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[Post release 0.28.0] remove deprecated blocks. (#8291)

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* remove deprecated blocks.

* update the location paths.
This commit is contained in:
Sayak Paul
2024-05-29 06:29:43 +05:30
committed by GitHub
parent 3393c01c9d
commit 3a28e36aa1
24 changed files with 69 additions and 879 deletions
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15
src/diffusers/models/autoencoders/autoencoder_kl.py
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@@ -245,11 +245,13 @@ class AutoencoderKL(ModelMixin, ConfigMixin, FromOriginalModelMixin):
Args:
x (`torch.Tensor`): Input batch of images.
return_dict (`bool`, *optional*, defaults to `True`):
Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
Whether to return a [`~models.autoencoders.autoencoder_kl.AutoencoderKLOutput`] instead of a plain
tuple.
Returns:
The latent representations of the encoded images. If `return_dict` is True, a
[`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
[`~models.autoencoders.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is
returned.
"""
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.tiled_encode(x, return_dict=return_dict)
@@ -331,12 +333,13 @@ class AutoencoderKL(ModelMixin, ConfigMixin, FromOriginalModelMixin):
Args:
x (`torch.Tensor`): Input batch of images.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
Whether or not to return a [`~models.autoencoders.autoencoder_kl.AutoencoderKLOutput`] instead of a
plain tuple.
Returns:
[`~models.autoencoder_kl.AutoencoderKLOutput`] or `tuple`:
If return_dict is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain
`tuple` is returned.
[`~models.autoencoders.autoencoder_kl.AutoencoderKLOutput`] or `tuple`:
If return_dict is True, a [`~models.autoencoders.autoencoder_kl.AutoencoderKLOutput`] is returned,
otherwise a plain `tuple` is returned.
"""
overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)

6
src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py
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@@ -323,11 +323,13 @@ class AutoencoderKLTemporalDecoder(ModelMixin, ConfigMixin):
Args:
x (`torch.Tensor`): Input batch of images.
return_dict (`bool`, *optional*, defaults to `True`):
Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
Whether to return a [`~models.autoencoders.autoencoder_kl.AutoencoderKLOutput`] instead of a plain
tuple.
Returns:
The latent representations of the encoded images. If `return_dict` is True, a
[`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
[`~models.autoencoders.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is
returned.
"""
h = self.encoder(x)
moments = self.quant_conv(h)

18
src/diffusers/models/autoencoders/consistency_decoder_vae.py
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@@ -284,13 +284,13 @@ class ConsistencyDecoderVAE(ModelMixin, ConfigMixin):
Args:
x (`torch.Tensor`): Input batch of images.
return_dict (`bool`, *optional*, defaults to `True`):
Whether to return a [`~models.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] instead of a plain
tuple.
Whether to return a [`~models.autoencoders.consistency_decoder_vae.ConsistencyDecoderVAEOutput`]
instead of a plain tuple.
Returns:
The latent representations of the encoded images. If `return_dict` is True, a
[`~models.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] is returned, otherwise a plain `tuple`
is returned.
[`~models.autoencoders.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] is returned, otherwise a
plain `tuple` is returned.
"""
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.tiled_encode(x, return_dict=return_dict)
@@ -382,13 +382,13 @@ class ConsistencyDecoderVAE(ModelMixin, ConfigMixin):
Args:
x (`torch.Tensor`): Input batch of images.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] instead of a
plain tuple.
Whether or not to return a [`~models.autoencoders.consistency_decoder_vae.ConsistencyDecoderVAEOutput`]
instead of a plain tuple.
Returns:
[`~models.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] or `tuple`:
If return_dict is True, a [`~models.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] is returned,
otherwise a plain `tuple` is returned.
[`~models.autoencoders.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] or `tuple`:
If return_dict is True, a [`~models.autoencoders.consistency_decoder_vae.ConsistencyDecoderVAEOutput`]
is returned, otherwise a plain `tuple` is returned.
"""
overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)

20
src/diffusers/models/dual_transformer_2d.py
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@@ -1,20 +0,0 @@
# Copyright 2024 The HuggingFace 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.
from ..utils import deprecate
from .transformers.dual_transformer_2d import DualTransformer2DModel
class DualTransformer2DModel(DualTransformer2DModel):
deprecation_message = "Importing `DualTransformer2DModel` from `diffusers.models.dual_transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.dual_transformer_2d import DualTransformer2DModel`, instead."
deprecate("DualTransformer2DModel", "0.29", deprecation_message)

12
src/diffusers/models/prior_transformer.py
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@@ -1,12 +0,0 @@
from ..utils import deprecate
from .transformers.prior_transformer import PriorTransformer, PriorTransformerOutput
class PriorTransformerOutput(PriorTransformerOutput):
deprecation_message = "Importing `PriorTransformerOutput` from `diffusers.models.prior_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.prior_transformer import PriorTransformerOutput`, instead."
deprecate("PriorTransformerOutput", "0.29", deprecation_message)
class PriorTransformer(PriorTransformer):
deprecation_message = "Importing `PriorTransformer` from `diffusers.models.prior_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.prior_transformer import PriorTransformer`, instead."
deprecate("PriorTransformer", "0.29", deprecation_message)

