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| # Copyright 2023 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. | |
| import flax.linen as nn | |
| import jax.numpy as jnp | |
| from ..attention_flax import FlaxTransformer2DModel | |
| from ..resnet_flax import FlaxDownsample2D, FlaxResnetBlock2D, FlaxUpsample2D | |
| class FlaxCrossAttnDownBlock2D(nn.Module): | |
| r""" | |
| Cross Attention 2D Downsizing block - original architecture from Unet transformers: | |
| https://arxiv.org/abs/2103.06104 | |
| Parameters: | |
| in_channels (:obj:`int`): | |
| Input channels | |
| out_channels (:obj:`int`): | |
| Output channels | |
| dropout (:obj:`float`, *optional*, defaults to 0.0): | |
| Dropout rate | |
| num_layers (:obj:`int`, *optional*, defaults to 1): | |
| Number of attention blocks layers | |
| num_attention_heads (:obj:`int`, *optional*, defaults to 1): | |
| Number of attention heads of each spatial transformer block | |
| add_downsample (:obj:`bool`, *optional*, defaults to `True`): | |
| Whether to add downsampling layer before each final output | |
| use_memory_efficient_attention (`bool`, *optional*, defaults to `False`): | |
| enable memory efficient attention https://arxiv.org/abs/2112.05682 | |
| split_head_dim (`bool`, *optional*, defaults to `False`): | |
| Whether to split the head dimension into a new axis for the self-attention computation. In most cases, | |
| enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL. | |
| dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): | |
| Parameters `dtype` | |
| """ | |
| in_channels: int | |
| out_channels: int | |
| dropout: float = 0.0 | |
| num_layers: int = 1 | |
| num_attention_heads: int = 1 | |
| add_downsample: bool = True | |
| use_linear_projection: bool = False | |
| only_cross_attention: bool = False | |
| use_memory_efficient_attention: bool = False | |
| split_head_dim: bool = False | |
| dtype: jnp.dtype = jnp.float32 | |
| transformer_layers_per_block: int = 1 | |
| def setup(self): | |
| resnets = [] | |
| attentions = [] | |
| for i in range(self.num_layers): | |
| in_channels = self.in_channels if i == 0 else self.out_channels | |
| res_block = FlaxResnetBlock2D( | |
| in_channels=in_channels, | |
| out_channels=self.out_channels, | |
| dropout_prob=self.dropout, | |
| dtype=self.dtype, | |
| ) | |
| resnets.append(res_block) | |
| attn_block = FlaxTransformer2DModel( | |
| in_channels=self.out_channels, | |
| n_heads=self.num_attention_heads, | |
| d_head=self.out_channels // self.num_attention_heads, | |
| depth=self.transformer_layers_per_block, | |
| use_linear_projection=self.use_linear_projection, | |
| only_cross_attention=self.only_cross_attention, | |
| use_memory_efficient_attention=self.use_memory_efficient_attention, | |
| split_head_dim=self.split_head_dim, | |
| dtype=self.dtype, | |
| ) | |
| attentions.append(attn_block) | |
| self.resnets = resnets | |
| self.attentions = attentions | |
| if self.add_downsample: | |
| self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype) | |
| def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True): | |
| output_states = () | |
| for resnet, attn in zip(self.resnets, self.attentions): | |
| hidden_states = resnet(hidden_states, temb, deterministic=deterministic) | |
| hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic) | |
| output_states += (hidden_states,) | |
| if self.add_downsample: | |
| hidden_states = self.downsamplers_0(hidden_states) | |
| output_states += (hidden_states,) | |
| return hidden_states, output_states | |
| class FlaxDownBlock2D(nn.Module): | |
| r""" | |
| Flax 2D downsizing block | |
| Parameters: | |
| in_channels (:obj:`int`): | |
| Input channels | |
| out_channels (:obj:`int`): | |
| Output channels | |
| dropout (:obj:`float`, *optional*, defaults to 0.0): | |
| Dropout rate | |
| num_layers (:obj:`int`, *optional*, defaults to 1): | |
| Number of attention blocks layers | |
| add_downsample (:obj:`bool`, *optional*, defaults to `True`): | |
| Whether to add downsampling layer before each final output | |
| dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): | |
| Parameters `dtype` | |
| """ | |
| in_channels: int | |
| out_channels: int | |
| dropout: float = 0.0 | |
| num_layers: int = 1 | |
| add_downsample: bool = True | |
| dtype: jnp.dtype = jnp.float32 | |
| def setup(self): | |
| resnets = [] | |
| for i in range(self.num_layers): | |
| in_channels = self.in_channels if i == 0 else self.out_channels | |
