chore: bump version to v5.0.2

For example, CLIP Vision models have no settings.

invokeai/app/invocations/flux_denoise.py

@@ -30,7 +30,7 @@ from invokeai.backend.flux.sampling_utils import (
     pack,
     unpack,
 )
-from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import FLUX_KOHYA_TRANFORMER_PREFIX
+from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
 from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 from invokeai.backend.lora.lora_patcher import LoRAPatcher
 from invokeai.backend.model_manager.config import ModelFormat
@@ -209,7 +209,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                     LoRAPatcher.apply_lora_patches(
                         model=transformer,
                         patches=self._lora_iterator(context),
-                        prefix=FLUX_KOHYA_TRANFORMER_PREFIX,
+                        prefix=FLUX_LORA_TRANSFORMER_PREFIX,
                         cached_weights=cached_weights,
                     )
                 )
@@ -220,7 +220,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                     LoRAPatcher.apply_lora_sidecar_patches(
                         model=transformer,
                         patches=self._lora_iterator(context),
-                        prefix=FLUX_KOHYA_TRANFORMER_PREFIX,
+                        prefix=FLUX_LORA_TRANSFORMER_PREFIX,
                         dtype=inference_dtype,
                     )
                 )

invokeai/app/invocations/flux_text_encoder.py

@@ -10,7 +10,7 @@ from invokeai.app.invocations.model import CLIPField, T5EncoderField
 from invokeai.app.invocations.primitives import FluxConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.flux.modules.conditioner import HFEncoder
-from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import FLUX_KOHYA_CLIP_PREFIX
+from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX
 from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 from invokeai.backend.lora.lora_patcher import LoRAPatcher
 from invokeai.backend.model_manager.config import ModelFormat
@@ -101,7 +101,7 @@ class FluxTextEncoderInvocation(BaseInvocation):
                     LoRAPatcher.apply_lora_patches(
                         model=clip_text_encoder,
                         patches=self._clip_lora_iterator(context),
-                        prefix=FLUX_KOHYA_CLIP_PREFIX,
+                        prefix=FLUX_LORA_CLIP_PREFIX,
                         cached_weights=cached_weights,
                     )
                 )

invokeai/backend/lora/conversions/flux_diffusers_lora_conversion_utils.py

@@ -2,6 +2,7 @@ from typing import Dict
 
 import torch
 
+from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
 from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
 from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
 from invokeai.backend.lora.layers.lora_layer import LoRALayer
@@ -189,7 +190,9 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
     # Assert that all keys were processed.
     assert len(grouped_state_dict) == 0
 
-    return LoRAModelRaw(layers=layers)
+    layers_with_prefix = {f"{FLUX_LORA_TRANSFORMER_PREFIX}{k}": v for k, v in layers.items()}
+
+    return LoRAModelRaw(layers=layers_with_prefix)
 
 
 def _group_by_layer(state_dict: Dict[str, torch.Tensor]) -> dict[str, dict[str, torch.Tensor]]:

invokeai/backend/lora/conversions/flux_kohya_lora_conversion_utils.py

@@ -3,6 +3,7 @@ from typing import Any, Dict, TypeVar
 
 import torch
 
+from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX, FLUX_LORA_TRANSFORMER_PREFIX
 from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
 from invokeai.backend.lora.layers.utils import any_lora_layer_from_state_dict
 from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
@@ -23,11 +24,6 @@ FLUX_KOHYA_TRANSFORMER_KEY_REGEX = (
 FLUX_KOHYA_CLIP_KEY_REGEX = r"lora_te1_text_model_encoder_layers_(\d+)_(mlp|self_attn)_(\w+)\.?.*"
 
 
-# Prefixes used to distinguish between transformer and CLIP text encoder keys in the InvokeAI LoRA format.
-FLUX_KOHYA_TRANFORMER_PREFIX = "lora_transformer-"
-FLUX_KOHYA_CLIP_PREFIX = "lora_clip-"
-
-
 def is_state_dict_likely_in_flux_kohya_format(state_dict: Dict[str, Any]) -> bool:
     """Checks if the provided state dict is likely in the Kohya FLUX LoRA format.
 