70
src/diffusers/models/t5_film_transformer.py
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@@ -1,70 +0,0 @@
# Copyright 2024 The HuggingFace 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.
from ..utils import deprecate
from .transformers.t5_film_transformer import (
DecoderLayer,
NewGELUActivation,
T5DenseGatedActDense,
T5FilmDecoder,
T5FiLMLayer,
T5LayerCrossAttention,
T5LayerFFCond,
T5LayerNorm,
T5LayerSelfAttentionCond,
)
class T5FilmDecoder(T5FilmDecoder):
deprecation_message = "Importing `T5FilmDecoder` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import T5FilmDecoder`, instead."
deprecate("T5FilmDecoder", "0.29", deprecation_message)
class DecoderLayer(DecoderLayer):
deprecation_message = "Importing `DecoderLayer` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import DecoderLayer`, instead."
deprecate("DecoderLayer", "0.29", deprecation_message)
class T5LayerSelfAttentionCond(T5LayerSelfAttentionCond):
deprecation_message = "Importing `T5LayerSelfAttentionCond` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import T5LayerSelfAttentionCond`, instead."
deprecate("T5LayerSelfAttentionCond", "0.29", deprecation_message)
class T5LayerCrossAttention(T5LayerCrossAttention):
deprecation_message = "Importing `T5LayerCrossAttention` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import T5LayerCrossAttention`, instead."
deprecate("T5LayerCrossAttention", "0.29", deprecation_message)
class T5LayerFFCond(T5LayerFFCond):
deprecation_message = "Importing `T5LayerFFCond` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import T5LayerFFCond`, instead."
deprecate("T5LayerFFCond", "0.29", deprecation_message)
class T5DenseGatedActDense(T5DenseGatedActDense):
deprecation_message = "Importing `T5DenseGatedActDense` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import T5DenseGatedActDense`, instead."
deprecate("T5DenseGatedActDense", "0.29", deprecation_message)
class T5LayerNorm(T5LayerNorm):
deprecation_message = "Importing `T5LayerNorm` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import T5LayerNorm`, instead."
deprecate("T5LayerNorm", "0.29", deprecation_message)
class NewGELUActivation(NewGELUActivation):
deprecation_message = "Importing `T5LayerNorm` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import NewGELUActivation`, instead."
deprecate("NewGELUActivation", "0.29", deprecation_message)
class T5FiLMLayer(T5FiLMLayer):
deprecation_message = "Importing `T5FiLMLayer` from `diffusers.models.t5_film_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.t5_film_transformer import T5FiLMLayer`, instead."
deprecate("T5FiLMLayer", "0.29", deprecation_message)

25
src/diffusers/models/transformer_2d.py
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@@ -1,25 +0,0 @@
# Copyright 2024 The HuggingFace 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.
from ..utils import deprecate
from .transformers.transformer_2d import Transformer2DModel, Transformer2DModelOutput
class Transformer2DModelOutput(Transformer2DModelOutput):
deprecation_message = "Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.transformer_2d import Transformer2DModelOutput`, instead."
deprecate("Transformer2DModelOutput", "0.29", deprecation_message)
class Transformer2DModel(Transformer2DModel):
deprecation_message = "Importing `Transformer2DModel` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.transformer_2d import Transformer2DModel`, instead."
deprecate("Transformer2DModel", "0.29", deprecation_message)

34
src/diffusers/models/transformer_temporal.py
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@@ -1,34 +0,0 @@
# Copyright 2024 The HuggingFace 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.
from ..utils import deprecate
from .transformers.transformer_temporal import (
TransformerSpatioTemporalModel,
TransformerTemporalModel,
TransformerTemporalModelOutput,
)
class TransformerTemporalModelOutput(TransformerTemporalModelOutput):
deprecation_message = "Importing `TransformerTemporalModelOutput` from `diffusers.models.transformer_temporal` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.transformer_temporal import TransformerTemporalModelOutput`, instead."
deprecate("TransformerTemporalModelOutput", "0.29", deprecation_message)
class TransformerTemporalModel(TransformerTemporalModel):
deprecation_message = "Importing `TransformerTemporalModel` from `diffusers.models.transformer_temporal` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.transformer_temporal import TransformerTemporalModel`, instead."
deprecate("TransformerTemporalModel", "0.29", deprecation_message)
class TransformerSpatioTemporalModel(TransformerSpatioTemporalModel):
deprecation_message = "Importing `TransformerSpatioTemporalModel` from `diffusers.models.transformer_temporal` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.transformer_temporal import TransformerSpatioTemporalModel`, instead."
deprecate("TransformerTemporalModelOutput", "0.29", deprecation_message)

6
src/diffusers/models/transformers/dual_transformer_2d.py
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@@ -123,9 +123,9 @@ class DualTransformer2DModel(nn.Module):
tuple.
Returns:
[`~models.transformer_2d.Transformer2DModelOutput`] or `tuple`:
[`~models.transformer_2d.Transformer2DModelOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is the sample tensor.
[`~models.transformers.transformer_2d.Transformer2DModelOutput`] or `tuple`:
[`~models.transformers.transformer_2d.Transformer2DModelOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is the sample tensor.
"""
input_states = hidden_states

