diff --git a/compare_samples.py b/compare_samples.py
index 9f186ab..e60ea1d 100644
--- a/compare_samples.py
+++ b/compare_samples.py
@@ -10,7 +10,9 @@ nominal = pv.read(VTKFILE_NOMINAL)
 
 # for each generated/sampled blade
 gen_files = Path("./output").glob("gen*.txt")
-for gen_file in gen_files:
+pc_files = Path("./output").glob("pc*.txt")
+files = list(gen_files) + list(pc_files)
+for gen_file in files:
     # load numpy txt
     blade = np.loadtxt(gen_file)
 
diff --git a/test_generation.py b/test_generation.py
index 8bf59cd..b018fb9 100644
--- a/test_generation.py
+++ b/test_generation.py
@@ -2,7 +2,6 @@ import argparse
 from pprint import pprint
 
 import datasets
-import pyvista as pv
 import torch
 import torch.nn as nn
 import torch.utils.data
@@ -502,8 +501,6 @@ def generate(model, opt):
     test_dataloader = torch.utils.data.DataLoader(
         test_dataset, batch_size=opt.batch_size, shuffle=False, num_workers=int(opt.workers), drop_last=False
     )
-    VTKFILE_NOMINAL = Path("~/data/stage-laurent-f/datasets/Rotor37/processed/nominal_blade_rotated.vtk")
-    nominal = pv.read(VTKFILE_NOMINAL)
 
     with torch.no_grad():
         samples = []
@@ -513,7 +510,8 @@ def generate(model, opt):
             x = data["positions"].transpose(1, 2)
             # m, s = data["mean"].float(), data["std"].float()
 
-            gen = model.gen_samples(x.shape, "cuda", clip_denoised=False).detach().cpu()
+            shape = torch.Size((*x.shape[:-1], 35000))
+            gen = model.gen_samples(shape, "cuda", clip_denoised=False).detach().cpu()
 
             gen = gen.transpose(1, 2).contiguous()
             x = x.transpose(1, 2).contiguous()
@@ -528,7 +526,6 @@ def generate(model, opt):
                 pc = blade
 
                 # unnormalize
-                pc = pc + nominal.points
                 pc = pc * STD + MEAN
 
                 print(f"Saving point cloud {idx}...")
diff --git a/train_generation.py b/train_generation.py
index 523361e..6d7587f 100644
--- a/train_generation.py
+++ b/train_generation.py
@@ -9,8 +9,6 @@ import torch.optim as optim
 import torch.utils.data
 from torch.distributions import Normal
 
-import pyvista as pv
-
 # from dataset.shapenet_data_pc import ShapeNet15kPointClouds
 from model.pvcnn_generation import PVCNN2Base
 from utils.file_utils import *
@@ -652,8 +650,6 @@ def train(gpu, opt, output_dir):
     """ data """
     train_dataset, _ = get_dataset(opt.dataroot, opt.npoints, opt.category)
     dataloader, _, train_sampler, _ = get_dataloader(opt, train_dataset, None)
-    VTKFILE_NOMINAL = Path("~/data/stage-laurent-f/datasets/Rotor37/processed/nominal_blade_rotated.vtk")
-    nominal = pv.read(VTKFILE_NOMINAL)
 
     """
     create networks
@@ -712,7 +708,6 @@ def train(gpu, opt, output_dir):
         lr_scheduler.step(epoch)
 
         for i, data in enumerate(dataloader):
-            x = data["positions"] - nominal.points
             x = data["positions"].transpose(1, 2)
             noises_batch = torch.randn_like(x)