[Better scheduler docs] Improve usage examples of schedulers

.github/workflows/pr_tests.yml

@@ -42,6 +42,8 @@ jobs:
         python utils/print_env.py
 
     - name: Run all fast tests on CPU
+      env:
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
       run: |
         python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=tests_torch_cpu tests/
 
@@ -91,6 +93,8 @@ jobs:
 
     - name: Run all fast tests on MPS
       shell: arch -arch arm64 bash {0}
+      env:
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
       run: |
         ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps tests/
 

README.md

@@ -142,11 +142,7 @@ it before the pipeline and pass it to `from_pretrained`.
 ```python
 from diffusers import LMSDiscreteScheduler
 
-lms = LMSDiscreteScheduler(
-    beta_start=0.00085, 
-    beta_end=0.012, 
-    beta_schedule="scaled_linear"
-)
+lms = LMSDiscreteScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
 pipe = StableDiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5", 

docs/source/quicktour.mdx

@@ -121,7 +121,7 @@ you could use it as follows:
 ```python
 >>> from diffusers import LMSDiscreteScheduler
 
->>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+>>> scheduler = LMSDiscreteScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
 >>> generator = StableDiffusionPipeline.from_pretrained(
 ...     "runwayml/stable-diffusion-v1-5", scheduler=scheduler, use_auth_token=AUTH_TOKEN

examples/dreambooth/train_dreambooth.py

@@ -469,9 +469,7 @@ def main(args):
         eps=args.adam_epsilon,
     )
 
-    noise_scheduler = DDPMScheduler(
-        beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000
-    )
+    noise_scheduler = DDPMScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
     train_dataset = DreamBoothDataset(
         instance_data_root=args.instance_data_dir,

examples/text_to_image/train_text_to_image.py

@@ -372,11 +372,7 @@ def main():
         weight_decay=args.adam_weight_decay,
         eps=args.adam_epsilon,
     )
-
-    # TODO (patil-suraj): load scheduler using args
-    noise_scheduler = DDPMScheduler(
-        beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000
-    )
+    noise_scheduler = DDPMScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
     # Get the datasets: you can either provide your own training and evaluation files (see below)
     # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
@@ -609,9 +605,7 @@ def collate_fn(examples):
             vae=vae,
             unet=unet,
             tokenizer=tokenizer,
-            scheduler=PNDMScheduler(
-                beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
-            ),
+            scheduler=PNDMScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler"),
             safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker"),
             feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
         )

examples/textual_inversion/textual_inversion.py

@@ -419,13 +419,7 @@ def main():
         eps=args.adam_epsilon,
     )
 
-    # TODO (patil-suraj): load scheduler using args
-    noise_scheduler = DDPMScheduler(
-        beta_start=0.00085,
-        beta_end=0.012,
-        beta_schedule="scaled_linear",
-        num_train_timesteps=1000,
-    )
+    noise_scheduler = DDPMScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
     train_dataset = TextualInversionDataset(
         data_root=args.train_data_dir,
@@ -558,9 +552,7 @@ def main():
             vae=vae,
             unet=unet,
             tokenizer=tokenizer,
-            scheduler=PNDMScheduler(
-                beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
-            ),
+            scheduler=PNDMScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler"),
             safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker"),
             feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
         )

src/diffusers/configuration_utils.py

@@ -16,6 +16,7 @@
 """ ConfigMixin base class and utilities."""
 import dataclasses
 import functools
+import importlib
 import inspect
 import json
 import os
@@ -48,9 +49,13 @@ class ConfigMixin:
           [`~ConfigMixin.save_config`] (should be overridden by parent class).
         - **ignore_for_config** (`List[str]`) -- A list of attributes that should not be saved in the config (should be
           overridden by parent class).
+        - **_compatible_classes** (`List[str]`) -- A list of classes that are compatible with the parent class, so that
+          `from_config` can be used from a class different than the one used to save the config (should be overridden
+          by parent class).
     """
     config_name = None
     ignore_for_config = []
+    _compatible_classes = []
 
     def register_to_config(self, **kwargs):
         if self.config_name is None:
@@ -280,9 +285,14 @@ def get_config_dict(
 
         return config_dict
 
+    @staticmethod
+    def _get_init_keys(cls):
+        return set(dict(inspect.signature(cls.__init__).parameters).keys())
+
     @classmethod
     def extract_init_dict(cls, config_dict, **kwargs):
-        expected_keys = set(dict(inspect.signature(cls.__init__).parameters).keys())
+        # 1. Retrieve expected config attributes from __init__ signature
+        expected_keys = cls._get_init_keys(cls)
         expected_keys.remove("self")
         # remove general kwargs if present in dict
         if "kwargs" in expected_keys:
@@ -292,9 +302,36 @@ def extract_init_dict(cls, config_dict, **kwargs):
             for arg in cls._flax_internal_args:
                 expected_keys.remove(arg)
 
