Update app.py

app.py

@@ -21,6 +21,261 @@ from segment_anything import sam_model_registry
 import easyocr
 import tts 
 
+###############################################################################
+############# this part is for 3D generate #############
+###############################################################################
+
+
+
+import spaces
+
+import os
+import imageio
+import numpy as np
+import torch
+import rembg
+from PIL import Image
+from torchvision.transforms import v2
+from pytorch_lightning import seed_everything
+from omegaconf import OmegaConf
+from einops import rearrange, repeat
+from tqdm import tqdm
+from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
+
+from src.utils.train_util import instantiate_from_config
+from src.utils.camera_util import (
+    FOV_to_intrinsics, 
+    get_zero123plus_input_cameras,
+    get_circular_camera_poses,
+)
+from src.utils.mesh_util import save_obj, save_glb
+from src.utils.infer_util import remove_background, resize_foreground, images_to_video
+
+import tempfile
+from functools import partial
+
+from huggingface_hub import hf_hub_download
+
+
+
+
+def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False):
+    """
+    Get the rendering camera parameters.
+    """
+    c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation)
+    if is_flexicubes:
+        cameras = torch.linalg.inv(c2ws)
+        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1)
+    else:
+        extrinsics = c2ws.flatten(-2)
+        intrinsics = FOV_to_intrinsics(50.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2)
+        cameras = torch.cat([extrinsics, intrinsics], dim=-1)
+        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1)
+    return cameras
+
+
+def images_to_video(images, output_path, fps=30):
+    # images: (N, C, H, W)
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    frames = []
+    for i in range(images.shape[0]):
+        frame = (images[i].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8).clip(0, 255)
+        assert frame.shape[0] == images.shape[2] and frame.shape[1] == images.shape[3], \
+            f"Frame shape mismatch: {frame.shape} vs {images.shape}"
+        assert frame.min() >= 0 and frame.max() <= 255, \
+            f"Frame value out of range: {frame.min()} ~ {frame.max()}"
+        frames.append(frame)
+    imageio.mimwrite(output_path, np.stack(frames), fps=fps, codec='h264')
+
+
+###############################################################################
+# Configuration.
+###############################################################################
+
+import shutil
+
+def find_cuda():
+    # Check if CUDA_HOME or CUDA_PATH environment variables are set
+    cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')
+
+    if cuda_home and os.path.exists(cuda_home):
+        return cuda_home
+
+    # Search for the nvcc executable in the system's PATH
+    nvcc_path = shutil.which('nvcc')
+
+    if nvcc_path:
+        # Remove the 'bin/nvcc' part to get the CUDA installation path
+        cuda_path = os.path.dirname(os.path.dirname(nvcc_path))
+        return cuda_path
+
+    return None
+
+cuda_path = find_cuda()
+
+if cuda_path:
+    print(f"CUDA installation found at: {cuda_path}")
+else:
+    print("CUDA installation not found")
+
+config_path = 'configs/instant-mesh-large.yaml'
+config = OmegaConf.load(config_path)
+config_name = os.path.basename(config_path).replace('.yaml', '')
+model_config = config.model_config
+infer_config = config.infer_config
+
+IS_FLEXICUBES = True if config_name.startswith('instant-mesh') else False
+
+device = torch.device('cuda')
+
+# load diffusion model
+print('Loading diffusion model ...')
+pipeline = DiffusionPipeline.from_pretrained(
+    "sudo-ai/zero123plus-v1.2", 
+    custom_pipeline="zero123plus",
+    torch_dtype=torch.float16,
+)
+pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
+    pipeline.scheduler.config, timestep_spacing='trailing'
+)
+
+# load custom white-background UNet
+unet_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="diffusion_pytorch_model.bin", repo_type="model")
+state_dict = torch.load(unet_ckpt_path, map_location='cpu')
+pipeline.unet.load_state_dict(state_dict, strict=True)
+
+pipeline = pipeline.to(device)
+
+# load reconstruction model
+print('Loading reconstruction model ...')
+model_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="instant_mesh_large.ckpt", repo_type="model")
+model = instantiate_from_config(model_config)
+state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict']
+state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.') and 'source_camera' not in k}
+model.load_state_dict(state_dict, strict=True)
+
+model = model.to(device)
+
+print('Loading Finished!')
+
+
+def check_input_image(input_image):
+    if input_image is None:
+        raise gr.Error("No image uploaded!")
+
+
+def preprocess(input_image, do_remove_background):
+
+    rembg_session = rembg.new_session() if do_remove_background else None
+
+    if do_remove_background:
+        input_image = remove_background(input_image, rembg_session)
+        input_image = resize_foreground(input_image, 0.85)
+
+    return input_image
+
+
+@spaces.GPU
+def generate_mvs(input_image, sample_steps, sample_seed):
+
+    seed_everything(sample_seed)
+    
+    # sampling
+    z123_image = pipeline(
+        input_image, 
+        num_inference_steps=sample_steps
+    ).images[0]
+
+    show_image = np.asarray(z123_image, dtype=np.uint8)
+    show_image = torch.from_numpy(show_image)     # (960, 640, 3)
+    show_image = rearrange(show_image, '(n h) (m w) c -> (n m) h w c', n=3, m=2)
+    show_image = rearrange(show_image, '(n m) h w c -> (n h) (m w) c', n=2, m=3)
+    show_image = Image.fromarray(show_image.numpy())
+
+    return z123_image, show_image
+
+
+@spaces.GPU
+def make3d(images):
+
+    global model
+    if IS_FLEXICUBES:
+        model.init_flexicubes_geometry(device, use_renderer=False)
+    model = model.eval()
+
+    images = np.asarray(images, dtype=np.float32) / 255.0
+    images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float()     # (3, 960, 640)
+    images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2)        # (6, 3, 320, 320)
+
+    input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0).to(device)
+    render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=IS_FLEXICUBES).to(device)
+
+    images = images.unsqueeze(0).to(device)
+    images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1)
+
+    mesh_fpath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name
+    print(mesh_fpath)
+    mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
+    mesh_dirname = os.path.dirname(mesh_fpath)
+    video_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.mp4")
+    mesh_glb_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb")
+
+    with torch.no_grad():
+        # get triplane
+        planes = model.forward_planes(images, input_cameras)
+
+        # # get video
+        # chunk_size = 20 if IS_FLEXICUBES else 1
+        # render_size = 384
+        
+        # frames = []
+        # for i in tqdm(range(0, render_cameras.shape[1], chunk_size)):
+        #     if IS_FLEXICUBES:
+        #         frame = model.forward_geometry(
+        #             planes,
+        #             render_cameras[:, i:i+chunk_size],
+        #             render_size=render_size,
+        #         )['img']
+        #     else:
+        #         frame = model.synthesizer(
+        #             planes,
+        #             cameras=render_cameras[:, i:i+chunk_size],
+        #             render_size=render_size,
+        #         )['images_rgb']
+        #     frames.append(frame)
+        # frames = torch.cat(frames, dim=1)
+
+        # images_to_video(
+        #     frames[0],
+        #     video_fpath,
+        #     fps=30,
+        # )
+
+        # print(f"Video saved to {video_fpath}")
+
+        # get mesh
+        mesh_out = model.extract_mesh(
+            planes,
+            use_texture_map=False,
+            **infer_config,
+        )
+
+        vertices, faces, vertex_colors = mesh_out
+        vertices = vertices[:, [1, 2, 0]]
+        
+        save_glb(vertices, faces, vertex_colors, mesh_glb_fpath)
+        save_obj(vertices, faces, vertex_colors, mesh_fpath)
+        
+        print(f"Mesh saved to {mesh_fpath}")
+
+    return mesh_fpath, mesh_glb_fpath
+
+
+###############################################################################
+############# above part is for 3D generate #############
+###############################################################################
+
 
