Create app.py

TODO: make sure source_camero have the right shape and value
TODO: instead of outputting .obj file -> directly output a 3d model

app.py

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+import torch
+import gradio as gr
+import os
+import numpy as np
+import trimesh
+import mcubes
+from torchvision.utils import save_image
+from PIL import Image
+from transformers import AutoModel, AutoConfig
+from rembg import remove, new_session
+from functools import partial
+from kiui.op import recenter
+import kiui
+
+
+# we load the pre-trained model from HF
+class LRMGeneratorWrapper:
+    def __init__(self):
+        self.config = AutoConfig.from_pretrained("jadechoghari/custom-llrm", trust_remote_code=True)
+        self.model = AutoModel.from_pretrained("jadechoghari/custom-llrm", trust_remote_code=True)
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.model.to(self.device)
+        self.model.eval()
+
+    def forward(self, image, camera):
+        return self.model(image, camera)
+
+model_wrapper = LRMGeneratorWrapper()
+
+
+def preprocess_image(image, source_size):
+    session = new_session("isnet-general-use")
+    rembg_remove = partial(remove, session=session)
+    image = np.array(image)
+    image = rembg_remove(image)
+    mask = rembg_remove(image, only_mask=True)
+    image = recenter(image, mask, border_ratio=0.20)
+    image = torch.tensor(image).permute(2, 0, 1).unsqueeze(0) / 255.0
+    if image.shape[1] == 4:
+        image = image[:, :3, ...] * image[:, 3:, ...] + (1 - image[:, 3:, ...])
+    image = torch.nn.functional.interpolate(image, size=(source_size, source_size), mode='bicubic', align_corners=True)
+    image = torch.clamp(image, 0, 1)
+    return image
+
+#Ref: https://github.com/jadechoghari/vfusion3d/blob/main/lrm/inferrer.py
+def generate_mesh(image, source_size=512, render_size=384, mesh_size=512, export_mesh=True):
+    image = preprocess_image(image, source_size).to(model_wrapper.device)
+
+    # TODO: make sure source_camero have the right shape and value
+    source_camera = torch.tensor([[0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1]], dtype=torch.float32).to(model_wrapper.device)
+
+    render_camera = torch.tensor([[0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1]], dtype=torch.float32).to(model_wrapper.device)
+
+    with torch.no_grad():
+        planes = model_wrapper.forward(image, source_camera)
+
+        if export_mesh:
+            grid_out = model_wrapper.model.synthesizer.forward_grid(planes=planes, grid_size=mesh_size)
+
+            vtx, faces = mcubes.marching_cubes(grid_out['sigma'].float().squeeze(0).squeeze(-1).cpu().numpy(), 1.0)
+            vtx = vtx / (mesh_size - 1) * 2 - 1
+            vtx_tensor = torch.tensor(vtx, dtype=torch.float32, device=model_wrapper.device).unsqueeze(0)
+            vtx_colors = model_wrapper.model.synthesizer.forward_points(planes, vtx_tensor)['rgb'].float().squeeze(0).cpu().numpy()
+
+            vtx_colors = (vtx_colors * 255).astype(np.uint8)
+            mesh = trimesh.Trimesh(vertices=vtx, faces=faces, vertex_colors=vtx_colors)
+
+            mesh_path = "awesome_mesh.obj"
+            mesh.export(mesh_path, 'obj')
+            return mesh_path
+
+# TODO: instead of outputting .obj file -> directly output a 3d model
+def gradio_interface(image):
+    mesh_file = generate_mesh(image)
+    print("Generated Mesh File Path:", mesh_file)
+    return mesh_file
+
+
+gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Image(type="pil", label="Input Image"),
+    outputs=gr.File(label="Awesome 3D Mesh (.obj)"),
+    title="3D Mesh Generator by FacebookAI",
+    description="Upload an image and generate a 3D mesh (.obj) file using VFusion3D by FacebookAI"
+).launch()