metadata
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
Mochi-1 Preview LoRA Finetune
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.
Download model
Download LoRA in the Files & Versions tab.
Usage
Requires the 🧨 Diffusers library installed.
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.
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]