---
base_model: mochi-1-preview
library_name: diffusers
license: apache-2.0
instance_prompt: a parrot flying in the blue skies, a grainy or noisy video effect in the background
widget:
- text: a parrot flying in the blue skies, a grainy or noisy video effect in the background
  output:
    url: replicate-parrot-vhs.mp4

tags:
- text-to-video
- diffusers-training
- diffusers
- lora
- mochi-1-preview
---

<!-- 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

This is a LoRA fine-tune of the Mochi-1 preview model. The model was trained using custom training data.

<Gallery />

## Usage

```python
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("lucataco/mochi-lora-vhs")
pipe.enable_model_cpu_offload()

video = pipe(
    prompt="your prompt here",
    guidance_scale=6.0,
    num_inference_steps=64,
    height=480,
    width=848,
    max_sequence_length=256,
).frames[0]

export_to_video(video, "output.mp4", fps=30)
```


## 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]