Bump to 0.10.0.dev0 + deprecations (#1490)

examples/unconditional_image_generation/train_unconditional.py

@@ -14,7 +14,6 @@ from datasets import load_dataset
 from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel, __version__
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import EMAModel
-from diffusers.utils import deprecate
 from huggingface_hub import HfFolder, Repository, whoami
 from packaging import version
 from torchvision.transforms import (
@@ -417,11 +416,7 @@ def main(args):
                     scheduler=noise_scheduler,
                 )
 
-                deprecate("todo: remove this check", "0.10.0", "when the most used version is >= 0.8.0")
+                generator = torch.Generator(device=pipeline.device).manual_seed(0)
-                if diffusers_version < version.parse("0.8.0"):
-                    generator = torch.manual_seed(0)
-                else:
-                    generator = torch.Generator(device=pipeline.device).manual_seed(0)
                 # run pipeline in inference (sample random noise and denoise)
                 images = pipeline(
                     generator=generator,

setup.py

@@ -214,7 +214,7 @@ install_requires = [
 
 setup(
     name="diffusers",
-    version="0.9.0",  # 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="0.10.0.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)
     description="Diffusers",
     long_description=open("README.md", "r", encoding="utf-8").read(),
     long_description_content_type="text/markdown",

src/diffusers/__init__.py

@@ -9,7 +9,7 @@ from .utils import (
 )
 
 
-__version__ = "0.9.0"
+__version__ = "0.10.0.dev0"
 
 from .configuration_utils import ConfigMixin
 from .onnx_utils import OnnxRuntimeModel

src/diffusers/hub_utils.py

@@ -15,16 +15,15 @@
 
 
 import os
-import shutil
 import sys
 from pathlib import Path
 from typing import Dict, Optional, Union
 from uuid import uuid4
 
-from huggingface_hub import HfFolder, Repository, whoami
+from huggingface_hub import HfFolder, whoami
 
 from . import __version__
-from .utils import ENV_VARS_TRUE_VALUES, deprecate, logging
+from .utils import ENV_VARS_TRUE_VALUES, logging
 from .utils.import_utils import (
     _flax_version,
     _jax_version,
@@ -83,121 +82,6 @@ def get_full_repo_name(model_id: str, organization: Optional[str] = None, token:
         return f"{organization}/{model_id}"
 
 
-def init_git_repo(args, at_init: bool = False):
-    """
-    Args:
-    Initializes a git repo in `args.hub_model_id`.
-        at_init (`bool`, *optional*, defaults to `False`):
-            Whether this function is called before any training or not. If `self.args.overwrite_output_dir` is `True`
-            and `at_init` is `True`, the path to the repo (which is `self.args.output_dir`) might be wiped out.
-    """
-    deprecation_message = (
-        "Please use `huggingface_hub.Repository`. "
-        "See `examples/unconditional_image_generation/train_unconditional.py` for an example."
-    )
-    deprecate("init_git_repo()", "0.10.0", deprecation_message)
-
-    if hasattr(args, "local_rank") and args.local_rank not in [-1, 0]:
-        return
-    hub_token = args.hub_token if hasattr(args, "hub_token") else None
-    use_auth_token = True if hub_token is None else hub_token
-    if not hasattr(args, "hub_model_id") or args.hub_model_id is None:
-        repo_name = Path(args.output_dir).absolute().name
-    else:
-        repo_name = args.hub_model_id
-    if "/" not in repo_name:
-        repo_name = get_full_repo_name(repo_name, token=hub_token)
-
-    try:
-        repo = Repository(
-            args.output_dir,
-            clone_from=repo_name,
-            use_auth_token=use_auth_token,
-            private=args.hub_private_repo,
-        )
-    except EnvironmentError:
-        if args.overwrite_output_dir and at_init:
-            # Try again after wiping output_dir
-            shutil.rmtree(args.output_dir)
-            repo = Repository(
-                args.output_dir,
-                clone_from=repo_name,
-                use_auth_token=use_auth_token,
-            )
-        else:
-            raise
-
-    repo.git_pull()
-
-    # By default, ignore the checkpoint folders
-    if not os.path.exists(os.path.join(args.output_dir, ".gitignore")):
-        with open(os.path.join(args.output_dir, ".gitignore"), "w", encoding="utf-8") as writer:
-            writer.writelines(["checkpoint-*/"])
-
-    return repo
-
-
-def push_to_hub(
-    args,
-    pipeline,
-    repo: Repository,
-    commit_message: Optional[str] = "End of training",
-    blocking: bool = True,
-    **kwargs,
-) -> str:
-    """
-    Parameters:
-    Upload *self.model* and *self.tokenizer* to the 🤗 model hub on the repo *self.args.hub_model_id*.
-        commit_message (`str`, *optional*, defaults to `"End of training"`):
-            Message to commit while pushing.
-        blocking (`bool`, *optional*, defaults to `True`):
-            Whether the function should return only when the `git push` has finished.
-        kwargs:
-            Additional keyword arguments passed along to [`create_model_card`].
-    Returns:
-        The url of the commit of your model in the given repository if `blocking=False`, a tuple with the url of the
-        commit and an object to track the progress of the commit if `blocking=True`
-    """
-    deprecation_message = (
-        "Please use `huggingface_hub.Repository` and `Repository.push_to_hub()`. "
-        "See `examples/unconditional_image_generation/train_unconditional.py` for an example."
-    )
-    deprecate("push_to_hub()", "0.10.0", deprecation_message)
-
-    if not hasattr(args, "hub_model_id") or args.hub_model_id is None:
-        model_name = Path(args.output_dir).name
-    else:
-        model_name = args.hub_model_id.split("/")[-1]
-
-    output_dir = args.output_dir
-    os.makedirs(output_dir, exist_ok=True)
-    logger.info(f"Saving pipeline checkpoint to {output_dir}")
-    pipeline.save_pretrained(output_dir)
-
-    # Only push from one node.
-    if hasattr(args, "local_rank") and args.local_rank not in [-1, 0]:
-        return
-
-    # Cancel any async push in progress if blocking=True. The commits will all be pushed together.
-    if (
-        blocking
-        and len(repo.command_queue) > 0
-        and repo.command_queue[-1] is not None
-        and not repo.command_queue[-1].is_done
-    ):
-        repo.command_queue[-1]._process.kill()
-
-    git_head_commit_url = repo.push_to_hub(commit_message=commit_message, blocking=blocking, auto_lfs_prune=True)
-    # push separately the model card to be independent from the rest of the model
-    create_model_card(args, model_name=model_name)
-    try:
-        repo.push_to_hub(commit_message="update model card README.md", blocking=blocking, auto_lfs_prune=True)
-    except EnvironmentError as exc:
-        logger.error(f"Error pushing update to the model card. Please read logs and retry.\n${exc}")
-
-    return git_head_commit_url
-
-
 def create_model_card(args, model_name):
     if not is_modelcards_available:
         raise ValueError(

