Fix issue with seamless context managers when seamless is not configured.

invokeai/app/invocations/latent.py

@@ -1,5 +1,6 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
+import contextlib
 from contextlib import ExitStack
 from functools import singledispatchmethod
 from typing import List, Literal, Optional, Union
@@ -716,10 +717,23 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 **self.unet.unet.model_dump(),
                 context=context,
             )
+
+            # Prepare seamless context, if configured.
+            seamless_context = contextlib.nullcontext()
+            seamless_config = self.unet.seamless
+            if seamless_config is not None:
+                seamless_context = set_seamless(
+                    model=unet_info.context.model,
+                    axes=seamless_config.axes,
+                    skipped_layers=seamless_config.skipped_layers,
+                    skip_second_resnet=seamless_config.skip_second_resnet,
+                    skip_conv2=seamless_config.skip_conv2,
+                )
+
             with (
                 ExitStack() as exit_stack,
                 ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config),
-                set_seamless(unet_info.context.model, self.unet.seamless_axes),
+                seamless_context,
                 unet_info as unet,
                 # Apply the LoRA after unet has been moved to its target device for faster patching.
                 ModelPatcher.apply_lora_unet(unet, _lora_loader()),
@@ -826,7 +840,19 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
             context=context,
         )
 
-        with set_seamless(vae_info.context.model, self.vae.seamless_axes), vae_info as vae:
+        # Prepare seamless context, if configured.
+        seamless_context = contextlib.nullcontext()
+        seamless_config = self.vae.seamless
+        if seamless_config is not None:
+            seamless_context = set_seamless(
+                model=vae_info.context.model,
+                axes=seamless_config.axes,
+                skipped_layers=seamless_config.skipped_layers,
+                skip_second_resnet=seamless_config.skip_second_resnet,
+                skip_conv2=seamless_config.skip_conv2,
+            )
+
+        with seamless_context, vae_info as vae:
             latents = latents.to(vae.device)
             if self.fp32:
                 vae.to(dtype=torch.float32)

invokeai/app/invocations/model.py

@@ -19,6 +19,13 @@ from .baseinvocation import (
 )
 
 
+class SeamlessSettings(BaseModel):
+    axes: List[str] = Field(description="Axes('x' and 'y') to which apply seamless")
+    skipped_layers: int = Field(description="How much down layers skip when applying seamless")
+    skip_second_resnet: bool = Field(description="Skip or not second resnet in down blocks when applying seamless")
+    skip_conv2: bool = Field(description="Skip or not conv2 in down blocks when applying seamless")
+
+
 class ModelInfo(BaseModel):
     model_name: str = Field(description="Info to load submodel")
     base_model: BaseModelType = Field(description="Base model")
@@ -36,8 +43,8 @@ class UNetField(BaseModel):
     unet: ModelInfo = Field(description="Info to load unet submodel")
     scheduler: ModelInfo = Field(description="Info to load scheduler submodel")
     loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
-    seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
     freeu_config: Optional[FreeUConfig] = Field(default=None, description="FreeU configuration")
+    seamless: Optional[SeamlessSettings] = Field(default=None, description="Seamless settings applied to model")
 
 
 class ClipField(BaseModel):
@@ -50,7 +57,7 @@ class ClipField(BaseModel):
 class VaeField(BaseModel):
     # TODO: better naming?
     vae: ModelInfo = Field(description="Info to load vae submodel")
-    seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
+    seamless: Optional[SeamlessSettings] = Field(default=None, description="Seamless settings applied to model")
 
 
 @invocation_output("unet_output")
@@ -451,6 +458,11 @@ class SeamlessModeInvocation(BaseInvocation):
     )
     seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless")
     seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless")
+    skipped_layers: int = InputField(default=0, input=Input.Any, description="How much model's down layers to skip")
+    skip_second_resnet: bool = InputField(
+        default=True, input=Input.Any, description="Skip or not second resnet in down layers"
+    )
+    skip_conv2: bool = InputField(default=True, input=Input.Any, description="Skip or not conv2 in down layers")
 
     def invoke(self, context: InvocationContext) -> SeamlessModeOutput:
         # Conditionally append 'x' and 'y' based on seamless_x and seamless_y
@@ -465,9 +477,19 @@ class SeamlessModeInvocation(BaseInvocation):
             seamless_axes_list.append("y")
 
