Mochi 1

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A state of the art video generation model by Genmo.

grid_output.mp4

News

⭐ November 26, 2024: Added support for LoRA fine-tuning
⭐ November 5, 2024: Consumer-GPU support for Mochi natively in ComfyUI

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.

Installation

Install using uv:

git clone https://github.com/genmoai/models
cd models 
pip install uv
uv venv .venv
source .venv/bin/activate
uv pip install setuptools
uv pip install -e . --no-build-isolation

If you want to install flash attention, you can use:

uv pip install -e .[flash] --no-build-isolation

You will also need to install FFMPEG to turn your outputs into videos.

Download Weights

Use download_weights.py to download the model + VAE to a local directory. Use it like this:

python3 ./scripts/download_weights.py weights/

Or, directly download the weights from Hugging Face 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

python3 ./demos/gradio_ui.py --model_dir weights/ --cpu_offload

Or generate videos directly from the CLI with

python3 ./demos/cli.py --model_dir weights/ --cpu_offload

If you have a fine-tuned LoRA in the safetensors format, you can add --lora_path <path/to/my_mochi_lora.safetensors> to either gradio_ui.py or cli.py.

API

This repository comes with a simple, composable API, so you can programmatically call the model. You can find a full example here. But, roughly, it looks like this:

from genmo.mochi_preview.pipelines import (
    DecoderModelFactory,
    DitModelFactory,
    MochiSingleGPUPipeline,
    T5ModelFactory,
    linear_quadratic_schedule,
)

pipeline = MochiSingleGPUPipeline(
    text_encoder_factory=T5ModelFactory(),
    dit_factory=DitModelFactory(
        model_path=f"weights/dit.safetensors", model_dtype="bf16"
    ),
    decoder_factory=DecoderModelFactory(
        model_path=f"weights/decoder.safetensors",
    ),
    cpu_offload=True,
    decode_type="tiled_spatial",
)

video = pipeline(
    height=480,
    width=848,
    num_frames=31,
    num_inference_steps=64,
    sigma_schedule=linear_quadratic_schedule(64, 0.025),
    cfg_schedule=[6.0] * 64,
    batch_cfg=False,
    prompt="your favorite prompt here ...",
    negative_prompt="",
    seed=12345,
)

Fine-tuning with LoRA

We provide an easy-to-use trainer that allows you to build LoRA fine-tunes of Mochi on your own videos. The model can be fine-tuned on one H100 or A100 80GB GPU.

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 Count	Enc Base Channels	Dec Base Channels	Latent Dim	Spatial Compression	Temporal 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 Count	Num Layers	Num Heads	Visual Dim	Text Dim	Visual Tokens	Text Tokens
10B	48	24	3072	1536	44520	256

Hardware Requirements

The repository supports both multi-GPU operation (splitting the model across multiple graphics cards) and single-GPU operation, though it requires approximately 60GB VRAM when running on a single GPU. While ComfyUI can optimize Mochi to run on less than 20GB VRAM, this implementation prioritizes flexibility over memory efficiency. When using this repository, we recommend using at least 1 H100 GPU.

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.

Related Work

ComfyUI-MochiWrapper adds ComfyUI support for Mochi. The integration of Pytorch's SDPA attention was based on their repository.
ComfyUI-MochiEdit adds ComfyUI nodes for video editing, such as object insertion and restyling.
mochi-xdit is a fork of this repository and improve the parallel inference speed with xDiT.
Modal script for fine-tuning Mochi on Modal GPUs.

BibTeX

@misc{genmo2024mochi,
      title={Mochi 1},
      author={Genmo Team},
      year={2024},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished={\url{https://github.com/genmoai/models}}
}

genmoai/mochi