[Flax] Make room for more frameworks

setup.py

@@ -68,6 +68,7 @@
 """
 
 import re
+import os
 from distutils.core import Command
 
 from setuptools import find_packages, setup
@@ -82,10 +83,13 @@
     "datasets",
     "filelock",
     "flake8>=3.8.3",
+    "flax>=0.4.1",
     "hf-doc-builder>=0.3.0",
     "huggingface-hub>=0.8.1",
     "importlib_metadata",
     "isort>=5.5.4",
+    "jax>=0.2.8,!=0.3.2,<=0.3.6",
+    "jaxlib>=0.1.65,<=0.3.6",
     "modelcards==0.1.4",
     "numpy",
     "pytest",
@@ -171,7 +175,14 @@ def run(self):
 extras["docs"] = ["hf-doc-builder"]
 extras["training"] = ["accelerate", "datasets", "tensorboard", "modelcards"]
 extras["test"] = ["datasets", "onnxruntime", "pytest", "pytest-timeout", "pytest-xdist", "scipy", "transformers"]
-extras["dev"] = extras["quality"] + extras["test"] + extras["training"] + extras["docs"]
+extras["torch"] = deps_list("torch")
+
+if os.name == "nt":  # windows
+    extras["flax"] = []  # jax is not supported on windows
+else:
+    extras["flax"] = deps_list("jax", "jaxlib", "flax")
+
+extras["dev"] = extras["quality"] + extras["test"] + extras["training"] + extras["docs"] + extras["torch"] + extras["flax"]
 
 install_requires = [
     deps["importlib_metadata"],
@@ -180,13 +191,12 @@ def run(self):
     deps["numpy"],
     deps["regex"],
     deps["requests"],
-    deps["torch"],
     deps["Pillow"],
 ]
 
 setup(
     name="diffusers",
-    version="0.4.0.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="0.4.0.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",
@@ -198,7 +208,7 @@ def run(self):
     package_dir={"": "src"},
     packages=find_packages("src"),
     include_package_data=True,
-    python_requires=">=3.6.0",
+    python_requires=">=3.7.0",
     install_requires=install_requires,
     extras_require=extras,
     entry_points={"console_scripts": ["diffusers-cli=diffusers.commands.diffusers_cli:main"]},

src/diffusers/__init__.py

@@ -2,6 +2,7 @@
     is_inflect_available,
     is_onnx_available,
     is_scipy_available,
+    is_torch_available,
     is_transformers_available,
     is_unidecode_available,
 )
@@ -10,51 +11,52 @@
 __version__ = "0.4.0.dev0"
 
 from .configuration_utils import ConfigMixin
-from .modeling_utils import ModelMixin
-from .models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel
 from .onnx_utils import OnnxRuntimeModel
-from .optimization import (
-    get_constant_schedule,
-    get_constant_schedule_with_warmup,
-    get_cosine_schedule_with_warmup,
-    get_cosine_with_hard_restarts_schedule_with_warmup,
-    get_linear_schedule_with_warmup,
-    get_polynomial_decay_schedule_with_warmup,
-    get_scheduler,
-)
-from .pipeline_utils import DiffusionPipeline
-from .pipelines import DDIMPipeline, DDPMPipeline, KarrasVePipeline, LDMPipeline, PNDMPipeline, ScoreSdeVePipeline
-from .schedulers import (
-    DDIMScheduler,
-    DDPMScheduler,
-    KarrasVeScheduler,
-    PNDMScheduler,
-    SchedulerMixin,
-    ScoreSdeVeScheduler,
-)
 from .utils import logging
 
 
-if is_scipy_available():
-    from .schedulers import LMSDiscreteScheduler
+if is_torch_available():
+    from .modeling_utils import ModelMixin
+    from .models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel
+    from .optimization import (
+        get_constant_schedule,
+        get_constant_schedule_with_warmup,
+        get_cosine_schedule_with_warmup,
+        get_cosine_with_hard_restarts_schedule_with_warmup,
+        get_linear_schedule_with_warmup,
+        get_polynomial_decay_schedule_with_warmup,
+        get_scheduler,
+    )
+    from .pipeline_utils import DiffusionPipeline
+    from .pipelines import DDIMPipeline, DDPMPipeline, KarrasVePipeline, LDMPipeline, PNDMPipeline, ScoreSdeVePipeline
+    from .schedulers import (
+        DDIMScheduler,
+        DDPMScheduler,
+        KarrasVeScheduler,
+        PNDMScheduler,
+        SchedulerMixin,
+        ScoreSdeVeScheduler,
+    )
+    from .training_utils import EMAModel
 else:
-    from .utils.dummy_scipy_objects import *  # noqa F403
-
-from .training_utils import EMAModel
+    from .utils.dummy_pt_objects import *  # noqa F403
 
+if is_torch_available() and is_scipy_available():
+    from .schedulers import LMSDiscreteScheduler
+else:
+    from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
 
-if is_transformers_available():
+if is_torch_available() and is_transformers_available():
     from .pipelines import (
         LDMTextToImagePipeline,
         StableDiffusionImg2ImgPipeline,
         StableDiffusionInpaintPipeline,
         StableDiffusionPipeline,
     )
 else:
-    from .utils.dummy_transformers_objects import *  # noqa F403
-
+    from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
 