10
src/diffusers/models/transformers/prior_transformer.py
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@@ -266,13 +266,13 @@ class PriorTransformer(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, Pef
attention_mask (`torch.BoolTensor` of shape `(batch_size, num_embeddings)`):
Text mask for the text embeddings.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.prior_transformer.PriorTransformerOutput`] instead of a plain
tuple.
Whether or not to return a [`~models.transformers.prior_transformer.PriorTransformerOutput`] instead of
a plain tuple.
Returns:
[`~models.prior_transformer.PriorTransformerOutput`] or `tuple`:
If return_dict is True, a [`~models.prior_transformer.PriorTransformerOutput`] is returned, otherwise a
tuple is returned where the first element is the sample tensor.
[`~models.transformers.prior_transformer.PriorTransformerOutput`] or `tuple`:
If return_dict is True, a [`~models.transformers.prior_transformer.PriorTransformerOutput`] is
returned, otherwise a tuple is returned where the first element is the sample tensor.
"""
batch_size = hidden_states.shape[0]

4
src/diffusers/models/transformers/transformer_2d.py
View File

@@ -377,8 +377,8 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
tuple.
Returns:
If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
`tuple` where the first element is the sample tensor.
If `return_dict` is True, an [`~models.transformers.transformer_2d.Transformer2DModelOutput`] is returned,
otherwise a `tuple` where the first element is the sample tensor.
"""
if cross_attention_kwargs is not None:
if cross_attention_kwargs.get("scale", None) is not None:

22
src/diffusers/models/transformers/transformer_temporal.py
View File

@@ -149,13 +149,14 @@ class TransformerTemporalModel(ModelMixin, ConfigMixin):
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
tuple.
Whether or not to return a [`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`]
instead of a plain tuple.
Returns:
[`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is
returned, otherwise a `tuple` where the first element is the sample tensor.
[`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
If `return_dict` is True, an
[`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`] is returned, otherwise a
`tuple` where the first element is the sample tensor.
"""
# 1. Input
batch_frames, channel, height, width = hidden_states.shape
@@ -294,13 +295,14 @@ class TransformerSpatioTemporalModel(nn.Module):
A tensor indicating whether the input contains only images. 1 indicates that the input contains only
images, 0 indicates that the input contains video frames.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.transformer_temporal.TransformerTemporalModelOutput`] instead of a
plain tuple.
Whether or not to return a [`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`]
instead of a plain tuple.
Returns:
[`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is
returned, otherwise a `tuple` where the first element is the sample tensor.
[`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
If `return_dict` is True, an
[`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`] is returned, otherwise a
`tuple` where the first element is the sample tensor.
"""
# 1. Input
batch_frames, _, height, width = hidden_states.shape

26
src/diffusers/models/unet_1d.py
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@@ -1,26 +0,0 @@
# Copyright 2024 The HuggingFace 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.
from ..utils import deprecate
from .unets.unet_1d import UNet1DModel, UNet1DOutput
class UNet1DOutput(UNet1DOutput):
deprecation_message = "Importing `UNet1DOutput` from `diffusers.models.unet_1d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d import UNet1DOutput`, instead."
deprecate("UNet1DOutput", "0.29", deprecation_message)
class UNet1DModel(UNet1DModel):
deprecation_message = "Importing `UNet1DModel` from `diffusers.models.unet_1d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d import UNet1DModel`, instead."
deprecate("UNet1DModel", "0.29", deprecation_message)

