Studio: tighten YAML import/save for vision_image_size

Two YAML-path asymmetries that could leak a stale image size into
training:

- parseYamlConfig now treats a missing training.vision_image_size as
  null. Without this, importing a YAML saved before this feature (or
  any config that omits the key) preserved whatever value the user had
  previously set on a different model. The model-defaults reload path
  still uses Object.hasOwn so same-model defaults reloads do not wipe
  a manual selection; only file import normalises the missing key.

- handleSaveConfig now passes a DeepSeek-OCR-specific guard to
  serializeConfigToYaml so saved YAML matches what the API mapper
  actually sends. Previously a state with visionImageSize set could
  emit the key even though Studio ignored it at training time for
  DeepSeek OCR, and a later import for a non-DeepSeek vision model
  would activate the stale value.

serializeConfigToYaml gains an optional third parameter
includeVisionImageSize defaulting to includeVisionFields, preserving
the existing 2-arg call signature for backwards compatibility.

studio/frontend/src/features/studio/sections/training-section.tsx

@@ -87,7 +87,18 @@ export function TrainingSection() {
     // vision_image_size choice in those windows.
     const includeVisionFields =
       store.isVisionModel && store.isDatasetImage !== false;
-    const yamlStr = serializeConfigToYaml(store, includeVisionFields);
+    // DeepSeek OCR ignores vision_image_size at training time (mappers.ts
+    // sends null), so do not emit it to YAML either; otherwise a stale
+    // value could later apply to a non-DeepSeek vision model.
+    const selectedModelLower = (store.selectedModel ?? "").toLowerCase();
+    const isDeepseekOcr =
+      selectedModelLower.includes("deepseek") &&
+      selectedModelLower.includes("ocr");
+    const yamlStr = serializeConfigToYaml(
+      store,
+      includeVisionFields,
+      includeVisionFields && !isDeepseekOcr,
+    );
     const blob = new Blob([yamlStr], { type: "text/yaml" });
     const url = URL.createObjectURL(blob);
     const a = document.createElement("a");

studio/frontend/src/features/training/lib/yaml-config.ts

@@ -27,8 +27,28 @@ export function parseYamlConfig(text: string): BackendModelConfig {
     console.warn("Ignored unknown YAML keys:", unknownKeys.join(", "));
   }
 
+  // YAML import means "use this config as authoritative". An absent
+  // vision_image_size should reset the in-memory value to Default, not
+  // preserve a stale one. Same-model defaults reloads (which also flow
+  // through the model-config mapper) skip the reset via Object.hasOwn
+  // in model-defaults.ts; here we forge the key so import always wins.
+  const trainingObj =
+    raw.training != null &&
+    typeof raw.training === "object" &&
+    !Array.isArray(raw.training)
+      ? { ...(raw.training as Record<string, unknown>) }
+      : raw.training;
+  if (
+    trainingObj != null &&
+    typeof trainingObj === "object" &&
+    !Array.isArray(trainingObj) &&
+    !Object.hasOwn(trainingObj, "vision_image_size")
+  ) {
+    (trainingObj as Record<string, unknown>).vision_image_size = null;
+  }
+
   return {
-    training: (raw.training ?? undefined) as BackendModelConfig["training"],
+    training: trainingObj as BackendModelConfig["training"],
     lora: (raw.lora ?? undefined) as BackendModelConfig["lora"],
     logging: (raw.logging ?? undefined) as BackendModelConfig["logging"],
   };
@@ -41,6 +61,7 @@ export function parseYamlConfig(text: string): BackendModelConfig {
 export function serializeConfigToYaml(
   state: TrainingConfigState,
   includeVisionFields: boolean,
+  includeVisionImageSize: boolean = includeVisionFields,
 ): string {
   const lora: Record<string, unknown> = {
     lora_r: state.loraRank,
@@ -77,7 +98,7 @@ export function serializeConfigToYaml(
     lr_scheduler_type: state.lrSchedulerType,
   };
 
-  if (includeVisionFields) {
+  if (includeVisionImageSize) {
     training.vision_image_size = state.visionImageSize;
   }