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import gradio as gr
import torch
from diffusers import StableDiffusionXLPipeline
from datetime import datetime
import os
class RafayyAI:
def __init__(self):
self.model = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16"
)
if torch.cuda.is_available():
self.model = self.model.to("cuda")
def generate_image(self, prompt, negative_prompt=""):
# Generate unique filename
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"generated_{timestamp}.png"
# Generate image
image = self.model(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=30,
guidance_scale=7.5
).images[0]
# Save image
image.save(filename)
return filename
# Initialize the AI
rafayy = RafayyAI()
# Create Gradio interface
def generate(prompt, negative_prompt=""):
return rafayy.generate_image(prompt, negative_prompt)
demo = gr.Interface(
fn=generate,
inputs=[
gr.Textbox(label="Prompt", placeholder="Describe the image you want to generate..."),
gr.Textbox(label="Negative Prompt (Optional)", placeholder="What you don't want in the image...")
],
outputs=gr.Image(label="Generated Image"),
title="Rafayy AI Image Generator",
description="Generate unique images from text descriptions",
examples=[
["A beautiful sunset over mountains", "blur, low quality"],
["A futuristic city at night", "dark, blurry"],
["A cute cat playing with yarn", "ugly, distorted"]
]
)
# Launch the app
if __name__ == "__main__":
demo.launch() |