3j]SSKrSSKJr SSKJrJrJr SSKJrJ r J r SSK J r J r JrJrJrJrJrJr SS KJr SS KJrJr SS KJrJrJr \R:"\5r"S S \5r "SS\5r!"SS\5r""SS\5r#"SS\5r$\ "S\"54S\ "54S\$"54S\"54/5r%"SS\5r&g)N)logging)AutoPipelineBlocksConditionalPipelineBlocksSequentialPipelineBlocks) InputParamInsertableDict OutputParam)HeliosAdditionalInputsStep HeliosAddNoiseToImageLatentsStep HeliosAddNoiseToVideoLatentsStepHeliosI2VSeedHistoryStepHeliosPrepareHistoryStepHeliosSetTimestepsStepHeliosTextInputStepHeliosV2VSeedHistoryStep)HeliosDecodeStep)HeliosChunkDenoiseStepHeliosI2VChunkDenoiseStep)HeliosImageVaeEncoderStepHeliosTextEncoderStepHeliosVideoVaeEncoderStepc@\rSrSrSr\\/rSS/rSS/r \ S5r Sr g ) HeliosAutoVaeEncoderStep,a Encoder step that encodes video or image inputs. This is an auto pipeline block. - `HeliosVideoVaeEncoderStep` (video_encoder) is used when `video` is provided. - `HeliosImageVaeEncoderStep` (image_encoder) is used when `image` is provided. - If neither is provided, step will be skipped. Components: vae (`AutoencoderKLWan`) video_processor (`VideoProcessor`) Inputs: video (`None`, *optional*): Input video for video-to-video generation height (`int`, *optional*, defaults to 384): The height in pixels of the generated image. width (`int`, *optional*, defaults to 640): The width in pixels of the generated image. num_latent_frames_per_chunk (`int`, *optional*, defaults to 9): Number of latent frames per temporal chunk. generator (`Generator`, *optional*): Torch generator for deterministic generation. image (`Image | list`, *optional*): Reference image(s) for denoising. Can be a single image or list of images. Outputs: image_latents (`Tensor`): The latent representation of the input image. video_latents (`Tensor`): Encoded video latents (chunked) fake_image_latents (`Tensor`): Fake image latents for history seeding video_encoder image_encodervideoimagecg)Na#Encoder step that encodes video or image inputs. This is an auto pipeline block. - `HeliosVideoVaeEncoderStep` (video_encoder) is used when `video` is provided. - `HeliosImageVaeEncoderStep` (image_encoder) is used when `image` is provided. - If neither is provided, step will be skipped.selfs r/home/wildlama/miniconda3/lib/python3.13/site-packages/diffusers/modular_pipelines/helios/modular_blocks_helios.py description$HeliosAutoVaeEncoderStep.descriptionQs  ? r#N) __name__ __module__ __qualname____firstlineno____doc__rr block_classes block_namesblock_trigger_inputspropertyr'__static_attributes__r#r)r&rr,s<@/0IJM"O4K#W-   r)rcP\rSrSrSrSr\\\\ /r /SQr \ S5r \ S5rSrg) HeliosCoreDenoiseStepbaz Denoise block that takes encoded conditions and runs the chunk-based denoising process. Components: transformer (`HeliosTransformer3DModel`) scheduler (`HeliosScheduler`) guider (`ClassifierFreeGuidance`) Inputs: num_videos_per_prompt (`int`, *optional*, defaults to 1): Number of videos to generate per prompt. prompt_embeds (`Tensor`): text embeddings used to guide the image generation. 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. height (`int`, *optional*, defaults to 384): The height in pixels of the generated image. width (`int`, *optional*, defaults to 640): The width in pixels of the generated image. num_frames (`int`, *optional*, defaults to 132): Total number of video frames to generate. num_latent_frames_per_chunk (`int`, *optional*, defaults to 9): Number of latent frames per temporal chunk. history_sizes (`list`, *optional*, defaults to [16, 2, 1]): Sizes of long/mid/short history buffers for temporal context. keep_first_frame (`bool`, *optional*, defaults to True): Whether to keep the first frame as a prefix in history. