diff --git a/point_e/examples/image2pointcloud.py b/point_e/examples/image2pointcloud.py
new file mode 100644
index 0000000..a0eeb0e
--- /dev/null
+++ b/point_e/examples/image2pointcloud.py
@@ -0,0 +1,71 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+
+from PIL import Image
+import torch
+from tqdm.auto import tqdm
+
+from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config
+from point_e.diffusion.sampler import PointCloudSampler
+from point_e.models.download import load_checkpoint
+from point_e.models.configs import MODEL_CONFIGS, model_from_config
+from point_e.util.plotting import plot_point_cloud
+
+
+# In[ ]:
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+print('creating base model...')
+base_name = 'base40M' # use base300M or base1B for better results
+base_model = model_from_config(MODEL_CONFIGS[base_name], device)
+base_model.eval()
+base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name])
+
+print('creating upsample model...')
+upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)
+upsampler_model.eval()
+upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])
+
+print('downloading base checkpoint...')
+base_model.load_state_dict(load_checkpoint(base_name, device))
+
+print('downloading upsampler checkpoint...')
+upsampler_model.load_state_dict(load_checkpoint('upsample', device))
+
+
+# In[ ]:
+
+
+sampler = PointCloudSampler(
+    device=device,
+    models=[base_model, upsampler_model],
+    diffusions=[base_diffusion, upsampler_diffusion],
+    num_points=[1024, 4096 - 1024],
+    aux_channels=['R', 'G', 'B'],
+    guidance_scale=[3.0, 3.0],
+)
+
+
+# In[ ]:
+
+
+# Load an image to condition on.
+img = Image.open('example_data/cube_stack.jpg')
+
+# Produce a sample from the model.
+samples = None
+for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(images=[img]))):
+    samples = x
+
+
+# In[ ]:
+
+
+pc = sampler.output_to_point_clouds(samples)[0]
+fig = plot_point_cloud(pc, grid_size=3, fixed_bounds=((-0.75, -0.75, -0.75),(0.75, 0.75, 0.75)))
+
diff --git a/point_e/examples/pointcloud2mesh.py b/point_e/examples/pointcloud2mesh.py
new file mode 100644
index 0000000..8fc6d0c
--- /dev/null
+++ b/point_e/examples/pointcloud2mesh.py
@@ -0,0 +1,62 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+
+from PIL import Image
+import torch
+import matplotlib.pyplot as plt
+from tqdm.auto import tqdm
+
+from point_e.models.download import load_checkpoint
+from point_e.models.configs import MODEL_CONFIGS, model_from_config
+from point_e.util.pc_to_mesh import marching_cubes_mesh
+from point_e.util.plotting import plot_point_cloud
+from point_e.util.point_cloud import PointCloud
+
+
+# In[ ]:
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+print('creating SDF model...')
+name = 'sdf'
+model = model_from_config(MODEL_CONFIGS[name], device)
+model.eval()
+
+print('loading SDF model...')
+model.load_state_dict(load_checkpoint(name, device))
+
+
+# In[ ]:
+
+
+# Load a point cloud we want to convert into a mesh.
+pc = PointCloud.load('example_data/pc_corgi.npz')
+
+# Plot the point cloud as a sanity check.
+fig = plot_point_cloud(pc, grid_size=2)
+
+
+# In[ ]:
+
+
+# Produce a mesh (with vertex colors)
+mesh = marching_cubes_mesh(
+    pc=pc,
+    model=model,
+    batch_size=4096,
+    grid_size=32, # increase to 128 for resolution used in evals
+    progress=True,
+)
+
+
+# In[ ]:
+
+
+# Write the mesh to a PLY file to import into some other program.
+with open('mesh.ply', 'wb') as f:
+    mesh.write_ply(f)
+
diff --git a/point_e/examples/text2pointcloud.py b/point_e/examples/text2pointcloud.py
new file mode 100644
index 0000000..4bd06b4
--- /dev/null
+++ b/point_e/examples/text2pointcloud.py
@@ -0,0 +1,71 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+
+import torch
+from tqdm.auto import tqdm
+
+from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config
+from point_e.diffusion.sampler import PointCloudSampler
+from point_e.models.download import load_checkpoint
+from point_e.models.configs import MODEL_CONFIGS, model_from_config
+from point_e.util.plotting import plot_point_cloud
+
+
+# In[ ]:
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+print('creating base model...')
+base_name = 'base40M-textvec'
+base_model = model_from_config(MODEL_CONFIGS[base_name], device)
+base_model.eval()
+base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name])
+
+print('creating upsample model...')
+upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)
+upsampler_model.eval()
+upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])
+
+print('downloading base checkpoint...')
+base_model.load_state_dict(load_checkpoint(base_name, device))
+
+print('downloading upsampler checkpoint...')
+upsampler_model.load_state_dict(load_checkpoint('upsample', device))
+
+
+# In[ ]:
+
+
+sampler = PointCloudSampler(
+    device=device,
+    models=[base_model, upsampler_model],
+    diffusions=[base_diffusion, upsampler_diffusion],
+    num_points=[1024, 4096 - 1024],
+    aux_channels=['R', 'G', 'B'],
+    guidance_scale=[3.0, 0.0],
+    model_kwargs_key_filter=('texts', ''), # Do not condition the upsampler at all
+)
+
+
+# In[ ]:
+
+
+# Set a prompt to condition on.
+prompt = 'a red motorcycle'
+
+# Produce a sample from the model.
+samples = None
+for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(texts=[prompt]))):
+    samples = x
+
+
+# In[ ]:
+
+
+pc = sampler.output_to_point_clouds(samples)[0]
+fig = plot_point_cloud(pc, grid_size=3, fixed_bounds=((-0.75, -0.75, -0.75),(0.75, 0.75, 0.75)))
+