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app.py

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+import sys
+import os 
+
+os.system("git clone https://github.com/3DTopia/3DTopia")
+sys.path.append("3DTopia")
+
+import os
+import cv2
+import time
+import json
+import torch
+import mcubes
+import trimesh
+import datetime
+import argparse
+import subprocess
+import numpy as np
+import gradio as gr
+from tqdm import tqdm
+import imageio.v2 as imageio
+import pytorch_lightning as pl
+from omegaconf import OmegaConf
+
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.models.diffusion.plms import PLMSSampler
+from ldm.models.diffusion.dpm_solver import DPMSolverSampler
+
+from utility.initialize import instantiate_from_config, get_obj_from_str
+from utility.triplane_renderer.eg3d_renderer import sample_from_planes, generate_planes
+from utility.triplane_renderer.renderer import get_rays, to8b
+from safetensors.torch import load_file
+from huggingface_hub import hf_hub_download
+
+import warnings
+warnings.filterwarnings("ignore", category=UserWarning)
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+def add_text(rgb, caption):
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    # org
+    gap = 10
+    org = (gap, gap)
+    # fontScale
+    fontScale = 0.3
+    # Blue color in BGR
+    color = (255, 0, 0)
+    # Line thickness of 2 px
+    thickness = 1
+    break_caption = []
+    for i in range(len(caption) // 30 + 1):
+        break_caption_i = caption[i*30:(i+1)*30]
+        break_caption.append(break_caption_i)
+    for i, bci in enumerate(break_caption):
+        cv2.putText(rgb, bci, (gap, gap*(i+1)), font, fontScale, color, thickness, cv2.LINE_AA)
+    return rgb
+
+config = "configs/default.yaml"
+local_ckpt = "checkpoints/3dtopia_diffusion_state_dict.ckpt"
+if os.path.exists(local_ckpt):
+    ckpt = local_ckpt
+else:
+    ckpt = hf_hub_download(repo_id="hongfz16/3DTopia", filename="model.safetensors")
+configs = OmegaConf.load(config)
+os.makedirs("tmp", exist_ok=True)
+
+if ckpt.endswith(".ckpt"):
+    model = get_obj_from_str(configs.model["target"]).load_from_checkpoint(ckpt, map_location='cpu', strict=False, **configs.model.params)
+elif ckpt.endswith(".safetensors"):
+    model = get_obj_from_str(configs.model["target"])(**configs.model.params)
+    model_ckpt = load_file(ckpt)
+    model.load_state_dict(model_ckpt)
+else:
+    raise NotImplementedError
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+model = model.to(device)
+sampler = DDIMSampler(model)
+
+img_size = configs.model.params.unet_config.params.image_size
+channels = configs.model.params.unet_config.params.in_channels
+shape = [channels, img_size, img_size * 3]
+
+pose_folder = 'assets/sample_data/pose'
+poses_fname = sorted([os.path.join(pose_folder, f) for f in os.listdir(pose_folder)])
+batch_rays_list = []
+H = 128
+ratio = 512 // H
+for p in poses_fname:
+    c2w = np.loadtxt(p).reshape(4, 4)
+    c2w[:3, 3] *= 2.2
+    c2w = np.array([
+        [1, 0, 0, 0],
+        [0, 0, -1, 0],
+        [0, 1, 0, 0],
+        [0, 0, 0, 1]
+    ]) @ c2w
+
+    k = np.array([
+        [560 / ratio, 0, H * 0.5],
+        [0, 560 / ratio, H * 0.5],
+        [0, 0, 1]
+    ])
+
+    rays_o, rays_d = get_rays(H, H, torch.Tensor(k), torch.Tensor(c2w[:3, :4]))
+    coords = torch.stack(torch.meshgrid(torch.linspace(0, H-1, H), torch.linspace(0, H-1, H), indexing='ij'), -1)
+    coords = torch.reshape(coords, [-1,2]).long()
+    rays_o = rays_o[coords[:, 0], coords[:, 1]]
+    rays_d = rays_d[coords[:, 0], coords[:, 1]]
+    batch_rays = torch.stack([rays_o, rays_d], 0)
+    batch_rays_list.append(batch_rays)
+batch_rays_list = torch.stack(batch_rays_list, 0)
+
+def marching_cube(b, text, global_info):
+    # prepare volumn for marching cube
