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app.py

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+
+import gradio as gr
+import torch
+from diffusers import StableDiffusionImg2ImgPipeline
+from PIL import Image
+
+# --- Model Loading --- #
+print('Loading Ghibli and Anime style models...')
+
+# Determine the device to run the model on
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print(f"Using device: {device}")
+
+# --- Load Ghibli Style Model ---
+ghibli_model_id = 'nitrosocke/Ghibli-Diffusion'
+
+# Note: `torch_dtype` is used here instead of `dtype` due to observed behavior in diffusers 0.36.0
+# where `dtype` was ignored and `torch_dtype` was effective despite deprecation warnings.
+if device == 'cuda':
+    ghibli_pipeline = StableDiffusionImg2ImgPipeline.from_pretrained(
+        ghibli_model_id,
+        torch_dtype=torch.float16,
+        cache_dir='./model_cache_ghibli'
+    )
+else:
+    ghibli_pipeline = StableDiffusionImg2ImgPipeline.from_pretrained(
+        ghibli_model_id,
+        torch_dtype=torch.float32,
+        cache_dir='./model_cache_ghibli'
+    )
+ghibli_pipeline.to(device)
+print(f'Ghibli Style Model ({ghibli_model_id}) loaded successfully.')
+
+# --- Load Anime Style Model ---
+anime_model_id = 'hakurei/waifu-diffusion'
+if device == 'cuda':
+    anime_pipeline = StableDiffusionImg2ImgPipeline.from_pretrained(
+        anime_model_id,
+        torch_dtype=torch.float16,
+        cache_dir='./model_cache_anime'
+    )
+else:
+    anime_pipeline = StableDiffusionImg2ImgPipeline.from_pretrained(
+        anime_model_id,
+        torch_dtype=torch.float32,
+        cache_dir='./model_cache_anime'
+    )
+anime_pipeline.to(device)
+print(f'Anime Style Model ({anime_model_id}) loaded successfully.')
+
+print('Both Ghibli and Anime Style Models loaded and moved to device successfully.')
+
+# --- Transformation Function ---
+def cartoon_transform(input_image: Image.Image, style: str) -> Image.Image:
+    """
+    Applies a cartoon-style transformation to the input image using the loaded Stable Diffusion pipelines.
+
+    Args:
+        input_image (PIL.Image.Image): The input image to transform.
+        style (str): The desired cartoon style ('Ghibli' or 'Anime').
+
+    Returns:
+        PIL.Image.Image: The transformed image in the selected cartoon style.
+    """
+    # Ensure the image is in RGB format
+    if input_image.mode != 'RGB':
+        input_image = input_image.convert('RGB')
+
+    # Set reasonable dimensions to avoid excessive memory usage and ensure reasonable processing time
+    # while maintaining aspect ratio
+    max_dim = 768 # Maximum dimension for processing
+    width, height = input_image.size
+    if max(width, height) > max_dim:
+        ratio = max_dim / max(width, height)
+        new_width = int(width * ratio)
+        new_height = int(height * ratio)
+        input_image = input_image.resize((new_width, new_height), Image.LANCZOS)
+
+    # Define pipelines, prompts, and parameters based on style
+    if style == 'Ghibli':
+        pipeline_to_use = ghibli_pipeline
+        prompt = "Studio Ghibli style, detailed, vibrant colors, fantasy, magical, serene"
+        strength = 0.75
+        guidance_scale = 7.5
+        num_inference_steps = 25 # Reduced for faster processing
+    elif style == 'Anime':
+        pipeline_to_use = anime_pipeline
+        prompt = "anime character, vibrant, digital art, high quality, detailed eyes"
+        strength = 0.8 # Slightly higher strength for a more pronounced anime effect
+        guidance_scale = 8.0
+        num_inference_steps = 25 # Reduced for faster processing
+    else:
+        raise ValueError(f"Unsupported style: {style}. Choose from 'Ghibli' or 'Anime'.")
+
+    # Run the image-to-image pipeline
+    transformed_image = pipeline_to_use(
+        prompt=prompt,
+        image=input_image,
+        strength=strength,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps
+    ).images[0]
+
+    print(f'Image transformed to {style} style.')
+    return transformed_image
+
+# --- Gradio Interface ---
+# Available cartoon styles
+cartoon_styles = ['Ghibli', 'Anime']
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=cartoon_transform,
+    inputs=[
+        gr.Image(type='pil', label='Upload your picture'),
+        gr.Dropdown(
+            choices=cartoon_styles,
+            label='Select Cartoon Style',
+            value='Ghibli' # Default selection
+        )
+    ],
+    outputs=gr.Image(type='pil', label='Transformed Image'),
+    title='Cartoon Style Image Transformer',
+    description='Upload a picture and transform it into various cartoon styles.'
+)
+
+# Launch the Gradio app - this part is typically removed or commented out when deploying to Hugging Face Spaces,
+# as Spaces handle the launch automatically.
+if __name__ == '__main__':
+    print('Launching Gradio interface locally...')
+    iface.launch(share=True) # share=True for Colab, change to False for local dev