mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-27 17:22:53 +03:00
[Core] introduce controlnet module (#8768)
* move vae flax module. * controlnet module. * prepare for PR. * revert a commit * gracefully deprecate controlnet deps. * fix * fix doc path * fix-copies * fix path * style * style * conflicts * fix * fix-copies * sparsectrl. * updates * fix * updates * updates * updates * fix --------- Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
This commit is contained in:
Sayak Paul
GitHub
@@ -39,7 +39,7 @@ pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=contro
|
||||
|
||||
## ControlNetOutput
|
||||
|
||||
[[autodoc]] models.controlnet.ControlNetOutput
|
||||
[[autodoc]] models.controlnets.controlnet.ControlNetOutput
|
||||
|
||||
## FlaxControlNetModel
|
||||
|
||||
@@ -47,4 +47,4 @@ pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=contro
|
||||
|
||||
## FlaxControlNetOutput
|
||||
|
||||
[[autodoc]] models.controlnet_flax.FlaxControlNetOutput
|
||||
[[autodoc]] models.controlnets.controlnet_flax.FlaxControlNetOutput
|
||||
|
||||
@@ -38,5 +38,5 @@ pipe = StableDiffusion3ControlNetPipeline.from_pretrained("stabilityai/stable-di
|
||||
|
||||
## SD3ControlNetOutput
|
||||
|
||||
[[autodoc]] models.controlnet_sd3.SD3ControlNetOutput
|
||||
[[autodoc]] models.controlnets.controlnet_sd3.SD3ControlNetOutput
|
||||
|
||||
|
||||
@@ -229,11 +229,11 @@ class PromptDiffusionControlNetModel(ControlNetModel):
|
||||
In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
|
||||
you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
|
||||
return_dict (`bool`, defaults to `True`):
|
||||
Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
|
||||
Whether or not to return a [`~models.controlnets.controlnet.ControlNetOutput`] instead of a plain tuple.
|
||||
|
||||
Returns:
|
||||
[`~models.controlnet.ControlNetOutput`] **or** `tuple`:
|
||||
If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
|
||||
[`~models.controlnets.controlnet.ControlNetOutput`] **or** `tuple`:
|
||||
If `return_dict` is `True`, a [`~models.controlnets.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
|
||||
returned where the first element is the sample tensor.
|
||||
"""
|
||||
# check channel order
|
||||
|
||||
@@ -487,7 +487,7 @@ except OptionalDependencyNotAvailable:
|
||||
|
||||
|
||||
else:
|
||||
_import_structure["models.controlnet_flax"] = ["FlaxControlNetModel"]
|
||||
_import_structure["models.controlnets.controlnet_flax"] = ["FlaxControlNetModel"]
|
||||
_import_structure["models.modeling_flax_utils"] = ["FlaxModelMixin"]
|
||||
_import_structure["models.unets.unet_2d_condition_flax"] = ["FlaxUNet2DConditionModel"]
|
||||
_import_structure["models.vae_flax"] = ["FlaxAutoencoderKL"]
|
||||
@@ -914,7 +914,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
|
||||
except OptionalDependencyNotAvailable:
|
||||
from .utils.dummy_flax_objects import * # noqa F403
|
||||
else:
|
||||
from .models.controlnet_flax import FlaxControlNetModel
|
||||
from .models.controlnets.controlnet_flax import FlaxControlNetModel
|
||||
from .models.modeling_flax_utils import FlaxModelMixin
|
||||
from .models.unets.unet_2d_condition_flax import FlaxUNet2DConditionModel
|
||||
from .models.vae_flax import FlaxAutoencoderKL
|
||||
|
||||
@@ -36,12 +36,16 @@ if is_torch_available():
|
||||
_import_structure["autoencoders.autoencoder_tiny"] = ["AutoencoderTiny"]
|
||||
_import_structure["autoencoders.consistency_decoder_vae"] = ["ConsistencyDecoderVAE"]
|
||||
_import_structure["autoencoders.vq_model"] = ["VQModel"]
|
||||
_import_structure["controlnet"] = ["ControlNetModel"]
|
||||
_import_structure["controlnet_flux"] = ["FluxControlNetModel", "FluxMultiControlNetModel"]
|
||||
_import_structure["controlnet_hunyuan"] = ["HunyuanDiT2DControlNetModel", "HunyuanDiT2DMultiControlNetModel"]
|
||||
_import_structure["controlnet_sd3"] = ["SD3ControlNetModel", "SD3MultiControlNetModel"]
|
||||
_import_structure["controlnet_sparsectrl"] = ["SparseControlNetModel"]
|
||||
_import_structure["controlnet_xs"] = ["ControlNetXSAdapter", "UNetControlNetXSModel"]
|
||||
_import_structure["controlnets.controlnet"] = ["ControlNetModel"]
|
||||
_import_structure["controlnets.controlnet_flux"] = ["FluxControlNetModel", "FluxMultiControlNetModel"]
|
||||
_import_structure["controlnets.controlnet_hunyuan"] = [
|
||||
"HunyuanDiT2DControlNetModel",
|
||||
"HunyuanDiT2DMultiControlNetModel",
|
||||
]
|
||||
_import_structure["controlnets.controlnet_sd3"] = ["SD3ControlNetModel", "SD3MultiControlNetModel"]
|
||||
_import_structure["controlnets.controlnet_sparsectrl"] = ["SparseControlNetModel"]
|
||||
_import_structure["controlnets.controlnet_xs"] = ["ControlNetXSAdapter", "UNetControlNetXSModel"]
|
||||
_import_structure["controlnets.multicontrolnet"] = ["MultiControlNetModel"]
|
||||
_import_structure["embeddings"] = ["ImageProjection"]
|
||||
_import_structure["modeling_utils"] = ["ModelMixin"]
|
||||
_import_structure["transformers.auraflow_transformer_2d"] = ["AuraFlowTransformer2DModel"]
|
||||
@@ -74,7 +78,7 @@ if is_torch_available():
|
||||
_import_structure["unets.uvit_2d"] = ["UVit2DModel"]
|
||||
|
||||
if is_flax_available():
|
||||
_import_structure["controlnet_flax"] = ["FlaxControlNetModel"]
|
||||
_import_structure["controlnets.controlnet_flax"] = ["FlaxControlNetModel"]
|
||||
_import_structure["unets.unet_2d_condition_flax"] = ["FlaxUNet2DConditionModel"]
|
||||
_import_structure["vae_flax"] = ["FlaxAutoencoderKL"]
|
||||
|
||||
@@ -94,12 +98,19 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
|
||||
ConsistencyDecoderVAE,
|
||||
VQModel,
|
||||
)
|
||||
from .controlnet import ControlNetModel
|
||||
from .controlnet_flux import FluxControlNetModel, FluxMultiControlNetModel
|
||||
from .controlnet_hunyuan import HunyuanDiT2DControlNetModel, HunyuanDiT2DMultiControlNetModel
|
||||
from .controlnet_sd3 import SD3ControlNetModel, SD3MultiControlNetModel
|
||||
from .controlnet_sparsectrl import SparseControlNetModel
|
||||
from .controlnet_xs import ControlNetXSAdapter, UNetControlNetXSModel
|
||||
from .controlnets import (
|
||||
ControlNetModel,
|
||||
ControlNetXSAdapter,
|
||||
FluxControlNetModel,
|
||||
FluxMultiControlNetModel,
|
||||
HunyuanDiT2DControlNetModel,
|
||||
HunyuanDiT2DMultiControlNetModel,
|
||||
MultiControlNetModel,
|
||||
SD3ControlNetModel,
|
||||
SD3MultiControlNetModel,
|
||||
SparseControlNetModel,
|
||||
UNetControlNetXSModel,
|
||||
)
|
||||
from .embeddings import ImageProjection
|
||||
from .modeling_utils import ModelMixin
|
||||
from .transformers import (
|
||||
@@ -137,7 +148,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
|
||||
)
|
||||
|
||||
if is_flax_available():
|
||||
from .controlnet_flax import FlaxControlNetModel
|
||||
from .controlnets import FlaxControlNetModel
|
||||
from .unets import FlaxUNet2DConditionModel
|
||||
from .vae_flax import FlaxAutoencoderKL
|
||||
|
||||
|
||||
@@ -11,860 +11,32 @@
|
||||
# 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 dataclasses import dataclass
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from ..configuration_utils import ConfigMixin, register_to_config
|
||||
from ..loaders.single_file_model import FromOriginalModelMixin
|
||||
from ..utils import BaseOutput, logging
|
||||
from .attention_processor import (
|
||||
ADDED_KV_ATTENTION_PROCESSORS,
|
||||
CROSS_ATTENTION_PROCESSORS,
|
||||
AttentionProcessor,
|
||||
AttnAddedKVProcessor,
|
||||
AttnProcessor,
|
||||
from ..utils import deprecate
|
||||
from .controlnets.controlnet import ( # noqa
|
||||
BaseOutput,
|
||||
ControlNetConditioningEmbedding,
|
||||
ControlNetModel,
|
||||
ControlNetOutput,
|
||||
zero_module,
|
||||
)
|
||||
from .embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps
|
||||
from .modeling_utils import ModelMixin
|
||||
from .unets.unet_2d_blocks import (
|
||||
CrossAttnDownBlock2D,
|
||||
DownBlock2D,
|
||||
UNetMidBlock2D,
|
||||
UNetMidBlock2DCrossAttn,
|
||||
get_down_block,
|
||||
)
|
||||
from .unets.unet_2d_condition import UNet2DConditionModel
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
||||
class ControlNetOutput(ControlNetOutput):
|
||||
def __init__(self, *args, **kwargs):
|
||||
deprecation_message = "Importing `ControlNetOutput` from `diffusers.models.controlnet` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet import ControlNetOutput`, instead."
