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import gradio as gr
from diffusers import StableDiffusionPipeline
import torch
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float32)
pipe.to("cuda" if torch.cuda.is_available() else "cpu")
pipe.safety_checker = lambda images, **kwargs: (images, False)
def infer(prompt, guidance_scale, num_inference_steps):
with torch.no_grad():
try:
image = pipe(prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
return image
except Exception as e:
return f"Error: {str(e)}"
with gr.Blocks() as demo:
gr.Markdown("🚀 Hyper-Sketch")
with gr.Row():
prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...", lines=2)
with gr.Row():
guidance_scale = gr.Slider(1.0, 20.0, value=7.5, step=0.1, label="Guidance Scale")
num_inference_steps = gr.Slider(10, 50, value=20, step=1, label="Inference Steps") # Lowered max steps
generate_button = gr.Button("Generate Image")
output_image = gr.Image(label="Generated Image", type="pil")
generate_button.click(infer, inputs=[prompt, guidance_scale, num_inference_steps], outputs=[output_image])
demo.launch(share=True)
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