diff --git a/.gitignore b/.gitignore
index e43b0f9..27cd6cf 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,9 @@
 .DS_Store
+tokenflow-results*/**
+venv/
+__pycache__/
+*.pyc
+latents/
+data/**
+!data/wolf.mp4
+!data/woman-running.mp4
\ No newline at end of file
diff --git a/README.md b/README.md
index 80ea028..fe19f78 100644
--- a/README.md
+++ b/README.md
@@ -32,11 +32,23 @@ For more see the [project webpage](https://diffusion-tokenflow.github.io).
 <td><img src="assets/videos.gif"></td>
 
 ## Environment
+
+Using conda
 ```
 conda create -n tokenflow python=3.9
 conda activate tokenflow
 pip install -r requirements.txt
 ```
+
+Using cpython Windows (powershell)
+```powershell
+python -m pip install virtualenv
+python -m virtualenv venv
+venv/Scripts/activate.ps1
+pip install -r requirements.txt
+pip install -r requirements-torch.txt
+```
+
 ## Preprocess
 
 Preprocess you video by running using the following command:
diff --git a/requirements-torch.txt b/requirements-torch.txt
new file mode 100644
index 0000000..224e56c
--- /dev/null
+++ b/requirements-torch.txt
@@ -0,0 +1,3 @@
+-i https://download.pytorch.org/whl/cu117
+torch==2.0.1+cu117
+torchvision==0.15.2+cu117
\ No newline at end of file
diff --git a/run_tokenflow_pnp.py b/run_tokenflow_pnp.py
index 037d4dc..6c83010 100644
--- a/run_tokenflow_pnp.py
+++ b/run_tokenflow_pnp.py
@@ -115,7 +115,9 @@ def get_latents_path(self):
         latents_path = os.path.join(config["latents_path"], f'sd_{config["sd_version"]}',
                              Path(config["data_path"]).stem, f'steps_{config["n_inversion_steps"]}')
         latents_path = [x for x in glob.glob(f'{latents_path}/*') if '.' not in Path(x).name]
-        n_frames = [int([x for x in latents_path[i].split('/') if 'nframes' in x][0].split('_')[1]) for i in range(len(latents_path))]
+        
+        n_frames = [int(os.path.basename(x).split('_')[1]) for x in latents_path if 'nframes_' in x]
+
         latents_path = latents_path[np.argmax(n_frames)]
         self.config["n_frames"] = min(max(n_frames), config["n_frames"])
         if self.config["n_frames"] % self.config["batch_size"] != 0: