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import gradio as gr
import spaces
from gradio_litmodel3d import LitModel3D
import os
from typing import *
import numpy as np
import imageio
import uuid
from PIL import Image
from trellis.pipelines import TrellisImageTo3DPipeline
from trellis.utils import render_utils, postprocessing_utils
def preprocess_image(image: Image.Image) -> Image.Image:
"""
Preprocess the input image.
Args:
image (Image.Image): The input image.
Returns:
Image.Image: The preprocessed image.
"""
return pipeline.preprocess_image(image)
@spaces.GPU
def image_to_3d(image: Image.Image) -> Tuple[dict, str]:
"""
Convert an image to a 3D model.
Args:
image (Image.Image): The input image.
Returns:
dict: The information of the generated 3D model.
str: The path to the video of the 3D model.
"""
outputs = pipeline(image, formats=["gaussian", "mesh"], preprocess_image=False)
video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
model_id = uuid.uuid4()
video_path = f"/tmp/Trellis-demo/{model_id}.mp4"
os.makedirs(os.path.dirname(video_path), exist_ok=True)
imageio.mimsave(video_path, video, fps=15)
model = {'gaussian': outputs['gaussian'][0], 'mesh': outputs['mesh'][0], 'model_id': model_id}
return model, video_path
@spaces.GPU
def extract_glb(model: dict, mesh_simplify: float, texture_size: int) -> Tuple[str, str]:
"""
Extract a GLB file from the 3D model.
Args:
model (dict): The generated 3D model.
mesh_simplify (float): The mesh simplification factor.
texture_size (int): The texture resolution.
Returns:
str: The path to the extracted GLB file.
"""
glb = postprocessing_utils.to_glb(model['gaussian'], model['mesh'], simplify=mesh_simplify, texture_size=texture_size)
glb_path = f"/tmp/Trellis-demo/{model['model_id']}.glb"
glb.export(glb_path)
return glb_path, glb_path
def activate_button() -> gr.Button:
return gr.Button(interactive=True)
def deactivate_button() -> gr.Button:
return gr.Button(interactive=False)
with gr.Blocks() as demo:
gr.Markdown("""
## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
* Upload an image and click "Generate" to create a 3D asset. If the image has alpha channel, it be used as the mask. Otherwise, we use `rembg` to remove the background.
* If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
""")
with gr.Row():
with gr.Column():
image_prompt = gr.Image(label="Image Prompt", image_mode="RGBA", type="pil", height=300)
generate_btn = gr.Button("Generate")
gr.Markdown("GLB Extraction Parameters:")
mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01)
texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
extract_glb_btn = gr.Button("Extract GLB", interactive=False)
with gr.Column():
video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
model_output = LitModel3D(label="Extracted GLB", exposure=20.0, height=300)
download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
# Example images at the bottom of the page
with gr.Row():
examples = gr.Examples(
examples=[
f'assets/example_image/{image}'
for image in os.listdir("assets/example_image")
],
inputs=[image_prompt],
fn=lambda image: preprocess_image(image),
outputs=[image_prompt],
run_on_click=True,
examples_per_page=64,
)
model = gr.State()
# Handlers
image_prompt.upload(
preprocess_image,
inputs=[image_prompt],
outputs=[image_prompt],
)
generate_btn.click(
image_to_3d,
inputs=[image_prompt],
outputs=[model, video_output],
).then(
activate_button,
outputs=[extract_glb_btn],
)
video_output.clear(
deactivate_button,
outputs=[extract_glb_btn],
)
extract_glb_btn.click(
extract_glb,
inputs=[model, mesh_simplify, texture_size],
outputs=[model_output, download_glb],
).then(
activate_button,
outputs=[download_glb],
)
model_output.clear(
deactivate_button,
outputs=[download_glb],
)
# Launch the Gradio app
if __name__ == "__main__":
pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
pipeline.cuda()
demo.launch()
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