update

src/diffusers/__init__.py

@@ -761,8 +761,8 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             LayerSkipConfig,
             PyramidAttentionBroadcastConfig,
             SmoothedEnergyGuidanceConfig,
-            apply_layer_skip,
             apply_faster_cache,
+            apply_layer_skip,
             apply_pyramid_attention_broadcast,
         )
         from .models import (
@@ -1085,6 +1085,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             StableDiffusionSAGPipeline,
             StableDiffusionUpscalePipeline,
             StableDiffusionXLAdapterPipeline,
+            StableDiffusionXLAutoPipeline,
             StableDiffusionXLControlNetImg2ImgPipeline,
             StableDiffusionXLControlNetInpaintPipeline,
             StableDiffusionXLControlNetPAGImg2ImgPipeline,
@@ -1102,7 +1103,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             StableDiffusionXLPAGInpaintPipeline,
             StableDiffusionXLPAGPipeline,
             StableDiffusionXLPipeline,
-            StableDiffusionXLAutoPipeline,
             StableUnCLIPImg2ImgPipeline,
             StableUnCLIPPipeline,
             StableVideoDiffusionPipeline,

src/diffusers/guiders/adaptive_projected_guidance.py

@@ -13,12 +13,13 @@
 # limitations under the License.
 
 import math
-from typing import Optional, List, TYPE_CHECKING
+from typing import TYPE_CHECKING, List, Optional
 
 import torch
 
 from .guider_utils import BaseGuidance, rescale_noise_cfg
 
+
 if TYPE_CHECKING:
     from ..pipelines.modular_pipeline import BlockState
 
@@ -119,19 +120,19 @@ class AdaptiveProjectedGuidance(BaseGuidance):
     def _is_apg_enabled(self) -> bool:
         if not self._enabled:
             return False
-        
+
         is_within_range = True
         if self._num_inference_steps is not None:
             skip_start_step = int(self._start * self._num_inference_steps)
             skip_stop_step = int(self._stop * self._num_inference_steps)
             is_within_range = skip_start_step <= self._step < skip_stop_step
-        
+
         is_close = False
         if self.use_original_formulation:
             is_close = math.isclose(self.guidance_scale, 0.0)
         else:
             is_close = math.isclose(self.guidance_scale, 1.0)
-        
+
         return is_within_range and not is_close
 
 
@@ -156,25 +157,25 @@ def normalized_guidance(
 ):
     diff = pred_cond - pred_uncond
     dim = [-i for i in range(1, len(diff.shape))]
-    
+
     if momentum_buffer is not None:
         momentum_buffer.update(diff)
         diff = momentum_buffer.running_average
-    
+
     if norm_threshold > 0:
         ones = torch.ones_like(diff)
         diff_norm = diff.norm(p=2, dim=dim, keepdim=True)
         scale_factor = torch.minimum(ones, norm_threshold / diff_norm)
         diff = diff * scale_factor
-    
+
     v0, v1 = diff.double(), pred_cond.double()
     v1 = torch.nn.functional.normalize(v1, dim=dim)
     v0_parallel = (v0 * v1).sum(dim=dim, keepdim=True) * v1
     v0_orthogonal = v0 - v0_parallel
     diff_parallel, diff_orthogonal = v0_parallel.type_as(diff), v0_orthogonal.type_as(diff)
     normalized_update = diff_orthogonal + eta * diff_parallel
-    
+
     pred = pred_cond if use_original_formulation else pred_uncond
     pred = pred + guidance_scale * normalized_update
-    
+
     return pred

src/diffusers/guiders/auto_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import List, Optional, Union, TYPE_CHECKING
+from typing import TYPE_CHECKING, List, Optional, Union
 
 import torch
 
@@ -21,6 +21,7 @@ from ..hooks import HookRegistry, LayerSkipConfig
 from ..hooks.layer_skip import _apply_layer_skip_hook
 from .guider_utils import BaseGuidance, rescale_noise_cfg
 
+
 if TYPE_CHECKING:
     from ..pipelines.modular_pipeline import BlockState
 
@@ -113,13 +114,13 @@ class AutoGuidance(BaseGuidance):
         if self._is_ag_enabled() and self.is_unconditional:
             for name, config in zip(self._auto_guidance_hook_names, self.auto_guidance_config):
                 _apply_layer_skip_hook(denoiser, config, name=name)
-    
+
     def cleanup_models(self, denoiser: torch.nn.Module) -> None:
         if self._is_ag_enabled() and self.is_unconditional:
             for name in self._auto_guidance_hook_names:
                 registry = HookRegistry.check_if_exists_or_initialize(denoiser)
                 registry.remove_hook(name, recurse=True)
-    
+
     def prepare_inputs(self, data: "BlockState") -> List["BlockState"]:
         tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
         data_batches = []
@@ -140,9 +141,9 @@ class AutoGuidance(BaseGuidance):
 
         if self.guidance_rescale > 0.0:
             pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
-        
+
         return pred, {}
-    
+
     @property
     def is_conditional(self) -> bool:
         return self._count_prepared == 1
@@ -157,17 +158,17 @@ class AutoGuidance(BaseGuidance):
     def _is_ag_enabled(self) -> bool:
         if not self._enabled:
             return False
-        
+
         is_within_range = True
         if self._num_inference_steps is not None:
             skip_start_step = int(self._start * self._num_inference_steps)
             skip_stop_step = int(self._stop * self._num_inference_steps)
             is_within_range = skip_start_step <= self._step < skip_stop_step
-        
+
         is_close = False
         if self.use_original_formulation:
             is_close = math.isclose(self.guidance_scale, 0.0)
         else:
             is_close = math.isclose(self.guidance_scale, 1.0)
-        
+
         return is_within_range and not is_close

src/diffusers/guiders/classifier_free_guidance.py

@@ -13,12 +13,13 @@
 # limitations under the License.
 
 import math
-from typing import Optional, List, TYPE_CHECKING
+from typing import TYPE_CHECKING, List, Optional
 
 import torch
 
 from .guider_utils import BaseGuidance, rescale_noise_cfg
 
+
 if TYPE_CHECKING:
     from ..pipelines.modular_pipeline import BlockState
 
@@ -74,7 +75,7 @@ class ClassifierFreeGuidance(BaseGuidance):
         self.guidance_scale = guidance_scale
         self.guidance_rescale = guidance_rescale
         self.use_original_formulation = use_original_formulation
-    
+
     def prepare_inputs(self, data: "BlockState") -> List["BlockState"]:
         tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
         data_batches = []
@@ -112,17 +113,17 @@ class ClassifierFreeGuidance(BaseGuidance):
     def _is_cfg_enabled(self) -> bool:
         if not self._enabled:
             return False
-        
+
         is_within_range = True
         if self._num_inference_steps is not None:
             skip_start_step = int(self._start * self._num_inference_steps)
             skip_stop_step = int(self._stop * self._num_inference_steps)
             is_within_range = skip_start_step <= self._step < skip_stop_step
-        
+
         is_close = False
         if self.use_original_formulation:
             is_close = math.isclose(self.guidance_scale, 0.0)
         else:
             is_close = math.isclose(self.guidance_scale, 1.0)
-        
+
         return is_within_range and not is_close

src/diffusers/guiders/classifier_free_zero_star_guidance.py

@@ -13,12 +13,13 @@
 # limitations under the License.
 
