| import gradio as gr | |
| import torch | |
| model_map = torch.hub.load('nateraw/image-generation:main', 'model_map') | |
| on_gpu = torch.cuda.is_available() | |
| print(f"GPU enabled? - {'🔴' if not on_gpu else '🟢'}") | |
| class InferenceWrapper: | |
| def __init__(self, model): | |
| self.model = model | |
| self.pipe = torch.hub.load('nateraw/image-generation:main', 'styleganv3', pretrained=self.model) | |
| def __call__(self, seed, model): | |
| if model != self.model: | |
| print(f"Loading model: {model}") | |
| self.model = model | |
| self.pipe = torch.hub.load('nateraw/image-generation:main', 'styleganv3', pretrained=self.model) | |
| else: | |
| print(f"Model '{model}' already loaded, reusing it.") | |
| return self.pipe(seed) | |
| wrapper = InferenceWrapper('wikiart-1024') | |
| def fn(seed, model): | |
| return wrapper(seed, model) | |
| gr.Interface( | |
| fn, | |
| inputs=[ | |
| gr.inputs.Slider(minimum=0, maximum=999999999, step=1, default=0, label='Random Seed'), | |
| gr.inputs.Radio(list(model_map), type="value", default='wikiart-1024', label='Pretrained Model') | |
| ], | |
| outputs='image', | |
| examples=[[343, 'wikiart-1024'], [456, 'landscapes-256'], [1234, 'stylegan3-r-ffhqu-256x256.pkl']], | |
| enable_queue=True | |
| ).launch() |