[Big refactor] move unets to unets module 🦋

docs/source/en/api/models/unet-motion.md

@@ -22,4 +22,4 @@ The abstract from the paper is:
 [[autodoc]] UNetMotionModel
 
 ## UNet3DConditionOutput
-[[autodoc]] models.unet_3d_condition.UNet3DConditionOutput
+[[autodoc]] models.unets.unet_3d_condition.UNet3DConditionOutput

docs/source/en/api/models/unet.md

@@ -22,4 +22,4 @@ The abstract from the paper is:
 [[autodoc]] UNet1DModel
 
 ## UNet1DOutput
-[[autodoc]] models.unet_1d.UNet1DOutput
+[[autodoc]] models.unets.unet_1d.UNet1DOutput

docs/source/en/api/models/unet2d-cond.md

@@ -22,10 +22,10 @@ The abstract from the paper is:
 [[autodoc]] UNet2DConditionModel
 
 ## UNet2DConditionOutput
-[[autodoc]] models.unet_2d_condition.UNet2DConditionOutput
+[[autodoc]] models.unets.unet_2d_condition.UNet2DConditionOutput
 
 ## FlaxUNet2DConditionModel
-[[autodoc]] models.unet_2d_condition_flax.FlaxUNet2DConditionModel
+[[autodoc]] models.unets.unet_2d_condition_flax.FlaxUNet2DConditionModel
 
 ## FlaxUNet2DConditionOutput
-[[autodoc]] models.unet_2d_condition_flax.FlaxUNet2DConditionOutput
+[[autodoc]] models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput

docs/source/en/api/models/unet2d.md

@@ -22,4 +22,4 @@ The abstract from the paper is:
 [[autodoc]] UNet2DModel
 
 ## UNet2DOutput
-[[autodoc]] models.unet_2d.UNet2DOutput
+[[autodoc]] models.unets.unet_2d.UNet2DOutput

docs/source/en/api/models/unet3d-cond.md

@@ -22,4 +22,4 @@ The abstract from the paper is:
 [[autodoc]] UNet3DConditionModel
 
 ## UNet3DConditionOutput
-[[autodoc]] models.unet_3d_condition.UNet3DConditionOutput
+[[autodoc]] models.unets.unet_3d_condition.UNet3DConditionOutput

examples/community/pipeline_animatediff_controlnet.py

@@ -26,7 +26,7 @@
 from diffusers.loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
 from diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel, UNetMotionModel
 from diffusers.models.lora import adjust_lora_scale_text_encoder
-from diffusers.models.unet_motion_model import MotionAdapter
+from diffusers.models.unets.unet_motion_model import MotionAdapter
 from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers import (

examples/community/stable_diffusion_controlnet_reference.py

@@ -8,7 +8,7 @@
 from diffusers import StableDiffusionControlNetPipeline
 from diffusers.models import ControlNetModel
 from diffusers.models.attention import BasicTransformerBlock
-from diffusers.models.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
+from diffusers.models.unets.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
 from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.utils import logging

examples/community/stable_diffusion_reference.py

@@ -7,7 +7,7 @@
 
 from diffusers import StableDiffusionPipeline
 from diffusers.models.attention import BasicTransformerBlock
-from diffusers.models.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
+from diffusers.models.unets.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import rescale_noise_cfg
 from diffusers.utils import PIL_INTERPOLATION, logging

examples/community/stable_diffusion_xl_reference.py

@@ -8,7 +8,7 @@
 
 from diffusers import StableDiffusionXLPipeline
 from diffusers.models.attention import BasicTransformerBlock
-from diffusers.models.unet_2d_blocks import (
+from diffusers.models.unets.unet_2d_blocks import (
     CrossAttnDownBlock2D,
     CrossAttnUpBlock2D,
     DownBlock2D,

examples/research_projects/controlnetxs/controlnetxs.py

@@ -26,7 +26,7 @@
 from diffusers.models.autoencoders import AutoencoderKL
 from diffusers.models.lora import LoRACompatibleConv
 from diffusers.models.modeling_utils import ModelMixin
-from diffusers.models.unet_2d_blocks import (
+from diffusers.models.unets.unet_2d_blocks import (
     CrossAttnDownBlock2D,
     CrossAttnUpBlock2D,
     DownBlock2D,
@@ -36,7 +36,7 @@
     UpBlock2D,
     Upsample2D,
 )
-from diffusers.models.unet_2d_condition import UNet2DConditionModel
+from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 from diffusers.utils import BaseOutput, logging
 