 import math
-from typing import Optional, List, TYPE_CHECKING
+from typing import TYPE_CHECKING, List, Optional
 
 import torch
 
 from .guider_utils import BaseGuidance, rescale_noise_cfg
 
+
 if TYPE_CHECKING:
     from ..pipelines.modular_pipeline import BlockState
 
@@ -72,7 +73,7 @@ class ClassifierFreeZeroStarGuidance(BaseGuidance):
         self.zero_init_steps = zero_init_steps
         self.guidance_rescale = guidance_rescale
         self.use_original_formulation = use_original_formulation
-    
+
     def prepare_inputs(self, data: "BlockState") -> List["BlockState"]:
         tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
         data_batches = []
@@ -102,7 +103,7 @@ class ClassifierFreeZeroStarGuidance(BaseGuidance):
             pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
 
         return pred, {}
-    
+
     @property
     def is_conditional(self) -> bool:
         return self._count_prepared == 1
@@ -117,19 +118,19 @@ class ClassifierFreeZeroStarGuidance(BaseGuidance):
     def _is_cfg_enabled(self) -> bool:
         if not self._enabled:
             return False
-        
+
         is_within_range = True
         if self._num_inference_steps is not None:
             skip_start_step = int(self._start * self._num_inference_steps)
             skip_stop_step = int(self._stop * self._num_inference_steps)
             is_within_range = skip_start_step <= self._step < skip_stop_step
-        
+
         is_close = False
         if self.use_original_formulation:
             is_close = math.isclose(self.guidance_scale, 0.0)
         else:
             is_close = math.isclose(self.guidance_scale, 1.0)
-        
+
         return is_within_range and not is_close
 
 

src/diffusers/guiders/guider_utils.py

@@ -58,10 +58,10 @@ class BaseGuidance:
 
     def disable(self):
         self._enabled = False
-    
+
     def enable(self):
         self._enabled = True
-    
+
     def set_state(self, step: int, num_inference_steps: int, timestep: torch.LongTensor) -> None:
         self._step = step
         self._num_inference_steps = num_inference_steps
@@ -104,14 +104,14 @@ class BaseGuidance:
                     f"Expected `set_input_fields` to be called with a string or a tuple of string with length 2, but got {type(value)} for key {key}."
                 )
         self._input_fields = kwargs
-    
+
     def prepare_models(self, denoiser: torch.nn.Module) -> None:
         """
         Prepares the models for the guidance technique on a given batch of data. This method should be overridden in
         subclasses to implement specific model preparation logic.
         """
         self._count_prepared += 1
-    
+
     def cleanup_models(self, denoiser: torch.nn.Module) -> None:
         """
         Cleans up the models for the guidance technique after a given batch of data. This method should be overridden in
@@ -119,7 +119,7 @@ class BaseGuidance:
         modifications made during `prepare_models`.
         """
         pass
-    
+
     def prepare_inputs(self, data: "BlockState") -> List["BlockState"]:
         raise NotImplementedError("BaseGuidance::prepare_inputs must be implemented in subclasses.")
 
@@ -139,15 +139,15 @@ class BaseGuidance:
     @property
     def is_conditional(self) -> bool:
         raise NotImplementedError("BaseGuidance::is_conditional must be implemented in subclasses.")
-    
+
     @property
     def is_unconditional(self) -> bool:
         return not self.is_conditional
-    
+
     @property
     def num_conditions(self) -> int:
         raise NotImplementedError("BaseGuidance::num_conditions must be implemented in subclasses.")
-    
+
     @classmethod
     def _prepare_batch(cls, input_fields: Dict[str, Union[str, Tuple[str, str]]], data: "BlockState", tuple_index: int, identifier: str) -> "BlockState":
         """

src/diffusers/guiders/skip_layer_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import List, Optional, Union, TYPE_CHECKING
+from typing import TYPE_CHECKING, List, Optional, Union
 
 import torch
 
@@ -21,6 +21,7 @@ from ..hooks import HookRegistry, LayerSkipConfig
 from ..hooks.layer_skip import _apply_layer_skip_hook
 from .guider_utils import BaseGuidance, rescale_noise_cfg
 
+
 if TYPE_CHECKING:
     from ..pipelines.modular_pipeline import BlockState
 
@@ -148,14 +149,14 @@ class SkipLayerGuidance(BaseGuidance):
         if self._is_slg_enabled() and self.is_conditional and self._count_prepared > 1:
             for name, config in zip(self._skip_layer_hook_names, self.skip_layer_config):
                 _apply_layer_skip_hook(denoiser, config, name=name)
-    
+
     def cleanup_models(self, denoiser: torch.nn.Module) -> None:
         if self._is_slg_enabled() and self.is_conditional and self._count_prepared > 1:
             registry = HookRegistry.check_if_exists_or_initialize(denoiser)
             # Remove the hooks after inference
             for hook_name in self._skip_layer_hook_names:
                 registry.remove_hook(hook_name, recurse=True)
-    
+
     def prepare_inputs(self, data: "BlockState") -> List["BlockState"]:
         if self.num_conditions == 1:
             tuple_indices = [0]
@@ -200,7 +201,7 @@ class SkipLayerGuidance(BaseGuidance):
             pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
 
         return pred, {}
-    
+
     @property
     def is_conditional(self) -> bool:
         return self._count_prepared == 1 or self._count_prepared == 3
@@ -217,31 +218,31 @@ class SkipLayerGuidance(BaseGuidance):
     def _is_cfg_enabled(self) -> bool:
         if not self._enabled:
             return False
-        
+
         is_within_range = True
         if self._num_inference_steps is not None:
             skip_start_step = int(self._start * self._num_inference_steps)
             skip_stop_step = int(self._stop * self._num_inference_steps)
             is_within_range = skip_start_step <= self._step < skip_stop_step
-        
+
         is_close = False
         if self.use_original_formulation:
             is_close = math.isclose(self.guidance_scale, 0.0)
         else:
             is_close = math.isclose(self.guidance_scale, 1.0)
-        
+
         return is_within_range and not is_close
 
     def _is_slg_enabled(self) -> bool:
         if not self._enabled:
             return False
-        
+
         is_within_range = True
         if self._num_inference_steps is not None:
             skip_start_step = int(self.skip_layer_guidance_start * self._num_inference_steps)
             skip_stop_step = int(self.skip_layer_guidance_stop * self._num_inference_steps)
             is_within_range = skip_start_step < self._step < skip_stop_step
-        
+
         is_zero = math.isclose(self.skip_layer_guidance_scale, 0.0)
-        
+
         return is_within_range and not is_zero

src/diffusers/guiders/smoothed_energy_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import List, Optional, Union, TYPE_CHECKING
+from typing import TYPE_CHECKING, List, Optional, Union
 
 import torch
 
@@ -21,6 +21,7 @@ from ..hooks import HookRegistry
 from ..hooks.smoothed_energy_guidance_utils import SmoothedEnergyGuidanceConfig, _apply_smoothed_energy_guidance_hook
 from .guider_utils import BaseGuidance, rescale_noise_cfg
 
+
 if TYPE_CHECKING:
     from ..pipelines.modular_pipeline import BlockState
 
@@ -141,14 +142,14 @@ class SmoothedEnergyGuidance(BaseGuidance):
         if self._is_seg_enabled() and self.is_conditional and self._count_prepared > 1:
             for name, config in zip(self._seg_layer_hook_names, self.seg_guidance_config):
                 _apply_smoothed_energy_guidance_hook(denoiser, config, self.seg_blur_sigma, name=name)
-    
+
     def cleanup_models(self, denoiser: torch.nn.Module):
         if self._is_seg_enabled() and self.is_conditional and self._count_prepared > 1:
             registry = HookRegistry.check_if_exists_or_initialize(denoiser)
             # Remove the hooks after inference
             for hook_name in self._seg_layer_hook_names:
                 registry.remove_hook(hook_name, recurse=True)
-    
+
     def prepare_inputs(self, data: "BlockState") -> List["BlockState"]:
         if self.num_conditions == 1:
             tuple_indices = [0]
@@ -193,7 +194,7 @@ class SmoothedEnergyGuidance(BaseGuidance):
             pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
 
         return pred, {}
-    
+
     @property
     def is_conditional(self) -> bool:
         return self._count_prepared == 1 or self._count_prepared == 3
@@ -210,31 +211,31 @@ class SmoothedEnergyGuidance(BaseGuidance):
     def _is_cfg_enabled(self) -> bool:
         if not self._enabled:
             return False
-        
+
         is_within_range = True
         if self._num_inference_steps is not None:
             skip_start_step = int(self._start * self._num_inference_steps)
             skip_stop_step = int(self._stop * self._num_inference_steps)
             is_within_range = skip_start_step <= self._step < skip_stop_step
-        
+
         is_close = False
         if self.use_original_formulation:
             is_close = math.isclose(self.guidance_scale, 0.0)
         else:
             is_close = math.isclose(self.guidance_scale, 1.0)
-        
+
         return is_within_range and not is_close
 
     def _is_seg_enabled(self) -> bool:
         if not self._enabled:
             return False
-        
+
         is_within_range = True
         if self._num_inference_steps is not None:
             skip_start_step = int(self.seg_guidance_start * self._num_inference_steps)
             skip_stop_step = int(self.seg_guidance_stop * self._num_inference_steps)
             is_within_range = skip_start_step < self._step < skip_stop_step
-        
+
         is_zero = math.isclose(self.seg_guidance_scale, 0.0)
-        
+
         return is_within_range and not is_zero

src/diffusers/guiders/tangential_classifier_free_guidance.py

@@ -13,12 +13,13 @@
 # limitations under the License.
 