|
||||
deprecate("ControlNetOutput", "0.34", deprecation_message)
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ControlNetOutput(BaseOutput):
|
||||
"""
|
||||
The output of [`ControlNetModel`].
|
||||
class ControlNetModel(ControlNetModel):
|
||||
def __init__(self, *args, **kwargs):
|
||||
deprecation_message = "Importing `ControlNetModel` from `diffusers.models.controlnet` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet import ControlNetModel`, instead."
|
||||
deprecate("ControlNetModel", "0.34", deprecation_message)
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
Args:
|
||||
down_block_res_samples (`tuple[torch.Tensor]`):
|
||||
A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
|
||||
be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
|
||||
used to condition the original UNet's downsampling activations.
|
||||
mid_down_block_re_sample (`torch.Tensor`):
|
||||
The activation of the middle block (the lowest sample resolution). Each tensor should be of shape
|
||||
`(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`.
|
||||
Output can be used to condition the original UNet's middle block activation.
|
||||
"""
|
||||
|
||||
down_block_res_samples: Tuple[torch.Tensor]
|
||||
mid_block_res_sample: torch.Tensor
|
||||
|
||||
|
||||
class ControlNetConditioningEmbedding(nn.Module):
|
||||
"""
|
||||
Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN
|
||||
[11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized
|
||||
training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the
|
||||
convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides
|
||||
(activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full
|
||||
model) to encode image-space conditions ... into feature maps ..."
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
conditioning_embedding_channels: int,
|
||||
conditioning_channels: int = 3,
|
||||
block_out_channels: Tuple[int, ...] = (16, 32, 96, 256),
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
|
||||
|
||||
self.blocks = nn.ModuleList([])
|
||||
|
||||
for i in range(len(block_out_channels) - 1):
|
||||
channel_in = block_out_channels[i]
|
||||
channel_out = block_out_channels[i + 1]
|
||||
self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
|
||||
self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
|
||||
|
||||
self.conv_out = zero_module(
|
||||
nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
|
||||
)
|
||||
|
||||
def forward(self, conditioning):
|
||||
embedding = self.conv_in(conditioning)
|
||||
embedding = F.silu(embedding)
|
||||
|
||||
for block in self.blocks:
|
||||
embedding = block(embedding)
|
||||
embedding = F.silu(embedding)
|
||||
|
||||
embedding = self.conv_out(embedding)
|
||||
|
||||
return embedding
|
||||
|
||||
|
||||
class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
|
||||
"""
|
||||
A ControlNet model.
|
||||
|
||||
Args:
|
||||
in_channels (`int`, defaults to 4):
|
||||
The number of channels in the input sample.
|
||||
flip_sin_to_cos (`bool`, defaults to `True`):
|
||||
Whether to flip the sin to cos in the time embedding.
|
||||
freq_shift (`int`, defaults to 0):
|
||||
The frequency shift to apply to the time embedding.
|
||||
down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
|
||||
The tuple of downsample blocks to use.
|
||||
only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
|
||||
block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
|
||||
The tuple of output channels for each block.
|
||||
layers_per_block (`int`, defaults to 2):
|
||||
The number of layers per block.
|
||||
downsample_padding (`int`, defaults to 1):
|
||||
The padding to use for the downsampling convolution.
|
||||
mid_block_scale_factor (`float`, defaults to 1):
|
||||
The scale factor to use for the mid block.
|
||||
act_fn (`str`, defaults to "silu"):
|
||||
The activation function to use.
|
||||
norm_num_groups (`int`, *optional*, defaults to 32):
|
||||
The number of groups to use for the normalization. If None, normalization and activation layers is skipped
|
||||
in post-processing.
|
||||
norm_eps (`float`, defaults to 1e-5):
|
||||
The epsilon to use for the normalization.
|
||||
cross_attention_dim (`int`, defaults to 1280):
|
||||
The dimension of the cross attention features.
|
||||
transformer_layers_per_block (`int` or `Tuple[int]`, *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`].
|
||||
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`.
|
||||
encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
|
||||
If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
|
||||
embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
|
||||
attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8):
|
||||
The dimension of the attention heads.
|
||||
use_linear_projection (`bool`, defaults to `False`):
|
||||
class_embed_type (`str`, *optional*, defaults to `None`):
|
||||
The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None,
|
||||
`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
|
||||
addition_embed_type (`str`, *optional*, defaults to `None`):
|
||||
Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
|
||||
"text". "text" will use the `TextTimeEmbedding` layer.
|
||||
num_class_embeds (`int`, *optional*, defaults to 0):
|
||||
Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
|
||||
class conditioning with `class_embed_type` equal to `None`.
|
||||
upcast_attention (`bool`, defaults to `False`):
|
||||
resnet_time_scale_shift (`str`, defaults to `"default"`):
|
||||
Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
|
||||
projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):
|
||||
The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
|
||||
`class_embed_type="projection"`.
|
||||
controlnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
|
||||
The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
|
||||
conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`):
|
||||
The tuple of output channel for each block in the `conditioning_embedding` layer.
|
||||
global_pool_conditions (`bool`, defaults to `False`):
|
||||
TODO(Patrick) - unused parameter.
|
||||
addition_embed_type_num_heads (`int`, defaults to 64):
|
||||
The number of heads to use for the `TextTimeEmbedding` layer.
