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app.py

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+import os
+import time
+import torch
+from diffusers import StableDiffusionXLImg2ImgPipeline
+from diffusers.utils import load_image
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+import json
+import gradio as gr
+import tempfile
+
+# Set environment variable to reduce memory fragmentation
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+
+# Check if CUDA is available, fallback to CPU
+device = "cuda" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if device == "cuda" else torch.float32
+
+# Load pipeline with error handling for HF Spaces
+try:
+    pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-xl-refiner-1.0",
+        torch_dtype=torch_dtype,
+        variant="fp16" if device == "cuda" else None,
+        use_safetensors=True
+    )
+    
+    # Move to device
+    pipe = pipe.to(device)
+    
+    # Enable optimizations based on available hardware
+    if device == "cuda":
+        # Use CPU offloading to reduce VRAM usage on GPU
+        pipe.enable_model_cpu_offload()
+        
+        # Try to enable memory efficient attention
+        try:
+            pipe.enable_xformers_memory_efficient_attention()
+        except (ModuleNotFoundError, ImportError):
+            print("xformers not available, using attention slicing")
+            pipe.enable_attention_slicing()
+    else:
+        # For CPU inference, enable attention slicing
+        pipe.enable_attention_slicing()
+        
+except Exception as e:
+    print(f"Error loading pipeline: {e}")
+    pipe = None
+
+
+def img2img(
+    uploaded_image,
+    image_url: str,
+    prompt: str,
+    negative_prompt: str = "",
+    strength: float = 0.7,
+    guidance_scale: float = 3.5,
+    num_inference_steps: int = 50,
+    seed: int = -1,
+):
+    if pipe is None:
+        return None, "❌ Model failed to load. Please try again later.", None
+        
+    try:
+        # Choose image source
+        if uploaded_image is not None:
+            init_image = Image.open(uploaded_image).convert("RGB")
+        elif image_url.strip() != "":
+            try:
+                init_image = load_image(image_url).convert("RGB")
+            except Exception as e:
+                return None, f"❌ Failed to load image from URL: {str(e)}", None
+        else:
+            return None, "❌ Please upload an image or enter a valid URL", None
+
+        # Resize image (keeping aspect ratio consideration for better results)
+        init_image.thumbnail((1024, 1024), Image.Resampling.LANCZOS)
+        
+        # Ensure dimensions are multiples of 8 for SDXL
+        width, height = init_image.size
+        width = (width // 8) * 8
+        height = (height // 8) * 8
+        init_image = init_image.resize((width, height))
+
+        # Set seed and generator
+        if seed == -1:
+            generator = torch.Generator(device=device)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+
+        # Validate inputs
+        if not prompt.strip():
+            return None, "❌ Please enter a prompt", None
+
+        # Run inference with progress tracking
+        with torch.inference_mode():
+            result = pipe(
+                prompt=prompt,
+                negative_prompt=negative_prompt if negative_prompt.strip() else None,
+                image=init_image,
+                strength=max(0.1, min(1.0, strength)),  # Clamp strength
+                guidance_scale=max(1.0, min(20.0, guidance_scale)),  # Clamp guidance
+                num_inference_steps=max(10, min(100, num_inference_steps)),  # Clamp steps
+                generator=generator
+            ).images[0]
+
+        used_seed = generator.initial_seed()
+
+        # Create metadata dictionary
+        metadata = {
+            "prompt": prompt,
+            "negative_prompt": negative_prompt,
+            "seed": used_seed,
+            "model": "stabilityai/stable-diffusion-xl-refiner-1.0",
+            "pipeline": "StableDiffusionXLImg2ImgPipeline",
+            "guidance_scale": guidance_scale,
+            "strength": strength,
+            "steps": num_inference_steps,
+            "width": result.width,
+            "height": result.height,
+            "device": device
+        }
+
+        # Save metadata into PNG
+        png_info = PngInfo()
+        png_info.add_text("parameters", json.dumps(metadata))
+
+        # Use temporary file for HF Spaces
+        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_file:
+            output_path = tmp_file.name
+            result.save(output_path, format="PNG", pnginfo=png_info)
+
+        # Build markdown preview of metadata
+        metadata_str = (
+            f"**Prompt:** {metadata['prompt']}\n\n"
+            f"**Negative Prompt:** {metadata['negative_prompt']}\n\n"
+            f"**Seed:** {metadata['seed']}\n\n"
+            f"**Model:** {metadata['model']}\n\n"
+            f"**Guidance Scale:** {metadata['guidance_scale']}\n\n"
+            f"**Strength:** {metadata['strength']}\n\n"
+            f"**Steps:** {metadata['steps']}\n\n"
+            f"**Dimensions:** {metadata['width']}x{metadata['height']}\n\n"
