blackify and rerun frontend build

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -215,10 +215,7 @@ class InvokeAIDiffuserComponent:
                                 dim=0,
                             ),
                         }
-                    (
+                    (encoder_hidden_states, encoder_attention_mask,) = self._concat_conditionings_for_batch(
-                        encoder_hidden_states,
-                        encoder_attention_mask,
-                    ) = self._concat_conditionings_for_batch(
                         conditioning_data.unconditioned_embeddings.embeds,
                         conditioning_data.text_embeddings.embeds,
                     )
@@ -280,10 +277,7 @@ class InvokeAIDiffuserComponent:
         wants_cross_attention_control = len(cross_attention_control_types_to_do) > 0
 
         if wants_cross_attention_control:
-            (
+            (unconditioned_next_x, conditioned_next_x,) = self._apply_cross_attention_controlled_conditioning(
-                unconditioned_next_x,
-                conditioned_next_x,
-            ) = self._apply_cross_attention_controlled_conditioning(
                 sample,
                 timestep,
                 conditioning_data,
@@ -291,10 +285,7 @@ class InvokeAIDiffuserComponent:
                 **kwargs,
             )
         elif self.sequential_guidance:
-            (
+            (unconditioned_next_x, conditioned_next_x,) = self._apply_standard_conditioning_sequentially(
-                unconditioned_next_x,
-                conditioned_next_x,
-            ) = self._apply_standard_conditioning_sequentially(
                 sample,
                 timestep,
                 conditioning_data,
@@ -302,10 +293,7 @@ class InvokeAIDiffuserComponent:
             )
 
         else:
-            (
+            (unconditioned_next_x, conditioned_next_x,) = self._apply_standard_conditioning(
-                unconditioned_next_x,
-                conditioned_next_x,
-            ) = self._apply_standard_conditioning(
                 sample,
                 timestep,
                 conditioning_data,

invokeai/backend/stable_diffusion/image_degradation/utils_image.py

@@ -562,14 +562,18 @@ def rgb2ycbcr(img, only_y=True):
     if only_y:
         rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
     else:
-        rlt = np.matmul(
+        rlt = (
+            np.matmul(
                 img,
                 [
                     [65.481, -37.797, 112.0],
                     [128.553, -74.203, -93.786],
                     [24.966, 112.0, -18.214],
                 ],
-        ) / 255.0 + [16, 128, 128]
+            )
+            / 255.0
+            + [16, 128, 128]
+        )
     if in_img_type == np.uint8:
         rlt = rlt.round()
     else:
@@ -588,14 +592,18 @@ def ycbcr2rgb(img):
     if in_img_type != np.uint8:
         img *= 255.0
     # convert
-    rlt = np.matmul(
+    rlt = (
+        np.matmul(
             img,
             [
                 [0.00456621, 0.00456621, 0.00456621],
                 [0, -0.00153632, 0.00791071],
                 [0.00625893, -0.00318811, 0],
             ],
-    ) * 255.0 + [-222.921, 135.576, -276.836]
+        )
+        * 255.0
+        + [-222.921, 135.576, -276.836]
+    )
     if in_img_type == np.uint8:
         rlt = rlt.round()
     else:
@@ -618,14 +626,18 @@ def bgr2ycbcr(img, only_y=True):
     if only_y:
         rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
     else:
-        rlt = np.matmul(
+        rlt = (
+            np.matmul(
                 img,
                 [
                     [24.966, 112.0, -18.214],
                     [128.553, -74.203, -93.786],
                     [65.481, -37.797, 112.0],
                 ],
-        ) / 255.0 + [16, 128, 128]
+            )
+            / 255.0
+            + [16, 128, 128]
+        )
     if in_img_type == np.uint8:
         rlt = rlt.round()
     else:

invokeai/backend/training/textual_inversion_training.py

@@ -475,10 +475,7 @@ class TextualInversionDataset(Dataset):
 
         if self.center_crop:
             crop = min(img.shape[0], img.shape[1])
-            (
+            (h, w,) = (
-                h,
-                w,
-            ) = (
                 img.shape[0],
                 img.shape[1],
             )

invokeai/frontend/web/dist/assets/ThemeLocaleProvider-707a230a.js

@@ -1,4 +1,4 @@
-import{x as m,h1 as Ze,w as y,Z as Ya,h2 as Za,a8 as ua,ac as d,h3 as b,h4 as o,h5 as Ja,h6 as h,h7 as fa,h8 as Qa,h9 as eo,aF as ro,ha as ao,a5 as oo,hb as to}from"./index-3a52d467.js";import{s as ha,n as t,t as io,o as ma,p as no,q as ga,v as ya,w as pa,x as lo,y as Sa,z as xa,A as xr,B as so,D as co,E as bo,F as $a,G as ka,H as _a,J as vo,K as wa,L as uo,M as fo,N as ho,O as mo,Q as za,R as go,S as yo,T as po,U as So,V as xo,W as $o,e as ko,X as _o}from"./menu-0ce947db.js";var Ca=String.raw,Aa=Ca`
+import{x as m,h1 as Ze,w as y,Z as Ya,h2 as Za,a8 as ua,ac as d,h3 as b,h4 as o,h5 as Ja,h6 as h,h7 as fa,h8 as Qa,h9 as eo,aF as ro,ha as ao,a5 as oo,hb as to}from"./index-08cda350.js";import{s as ha,n as t,t as io,o as ma,p as no,q as ga,v as ya,w as pa,x as lo,y as Sa,z as xa,A as xr,B as so,D as co,E as bo,F as $a,G as ka,H as _a,J as vo,K as wa,L as uo,M as fo,N as ho,O as mo,Q as za,R as go,S as yo,T as po,U as So,V as xo,W as $o,e as ko,X as _o}from"./menu-3d10c968.js";var Ca=String.raw,Aa=Ca`
   :root,
   :host {
     --chakra-vh: 100vh;

invokeai/frontend/web/dist/index.html

@@ -12,7 +12,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-3a52d467.js"></script>
+    <script type="module" crossorigin src="./assets/index-08cda350.js"></script>
   </head>
 
   <body dir="ltr">