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from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
from tuneavideo.models.unet import UNet3DConditionModel
from tuneavideo.util import save_videos_grid
import torch
import gradio as gr
def tune_video_predict(
prompt: str,
video_length: int,
height: int,
width: int,
num_inference_steps: int,
guidance_scale: float,
):
unet = UNet3DConditionModel.from_pretrained('Tune-A-Video-library/a-man-is-surfing', subfolder='unet', torch_dtype=torch.float16).to('cuda')
pipe = TuneAVideoPipeline.from_pretrained('CompVis/stable-diffusion-v1-4', unet=unet, torch_dtype=torch.float16).to("cuda")
video = pipe(prompt, video_length=video_length, height=height, width=width, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale).videos
output_path = save_videos_grid(video, save_path='output', path=f"{prompt}.gif")
return output_path
demo_inputs = [
gr.inputs.Textbox(
label="Prompt",
default='a panda is surfing'
),
gr.inputs.Slider(
label="Video Length",
minimum=1,
maximum=50,
default=4,
step=1,
),
gr.inputs.Slider(
label="Height",
minimum=128,
maximum=1280,
default=128,
step=32,
),
gr.inputs.Slider(
label="Width",
minimum=128,
maximum=1280,
default=128,
step=32,
),
gr.inputs.Slider(
label="Num Inference Steps",
minimum=1,
maximum=100,
default=10,
step=1,
),
gr.inputs.Slider(
label="Guidance Scale",
minimum=0.0,
maximum=50,
default=7.5,
step=0.5,
)
]
demo_outputs = gr.outputs.Video(type="gif", label="Output")
examples = [
["a panda is surfing", 4, 128, 128, 10, 7.5]
]
description = "This generates video from an input text, using [one-shot tuning of diffusion models](https://arxiv.org/abs/2212.11565). To use it, simply input a text."
demo_app = gr.Interface(
fn=tune_video_predict,
inputs=demo_inputs,
outputs=demo_outputs,
examples=examples,
cache_examples=False,
title="Tune-A-Video",
theme="huggingface",
description=description
)
demo_app.launch(debug=True, enable_queue=True)
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