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import gradio as gr | |
import subprocess | |
import shutil | |
import os | |
is_shared_ui = True if "fffiloni/Go-With-The-Flow" in os.environ['SPACE_ID'] else False | |
from huggingface_hub import snapshot_download | |
# Define the folder name | |
folder_name = "lora_models" | |
# Create the folder | |
os.makedirs(folder_name, exist_ok=True) | |
# Download models | |
snapshot_download( | |
repo_id = "Eyeline-Research/Go-with-the-Flow", | |
local_dir = folder_name | |
) | |
def process_video(video_path, prompt, num_steps, degradation_level): | |
output_folder="noise_warp_output_folder" | |
if os.path.exists(output_folder): | |
# Delete the folder and its contents | |
shutil.rmtree(output_folder) | |
# Check if the file exists and delete it | |
if os.path.exists("output.mp4"): | |
os.remove("output.mp4") | |
output_video="output.mp4" | |
device="cuda" | |
try: | |
# Step 1: Warp the noise | |
gr.Info("Step 1: Warp the noise...") | |
warp_command = [ | |
"python", "make_warped_noise.py", video_path, | |
"--output_folder", output_folder | |
] | |
subprocess.run(warp_command, check=True) | |
warped_vid_path = os.path.join(output_folder, "input.mp4") | |
# Step 2: Run inference | |
gr.Info("Step 2: Run inference...") | |
inference_command = [ | |
"python", "cut_and_drag_inference.py", output_folder, | |
"--prompt", prompt, | |
"--degradation", str(degradation_level), | |
"--output_mp4_path", output_video, | |
"--device", device, | |
"--num_inference_steps", str(num_steps) | |
] | |
subprocess.run(inference_command, check=True) | |
# Return the path to the output video | |
gr.Success("Done!") | |
return output_video | |
except subprocess.CalledProcessError as e: | |
raise gr.Error(f"An error occurred: {str(e)}") | |
css=""" | |
div#follow-div{ | |
text-decoration: none !important; | |
display: flex; | |
column-gap: 5px; | |
font-size: 0.8em; | |
} | |
""" | |
with gr.Blocks(css=css) as demo: | |
with gr.Column(): | |
gr.Markdown("# Go-With-The-Flow • Cut and Drag") | |
gr.HTML(""" | |
<div style="display:flex;column-gap:4px;"> | |
<a href="https://github.com/Eyeline-Research/Go-with-the-Flow"> | |
<img src='https://img.shields.io/badge/GitHub-Repo-blue'> | |
</a> | |
<a href="https://arxiv.org/abs/2501.08331"> | |
<img src='https://img.shields.io/badge/ArXiv-Paper-red'> | |
</a> | |
<a href="https://eyeline-research.github.io/Go-with-the-Flow/"> | |
<img src='https://img.shields.io/badge/Project-Page-green'> | |
</a> | |
<a href="https://huggingface.co/spaces/fffiloni/Go-With-The-Flow?duplicate=true"> | |
<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-sm.svg" alt="Duplicate this Space"> | |
</a> | |
</div> | |
""") | |
with gr.Row(): | |
with gr.Column(): | |
input_video = gr.Video(label="Input Video") | |
prompt = gr.Textbox(label="Prompt") | |
with gr.Row(): | |
if is_shared_ui: | |
num_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=30, value=5, step=1, interactive=False) | |
degradation = gr.Slider(label="Noise Degradation", minimum=0, maximum=1, value=0.5, step=0.1, interactive=False) | |
else: | |
num_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=30, value=20, step=1, interactive=True) | |
degradation = gr.Slider(label="Noise Degradation", minimum=0, maximum=1, value=0.5, step=0.1, interactive=True) | |
submit_btn = gr.Button("Submit") | |
gr.Examples( | |
examples = [ | |
["./examples/example_1.mp4", "yellow plastic duck is swimming and jumping in the water"], | |
["./examples/example_2.mp4", "a car enters the frame and goes forward to the end of the street"] | |
], | |
inputs = [input_video, prompt] | |
) | |
with gr.Column(): | |
output_video = gr.Video(label="Result") | |
gr.HTML(""" | |
<div id="follow-div"> | |
<a href="https://huggingface.co/fffiloni"> | |
<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/follow-me-on-HF-sm-dark.svg" alt="Follow me on HF"> | |
</a> | |
<p>for space updates</p> | |
""") | |
submit_btn.click( | |
fn = process_video, | |
inputs = [input_video, prompt, num_steps, degradation], | |
outputs = [output_video] | |
) | |
demo.queue().launch(show_api=False) |