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. sigmas (`list`, *optional*): Custom sigmas for the denoising process. generator (`Generator`, *optional*): Torch generator for deterministic generation. latents (`Tensor`, *optional*): Pre-generated noisy latents for image generation. timesteps (`Tensor`, *optional*): Timesteps for the denoising process. **denoiser_input_fields (`None`, *optional*): conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc. attention_kwargs (`dict`, *optional*): Additional kwargs for attention processors. Outputs: latent_chunks (`list`): List of per-chunk denoised latent tensors helios)inputprepare_history set_timesteps chunk_denoisecg)NzWDenoise block that takes encoded conditions and runs the chunk-based denoising process.r#r$s r&r'!HeliosCoreDenoiseStep.descriptionshr)c"[S[SS9/$N latent_chunksz)List of per-chunk denoised latent tensors type_hintr'r listr$s r&outputsHeliosCoreDenoiseStep.outputsOtItuvvr)r#N)r*r+r,r-r. model_namerrrrr/r0r2r'rEr3r#r)r&r5r5bsT+ZJ  M QK iiwwr)r5c \rSrSrSrSr\\"\R"S5/\"S\ RSS9/S9\ \ \\\/r/S Qr\S 5r\S 5rS rg )HeliosI2VCoreDenoiseStepa I2V denoise block that seeds history with image latents and uses I2V-aware chunk preparation. Components: transformer (`HeliosTransformer3DModel`) scheduler (`HeliosScheduler`) guider (`ClassifierFreeGuidance`) Inputs: num_videos_per_prompt (`int`, *optional*, defaults to 1): Number of videos to generate per prompt. prompt_embeds (`Tensor`): text embeddings used to guide the image generation. 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. image_latents (`Tensor`): image latents used to guide the image generation. Can be generated from vae_encoder step. fake_image_latents (`Tensor`, *optional*): Fake image latents used as history seed for I2V generation. image_noise_sigma_min (`float`, *optional*, defaults to 0.111): Minimum sigma for image latent noise. image_noise_sigma_max (`float`, *optional*, defaults to 0.135): Maximum sigma for image latent noise. video_noise_sigma_min (`float`, *optional*, defaults to 0.111): Minimum sigma for video/fake-image latent noise. video_noise_sigma_max (`float`, *optional*, defaults to 0.135): Maximum sigma for video/fake-image latent noise. generator (`Generator`, *optional*): Torch generator for deterministic generation. num_frames (`int`, *optional*, defaults to 132): Total number of video frames to generate. num_latent_frames_per_chunk (`int`, *optional*, defaults to 9): Number of latent frames per temporal chunk. history_sizes (`list`, *optional*, defaults to [16, 2, 1]): Sizes of long/mid/short history buffers for temporal context. keep_first_frame (`bool`, *optional*, defaults to True): Whether to keep the first frame as a prefix in history. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. sigmas (`list`, *optional*): Custom sigmas for the denoising process. latents (`Tensor`, *optional*): Pre-generated noisy latents for image generation. timesteps (`Tensor`, *optional*): Timesteps for the denoising process. **denoiser_input_fields (`None`, *optional*): conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc. attention_kwargs (`dict`, *optional*): Additional kwargs for attention processors. Outputs: latent_chunks (`list`): List of per-chunk denoised latent tensors r7 image_latentsfake_image_latentsz;Fake image latents used as history seed for I2V generation.rAimage_latent_inputsadditional_batch_inputs)r8additional_inputsadd_noise_imager9 seed_historyr:r;cg)Nz]I2V denoise block that seeds history with image latents and uses I2V-aware chunk preparation.r#r$s r&r'$HeliosI2VCoreDenoiseStep.descriptionnr)c"[S[SS9/$r?rCr$s r&rE HeliosI2VCoreDenoiseStep.outputsrGr)r#N)r*r+r,r-r.rHrr r templatetorchTensorrrrrrr/r0r2r'rEr3r#r)r&rJrJs3jJ"!+!4!4_!E F(#ll ]%  )  !!M$Koowwr)rJc \rSrSrSrSr\\"\R"S5/\"S\ RSS9/S9\ \ \\\/r/S Qr\S 5r\S 5rS rg )HeliosV2VCoreDenoiseStepiav V2V denoise block that seeds history with video latents and uses I2V-aware chunk preparation. Components: transformer (`HeliosTransformer3DModel`) scheduler (`HeliosScheduler`) guider (`ClassifierFreeGuidance`) Inputs: num_videos_per_prompt (`int`, *optional*, defaults to 1): Number of videos to generate per prompt. prompt_embeds (`Tensor`): text embeddings used to guide the image generation. 