|
||||
"""
|
||||
|
||||
_supports_gradient_checkpointing = True
|
||||
|
||||
@register_to_config
|
||||
def __init__(
|
||||
self,
|
||||
in_channels: int = 4,
|
||||
conditioning_channels: int = 3,
|
||||
flip_sin_to_cos: bool = True,
|
||||
freq_shift: int = 0,
|
||||
down_block_types: Tuple[str, ...] = (
|
||||
"CrossAttnDownBlock2D",
|
||||
"CrossAttnDownBlock2D",
|
||||
"CrossAttnDownBlock2D",
|
||||
"DownBlock2D",
|
||||
),
|
||||
mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
|
||||
only_cross_attention: Union[bool, Tuple[bool]] = False,
|
||||
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
|
||||
layers_per_block: int = 2,
|
||||
downsample_padding: int = 1,
|
||||
mid_block_scale_factor: float = 1,
|
||||
act_fn: str = "silu",
|
||||
norm_num_groups: Optional[int] = 32,
|
||||
norm_eps: float = 1e-5,
|
||||
cross_attention_dim: int = 1280,
|
||||
transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
|
||||
encoder_hid_dim: Optional[int] = None,
|
||||
encoder_hid_dim_type: Optional[str] = None,
|
||||
attention_head_dim: Union[int, Tuple[int, ...]] = 8,
|
||||
num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,
|
||||
use_linear_projection: bool = False,
|
||||
class_embed_type: Optional[str] = None,
|
||||
addition_embed_type: Optional[str] = None,
|
||||
addition_time_embed_dim: Optional[int] = None,
|
||||
num_class_embeds: Optional[int] = None,
|
||||
upcast_attention: bool = False,
|
||||
resnet_time_scale_shift: str = "default",
|
||||
projection_class_embeddings_input_dim: Optional[int] = None,
|
||||
controlnet_conditioning_channel_order: str = "rgb",
|
||||
conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
|
||||
global_pool_conditions: bool = False,
|
||||
addition_embed_type_num_heads: int = 64,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
# 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 = num_attention_heads or attention_head_dim
|
||||
|
||||
# Check inputs
|
||||
if len(block_out_channels) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if isinstance(transformer_layers_per_block, int):
|
||||
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
|
||||
|
||||
# input
|
||||
conv_in_kernel = 3
|
||||
conv_in_padding = (conv_in_kernel - 1) // 2
|
||||
self.conv_in = nn.Conv2d(
|
||||
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
|
||||
)
|
||||
|
||||
# time
|
||||
time_embed_dim = block_out_channels[0] * 4
|
||||
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
|
||||
timestep_input_dim = block_out_channels[0]
|
||||
self.time_embedding = TimestepEmbedding(
|
||||
timestep_input_dim,
|
||||
time_embed_dim,
|
||||
act_fn=act_fn,
|
||||
)
|
||||
|
||||
if encoder_hid_dim_type is None and encoder_hid_dim is not None:
|
||||
encoder_hid_dim_type = "text_proj"
|
||||
self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
|
||||
logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
|
||||
|
||||
if encoder_hid_dim is None and encoder_hid_dim_type is not None:
|
||||
raise ValueError(
|
||||
f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
|
||||
)
|
||||
|
||||
if encoder_hid_dim_type == "text_proj":
|
||||
self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
|
||||
elif encoder_hid_dim_type == "text_image_proj":
|
||||
# image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
|
||||
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
|
||||
# case when `addition_embed_type == "text_image_proj"` (Kandinsky 2.1)`
|
||||
self.encoder_hid_proj = TextImageProjection(
|
||||
text_embed_dim=encoder_hid_dim,
|
||||
image_embed_dim=cross_attention_dim,
|
||||
cross_attention_dim=cross_attention_dim,
|
||||
)
|
||||
|
||||
elif encoder_hid_dim_type is not None:
|
||||
raise ValueError(
|
||||
f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
|
||||
)
|
||||
else:
|
||||
self.encoder_hid_proj = None
|
||||
|
||||
# class embedding
|
||||
if class_embed_type is None and num_class_embeds is not None:
|
||||
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
|
||||
elif class_embed_type == "timestep":
|
||||
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
|
||||
elif class_embed_type == "identity":
|
||||
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
|
||||
elif class_embed_type == "projection":
|
||||
if projection_class_embeddings_input_dim is None:
|
||||
raise ValueError(
|
||||
"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
|
||||
)
|
||||
# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
|
||||
# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
|
||||
# 2. it projects from an arbitrary input dimension.
|
||||
#
|
||||
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
|
||||
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
|
||||
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
|
||||
self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
||||
else:
|
||||
self.class_embedding = None
|
||||
|
||||
if addition_embed_type == "text":
|
||||
if encoder_hid_dim is not None:
|
||||
text_time_embedding_from_dim = encoder_hid_dim
|
||||
else:
|
||||
text_time_embedding_from_dim = cross_attention_dim
|
||||
|
||||
self.add_embedding = TextTimeEmbedding(
|
||||
text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
|
||||
)
|
||||
elif addition_embed_type == "text_image":
|
||||
# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
|
||||
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
|
||||
# case when `addition_embed_type == "text_image"` (Kandinsky 2.1)`
|
||||
self.add_embedding = TextImageTimeEmbedding(
|
||||
text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
|
||||
)
|
||||
elif addition_embed_type == "text_time":
|
||||
self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
|
||||
self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
||||
|
||||
elif addition_embed_type is not None:
|
||||
raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
|
||||
|
||||
# control net conditioning embedding
|
||||
self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
|
||||
conditioning_embedding_channels=block_out_channels[0],
|
||||
block_out_channels=conditioning_embedding_out_channels,
|
||||
conditioning_channels=conditioning_channels,
|
||||
)
|
||||
|
||||
self.down_blocks = nn.ModuleList([])
|
||||
self.controlnet_down_blocks = nn.ModuleList([])
|
||||
|
||||
if isinstance(only_cross_attention, bool):
|
||||
only_cross_attention = [only_cross_attention] * len(down_block_types)
|
||||
|
||||
if isinstance(attention_head_dim, int):
|
||||
attention_head_dim = (attention_head_dim,) * len(down_block_types)
|
||||
|
||||
if isinstance(num_attention_heads, int):
|
||||
num_attention_heads = (num_attention_heads,) * len(down_block_types)
|
||||
|
||||
# down
|
||||
output_channel = block_out_channels[0]
|
||||
|
||||
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_down_blocks.append(controlnet_block)
|
||||
|
||||
for i, down_block_type in enumerate(down_block_types):
|
||||
input_channel = output_channel
|
||||
output_channel = block_out_channels[i]
|
||||
is_final_block = i == len(block_out_channels) - 1
|
||||
|
||||
down_block = get_down_block(
|
||||
down_block_type,
|
||||
num_layers=layers_per_block,
|
||||
transformer_layers_per_block=transformer_layers_per_block[i],
|
||||
in_channels=input_channel,
|
||||
out_channels=output_channel,
|
||||
temb_channels=time_embed_dim,
|
||||
add_downsample=not is_final_block,
|
||||
resnet_eps=norm_eps,
|
||||
resnet_act_fn=act_fn,
|
||||
resnet_groups=norm_num_groups,
|
||||
cross_attention_dim=cross_attention_dim,
|
||||
num_attention_heads=num_attention_heads[i],
|
||||
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
|
||||
downsample_padding=downsample_padding,
|
||||
use_linear_projection=use_linear_projection,
|
||||
only_cross_attention=only_cross_attention[i],
|
||||
upcast_attention=upcast_attention,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
)
|
||||
self.down_blocks.append(down_block)
|
||||
|
||||
for _ in range(layers_per_block):
|
||||
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_down_blocks.append(controlnet_block)
|
||||
|
||||
if not is_final_block:
|
||||
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_down_blocks.append(controlnet_block)
|
||||
|
||||
# mid
|
||||
mid_block_channel = block_out_channels[-1]
|
||||
|
||||
controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_mid_block = controlnet_block
|
||||
|
||||
if mid_block_type == "UNetMidBlock2DCrossAttn":
|
||||
self.mid_block = UNetMidBlock2DCrossAttn(
|
||||
transformer_layers_per_block=transformer_layers_per_block[-1],
|
||||
in_channels=mid_block_channel,
|
||||
temb_channels=time_embed_dim,
|
||||
resnet_eps=norm_eps,
|
||||
resnet_act_fn=act_fn,
|
||||
output_scale_factor=mid_block_scale_factor,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
cross_attention_dim=cross_attention_dim,
|
||||
num_attention_heads=num_attention_heads[-1],
|
||||
resnet_groups=norm_num_groups,
|
||||
use_linear_projection=use_linear_projection,
|
||||
upcast_attention=upcast_attention,
|
||||
)
|
||||
elif mid_block_type == "UNetMidBlock2D":
|
||||
self.mid_block = UNetMidBlock2D(
|
||||
in_channels=block_out_channels[-1],
|
||||
temb_channels=time_embed_dim,
|
||||
num_layers=0,
|
||||
resnet_eps=norm_eps,
|
||||
resnet_act_fn=act_fn,
|
||||
output_scale_factor=mid_block_scale_factor,
|
||||
resnet_groups=norm_num_groups,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
add_attention=False,
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"unknown mid_block_type : {mid_block_type}")
|
||||
|
||||
@classmethod
|
||||
def from_unet(
|
||||
cls,
|
||||
unet: UNet2DConditionModel,
|
||||
controlnet_conditioning_channel_order: str = "rgb",
|
||||
conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
|
||||
load_weights_from_unet: bool = True,
|
||||
conditioning_channels: int = 3,
|
||||
):
|
||||
r"""
|
||||
Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`].
|
||||
|
||||
Parameters:
|
||||
unet (`UNet2DConditionModel`):
|
||||
The UNet model weights to copy to the [`ControlNetModel`]. All configuration options are also copied
|
||||
where applicable.
|
||||
"""
|
||||
transformer_layers_per_block = (
|
||||
unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1
|
||||
)
|
||||
encoder_hid_dim = unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None
|
||||
encoder_hid_dim_type = unet.config.encoder_hid_dim_type if "encoder_hid_dim_type" in unet.config else None
|
||||
addition_embed_type = unet.config.addition_embed_type if "addition_embed_type" in unet.config else None
|
||||
addition_time_embed_dim = (
|
||||
unet.config.addition_time_embed_dim if "addition_time_embed_dim" in unet.config else None
|
||||
)
|
||||
|
||||
controlnet = cls(
|
||||
encoder_hid_dim=encoder_hid_dim,
|
||||
encoder_hid_dim_type=encoder_hid_dim_type,
|
||||
addition_embed_type=addition_embed_type,
|
||||
addition_time_embed_dim=addition_time_embed_dim,
|
||||
transformer_layers_per_block=transformer_layers_per_block,
|
||||
in_channels=unet.config.in_channels,
|
||||
flip_sin_to_cos=unet.config.flip_sin_to_cos,
|
||||
freq_shift=unet.config.freq_shift,
|
||||
down_block_types=unet.config.down_block_types,
|
||||
only_cross_attention=unet.config.only_cross_attention,
|
||||
block_out_channels=unet.config.block_out_channels,
|
||||
layers_per_block=unet.config.layers_per_block,
|
||||
downsample_padding=unet.config.downsample_padding,
|
||||
mid_block_scale_factor=unet.config.mid_block_scale_factor,
|
||||
act_fn=unet.config.act_fn,
|
||||
norm_num_groups=unet.config.norm_num_groups,
|
||||
norm_eps=unet.config.norm_eps,
|
||||
cross_attention_dim=unet.config.cross_attention_dim,
|
||||
attention_head_dim=unet.config.attention_head_dim,
|
||||
num_attention_heads=unet.config.num_attention_heads,
|
||||
use_linear_projection=unet.config.use_linear_projection,
|
||||
class_embed_type=unet.config.class_embed_type,
|
||||
num_class_embeds=unet.config.num_class_embeds,
|
||||
upcast_attention=unet.config.upcast_attention,
|
||||
resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
|
||||
projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
|
||||
mid_block_type=unet.config.mid_block_type,
|
||||
controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
|
||||
conditioning_embedding_out_channels=conditioning_embedding_out_channels,
|
||||
conditioning_channels=conditioning_channels,
|
||||
)
|
||||
|
||||
if load_weights_from_unet:
|
||||
controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())
|
||||
controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())
|
||||
controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())
|
||||
|
||||
if controlnet.class_embedding:
|
||||
controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())
|
||||
|
||||
if hasattr(controlnet, "add_embedding"):
|
||||
controlnet.add_embedding.load_state_dict(unet.add_embedding.state_dict())
|
||||
|
||||
controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())
|
||||
controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())
|
||||
|
||||
return controlnet
|
||||
|
||||
@property
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
|
||||
def attn_processors(self) -> Dict[str, AttentionProcessor]:
|
||||
r"""
|
||||
Returns:
|
||||
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
||||
indexed by its weight name.
|
||||
"""
|
||||
# set recursively
|
||||
processors = {}
|
||||
|
||||
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
||||
if hasattr(module, "get_processor"):
|
||||
processors[f"{name}.processor"] = module.get_processor()
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
||||
|
||||
return processors
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_add_processors(name, module, processors)
|
||||
|
||||
return processors
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
|
||||
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
|
||||
r"""
|
||||
Sets the attention processor to use to compute attention.
|
||||
|
||||
Parameters:
|
||||
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
|
||||
The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
||||
for **all** `Attention` layers.
|
||||
|
||||
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
|
||||
processor. This is strongly recommended when setting trainable attention processors.
|
||||
|
||||
"""
|
||||
count = len(self.attn_processors.keys())
|
||||
|
||||
if isinstance(processor, dict) and len(processor) != count:
|
||||
raise ValueError(
|
||||
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
||||
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
||||
)
|
||||
|
||||
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
||||
if hasattr(module, "set_processor"):
|
||||
if not isinstance(processor, dict):
|
||||
module.set_processor(processor)
|
||||
else:
|
||||
module.set_processor(processor.pop(f"{name}.processor"))
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_attn_processor(name, module, processor)
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
|
||||
def set_default_attn_processor(self):
|
||||
"""
|
||||
Disables custom attention processors and sets the default attention implementation.
|
||||
"""
|
||||
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
||||
processor = AttnAddedKVProcessor()
|
||||
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
||||
processor = AttnProcessor()
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
|
||||
)
|
||||
|
||||
self.set_attn_processor(processor)
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice
|
||||
def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:
|
||||
r"""
|
||||
Enable sliced attention computation.
|
||||
|
||||
When this option is enabled, the attention module splits the input tensor in slices to compute attention in
|
||||
several steps. This is useful for saving some memory in exchange for a small decrease in speed.
|
||||
|
||||
Args:
|
||||
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
|
||||
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
|
||||
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
|
||||
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
|
||||
must be a multiple of `slice_size`.
