revert name

Author: SunMarc

scripts/convert_sana_to_diffusers.py

@@ -18,7 +18,7 @@
     SanaPipeline,
     SanaTransformer2DModel,
 )
-from diffusers.models.modeling_utils import load_state_dict_into_meta
+from diffusers.models.modeling_utils import load_model_dict_into_meta
 from diffusers.utils.import_utils import is_accelerate_available
 
 
@@ -189,7 +189,7 @@ def main(args):
         )
 
     if is_accelerate_available():
-        load_state_dict_into_meta(transformer, converted_state_dict)
+        load_model_dict_into_meta(transformer, converted_state_dict)
     else:
         transformer.load_state_dict(converted_state_dict, strict=True, assign=True)
 

scripts/convert_sd3_to_diffusers.py

@@ -7,7 +7,7 @@
 
 from diffusers import AutoencoderKL, SD3Transformer2DModel
 from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint
-from diffusers.models.modeling_utils import load_state_dict_into_meta
+from diffusers.models.modeling_utils import load_model_dict_into_meta
 from diffusers.utils.import_utils import is_accelerate_available
 
 
@@ -319,7 +319,7 @@ def main(args):
             dual_attention_layers=attn2_layers,
         )
     if is_accelerate_available():
-        load_state_dict_into_meta(transformer, converted_transformer_state_dict)
+        load_model_dict_into_meta(transformer, converted_transformer_state_dict)
     else:
         transformer.load_state_dict(converted_transformer_state_dict, strict=True)
 
@@ -339,7 +339,7 @@ def main(args):
             )
         converted_vae_state_dict = convert_ldm_vae_checkpoint(original_ckpt, vae.config)
         if is_accelerate_available():
-            load_state_dict_into_meta(vae, converted_vae_state_dict)
+            load_model_dict_into_meta(vae, converted_vae_state_dict)
         else:
             vae.load_state_dict(converted_vae_state_dict, strict=True)
 

scripts/convert_stable_audio.py

@@ -18,7 +18,7 @@
     StableAudioPipeline,
     StableAudioProjectionModel,
 )
-from diffusers.models.modeling_utils import load_state_dict_into_meta
+from diffusers.models.modeling_utils import load_model_dict_into_meta
 from diffusers.utils import is_accelerate_available
 
 
@@ -221,7 +221,7 @@ def convert_stable_audio_state_dict_to_diffusers(state_dict, num_autoencoder_lay
         ],  # assume `seconds_start` and `seconds_total` have the same min / max values.
     )
 if is_accelerate_available():
-    load_state_dict_into_meta(projection_model, projection_model_state_dict)
+    load_model_dict_into_meta(projection_model, projection_model_state_dict)
 else:
     projection_model.load_state_dict(projection_model_state_dict)
 
@@ -242,7 +242,7 @@ def convert_stable_audio_state_dict_to_diffusers(state_dict, num_autoencoder_lay
         cross_attention_input_dim=model_config["cond_token_dim"],
     )
 if is_accelerate_available():
-    load_state_dict_into_meta(model, model_state_dict)
+    load_model_dict_into_meta(model, model_state_dict)
 else:
     model.load_state_dict(model_state_dict)
 
@@ -260,7 +260,7 @@ def convert_stable_audio_state_dict_to_diffusers(state_dict, num_autoencoder_lay
     )
 
 if is_accelerate_available():
-    load_state_dict_into_meta(autoencoder, autoencoder_state_dict)
+    load_model_dict_into_meta(autoencoder, autoencoder_state_dict)
 else:
     autoencoder.load_state_dict(autoencoder_state_dict)
 

scripts/convert_stable_cascade.py

@@ -20,7 +20,7 @@
 )
 from diffusers.loaders.single_file_utils import convert_stable_cascade_unet_single_file_to_diffusers
 from diffusers.models import StableCascadeUNet
-from diffusers.models.modeling_utils import load_state_dict_into_meta
+from diffusers.models.modeling_utils import load_model_dict_into_meta
 from diffusers.pipelines.wuerstchen import PaellaVQModel
 from diffusers.utils import is_accelerate_available
 
@@ -126,7 +126,7 @@
             switch_level=[False],
         )
     if is_accelerate_available():
-        load_state_dict_into_meta(prior_model, prior_state_dict)
+        load_model_dict_into_meta(prior_model, prior_state_dict)
     else:
         prior_model.load_state_dict(prior_state_dict)
 
@@ -181,7 +181,7 @@
         )
 
     if is_accelerate_available():
-        load_state_dict_into_meta(decoder, decoder_state_dict)
+        load_model_dict_into_meta(decoder, decoder_state_dict)
     else:
         decoder.load_state_dict(decoder_state_dict)
 

scripts/convert_stable_cascade_lite.py

@@ -20,7 +20,7 @@
 )
 from diffusers.loaders.single_file_utils import convert_stable_cascade_unet_single_file_to_diffusers
 from diffusers.models import StableCascadeUNet
-from diffusers.models.modeling_utils import load_state_dict_into_meta
+from diffusers.models.modeling_utils import load_model_dict_into_meta
 from diffusers.pipelines.wuerstchen import PaellaVQModel
 from diffusers.utils import is_accelerate_available
 
@@ -133,7 +133,7 @@
         )
 
     if is_accelerate_available():
-        load_state_dict_into_meta(prior_model, prior_state_dict)
+        load_model_dict_into_meta(prior_model, prior_state_dict)
     else:
         prior_model.load_state_dict(prior_state_dict)
 