| res_block = FlaxResnetBlock2D( | |
| in_channels=in_channels, | |
| out_channels=self.out_channels, | |
| dropout_prob=self.dropout, | |
| dtype=self.dtype, | |
| ) | |
| resnets.append(res_block) | |
| self.resnets = resnets | |
| if self.add_downsample: | |
| self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype) | |
| def __call__(self, hidden_states, temb, deterministic=True): | |
| output_states = () | |
| for resnet in self.resnets: | |
| hidden_states = resnet(hidden_states, temb, deterministic=deterministic) | |
| output_states += (hidden_states,) | |
| if self.add_downsample: | |
| hidden_states = self.downsamplers_0(hidden_states) | |
| output_states += (hidden_states,) | |
| return hidden_states, output_states | |
| class FlaxCrossAttnUpBlock2D(nn.Module): | |
| r""" | |
| Cross Attention 2D Upsampling block - original architecture from Unet transformers: | |
| https://arxiv.org/abs/2103.06104 | |
| Parameters: | |
| in_channels (:obj:`int`): | |
| Input channels | |
| out_channels (:obj:`int`): | |
| Output channels | |
| dropout (:obj:`float`, *optional*, defaults to 0.0): | |
| Dropout rate | |
| num_layers (:obj:`int`, *optional*, defaults to 1): | |
| Number of attention blocks layers | |
| num_attention_heads (:obj:`int`, *optional*, defaults to 1): | |
| Number of attention heads of each spatial transformer block | |
| add_upsample (:obj:`bool`, *optional*, defaults to `True`): | |
| Whether to add upsampling layer before each final output | |
| use_memory_efficient_attention (`bool`, *optional*, defaults to `False`): | |
| enable memory efficient attention https://arxiv.org/abs/2112.05682 | |
| split_head_dim (`bool`, *optional*, defaults to `False`): | |
| Whether to split the head dimension into a new axis for the self-attention computation. In most cases, | |
| enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL. | |
| dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): | |
| Parameters `dtype` | |
| """ | |
| in_channels: int | |
| out_channels: int | |
| prev_output_channel: int | |
| dropout: float = 0.0 | |
| num_layers: int = 1 | |
| num_attention_heads: int = 1 | |
| add_upsample: bool = True | |
| use_linear_projection: bool = False | |
| only_cross_attention: bool = False | |
| use_memory_efficient_attention: bool = False | |
| split_head_dim: bool = False | |
| dtype: jnp.dtype = jnp.float32 | |
| transformer_layers_per_block: int = 1 | |
| def setup(self): | |
| resnets = [] | |
| attentions = [] | |
| for i in range(self.num_layers): | |
| res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels | |
| resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels | |
| res_block = FlaxResnetBlock2D( | |
| in_channels=resnet_in_channels + res_skip_channels, | |
| out_channels=self.out_channels, | |
| dropout_prob=self.dropout, | |
| dtype=self.dtype, | |
| ) | |
| resnets.append(res_block) | |
| attn_block = FlaxTransformer2DModel( | |
| in_channels=self.out_channels, | |
| n_heads=self.num_attention_heads, | |
| d_head=self.out_channels // self.num_attention_heads, | |
| depth=self.transformer_layers_per_block, | |
| use_linear_projection=self.use_linear_projection, | |
| only_cross_attention=self.only_cross_attention, | |
| use_memory_efficient_attention=self.use_memory_efficient_attention, | |
| split_head_dim=self.split_head_dim, | |
| dtype=self.dtype, | |
| ) | |
| attentions.append(attn_block) | |
| self.resnets = resnets | |
| self.attentions = attentions | |
| if self.add_upsample: | |
| self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype) | |
| def __call__(self, hidden_states, res_hidden_states_tuple, temb, encoder_hidden_states, deterministic=True): | |
| for resnet, attn in zip(self.resnets, self.attentions): | |
| # pop res hidden states | |
| res_hidden_states = res_hidden_states_tuple[-1] | |
| res_hidden_states_tuple = res_hidden_states_tuple[:-1] | |
| hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1) | |
| hidden_states = resnet(hidden_states, temb, deterministic=deterministic) | |
| hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic) | |
| if self.add_upsample: | |
| hidden_states = self.upsamplers_0(hidden_states) | |
| return hidden_states | |
| class FlaxUpBlock2D(nn.Module): | |
| r""" | |
| Flax 2D upsampling block | |
| Parameters: | |
| in_channels (:obj:`int`): | |
| Input channels | |
| out_channels (:obj:`int`): | |
| Output channels | |
| prev_output_channel (:obj:`int`): | |
| Output channels from the previous block | |