@@ -67,9 +63,9 @@ def lora_model_from_flux_kohya_state_dict(state_dict: Dict[str, torch.Tensor]) -
     # Create LoRA layers.
     layers: dict[str, AnyLoRALayer] = {}
     for layer_key, layer_state_dict in transformer_grouped_sd.items():
-        layers[FLUX_KOHYA_TRANFORMER_PREFIX + layer_key] = any_lora_layer_from_state_dict(layer_state_dict)
+        layers[FLUX_LORA_TRANSFORMER_PREFIX + layer_key] = any_lora_layer_from_state_dict(layer_state_dict)
     for layer_key, layer_state_dict in clip_grouped_sd.items():
-        layers[FLUX_KOHYA_CLIP_PREFIX + layer_key] = any_lora_layer_from_state_dict(layer_state_dict)
+        layers[FLUX_LORA_CLIP_PREFIX + layer_key] = any_lora_layer_from_state_dict(layer_state_dict)
 
     # Create and return the LoRAModelRaw.
     return LoRAModelRaw(layers=layers)

invokeai/backend/lora/conversions/flux_lora_constants.py

@@ -0,0 +1,3 @@
+# Prefixes used to distinguish between transformer and CLIP text encoder keys in the FLUX InvokeAI LoRA format.
+FLUX_LORA_TRANSFORMER_PREFIX = "lora_transformer-"
+FLUX_LORA_CLIP_PREFIX = "lora_clip-"

invokeai/frontend/web/src/features/modelManagerV2/store/modelManagerV2Slice.ts

@@ -3,7 +3,7 @@ import { createSelector, createSlice } from '@reduxjs/toolkit';
 import type { PersistConfig, RootState } from 'app/store/store';
 import type { ModelType } from 'services/api/types';
 
-export type FilterableModelType = Exclude<ModelType, 'onnx' | 'clip_vision'> | 'refiner';
+export type FilterableModelType = Exclude<ModelType, 'onnx'> | 'refiner';
 
 type ModelManagerState = {
   _version: 1;

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelManagerPanel/ModelList.tsx

@@ -10,6 +10,7 @@ import { memo, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import {
   useCLIPEmbedModels,
+  useCLIPVisionModels,
   useControlNetModels,
   useEmbeddingModels,
   useIPAdapterModels,
@@ -73,6 +74,12 @@ const ModelList = () => {
     [ipAdapterModels, searchTerm, filteredModelType]
   );
 
+  const [clipVisionModels, { isLoading: isLoadingCLIPVisionModels }] = useCLIPVisionModels();
+  const filteredCLIPVisionModels = useMemo(
+    () => modelsFilter(clipVisionModels, searchTerm, filteredModelType),
+    [clipVisionModels, searchTerm, filteredModelType]
+  );
+
   const [vaeModels, { isLoading: isLoadingVAEModels }] = useVAEModels();
   const filteredVAEModels = useMemo(
     () => modelsFilter(vaeModels, searchTerm, filteredModelType),
@@ -107,6 +114,7 @@ const ModelList = () => {
       filteredControlNetModels.length +
       filteredT2IAdapterModels.length +
       filteredIPAdapterModels.length +
+      filteredCLIPVisionModels.length +
       filteredVAEModels.length +
       filteredSpandrelImageToImageModels.length +
       t5EncoderModels.length +
@@ -116,6 +124,7 @@ const ModelList = () => {
     filteredControlNetModels.length,
     filteredEmbeddingModels.length,
     filteredIPAdapterModels.length,
+    filteredCLIPVisionModels.length,
     filteredLoRAModels.length,
     filteredMainModels.length,
     filteredRefinerModels.length,
@@ -171,6 +180,11 @@ const ModelList = () => {
         {!isLoadingIPAdapterModels && filteredIPAdapterModels.length > 0 && (
           <ModelListWrapper title={t('common.ipAdapter')} modelList={filteredIPAdapterModels} key="ip-adapters" />
         )}
+        {/* CLIP Vision List */}
+        {isLoadingCLIPVisionModels && <FetchingModelsLoader loadingMessage="Loading CLIP Vision Models..." />}
+        {!isLoadingCLIPVisionModels && filteredCLIPVisionModels.length > 0 && (
+          <ModelListWrapper title="CLIP Vision" modelList={filteredCLIPVisionModels} key="clip-vision" />
+        )}
         {/* T2I Adapters List */}
         {isLoadingT2IAdapterModels && <FetchingModelsLoader loadingMessage="Loading T2I Adapters..." />}
         {!isLoadingT2IAdapterModels && filteredT2IAdapterModels.length > 0 && (

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelManagerPanel/ModelTypeFilter.tsx

