# Copyright 2024 Anton Obukhov, ETH Zurich. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------------------------
# If you find this code useful, we kindly ask you to cite our paper in your work.
# Please find bibtex at: https://github.com/prs-eth/Marigold#-citation
# More information about the method can be found at https://marigoldmonodepth.github.io
# --------------------------------------------------------------------------
from __future__ import annotations
import functools
import os
import tempfile
import diffusers
import gradio as gr
import imageio as imageio
import numpy as np
import spaces
import torch as torch
torch.backends.cuda.matmul.allow_tf32 = True
from PIL import Image
from gradio_imageslider import ImageSlider
from tqdm import tqdm
from pathlib import Path
import gradio
from gradio.utils import get_cache_folder
from stablenormal.pipeline_yoso_normal import YOSONormalsPipeline
from stablenormal.pipeline_stablenormal import StableNormalPipeline
from stablenormal.scheduler.heuristics_ddimsampler import HEURI_DDIMScheduler
class Examples(gradio.helpers.Examples):
def __init__(self, *args, directory_name=None, **kwargs):
super().__init__(*args, **kwargs, _initiated_directly=False)
if directory_name is not None:
self.cached_folder = get_cache_folder() / directory_name
self.cached_file = Path(self.cached_folder) / "log.csv"
self.create()
default_seed = 2024
default_batch_size = 1
default_image_processing_resolution = 768
default_video_num_inference_steps = 10
default_video_processing_resolution = 768
default_video_out_max_frames = 60
def process_image_check(path_input):
if path_input is None:
raise gr.Error(
"Missing image in the first pane: upload a file or use one from the gallery below."
)
def resize_image(input_image, resolution):
# Ensure input_image is a PIL Image object
if not isinstance(input_image, Image.Image):
raise ValueError("input_image should be a PIL Image object")
# Convert image to numpy array
input_image_np = np.asarray(input_image)
# Get image dimensions
H, W, C = input_image_np.shape
H = float(H)
W = float(W)
# Calculate the scaling factor
k = float(resolution) / min(H, W)
# Determine new dimensions
H *= k
W *= k
H = int(np.round(H / 64.0)) * 64
W = int(np.round(W / 64.0)) * 64
# Resize the image using PIL's resize method
img = input_image.resize((W, H), Image.Resampling.LANCZOS)
return img
def process_image(
pipe,
path_input,
):
name_base, name_ext = os.path.splitext(os.path.basename(path_input))
print(f"Processing image {name_base}{name_ext}")
path_output_dir = tempfile.mkdtemp()
path_out_png = os.path.join(path_output_dir, f"{name_base}_normal_colored.png")
input_image = Image.open(path_input)
input_image = resize_image(input_image, default_image_processing_resolution)
pipe_out = pipe(
input_image,
match_input_resolution=False,
processing_resolution=max(input_image.size)
)
normal_pred = pipe_out.prediction[0, :, :]
normal_colored = pipe.image_processor.visualize_normals(pipe_out.prediction)
normal_colored[-1].save(path_out_png)
yield [input_image, path_out_png]
def center_crop(img):
# Open the image file
img_width, img_height = img.size
crop_width =min(img_width, img_height)
# Calculate the cropping box
left = (img_width - crop_width) / 2
top = (img_height - crop_width) / 2
right = (img_width + crop_width) / 2
bottom = (img_height + crop_width) / 2
# Crop the image
img_cropped = img.crop((left, top, right, bottom))
return img_cropped
def process_video(
pipe,
path_input,
out_max_frames=default_video_out_max_frames,
target_fps=10,
progress=gr.Progress(),
):
if path_input is None:
raise gr.Error(
"Missing video in the first pane: upload a file or use one from the gallery below."
)
name_base, name_ext = os.path.splitext(os.path.basename(path_input))
print(f"Processing video {name_base}{name_ext}")
path_output_dir = tempfile.mkdtemp()
path_out_vis = os.path.join(path_output_dir, f"{name_base}_normal_colored.mp4")
init_latents = None
reader, writer = None, None
try:
reader = imageio.get_reader(path_input)
meta_data = reader.get_meta_data()
fps = meta_data["fps"]
size = meta_data["size"]
duration_sec = meta_data["duration"]
writer = imageio.get_writer(path_out_vis, fps=target_fps)
out_frame_id = 0
pbar = tqdm(desc="Processing Video", total=duration_sec)
for frame_id, frame in enumerate(reader):
if frame_id % (fps // target_fps) != 0:
continue
else:
out_frame_id += 1
pbar.update(1)
if out_frame_id > out_max_frames:
break
frame_pil = Image.fromarray(frame)
frame_pil = center_crop(frame_pil)
pipe_out = pipe(
frame_pil,
match_input_resolution=False,
latents=init_latents
)
if init_latents is None:
init_latents = pipe_out.gaus_noise
processed_frame = pipe.image_processor.visualize_normals( # noqa
pipe_out.prediction
)[0]
processed_frame = np.array(processed_frame)
_processed_frame = imageio.core.util.Array(processed_frame)
writer.append_data(_processed_frame)
yield (
[frame_pil, processed_frame],
None,
)
finally:
if writer is not None:
writer.close()
if reader is not None:
reader.close()
yield (
[frame_pil, processed_frame],
[path_out_vis,]
)
def run_demo_server(pipe):
