---
base_model: genmo/mochi-1-preview
library_name: diffusers
license: apache-2.0
instance_prompt: There is a *crab* blending into a +rocky ocean floor+ where the crab's
  mottled brown shell, rough texture, and uneven shape closely match the scattered
  rocks and coarse sand, all in muted brown and grey tones. The crab moves slowly
  and subtly, making it difficult to distinguish as its rough brown pattern looks
  just like a piece of rock among the uneven, similarly colored stones and patches
  of sand.
widget:
- text: There is a *crab* blending into a +rocky ocean floor+ where the crab's mottled
    brown shell, rough texture, and uneven shape closely match the scattered rocks
    and coarse sand, all in muted brown and grey tones. The crab moves slowly and
    subtly, making it difficult to distinguish as its rough brown pattern looks just
    like a piece of rock among the uneven, similarly colored stones and patches of
    sand.
  output:
    url: final_video_0.mp4
tags:
- text-to-video
- diffusers-training
- diffusers
- lora
- mochi-1-preview
- mochi-1-preview-diffusers
- template:sd-lora
- text-to-video
- diffusers-training
- diffusers
- lora
- mochi-1-preview
- mochi-1-preview-diffusers
- template:sd-lora
---

<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->


# Mochi-1 Preview LoRA Finetune

<Gallery />

## Model description

This is a lora finetune of the Mochi-1 preview model `genmo/mochi-1-preview`.

The model was trained using [CogVideoX Factory](https://github.com/a-r-r-o-w/cogvideox-factory) - a repository containing memory-optimized training scripts for the CogVideoX and Mochi family of models using [TorchAO](https://github.com/pytorch/ao) and [DeepSpeed](https://github.com/microsoft/DeepSpeed). The scripts were adopted from [CogVideoX Diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/cogvideo/train_cogvideox_lora.py).

## Download model

[Download LoRA](weathon/mochi-lora/tree/main) in the Files & Versions tab.

## Usage

Requires the [🧨 Diffusers library](https://github.com/huggingface/diffusers) installed.

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

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

with torch.autocast("cuda", torch.bfloat16):
    video = pipe(
        prompt="CHANGE_ME",
        guidance_scale=6.0,
        num_inference_steps=64,
        height=480,
        width=848,
        max_sequence_length=256,
        output_type="np"
    ).frames[0]
export_to_video(video)
```

For more details, including weighting, merging and fusing LoRAs, check the [documentation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) on loading LoRAs in diffusers.



## Intended uses & limitations

#### How to use

```python
# TODO: add an example code snippet for running this diffusion pipeline
```

#### Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

## Training details

[TODO: describe the data used to train the model]