[Better scheduler docs] Improve usage examples of schedulers (#890)

* [Better scheduler docs] Improve usage examples of schedulers

* finish

* fix warnings and add test

* finish

* more replacements

* adapt fast tests hf token

* correct more

* Apply suggestions from code review

Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Integrate compatibility with euler

Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

.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 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"),
         )

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 @@ class ConfigMixin:
 
         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 @@ class ConfigMixin:
             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:
@@ -304,8 +341,7 @@ class ConfigMixin:
                 # 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__}, "
@@ -313,14 +349,16 @@ class ConfigMixin:
                 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 @@ class FlaxDiffusionPipeline(ConfigMixin):
         >>> # 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 @@ class DiffusionPipeline(ConfigMixin):
         >>> # 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 @@ class DiffusionPipeline(ConfigMixin):
         ...     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 @@ import numpy as np
 
 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 @@ class OnnxStableDiffusionPipeline(DiffusionPipeline):
         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 @@ class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
             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 @@ class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline):
             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 @@ class StableDiffusionPipeline(DiffusionPipeline):
             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 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
             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_inpaint.py

@@ -90,6 +90,19 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
             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_inpaint_legacy.py

@@ -96,6 +96,19 @@ class StableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
             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/schedulers/scheduling_ddim.py

@@ -109,6 +109,14 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
 
     """
 
+    _compatible_classes = [
+        "PNDMScheduler",
+        "DDPMScheduler",
+        "LMSDiscreteScheduler",
+        "EulerDiscreteScheduler",
+        "EulerAncestralDiscreteScheduler",
+    ]
+
     @register_to_config
     def __init__(
         self,

src/diffusers/schedulers/scheduling_ddpm.py

@@ -102,6 +102,14 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
 
     """
 
+    _compatible_classes = [
+        "DDIMScheduler",
+        "PNDMScheduler",
+        "LMSDiscreteScheduler",
+        "EulerDiscreteScheduler",
+        "EulerAncestralDiscreteScheduler",
+    ]
+
     @register_to_config
     def __init__(
         self,

src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py

@@ -67,6 +67,14 @@ class EulerAncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
 
     """
 
+    _compatible_classes = [
+        "DDIMScheduler",
+        "DDPMScheduler",
+        "LMSDiscreteScheduler",
+        "PNDMScheduler",
+        "EulerDiscreteScheduler",
+    ]
+
     @register_to_config
     def __init__(
         self,

src/diffusers/schedulers/scheduling_euler_discrete.py

@@ -68,6 +68,14 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
 
     """
 
+    _compatible_classes = [
+        "DDIMScheduler",
+        "DDPMScheduler",
+        "LMSDiscreteScheduler",
+        "PNDMScheduler",
+        "EulerAncestralDiscreteScheduler",
+    ]
+
     @register_to_config
     def __init__(
         self,

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -67,6 +67,14 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
 
     """
 
+    _compatible_classes = [
+        "DDIMScheduler",
+        "DDPMScheduler",
+        "PNDMScheduler",
+        "EulerDiscreteScheduler",
+        "EulerAncestralDiscreteScheduler",
+    ]
+
     @register_to_config
     def __init__(
         self,

src/diffusers/schedulers/scheduling_pndm.py

@@ -88,6 +88,14 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
 
     """
 
+    _compatible_classes = [
+        "DDIMScheduler",
+        "DDPMScheduler",
+        "LMSDiscreteScheduler",
+        "EulerDiscreteScheduler",
+        "EulerAncestralDiscreteScheduler",
+    ]
+
     @register_to_config
     def __init__(
         self,

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -644,13 +644,7 @@ class StableDiffusionPipelineIntegrationTests(unittest.TestCase):
         sd_pipe = sd_pipe.to(torch_device)
         sd_pipe.set_progress_bar_config(disable=None)
 
-        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-v1-1", subfolder="scheduler")
         sd_pipe.scheduler = scheduler
 
         prompt = "A painting of a squirrel eating a burger"

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -523,9 +523,8 @@ class StableDiffusionImg2ImgPipelineIntegrationTests(unittest.TestCase):
         init_image = init_image.resize((768, 512))
         expected_image = np.array(expected_image, dtype=np.float32) / 255.0
 
-        lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
-
         model_id = "CompVis/stable-diffusion-v1-4"
+        lms = LMSDiscreteScheduler.from_config(model_id, subfolder="scheduler")
         pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
             model_id,
             scheduler=lms,

