Add application file

app.py

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+import gradio as gr
+from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler, DDIMScheduler, PNDMScheduler
+import torch
+from PIL import ImageEnhance, Image
+import numpy as np
+
+# Load Stable Diffusion pipeline
+model_id = "CompVis/stable-diffusion-v1-4"
+default_scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=default_scheduler, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+# Scheduler options
+schedulers = {
+    "Artistic & Imaginative (Euler Ancestral) - Recommended for creative scenes, moderate speed": EulerAncestralDiscreteScheduler,
+    "Photo-Realistic (PNDM) - Best for realistic details, moderate speed": PNDMScheduler,
+    "High-Definition & Fast (DDIM) - Good quality with fastest speed": DDIMScheduler,
+}
+
+# Main image generation function with dynamic scheduling and size option
+def generate_image(prompt, use_categories, genre, style, theme, lighting, scheduler_choice, quality, size):
+    # Check if additional categories should be added to the prompt
+    if use_categories:
+        prompt_text = (
+            f"{prompt.strip()} in a {genre.lower()} wallpaper style, "
+            f"with {style.lower()} visuals, focusing on a {theme.lower()} theme "
+            f"and {lighting.lower()} lighting."
+        )
+    else:
+        prompt_text = prompt.strip()
+
+    # Set the scheduler based on user choice
+    scheduler = schedulers[scheduler_choice].from_pretrained(model_id, subfolder="scheduler")
+    pipe.scheduler = scheduler
+
+    # Set output size based on selection
+    image_size = (512, 512) if size == "Profile Picture" else (1024, 768)
+
+    # Generate image with specified quality and size
+    with torch.no_grad():
+        image = pipe(prompt_text, num_inference_steps=quality, guidance_scale=7.5).images[0]
+        image = image.resize(image_size)  # Resize image to fit selected dimensions
+
+    return np.array(image)  # Return as NumPy array for Gradio
+
+# Post-processing function for brightness and contrast
+def adjust_brightness_contrast(image, brightness, contrast):
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
+    image = ImageEnhance.Brightness(image).enhance(brightness)
+    image = ImageEnhance.Contrast(image).enhance(contrast)
+    return np.array(image)
+
+# Warning function to show a message if the user selects a high value for quality
+def show_warning(quality):
+    if quality > 80:
+        return "⚠️ High Quality: This setting may slow down generation and might not provide additional visual improvement. Consider using 50-80 steps for best results."
+    return ""
+
+# Build Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# ✨ AI-Powered Wallpaper/Profile Picture Generator\n🖼️ A tool to generate and fine-tune AI-created wallpapers and profile pictures with adjustable styles and effects.")
+    gr.Markdown("⚠️ **Live effects and advanced prompt engineering coming soon! Disclaimer**: Results may not always be accurate or perfectly aligned with your prompt. Experiment with prompt adjustments and settings to get the best results.")
+    
+    # Image Generation Section
+    with gr.Tab("Image Generator"):
+        gr.Markdown("## Generate an Image")
+        
+        with gr.Row():
+            with gr.Column():
+                custom_prompt = gr.Textbox(label="Custom Prompt", placeholder="Describe your image (e.g., 'A forest at sunset')")
+                
+                # Toggle for using additional categories
+                use_categories = gr.Checkbox(label="Enable Advanced Settings (Genre, Style, Theme, Lighting)", value=False)
+                
+                # Additional categories, hidden by default and shown only if use_categories is checked
+                with gr.Accordion("Advanced Settings", open=False):
+                    genre = gr.Dropdown(["Futuristic", "Nature", "Abstract", "Fantasy", "Sci-Fi", "Cyberpunk"], label="Genre")
+                    style = gr.Dropdown(["Realistic", "Surreal", "Digital Art", "Cartoon", "Photorealistic"], label="Style")
+                    theme = gr.Dropdown(["Landscape", "Portrait", "Abstract Patterns", "Architecture"], label="Theme")
+                    lighting = gr.Dropdown(["Warm", "Cool", "Cinematic", "Soft", "Neon"], label="Lighting")
+
+                quality = gr.Slider(20, 150, value=80, step=10, label="Image Quality", info="Higher values yield more detail but take longer to generate.")
+                warning_message = gr.Markdown("")
+                
+                # Scheduler selection with default option
+                scheduler_choice = gr.Dropdown(
+                    [
+                    "Artistic & Imaginative (Euler Ancestral) - Recommended for creative scenes, moderate speed",
+                    "Photo-Realistic (PNDM) - Best for realistic details, moderate speed",
+                    "High-Definition & Fast (DDIM) - Good quality with fastest speed"
+                    ],
+                    label="Artistic Style & Speed",
+                    value="Artistic & Imaginative (Euler Ancestral) - Recommended for creative scenes, moderate speed"
+                )
+                
+                size = gr.Dropdown(["Profile Picture", "Wallpaper"], label="Image Size", value="Profile Picture")
+                generate_button = gr.Button("Generate Image")
+
+            with gr.Column():
+                generated_image = gr.Image(label="Generated Image", interactive=False)
+
+        # Display warning message for high-quality settings
+        quality.change(show_warning, inputs=[quality], outputs=warning_message)
+
+        # Bind the generate function to the generate button
+        generate_button.click(
+            fn=generate_image,
+            inputs=[custom_prompt, use_categories, genre, style, theme, lighting, scheduler_choice, quality, size],
+            outputs=generated_image
+        )
+
+    # Post-Processing Section
+    with gr.Tab("Edit Generated Image"):
+        gr.Markdown("## Adjust Brightness & Contrast")
+        
+        with gr.Row():
+            with gr.Column():
+                brightness_slider = gr.Slider(0.5, 2.0, value=1.0, label="Brightness")
+                contrast_slider = gr.Slider(0.5, 2.0, value=1.0, label="Contrast")
+                apply_adjustments = gr.Button("Apply Adjustments")
+
+            with gr.Column():
+                output_image = gr.Image(label="Adjusted Image", interactive=False)
+
+        # Bind the brightness and contrast adjustment function to the Apply Adjustments button
+        apply_adjustments.click(
+            fn=adjust_brightness_contrast,
+            inputs=[generated_image, brightness_slider, contrast_slider],
+            outputs=output_image
+        )
+
+# Launch with a public shareable link
+demo.launch(share=True)

requirements.txt

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+diffusers
+torch
+gradio
+pillow