3j SSKrSSKJr SSKJrJrJr SSKJrJ r J r SSK J r J r JrJrJrJrJr SS KJrJrJrJr SS KJrJrJrJr SS KJrJrJ r J!r!J"r" SS K#J$r$J%r%J&r& \RN"\(5r)"S S\5r*"SS\5r+"SS\5r,"SS\5r-"SS\5r."SS\5r/"SS\5r0"SS\5r1"SS\5r2"SS \5r3"S!S"\5r4"S#S$\5r5"S%S&\5r6"S'S(\5r7"S)S*\5r8"S+S,\5r9"S-S.\5r:"S/S0\5r;\ "S1\*"54S2\-"54S3\."54S4\8"54S5\;"54/5r<"S6S7\5r=g)8N)logging)AutoPipelineBlocksConditionalPipelineBlocksSequentialPipelineBlocks) InputParamInsertableDict OutputParam)%QwenImageControlNetBeforeDenoiserStepQwenImageCreateMaskLatentsStepQwenImagePrepareLatentsStep'QwenImagePrepareLatentsWithStrengthStepQwenImageRoPEInputsStepQwenImageSetTimestepsStep%QwenImageSetTimestepsWithStrengthStep)QwenImageAfterDenoiseStepQwenImageDecoderStep'QwenImageInpaintProcessImagesOutputStep QwenImageProcessImagesOutputStep)QwenImageControlNetDenoiseStepQwenImageDenoiseStep%QwenImageInpaintControlNetDenoiseStepQwenImageInpaintDenoiseStep)!QwenImageControlNetVaeEncoderStep&QwenImageInpaintProcessImagesInputStepQwenImageProcessImagesInputStepQwenImageTextEncoderStepQwenImageVaeEncoderStep)QwenImageAdditionalInputsStepQwenImageControlNetInputsStepQwenImageTextInputsStepcP\rSrSrSrSr\"5/rS/rS/r \ S\ 4Sj5r Sr g ) QwenImageAutoTextEncoderStep@a Text encoder step that encodes the text prompt into a text embedding. This is an auto pipeline block. Components: text_encoder (`Qwen2_5_VLForConditionalGeneration`): The text encoder to use tokenizer (`Qwen2Tokenizer`): The tokenizer to use guider (`ClassifierFreeGuidance`) Inputs: prompt (`str`, *optional*): The prompt or prompts to guide image generation. negative_prompt (`str`, *optional*): The prompt or prompts not to guide the image generation. max_sequence_length (`int`, *optional*, defaults to 1024): Maximum sequence length for prompt encoding. Outputs: prompt_embeds (`Tensor`): The prompt embeddings. prompt_embeds_mask (`Tensor`): The encoder attention mask. negative_prompt_embeds (`Tensor`): The negative prompt embeddings. negative_prompt_embeds_mask (`Tensor`): The negative prompt embeddings mask. qwenimage text_encoderpromptreturncg)NzeText encoder step that encodes the text prompt into a text embedding. This is an auto pipeline block.selfs x/home/wildlama/miniconda3/lib/python3.13/site-packages/diffusers/modular_pipelines/qwenimage/modular_blocks_qwenimage.py description(QwenImageAutoTextEncoderStep.description`svr,N)__name__ __module__ __qualname____firstlineno____doc__ model_namer block_classes block_namesblock_trigger_inputspropertystrr0__static_attributes__r,r2r/r%r%@sE4J-/0M!"K$: @S@@r2r%cX\rSrSrSrSr\"5\"5/rSS/r \ S\ 4Sj5r Sr g ) QwenImageInpaintVaeEncoderStepma This step is used for processing image and mask inputs for inpainting tasks. It: - Resizes the image to the target size, based on `height` and `width`. - Processes and updates `image` and `mask_image`. - Creates `image_latents`. Components: image_mask_processor (`InpaintProcessor`) vae (`AutoencoderKLQwenImage`) Inputs: mask_image (`Image`): Mask image for inpainting. image (`Image | list`): Reference image(s) for denoising. Can be a single image or list of images. height (`int`, *optional*): The height in pixels of the generated image. width (`int`, *optional*): The width in pixels of the generated image. padding_mask_crop (`int`, *optional*): Padding for mask cropping in inpainting. generator (`Generator`, *optional*): Torch generator for deterministic generation. Outputs: processed_image (`Tensor`): The processed image processed_mask_image (`Tensor`): The processed mask image mask_overlay_kwargs (`dict`): The kwargs for the postprocess step to apply the mask overlay image_latents (`Tensor`): The latent representation of the input image. r' preprocessencoder*cg)NzThis step is used for processing image and mask inputs for inpainting tasks. It: - Resizes the image to the target size, based on `height` and `width`. - Processes and updates `image` and `mask_image`. - Creates `image_latents`.r,r-s r/r0*QwenImageInpaintVaeEncoderStep.descriptions  * r2r,N)r3r4r5r6r7r8rr r9r:r<r=r0r>r,r2r/r@r@msC DJ;=?V?XYM*K  S  r2r@cX\rSrSrSrSr\"5\"5/rSS/r \ S\ 4Sj5r Sr g ) QwenImageImg2ImgVaeEncoderStepa Vae encoder step that preprocess andencode the image inputs into their latent representations. Components: image_processor (`VaeImageProcessor`) vae (`AutoencoderKLQwenImage`) Inputs: image (`Image | list`): Reference image(s) for denoising. Can be a