+    res = 128
+    assert 'decode_res' in global_info
+    decode_res = global_info['decode_res']
+    c_list = torch.linspace(-1.2, 1.2, steps=res)
+    grid_x, grid_y, grid_z = torch.meshgrid(
+        c_list, c_list, c_list, indexing='ij'
+    )
+    coords = torch.stack([grid_x, grid_y, grid_z], -1).to(device)
+    plane_axes = generate_planes()
+    feats = sample_from_planes(
+        plane_axes, decode_res[b:b+1].reshape(1, 3, -1, 256, 256), coords.reshape(1, -1, 3), padding_mode='zeros', box_warp=2.4
+    )
+    fake_dirs = torch.zeros_like(coords)
+    fake_dirs[..., 0] = 1
+    out = model.first_stage_model.triplane_decoder.decoder(feats, fake_dirs)
+    u = out['sigma'].reshape(res, res, res).detach().cpu().numpy()
+    del out
+
+    # marching cube
+    vertices, triangles = mcubes.marching_cubes(u, 10)
+    min_bound = np.array([-1.2, -1.2, -1.2])
+    max_bound = np.array([1.2, 1.2, 1.2])
+    vertices = vertices / (res - 1) * (max_bound - min_bound)[None, :] + min_bound[None, :]
+    pt_vertices = torch.from_numpy(vertices).to(device)
+
+    # extract vertices color
+    res_triplane = 256
+    render_kwargs = {
+        'depth_resolution': 128,
+        'disparity_space_sampling': False,
+        'box_warp': 2.4,
+        'depth_resolution_importance': 128,
+        'clamp_mode': 'softplus',
+        'white_back': True,
+        'det': True
+    }
+    rays_o_list = [
+        np.array([0, 0, 2]),
+        np.array([0, 0, -2]),
+        np.array([0, 2, 0]),
+        np.array([0, -2, 0]),
+        np.array([2, 0, 0]),
+        np.array([-2, 0, 0]),
+    ]
+    rgb_final = None
+    diff_final = None
+    for rays_o in tqdm(rays_o_list):
+        rays_o = torch.from_numpy(rays_o.reshape(1, 3)).repeat(vertices.shape[0], 1).float().to(device)
+        rays_d = pt_vertices.reshape(-1, 3) - rays_o
+        rays_d = rays_d / torch.norm(rays_d, dim=-1).reshape(-1, 1)
+        dist = torch.norm(pt_vertices.reshape(-1, 3) - rays_o, dim=-1).cpu().numpy().reshape(-1)
+
+        render_out = model.first_stage_model.triplane_decoder(
+            decode_res[b:b+1].reshape(1, 3, -1, res_triplane, res_triplane),
+            rays_o.unsqueeze(0), rays_d.unsqueeze(0), render_kwargs,
+            whole_img=False, tvloss=False
+        )
+        rgb = render_out['rgb_marched'].reshape(-1, 3).detach().cpu().numpy()
+        depth = render_out['depth_final'].reshape(-1).detach().cpu().numpy()
+        depth_diff = np.abs(dist - depth)
+
+        if rgb_final is None:
+            rgb_final = rgb.copy()
+            diff_final = depth_diff.copy()
+
+        else:
+            ind = diff_final > depth_diff
+            rgb_final[ind] = rgb[ind]
+            diff_final[ind] = depth_diff[ind]
+
+    # bgr to rgb
+    rgb_final = np.stack([
+        rgb_final[:, 2], rgb_final[:, 1], rgb_final[:, 0]
+    ], -1)
+
+    # export to ply
+    mesh = trimesh.Trimesh(vertices, triangles, vertex_colors=(rgb_final * 255).astype(np.uint8))
+    path = os.path.join('tmp', f"{text.replace(' ', '_')}_{str(datetime.datetime.now()).replace(' ', '_')}.ply")
+    trimesh.exchange.export.export_mesh(mesh, path, file_type='ply')
+
+    del vertices, triangles, rgb_final
+    torch.cuda.empty_cache()
+
+    return path
+
+def infer(prompt, samples, steps, scale, seed, global_info):
+    prompt = prompt.replace('/', '')
+    pl.seed_everything(seed)
+    batch_size = samples
+    with torch.no_grad():
+        noise = None
+        c = model.get_learned_conditioning([prompt])
+        unconditional_c = torch.zeros_like(c)
+        sample, _ = sampler.sample(
+            S=steps,
+            batch_size=batch_size,
+            shape=shape,
+            verbose=False,
+            x_T = noise,
+            conditioning = c.repeat(batch_size, 1, 1),
