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import gradio as gr
import torch
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
from PIL import Image
import time
# Load CPU-optimized model
model_id = "OFA-Sys/small-stable-diffusion-v0" # Smaller model for CPU
pipe = StableDiffusionPipeline.from_pretrained(
model_id,
torch_dtype=torch.float32 # Force float32 for CPU
)
# Use DPMSolver for better CPU performance
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cpu")
def generate_image(text):
if not text:
return None, "Please enter some text first!"
start_time = time.time()
try:
# Generate with reduced steps for faster processing
image = pipe(
text,
num_inference_steps=20, # Reduced from typical 50 steps
guidance_scale=7.5
).images[0]
if image.mode != "RGB":
image = image.convert("RGB")
gen_time = time.time() - start_time
return image, f"Generated in {gen_time:.1f} seconds"
except Exception as e:
return None, f"Error: {str(e)}"
# Create Gradio interface with loading states
with gr.Blocks(title="CPU Poetry to Image") as demo:
gr.Markdown("# 💖 CPU-Friendly Poetry to Image")
gr.Markdown("Note: Generation may take 2-5 minutes on CPU")
with gr.Row():
with gr.Column():
input_text = gr.Textbox(
label="Your Romantic Text",
placeholder="e.g., 'Your eyes sparkle like stars'",
lines=3
)
generate_btn = gr.Button("Create Magic ✨")
with gr.Column():
output_image = gr.Image(label="Your Generated Art")
time_info = gr.Textbox(label="Generation Time")
examples = gr.Examples(
examples=[
["A moonlit beach with heart-shaped waves"],
["Two roses intertwined with golden light"],
["A love letter floating in the clouds"]
],
inputs=[input_text]
)
generate_btn.click(
fn=generate_image,
inputs=[input_text],
outputs=[output_image, time_info],
api_name="generate"
)
if __name__ == "__main__":
demo.launch() |