 import math
-from typing import Optional, List, TYPE_CHECKING
+from typing import TYPE_CHECKING, List, Optional
 
 import torch
 
 from .guider_utils import BaseGuidance, rescale_noise_cfg
 
+
 if TYPE_CHECKING:
     from ..pipelines.modular_pipeline import BlockState
 
@@ -97,24 +98,24 @@ class TangentialClassifierFreeGuidance(BaseGuidance):
     def _is_tcfg_enabled(self) -> bool:
         if not self._enabled:
             return False
-        
+
         is_within_range = True
         if self._num_inference_steps is not None:
             skip_start_step = int(self._start * self._num_inference_steps)
             skip_stop_step = int(self._stop * self._num_inference_steps)
             is_within_range = skip_start_step <= self._step < skip_stop_step
-        
+
         is_close = False
         if self.use_original_formulation:
             is_close = math.isclose(self.guidance_scale, 0.0)
         else:
             is_close = math.isclose(self.guidance_scale, 1.0)
-        
+
         return is_within_range and not is_close
 
 
 def normalized_guidance(pred_cond: torch.Tensor, pred_uncond: torch.Tensor, guidance_scale: float, use_original_formulation: bool = False) -> torch.Tensor:
-    cond_dtype = pred_cond.dtype    
+    cond_dtype = pred_cond.dtype
     preds = torch.stack([pred_cond, pred_uncond], dim=1).float()
     preds = preds.flatten(2)
     U, S, Vh = torch.linalg.svd(preds, full_matrices=False)
@@ -125,9 +126,9 @@ def normalized_guidance(pred_cond: torch.Tensor, pred_uncond: torch.Tensor, guid
     x_Vh = torch.matmul(uncond_flat, Vh.transpose(-2, -1))
     x_Vh_V = torch.matmul(x_Vh, Vh_modified)
     pred_uncond = x_Vh_V.reshape(pred_uncond.shape).to(cond_dtype)
-    
+
     pred = pred_cond if use_original_formulation else pred_uncond
     shift = pred_cond - pred_uncond
     pred = pred + guidance_scale * shift
-    
+
     return pred

src/diffusers/hooks/layer_skip.py

@@ -20,7 +20,12 @@ import torch
 
 from ..utils import get_logger
 from ..utils.torch_utils import unwrap_module
-from ._common import _ALL_TRANSFORMER_BLOCK_IDENTIFIERS, _ATTENTION_CLASSES, _FEEDFORWARD_CLASSES, _get_submodule_from_fqn
+from ._common import (
+    _ALL_TRANSFORMER_BLOCK_IDENTIFIERS,
+    _ATTENTION_CLASSES,
+    _FEEDFORWARD_CLASSES,
+    _get_submodule_from_fqn,
+)
 from ._helpers import AttentionProcessorRegistry, TransformerBlockRegistry
 from .hooks import HookRegistry, ModelHook
 
@@ -196,15 +201,15 @@ def _apply_layer_skip_hook(module: torch.nn.Module, config: LayerSkipConfig, nam
     for i, block in enumerate(transformer_blocks):
         if i not in config.indices:
             continue
-        
+
         blocks_found = True
-        
+
         if config.skip_attention and config.skip_ff:
             logger.debug(f"Applying TransformerBlockSkipHook to '{config.fqn}.{i}'")
             registry = HookRegistry.check_if_exists_or_initialize(block)
             hook = TransformerBlockSkipHook(config.dropout)
             registry.register_hook(hook, name)
-        
+
         elif config.skip_attention or config.skip_attention_scores:
             for submodule_name, submodule in block.named_modules():
                 if isinstance(submodule, _ATTENTION_CLASSES) and not submodule.is_cross_attention:
@@ -213,7 +218,7 @@ def _apply_layer_skip_hook(module: torch.nn.Module, config: LayerSkipConfig, nam
                     registry = HookRegistry.check_if_exists_or_initialize(submodule)
                     hook = AttentionProcessorSkipHook(output_fn, config.skip_attention_scores, config.dropout)
                     registry.register_hook(hook, name)
-        
+
         if config.skip_ff:
             for submodule_name, submodule in block.named_modules():
                 if isinstance(submodule, _FEEDFORWARD_CLASSES):

src/diffusers/hooks/smoothed_energy_guidance_utils.py

@@ -14,7 +14,7 @@
 
 import math
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple
+from typing import List, Optional
 
 import torch
 import torch.nn.functional as F
@@ -67,7 +67,7 @@ class SmoothedEnergyGuidanceHook(ModelHook):
 
 def _apply_smoothed_energy_guidance_hook(module: torch.nn.Module, config: SmoothedEnergyGuidanceConfig, blur_sigma: float, name: Optional[str] = None) -> None:
     name = name or _SMOOTHED_ENERGY_GUIDANCE_HOOK
-    
+
     if config.fqn == "auto":
         for identifier in _ALL_TRANSFORMER_BLOCK_IDENTIFIERS:
             if hasattr(module, identifier):
@@ -78,18 +78,18 @@ def _apply_smoothed_energy_guidance_hook(module: torch.nn.Module, config: Smooth
                 "Could not find a suitable identifier for the transformer blocks automatically. Please provide a valid "
                 "`fqn` (fully qualified name) that identifies a stack of transformer blocks."
             )
-    
+
     if config._query_proj_identifiers is None:
         config._query_proj_identifiers = ["to_q"]
-    
+
     transformer_blocks = _get_submodule_from_fqn(module, config.fqn)
     blocks_found = False
     for i, block in enumerate(transformer_blocks):
         if i not in config.indices:
             continue
-        
+
         blocks_found = True
-        
+
         for submodule_name, submodule in block.named_modules():
             if not isinstance(submodule, _ATTENTION_CLASSES) or submodule.is_cross_attention:
                 continue
@@ -103,7 +103,7 @@ def _apply_smoothed_energy_guidance_hook(module: torch.nn.Module, config: Smooth
                 registry = HookRegistry.check_if_exists_or_initialize(query_proj)
                 hook = SmoothedEnergyGuidanceHook(blur_sigma)
                 registry.register_hook(hook, name)
-    
+
     if not blocks_found:
         raise ValueError(
             f"Could not find any transformer blocks matching the provided indices {config.indices} and "
@@ -124,7 +124,7 @@ def _gaussian_blur_2d(query: torch.Tensor, kernel_size: int, sigma: float, sigma
     in the future without warning or guarantee of reproducibility.
     """
     assert query.ndim == 3
-    
+
     is_inf = sigma > sigma_threshold_inf
     batch_size, seq_len, embed_dim = query.shape
 
