Merge branch 'main' into tests-load-components

* hoping to fix is_flaky with wanvace.

* revert changes in src/diffusers/utils/testing_utils.py and propagate them to tests/testing_utils.py.

* up

.ai/review-rules.md

@@ -1,34 +0,0 @@
-# PR Review Rules
-
-Rules for Claude to check during PR reviews. Focus on correctness — style is handled by ruff.
-
-## Code style
-- Inline logic — minimize small helper/utility functions. A reader should follow the full flow without jumping between functions.
-- No defensive code or unused code paths — no fallback paths, safety checks, or config options "just in case".
-- No silent fallbacks — raise a concise error for unsupported cases rather than guessing user intent.
-
-## Dependencies
-- No new mandatory dependencies without prior discussion.
-- Optional deps must be guarded with `is_X_available()` and have a dummy in `utils/dummy_*.py`.
-- Never use `einops` — rewrite with native PyTorch (`reshape`, `permute`, `unflatten`).
-
-## Models
-- All layer calls must be visible directly in `forward()` — no helper functions hiding `nn.Module` calls.
-- No NumPy operations in `forward()` — breaks `torch.compile` with `fullgraph=True`.
-- No hardcoded dtypes (e.g. `torch.float32`, `torch.bfloat16`) in forward — use input tensor dtype or `self.dtype`.
-- Attention must use `dispatch_attention_fn`, not `F.scaled_dot_product_attention` directly.
-- Every `__init__` parameter in a `ModelMixin` subclass must be captured by `register_to_config`.
-- New classes must be registered in `__init__.py` with lazy imports (both `_import_structure` and `_lazy_modules`).
-
-## Pipelines
-- Must inherit from `DiffusionPipeline`.
-- `@torch.no_grad()` on pipeline `__call__` — forgetting this causes OOM from gradient accumulation.
-- Do NOT subclass an existing pipeline for a variant (e.g. don't subclass `FluxPipeline` for `FluxImg2ImgPipeline`).
-- Support `output_type="latent"` for skipping VAE decode.
-- Support `generator` parameter for reproducibility.
-
-## Copied code
-- Never edit a `# Copied from` block directly — run `make fix-copies` to propagate changes from the source.
-- Remove the `# Copied from` header to intentionally break the sync link.
-
-## Common mistakes (add new rules below this line)

.github/workflows/claude_review.yml

@@ -1,27 +0,0 @@
-name: Claude PR Review
-
-on:
-  issue_comment:
-    types: [created]
-
-permissions:
-  contents: write
-  pull-requests: write
-  issues: read
-
-jobs:
-  claude-review:
-    if: |
-      github.event.issue.pull_request &&
-      github.event.issue.state == 'open' &&
-      contains(github.event.comment.body, '@claude') &&
-      (github.event.comment.author_association == 'MEMBER' ||
-       github.event.comment.author_association == 'OWNER' ||
-       github.event.comment.author_association == 'COLLABORATOR')
-    runs-on: ubuntu-latest
-    steps:
-      - uses: anthropics/claude-code-action@v1
-        with:
-          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
-          claude_args: |
-            --append-system-prompt "Review this PR against the rules in .ai/review-rules.md. Focus on correctness, not style (ruff handles style). Only review changes under src/diffusers/."

tests/lora/test_lora_layers_wanvace.py

@@ -28,7 +28,6 @@ from diffusers.utils.import_utils import is_peft_available
 
 from ..testing_utils import (
     floats_tensor,
-    is_flaky,
     require_peft_backend,
     require_peft_version_greater,
     skip_mps,
@@ -46,7 +45,6 @@ from .utils import PeftLoraLoaderMixinTests  # noqa: E402
 
 @require_peft_backend
 @skip_mps
-@is_flaky(max_attempts=10, description="very flaky class")
 class WanVACELoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
     pipeline_class = WanVACEPipeline
     scheduler_cls = FlowMatchEulerDiscreteScheduler
@@ -73,8 +71,8 @@ class WanVACELoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
         "base_dim": 3,
         "z_dim": 4,
         "dim_mult": [1, 1, 1, 1],
-        "latents_mean": torch.randn(4).numpy().tolist(),
-        "latents_std": torch.randn(4).numpy().tolist(),
+        "latents_mean": [-0.7571, -0.7089, -0.9113, -0.7245],
+        "latents_std": [2.8184, 1.4541, 2.3275, 2.6558],
         "num_res_blocks": 1,
         "temperal_downsample": [False, True, True],
     }

tests/modular_pipelines/test_modular_pipelines_common.py

@@ -5,6 +5,7 @@ from typing import Callable
 
 import pytest
 import torch
+from huggingface_hub import hf_hub_download
 
 import diffusers
 from diffusers import AutoModel, ComponentsManager, ModularPipeline, ModularPipelineBlocks
@@ -32,6 +33,33 @@ from ..testing_utils import (
 )
 
 
+def _get_specified_components(path_or_repo_id, cache_dir=None):
+    if os.path.isdir(path_or_repo_id):
+        config_path = os.path.join(path_or_repo_id, "modular_model_index.json")
+    else:
+        try:
+            config_path = hf_hub_download(
+                repo_id=path_or_repo_id,
+                filename="modular_model_index.json",
+                local_dir=cache_dir,
+            )
+        except Exception:
+            return None
+
+    with open(config_path) as f:
+        config = json.load(f)
+
+    components = set()
+    for k, v in config.items():
+        if isinstance(v, (str, int, float, bool)):
+            continue
+        for entry in v:
+            if isinstance(entry, dict) and (entry.get("repo") or entry.get("pretrained_model_name_or_path")):
+                components.add(k)
+                break
+    return components
+
+
 class ModularPipelineTesterMixin:
     """
     It provides a set of common tests for each modular pipeline,
@@ -360,6 +388,39 @@ class ModularPipelineTesterMixin:
 
         assert torch.abs(image_slices[0] - image_slices[1]).max() < 1e-3
 
+    def test_load_expected_components_from_pretrained(self, tmp_path):
+        pipe = self.get_pipeline()
+        expected = _get_specified_components(self.pretrained_model_name_or_path, cache_dir=tmp_path)
+        if not expected:
+            pytest.skip("Skipping test as we couldn't fetch the expected components.")
+
+        actual = {
+            name
+            for name in pipe.components
+            if getattr(pipe, name, None) is not None
+            and getattr(getattr(pipe, name), "_diffusers_load_id", None) not in (None, "null")
+        }
+        assert expected == actual, f"Component mismatch: missing={expected - actual}, unexpected={actual - expected}"
+
+    def test_load_expected_components_from_save_pretrained(self, tmp_path):
+        pipe = self.get_pipeline()
+        save_dir = str(tmp_path / "saved-pipeline")
+        pipe.save_pretrained(save_dir)
+
+        expected = _get_specified_components(save_dir)
+        loaded_pipe = ModularPipeline.from_pretrained(save_dir)
+        loaded_pipe.load_components(torch_dtype=torch.float32)
+
+        actual = {
+            name
+            for name in loaded_pipe.components
+            if getattr(loaded_pipe, name, None) is not None
+            and getattr(getattr(loaded_pipe, name), "_diffusers_load_id", None) not in (None, "null")
+        }
+        assert expected == actual, (
+            f"Component mismatch after save/load: missing={expected - actual}, unexpected={actual - expected}"
+        )
+
     def test_modular_index_consistency(self, tmp_path):
         pipe = self.get_pipeline()
         components_spec = pipe._component_specs