Merge branch 'main' into lora-classification

invokeai/app/invocations/image.py

@@ -1089,7 +1089,7 @@ class CanvasV2MaskAndCropInvocation(BaseInvocation, WithMetadata, WithBoard):
 
 
 @invocation(
-    "expand_mask_with_fade", title="Expand Mask with Fade", tags=["image", "mask"], category="image", version="1.0.0"
+    "expand_mask_with_fade", title="Expand Mask with Fade", tags=["image", "mask"], category="image", version="1.0.1"
 )
 class ExpandMaskWithFadeInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Expands a mask with a fade effect. The mask uses black to indicate areas to keep from the generated image and white for areas to discard.
@@ -1147,8 +1147,21 @@ class ExpandMaskWithFadeInvocation(BaseInvocation, WithMetadata, WithBoard):
         coeffs = numpy.polyfit(x_control, y_control, 3)
         poly = numpy.poly1d(coeffs)
 
-        # Evaluate and clip the smooth mapping
-        feather = numpy.clip(poly(d_norm), 0, 1)
+        # Evaluate the polynomial
+        feather = poly(d_norm)
+
+        # The polynomial fit isn't perfect. Points beyond the fade distance are likely to be slightly less than 1.0,
+        # even though the control points indicate that they should be exactly 1.0. This is due to the nature of the
+        # polynomial fit, which is a best approximation of the control points but not an exact match.
+
+        # When this occurs, the area outside the mask and fade-out will not be 100% transparent. For example, it may
+        # have an alpha value of 1 instead of 0. So we must force pixels at or beyond the fade distance to exactly 1.0.
+
+        # Force pixels at or beyond the fade distance to exactly 1.0
+        feather = numpy.where(d_norm >= 1.0, 1.0, feather)
+
+        # Clip any other values to ensure they're in the valid range [0,1]
+        feather = numpy.clip(feather, 0, 1)
 
         # Build final image.
         np_result = numpy.where(black_mask == 1, 0, (feather * 255).astype(numpy.uint8))

invokeai/backend/flux/flux_state_dict_utils.py

@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from invokeai.backend.model_manager.legacy_probe import CkptType
+
+
+def get_flux_in_channels_from_state_dict(state_dict: "CkptType") -> int | None:
+    """Gets the in channels from the state dict."""
+
+    # "Standard" FLUX models use "img_in.weight", but some community fine tunes use
+    # "model.diffusion_model.img_in.weight". Known models that use the latter key:
+    # - https://civitai.com/models/885098?modelVersionId=990775
+    # - https://civitai.com/models/1018060?modelVersionId=1596255
+    # - https://civitai.com/models/978314/ultrareal-fine-tune?modelVersionId=1413133
+
+    keys = {"img_in.weight", "model.diffusion_model.img_in.weight"}
+
+    for key in keys:
+        val = state_dict.get(key)
+        if val is not None:
+            return val.shape[1]
+
+    return None

invokeai/backend/model_manager/legacy_probe.py

@@ -14,6 +14,7 @@ from invokeai.backend.flux.controlnet.state_dict_utils import (
     is_state_dict_instantx_controlnet,
     is_state_dict_xlabs_controlnet,
 )
+from invokeai.backend.flux.flux_state_dict_utils import get_flux_in_channels_from_state_dict
 from invokeai.backend.flux.ip_adapter.state_dict_utils import is_state_dict_xlabs_ip_adapter
 from invokeai.backend.flux.redux.flux_redux_state_dict_utils import is_state_dict_likely_flux_redux
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
@@ -564,7 +565,14 @@ class CheckpointProbeBase(ProbeBase):
         state_dict = self.checkpoint.get("state_dict") or self.checkpoint
 
         if base_type == BaseModelType.Flux:
-            in_channels = state_dict["img_in.weight"].shape[1]
+            in_channels = get_flux_in_channels_from_state_dict(state_dict)
+
+            if in_channels is None:
+                # If we cannot find the in_channels, we assume that this is a normal variant. Log a warning.
+                logger.warning(
+                    f"{self.model_path} does not have img_in.weight or model.diffusion_model.img_in.weight key. Assuming normal variant."
+                )
+                return ModelVariantType.Normal
 
             # FLUX Model variant types are distinguished by input channels:
             # - Unquantized Dev and Schnell have in_channels=64

invokeai/frontend/web/src/features/controlLayers/components/ParamDenoisingStrength.tsx

@@ -14,8 +14,9 @@ import WavyLine from 'common/components/WavyLine';
 import { selectImg2imgStrength, setImg2imgStrength } from 'features/controlLayers/store/paramsSlice';
 import { selectActiveRasterLayerEntities } from 'features/controlLayers/store/selectors';
 import { selectImg2imgStrengthConfig } from 'features/system/store/configSlice';
-import { memo, useCallback } from 'react';
+import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
+import { useSelectedModelConfig } from 'services/api/hooks/useSelectedModelConfig';
 
 const selectHasRasterLayersWithContent = createSelector(
   selectActiveRasterLayerEntities,
@@ -26,6 +27,7 @@ export const ParamDenoisingStrength = memo(() => {
   const img2imgStrength = useAppSelector(selectImg2imgStrength);
   const dispatch = useAppDispatch();
   const hasRasterLayersWithContent = useAppSelector(selectHasRasterLayersWithContent);
+  const selectedModelConfig = useSelectedModelConfig();
 
   const onChange = useCallback(
     (v: number) => {
@@ -39,8 +41,24 @@ export const ParamDenoisingStrength = memo(() => {
 
   const [invokeBlue300] = useToken('colors', ['invokeBlue.300']);
 
+  const isDisabled = useMemo(() => {
+    if (!hasRasterLayersWithContent) {
+      // Denoising strength does nothing if there are no raster layers w/ content
+      return true;
+    }
+    if (
+      selectedModelConfig?.type === 'main' &&
+      selectedModelConfig?.base === 'flux' &&
+      selectedModelConfig.variant === 'inpaint'
+    ) {
+      // Denoising strength is ignored by FLUX Fill, which is indicated by the variant being 'inpaint'
+      return true;
+    }
+    return false;
+  }, [hasRasterLayersWithContent, selectedModelConfig]);
+
   return (
-    <FormControl isDisabled={!hasRasterLayersWithContent} p={1} justifyContent="space-between" h={8}>
+    <FormControl isDisabled={isDisabled} p={1} justifyContent="space-between" h={8}>
       <Flex gap={3} alignItems="center">
         <InformationalPopover feature="paramDenoisingStrength">
           <FormLabel mr={0}>{`${t('parameters.denoisingStrength')}`}</FormLabel>
@@ -49,7 +67,7 @@ export const ParamDenoisingStrength = memo(() => {
           <WavyLine amplitude={img2imgStrength * 10} stroke={invokeBlue300} strokeWidth={1} width={40} height={14} />
         )}
       </Flex>
-      {hasRasterLayersWithContent ? (
+      {!isDisabled ? (
         <>
           <CompositeSlider
             step={config.coarseStep}

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "5.9.0"
+__version__ = "5.9.1"