-if is_transformers_available() and is_onnx_available():
+if is_torch_available() and is_transformers_available() and is_onnx_available():
     from .pipelines import StableDiffusionOnnxPipeline
 else:
-    from .utils.dummy_transformers_and_onnx_objects import *  # noqa F403
+    from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403

src/diffusers/dependency_versions_table.py

@@ -8,10 +8,13 @@
     "datasets": "datasets",
     "filelock": "filelock",
     "flake8": "flake8>=3.8.3",
+    "flax": "flax>=0.4.1",
     "hf-doc-builder": "hf-doc-builder>=0.3.0",
     "huggingface-hub": "huggingface-hub>=0.8.1",
     "importlib_metadata": "importlib_metadata",
     "isort": "isort>=5.5.4",
+    "jax": "jax>=0.2.8,!=0.3.2,<=0.3.6",
+    "jaxlib": "jaxlib>=0.1.65,<=0.3.6",
     "modelcards": "modelcards==0.1.4",
     "numpy": "numpy",
     "pytest": "pytest",

src/diffusers/utils/dummy_pt_objects.py

@@ -0,0 +1,165 @@
+# This file is autogenerated by the command `make fix-copies`, do not edit.
+# flake8: noqa
+
+from ..utils import DummyObject, requires_backends
+
+
+class ModelMixin(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class AutoencoderKL(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class UNet2DConditionModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class UNet2DModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class VQModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+def get_constant_schedule(*args, **kwargs):
+    requires_backends(get_constant_schedule, ["torch"])
+
+
+def get_constant_schedule_with_warmup(*args, **kwargs):
+    requires_backends(get_constant_schedule_with_warmup, ["torch"])
+
+
+def get_cosine_schedule_with_warmup(*args, **kwargs):
+    requires_backends(get_cosine_schedule_with_warmup, ["torch"])
+
+
+def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs):
+    requires_backends(get_cosine_with_hard_restarts_schedule_with_warmup, ["torch"])
+
+
+def get_linear_schedule_with_warmup(*args, **kwargs):
+    requires_backends(get_linear_schedule_with_warmup, ["torch"])
+
+
+def get_polynomial_decay_schedule_with_warmup(*args, **kwargs):
+    requires_backends(get_polynomial_decay_schedule_with_warmup, ["torch"])
+
+
+def get_scheduler(*args, **kwargs):
+    requires_backends(get_scheduler, ["torch"])
+
+
+class DiffusionPipeline(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class DDIMPipeline(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class DDPMPipeline(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class KarrasVePipeline(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class LDMPipeline(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class PNDMPipeline(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class ScoreSdeVePipeline(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class DDIMScheduler(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class DDPMScheduler(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class KarrasVeScheduler(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class PNDMScheduler(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class SchedulerMixin(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class ScoreSdeVeScheduler(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class EMAModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])

src/diffusers/utils/dummy_scipy_objects.py → src/diffusers/utils/dummy_torch_and_scipy_objects.py

@@ -5,7 +5,7 @@
 
 
 class LMSDiscreteScheduler(metaclass=DummyObject):
-    _backends = ["scipy"]
+    _backends = ["torch", "scipy"]
 
     def __init__(self, *args, **kwargs):
-        requires_backends(self, ["scipy"])
+        requires_backends(self, ["torch", "scipy"])

src/diffusers/utils/dummy_transformers_and_onnx_objects.py → src/diffusers/utils/dummy_torch_and_transformers_and_onnx_objects.py

@@ -5,7 +5,7 @@
 
 
 class StableDiffusionOnnxPipeline(metaclass=DummyObject):
-    _backends = ["transformers", "onnx"]
+    _backends = ["torch", "transformers", "onnx"]
 
     def __init__(self, *args, **kwargs):
-        requires_backends(self, ["transformers", "onnx"])
+        requires_backends(self, ["torch", "transformers", "onnx"])

src/diffusers/utils/dummy_transformers_objects.py → src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -5,28 +5,28 @@
 
 
 class LDMTextToImagePipeline(metaclass=DummyObject):
-    _backends = ["transformers"]
+    _backends = ["torch", "transformers"]
 
     def __init__(self, *args, **kwargs):
-        requires_backends(self, ["transformers"])
+        requires_backends(self, ["torch", "transformers"])
 
 
 class StableDiffusionImg2ImgPipeline(metaclass=DummyObject):
-    _backends = ["transformers"]
+    _backends = ["torch", "transformers"]
 
     def __init__(self, *args, **kwargs):
-        requires_backends(self, ["transformers"])
+        requires_backends(self, ["torch", "transformers"])
 
 
 class StableDiffusionInpaintPipeline(metaclass=DummyObject):
-    _backends = ["transformers"]
+    _backends = ["torch", "transformers"]
 
     def __init__(self, *args, **kwargs):
-        requires_backends(self, ["transformers"])
+        requires_backends(self, ["torch", "transformers"])
 
 
 class StableDiffusionPipeline(metaclass=DummyObject):
-    _backends = ["transformers"]
+    _backends = ["torch", "transformers"]
 
     def __init__(self, *args, **kwargs):
-        requires_backends(self, ["transformers"])
+        requires_backends(self, ["torch", "transformers"])