124 lines
3.9 KiB
Python
124 lines
3.9 KiB
Python
# Copyright 2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import flax.linen as nn
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import jax
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import jax.numpy as jnp
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class FlaxUpsample2D(nn.Module):
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out_channels: int
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dtype: jnp.dtype = jnp.float32
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def setup(self):
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self.conv = nn.Conv(
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self.out_channels,
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kernel_size=(3, 3),
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strides=(1, 1),
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padding=((1, 1), (1, 1)),
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dtype=self.dtype,
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)
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def __call__(self, hidden_states):
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batch, height, width, channels = hidden_states.shape
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hidden_states = jax.image.resize(
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hidden_states,
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shape=(batch, height * 2, width * 2, channels),
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method="nearest",
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)
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hidden_states = self.conv(hidden_states)
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return hidden_states
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class FlaxDownsample2D(nn.Module):
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out_channels: int
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dtype: jnp.dtype = jnp.float32
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def setup(self):
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self.conv = nn.Conv(
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self.out_channels,
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kernel_size=(3, 3),
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strides=(2, 2),
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padding=((1, 1), (1, 1)), # padding="VALID",
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dtype=self.dtype,
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)
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def __call__(self, hidden_states):
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# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
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# hidden_states = jnp.pad(hidden_states, pad_width=pad)
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hidden_states = self.conv(hidden_states)
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return hidden_states
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class FlaxResnetBlock2D(nn.Module):
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in_channels: int
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out_channels: int = None
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dropout_prob: float = 0.0
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use_nin_shortcut: bool = None
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dtype: jnp.dtype = jnp.float32
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def setup(self):
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out_channels = self.in_channels if self.out_channels is None else self.out_channels
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self.norm1 = nn.GroupNorm(num_groups=32, epsilon=1e-5)
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self.conv1 = nn.Conv(
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out_channels,
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kernel_size=(3, 3),
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strides=(1, 1),
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padding=((1, 1), (1, 1)),
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dtype=self.dtype,
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)
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self.time_emb_proj = nn.Dense(out_channels, dtype=self.dtype)
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self.norm2 = nn.GroupNorm(num_groups=32, epsilon=1e-5)
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self.dropout = nn.Dropout(self.dropout_prob)
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self.conv2 = nn.Conv(
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out_channels,
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kernel_size=(3, 3),
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strides=(1, 1),
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padding=((1, 1), (1, 1)),
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dtype=self.dtype,
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)
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use_nin_shortcut = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
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self.conv_shortcut = None
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if use_nin_shortcut:
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self.conv_shortcut = nn.Conv(
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out_channels,
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kernel_size=(1, 1),
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strides=(1, 1),
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padding="VALID",
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dtype=self.dtype,
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)
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def __call__(self, hidden_states, temb, deterministic=True):
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residual = hidden_states
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hidden_states = self.norm1(hidden_states)
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hidden_states = nn.swish(hidden_states)
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hidden_states = self.conv1(hidden_states)
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temb = self.time_emb_proj(nn.swish(temb))
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temb = jnp.expand_dims(jnp.expand_dims(temb, 1), 1)
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hidden_states = hidden_states + temb
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hidden_states = self.norm2(hidden_states)
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hidden_states = nn.swish(hidden_states)
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hidden_states = self.dropout(hidden_states, deterministic)
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hidden_states = self.conv2(hidden_states)
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if self.conv_shortcut is not None:
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residual = self.conv_shortcut(residual)
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return hidden_states + residual
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