retrieve the unet from create_diffusers_unet_model_from_ldm

src/diffusers/loaders/__init__.py

@@ -57,7 +57,7 @@ if is_torch_available():
     _import_structure["autoencoder"] = ["FromOriginalVAEMixin"]
 
     _import_structure["controlnet"] = ["FromOriginalControlNetMixin"]
-    _import_structure["unet"] = ["UNet2DConditionLoadersMixin"]
+    _import_structure["unet"] = ["FromOriginalUNetMixin", "UNet2DConditionLoadersMixin"]
     _import_structure["utils"] = ["AttnProcsLayers"]
     if is_transformers_available():
         _import_structure["single_file"] = ["FromSingleFileMixin"]
@@ -72,7 +72,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     if is_torch_available():
         from .autoencoder import FromOriginalVAEMixin
         from .controlnet import FromOriginalControlNetMixin
-        from .unet import UNet2DConditionLoadersMixin
+        from .unet import FromOriginalUNetMixin, UNet2DConditionLoadersMixin
         from .utils import AttnProcsLayers
 
         if is_transformers_available():

src/diffusers/loaders/single_file_utils.py

@@ -50,6 +50,8 @@ if is_transformers_available():
 if is_accelerate_available():
     from accelerate import init_empty_weights
 
+    from ..models.modeling_utils import load_model_dict_into_meta
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 CONFIG_URLS = {
@@ -1274,6 +1276,59 @@ def create_text_encoder_from_open_clip_checkpoint(
     return text_model
 
 
+def create_diffusers_unet_from_stable_cascade(
+    cls,
+    pretrained_model_link_or_path,
+    config,
+    resume_download,
+    force_download,
+    proxies,
+    token,
+    cache_dir,
+    local_files_only,
+    revision,
+    torch_dtype,
+    **kwargs,
+):
+    checkpoint = load_single_file_model_checkpoint(
+        pretrained_model_link_or_path,
+        resume_download=resume_download,
+        force_download=force_download,
+        proxies=proxies,
+        token=token,
+        cache_dir=cache_dir,
+        local_files_only=local_files_only,
+        revision=revision,
+    )
+
+    if config is None:
+        config = infer_stable_cascade_single_file_config(checkpoint)
+        model_config = cls.load_config(**config, **kwargs)
+    else:
+        model_config = config
+
+    ctx = init_empty_weights if is_accelerate_available() else nullcontext
+    with ctx():
+        model = cls.from_config(model_config, **kwargs)
+
+    diffusers_format_checkpoint = convert_stable_cascade_unet_single_file_to_diffusers(checkpoint)
+
+    if is_accelerate_available():
+        unexpected_keys = load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)
+        if len(unexpected_keys) > 0:
+            logger.warn(
+                f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
+            )
+
+    else:
+        model.load_state_dict(diffusers_format_checkpoint)
+
+    if torch_dtype is not None:
+        model.to(torch_dtype)
+
+    return model
+
+
 def create_diffusers_unet_model_from_ldm(
     pipeline_class_name,
     original_config,

src/diffusers/loaders/unet.py

@@ -43,9 +43,9 @@ from ..utils import (
     set_weights_and_activate_adapters,
 )
 from .single_file_utils import (
-    convert_stable_cascade_unet_single_file_to_diffusers,
-    infer_stable_cascade_single_file_config,
-    load_single_file_model_checkpoint,
+    create_diffusers_unet_from_stable_cascade,
+    create_diffusers_unet_model_from_ldm,
+    fetch_ldm_config_and_checkpoint,
 )
 from .utils import AttnProcsLayers
 
@@ -66,6 +66,8 @@ LORA_WEIGHT_NAME_SAFE = "pytorch_lora_weights.safetensors"
 CUSTOM_DIFFUSION_WEIGHT_NAME = "pytorch_custom_diffusion_weights.bin"
 CUSTOM_DIFFUSION_WEIGHT_NAME_SAFE = "pytorch_custom_diffusion_weights.safetensors"
 
+COMPATIBLE_SINGLE_FILE_CLASSES = ["StableCascadeUNet", "UNet2DConditionModel"]
+
 
 class UNet2DConditionLoadersMixin:
     """
@@ -912,8 +914,9 @@ class FromOriginalUNetMixin:
     @validate_hf_hub_args
     def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
         r"""
-        Instantiate a [`StableCascadeUNet`] from pretrained StableCascadeUNet weights saved in the original `.ckpt` or
-        `.safetensors` format. The pipeline is set in evaluation mode (`model.eval()`) by default.
+        Instantiate a UNet from pretrained weights saved in the original `.ckpt`, `.bin`, or
+        `.safetensors` format. The model is set in evaluation mode (`model.eval()`) by default.
+        Currently supported checkpoints: StableCascade, SDXL, SD, Playground v2.5, etc.
 