|
||||
"""
|
||||
sliceable_head_dims = []
|
||||
|
||||
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
|
||||
if hasattr(module, "set_attention_slice"):
|
||||
sliceable_head_dims.append(module.sliceable_head_dim)
|
||||
|
||||
for child in module.children():
|
||||
fn_recursive_retrieve_sliceable_dims(child)
|
||||
|
||||
# retrieve number of attention layers
|
||||
for module in self.children():
|
||||
fn_recursive_retrieve_sliceable_dims(module)
|
||||
|
||||
num_sliceable_layers = len(sliceable_head_dims)
|
||||
|
||||
if slice_size == "auto":
|
||||
# half the attention head size is usually a good trade-off between
|
||||
# speed and memory
|
||||
slice_size = [dim // 2 for dim in sliceable_head_dims]
|
||||
elif slice_size == "max":
|
||||
# make smallest slice possible
|
||||
slice_size = num_sliceable_layers * [1]
|
||||
|
||||
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
|
||||
|
||||
if len(slice_size) != len(sliceable_head_dims):
|
||||
raise ValueError(
|
||||
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
|
||||
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
|
||||
)
|
||||
|
||||
for i in range(len(slice_size)):
|
||||
size = slice_size[i]
|
||||
dim = sliceable_head_dims[i]
|
||||
if size is not None and size > dim:
|
||||
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
|
||||
|
||||
# Recursively walk through all the children.
|
||||
# Any children which exposes the set_attention_slice method
|
||||
# gets the message
|
||||
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
|
||||
if hasattr(module, "set_attention_slice"):
|
||||
module.set_attention_slice(slice_size.pop())
|
||||
|
||||
for child in module.children():
|
||||
fn_recursive_set_attention_slice(child, slice_size)
|
||||
|
||||
reversed_slice_size = list(reversed(slice_size))
|
||||
for module in self.children():
|
||||
fn_recursive_set_attention_slice(module, reversed_slice_size)
|
||||
|
||||
def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
|
||||
if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):
|
||||
module.gradient_checkpointing = value
|
||||
|
||||
def forward(
|
||||
self,
|
||||
sample: torch.Tensor,
|
||||
timestep: Union[torch.Tensor, float, int],
|
||||
encoder_hidden_states: torch.Tensor,
|
||||
controlnet_cond: torch.Tensor,
|
||||
conditioning_scale: float = 1.0,
|
||||
class_labels: Optional[torch.Tensor] = None,
|
||||
timestep_cond: Optional[torch.Tensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
|
||||
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
guess_mode: bool = False,
|
||||
return_dict: bool = True,
|
||||
) -> Union[ControlNetOutput, Tuple[Tuple[torch.Tensor, ...], torch.Tensor]]:
|
||||
"""
|
||||
The [`ControlNetModel`] forward method.
|
||||
|
||||
Args:
|
||||
sample (`torch.Tensor`):
|
||||
The noisy input tensor.
|
||||
timestep (`Union[torch.Tensor, float, int]`):
|
||||
The number of timesteps to denoise an input.
|
||||
encoder_hidden_states (`torch.Tensor`):
|
||||
The encoder hidden states.
|
||||
controlnet_cond (`torch.Tensor`):
|
||||
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
|
||||
conditioning_scale (`float`, defaults to `1.0`):
|
||||
The scale factor for ControlNet outputs.
|
||||
class_labels (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
|
||||
timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
|
||||
timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
|
||||
embeddings.
|
||||
attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
|
||||
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
|
||||
negative values to the attention scores corresponding to "discard" tokens.
|
||||
added_cond_kwargs (`dict`):
|
||||
Additional conditions for the Stable Diffusion XL UNet.
|
||||
cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
|
||||
A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
|
||||
guess_mode (`bool`, defaults to `False`):
|
||||
In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
|
||||
you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
|
||||
return_dict (`bool`, defaults to `True`):
|
||||
Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
|
||||
|
||||
Returns:
|
||||
[`~models.controlnet.ControlNetOutput`] **or** `tuple`:
|
||||
If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
|
||||
returned where the first element is the sample tensor.
|
||||
"""
|
||||
# check channel order
|
||||
channel_order = self.config.controlnet_conditioning_channel_order
|
||||
|
||||
if channel_order == "rgb":
|
||||
# in rgb order by default
|
||||
...
|
||||
elif channel_order == "bgr":
|
||||
controlnet_cond = torch.flip(controlnet_cond, dims=[1])
|
||||
else:
|
||||
raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
|
||||
|
||||
# prepare attention_mask
|
||||
if attention_mask is not None:
|
||||
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
||||
attention_mask = attention_mask.unsqueeze(1)
|
||||
|
||||
# 1. time
|
||||
timesteps = timestep
|
||||
if not torch.is_tensor(timesteps):
|
||||
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
|
||||
# This would be a good case for the `match` statement (Python 3.10+)
|
||||
is_mps = sample.device.type == "mps"
|
||||
if isinstance(timestep, float):
|
||||
dtype = torch.float32 if is_mps else torch.float64
|
||||
else:
|
||||
dtype = torch.int32 if is_mps else torch.int64
|
||||
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
||||
elif len(timesteps.shape) == 0:
|
||||
timesteps = timesteps[None].to(sample.device)
|
||||
|
||||
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
||||
timesteps = timesteps.expand(sample.shape[0])
|
||||
|
||||
t_emb = self.time_proj(timesteps)
|
||||
|
||||
# timesteps does not contain any weights and will always return f32 tensors
|
||||
# but time_embedding might actually be running in fp16. so we need to cast here.
|
||||
# there might be better ways to encapsulate this.
|
||||
t_emb = t_emb.to(dtype=sample.dtype)
|
||||
|
||||
emb = self.time_embedding(t_emb, timestep_cond)
|
||||
aug_emb = None
|
||||
|
||||
if self.class_embedding is not None:
|
||||
if class_labels is None:
|
||||
raise ValueError("class_labels should be provided when num_class_embeds > 0")
|
||||
|
||||
if self.config.class_embed_type == "timestep":
|
||||
class_labels = self.time_proj(class_labels)
|
||||
|
||||
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
|
||||
emb = emb + class_emb
|
||||
|
||||
if self.config.addition_embed_type is not None:
|
||||
if self.config.addition_embed_type == "text":
|
||||
aug_emb = self.add_embedding(encoder_hidden_states)
|
||||
|
||||
elif self.config.addition_embed_type == "text_time":
|
||||
if "text_embeds" not in added_cond_kwargs:
|
||||
raise ValueError(
|
||||
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
|
||||
)
|
||||
text_embeds = added_cond_kwargs.get("text_embeds")
|
||||
if "time_ids" not in added_cond_kwargs:
|
||||
raise ValueError(
|
||||
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
|
||||
)
|
||||
time_ids = added_cond_kwargs.get("time_ids")
|
||||
time_embeds = self.add_time_proj(time_ids.flatten())
|
||||
time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
|
||||
|
||||
add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
|
||||
add_embeds = add_embeds.to(emb.dtype)
|
||||
aug_emb = self.add_embedding(add_embeds)
|
||||
|
||||
emb = emb + aug_emb if aug_emb is not None else emb
|
||||
|
||||
# 2. pre-process
|
||||
sample = self.conv_in(sample)
|
||||
|
||||
controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
|
||||
sample = sample + controlnet_cond
|
||||
|
||||
# 3. down
|
||||
down_block_res_samples = (sample,)
|
||||
for downsample_block in self.down_blocks:
|
||||
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
||||
sample, res_samples = downsample_block(
|
||||
hidden_states=sample,
|
||||
temb=emb,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
)
|
||||
else:
|
||||
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
|
||||
|
||||
down_block_res_samples += res_samples
|
||||
|
||||
# 4. mid
|
||||
if self.mid_block is not None:
|
||||
if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
|
||||
sample = self.mid_block(
|
||||
sample,
|
||||
emb,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
)
|
||||
else:
|
||||
sample = self.mid_block(sample, emb)
|
||||
|
||||
# 5. Control net 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 = 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
|
||||
if guess_mode and not self.config.global_pool_conditions:
|
||||
scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0
|
||||
scales = scales * conditioning_scale
|
||||
down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
|
||||
mid_block_res_sample = mid_block_res_sample * scales[-1] # last one
|
||||
else:
|
||||
down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
|
||||
mid_block_res_sample = mid_block_res_sample * conditioning_scale
|
||||
|
||||
if self.config.global_pool_conditions:
|
||||
down_block_res_samples = [
|
||||
torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
|
||||
]
|
||||
mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)
|
||||
|
||||
if not return_dict:
|
||||
return (down_block_res_samples, mid_block_res_sample)
|
||||
|
||||
return ControlNetOutput(
|
||||
down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
|
||||
)
|
||||
|
||||
|
||||
def zero_module(module):
|
||||
for p in module.parameters():
|
||||
nn.init.zeros_(p)
|
||||
return module
|
||||
class ControlNetConditioningEmbedding(ControlNetConditioningEmbedding):
|
||||
def __init__(self, *args, **kwargs):
|
||||
deprecation_message = "Importing `ControlNetConditioningEmbedding` from `diffusers.models.controlnet` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet import ControlNetConditioningEmbedding`, instead."