@@ -22,6 +22,7 @@ export const ModelTypeFilter = memo(() => {
       t5_encoder: t('modelManager.t5Encoder'),
       clip_embed: t('modelManager.clipEmbed'),
       ip_adapter: t('common.ipAdapter'),
+      clip_vision: 'CLIP Vision',
       spandrel_image_to_image: t('modelManager.spandrelImageToImage'),
     }),
     [t]

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/ModelEdit.tsx

@@ -120,14 +120,18 @@ export const ModelEdit = memo(({ modelConfig }: Props) => {
                 <Textarea {...form.register('description')} minH={32} />
               </FormControl>
             </Flex>
-            <Heading as="h3" fontSize="md" mt="4">
-              {t('modelManager.modelSettings')}
-            </Heading>
+            {modelConfig.type !== 'clip_vision' && (
+              <Heading as="h3" fontSize="md" mt="4">
+                {t('modelManager.modelSettings')}
+              </Heading>
+            )}
             <SimpleGrid columns={2} gap={4}>
-              <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                <FormLabel>{t('modelManager.baseModel')}</FormLabel>
-                <BaseModelSelect control={form.control} />
-              </FormControl>
+              {modelConfig.type !== 'clip_vision' && (
+                <FormControl flexDir="column" alignItems="flex-start" gap={1}>
+                  <FormLabel>{t('modelManager.baseModel')}</FormLabel>
+                  <BaseModelSelect control={form.control} />
+                </FormControl>
+              )}
               {modelConfig.type === 'main' && (
                 <FormControl flexDir="column" alignItems="flex-start" gap={1}>
                   <FormLabel>{t('modelManager.variant')}</FormLabel>

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/ModelView.tsx

@@ -4,7 +4,7 @@ import { ModelConvertButton } from 'features/modelManagerV2/subpanels/ModelPanel
 import { ModelEditButton } from 'features/modelManagerV2/subpanels/ModelPanel/ModelEditButton';
 import { ModelHeader } from 'features/modelManagerV2/subpanels/ModelPanel/ModelHeader';
 import { TriggerPhrases } from 'features/modelManagerV2/subpanels/ModelPanel/TriggerPhrases';
-import { memo } from 'react';
+import { memo, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import type { AnyModelConfig } from 'services/api/types';
 
@@ -17,6 +17,20 @@ type Props = {
 
 export const ModelView = memo(({ modelConfig }: Props) => {
   const { t } = useTranslation();
+  const withSettings = useMemo(() => {
+    if (modelConfig.type === 'main' && modelConfig.base !== 'sdxl-refiner') {
+      return true;
+    }
+    if (modelConfig.type === 'controlnet' || modelConfig.type === 't2i_adapter') {
+      return true;
+    }
+    if (modelConfig.type === 'main' || modelConfig.type === 'lora') {
+      return true;
+    }
+
+    return false;
+  }, [modelConfig.base, modelConfig.type]);
+
   return (
     <Flex flexDir="column" gap={4}>
       <ModelHeader modelConfig={modelConfig}>
@@ -50,15 +64,19 @@ export const ModelView = memo(({ modelConfig }: Props) => {
             )}
           </SimpleGrid>
         </Box>
-        <Box layerStyle="second" borderRadius="base" p={4}>
-          {modelConfig.type === 'main' && modelConfig.base !== 'sdxl-refiner' && (
-            <MainModelDefaultSettings modelConfig={modelConfig} />
-          )}
-          {(modelConfig.type === 'controlnet' || modelConfig.type === 't2i_adapter') && (
-            <ControlNetOrT2IAdapterDefaultSettings modelConfig={modelConfig} />
-          )}
-          {(modelConfig.type === 'main' || modelConfig.type === 'lora') && <TriggerPhrases modelConfig={modelConfig} />}
-        </Box>
+        {withSettings && (
+          <Box layerStyle="second" borderRadius="base" p={4}>
+            {modelConfig.type === 'main' && modelConfig.base !== 'sdxl-refiner' && (
+              <MainModelDefaultSettings modelConfig={modelConfig} />
+            )}
+            {(modelConfig.type === 'controlnet' || modelConfig.type === 't2i_adapter') && (
+              <ControlNetOrT2IAdapterDefaultSettings modelConfig={modelConfig} />
+            )}
+            {(modelConfig.type === 'main' || modelConfig.type === 'lora') && (
+              <TriggerPhrases modelConfig={modelConfig} />
+            )}
+          </Box>
+        )}
       </Flex>
     </Flex>
   );

