Create app.py

app.py

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+import torch
+from diffusers import StableDiffusionPipeline
+import gradio as gr
+
+# Use GPU if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load Stable Diffusion v1.5 from Hugging Face
+pipe = StableDiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-v1-5",
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+    revision="fp16" if device == "cuda" else None,
+    use_safetensors=True
+)
+pipe = pipe.to(device)
+
+# Inference function
+def generate(prompt, guidance, steps, width, height):
+    image = pipe(prompt=prompt, guidance_scale=guidance, num_inference_steps=steps, height=height, width=width).images[0]
+    return image
+
+# Gradio UI
+title = "🎨 Offline Text-to-Image Generator (Stable Diffusion v1.5)"
+description = "Generate images from text prompts using a fully self-hosted Stable Diffusion model."
+
+with gr.Blocks() as demo:
+    gr.Markdown(f"# {title}")
+    gr.Markdown(description)
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Enter your prompt", placeholder="A steampunk dragon flying over a futuristic city")
+            guidance = gr.Slider(1, 20, value=7.5, step=0.5, label="Guidance Scale")
+            steps = gr.Slider(10, 100, value=30, step=5, label="Inference Steps")
+            width = gr.Slider(256, 768, value=512, step=64, label="Image Width")
+            height = gr.Slider(256, 768, value=512, step=64, label="Image Height")
+            submit = gr.Button("Generate Image")
+
+        with gr.Column():
+            output = gr.Image(label="Generated Image")
+
+    submit.click(fn=generate, inputs=[prompt, guidance, steps, width, height], outputs=output)
+
+demo.launch()