resolve conflicts.

src/diffusers/loaders/autoencoder.py

@@ -143,4 +143,5 @@ class FromOriginalVAEMixin:
         if torch_dtype is not None:
             vae = vae.to(torch_dtype)
 
+        vae.eval()
         return vae

src/diffusers/loaders/controlnet.py

@@ -133,4 +133,5 @@ class FromOriginalControlNetMixin:
         if torch_dtype is not None:
             controlnet = controlnet.to(torch_dtype)
 
+        controlnet.eval()
         return controlnet

src/diffusers/models/modeling_utils.py

@@ -39,6 +39,7 @@ from ..utils import (
     _get_model_file,
     deprecate,
     is_accelerate_available,
+    is_single_file_checkpoint,
     is_torch_version,
     logging,
 )
@@ -48,6 +49,8 @@ from ..utils.hub_utils import PushToHubMixin, load_or_create_model_card, populat
 logger = logging.get_logger(__name__)
 
 
+SINGLE_FILE_LOADABLE_CLASSES = {"ControlNetModel", "AutoencoderKL"}
+
 if is_torch_version(">=", "1.9.0"):
     _LOW_CPU_MEM_USAGE_DEFAULT = True
 else:
@@ -497,6 +500,16 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
         ```
         """
+        if is_single_file_checkpoint(pretrained_model_name_or_path):
+            if cls.__name__ not in SINGLE_FILE_LOADABLE_CLASSES:
+                raise ValueError(
+                    f"{cls.__name__} is not supported. Supported classes are: {' '.join(list(SINGLE_FILE_LOADABLE_CLASSES))}."
+                )
+            logger.info("Single file checkpoint detected...")
+            model = cls.from_single_file(pretrained_model_name_or_path, **kwargs)
+            model = model.eval()
+            return model
+        else:
             cache_dir = kwargs.pop("cache_dir", None)
             ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False)
             force_download = kwargs.pop("force_download", False)

src/diffusers/pipelines/pipeline_utils.py

@@ -57,6 +57,7 @@ from ..utils import (
     is_accelerate_available,
     is_accelerate_version,
     is_peft_available,
+    is_single_file_checkpoint,
     is_torch_version,
     is_transformers_available,
     logging,
@@ -110,6 +111,20 @@ LOADABLE_CLASSES = {
     },
 }
 
+SINGLE_FILE_LOADABLE_CLASSES = {
+    "StableDiffusionPipeline",
+    "StableDiffusionImg2ImgPipeline",
+    "StableDiffusionInpaintPipeline",
+    "StableDiffusionUpscalePipeline",
+    "StableDiffusionControlNetPipeline",
+    "StableDiffusionControlNetImg2ImgPipeline",
+    "StableDiffusionControlNetInpaintPipeline",
+    "StableDiffusionXLPipeline",
+    "StableDiffusionXLImg2ImgPipeline",
+    "StableDiffusionXLInpaintPipeline",
+    "StableDiffusionXLControlNetImg2ImgPipeline",
+}
+
 ALL_IMPORTABLE_CLASSES = {}
 for library in LOADABLE_CLASSES:
     ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])
@@ -1056,6 +1071,18 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
         >>> pipeline.scheduler = scheduler
         ```
         """
+        if is_single_file_checkpoint(pretrained_model_name_or_path):
+            if cls.__name__ not in SINGLE_FILE_LOADABLE_CLASSES:
+                raise ValueError(
+                    f'The provided pretrained_model_name_or_path "{pretrained_model_name_or_path}"'
+                    " is neither a valid local path nor a valid repo id. Please check the parameter."
+                    f"{cls.__name__} is not supported. Supported classes are: {' '.join(list(SINGLE_FILE_LOADABLE_CLASSES))}."
+                )
+            logger.info("Single file checkpoint detected...")
+            model = cls.from_single_file(pretrained_model_name_or_path, **kwargs)
+            return model
+
+        else:
             cache_dir = kwargs.pop("cache_dir", None)
             resume_download = kwargs.pop("resume_download", False)
             force_download = kwargs.pop("force_download", False)
@@ -1242,7 +1269,9 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
             from diffusers import pipelines
 
             # 6. Load each module in the pipeline
-        for name, (library_name, class_name) in logging.tqdm(init_dict.items(), desc="Loading pipeline components..."):
+            for name, (library_name, class_name) in logging.tqdm(
+                init_dict.items(), desc="Loading pipeline components..."
+            ):
                 # 6.1 - now that JAX/Flax is an official framework of the library, we might load from Flax names
                 class_name = class_name[4:] if class_name.startswith("Flax") else class_name
 
