diff --git a/api/onnx_web/chain/blend_img2img.py b/api/onnx_web/chain/blend_img2img.py
index 72bad324..0a26650a 100644
--- a/api/onnx_web/chain/blend_img2img.py
+++ b/api/onnx_web/chain/blend_img2img.py
@@ -1,5 +1,4 @@
 from logging import getLogger
-from typing import Optional
 
 import numpy as np
 import torch
@@ -25,7 +24,9 @@ def blend_img2img(
     **kwargs,
 ) -> Image.Image:
     params = params.with_args(**kwargs)
-    logger.info("blending image using img2img, %s steps: %s", params.steps, params.prompt)
+    logger.info(
+        "blending image using img2img, %s steps: %s", params.steps, params.prompt
+    )
 
     pipe = load_pipeline(
         server,
diff --git a/api/onnx_web/chain/blend_inpaint.py b/api/onnx_web/chain/blend_inpaint.py
index a7c7a23c..11e042f5 100644
--- a/api/onnx_web/chain/blend_inpaint.py
+++ b/api/onnx_web/chain/blend_inpaint.py
@@ -56,14 +56,14 @@ def blend_inpaint(
         save_image(server, "last-mask.png", mask)
         save_image(server, "last-noise.png", noise)
 
-    def outpaint(image: Image.Image, dims: Tuple[int, int, int]):
+    def outpaint(tile_source: Image.Image, dims: Tuple[int, int, int]):
         left, top, tile = dims
-        size = Size(*image.size)
-        mask = mask.crop((left, top, left + tile, top + tile))
+        size = Size(*tile_source.size)
+        tile_mask = mask.crop((left, top, left + tile, top + tile))
 
         if is_debug():
-            save_image(server, "tile-source.png", image)
-            save_image(server, "tile-mask.png", mask)
+            save_image(server, "tile-source.png", tile_source)
+            save_image(server, "tile-mask.png", tile_mask)
 
         latents = get_latents_from_seed(params.seed, size)
         pipe = load_pipeline(
@@ -83,9 +83,9 @@ def blend_inpaint(
                 generator=rng,
                 guidance_scale=params.cfg,
                 height=size.height,
-                image=image,
+                image=tile_source,
                 latents=latents,
-                mask=mask,
+                mask=tile_mask,
                 negative_prompt=params.negative_prompt,
                 num_inference_steps=params.steps,
                 width=size.width,
@@ -98,7 +98,7 @@ def blend_inpaint(
                 generator=rng,
                 guidance_scale=params.cfg,
                 height=size.height,
-                image=image,
+                image=tile_source,
                 latents=latents,
                 mask=mask,
                 negative_prompt=params.negative_prompt,
@@ -109,9 +109,7 @@ def blend_inpaint(
 
         return result.images[0]
 
-    output = process_tile_order(
-        stage.tile_order, source, SizeChart.auto, 1, [outpaint]
-    )
+    output = process_tile_order(stage.tile_order, source, SizeChart.auto, 1, [outpaint])
 
     logger.info("final output image size", output.size)
     return output
diff --git a/api/onnx_web/chain/source_txt2img.py b/api/onnx_web/chain/source_txt2img.py
index 9668153d..56be4686 100644
--- a/api/onnx_web/chain/source_txt2img.py
+++ b/api/onnx_web/chain/source_txt2img.py
@@ -26,7 +26,9 @@ def source_txt2img(
 ) -> Image.Image:
     params = params.with_args(**kwargs)
     size = size.with_args(**kwargs)
-    logger.info("generating image using txt2img, %s steps: %s", params.steps, params.prompt)
+    logger.info(
+        "generating image using txt2img, %s steps: %s", params.steps, params.prompt
+    )
 
     if source is not None:
         logger.warn(
diff --git a/api/onnx_web/chain/upscale_outpaint.py b/api/onnx_web/chain/upscale_outpaint.py
index 6a33d883..875b9052 100644
--- a/api/onnx_web/chain/upscale_outpaint.py
+++ b/api/onnx_web/chain/upscale_outpaint.py
@@ -62,14 +62,14 @@ def upscale_outpaint(
         save_image(server, "last-mask.png", mask)
         save_image(server, "last-noise.png", noise)
 
-    def outpaint(image: Image.Image, dims: Tuple[int, int, int]):
+    def outpaint(tile_source: Image.Image, dims: Tuple[int, int, int]):
         left, top, tile = dims
-        size = Size(*image.size)
-        mask = mask.crop((left, top, left + tile, top + tile))
+        size = Size(*tile_source.size)
+        tile_mask = mask.crop((left, top, left + tile, top + tile))
 
         if is_debug():
-            save_image(server, "tile-source.png", image)
-            save_image(server, "tile-mask.png", mask)
+            save_image(server, "tile-source.png", tile_source)
+            save_image(server, "tile-mask.png", tile_mask)
 
         latents = get_tile_latents(full_latents, dims)
         pipe = load_pipeline(
@@ -84,8 +84,8 @@ def upscale_outpaint(
             logger.debug("using LPW pipeline for inpaint")
             rng = torch.manual_seed(params.seed)
             result = pipe.inpaint(
-                image,
-                mask,
+                tile_source,
+                tile_mask,
                 prompt,
                 generator=rng,
                 guidance_scale=params.cfg,
@@ -100,12 +100,12 @@ def upscale_outpaint(
             rng = np.random.RandomState(params.seed)
             result = pipe(
                 prompt,
-                image,
+                tile_source,
                 generator=rng,
                 guidance_scale=params.cfg,
                 height=size.height,
                 latents=latents,
-                mask=mask,
+                mask=tile_mask,
                 negative_prompt=params.negative_prompt,
                 num_inference_steps=params.steps,
                 width=size.width,
diff --git a/api/onnx_web/chain/upscale_stable_diffusion.py b/api/onnx_web/chain/upscale_stable_diffusion.py
index a92bd353..4ff0e245 100644
--- a/api/onnx_web/chain/upscale_stable_diffusion.py
+++ b/api/onnx_web/chain/upscale_stable_diffusion.py
@@ -74,7 +74,9 @@ def upscale_stable_diffusion(
 ) -> Image.Image:
     params = params.with_args(**kwargs)
     upscale = upscale.with_args(**kwargs)
-    logger.info("upscaling with Stable Diffusion, %s steps: %s", params.steps, params.prompt)
+    logger.info(
+        "upscaling with Stable Diffusion, %s steps: %s", params.steps, params.prompt
+    )
 
     pipeline = load_stable_diffusion(server, upscale, job.get_device())
     generator = torch.manual_seed(params.seed)