+            unconditional_guidance_scale=scale,
+            unconditional_conditioning=unconditional_c.repeat(batch_size, 1, 1)
+        )
+        decode_res = model.decode_first_stage(sample)
+
+        big_video_list = []
+
+        global_info['decode_res'] = decode_res
+
+        for b in range(batch_size):
+            def render_img(v):
+                rgb_sample, _ = model.first_stage_model.render_triplane_eg3d_decoder(
+                    decode_res[b:b+1], batch_rays_list[v:v+1].to(device), torch.zeros(1, H, H, 3).to(device),
+                )
+                rgb_sample = to8b(rgb_sample.detach().cpu().numpy())[0]
+                rgb_sample = np.stack(
+                    [rgb_sample[..., 2], rgb_sample[..., 1], rgb_sample[..., 0]], -1
+                )
+                rgb_sample = add_text(rgb_sample, str(b))
+                return rgb_sample
+
+            view_num = len(batch_rays_list)
+            video_list = []
+            for v in tqdm(range(view_num//8*3, view_num//8*5, 2)):
+                rgb_sample = render_img(v)
+                video_list.append(rgb_sample)
+            big_video_list.append(video_list)
+        # if batch_size == 2:
+        #     cat_video_list = [
+        #         np.concatenate([big_video_list[j][i] for j in range(len(big_video_list))], 1) \
+        #         for i in range(len(big_video_list[0]))
+        #     ]
+        # elif batch_size > 2:
+        #     if batch_size == 3:
+        #         big_video_list.append(
+        #             [np.zeros_like(f) for f in big_video_list[0]]
+        #         )
+        #     cat_video_list = [
+        #         np.concatenate([
+        #             np.concatenate([big_video_list[0][i], big_video_list[1][i]], 1),
+        #             np.concatenate([big_video_list[2][i], big_video_list[3][i]], 1),
+        #         ], 0) \
+        #         for i in range(len(big_video_list[0]))
+        #     ]
+        # else:
+        #     cat_video_list = big_video_list[0]
+
+        for _ in range(4 - batch_size):
+            big_video_list.append(
+                [np.zeros_like(f) + 255 for f in big_video_list[0]]
+            )
+        cat_video_list = [
+            np.concatenate([
+                np.concatenate([big_video_list[0][i], big_video_list[1][i]], 1),
+                np.concatenate([big_video_list[2][i], big_video_list[3][i]], 1),
+            ], 0) \
+            for i in range(len(big_video_list[0]))
+        ]
+
+        path = f"tmp/{prompt.replace(' ', '_')}_{str(datetime.datetime.now()).replace(' ', '_')}.mp4"
+        imageio.mimwrite(path, np.stack(cat_video_list, 0))
+
+    return global_info, path
+
+def infer_stage2(prompt, selection, seed, global_info):
+    prompt = prompt.replace('/', '')
+    mesh_path = marching_cube(int(selection), prompt, global_info)
+    mesh_name = mesh_path.split('/')[-1][:-4]
+    
+    if2_cmd = f"threefiner if2 --mesh {mesh_path} --prompt \"{prompt}\" --outdir tmp --save {mesh_name}_if2.glb --text_dir --front_dir=-y"
+    print(if2_cmd)
+    # os.system(if2_cmd)
+    subprocess.Popen(if2_cmd, shell=True).wait()
+    torch.cuda.empty_cache()
+
+    video_path = f"tmp/{prompt.replace(' ', '_')}_{str(datetime.datetime.now()).replace(' ', '_')}.mp4"
+    render_cmd = f"kire {os.path.join('tmp', mesh_name + '_if2.glb')} --save_video {video_path} --wogui --force_cuda_rast --H 256 --W 256"
+    print(render_cmd)
+    # os.system(render_cmd)
+    subprocess.Popen(render_cmd, shell=True).wait()
+    torch.cuda.empty_cache()
+
+    return video_path, os.path.join('tmp', mesh_name + '_if2.glb')
+
+markdown=f'''
+  # 3DTopia
+  A two-stage text-to-3D generation model. The first stage uses diffusion model to quickly generate candidates. The second stage refines the assets chosen from the first stage.
