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# 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, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unets.unet_2d_blocks_flax import (
FlaxCrossAttnDownBlock2D,
FlaxDownBlock2D,
FlaxUNetMidBlock2DCrossAttn,
)
@flax.struct.dataclass
class FlaxControlNetOutput(BaseOutput):
"""
The output of [`FlaxControlNetModel`].
Args:
down_block_res_samples (`jnp.ndarray`):
mid_block_res_sample (`jnp.ndarray`):
"""
down_block_res_samples: jnp.ndarray
mid_block_res_sample: jnp.ndarray
class FlaxControlNetConditioningEmbedding(nn.Module):
conditioning_embedding_channels: int
block_out_channels: Tuple[int, ...] = (16, 32, 96, 256)
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.conv_in = nn.Conv(
self.block_out_channels[0],
kernel_size=(3, 3),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
blocks = []
for i in range(len(self.block_out_channels) - 1):
channel_in = self.block_out_channels[i]
channel_out = self.block_out_channels[i + 1]
conv1 = nn.Conv(
channel_in,
kernel_size=(3, 3),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
blocks.append(conv1)
conv2 = nn.Conv(
channel_out,
kernel_size=(3, 3),
strides=(2, 2),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
blocks.append(conv2)
self.blocks = blocks
self.conv_out = nn.Conv(
self.conditioning_embedding_channels,
kernel_size=(3, 3),
padding=((1, 1), (1, 1)),
kernel_init=nn.initializers.zeros_init(),
bias_init=nn.initializers.zeros_init(),
dtype=self.dtype,
)
def __call__(self, conditioning: jnp.ndarray) -> jnp.ndarray:
embedding = self.conv_in(conditioning)
embedding = nn.silu(embedding)
for block in self.blocks:
embedding = block(embedding)
embedding = nn.silu(embedding)
embedding = self.conv_out(embedding)
return embedding
@flax_register_to_config
class FlaxControlNetModel(nn.Module, FlaxModelMixin, ConfigMixin):
r"""
A ControlNet model.
This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for it’s generic methods
implemented for all models (such as downloading or saving).
This model is also a Flax Linen [`flax.linen.Module`](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
subclass. Use it as a regular Flax Linen module and refer to the Flax documentation for all matters related to its
general usage and behavior.
Inherent JAX features such as the following are supported:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
sample_size (`int`, *optional*):
The size of the input sample.
in_channels (`int`, *optional*, defaults to 4):
The number of channels in the input sample.
down_block_types (`Tuple[str]`, *optional*, defaults to `("FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxDownBlock2D")`):
The tuple of downsample blocks to use.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block.
layers_per_block (`int`, *optional*, defaults to 2):
The number of layers per block.
attention_head_dim (`int` or `Tuple[int]`, *optional*, defaults to 8):
The dimension of the attention heads.
num_attention_heads (`int` or `Tuple[int]`, *optional*):
The number of attention heads.
cross_attention_dim (`int`, *optional*, defaults to 768):
The dimension of the cross attention features.
dropout (`float`, *optional*, defaults to 0):
Dropout probability for down, up and bottleneck blocks.
flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
Whether to flip the sin to cos in the time embedding.
freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
controlnet_conditioning_channel_order (`str`, *optional*, defaults to `rgb`):
The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
conditioning_embedding_out_channels (`tuple`, *optional*, defaults to `(16, 32, 96, 256)`):
The tuple of output channel for each block in the `conditioning_embedding` layer.
"""
sample_size: int = 32
in_channels: int = 4
down_block_types: Tuple[str, ...] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
only_cross_attention: Union[bool, Tuple[bool, ...]] = False
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280)
layers_per_block: int = 2
attention_head_dim: Union[int, Tuple[int, ...]] = 8
num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None
cross_attention_dim: int = 1280
dropout: float = 0.0
use_linear_projection: bool = False
dtype: jnp.dtype = jnp.float32
flip_sin_to_cos: bool = True
freq_shift: int = 0
controlnet_conditioning_channel_order: str = "rgb"
conditioning_embedding_out_channels: Tuple[int, ...] = (16, 32, 96, 256)
def init_weights(self, rng: jax.Array) -> FrozenDict:
# init input tensors
sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
sample = jnp.zeros(sample_shape, dtype=jnp.float32)
timesteps = jnp.ones((1,), dtype=jnp.int32)
encoder_hidden_states = jnp.zeros((1, 1, self.cross_attention_dim), dtype=jnp.float32)
controlnet_cond_shape = (1, 3, self.sample_size * 8, self.sample_size * 8)
controlnet_cond = jnp.zeros(controlnet_cond_shape, dtype=jnp.float32)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
return self.init(rngs, sample, timesteps, encoder_hidden_states, controlnet_cond)["params"]
def setup(self) -> None:
block_out_channels = self.block_out_channels
time_embed_dim = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
num_attention_heads = self.num_attention_heads or self.attention_head_dim
# input
self.conv_in = nn.Conv(
block_out_channels[0],
kernel_size=(3, 3),
strides=(1, 1),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
# time
self.time_proj = FlaxTimesteps(