process_pipe_image = spaces.GPU(functools.partial(process_image, pipe))
process_pipe_video = spaces.GPU(
functools.partial(process_video, pipe), duration=120
)
gradio_theme = gr.themes.Default()
with gr.Blocks(
theme=gradio_theme,
title="Stable Normal Estimation",
css="""
#download {
height: 118px;
}
.slider .inner {
width: 5px;
background: #FFF;
}
.viewport {
aspect-ratio: 4/3;
}
.tabs button.selected {
font-size: 20px !important;
color: crimson !important;
}
h1 {
text-align: center;
display: block;
}
h2 {
text-align: center;
display: block;
}
h3 {
text-align: center;
display: block;
}
.md_feedback li {
margin-bottom: 0px !important;
}
""",
head="""
<script async src="https://www.googletagmanager.com/gtag/js?id=G-1FWSVCGZTG"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag() {dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'G-1FWSVCGZTG');
</script>
""",
) as demo:
gr.Markdown(
"""
# StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal
<p align="center">
<a title="Website" href="https://stable-x.github.io/StableNormal/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
<img src="https://www.obukhov.ai/img/badges/badge-website.svg">
</a>
<a title="arXiv" href="https://arxiv.org/abs/2406.16864" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
<img src="https://www.obukhov.ai/img/badges/badge-pdf.svg">
</a>
<a title="Github" href="https://github.com/Stable-X/StableNormal" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
<img src="https://img.shields.io/github/stars/Stable-X/StableDelight?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="badge-github-stars">
</a>
<a title="Social" href="https://x.com/ychngji6" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
<img src="https://www.obukhov.ai/img/badges/badge-social.svg" alt="social">
</a>
"""
)
with gr.Tabs(elem_classes=["tabs"]):
with gr.Tab("Image"):
with gr.Row():
with gr.Column():
image_input = gr.Image(
label="Input Image",
type="filepath",
)
with gr.Row():
image_submit_btn = gr.Button(
value="Compute Normal", variant="primary"
)
image_reset_btn = gr.Button(value="Reset")
with gr.Column():
image_output_slider = ImageSlider(
label="Normal outputs",
type="filepath",
show_download_button=True,
show_share_button=True,
interactive=False,
elem_classes="slider",
position=0.25,
)
Examples(
fn=process_pipe_image,
examples=sorted([
os.path.join("files", "image", name)
for name in os.listdir(os.path.join("files", "image"))
]),
inputs=[image_input],
outputs=[image_output_slider],
cache_examples=True,
directory_name="examples_image",
)
with gr.Tab("Video"):
with gr.Row():
with gr.Column():
video_input = gr.Video(
label="Input Video",
sources=["upload", "webcam"],
)
with gr.Row():
video_submit_btn = gr.Button(
value="Compute Normal", variant="primary"
)
video_reset_btn = gr.Button(value="Reset")
with gr.Column():
processed_frames = ImageSlider(
label="Realtime Visualization",
type="filepath",
show_download_button=True,
show_share_button=True,
interactive=False,
elem_classes="slider",
position=0.25,
)
video_output_files = gr.Files(
label="Normal outputs",
elem_id="download",
interactive=False,
)
Examples(
fn=process_pipe_video,
examples=sorted([
os.path.join("files", "video", name)
for name in os.listdir(os.path.join("files", "video"))
]),
inputs=[video_input],
outputs=[processed_frames, video_output_files],
directory_name="examples_video",
cache_examples=False,
)
with gr.Tab("Panorama"):
with gr.Column():
gr.Markdown("Coming soon")
with gr.Tab("4K Image"):
with gr.Column():
gr.Markdown("Coming soon")
### Image tab
image_submit_btn.click(
fn=process_image_check,
inputs=image_input,
outputs=None,
preprocess=False,
queue=False,
).success(
fn=process_pipe_image,
inputs=[
image_input,
],
outputs=[image_output_slider],
concurrency_limit=1,
)
image_reset_btn.click(
fn=lambda: (
None,
None,
None,
),
inputs=[],
outputs=[
image_input,
image_output_slider,
],
queue=False,
)
### Video tab
video_submit_btn.click(
fn=process_pipe_video,
inputs=[video_input],
outputs=[processed_frames, video_output_files],
concurrency_limit=1,
)
video_reset_btn.click(
fn=lambda: (None, None, None),
inputs=[],
outputs=[video_input, processed_frames, video_output_files],
concurrency_limit=1,
)
### Server launch
demo.queue(
api_open=False,
).launch(
server_name="0.0.0.0",
server_port=7860,
)
def main():
os.system("pip freeze")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
x_start_pipeline = YOSONormalsPipeline.from_pretrained(
'Stable-X/yoso-normal-v0-3', trust_remote_code=True, variant="fp16", torch_dtype=torch.float16).to(device)
pipe = StableNormalPipeline.from_pretrained('Stable-X/stable-normal-v0-1', trust_remote_code=True,
variant="fp16", torch_dtype=torch.float16,
scheduler=HEURI_DDIMScheduler(prediction_type='sample',
beta_start=0.00085, beta_end=0.0120,
beta_schedule = "scaled_linear"))
pipe.x_start_pipeline = x_start_pipeline
pipe.to(device)
pipe.prior.to(device, torch.float16)
try:
import xformers
pipe.enable_xformers_memory_efficient_attention()
except:
pass # run without xformers
run_demo_server(pipe)
if __name__ == "__main__":
main()