22

app.py

@@ -108,11 +108,12 @@ def inference(
     mask_image,
     control_context_scale = 0.75,
     seed=42,
-    randomize_seed=True,
-    guidance_scale=1.5,
+    randomize_seed=False,
+    # guidance_scale=1,
     num_inference_steps=8,
     progress=gr.Progress(track_tqdm=True),
 ):
+    guidance_scale=1
     timestamp = time.time()
     print(f"timestamp: {timestamp}")
 
@@ -141,13 +142,13 @@ def inference(
     else:
         mask_image = torch.ones([1, 1, sample_size[0], sample_size[1]]) * 255
 
-    print("DEBUG: control_image_torch")
-    processor = Processor('canny')
-    control_image, w, h = image_utils.rescale_image(input_image, scale_target, 8, max_size=1280)
-    control_image = control_image.resize((1024, 1024))
-    control_image = processor(control_image, to_pil=True)
-    control_image = control_image.resize((width, height))
-    control_image_torch = get_image_latent(control_image, sample_size=sample_size)[:, :, 0]
+    # print("DEBUG: control_image_torch")
+    # processor = Processor('canny')
+    # control_image, w, h = image_utils.rescale_image(input_image, scale_target, 8, max_size=1280)
+    # control_image = control_image.resize((1024, 1024))
+    # control_image = processor(control_image, to_pil=True)
+    # control_image = control_image.resize((width, height))
+    # control_image_torch = get_image_latent(control_image, sample_size=sample_size)[:, :, 0]
 
     # generation
     if randomize_seed: seed = random.randint(0, MAX_SEED)
@@ -160,9 +161,9 @@ def inference(
         width=width,
         generator=generator,
         guidance_scale=guidance_scale,
-        image               = inpaint_image,
-        mask_image          = mask_image,
-        control_image=control_image_torch,
+        image = inpaint_image,
+        mask_image = mask_image,
+        # control_image=control_image_torch,
         num_inference_steps=num_inference_steps,
         control_context_scale=control_context_scale,
     ).images[0]
@@ -235,13 +236,13 @@ with gr.Blocks() as demo:
                         step=0.01,
                         value=0.40,
                     )
-                    guidance_scale = gr.Slider(
-                        label="Guidance scale",
-                        minimum=0.0,
-                        maximum=10.0,
-                        step=0.1,
-                        value=1.0,
-                    )
+                    # guidance_scale = gr.Slider(
+                    #     label="Guidance scale",
+                    #     minimum=0.0,
+                    #     maximum=10.0,
+                    #     step=0.1,
+                    #     value=1.0,
+                    # )
 
                     seed = gr.Slider(
                         label="Seed",
@@ -286,7 +287,7 @@ with gr.Blocks() as demo:
             control_context_scale,
             seed,
             randomize_seed,
-            guidance_scale,
+            # guidance_scale,
             num_inference_steps,
         ],
         outputs=[output_image, seed, control_image],