src/diffusers/modeling_utils.py

@@ -666,20 +666,6 @@ class ModelMixin(torch.nn.Module):
             return sum(p.numel() for p in self.parameters() if p.requires_grad or not only_trainable)
 
 
-def unwrap_model(model: torch.nn.Module) -> torch.nn.Module:
-    """
-    Recursively unwraps a model from potential containers (as used in distributed training).
-
-    Args:
-        model (`torch.nn.Module`): The model to unwrap.
-    """
-    # since there could be multiple levels of wrapping, unwrap recursively
-    if hasattr(model, "module"):
-        return unwrap_model(model.module)
-    else:
-        return model
-
-
 def _get_model_file(
     pretrained_model_name_or_path,
     *,

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -73,7 +73,7 @@ class DDPMPipeline(DiffusionPipeline):
             "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
             " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
         )
-        predict_epsilon = deprecate("predict_epsilon", "0.10.0", message, take_from=kwargs)
+        predict_epsilon = deprecate("predict_epsilon", "0.11.0", message, take_from=kwargs)
 
         if predict_epsilon is not None:
             new_config = dict(self.scheduler.config)

src/diffusers/schedulers/scheduling_ddim.py

@@ -134,7 +134,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
             "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
             " DDIMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
         )
-        predict_epsilon = deprecate("predict_epsilon", "0.10.0", message, take_from=kwargs)
+        predict_epsilon = deprecate("predict_epsilon", "0.11.0", message, take_from=kwargs)
         if predict_epsilon is not None:
             self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
 

src/diffusers/schedulers/scheduling_ddim_flax.py

@@ -138,7 +138,7 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
             "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
             " FlaxDDIMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
         )
-        predict_epsilon = deprecate("predict_epsilon", "0.10.0", message, take_from=kwargs)
+        predict_epsilon = deprecate("predict_epsilon", "0.11.0", message, take_from=kwargs)
         if predict_epsilon is not None:
             self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
 

src/diffusers/schedulers/scheduling_ddpm.py

@@ -125,7 +125,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
             "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
             " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
         )
-        predict_epsilon = deprecate("predict_epsilon", "0.10.0", message, take_from=kwargs)
+        predict_epsilon = deprecate("predict_epsilon", "0.11.0", message, take_from=kwargs)
         if predict_epsilon is not None:
             self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
 