single image or list of images. height (`int`, *optional*): The height in pixels of the generated image. width (`int`, *optional*): The width in pixels of the generated image. generator (`Generator`, *optional*): Torch generator for deterministic generation. Outputs: processed_image (`Tensor`): The processed image image_latents (`Tensor`): The latent representation of the input image. r'rBrCr*cg)Nz^Vae encoder step that preprocess andencode the image inputs into their latent representations.r,r-s r/r0*QwenImageImg2ImgVaeEncoderStep.descriptionsor2r,N)r3r4r5r6r7r8rr r9r:r<r=r0r>r,r2r/rGrGsE.J468O8QRM*K pSppr2rGc<\rSrSr\\/rSS/rSS/r\ S5r Sr g) QwenImageAutoVaeEncoderStepinpaintimg2img mask_imageimagecg)NaZVae encoder step that encode the image inputs into their latent representations. This is an auto pipeline block. - `QwenImageInpaintVaeEncoderStep` (inpaint) is used when `mask_image` is provided. - `QwenImageImg2ImgVaeEncoderStep` (img2img) is used when `image` is provided. - if `mask_image` or `image` is not provided, step will be skipped.r,r-s r/r0'QwenImageAutoVaeEncoderStep.descriptions  U r2r,N) r3r4r5r6r@rGr9r:r;r<r0r>r,r2r/rLrLs435STMi(K('2   r2rLc:\rSrSrSr\/rS/rS/r\ S5r Sr g))QwenImageOptionalControlNetVaeEncoderStepa Vae encoder step that encode the image inputs into their latent representations. This is an auto pipeline block. - `QwenImageControlNetVaeEncoderStep` (controlnet) is used when `control_image` is provided. - if `control_image` is not provided, step will be skipped. Components: vae (`AutoencoderKLQwenImage`) controlnet (`QwenImageControlNetModel`) control_image_processor (`VaeImageProcessor`) Inputs: control_image (`Image`, *optional*): Control image for ControlNet conditioning. height (`int`, *optional*): The height in pixels of the generated image. width (`int`, *optional*): The width in pixels of the generated image. generator (`Generator`, *optional*): Torch generator for deterministic generation. Outputs: control_image_latents (`Tensor`): The latents representing the control image controlnet control_imagecg)Na Vae encoder step that encode the image inputs into their latent representations. This is an auto pipeline block. - `QwenImageControlNetVaeEncoderStep` (controlnet) is used when `control_image` is provided. - if `control_image` is not provided, step will be skipped.r,r-s r/r05QwenImageOptionalControlNetVaeEncoderStep.descriptions  M r2r,N) r3r4r5r6r7rr9r:r;r<r0r>r,r2r/rUrUs3277M.K+,   r2rUcP\rSrSrSrSr\"5\"5/rSS/r \ S5r Sr g) QwenImageImg2ImgInputStepia Input step that prepares the inputs for the img2img denoising step. It: Components: pachifier (`QwenImagePachifier`) Inputs: num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. prompt_embeds (`Tensor`): text embeddings used to guide the image generation. Can be generated from text_encoder step. prompt_embeds_mask (`Tensor`): mask for the text embeddings. Can be generated from text_encoder step. negative_prompt_embeds (`Tensor`, *optional*): negative text embeddings used to guide the image generation. Can be generated from text_encoder step. negative_prompt_embeds_mask (`Tensor`, *optional*): mask for the negative text embeddings. Can be generated from text_encoder step. height (`int`, *optional*): The height in pixels of the generated image. width (`int`, *optional*): The width in pixels of the generated image. image_latents (`Tensor`): image latents used to guide the image generation. Can be generated from vae_encoder step. Outputs: batch_size (`int`): The batch size of the prompt embeddings dtype (`dtype`): The data type of the prompt embeddings prompt_embeds (`Tensor`): The prompt embeddings. (batch-expanded) prompt_embeds_mask (`Tensor`): The encoder attention mask. (batch-expanded) negative_prompt_embeds (`Tensor`): The negative prompt embeddings. (batch-expanded) negative_prompt_embeds_mask (`Tensor`): The negative prompt embeddings mask. (batch-expanded) image_height (`int`): The image height calculated from the image latents dimension image_width (`int`): The image width calculated from the image latents dimension height (`int`): if not provided, updated to image height width (`int`): if not provided, updated to image width image_latents (`Tensor`): image latents used to guide the image generation. Can be generated from vae_encoder step. (patchified and batch-expanded) r' text_inputsadditional_inputscg)NzHInput