         if unet is not None:
-            unet.seamless_axes = seamless_axes_list
+            unet.seamless = SeamlessSettings(
+                axes=seamless_axes_list,
+                skipped_layers=self.skipped_layers,
+                skip_second_resnet=self.skip_second_resnet,
+                skip_conv2=self.skip_conv2,
+            )
         if vae is not None:
-            vae.seamless_axes = seamless_axes_list
+            vae.seamless = SeamlessSettings(
+                axes=seamless_axes_list,
+                skipped_layers=self.skipped_layers,
+                skip_second_resnet=self.skip_second_resnet,
+                skip_conv2=self.skip_conv2,
+            )
 
         return SeamlessModeOutput(unet=unet, vae=vae)
 

invokeai/backend/model_management/lora.py

@@ -13,7 +13,6 @@ from safetensors.torch import load_file
 from transformers import CLIPTextModel, CLIPTokenizer
 
 from invokeai.app.shared.models import FreeUConfig
-from invokeai.backend.model_management.model_load_optimizations import skip_torch_weight_init
 
 from .models.lora import LoRAModel
 
@@ -212,12 +211,8 @@ class ModelPatcher:
                 for i in range(ti_embedding.shape[0]):
                     new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
 
-            # Modify text_encoder.
-            # resize_token_embeddings(...) constructs a new torch.nn.Embedding internally. Initializing the weights of
-            # this embedding is slow and unnecessary, so we wrap this step in skip_torch_weight_init() to save some
-            # time.
-            with skip_torch_weight_init():
-                text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added, pad_to_multiple_of)
+            # modify text_encoder
+            text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added, pad_to_multiple_of)
             model_embeddings = text_encoder.get_input_embeddings()
 
             for ti_name, ti in ti_list:

invokeai/backend/model_management/seamless.py

@@ -25,71 +25,55 @@ def _conv_forward_asymmetric(self, input, weight, bias):
 
 
 @contextmanager
-def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL], seamless_axes: List[str]):
+def set_seamless(
+    model: Union[UNet2DConditionModel, AutoencoderKL],
+    axes: List[str],
+    skipped_layers: int,
+    skip_second_resnet: bool,
+    skip_conv2: bool,
+):
     try:
         to_restore = []
 
         for m_name, m in model.named_modules():
-            if isinstance(model, UNet2DConditionModel):
-                if ".attentions." in m_name:
+            if not isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+                continue
+
+            if isinstance(model, UNet2DConditionModel) and m_name.startswith("down_blocks.") and ".resnets." in m_name:
+                # down_blocks.1.resnets.1.conv1
+                _, block_num, _, resnet_num, submodule_name = m_name.split(".")
+                block_num = int(block_num)
+                resnet_num = int(resnet_num)
+
+                # if block_num >= seamless_down_blocks:
+                if block_num >= len(model.down_blocks) - skipped_layers:
                     continue
 
-                if ".resnets." in m_name:
-                    if ".conv2" in m_name:
-                        continue
-                    if ".conv_shortcut" in m_name:
-                        continue
-
-            """
-            if isinstance(model, UNet2DConditionModel):
-                if False and ".upsamplers." in m_name:
+                if resnet_num > 0 and skip_second_resnet:
                     continue
 
-                if False and ".downsamplers." in m_name:
+                if submodule_name == "conv2" and skip_conv2:
                     continue
 
-                if True and ".resnets." in m_name:
-                    if True and ".conv1" in m_name:
-                        if False and "down_blocks" in m_name:
-                            continue
-                        if False and "mid_block" in m_name:
-                            continue
-                        if False and "up_blocks" in m_name:
-                            continue
+            m.asymmetric_padding_mode = {}
+            m.asymmetric_padding = {}
+            m.asymmetric_padding_mode["x"] = "circular" if ("x" in axes) else "constant"
+            m.asymmetric_padding["x"] = (
+                m._reversed_padding_repeated_twice[0],
+                m._reversed_padding_repeated_twice[1],
+                0,
+                0,
+            )
+            m.asymmetric_padding_mode["y"] = "circular" if ("y" in axes) else "constant"
+            m.asymmetric_padding["y"] = (
+                0,
+                0,
+                m._reversed_padding_repeated_twice[2],
+                m._reversed_padding_repeated_twice[3],
+            )
 
