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from typing import List, Literal
from pathlib import Path
from functools import partial
import spaces
import gradio as gr
import numpy as np
import torch
from torchvision.datasets.utils import download_and_extract_archive
from einops import repeat
from omegaconf import OmegaConf
from modeling.pipeline import VMemPipeline
from diffusers.utils import export_to_video, export_to_gif
from scipy.spatial.transform import Rotation, Slerp
from navigation import Navigator
from PIL import Image
from utils import tensor_to_pil, encode_vae_image, encode_image, get_default_intrinsics, load_img_and_K, transform_img_and_K
import os
import glob
CONFIG_PATH = "configs/inference/inference.yaml"
CONFIG = OmegaConf.load(CONFIG_PATH)
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
MODEL = VMemPipeline(CONFIG, DEVICE)
NAVIGATORS = []
NAVIGATION_FPS = 3
WIDTH = 576
HEIGHT = 576
IMAGE_PATHS = ['test_samples/changi.jpg', 'test_samples/oxford.jpeg', 'test_samples/open_door.jpg', 'test_samples/jesus.jpg', 'test_samples/friends.jpg']
# for asset_dir in ASSET_DIRS:
# if os.path.exists(asset_dir):
# for ext in ["*.jpg", "*.jpeg", "*.png"]:
# IMAGE_PATHS.extend(glob.glob(os.path.join(asset_dir, ext)))
# If no images found, create placeholders
if not IMAGE_PATHS:
def create_placeholder_images(num_samples=5, height=HEIGHT, width=WIDTH):
"""Create placeholder images for the demo"""
images = []
for i in range(num_samples):
# Create a gradient image as placeholder
img = np.zeros((height, width, 3), dtype=np.uint8)
for h in range(height):
for w in range(width):
img[h, w, 0] = int(255 * h / height) # Red gradient
img[h, w, 1] = int(255 * w / width) # Green gradient
img[h, w, 2] = int(255 * (i+1) / num_samples) # Blue varies by image
images.append(img)
return images
# Create placeholder video frames and poses
def create_placeholder_video_and_poses(num_samples=5, num_frames=1, height=HEIGHT, width=WIDTH):
"""Create placeholder videos and poses for the demo"""
videos = []
poses = []
for i in range(num_samples):
# Create a simple video (just one frame initially for each sample)
frames = []
for j in range(num_frames):
# Create a gradient frame
img = np.zeros((height, width, 3), dtype=np.uint8)
for h in range(height):
for w in range(width):
img[h, w, 0] = int(255 * h / height) # Red gradient
img[h, w, 1] = int(255 * w / width) # Green gradient
img[h, w, 2] = int(255 * (i+1) / num_samples) # Blue varies by video
# Convert to torch tensor [C, H, W] with normalized values
frame = torch.from_numpy(img.transpose(2, 0, 1)).float() / 255.0
frames.append(frame)
video = torch.stack(frames)
videos.append(video)
# Create placeholder poses (identity matrices flattened)
# This creates a 4x4 identity matrix flattened to match expected format
# pose = torch.eye(4).flatten()[:-4] # Remove last row of 4x4 matrix
poses.append(torch.eye(4).unsqueeze(0).repeat(num_frames, 1, 1))
return videos, poses
first_frame_list = create_placeholder_images(num_samples=5)
video_list, poses_list = create_placeholder_video_and_poses(num_samples=5)
# Function to load image from path
def load_image_for_navigation(image_path):
"""Load image from path and prepare for navigation"""
# Load image and get default intrinsics
image, _ = load_img_and_K(image_path, None, K=None, device=DEVICE)
# Transform image to the target size
config = OmegaConf.load(CONFIG_PATH)
image, _ = transform_img_and_K(image, (config.model.height, config.model.width), mode="crop", K=None)
# Create initial video with single frame and pose
video = image
pose = torch.eye(4).unsqueeze(0) # [1, 4, 4]
return {
"image": tensor_to_pil(image),
"video": video,
"pose": pose
}
class CustomProgressBar:
def __init__(self, pbar):
self.pbar = pbar
def set_postfix(self, **kwargs):
pass
def __getattr__(self, attr):
return getattr(self.pbar, attr)
def get_duration_navigate_video(video: torch.Tensor,
poses: torch.Tensor,
x_angle: float,
y_angle: float,
distance: float
):
# Estimate processing time based on navigation complexity and number of frames
base_duration = 15 # Base duration in seconds
# Add time for more complex navigation operations
if abs(x_angle) > 20 or abs(y_angle) > 30:
base_duration += 10 # More time for sharp turns
if distance > 100:
base_duration += 10 # More time for longer distances
# Add time proportional to existing video length (more frames = more processing)
base_duration += min(10, len(video))
return base_duration
@spaces.GPU(duration=get_duration_navigate_video)
@torch.autocast("cuda")
@torch.no_grad()
def navigate_video(
video: torch.Tensor,
poses: torch.Tensor,
x_angle: float,
y_angle: float,
distance: float,
):
"""
Generate new video frames by navigating in the 3D scene.
