---
base_model: genmo/mochi-1-preview
library_name: diffusers
license: apache-2.0
instance_prompt: A black and white animated scene unfolds with an anthropomorphic
  goat surrounded by musical notes and symbols, suggesting a playful environment.
  Mickey Mouse appears, leaning forward in curiosity as the goat remains still. The
  goat then engages with Mickey, who bends down to converse or react. The dynamics
  shift as Mickey grabs the goat, potentially in surprise or playfulness, amidst a
  minimalistic background. The scene captures the evolving relationship between the
  two characters in a whimsical, animated setting, emphasizing their interactions
  and emotions
widget:
- text: A black and white animated scene unfolds with an anthropomorphic goat surrounded
    by musical notes and symbols, suggesting a playful environment. Mickey Mouse appears,
    leaning forward in curiosity as the goat remains still. The goat then engages
    with Mickey, who bends down to converse or react. The dynamics shift as Mickey
    grabs the goat, potentially in surprise or playfulness, amidst a minimalistic
    background. The scene captures the evolving relationship between the two characters
    in a whimsical, animated setting, emphasizing their interactions and emotions
  output:
    url: final_video_0.mp4
tags:
- 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](sayakpaul/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]