203
src/diffusers/models/unet_1d_blocks.py
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@@ -1,203 +0,0 @@
# Copyright 2024 The HuggingFace 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.
from ..utils import deprecate
from .unets.unet_1d_blocks import (
AttnDownBlock1D,
AttnUpBlock1D,
DownBlock1D,
DownBlock1DNoSkip,
DownResnetBlock1D,
Downsample1d,
MidResTemporalBlock1D,
OutConv1DBlock,
OutValueFunctionBlock,
ResConvBlock,
SelfAttention1d,
UNetMidBlock1D,
UpBlock1D,
UpBlock1DNoSkip,
UpResnetBlock1D,
Upsample1d,
ValueFunctionMidBlock1D,
)
class DownResnetBlock1D(DownResnetBlock1D):
deprecation_message = "Importing `DownResnetBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import DownResnetBlock1D`, instead."
deprecate("DownResnetBlock1D", "0.29", deprecation_message)
class UpResnetBlock1D(UpResnetBlock1D):
deprecation_message = "Importing `UpResnetBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import UpResnetBlock1D`, instead."
deprecate("UpResnetBlock1D", "0.29", deprecation_message)
class ValueFunctionMidBlock1D(ValueFunctionMidBlock1D):
deprecation_message = "Importing `ValueFunctionMidBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import ValueFunctionMidBlock1D`, instead."
deprecate("ValueFunctionMidBlock1D", "0.29", deprecation_message)
class OutConv1DBlock(OutConv1DBlock):
deprecation_message = "Importing `OutConv1DBlock` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import OutConv1DBlock`, instead."
deprecate("OutConv1DBlock", "0.29", deprecation_message)
class OutValueFunctionBlock(OutValueFunctionBlock):
deprecation_message = "Importing `OutValueFunctionBlock` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import OutValueFunctionBlock`, instead."
deprecate("OutValueFunctionBlock", "0.29", deprecation_message)
class Downsample1d(Downsample1d):
deprecation_message = "Importing `Downsample1d` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import Downsample1d`, instead."
deprecate("Downsample1d", "0.29", deprecation_message)
class Upsample1d(Upsample1d):
deprecation_message = "Importing `Upsample1d` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import Upsample1d`, instead."
deprecate("Upsample1d", "0.29", deprecation_message)
class SelfAttention1d(SelfAttention1d):
deprecation_message = "Importing `SelfAttention1d` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import SelfAttention1d`, instead."
deprecate("SelfAttention1d", "0.29", deprecation_message)
class ResConvBlock(ResConvBlock):
deprecation_message = "Importing `ResConvBlock` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import ResConvBlock`, instead."
deprecate("ResConvBlock", "0.29", deprecation_message)
class UNetMidBlock1D(UNetMidBlock1D):
deprecation_message = "Importing `UNetMidBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import UNetMidBlock1D`, instead."
deprecate("UNetMidBlock1D", "0.29", deprecation_message)
class AttnDownBlock1D(AttnDownBlock1D):
deprecation_message = "Importing `AttnDownBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import AttnDownBlock1D`, instead."
deprecate("AttnDownBlock1D", "0.29", deprecation_message)
class DownBlock1D(DownBlock1D):
deprecation_message = "Importing `DownBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import DownBlock1D`, instead."
deprecate("DownBlock1D", "0.29", deprecation_message)
class DownBlock1DNoSkip(DownBlock1DNoSkip):
deprecation_message = "Importing `DownBlock1DNoSkip` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import DownBlock1DNoSkip`, instead."
deprecate("DownBlock1DNoSkip", "0.29", deprecation_message)
class AttnUpBlock1D(AttnUpBlock1D):
deprecation_message = "Importing `AttnUpBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import AttnUpBlock1D`, instead."
deprecate("AttnUpBlock1D", "0.29", deprecation_message)
class UpBlock1D(UpBlock1D):
deprecation_message = "Importing `UpBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import UpBlock1D`, instead."
deprecate("UpBlock1D", "0.29", deprecation_message)
class UpBlock1DNoSkip(UpBlock1DNoSkip):
deprecation_message = "Importing `UpBlock1DNoSkip` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import UpBlock1DNoSkip`, instead."
deprecate("UpBlock1DNoSkip", "0.29", deprecation_message)
class MidResTemporalBlock1D(MidResTemporalBlock1D):
deprecation_message = "Importing `MidResTemporalBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import MidResTemporalBlock1D`, instead."
deprecate("MidResTemporalBlock1D", "0.29", deprecation_message)
def get_down_block(
down_block_type: str,
num_layers: int,
in_channels: int,
out_channels: int,
temb_channels: int,
add_downsample: bool,
):
deprecation_message = "Importing `get_down_block` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import get_down_block`, instead."
deprecate("get_down_block", "0.29", deprecation_message)
from .unets.unet_1d_blocks import get_down_block
return get_down_block(
down_block_type=down_block_type,
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
)
def get_up_block(
up_block_type: str, num_layers: int, in_channels: int, out_channels: int, temb_channels: int, add_upsample: bool
):
deprecation_message = "Importing `get_up_block` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import get_up_block`, instead."
deprecate("get_up_block", "0.29", deprecation_message)
from .unets.unet_1d_blocks import get_up_block
return get_up_block(
up_block_type=up_block_type,
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_upsample=add_upsample,
)
def get_mid_block(
mid_block_type: str,
num_layers: int,
in_channels: int,
mid_channels: int,
out_channels: int,
embed_dim: int,
add_downsample: bool,
):
deprecation_message = "Importing `get_mid_block` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import get_mid_block`, instead."
deprecate("get_mid_block", "0.29", deprecation_message)
from .unets.unet_1d_blocks import get_mid_block
return get_mid_block(
mid_block_type=mid_block_type,
num_layers=num_layers,
in_channels=in_channels,
mid_channels=mid_channels,
out_channels=out_channels,
embed_dim=embed_dim,
add_downsample=add_downsample,
)
def get_out_block(
*, out_block_type: str, num_groups_out: int, embed_dim: int, out_channels: int, act_fn: str, fc_dim: int
):
deprecation_message = "Importing `get_out_block` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import get_out_block`, instead."
deprecate("get_out_block", "0.29", deprecation_message)
from .unets.unet_1d_blocks import get_out_block
return get_out_block(
out_block_type=out_block_type,
num_groups_out=num_groups_out,
embed_dim=embed_dim,
out_channels=out_channels,
act_fn=act_fn,
fc_dim=fc_dim,
)

