Text-to-video generation with Mochi

This example demonstrates how to run the Mochi 1 video generation model by Genmo on Modal.

Here’s one that we generated, inspired by our logo:

Note that the Mochi model, at time of writing, requires several minutes on one H100 to produce a high-quality clip of even a few seconds. So a single video generation therefore costs about $0.33 at our ~$5/hr rate for H100s.

Keep your eyes peeled for improved efficiency as the open source community works on this new model. We welcome PRs to improve the performance of this example!

Setting up the environment for Mochi

At the time of writing, Mochi is supported natively in the diffusers library, but only in a pre-release version. So we’ll need to install diffusers and transformers from GitHub.

import string
import time
from pathlib import Path

import modal

app = modal.App()

image = (
    modal.Image.debian_slim(python_version="3.11")
    .apt_install("git")
    .pip_install(
        "torch==2.5.1",
        "accelerate==1.1.1",
        "hf_transfer==0.1.8",
        "sentencepiece==0.2.0",
        "imageio==2.36.0",
        "imageio-ffmpeg==0.5.1",
        "git+https://github.com/huggingface/transformers@30335093276212ce74938bdfd85bfd5df31a668a",
        "git+https://github.com/huggingface/diffusers@99c0483b67427de467f11aa35d54678fd36a7ea2",
    )
    .env(
        {
            "HF_HUB_ENABLE_HF_TRANSFER": "1",
            "HF_HOME": "/models",
        }
    )
)

Saving outputs

On Modal, we save large or expensive-to-compute data to distributed Volumes

We’ll use this for saving our Mochi weights, as well as our video outputs.

VOLUME_NAME = "mochi-outputs"
outputs = modal.Volume.from_name(VOLUME_NAME, create_if_missing=True)
OUTPUTS_PATH = Path("/outputs")  # remote path for saving video outputs

MODEL_VOLUME_NAME = "mochi-model"
model = modal.Volume.from_name(MODEL_VOLUME_NAME, create_if_missing=True)
MODEL_PATH = Path("/models")  # remote path for saving model weights

MINUTES = 60
HOURS = 60 * MINUTES

Downloading the model

We download the model weights into Volume cache to speed up cold starts.

This download takes five minutes or more, depending on traffic and network speed.

If you want to launch the download first, before running the rest of the code, use the following command from the folder containing this file:

modal run --detach mochi::download_model

The --detach flag ensures the download will continue even if you close your terminal or shut down your computer while it’s running.

with image.imports():
    import torch
    from diffusers import MochiPipeline
    from diffusers.utils import export_to_video


@app.function(
    image=image,
    volumes={
        MODEL_PATH: model,
    },
    timeout=20 * MINUTES,
)
def download_model(revision="83359d26a7e2bbe200ecbfda8ebff850fd03b545"):
    # uses HF_HOME to point download to the model volume
    MochiPipeline.from_pretrained(
        "genmo/mochi-1-preview",
        torch_dtype=torch.bfloat16,
        revision=revision,
    )

Setting up our Mochi class

We’ll use the @cls decorator to define a Modal Class which we use to control the lifecycle of our cloud container.

We configure it to use our image, the distributed volume, and a single H100 GPU.

@app.cls(
    image=image,
    volumes={
        OUTPUTS_PATH: outputs,  # videos will be saved to a distributed volume
        MODEL_PATH: model,
    },
    gpu=modal.gpu.H100(count=1),
    timeout=1 * HOURS,
)
class Mochi:
    @modal.enter()
    def load_model(self):
        # our HF_HOME env var points to the model volume as the cache
        self.pipe = MochiPipeline.from_pretrained(
            "genmo/mochi-1-preview",
            torch_dtype=torch.bfloat16,
        )
        self.pipe.enable_model_cpu_offload()
        self.pipe.enable_vae_tiling()

    @modal.method()
    def generate(
        self,
        prompt,
        negative_prompt="",
        num_inference_steps=200,
        guidance_scale=4.5,
        num_frames=19,
    ):
        frames = self.pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            num_frames=num_frames,
        ).frames[0]

        # save to disk using prompt as filename
        mp4_name = slugify(prompt)
        export_to_video(frames, Path(OUTPUTS_PATH) / mp4_name)
        outputs.commit()
        return mp4_name

Running Mochi inference

We can trigger Mochi inference from our local machine by running the code in the local entrypoint below.

It ensures the model is downloaded to a remote volume, spins up a new replica to generate a video, also saved remotely, and then downloads the video to the local machine.

You can trigger it with:

modal run --detach mochi

Optional command line flags can be viewed with:

modal run mochi --help

Using these flags, you can tweak your generation from the command line:

modal run --detach mochi --prompt="a cat playing drums in a jazz ensemble" --num-inference-steps=64

@app.local_entrypoint()
def main(
    prompt="Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k.",
    negative_prompt="",
    num_inference_steps=200,
    guidance_scale=4.5,
    num_frames=19,  # produces ~1s of video
):
    mochi = Mochi()
    mp4_name = mochi.generate.remote(
        prompt=str(prompt),
        negative_prompt=str(negative_prompt),
        num_inference_steps=int(num_inference_steps),
        guidance_scale=float(guidance_scale),
        num_frames=int(num_frames),
    )
    print(f"🍡 video saved to volume at {mp4_name}")

    local_dir = Path("/tmp/mochi")
    local_dir.mkdir(exist_ok=True, parents=True)
    local_path = local_dir / mp4_name
    local_path.write_bytes(b"".join(outputs.read_file(mp4_name)))
    print(f"🍡 video saved locally at {local_path}")

Addenda

The remainder of the code in this file is utility code.

def slugify(prompt):
    for char in string.punctuation:
        prompt = prompt.replace(char, "")
    prompt = prompt.replace(" ", "_")
    prompt = prompt[:230]  # since filenames can't be longer than 255 characters
    mp4_name = str(int(time.time())) + "_" + prompt + ".mp4"
    return mp4_name