Add FLUX Control LoRA weight param

invokeai/app/invocations/flux_control_lora_loader.py

@@ -24,7 +24,7 @@ class FluxControlLoRALoaderOutput(BaseInvocationOutput):
     title="Flux Control LoRA",
     tags=["lora", "model", "flux"],
     category="model",
-    version="1.0.0",
+    version="1.1.0",
     classification=Classification.Prototype,
 )
 class FluxControlLoRALoaderInvocation(BaseInvocation):
@@ -34,6 +34,7 @@ class FluxControlLoRALoaderInvocation(BaseInvocation):
         description=FieldDescriptions.control_lora_model, title="Control LoRA", ui_type=UIType.ControlLoRAModel
     )
     image: ImageField = InputField(description="The image to encode.")
+    weight: float = InputField(description="The weight of the LoRA.", default=1.0)
 
     def invoke(self, context: InvocationContext) -> FluxControlLoRALoaderOutput:
         if not context.models.exists(self.lora.key):
@@ -43,6 +44,6 @@ def invoke(self, context: InvocationContext) -> FluxControlLoRALoaderOutput:
             control_lora=ControlLoRAField(
                 lora=self.lora,
                 img=self.image,
-                weight=1,
+                weight=self.weight,
             )
         )

invokeai/backend/patches/layers/set_parameter_layer.py

@@ -15,8 +15,10 @@ def __init__(self, param_name: str, weight: torch.Tensor):
         self.param_name = param_name
 
     def get_parameters(self, orig_module: torch.nn.Module, weight: float) -> dict[str, torch.Tensor]:
+        # Note: We intentionally ignore the weight parameter here. This matches the behavior in the official FLUX
+        # Control LoRA implementation.
         diff = self.weight - orig_module.get_parameter(self.param_name)
-        return {self.param_name: diff * weight}
+        return {self.param_name: diff}
 
     def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
         self.weight = self.weight.to(device=device, dtype=dtype)

invokeai/frontend/web/src/features/controlLayers/components/ControlLayer/ControlLayerControlAdapter.tsx

@@ -162,7 +162,7 @@ export const ControlLayerControlAdapter = memo(() => {
         />
         <input {...uploadApi.getUploadInputProps()} />
       </Flex>
-      {controlAdapter.type !== 'control_lora' && <Weight weight={controlAdapter.weight} onChange={onChangeWeight} />}
+      <Weight weight={controlAdapter.weight} onChange={onChangeWeight} />
       {controlAdapter.type !== 'control_lora' && (
         <BeginEndStepPct beginEndStepPct={controlAdapter.beginEndStepPct} onChange={onChangeBeginEndStepPct} />
       )}

invokeai/frontend/web/src/features/controlLayers/store/canvasSlice.ts

@@ -74,6 +74,7 @@ import {
   getReferenceImageState,
   getRegionalGuidanceState,
   imageDTOToImageWithDims,
+  initialControlLoRA,
   initialControlNet,
   initialIPAdapter,
   initialT2IAdapter,
@@ -462,38 +463,64 @@ export const canvasSlice = createSlice({
       }
       layer.controlAdapter.model = zModelIdentifierField.parse(modelConfig);
 
+      // When converting between control layer types, we may need to add or remove properties. For example, ControlNet
+      // has a control mode, while T2I Adapter does not - otherwise they are the same.
+
       switch (layer.controlAdapter.model.type) {
+        // Converting to T2I adapter from...
         case 't2i_adapter': {
           if (layer.controlAdapter.type === 'controlnet') {
+            // T2I Adapters have all the ControlNet properties, minus control mode - strip it
             const { controlMode: _, ...rest } = layer.controlAdapter;
-            const t2iAdapterConfig: T2IAdapterConfig = { ...rest, type: 't2i_adapter' };
+            const t2iAdapterConfig: T2IAdapterConfig = { ...initialT2IAdapter, ...rest, type: 't2i_adapter' };
             layer.controlAdapter = t2iAdapterConfig;
           } else if (layer.controlAdapter.type === 'control_lora') {
-            const t2iAdapterConfig: T2IAdapterConfig = { ...layer.controlAdapter, ...initialT2IAdapter };
+            // Control LoRAs have only model and weight
+            const t2iAdapterConfig: T2IAdapterConfig = {
+              ...initialT2IAdapter,
+              ...layer.controlAdapter,
+              type: 't2i_adapter',
+            };
             layer.controlAdapter = t2iAdapterConfig;
           }
           break;
         }
 