27
src/diffusers/models/unet_2d.py
View File

@@ -1,27 +0,0 @@
# Copyright 2024 The HuggingFace 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.
from ..utils import deprecate
from .unets.unet_2d import UNet2DModel, UNet2DOutput
class UNet2DOutput(UNet2DOutput):
deprecation_message = "Importing `UNet2DOutput` from `diffusers.models.unet_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d import UNet2DOutput`, instead."
deprecate("UNet2DOutput", "0.29", deprecation_message)
class UNet2DModel(UNet2DModel):
deprecation_message = "Importing `UNet2DModel` from `diffusers.models.unet_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d import UNet2DModel`, instead."
deprecate("UNet2DModel", "0.29", deprecation_message)

375
src/diffusers/models/unet_2d_blocks.py
View File

@@ -1,375 +0,0 @@
# Copyright 2024 The HuggingFace 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.
from typing import Optional
from ..utils import deprecate
from .unets.unet_2d_blocks import (
AttnDownBlock2D,
AttnDownEncoderBlock2D,
AttnSkipDownBlock2D,
AttnSkipUpBlock2D,
AttnUpBlock2D,
AttnUpDecoderBlock2D,
AutoencoderTinyBlock,
CrossAttnDownBlock2D,
CrossAttnUpBlock2D,
DownBlock2D,
KAttentionBlock,
KCrossAttnDownBlock2D,
KCrossAttnUpBlock2D,
KDownBlock2D,
KUpBlock2D,
ResnetDownsampleBlock2D,
ResnetUpsampleBlock2D,
SimpleCrossAttnDownBlock2D,
SimpleCrossAttnUpBlock2D,
SkipDownBlock2D,
SkipUpBlock2D,
UNetMidBlock2D,
UNetMidBlock2DCrossAttn,
UNetMidBlock2DSimpleCrossAttn,
UpBlock2D,
UpDecoderBlock2D,
)
def get_down_block(
down_block_type: str,
num_layers: int,
in_channels: int,
out_channels: int,
temb_channels: int,
add_downsample: bool,
resnet_eps: float,
resnet_act_fn: str,
transformer_layers_per_block: int = 1,
num_attention_heads: Optional[int] = None,
resnet_groups: Optional[int] = None,
cross_attention_dim: Optional[int] = None,
downsample_padding: Optional[int] = None,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
only_cross_attention: bool = False,
upcast_attention: bool = False,
resnet_time_scale_shift: str = "default",
attention_type: str = "default",
resnet_skip_time_act: bool = False,
resnet_out_scale_factor: float = 1.0,
cross_attention_norm: Optional[str] = None,
attention_head_dim: Optional[int] = None,
downsample_type: Optional[str] = None,
dropout: float = 0.0,
):
deprecation_message = "Importing `get_down_block` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import get_down_block`, instead."
deprecate("get_down_block", "0.29", deprecation_message)
from .unets.unet_2d_blocks import get_down_block
return get_down_block(
down_block_type=down_block_type,
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
transformer_layers_per_block=transformer_layers_per_block,
num_attention_heads=num_attention_heads,
resnet_groups=resnet_groups,
cross_attention_dim=cross_attention_dim,
downsample_padding=downsample_padding,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
attention_type=attention_type,
resnet_skip_time_act=resnet_skip_time_act,
resnet_out_scale_factor=resnet_out_scale_factor,
cross_attention_norm=cross_attention_norm,
attention_head_dim=attention_head_dim,
downsample_type=downsample_type,
dropout=dropout,
)
def get_mid_block(
mid_block_type: str,
temb_channels: int,
in_channels: int,
resnet_eps: float,
resnet_act_fn: str,
resnet_groups: int,
output_scale_factor: float = 1.0,
transformer_layers_per_block: int = 1,
num_attention_heads: Optional[int] = None,
cross_attention_dim: Optional[int] = None,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
mid_block_only_cross_attention: bool = False,
upcast_attention: bool = False,
resnet_time_scale_shift: str = "default",
attention_type: str = "default",
resnet_skip_time_act: bool = False,
cross_attention_norm: Optional[str] = None,
attention_head_dim: Optional[int] = 1,
dropout: float = 0.0,
):
if mid_block_type == "UNetMidBlock2DCrossAttn":
return UNetMidBlock2DCrossAttn(
transformer_layers_per_block=transformer_layers_per_block,
in_channels=in_channels,
temb_channels=temb_channels,
dropout=dropout,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
output_scale_factor=output_scale_factor,
resnet_time_scale_shift=resnet_time_scale_shift,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads,
resnet_groups=resnet_groups,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
upcast_attention=upcast_attention,
attention_type=attention_type,
)
elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
return UNetMidBlock2DSimpleCrossAttn(
