diff --git a/modules/scripts.py b/modules/scripts.py
index c60e1b28d..59eb4c82b 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -476,6 +476,9 @@ class ScriptRunner:
         script = self.selectable_scripts[script_index-1]
         if script is None:
             return None
+        if 'upscale' in script.title():
+            if not hasattr(p, 'init_images') and p.task_args.get('image', None) is not None:
+                p.init_images = p.task_args['image']
         parsed = p.per_script_args.get(script.title(), args[script.args_from:script.args_to])
         processed = script.run(p, *parsed)
         s.record(script.title())
diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py
index 0799e2c79..cc598bfd1 100644
--- a/scripts/sd_upscale.py
+++ b/scripts/sd_upscale.py
@@ -22,65 +22,62 @@ class Script(scripts.Script):
             scale_factor = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label='Scale Factor', value=2.0, elem_id=self.elem_id("scale_factor"))
         with gr.Row():
             upscaler_index = gr.Dropdown(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index", elem_id=self.elem_id("upscaler_index"))
-
         return [info, overlap, upscaler_index, scale_factor]
 
     def run(self, p, _, overlap, upscaler_index, scale_factor): # pylint: disable=arguments-differ
+        init_img = None
+        if hasattr(p, 'init_images') and p.init_images is not None:
+            init_img = p.init_images[0]
+        elif hasattr(p.task_args, 'image') and p.task_args['image'] is not None:
+            init_img = p.task_args['image'][0]
+
+        if init_img is None:
+            return None
+        init_img = images.flatten(init_img, opts.img2img_background_color)
+
         if isinstance(upscaler_index, str):
             upscaler_index = [x.name.lower() for x in shared.sd_upscalers].index(upscaler_index.lower())
         processing.fix_seed(p)
         upscaler = shared.sd_upscalers[upscaler_index]
-
         p.extra_generation_params["SD upscale overlap"] = overlap
         p.extra_generation_params["SD upscale upscaler"] = upscaler.name
-
         initial_info = None
         seed = p.seed
-
-        init_img = p.init_images[0]
-        init_img = images.flatten(init_img, opts.img2img_background_color)
-
         if upscaler.name != "None":
             img = upscaler.scaler.upscale(init_img, scale_factor, upscaler.data_path)
         else:
             img = init_img
-
         devices.torch_gc()
-
         grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=overlap)
-
         batch_size = p.batch_size
         upscale_count = p.n_iter
         p.n_iter = 1
         p.do_not_save_grid = True
         p.do_not_save_samples = True
-
         work = []
-
+        i = 1
         for _y, _h, row in grid.tiles:
             for tiledata in row:
                 work.append(tiledata[2])
+                tiledata[2].save(f'/tmp/tile{i}.png')
+                i += 1
 
         batch_count = math.ceil(len(work) / batch_size)
         state.job_count = batch_count * upscale_count
-
         log.info(f"SD upscale: images={len(work)} tile={len(grid.tiles[0][2])}x{len(grid.tiles)} batches={state.job_count}")
 
         result_images = []
         for n in range(upscale_count):
             start_seed = seed + n
             p.seed = start_seed
-
             work_results = []
             for i in range(batch_count):
                 p.batch_size = batch_size
                 p.init_images = work[i * batch_size:(i + 1) * batch_size]
                 state.job = f"upscale batch {i+1+n*batch_count}/{state.job_count}"
                 processed = processing.process_images(p)
-
                 if initial_info is None:
                     initial_info = processed.info
-
                 p.seed = processed.seed + 1
                 work_results += processed.images
 
@@ -97,5 +94,4 @@ class Script(scripts.Script):
                 images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
 
         processed = Processed(p, result_images, seed, initial_info)
-
         return processed