update

app.py

@@ -102,14 +102,14 @@ def image_generation(input_image, quality, random_perturbation, sigma, dimension
             updated_feature = updated_feature / np.linalg.norm(updated_feature, 2, 1, True) * norm
             features.append(updated_feature)
         features = torch.tensor(np.vstack(features)).float().to(device)
-        if quality > 25:
+        if quality > 22:
             images, _ = generator.gen_image(features, quality_model, id_model, q_target=quality)
         else:
             _, _, images, *_ = generator(features)
     else:
         features = torch.repeat_interleave(torch.tensor(feature), 3, dim=0)
         features = sample_nearby_vectors(features, [sigma], [1]).float().to(device)
-        if quality > 25:
+        if quality > 22:
             images, _ = generator.gen_image(features, quality_model, id_model, q_target=quality, class_rep=features)
         else:
             _, _, images, *_ = generator(features)
@@ -194,7 +194,7 @@ def main():
                     # num8 = gr.Number(label="Dimension 8", value=0, minimum=0, maximum=511, step=1)
 
                 random_seed = gr.Number(label="Random Seed", value=42, minimum=0, maximum=MAX_SEED, step=1)
-                target_quality = gr.Slider(label="Minimum Quality", minimum=22, maximum=30, step=1, value=24)
+                target_quality = gr.Slider(label="Minimum Quality", minimum=22, maximum=30, step=1, value=22)
 
                 with gr.Row():
                     random_perturbation = gr.Checkbox(label="Random Perturbation")