+        # 2. Remove attributes that cannot be expected from expected config attributes
         # remove keys to be ignored
         if len(cls.ignore_for_config) > 0:
             expected_keys = expected_keys - set(cls.ignore_for_config)
+
+        # load diffusers library to import compatible and original scheduler
+        diffusers_library = importlib.import_module(__name__.split(".")[0])
+
+        # remove attributes from compatible classes that orig cannot expect
+        compatible_classes = [getattr(diffusers_library, c, None) for c in cls._compatible_classes]
+        # filter out None potentially undefined dummy classes
+        compatible_classes = [c for c in compatible_classes if c is not None]
+        expected_keys_comp_cls = set()
+        for c in compatible_classes:
+            expected_keys_c = cls._get_init_keys(c)
+            expected_keys_comp_cls = expected_keys_comp_cls.union(expected_keys_c)
+        expected_keys_comp_cls = expected_keys_comp_cls - cls._get_init_keys(cls)
+        config_dict = {k: v for k, v in config_dict.items() if k not in expected_keys_comp_cls}
+
+        # remove attributes from orig class that cannot be expected
+        orig_cls_name = config_dict.pop("_class_name", cls.__name__)
+        if orig_cls_name != cls.__name__:
+            orig_cls = getattr(diffusers_library, orig_cls_name)
+            unexpected_keys_from_orig = cls._get_init_keys(orig_cls) - expected_keys
+            config_dict = {k: v for k, v in config_dict.items() if k not in unexpected_keys_from_orig}
+
+        # remove private attributes
+        config_dict = {k: v for k, v in config_dict.items() if not k.startswith("_")}
+
+        # 3. Create keyword arguments that will be passed to __init__ from expected keyword arguments
         init_dict = {}
         for key in expected_keys:
             if key in kwargs:
@@ -304,23 +341,24 @@ def extract_init_dict(cls, config_dict, **kwargs):
                 # use value from config dict
                 init_dict[key] = config_dict.pop(key)
 
-        config_dict = {k: v for k, v in config_dict.items() if not k.startswith("_")}
-
+        # 4. Give nice warning if unexpected values have been passed
         if len(config_dict) > 0:
             logger.warning(
                 f"The config attributes {config_dict} were passed to {cls.__name__}, "
                 "but are not expected and will be ignored. Please verify your "
                 f"{cls.config_name} configuration file."
             )
 
-        unused_kwargs = {**config_dict, **kwargs}
-
+        # 5. Give nice info if config attributes are initiliazed to default because they have not been passed
         passed_keys = set(init_dict.keys())
         if len(expected_keys - passed_keys) > 0:
             logger.info(
                 f"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values."
             )
 
+        # 6. Define unused keyword arguments
+        unused_kwargs = {**config_dict, **kwargs}
+
         return init_dict, unused_kwargs
 
     @classmethod

src/diffusers/pipeline_flax_utils.py

@@ -272,7 +272,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         >>> # Download pipeline, but overwrite scheduler
         >>> from diffusers import LMSDiscreteScheduler
 
-        >>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+        >>> scheduler = LMSDiscreteScheduler.from_config("runwayml/stable-diffusion-v1-5", subfolder="scheduler")
         >>> pipeline = FlaxDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", scheduler=scheduler)
         ```
         """

src/diffusers/pipeline_utils.py

@@ -360,7 +360,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         >>> # Download pipeline, but overwrite scheduler
         >>> from diffusers import LMSDiscreteScheduler
 
-        >>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+        >>> scheduler = LMSDiscreteScheduler.from_config("runwayml/stable-diffusion-v1-5", subfolder="scheduler")
         >>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", scheduler=scheduler)
         ```
         """
@@ -602,7 +602,7 @@ def components(self) -> Dict[str, Any]:
         ...     StableDiffusionInpaintPipeline,
         ... )
 
-        >>> img2text = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+        >>> img2text = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
         >>> img2img = StableDiffusionImg2ImgPipeline(**img2text.components)
         >>> inpaint = StableDiffusionInpaintPipeline(**img2text.components)
         ```

src/diffusers/pipelines/stable_diffusion/README.md

@@ -72,7 +72,7 @@ image.save("astronaut_rides_horse.png")
 # make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline, DDIMScheduler
 
-scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
+scheduler =  DDIMScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
 pipe = StableDiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5", 
@@ -91,11 +91,7 @@ image.save("astronaut_rides_horse.png")
 # make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
 
-lms = LMSDiscreteScheduler(
-    beta_start=0.00085, 
-    beta_end=0.012, 
-    beta_schedule="scaled_linear"
-)
+lms = LMSDiscreteScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
 pipe = StableDiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5", 

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py

@@ -5,6 +5,7 @@
 
 from transformers import CLIPFeatureExtractor, CLIPTokenizer
 
+from ...configuration_utils import FrozenDict
 from ...onnx_utils import OnnxRuntimeModel
 from ...pipeline_utils import DiffusionPipeline
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
@@ -36,6 +37,34 @@ def __init__(
         feature_extractor: CLIPFeatureExtractor,
     ):
         super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
         self.register_modules(
             vae_encoder=vae_encoder,
             vae_decoder=vae_decoder,

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py

@@ -90,6 +90,19 @@ def __init__(
             new_config["steps_offset"] = 1
             scheduler._internal_dict = FrozenDict(new_config)
 
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
         if safety_checker is None:
             logger.warning(
                 f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py

@@ -104,6 +104,19 @@ def __init__(
             new_config["steps_offset"] = 1
             scheduler._internal_dict = FrozenDict(new_config)
 
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
         if safety_checker is None:
             logger.warning(
                 f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -80,6 +80,19 @@ def __init__(
             new_config["steps_offset"] = 1
             scheduler._internal_dict = FrozenDict(new_config)
 
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
         if safety_checker is None:
             logger.warn(
                 f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -91,6 +91,19 @@ def __init__(
             new_config["steps_offset"] = 1
             scheduler._internal_dict = FrozenDict(new_config)
 
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
         if safety_checker is None:
             logger.warn(
                 f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"