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import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/sdxl-turbo" # Replace with the model you want to use
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)
MAX_SEED = np.iinfo(np.int32).max
def generate_cat_image():
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
image = pipe(
prompt="A cute cat, highly detailed, 8k",
negative_prompt="",
guidance_scale=7.5,
num_inference_steps=20,
width=512,
height=512,
generator=generator,
).images[0]
return image
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(" # Cat Image Generator 🐱")
result = gr.Image(label="Generated Cat Image", show_label=False)
run_button = gr.Button("Generate New Cat")
run_button.click(fn=generate_cat_image, inputs=[], outputs=[result])
if __name__ == "__main__":
demo.launch()
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