This function uses the Navigator class from navigation.py to handle movement:
- y_angle parameter controls left/right turning (turn_left/turn_right methods)
- distance parameter controls forward movement (move_forward method)
- x_angle parameter controls vertical angle (not directly implemented in Navigator)
Each Navigator instance is stored based on the video session to maintain state.
"""
try:
# Convert first frame to PIL Image for navigator
initial_frame = tensor_to_pil(video[0])
# Initialize the navigator for this session if not already done
if len(NAVIGATORS) == 0:
# Create a new navigator instance
NAVIGATORS.append(Navigator(MODEL, step_size=0.1, num_interpolation_frames=4))
# Get the initial pose and convert to numpy
initial_pose = poses[0].cpu().numpy().reshape(4, 4)
# Default camera intrinsics if not available
initial_K = np.array(get_default_intrinsics()[0])
# Initialize the navigator
NAVIGATORS[0].initialize(initial_frame, initial_pose, initial_K)
navigator = NAVIGATORS[0]
# Generate new frames based on navigation commands
new_frames = []
# First handle any x-angle (vertical angle) adjustments
# Note: This is approximated as Navigator doesn't directly support this
if abs(x_angle) > 0:
# Implementation for x-angle could be added here
# For now, we'll skip this as it's not directly supported
pass
# Next handle y-angle (turning left/right)
if abs(y_angle) > 0:
# Use Navigator's turn methods
if y_angle > 0:
new_frames = navigator.turn_left(abs(y_angle//2))
else:
new_frames = navigator.turn_right(abs(y_angle//2))
# Finally handle distance (moving forward)
elif distance > 0:
# Calculate number of steps based on distance
steps = max(1, int(distance / 10))
new_frames = navigator.move_forward(steps)
elif distance < 0:
# Handle moving backward if needed
steps = max(1, int(abs(distance) / 10))
new_frames = navigator.move_backward(steps)
if not new_frames:
# If no new frames were generated, return the current state
return video, poses, tensor_to_pil(video[-1]), export_to_video([tensor_to_pil(video[i]) for i in range(len(video))], fps=NAVIGATION_FPS), [(tensor_to_pil(video[i]), f"t={i}") for i in range(len(video))]
# Convert PIL images to tensors
new_frame_tensors = []
for frame in new_frames:
# Convert PIL Image to tensor [C, H, W]
frame_np = np.array(frame) / 255.0
# Convert to [-1, 1] range to match the expected format
frame_tensor = torch.from_numpy(frame_np.transpose(2, 0, 1)).float() * 2.0 - 1.0
new_frame_tensors.append(frame_tensor)
new_frames_tensor = torch.stack(new_frame_tensors)
# Get the updated camera poses from the navigator
current_pose = navigator.current_pose
new_poses = torch.from_numpy(current_pose).float().unsqueeze(0).repeat(len(new_frames), 1, 1)
# Reshape the poses to match the expected format
new_poses = new_poses.view(len(new_frames), 4, 4)
# Concatenate new frames and poses with existing ones
updated_video = torch.cat([video.cpu(), new_frames_tensor], dim=0)
updated_poses = torch.cat([poses.cpu(), new_poses], dim=0)
# Create output images for gallery
all_images = [(tensor_to_pil(updated_video[i]), f"t={i}") for i in range(len(updated_video))]
updated_video_pil = [tensor_to_pil(updated_video[i]) for i in range(len(updated_video))]
return (
updated_video,
updated_poses,
tensor_to_pil(updated_video[-1]), # Current view
export_to_video(updated_video_pil, fps=NAVIGATION_FPS), # Video
all_images, # Gallery
)
except Exception as e:
print(f"Error in navigate_video: {e}")
gr.Warning(f"Navigation error: {e}")
# Return the original inputs to avoid crashes
current_frame = tensor_to_pil(video[-1]) if len(video) > 0 else None
all_frames = [(tensor_to_pil(video[i]), f"t={i}") for i in range(len(video))]
video_frames = [tensor_to_pil(video[i]) for i in range(len(video))]
video_output = export_to_video(video_frames, fps=NAVIGATION_FPS) if video_frames else None
return video, poses, current_frame, video_output, all_frames
def undo_navigation(
video: torch.Tensor,
poses: torch.Tensor,
):
"""
Undo the last navigation step by removing the last set of frames.