block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift
)
self.time_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype)
self.controlnet_cond_embedding = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0],
block_out_channels=self.conditioning_embedding_out_channels,
)
only_cross_attention = self.only_cross_attention
if isinstance(only_cross_attention, bool):
only_cross_attention = (only_cross_attention,) * len(self.down_block_types)
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(self.down_block_types)
# down
down_blocks = []
controlnet_down_blocks = []
output_channel = block_out_channels[0]
controlnet_block = nn.Conv(
output_channel,
kernel_size=(1, 1),
padding="VALID",
kernel_init=nn.initializers.zeros_init(),
bias_init=nn.initializers.zeros_init(),
dtype=self.dtype,
)
controlnet_down_blocks.append(controlnet_block)
for i, down_block_type in enumerate(self.down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
if down_block_type == "CrossAttnDownBlock2D":
down_block = FlaxCrossAttnDownBlock2D(
in_channels=input_channel,
out_channels=output_channel,
dropout=self.dropout,
num_layers=self.layers_per_block,
num_attention_heads=num_attention_heads[i],
add_downsample=not is_final_block,
use_linear_projection=self.use_linear_projection,
only_cross_attention=only_cross_attention[i],
dtype=self.dtype,
)
else:
down_block = FlaxDownBlock2D(
in_channels=input_channel,
out_channels=output_channel,
dropout=self.dropout,
num_layers=self.layers_per_block,
add_downsample=not is_final_block,
dtype=self.dtype,
)
down_blocks.append(down_block)
for _ in range(self.layers_per_block):
controlnet_block = nn.Conv(
output_channel,
kernel_size=(1, 1),
padding="VALID",
kernel_init=nn.initializers.zeros_init(),
bias_init=nn.initializers.zeros_init(),
dtype=self.dtype,
)
controlnet_down_blocks.append(controlnet_block)
if not is_final_block:
controlnet_block = nn.Conv(
output_channel,
kernel_size=(1, 1),
padding="VALID",
kernel_init=nn.initializers.zeros_init(),
bias_init=nn.initializers.zeros_init(),
dtype=self.dtype,
)
controlnet_down_blocks.append(controlnet_block)
self.down_blocks = down_blocks
self.controlnet_down_blocks = controlnet_down_blocks
# mid
mid_block_channel = block_out_channels[-1]
self.mid_block = FlaxUNetMidBlock2DCrossAttn(
in_channels=mid_block_channel,
dropout=self.dropout,
num_attention_heads=num_attention_heads[-1],
use_linear_projection=self.use_linear_projection,
dtype=self.dtype,
)
self.controlnet_mid_block = nn.Conv(
mid_block_channel,
kernel_size=(1, 1),
padding="VALID",
kernel_init=nn.initializers.zeros_init(),
bias_init=nn.initializers.zeros_init(),
dtype=self.dtype,
)
def __call__(
self,
sample: jnp.ndarray,
timesteps: Union[jnp.ndarray, float, int],
encoder_hidden_states: jnp.ndarray,
controlnet_cond: jnp.ndarray,
conditioning_scale: float = 1.0,
return_dict: bool = True,
train: bool = False,
) -> Union[FlaxControlNetOutput, Tuple[Tuple[jnp.ndarray, ...], jnp.ndarray]]:
r"""
Args:
sample (`jnp.ndarray`): (batch, channel, height, width) noisy inputs tensor
timestep (`jnp.ndarray` or `float` or `int`): timesteps
encoder_hidden_states (`jnp.ndarray`): (batch_size, sequence_length, hidden_size) encoder hidden states
controlnet_cond (`jnp.ndarray`): (batch, channel, height, width) the conditional input tensor
conditioning_scale (`float`, *optional*, defaults to `1.0`): the scale factor for controlnet outputs
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] instead of
a plain tuple.
train (`bool`, *optional*, defaults to `False`):
Use deterministic functions and disable dropout when not training.
Returns:
[`~models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] or `tuple`:
[`~models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] if `return_dict` is True, otherwise
a `tuple`. When returning a tuple, the first element is the sample tensor.
"""
channel_order = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
controlnet_cond = jnp.flip(controlnet_cond, axis=1)
# 1. time
if not isinstance(timesteps, jnp.ndarray):
timesteps = jnp.array([timesteps], dtype=jnp.int32)
elif isinstance(timesteps, jnp.ndarray) and len(timesteps.shape) == 0:
timesteps = timesteps.astype(dtype=jnp.float32)
timesteps = jnp.expand_dims(timesteps, 0)
t_emb = self.time_proj(timesteps)
t_emb = self.time_embedding(t_emb)
# 2. pre-process
sample = jnp.transpose(sample, (0, 2, 3, 1))
sample = self.conv_in(sample)
controlnet_cond = jnp.transpose(controlnet_cond, (0, 2, 3, 1))
controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
sample += controlnet_cond
# 3. down
down_block_res_samples = (sample,)
for down_block in self.down_blocks:
if isinstance(down_block, FlaxCrossAttnDownBlock2D):
sample, res_samples = down_block(sample, t_emb, encoder_hidden_states, deterministic=not train)
else:
sample, res_samples = down_block(sample, t_emb, deterministic=not train)
down_block_res_samples += res_samples
# 4. mid
sample = self.mid_block(sample, t_emb, encoder_hidden_states, deterministic=not train)
# 5. contronet blocks
controlnet_down_block_res_samples = ()
for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
down_block_res_sample = controlnet_block(down_block_res_sample)
controlnet_down_block_res_samples += (down_block_res_sample,)
down_block_res_samples = controlnet_down_block_res_samples
mid_block_res_sample = self.controlnet_mid_block(sample)
# 6. scaling
down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
)
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