Change TripoSR to InstantMesh

* Replace generative model
* added instant-mesh utils

app.py

@@ -1,6 +1,7 @@
+import spaces
 import gradio as gr
+
 import numpy as np
-import spaces
 import torch
 import rembg
 from PIL import Image
@@ -12,11 +13,51 @@ import shlex
 import subprocess
 import tempfile
 import time
+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
+
+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
 
+from instant-mesh import get_render_cameras, find_cuda, check_input_image, generate_mvs, make3d
+
+
+# This was the code needed for TripoSR
+"""
 subprocess.run(shlex.split('pip install wheel/torchmcubes-0.1.0-cp310-cp310-linux_x86_64.whl'))
 
 from tsr.system import TSR
 from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation
+"""
 
 HEADER = """
 # Generate 3D Assets for Roblox
@@ -37,7 +78,7 @@ We wrote a tutorial here
 STEP1_HEADER = """
 ## Step 1: Generate the 3D Mesh
 
-For this step, we use TripoSR, an 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/).
+For this step, we use <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>InstantMesh</a>, an open-source model for **fast** feedforward 3D mesh generation from a single image.
 
 During this step, you need to upload an image of what you want to generate a 3D Model from.
 
@@ -46,10 +87,7 @@ During this step, you need to upload an image of what you want to generate a 3D
 
 -  If there's a background, ✅ Remove background.
 
-- If you find the result is unsatisfied, please try to change the foreground ratio. It might improve the results.
-
-- To know more about what is the Marching Cubes Resolution check this : https://huggingface.co/learn/ml-for-3d-course/en/unit4/marching-cubes#marching-cubes
-
+- The 3D mesh generation results highly depend on the quality of generated multi-view images. Please try a different **seed value** if the result is unsatisfying (Default: 42).
 """
 
 STEP2_HEADER = """
@@ -86,7 +124,8 @@ STEP4_HEADER = """
 
 """
 
-
+# Code for TripoSR
+"""
 # These part of the code (check_input_image and preprocess were taken from https://huggingface.co/spaces/stabilityai/TripoSR/blob/main/app.py)
 if torch.cuda.is_available():
     device = "cuda:0"
@@ -142,6 +181,61 @@ def generate(image, mc_resolution, formats=["obj", "glb"]):
     mesh.export(mesh_path_obj.name)
     
     return mesh_path_obj.name, mesh_path_glb.name
+"""
+
+
+
+###############################################################################
+# Configuration for InstantMesh
+# All this code is from https://huggingface.co/spaces/TencentARC/InstantMesh/blob/main/app.py
+###############################################################################
+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!')
+
 
 
 with gr.Blocks() as demo:
@@ -155,30 +249,41 @@ with gr.Blocks() as demo:
 					image_mode = "RGBA",
 					sources = "upload",
 					type="pil",
-					elem_id="content_image")
-				processed_image = gr.Image(label="Processed Image", interactive=False)
+					elem_id="content_image"
+				)
+				processed_image = gr.Image(label="Processed Image",
+					image_mode="RGBA",
+					type="pil",
+					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,
-						)
-					mc_resolution = gr.Slider(
-						label="Marching Cubes Resolution",
-						minimum=32,
-						maximum=320,
-						value=256,
-						step=32
-						)
+					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():
 				step1_submit = gr.Button("Generate", elem_id="generate", variant="primary")
-
+		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():
 			with gr.Tab("OBJ"):
 				output_model_obj = gr.Model3D(
@@ -192,15 +297,23 @@ with gr.Blocks() as demo:
                     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()
 
 	step1_submit.click(fn=check_input_image, inputs=[input_image]).success(
         fn=preprocess,
-        inputs=[input_image, do_remove_background, foreground_ratio],
+        inputs=[input_image, do_remove_background],
         outputs=[processed_image],
     ).success(
-        fn=generate,
-        inputs=[processed_image, mc_resolution],
-        outputs=[output_model_obj, output_model_glb],
+        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]
     )
 	gr.Markdown(STEP2_HEADER)
 	gr.Markdown(STEP3_HEADER)

instant-mesh/utils.py

@@ -0,0 +1,178 @@
+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
+
+import gradio as gr
+import shutil
+import spaces
+
+
+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
+
+
+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
+
+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
+