[v0.4.0] Temporarily remove Flax modules from the public API (#755)

Temporarily remove Flax modules from the public API

docs/source/api/models.mdx

@@ -45,21 +45,3 @@ The models are built on the base class ['ModelMixin'] that is a `torch.nn.module
 
 ## AutoencoderKL
 [[autodoc]] AutoencoderKL
-
-## FlaxModelMixin
-[[autodoc]] FlaxModelMixin
-
-## FlaxUNet2DConditionOutput
-[[autodoc]] models.unet_2d_condition_flax.FlaxUNet2DConditionOutput
-
-## FlaxUNet2DConditionModel
-[[autodoc]] FlaxUNet2DConditionModel
-
-## FlaxDecoderOutput
-[[autodoc]] models.vae_flax.FlaxDecoderOutput
-
-## FlaxAutoencoderKLOutput
-[[autodoc]] models.vae_flax.FlaxAutoencoderKLOutput
-
-## FlaxAutoencoderKL
-[[autodoc]] FlaxAutoencoderKL

docs/source/api/schedulers.mdx

@@ -36,7 +36,7 @@ This allows for rapid experimentation and cleaner abstractions in the code, wher
 To this end, the design of schedulers is such that:
 
 - Schedulers can be used interchangeably between diffusion models in inference to find the preferred trade-off between speed and generation quality.
-- Schedulers are currently by default in PyTorch, but are designed to be framework independent (partial Jax support currently exists).
+- Schedulers are currently by default in PyTorch.
 
 
 ## API

setup.py

@@ -84,13 +84,10 @@ _deps = [
     "datasets",
     "filelock",
     "flake8>=3.8.3",
-    "flax>=0.4.1",
     "hf-doc-builder>=0.3.0",
     "huggingface-hub>=0.10.0",
     "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",
     "onnxruntime",
@@ -188,15 +185,9 @@ extras["test"] = deps_list(
     "torchvision",
     "transformers"
 )
-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"]
+    extras["quality"] + extras["test"] + extras["training"] + extras["docs"]
 )
 
 install_requires = [
@@ -207,6 +198,7 @@ install_requires = [
     deps["regex"],
     deps["requests"],
     deps["Pillow"],
+    deps["torch"]
 ]
 
 setup(

src/diffusers/__init__.py

 from .utils import (
-    is_flax_available,
     is_inflect_available,
     is_onnx_available,
     is_scipy_available,
@@ -61,25 +60,3 @@ if is_torch_available() and is_transformers_available() and is_onnx_available():
     from .pipelines import StableDiffusionOnnxPipeline
 else:
     from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
-
-if is_flax_available():
-    from .modeling_flax_utils import FlaxModelMixin
-    from .models.unet_2d_condition_flax import FlaxUNet2DConditionModel
-    from .models.vae_flax import FlaxAutoencoderKL
-    from .pipeline_flax_utils import FlaxDiffusionPipeline
-    from .schedulers import (
-        FlaxDDIMScheduler,
-        FlaxDDPMScheduler,
-        FlaxKarrasVeScheduler,
-        FlaxLMSDiscreteScheduler,
-        FlaxPNDMScheduler,
-        FlaxSchedulerMixin,
-        FlaxScoreSdeVeScheduler,
-    )
-else:
-    from .utils.dummy_flax_objects import *  # noqa F403
-
-if is_flax_available() and is_transformers_available():
-    from .pipelines import FlaxStableDiffusionPipeline
-else:
-    from .utils.dummy_flax_and_transformers_objects import *  # noqa F403

src/diffusers/dependency_versions_table.py

@@ -8,13 +8,10 @@ deps = {
     "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>=0.10.0",
     "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",
     "onnxruntime": "onnxruntime",

src/diffusers/models/__init__.py

@@ -12,14 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ..utils import is_flax_available, is_torch_available
+from ..utils import is_torch_available
 
 
 if is_torch_available():
     from .unet_2d import UNet2DModel
     from .unet_2d_condition import UNet2DConditionModel
     from .vae import AutoencoderKL, VQModel
-
-if is_flax_available():
-    from .unet_2d_condition_flax import FlaxUNet2DConditionModel
-    from .vae_flax import FlaxAutoencoderKL

src/diffusers/pipelines/__init__.py

@@ -21,6 +21,3 @@ if is_torch_available() and is_transformers_available():
 
 if is_transformers_available() and is_onnx_available():
     from .stable_diffusion import StableDiffusionOnnxPipeline
-
-if is_transformers_available() and is_flax_available():
-    from .stable_diffusion import FlaxStableDiffusionPipeline

src/diffusers/pipelines/stable_diffusion/__init__.py

@@ -6,7 +6,7 @@ import numpy as np
 import PIL
 from PIL import Image
 
-from ...utils import BaseOutput, is_flax_available, is_onnx_available, is_torch_available, is_transformers_available
+from ...utils import BaseOutput, is_onnx_available, is_torch_available, is_transformers_available
 
 
 @dataclass
@@ -35,27 +35,3 @@ if is_transformers_available() and is_torch_available():
 
 if is_transformers_available() and is_onnx_available():
     from .pipeline_stable_diffusion_onnx import StableDiffusionOnnxPipeline
-
-if is_transformers_available() and is_flax_available():
-    import flax
-
-    @flax.struct.dataclass
-    class FlaxStableDiffusionPipelineOutput(BaseOutput):
-        """
-        Output class for Stable Diffusion pipelines.
-
-        Args:
-            images (`List[PIL.Image.Image]` or `np.ndarray`)
-                List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
-                num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
-            nsfw_content_detected (`List[bool]`)
-                List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work"
-                (nsfw) content.
-        """
-
-        images: Union[List[PIL.Image.Image], np.ndarray]
-        nsfw_content_detected: List[bool]
-
-    from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
-    from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
-    from .safety_checker_flax import FlaxStableDiffusionSafetyChecker

src/diffusers/schedulers/__init__.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 
-from ..utils import is_flax_available, is_scipy_available, is_torch_available
+from ..utils import is_scipy_available, is_torch_available
 
 
 if is_torch_available():
@@ -27,17 +27,6 @@ if is_torch_available():
 else:
     from ..utils.dummy_pt_objects import *  # noqa F403
 
-if is_flax_available():
-    from .scheduling_ddim_flax import FlaxDDIMScheduler
-    from .scheduling_ddpm_flax import FlaxDDPMScheduler
-    from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
-    from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
-    from .scheduling_pndm_flax import FlaxPNDMScheduler
-    from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
-    from .scheduling_utils_flax import FlaxSchedulerMixin
-else:
-    from ..utils.dummy_flax_objects import *  # noqa F403
-
 
 if is_scipy_available() and is_torch_available():
     from .scheduling_lms_discrete import LMSDiscreteScheduler