Let Image v2 pick a LoRA or LoRA chain.

Co-authored-by: Cursor <cursoragent@cursor.com>

server/utils/loras.ts

@@ -4,7 +4,9 @@ import {
   XAIGEN_LORA_MESSAGE,
   MINIMAX_TURBO_LORA,
   filterLorasForStudio,
+  isKleinImageLora,
   isXaigenOnlyLora,
+  kleinLoraOnKreaMessage,
   loraIdentityKey,
   normalizeLoraName,
   normalizeLoraStack,
@@ -378,6 +380,106 @@ export function applyOptionalLoraLoaders(graph: WorkflowGraph, stack?: unknown,
   }
 }
 
+function linkRef(value: unknown): [string, number] | null {
+  return Array.isArray(value) && typeof value[0] === 'string' && Number.isFinite(Number(value[1]))
+    ? [value[0], Number(value[1])]
+    : null
+}
+
+function currentModelClipFeeds(graph: WorkflowGraph) {
+  const prompt = Object.values(graph).find(node => node.class_type === 'CLIPTextEncode' && linkRef(node.inputs.clip))
+  const modelNode = Object.values(graph).find(node => MODEL_FEED_CLASSES.has(node.class_type) && linkRef(node.inputs.model))
+  const model = linkRef(modelNode?.inputs.model)
+  const clip = linkRef(prompt?.inputs.clip)
+  return {
+    model: model && graph[model[0]] ? model : null,
+    clip: clip && graph[clip[0]] ? clip : null
+  }
+}
+
+export function assertImageV2LoraStack(stack: unknown, engine: 'flux' | 'krea') {
+  const items = normalizeLoraStack(stack)
+  if (engine !== 'krea') return items
+  const klein = items.find(item => isKleinImageLora(item.name))
+  if (klein) {
+    throw createError({ statusCode: 400, statusMessage: kleinLoraOnKreaMessage(klein.name) })
+  }
+  return items
+}
+
+/**
+ * Append a user LoRA chain after the current model/CLIP feed.
+ * Flux v2 already has Concept/Consistency loaders; this stacks after those.
+ * Empty stack is a no-op. Klein LoRAs on a Krea graph throw.
+ */
+export function applyImageV2UserLoras(
+  graph: WorkflowGraph,
+  stack?: unknown,
+  engine: 'flux' | 'krea' = 'flux'
+) {
+  const xaigen = isXaigenStudio()
+  const items = normalizeLoraStack(stack)
+    .filter(item => xaigen || !isXaigenOnlyLora(item.name))
+    .map(item => ({
+      ...item,
+      name: resolveUserLoraName(item.name, 'image')
+    }))
+    .filter(item => item.name && (xaigen || !isXaigenOnlyLora(item.name)) && !alreadyHasLora(graph, item.name))
+  if (!items.length) return [] as LoraStackItem[]
+
+  if (engine === 'krea') {
+    const klein = items.find(item => isKleinImageLora(item.name))
+    if (klein) {
+      throw createError({ statusCode: 400, statusMessage: kleinLoraOnKreaMessage(klein.name) })
+    }
+  }
+
+  const feeds = currentModelClipFeeds(graph)
+  const modelFrom = feeds.model || (graph['4'] ? ['4', 0] as [string, number] : null)
+  const clipFrom = feeds.clip || (graph['5'] ? ['5', 0] as [string, number] : null)
+  if (!modelFrom || !clipFrom) {
+    throw createError({ statusCode: 500, statusMessage: 'v2 graph has no model/CLIP feed for user LoRAs.' })
+  }
+
+  const firstId = 'user:lora:0'
+  graph[firstId] = {
+    class_type: LORA_LOADER,
+    inputs: {
+      lora_name: items[0].name,
+      strength_model: items[0].strengthModel,
+      strength_clip: items[0].strengthClip,
+      model: modelFrom,
+      clip: clipFrom
+    },
+    _meta: { title: items.length === 1 ? 'User LoRA' : 'User LoRA 1' }
+  }
+  for (const [id, node] of Object.entries(graph)) {
+    if (id === firstId) continue
+    for (const [key, value] of Object.entries(node.inputs)) {
+      if (!Array.isArray(value)) continue
+      if (value[0] === modelFrom[0] && value[1] === modelFrom[1]) node.inputs[key] = [firstId, 0]
+      else if (value[0] === clipFrom[0] && value[1] === clipFrom[1]) node.inputs[key] = [firstId, 1]
+    }
+  }
+  let sourceId = firstId
+  for (const [index, item] of items.slice(1).entries()) {
+    const nodeId = `user:lora:${index + 1}`
+    injectAfter(graph, sourceId, nodeId, {
+      class_type: LORA_LOADER,
+      inputs: {
+        lora_name: item.name,
+        strength_model: item.strengthModel,
+        strength_clip: item.strengthClip,
+        model: [sourceId, 0],
+        clip: [sourceId, 1]
+      },
+      _meta: { title: `User LoRA ${index + 2}` }
+    })
+    sourceId = nodeId
+  }
+  return items
+}
+
 export function applyUserLoraToGraph(graph: WorkflowGraph, stack?: unknown) {
   const xaigen = isXaigenStudio()
   const items = normalizeLoraStack(stack)