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app.py

@@ -4,50 +4,53 @@ import gradio as gr
 from diffusers import StableDiffusionImg2ImgPipeline
 from PIL import Image
 
-# Force CUDA usage
+# Force CUDA usage if available
 os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 torch.backends.cudnn.benchmark = True
 torch.backends.cuda.matmul.allow_tf32 = True
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
+# Check if GPU is available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"🚀 Using device: {device}")
 
-# Ensure torch is properly installed
+# Ensure Torch is correctly initialized
 try:
     torch.zeros(1).to(device)
-    print("Torch initialized successfully on", device)
+    print("✅ Torch initialized successfully on", device)
 except Exception as e:
-    print("Torch initialization error:", e)
+    print("⚠️ Torch initialization error:", e)
 
 # Load the optimized Stable Diffusion model
 model_id = "nitrosocke/Ghibli-Diffusion"
 
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
     model_id,
-    torch_dtype=torch.float16,
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
     use_safetensors=True,
     low_cpu_mem_usage=True
-).to("cuda")
+).to(device)
 
-# Try enabling xformers, but fail gracefully
+# Try enabling xFormers for memory efficiency
 try:
     pipe.enable_xformers_memory_efficient_attention()
     print("✅ xFormers enabled!")
 except Exception as e:
     print(f"⚠️ xFormers not available: {e}")
-pipe.enable_xformers_memory_efficient_attention()
+
+# Apply additional optimizations for performance
 pipe.enable_model_cpu_offload()
 pipe.enable_vae_slicing()
 pipe.enable_attention_slicing()
 
-# Enhanced prompt for Studio Ghibli-style transformation
+# Enhanced Studio Ghibli-style transformation prompt
 prompt = (
-    "Beautiful Studio Ghibli anime-style portrait, breathtaking landscape background, "
-    "soft pastel colors, hand-painted texture, cinematic lighting, dreamy atmosphere, "
-    "vibrant and rich details, Miyazaki aesthetic, magical realism, watercolor effect, "
-    "warm sunlight, stunning composition, high detail, fantasy world."
+    "Studio Ghibli anime-style illustration, magical landscape, soft pastel colors, "
+    "hand-painted textures, cinematic lighting, dreamy atmosphere, vibrant and rich details, "
+    "Miyazaki-inspired fantasy world, watercolor aesthetic, warm sunlight, intricate composition, "
+    "high detail, whimsical and nostalgic beauty."
 )
 
+# Image transformation function
 def transform_image(input_image):
     input_image = input_image.resize((512, 512))
 
@@ -55,8 +58,8 @@ def transform_image(input_image):
         prompt=prompt,
         image=input_image,
         strength=0.65,
-        guidance_scale=4.5,
-        num_inference_steps=20,
+        guidance_scale=5.0,  # Slightly increased for better stylization
+        num_inference_steps=25,  # More steps for higher quality output
     )
 
     return output.images[0]
@@ -64,10 +67,10 @@ def transform_image(input_image):
 # Gradio UI
 demo = gr.Interface(
     fn=transform_image,
-    inputs=gr.Image(type="pil", label="Upload your portrait/photo"),
-    outputs=gr.Image(type="pil", label="Studio Ghibli Style Output"),
-    title="Studio Ghibli AI Converter",
-    description="Upload a portrait or photo to transform it into a Studio Ghibli-style image.",
+    inputs=gr.Image(type="pil", label="Upload a Portrait/Photo"),
+    outputs=gr.Image(type="pil", label="Studio Ghibli-Style Output"),
+    title="🎨 Studio Ghibli AI Art Generator",
+    description="Upload a portrait or a photo and transform it into a breathtaking Studio Ghibli-style masterpiece!",
 )
 
 if __name__ == "__main__":