@@ -133,7 +133,7 @@ def _gaussian_blur_2d(query: torch.Tensor, kernel_size: int, sigma: float, sigma
     query_slice = query[:, :num_square_tokens, :]
     query_slice = query_slice.permute(0, 2, 1)
     query_slice = query_slice.reshape(batch_size, embed_dim, seq_len_sqrt, seq_len_sqrt)
-    
+
     if is_inf:
         kernel_size = min(kernel_size, seq_len_sqrt - (seq_len_sqrt % 2 - 1))
         kernel_size_half = (kernel_size - 1) / 2
@@ -154,5 +154,5 @@ def _gaussian_blur_2d(query: torch.Tensor, kernel_size: int, sigma: float, sigma
     query_slice = query_slice.reshape(batch_size, embed_dim, num_square_tokens)
     query_slice = query_slice.permute(0, 2, 1)
     query[:, :num_square_tokens, :] = query_slice.clone()
-    
+
     return query

src/diffusers/loaders/__init__.py

@@ -102,8 +102,8 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             from .ip_adapter import (
                 FluxIPAdapterMixin,
                 IPAdapterMixin,
-                SD3IPAdapterMixin,
                 ModularIPAdapterMixin,
+                SD3IPAdapterMixin,
             )
             from .lora_pipeline import (
                 AmusedLoraLoaderMixin,

src/diffusers/pipelines/__init__.py

@@ -703,12 +703,12 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .stable_diffusion_safe import StableDiffusionPipelineSafe
         from .stable_diffusion_sag import StableDiffusionSAGPipeline
         from .stable_diffusion_xl import (
+            StableDiffusionXLAutoPipeline,
             StableDiffusionXLImg2ImgPipeline,
             StableDiffusionXLInpaintPipeline,
             StableDiffusionXLInstructPix2PixPipeline,
             StableDiffusionXLModularLoader,
             StableDiffusionXLPipeline,
-            StableDiffusionXLAutoPipeline,
         )
         from .stable_video_diffusion import StableVideoDiffusionPipeline
         from .t2i_adapter import (

src/diffusers/pipelines/components_manager.py

@@ -12,21 +12,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import copy
+import time
 from collections import OrderedDict
 from itertools import combinations
-from typing import List, Optional, Union, Dict, Any
-import copy
+from typing import Any, Dict, List, Optional, Union
 
 import torch
-import time
-from dataclasses import dataclass
 
 from ..utils import (
     is_accelerate_available,
     logging,
 )
-from ..models.modeling_utils import ModelMixin
-from .modular_pipeline_utils import ComponentSpec
 
 
 if is_accelerate_available():
@@ -231,17 +228,18 @@ class AutoOffloadStrategy:
 
 
 
-from .modular_pipeline_utils import ComponentSpec
 import uuid
+
+
 class ComponentsManager:
     def __init__(self):
         self.components = OrderedDict()
-        self.added_time = OrderedDict()  # Store when components were added 
+        self.added_time = OrderedDict()  # Store when components were added
         self.collections = OrderedDict() # collection_name -> set of component_names
         self.model_hooks = None
         self._auto_offload_enabled = False
 
-    
+
     def _get_by_collection(self, collection: str):
         """
         Select components by collection name.
@@ -252,8 +250,8 @@ class ComponentsManager:
             for component_id in component_ids:
                 selected_components[component_id] = self.components[component_id]
         return selected_components
-        
-    
+
+
     def _get_by_load_id(self, load_id: str):
         """
         Select components by its load_id.
@@ -263,8 +261,8 @@ class ComponentsManager:
             if hasattr(component, "_diffusers_load_id") and component._diffusers_load_id == load_id:
                 selected_components[name] = component
         return selected_components
-    
-    
+
+
     def add(self, name, component, collection: Optional[str] = None):
 
         for comp_id, comp in self.components.items():
@@ -282,7 +280,7 @@ class ComponentsManager:
                     f"Component '{name}' has duplicate load_id '{component._diffusers_load_id}' with existing components: {existing}. "
                     f"To remove a duplicate, call `components_manager.remove('<component_name>')`."
                 )
-        
+
 
         # add component to components manager
         self.components[component_id] = component
@@ -293,8 +291,8 @@ class ComponentsManager:
             self.collections[collection].add(component_id)
 
         if self._auto_offload_enabled:
-            self.enable_auto_cpu_offload(self._auto_offload_device)   
-        
+            self.enable_auto_cpu_offload(self._auto_offload_device)
+
         logger.info(f"Added component '{name}' to ComponentsManager as '{component_id}'")
         return component_id
 
@@ -304,14 +302,14 @@ class ComponentsManager:
         if name not in self.components:
             logger.warning(f"Component '{name}' not found in ComponentsManager")
             return
-            
+
         self.components.pop(name)
         self.added_time.pop(name)
 
         for collection in self.collections:
             if name in self.collections[collection]:
                 self.collections[collection].remove(name)
-        
+
         if self._auto_offload_enabled:
             self.enable_auto_cpu_offload(self._auto_offload_device)
 
@@ -341,7 +339,7 @@ class ComponentsManager:
             Dictionary mapping component IDs to components, 
             or list of (base_name, component) tuples if as_name_component_tuples=True
         """
-        
+
         if collection:
             if collection not in self.collections:
                 logger.warning(f"Collection '{collection}' not found in ComponentsManager")
@@ -360,16 +358,16 @@ class ComponentsManager:
             if len(parts) > 1 and len(parts[-1]) >= 8 and '-' in parts[-1]:
                 return '_'.join(parts[:-1])
             return component_id
-            
+
         if names is None:
             if as_name_component_tuples:
                 return [(get_base_name(comp_id), comp) for comp_id, comp in components.items()]
             else:
                 return components
-        
+
         # Create mapping from component_id to base_name for all components
         base_names = {comp_id: get_base_name(comp_id) for comp_id in components.keys()}
-        
+
         def matches_pattern(component_id, pattern, exact_match=False):
             """
             Helper function to check if a component matches a pattern based on its base name.
@@ -380,124 +378,124 @@ class ComponentsManager:
                 exact_match: If True, only exact matches to base_name are considered
             """
             base_name = base_names[component_id]
-            
+
             # Exact match with base name
             if exact_match:
                 return pattern == base_name
-                
+
             # Prefix match (ends with *)
             elif pattern.endswith('*'):
                 prefix = pattern[:-1]
                 return base_name.startswith(prefix)
-                
+
             # Contains match (starts with *)
             elif pattern.startswith('*'):
                 search = pattern[1:-1] if pattern.endswith('*') else pattern[1:]
                 return search in base_name
-                
+
             # Exact match (no wildcards)
             else:
                 return pattern == base_name
-        
+
         if isinstance(names, str):
             # Check if this is a "not" pattern
             is_not_pattern = names.startswith('!')
             if is_not_pattern:
                 names = names[1:]  # Remove the ! prefix
-            
+
             # Handle OR patterns (containing |)
             if '|' in names:
                 terms = names.split('|')
                 matches = {}
-                
+
                 for comp_id, comp in components.items():
                     # For OR patterns with exact names (no wildcards), we do exact matching on base names
                     exact_match = all(not (term.startswith('*') or term.endswith('*')) for term in terms)
-                    
+
                     # Check if any of the terms match this component
                     should_include = any(matches_pattern(comp_id, term, exact_match) for term in terms)
-                    
+
                     # Flip the decision if this is a NOT pattern
                     if is_not_pattern:
                         should_include = not should_include
-                        
+
                     if should_include:
                         matches[comp_id] = comp
-                
+
                 log_msg = "NOT " if is_not_pattern else ""
                 match_type = "exactly matching" if exact_match else "matching any of patterns"
                 logger.info(f"Getting components {log_msg}{match_type} {terms}: {list(matches.keys())}")
-            
+
             # Try exact match with a base name
             elif any(names == base_name for base_name in base_names.values()):
                 # Find all components with this base name
                 matches = {
-                    comp_id: comp for comp_id, comp in components.items() 
+                    comp_id: comp for comp_id, comp in components.items()
                     if (base_names[comp_id] == names) != is_not_pattern
                 }
-                
+
                 if is_not_pattern:
                     logger.info(f"Getting all components except those with base name '{names}': {list(matches.keys())}")
                 else:
                     logger.info(f"Getting components with base name '{names}': {list(matches.keys())}")
-            
+
             # Prefix match (ends with *)
             elif names.endswith('*'):
                 prefix = names[:-1]
                 matches = {
-                    comp_id: comp for comp_id, comp in components.items() 
+                    comp_id: comp for comp_id, comp in components.items()
                     if base_names[comp_id].startswith(prefix) != is_not_pattern
                 }
                 if is_not_pattern:
                     logger.info(f"Getting components NOT starting with '{prefix}': {list(matches.keys())}")
                 else:
                     logger.info(f"Getting components starting with '{prefix}': {list(matches.keys())}")
-            
+
             # Contains match (starts with *)
             elif names.startswith('*'):
                 search = names[1:-1] if names.endswith('*') else names[1:]
                 matches = {
-                    comp_id: comp for comp_id, comp in components.items() 
+                    comp_id: comp for comp_id, comp in components.items()
                     if (search in base_names[comp_id]) != is_not_pattern
                 }
                 if is_not_pattern:
                     logger.info(f"Getting components NOT containing '{search}': {list(matches.keys())}")
                 else:
                     logger.info(f"Getting components containing '{search}': {list(matches.keys())}")
-            
+
             # Substring match (no wildcards, but not an exact component name)
             elif any(names in base_name for base_name in base_names.values()):
                 matches = {
-                    comp_id: comp for comp_id, comp in components.items() 
+                    comp_id: comp for comp_id, comp in components.items()
                     if (names in base_names[comp_id]) != is_not_pattern
                 }
                 if is_not_pattern:
                     logger.info(f"Getting components NOT containing '{names}': {list(matches.keys())}")
                 else:
                     logger.info(f"Getting components containing '{names}': {list(matches.keys())}")
-            
+
             else:
                 raise ValueError(f"Component or pattern '{names}' not found in ComponentsManager")
-            
+
             if not matches:
                 raise ValueError(f"No components found matching pattern '{names}'")
-            
+
             if as_name_component_tuples:
                 return [(base_names[comp_id], comp) for comp_id, comp in matches.items()]
             else:
                 return matches
-        
+
         elif isinstance(names, list):
             results = {}
             for name in names:
                 result = self.get(name, collection, load_id, as_name_component_tuples=False)
                 results.update(result)
-                
+
             if as_name_component_tuples:
                 return [(base_names[comp_id], comp) for comp_id, comp in results.items()]
             else:
                 return results
-        
+
         else:
             raise ValueError(f"Invalid type for names: {type(names)}")
 
