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library_name: diffusers
tags:
- modular-diffusers
- diffusers
- helios-pyramid
---
This is a modular diffusion pipeline built with 🧨 Diffusers' modular pipeline framework.
**Pipeline Type**: HeliosPyramidAutoBlocks
**Description**: Auto Modular pipeline for pyramid progressive generation (T2V/I2V/V2V) using Helios.
This pipeline uses a 4-block architecture that can be customized and extended.
## Example Usage
[TODO]
## Pipeline Architecture
This modular pipeline is composed of the following blocks:
1. **text_encoder** (`HeliosTextEncoderStep`)
- Text Encoder step that generates text embeddings to guide the video generation
2. **vae_encoder** (`HeliosPyramidAutoVaeEncoderStep`)
- Encoder step that encodes video or image inputs. This is an auto pipeline block.
3. **denoise** (`HeliosPyramidAutoCoreDenoiseStep`)
- Pyramid core denoise step that selects the appropriate denoising block.
4. **decode** (`HeliosDecodeStep`)
- Decodes all chunk latents with the VAE, concatenates them, trims to the target frame count, and postprocesses into the final video output.
## Model Components
1. text_encoder (`UMT5EncoderModel`)
2. tokenizer (`AutoTokenizer`)
3. guider (`ClassifierFreeGuidance`)
4. vae (`AutoencoderKLWan`)
5. video_processor (`VideoProcessor`)
6. transformer (`HeliosTransformer3DModel`)
7. scheduler (`HeliosScheduler`)
## Workflow Input Specification
<details>
<summary><strong>text2video</strong></summary>
- `prompt` (`str`): The prompt or prompts to guide image generation.
</details>
<details>
<summary><strong>image2video</strong></summary>
- `prompt` (`str`): The prompt or prompts to guide image generation.
- `image` (`Image | list`): Reference image(s) for denoising. Can be a single image or list of images.
</details>
<details>
<summary><strong>video2video</strong></summary>
- `prompt` (`str`): The prompt or prompts to guide image generation.
- `video` (`None`): Input video for video-to-video generation
</details>
## Input/Output Specification
**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.
- `pyramid_num_inference_steps_list` (`list`, *optional*, defaults to `[10, 10, 10]`): Number of denoising steps per pyramid stage.
- `latents` (`Tensor`, *optional*): Pre-generated noisy latents for image generation.
- `**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.
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