| 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 | |