# Mochi 1
[Blog](https://www.genmo.ai/blog) | [Hugging Face](https://huggingface.co/genmo/mochi-1-preview) | [Playground](https://www.genmo.ai/play) | [Careers](https://jobs.ashbyhq.com/genmo)

A state of the art video generation model by [Genmo](https://genmo.ai).

https://github.com/user-attachments/assets/4d268d02-906d-4cb0-87cc-f467f1497108

## Overview

Mochi 1 preview is an open state-of-the-art video generation model with high-fidelity motion and strong prompt adherence in preliminary evaluation. This model dramatically closes the gap between closed and open video generation systems. We’re releasing the model under a permissive Apache 2.0 license. Try this model for free on [our playground](https://genmo.ai/play).

## Installation

Install using [uv](https://github.com/astral-sh/uv):

```bash
git clone https://github.com/genmoai/models
cd models 
pip install uv
uv venv .venv
source .venv/bin/activate
uv pip install -e . --no-build-isolation
```
N.B. On Windows the **activate** command is
```bash
.venv/Scripts/activate
```

## Extra notes on the Windows install

If you can not get flash-attn to play nice from source, downloaded this one https://huggingface.co/Kijai/Mochi_preview_comfy/resolve/main/flash_attn-2.6.3-cp310-cp310-win_amd64.whl
and install it like so
```bash
uv pip install C:/Users/new/Downloads/flash_attn-2.6.3-cp310-cp310-win_amd64.whl
```
same for any missing things - Setuptools, 
example: install from github source in the .venv using uv
```bash
git clone https://github.com/gpuopenanalytics/pynvml
cd pynvml
uv pip install .
```
when you can finally run the following command succesfully 
```bash
uv pip install -e . --no-build-isolation
```
you should be good to go.

## Download Weights

Download the weights from [Hugging Face](https://huggingface.co/genmo/mochi-1-preview/tree/main) or via `magnet:?xt=urn:btih:441da1af7a16bcaa4f556964f8028d7113d21cbb&dn=weights&tr=udp://tracker.opentrackr.org:1337/announce` to a folder on your computer.

## Running

Start the gradio UI with

```bash
python3 -m mochi_preview.gradio_ui --model_dir "<path_to_downloaded_directory>"
```

**python or python3**

NB On Windows use python, not python 3 as you may access the wrong python or none whatsoever
```bash
python -m mochi_preview.gradio_ui --model_dir "<path_to_downloaded_directory>"
```
this follow for all the subsequent commands.  On Windows elide the "3".

Or generate videos directly from the CLI with

```bash
python3 -m mochi_preview.infer --prompt "A hand with delicate fingers picks up a bright yellow lemon from a wooden bowl filled with lemons and sprigs of mint against a peach-colored background. The hand gently tosses the lemon up and catches it, showcasing its smooth texture. A beige string bag sits beside the bowl, adding a rustic touch to the scene. Additional lemons, one halved, are scattered around the base of the bowl. The even lighting enhances the vibrant colors and creates a fresh, inviting atmosphere." --seed 1710977262 --cfg_scale 4.5 --model_dir "<path_to_downloaded_directory>"
```

Replace `<path_to_downloaded_directory>` with the path to your model directory.

## Model Architecture

Mochi 1 represents a significant advancement in open-source video generation, featuring a 10 billion parameter diffusion model built on our novel Asymmetric Diffusion Transformer (AsymmDiT) architecture. Trained entirely from scratch, it is the largest video generative model ever openly released. And best of all, it’s a simple, hackable architecture. Additionally, we are releasing an inference harness that includes an efficient context parallel implementation. 

Alongside Mochi, we are open-sourcing our video AsymmVAE. We use an asymmetric encoder-decoder structure to build an efficient high quality compression model. Our AsymmVAE causally compresses videos to a 128x smaller size, with an 8x8 spatial and a 6x temporal compression to a 12-channel latent space. 

### AsymmVAE Model Specs
|Params <br> Count | Enc Base <br>  Channels | Dec Base <br> Channels |Latent <br> Dim | Spatial <br> Compression | Temporal <br> Compression | 
|:--:|:--:|:--:|:--:|:--:|:--:|
|362M   | 64  | 128  | 12   | 8x8   | 6x   | 

An AsymmDiT efficiently processes user prompts alongside compressed video tokens by streamlining text processing and focusing neural network capacity on visual reasoning. AsymmDiT jointly attends to text and visual tokens with multi-modal self-attention and learns separate MLP layers for each modality, similar to Stable Diffusion 3. However, our visual stream has nearly 4 times as many parameters as the text stream via a larger hidden dimension. To unify the modalities in self-attention, we use non-square QKV and output projection layers. This asymmetric design reduces inference memory requirements.
Many modern diffusion models use multiple pretrained language models to represent user prompts. In contrast, Mochi 1 simply encodes prompts with a single T5-XXL language model.

### AsymmDiT Model Specs
|Params <br> Count | Num <br> Layers | Num <br> Heads | Visual <br> Dim | Text <br> Dim | Visual <br> Tokens | Text <br> Tokens | 
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
|10B   | 48   | 24   | 3072   | 1536   | 44520   |   256   |

## Hardware Requirements

The model requires at least 4 H100 GPUs to run. We welcome contributions from the community to reduce this requirement.

## Safety
Genmo video models are general text-to-video diffusion models that inherently reflect the biases and preconceptions found in their training data. While steps have been taken to limit NSFW content, organizations should implement additional safety protocols and careful consideration before deploying these model weights in any commercial services or products.

## Limitations
Under the research preview, Mochi 1 is a living and evolving checkpoint. There are a few known limitations. The initial release generates videos at 480p today. In some edge cases with extreme motion, minor warping and distortions can also occur. Mochi 1 is also optimized for photorealistic styles so does not perform well with animated content. We also anticipate that the community will fine-tune the model to suit various aesthetic preferences.


## BibTeX
```
@misc{genmo2024mochi,
      title={Mochi},
      author={Genmo Team},
      year={2024}
}
```