks to prepare inputs for the denoising step: 1. For encoded image latents, use it update height/width if None, patchifies, and expands batch size 2. For additional_batch_inputs: Only expands batch dimensions to match final batch size This is a dynamic block that allows you to configure which inputs to process. Args: image_latent_inputs (List[str], optional): Names of image latent tensors to process. These will be used to determine height/width, patchified, and batch-expanded. Can be a single string or list of strings. Defaults to ["image_latents"]. Examples: ["image_latents"], ["control_image_latents"] additional_batch_inputs (List[str], optional): Names of additional conditional input tensors to expand batch size. These tensors will only have their batch dimensions adjusted to match the final batch size. Can be a single string or list of strings. Defaults to []. Examples: ["processed_mask_image"] Examples: # Configure to process image_latents (default behavior) QwenImageInputsDynamicStep() # Configure to process multiple image latent inputs QwenImageInputsDynamicStep(image_latent_inputs=["image_latents", "control_image_latents"]) # Configure to process image latents and additional batch inputs QwenImageInputsDynamicStep( image_latent_inputs=["image_latents"], additional_batch_inputs=["processed_mask_image"] ) N)rlist_image_latent_inputs_additional_batch_inputssuper__init__)r3rcrd __class__s rrjz#QwenImageInputsDynamicStep.__init__sI@-t4#6"7 148'>&? #$7!(?% r!rcd}d}|js |jr>d}|jr|d|jz }|jr|d|jz }d}||z|zS)NzInput processing step that: 1. For image latent inputs: Updates height/width if None, patchifies latents, and expands batch size 2. For additional batch inputs: Expands batch dimensions to match final batch sizez Configured inputs:z - Image latent inputs: z - Additional batch inputs: zN This block should be placed after the encoder steps and the text input step.)rgrh)r3r4 inputs_infor5s rr6z&QwenImageInputsDynamicStep.description s c   $ $(E(E2K((!20&1&G&G&11 #  K!8:M N5 O:77 ;J"; ;L#7%)&1&G&G&11 L K\ : ; UK05  r!)rYrZr[r\rr^rjr]r6r rDr rIr r{rr rX __classcell__)rks@rrarasJ+:):-/'!#Y'"&c'RASAA,Z($ d;&7   T-%8  1!#;1!M1!Vc1!r!rac~eZdZdZedefdZedeefdZ e jde de de fdZy) QwenImageControlNetInputsStepr0rcy)Nz\prepare the `control_image_latents` for controlnet. Insert after all the other inputs steps.r2rCs rr6z)QwenImageControlNetInputsStep.descriptionzsmr!cztddtddtddtd td gS) Ncontrol_image_latentsTrprrr r8r+rqr,rBrCs rrDz$QwenImageControlNetInputsStep.inputs~s> 3d C L4 8 3Q ? 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