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. image_latents (`Tensor`, *optional*): image latents used to guide the image generation. Can be generated from vae_encoder step. video_latents (`Tensor`, *optional*): Encoded video latents for V2V generation. num_latent_frames_per_chunk (`int`, *optional*, defaults to 9): Number of latent frames per temporal chunk. image_noise_sigma_min (`float`, *optional*, defaults to 0.111): Minimum sigma for image latent noise. image_noise_sigma_max (`float`, *optional*, defaults to 0.135): Maximum sigma for image latent noise. video_noise_sigma_min (`float`, *optional*, defaults to 0.111): Minimum sigma for video latent noise. video_noise_sigma_max (`float`, *optional*, defaults to 0.135): Maximum sigma for video latent noise. generator (`Generator`, *optional*): Torch generator for deterministic generation. num_frames (`int`, *optional*, defaults to 132): Total number of video frames to generate. history_sizes (`list`, *optional*, defaults to [16, 2, 1]): Sizes of long/mid/short history buffers for temporal context. keep_first_frame (`bool`, *optional*, defaults to True): Whether to keep the first frame as a prefix in history. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. sigmas (`list`, *optional*): Custom sigmas for the denoising process. latents (`Tensor`, *optional*): Pre-generated noisy latents for image generation. timesteps (`Tensor`, *optional*): Timesteps for the denoising process. **denoiser_input_fields (`None`, *optional*): conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc. attention_kwargs (`dict`, *optional*): Additional kwargs for attention processors. Outputs: latent_chunks (`list`): List of per-chunk denoised latent tensors r7rL video_latentsz)Encoded video latents for V2V generation.rArN)r8rQadd_noise_videor9rSr:r;cg)Nz]V2V denoise block that seeds history with video latents and uses I2V-aware chunk preparation.r#r$s r&r'$HeliosV2VCoreDenoiseStep.descriptionSrVr)c"[S[SS9/$r?rCr$s r&rE HeliosV2VCoreDenoiseStep.outputsWrGr)r#N)r*r+r,r-r.rHrr r rYrZr[rrrrrr/r0r2r'rEr3r#r)r&r]r]s3jJ"!+!4!4_!E F#u||It%  )  !M Koowwr)r]cP\rSrSrSr\\\/r/SQr SS/r Sr S Sjr \ S 5rS rg) HeliosAutoCoreDenoiseStepi^aD Core denoise step that selects the appropriate denoising block. - `HeliosV2VCoreDenoiseStep` (video2video) for video-to-video tasks. - `HeliosI2VCoreDenoiseStep` (image2video) for image-to-video tasks. - `HeliosCoreDenoiseStep` (text2video) for text-to-video tasks. Components: transformer (`HeliosTransformer3DModel`) scheduler (`HeliosScheduler`) guider (`ClassifierFreeGuidance`) Inputs: num_videos_per_prompt (`int`, *optional*, defaults to 1): Number of videos to generate per prompt. prompt_embeds (`Tensor`): text embeddings used to guide the image generation. 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. image_latents (`Tensor`, *optional*): image latents used to guide the image generation. Can be generated from vae_encoder step. video_latents (`Tensor`, *optional*): Encoded video latents for V2V generation. num_latent_frames_per_chunk (`int`, *optional*, defaults to 9): Number of latent frames per temporal chunk. image_noise_sigma_min (`float`, *optional*, defaults to 0.111): Minimum sigma for image latent noise. image_noise_sigma_max (`float`, *optional*, defaults to 0.135): Maximum sigma for image latent noise. video_noise_sigma_min (`float`, *optional*, defaults to 0.111): Minimum sigma for video latent noise. video_noise_sigma_max (`float`, *optional*, defaults to 0.135): Maximum sigma for video latent noise. generator (`Generator`, *optional*): Torch generator for deterministic generation. num_frames (`int`, *optional*, defaults to 132): Total number of video frames to generate. history_sizes (`list`): Sizes of long/mid/short history buffers for temporal context. keep_first_frame (`bool`, *optional*, defaults to True): Whether to keep the first frame as a prefix in history. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. sigmas (`list`): Custom sigmas for the denoising process. latents (`Tensor`, *optional*): Pre-generated noisy latents for image generation. timesteps (`Tensor`, *optional*): Timesteps for the denoising process. **denoiser_input_fields (`None`, *optional*): conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc. attention_kwargs (`dict`, *optional*): Additional kwargs for attention processors. fake_image_latents (`Tensor`, *optional*): Fake image latents used as history seed for I2V generation. height (`int`, *optional*, defaults to 384): The height in pixels of the generated image. width (`int`, *optional*, defaults to 640): The width