 

scripts/convert_amused.py

@@ -10,7 +10,7 @@
 
 from diffusers import VQModel
 from diffusers.models.attention_processor import AttnProcessor
-from diffusers.models.uvit_2d import UVit2DModel
+from diffusers.models.unets.uvit_2d import UVit2DModel
 from diffusers.pipelines.amused.pipeline_amused import AmusedPipeline
 from diffusers.schedulers import AmusedScheduler
 

scripts/convert_consistency_decoder.py

@@ -14,7 +14,7 @@
 from diffusers import AutoencoderKL, ConsistencyDecoderVAE, DiffusionPipeline, StableDiffusionPipeline, UNet2DModel
 from diffusers.models.autoencoders.vae import Encoder
 from diffusers.models.embeddings import TimestepEmbedding
-from diffusers.models.unet_2d_blocks import ResnetDownsampleBlock2D, ResnetUpsampleBlock2D, UNetMidBlock2D
+from diffusers.models.unets.unet_2d_blocks import ResnetDownsampleBlock2D, ResnetUpsampleBlock2D, UNetMidBlock2D
 
 
 args = ArgumentParser()

src/diffusers/__init__.py

@@ -382,7 +382,7 @@
 else:
     _import_structure["models.controlnet_flax"] = ["FlaxControlNetModel"]
     _import_structure["models.modeling_flax_utils"] = ["FlaxModelMixin"]
-    _import_structure["models.unet_2d_condition_flax"] = ["FlaxUNet2DConditionModel"]
+    _import_structure["models.unets.unet_2d_condition_flax"] = ["FlaxUNet2DConditionModel"]
     _import_structure["models.vae_flax"] = ["FlaxAutoencoderKL"]
     _import_structure["pipelines"].extend(["FlaxDiffusionPipeline"])
     _import_structure["schedulers"].extend(
@@ -711,7 +711,7 @@
     else:
         from .models.controlnet_flax import FlaxControlNetModel
         from .models.modeling_flax_utils import FlaxModelMixin
-        from .models.unet_2d_condition_flax import FlaxUNet2DConditionModel
+        from .models.unets.unet_2d_condition_flax import FlaxUNet2DConditionModel
         from .models.vae_flax import FlaxAutoencoderKL
         from .pipelines import FlaxDiffusionPipeline
         from .schedulers import (

src/diffusers/experimental/rl/value_guided_sampling.py

@@ -16,7 +16,7 @@
 import torch
 import tqdm
 
-from ...models.unet_1d import UNet1DModel
+from ...models.unets.unet_1d import UNet1DModel
 from ...pipelines import DiffusionPipeline
 from ...utils.dummy_pt_objects import DDPMScheduler
 from ...utils.torch_utils import randn_tensor

src/diffusers/models/__init__.py

@@ -39,19 +39,19 @@
     _import_structure["t5_film_transformer"] = ["T5FilmDecoder"]
     _import_structure["transformer_2d"] = ["Transformer2DModel"]
     _import_structure["transformer_temporal"] = ["TransformerTemporalModel"]
-    _import_structure["unet_1d"] = ["UNet1DModel"]
-    _import_structure["unet_2d"] = ["UNet2DModel"]
-    _import_structure["unet_2d_condition"] = ["UNet2DConditionModel"]
-    _import_structure["unet_3d_condition"] = ["UNet3DConditionModel"]
-    _import_structure["unet_kandinsky3"] = ["Kandinsky3UNet"]
-    _import_structure["unet_motion_model"] = ["MotionAdapter", "UNetMotionModel"]
-    _import_structure["unet_spatio_temporal_condition"] = ["UNetSpatioTemporalConditionModel"]
-    _import_structure["uvit_2d"] = ["UVit2DModel"]
+    _import_structure["unets.unet_1d"] = ["UNet1DModel"]
+    _import_structure["unets.unet_2d"] = ["UNet2DModel"]
+    _import_structure["unets.unet_2d_condition"] = ["UNet2DConditionModel"]
+    _import_structure["unets.unet_3d_condition"] = ["UNet3DConditionModel"]
+    _import_structure["unets.unet_kandinsky3"] = ["Kandinsky3UNet"]
+    _import_structure["unets.unet_motion_model"] = ["MotionAdapter", "UNetMotionModel"]
+    _import_structure["unets.unet_spatio_temporal_condition"] = ["UNetSpatioTemporalConditionModel"]
+    _import_structure["unets.uvit_2d"] = ["UVit2DModel"]
     _import_structure["vq_model"] = ["VQModel"]
 
