[Refactor autoencoders] feat: introduce autoencoders module

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

@@ -49,12 +49,12 @@ make_image_grid([original_image, mask_image, image], rows=1, cols=3)
 
 ## AsymmetricAutoencoderKL
 
-[[autodoc]] models.autoencoder_asym_kl.AsymmetricAutoencoderKL
+[[autodoc]] models.autoencoders.autoencoder_asym_kl.AsymmetricAutoencoderKL
 
 ## AutoencoderKLOutput
 
-[[autodoc]] models.autoencoder_kl.AutoencoderKLOutput
+[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
 
 ## DecoderOutput
 
-[[autodoc]] models.vae.DecoderOutput
+[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -54,4 +54,4 @@ image
 
 ## AutoencoderTinyOutput
 
-[[autodoc]] models.autoencoder_tiny.AutoencoderTinyOutput
+[[autodoc]] models.autoencoders.autoencoder_tiny.AutoencoderTinyOutput

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

@@ -36,11 +36,11 @@ model = AutoencoderKL.from_single_file(url)
 
 ## AutoencoderKLOutput
 
-[[autodoc]] models.autoencoder_kl.AutoencoderKLOutput
+[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
 
 ## DecoderOutput
 
-[[autodoc]] models.vae.DecoderOutput
+[[autodoc]] models.autoencoders.vae.DecoderOutput
 
 ## FlaxAutoencoderKL
 

scripts/convert_consistency_decoder.py

@@ -12,9 +12,9 @@
 from tqdm import tqdm
 
 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.vae import Encoder
 
 
 args = ArgumentParser()

src/diffusers/models/__init__.py

@@ -26,11 +26,11 @@
 
 if is_torch_available():
     _import_structure["adapter"] = ["MultiAdapter", "T2IAdapter"]
-    _import_structure["autoencoder_asym_kl"] = ["AsymmetricAutoencoderKL"]
-    _import_structure["autoencoder_kl"] = ["AutoencoderKL"]
-    _import_structure["autoencoder_kl_temporal_decoder"] = ["AutoencoderKLTemporalDecoder"]
-    _import_structure["autoencoder_tiny"] = ["AutoencoderTiny"]
-    _import_structure["consistency_decoder_vae"] = ["ConsistencyDecoderVAE"]
+    _import_structure["autoencoders.autoencoder_asym_kl"] = ["AsymmetricAutoencoderKL"]
+    _import_structure["autoencoders.autoencoder_kl"] = ["AutoencoderKL"]
+    _import_structure["autoencoders.autoencoder_kl_temporal_decoder"] = ["AutoencoderKLTemporalDecoder"]
+    _import_structure["autoencoders.autoencoder_tiny"] = ["AutoencoderTiny"]
+    _import_structure["autoencoders.consistency_decoder_vae"] = ["ConsistencyDecoderVAE"]
     _import_structure["controlnet"] = ["ControlNetModel"]
     _import_structure["controlnetxs"] = ["ControlNetXSModel"]
     _import_structure["dual_transformer_2d"] = ["DualTransformer2DModel"]
@@ -58,11 +58,13 @@
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     if is_torch_available():
         from .adapter import MultiAdapter, T2IAdapter
-        from .autoencoder_asym_kl import AsymmetricAutoencoderKL
-        from .autoencoder_kl import AutoencoderKL
-        from .autoencoder_kl_temporal_decoder import AutoencoderKLTemporalDecoder
-        from .autoencoder_tiny import AutoencoderTiny
-        from .consistency_decoder_vae import ConsistencyDecoderVAE
+        from .autoencoders import (
+            AsymmetricAutoencoderKL,
+            AutoencoderKL,
+            AutoencoderKLTemporalDecoder,
+            AutoencoderTiny,
+            ConsistencyDecoderVAE,
+        )
         from .controlnet import ControlNetModel
         from .controlnetxs import ControlNetXSModel
         from .dual_transformer_2d import DualTransformer2DModel

