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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 | |
| import jax.numpy as jnp | |
| class FlaxUpsample2D(nn.Module): | |
| out_channels: int | |
| dtype: jnp.dtype = jnp.float32 | |
| def setup(self): | |
| self.conv = nn.Conv( | |
| self.out_channels, | |
| kernel_size=(3, 3), | |
| strides=(1, 1), | |
| padding=((1, 1), (1, 1)), | |
| dtype=self.dtype, | |
| ) | |
| def __call__(self, hidden_states): | |
| batch, height, width, channels = hidden_states.shape | |
| hidden_states = jax.image.resize( | |
| hidden_states, | |
| shape=(batch, height * 2, width * 2, channels), | |
| method="nearest", | |
| ) | |
| hidden_states = self.conv(hidden_states) | |
| return hidden_states | |
| class FlaxDownsample2D(nn.Module): | |
| out_channels: int | |
| dtype: jnp.dtype = jnp.float32 | |
| def setup(self): | |
| self.conv = nn.Conv( | |
| self.out_channels, | |
| kernel_size=(3, 3), | |
| strides=(2, 2), | |
| padding=((1, 1), (1, 1)), # padding="VALID", | |
| dtype=self.dtype, | |
| ) | |
| def __call__(self, hidden_states): | |
| # pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim | |
| # hidden_states = jnp.pad(hidden_states, pad_width=pad) | |
| hidden_states = self.conv(hidden_states) | |
| return hidden_states | |
| class FlaxResnetBlock2D(nn.Module): | |
| in_channels: int | |
| out_channels: int = None | |
| dropout_prob: float = 0.0 | |
| use_nin_shortcut: bool = None | |
| dtype: jnp.dtype = jnp.float32 | |
| def setup(self): | |
| out_channels = self.in_channels if self.out_channels is None else self.out_channels | |
| self.norm1 = nn.GroupNorm(num_groups=32, epsilon=1e-5) | |
| self.conv1 = nn.Conv( | |
| out_channels, | |
| kernel_size=(3, 3), | |
| strides=(1, 1), | |
| padding=((1, 1), (1, 1)), | |
| dtype=self.dtype, | |
| ) | |
| self.time_emb_proj = nn.Dense(out_channels, dtype=self.dtype) | |
| self.norm2 = nn.GroupNorm(num_groups=32, epsilon=1e-5) | |
| self.dropout = nn.Dropout(self.dropout_prob) | |
| self.conv2 = nn.Conv( | |
| out_channels, | |
| kernel_size=(3, 3), | |
| strides=(1, 1), | |
| padding=((1, 1), (1, 1)), | |
| dtype=self.dtype, | |
| ) | |
| use_nin_shortcut = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut | |
| self.conv_shortcut = None | |
| if use_nin_shortcut: | |
| self.conv_shortcut = nn.Conv( | |
| out_channels, | |
| kernel_size=(1, 1), | |
| strides=(1, 1), | |
| padding="VALID", | |
| dtype=self.dtype, | |
| ) | |
| def __call__(self, hidden_states, temb, deterministic=True): | |
| residual = hidden_states | |
| hidden_states = self.norm1(hidden_states) | |
| hidden_states = nn.swish(hidden_states) | |
| hidden_states = self.conv1(hidden_states) | |
| temb = self.time_emb_proj(nn.swish(temb)) | |
| temb = jnp.expand_dims(jnp.expand_dims(temb, 1), 1) | |
| hidden_states = hidden_states + temb | |
| hidden_states = self.norm2(hidden_states) | |
| hidden_states = nn.swish(hidden_states) | |
| hidden_states = self.dropout(hidden_states, deterministic) | |
| hidden_states = self.conv2(hidden_states) | |
| if self.conv_shortcut is not None: | |
| residual = self.conv_shortcut(residual) | |
| return hidden_states + residual | |