| dropout (:obj:`float`, *optional*, defaults to 0.0): | |
| Dropout rate | |
| num_layers (:obj:`int`, *optional*, defaults to 1): | |
| Number of attention blocks layers | |
| add_downsample (:obj:`bool`, *optional*, defaults to `True`): | |
| Whether to add downsampling layer before each final output | |
| dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): | |
| Parameters `dtype` | |
| """ | |
| in_channels: int | |
| out_channels: int | |
| prev_output_channel: int | |
| dropout: float = 0.0 | |
| num_layers: int = 1 | |
| add_upsample: bool = True | |
| dtype: jnp.dtype = jnp.float32 | |
| def setup(self): | |
| resnets = [] | |
| for i in range(self.num_layers): | |
| res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels | |
| resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels | |
| res_block = FlaxResnetBlock2D( | |
| in_channels=resnet_in_channels + res_skip_channels, | |
| out_channels=self.out_channels, | |
| dropout_prob=self.dropout, | |
| dtype=self.dtype, | |
| ) | |
| resnets.append(res_block) | |
| self.resnets = resnets | |
| if self.add_upsample: | |
| self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype) | |
| def __call__(self, hidden_states, res_hidden_states_tuple, temb, deterministic=True): | |
| for resnet in self.resnets: | |
| # pop res hidden states | |
| res_hidden_states = res_hidden_states_tuple[-1] | |
| res_hidden_states_tuple = res_hidden_states_tuple[:-1] | |
| hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1) | |
| hidden_states = resnet(hidden_states, temb, deterministic=deterministic) | |
| if self.add_upsample: | |
| hidden_states = self.upsamplers_0(hidden_states) | |
| return hidden_states | |
| class FlaxUNetMidBlock2DCrossAttn(nn.Module): | |
| r""" | |
| Cross Attention 2D Mid-level block - original architecture from Unet transformers: https://arxiv.org/abs/2103.06104 | |
| Parameters: | |
| in_channels (:obj:`int`): | |
| Input channels | |
| dropout (:obj:`float`, *optional*, defaults to 0.0): | |
| Dropout rate | |
| num_layers (:obj:`int`, *optional*, defaults to 1): | |
| Number of attention blocks layers | |
| num_attention_heads (:obj:`int`, *optional*, defaults to 1): | |
| Number of attention heads of each spatial transformer block | |
| use_memory_efficient_attention (`bool`, *optional*, defaults to `False`): | |
| enable memory efficient attention https://arxiv.org/abs/2112.05682 | |
| split_head_dim (`bool`, *optional*, defaults to `False`): | |
| Whether to split the head dimension into a new axis for the self-attention computation. In most cases, | |
| enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL. | |
| dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32): | |
| Parameters `dtype` | |
| """ | |
| in_channels: int | |
| dropout: float = 0.0 | |
| num_layers: int = 1 | |
| num_attention_heads: int = 1 | |
| use_linear_projection: bool = False | |
| use_memory_efficient_attention: bool = False | |
| split_head_dim: bool = False | |
| dtype: jnp.dtype = jnp.float32 | |
| transformer_layers_per_block: int = 1 | |
| def setup(self): | |
| # there is always at least one resnet | |
| resnets = [ | |
| FlaxResnetBlock2D( | |
| in_channels=self.in_channels, | |
| out_channels=self.in_channels, | |
| dropout_prob=self.dropout, | |
| dtype=self.dtype, | |
| ) | |
| ] | |
| attentions = [] | |
| for _ in range(self.num_layers): | |
| attn_block = FlaxTransformer2DModel( | |
| in_channels=self.in_channels, | |
| n_heads=self.num_attention_heads, | |
| d_head=self.in_channels // self.num_attention_heads, | |
| depth=self.transformer_layers_per_block, | |
| use_linear_projection=self.use_linear_projection, | |
| use_memory_efficient_attention=self.use_memory_efficient_attention, | |
| split_head_dim=self.split_head_dim, | |
| dtype=self.dtype, | |
| ) | |
| attentions.append(attn_block) | |
| res_block = FlaxResnetBlock2D( | |
| in_channels=self.in_channels, | |
| out_channels=self.in_channels, | |
| dropout_prob=self.dropout, | |
| dtype=self.dtype, | |
| ) | |
| resnets.append(res_block) | |
| self.resnets = resnets | |
| self.attentions = attentions | |
| def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True): | |
| hidden_states = self.resnets[0](hidden_states, temb) | |
| for attn, resnet in zip(self.attentions, self.resnets[1:]): | |
| hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic) | |
| hidden_states = resnet(hidden_states, temb, deterministic=deterministic) | |
| return hidden_states | |