op

app.py

@@ -1,15 +1,24 @@
 import os
 import torch
 import gradio as gr
-from diffusers import StableDiffusionImg2ImgPipeline
+from diffusers import DiffusionPipeline
 from PIL import Image
 
-# Force CUDA usage if available
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-torch.backends.cudnn.benchmark = True
-torch.backends.cuda.matmul.allow_tf32 = True
+# --- Configuration ---
+SPACE_TITLE = "🎨 Enhanced Studio Ghibli AI Art Generator (LoRA)"
+SPACE_DESCRIPTION = "Upload a portrait or a photo and transform it into a breathtaking Studio Ghibli-style masterpiece using a LoRA for fine-tuned results."
+BASE_MODEL_ID = "black-forest-labs/FLUX.1-dev"
+LORA_REPO_ID = "strangerzonehf/Flux-Ghibli-Art-LoRA"
+TRIGGER_WORD = "Ghibli Art"
+STRENGTH = 0.60  # Adjust for better balance between input and style
+GUIDANCE_SCALE = 7.5  # Increased for better prompt adherence
+NUM_INFERENCE_STEPS = 30  # Increased for potentially higher quality
+INPUT_IMAGE_SIZE = (512, 512)
+PROMPT_PREFIX = ""  # No need for separate prefix as LoRA is targeted
+NEGATIVE_PROMPT = "ugly, deformed, blurry, low quality, bad anatomy, bad proportions, disfigured, poorly drawn face, mutation, mutated, extra limbs, extra fingers, body horror, glitchy, tiling"
 
-# Check if GPU is available
+# --- Device Setup ---
+# Attempt to use CUDA if available, otherwise fallback to CPU
 device = "cuda" if torch.cuda.is_available() else "cpu"
 print(f"🚀 Using device: {device}")
 
@@ -18,61 +27,87 @@ try:
     torch.zeros(1).to(device)
     print("✅ Torch initialized successfully on", device)
 except Exception as e:
-    print("⚠️ Torch initialization error:", e)
+    print(f"⚠️ Torch initialization error: {e}")
 
-# Load the correct Stable Diffusion pipeline
-model_id = "nitrosocke/Ghibli-Diffusion"
-
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    model_id,
-    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
-    use_safetensors=True,
-    low_cpu_mem_usage=True
-).to(device)
+# --- Model Loading ---
+try:
+    pipe = DiffusionPipeline.from_pretrained(BASE_MODEL_ID, torch_dtype=torch.bfloat16)
+except ValueError as e:
+    if "sentencepiece" in str(e):
+        print("⚠️ Error: sentencepiece is not installed. Please install it with: pip install sentencepiece")
+        raise
+    else:
+        raise e
 
-# Try enabling xFormers for memory efficiency
 try:
-    pipe.enable_xformers_memory_efficient_attention()
-    print("✅ xFormers enabled!")
+    pipe.load_lora_weights(LORA_REPO_ID)
+    print(f"✅ LoRA weights loaded from {LORA_REPO_ID}")
 except Exception as e:
-    print(f"⚠️ xFormers not available: {e}")
+    print(f"⚠️ Error loading LoRA weights: {e}")
 
-# Apply additional optimizations for performance
-pipe.enable_model_cpu_offload()
+pipe.to(device)
+
+# --- Optimization (Conditional for CUDA) ---
+if device == "cuda":
+    try:
+        pipe.enable_xformers_memory_efficient_attention()
+        print("✅ xFormers enabled!")
+    except Exception as e:
+        print(f"⚠️ xFormers not available: {e}")
+    pipe.enable_model_cpu_offload()
 pipe.enable_vae_slicing()
 pipe.enable_attention_slicing()
 
-# Enhanced Studio Ghibli-style transformation prompt
-prompt = (
-    "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
+# --- Image Transformation Function ---
 def transform_image(input_image):
-    input_image = input_image.resize((512, 512))
+    if input_image is None:
+        return None
+
+    try:
+        input_image = input_image.resize(INPUT_IMAGE_SIZE)
 
-    # Pass the image as `init_image`
-    output = pipe(
-        prompt=prompt,
-        init_image=input_image,  # ✅ FIXED: Changed from "image" to "init_image"
-        strength=0.65,
-        guidance_scale=5.0,  # Slightly increased for better stylization
-        num_inference_steps=25,  # More steps for higher quality output
-    )
+        prompt = f"{PROMPT_PREFIX} {TRIGGER_WORD}, portrait of a person" # Incorporate trigger word
 
-    return output.images[0]
+        output = pipe(
+            prompt=prompt,
+            image=input_image,
+            strength=STRENGTH,
+            guidance_scale=GUIDANCE_SCALE,
+            num_inference_steps=NUM_INFERENCE_STEPS,
+            negative_prompt=NEGATIVE_PROMPT,
+        ).images[0]
 
-# Gradio UI
-demo = gr.Interface(
+        return output
+    except Exception as e:
+        print(f"❌ Error during image transformation: {e}")
+        return None
+
+# --- Gradio UI ---
+iface = gr.Interface(
     fn=transform_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!",
+    title=SPACE_TITLE,
+    description=SPACE_DESCRIPTION,
+    examples=[
+        "examples/portrait1.jpg",
+        "examples/photo1.jpg",
+        "examples/landscape1.jpg",
+    ],
 )
 
+# --- Main Execution ---
 if __name__ == "__main__":
-    demo.launch()
+    # Create an 'examples' directory if it doesn't exist and add some sample images
+    if not os.path.exists("examples"):
+        os.makedirs("examples")
+        # You'll need to download or create these example images
+        # and place them in the 'examples' folder.
+        # Example:
+        # from urllib.request import urlretrieve
+        # urlretrieve("URL_TO_YOUR_EXAMPLE_IMAGE_1", "examples/portrait1.jpg")
+        # urlretrieve("URL_TO_YOUR_EXAMPLE_IMAGE_2", "examples/photo1.jpg")
+        # urlretrieve("URL_TO_YOUR_EXAMPLE_IMAGE_3", "examples/landscape1.jpg")
+        print("ℹ️ Created 'examples' directory. Please add sample images.")
+
+    iface.launch()