+
+  ### Usage:
+  First enter prompt for a 3D object, hit "Generate 3D". Then choose one candidate from the dropdown options for the second stage refinement and hit "Start Refinement". The final mesh can be downloaded from the bottom right box.
+  
+  ### Runtime:
+  The first stage takes 30s if generating 4 samples. The second stage takes roughly 3 min.
+
+  ### Useful links:
+  [Github Repo](https://github.com/3DTopia/3DTopia)
+'''
+
+block = gr.Blocks()
+
+with block:
+    global_info = gr.State(dict())
+    gr.Markdown(markdown)
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                text = gr.Textbox(
+                    label = "Enter your prompt",
+                    max_lines = 1,
+                    placeholder = "Enter your prompt",
+                    container = False,
+                )
+                btn = gr.Button("Generate 3D")
+            gallery = gr.Video(height=512)
+            # advanced_button = gr.Button("Advanced Options", elem_id="advanced-btn")
+            with gr.Row(elem_id="advanced-options"):
+                with gr.Tab("Advanced options"):
+                    samples = gr.Slider(label="Number of Samples", minimum=1, maximum=4, value=4, step=1)
+                    steps = gr.Slider(label="Steps", minimum=1, maximum=500, value=50, step=1)
+                    scale = gr.Slider(
+                        label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
+                    )
+                    seed = gr.Slider(
+                        label="Seed",
+                        minimum=0,
+                        maximum=2147483647,
+                        step=1,
+                        randomize=True,
+                    )
+            gr.on([text.submit, btn.click], infer, inputs=[text, samples, steps, scale, seed, global_info], outputs=[global_info, gallery])
+            # advanced_button.click(
+            #     None,
+            #     [],
+            #     text,
+            # )
+        with gr.Column():
+            with gr.Row():
+                dropdown = gr.Dropdown(
+                    ['0', '1', '2', '3'], label="Choose a candidate for stage2", value='0'
+                )
+                btn_stage2 = gr.Button("Start Refinement")
+            gallery = gr.Video(height=512)
+            download = gr.File(label="Download mesh", file_count="single", height=100)
+            gr.on([btn_stage2.click], infer_stage2, inputs=[text, dropdown, seed, global_info], outputs=[gallery, download])
+
+block.launch(share=True)

requirements.txt

@@ -0,0 +1,57 @@
+--extra-index-url https://download.pytorch.org/whl/cu113
+torch==1.12.0+cu113
+torchvision==0.13.0+cu113
+torchaudio==0.12.0
+numpy
+tqdm
+yaml
+git+https://github.com/openai/CLIP.git
+einops==1.2.0
+huggingface-hub==0.16.4
+imageio==2.31.0
+imageio-ffmpeg==0.4.8
+importlib-resources==6.1.0
+ipdb==0.13.13
+ipython==8.12.2
+jedi==0.19.0
+kiwisolver==1.4.5
+kornia==0.6.0
+lpips==0.1.4
+matplotlib==3.7.3
+matplotlib-inline==0.1.6
+omegaconf==2.3.0
+open-clip-torch==2.20.0
+opencv-python==4.7.0.72
+parso==0.8.3
+pathtools==0.1.2
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow==9.5.0
+prompt-toolkit==3.0.39
+protobuf==3.20.3
+psutil==5.9.5
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pygments==2.16.1
+pymcubes==0.1.4
+pyparsing==3.1.1
+pytorch-fid==0.3.0
+pytorch-msssim==1.0.0
+regex==2023.6.3
+safetensors==0.3.3
+scipy==1.10.1
+sentencepiece==0.1.99
+sentry-sdk==1.25.0
+setproctitle==1.3.2
+smmap==5.0.0
+stack-data==0.6.2
+timm==0.9.7
+tokenizers==0.12.1
+tomli==2.0.1
+traitlets==5.9.0
+transformers
+trimesh==4.0.2
+vit-pytorch==1.2.2
+wandb==0.15.3
+wcwidth==0.2.6
+zipp==3.17.0