Patch release: v0.21.1

* [Import] Add missing settings

* up

* up

* up

examples/controlnet/train_controlnet.py

@@ -56,7 +56,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = get_logger(__name__)
 

examples/controlnet/train_controlnet_flax.py

@@ -59,7 +59,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = logging.getLogger(__name__)
 

examples/controlnet/train_controlnet_sdxl.py

@@ -58,7 +58,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = get_logger(__name__)
 

examples/custom_diffusion/train_custom_diffusion.py

@@ -58,7 +58,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth.py

@@ -60,7 +60,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth_flax.py

@@ -36,7 +36,7 @@ from diffusers.utils import check_min_version
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 # Cache compiled models across invocations of this script.
 cc.initialize_cache(os.path.expanduser("~/.cache/jax/compilation_cache"))

examples/dreambooth/train_dreambooth_lora.py

@@ -70,7 +70,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -58,7 +58,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = get_logger(__name__)
 

examples/instruct_pix2pix/train_instruct_pix2pix.py

@@ -52,7 +52,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py

@@ -55,7 +55,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/t2i_adapter/train_t2i_adapter_sdxl.py

@@ -58,7 +58,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = get_logger(__name__)
 

examples/text_to_image/train_text_to_image.py

@@ -53,7 +53,7 @@ if is_wandb_available():
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/text_to_image/train_text_to_image_flax.py

@@ -33,7 +33,7 @@ from diffusers.utils import check_min_version
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = logging.getLogger(__name__)
 

examples/text_to_image/train_text_to_image_lora.py

@@ -48,7 +48,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/text_to_image/train_text_to_image_lora_sdxl.py

@@ -57,7 +57,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = get_logger(__name__)
 

examples/text_to_image/train_text_to_image_sdxl.py

@@ -57,7 +57,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = get_logger(__name__)
 

examples/textual_inversion/textual_inversion.py

@@ -79,7 +79,7 @@ else:
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = get_logger(__name__)
 

examples/textual_inversion/textual_inversion_flax.py

@@ -56,7 +56,7 @@ else:
 # ------------------------------------------------------------------------------
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = logging.getLogger(__name__)
 

examples/unconditional_image_generation/train_unconditional.py

@@ -30,7 +30,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.21.0")
 
 logger = get_logger(__name__, log_level="INFO")
 

scripts/convert_original_stable_diffusion_to_diffusers.py

@@ -154,6 +154,7 @@ if __name__ == "__main__":
     pipe = download_from_original_stable_diffusion_ckpt(
         checkpoint_path_or_dict=args.checkpoint_path,
         original_config_file=args.original_config_file,
+        config_files=args.config_files,
         image_size=args.image_size,
         prediction_type=args.prediction_type,
         model_type=args.pipeline_type,

setup.py

@@ -244,7 +244,7 @@ install_requires = [
 
 setup(
     name="diffusers",
-    version="0.21.0.dev0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
+    version="0.21.1",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
     description="State-of-the-art diffusion in PyTorch and JAX.",
     long_description=open("README.md", "r", encoding="utf-8").read(),
     long_description_content_type="text/markdown",

src/diffusers/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.21.0.dev0"
+__version__ = "0.21.1"
 
 from typing import TYPE_CHECKING
 

src/diffusers/loaders.py

@@ -41,7 +41,7 @@ from .utils.import_utils import BACKENDS_MAPPING
 
 
 if is_transformers_available():
-    from transformers import CLIPTextModel, CLIPTextModelWithProjection, PreTrainedModel, PreTrainedTokenizer
+    from transformers import CLIPTextModel, CLIPTextModelWithProjection
 
 if is_accelerate_available():
     from accelerate import init_empty_weights
@@ -627,7 +627,7 @@ class TextualInversionLoaderMixin:
     Load textual inversion tokens and embeddings to the tokenizer and text encoder.
     """
 
-    def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: "PreTrainedTokenizer"):
+    def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: "PreTrainedTokenizer"):  # noqa: F821
         r"""
         Processes prompts that include a special token corresponding to a multi-vector textual inversion embedding to
         be replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual
@@ -654,7 +654,7 @@ class TextualInversionLoaderMixin:
 
         return prompts
 
-    def _maybe_convert_prompt(self, prompt: str, tokenizer: "PreTrainedTokenizer"):
+    def _maybe_convert_prompt(self, prompt: str, tokenizer: "PreTrainedTokenizer"):  # noqa: F821
         r"""
         Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds
         to a multi-vector textual inversion embedding, this function will process the prompt so that the special token
@@ -688,8 +688,8 @@ class TextualInversionLoaderMixin:
         self,
         pretrained_model_name_or_path: Union[str, List[str], Dict[str, torch.Tensor], List[Dict[str, torch.Tensor]]],
         token: Optional[Union[str, List[str]]] = None,
-        tokenizer: Optional[PreTrainedTokenizer] = None,
+        tokenizer: Optional["PreTrainedTokenizer"] = None,  # noqa: F821
-        text_encoder: Optional[PreTrainedModel] = None,
+        text_encoder: Optional["PreTrainedModel"] = None,  # noqa: F821
         **kwargs,
     ):
         r"""
@@ -2098,6 +2098,7 @@ class FromSingleFileMixin:
         from .pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
 