Uses the Navigator's undo method which in turn uses the pipeline's undo_latest_move
to properly handle surfels and state management.
"""
if len(NAVIGATORS) > 0:
navigator = NAVIGATORS[0]
# Call the Navigator's undo method to handle the operation
success = navigator.undo()
if success:
# Since the navigator has handled the frame removal internally,
# we need to update our video and poses tensors to match
updated_video = video[:len(navigator.frames)]
updated_poses = poses[:len(navigator.frames)]
# Create gallery images
all_images = [(tensor_to_pil(updated_video[i]), f"t={i}") for i in range(len(updated_video))]
return (
updated_video,
updated_poses,
tensor_to_pil(updated_video[-1]),
export_to_video([tensor_to_pil(updated_video[i]) for i in range(len(updated_video))], fps=NAVIGATION_FPS),
all_images,
)
else:
gr.Warning("You have no moves left to undo!")
else:
gr.Warning("No navigation session available!")
# If undo wasn't successful or no navigator exists, return original state
all_images = [(tensor_to_pil(video[i]), f"t={i}") for i in range(len(video))]
return (
video,
poses,
tensor_to_pil(video[-1]),
export_to_video([tensor_to_pil(video[i]) for i in range(len(video))], fps=NAVIGATION_FPS),
all_images,
)
def render_demo3(
s: Literal["Selection", "Generation"],
idx: int,
demo3_stage: gr.State,
demo3_selected_index: gr.State,
demo3_current_video: gr.State,
demo3_current_poses: gr.State
):
gr.Markdown(
"""
## Single Image → Consistent Scene Navigation
> #### _Select an image and navigate through the scene by controlling camera movements._
""",
elem_classes=["task-title"]
)
match s:
case "Selection":
with gr.Group():
# Add upload functionality
with gr.Group(elem_classes=["gradio-box"]):
gr.Markdown("### Upload Your Own Image")
gr.Markdown("_Upload an image to navigate through its 3D scene_")
with gr.Row():
with gr.Column(scale=3):
upload_image = gr.Image(
label="Upload an image",
type="filepath",
height=300,
elem_id="upload-image"
)
with gr.Column(scale=1):
gr.Markdown("#### Instructions:")
gr.Markdown("1. Upload a clear, high-quality image")
gr.Markdown("2. Images with distinct visual features work best")
gr.Markdown("3. Landscape or architectural scenes are ideal")
upload_btn = gr.Button("Start Navigation", variant="primary", size="lg")
def process_uploaded_image(image_path):
if image_path is None:
gr.Warning("Please upload an image first")
return "Selection", None, None, None
try:
# Load image and prepare for navigation
result = load_image_for_navigation(image_path)
# Clear any existing navigators
global NAVIGATORS
NAVIGATORS = []
return (
"Generation",
None, # No predefined index for uploaded images
result["video"],
result["pose"],
)
except Exception as e:
print(f"Error in process_uploaded_image: {e}")
gr.Warning(f"Error processing uploaded image: {e}")
return "Selection", None, None, None
upload_btn.click(
fn=process_uploaded_image,
inputs=[upload_image],
outputs=[demo3_stage, demo3_selected_index, demo3_current_video, demo3_current_poses]
)
gr.Markdown("### Or Choose From Our Examples")
# Define image captions
image_captions = {
'test_samples/changi.jpg': 'Changi Airport',
'test_samples/oxford.jpeg': 'Oxford University',
'test_samples/open_door.jpg': 'Bedroom Interior',
'test_samples/jesus.jpg': 'Jesus College',
'test_samples/friends.jpg': 'Friends Café'
}
# Load all images for the gallery with captions
gallery_images = []
for img_path in IMAGE_PATHS:
try:
# Get caption or default to basename
caption = image_captions.get(img_path, os.path.basename(img_path))
gallery_images.append((img_path, caption))
except Exception as e:
print(f"Error loading image {img_path}: {e}")
# Show image gallery for selection
demo3_image_gallery = gr.Gallery(