step that prepares the inputs for the img2img denoising step. It: r,r-s r/r0%QwenImageImg2ImgInputStep.description9sZr2r,N) r3r4r5r6r7r8r#r!r9r:r<r0r>r,r2r/r\r\s?0dJ,.0M0OPM "56K RRr2r\c p\rSrSrSrSr\"5\"\"S\ RSS9/S9/r SS /r \ S 5rS rg ) QwenImageInpaintInputStepiAaM Input step that prepares the inputs for the inpainting denoising step. It: Components: pachifier (`QwenImagePachifier`) Inputs: num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. prompt_embeds (`Tensor`): text embeddings used to guide the image generation. Can be generated from text_encoder step. prompt_embeds_mask (`Tensor`): mask for the text embeddings. Can be generated from text_encoder step. negative_prompt_embeds (`Tensor`, *optional*): negative text embeddings used to guide the image generation. Can be generated from text_encoder step. negative_prompt_embeds_mask (`Tensor`, *optional*): mask for the negative text embeddings. Can be generated from text_encoder step. height (`int`, *optional*): The height in pixels of the generated image. width (`int`, *optional*): The width in pixels of the generated image. image_latents (`Tensor`, *optional*): image latents used to guide the image generation. Can be generated from vae_encoder step. processed_mask_image (`Tensor`, *optional*): The processed mask image Outputs: batch_size (`int`): The batch size of the prompt embeddings dtype (`dtype`): The data type of the prompt embeddings prompt_embeds (`Tensor`): The prompt embeddings. (batch-expanded) prompt_embeds_mask (`Tensor`): The encoder attention mask. (batch-expanded) negative_prompt_embeds (`Tensor`): The negative prompt embeddings. (batch-expanded) negative_prompt_embeds_mask (`Tensor`): The negative prompt embeddings mask. (batch-expanded) image_height (`int`): The image height calculated from the image latents dimension image_width (`int`): The image width calculated from the image latents dimension height (`int`): if not provided, updated to image height width (`int`): if not provided, updated to image width image_latents (`Tensor`): image latents used to guide the image generation. Can be generated from vae_encoder step. (patchified and batch-expanded) processed_mask_image (`Tensor`): The processed mask image (batch-expanded) r'processed_mask_imagezThe processed mask image)name type_hintr0)additional_batch_inputsr]r^cg)NzKInput step that prepares the inputs for the inpainting denoising step. It: r,r-s r/r0%QwenImageInpaintInputStep.descriptions]r2r,N)r3r4r5r6r7r8r#r!r torchTensorr9r:r<r0r>r,r2r/rbrbAs\4lJ!% 6%,,\vw% M!"56K RRr2rbcX\rSrSrSrSr\"5\"5/rSS/r \ S\ 4Sj5r Sr g ) "QwenImageInpaintPrepareLatentsStepia This step prepares the latents/image_latents and mask inputs for the inpainting denoising step. It: - Add noise to the image latents to create the latents input for the denoiser. - Create the pachified latents `mask` based on the processedmask image. Components: scheduler (`FlowMatchEulerDiscreteScheduler`) pachifier (`QwenImagePachifier`) Inputs: latents (`Tensor`): The initial random noised, can be generated in prepare latent step. image_latents (`Tensor`): image latents used to guide the image generation. Can be generated from vae_encoder step. (Can be generated from vae encoder and updated in input step.) timesteps (`Tensor`): The timesteps to use for the denoising process. Can be generated in set_timesteps step. processed_mask_image (`Tensor`): The processed mask to use for the inpainting process. height (`int`): The height in pixels of the generated image. width (`int`): The width in pixels of the generated image. dtype (`dtype`, *optional*, defaults to torch.float32): The dtype of the model inputs, can be generated in input step. Outputs: initial_noise (`Tensor`): The initial random noised used for inpainting denoising. latents (`Tensor`): The scaled noisy latents to use for inpainting/image-to-image denoising. mask (`Tensor`): The mask to use for the inpainting process. r'add_noise_to_latentscreate_mask_latentsr*cg)NzThis step prepares the latents/image_latents and mask inputs for the inpainting denoising step. It: - Add noise to the image latents to create the latents input for the denoiser. - Create the pachified latents `mask` based on the processedmask image. r,r-s r/r0.QwenImageInpaintPrepareLatentsStep.descriptions  Y r2r,N)r3r4r5r6r7r8rrr9r:r<r=r0r>r,r2r/rlrlsD DJ<>@^@`aM)+@AK  S  