| from diffusers import StableDiffusionPipeline | |
| import torch | |
| import gradio as gr | |
| print("Loading the Stable Diffusion model (this may take a minute)...") | |
| pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) | |
| pipeline.to("cuda") | |
| print("Model loaded successfully!") | |
| def generate_image(prompt, guidance_scale): | |
| """ | |
| Generate an image from a text prompt using Stable Diffusion. | |
| Args: | |
| prompt (str): The text prompt for generating the image. | |
| guidance_scale (float): Controls how closely the image matches the prompt. | |
| Returns: | |
| PIL.Image.Image: The generated image. | |
| """ | |
| if not prompt.strip(): | |
| return "Error: Prompt cannot be empty!" | |
| try: | |
| image = pipeline(prompt, guidance_scale=guidance_scale).images[0] | |
| return image | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 🖼 Text-to-Image Generator by Abhinav ") | |
| gr.Markdown("Enter a text description below to generate an image.") | |
| with gr.Row(): | |
| prompt_input = gr.Textbox(label="Enter Image Prompt", placeholder="e.g., A beautiful place in Kerala", lines=2) | |
| guidance_slider = gr.Slider(label="Guidance Scale", minimum=5.0, maximum=15.0, value=7.5, step=0.5) | |
| generate_button = gr.Button("Generate Image") | |
| output_image = gr.Image(label="Generated Image") | |
| generate_button.click( | |
| fn=generate_image, | |
| inputs=[prompt_input, guidance_slider], | |
| outputs=output_image | |
| ) | |
| demo.launch() | |