Make repo structure consistent (#1862)

* move files a bit

* more refactors

* fix more

* more fixes

* fix more onnx

* make style

* upload

* fix

* up

* fix more

* up again

* up

* small fix

* Update src/diffusers/__init__.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

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

src/diffusers/utils/constants.py

+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+
+
+hf_cache_home = os.path.expanduser(
+    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
+)
+default_cache_path = os.path.join(hf_cache_home, "diffusers")
+
+
+CONFIG_NAME = "config.json"
+WEIGHTS_NAME = "diffusion_pytorch_model.bin"
+FLAX_WEIGHTS_NAME = "diffusion_flax_model.msgpack"
+ONNX_WEIGHTS_NAME = "model.onnx"
+SAFETENSORS_WEIGHTS_NAME = "diffusion_pytorch_model.safetensors"
+ONNX_EXTERNAL_WEIGHTS_NAME = "weights.pb"
+HUGGINGFACE_CO_RESOLVE_ENDPOINT = "https://huggingface.co"
+DIFFUSERS_CACHE = default_cache_path
+DIFFUSERS_DYNAMIC_MODULE_NAME = "diffusers_modules"
+HF_MODULES_CACHE = os.getenv("HF_MODULES_CACHE", os.path.join(hf_cache_home, "modules"))
+
+_COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS = [
+    "DDIMScheduler",
+    "DDPMScheduler",
+    "PNDMScheduler",
+    "LMSDiscreteScheduler",
+    "EulerDiscreteScheduler",
+    "HeunDiscreteScheduler",
+    "EulerAncestralDiscreteScheduler",
+    "DPMSolverMultistepScheduler",
+    "DPMSolverSinglestepScheduler",
+]

src/diffusers/utils/dummy_onnx_objects.py

+# This file is autogenerated by the command `make fix-copies`, do not edit.
+# flake8: noqa
+
+from ..utils import DummyObject, requires_backends
+
+
+class OnnxRuntimeModel(metaclass=DummyObject):
+    _backends = ["onnx"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["onnx"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["onnx"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["onnx"])

src/diffusers/utils/dummy_pt_objects.py

@@ -4,7 +4,7 @@
 from ..utils import DummyObject, requires_backends
 
 
-class ModelMixin(metaclass=DummyObject):
+class AutoencoderKL(metaclass=DummyObject):
     _backends = ["torch"]
 
     def __init__(self, *args, **kwargs):
@@ -19,7 +19,7 @@ class ModelMixin(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
-class AutoencoderKL(metaclass=DummyObject):
+class ModelMixin(metaclass=DummyObject):
     _backends = ["torch"]
 
     def __init__(self, *args, **kwargs):
@@ -152,7 +152,7 @@ def get_scheduler(*args, **kwargs):
     requires_backends(get_scheduler, ["torch"])
 
 
-class DiffusionPipeline(metaclass=DummyObject):
+class AudioPipelineOutput(metaclass=DummyObject):
     _backends = ["torch"]
 
     def __init__(self, *args, **kwargs):
@@ -212,6 +212,36 @@ class DDPMPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class DiffusionPipeline(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
+class ImagePipelineOutput(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 class KarrasVePipeline(metaclass=DummyObject):
     _backends = ["torch"]
 

src/diffusers/dynamic_modules_utils.py → src/diffusers/utils/dynamic_modules_utils.py

@@ -28,8 +28,8 @@ from urllib import request
 
 from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
 
-from . import __version__
-from .utils import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
+from .. import __version__
+from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
 