+        // Converting to ControlNet from...
         case 'controlnet': {
           if (layer.controlAdapter.type === 't2i_adapter') {
+            // ControlNets have all the T2I Adapter properties, plus control mode
             const controlNetConfig: ControlNetConfig = {
+              ...initialControlNet,
               ...layer.controlAdapter,
               type: 'controlnet',
-              controlMode: initialControlNet.controlMode,
             };
             layer.controlAdapter = controlNetConfig;
           } else if (layer.controlAdapter.type === 'control_lora') {
-            const controlNetConfig: ControlNetConfig = { ...layer.controlAdapter, ...initialControlNet };
+            // ControlNets have all the Control LoRA properties, plus control mode and begin/end step pct
+            const controlNetConfig: ControlNetConfig = {
+              ...initialControlNet,
+              ...layer.controlAdapter,
+              type: 'controlnet',
+            };
             layer.controlAdapter = controlNetConfig;
           }
           break;
         }
 
+        // Converting to ControlLoRA from...
         case 'control_lora': {
-          const controlLoraConfig: ControlLoRAConfig = { ...layer.controlAdapter, type: 'control_lora' };
-          layer.controlAdapter = controlLoraConfig;
-
+          if (layer.controlAdapter.type === 'controlnet') {
+            // We only need the model and weight for Control LoRA
+            const { model, weight } = layer.controlAdapter;
+            const controlNetConfig: ControlLoRAConfig = { ...initialControlLoRA, model, weight };
+            layer.controlAdapter = controlNetConfig;
+          } else if (layer.controlAdapter.type === 't2i_adapter') {
+            // We only need the model and weight for Control LoRA
+            const { model, weight } = layer.controlAdapter;
+            const t2iAdapterConfig: ControlLoRAConfig = { ...initialControlLoRA, model, weight };
+            layer.controlAdapter = t2iAdapterConfig;
+          }
           break;
         }
 
@@ -518,7 +545,7 @@ export const canvasSlice = createSlice({
     ) => {
       const { entityIdentifier, weight } = action.payload;
       const layer = selectEntity(state, entityIdentifier);
-      if (!layer || !layer.controlAdapter || layer.controlAdapter.type === 'control_lora') {
+      if (!layer || !layer.controlAdapter) {
         return;
       }
       layer.controlAdapter.weight = weight;

invokeai/frontend/web/src/features/controlLayers/store/types.ts

@@ -298,6 +298,7 @@ export type T2IAdapterConfig = z.infer<typeof zT2IAdapterConfig>;
 
 const zControlLoRAConfig = z.object({
   type: z.literal('control_lora'),
+  weight: z.number().gte(-1).lte(2),
   model: zServerValidatedModelIdentifierField.nullable(),
 });
 export type ControlLoRAConfig = z.infer<typeof zControlLoRAConfig>;

invokeai/frontend/web/src/features/controlLayers/store/util.ts

@@ -7,6 +7,7 @@ import type {
   CanvasRasterLayerState,
   CanvasReferenceImageState,
   CanvasRegionalGuidanceState,
+  ControlLoRAConfig,
   ControlNetConfig,
   ImageWithDims,
   IPAdapterConfig,
@@ -82,6 +83,11 @@ export const initialControlNet: ControlNetConfig = {
   beginEndStepPct: [0, 0.75],
   controlMode: 'balanced',
 };
+export const initialControlLoRA: ControlLoRAConfig = {
+  type: 'control_lora',
+  model: null,
+  weight: 0.75,
+};
 
 export const getReferenceImageState = (
   id: string,

invokeai/frontend/web/src/features/nodes/util/graph/generation/addControlAdapters.ts

@@ -207,7 +207,7 @@ const addControlLoRAToGraph = (
 ) => {
   const { id, controlAdapter } = layer;
   assert(controlAdapter.type === 'control_lora');
-  const { model } = controlAdapter;
+  const { model, weight } = controlAdapter;
   assert(model !== null);
   const { image_name } = imageDTO;
 
@@ -216,6 +216,7 @@ const addControlLoRAToGraph = (
     type: 'flux_control_lora_loader',
     lora: model,
     image: { image_name },
+    weight: weight,
   });
 
   g.addEdge(controlLoRA, 'control_lora', denoise, 'control_lora');

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

@@ -6708,6 +6708,12 @@ export type components = {
              * @default null
              */
             image?: components["schemas"]["ImageField"];
+            /**
+             * Weight
+             * @description The weight of the LoRA.
+             * @default 1
+             */
+            weight?: number;
             /**
              * type
              * @default flux_control_lora_loader
@@ -6722,11 +6728,11 @@ export type components = {
          */
         FluxControlLoRALoaderOutput: {
             /**
-             * Flux Control Lora
+             * Flux Control LoRA
              * @description Control LoRAs to apply on model loading
              * @default null
              */
-            control_lora: components["schemas"]["ControlLoRAField"] | null;
+            control_lora: components["schemas"]["ControlLoRAField"];
             /**
              * type
              * @default flux_control_lora_loader_output
@@ -6926,7 +6932,7 @@ export type components = {
              */
             transformer?: components["schemas"]["TransformerField"];
             /**
-             * Control Lora
+             * Control LoRA
              * @description Control LoRA model to load
              * @default null
              */