value=gallery_images,
label="Select an Image to Start Navigation",
columns=len(gallery_images),
height=400,
allow_preview=True,
preview=False,
elem_id="navigation-gallery"
)
gr.Markdown("_Click on an image to begin navigation_")
def start_navigation(evt: gr.SelectData):
try:
# Get the selected image path
selected_path = IMAGE_PATHS[evt.index]
# Load image and prepare for navigation
result = load_image_for_navigation(selected_path)
# Clear any existing navigators
global NAVIGATORS
NAVIGATORS = []
return (
"Generation",
evt.index,
result["video"],
result["pose"],
)
except Exception as e:
print(f"Error in start_navigation: {e}")
gr.Warning(f"Error starting navigation: {e}")
return "Selection", None, None, None
demo3_image_gallery.select(
fn=start_navigation,
inputs=None,
outputs=[demo3_stage, demo3_selected_index, demo3_current_video, demo3_current_poses]
)
case "Generation":
with gr.Row():
with gr.Column(scale=3):
with gr.Row():
demo3_current_view = gr.Image(
label="Current View",
width=256,
height=256,
)
demo3_video = gr.Video(
label="Generated Video",
width=256,
height=256,
autoplay=True,
loop=True,
show_share_button=True,
show_download_button=True,
)
demo3_generated_gallery = gr.Gallery(
value=[],
label="Generated Frames",
columns=[6],
)
# Initialize the current view with the selected image if available
if idx is not None:
try:
selected_path = IMAGE_PATHS[idx]
result = load_image_for_navigation(selected_path)
demo3_current_view.value = result["image"]
except Exception as e:
print(f"Error initializing current view: {e}")
with gr.Column():
gr.Markdown("### Navigation Controls ↓")
with gr.Accordion("Instructions", open=False):
gr.Markdown("""
- **The model will predict the next few frames based on your camera movements. Repeat the process to continue navigating through the scene.**
- **Use the navigation controls to move forward/backward and turn left/right.**
- **At the end of your navigation, you can save your camera path for later use.**
""")
# with gr.Tab("Basic", elem_id="basic-controls-tab"):
with gr.Group():
gr.Markdown("_**Select a direction to move:**_")
# First row: Turn left/right
with gr.Row(elem_id="basic-controls"):
gr.Button(
"↰20°\nVeer",
size="sm",
min_width=0,
variant="primary",
).click(
fn=partial(
navigate_video,
x_angle=0,
y_angle=20,
distance=0,
),
inputs=[
demo3_current_video,
demo3_current_poses,
],
outputs=[
demo3_current_video,
demo3_current_poses,
demo3_current_view,
demo3_video,
demo3_generated_gallery,
],
)
gr.Button(
"↖10°\nTurn",
size="sm",
min_width=0,
variant="primary",
).click(
fn=partial(
navigate_video,
x_angle=0,
y_angle=10,
distance=0,
),
inputs=[
demo3_current_video,
demo3_current_poses,
],
outputs=[
demo3_current_video,
demo3_current_poses,
demo3_current_view,
demo3_video,
demo3_generated_gallery,
],
)
# gr.Button(
# "↑0°\nAhead",
# size="sm",
# min_width=0,
# variant="primary",
# ).click(
# fn=partial(
# navigate_video,
# x_angle=0,
# y_angle=0,
# distance=10,
# ),
# inputs=[
# demo3_current_video,
# demo3_current_poses,
# ],
# outputs=[
# demo3_current_video,
# demo3_current_poses,
# demo3_current_view,
# demo3_video,
# demo3_generated_gallery,
# ],
# )
gr.Button(
"↗10°\nTurn",
size="sm",
min_width=0,
variant="primary",
).click(
fn=partial(
navigate_video,
x_angle=0,
y_angle=-10,
distance=0,
),
inputs=[
demo3_current_video,
demo3_current_poses,
],
outputs=[
demo3_current_video,
demo3_current_poses,
demo3_current_view,
demo3_video,
demo3_generated_gallery,
],
)
gr.Button(
"↱\n20° Veer",
size="sm",
min_width=0,
variant="primary",
).click(
fn=partial(
navigate_video,
x_angle=0,
y_angle=-20,
distance=0,
),
inputs=[
demo3_current_video,
demo3_current_poses,
],
outputs=[
demo3_current_video,