         Parameters:
             pretrained_model_link_or_path (`str` or `os.PathLike`, *optional*):
@@ -952,8 +955,10 @@ class FromOriginalUNetMixin:
 
         """
         class_name = cls.__name__
-        if class_name != "StableCascadeUNet":
-            raise ValueError("FromOriginalUNetMixin is currently only compatible with StableCascadeUNet")
+        if class_name not in COMPATIBLE_SINGLE_FILE_CLASSES:
+            raise ValueError(
+                f"FromOriginalUNetMixin is currently only compatible with {', '.join(COMPATIBLE_SINGLE_FILE_CLASSES)}"
+            )
 
         config = kwargs.pop("config", None)
         resume_download = kwargs.pop("resume_download", False)
@@ -965,39 +970,42 @@ class FromOriginalUNetMixin:
         revision = kwargs.pop("revision", None)
         torch_dtype = kwargs.pop("torch_dtype", None)
 
-        checkpoint = load_single_file_model_checkpoint(
-            pretrained_model_link_or_path,
-            resume_download=resume_download,
-            force_download=force_download,
-            proxies=proxies,
-            token=token,
-            cache_dir=cache_dir,
-            local_files_only=local_files_only,
-            revision=revision,
-        )
-
-        if config is None:
-            config = infer_stable_cascade_single_file_config(checkpoint)
-            model_config = cls.load_config(**config, **kwargs)
+        if class_name == "StableCascadeUNet":
+            return create_diffusers_unet_from_stable_cascade(
+                cls,
+                pretrained_model_link_or_path,
+                config,
+                resume_download,
+                force_download,
+                proxies,
+                token,
+                cache_dir,
+                local_files_only,
+                revision,
+                torch_dtype,
+                **kwargs,
+            )
         else:
-            model_config = config
-
-        ctx = init_empty_weights if is_accelerate_available() else nullcontext
-        with ctx():
-            model = cls.from_config(model_config, **kwargs)
-
-        diffusers_format_checkpoint = convert_stable_cascade_unet_single_file_to_diffusers(checkpoint)
-        if is_accelerate_available():
-            unexpected_keys = load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)
-            if len(unexpected_keys) > 0:
-                logger.warn(
-                    f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
-                )
-
-        else:
-            model.load_state_dict(diffusers_format_checkpoint)
-
-        if torch_dtype is not None:
-            model.to(torch_dtype)
-
-        return model
+            original_config, checkpoint = fetch_ldm_config_and_checkpoint(
+                pretrained_model_link_or_path=pretrained_model_link_or_path,
+                class_name=kwargs.get("pipeline_class_name", None),
+                original_config_file=kwargs.get("original_config_file", None),
+                resume_download=resume_download,
+                force_download=force_download,
+                proxies=proxies,
+                token=token,
+                revision=revision,
+                local_files_only=local_files_only,
+                cache_dir=cache_dir,
+            )
+            return create_diffusers_unet_model_from_ldm(
+                pipeline_class_name=kwargs.get("pipeline_class_name", None),
+                original_config=original_config,
+                checkpoint=checkpoint,
+                num_in_channels=kwargs.get("num_in_channels", 4),
+                upcast_attention=kwargs.get("upcast_attention", None),
+                extract_ema=kwargs.get("upcast_attention", False),
+                image_size=kwargs.get("image_size", None),
+                torch_dtype=torch_dtype,
+                model_type=kwargs.pop("model_type", None),
+            )["unet"]

src/diffusers/models/unets/unet_2d_condition.py

@@ -19,7 +19,7 @@ import torch.nn as nn
 import torch.utils.checkpoint
 
 from ...configuration_utils import ConfigMixin, register_to_config
-from ...loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin
+from ...loaders import FromOriginalUNetMixin, PeftAdapterMixin, UNet2DConditionLoadersMixin
 from ...utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers
 from ..activations import get_activation
 from ..attention_processor import (
@@ -66,7 +66,9 @@ class UNet2DConditionOutput(BaseOutput):
     sample: torch.FloatTensor = None
 
 
-class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin):
+class UNet2DConditionModel(
+    ModelMixin, ConfigMixin, FromOriginalUNetMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin
+):
     r"""
     A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
     shaped output.