r2rlc\rSrSrSrSr\"5\"5\"5\ "5\ "5\ "5/r /SQr \S5r\S5rSrg) QwenImageCoreDenoiseStepia step that denoise noise into image for text2image task. It includes the denoise loop, as well as prepare the inputs (timesteps, latents, rope inputs etc.). Components: pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) guider (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`) Inputs: num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. prompt_embeds (`Tensor`): text embeddings used to guide the image generation. Can be generated from text_encoder step. prompt_embeds_mask (`Tensor`): mask for the text embeddings. Can be generated from text_encoder step. negative_prompt_embeds (`Tensor`, *optional*): negative text embeddings used to guide the image generation. Can be generated from text_encoder step. negative_prompt_embeds_mask (`Tensor`, *optional*): mask for the negative text embeddings. Can be generated from text_encoder step. latents (`Tensor`, *optional*): Pre-generated noisy latents for image generation. height (`int`, *optional*): The height in pixels of the generated image. width (`int`, *optional*): The width in pixels of the generated image. generator (`Generator`, *optional*): Torch generator for deterministic generation. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. sigmas (`list`, *optional*): Custom sigmas for the denoising process. attention_kwargs (`dict`, *optional*): Additional kwargs for attention processors. **denoiser_input_fields (`None`, *optional*): conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc. Outputs: latents (`Tensor`): Denoised latents. r')inputprepare_latents set_timestepsprepare_rope_inputsdenoise after_denoisecgNzstep that denoise noise into image for text2image task. It includes the denoise loop, as well as prepare the inputs (timesteps, latents, rope inputs etc.).r,r-s r/r0$QwenImageCoreDenoiseStep.descriptionmr2c0[R"S5/$Nlatentsr templater-s r/outputs QwenImageCoreDenoiseStep.outputs   +  r2r,N)r3r4r5r6r7r8r#rrrrrr9r:r<r0rr>r,r2r/rrrrsk'RJ!#%!#!!# MKmm  r2rrc\rSrSrSrSr\"5\"5\"5\ "5\ "5\ "5\ "5/r /SQr\S5r\S5rSrg) QwenImageInpaintCoreDenoiseStepi a Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for inpaint task. Components: pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) guider (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`) Inputs: num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. prompt_embeds (`Tensor`): text embeddings used to guide the image generation. Can be generated from text_encoder step. prompt_embeds_mask (`Tensor`): mask for the text embeddings. Can be generated from text_encoder step. negative_prompt_embeds (`Tensor`, *optional*): negative text embeddings used to guide the image generation. Can be generated from text_encoder step. negative_prompt_embeds_mask (`Tensor`, *optional*): mask for the negative text embeddings. Can be generated from text_encoder step. height (`int`, *optional*): The height in pixels of the generated image. width (`int`, *optional*): The width in pixels of the generated image. image_latents (`Tensor`, *optional*): image latents used to guide the image generation. Can be generated from vae_encoder step. processed_mask_image (`Tensor`, *optional*): The processed mask image latents (`Tensor`, *optional*): Pre-generated noisy latents for image generation. generator (`Generator`, *optional*): Torch generator for deterministic generation. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. sigmas (`list`, *optional*): Custom sigmas for the denoising process. strength (`float`, *optional*, defaults to 0.9): Strength for img2img/inpainting. attention_kwargs (`dict`, *optional*): Additional kwargs for attention processors. **denoiser_input_fields (`None`, *optional*): conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc. Outputs: latents (`Tensor`): Denoised latents. r')rsrtruprepare_inpaint_latentsrvrwrxcgNzyBefore denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for inpaint task.r,r-s r/r0+QwenImageInpaintCoreDenoiseStep.descriptionNKr2c0[R"S5/$r~rr-s r/r'QwenImageInpaintCoreDenoiseStep.outputsRrr2r,N)r3r4r5r6r7r8rbrrrlrrrr9r:r<r0rr>r,r2r/rr sq-^J!##%-/*,!#%!