demo3_current_poses,
demo3_current_view,
demo3_video,
demo3_generated_gallery,
],
)
# Second row: Forward/Backward movement
with gr.Row(elem_id="forward-backward-controls"):
gr.Button(
"↓\nBackward",
size="sm",
min_width=0,
variant="secondary",
).click(
fn=partial(
navigate_video,
x_angle=0,
y_angle=0,
distance=-10,
),
inputs=[
demo3_current_video,
demo3_current_poses,
],
outputs=[
demo3_current_video,
demo3_current_poses,
demo3_current_view,
demo3_video,
demo3_generated_gallery,
],
)
gr.Button(
"↑\nForward",
size="sm",
min_width=0,
variant="secondary",
).click(
fn=partial(
navigate_video,
x_angle=0,
y_angle=0,
distance=10,
),
inputs=[
demo3_current_video,
demo3_current_poses,
],
outputs=[
demo3_current_video,
demo3_current_poses,
demo3_current_view,
demo3_video,
demo3_generated_gallery,
],
)
# with gr.Tab("Advanced", elem_id="advanced-controls-tab"):
# with gr.Group():
# gr.Markdown("_**Select angles and distance:**_")
# demo3_y_angle = gr.Slider(
# minimum=-90,
# maximum=90,
# value=0,
# step=10,
# label="Horizontal Angle",
# interactive=True,
# )
# demo3_x_angle = gr.Slider(
# minimum=-40,
# maximum=40,
# value=0,
# step=10,
# label="Vertical Angle",
# interactive=True,
# )
# demo3_distance = gr.Slider(
# minimum=-200,
# maximum=200,
# value=100,
# step=10,
# label="Distance (negative = backward)",
# interactive=True,
# )
# gr.Button(
# "Generate Next Move", variant="primary"
# ).click(
# fn=navigate_video,
# inputs=[
# demo3_current_video,
# demo3_current_poses,
# demo3_x_angle,
# demo3_y_angle,
# demo3_distance,
# ],
# outputs=[
# demo3_current_video,
# demo3_current_poses,
# demo3_current_view,
# demo3_video,
# demo3_generated_gallery,
# ],
# )
gr.Markdown("---")
with gr.Group():
gr.Markdown("_**Navigation controls:**_")
with gr.Row():
gr.Button("Undo Last Move", variant="huggingface").click(
fn=undo_navigation,
inputs=[demo3_current_video, demo3_current_poses],
outputs=[
demo3_current_video,
demo3_current_poses,
demo3_current_view,
demo3_video,
demo3_generated_gallery,
],
)
# Add a function to save camera poses
def save_camera_poses(video, poses):
if len(NAVIGATORS) > 0:
navigator = NAVIGATORS[0]
# Create a directory for saved poses
os.makedirs("./visualization", exist_ok=True)
save_path = f"./visualization/transforms_{len(navigator.frames)}_frames.json"
navigator.save_camera_poses(save_path)
return gr.Info(f"Camera poses saved to {save_path}")
return gr.Warning("No navigation instance found")
gr.Button("Save Camera", variant="huggingface").click(
fn=save_camera_poses,
inputs=[demo3_current_video, demo3_current_poses],
outputs=[]
)
# Add a button to return to image selection
def reset_navigation():
# Clear current navigator
global NAVIGATORS
NAVIGATORS = []
return "Selection", None, None, None
gr.Button("Choose New Image", variant="secondary").click(
fn=reset_navigation,
inputs=[],
outputs=[demo3_stage, demo3_selected_index, demo3_current_video, demo3_current_poses]
)
# Create the Gradio Blocks
with gr.Blocks(theme=gr.themes.Base(primary_hue="teal")) as demo:
gr.HTML(
"""
<style>
[data-tab-id="task-1"], [data-tab-id="task-2"], [data-tab-id="task-3"] {
font-size: 16px !important;
font-weight: bold;
}
#page-title h1 {
color: #0D9488 !important;
}
.task-title h2 {
color: #F59E0C !important;
}
.header-button-row {
gap: 4px !important;
}
.header-button-row div {
width: 131.0px !important;
}
.header-button-column {
width: 131.0px !important;
gap: 5px !important;
}
.header-button a {
border: 1px solid #e4e4e7;
}
.header-button .button-icon {
margin-right: 8px;
}
.demo-button-column .gap {
gap: 5px !important;
}
#basic-controls {
column-gap: 0px;
}
#basic-controls-tab {
padding: 0px;
}
#advanced-controls-tab {
padding: 0px;
}
