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Running
on
Zero
| import spaces | |
| import os | |
| import gradio as gr | |
| from PIL import Image | |
| from pytorch3d.structures import Meshes | |
| from gradio_app.utils import clean_up | |
| from gradio_app.custom_models.mvimg_prediction import run_mvprediction | |
| from gradio_app.custom_models.normal_prediction import predict_normals | |
| from scripts.refine_lr_to_sr import run_sr_fast | |
| from scripts.utils import save_glb_and_video | |
| from scripts.multiview_inference import geo_reconstruct | |
| def generate3dv2(preview_img, input_processing, seed, render_video=True, do_refine=True, expansion_weight=0.1, init_type="std"): | |
| if preview_img is None: | |
| raise gr.Error("preview_img is none") | |
| if isinstance(preview_img, str): | |
| preview_img = Image.open(preview_img) | |
| if preview_img.size[0] <= 512: | |
| preview_img = run_sr_fast([preview_img])[0] | |
| rgb_pils, front_pil = run_mvprediction(preview_img, remove_bg=input_processing, seed=int(seed)) # 6s | |
| new_meshes = geo_reconstruct(rgb_pils, None, front_pil, do_refine=do_refine, predict_normal=True, expansion_weight=expansion_weight, init_type=init_type) | |
| vertices = new_meshes.verts_packed() | |
| vertices = vertices / 2 * 1.35 | |
| vertices[..., [0, 2]] = - vertices[..., [0, 2]] | |
| new_meshes = Meshes(verts=[vertices], faces=new_meshes.faces_list(), textures=new_meshes.textures) | |
| ret_mesh, video = save_glb_and_video("/tmp/gradio/generated", new_meshes, with_timestamp=True, dist=3.5, fov_in_degrees=2 / 1.35, cam_type="ortho", export_video=render_video) | |
| return ret_mesh, video | |
| ####################################### | |
| def create_ui(concurrency_id="wkl"): | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| input_image = gr.Image(type='pil', image_mode='RGBA', label='Frontview') | |
| example_folder = os.path.join(os.path.dirname(__file__), "./examples") | |
| example_fns = sorted([os.path.join(example_folder, example) for example in os.listdir(example_folder)]) | |
| gr.Examples( | |
| examples=example_fns, | |
| inputs=[input_image], | |
| cache_examples=False, | |
| label='Examples', | |
| examples_per_page=12 | |
| ) | |
| with gr.Column(scale=3): | |
| # export mesh display | |
| output_mesh = gr.Model3D(value=None, label="Mesh Model", show_label=True, height=320) | |
| output_video = gr.Video(label="Preview", show_label=True, show_share_button=True, height=320, visible=False) | |
| input_processing = gr.Checkbox( | |
| value=True, | |
| label='Remove Background', | |
| visible=True, | |
| ) | |
| do_refine = gr.Checkbox(value=True, label="Refine Multiview Details", visible=False) | |
| expansion_weight = gr.Slider(minimum=-1., maximum=1.0, value=0.1, step=0.1, label="Expansion Weight", visible=False) | |
| init_type = gr.Dropdown(choices=["std", "thin"], label="Mesh Initialization", value="std", visible=False) | |
| setable_seed = gr.Slider(-1, 1000000000, -1, step=1, visible=True, label="Seed") | |
| render_video = gr.Checkbox(value=False, visible=False, label="generate video") | |
| fullrunv2_btn = gr.Button('Generate 3D', interactive=True) | |
| fullrunv2_btn.click( | |
| fn = generate3dv2, | |
| inputs=[input_image, input_processing, setable_seed, render_video, do_refine, expansion_weight, init_type], | |
| outputs=[output_mesh, output_video], | |
| concurrency_id=concurrency_id, | |
| api_name="generate3dv2", | |
| ).success(clean_up, api_name=False) | |
| return input_image | |