Remove sample size argument

app.py

@@ -29,11 +29,13 @@ current_image_size = 256
 current_vae_model = "stabilityai/sd-vae-ft-mse"
 
 
-def generate(image_size, vae_model, class_label, cfg_scale, num_sampling_steps, n, seed):
+def generate(image_size, vae_model, class_label, cfg_scale, num_sampling_steps, seed):
+    n = 1
     image_size = int(image_size.split("x")[0])
     global current_image_size
     if image_size != current_image_size:
         global model
+        model = model.to("cpu")
         del model
         if device == "cuda":
             torch.cuda.empty_cache()
@@ -126,15 +128,15 @@ with gr.Blocks() as demo:
                         )
                     cfg_scale = gr.inputs.Slider(minimum=1, maximum=25, step=0.1, default=4.0, label='Classifier-free Guidance Scale')
                     steps = gr.inputs.Slider(minimum=4, maximum=1000, step=1, default=75, label='Sampling Steps')
-                    n = gr.inputs.Slider(minimum=1, maximum=16, step=1, default=1, label='Number of Samples')
+                    # n = gr.inputs.Slider(minimum=1, maximum=16, step=1, default=1, label='Number of Samples')
                     seed = gr.inputs.Number(default=0, label='Seed')
                     button = gr.Button("Generate", variant="primary")
                 with gr.Column():
                     output = gr.Gallery(label='Generated Images').style(grid=[2], height="auto")
-                    button.click(generate, inputs=[image_size, vae_model, i1k_class, cfg_scale, steps, n, seed], outputs=[output])
+                    button.click(generate, inputs=[image_size, vae_model, i1k_class, cfg_scale, steps, seed], outputs=[output])
             with gr.Row():
                 ex = gr.Examples(examples=examples, fn=generate,
-                                 inputs=[image_size, vae_model, i1k_class, cfg_scale, steps, n, seed],
+                                 inputs=[image_size, vae_model, i1k_class, cfg_scale, steps, seed],
                                  outputs=[output],
                                  cache_examples=True)