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A10G
Running
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A10G
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import gradio as gr
from model import Model
import os
from hf_utils import get_model_list
on_huggingspace = os.environ.get("SPACE_AUTHOR_NAME") == "PAIR"
examples = [
["an astronaut waving the arm on the moon"],
["a sloth surfing on a wakeboard"],
["an astronaut walking on a street"],
["a cute cat walking on grass"],
["a horse is galloping on a street"],
["an astronaut is skiing down the hill"],
["a gorilla walking alone down the street"],
["a gorilla dancing on times square"],
["A panda dancing dancing like crazy on Times Square"],
]
def create_demo(model: Model):
with gr.Blocks() as demo:
with gr.Row():
gr.Markdown('## Text2Video-Zero: Video Generation')
with gr.Row():
gr.HTML(
"""
<div style="text-align: left; auto;">
<h2 style="font-weight: 450; font-size: 1rem; margin: 0rem">
Description: Simply input <b>any textual prompt</b> to generate videos right away and unleash your creativity and imagination! You can also select from the examples below. For performance purposes, our current preview release allows to generate up to 16 frames, which can be configured in the Advanced Options.
</h3>
</div>
""")
with gr.Row():
with gr.Column():
model_name = gr.Dropdown(
label="Model",
choices=get_model_list(),
value="dreamlike-art/dreamlike-photoreal-2.0",
)
prompt = gr.Textbox(label='Prompt')
run_button = gr.Button(label='Run')
with gr.Accordion('Advanced options', open=False):
watermark = gr.Radio(["Picsart AI Research", "Text2Video-Zero",
"None"], label="Watermark", value='Picsart AI Research')
if on_huggingspace:
video_length = gr.Slider(
label="Video length", minimum=8, maximum=16, step=1)
else:
video_length = gr.Number(
label="Video length", value=8, precision=0)
n_prompt = gr.Textbox(
label="Optional Negative Prompt", value='')
seed = gr.Slider(label='Seed',
info="-1 for random seed on each run. Otherwise, the seed will be fixed.",
minimum=-1,
maximum=65536,
value=0,
step=1)
motion_field_strength_x = gr.Slider(
label='Global Translation $\\delta_{x}$', minimum=-20, maximum=20,
value=12,
step=1)
motion_field_strength_y = gr.Slider(
label='Global Translation $\\delta_{y}$', minimum=-20, maximum=20,
value=12,
step=1)
t0 = gr.Slider(label="Timestep t0", minimum=0,
maximum=47, value=44, step=1,
info="Perform DDPM steps from t0 to t1. The larger the gap between t0 and t1, the more variance between the frames. Ensure t0 < t1 ",
)
t1 = gr.Slider(label="Timestep t1", minimum=1,
info="Perform DDPM steps from t0 to t1. The larger the gap between t0 and t1, the more variance between the frames. Ensure t0 < t1",
maximum=48, value=47, step=1)
chunk_size = gr.Slider(
label="Chunk size", minimum=2, maximum=16, value=2, step=1, visible=not on_huggingspace,
info="Number of frames processed at once. Reduce for lower memory usage."
)
merging_ratio = gr.Slider(
label="Merging ratio", minimum=0.0, maximum=0.9, step=0.1, value=0.0, visible=not on_huggingspace,
info="Ratio of how many tokens are merged. The higher the more compression (less memory and faster inference)."
)
with gr.Column():
result = gr.Video(label="Generated Video")
inputs = [
prompt,
model_name,
motion_field_strength_x,
motion_field_strength_y,
t0,
t1,
n_prompt,
chunk_size,
video_length,
watermark,
merging_ratio,
seed,
]
gr.Examples(examples=examples,
inputs=inputs,
outputs=result,
fn=model.process_text2video,
run_on_click=False,
cache_examples=on_huggingspace,
)
run_button.click(fn=model.process_text2video,
inputs=inputs,
outputs=result,)
return demo
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