@@ -558,14 +556,14 @@ class ComponentsManager:
             raise ValueError(f"Component '{name}' not found in ComponentsManager")
 
         component = self.components[name]
-        
+
         # Build complete info dict first
         info = {
             "model_id": name,
             "added_time": self.added_time[name],
             "collection": next((coll for coll, comps in self.collections.items() if name in comps), None),
         }
-        
+
         # Additional info for torch.nn.Module components
         if isinstance(component, torch.nn.Module):
             # Check for hook information
@@ -573,7 +571,7 @@ class ComponentsManager:
             execution_device = None
             if has_hook and hasattr(component._hf_hook, "execution_device"):
                 execution_device = component._hf_hook.execution_device
-                
+
             info.update({
                 "class_name": component.__class__.__name__,
                 "size_gb": get_memory_footprint(component) / (1024**3),
@@ -594,8 +592,8 @@ class ComponentsManager:
                 if any("IPAdapter" in ptype for ptype in processor_types):
                     # Then get scales only from IP-Adapter processors
                     scales = {
-                        k: v.scale 
-                        for k, v in processors.items() 
+                        k: v.scale
+                        for k, v in processors.items()
                         if hasattr(v, "scale") and "IPAdapter" in v.__class__.__name__
                     }
                     if scales:
@@ -609,7 +607,7 @@ class ComponentsManager:
             else:
                 # List of fields requested, return dict with just those fields
                 return {k: v for k, v in info.items() if k in fields}
-            
+
         return info
 
     def __repr__(self):
@@ -622,13 +620,13 @@ class ComponentsManager:
             if len(parts) > 1 and len(parts[-1]) >= 8 and '-' in parts[-1]:
                 return '_'.join(parts[:-1])
             return name
-            
+
         # Extract load_id if available
         def get_load_id(component):
             if hasattr(component, "_diffusers_load_id"):
                 return component._diffusers_load_id
             return "N/A"
-            
+
         # Format device info compactly
         def format_device(component, info):
             if not info["has_hook"]:
@@ -637,24 +635,24 @@ class ComponentsManager:
                 device = str(getattr(component, 'device', 'N/A'))
                 exec_device = str(info['execution_device'] or 'N/A')
                 return f"{device}({exec_device})"
-                
+
         # Get all simple names to calculate width
         simple_names = [get_simple_name(id) for id in self.components.keys()]
-        
+
         # Get max length of load_ids for models
         load_ids = [
-            get_load_id(component) 
-            for component in self.components.values() 
+            get_load_id(component)
+            for component in self.components.values()
             if isinstance(component, torch.nn.Module) and hasattr(component, "_diffusers_load_id")
         ]
         max_load_id_len = max([15] + [len(str(lid)) for lid in load_ids]) if load_ids else 15
-        
+
         # Collection names
         collection_names = [
             next((coll for coll, comps in self.collections.items() if name in comps), "N/A")
             for name in self.components.keys()
         ]
-        
+
         col_widths = {
             "name": max(15, max(len(name) for name in simple_names)),
             "class": max(25, max(len(component.__class__.__name__) for component in self.components.values())),
@@ -692,7 +690,7 @@ class ComponentsManager:
                 dtype = str(component.dtype) if hasattr(component, "dtype") else "N/A"
                 load_id = get_load_id(component)
                 collection = info["collection"] or "N/A"
-                
+
                 output += f"{simple_name:<{col_widths['name']}} | {info['class_name']:<{col_widths['class']}} | "
                 output += f"{device_str:<{col_widths['device']}} | {dtype:<{col_widths['dtype']}} | "
                 output += f"{info['size_gb']:<{col_widths['size']}.2f} | {load_id:<{col_widths['load_id']}} | {collection}\n"
@@ -712,7 +710,7 @@ class ComponentsManager:
                 info = self.get_model_info(name)
                 simple_name = get_simple_name(name)
                 collection = info["collection"] or "N/A"
-                
+
                 output += f"{simple_name:<{col_widths['name']}} | {component.__class__.__name__:<{col_widths['class']}} | {collection}\n"
             output += dash_line
 
@@ -726,9 +724,9 @@ class ComponentsManager:
                 if info.get("adapters") is not None:
                     output += f"  Adapters: {info['adapters']}\n"
                 if info.get("ip_adapter"):
-                    output += f"  IP-Adapter: Enabled\n"
+                    output += "  IP-Adapter: Enabled\n"
                 output += f"  Added Time: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(info['added_time']))}\n"
-        
+
         return output
 
     def from_pretrained(self, pretrained_model_name_or_path, prefix: Optional[str] = None, **kwargs):
@@ -759,13 +757,13 @@ class ComponentsManager:
             from ..pipelines.pipeline_utils import DiffusionPipeline
             pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, **kwargs)
             for name, component in pipe.components.items():
-                    
+
                 if component is None:
                     continue
-                
+
                 # Add prefix if specified
                 component_name = f"{prefix}_{name}" if prefix else name
-                
+
                 if component_name not in self.components:
                     self.add(component_name, component)
                 else:
@@ -791,13 +789,13 @@ class ComponentsManager:
             ValueError: If no components match or multiple components match
         """
         results = self.get(name, collection, load_id)
-        
+
         if not results:
             raise ValueError(f"No components found matching '{name}'")
-            
+
         if len(results) > 1:
             raise ValueError(f"Multiple components found matching '{name}': {list(results.keys())}")
-            
+
         return next(iter(results.values()))
 
 def summarize_dict_by_value_and_parts(d: Dict[str, Any]) -> Dict[str, Any]:
@@ -823,17 +821,17 @@ def summarize_dict_by_value_and_parts(d: Dict[str, Any]) -> Dict[str, Any]:
         if value_tuple not in value_to_keys:
             value_to_keys[value_tuple] = []
         value_to_keys[value_tuple].append(key)
-    
+
     def find_common_prefix(keys: List[str]) -> str:
         """Find the shortest common prefix among a list of dot-separated keys."""
         if not keys:
             return ""
         if len(keys) == 1:
             return keys[0]
-            
+
         # Split all keys into parts
         key_parts = [k.split('.') for k in keys]
-        
+
         # Find how many initial parts are common
         common_length = 0
         for parts in zip(*key_parts):
@@ -841,10 +839,10 @@ def summarize_dict_by_value_and_parts(d: Dict[str, Any]) -> Dict[str, Any]:
                 common_length += 1
             else:
                 break
-                
+
         if common_length == 0:
             return ""
-            
+
         # Return the common prefix
         return '.'.join(key_parts[0][:common_length])
 