src/diffusers/models/autoencoders/__init__.py

@@ -0,0 +1,5 @@
+from .autoencoder_asym_kl import AsymmetricAutoencoderKL
+from .autoencoder_kl import AutoencoderKL
+from .autoencoder_kl_temporal_decoder import AutoencoderKLTemporalDecoder
+from .autoencoder_tiny import AutoencoderTiny
+from .consistency_decoder_vae import ConsistencyDecoderVAE

src/diffusers/models/autoencoder_asym_kl.py → src/diffusers/models/autoencoders/autoencoder_asym_kl.py

@@ -16,10 +16,10 @@
 import torch
 import torch.nn as nn
 
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils.accelerate_utils import apply_forward_hook
-from .modeling_outputs import AutoencoderKLOutput
-from .modeling_utils import ModelMixin
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...utils.accelerate_utils import apply_forward_hook
+from ..modeling_outputs import AutoencoderKLOutput
+from ..modeling_utils import ModelMixin
 from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder, MaskConditionDecoder
 
 

src/diffusers/models/autoencoder_kl.py → src/diffusers/models/autoencoders/autoencoder_kl.py

@@ -16,19 +16,19 @@
 import torch
 import torch.nn as nn
 
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..loaders import FromOriginalVAEMixin
-from ..utils.accelerate_utils import apply_forward_hook
-from .attention_processor import (
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...loaders import FromOriginalVAEMixin
+from ...utils.accelerate_utils import apply_forward_hook
+from ..attention_processor import (
     ADDED_KV_ATTENTION_PROCESSORS,
     CROSS_ATTENTION_PROCESSORS,
     Attention,
     AttentionProcessor,
     AttnAddedKVProcessor,
     AttnProcessor,
 )
-from .modeling_outputs import AutoencoderKLOutput
-from .modeling_utils import ModelMixin
+from ..modeling_outputs import AutoencoderKLOutput
+from ..modeling_utils import ModelMixin
 from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
 
 

src/diffusers/models/autoencoder_kl_temporal_decoder.py → src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py

@@ -16,14 +16,14 @@
 import torch
 import torch.nn as nn
 
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..loaders import FromOriginalVAEMixin
-from ..utils import is_torch_version
-from ..utils.accelerate_utils import apply_forward_hook
-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 ...configuration_utils import ConfigMixin, register_to_config
+from ...loaders import FromOriginalVAEMixin
+from ...utils import is_torch_version
+from ...utils.accelerate_utils import apply_forward_hook
+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 .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder
 
 

src/diffusers/models/autoencoder_tiny.py → src/diffusers/models/autoencoders/autoencoder_tiny.py

@@ -18,10 +18,10 @@
 
 import torch
 
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
-from ..utils.accelerate_utils import apply_forward_hook
-from .modeling_utils import ModelMixin
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...utils import BaseOutput
+from ...utils.accelerate_utils import apply_forward_hook
+from ..modeling_utils import ModelMixin
 from .vae import DecoderOutput, DecoderTiny, EncoderTiny
 
 

src/diffusers/models/consistency_decoder_vae.py → src/diffusers/models/autoencoders/consistency_decoder_vae.py

@@ -18,20 +18,20 @@
 import torch.nn.functional as F
 from torch import nn
 
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..schedulers import ConsistencyDecoderScheduler
-from ..utils import BaseOutput
-from ..utils.accelerate_utils import apply_forward_hook
-from ..utils.torch_utils import randn_tensor
-from .attention_processor import (
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...schedulers import ConsistencyDecoderScheduler
+from ...utils import BaseOutput
+from ...utils.accelerate_utils import apply_forward_hook
+from ...utils.torch_utils import randn_tensor
+from ..attention_processor import (
     ADDED_KV_ATTENTION_PROCESSORS,
     CROSS_ATTENTION_PROCESSORS,
     AttentionProcessor,
     AttnAddedKVProcessor,
     AttnProcessor,
 )
-from .modeling_utils import ModelMixin
-from .unet_2d import UNet2DModel
+from ..modeling_utils import ModelMixin
+from ..unet_2d import UNet2DModel
 from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder
 
