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import torch
import gradio as gr
from torchvision import transforms
from PIL import Image
from model import model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
latent_dim = model.config.latent_dim
def generate_from_noise():
z = torch.randn(1, latent_dim).to(device)
with torch.no_grad():
generated = model.decode(z)
gen_img = generated.squeeze(0).permute(1, 2, 0).cpu().numpy()
gen_pil = Image.fromarray((gen_img * 255).astype("uint8")).resize((512, 512))
return gen_pil
def get_interface():
with gr.Blocks() as iface:
gr.Markdown("## Generate from Random Noise")
generate_button = gr.Button("Generate Image")
output_image = gr.Image(label="Generated Image", type="pil")
generate_button.click(fn=generate_from_noise, inputs=[], outputs=output_image)
examples = [[]]
gr.Examples(
examples=examples,
inputs=[],
outputs=output_image,
fn=generate_from_noise,
cache_examples=False
)
return iface
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