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 functools import partial from tsr.system import TSR from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation import argparse 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", ) # adjust the chunk size to balance between speed and memory usage model.renderer.set_chunk_size(8192) 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, mc_resolution, formats=["obj", "glb"]): print(image.shape, image.min(), image.max()) scene_codes = model(image, device=device) mesh = model.extract_mesh(scene_codes, resolution=mc_resolution)[0] mesh = to_gradio_3d_orientation(mesh) rv = [] for format in formats: mesh_path = tempfile.NamedTemporaryFile(suffix=f".{format}", delete=False) mesh.export(mesh_path.name) rv.append(mesh_path.name) return rv def run_example(image_pil): preprocessed = preprocess(image_pil, False, 0.9) mesh_name_obj, mesh_name_glb = generate(preprocessed, 256, ["obj", "glb"]) return preprocessed, mesh_name_obj, mesh_name_glb with gr.Blocks(title="TripoSR") as interface: 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/). **Tips:** 1. If you find the result is unsatisfied, please try to change the foreground ratio. It might improve the results. 2. You can disable "Remove Background" for the provided examples since they have been already preprocessed. 3. Otherwise, please disable "Remove Background" option only if your input image is RGBA with transparent background, image contents are centered and occupy more than 70% of image width or height. """ ) 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, ) mc_resolution = gr.Slider( label="Marching Cubes Resolution", minimum=32, maximum=320, value=256, step=32 ) with gr.Row(): submit = gr.Button("Generate", elem_id="generate", variant="primary") with gr.Column(): with gr.Tab("OBJ"): output_model_obj = gr.Model3D( label="Output Model (OBJ Format)", interactive=False, ) gr.Markdown("Note: The model shown here is flipped. Download to get correct results.") with gr.Tab("GLB"): output_model_glb = gr.Model3D( label="Output Model (GLB Format)", interactive=False, ) gr.Markdown("Note: The model shown here has a darker appearance. Download to get correct results.") with gr.Row(variant="panel"): gr.Examples( examples=[ "examples/hamburger.png", "examples/poly_fox.png", "examples/robot.png", "examples/teapot.png", "examples/tiger_girl.png", "examples/horse.png", "examples/flamingo.png", "examples/unicorn.png", "examples/chair.png", "examples/iso_house.png", "examples/marble.png", "examples/police_woman.png", "examples/captured_p.png", ], inputs=[input_image], outputs=[processed_image, output_model_obj, output_model_glb], cache_examples=False, fn=partial(run_example), label="Examples", examples_per_page=20, ) 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, mc_resolution], outputs=[output_model_obj, output_model_glb], ) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--username', type=str, default=None, help='Username for authentication') parser.add_argument('--password', type=str, default=None, help='Password for authentication') parser.add_argument('--port', type=int, default=7860, help='Port to run the server listener on') parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests") parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site") parser.add_argument("--queuesize", type=int, default=1, help="launch gradio queue max_size") args = parser.parse_args() interface.queue(max_size=args.queuesize) interface.launch( auth=(args.username, args.password) if (args.username and args.password) else None, share=args.share, server_name="0.0.0.0" if args.listen else None, server_port=args.port )