Create app.py

app.py

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+import torch
+import gradio as gr
+from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+
+# Initialize model and pipeline once at startup
+base = "stabilityai/stable-diffusion-xl-base-1.0"
+repo = "ByteDance/SDXL-Lightning"
+ckpt = "sdxl_lightning_4step_unet.safetensors"
+
+# Load model with float32 precision for CPU compatibility
+unet = UNet2DConditionModel.from_config(base, subfolder="unet").to(torch.float32)
+unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cpu"))
+
+# Create pipeline with CPU configuration
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    base, 
+    unet=unet, 
+    torch_dtype=torch.float32
+).to("cpu")
+
+# Configure scheduler
+pipe.scheduler = EulerDiscreteScheduler.from_config(
+    pipe.scheduler.config, 
+    timestep_spacing="trailing"
+)
+
+# Expanded list of predefined elements
+elements_list = [
+    "Kittens", "Tea", "Home", "Snow", "Young Girl", "Stars",
+    "Blanket", "Books", "Candles", "Flowers", "Moon", "Cookies",
+    "Fireplace", "Pillows", "Mittens", "Lanterns", "Socks",
+    "Hot Chocolate", "Snowflakes", "Winter Scarf", "Marshmallows",
+    "Vintage Clock", "Knitted Sweater", "Fairy Lights", "Porcelain Cup"
+]
+
+def generate_image(custom_text, elements, steps):
+    """Generate image using the provided text, selected elements, and steps"""
+    # Construct the prompt
+    prompt_parts = []
+    if custom_text.strip():
+        prompt_parts.append(custom_text)
+    if elements:
+        prompt_parts.append(", ".join(elements))
+    
+    prompt = ", ".join(prompt_parts) or "a beautiful image" 
+    
+    image = pipe(
+        prompt,
+        num_inference_steps=int(steps),
+        guidance_scale=0,
+        width=768,
+        height=960
+    ).images[0]
+    
+    return image
+
+# Create Gradio interface
+with gr.Blocks(title="Good Night Image Diffuser") as demo:
+    gr.Markdown("# 🌙 Generate Good Night Wish Images")
+    gr.Markdown("Create personalized good night images with your message and favorite elements!")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            custom_text = gr.Textbox(
+                label="Your Message",
+                value="Create a cozy and heartwarming scene. Use a warm, pastel color palette with soft shadows and subtle textures to evoke comfort and nostalgia. Additional elements to include:",
+                max_lines=3
+            )
+            elements = gr.CheckboxGroup(
+                label="Image Elements",
+                choices=elements_list,
+                value=["Kittens", "Moon"],
+                info="Select elements to include in your image"
+            )
+            steps_slider = gr.Slider(
+                label="Number of Inference Steps",
+                minimum=1,
+                maximum=8,
+                value=4,
+                step=2,
+                info="Adjust the number of denoising steps (more steps can improve quality but take longer)"
+            )
+            generate_btn = gr.Button("✨ Generate Image", variant="primary")
+        
+        with gr.Column(scale=1):
+            output_image = gr.Image(
+                label="Generated Image",
+                width=768,
+                height=960,
+                elem_id="output-image"
+            )
+
+    # Connect components
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[custom_text, elements, steps_slider],
+        outputs=output_image,
+        api_name="generate"
+    )
+
+if __name__ == "__main__":
+    demo.launch()