🎯 What problem does this PR solve?

• FLUX.2 supports conditioning on multiple input images ("multi-reference fusion"), but the SDK's diffusion() API only accepted a single init_image.
• Users had no way to apply a LoRA adapter on a per-generation basis through the SDK, nor to control whether the adapter persists across subsequent calls.

📝 How does it solve it?

• Adds init_images: Uint8Array[] to DiffusionClientParams for FLUX.2 fusion; mutually exclusive with init_image (enforced both at compile time via discriminated union and at runtime via Zod refine).
• Adds FLUX.2 reference-image tunables increase_ref_index and auto_resize_ref_image.
• Adds per-call lora field, validated as an absolute filesystem path (POSIX, Windows drive-letter, and UNC). Relative paths are rejected because the SDK runs across processes with differing cwds.
• Adds lora_apply_mode: "auto" | "immediately" | "at_runtime" to sdcppConfigSchema (set at loadModel time) controlling whether the adapter is fused permanently or applied per-call.
• Bumps @qvac/diffusion-cpp to ^0.5.0 for the underlying addon support and wires the new fields through the bare plugin op and the desktop / mobile / shared test executors.

🧪 How was it tested?

• Unit tests in sdcpp-plugin.test.ts cover: lora absolute-path validation (POSIX / Windows / UNC accepted; relative, bare-filename, empty, wrong-type rejected), init_images shape validation, increase_ref_index / auto_resize_ref_image type checks, init_image ↔ init_images mutual exclusion, and lora_apply_mode enum acceptance.
• E2E test diffusion-fusion-flux2-basic added to tests-qvac covering FLUX.2 fusion with two reference images.

🔌 API Changes

// FLUX.2 multi-reference fusion (mutually exclusive with init_image)
const refA = fs.readFileSync("scientist-a.jpg");
const refB = fs.readFileSync("scientist-b.jpg");
const { outputs } = diffusion({
  modelId,
  prompt: "a portrait using most visual traits from @image1 and the eyes from @image2",
  init_images: [refA, refB],
  width: 768,
  height: 768,
});

// Per-call LoRA adapter (absolute path required)
const { outputs } = diffusion({
  modelId,
  prompt: "a watercolor cat",
  lora: "/home/user/loras/watercolor.safetensors",
});

// LoRA persistence mode is selected at loadModel time
await loadModel(modelSrc, {
  modelType: "diffusion",
  modelConfig: { prediction: "flux2_flow", lora_apply_mode: "immediately" },
});