metadata
license: creativeml-openrail-m
base_model: nitrosocke/redshift-diffusion
training_prompt: A man is skiing.
tags:
- tune-a-video
- text-to-video
- diffusers
inference: false
Tune-A-Video - Redshift
Model Description
- Base model: nitrosocke/redshift-diffusion
- Training prompt: a man is skiing.
Samples
Test prompt: (redshift style) [spider man/black widow/batman/hulk] is skiing.
Usage
Clone the github repo
git clone https://github.com/showlab/Tune-A-Video.git
Run inference code
from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
from tuneavideo.models.unet import UNet3DConditionModel
from tuneavideo.util import save_videos_grid
import torch
pretrained_model_path = "nitrosocke/redshift-diffusion"
unet_model_path = "Tune-A-Video-library/redshift-man-skiing"
unet = UNet3DConditionModel.from_pretrained(unet_model_path, subfolder='unet', torch_dtype=torch.float16).to('cuda')
pipe = TuneAVideoPipeline.from_pretrained(pretrained_model_path, unet=unet, torch_dtype=torch.float16).to("cuda")
pipe.enable_xformers_memory_efficient_attention()
prompt = "(redshift style) spider man is skiing"
video = pipe(prompt, video_length=8, height=512, width=512, num_inference_steps=50, guidance_scale=7.5).videos
save_videos_grid(video, f"./{prompt}.gif")
Related Papers:
- Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
- Stable Diffusion: High-Resolution Image Synthesis with Latent Diffusion Models