Mochi-Lora Open-Source Model - Free Deployment, Helping Easily Generate Wonderful Videos from Texts

Mochi Lora

Developed by weathon

A LoRA fine-tuned version based on the Mochi-1 preview model, focusing on text-to-video generation tasks

Text-to-Video Open Source License:Apache-2.0 #Video Generation-LoRA Fine-tuning #Dynamic Scene Simulation #Biomimetic Rendering

Downloads 112

Release Time : 5/15/2025

Model Overview

This is a LoRA fine-tuned Mochi-1 preview model, primarily used for converting text descriptions into video content, especially skilled at generating videos with specific styles and themes

Model Features

LoRA Fine-tuning Technology

Uses LoRA (Low-Rank Adaptation) technology for efficient fine-tuning of the base model, preserving the original model's capabilities while adapting to specific tasks

Text-to-Video Generation

Capable of generating high-quality video content based on text prompts, such as the example of a crab blending into a rocky seabed scene

Strong Style Adaptability

Can generate videos with specific styles and color tones, such as the soft brown-gray tones in the example

Model Capabilities

Text-to-Video Conversion

Stylized Video Generation

Scene Detail Rendering

Use Cases

Creative Content Generation

Natural Scene Simulation

Generate realistic natural scene videos, such as marine life blending into the environment

Example video of a crab blending into a rocky seabed

Educational Demonstrations

Animal Camouflage Display

Generate educational videos showcasing animal camouflage abilities

🚀 Mochi-1 Preview LoRA Finetune

This is a LoRA finetune of the Mochi-1 preview model, offering enhanced capabilities for text - to - video tasks.

🚀 Quick Start

To start using this model, you need to download the LoRA weights and have the necessary libraries installed.

Download the Model

You can Download LoRA in the Files & Versions tab.

Installation

Requires the 🧨 Diffusers library installed.

Usage Example

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 on loading LoRAs in diffusers.

✨ Features

Based on Mochi-1 Preview: This is a LoRA finetune of the Mochi-1 preview model genmo/mochi-1-preview.
Optimized Training: The model was trained using CogVideoX Factory, which contains memory-optimized training scripts for the CogVideoX and Mochi family of models using TorchAO and DeepSpeed. The scripts were adopted from CogVideoX Diffusers trainer.

📚 Documentation

Model description

This is a lora finetune of the Mochi-1 preview model genmo/mochi-1-preview. The model was trained using CogVideoX Factory - a repository containing memory-optimized training scripts for the CogVideoX and Mochi family of models using TorchAO and DeepSpeed. The scripts were adopted from CogVideoX Diffusers trainer.

Intended uses & limitations

How to use

# 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]

📄 License

The model uses the apache-2.0 license.

Model Information

Property	Details
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.
Tags	text-to-video, diffusers-training, diffusers, lora, mochi-1-preview, mochi-1-preview-diffusers, template:sd-lora

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