in_channels=in_channels,
temb_channels=temb_channels,
dropout=dropout,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
output_scale_factor=output_scale_factor,
cross_attention_dim=cross_attention_dim,
attention_head_dim=attention_head_dim,
resnet_groups=resnet_groups,
resnet_time_scale_shift=resnet_time_scale_shift,
skip_time_act=resnet_skip_time_act,
only_cross_attention=mid_block_only_cross_attention,
cross_attention_norm=cross_attention_norm,
)
elif mid_block_type == "UNetMidBlock2D":
return UNetMidBlock2D(
in_channels=in_channels,
temb_channels=temb_channels,
dropout=dropout,
num_layers=0,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
output_scale_factor=output_scale_factor,
resnet_groups=resnet_groups,
resnet_time_scale_shift=resnet_time_scale_shift,
add_attention=False,
)
elif mid_block_type is None:
return None
else:
raise ValueError(f"unknown mid_block_type : {mid_block_type}")
def get_up_block(
up_block_type: str,
num_layers: int,
in_channels: int,
out_channels: int,
prev_output_channel: int,
temb_channels: int,
add_upsample: bool,
resnet_eps: float,
resnet_act_fn: str,
resolution_idx: Optional[int] = None,
transformer_layers_per_block: int = 1,
num_attention_heads: Optional[int] = None,
resnet_groups: Optional[int] = None,
cross_attention_dim: Optional[int] = None,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
only_cross_attention: bool = False,
upcast_attention: bool = False,
resnet_time_scale_shift: str = "default",
attention_type: str = "default",
resnet_skip_time_act: bool = False,
resnet_out_scale_factor: float = 1.0,
cross_attention_norm: Optional[str] = None,
attention_head_dim: Optional[int] = None,
upsample_type: Optional[str] = None,
dropout: float = 0.0,
):
deprecation_message = "Importing `get_up_block` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import get_up_block`, instead."
deprecate("get_up_block", "0.29", deprecation_message)
from .unets.unet_2d_blocks import get_up_block
return get_up_block(
up_block_type=up_block_type,
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resolution_idx=resolution_idx,
transformer_layers_per_block=transformer_layers_per_block,
num_attention_heads=num_attention_heads,
resnet_groups=resnet_groups,
cross_attention_dim=cross_attention_dim,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
attention_type=attention_type,
resnet_skip_time_act=resnet_skip_time_act,
resnet_out_scale_factor=resnet_out_scale_factor,
cross_attention_norm=cross_attention_norm,
attention_head_dim=attention_head_dim,
upsample_type=upsample_type,
dropout=dropout,
)
class AutoencoderTinyBlock(AutoencoderTinyBlock):
deprecation_message = "Importing `AutoencoderTinyBlock` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import AutoencoderTinyBlock`, instead."
deprecate("AutoencoderTinyBlock", "0.29", deprecation_message)
class UNetMidBlock2D(UNetMidBlock2D):
deprecation_message = "Importing `UNetMidBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2D`, instead."
deprecate("UNetMidBlock2D", "0.29", deprecation_message)
class UNetMidBlock2DCrossAttn(UNetMidBlock2DCrossAttn):
deprecation_message = "Importing `UNetMidBlock2DCrossAttn` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2DCrossAttn`, instead."
deprecate("UNetMidBlock2DCrossAttn", "0.29", deprecation_message)
class UNetMidBlock2DSimpleCrossAttn(UNetMidBlock2DSimpleCrossAttn):
deprecation_message = "Importing `UNetMidBlock2DSimpleCrossAttn` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2DSimpleCrossAttn`, instead."
deprecate("UNetMidBlock2DSimpleCrossAttn", "0.29", deprecation_message)
class AttnDownBlock2D(AttnDownBlock2D):
deprecation_message = "Importing `AttnDownBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import AttnDownBlock2D`, instead."
deprecate("AttnDownBlock2D", "0.29", deprecation_message)
class CrossAttnDownBlock2D(CrossAttnDownBlock2D):
deprecation_message = "Importing `AttnDownBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import CrossAttnDownBlock2D`, instead."
deprecate("CrossAttnDownBlock2D", "0.29", deprecation_message)
class DownBlock2D(DownBlock2D):
deprecation_message = "Importing `DownBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import DownBlock2D`, instead."
deprecate("DownBlock2D", "0.29", deprecation_message)
class AttnDownEncoderBlock2D(AttnDownEncoderBlock2D):
deprecation_message = "Importing `AttnDownEncoderBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import AttnDownEncoderBlock2D`, instead."
