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 HF_TOKEN = os.getenv("HF_TOKEN") HEADER = """ # TripoSR Demo
[Tech Report pdf]
**TripoSR** is a state-of-the-art open-source model for **fast** feedforward 3D reconstruction from a single image, developed in collaboration between [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. """ 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 = to_gradio_3d_orientation(mesh) mesh_path = tempfile.NamedTemporaryFile(suffix=".obj", delete=False) mesh.export(mesh_path.name) return mesh_path.name def run_example(image_pil): preprocessed = preprocess(image_pil, False, 0.9) mesh_name = generate(preprocessed) return preprocessed, mesh_name with gr.Blocks() as demo: gr.Markdown(HEADER) 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, ) with gr.Row(variant="panel"): gr.Examples( examples=[ os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples")) ], inputs=[input_image], outputs=[processed_image, output_model], cache_examples=True, 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], outputs=[output_model], ) demo.queue(max_size=10) demo.launch()