#forward-backward-controls {
column-gap: 0px;
justify-content: center;
margin-top: 8px;
}
#selected-demo-button {
color: #F59E0C;
text-decoration: underline;
}
.demo-button {
text-align: left !important;
display: block !important;
}
#navigation-gallery {
margin-bottom: 15px;
}
#navigation-gallery .gallery-item {
cursor: pointer;
border-radius: 6px;
transition: transform 0.2s, box-shadow 0.2s;
}
#navigation-gallery .gallery-item:hover {
transform: scale(1.02);
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
}
#navigation-gallery .gallery-item.selected {
border: 3px solid #0D9488;
}
/* Upload image styling */
#upload-image {
border-radius: 8px;
border: 2px dashed #0D9488;
padding: 10px;
transition: all 0.3s ease;
}
#upload-image:hover {
border-color: #F59E0C;
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
}
/* Box styling */
.gradio-box {
border-radius: 10px;
margin-bottom: 20px;
padding: 15px;
background-color: #f8f9fa;
border: 1px solid #e9ecef;
}
</style>
"""
)
demo_idx = gr.State(value=3)
with gr.Sidebar():
gr.Markdown("# VMem: Consistent Scene Generation with Surfel Memory of Views", elem_id="page-title")
gr.Markdown(
"### Official Interactive Demo for [_VMem_](https://arxiv.org/abs/2502.06764)"
)
gr.Markdown("---")
gr.Markdown("#### Links ↓")
with gr.Row(elem_classes=["header-button-row"]):
with gr.Column(elem_classes=["header-button-column"], min_width=0):
gr.Button(
value="Website",
link="https://v-mem.github.io/",
icon="https://simpleicons.org/icons/googlechrome.svg",
elem_classes=["header-button"],
size="md",
min_width=0,
)
gr.Button(
value="Paper",
link="https://arxiv.org/abs/2502.06764",
icon="https://simpleicons.org/icons/arxiv.svg",
elem_classes=["header-button"],
size="md",
min_width=0,
)
with gr.Column(elem_classes=["header-button-column"], min_width=0):
gr.Button(
value="Code",
link="https://github.com/kwsong0113/diffusion-forcing-transformer",
icon="https://simpleicons.org/icons/github.svg",
elem_classes=["header-button"],
size="md",
min_width=0,
)
gr.Button(
value="Weights",
link="https://huggingface.co/liguang0115/vmem",
icon="https://simpleicons.org/icons/huggingface.svg",
elem_classes=["header-button"],
size="md",
min_width=0,
)
gr.Markdown("---")
gr.Markdown("#### Choose a Demo ↓")
with gr.Column(elem_classes=["demo-button-column"]):
@gr.render(inputs=[demo_idx])
def render_demo_tabs(idx):
demo_tab_button3 = gr.Button(
"Navigate Image",
size="md", elem_classes=["demo-button"], **{"elem_id": "selected-demo-button"} if idx == 3 else {}
).click(
fn=lambda: 3,
outputs=demo_idx
)
gr.Markdown("---")
gr.Markdown("#### Troubleshooting ↓")
with gr.Group():
with gr.Accordion("Error or Unexpected Results?", open=False):
gr.Markdown("Please try again after refreshing the page and ensure you do not click the same button multiple times.")
with gr.Accordion("Too Slow or No GPU Allocation?", open=False):
gr.Markdown(
"Consider running the demo locally (click the dots in the top-right corner). Alternatively, you can subscribe to Hugging Face Pro for an increased GPU quota."
)
demo3_stage = gr.State(value="Selection")
demo3_selected_index = gr.State(value=None)
demo3_current_video = gr.State(value=None)
demo3_current_poses = gr.State(value=None)
@gr.render(inputs=[demo_idx, demo3_stage, demo3_selected_index])
def render_demo(
_demo_idx, _demo3_stage, _demo3_selected_index
):
match _demo_idx:
case 3:
render_demo3(_demo3_stage, _demo3_selected_index, demo3_stage, demo3_selected_index, demo3_current_video, demo3_current_poses)
if __name__ == "__main__":
demo.launch(debug=True,
share=True,
max_threads=1, # Limit concurrent processing
show_error=True, # Show detailed error messages
)
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