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#!/usr/bin/env python
from __future__ import annotations
import os
import gradio as gr
import torch
from inference import InferencePipeline
class InferenceUtil:
def __init__(self, hf_token: str | None):
self.hf_token = hf_token
def load_model_info(self, model_id: str) -> tuple[str, str]:
try:
card = InferencePipeline.get_model_card(model_id, self.hf_token)
except Exception:
return "", ""
base_model = getattr(card.data, "base_model", "")
training_prompt = getattr(card.data, "training_prompt", "")
return base_model, training_prompt
DESCRIPTION = "# [Tune-A-Video](https://tuneavideo.github.io/)"
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
HF_TOKEN = os.getenv("HF_TOKEN")
pipe = InferencePipeline(HF_TOKEN)
app = InferenceUtil(HF_TOKEN)
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Column():
with gr.Box():
model_id = gr.Dropdown(
label="Model ID",
choices=[
"Tune-A-Video-library/a-man-is-surfing",
"Tune-A-Video-library/mo-di-bear-guitar",
"Tune-A-Video-library/redshift-man-skiing",
],
value="Tune-A-Video-library/a-man-is-surfing",
)
with gr.Accordion(label="Model info (Base model and prompt used for training)", open=False):
with gr.Row():
base_model_used_for_training = gr.Text(label="Base model", interactive=False)
prompt_used_for_training = gr.Text(label="Training prompt", interactive=False)
prompt = gr.Textbox(label="Prompt", max_lines=1, placeholder='Example: "A panda is surfing"')
video_length = gr.Slider(label="Video length", minimum=4, maximum=12, step=1, value=8)
fps = gr.Slider(label="FPS", minimum=1, maximum=12, step=1, value=1)
seed = gr.Slider(label="Seed", minimum=0, maximum=100000, step=1, value=0)
with gr.Accordion("Other Parameters", open=False):
num_steps = gr.Slider(label="Number of Steps", minimum=0, maximum=100, step=1, value=50)
guidance_scale = gr.Slider(label="CFG Scale", minimum=0, maximum=50, step=0.1, value=7.5)
run_button = gr.Button("Generate")
gr.Markdown(
"""
- It takes a few minutes to download model first.
- Expected time to generate an 8-frame video: 70 seconds with T4, 24 seconds with A10G, (10 seconds with A100)
"""
)
with gr.Column():
result = gr.Video(label="Result")
with gr.Row():
examples = [
[
"Tune-A-Video-library/a-man-is-surfing",
"A panda is surfing.",
8,
1,
3,
50,
7.5,
],
[
"Tune-A-Video-library/a-man-is-surfing",
"A racoon is surfing, cartoon style.",
8,
1,
3,
50,
7.5,
],
[
"Tune-A-Video-library/mo-di-bear-guitar",
"a handsome prince is playing guitar, modern disney style.",
8,
1,
123,
50,
7.5,
],
[
"Tune-A-Video-library/mo-di-bear-guitar",
"a magical princess is playing guitar, modern disney style.",
8,
1,
123,
50,
7.5,
],
[
"Tune-A-Video-library/mo-di-bear-guitar",
"a rabbit is playing guitar, modern disney style.",
8,
1,
123,
50,
7.5,
],
[
"Tune-A-Video-library/mo-di-bear-guitar",
"a baby is playing guitar, modern disney style.",
8,
1,
123,
50,
7.5,
],
[
"Tune-A-Video-library/redshift-man-skiing",
"(redshift style) spider man is skiing.",
8,
1,
123,
50,
7.5,
],
[
"Tune-A-Video-library/redshift-man-skiing",
"(redshift style) black widow is skiing.",
8,
1,
123,
50,
7.5,
],
[
"Tune-A-Video-library/redshift-man-skiing",
"(redshift style) batman is skiing.",
8,
1,
123,
50,
7.5,
],
[
"Tune-A-Video-library/redshift-man-skiing",
"(redshift style) hulk is skiing.",
8,
1,
123,
50,
7.5,
],
]
gr.Examples(
examples=examples,
inputs=[
model_id,
prompt,
video_length,
fps,
seed,
num_steps,
guidance_scale,
],
outputs=result,
fn=pipe.run,
cache_examples=CACHE_EXAMPLES,
)
model_id.change(
fn=app.load_model_info,
inputs=model_id,
outputs=[
base_model_used_for_training,
prompt_used_for_training,
],
)
inputs = [
model_id,
prompt,
video_length,
fps,
seed,
num_steps,
guidance_scale,
]
prompt.submit(fn=pipe.run, inputs=inputs, outputs=result)
run_button.click(fn=pipe.run, inputs=inputs, outputs=result)
demo.queue().launch()
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