         original_config_file = kwargs.pop("original_config_file", None)
+        config_files = kwargs.pop("config_files", None)
         cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
         resume_download = kwargs.pop("resume_download", False)
         force_download = kwargs.pop("force_download", False)
@@ -2215,6 +2216,7 @@ class FromSingleFileMixin:
             vae=vae,
             tokenizer=tokenizer,
             original_config_file=original_config_file,
+            config_files=config_files,
         )
 
         if torch_dtype is not None:

src/diffusers/pipelines/pipeline_utils.py

@@ -1255,7 +1255,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
         self._all_hooks = []
         hook = None
         for model_str in self.model_cpu_offload_seq.split("->"):
-            model = all_model_components.pop(model_str)
+            model = all_model_components.pop(model_str, None)
             if not isinstance(model, torch.nn.Module):
                 continue
 

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -1256,25 +1256,37 @@ def download_from_original_stable_diffusion_ckpt(
         key_name_v2_1 = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
         key_name_sd_xl_base = "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias"
         key_name_sd_xl_refiner = "conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias"
+        config_url = None
 
         # model_type = "v1"
-        config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
+        if config_files is not None and "v1" in config_files:
+            original_config_file = config_files["v1"]
+        else:
+            config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
 
         if key_name_v2_1 in checkpoint and checkpoint[key_name_v2_1].shape[-1] == 1024:
             # model_type = "v2"
-            config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml"
+            if config_files is not None and "v2" in config_files:
-
+                original_config_file = config_files["v2"]
+            else:
+                config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml"
             if global_step == 110000:
                 # v2.1 needs to upcast attention
                 upcast_attention = True
         elif key_name_sd_xl_base in checkpoint:
             # only base xl has two text embedders
-            config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml"
+            if config_files is not None and "xl" in config_files:
+                original_config_file = config_files["xl"]
+            else:
+                config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml"
         elif key_name_sd_xl_refiner in checkpoint:
             # only refiner xl has embedder and one text embedders
-            config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_refiner.yaml"
+            if config_files is not None and "xl_refiner" in config_files:
-
+                original_config_file = config_files["xl_refiner"]
-        original_config_file = BytesIO(requests.get(config_url).content)
+            else:
+                config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_refiner.yaml"
+        if config_url is not None:
+            original_config_file = BytesIO(requests.get(config_url).content)
 
     original_config = OmegaConf.load(original_config_file)
 

src/diffusers/pipelines/stable_diffusion_safe/__init__.py

@@ -50,13 +50,26 @@ class SafetyConfig(object):
 
 _dummy_objects = {}
 _additional_imports = {}
-_import_structure = {
+_import_structure = {}
-    "pipeline_output": ["StableDiffusionSafePipelineOutput"],
+
-    "pipeline_stable_diffusion_safe": ["StableDiffusionPipelineSafe"],
-    "safety_checker": ["StableDiffusionSafetyChecker"],
-}
 _additional_imports.update({"SafetyConfig": SafetyConfig})
 
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure.update(
+        {
+            "pipeline_output": ["StableDiffusionSafePipelineOutput"],
+            "pipeline_stable_diffusion_safe": ["StableDiffusionPipelineSafe"],
+            "safety_checker": ["StableDiffusionSafetyChecker"],
+        }
+    )
+
 
 if TYPE_CHECKING:
     try:
@@ -70,25 +83,16 @@ if TYPE_CHECKING:
         from .safety_checker import SafeStableDiffusionSafetyChecker
 
 else:
-    try:
+    import sys
-        if not (is_transformers_available() and is_torch_available()):
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        from ...utils import dummy_torch_and_transformers_objects
 
-        _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
 
-    else:
+    for name, value in _dummy_objects.items():
-        import sys
+        setattr(sys.modules[__name__], name, value)
-
+    for name, value in _additional_imports.items():
-        sys.modules[__name__] = _LazyModule(
+        setattr(sys.modules[__name__], name, value)
-            __name__,
-            globals()["__file__"],
-            _import_structure,
-            module_spec=__spec__,
-        )
-
-        for name, value in _dummy_objects.items():
-            setattr(sys.modules[__name__], name, value)
-        for name, value in _additional_imports.items():
-            setattr(sys.modules[__name__], name, value)

src/diffusers/pipelines/text_to_video_synthesis/__init__.py

@@ -47,3 +47,5 @@ else:
         _import_structure,
         module_spec=__spec__,
     )
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

src/diffusers/pipelines/vq_diffusion/__init__.py

@@ -51,3 +51,6 @@ else:
         _import_structure,
         module_spec=__spec__,
     )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

src/diffusers/pipelines/wuerstchen/__init__.py

@@ -41,7 +41,6 @@ if TYPE_CHECKING:
         from .pipeline_wuerstchen import WuerstchenDecoderPipeline
         from .pipeline_wuerstchen_combined import WuerstchenCombinedPipeline
         from .pipeline_wuerstchen_prior import WuerstchenPriorPipeline
-
 else:
     import sys
 
@@ -51,3 +50,6 @@ else:
         _import_structure,
         module_spec=__spec__,
     )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)