@@ -189,7 +189,7 @@
         )
 
     if is_accelerate_available():
-        load_state_dict_into_meta(decoder, decoder_state_dict)
+        load_model_dict_into_meta(decoder, decoder_state_dict)
     else:
         decoder.load_state_dict(decoder_state_dict)
 

src/diffusers/loaders/single_file_utils.py

@@ -53,7 +53,7 @@
 if is_accelerate_available():
     from accelerate import init_empty_weights
 
-    from ..models.modeling_utils import load_state_dict_into_meta
+    from ..models.modeling_utils import load_model_dict_into_meta
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -1588,7 +1588,7 @@ def create_diffusers_clip_model_from_ldm(
         raise ValueError("The provided checkpoint does not seem to contain a valid CLIP model.")
 
     if is_accelerate_available():
-        load_state_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)
+        load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)
     else:
         model.load_state_dict(diffusers_format_checkpoint, strict=False)
 
@@ -2047,7 +2047,7 @@ def create_diffusers_t5_model_from_checkpoint(
     diffusers_format_checkpoint = convert_sd3_t5_checkpoint_to_diffusers(checkpoint)
 
     if is_accelerate_available():
-        load_state_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)
+        load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)
     else:
         model.load_state_dict(diffusers_format_checkpoint)
 

src/diffusers/loaders/transformer_flux.py

@@ -17,7 +17,7 @@
     ImageProjection,
     MultiIPAdapterImageProjection,
 )
-from ..models.modeling_utils import load_state_dict_into_meta
+from ..models.modeling_utils import load_model_dict_into_meta
 from ..utils import (
     is_accelerate_available,
     is_torch_version,
@@ -82,7 +82,7 @@ def _convert_ip_adapter_image_proj_to_diffusers(self, state_dict, low_cpu_mem_us
         if not low_cpu_mem_usage:
             image_projection.load_state_dict(updated_state_dict, strict=True)
         else:
-            load_state_dict_into_meta(image_projection, updated_state_dict, device=self.device, dtype=self.dtype)
+            load_model_dict_into_meta(image_projection, updated_state_dict, device=self.device, dtype=self.dtype)
 
         return image_projection
 
@@ -153,7 +153,7 @@ def _convert_ip_adapter_attn_to_diffusers(self, state_dicts, low_cpu_mem_usage=F
                 else:
                     device = self.device
                     dtype = self.dtype
-                    load_state_dict_into_meta(attn_procs[name], value_dict, device=device, dtype=dtype)
+                    load_model_dict_into_meta(attn_procs[name], value_dict, device=device, dtype=dtype)
 
                 key_id += 1
 

src/diffusers/loaders/transformer_sd3.py

@@ -15,7 +15,7 @@
 
 from ..models.attention_processor import SD3IPAdapterJointAttnProcessor2_0
 from ..models.embeddings import IPAdapterTimeImageProjection
-from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_state_dict_into_meta
+from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_model_dict_into_meta
 
 
 class SD3Transformer2DLoadersMixin:
@@ -59,7 +59,7 @@ def _load_ip_adapter_weights(self, state_dict: Dict, low_cpu_mem_usage: bool = _
             if not low_cpu_mem_usage:
                 attn_procs[name].load_state_dict(layer_state_dict[idx], strict=True)
             else:
-                load_state_dict_into_meta(
+                load_model_dict_into_meta(
                     attn_procs[name], layer_state_dict[idx], device=self.device, dtype=self.dtype
                 )
 
@@ -86,4 +86,4 @@ def _load_ip_adapter_weights(self, state_dict: Dict, low_cpu_mem_usage: bool = _
         if not low_cpu_mem_usage:
             self.image_proj.load_state_dict(state_dict["image_proj"], strict=True)
         else:
-            load_state_dict_into_meta(self.image_proj, state_dict["image_proj"], device=self.device, dtype=self.dtype)
+            load_model_dict_into_meta(self.image_proj, state_dict["image_proj"], device=self.device, dtype=self.dtype)

src/diffusers/loaders/unet.py

@@ -30,7 +30,7 @@
     IPAdapterPlusImageProjection,
     MultiIPAdapterImageProjection,
 )
-from ..models.modeling_utils import load_state_dict, load_state_dict_into_meta
+from ..models.modeling_utils import load_state_dict, load_model_dict_into_meta
 from ..utils import (
     USE_PEFT_BACKEND,
     _get_model_file,
@@ -753,7 +753,7 @@ def _convert_ip_adapter_image_proj_to_diffusers(self, state_dict, low_cpu_mem_us
         if not low_cpu_mem_usage:
             image_projection.load_state_dict(updated_state_dict, strict=True)
         else:
-            load_state_dict_into_meta(image_projection, updated_state_dict, device=self.device, dtype=self.dtype)
+            load_model_dict_into_meta(image_projection, updated_state_dict, device=self.device, dtype=self.dtype)
 
         return image_projection
 
@@ -846,7 +846,7 @@ def _convert_ip_adapter_attn_to_diffusers(self, state_dicts, low_cpu_mem_usage=F
                 else:
                     device = next(iter(value_dict.values())).device
                     dtype = next(iter(value_dict.values())).dtype
-                    load_state_dict_into_meta(attn_procs[name], value_dict, device=device, dtype=dtype)
+                    load_model_dict_into_meta(attn_procs[name], value_dict, device=device, dtype=dtype)
 
                 key_id += 2
 

src/diffusers/models/model_loading_utils.py

@@ -201,7 +201,7 @@ def load_state_dict(
             )
 
 
-def load_state_dict_into_meta(
+def load_model_dict_into_meta(
     model,
     state_dict: OrderedDict,
     dtype: Optional[Union[str, torch.dtype]] = None,