README.md

---
library_name: diffusers
tags:
- modular-diffusers
- diffusers
- helios
- text-to-image
- modular-diffusers
- diffusers
- helios
---
This is a modular diffusion pipeline built with 🧨 Diffusers' modular pipeline framework.

**Pipeline Type**: HeliosAutoBlocks

**Description**: Auto Modular pipeline for text-to-video, image-to-video, and video-to-video tasks 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** (`HeliosAutoVaeEncoderStep`)
   - Encoder step that encodes video or image inputs. This is an auto pipeline block.
   - *video_encoder*: `HeliosVideoVaeEncoderStep`
     - Video Encoder step that encodes an input video into VAE latent space, producing image_latents (first frame) and video_latents (chunked video frames) for video-to-video generation.
   - *image_encoder*: `HeliosImageVaeEncoderStep`
     - Image Encoder step that encodes an input image into VAE latent space, producing image_latents (first frame prefix) and fake_image_latents (history seed) for image-to-video generation.
3. **denoise** (`HeliosAutoCoreDenoiseStep`)
   - Core denoise step that selects the appropriate denoising block.
   - *video2video*: `HeliosV2VCoreDenoiseStep`
     - V2V denoise block that seeds history with video latents and uses I2V-aware chunk preparation.
   - *image2video*: `HeliosI2VCoreDenoiseStep`
     - I2V denoise block that seeds history with image latents and uses I2V-aware chunk preparation.
   - *text2video*: `HeliosCoreDenoiseStep`
     - Denoise block that takes encoded conditions and runs the chunk-based denoising process.
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`) 

## Input/Output Specification

### Inputs **Required:**

- `prompt` (`str`): The prompt or prompts to guide image generation.
- `history_sizes` (`list`): Sizes of long/mid/short history buffers for temporal context.
- `sigmas` (`list`): Custom sigmas for the denoising process.

**Optional:**

- `negative_prompt` (`str`): The prompt or prompts not to guide the image generation.
- `max_sequence_length` (`int`), default: `512`: Maximum sequence length for prompt encoding.
- `video` (`Any`): Input video for video-to-video generation
- `height` (`int`), default: `384`: The height in pixels of the generated image.
- `width` (`int`), default: `640`: The width in pixels of the generated image.
- `num_latent_frames_per_chunk` (`int`), default: `9`: Number of latent frames per temporal chunk.
- `generator` (`Generator`): Torch generator for deterministic generation.
- `image` (`PIL.Image.Image | list[PIL.Image.Image]`): Reference image(s) for denoising. Can be a single image or list of images.
- `num_videos_per_prompt` (`int`), default: `1`: Number of videos to generate per prompt.
- `image_latents` (`Tensor`): image latents used to guide the image generation. Can be generated from vae_encoder step.
- `video_latents` (`Tensor`): Encoded video latents for V2V generation.
- `image_noise_sigma_min` (`float`), default: `0.111`: Minimum sigma for image latent noise.
- `image_noise_sigma_max` (`float`), default: `0.135`: Maximum sigma for image latent noise.
- `video_noise_sigma_min` (`float`), default: `0.111`: Minimum sigma for video latent noise.
- `video_noise_sigma_max` (`float`), default: `0.135`: Maximum sigma for video latent noise.
- `num_frames` (`int`), default: `132`: Total number of video frames to generate.
- `keep_first_frame` (`bool`), default: `True`: Whether to keep the first frame as a prefix in history.
- `num_inference_steps` (`int`), default: `50`: The number of denoising steps.
- `latents` (`Tensor`): Pre-generated noisy latents for image generation.
- `timesteps` (`Tensor`): Timesteps for the denoising process.
- `None` (`Any`): conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
- `attention_kwargs` (`dict`): Additional kwargs for attention processors.
- `fake_image_latents` (`Tensor`): Fake image latents used as history seed for I2V generation.
- `output_type` (`str`), default: `np`: Output format: 'pil', 'np', 'pt'.

### Outputs - `videos` (`list`): The generated videos.