o GÆÏiD0ã @s¢ddlmZddlmZmZddlmZmZddlm Z m Z m Z m Z m Z ddlmZmZddlmZdd lmZmZdd lmZmZe e¡ZGd d „d eƒZGd d„deƒZedeƒfde ƒfde ƒfde ƒfde ƒfdeƒfdeƒfgƒZGdd„deƒZedeƒfde ƒfde ƒfde ƒfde ƒfde ƒfdeƒfdeƒfgƒZ Gdd„deƒZ!Gdd„deƒZ"edeƒfdeƒfde"ƒfdeƒfgƒZ#Gd d!„d!eƒZ$d"S)#é)Úloggingé)ÚAutoPipelineBlocksÚSequentialPipelineBlocks)ÚInsertableDictÚ OutputParamé)ÚFlux2PrepareGuidanceStepÚFlux2PrepareImageLatentsStepÚFlux2PrepareLatentsStepÚFlux2RoPEInputsStepÚFlux2SetTimestepsStep)ÚFlux2DecodeStepÚFlux2UnpackLatentsStep)ÚFlux2DenoiseStep)ÚFlux2TextEncoderStepÚFlux2VaeEncoderStep)ÚFlux2ProcessImagesInputStepÚFlux2TextInputStepc@s:eZdZdZdZeƒeƒgZddgZe de fdd„ƒZ dS) ÚFlux2VaeEncoderSequentialStepaË VAE encoder step that preprocesses, encodes, and prepares image latents for Flux2 conditioning. Components: image_processor (`Flux2ImageProcessor`) vae (`AutoencoderKLFlux2`) Inputs: image (`None`, *optional*): TODO: Add description. height (`None`, *optional*): TODO: Add description. width (`None`, *optional*): TODO: Add description. generator (`None`, *optional*): TODO: Add description. Outputs: condition_images (`list`): TODO: Add description. image_latents (`list`): List of latent representations for each reference image Úflux2Ú preprocessÚencodeÚreturncCódS)Nz_VAE encoder step that preprocesses, encodes, and prepares image latents for Flux2 conditioning.©©Úselfrrúj/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/modular_pipelines/flux2/modular_blocks_flux2.pyÚ descriptionGóz)Flux2VaeEncoderSequentialStep.descriptionN) Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_namerrÚ block_classesÚ block_namesÚpropertyÚstrrrrrrr*s rc@s.eZdZdZegZdgZdgZedd„ƒZ dS)ÚFlux2AutoVaeEncoderStepa‘ VAE encoder step that encodes the image inputs into their latent representations. This is an auto pipeline block that works for image conditioning tasks. - `Flux2VaeEncoderSequentialStep` is used when `image` is provided. - If `image` is not provided, step will be skipped. Components: image_processor (`Flux2ImageProcessor`) vae (`AutoencoderKLFlux2`) Inputs: image (`None`, *optional*): TODO: Add description. height (`None`, *optional*): TODO: Add description. width (`None`, *optional*): TODO: Add description. generator (`None`, *optional*): TODO: Add description. Outputs: condition_images (`list`): TODO: Add description. image_latents (`list`): List of latent representations for each reference image Úimg_conditioningÚimagecCó dS)NaVAE encoder step that encodes the image inputs into their latent representations. This is an auto pipeline block that works for image conditioning tasks. - `Flux2VaeEncoderSequentialStep` is used when `image` is provided. - If `image` is not provided, step will be skipped.rrrrrrlóÿz#Flux2AutoVaeEncoderStep.descriptionN) r!r"r#r$rr&r'Úblock_trigger_inputsr(rrrrrr*Msr*ÚinputÚprepare_latentsÚ set_timestepsÚprepare_guidanceÚprepare_rope_inputsÚdenoiseÚ after_denoisec@ó<eZdZdZdZe ¡Ze ¡Z e dd„ƒZ e dd„ƒZ dS)ÚFlux2CoreDenoiseStepa Core denoise step that performs the denoising process for Flux2-dev. Components: scheduler (`FlowMatchEulerDiscreteScheduler`) transformer (`Flux2Transformer2DModel`) Inputs: num_images_per_prompt (`None`, *optional*, defaults to 1): TODO: Add description. prompt_embeds (`Tensor`): Pre-generated text embeddings. Can be generated from text_encoder step. height (`int`, *optional*): TODO: Add description. width (`int`, *optional*): TODO: Add description. latents (`Tensor | NoneType`, *optional*): TODO: Add description. generator (`None`, *optional*): TODO: Add description. num_inference_steps (`None`, *optional*, defaults to 50): TODO: Add description. timesteps (`None`, *optional*): TODO: Add description. sigmas (`None`, *optional*): TODO: Add description. guidance_scale (`None`, *optional*, defaults to 4.0): TODO: Add description. joint_attention_kwargs (`None`, *optional*): TODO: Add description. image_latents (`Tensor`, *optional*): Packed image latents for conditioning. Shape: (B, img_seq_len, C) image_latent_ids (`Tensor`, *optional*): Position IDs for image latents. Shape: (B, img_seq_len, 4) Outputs: latents (`Tensor`): Denoised latents. rcCr)NzDCore denoise step that performs the denoising process for Flux2-dev.rrrrrr±r z