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import numpy as np
import gradio as gr
from datasets import load_dataset
def generate_random_data():
# Load the dataset with the `large_random_1k` subset
dataset = load_dataset('poloclub/diffusiondb', 'large_random_1k')
# All data are stored in the `train` split
my_1k_data = dataset['train']
random_i = np.random.choice(range(my_1k_data.num_rows))
prompt = my_1k_data['prompt'][random_i]
image = my_1k_data['image'][random_i]
seed = my_1k_data['seed'][random_i]
step = my_1k_data['step'][random_i]
cfg = my_1k_data['cfg'][random_i]
sampler = my_1k_data['sampler'][random_i]
return prompt, image, seed, step, cfg, sampler
def random_data():
prompt, image, seed, step, cfg, sampler = generate_random_data()
data = {
'Prompt': prompt,
'Seed': seed,
'Step': step,
'CFG': cfg,
'Sampler': sampler
}
with open("random_data.txt", "w") as file:
for key, value in data.items():
file.write(f"{key}: {value}\n")
return prompt, image, seed, step, cfg, sampler
iface = gr.Interface(fn=random_data, inputs=None, outputs=[
gr.outputs.Textbox(label="Prompt"),
gr.outputs.Image(label="Image", type="pil"),
gr.outputs.Textbox(label="Seed"),
gr.outputs.Textbox(label="Step"),
gr.outputs.Textbox(label="CFG"),
gr.outputs.Textbox(label="Sampler")
], title="Stable Diffusion DB", description="By Falah.G.S AI Developer")
iface.launch(debug=True)
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