deprecate("AttnDownEncoderBlock2D", "0.29", deprecation_message)
class AttnSkipDownBlock2D(AttnSkipDownBlock2D):
deprecation_message = "Importing `AttnSkipDownBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import AttnSkipDownBlock2D`, instead."
deprecate("AttnSkipDownBlock2D", "0.29", deprecation_message)
class SkipDownBlock2D(SkipDownBlock2D):
deprecation_message = "Importing `SkipDownBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import SkipDownBlock2D`, instead."
deprecate("SkipDownBlock2D", "0.29", deprecation_message)
class ResnetDownsampleBlock2D(ResnetDownsampleBlock2D):
deprecation_message = "Importing `ResnetDownsampleBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import ResnetDownsampleBlock2D`, instead."
deprecate("ResnetDownsampleBlock2D", "0.29", deprecation_message)
class SimpleCrossAttnDownBlock2D(SimpleCrossAttnDownBlock2D):
deprecation_message = "Importing `SimpleCrossAttnDownBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import SimpleCrossAttnDownBlock2D`, instead."
deprecate("SimpleCrossAttnDownBlock2D", "0.29", deprecation_message)
class KDownBlock2D(KDownBlock2D):
deprecation_message = "Importing `KDownBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import KDownBlock2D`, instead."
deprecate("KDownBlock2D", "0.29", deprecation_message)
class KCrossAttnDownBlock2D(KCrossAttnDownBlock2D):
deprecation_message = "Importing `KCrossAttnDownBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import KCrossAttnDownBlock2D`, instead."
deprecate("KCrossAttnDownBlock2D", "0.29", deprecation_message)
class AttnUpBlock2D(AttnUpBlock2D):
deprecation_message = "Importing `AttnUpBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import AttnUpBlock2D`, instead."
deprecate("AttnUpBlock2D", "0.29", deprecation_message)
class CrossAttnUpBlock2D(CrossAttnUpBlock2D):
deprecation_message = "Importing `CrossAttnUpBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import CrossAttnUpBlock2D`, instead."
deprecate("CrossAttnUpBlock2D", "0.29", deprecation_message)
class UpBlock2D(UpBlock2D):
deprecation_message = "Importing `UpBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import UpBlock2D`, instead."
deprecate("UpBlock2D", "0.29", deprecation_message)
class UpDecoderBlock2D(UpDecoderBlock2D):
deprecation_message = "Importing `UpDecoderBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import UpDecoderBlock2D`, instead."
deprecate("UpDecoderBlock2D", "0.29", deprecation_message)
class AttnUpDecoderBlock2D(AttnUpDecoderBlock2D):
deprecation_message = "Importing `AttnUpDecoderBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import AttnUpDecoderBlock2D`, instead."
deprecate("AttnUpDecoderBlock2D", "0.29", deprecation_message)
class AttnSkipUpBlock2D(AttnSkipUpBlock2D):
deprecation_message = "Importing `AttnSkipUpBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import AttnSkipUpBlock2D`, instead."
deprecate("AttnSkipUpBlock2D", "0.29", deprecation_message)
class SkipUpBlock2D(SkipUpBlock2D):
deprecation_message = "Importing `SkipUpBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import SkipUpBlock2D`, instead."
deprecate("SkipUpBlock2D", "0.29", deprecation_message)
class ResnetUpsampleBlock2D(ResnetUpsampleBlock2D):
deprecation_message = "Importing `ResnetUpsampleBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import ResnetUpsampleBlock2D`, instead."
deprecate("ResnetUpsampleBlock2D", "0.29", deprecation_message)
class SimpleCrossAttnUpBlock2D(SimpleCrossAttnUpBlock2D):
deprecation_message = "Importing `SimpleCrossAttnUpBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import SimpleCrossAttnUpBlock2D`, instead."
deprecate("SimpleCrossAttnUpBlock2D", "0.29", deprecation_message)
class KUpBlock2D(KUpBlock2D):
deprecation_message = "Importing `KUpBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import KUpBlock2D`, instead."
deprecate("KUpBlock2D", "0.29", deprecation_message)
class KCrossAttnUpBlock2D(KCrossAttnUpBlock2D):
deprecation_message = "Importing `KCrossAttnUpBlock2D` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import KCrossAttnUpBlock2D`, instead."
deprecate("KCrossAttnUpBlock2D", "0.29", deprecation_message)
# can potentially later be renamed to `No-feed-forward` attention
class KAttentionBlock(KAttentionBlock):
deprecation_message = "Importing `KAttentionBlock` from `diffusers.models.unet_2d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_blocks import KAttentionBlock`, instead."
deprecate("KAttentionBlock", "0.29", deprecation_message)

