o Gi@s.ddlZddlmZddlmZmZmZddlmZm Z m Z ddl m Z m Z mZmZmZmZmZdd lmZmZmZmZdd lmZmZmZmZdd lmZmZm Z m!Z!m"Z"dd l#m$Z$m%Z%m&Z&e'e(Z)Gd ddeZ*GdddeZ+GdddeZ,GdddeZ-GdddeZ.GdddeZ/GdddeZ0GdddeZ1GdddeZ2Gdd d eZ3Gd!d"d"eZ4Gd#d$d$eZ5Gd%d&d&eZ6Gd'd(d(eZ7Gd)d*d*eZ8Gd+d,d,eZ9Gd-d.d.eZ:Gd/d0d0eZ;e d1e*fd2e-fd3e.fd4e8fd5e;fgZz'QwenImageAutoVaeEncoderStep.descriptionN) r/r0r1r:r@r4r5r6r7r-r)r)r)r,rA rAc@s.eZdZdZegZdgZdgZeddZ dS))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_imagecCr=)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*r)r)r,r-r>z5QwenImageOptionalControlNetVaeEncoderStep.descriptionN) r/r0r1r2rr4r5r6r7r-r)r)r)r,rGsrGc@4eZdZdZdZeegZddgZe ddZ dS)QwenImageImg2ImgInputStepaA 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_inputscCr()NzHInput step that prepares the inputs for the img2img denoising step. It: r)r*r)r)r,r-9r.z%QwenImageImg2ImgInputStep.descriptionN) r/r0r1r2r3r"r r4r5r7r-r)r)r)r,rKs2 rKc@sFeZdZdZdZeeedej ddgdgZ ddgZ e d d Z d S) QwenImageInpaintInputStepa 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$processed_mask_imagezThe processed mask image)name type_hintr-)additional_batch_inputsrLrMcCr()NzKInput step that prepares the inputs for the inpainting denoising step. It: r)r*r)r)r,r-r.z%QwenImageInpaintInputStep.descriptionN)r/r0r1r2r3r"r rtorchTensorr4r5r7r-r)r)r)r,rNAs6rNc@r9) "QwenImageInpaintPrepareLatentsStepaG This step prepares the latents/image_latents and mask inputs for the inpainting denoising step. It: - Add noise to the image latents to create the latents input for the denoiser. - Create the pachified latents `mask` based on the processedmask image. Components: scheduler (`FlowMatchEulerDiscreteScheduler`) pachifier (`QwenImagePachifier`) Inputs: latents (`Tensor`): The initial random noised, can be generated in prepare latent step. image_latents (`Tensor`): image latents used to guide the image generation. Can be generated from vae_encoder step. (Can be generated from vae encoder and updated in input step.) timesteps (`Tensor`): The timesteps to use for the denoising process. Can be generated in set_timesteps step. processed_mask_image (`Tensor`): The processed mask to use for the inpainting process. height (`int`): The height in pixels of the generated image. width (`int`): The width in pixels of the generated image. dtype (`dtype`, *optional*, defaults to torch.float32): The dtype of the model inputs, can be generated in input step. Outputs: initial_noise (`Tensor`): The initial random noised used for inpainting denoising. latents (`Tensor`): The scaled noisy latents to use for inpainting/image-to-image denoising. mask (`Tensor`): The mask to use for the inpainting process. r$add_noise_to_latentscreate_mask_latentsr'cCr=)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*r)r)r,r-r>z.QwenImageInpaintPrepareLatentsStep.descriptionN) r/r0r1r2r3rr r4r5r7r8r-r)r)r)r,rUr?rUc@sPeZdZdZdZeeeee e gZ gdZ e ddZe ddZdS) QwenImageCoreDenoiseStepa 