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Delete gradio_app.py
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gradio_app.py
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# -*- coding: utf-8 -*-
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import os
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import time
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from collections import OrderedDict
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from PIL import Image
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import torch
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import trimesh
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from typing import Optional, List
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from einops import repeat, rearrange
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import numpy as np
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from michelangelo.models.tsal.tsal_base import Latent2MeshOutput
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from michelangelo.utils.misc import get_config_from_file, instantiate_from_config
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from michelangelo.utils.visualizers.pythreejs_viewer import PyThreeJSViewer
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from michelangelo.utils.visualizers import html_util
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import gradio as gr
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gradio_cached_dir = "./gradio_cached_dir"
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os.makedirs(gradio_cached_dir, exist_ok=True)
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save_mesh = False
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state = ""
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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box_v = 1.1
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viewer = PyThreeJSViewer(settings={}, render_mode="WEBSITE")
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image_model_config_dict = OrderedDict({
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"ASLDM-256-obj": {
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"config": "./configs/image_cond_diffuser_asl/image-ASLDM-256.yaml",
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"ckpt_path": "./checkpoints/image_cond_diffuser_asl/image-ASLDM-256.ckpt",
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},
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})
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text_model_config_dict = OrderedDict({
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"ASLDM-256": {
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"config": "./configs/text_cond_diffuser_asl/text-ASLDM-256.yaml",
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"ckpt_path": "./checkpoints/text_cond_diffuser_asl/text-ASLDM-256.ckpt",
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},
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})
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class InferenceModel(object):
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model = None
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name = ""
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text2mesh_model = InferenceModel()
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image2mesh_model = InferenceModel()
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def set_state(s):
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global state
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state = s
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print(s)
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def output_to_html_frame(mesh_outputs: List[Latent2MeshOutput], bbox_size: float,
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image: Optional[np.ndarray] = None,
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html_frame: bool = False):
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global viewer
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for i in range(len(mesh_outputs)):
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mesh = mesh_outputs[i]
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if mesh is None:
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continue
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mesh_v = mesh.mesh_v.copy()
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mesh_v[:, 0] += i * np.max(bbox_size)
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mesh_v[:, 2] += np.max(bbox_size)
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viewer.add_mesh(mesh_v, mesh.mesh_f)
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mesh_tag = viewer.to_html(html_frame=False)
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if image is not None:
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image_tag = html_util.to_image_embed_tag(image)
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frame = f"""
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<table border = "1">
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<tr>
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<td>{image_tag}</td>
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<td>{mesh_tag}</td>
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</tr>
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</table>
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"""
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else:
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frame = mesh_tag
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if html_frame:
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frame = html_util.to_html_frame(frame)
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viewer.reset()
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return frame
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def load_model(model_name: str, model_config_dict: dict, inference_model: InferenceModel):
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global device
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if inference_model.name == model_name:
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model = inference_model.model
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else:
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assert model_name in model_config_dict
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if inference_model.model is not None:
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del inference_model.model
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config_ckpt_path = model_config_dict[model_name]
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model_config = get_config_from_file(config_ckpt_path["config"])
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if hasattr(model_config, "model"):
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model_config = model_config.model
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model = instantiate_from_config(model_config, ckpt_path=config_ckpt_path["ckpt_path"])
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model = model.to(device)
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model = model.eval()
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inference_model.model = model
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inference_model.name = model_name
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return model
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def prepare_img(image: np.ndarray):
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image_pt = torch.tensor(image).float()
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image_pt = image_pt / 255 * 2 - 1
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image_pt = rearrange(image_pt, "h w c -> c h w")
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return image_pt
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def prepare_model_viewer(fp):
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content = f"""
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<head>
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<script
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type="module" src="https://ajax.googleapis.com/ajax/libs/model-viewer/3.1.1/model-viewer.min.js">
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</script>
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</head>
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<body>
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<model-viewer
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style="height: 150px; width: 150px;"
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rotation-per-second="10deg"
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id="t1"
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src="file/gradio_cached_dir/{fp}"
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environment-image="neutral"
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camera-target="0m 0m 0m"
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orientation="0deg 90deg 170deg"
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shadow-intensity="1"
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ar:true
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auto-rotate
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camera-controls>
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</model-viewer>
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</body>
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"""
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return content
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def prepare_html_frame(content):
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frame = f"""
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<html>
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<body>
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{content}
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</body>
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</html>
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"""
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return frame
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def prepare_html_body(content):
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frame = f"""
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<body>
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{content}
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</body>
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"""
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return frame
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def post_process_mesh_outputs(mesh_outputs):
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# html_frame = output_to_html_frame(mesh_outputs, 2 * box_v, image=None, html_frame=True)
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html_content = output_to_html_frame(mesh_outputs, 2 * box_v, image=None, html_frame=False)
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html_frame = prepare_html_frame(html_content)
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# filename = f"{time.time()}.html"
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filename = f"text-256-{time.time()}.html"
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html_filepath = os.path.join(gradio_cached_dir, filename)
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with open(html_filepath, "w") as writer:
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writer.write(html_frame)
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'''
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Bug: The iframe tag does not work in Gradio.
