log_information

app.py

@@ -204,12 +204,12 @@ def restore_in_Xmin(
         input_height, input_width, input_channel = denoise_image.shape
         denoise_image = denoise_image.resize((input_width // downscale, input_height // downscale), Image.LANCZOS)
 
+    denoise_image = HWC3(np.array(denoise_image))
+
     if torch.cuda.device_count() == 0:
         gr.Warning('Set this space to GPU config to make it work.')
         return [noisy_image, denoise_image], [denoise_image], None, None
 
-    denoise_image = HWC3(np.array(denoise_image))
-
     if model_select != model.current_model:
         print('load ' + model_select)
         if model_select == 'v0-Q':
@@ -218,6 +218,9 @@ def restore_in_Xmin(
             model.load_state_dict(ckpt_F, strict=False)
         model.current_model = model_select
 
+    model.ae_dtype = convert_dtype(ae_dtype)
+    model.model.dtype = convert_dtype(diff_dtype)
+
     # Allocation
     if allocation == 1:
         return restore_in_1min(
@@ -335,9 +338,6 @@ def restore(
 
     torch.cuda.set_device(SUPIR_device)
 
-    model.ae_dtype = convert_dtype(ae_dtype)
-    model.model.dtype = convert_dtype(diff_dtype)
-
     input_image = upscale_image(input_image, upscale, unit_resolution=32, min_size=min_size)
     LQ = np.array(input_image) / 255.0
     LQ = np.power(LQ, gamma_correction)
@@ -776,7 +776,7 @@ with gr.Blocks() as interface:
         result_slider,
         result_gallery,
         restore_information
-    ]).success(fn = log_information, inputs = [
+    ]).then(fn = log_information, inputs = [
         result_gallery
     ], outputs = [], queue = False, show_progress = False)