Update README.md

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
linkcode
from diffusers import DiffusionPipeline
import torch

# Load the model
pipe = DiffusionPipeline.from_pretrained(
"VERUMNNODE/OS",
torch_dtype=torch.float16,
use_safetensors=True
)

# Move to GPU ifailable
if torch.cuda.is_available():
pipe = pipe.to("cuda")

README.md

@@ -234,4 +234,35 @@ If you use this model in your research or projects, please cite:
   publisher={Hugging Face},
   url={https://huggingface.co/VERUMNNODE/OS}
 }
-kaggle kernels output nina6923/notebook15ab497e3e -p /path/to/dest
+kaggle kernels output nina6923/notebook15ab497e3e -p /path/to/dest
+# This Python 3 environment comes with many helpful analytics libraries installed
+# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
+# For example, here's several helpful packages to load
+
+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+
+# Input data files are available in the read-only "../input/" directory
+# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
+
+import os
+for dirname, _, filenames in os.walk('/kaggle/input'):
+    for filename in filenames:
+        print(os.path.join(dirname, filename))
+
+# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
+# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
+linkcode
+from diffusers import DiffusionPipeline
+import torch
+
+# Load the model
+pipe = DiffusionPipeline.from_pretrained(
+    "VERUMNNODE/OS",
+    torch_dtype=torch.float16,
+    use_safetensors=True
+)
+
+# Move to GPU ifailable
+if torch.cuda.is_available():
+    pipe = pipe.to("cuda")