Mochi 1 Preview

A state-of-the-art video generation model by Genmo.

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

Clone the repository and install it in editable mode:

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 -e .

Download Weights

Download the weights from Hugging Face or via magnet:?xt=urn:btih:441da1af7a16bcaa4f556964f8028d7113d21cbb&dn=weights&tr=udp://tracker.opentrackr.org:1337/announce.

Running

Start the gradio UI with

python3 -m mochi_preview.gradio_ui --model_dir "<path_to_model_directory>"

Or generate videos directly from the CLI with

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_model_directory>"

Replace <path_to_model_directory> with the path to your model directory.

Running with Diffusers

Install the latest version of Diffusers

pip install git+https://github.com/huggingface/diffusers.git

The following example requires 42GB VRAM but ensures the highest quality output.

import torch
from diffusers import MochiPipeline
from diffusers.utils import export_to_video

pipe = MochiPipeline.from_pretrained("genmo/mochi-1-preview")

# Enable memory savings
pipe.enable_model_cpu_offload()
pipe.enable_vae_tiling()

prompt = "Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k."

with torch.autocast("cuda", torch.bfloat16, cache_enabled=False):
      frames = pipe(prompt, num_frames=84).frames[0]

export_to_video(frames, "mochi.mp4", fps=30)

Using a lower precision variant to save memory

The following example will use the bfloat16 variant of the model and requires 22GB VRAM to run. There is a slight drop in the quality of the generated video as a result.

import torch
from diffusers import MochiPipeline
from diffusers.utils import export_to_video

pipe = MochiPipeline.from_pretrained("genmo/mochi-1-preview", variant="bf16", torch_dtype=torch.bfloat16)

# Enable memory savings
pipe.enable_model_cpu_offload()
pipe.enable_vae_tiling()

prompt = "Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k."
frames = pipe(prompt, num_frames=84).frames[0]

export_to_video(frames, "mochi.mp4", fps=30)

To learn more check out the Diffusers documentation

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.

Alongside Mochi, we are open-sourcing our video VAE. Our VAE causally compresses videos to a 96x smaller size, with an 8x8 spatial and a 6x temporal compression to a 12-channel latent space.

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.

Hardware Requirements

Mochi 1 supports a variety of hardware platforms depending on quantization level, ranging from a single 3090 GPU up to multiple H100 GPUs.

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

genmo
/

mochi-1-preview