Add seeded Text LLM sampling

[JPPhoto]

Summary

Adds controlled seeded sampling to the Text LLM and Text LLM with System Prompt Preset nodes.

• Exposes a validated seed input and bumps both nodes to version 1.1.0.
• Uses invocation-local generators to prevent concurrent requests from interfering with each other's RNG state.
• Preserves varied Expand Prompt results by selecting a fresh seed per request.
• Documents seed behavior and reproducibility boundaries.

Related Issues / Discussions

Discord.

QA Instructions

1. Run:
   pytest -n auto --no-cov tests/backend/text_llm tests/app/invocations/test_text_llm_with_preset.py
2. Confirm all tests pass.
3. Run the same Text LLM node twice with identical inputs and seed; confirm identical output on the same platform.
4. Change the seed; confirm sampling can produce different output.
5. Run concurrent seeded CPU sampling; confirm each request matches its isolated sequence.

Merge Plan

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)

[Pfannkuchensack]

PR #9451 — Add seeded Text LLM sampling

• The tests do not prove the feature. Two independent one-line mutations destroy seeded sampling and leave all 30 tests green:
  • removing the self._generators cache (invokeai/backend/text_llm_pipeline.py:40-44) so the RNG never advances — measured effect: torch.multinomial returns the same value 10/10 times, i.e. a real model emits one repeated token;
  • moving with _SeededMultinomialMode(seed): (:127) out of the worker closure — TorchFunctionMode is thread-local, so the mode never covers generate and the seed does nothing.
    Cause: the RNG tests call torch.multinomial by hand inside the mode and never go through run(); everything that does go through run() uses a MagicMock model that never samples. An end-to-end test with a tiny randomly-initialised causal LM costs 0.18 s for three CPU runs and kills both mutations.
• Runtime cost: +19.4% on every text-LLM invocation. Measured on real Qwen2.5-1.5B-Instruct weights (RTX 4090, bf16, 128 tokens, median of 5, non-overlapping ranges): 19.81 → 23.66 ms/token, +3.85 ms/token, from 1931 intercepted torch calls per token. That is ~+1.16 s at the node default max_tokens=300 and ~+6 s at the 2048 cap, on a path where the user watches tokens stream. The mode wraps the whole forward pass, not just sampling — narrowing it to the sampling step (a custom LogitsProcessor with explicit Temperature/TopP warpers) would be O(1) per token. If the cost is accepted, put it in the PR description.
• Saved workflows silently become deterministic. The 1.0.0 → 1.1.0 bump makes validateWorkflow auto-run updateNode, whose defaultsDeep fills the new seed with the template default 0. A workflow that previously varied on each run now returns byte-identical text forever, with no signal to the user and no way back except manually wiring a rand_int. (Masked inside one server process by the node cache; visible across restarts.) Add a vitest case pinning the updateNode outcome for a stored text_llm node.
• func is torch.multinomial misses the method form. p.multinomial(...) bypasses seeding entirely (verified: reproducible via the function form, not via the method form). Unreachable today — all four transformers sampling paths were checked and ruled out — but the method form already exists elsewhere in the same transformers version, so an upstream refactor would ship broken reproducibility with a fully green suite.
• Expand Prompt cannot reproduce anything. invokeai/app/api/routers/utilities.py:170 draws a fresh seed that is never returned, logged, or accepted back; ExpandPromptRequest/ExpandPromptResponse have no seed field. The route already accepts max_tokens and system_prompt from the client, so accepting an optional seed is zero-risk. Decide explicitly.
• MPS path unverified (no Apple Silicon available). Static review of the .cpu() / .to(device) rewrite is clean and num_samples/replacement survive, but no test covers the branch and the per-token .cpu() hop forces a device sync. Someone on an M-series machine should run one expansion twice with the same seed before merge.