@@ -858,5 +856,5 @@ def summarize_dict_by_value_and_parts(d: Dict[str, Any]) -> Dict[str, Any]:
             summary[prefix] = value
         else:
             summary[""] = value  # Use empty string if no common prefix
-            
+
     return summary

src/diffusers/pipelines/modular_pipeline_utils.py

@@ -12,13 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import re
 import inspect
-from dataclasses import dataclass, asdict, field, fields
-from typing import Any, Dict, List, Optional, Tuple, Type, Union, Literal
+import re
+from dataclasses import dataclass, field, fields
+from typing import Any, Dict, List, Literal, Optional, Type, Union
 
+from ..configuration_utils import ConfigMixin, FrozenDict
 from ..utils.import_utils import is_torch_available
-from ..configuration_utils import FrozenDict, ConfigMixin
+
 
 if is_torch_available():
     import torch
@@ -56,50 +57,50 @@ class ComponentSpec:
     variant: Optional[str] = field(default=None, metadata={"loading": True})
     revision: Optional[str] = field(default=None, metadata={"loading": True})
     default_creation_method: Literal["from_config", "from_pretrained"] = "from_pretrained"
-    
-    
+
+
     def __hash__(self):
         """Make ComponentSpec hashable, using load_id as the hash value."""
         return hash((self.name, self.load_id, self.default_creation_method))
-    
+
     def __eq__(self, other):
         """Compare ComponentSpec objects based on name and load_id."""
         if not isinstance(other, ComponentSpec):
             return False
-        return (self.name == other.name and 
-                self.load_id == other.load_id and 
+        return (self.name == other.name and
+                self.load_id == other.load_id and
                 self.default_creation_method == other.default_creation_method)
-    
+
     @classmethod
     def from_component(cls, name: str, component: torch.nn.Module) -> Any:
         """Create a ComponentSpec from a Component created by `create` method."""
-        
+
         if not hasattr(component, "_diffusers_load_id"):
             raise ValueError("Component is not created by `create` method")
-        
+
         type_hint = component.__class__
-        
+
         if component._diffusers_load_id == "null" and isinstance(component, ConfigMixin):
             config = component.config
         else:
             config = None
-     
+
         load_spec = cls.decode_load_id(component._diffusers_load_id)
-    
+
         return cls(name=name, type_hint=type_hint, config=config, **load_spec)
-    
+
     @classmethod
     def from_load_id(cls, load_id: str, name: Optional[str] = None) -> Any:
         """Create a ComponentSpec from a load_id string."""
         if load_id == "null":
             raise ValueError("Cannot create ComponentSpec from null load_id")
-        
+
         # Decode the load_id into a dictionary of loading fields
         load_fields = cls.decode_load_id(load_id)
-        
+
         # Create a new ComponentSpec instance with the decoded fields
         return cls(name=name, **load_fields)
-        
+
     @classmethod
     def loading_fields(cls) -> List[str]:
         """
@@ -107,8 +108,8 @@ class ComponentSpec:
         (i.e. those whose field.metadata["loading"] is True).
         """
         return [f.name for f in fields(cls) if f.metadata.get("loading", False)]
-    
-    
+
+
     @property
     def load_id(self) -> str:
         """
@@ -118,7 +119,7 @@ class ComponentSpec:
         parts = [getattr(self, k) for k in self.loading_fields()]
         parts = ["null" if p is None else p for p in parts]
         return "|".join(p for p in parts if p)
-    
+
     @classmethod
     def decode_load_id(cls, load_id: str) -> Dict[str, Optional[str]]:
         """
@@ -139,29 +140,29 @@ class ComponentSpec:
             If a segment value is "null", it's replaced with None.
             Returns None if load_id is "null" (indicating component not loaded from pretrained).
         """
-            
+
         # Get all loading fields in order
         loading_fields = cls.loading_fields()
         result = {f: None for f in loading_fields}
 
         if load_id == "null":
             return result
-        
+
         # Split the load_id
         parts = load_id.split("|")
-        
+
         # Map parts to loading fields by position
         for i, part in enumerate(parts):
             if i < len(loading_fields):
                 # Convert "null" string back to None
                 result[loading_fields[i]] = None if part == "null" else part
-        
+
         return result
-    
+
     # YiYi TODO: add validator
     def create(self, **kwargs) -> Any:
         """Create the component using the preferred creation method."""
-          
+
         # from_pretrained creation
         if self.default_creation_method == "from_pretrained":
             return self.create_from_pretrained(**kwargs)
@@ -170,17 +171,17 @@ class ComponentSpec:
             return self.create_from_config(**kwargs)
         else:
             raise ValueError(f"Invalid creation method: {self.default_creation_method}")
-    
+
     def create_from_config(self, config: Optional[Union[FrozenDict, Dict[str, Any]]] = None, **kwargs) -> Any:
         """Create component using from_config with config."""
 
         if self.type_hint is None or not isinstance(self.type_hint, type):
             raise ValueError(
-                f"`type_hint` is required when using from_config creation method."
+                "`type_hint` is required when using from_config creation method."
             )
-    
+
         config = config or self.config or {}
-    
+
         if issubclass(self.type_hint, ConfigMixin):
             component = self.type_hint.from_config(config, **kwargs)
         else:
@@ -193,24 +194,24 @@ class ComponentSpec:
                 if k in signature_params:
                     init_kwargs[k] = v
             component = self.type_hint(**init_kwargs)
-        
+
         component._diffusers_load_id = "null"
         if hasattr(component, "config"):
             self.config = component.config
-        
+
         return component
-    
+
     # YiYi TODO: add guard for type of model, if it is supported by from_pretrained
     def create_from_pretrained(self, **kwargs) -> Any:
         """Create component using from_pretrained."""
-        
+
         passed_loading_kwargs = {key: kwargs.pop(key) for key in self.loading_fields() if key in kwargs}
         load_kwargs = {key: passed_loading_kwargs.get(key, getattr(self, key)) for key in self.loading_fields()}
         # repo is a required argument for from_pretrained, a.k.a. pretrained_model_name_or_path
         repo = load_kwargs.pop("repo", None)
         if repo is None:
-            raise ValueError(f"`repo` info is required when using from_pretrained creation method (you can directly set it in `repo` field of the ComponentSpec or pass it as an argument)")
-        
+            raise ValueError("`repo` info is required when using from_pretrained creation method (you can directly set it in `repo` field of the ComponentSpec or pass it as an argument)")
+
         if self.type_hint is None:
             try:
                 from diffusers import AutoModel
@@ -223,19 +224,19 @@ class ComponentSpec:
                 component = self.type_hint.from_pretrained(repo, **load_kwargs, **kwargs)
             except Exception as e:
                 raise ValueError(f"Error creating {self.name}[{self.type_hint.__name__}] from pretrained: {e}")
-        
+
         if repo != self.repo:
             self.repo = repo
         for k, v in passed_loading_kwargs.items():
             if v is not None:
                 setattr(self, k, v)
         component._diffusers_load_id = self.load_id
-            
+
         return component
-    
 
 
-@dataclass 
+
+@dataclass
 class ConfigSpec:
     """Specification for a pipeline configuration parameter."""
     name: str
@@ -254,7 +255,7 @@ class InputParam:
         return f"<{self.name}: {'required' if self.required else 'optional'}, default={self.default}>"
 
 
-@dataclass 
+@dataclass
 class OutputParam:
     """Specification for an output parameter."""
     name: str
@@ -287,14 +288,14 @@ def format_inputs_short(inputs):
     """
     required_inputs = [param for param in inputs if param.required]
     optional_inputs = [param for param in inputs if not param.required]
-    
+
     required_str = ", ".join(param.name for param in required_inputs)
     optional_str = ", ".join(f"{param.name}={param.default}" for param in optional_inputs)
-    
+
     inputs_str = required_str
     if optional_str:
         inputs_str = f"{inputs_str}, {optional_str}" if required_str else optional_str
-        
+
     return inputs_str
 