#MKKK  r2rc\rSrSrSrSr\"5\"5\"5\ "5\ "5\ "5\ "5/r /SQr\S5r\S5rSrg) QwenImageImg2ImgCoreDenoiseStepi[aC Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for img2img task. Components: pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) guider (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`) Inputs: num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. prompt_embeds (`Tensor`): text embeddings used to guide the image generation. Can be generated from text_encoder step. prompt_embeds_mask (`Tensor`): mask for the text embeddings. Can be generated from text_encoder step. negative_prompt_embeds (`Tensor`, *optional*): negative text embeddings used to guide the image generation. Can be generated from text_encoder step. negative_prompt_embeds_mask (`Tensor`, *optional*): mask for the negative text embeddings. Can be generated from text_encoder step. height (`int`, *optional*): The height in pixels of the generated image. width (`int`, *optional*): The width in pixels of the generated image. image_latents (`Tensor`): image latents used to guide the image generation. Can be generated from vae_encoder step. latents (`Tensor`, *optional*): Pre-generated noisy latents for image generation. generator (`Generator`, *optional*): Torch generator for deterministic generation. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. sigmas (`list`, *optional*): Custom sigmas for the denoising process. strength (`float`, *optional*, defaults to 0.9): Strength for img2img/inpainting. attention_kwargs (`dict`, *optional*): Additional kwargs for attention processors. **denoiser_input_fields (`None`, *optional*): conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc. Outputs: latents (`Tensor`): Denoised latents. r')rsrtruprepare_img2img_latentsrvrwrxcgNzyBefore denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for img2img task.r,r-s r/r0+QwenImageImg2ImgCoreDenoiseStep.descriptionrr2c0[R"S5/$r~rr-s r/r'QwenImageImg2ImgCoreDenoiseStep.outputsrr2r,N)r3r4r5r6r7r8r\rrrrrrr9r:r<r0rr>r,r2r/rr[sq+ZJ!##%-//1!!#MKKK  r2rc \rSrSrSrSr\"5\"5\"5\ "5\ "5\ "5\ "5\ "5/r/SQr\S5r\S5rSrg) "QwenImageControlNetCoreDenoiseStepia step that denoise noise into image for text2image task. It includes the denoise loop, as well as prepare the inputs (timesteps, latents, rope inputs etc.). Components: pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) controlnet (`QwenImageControlNetModel`) guider (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`) Inputs: num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. prompt_embeds (`Tensor`): text embeddings used to guide the image generation. Can be generated from text_encoder step. prompt_embeds_mask (`Tensor`): mask for the text embeddings. Can be generated from text_encoder step. negative_prompt_embeds (`Tensor`, *optional*): negative text embeddings used to guide the image generation. Can be generated from text_encoder step. negative_prompt_embeds_mask (`Tensor`, *optional*): mask for the negative text embeddings. Can be generated from text_encoder step. control_image_latents (`Tensor`): The control image latents to use for the denoising process. Can be generated in controlnet vae encoder step. height (`int`, *optional*): The height in pixels of the generated image. width (`int`, *optional*): The width in pixels of the generated image. latents (`Tensor`, *optional*): Pre-generated noisy latents for image generation. generator (`Generator`, *optional*): Torch generator for deterministic generation. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. sigmas (`list`, *optional*): Custom sigmas for the denoising process. control_guidance_start (`float`, *optional*, defaults to 0.0): When to start applying ControlNet. control_guidance_end (`float`, *optional*, defaults to 1.0): When to stop applying ControlNet. controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0): Scale for ControlNet conditioning. attention_kwargs (`dict`, *optional*): Additional kwargs for attention processors. **denoiser_input_fields (`None`, *optional*): conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc. Outputs: latents (`Tensor`): Denoised latents. r')rscontrolnet_inputrtrurvcontrolnet_before_denoisecontrolnet_denoiserxcgrzr,r-s r/r0.QwenImageControlNetCoreDenoiseStep.descriptionr|r2c0[R"S5/$r~rr-s r/r*QwenImageControlNetCoreDenoiseStep.outputsrr2r,N)r3r4r5r6r7r8r#r"rrrr rrr9r:r<r0rr>r,r2r/rrsw0dJ!%'#%!#!-/&(!