Fix shared LoRA component weight broadcasting - #15128

DIFFUSERS #15127 — FINAL SELF-REVIEW (SIMPLIFIED TESTS)
  Base: aaf57d03a3edfbd9de457d2872417a92f149a91d
  Branch: fix-lora-component-weight-broadcast
  Patch SHA-256: a940c6b9f265fcffb40afac6e4ba286f892752fc96bdb63db1275340a11c5339

  Applied .ai/skills/self-review/SKILL.md to the complete uncommitted git diff HEAD,
  using .ai/references/review-rules.md and the applicable code-style, shared-test,
  pipeline/model, nested-scale, and numerical guidance. This is a Codex self-review.

  Blocking findings: none identified.

  Non-blocking suggestion:
  1. Existing docstring underspecifies supported weight forms.
     src/diffusers/loaders/lora_base.py:709 still describes list weights and UNet
     only. Per review-rules.md (Documentation impact), optionally clarify component
     dictionary broadcasting. Left unchanged to keep this bug fix focused; no new
     API is introduced. No agent instructions or configuration were modified.

  Correctness:
  - Production fix is unchanged: weights.pop(component, None) becomes
    weights.get(component, None). No later code relies on consumed component keys.
  - Scalar/list handling, validation, filtering, optional components, ownership
    warnings, and None defaults keep their existing control flow.
  - Both ModelMixin.set_adapters and text-encoder helper paths were checked.
    Nested UNet expansion deep-copies its input; activation reads weights.
    The existing outer deepcopy remains in place.

  Test proportionality and conventions:
  - Two shared-mixin test methods, with three general parameter pairs: broadcast
    0.5, broadcast 0.0, and repeated-reference list 0.5. Executed for SDXL and Flux.
  - One broadcast nested UNet block-weight case in SDXL. Seven retained cases
    across the two validated classes; each fails against original upstream code.
  - Independent-dictionary reference outputs, original caller configuration,
    reversed adapter order, and actual inference comparisons are retained.
  - Removed unit-weight cases, redundant alias/zero and nested alias combinations,
    and repeated scalar-reference inference. No bespoke fixtures or test hooks.
  - Existing real tiny pipeline builders, nonzero adapter helper, seeded run_pipe,
    and tensor assertions follow testing.md. Shared tests also collect for other
    compatible pipelines; only SDXL and Flux classes were executed here.

  Validation:
  - Baseline production file verified byte-for-byte against HEAD in a separate
    worktree; baseline imported its own src/diffusers package. Same simplified tests.
  - Baseline regressions: 7 failed, 70 deselected, 19 warnings in 6.55s (exit 1).
  - Fixed regressions: 7 passed, 70 deselected, 22 warnings in 4.80s (exit 0).
  - Complete SDXL and Flux LoRA classes: 77 passed, 110 warnings in 40.31s (exit 0).
    This comprises 70 pre-existing cases and seven retained new cases.
  - make style, make fix-copies, make quality, git diff --check: all exit 0.
  - make style collapsed the parameter decorator without changing its AST. Final
    tests have identical AST to the baseline/fixed runs. The production file stayed
    byte-identical. An unrelated generated loader import reorder was restored.

  Dead-code advisory:
  Path:line | Status | Reason
  src/diffusers/loaders/lora_base.py:780 | Used | Pipeline component-weight lookup.
  tests/pipelines/testing_utils/lora.py:705 | Used | Six collected cases across SDXL/Flux.
  tests/pipelines/testing_utils/lora.py:1227 | Used | One collected nested UNet case in SDXL.
  No dead code identified; no new models, dependencies, or fallback branches.

  Verdict: READY for human review.
  Fix before submitting: no blocking findings or dead code identified.
  Leave for maintainer review: optional existing docstring clarification above.
  Limits: CPU float32 tiny fixtures; no GPU, mixed precision, quantization,
  full-size model, offloading class, or entire-library validation.
  Final self-review notes belong in the eventual PR description/comment, outside
  its code diff. The PR draft includes the findings and limitations above.