Create app.py

app.py

@@ -0,0 +1,31 @@
+import gradio as gr
+import torch
+model_map = torch.hub.load('nateraw/image-generation:main', 'model_map')
+class InferenceWrapper:
+    def __init__(self, model):
+        self.model = model
+        self.pipe = torch.hub.load('nateraw/image-generation:main', 'styleganv3', pretrained=self.model, videos=True)
+    def __call__(self, seed1, seed2, seed3, w_frames, 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, videos=True)
+        else:
+            print(f"Model '{model}' already loaded, reusing it.")
+        return self.pipe([seed1, seed2, seed3], w_frames=w_frames)
+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 For Image 1'),
+        gr.inputs.Slider(minimum=0, maximum=999999999, step=1, default=0, label='Random Seed For Image 2'),
+        gr.inputs.Slider(minimum=0, maximum=999999999, step=1, default=0, label='Random Seed For Image 3'),
+        gr.inputs.Radio([60, 120, 240], type="value", default=60, label='Frames'),
+        gr.inputs.Radio(list(model_map), type="value", default='wikiart-1024', label='Pretrained Model')
+    ],
+    outputs='image',
+    examples=[[0, 1, 2, 60, 'wikiart-1024']],
+    enable_queue=True
+).launch()