update example, fix vae progressbar

hy3dgen/shapegen/models/vae.py

@@ -580,7 +580,7 @@ class ShapeVAE(nn.Module):
         # 2. latents to 3d volume
         batch_logits = []
         batch_size = latents.shape[0]
-        comfy_pbar = ProgressBar(num_chunks * xyz_samples.shape[0])
+        comfy_pbar = ProgressBar(xyz_samples.shape[0])
         for start in tqdm(range(0, xyz_samples.shape[0], num_chunks),
                           desc=f"MC Level {mc_level} Implicit Function:"):
             queries = xyz_samples[start: start + num_chunks, :].to(device)
@@ -593,7 +593,7 @@ class ShapeVAE(nn.Module):
                 logits = torch.sigmoid(logits) * 2 - 1
                 print(f'Training with soft labels, inference with sigmoid and marching cubes level 0.')
             batch_logits.append(logits)
-            comfy_pbar.update(1)
+            comfy_pbar.update(num_chunks)
         grid_logits = torch.cat(batch_logits, dim=1)
         grid_logits = grid_logits.view((batch_size, grid_size[0], grid_size[1], grid_size[2])).float()
 

nodes.py

@@ -380,6 +380,9 @@ class Hy3DGenerateMesh:
 
         image = image.permute(0, 3, 1, 2).to(device)
 
+        if mask is not None:
+            mask = mask.unsqueeze(0).to(device)
+
         pipeline.to(device)
 
         mesh = pipeline(

readme.md

@@ -42,4 +42,4 @@ cd hy3dgen/texgen/differentiable_renderer
 python setup.py build_ext --inplace
 ```
 
-![alt text](image.png)
+![alt text](example_workflows/example_workflow.png)

requirements.txt

@@ -9,4 +9,5 @@ xatlas
 pymeshlab
 pygltflib
 scikit-learn
-scikit-image
+scikit-image
+pybind11