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import os
import torch
import gradio as gr
from huggingface_hub import snapshot_download
os.makedirs("checkpoints", exist_ok=True)
snapshot_download("alex4727/InstantDrag", local_dir="./checkpoints")
from demo_utils import (
process_img,
get_points,
undo_points_image,
clear_all,
InstantDragPipeline,
)
LENGTH = 480 # Length of the square area displaying/editing images
with gr.Blocks() as demo:
pipeline = InstantDragPipeline(seed=42, device="cuda", dtype=torch.float16)
# Layout definition
with gr.Row():
gr.Markdown(
"""
# InstantDrag: Improving Interactivity in Drag-based Image Editing
"""
)
with gr.Tab(label="InstantDrag Demo"):
selected_points = gr.State([]) # Store points
original_image = gr.State(value=None) # Store original input image
with gr.Row():
# Upload & Preprocess Image Column
with gr.Column():
gr.Markdown(
"""<p style="text-align: center; font-size: 20px">Upload & Preprocess Image</p>"""
)
canvas = gr.ImageEditor(
height=LENGTH,
width=LENGTH,
type="numpy",
image_mode="RGB",
label="Preprocess Image",
show_label=True,
interactive=True,
)
with gr.Row():
save_results = gr.Checkbox(
value=False,
label="Save Results",
scale=1,
)
undo_button = gr.Button("Undo Clicked Points", scale=3)
# Click Points Column
with gr.Column():
gr.Markdown(
"""<p style="text-align: center; font-size: 20px">Click Points</p>"""
)
input_image = gr.Image(
type="numpy",
label="Click Points",
show_label=True,
height=LENGTH,
width=LENGTH,
interactive=False,
show_fullscreen_button=False,
)
with gr.Row():
run_button = gr.Button("Run")
# Editing Results Column
with gr.Column():
gr.Markdown(
"""<p style="text-align: center; font-size: 20px">Editing Results</p>"""
)
edited_image = gr.Image(
type="numpy",
label="Editing Results",
show_label=True,
height=LENGTH,
width=LENGTH,
interactive=False,
show_fullscreen_button=False,
)
with gr.Row():
clear_all_button = gr.Button("Clear All")
with gr.Tab("Configs - make sure to check README for details"):
with gr.Row():
with gr.Column():
with gr.Row():
flowgen_choices = sorted(
[model for model in os.listdir("checkpoints/") if "flowgen" in model]
)
flowgen_ckpt = gr.Dropdown(
value=flowgen_choices[0],
label="Select FlowGen to use",
choices=flowgen_choices,
info="config2 for most cases, config3 for more fine-grained dragging",
scale=2,
)
flowdiffusion_choices = sorted(
[model for model in os.listdir("checkpoints/") if "flowdiffusion" in model]
)
flowdiffusion_ckpt = gr.Dropdown(
value=flowdiffusion_choices[0],
label="Select FlowDiffusion to use",
choices=flowdiffusion_choices,
info="single model for all cases",
scale=1,
)
image_guidance = gr.Number(
value=1.5,
label="Image Guidance Scale",
precision=2,
step=0.1,
scale=1,
info="typically between 1.0-2.0.",
)
flow_guidance = gr.Number(
value=1.5,
label="Flow Guidance Scale",
precision=2,
step=0.1,
scale=1,
info="typically between 1.0-5.0",
)
num_steps = gr.Number(
value=20,
label="Inference Steps",
precision=0,
step=1,
scale=1,
info="typically between 20-50, 20 is usually enough",
)
flowgen_output_scale = gr.Number(
value=-1.0,
label="FlowGen Output Scale",
precision=1,
step=0.1,
scale=2,
info="-1.0, by default, forces flowgen's output to [-1, 1], could be adjusted to [0, ∞] for stronger/weaker effects",
)
gr.Markdown(
"""
<p style="text-align: center; font-size: 18px;">Examples</p>
"""
)
with gr.Row():
gr.Examples(
examples=[
"samples/airplane.jpg",
"samples/anime.jpg",
"samples/caligraphy.jpg",
"samples/crocodile.jpg",
"samples/elephant.jpg",
"samples/meteor.jpg",
"samples/monalisa.jpg",
"samples/portrait.jpg",
"samples/sketch.jpg",
"samples/surreal.jpg",
],
inputs=[canvas],
outputs=[original_image, selected_points, input_image],
fn=process_img,
cache_examples=False,
examples_per_page=10,
)
gr.Markdown(
"""
<p style="text-align: center; font-size: 9">[Important] Our base models are solely trained on real-world talking head (facial) videos, with a focus on achieving fine-grained facial editing. <br>
Their application to other types of scenes, without fine-tuning, should be considered more of an experimental byproduct and may not perform well in many cases (we currently support only square images).</p>
"""
)
# Event Handlers
canvas.change(
process_img,
[canvas],
[original_image, selected_points, input_image],
)
input_image.select(
get_points,
[input_image, selected_points],
[input_image],
)
undo_button.click(
undo_points_image,
[original_image],
[input_image, selected_points],
)
run_button.click(
pipeline.run,
[
original_image,
selected_points,
flowgen_ckpt,
flowdiffusion_ckpt,
image_guidance,
flow_guidance,
flowgen_output_scale,
num_steps,
save_results,
],
[edited_image],
)
clear_all_button.click(
clear_all,
[],
[
canvas,
input_image,
edited_image,
selected_points,
original_image,
],
)
demo.queue().launch(share=False, debug=True)
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