 
@@ -321,18 +322,18 @@ def format_intermediates_short(intermediates_inputs, required_intermediates_inpu
             input_parts.append(f"Required({inp.name})")
         else:
             input_parts.append(inp.name)
-    
+
     # Handle modified variables (appear in both inputs and outputs)
     inputs_set = {inp.name for inp in intermediates_inputs}
     modified_parts = []
     new_output_parts = []
-    
+
     for out in intermediates_outputs:
         if out.name in inputs_set:
             modified_parts.append(out.name)
         else:
             new_output_parts.append(out.name)
-    
+
     result = []
     if input_parts:
         result.append(f"    - inputs: {', '.join(input_parts)}")
@@ -340,7 +341,7 @@ def format_intermediates_short(intermediates_inputs, required_intermediates_inpu
         result.append(f"    - modified: {', '.join(modified_parts)}")
     if new_output_parts:
         result.append(f"    - outputs: {', '.join(new_output_parts)}")
-        
+
     return "\n".join(result) if result else "    (none)"
 
 
@@ -358,18 +359,18 @@ def format_params(params, header="Args", indent_level=4, max_line_length=115):
     """
     if not params:
         return ""
-        
+
     base_indent = " " * indent_level
     param_indent = " " * (indent_level + 4)
     desc_indent = " " * (indent_level + 8)
     formatted_params = []
-    
+
     def get_type_str(type_hint):
         if hasattr(type_hint, "__origin__") and type_hint.__origin__ is Union:
             types = [t.__name__ if hasattr(t, "__name__") else str(t) for t in type_hint.__args__]
             return f"Union[{', '.join(types)}]"
         return type_hint.__name__ if hasattr(type_hint, "__name__") else str(type_hint)
-    
+
     def wrap_text(text, indent, max_length):
         """Wrap text while preserving markdown links and maintaining indentation."""
         words = text.split()
@@ -379,7 +380,7 @@ def format_params(params, header="Args", indent_level=4, max_line_length=115):
 
         for word in words:
             word_length = len(word) + (1 if current_line else 0)
-            
+
             if current_line and current_length + word_length > max_length:
                 lines.append(" ".join(current_line))
                 current_line = [word]
@@ -387,20 +388,20 @@ def format_params(params, header="Args", indent_level=4, max_line_length=115):
             else:
                 current_line.append(word)
                 current_length += word_length
-        
+
         if current_line:
             lines.append(" ".join(current_line))
-            
+
         return f"\n{indent}".join(lines)
-    
+
     # Add the header
     formatted_params.append(f"{base_indent}{header}:")
-    
+
     for param in params:
         # Format parameter name and type
         type_str = get_type_str(param.type_hint) if param.type_hint != Any else ""
         param_str = f"{param_indent}{param.name} (`{type_str}`"
-        
+
         # Add optional tag and default value if parameter is an InputParam and optional
         if hasattr(param, "required"):
             if not param.required:
@@ -408,7 +409,7 @@ def format_params(params, header="Args", indent_level=4, max_line_length=115):
                 if param.default is not None:
                     param_str += f", defaults to {param.default}"
         param_str += "):"
-            
+
         # Add description on a new line with additional indentation and wrapping
         if param.description:
             desc = re.sub(
@@ -418,9 +419,9 @@ def format_params(params, header="Args", indent_level=4, max_line_length=115):
             )
             wrapped_desc = wrap_text(desc, desc_indent, max_line_length)
             param_str += f"\n{desc_indent}{wrapped_desc}"
-            
+
         formatted_params.append(param_str)
-    
+
     return "\n\n".join(formatted_params)
 
 
@@ -466,42 +467,42 @@ def format_components(components, indent_level=4, max_line_length=115, add_empty
     """
     if not components:
         return ""
-        
+
     base_indent = " " * indent_level
     component_indent = " " * (indent_level + 4)
     formatted_components = []
-    
+
     # Add the header
     formatted_components.append(f"{base_indent}Components:")
     if add_empty_lines:
         formatted_components.append("")
-    
+
     # Add each component with optional empty lines between them
     for i, component in enumerate(components):
         # Get type name, handling special cases
         type_name = component.type_hint.__name__ if hasattr(component.type_hint, "__name__") else str(component.type_hint)
-        
+
         component_desc = f"{component_indent}{component.name} (`{type_name}`)"
         if component.description:
             component_desc += f": {component.description}"
-        
+
         # Get the loading fields dynamically
         loading_field_values = []
         for field_name in component.loading_fields():
             field_value = getattr(component, field_name)
             if field_value is not None:
                 loading_field_values.append(f"{field_name}={field_value}")
-        
+
         # Add loading field information if available
         if loading_field_values:
             component_desc += f" [{', '.join(loading_field_values)}]"
-            
+
         formatted_components.append(component_desc)
-        
+
         # Add an empty line after each component except the last one
         if add_empty_lines and i < len(components) - 1:
             formatted_components.append("")
-    
+
     return "\n".join(formatted_components)
 
 
@@ -519,27 +520,27 @@ def format_configs(configs, indent_level=4, max_line_length=115, add_empty_lines
     """
     if not configs:
         return ""
-        
+
     base_indent = " " * indent_level
     config_indent = " " * (indent_level + 4)
     formatted_configs = []
-    
+
     # Add the header
     formatted_configs.append(f"{base_indent}Configs:")
     if add_empty_lines:
         formatted_configs.append("")
-    
+
     # Add each config with optional empty lines between them
     for i, config in enumerate(configs):
         config_desc = f"{config_indent}{config.name} (default: {config.default})"
         if config.description:
             config_desc += f": {config.description}"
         formatted_configs.append(config_desc)
-        
+
         # Add an empty line after each config except the last one
         if add_empty_lines and i < len(configs) - 1:
             formatted_configs.append("")
-    
+
     return "\n".join(formatted_configs)
 
 
@@ -584,9 +585,9 @@ def make_doc_string(inputs, intermediates_inputs, outputs, description="", class
 
     # Add inputs section
     output += format_input_params(inputs + intermediates_inputs, indent_level=2)
-    
+
     # Add outputs section
     output += "\n\n"
     output += format_output_params(outputs, indent_level=2)
 
-    return output 
+    return output

src/diffusers/pipelines/pipeline_loading_utils.py

@@ -334,6 +334,7 @@ def maybe_raise_or_warn(
 # a simpler version of get_class_obj_and_candidates, it won't work with custom code
 def simple_get_class_obj(library_name, class_name):
     from diffusers import pipelines
+
     is_pipeline_module = hasattr(pipelines, library_name)
 
     if is_pipeline_module:
@@ -345,6 +346,7 @@ def simple_get_class_obj(library_name, class_name):
 
     return class_obj
 
+
 def get_class_obj_and_candidates(
     library_name, class_name, importable_classes, pipelines, is_pipeline_module, component_name=None, cache_dir=None
 ):

src/diffusers/pipelines/pipeline_utils.py

@@ -1120,7 +1120,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                 The PyTorch device type of the accelerator that shall be used in inference. If not specified, it will
                 automatically detect the available accelerator and use.
         """
- 
+
         self._maybe_raise_error_if_group_offload_active(raise_error=True)
 
         is_pipeline_device_mapped = hasattr(self, "hf_device_map") and self.hf_device_map is not None and len(self.hf_device_map) > 1

src/diffusers/pipelines/stable_diffusion_xl/__init__.py

@@ -61,6 +61,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .pipeline_stable_diffusion_xl_inpaint import StableDiffusionXLInpaintPipeline
         from .pipeline_stable_diffusion_xl_instruct_pix2pix import StableDiffusionXLInstructPix2PixPipeline
         from .pipeline_stable_diffusion_xl_modular import (
+            StableDiffusionXLAutoPipeline,
             StableDiffusionXLControlNetDenoiseStep,
             StableDiffusionXLDecodeLatentsStep,
             StableDiffusionXLDenoiseStep,
@@ -70,7 +71,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             StableDiffusionXLPrepareLatentsStep,
             StableDiffusionXLSetTimestepsStep,
             StableDiffusionXLTextEncoderStep,
-            StableDiffusionXLAutoPipeline,
         )
 
     try:

src/diffusers/utils/dynamic_modules_utils.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 """Utilities to dynamically load objects from the Hub."""
 