|
||||
deprecate("ControlNetConditioningEmbedding", "0.34", deprecation_message)
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
@@ -12,525 +12,30 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from ..configuration_utils import ConfigMixin, register_to_config
|
||||
from ..loaders import PeftAdapterMixin
|
||||
from ..models.attention_processor import AttentionProcessor
|
||||
from ..models.modeling_utils import ModelMixin
|
||||
from ..utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
|
||||
from .controlnet import BaseOutput, ControlNetConditioningEmbedding, zero_module
|
||||
from .embeddings import CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings, FluxPosEmbed
|
||||
from .modeling_outputs import Transformer2DModelOutput
|
||||
from .transformers.transformer_flux import FluxSingleTransformerBlock, FluxTransformerBlock
|
||||
from ..utils import deprecate, logging
|
||||
from .controlnets.controlnet_flux import FluxControlNetModel, FluxControlNetOutput, FluxMultiControlNetModel
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
||||
|
||||
|
||||
@dataclass
|
||||
class FluxControlNetOutput(BaseOutput):
|
||||
controlnet_block_samples: Tuple[torch.Tensor]
|
||||
controlnet_single_block_samples: Tuple[torch.Tensor]
|
||||
class FluxControlNetOutput(FluxControlNetOutput):
|
||||
def __init__(self, *args, **kwargs):
|
||||
deprecation_message = "Importing `FluxControlNetOutput` from `diffusers.models.controlnet_flux` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet_flux import FluxControlNetOutput`, instead."
|
||||
deprecate("FluxControlNetOutput", "0.34", deprecation_message)
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
|
||||
class FluxControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
|
||||
_supports_gradient_checkpointing = True
|
||||
class FluxControlNetModel(FluxControlNetModel):
|
||||
def __init__(self, *args, **kwargs):
|
||||
deprecation_message = "Importing `FluxControlNetModel` from `diffusers.models.controlnet_flux` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet_flux import FluxControlNetModel`, instead."
|
||||
deprecate("FluxControlNetModel", "0.34", deprecation_message)
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
@register_to_config
|
||||
def __init__(
|
||||
self,
|
||||
patch_size: int = 1,
|
||||
in_channels: int = 64,
|
||||
num_layers: int = 19,
|
||||
num_single_layers: int = 38,
|
||||
attention_head_dim: int = 128,
|
||||
num_attention_heads: int = 24,
|
||||
joint_attention_dim: int = 4096,
|
||||
pooled_projection_dim: int = 768,
|
||||
guidance_embeds: bool = False,
|
||||
axes_dims_rope: List[int] = [16, 56, 56],
|
||||
num_mode: int = None,
|
||||
conditioning_embedding_channels: int = None,
|
||||
):
|
||||
super().__init__()
|
||||
self.out_channels = in_channels
|
||||
self.inner_dim = num_attention_heads * attention_head_dim
|
||||
|
||||
self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope)
|
||||
text_time_guidance_cls = (
|
||||
CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings
|
||||
)
|
||||
self.time_text_embed = text_time_guidance_cls(
|
||||
embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
|
||||
)
|
||||
|
||||
self.context_embedder = nn.Linear(joint_attention_dim, self.inner_dim)
|
||||
self.x_embedder = torch.nn.Linear(in_channels, self.inner_dim)
|
||||
|
||||
self.transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
FluxTransformerBlock(
|
||||
dim=self.inner_dim,
|
||||
num_attention_heads=num_attention_heads,
|
||||
attention_head_dim=attention_head_dim,
|
||||
)
|
||||
for i in range(num_layers)
|
||||
]
|
||||
)
|
||||
|
||||
self.single_transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
FluxSingleTransformerBlock(
|
||||
dim=self.inner_dim,
|
||||
num_attention_heads=num_attention_heads,
|
||||
attention_head_dim=attention_head_dim,
|
||||
)
|
||||
for i in range(num_single_layers)
|
||||
]
|
||||
)
|
||||
|
||||
# controlnet_blocks
|
||||
self.controlnet_blocks = nn.ModuleList([])
|
||||
for _ in range(len(self.transformer_blocks)):
|
||||
self.controlnet_blocks.append(zero_module(nn.Linear(self.inner_dim, self.inner_dim)))
|
||||
|
||||
self.controlnet_single_blocks = nn.ModuleList([])
|
||||
for _ in range(len(self.single_transformer_blocks)):
|
||||
self.controlnet_single_blocks.append(zero_module(nn.Linear(self.inner_dim, self.inner_dim)))
|
||||
|
||||
self.union = num_mode is not None
|
||||
if self.union:
|
||||
self.controlnet_mode_embedder = nn.Embedding(num_mode, self.inner_dim)
|
||||
|
||||
if conditioning_embedding_channels is not None:
|
||||
self.input_hint_block = ControlNetConditioningEmbedding(
|
||||
conditioning_embedding_channels=conditioning_embedding_channels, block_out_channels=(16, 16, 16, 16)
|
||||
)
|
||||
self.controlnet_x_embedder = torch.nn.Linear(in_channels, self.inner_dim)
|
||||
else:
|
||||
self.input_hint_block = None
|
||||
self.controlnet_x_embedder = zero_module(torch.nn.Linear(in_channels, self.inner_dim))
|
||||
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
@property
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
|
||||
def attn_processors(self):
|
||||
r"""
|
||||
Returns:
|
||||
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
||||
indexed by its weight name.
|
||||
"""
|
||||
# set recursively
|
||||
processors = {}
|
||||
|
||||
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
||||
if hasattr(module, "get_processor"):
|
||||
processors[f"{name}.processor"] = module.get_processor()
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
||||
|
||||
return processors
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_add_processors(name, module, processors)
|
||||
|
||||
return processors
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
|
||||
def set_attn_processor(self, processor):
|
||||
r"""
|
||||
Sets the attention processor to use to compute attention.
|
||||
|
||||
Parameters:
|
||||
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
|
||||
The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
||||
for **all** `Attention` layers.
|
||||
|
||||
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
|
||||
processor. This is strongly recommended when setting trainable attention processors.
