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---
base_model: genmo/mochi-1-preview
library_name: diffusers
license: apache-2.0
widget: []
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](soumildatta/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] |