+import hashlib
 import importlib
 import inspect
 import json
@@ -21,8 +22,9 @@ import os
 import re
 import shutil
 import sys
+import threading
 from pathlib import Path
-from typing import Dict, Optional, Union
+from typing import Dict, ModuleType, Optional, Union
 from urllib import request
 
 from huggingface_hub import hf_hub_download, model_info
@@ -37,6 +39,7 @@ logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 # See https://huggingface.co/datasets/diffusers/community-pipelines-mirror
 COMMUNITY_PIPELINES_MIRROR_ID = "diffusers/community-pipelines-mirror"
+_HF_REMOTE_CODE_LOCK = threading.Lock()
 
 
 def get_diffusers_versions():
@@ -154,15 +157,132 @@ def check_imports(filename):
     return get_relative_imports(filename)
 
 
-def get_class_in_module(class_name, module_path):
+def resolve_trust_remote_code(trust_remote_code, model_name, has_local_code, has_remote_code):
+    if trust_remote_code is None:
+        if has_local_code:
+            trust_remote_code = False
+        elif has_remote_code and TIME_OUT_REMOTE_CODE > 0:
+            prev_sig_handler = None
+            try:
+                prev_sig_handler = signal.signal(signal.SIGALRM, _raise_timeout_error)
+                signal.alarm(TIME_OUT_REMOTE_CODE)
+                while trust_remote_code is None:
+                    answer = input(
+                        f"The repository for {model_name} contains custom code which must be executed to correctly "
+                        f"load the model. You can inspect the repository content at https://hf.co/{model_name}.\n"
+                        f"You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n\n"
+                        f"Do you wish to run the custom code? [y/N] "
+                    )
+                    if answer.lower() in ["yes", "y", "1"]:
+                        trust_remote_code = True
+                    elif answer.lower() in ["no", "n", "0", ""]:
+                        trust_remote_code = False
+                signal.alarm(0)
+            except Exception:
+                # OS which does not support signal.SIGALRM
+                raise ValueError(
+                    f"The repository for {model_name} contains custom code which must be executed to correctly "
+                    f"load the model. You can inspect the repository content at https://hf.co/{model_name}.\n"
+                    f"Please pass the argument `trust_remote_code=True` to allow custom code to be run."
+                )
+            finally:
+                if prev_sig_handler is not None:
+                    signal.signal(signal.SIGALRM, prev_sig_handler)
+                    signal.alarm(0)
+        elif has_remote_code:
+            # For the CI which puts the timeout at 0
+            _raise_timeout_error(None, None)
+
+    if has_remote_code and not has_local_code and not trust_remote_code:
+        raise ValueError(
+            f"Loading {model_name} requires you to execute the configuration file in that"
+            " repo on your local machine. Make sure you have read the code there to avoid malicious use, then"
+            " set the option `trust_remote_code=True` to remove this error."
+        )
+
+    return trust_remote_code
+
+
+def get_class_in_modular_module(
+    class_name: str,
+    module_path: Union[str, os.PathLike],
+    *,
+    force_reload: bool = False,
+) -> type:
+    """
+    Import a module on the cache directory for modules and extract a class from it.
+
+    Args:
+        class_name (`str`): The name of the class to import.
+        module_path (`str` or `os.PathLike`): The path to the module to import.
+        force_reload (`bool`, *optional*, defaults to `False`):
+            Whether to reload the dynamic module from file if it already exists in `sys.modules`.
+            Otherwise, the module is only reloaded if the file has changed.
+
+    Returns:
+        `typing.Type`: The class looked for.
+    """
+    name = os.path.normpath(module_path)
+    if name.endswith(".py"):
+        name = name[:-3]
+    name = name.replace(os.path.sep, ".")
+    module_file: Path = Path(HF_MODULES_CACHE) / module_path
+    with _HF_REMOTE_CODE_LOCK:
+        if force_reload:
+            sys.modules.pop(name, None)
+            importlib.invalidate_caches()
+        cached_module: Optional[ModuleType] = sys.modules.get(name)
+        module_spec = importlib.util.spec_from_file_location(name, location=module_file)
+
+        # Hash the module file and all its relative imports to check if we need to reload it
+        module_files: list[Path] = [module_file] + sorted(map(Path, get_relative_import_files(module_file)))
+        module_hash: str = hashlib.sha256(b"".join(bytes(f) + f.read_bytes() for f in module_files)).hexdigest()
+
+        module: ModuleType
+        if cached_module is None:
+            module = importlib.util.module_from_spec(module_spec)
+            # insert it into sys.modules before any loading begins
+            sys.modules[name] = module
+        else:
+            module = cached_module
+        # reload in both cases, unless the module is already imported and the hash hits
+        if getattr(module, "__transformers_module_hash__", "") != module_hash:
+            module_spec.loader.exec_module(module)
+            module.__transformers_module_hash__ = module_hash
+
+        return getattr(module, class_name)
+
+
+def get_class_in_module(class_name, module_path, force_reload=False):
     """
     Import a module on the cache directory for modules and extract a class from it.
     """
-    module_path = module_path.replace(os.path.sep, ".")
-    module = importlib.import_module(module_path)
+    name = os.path.normpath(module_path)
+    if name.endswith(".py"):
+        name = name[:-3]
+    name = name.replace(os.path.sep, ".")
+    module_file: Path = Path(HF_MODULES_CACHE) / module_path
+
+    with _HF_REMOTE_CODE_LOCK:
+        if force_reload:
+            sys.modules.pop(name, None)
+            importlib.invalidate_caches()
+        cached_module: Optional[ModuleType] = sys.modules.get(name)
+        module_spec = importlib.util.spec_from_file_location(name, location=module_file)
+
+        module: ModuleType
+        if cached_module is None:
+            module = importlib.util.module_from_spec(module_spec)
+            # insert it into sys.modules before any loading begins
+            sys.modules[name] = module
+        else:
+            module = cached_module
+
+        module_spec.loader.exec_module(module)
 
     if class_name is None:
         return find_pipeline_class(module)
+
     return getattr(module, class_name)
 
 
@@ -203,6 +323,7 @@ def get_cached_module_file(
     token: Optional[Union[bool, str]] = None,
     revision: Optional[str] = None,
     local_files_only: bool = False,
+    is_modular: bool = False,
 ):
     """
     Prepares Downloads a module from a local folder or a distant repo and returns its path inside the cached
@@ -257,7 +378,7 @@ def get_cached_module_file(
     if os.path.isfile(module_file_or_url):
         resolved_module_file = module_file_or_url
         submodule = "local"
-    elif pretrained_model_name_or_path.count("/") == 0:
+    elif pretrained_model_name_or_path.count("/") == 0 and not is_modular:
         available_versions = get_diffusers_versions()
         # cut ".dev0"
         latest_version = "v" + ".".join(__version__.split(".")[:3])
@@ -297,6 +418,24 @@ def get_cached_module_file(
         except EnvironmentError:
             logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
             raise
+
+    elif is_modular:
+        try:
+            # Load from URL or cache if already cached
+            resolved_module_file = hf_hub_download(
+                pretrained_model_name_or_path,
+                module_file,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                token=token,
+            )
+            submodule = pretrained_model_name_or_path.replace("/", os.path.sep)
+        except EnvironmentError:
+            logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
+            raise
+
     else:
         try:
             # Load from URL or cache if already cached
@@ -381,6 +520,7 @@ def get_class_from_dynamic_module(
     token: Optional[Union[bool, str]] = None,
     revision: Optional[str] = None,
     local_files_only: bool = False,
+    is_modular: bool = False,
     **kwargs,
 ):
     """
@@ -453,5 +593,7 @@ def get_class_from_dynamic_module(
         token=token,
         revision=revision,
         local_files_only=local_files_only,
+        is_modular=is_modular,
     )
-    return get_class_in_module(class_name, final_module.replace(".py", ""))
+    __import__("ipdb").set_trace()
+    return get_class_in_module(class_name, final_module)