 
@@ -153,7 +153,7 @@ def __init__(
         self.use_slicing = False
         self.use_tiling = False
 
-    # Copied from diffusers.models.autoencoder_kl.AutoencoderKL.enable_tiling
+    # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.enable_tiling
     def enable_tiling(self, use_tiling: bool = True):
         r"""
         Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
@@ -162,23 +162,23 @@ def enable_tiling(self, use_tiling: bool = True):
         """
         self.use_tiling = use_tiling
 
-    # Copied from diffusers.models.autoencoder_kl.AutoencoderKL.disable_tiling
+    # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.disable_tiling
     def disable_tiling(self):
         r"""
         Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
         decoding in one step.
         """
         self.enable_tiling(False)
 
-    # Copied from diffusers.models.autoencoder_kl.AutoencoderKL.enable_slicing
+    # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.enable_slicing
     def enable_slicing(self):
         r"""
         Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
         compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
         """
         self.use_slicing = True
 
-    # Copied from diffusers.models.autoencoder_kl.AutoencoderKL.disable_slicing
+    # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.disable_slicing
     def disable_slicing(self):
         r"""
         Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
@@ -333,14 +333,14 @@ def decode(
 
         return DecoderOutput(sample=x_0)
 
-    # Copied from diffusers.models.autoencoder_kl.AutoencoderKL.blend_v
+    # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.blend_v
     def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
         blend_extent = min(a.shape[2], b.shape[2], blend_extent)
         for y in range(blend_extent):
             b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
         return b
 
-    # Copied from diffusers.models.autoencoder_kl.AutoencoderKL.blend_h
+    # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.blend_h
     def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
         blend_extent = min(a.shape[3], b.shape[3], blend_extent)
         for x in range(blend_extent):

src/diffusers/models/vae.py → src/diffusers/models/autoencoders/vae.py

@@ -18,11 +18,11 @@
 import torch
 import torch.nn as nn
 
-from ..utils import BaseOutput, is_torch_version
-from ..utils.torch_utils import randn_tensor
-from .activations import get_activation
-from .attention_processor import SpatialNorm
-from .unet_2d_blocks import (
+from ...utils import BaseOutput, is_torch_version
+from ...utils.torch_utils import randn_tensor
+from ..activations import get_activation
+from ..attention_processor import SpatialNorm
+from ..unet_2d_blocks import (
     AutoencoderTinyBlock,
     UNetMidBlock2D,
     get_down_block,

src/diffusers/models/controlnetxs.py

@@ -26,7 +26,7 @@
 from .attention_processor import (
     AttentionProcessor,
 )
-from .autoencoder_kl import AutoencoderKL
+from .autoencoders import AutoencoderKL
 from .lora import LoRACompatibleConv
 from .modeling_utils import ModelMixin
 from .unet_2d_blocks import (

src/diffusers/models/vq_model.py

@@ -20,8 +20,8 @@
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..utils import BaseOutput
 from ..utils.accelerate_utils import apply_forward_hook
+from .autoencoders.vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
 from .modeling_utils import ModelMixin
-from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
 
 
 @dataclass

src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py

@@ -19,8 +19,8 @@
 import torch.nn as nn
 
 from ...configuration_utils import ConfigMixin, register_to_config
+from ...models.autoencoders.vae import DecoderOutput, VectorQuantizer
 from ...models.modeling_utils import ModelMixin
-from ...models.vae import DecoderOutput, VectorQuantizer
 from ...models.vq_model import VQEncoderOutput
 from ...utils.accelerate_utils import apply_forward_hook