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$)inputprepare_latents set_timestepsprepare_rope_inputsdenoise after_denoisecCr(Nzstep that denoise noise into image for text2image task. It includes the denoise loop, as well as prepare the inputs (timesteps, latents, rope inputs etc.).r)r*r)r)r,r-r.z$QwenImageCoreDenoiseStep.descriptioncC tdgSNlatentsr templater*r)r)r,outputsz QwenImageCoreDenoiseStep.outputsN)r/r0r1r2r3r"rrrrrr4r5r7r-rer)r)r)r,rXs) rXc@TeZdZdZdZeeeee e e gZ gdZ eddZeddZdS) QwenImageInpaintCoreDenoiseStepaM 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$)rYrZr[prepare_inpaint_latentsr\r]r^cCr(NzyBefore denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for inpaint task.r)r*r)r)r,r-Nr.z+QwenImageInpaintCoreDenoiseStep.descriptioncCr`rarcr*r)r)r,reRrfz'QwenImageInpaintCoreDenoiseStep.outputsN)r/r0r1r2r3rNrrrUrrrr4r5r7r-rer)r)r)r,rh s /  rhc@rg) QwenImageImg2ImgCoreDenoiseStepa 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$)rYrZr[prepare_img2img_latentsr\r]r^cCr(NzyBefore denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for img2img task.r)r*r)r)r,r-r.z+QwenImageImg2ImgCoreDenoiseStep.descriptioncCr`rarcr*r)r)r,rerfz'QwenImageImg2ImgCoreDenoiseStep.outputsN)r/r0r1r2r3rKrrrrrrr4r5r7r-rer)r)r)r,rk[s -  rkc@sXeZdZdZdZeeeee e e e gZ gdZeddZeddZdS) "QwenImageControlNetCoreDenoiseStepa^ 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$)rYcontrolnet_inputrZr[r\controlnet_before_denoisecontrolnet_denoiser^cCr(r_r)r*r)r)r,r-r.z.QwenImageControlNetCoreDenoiseStep.descriptioncCr`rarcr*r)r)r,rerfz*QwenImageControlNetCoreDenoiseStep.outputsN)r/r0r1r2r3r"r!rrrr rrr4r5r7r-rer)r)r)r,rns"2  rnc @\eZdZdZdZeeeee e e e e g ZgdZeddZeddZdS) )QwenImageControlNetInpaintCoreDenoiseStepa 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$) rYrorZr[rir\rprqr^cCr(rjr)r*r)r)r,r-Qr.z5QwenImageControlNetInpaintCoreDenoiseStep.descriptioncCr`rarcr*r)r)r,reUrfz1QwenImageControlNetInpaintCoreDenoiseStep.outputsN)r/r0r1r2r3rNr!rrrUrr rrr4r5r7r-rer)r)r)r,rss$8  rsc @rr) )QwenImageControlNetImg2ImgCoreDenoiseStepa2 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$) rYrorZr[rlr\rprqr^cCr(rmr)r*r)r)r,r-r.z5QwenImageControlNetImg2ImgCoreDenoiseStep.descriptioncCr`rarcr*r)r)r,rerfz1QwenImageControlNetImg2ImgCoreDenoiseStep.outputsN)r/r0r1r2r3rKr!rrrrr rrr4r5r7r-rer)r)r)r,rt^s$6  rtc@sReZdZeeeeeegZ gdZ gdZ dZ d ddZ eddZed d ZdS) QwenImageAutoCoreDenoiseStep) text2imagerBrCcontrolnet_text2imagecontrolnet_inpaintcontrolnet_img2img)control_image_latentsrO image_latentsrvNcCs@|dur|dur dS|durdSdS|durdS|durdSdS)NrxryrwrBrCrvr))r+rzrOr{r)r)r, select_blocksz)QwenImageAutoCoreDenoiseStep.select_blockcCr=)NaCore step that performs the denoising process. - `QwenImageCoreDenoiseStep` (text2image) for text2image tasks. - `QwenImageInpaintCoreDenoiseStep` (inpaint) for inpaint tasks. - `QwenImageImg2ImgCoreDenoiseStep` (img2img) for img2img tasks. - `QwenImageControlNetCoreDenoiseStep` (controlnet_text2image) for text2image tasks with