import logging import os import tempfile import time import gradio as gr import numpy as np import rembg import torch from PIL import Image from tsr.system import TSR from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation HF_TOKEN = os.getenv("HF_TOKEN") if torch.cuda.is_available(): device = "cuda:0" else: device = "cpu" model = TSR.from_pretrained( "stabilityai/TripoSR", config_name="config.yaml", weight_name="model.ckpt", token=HF_TOKEN ) model.renderer.set_chunk_size(131072) model.to(device) rembg_session = rembg.new_session() def check_input_image(input_image): if input_image is None: raise gr.Error("No image uploaded!") def preprocess(input_image, do_remove_background, foreground_ratio): def fill_background(image): image = np.array(image).astype(np.float32) / 255.0 image = image[:, :, :3] * image[:, :, 3:4] + (1 - image[:, :, 3:4]) * 0.5 image = Image.fromarray((image * 255.0).astype(np.uint8)) return image if do_remove_background: image = input_image.convert("RGB") image = remove_background(image, rembg_session) image = resize_foreground(image, foreground_ratio) image = fill_background(image) else: image = input_image if image.mode == "RGBA": image = fill_background(image) return image def generate(image): scene_codes = model(image, device=device) mesh = model.extract_mesh(scene_codes)[0] mesh.vertices = to_gradio_3d_orientation(mesh.vertices) mesh_path = tempfile.NamedTemporaryFile(suffix=".obj", delete=False) mesh.export(mesh_path.name) return mesh_path.name with gr.Blocks() as demo: gr.Markdown( """ ## TripoSR Demo [TripoSR](https://github.com/VAST-AI-Research/TripoSR) is a state-of-the-art open-source model for **fast** feedforward 3D reconstruction from a single image, collaboratively developed by [Tripo AI](https://www.tripo3d.ai/) and [Stability AI](https://stability.ai/). """ ) with gr.Row(variant="panel"): with gr.Column(): with gr.Row(): input_image = gr.Image( label="Input Image", image_mode="RGBA", sources="upload", type="pil", elem_id="content_image", ) processed_image = gr.Image(label="Processed Image", interactive=False) with gr.Row(): with gr.Group(): do_remove_background = gr.Checkbox( label="Remove Background", value=True ) foreground_ratio = gr.Slider( label="Foreground Ratio", minimum=0.5, maximum=1.0, value=0.85, step=0.05, ) with gr.Row(): submit = gr.Button("Generate", elem_id="generate", variant="primary") with gr.Column(): with gr.Tab("Model"): output_model = gr.Model3D( label="Output Model", interactive=False, ) gr.Markdown( """ Note: The model shown here will be flipped due to some visualization issues. Please download to get the correct result. """ ) submit.click(fn=check_input_image, inputs=[input_image]).success( fn=preprocess, inputs=[input_image, do_remove_background, foreground_ratio], outputs=[processed_image], ).success( fn=generate, inputs=[processed_image], outputs=[output_model], ) demo.queue(max_size=10) demo.launch()