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -366,8 +366,8 @@ class StableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
         )
         expected_image = np.array(expected_image, dtype=np.float32) / 255.0
 
-        pndm = PNDMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True)
         model_id = "runwayml/stable-diffusion-inpainting"
+        pndm = PNDMScheduler.from_config(model_id, subfolder="scheduler")
         pipe = StableDiffusionInpaintPipeline.from_pretrained(
             model_id, safety_checker=None, scheduler=pndm, device_map="auto"
         )

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint_legacy.py

@@ -407,9 +407,8 @@ class StableDiffusionInpaintLegacyPipelineIntegrationTests(unittest.TestCase):
         )
         expected_image = np.array(expected_image, dtype=np.float32) / 255.0
 
-        lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
-
         model_id = "CompVis/stable-diffusion-v1-4"
+        lms = LMSDiscreteScheduler.from_config(model_id, subfolder="scheduler")
         pipe = StableDiffusionInpaintPipeline.from_pretrained(
             model_id,
             scheduler=lms,

tests/test_config.py

@@ -13,10 +13,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import json
+import os
 import tempfile
 import unittest
 
+import diffusers
+from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, PNDMScheduler, logging
 from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.utils.testing_utils import CaptureLogger
 
 
 class SampleObject(ConfigMixin):
@@ -34,6 +39,37 @@ class SampleObject(ConfigMixin):
         pass
 