 
 COMMUNITY_PIPELINES_URL = (
@@ -172,7 +172,7 @@ def find_pipeline_class(loaded_module):
     Retrieve pipeline class that inherits from `DiffusionPipeline`. Note that there has to be exactly one class
     inheriting from `DiffusionPipeline`.
     """
-    from .pipeline_utils import DiffusionPipeline
+    from ..pipelines import DiffusionPipeline
 
     cls_members = dict(inspect.getmembers(loaded_module, inspect.isclass))
 

src/diffusers/hub_utils.py → src/diffusers/utils/hub_utils.py

@@ -22,9 +22,10 @@ from uuid import uuid4
 
 from huggingface_hub import HfFolder, whoami
 
-from . import __version__
-from .utils import ENV_VARS_TRUE_VALUES, HUGGINGFACE_CO_RESOLVE_ENDPOINT, logging
-from .utils.import_utils import (
+from .. import __version__
+from .constants import HUGGINGFACE_CO_RESOLVE_ENDPOINT
+from .import_utils import (
+    ENV_VARS_TRUE_VALUES,
     _flax_version,
     _jax_version,
     _onnxruntime_version,
@@ -34,13 +35,14 @@ from .utils.import_utils import (
     is_onnx_available,
     is_torch_available,
 )
+from .logging import get_logger
 
 
 if is_modelcards_available():
     from modelcards import CardData, ModelCard
 
 
-logger = logging.get_logger(__name__)
+logger = get_logger(__name__)
 
 
 MODEL_CARD_TEMPLATE_PATH = Path(__file__).parent / "utils" / "model_card_template.md"

tests/fixtures/custom_pipeline/pipeline.py

@@ -18,7 +18,7 @@ from typing import Optional, Tuple, Union
 
 import torch
 
-from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+from diffusers import DiffusionPipeline, ImagePipelineOutput
 
 
 class CustomLocalPipeline(DiffusionPipeline):
@@ -63,10 +63,10 @@ class CustomLocalPipeline(DiffusionPipeline):
                 The output format of the generate image. Choose between
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
+                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
 
         Returns:
-            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
+            [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
             `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
             generated images.
         """

tests/models/test_models_vae.py

@@ -19,7 +19,7 @@ import unittest
 import torch
 
 from diffusers import AutoencoderKL
-from diffusers.modeling_utils import ModelMixin
+from diffusers.models import ModelMixin
 from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
 from parameterized import parameterized
 

tests/pipelines/unclip/test_unclip_image_variation.py

@@ -20,8 +20,13 @@ import unittest
 import numpy as np
 import torch
 
-from diffusers import UnCLIPImageVariationPipeline, UnCLIPScheduler, UNet2DConditionModel, UNet2DModel
-from diffusers.pipeline_utils import DiffusionPipeline
+from diffusers import (
+    DiffusionPipeline,
+    UnCLIPImageVariationPipeline,
+    UnCLIPScheduler,
+    UNet2DConditionModel,
+    UNet2DModel,
+)
 from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
 from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
 from diffusers.utils.testing_utils import load_image, require_torch_gpu

tests/test_modeling_common.py

@@ -21,7 +21,7 @@ from typing import Dict, List, Tuple
 import numpy as np
 import torch
 
-from diffusers.modeling_utils import ModelMixin
+from diffusers.models import ModelMixin
 from diffusers.training_utils import EMAModel
 from diffusers.utils import torch_device
 

tests/test_pipelines.py

@@ -33,6 +33,7 @@ from diffusers import (
     DDIMScheduler,
     DDPMPipeline,
     DDPMScheduler,
+    DiffusionPipeline,
     DPMSolverMultistepScheduler,
     EulerAncestralDiscreteScheduler,
     EulerDiscreteScheduler,
@@ -45,7 +46,6 @@ from diffusers import (
     UNet2DModel,
     logging,
 )
-from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
 from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, nightly, slow, torch_device
 from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir, require_torch_gpu
@@ -704,7 +704,7 @@ class PipelineSlowTests(unittest.TestCase):
 
     def test_warning_unused_kwargs(self):
         model_id = "hf-internal-testing/unet-pipeline-dummy"
-        logger = logging.get_logger("diffusers.pipeline_utils")
+        logger = logging.get_logger("diffusers.pipelines")
         with tempfile.TemporaryDirectory() as tmpdirname:
             with CaptureLogger(logger) as cap_logger:
                 DiffusionPipeline.from_pretrained(