|
||||
|
||||
"""
|
||||
count = len(self.attn_processors.keys())
|
||||
|
||||
if isinstance(processor, dict) and len(processor) != count:
|
||||
raise ValueError(
|
||||
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
||||
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
||||
)
|
||||
|
||||
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
||||
if hasattr(module, "set_processor"):
|
||||
if not isinstance(processor, dict):
|
||||
module.set_processor(processor)
|
||||
else:
|
||||
module.set_processor(processor.pop(f"{name}.processor"))
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_attn_processor(name, module, processor)
|
||||
|
||||
def _set_gradient_checkpointing(self, module, value=False):
|
||||
if hasattr(module, "gradient_checkpointing"):
|
||||
module.gradient_checkpointing = value
|
||||
|
||||
@classmethod
|
||||
def from_transformer(
|
||||
cls,
|
||||
transformer,
|
||||
num_layers: int = 4,
|
||||
num_single_layers: int = 10,
|
||||
attention_head_dim: int = 128,
|
||||
num_attention_heads: int = 24,
|
||||
load_weights_from_transformer=True,
|
||||
):
|
||||
config = transformer.config
|
||||
config["num_layers"] = num_layers
|
||||
config["num_single_layers"] = num_single_layers
|
||||
config["attention_head_dim"] = attention_head_dim
|
||||
config["num_attention_heads"] = num_attention_heads
|
||||
|
||||
controlnet = cls(**config)
|
||||
|
||||
if load_weights_from_transformer:
|
||||
controlnet.pos_embed.load_state_dict(transformer.pos_embed.state_dict())
|
||||
controlnet.time_text_embed.load_state_dict(transformer.time_text_embed.state_dict())
|
||||
controlnet.context_embedder.load_state_dict(transformer.context_embedder.state_dict())
|
||||
controlnet.x_embedder.load_state_dict(transformer.x_embedder.state_dict())
|
||||
controlnet.transformer_blocks.load_state_dict(transformer.transformer_blocks.state_dict(), strict=False)
|
||||
controlnet.single_transformer_blocks.load_state_dict(
|
||||
transformer.single_transformer_blocks.state_dict(), strict=False
|
||||
)
|
||||
|
||||
controlnet.controlnet_x_embedder = zero_module(controlnet.controlnet_x_embedder)
|
||||
|
||||
return controlnet
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
controlnet_cond: torch.Tensor,
|
||||
controlnet_mode: torch.Tensor = None,
|
||||
conditioning_scale: float = 1.0,
|
||||
encoder_hidden_states: torch.Tensor = None,
|
||||
pooled_projections: torch.Tensor = None,
|
||||
timestep: torch.LongTensor = None,
|
||||
img_ids: torch.Tensor = None,
|
||||
txt_ids: torch.Tensor = None,
|
||||
guidance: torch.Tensor = None,
|
||||
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
return_dict: bool = True,
|
||||
) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
|
||||
"""
|
||||
The [`FluxTransformer2DModel`] forward method.
|
||||
|
||||
Args:
|
||||
hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
|
||||
Input `hidden_states`.
|
||||
controlnet_cond (`torch.Tensor`):
|
||||
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
|
||||
controlnet_mode (`torch.Tensor`):
|
||||
The mode tensor of shape `(batch_size, 1)`.
|
||||
conditioning_scale (`float`, defaults to `1.0`):
|
||||
The scale factor for ControlNet outputs.
|
||||
encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
|
||||
Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
|
||||
pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
|
||||
from the embeddings of input conditions.
|
||||
timestep ( `torch.LongTensor`):
|
||||
Used to indicate denoising step.
|
||||
block_controlnet_hidden_states: (`list` of `torch.Tensor`):
|
||||
A list of tensors that if specified are added to the residuals of transformer blocks.
|
||||
joint_attention_kwargs (`dict`, *optional*):
|
||||
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
||||
`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.transformer_2d.Transformer2DModelOutput`] instead of a plain
|
||||
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 joint_attention_kwargs is not None:
|
||||
joint_attention_kwargs = joint_attention_kwargs.copy()
|
||||
lora_scale = joint_attention_kwargs.pop("scale", 1.0)
|
||||
else:
|
||||
lora_scale = 1.0
|
||||
|
||||
if USE_PEFT_BACKEND:
|
||||
# weight the lora layers by setting `lora_scale` for each PEFT layer
|
||||
scale_lora_layers(self, lora_scale)
|
||||
else:
|
||||
if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
|
||||
logger.warning(
|
||||
"Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
|
||||
)
|
||||
hidden_states = self.x_embedder(hidden_states)
|
||||
|
||||
if self.input_hint_block is not None:
|
||||
controlnet_cond = self.input_hint_block(controlnet_cond)
|
||||
batch_size, channels, height_pw, width_pw = controlnet_cond.shape
|
||||
height = height_pw // self.config.patch_size
|
||||
width = width_pw // self.config.patch_size
|
||||
controlnet_cond = controlnet_cond.reshape(
|
||||
batch_size, channels, height, self.config.patch_size, width, self.config.patch_size
|
||||
)
|
||||
controlnet_cond = controlnet_cond.permute(0, 2, 4, 1, 3, 5)
|
||||
controlnet_cond = controlnet_cond.reshape(batch_size, height * width, -1)
|
||||
# add
|
||||
hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_cond)
|
||||
|
||||
timestep = timestep.to(hidden_states.dtype) * 1000
|
||||
if guidance is not None:
|
||||
guidance = guidance.to(hidden_states.dtype) * 1000
|
||||
else:
|
||||
guidance = None
|
||||
temb = (
|
||||
self.time_text_embed(timestep, pooled_projections)
|
||||
if guidance is None
|
||||
else self.time_text_embed(timestep, guidance, pooled_projections)
|
||||
)
|
||||
encoder_hidden_states = self.context_embedder(encoder_hidden_states)
|
||||
|
||||
if self.union:
|
||||
# union mode
|
||||
if controlnet_mode is None:
|
||||
raise ValueError("`controlnet_mode` cannot be `None` when applying ControlNet-Union")
|
||||
# union mode emb
|
||||
controlnet_mode_emb = self.controlnet_mode_embedder(controlnet_mode)
|
||||
encoder_hidden_states = torch.cat([controlnet_mode_emb, encoder_hidden_states], dim=1)
|
||||
txt_ids = torch.cat([txt_ids[:1], txt_ids], dim=0)
|
||||
|
||||
if txt_ids.ndim == 3:
|
||||
logger.warning(
|
||||
"Passing `txt_ids` 3d torch.Tensor is deprecated."
|
||||
"Please remove the batch dimension and pass it as a 2d torch Tensor"
|
||||
)
|
||||
txt_ids = txt_ids[0]
|
||||
if img_ids.ndim == 3:
|
||||
logger.warning(
|
||||
"Passing `img_ids` 3d torch.Tensor is deprecated."