Flux2CoreDenoiseStep.descriptioncCó t d¡gS©NÚlatents©rÚtemplaterrrrÚoutputsµóÿzFlux2CoreDenoiseStep.outputsN) r!r"r#r$r%ÚFlux2CoreDenoiseBlocksÚvaluesr&Úkeysr'r(rr>rrrrr8„s' r8Úprepare_image_latentsc@r7)Ú$Flux2ImageConditionedCoreDenoiseStepap Core denoise step that performs the denoising process for Flux2-dev with image conditioning. Components: scheduler (`FlowMatchEulerDiscreteScheduler`) transformer (`Flux2Transformer2DModel`) Inputs: num_images_per_prompt (`None`, *optional*, defaults to 1): TODO: Add description. prompt_embeds (`Tensor`): Pre-generated text embeddings. Can be generated from text_encoder step. image_latents (`list`, *optional*): TODO: Add description. height (`int`, *optional*): TODO: Add description. width (`int`, *optional*): TODO: Add description. latents (`Tensor | NoneType`, *optional*): TODO: Add description. generator (`None`, *optional*): TODO: Add description. num_inference_steps (`None`, *optional*, defaults to 50): TODO: Add description. timesteps (`None`, *optional*): TODO: Add description. sigmas (`None`, *optional*): TODO: Add description. guidance_scale (`None`, *optional*, defaults to 4.0): TODO: Add description. joint_attention_kwargs (`None`, *optional*): TODO: Add description. Outputs: latents (`Tensor`): Denoised latents. rcCr)Nz\Core denoise step that performs the denoising process for Flux2-dev with image conditioning.rrrrrrör z0Flux2ImageConditionedCoreDenoiseStep.descriptioncCr9r:r<rrrrr>úr?z,Flux2ImageConditionedCoreDenoiseStep.outputsN) r!r"r#r$r%Ú&Flux2ImageConditionedCoreDenoiseBlocksrAr&rBr'r(rr>rrrrrDËs% rDc@s4eZdZdZeegZddgZddgZe dd„ƒZ dS)ÚFlux2AutoCoreDenoiseSteprÚimage_conditionedÚ text2imageÚ image_latentsNcCr-)Na:Auto core denoise step that performs the denoising process for Flux2-dev.This is an auto pipeline block that works for text-to-image and image-conditioned generation. - `Flux2CoreDenoiseStep` is used for text-to-image generation. - `Flux2ImageConditionedCoreDenoiseStep` is used for image-conditioned generation. rrrrrrr.z$Flux2AutoCoreDenoiseStep.description) r!r"r#r%rDr8r&r'r/r(rrrrrrFsrFÚ text_encoderÚ vae_encoderÚdecodec@sPeZdZdZdZe ¡Ze ¡Z ddidddœdœZ e dd„ƒZ e d d „ƒZ d S) ÚFlux2AutoBlocksa5 Auto Modular pipeline for text-to-image and image-conditioned generation using Flux2. Supported workflows: - `text2image`: requires `prompt` - `image_conditioned`: requires `image`, `prompt` Components: text_encoder (`Mistral3ForConditionalGeneration`) tokenizer (`AutoProcessor`) image_processor (`Flux2ImageProcessor`) vae (`AutoencoderKLFlux2`) scheduler (`FlowMatchEulerDiscreteScheduler`) transformer (`Flux2Transformer2DModel`) Inputs: prompt (`None`, *optional*): TODO: Add description. max_sequence_length (`int`, *optional*, defaults to 512): TODO: Add description. text_encoder_out_layers (`tuple`, *optional*, defaults to (10, 20, 30)): TODO: Add description. image (`None`, *optional*): TODO: Add description. height (`None`, *optional*): TODO: Add description. width (`None`, *optional*): TODO: Add description. generator (`None`, *optional*): TODO: Add description. num_images_per_prompt (`None`, *optional*, defaults to 1): TODO: Add description. image_latents (`list`, *optional*): TODO: Add description. latents (`Tensor | NoneType`): TODO: Add description. num_inference_steps (`None`): TODO: Add description. timesteps (`None`): TODO: Add description. sigmas (`None`, *optional*): TODO: Add description. guidance_scale (`None`, *optional*, defaults to 4.0): TODO: Add description. joint_attention_kwargs (`None`, *optional*): TODO: Add description. image_latent_ids (`Tensor`, *optional*): Position IDs for image latents. Shape: (B, img_seq_len, 4) output_type (`None`, *optional*, defaults to pil): TODO: Add description. Outputs: images (`list`): Generated images. rÚpromptT)r,rN)rHrGcCr)NzUAuto Modular pipeline for text-to-image and image-conditioned generation using Flux2.rrrrrr\r zFlux2AutoBlocks.descriptioncCr9)NÚimagesr<rrrrr>`r?zFlux2AutoBlocks.outputsN)r!r"r#r$r%Ú AUTO_BLOCKSrAr&rBr'Ú _workflow_mapr(rr>rrrrrMs5þ rMN)%ÚutilsrÚmodular_pipelinerrÚmodular_pipeline_utilsrrÚbefore_denoiser r r r r Údecodersrrr5rÚencodersrrÚinputsrrÚ get_loggerr!Úloggerrr*r@r8rErDrFrPrMrrrrÚsV   #)ùÿ8øÿ6üÿ