update

.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/."

docs/source/en/optimization/attention_backends.md

@@ -143,7 +143,6 @@ Refer to the table below for a complete list of available attention backends and
 | `flash_varlen` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention |
 | `flash_varlen_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention from kernels |
 | `aiter` | [AI Tensor Engine for ROCm](https://github.com/ROCm/aiter) | FlashAttention for AMD ROCm |
-| `flash_4_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-4 |
 | `_flash_3` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-3 |
 | `_flash_varlen_3` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention-3 |
 | `_flash_3_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-3 from kernels |

src/diffusers/models/attention_dispatch.py

@@ -229,7 +229,6 @@ class AttentionBackendName(str, Enum):
     FLASH_HUB = "flash_hub"
     FLASH_VARLEN = "flash_varlen"
     FLASH_VARLEN_HUB = "flash_varlen_hub"
-    FLASH_4_HUB = "flash_4_hub"
     _FLASH_3 = "_flash_3"
     _FLASH_VARLEN_3 = "_flash_varlen_3"
     _FLASH_3_HUB = "_flash_3_hub"
@@ -359,11 +358,6 @@ _HUB_KERNELS_REGISTRY: dict["AttentionBackendName", _HubKernelConfig] = {
         function_attr="sageattn",
         version=1,
     ),
-    AttentionBackendName.FLASH_4_HUB: _HubKernelConfig(
-        repo_id="kernels-staging/flash-attn4",
-        function_attr="flash_attn_func",
-        version=0,
-    ),
 }
 
 
@@ -527,7 +521,6 @@ def _check_attention_backend_requirements(backend: AttentionBackendName) -> None
         AttentionBackendName._FLASH_3_HUB,
         AttentionBackendName._FLASH_3_VARLEN_HUB,
         AttentionBackendName.SAGE_HUB,
-        AttentionBackendName.FLASH_4_HUB,
     ]:
         if not is_kernels_available():
             raise RuntimeError(
@@ -538,11 +531,6 @@ def _check_attention_backend_requirements(backend: AttentionBackendName) -> None
                 f"Backend '{backend.value}' needs to be used with a `kernels` version of at least 0.12. Please update with `pip install -U kernels`."
             )
 
-        if backend == AttentionBackendName.FLASH_4_HUB and not is_kernels_available(">=", "0.12.3"):
-            raise RuntimeError(
-                f"Backend '{backend.value}' needs to be used with a `kernels` version of at least 0.12.3. Please update with `pip install -U kernels`."
-            )
-
     elif backend == AttentionBackendName.AITER:
         if not _CAN_USE_AITER_ATTN:
             raise RuntimeError(
@@ -2688,37 +2676,6 @@ def _flash_attention_3_varlen_hub(
     return (out, lse) if return_lse else out
 
 
-@_AttentionBackendRegistry.register(
-    AttentionBackendName.FLASH_4_HUB,
-    constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],
-    supports_context_parallel=False,
-)
-def _flash_attention_4_hub(
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: torch.Tensor | None = None,
-    scale: float | None = None,
-    is_causal: bool = False,
-    return_lse: bool = False,
-    _parallel_config: "ParallelConfig" | None = None,
-) -> torch.Tensor:
-    if attn_mask is not None:
-        raise ValueError("`attn_mask` is not supported for flash-attn 4.")
-
-    func = _HUB_KERNELS_REGISTRY[AttentionBackendName.FLASH_4_HUB].kernel_fn
-    out = func(
-        q=query,
-        k=key,
-        v=value,
-        softmax_scale=scale,
-        causal=is_causal,
-    )
-    if isinstance(out, tuple):
-        return (out[0], out[1]) if return_lse else out[0]
-    return out
-
-
 @_AttentionBackendRegistry.register(
     AttentionBackendName._FLASH_VARLEN_3,
     constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],

src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py

@@ -324,18 +324,17 @@ class AudioLDM2Pipeline(DiffusionPipeline):
             `inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
                 The sequence of generated hidden-states.
         """
+        cache_position_kwargs = {}
+        if is_transformers_version("<", "4.52.1"):
+            cache_position_kwargs["input_ids"] = inputs_embeds
+        else:
+            cache_position_kwargs["seq_length"] = inputs_embeds.shape[0]
+            cache_position_kwargs["device"] = (
+                self.language_model.device if getattr(self, "language_model", None) is not None else self.device
+            )
+        cache_position_kwargs["model_kwargs"] = model_kwargs
         max_new_tokens = max_new_tokens if max_new_tokens is not None else self.language_model.config.max_new_tokens
-        if hasattr(self.language_model, "_get_initial_cache_position"):
-            cache_position_kwargs = {}
-            if is_transformers_version("<", "4.52.1"):
-                cache_position_kwargs["input_ids"] = inputs_embeds
-            else:
-                cache_position_kwargs["seq_length"] = inputs_embeds.shape[0]
-                cache_position_kwargs["device"] = (
-                    self.language_model.device if getattr(self, "language_model", None) is not None else self.device
-                )
-            cache_position_kwargs["model_kwargs"] = model_kwargs
-            model_kwargs = self.language_model._get_initial_cache_position(**cache_position_kwargs)
+        model_kwargs = self.language_model._get_initial_cache_position(**cache_position_kwargs)
 
         for _ in range(max_new_tokens):
             # prepare model inputs

tests/lora/test_lora_layers_wanvace.py

@@ -28,6 +28,7 @@ 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,
@@ -45,6 +46,7 @@ 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
@@ -71,8 +73,8 @@ class WanVACELoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
         "base_dim": 3,
         "z_dim": 4,
         "dim_mult": [1, 1, 1, 1],
-        "latents_mean": [-0.7571, -0.7089, -0.9113, -0.7245],
-        "latents_std": [2.8184, 1.4541, 2.3275, 2.6558],
+        "latents_mean": torch.randn(4).numpy().tolist(),
+        "latents_std": torch.randn(4).numpy().tolist(),
         "num_res_blocks": 1,
         "temperal_downsample": [False, True, True],
     }

tests/modular_pipelines/test_modular_pipelines_common.py

@@ -5,7 +5,6 @@ from typing import Callable
 
 import pytest
 import torch
-from huggingface_hub import hf_hub_download
 
 import diffusers
 from diffusers import AutoModel, ComponentsManager, ModularPipeline, ModularPipelineBlocks
@@ -33,33 +32,6 @@ 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,
@@ -388,39 +360,6 @@ 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