 if is_flax_available():
     _import_structure["controlnet_flax"] = ["FlaxControlNetModel"]
-    _import_structure["unet_2d_condition_flax"] = ["FlaxUNet2DConditionModel"]
+    _import_structure["unets.unet_2d_condition_flax"] = ["FlaxUNet2DConditionModel"]
     _import_structure["vae_flax"] = ["FlaxAutoencoderKL"]
 
 
@@ -73,19 +73,22 @@
         from .t5_film_transformer import T5FilmDecoder
         from .transformer_2d import Transformer2DModel
         from .transformer_temporal import TransformerTemporalModel
-        from .unet_1d import UNet1DModel
-        from .unet_2d import UNet2DModel
-        from .unet_2d_condition import UNet2DConditionModel
-        from .unet_3d_condition import UNet3DConditionModel
-        from .unet_kandinsky3 import Kandinsky3UNet
-        from .unet_motion_model import MotionAdapter, UNetMotionModel
-        from .unet_spatio_temporal_condition import UNetSpatioTemporalConditionModel
-        from .uvit_2d import UVit2DModel
+        from .unets import (
+            Kandinsky3UNet,
+            MotionAdapter,
+            UNet1DModel,
+            UNet2DConditionModel,
+            UNet2DModel,
+            UNet3DConditionModel,
+            UNetMotionModel,
+            UNetSpatioTemporalConditionModel,
+            UVit2DModel,
+        )
         from .vq_model import VQModel
 
     if is_flax_available():
         from .controlnet_flax import FlaxControlNetModel
-        from .unet_2d_condition_flax import FlaxUNet2DConditionModel
+        from .unets import FlaxUNet2DConditionModel
         from .vae_flax import FlaxAutoencoderKL
 
 else:

src/diffusers/models/autoencoders/autoencoder_kl.py

@@ -157,7 +157,7 @@ def disable_slicing(self):
         self.use_slicing = False
 
     @property
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
     def attn_processors(self) -> Dict[str, AttentionProcessor]:
         r"""
         Returns:
@@ -181,7 +181,7 @@ def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors:
 
         return processors
 
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
     def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
         r"""
         Sets the attention processor to use to compute attention.
@@ -216,7 +216,7 @@ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
         for name, module in self.named_children():
             fn_recursive_attn_processor(name, module, processor)
 
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
     def set_default_attn_processor(self):
         """
         Disables custom attention processors and sets the default attention implementation.
@@ -448,7 +448,7 @@ def forward(
 
         return DecoderOutput(sample=dec)
 
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
     def fuse_qkv_projections(self):
         """
         Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query,
@@ -472,7 +472,7 @@ def fuse_qkv_projections(self):
             if isinstance(module, Attention):
                 module.fuse_projections(fuse=True)
 
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
     def unfuse_qkv_projections(self):
         """Disables the fused QKV projection if enabled.
 

src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py

@@ -23,7 +23,7 @@
 from ..attention_processor import CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnProcessor
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from ..unet_3d_blocks import MidBlockTemporalDecoder, UpBlockTemporalDecoder
+from ..unets.unet_3d_blocks import MidBlockTemporalDecoder, UpBlockTemporalDecoder
 from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder
 
 
@@ -242,7 +242,7 @@ def _set_gradient_checkpointing(self, module, value=False):
             module.gradient_checkpointing = value
 