@@ -1256,7 +1285,13 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                     # if the model is in a pipeline module, then we load it from the pipeline
                     # check that passed_class_obj has correct parent class
                     maybe_raise_or_warn(
-                    library_name, library, class_name, importable_classes, passed_class_obj, name, is_pipeline_module
+                        library_name,
+                        library,
+                        class_name,
+                        importable_classes,
+                        passed_class_obj,
+                        name,
+                        is_pipeline_module,
                     )
 
                     loaded_sub_model = passed_class_obj[name]
@@ -1291,7 +1326,9 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
 
             if pipeline_class._load_connected_pipes and os.path.isfile(os.path.join(cached_folder, "README.md")):
                 modelcard = ModelCard.load(os.path.join(cached_folder, "README.md"))
-            connected_pipes = {prefix: getattr(modelcard.data, prefix, [None])[0] for prefix in CONNECTED_PIPES_KEYS}
+                connected_pipes = {
+                    prefix: getattr(modelcard.data, prefix, [None])[0] for prefix in CONNECTED_PIPES_KEYS
+                }
                 load_kwargs = {
                     "cache_dir": cache_dir,
                     "resume_download": resume_download,
@@ -1316,10 +1353,14 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
 
                 def get_connected_passed_kwargs(prefix):
                     connected_passed_class_obj = {
-                    k.replace(f"{prefix}_", ""): w for k, w in passed_class_obj.items() if k.split("_")[0] == prefix
+                        k.replace(f"{prefix}_", ""): w
+                        for k, w in passed_class_obj.items()
+                        if k.split("_")[0] == prefix
                     }
                     connected_passed_pipe_kwargs = {
-                    k.replace(f"{prefix}_", ""): w for k, w in passed_pipe_kwargs.items() if k.split("_")[0] == prefix
+                        k.replace(f"{prefix}_", ""): w
+                        for k, w in passed_pipe_kwargs.items()
+                        if k.split("_")[0] == prefix
                     }
 
                     connected_passed_kwargs = {**connected_passed_class_obj, **connected_passed_pipe_kwargs}

src/diffusers/utils/__init__.py

@@ -19,6 +19,7 @@ from packaging import version
 
 from .. import __version__
 from .constants import (
+    _ACCEPTED_SINGLE_FILE_FORMATS,
     CONFIG_NAME,
     DEPRECATED_REVISION_ARGS,
     DIFFUSERS_DYNAMIC_MODULE_NAME,
@@ -83,7 +84,7 @@ from .import_utils import (
     is_xformers_available,
     requires_backends,
 )
-from .loading_utils import load_image
+from .loading_utils import is_single_file_checkpoint, load_image
 from .logging import get_logger
 from .outputs import BaseOutput
 from .peft_utils import (

src/diffusers/utils/constants.py

@@ -37,6 +37,7 @@ HUGGINGFACE_CO_RESOLVE_ENDPOINT = os.environ.get("HF_ENDPOINT", "https://hugging
 DIFFUSERS_DYNAMIC_MODULE_NAME = "diffusers_modules"
 HF_MODULES_CACHE = os.getenv("HF_MODULES_CACHE", os.path.join(HF_HOME, "modules"))
 DEPRECATED_REVISION_ARGS = ["fp16", "non-ema"]
+_ACCEPTED_SINGLE_FILE_FORMATS = (".safetensors", ".ckpt", ".bin", ".pth", ".pt")
 
 # Below should be `True` if the current version of `peft` and `transformers` are compatible with
 # PEFT backend. Will automatically fall back to PEFT backend if the correct versions of the libraries are

src/diffusers/utils/loading_utils.py

@@ -1,10 +1,28 @@
 import os
 from typing import Callable, Union
+from urllib.parse import urlparse
 
 import PIL.Image
 import PIL.ImageOps
 import requests
 
+from ..utils.constants import _ACCEPTED_SINGLE_FILE_FORMATS
+
+
+def is_single_file_checkpoint(filepath):
+    def is_valid_url(url):
+        result = urlparse(url)
+        if result.scheme and result.netloc:
+            return True
+
+    filepath = str(filepath)
+    if filepath.endswith(_ACCEPTED_SINGLE_FILE_FORMATS):
+        if is_valid_url(filepath):
+            return True
+        elif os.path.isfile(filepath):
+            return True
+    return False
+
 
 def load_image(
     image: Union[str, PIL.Image.Image], convert_method: Callable[[PIL.Image.Image], PIL.Image.Image] = None