update

app.py

@@ -25,11 +25,11 @@ def clear_generation_time():
 
 
 def generating():
-    return "**Generating images...**"
+    return "Generating images..."
 
 
 def done():
-    return "**Done!**"
+    return "Done!"
 
 
 def sample_nearby_vectors(base_vector, epsilons=[0.3, 0.5, 0.7], percentages=[0.4, 0.4, 0.2]):
@@ -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 > 22:
+        if quality > 25:
             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 > 22:
+        if quality > 25:
             images, _ = generator.gen_image(features, quality_model, id_model, q_target=quality, class_rep=features)
         else:
             _, _, images, *_ = generator(features)
@@ -121,7 +121,7 @@ def image_generation(input_image, quality, random_perturbation, sigma, dimension
     return generated_images
 
 @spaces.GPU
-def process_input(image_input, num1, num2, num3, num4, num5, num6, num7, num8, random_seed, target_quality, random_perturbation, sigma, progress=gr.Progress()):
+def process_input(image_input, num1, num2, num3, num4, random_seed, target_quality, random_perturbation, sigma, progress=gr.Progress()):
     # Ensure all dimension numbers are within [0, 512)
     num1, num2, num3, num4 = [max(0, min(int(n), 511)) for n in [num1, num2, num3, num4]]
 
@@ -135,7 +135,7 @@ def process_input(image_input, num1, num2, num3, num4, num5, num6, num7, num8, r
         input_data = Image.open(image_input)
         input_data = np.array(input_data.resize((112, 112)))
 
-    generated_images = image_generation(input_data, target_quality, random_perturbation, sigma, [num1, num2, num3, num4, num5, num6, num7, num8], progress)
+    generated_images = image_generation(input_data, target_quality, random_perturbation, sigma, [num1, num2, num3, num4], progress)
 
     return generated_images
 
@@ -152,10 +152,6 @@ def toggle_inputs(random_perturbation):
         gr.update(interactive=not random_perturbation),  # num2
         gr.update(interactive=not random_perturbation),  # num3
         gr.update(interactive=not random_perturbation),  # num4
-        gr.update(interactive=not random_perturbation),  # num5
-        gr.update(interactive=not random_perturbation),  # num6
-        gr.update(interactive=not random_perturbation),  # num7
-        gr.update(interactive=not random_perturbation),  # num8
     ]
 
 
@@ -189,13 +185,13 @@ def main():
 
                 with gr.Row():
                     num1 = gr.Number(label="Dimension 1", value=0, minimum=0, maximum=511, step=1)
-                    num2 = gr.Number(label="Dimension 2", value=25, minimum=0, maximum=511, step=1)
-                    num3 = gr.Number(label="Dimension 3", value=50, minimum=0, maximum=511, step=1)
-                    num4 = gr.Number(label="Dimension 4", value=75, minimum=0, maximum=511, step=1)
-                    num5 = gr.Number(label="Dimension 5", value=100, minimum=0, maximum=511, step=1)
-                    num6 = gr.Number(label="Dimension 6", value=125, minimum=0, maximum=511, step=1)
-                    num7 = gr.Number(label="Dimension 7", value=150, minimum=0, maximum=511, step=1)
-                    num8 = gr.Number(label="Dimension 8", value=200, minimum=0, maximum=511, step=1)
+                    num2 = gr.Number(label="Dimension 2", value=0, minimum=0, maximum=511, step=1)
+                    num3 = gr.Number(label="Dimension 3", value=0, minimum=0, maximum=511, step=1)
+                    num4 = gr.Number(label="Dimension 4", value=0, minimum=0, maximum=511, step=1)
+                    # num5 = gr.Number(label="Dimension 5", value=0, minimum=0, maximum=511, step=1)
+                    # num6 = gr.Number(label="Dimension 6", value=0, minimum=0, maximum=511, step=1)
+                    # num7 = gr.Number(label="Dimension 7", value=0, minimum=0, maximum=511, step=1)
+                    # 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=35, step=1, value=24)
@@ -227,7 +223,7 @@ def main():
         random_perturbation.change(
             fn=toggle_inputs,
             inputs=[random_perturbation],
-            outputs=[sigma, num1, num2, num3, num4, num5, num6, num7, num8]
+            outputs=[sigma, num1, num2, num3, num4]
         )
 
         generated_images = gr.State([])
@@ -242,7 +238,7 @@ def main():
             outputs=[generation_time]
         ).then(
             fn=process_input,
-            inputs=[image_file, num1, num2, num3, num4, num5, num6, num7, num8, random_seed, target_quality, random_perturbation, sigma],
+            inputs=[image_file, num1, num2, num3, num4, random_seed, target_quality, random_perturbation, sigma],
             outputs=[generated_images]
         ).then(
             fn=done,