invokeai/frontend/web/src/services/api/hooks/modelsByType.ts

@@ -10,6 +10,7 @@ import {
 import type { AnyModelConfig } from 'services/api/types';
 import {
   isCLIPEmbedModelConfig,
+  isCLIPVisionModelConfig,
   isControlNetModelConfig,
   isControlNetOrT2IAdapterModelConfig,
   isFluxMainModelModelConfig,
@@ -58,6 +59,7 @@ export const useIPAdapterModels = buildModelsHook(isIPAdapterModelConfig);
 export const useEmbeddingModels = buildModelsHook(isTIModelConfig);
 export const useVAEModels = buildModelsHook(isVAEModelConfig);
 export const useFluxVAEModels = buildModelsHook(isFluxVAEModelConfig);
+export const useCLIPVisionModels = buildModelsHook(isCLIPVisionModelConfig);
 
 // const buildModelsSelector =
 //   <T extends AnyModelConfig>(typeGuard: (config: AnyModelConfig) => config is T): Selector<RootState, T[]> =>

invokeai/frontend/web/src/services/api/types.ts

@@ -99,6 +99,10 @@ export const isIPAdapterModelConfig = (config: AnyModelConfig): config is IPAdap
   return config.type === 'ip_adapter';
 };
 
+export const isCLIPVisionModelConfig = (config: AnyModelConfig): config is CLIPVisionDiffusersConfig => {
+  return config.type === 'clip_vision';
+};
+
 export const isT2IAdapterModelConfig = (config: AnyModelConfig): config is T2IAdapterModelConfig => {
   return config.type === 't2i_adapter';
 };

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "5.0.1"
+__version__ = "5.0.2"

tests/backend/lora/conversions/test_flux_diffusers_lora_conversion_utils.py

@@ -5,6 +5,7 @@ from invokeai.backend.lora.conversions.flux_diffusers_lora_conversion_utils impo
     is_state_dict_likely_in_flux_diffusers_format,
     lora_model_from_flux_diffusers_state_dict,
 )
+from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
 from tests.backend.lora.conversions.lora_state_dicts.flux_lora_diffusers_format import (
     state_dict_keys as flux_diffusers_state_dict_keys,
 )
@@ -50,6 +51,7 @@ def test_lora_model_from_flux_diffusers_state_dict():
     concatenated_weights = ["to_k", "to_v", "proj_mlp", "add_k_proj", "add_v_proj"]
     expected_lora_layers = {k for k in expected_lora_layers if not any(w in k for w in concatenated_weights)}
     assert len(model.layers) == len(expected_lora_layers)
+    assert all(k.startswith(FLUX_LORA_TRANSFORMER_PREFIX) for k in model.layers.keys())
 
 
 def test_lora_model_from_flux_diffusers_state_dict_extra_keys_error():

tests/backend/lora/conversions/test_flux_kohya_lora_conversion_utils.py

@@ -5,12 +5,11 @@ import torch
 from invokeai.backend.flux.model import Flux
 from invokeai.backend.flux.util import params
 from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import (
-    FLUX_KOHYA_CLIP_PREFIX,
-    FLUX_KOHYA_TRANFORMER_PREFIX,
     _convert_flux_transformer_kohya_state_dict_to_invoke_format,
     is_state_dict_likely_in_flux_kohya_format,
     lora_model_from_flux_kohya_state_dict,
 )
+from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX, FLUX_LORA_TRANSFORMER_PREFIX
 from tests.backend.lora.conversions.lora_state_dicts.flux_lora_diffusers_format import (
     state_dict_keys as flux_diffusers_state_dict_keys,
 )
@@ -95,8 +94,8 @@ def test_lora_model_from_flux_kohya_state_dict(sd_keys: list[str]):
     expected_layer_keys: set[str] = set()
     for k in sd_keys:
         # Replace prefixes.
-        k = k.replace("lora_unet_", FLUX_KOHYA_TRANFORMER_PREFIX)
-        k = k.replace("lora_te1_", FLUX_KOHYA_CLIP_PREFIX)
+        k = k.replace("lora_unet_", FLUX_LORA_TRANSFORMER_PREFIX)
+        k = k.replace("lora_te1_", FLUX_LORA_CLIP_PREFIX)
         # Remove suffixes.
         k = k.replace(".lora_up.weight", "")
         k = k.replace(".lora_down.weight", "")