+            f"**Device:** {metadata['device']}"
+        )
+
+        return output_path, f"✅ **Generation Complete!**\n\n{metadata_str}", output_path
+
+    except torch.cuda.OutOfMemoryError:
+        return None, "❌ GPU out of memory. Try reducing image size or inference steps.", None
+    except Exception as e:
+        return None, f"❌ Error during generation: {str(e)}", None
+
+
+# Define UI components with better styling
+title = "🎨 SDXL Image-to-Image Editor"
+description = """
+Transform your images with AI! Upload an image and describe the changes you want to make.
+
+**Tips:**
+- Use detailed prompts for better results
+- Lower strength values preserve more of the original image
+- Higher guidance scale follows your prompt more closely
+"""
+
+# Custom CSS for better appearance
+css = """
+.gradio-container {
+    font-family: 'IBM Plex Sans', sans-serif;
+}
+.gr-button {
+    color: white;
+    background: linear-gradient(90deg, #4f46e5, #7c3aed);
+    border: none;
+}
+.gr-button:hover {
+    background: linear-gradient(90deg, #4338ca, #6d28d9);
+}
+"""
+
+with gr.Blocks(title=title, css=css, theme=gr.themes.Soft()) as demo:
+    gr.Markdown(f"# {title}")
+    gr.Markdown(description)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 📸 Input Image")
+            uploaded_image = gr.Image(
+                label="Upload Image", 
+                type="filepath",
+                height=300
+            )
+            
+            gr.Markdown("**Or**")
+            image_url = gr.Textbox(
+                label="Image URL", 
+                placeholder="https://example.com/image.jpg",
+                info="Paste a direct link to an image"
+            )
+
+            gr.Markdown("### ✍️ Prompts")
+            prompt = gr.Textbox(
+                label="Prompt", 
+                placeholder="a beautiful sunset over mountains, photorealistic, detailed",
+                lines=3,
+                info="Describe what you want to see"
+            )
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt", 
+                placeholder="blurry, low quality, distorted",
+                lines=2,
+                info="What to avoid in the image"
+            )
+
+            gr.Markdown("### ⚙️ Settings")
+            with gr.Row():
+                strength = gr.Slider(
+                    minimum=0.1, maximum=1.0, value=0.7, step=0.05,
+                    label="Transformation Strength",
+                    info="0.1 = subtle changes, 1.0 = major changes"
+                )
+                guidance_scale = gr.Slider(
+                    minimum=1.0, maximum=20.0, value=7.5, step=0.5,
+                    label="Guidance Scale",
+                    info="How closely to follow the prompt"
+                )
+                
+            with gr.Row():
+                num_inference_steps = gr.Slider(
+                    minimum=10, maximum=50, step=5, value=30,
+                    label="Quality Steps",
+                    info="More steps = higher quality but slower"
+                )
+                seed = gr.Slider(
+                    minimum=-1, maximum=999999, step=1, value=-1,
+                    label="Seed",
+                    info="-1 for random"
+                )
+
+            submit_btn = gr.Button("🚀 Generate Image", variant="primary", size="lg")
+
+        with gr.Column(scale=1):
+            gr.Markdown("### 🖼️ Result")
+            image_output = gr.Image(label="Generated Image", height=400)
+            download_button = gr.File(label="📥 Download Full Resolution", visible=False)
+            
+            gr.Markdown("### 📊 Generation Details")
+            metadata_output = gr.Markdown()
+
+    # Event handlers
+    submit_btn.click(
+        fn=img2img,
+        inputs=[
+            uploaded_image,
+            image_url,
+            prompt,
+            negative_prompt,
+            strength,
+            guidance_scale,
+            num_inference_steps,
+            seed
+        ],
+        outputs=[image_output, metadata_output, download_button]
+    ).then(
+        lambda x: gr.update(visible=x is not None),
+        inputs=[image_output],
+        outputs=[download_button]
+    )
+
+    # Examples
+    gr.Markdown("### 🎯 Examples")
+    gr.Examples(
+        examples=[
+            ["", "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png", "make it a van gogh painting", "blurry, low quality", 0.8, 7.5, 30, 42],
+            ["", "", "turn into a cyberpunk cityscape", "blurry, distorted", 0.9, 8.0, 30, 123],
+        ],
+        inputs=[uploaded_image, image_url, prompt, negative_prompt, strength, guidance_scale, num_inference_steps, seed],
+    )
+
+# Launch configuration for HF Spaces
+if __name__ == "__main__":
+    demo.queue(max_size=20)  # Enable queuing for better performance
+    demo.launch(
+        show_error=True,
+        share=False,  # Don't create gradio.live links in HF Spaces
+        inbrowser=False,  # Don't try to open browser in cloud environment
+        quiet=False
+    )

requirements.txt

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+torch>=2.0.0
+diffusers>=0.24.0
+transformers>=4.25.0
+accelerate>=0.20.0
+gradio>=4.0.0
+Pillow>=9.0.0
+xformers>=0.0.20