 
+class SampleObject2(ConfigMixin):
+    config_name = "config.json"
+
+    @register_to_config
+    def __init__(
+        self,
+        a=2,
+        b=5,
+        c=(2, 5),
+        d="for diffusion",
+        f=[1, 3],
+    ):
+        pass
+
+
+class SampleObject3(ConfigMixin):
+    config_name = "config.json"
+
+    @register_to_config
+    def __init__(
+        self,
+        a=2,
+        b=5,
+        c=(2, 5),
+        d="for diffusion",
+        e=[1, 3],
+        f=[1, 3],
+    ):
+        pass
+
+
 class ConfigTester(unittest.TestCase):
     def test_load_not_from_mixin(self):
         with self.assertRaises(ValueError):
@@ -97,3 +133,151 @@ class ConfigTester(unittest.TestCase):
         assert config.pop("c") == (2, 5)  # instantiated as tuple
         assert new_config.pop("c") == [2, 5]  # saved & loaded as list because of json
         assert config == new_config
+
+    def test_save_load_from_different_config(self):
+        obj = SampleObject()
+
+        # mock add obj class to `diffusers`
+        setattr(diffusers, "SampleObject", SampleObject)
+        logger = logging.get_logger("diffusers.configuration_utils")
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            obj.save_config(tmpdirname)
+            with CaptureLogger(logger) as cap_logger_1:
+                new_obj_1 = SampleObject2.from_config(tmpdirname)
+
+            # now save a config parameter that is not expected
+            with open(os.path.join(tmpdirname, SampleObject.config_name), "r") as f:
+                data = json.load(f)
+                data["unexpected"] = True
+
+            with open(os.path.join(tmpdirname, SampleObject.config_name), "w") as f:
+                json.dump(data, f)
+
+            with CaptureLogger(logger) as cap_logger_2:
+                new_obj_2 = SampleObject.from_config(tmpdirname)
+
+            with CaptureLogger(logger) as cap_logger_3:
+                new_obj_3 = SampleObject2.from_config(tmpdirname)
+
+        assert new_obj_1.__class__ == SampleObject2
+        assert new_obj_2.__class__ == SampleObject
+        assert new_obj_3.__class__ == SampleObject2
+
+        assert cap_logger_1.out == ""
+        assert (
+            cap_logger_2.out
+            == "The config attributes {'unexpected': True} were passed to SampleObject, but are not expected and will"
+            " be ignored. Please verify your config.json configuration file.\n"
+        )
+        assert cap_logger_2.out.replace("SampleObject", "SampleObject2") == cap_logger_3.out
+
+    def test_save_load_compatible_schedulers(self):
+        SampleObject2._compatible_classes = ["SampleObject"]
+        SampleObject._compatible_classes = ["SampleObject2"]
+
+        obj = SampleObject()
+
+        # mock add obj class to `diffusers`
+        setattr(diffusers, "SampleObject", SampleObject)
+        setattr(diffusers, "SampleObject2", SampleObject2)
+        logger = logging.get_logger("diffusers.configuration_utils")
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            obj.save_config(tmpdirname)
+
+            # now save a config parameter that is expected by another class, but not origin class
+            with open(os.path.join(tmpdirname, SampleObject.config_name), "r") as f:
+                data = json.load(f)
+                data["f"] = [0, 0]
+                data["unexpected"] = True
+
+            with open(os.path.join(tmpdirname, SampleObject.config_name), "w") as f:
+                json.dump(data, f)
+
+            with CaptureLogger(logger) as cap_logger:
+                new_obj = SampleObject.from_config(tmpdirname)
+
+        assert new_obj.__class__ == SampleObject
+
+        assert (
+            cap_logger.out
+            == "The config attributes {'unexpected': True} were passed to SampleObject, but are not expected and will"
+            " be ignored. Please verify your config.json configuration file.\n"
+        )
+
+    def test_save_load_from_different_config_comp_schedulers(self):
+        SampleObject3._compatible_classes = ["SampleObject", "SampleObject2"]
+        SampleObject2._compatible_classes = ["SampleObject", "SampleObject3"]
+        SampleObject._compatible_classes = ["SampleObject2", "SampleObject3"]
+
+        obj = SampleObject()
+
+        # mock add obj class to `diffusers`
+        setattr(diffusers, "SampleObject", SampleObject)
+        setattr(diffusers, "SampleObject2", SampleObject2)
+        setattr(diffusers, "SampleObject3", SampleObject3)
+        logger = logging.get_logger("diffusers.configuration_utils")
+        logger.setLevel(diffusers.logging.INFO)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            obj.save_config(tmpdirname)
+
+            with CaptureLogger(logger) as cap_logger_1:
+                new_obj_1 = SampleObject.from_config(tmpdirname)
+
+            with CaptureLogger(logger) as cap_logger_2:
+                new_obj_2 = SampleObject2.from_config(tmpdirname)
+
+            with CaptureLogger(logger) as cap_logger_3:
+                new_obj_3 = SampleObject3.from_config(tmpdirname)
+
+        assert new_obj_1.__class__ == SampleObject
+        assert new_obj_2.__class__ == SampleObject2
+        assert new_obj_3.__class__ == SampleObject3
+
+        assert cap_logger_1.out == ""
+        assert cap_logger_2.out == "{'f'} was not found in config. Values will be initialized to default values.\n"
+        assert cap_logger_3.out == "{'f'} was not found in config. Values will be initialized to default values.\n"
+
+    def test_load_ddim_from_pndm(self):
+        logger = logging.get_logger("diffusers.configuration_utils")
+
+        with CaptureLogger(logger) as cap_logger:
+            ddim = DDIMScheduler.from_config("runwayml/stable-diffusion-v1-5", subfolder="scheduler")
+
+        assert ddim.__class__ == DDIMScheduler
+        # no warning should be thrown
+        assert cap_logger.out == ""
+
+    def test_load_ddim_from_euler(self):
+        logger = logging.get_logger("diffusers.configuration_utils")
+
+        with CaptureLogger(logger) as cap_logger:
+            euler = EulerDiscreteScheduler.from_config("runwayml/stable-diffusion-v1-5", subfolder="scheduler")
+
+        assert euler.__class__ == EulerDiscreteScheduler
+        # no warning should be thrown
+        assert cap_logger.out == ""
+
+    def test_load_ddim_from_euler_ancestral(self):
+        logger = logging.get_logger("diffusers.configuration_utils")
+
+        with CaptureLogger(logger) as cap_logger:
+            euler = EulerAncestralDiscreteScheduler.from_config(
+                "runwayml/stable-diffusion-v1-5", subfolder="scheduler"
+            )
+
+        assert euler.__class__ == EulerAncestralDiscreteScheduler
+        # no warning should be thrown
+        assert cap_logger.out == ""
+
+    def test_load_pndm(self):
+        logger = logging.get_logger("diffusers.configuration_utils")
+
+        with CaptureLogger(logger) as cap_logger:
+            pndm = PNDMScheduler.from_config("runwayml/stable-diffusion-v1-5", subfolder="scheduler")
+
+        assert pndm.__class__ == PNDMScheduler
+        # no warning should be thrown
+        assert cap_logger.out == ""