Update app.py

app.py

@@ -35,27 +35,42 @@ def single_condition_generate_image(prompt, spatial_img, height, width, seed, co
     if API_TOKEN and api_token != API_TOKEN:
         return "ERROR: Invalid API token. Please provide a valid token to generate images."
     
-    # Set the control type
-    if control_type == "Ghibli":
-        lora_path = os.path.join(lora_base_path, "Ghibli.safetensors")
-    set_single_lora(pipe.transformer, lora_path, lora_weights=[1], cond_size=512)
-    
-    # Process the image
-    spatial_imgs = [spatial_img] if spatial_img else []
-    image = pipe(
-        prompt,
-        height=int(height),
-        width=int(width),
-        guidance_scale=3.5,
-        num_inference_steps=25,
-        max_sequence_length=512,
-        generator=torch.Generator("cpu").manual_seed(seed), 
-        subject_images=[],
-        spatial_images=spatial_imgs,
-        cond_size=512,
-    ).images[0]
-    clear_cache(pipe.transformer)
-    return image
+    try:
+        # Ensure height and width are divisible by 8
+        height = int(height)
+        width = int(width)
+        
+        if height % 8 != 0 or width % 8 != 0:
+            # Adjust to nearest multiple of 8
+            height = (height // 8) * 8
+            width = (width // 8) * 8
+            print(f"Dimensions adjusted to be divisible by 8: {height}x{width}")
+        
+        # Set the control type
+        if control_type == "Ghibli":
+            lora_path = os.path.join(lora_base_path, "Ghibli.safetensors")
+        set_single_lora(pipe.transformer, lora_path, lora_weights=[1], cond_size=512)
+        
+        # Process the image
+        spatial_imgs = [spatial_img] if spatial_img else []
+        image = pipe(
+            prompt,
+            height=height,
+            width=width,
+            guidance_scale=3.5,
+            num_inference_steps=25,
+            max_sequence_length=512,
+            generator=torch.Generator("cpu").manual_seed(seed), 
+            subject_images=[],
+            spatial_images=spatial_imgs,
+            cond_size=512,
+        ).images[0]
+        clear_cache(pipe.transformer)
+        return image
+    except Exception as e:
+        error_message = f"Error during generation: {str(e)}"
+        print(error_message)
+        return f"ERROR: {error_message}"
 
 # Define the Gradio interface components
 control_types = ["Ghibli"]
@@ -76,14 +91,15 @@ with gr.Blocks() as demo:
     
     gr.Markdown("**[Attention!!]**：The recommended prompts for using Ghibli Control LoRA should include the trigger words: `Ghibli Studio style, Charming hand-drawn anime-style illustration`")
     gr.Markdown("😊😊If you like this demo, please give us a star (github: [EasyControl](https://github.com/Xiaojiu-z/EasyControl))")
+    gr.Markdown("**NOTE**: Both height and width must be divisible by 8. Values will be automatically adjusted if needed.")
 
     with gr.Tab("Ghibli Condition Generation"):
         with gr.Row():
             with gr.Column():
                 prompt = gr.Textbox(label="Prompt", value="Ghibli Studio style, Charming hand-drawn anime-style illustration")
                 spatial_img = gr.Image(label="Ghibli Image", type="pil")
-                height = gr.Slider(minimum=256, maximum=1024, step=64, label="Height", value=768)
-                width = gr.Slider(minimum=256, maximum=1024, step=64, label="Width", value=768)
+                height = gr.Slider(minimum=256, maximum=1024, step=8, label="Height", value=768)
+                width = gr.Slider(minimum=256, maximum=1024, step=8, label="Width", value=768)
                 seed = gr.Number(label="Seed", value=42)
                 control_type = gr.Dropdown(choices=control_types, label="Control Type", value="Ghibli")
                 single_generate_btn = gr.Button("Generate Image")