controlnet. - `QwenImageControlNetInpaintCoreDenoiseStep` (controlnet_inpaint) for inpaint tasks with controlnet. - `QwenImageControlNetImg2ImgCoreDenoiseStep` (controlnet_img2img) for img2img tasks with controlnet. This step support text-to-image, image-to-image, inpainting, and controlnet tasks for QwenImage: - for image-to-image generation, you need to provide `image_latents` - for inpainting, you need to provide `processed_mask_image` and `image_latents` - to run the controlnet workflow, you need to provide `control_image_latents` - for text-to-image generation, all you need to provide is prompt embeddingsr)r*r)r)r,r-r>z(QwenImageAutoCoreDenoiseStep.descriptioncCr`rarcr*r)r)r,rerfz$QwenImageAutoCoreDenoiseStep.outputs)NNN)r/r0r1rXrhrkrnrsrtr4r5r6default_block_namer|r7r-rer)r)r)r,rus   ruc@rJ)QwenImageDecodeStepaP 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$decode postprocesscCr()NzSDecode step that decodes the latents to images and postprocess the generated image.r)r*r)r)r,r-r.zQwenImageDecodeStep.descriptionN) r/r0r1r2r3rrr4r5r7r-r)r)r)r,r~s r~c@rJ)QwenImageInpaintDecodeStepaK Decode step that decodes the latents to images and postprocess the generated image, optional apply the mask overally to the original image. Components: vae (`AutoencoderKLQwenImage`) image_mask_processor (`InpaintProcessor`) Inputs: latents (`Tensor`): The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise step. output_type (`str`, *optional*, defaults to pil): Output format: 'pil', 'np', 'pt'. mask_overlay_kwargs (`dict`, *optional*): The kwargs for the postprocess step to apply the mask overlay. generated in InpaintProcessImagesInputStep. Outputs: images (`list`): Generated images. (tensor output of the vae decoder.) r$rrcCr()NzDecode step that decodes the latents to images and postprocess the generated image, optional apply the mask overally to the original image.r)r*r)r)r,r-5r.z&QwenImageInpaintDecodeStep.descriptionN) r/r0r1r2r3rrr4r5r7r-r)r)r)r,rs rc@s0eZdZeegZddgZddgZeddZ dS)QwenImageAutoDecodeStepinpaint_decodermaskNcCr=)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*r)r)r,r-@r>z#QwenImageAutoDecodeStep.description) r/r0r1rr~r4r5r6r7r-r)r)r)r,r;rFrr% vae_encodercontrolnet_vae_encoderr]rc @sxeZdZdZdZeZeZ ddiddddddddddddddddddd d Z e d d Z e d dZ dS)QwenImageAutoBlocksa 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&rE)r&rDrE)r&rI)r&rErI)r&rDrErI)rv image2image inpaintingrwcontrolnet_image2imagecontrolnet_inpaintingcCr()NzjAuto Modular pipeline for text-to-image, image-to-image, inpainting, and controlnet tasks using QwenImage.r)r*r)r)r,r-r.zQwenImageAutoBlocks.descriptioncCr`)Nimagesrcr*r)r)r,res zQwenImageAutoBlocks.outputsN)r/r0r1r2r3 AUTO_BLOCKSvaluesr4keysr5 _workflow_mapr7r-rer)r)r)r,rYs U    r)>rSutilsrmodular_pipelinerrrmodular_pipeline_utilsrr r before_denoiser r rrrrrdecodersrrrrr]rrrrencodersrrrrrinputsr r!r" get_loggerr/loggerr#r:r@rArGrKrNrUrXrhrkrnrsrtrur~rrrrr)r)r)r,sJ $   -2"/?K5IQOV^[C!