# M Kmm  r2rc \rSrSrSrSr\"5\"5\"5\ "5\ "5\ "5\ "5\ "5\"5/ r/SQr\S5r\S5rSrg) )QwenImageControlNetInpaintCoreDenoiseStepia Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for inpaint task. Components: pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) controlnet (`QwenImageControlNetModel`) guider (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`) Inputs: num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. prompt_embeds (`Tensor`): text embeddings used to guide the image generation. Can be generated from text_encoder step. prompt_embeds_mask (`Tensor`): mask for the text embeddings. Can be generated from text_encoder step. negative_prompt_embeds (`Tensor`, *optional*): negative text embeddings used to guide the image generation. Can be generated from text_encoder step. negative_prompt_embeds_mask (`Tensor`, *optional*): mask for the negative text embeddings. Can be generated from text_encoder step. height (`int`, *optional*): The height in pixels of the generated image. width (`int`, *optional*): The width in pixels of the generated image. image_latents (`Tensor`, *optional*): image latents used to guide the image generation. Can be generated from vae_encoder step. processed_mask_image (`Tensor`, *optional*): The processed mask image control_image_latents (`Tensor`): The control image latents to use for the denoising process. Can be generated in controlnet vae encoder step. latents (`Tensor`, *optional*): Pre-generated noisy latents for image generation. generator (`Generator`, *optional*): Torch generator for deterministic generation. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. sigmas (`list`, *optional*): Custom sigmas for the denoising process. strength (`float`, *optional*, defaults to 0.9): Strength for img2img/inpainting. control_guidance_start (`float`, *optional*, defaults to 0.0): When to start applying ControlNet. control_guidance_end (`float`, *optional*, defaults to 1.0): When to stop applying ControlNet. controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0): Scale for ControlNet conditioning. attention_kwargs (`dict`, *optional*): Additional kwargs for attention processors. **denoiser_input_fields (`None`, *optional*): conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc. Outputs: latents (`Tensor`): Denoised latents. r') rsrrtrurrvrrrxcgrr,r-s r/r05QwenImageControlNetInpaintCoreDenoiseStep.descriptionQrr2c0[R"S5/$r~rr-s r/r1QwenImageControlNetInpaintCoreDenoiseStep.outputsUrr2r,N)r3r4r5r6r7r8rbr"rrrlrr rrr9r:r<r0rr>r,r2r/rrs}6pJ!#%'#%-/*,!-/-/!# M KKK  r2rc \rSrSrSrSr\"5\"5\"5\ "5\ "5\ "5\ "5\ "5\"5/ r/SQr\S5r\S5rSrg) )QwenImageControlNetImg2ImgCoreDenoiseStepi^an Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for img2img task. Components: pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) controlnet (`QwenImageControlNetModel`) guider (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`) Inputs: num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. prompt_embeds (`Tensor`): text embeddings used to guide the image generation. Can be generated from text_encoder step. prompt_embeds_mask (`Tensor`): mask for the text embeddings. Can be generated from text_encoder step. negative_prompt_embeds (`Tensor`, *optional*): negative text embeddings used to guide the image generation. Can be generated from text_encoder step. negative_prompt_embeds_mask (`Tensor`, *optional*): mask for the negative text embeddings. Can be generated from text_encoder step. height (`int`, *optional*): The height in pixels of the generated image. width (`int`, *optional*): The width in pixels of the generated image. image_latents (`Tensor`): image latents used to guide the image generation. Can be generated from vae_encoder step. control_image_latents (`Tensor`): The control image latents to use for the denoising process. Can be generated in controlnet vae encoder step. latents (`Tensor`, *optional*): Pre-generated noisy latents for image generation. generator (`Generator`, *optional*): Torch generator for deterministic generation. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. sigmas (`list`, *optional*): Custom sigmas for the denoising process. strength (`float`, *optional*, defaults to 0.9): Strength for img2img/inpainting. control_guidance_start (`float`, *optional*, defaults to 0.0): When to start applying ControlNet. control_guidance_end (`float`, *optional*, defaults to 1.0): When to stop applying ControlNet. controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0): Scale for ControlNet conditioning. attention_kwargs (`dict`, *optional*): Additional kwargs for attention processors. **denoiser_input_fields (`None`, *optional*): conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc. Outputs: latents (`Tensor`): Denoised latents. r') rsrrtrurrvrrrxcgrr,r-s r/r05QwenImageControlNetImg2ImgCoreDenoiseStep.descriptionrr2c0[R"S5/$r~rr-s r/r1QwenImageControlNetImg2ImgCoreDenoiseStep.outputsrr2r,N)r3r4r5r6r7r8r\r"rrrrr rrr9r:r<r0rr>r,r2r/rr^s}4lJ!