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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()
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