@@ -255,7 +255,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
             "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
             " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
         )
-        predict_epsilon = deprecate("predict_epsilon", "0.10.0", message, take_from=kwargs)
+        predict_epsilon = deprecate("predict_epsilon", "0.11.0", message, take_from=kwargs)
         if predict_epsilon is not None:
             new_config = dict(self.config)
             new_config["prediction_type"] = "epsilon" if predict_epsilon else "sample"

src/diffusers/schedulers/scheduling_ddpm_flax.py

@@ -132,7 +132,7 @@ class FlaxDDPMScheduler(FlaxSchedulerMixin, ConfigMixin):
             "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
             " FlaxDDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
         )
-        predict_epsilon = deprecate("predict_epsilon", "0.10.0", message, take_from=kwargs)
+        predict_epsilon = deprecate("predict_epsilon", "0.11.0", message, take_from=kwargs)
         if predict_epsilon is not None:
             self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
 
@@ -239,7 +239,7 @@ class FlaxDDPMScheduler(FlaxSchedulerMixin, ConfigMixin):
             "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
             " FlaxDDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
         )
-        predict_epsilon = deprecate("predict_epsilon", "0.10.0", message, take_from=kwargs)
+        predict_epsilon = deprecate("predict_epsilon", "0.11.0", message, take_from=kwargs)
         if predict_epsilon is not None:
             new_config = dict(self.config)
             new_config["prediction_type"] = "epsilon" if predict_epsilon else "sample"

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -142,7 +142,7 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
             "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
             " DPMSolverMultistepScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
         )
-        predict_epsilon = deprecate("predict_epsilon", "0.10.0", message, take_from=kwargs)
+        predict_epsilon = deprecate("predict_epsilon", "0.11.0", message, take_from=kwargs)
         if predict_epsilon is not None:
             self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
 

src/diffusers/schedulers/scheduling_dpmsolver_multistep_flax.py

@@ -177,7 +177,7 @@ class FlaxDPMSolverMultistepScheduler(FlaxSchedulerMixin, ConfigMixin):
             "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
             " FlaxDPMSolverMultistepScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
         )
-        predict_epsilon = deprecate("predict_epsilon", "0.10.0", message, take_from=kwargs)
+        predict_epsilon = deprecate("predict_epsilon", "0.11.0", message, take_from=kwargs)
         if predict_epsilon is not None:
             self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
 

tests/pipelines/ddpm/test_ddpm.py

@@ -69,7 +69,7 @@ class DDPMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
 
     def test_inference_deprecated_predict_epsilon(self):
-        deprecate("remove this test", "0.10.0", "remove")
+        deprecate("remove this test", "0.11.0", "remove")
         unet = self.dummy_uncond_unet
         scheduler = DDPMScheduler(predict_epsilon=False)
 

tests/test_config.py

@@ -203,7 +203,7 @@ class ConfigTester(unittest.TestCase):
             ddpm_2 = DDPMScheduler.from_pretrained("google/ddpm-celebahq-256", beta_start=88)
 
         with CaptureLogger(logger) as cap_logger:
-            deprecate("remove this case", "0.10.0", "remove")
+            deprecate("remove this case", "0.11.0", "remove")
             ddpm_3 = DDPMScheduler.from_pretrained(
                 "hf-internal-testing/tiny-stable-diffusion-torch",
                 subfolder="scheduler",

tests/test_scheduler.py

@@ -639,12 +639,12 @@ class DDPMSchedulerTest(SchedulerCommonTest):
             self.check_over_configs(prediction_type=prediction_type)
 
     def test_deprecated_predict_epsilon(self):
-        deprecate("remove this test", "0.10.0", "remove")
+        deprecate("remove this test", "0.11.0", "remove")
         for predict_epsilon in [True, False]:
             self.check_over_configs(predict_epsilon=predict_epsilon)
 
     def test_deprecated_epsilon(self):
-        deprecate("remove this test", "0.10.0", "remove")
+        deprecate("remove this test", "0.11.0", "remove")
         scheduler_class = self.scheduler_classes[0]
         scheduler_config = self.get_scheduler_config()
 

tests/test_scheduler_flax.py

@@ -626,12 +626,12 @@ class FlaxDDIMSchedulerTest(FlaxSchedulerCommonTest):
             self.check_over_configs(prediction_type=prediction_type)
 
     def test_deprecated_predict_epsilon(self):
-        deprecate("remove this test", "0.10.0", "remove")
+        deprecate("remove this test", "0.11.0", "remove")
         for predict_epsilon in [True, False]:
             self.check_over_configs(predict_epsilon=predict_epsilon)
 
     def test_deprecated_predict_epsilon_to_prediction_type(self):
-        deprecate("remove this test", "0.10.0", "remove")
+        deprecate("remove this test", "0.11.0", "remove")
         for scheduler_class in self.scheduler_classes:
             scheduler_config = self.get_scheduler_config(predict_epsilon=True)
             scheduler = scheduler_class.from_config(scheduler_config)