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The chrome returns "No resource with given URL found"
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Solutions:
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https://github.com/gradio-app/gradio/issues/884
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Due to the security bitches, the server can only find files parallel to the gradio_app.py.
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The path has format "file/TARGET_FILE_PATH"
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'''
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iframe_tag = f'<iframe src="file/gradio_cached_dir/{filename}" width="600%" height="400" frameborder="0"></iframe>'
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filelist = []
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filenames = []
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for i, mesh in enumerate(mesh_outputs):
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mesh.mesh_f = mesh.mesh_f[:, ::-1]
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mesh_output = trimesh.Trimesh(mesh.mesh_v, mesh.mesh_f)
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name = str(i) + "_out_mesh.obj"
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filepath = gradio_cached_dir + "/" + name
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mesh_output.export(filepath, include_normals=True)
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filelist.append(filepath)
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filenames.append(name)
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filelist.append(html_filepath)
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return iframe_tag, filelist
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def image2mesh(image: np.ndarray,
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model_name: str = "subsp+pk_asl_perceiver=01_01_udt=03",
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num_samples: int = 4,
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guidance_scale: int = 7.5,
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octree_depth: int = 7):
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global device, gradio_cached_dir, image_model_config_dict, box_v
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# load model
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model = load_model(model_name, image_model_config_dict, image2mesh_model)
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# prepare image inputs
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image_pt = prepare_img(image)
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image_pt = repeat(image_pt, "c h w -> b c h w", b=num_samples)
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sample_inputs = {
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"image": image_pt
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}
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mesh_outputs = model.sample(
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sample_inputs,
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sample_times=1,
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guidance_scale=guidance_scale,
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return_intermediates=False,
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bounds=[-box_v, -box_v, -box_v, box_v, box_v, box_v],
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octree_depth=octree_depth,
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)[0]
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iframe_tag, filelist = post_process_mesh_outputs(mesh_outputs)
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return iframe_tag, gr.update(value=filelist, visible=True)
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def text2mesh(text: str,
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model_name: str = "subsp+pk_asl_perceiver=01_01_udt=03",
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num_samples: int = 4,
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guidance_scale: int = 7.5,
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octree_depth: int = 7):
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global device, gradio_cached_dir, text_model_config_dict, text2mesh_model, box_v
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# load model
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model = load_model(model_name, text_model_config_dict, text2mesh_model)
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# prepare text inputs
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sample_inputs = {
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"text": [text] * num_samples
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}
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mesh_outputs = model.sample(
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sample_inputs,
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sample_times=1,
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guidance_scale=guidance_scale,
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return_intermediates=False,
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bounds=[-box_v, -box_v, -box_v, box_v, box_v, box_v],
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octree_depth=octree_depth,
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)[0]
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iframe_tag, filelist = post_process_mesh_outputs(mesh_outputs)
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return iframe_tag, gr.update(value=filelist, visible=True)
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example_dir = './gradio_cached_dir/example/img_example'
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first_page_items = [
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'alita.jpg',
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'burger.jpg'
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'loopy.jpg'
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'building.jpg',
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'mario.jpg',
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'car.jpg',
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'airplane.jpg',
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'bag.jpg',
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'bench.jpg',
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'ship.jpg'
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]
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raw_example_items = [
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# (os.path.join(example_dir, x), x)
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os.path.join(example_dir, x)
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for x in os.listdir(example_dir)
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if x.endswith(('.jpg', '.png'))
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]
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example_items = [x for x in raw_example_items if os.path.basename(x) in first_page_items] + [x for x in raw_example_items if os.path.basename(x) not in first_page_items]
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example_text = [
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["A 3D model of a car; Audi A6."],
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["A 3D model of police car; Highway Patrol Charger"]
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],
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def set_cache(data: gr.SelectData):
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img_name = os.path.basename(example_items[data.index])
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return os.path.join(example_dir, img_name), os.path.join(img_name)
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def disable_cache():
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return ""
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with gr.Blocks() as app:
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gr.Markdown("# Michelangelo")
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gr.Markdown("## [Github](https://github.com/NeuralCarver/Michelangelo) | [Arxiv](https://arxiv.org/abs/2306.17115) | [Project Page](https://neuralcarver.github.io/michelangelo/)")
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gr.Markdown("Michelangelo is a conditional 3D shape generation system that trains based on the shape-image-text aligned latent representation.")