-                    if True and ".conv2" in m_name:
-                        continue
-
-                    if True and ".conv_shortcut" in m_name:
-                        continue
-
-                if True and ".attentions." in m_name:
-                    continue
-
-                if False and m_name in ["conv_in", "conv_out"]:
-                    continue
-            """
-
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                m.asymmetric_padding_mode = {}
-                m.asymmetric_padding = {}
-                m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
-                m.asymmetric_padding["x"] = (
-                    m._reversed_padding_repeated_twice[0],
-                    m._reversed_padding_repeated_twice[1],
-                    0,
-                    0,
-                )
-                m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
-                m.asymmetric_padding["y"] = (
-                    0,
-                    0,
-                    m._reversed_padding_repeated_twice[2],
-                    m._reversed_padding_repeated_twice[3],
-                )
-
-                to_restore.append((m, m._conv_forward))
-                m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
+            to_restore.append((m, m._conv_forward))
+            m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
 
         yield
 

invokeai/frontend/web/favicon-outline.svg

@@ -1,3 +0,0 @@
-<svg width="44" height="44" viewBox="0 0 44 44" fill="none" xmlns="http://www.w3.org/2000/svg">
-<path d="M29.1951 10.6667H42V2H2V10.6667H14.8049L29.1951 33.3333H42V42H2V33.3333H14.8049" stroke="#E6FD13" stroke-width="2.8"/>
-</svg>

invokeai/frontend/web/index.html

@@ -8,8 +8,8 @@
   <meta http-equiv="Pragma" content="no-cache">
   <meta http-equiv="Expires" content="0">
   <title>InvokeAI - A Stable Diffusion Toolkit</title>
-  <link rel="mask-icon" type="icon" href="favicon-outline.svg" color="#E6FD13" sizes="any" />
-  <link rel="icon" type="icon" href="favicon-key.svg" />
+  <link rel="mask-icon" href="/invoke-key-ylw-sm.svg" color="#E6FD13" sizes="any" />
+  <link rel="icon" href="/invoke-key-char-on-ylw.svg" />
   <style>
     html,
     body {

invokeai/frontend/web/src/common/components/InvControl/InvControl.tsx

@@ -45,7 +45,6 @@ export const InvControl = memo(
           orientation={orientation}
           isDisabled={isDisabled}
           {...formControlProps}
-          {...ctx.controlProps}
         >
           <Flex className="invcontrol-label-wrapper">
             {label && (

invokeai/frontend/web/src/common/components/InvControl/InvControlGroup.tsx

@@ -1,10 +1,9 @@
-import type { FormControlProps, FormLabelProps } from '@chakra-ui/react';
+import type { FormLabelProps } from '@chakra-ui/react';
 import type { PropsWithChildren } from 'react';
 import { createContext, memo } from 'react';
 
 export type InvControlGroupProps = {
   labelProps?: FormLabelProps;
-  controlProps?: FormControlProps;
   isDisabled?: boolean;
   orientation?: 'horizontal' | 'vertical';
 };

invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/NoBoardBoard.tsx

@@ -1,6 +1,6 @@
 import { Box, Flex, Image } from '@chakra-ui/react';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import InvokeLogoSVG from 'assets/images/invoke-key-wht-lrg.svg';
+import InvokeAILogoImage from 'assets/images/logo.png';
 import IAIDroppable from 'common/components/IAIDroppable';
 import { InvText } from 'common/components/InvText/wrapper';
 import { InvTooltip } from 'common/components/InvTooltip/InvTooltip';
@@ -101,10 +101,10 @@ const NoBoardBoard = memo(({ isSelected }: Props) => {
                   alignItems="center"
                 >
                   <Image
-                    src={InvokeLogoSVG}
+                    src={InvokeAILogoImage}
                     alt="invoke-ai-logo"
-                    opacity={0.7}
-                    mixBlendMode="overlay"
+                    opacity={0.4}
+                    filter="grayscale(1)"
                     mt={-6}
                     w={16}
                     h={16}