25
src/diffusers/models/unet_2d_condition.py
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@@ -1,25 +0,0 @@
# Copyright 2024 The HuggingFace 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.
from ..utils import deprecate
from .unets.unet_2d_condition import UNet2DConditionModel, UNet2DConditionOutput
class UNet2DConditionOutput(UNet2DConditionOutput):
deprecation_message = "Importing `UNet2DConditionOutput` from `diffusers.models.unet_2d_condition` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_condition import UNet2DConditionOutput`, instead."
deprecate("UNet2DConditionOutput", "0.29", deprecation_message)
class UNet2DConditionModel(UNet2DConditionModel):
deprecation_message = "Importing `UNet2DConditionModel` from `diffusers.models.unet_2d_condition` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel`, instead."
deprecate("UNet2DConditionModel", "0.29", deprecation_message)

6
src/diffusers/models/unets/unet_1d.py
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@@ -206,11 +206,11 @@ class UNet1DModel(ModelMixin, ConfigMixin):
The noisy input tensor with the following shape `(batch_size, num_channels, sample_size)`.
timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unet_1d.UNet1DOutput`] instead of a plain tuple.
Whether or not to return a [`~models.unets.unet_1d.UNet1DOutput`] instead of a plain tuple.
Returns:
[`~models.unet_1d.UNet1DOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unet_1d.UNet1DOutput`] is returned, otherwise a `tuple` is
[`~models.unets.unet_1d.UNet1DOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unets.unet_1d.UNet1DOutput`] is returned, otherwise a `tuple` is
returned where the first element is the sample tensor.
"""

6
src/diffusers/models/unets/unet_2d.py
View File

@@ -257,11 +257,11 @@ class UNet2DModel(ModelMixin, ConfigMixin):
class_labels (`torch.Tensor`, *optional*, defaults to `None`):
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unet_2d.UNet2DOutput`] instead of a plain tuple.
Whether or not to return a [`~models.unets.unet_2d.UNet2DOutput`] instead of a plain tuple.
Returns:
[`~models.unet_2d.UNet2DOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unet_2d.UNet2DOutput`] is returned, otherwise a `tuple` is
[`~models.unets.unet_2d.UNet2DOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unets.unet_2d.UNet2DOutput`] is returned, otherwise a `tuple` is
returned where the first element is the sample tensor.
"""
# 0. center input if necessary

8
src/diffusers/models/unets/unet_2d_condition.py
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@@ -110,13 +110,13 @@ class UNet2DConditionModel(
The dimension of the cross attention features.
transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
[`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
[`~models.unets.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unets.unet_2d_blocks.CrossAttnUpBlock2D`],
[`~models.unets.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling
blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for
[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
[`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
[`~models.unets.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unets.unet_2d_blocks.CrossAttnUpBlock2D`],
[`~models.unets.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
encoder_hid_dim (`int`, *optional*, defaults to None):
If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
dimension to `cross_attention_dim`.

8
src/diffusers/models/unets/unet_3d_condition.py
View File

@@ -598,15 +598,15 @@ class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
mid_block_additional_residual: (`torch.Tensor`, *optional*):
A tensor that if specified is added to the residual of the middle unet block.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unet_3d_condition.UNet3DConditionOutput`] instead of a plain
Whether or not to return a [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] instead of a plain
tuple.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
Returns:
[`~models.unet_3d_condition.UNet3DConditionOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unet_3d_condition.UNet3DConditionOutput`] is returned, otherwise
a `tuple` is returned where the first element is the sample tensor.
[`~models.unets.unet_3d_condition.UNet3DConditionOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] is returned,
otherwise a `tuple` is returned where the first element is the sample tensor.
"""
# By default samples have to be AT least a multiple of the overall upsampling factor.
# The overall upsampling factor is equal to 2 ** (# num of upsampling layears).

8
src/diffusers/models/unets/unet_i2vgen_xl.py
View File

@@ -542,13 +542,13 @@ class I2VGenXLUNet(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unet_3d_condition.UNet3DConditionOutput`] instead of a plain
Whether or not to return a [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] instead of a plain
tuple.
Returns:
[`~models.unet_3d_condition.UNet3DConditionOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unet_3d_condition.UNet3DConditionOutput`] is returned, otherwise
a `tuple` is returned where the first element is the sample tensor.
[`~models.unets.unet_3d_condition.UNet3DConditionOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] is returned,
otherwise a `tuple` is returned where the first element is the sample tensor.
"""
batch_size, channels, num_frames, height, width = sample.shape

8
src/diffusers/models/unets/unet_motion_model.py
View File

@@ -856,13 +856,13 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
mid_block_additional_residual: (`torch.Tensor`, *optional*):
A tensor that if specified is added to the residual of the middle unet block.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unet_3d_condition.UNet3DConditionOutput`] instead of a plain
Whether or not to return a [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] instead of a plain
tuple.
Returns:
[`~models.unet_3d_condition.UNet3DConditionOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unet_3d_condition.UNet3DConditionOutput`] is returned, otherwise
a `tuple` is returned where the first element is the sample tensor.
[`~models.unets.unet_3d_condition.UNet3DConditionOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] is returned,
otherwise a `tuple` is returned where the first element is the sample tensor.
"""
# By default samples have to be AT least a multiple of the overall upsampling factor.
# The overall upsampling factor is equal to 2 ** (# num of upsampling layears).

6
src/diffusers/models/unets/unet_spatio_temporal_condition.py
View File

@@ -57,9 +57,9 @@ class UNetSpatioTemporalConditionModel(ModelMixin, ConfigMixin, UNet2DConditionL
The dimension of the cross attention features.
transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
[`~models.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal`],
[`~models.unet_3d_blocks.CrossAttnUpBlockSpatioTemporal`],
[`~models.unet_3d_blocks.UNetMidBlockSpatioTemporal`].
[`~models.unets.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal`],
[`~models.unets.unet_3d_blocks.CrossAttnUpBlockSpatioTemporal`],
[`~models.unets.unet_3d_blocks.UNetMidBlockSpatioTemporal`].
num_attention_heads (`int`, `Tuple[int]`, defaults to `(5, 10, 10, 20)`):
The number of attention heads.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.