     @property
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
     def attn_processors(self) -> Dict[str, AttentionProcessor]:
         r"""
         Returns:
@@ -266,7 +266,7 @@ def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors:
 
         return processors
 
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
     def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
         r"""
         Sets the attention processor to use to compute attention.

src/diffusers/models/autoencoders/consistency_decoder_vae.py

@@ -31,7 +31,7 @@
     AttnProcessor,
 )
 from ..modeling_utils import ModelMixin
-from ..unet_2d import UNet2DModel
+from ..unets.unet_2d import UNet2DModel
 from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder
 
 
@@ -187,7 +187,7 @@ def disable_slicing(self):
         self.use_slicing = False
 
     @property
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
     def attn_processors(self) -> Dict[str, AttentionProcessor]:
         r"""
         Returns:
@@ -211,7 +211,7 @@ def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors:
 
         return processors
 
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
     def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
         r"""
         Sets the attention processor to use to compute attention.
@@ -246,7 +246,7 @@ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
         for name, module in self.named_children():
             fn_recursive_attn_processor(name, module, processor)
 
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
     def set_default_attn_processor(self):
         """
         Disables custom attention processors and sets the default attention implementation.

src/diffusers/models/autoencoders/vae.py

@@ -22,7 +22,7 @@
 from ...utils.torch_utils import randn_tensor
 from ..activations import get_activation
 from ..attention_processor import SpatialNorm
-from ..unet_2d_blocks import (
+from ..unets.unet_2d_blocks import (
     AutoencoderTinyBlock,
     UNetMidBlock2D,
     get_down_block,

src/diffusers/models/controlnet.py

@@ -30,8 +30,14 @@
 )
 from .embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps
 from .modeling_utils import ModelMixin
-from .unet_2d_blocks import CrossAttnDownBlock2D, DownBlock2D, UNetMidBlock2D, UNetMidBlock2DCrossAttn, get_down_block
-from .unet_2d_condition import UNet2DConditionModel
+from .unets.unet_2d_blocks import (
+    CrossAttnDownBlock2D,
+    DownBlock2D,
+    UNetMidBlock2D,
+    UNetMidBlock2DCrossAttn,
+    get_down_block,
+)
+from .unets.unet_2d_condition import UNet2DConditionModel
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -509,7 +515,7 @@ def from_unet(
         return controlnet
 
     @property
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
     def attn_processors(self) -> Dict[str, AttentionProcessor]:
         r"""
         Returns:
@@ -533,7 +539,7 @@ def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors:
 
         return processors
 
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
     def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
         r"""
         Sets the attention processor to use to compute attention.
@@ -568,7 +574,7 @@ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
         for name, module in self.named_children():
             fn_recursive_attn_processor(name, module, processor)
 
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
     def set_default_attn_processor(self):
         """
         Disables custom attention processors and sets the default attention implementation.
@@ -584,7 +590,7 @@ def set_default_attn_processor(self):
 
         self.set_attn_processor(processor)
 
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice
     def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:
         r"""
         Enable sliced attention computation.

src/diffusers/models/controlnet_flax.py

@@ -23,7 +23,7 @@
 from ..utils import BaseOutput
 from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
 from .modeling_flax_utils import FlaxModelMixin
-from .unet_2d_blocks_flax import (
+from .unets.unet_2d_blocks_flax import (
     FlaxCrossAttnDownBlock2D,
     FlaxDownBlock2D,
     FlaxUNetMidBlock2DCrossAttn,
@@ -329,14 +329,14 @@ def __call__(
             controlnet_cond (`jnp.ndarray`): (batch, channel, height, width) the conditional input tensor
             conditioning_scale (`float`, *optional*, defaults to `1.0`): the scale factor for controlnet outputs
             return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] instead of a
+                Whether or not to return a [`models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] instead of a
                 plain tuple.
             train (`bool`, *optional*, defaults to `False`):
                 Use deterministic functions and disable dropout when not training.
 
         Returns:
-            [`~models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] or `tuple`:
-                [`~models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] if `return_dict` is True, otherwise a
+            [`~models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] or `tuple`:
+                [`~models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] if `return_dict` is True, otherwise a
                 `tuple`. When returning a tuple, the first element is the sample tensor.
         """
         channel_order = self.controlnet_conditioning_channel_order