#%'#%-//1!-/&(!# M KKK  r2rcb\rSrSr\\\\\\ /r /SQr /SQr Sr S Sjr\S5r\S5rS rg) QwenImageAutoCoreDenoiseStepi) text2imagerNrOcontrolnet_text2imagecontrolnet_inpaintcontrolnet_img2img)control_image_latentsrc image_latentsrNc,Ub UbgUbggUbgUbgg)NrrrrNrOrr,)r.rrcrs r/ select_block)QwenImageAutoCoreDenoiseStep.select_blocks2 ,#/+*+.#/ * #r2cg)NaCore step that performs the denoising process. - `QwenImageCoreDenoiseStep` (text2image) for text2image tasks. - `QwenImageInpaintCoreDenoiseStep` (inpaint) for inpaint tasks. - `QwenImageImg2ImgCoreDenoiseStep` (img2img) for img2img tasks. - `QwenImageControlNetCoreDenoiseStep` (controlnet_text2image) for text2image tasks with controlnet. - `QwenImageControlNetInpaintCoreDenoiseStep` (controlnet_inpaint) for inpaint tasks with controlnet. - `QwenImageControlNetImg2ImgCoreDenoiseStep` (controlnet_img2img) for img2img tasks with controlnet. This step support text-to-image, image-to-image, inpainting, and controlnet tasks for QwenImage: - for image-to-image generation, you need to provide `image_latents` - for inpainting, you need to provide `processed_mask_image` and `image_latents` - to run the controlnet workflow, you need to provide `control_image_latents` - for text-to-image generation, all you need to provide is prompt embeddingsr,r-s r/r0(QwenImageAutoCoreDenoiseStep.descriptions  ^ r2c0[R"S5/$r~rr-s r/r$QwenImageAutoCoreDenoiseStep.outputsrr2r,)NNN)r3r4r5r6rrrrrrrr9r:r;default_block_namerr<r0rr>r,r2r/rrs^ ''*11 MK^%$     r2rcP\rSrSrSrSr\"5\"5/rSS/r \ S5r Sr g) QwenImageDecodeStepia Decode step that decodes the latents to images and postprocess the generated image. Components: vae (`AutoencoderKLQwenImage`) image_processor (`VaeImageProcessor`) Inputs: latents (`Tensor`): The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise step. output_type (`str`, *optional*, defaults to pil): Output format: 'pil', 'np', 'pt'. Outputs: images (`list`): Generated images. (tensor output of the vae decoder.) r'decode postprocesscg)NzSDecode step that decodes the latents to images and postprocess the generated image.r,r-s r/r0QwenImageDecodeStep.descriptionsdr2r,N) r3r4r5r6r7r8rrr9r:r<r0r>r,r2r/rrs=$J)+-M-OPM]+K eer2rcP\rSrSrSrSr\"5\"5/rSS/r \ S5r Sr g) QwenImageInpaintDecodeStepia Decode step that decodes the latents to images and postprocess the generated image, optional apply the mask overally to the original image. Components: vae (`AutoencoderKLQwenImage`) image_mask_processor (`InpaintProcessor`) Inputs: latents (`Tensor`): The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise step. output_type (`str`, *optional*, defaults to pil): Output format: 'pil', 'np', 'pt'. mask_overlay_kwargs (`dict`, *optional*): The kwargs for the postprocess step to apply the mask overlay. generated in InpaintProcessImagesInputStep. Outputs: images (`list`): Generated images. (tensor output of the vae decoder.) r'rrcg)NzDecode step that decodes the latents to images and postprocess the generated image, optional apply the mask overally to the original image.r,r-s r/r0&QwenImageInpaintDecodeStep.description5s]r2r,N) r3r4r5r6r7r8rrr9r:r<r0r>r,r2r/rrs=,J)+-T-VWM]+K ]]r2rc<\rSrSr\\/rSS/rSS/r\ S5r Sr g)QwenImageAutoDecodeStepi;inpaint_decodermaskNcg)NaHDecode step that decode the latents into images. This is an auto pipeline block that works for inpaint/text2image/img2img tasks, for both QwenImage and QwenImage-Edit. - `QwenImageInpaintDecodeStep` (inpaint) is used when `mask` is provided. - `QwenImageDecodeStep` (text2image/img2img) is used when `mask` is not provided. r,r-s r/r0#QwenImageAutoDecodeStep.description@s  e r2r,) r3r4r5r6rrr9r:r;r<r0r>r,r2r/rr;s3/1DEM#X.K"D>   r2rr( vae_encodercontrolnet_vae_encoderrwrc \rSrSrSrSr\R5r\R5r SS0SSS.SSSS.SSS.SSSS .SSSSS .S .r \ S 5r \ S 5rSrg)QwenImageAutoBlocksiYaE Auto Modular pipeline for text-to-image, image-to-image, inpainting, and controlnet tasks using QwenImage. Supported workflows: - `text2image`: requires `prompt` - `image2image`: requires `prompt`, `image` - `inpainting`: requires `prompt`, `mask_image`, `image` - `controlnet_text2image`: requires `prompt`, `control_image` - `controlnet_image2image`: requires `prompt`, `image`, `control_image` - `controlnet_inpainting`: requires `prompt`, `mask_image`, `image`, `control_image` Components: text_encoder (`Qwen2_5_VLForConditionalGeneration`): The text encoder to use tokenizer (`Qwen2Tokenizer`): The tokenizer to use guider (`ClassifierFreeGuidance`) image_mask_processor (`InpaintProcessor`) vae (`AutoencoderKLQwenImage`) image_processor (`VaeImageProcessor`) controlnet (`QwenImageControlNetModel`) control_image_processor (`VaeImageProcessor`) pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) transformer (`QwenImageTransformer2DModel`) Inputs: prompt (`str`, *optional*): The prompt or prompts to guide image generation. negative_prompt (`str`, *optional*): The prompt or prompts not to guide the image generation. max_sequence_length (`int`, *optional*, defaults to 1024): Maximum sequence length for prompt encoding. mask_image (`Image`, *optional*): Mask image for inpainting. image (`Image | list`, *optional*): Reference image(s) for denoising. Can be a single image or list of images. height (`int`, *optional*): The height in pixels of the generated image. width (`int`, *optional*): The width in pixels of the generated image. padding_mask_crop (`int`, *optional*): Padding for mask cropping in inpainting. generator (`Generator`, *optional*): Torch generator for deterministic generation. control_image (`Image`, *optional*): Control image for ControlNet conditioning. num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. prompt_embeds (`Tensor`): text embeddings used to guide the image generation. Can be generated from text_encoder step. prompt_embeds_mask (`Tensor`): mask for the text embeddings. Can be generated from text_encoder step. negative_prompt_embeds (`Tensor`, *optional*): negative text embeddings used to guide the image generation. Can be generated from text_encoder step. negative_prompt_embeds_mask (`Tensor`, *optional*): mask for the negative text embeddings. Can be generated from text_encoder step. latents (`Tensor`): Pre-generated noisy latents for image generation. num_inference_steps (`int`): The number of denoising steps. sigmas (`list`, *optional*): Custom sigmas for the denoising process. attention_kwargs (`dict`, *optional*): Additional kwargs for attention processors. **denoiser_input_fields (`None`, *optional*): conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc. image_latents (`Tensor`, *optional*): image latents used to guide the image generation. Can be generated from vae_encoder step. processed_mask_image (`Tensor`, *optional*): The processed mask image strength (`float`, *optional*, defaults to 0.9): Strength for img2img/inpainting. control_image_latents (`Tensor`, *optional*): The control image latents to use for the denoising process. Can be generated in controlnet vae encoder step. control_guidance_start (`float`, *optional*, defaults to 0.0): When to start applying ControlNet. control_guidance_end (`float`, *optional*, defaults to 1.0): When to stop applying ControlNet. controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0): Scale for ControlNet conditioning. output_type (`str`, *optional*, defaults to pil): Output format: 'pil', 'np', 'pt'. mask_overlay_kwargs (`dict`, *optional*): The kwargs for the postprocess step to apply the mask overlay. generated in InpaintProcessImagesInputStep. Outputs: images (`list`): Generated images. r'r)T)r)rQ)r)rPrQ)r)rX)r)rQrX)r)rPrQrX)r image2image inpaintingrcontrolnet_image2imagecontrolnet_inpaintingcg)NzjAuto Modular pipeline for text-to-image, image-to-image, inpainting, and controlnet tasks using QwenImage.r,r-s r/r0QwenImageAutoBlocks.descriptions{r2c0[R"S5/$)Nimagesrr-s r/rQwenImageAutoBlocks.outputss$$X.//r2r,N)r3r4r5r6r7r8 AUTO_BLOCKSvaluesr9keysr: _workflow_mapr<r0rr>r,r2r/rrYsSjJ&&(M""$K &"&6!%TDI,04!H-1DSW"X,0tfj!k M||00r2r)>riutilsrmodular_pipelinerrrmodular_pipeline_utilsr r r before_denoiser rrrrrrdecodersrrrrrwrrrrencodersrrrrr inputsr!r"r# get_loggerr3loggerr%r@rGrLrUr\rbrlrrrrrrrrrrrrrr,r2r/rs ffLL     H %$@#5$@Z. %=. dp%=pD  "4  $% 0B% ^;R 8;R~FR 8FRV- )A- jD 7D RL &>L bJ &>J ^Q )AQ lY 0HY |W 0HW v9 #<9 Fe2e<]!9]B  0  $ 578 356 !#L#NO 023 *,-  o02o0r2