 gpt_state = 0
 
@@ -735,7 +990,117 @@ def create_ui():
                         submit_tts = gr.Button(value="Submit", interactive=True)
                         clear_tts = gr.Button(value="Clear", interactive=True)
 
-
+        ###############################################################################
+        # this part is for 3d generate.
+        ###############################################################################
+
+        with gr.Row(variant="panel"):
+            with gr.Column():
+                with gr.Row():
+                    input_image = gr.Image(
+                        label="Input Image",
+                        image_mode="RGBA",
+                        sources="upload",
+                        #width=256,
+                        #height=256,
+                        type="pil",
+                        elem_id="content_image",
+                    )
+                    processed_image = gr.Image(
+                        label="Processed Image", 
+                        image_mode="RGBA", 
+                        #width=256,
+                        #height=256,
+                        type="pil", 
+                        interactive=False
+                    )
+                with gr.Row():
+                    with gr.Group():
+                        do_remove_background = gr.Checkbox(
+                            label="Remove Background", value=True
+                        )
+                        sample_seed = gr.Number(value=42, label="Seed Value", precision=0)
+    
+                        sample_steps = gr.Slider(
+                            label="Sample Steps",
+                            minimum=30,
+                            maximum=75,
+                            value=75,
+                            step=5
+                        )
+    
+                with gr.Row():
+                    submit = gr.Button("Generate", elem_id="generate", variant="primary")
+    
+                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],
+                        label="Examples",
+                        cache_examples=False,
+                        examples_per_page=16
+                    )
+
+            with gr.Column():
+    
+                with gr.Row():
+    
+                    with gr.Column():
+                        mv_show_images = gr.Image(
+                            label="Generated Multi-views",
+                            type="pil",
+                            width=379,
+                            interactive=False
+                        )
+    
+                    # with gr.Column():
+                    #     output_video = gr.Video(
+                    #         label="video", format="mp4",
+                    #         width=379,
+                    #         autoplay=True,
+                    #         interactive=False
+                    #     )
+    
+                with gr.Row():
+                    with gr.Tab("OBJ"):
+                        output_model_obj = gr.Model3D(
+                            label="Output Model (OBJ Format)",
+                            interactive=False,
+                        )
+                        gr.Markdown("Note: Downloaded .obj model will be flipped. Export .glb instead or manually flip it before usage.")
+                    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():
+                    gr.Markdown('''Try a different <b>seed value</b> if the result is unsatisfying (Default: 42).''')
+
+        mv_images = gr.State()
+
+        submit.click(fn=check_input_image, inputs=[input_image]).success(
+            fn=preprocess,
+            inputs=[input_image, do_remove_background],
+            outputs=[processed_image],
+        ).success(
+            fn=generate_mvs,
+            inputs=[processed_image, sample_steps, sample_seed],
+            outputs=[mv_images, mv_show_images]
+            
+        ).success(
+            fn=make3d,
+            inputs=[mv_images],
+            outputs=[output_model_obj, output_model_glb]
+        )
+        ###############################################################################
+        # above part is for 3d generate.
+        ###############################################################################
+        
+        
         def clear_tts_fields():
             return [gr.update(value=""), gr.update(value=""), None, None, gr.update(value=False), gr.update(value=True), None, None]