Update app.py

app.py

@@ -17,16 +17,12 @@ else:
     torch_dtype = torch.float32
 
 def split_image(input_image, num_splits=4):
-    # Create a list to store the output images
     output_images = []
-
-    # Split the image into four 256x256 sections
     for i in range(num_splits):
         left = i * 320
         right = (i + 1) * 320
         box = (left, 0, right, 320)
         output_images.append(input_image.crop(box))
-
     return output_images
 
 pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch_dtype).to(device)
@@ -52,14 +48,13 @@ def infer(prompt, seed=1, randomize_seed=False, num_inference_steps=28):
     ).images[0]
     
     gif_name = f"{uuid.uuid4().hex}-flux.gif"
-    
     export_to_gif(split_image(image, 4), gif_name, fps=4)
     
     return gif_name, image, seed
-    
+
 examples = [
     "a red panda playing with a bamboo stick in the snow",
-    "a astronaut breakdancing on the moon",
+    "an astronaut breakdancing on the moon",
     "a magical butterfly transforming into sparkles",
     "a robot learning to paint like Van Gogh",
     "a dragon hatching from a crystal egg",
@@ -107,7 +102,7 @@ footer {visibility: hidden}
 }
 """
 
-with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:
+with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css=css) as demo:
     gr.HTML("""
         <div style="text-align: center; max-width: 800px; margin: 0 auto;">
             <h1 style="font-size: 3rem; font-weight: 700; margin-bottom: 1rem;">
@@ -120,24 +115,21 @@ with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:
     """)
     
     with gr.Column(elem_id="col-container"):
-            with gr.Row():
-                prompt = gr.Text(
-                    label="Your Animation Prompt",
-                    show_label=True,
-                    max_lines=1,
-                    placeholder="Describe the animation you want to create...",
-                    container=True,
-                    elem_id="prompt-input"
-                )
-                
-                run_button = gr.Button("✨ Generate", scale=0, variant="primary")
-
-
+        with gr.Row():
+            prompt = gr.Text(
+                label="Your Animation Prompt",
+                show_label=True,
+                max_lines=1,
+                placeholder="Describe the animation you want to create...",
+                container=True,
+                elem_id="prompt-input"
+            )
+            run_button = gr.Button("✨ Generate", scale=0, variant="primary")
         
         result = gr.Image(label="Result", show_label=False)
         result_full = gr.Image(label="Gif Strip", elem_id="strip")
+        
         with gr.Accordion("Advanced Settings", open=False):
-            
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -145,31 +137,28 @@ with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:
                 step=1,
                 value=0,
             )
-            
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
             num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=32,
-                    step=1,
-                    value=28, 
+                label="Number of inference steps",
+                minimum=1,
+                maximum=32,
+                step=1,
+                value=28, 
             )    
-                
         
         gr.Examples(
-            examples = examples,
-            inputs = [prompt],
-            outputs = [result, result_full, seed],
+            examples=examples,
+            inputs=[prompt],
+            outputs=[result, result_full, seed],
             fn=infer,
             cache_examples="lazy"
         )
-        
+    
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, seed, randomize_seed, num_inference_steps],
-        outputs = [result, result_full, seed]
+        fn=infer,
+        inputs=[prompt, seed, randomize_seed, num_inference_steps],
+        outputs=[result, result_full, seed]
     )
 
 demo.queue().launch()