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gr.Markdown("### Hint:")
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gr.Markdown("1. We provide two APIs: Image-conditioned generation and Text-conditioned generation")
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gr.Markdown("2. Note that the Image-conditioned model is trained on multiple 3D datasets like ShapeNet and Objaverse")
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gr.Markdown("3. We provide some examples for you to try. You can also upload images or text as input.")
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gr.Markdown("4. Welcome to share your amazing results with us, and thanks for your interest in our work!")
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with gr.Row():
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with gr.Column():
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with gr.Tab("Image to 3D"):
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img = gr.Image(label="Image")
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gr.Markdown("For the best results, we suggest that the images uploaded meet the following three criteria: 1. The object is positioned at the center of the image, 2. The image size is square, and 3. The background is relatively clean.")
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btn_generate_img2obj = gr.Button(value="Generate")
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with gr.Accordion("Advanced settings", open=False):
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image_dropdown_models = gr.Dropdown(label="Model", value="ASLDM-256-obj",choices=list(image_model_config_dict.keys()))
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num_samples = gr.Slider(label="samples", value=4, minimum=1, maximum=8, step=1)
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guidance_scale = gr.Slider(label="Guidance scale", value=7.5, minimum=3.0, maximum=10.0, step=0.1)
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octree_depth = gr.Slider(label="Octree Depth (for 3D model)", value=7, minimum=4, maximum=8, step=1)
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cache_dir = gr.Textbox(value="", visible=False)
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examples = gr.Gallery(label='Examples', value=example_items, elem_id="gallery", allow_preview=False, columns=[4], object_fit="contain")
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with gr.Tab("Text to 3D"):
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prompt = gr.Textbox(label="Prompt", placeholder="A 3D model of motorcar; Porche Cayenne Turbo.")
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gr.Markdown("For the best results, we suggest that the prompt follows 'A 3D model of CATEGORY; DESCRIPTION'. For example, A 3D model of motorcar; Porche Cayenne Turbo.")
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btn_generate_txt2obj = gr.Button(value="Generate")
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with gr.Accordion("Advanced settings", open=False):
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text_dropdown_models = gr.Dropdown(label="Model", value="ASLDM-256",choices=list(text_model_config_dict.keys()))
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num_samples = gr.Slider(label="samples", value=4, minimum=1, maximum=8, step=1)
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guidance_scale = gr.Slider(label="Guidance scale", value=7.5, minimum=3.0, maximum=10.0, step=0.1)
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octree_depth = gr.Slider(label="Octree Depth (for 3D model)", value=7, minimum=4, maximum=8, step=1)
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gr.Markdown("#### Examples:")
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gr.Markdown("1. A 3D model of a coupe; Audi A6.")
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gr.Markdown("2. A 3D model of a motorcar; Hummer H2 SUT.")
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gr.Markdown("3. A 3D model of an airplane; Airbus.")
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gr.Markdown("4. A 3D model of a fighter aircraft; Attack Fighter.")
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gr.Markdown("5. A 3D model of a chair; Simple Wooden Chair.")
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gr.Markdown("6. A 3D model of a laptop computer; Dell Laptop.")
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gr.Markdown("7. A 3D model of a lamp; ceiling light.")
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gr.Markdown("8. A 3D model of a rifle; AK47.")
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gr.Markdown("9. A 3D model of a knife; Sword.")
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gr.Markdown("10. A 3D model of a vase; Plant in pot.")
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with gr.Column():
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model_3d = gr.HTML()
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file_out = gr.File(label="Files", visible=False)
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outputs = [model_3d, file_out]
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img.upload(disable_cache, outputs=cache_dir)
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examples.select(set_cache, outputs=[img, cache_dir])
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print(f'line:404: {cache_dir}')
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btn_generate_img2obj.click(image2mesh, inputs=[img, image_dropdown_models, num_samples,
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guidance_scale, octree_depth],
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outputs=outputs, api_name="generate_img2obj")
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btn_generate_txt2obj.click(text2mesh, inputs=[prompt, text_dropdown_models, num_samples,
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guidance_scale, octree_depth],
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outputs=outputs, api_name="generate_txt2obj")
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app.launch(server_name="0.0.0.0", server_port=8008, share=False)
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