in pixels of the generated image. Outputs: latent_chunks (`list`): List of per-chunk denoised latent tensors ) video2video image2video text2videor^rMrhNcUbgUbgg)Nrfrgr#)r%r^rMs r& select_block&HeliosAutoCoreDenoiseStep.select_blocks  $  + r)cg)Na Core denoise step that selects the appropriate denoising block. - `HeliosV2VCoreDenoiseStep` (video2video) for video-to-video tasks. - `HeliosI2VCoreDenoiseStep` (image2video) for image-to-video tasks. - `HeliosCoreDenoiseStep` (text2video) for text-to-video tasks.r#r$s r&r'%HeliosAutoCoreDenoiseStep.descriptions  O r)r#)NN)r*r+r,r-r.r]rJr5r/r0r1default_block_namerjr2r'r3r#r)r&rere^sH<|./GI^_M>K+-AB%  r)re text_encoder vae_encoderdenoisedecodec\rSrSrSrSr\R5r\R5r SS0SSS.SSS.S.r \ S 5r \ S 5rS rg ) HeliosAutoBlocksia Auto Modular pipeline for text-to-video, image-to-video, and video-to-video tasks using Helios. Supported workflows: - `text2video`: requires `prompt` - `image2video`: requires `prompt`, `image` - `video2video`: requires `prompt`, `video` Components: text_encoder (`UMT5EncoderModel`) tokenizer (`AutoTokenizer`) guider (`ClassifierFreeGuidance`) vae (`AutoencoderKLWan`) video_processor (`VideoProcessor`) transformer (`HeliosTransformer3DModel`) scheduler (`HeliosScheduler`) Inputs: prompt (`str`): 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 512): Maximum sequence length for prompt encoding. video (`None`, *optional*): Input video for video-to-video generation height (`int`, *optional*, defaults to 384): The height in pixels of the generated image. width (`int`, *optional*, defaults to 640): The width in pixels of the generated image. num_latent_frames_per_chunk (`int`, *optional*, defaults to 9): Number of latent frames per temporal chunk. generator (`Generator`, *optional*): Torch generator for deterministic generation. image (`Image | list`, *optional*): Reference image(s) for denoising. Can be a single image or list of images. num_videos_per_prompt (`int`, *optional*, defaults to 1): Number of videos to generate per prompt. image_latents (`Tensor`, *optional*): image latents used to guide the image generation. Can be generated from vae_encoder step. video_latents (`Tensor`, *optional*): Encoded video latents for V2V generation. image_noise_sigma_min (`float`, *optional*, defaults to 0.111): Minimum sigma for image latent noise. image_noise_sigma_max (`float`, *optional*, defaults to 0.135): Maximum sigma for image latent noise. video_noise_sigma_min (`float`, *optional*, defaults to 0.111): Minimum sigma for video latent noise. video_noise_sigma_max (`float`, *optional*, defaults to 0.135): Maximum sigma for video latent noise. num_frames (`int`, *optional*, defaults to 132): Total number of video frames to generate. history_sizes (`list`): Sizes of long/mid/short history buffers for temporal context. keep_first_frame (`bool`, *optional*, defaults to True): Whether to keep the first frame as a prefix in history. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. sigmas (`list`): Custom sigmas for the denoising process. latents (`Tensor`, *optional*): Pre-generated noisy latents for image generation. timesteps (`Tensor`, *optional*): Timesteps for the denoising process. **denoiser_input_fields (`None`, *optional*): conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc. attention_kwargs (`dict`, *optional*): Additional kwargs for attention processors. fake_image_latents (`Tensor`, *optional*): Fake image latents used as history seed for I2V generation. output_type (`str`, *optional*, defaults to np): Output format: 'pil', 'np', 'pt'. Outputs: videos (`list`): The generated videos. r7promptT)rur!)rur )rhrgrfcg)Nz_Auto Modular pipeline for text-to-video, image-to-video, and video-to-video tasks using Helios.r#r$s r&r'HeliosAutoBlocks.descriptionspr)c0[R"S5/$)Nvideos)r rYr$s r&rEHeliosAutoBlocks.outputss$$X.//r)r#N)r*r+r,r-r.rH AUTO_BLOCKSvaluesr/keysr0 _workflow_mapr2r'rEr3r#r)r&rtrtswHTJ&&(M""$K &"&6"&6M qq00r)rt)'rZutilsrmodular_pipelinerrrmodular_pipeline_utilsr r r before_denoiser rrrrrrrdecodersrrqrrencodersrrr get_loggerr*loggerrr5rJr]rer{rtr#r)r&rs ffLL   'Faa   H %, 1, l=w4=wDYw7Yw|Ww7WwxR 9R j .01 023 -/0 #%&  \0/\0r)