Update app.py

app.py

@@ -4,33 +4,36 @@ import torch
 from PIL import Image
 import numpy as np
 
-# Load the model (this runs when the Space starts)
+# Load the model
 model_id = "nitrosocke/Ghibli-Diffusion"
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
 
 # Define the inference function
 def ghibli_transform(input_image, prompt="ghibli style", strength=0.75, guidance_scale=7.5):
-    # Check if input_image is valid
+    # Debug: Check input type and value
+    print(f"Input type: {type(input_image)}")
     if input_image is None:
         raise gr.Error("Please upload an image before clicking Transform!")
     
-    # Ensure input_image is a NumPy array and convert to PIL
-    if not isinstance(input_image, np.ndarray):
-        raise gr.Error("Input image format is invalid. Expected a NumPy array.")
-    
+    # Since input is now PIL, just resize and ensure RGB
     try:
-        init_image = Image.fromarray(input_image).convert("RGB").resize((768, 768))
+        init_image = input_image.resize((768, 768)).convert("RGB")
+        print(f"Converted to PIL Image: {type(init_image)}")
     except Exception as e:
         raise gr.Error(f"Failed to process image: {str(e)}")
     
     # Generate the Ghibli-style image
-    output = pipe(
-        prompt=prompt,
-        init_image=init_image,
-        strength=strength,
-        guidance_scale=guidance_scale,
-        num_inference_steps=50
-    ).images[0]
+    try:
+        output = pipe(
+            prompt=prompt,
+            init_image=init_image,
+            strength=strength,
+            guidance_scale=guidance_scale,
+            num_inference_steps=50
+        ).images[0]
+        print("Pipeline executed successfully")
+    except Exception as e:
+        raise gr.Error(f"Pipeline error: {str(e)}")
     
     return output
 
@@ -41,7 +44,7 @@ with gr.Blocks(title="Ghibli Diffusion Image Transformer") as demo:
     
     with gr.Row():
         with gr.Column():
-            input_img = gr.Image(label="Upload Image", type="numpy")
+            input_img = gr.Image(label="Upload Image", type="pil")  # Changed to "pil"
             prompt = gr.Textbox(label="Prompt", value="ghibli style")
             strength = gr.Slider(0, 1, value=0.75, step=0.05, label="Strength (How much to transform)")
             guidance = gr.Slider(1, 20, value=7.5, step=0.5, label="Guidance Scale")