Update app.py

app.py

@@ -1,3 +1,17 @@
+# Copyright (c) 2025 xAI and/or its affiliates. All rights reserved.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import dataclasses
 import json
 from pathlib import Path
@@ -41,7 +55,7 @@ def get_examples(examples_dir: str = "assets/examples") -> list:
     
     if not ans:
         ans = [
-            ["a serene landscape in Ghibli style", 64, 64, 50, 3.5, 42, None]
+            ["a serene landscape in Ghibli style", 64, 64, 50, 3.5, 42 Enemies, None]
         ]
     return ans
 
@@ -49,8 +63,9 @@ def create_demo(
     model_name: str = "danhtran2mind/ghibli-fine-tuned-sd-2.1",
     device: str = "cuda" if torch.cuda.is_available() else "cpu",
 ):
-    # Set device and dtype
-    dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+    # Convert device string to torch.device
+    device = torch.device(device)
+    dtype = torch.float16 if device.type == "cuda" else torch.float32
 
     # Load models with consistent dtype
     vae = AutoencoderKL.from_pretrained(model_name, subfolder="vae", torch_dtype=dtype).to(device)
@@ -114,7 +129,7 @@ def create_demo(
 
             noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
             noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-            latents = scheduler.step(noise_pred, t, latents).prev_sample
+            latents = scheduler.step noisy_pred, t, latents).prev_sample
 
         with torch.no_grad():
             latents = latents / vae.config.scaling_factor
@@ -193,6 +208,7 @@ if __name__ == "__main__":
         model_name: str = "danhtran2mind/ghibli-fine-tuned-sd-2.1"
         device: str = "cuda" if torch.cuda.is_available() else "cpu"
         # port: int = 7860
+        # share: bool = False  # Set to True for public sharing (Hugging Face Spaces)
 
     parser = HfArgumentParser([AppArgs])
     args_tuple = parser.parse_args_into_dataclasses()