feat(vae): support running VAEs on CPU via cpu_only setting

[Pfannkuchensack]

Summary

Extends the cpu_only mechanism from #8777 (text encoders) to VAE decode. Adds a cpu_only field to all standalone VAE configs; the loader already forces standalone configs with cpu_only=True onto the CPU. The 7 decode invocations now move latents to the VAE's effective device instead of hard-coding CUDA, and the SD/SDXL path falls back to fp32 on CPU (fp16 conv is unsupported there). Adds a "Run on CPU" toggle to the VAE model settings panel and regenerates the API schema.

Decode-only for now; encode and main-model VAE submodels are unchanged.

Backend

• cpu_only field added to all standalone VAE configs (SD1/SD2/SDXL/FLUX checkpoint, SD1/SDXL diffusers, FLUX.2/Qwen-Image/Anima, FLUX.2 diffusers).
• No loader change needed — _get_execution_device already returns cpu for any standalone config with cpu_only=True.
• All 7 latents-to-image invocations (l2i, FLUX, FLUX.2, SD3, CogView4, Qwen-Image, Z-Image, Anima) now send latents to get_effective_device(vae) rather than TorchDevice.choose_torch_device().
• SD/SDXL decode is forced to fp32 when the VAE is on CPU (fp16 conv is not implemented on CPU).
• Cache invalidation on toggle is already covered by the existing _LOAD_AFFECTING_SETTINGS (cpu_only is in it).

Frontend

• New VAEModelSettings panel + useVAEModelSettings hook (mirrors the encoder panel, reuses the shared form-data type).
• ModelView renders the panel for type === 'vae'.

Related Issues / Discussions

Closes #7276 (VAE part — the CLIP/text-encoder part was delivered in #8777)

QA Instructions

1. In Model Manager, select a standalone VAE model → the settings panel now shows a Run on CPU toggle. Enable it and save.
2. Run a generation that decodes with that VAE (e.g. SDXL → Latents to Image). Confirm:
   • The image decodes correctly (no device-mismatch / "Half not implemented on CPU" errors).
   • During decode, VRAM usage stays flat for the VAE (it loads into system RAM, not VRAM) — visible as reduced peak VRAM on low-memory GPUs.
3. Toggle the setting off, save, regenerate → decode runs on GPU again (no restart needed; the cached entry is evicted on the settings change).
4. Repeat the smoke test across architectures: SD1/SDXL (l2i), FLUX, FLUX.2, SD3, CogView4, Qwen-Image, Z-Image, Anima.
5. Expected trade-off: CPU decode is noticeably slower (esp. SDXL at high resolution, now fp32) — this is intentional (VRAM savings vs. speed).

Tested on SD1,SDXL,Flux1,Flux2klein9b,Anima,Zimage. Open are SD3,CogView4, Qwen-Image,Flux2klein4b(should work). Anima is really slow on CPU, anything else is ok.

Automated:

• pytest tests/backend/model_manager/load/test_load_default_cpu_only.py tests/app/routers/test_update_model_record_cache_invalidation.py
• Frontend: pnpm lint + pnpm test:no-watch (all green).

Merge Plan

Standard merge. No DB schema or redux migration.

Checklist

• The PR has a short but descriptive title, suitable for a changelog
• Tests added / updated (if applicable)
• ❗Changes to a redux slice have a corresponding migration
• Documentation added / updated (if applicable)
• Updated What's New copy (if doing a release after this PR)