add width and height

app.py

@@ -39,45 +39,45 @@ for model_name, model_path in lora_models.items():
 
 lora_models["None"] = None
 
-def calculate_optimal_dimensions(image: Image.Image):
-    # Extract the original dimensions
-    original_width, original_height = image.size
-
-    # Set constants
-    MIN_ASPECT_RATIO = 9 / 16
-    MAX_ASPECT_RATIO = 16 / 9
-    FIXED_DIMENSION = 1024
-
-    # Calculate the aspect ratio of the original image
-    original_aspect_ratio = original_width / original_height
-
-    # Determine which dimension to fix
-    if original_aspect_ratio > 1:  # Wider than tall
-        width = FIXED_DIMENSION
-        height = round(FIXED_DIMENSION / original_aspect_ratio)
-    else:  # Taller than wide
-        height = FIXED_DIMENSION
-        width = round(FIXED_DIMENSION * original_aspect_ratio)
-
-    # Ensure dimensions are multiples of 8
-    width = (width // 8) * 8
-    height = (height // 8) * 8
-
-    # Enforce aspect ratio limits
-    calculated_aspect_ratio = width / height
-    if calculated_aspect_ratio > MAX_ASPECT_RATIO:
-        width = (height * MAX_ASPECT_RATIO // 8) * 8
-    elif calculated_aspect_ratio < MIN_ASPECT_RATIO:
-        height = (width / MIN_ASPECT_RATIO // 8) * 8
-
-    # Ensure width and height remain above the minimum dimensions
-    width = max(width, 576) if width == FIXED_DIMENSION else width
-    height = max(height, 576) if height == FIXED_DIMENSION else height
-
-    return width, height
+# def calculate_optimal_dimensions(image: Image.Image):
+#     # Extract the original dimensions
+#     original_width, original_height = image.size
+
+#     # Set constants
+#     MIN_ASPECT_RATIO = 9 / 16
+#     MAX_ASPECT_RATIO = 16 / 9
+#     FIXED_DIMENSION = 1024
+
+#     # Calculate the aspect ratio of the original image
+#     original_aspect_ratio = original_width / original_height
+
+#     # Determine which dimension to fix
+#     if original_aspect_ratio > 1:  # Wider than tall
+#         width = FIXED_DIMENSION
+#         height = round(FIXED_DIMENSION / original_aspect_ratio)
+#     else:  # Taller than wide
+#         height = FIXED_DIMENSION
+#         width = round(FIXED_DIMENSION * original_aspect_ratio)
+
+#     # Ensure dimensions are multiples of 8
+#     width = (width // 8) * 8
+#     height = (height // 8) * 8
+
+#     # Enforce aspect ratio limits
+#     calculated_aspect_ratio = width / height
+#     if calculated_aspect_ratio > MAX_ASPECT_RATIO:
+#         width = (height * MAX_ASPECT_RATIO // 8) * 8
+#     elif calculated_aspect_ratio < MIN_ASPECT_RATIO:
+#         height = (width / MIN_ASPECT_RATIO // 8) * 8
+
+#     # Ensure width and height remain above the minimum dimensions
+#     width = max(width, 576) if width == FIXED_DIMENSION else width
+#     height = max(height, 576) if height == FIXED_DIMENSION else height
+
+#     return width, height
 
 @spaces.GPU(durations=300)
-def infer(edit_images, prompt, lora_model, strength, seed=42, randomize_seed=False, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+def infer(edit_images, prompt, width, height, lora_model, strength, seed=42, randomize_seed=False, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
     # pipe.enable_xformers_memory_efficient_attention()
     gr.Info("Infering")
 
@@ -95,7 +95,7 @@ def infer(edit_images, prompt, lora_model, strength, seed=42, randomize_seed=Fal
         return None, None
 
 
-    width, height = calculate_optimal_dimensions(image)
+    # width, height = calculate_optimal_dimensions(image)
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
@@ -261,26 +261,28 @@ with gr.Blocks(css=css) as demo:
                     value=0.85,
                 )
 
-            #     width = gr.Slider(
-            #         label="width",
-            #         minimum=512,
-            #         maximum=3072,
-            #         step=1,
-            #         value=1024,
-            #     )
-
-            #     height = gr.Slider(
-            #         label="height",
-            #         minimum=512,
-            #         maximum=3072,
-            #         step=1,
-            #         value=1024,
-            #     )
+            with gr.Row():
+
+                width = gr.Slider(
+                    label="width",
+                    minimum=512,
+                    maximum=3072,
+                    step=1,
+                    value=1024,
+                )
+
+                height = gr.Slider(
+                    label="height",
+                    minimum=512,
+                    maximum=3072,
+                    step=1,
+                    value=1024,
+                )
 
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn = infer,
-        inputs = [edit_image, prompt, lora_model, strength, seed, randomize_seed, guidance_scale, num_inference_steps],
+        inputs = [edit_image, prompt, width, height, lora_model, strength, seed, randomize_seed, guidance_scale, num_inference_steps],
         outputs = [result, seed]
     )
 
@@ -324,4 +326,4 @@ def authenticate(username, password):
         return False
 # Launch the app with authentication
 
-demo.launch(debug=True, auth=authenticate)
+demo.launch(debug=True, auth=authenticate)