invokeai/frontend/web/src/features/lora/components/LoRACard.tsx

@@ -4,10 +4,10 @@ import {
   InvCardBody,
   InvCardHeader,
 } from 'common/components/InvCard/wrapper';
-import { InvLabel } from 'common/components/InvControl/InvLabel';
 import { InvIconButton } from 'common/components/InvIconButton/InvIconButton';
 import { InvNumberInput } from 'common/components/InvNumberInput/InvNumberInput';
 import { InvSlider } from 'common/components/InvSlider/InvSlider';
+import { InvText } from 'common/components/InvText/wrapper';
 import type { LoRA } from 'features/lora/store/loraSlice';
 import { loraRemoved, loraWeightChanged } from 'features/lora/store/loraSlice';
 import { memo, useCallback } from 'react';
@@ -35,9 +35,9 @@ export const LoRACard = memo((props: LoRACardProps) => {
   return (
     <InvCard variant="lora">
       <InvCardHeader>
-        <InvLabel noOfLines={1} wordBreak="break-all">
+        <InvText noOfLines={1} wordBreak="break-all">
           {lora.model_name}
-        </InvLabel>
+        </InvText>
         <InvIconButton
           aria-label="Remove LoRA"
           variant="ghost"

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/InvocationNodeFooter.tsx

@@ -1,6 +1,4 @@
-import type { ChakraProps } from '@chakra-ui/react';
 import { Flex } from '@chakra-ui/react';
-import { InvControlGroup } from 'common/components/InvControl/InvControlGroup';
 import { useHasImageOutput } from 'features/nodes/hooks/useHasImageOutput';
 import { DRAG_HANDLE_CLASSNAME } from 'features/nodes/types/constants';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
@@ -13,8 +11,6 @@ type Props = {
   nodeId: string;
 };
 
-const props: ChakraProps = { w: 'unset' };
-
 const InvocationNodeFooter = ({ nodeId }: Props) => {
   const hasImageOutput = useHasImageOutput(nodeId);
   const isCacheEnabled = useFeatureStatus('invocationCache').isFeatureEnabled;
@@ -24,16 +20,13 @@ const InvocationNodeFooter = ({ nodeId }: Props) => {
       layerStyle="nodeFooter"
       w="full"
       borderBottomRadius="base"
-      gap={4}
       px={2}
       py={0}
       h={8}
       justifyContent="space-between"
     >
-      <InvControlGroup controlProps={props} labelProps={props}>
-        {isCacheEnabled && <UseCacheCheckbox nodeId={nodeId} />}
-        {hasImageOutput && <SaveToGalleryCheckbox nodeId={nodeId} />}
-      </InvControlGroup>
+      {isCacheEnabled && <UseCacheCheckbox nodeId={nodeId} />}
+      {hasImageOutput && <SaveToGalleryCheckbox nodeId={nodeId} />}
     </Flex>
   );
 };

invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsModal.tsx

@@ -37,10 +37,11 @@ import { SettingsLanguageSelect } from './SettingsLanguageSelect';
 import { SettingsLogLevelSelect } from './SettingsLogLevelSelect';
 
 type ConfigOptions = {
-  shouldShowDeveloperSettings?: boolean;
-  shouldShowResetWebUiText?: boolean;
-  shouldShowClearIntermediates?: boolean;
-  shouldShowLocalizationToggle?: boolean;
+  shouldShowDeveloperSettings: boolean;
+  shouldShowResetWebUiText: boolean;
+  shouldShowAdvancedOptionsSettings: boolean;
+  shouldShowClearIntermediates: boolean;
+  shouldShowLocalizationToggle: boolean;
 };
 
 type SettingsModalProps = {
@@ -83,7 +84,7 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => {
     hasPendingItems,
     intermediatesCount,
     isLoading: isLoadingClearIntermediates,
-  } = useClearIntermediates(shouldShowClearIntermediates);
+  } = useClearIntermediates();
 
   const {
     isOpen: isSettingsModalOpen,

invokeai/frontend/web/src/features/system/components/SettingsModal/useClearIntermediates.ts

@@ -17,9 +17,7 @@ export type UseClearIntermediatesReturn = {
   hasPendingItems: boolean;
 };
 
-export const useClearIntermediates = (
-  shouldShowClearIntermediates: boolean
-): UseClearIntermediatesReturn => {
+export const useClearIntermediates = (): UseClearIntermediatesReturn => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
 
@@ -27,7 +25,6 @@ export const useClearIntermediates = (
     undefined,
     {
       refetchOnMountOrArgChange: true,
-      skip: !shouldShowClearIntermediates,
     }
   );
 

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "3.6.0rc4"
+__version__ = "3.6.0rc3"