|
||||
"Please remove the batch dimension and pass it as a 2d torch Tensor"
|
||||
)
|
||||
img_ids = img_ids[0]
|
||||
|
||||
ids = torch.cat((txt_ids, img_ids), dim=0)
|
||||
image_rotary_emb = self.pos_embed(ids)
|
||||
|
||||
block_samples = ()
|
||||
for index_block, block in enumerate(self.transformer_blocks):
|
||||
if self.training and self.gradient_checkpointing:
|
||||
|
||||
def create_custom_forward(module, return_dict=None):
|
||||
def custom_forward(*inputs):
|
||||
if return_dict is not None:
|
||||
return module(*inputs, return_dict=return_dict)
|
||||
else:
|
||||
return module(*inputs)
|
||||
|
||||
return custom_forward
|
||||
|
||||
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
|
||||
encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
hidden_states,
|
||||
encoder_hidden_states,
|
||||
temb,
|
||||
image_rotary_emb,
|
||||
**ckpt_kwargs,
|
||||
)
|
||||
|
||||
else:
|
||||
encoder_hidden_states, hidden_states = block(
|
||||
hidden_states=hidden_states,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
temb=temb,
|
||||
image_rotary_emb=image_rotary_emb,
|
||||
)
|
||||
block_samples = block_samples + (hidden_states,)
|
||||
|
||||
hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
|
||||
|
||||
single_block_samples = ()
|
||||
for index_block, block in enumerate(self.single_transformer_blocks):
|
||||
if self.training and self.gradient_checkpointing:
|
||||
|
||||
def create_custom_forward(module, return_dict=None):
|
||||
def custom_forward(*inputs):
|
||||
if return_dict is not None:
|
||||
return module(*inputs, return_dict=return_dict)
|
||||
else:
|
||||
return module(*inputs)
|
||||
|
||||
return custom_forward
|
||||
|
||||
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
|
||||
hidden_states = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
hidden_states,
|
||||
temb,
|
||||
image_rotary_emb,
|
||||
**ckpt_kwargs,
|
||||
)
|
||||
|
||||
else:
|
||||
hidden_states = block(
|
||||
hidden_states=hidden_states,
|
||||
temb=temb,
|
||||
image_rotary_emb=image_rotary_emb,
|
||||
)
|
||||
single_block_samples = single_block_samples + (hidden_states[:, encoder_hidden_states.shape[1] :],)
|
||||
|
||||
# controlnet block
|
||||
controlnet_block_samples = ()
|
||||
for block_sample, controlnet_block in zip(block_samples, self.controlnet_blocks):
|
||||
block_sample = controlnet_block(block_sample)
|
||||
controlnet_block_samples = controlnet_block_samples + (block_sample,)
|
||||
|
||||
controlnet_single_block_samples = ()
|
||||
for single_block_sample, controlnet_block in zip(single_block_samples, self.controlnet_single_blocks):
|
||||
single_block_sample = controlnet_block(single_block_sample)
|
||||
controlnet_single_block_samples = controlnet_single_block_samples + (single_block_sample,)
|
||||
|
||||
# scaling
|
||||
controlnet_block_samples = [sample * conditioning_scale for sample in controlnet_block_samples]
|
||||
controlnet_single_block_samples = [sample * conditioning_scale for sample in controlnet_single_block_samples]
|
||||
|
||||
controlnet_block_samples = None if len(controlnet_block_samples) == 0 else controlnet_block_samples
|
||||
controlnet_single_block_samples = (
|
||||
None if len(controlnet_single_block_samples) == 0 else controlnet_single_block_samples
|
||||
)
|
||||
|
||||
if USE_PEFT_BACKEND:
|
||||
# remove `lora_scale` from each PEFT layer
|
||||
unscale_lora_layers(self, lora_scale)
|
||||
|
||||
if not return_dict:
|
||||
return (controlnet_block_samples, controlnet_single_block_samples)
|
||||
|
||||
return FluxControlNetOutput(
|
||||
controlnet_block_samples=controlnet_block_samples,
|
||||
controlnet_single_block_samples=controlnet_single_block_samples,
|
||||
)
|
||||
|
||||
|
||||
class FluxMultiControlNetModel(ModelMixin):
|
||||
r"""
|
||||
`FluxMultiControlNetModel` wrapper class for Multi-FluxControlNetModel
|
||||
|
||||
This module is a wrapper for multiple instances of the `FluxControlNetModel`. The `forward()` API is designed to be
|
||||
compatible with `FluxControlNetModel`.
|
||||
|
||||
Args:
|
||||
controlnets (`List[FluxControlNetModel]`):
|
||||
Provides additional conditioning to the unet during the denoising process. You must set multiple
|
||||
`FluxControlNetModel` as a list.
|
||||
"""
|
||||
|
||||
def __init__(self, controlnets):
|
||||
super().__init__()
|
||||
self.nets = nn.ModuleList(controlnets)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.FloatTensor,
|
||||
controlnet_cond: List[torch.tensor],
|
||||
controlnet_mode: List[torch.tensor],
|
||||
conditioning_scale: List[float],
|
||||
encoder_hidden_states: torch.Tensor = None,
|
||||
pooled_projections: torch.Tensor = None,
|
||||
timestep: torch.LongTensor = None,
|
||||
img_ids: torch.Tensor = None,
|
||||
txt_ids: torch.Tensor = None,
|
||||
guidance: torch.Tensor = None,
|
||||
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
return_dict: bool = True,
|
||||
) -> Union[FluxControlNetOutput, Tuple]:
|
||||
# ControlNet-Union with multiple conditions
|
||||
# only load one ControlNet for saving memories
|
||||
if len(self.nets) == 1 and self.nets[0].union:
|
||||
controlnet = self.nets[0]
|
||||
|
||||
for i, (image, mode, scale) in enumerate(zip(controlnet_cond, controlnet_mode, conditioning_scale)):
|
||||
block_samples, single_block_samples = controlnet(
|
||||
hidden_states=hidden_states,
|
||||
controlnet_cond=image,
|
||||
controlnet_mode=mode[:, None],
|
||||
conditioning_scale=scale,
|
||||
timestep=timestep,
|
||||
guidance=guidance,
|
||||
pooled_projections=pooled_projections,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
txt_ids=txt_ids,
|
||||
img_ids=img_ids,
|
||||
joint_attention_kwargs=joint_attention_kwargs,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
|
||||
# merge samples
|
||||
if i == 0:
|
||||
control_block_samples = block_samples
|
||||
control_single_block_samples = single_block_samples
|
||||
else:
|
||||
control_block_samples = [
|
||||
control_block_sample + block_sample
|
||||
for control_block_sample, block_sample in zip(control_block_samples, block_samples)
|
||||
]
|
||||
|
||||
control_single_block_samples = [
|
||||
control_single_block_sample + block_sample
|
||||
for control_single_block_sample, block_sample in zip(
|
||||
control_single_block_samples, single_block_samples
|
||||
)
|
||||
]
|
||||
|
||||
# Regular Multi-ControlNets
|
||||
# load all ControlNets into memories
|
||||
else:
|
||||
for i, (image, mode, scale, controlnet) in enumerate(
|
||||
zip(controlnet_cond, controlnet_mode, conditioning_scale, self.nets)
|
||||
):
|
||||
block_samples, single_block_samples = controlnet(
|
||||
hidden_states=hidden_states,
|
||||
controlnet_cond=image,
|
||||
controlnet_mode=mode[:, None],
|
||||
conditioning_scale=scale,
|
||||
timestep=timestep,
|
||||
guidance=guidance,
|
||||
pooled_projections=pooled_projections,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
txt_ids=txt_ids,
|
||||
img_ids=img_ids,
|
||||
joint_attention_kwargs=joint_attention_kwargs,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
|
||||
# merge samples
|
||||
if i == 0:
|
||||
control_block_samples = block_samples
|
||||
control_single_block_samples = single_block_samples
|
||||
else:
|
||||
if block_samples is not None and control_block_samples is not None:
|
||||
control_block_samples = [
|
||||
control_block_sample + block_sample
|
||||
for control_block_sample, block_sample in zip(control_block_samples, block_samples)
|
||||
]
|
||||
if single_block_samples is not None and control_single_block_samples is not None:
|
||||
control_single_block_samples = [
|
||||
control_single_block_sample + block_sample
|
||||
for control_single_block_sample, block_sample in zip(
|
||||
control_single_block_samples, single_block_samples
|
||||
)
|
||||
]
|
||||
|
||||
return control_block_samples, control_single_block_samples
|
||||
class FluxMultiControlNetModel(FluxMultiControlNetModel):
|
||||
def __init__(self, *args, **kwargs):
|
||||
deprecation_message = "Importing `FluxMultiControlNetModel` from `diffusers.models.controlnet_flux` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet_flux import FluxMultiControlNetModel`, instead."
|
||||
deprecate("FluxMultiControlNetModel", "0.34", deprecation_message)
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
@@ -13,410 +13,29 @@
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from ..configuration_utils import ConfigMixin, register_to_config
|
||||
from ..loaders import FromOriginalModelMixin, PeftAdapterMixin
|
||||
from ..models.attention import JointTransformerBlock
|
||||
from ..models.attention_processor import Attention, AttentionProcessor, FusedJointAttnProcessor2_0
|
||||
from ..models.modeling_outputs import Transformer2DModelOutput
|
||||
from ..models.modeling_utils import ModelMixin
|
||||
from ..utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
|
||||
from .controlnet import BaseOutput, zero_module
|
||||
from .embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed
|
||||
from ..utils import deprecate, logging
|
||||
from .controlnets.controlnet_sd3 import SD3ControlNetModel, SD3ControlNetOutput, SD3MultiControlNetModel
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
||||
|
||||
|
||||
@dataclass
|
||||
class SD3ControlNetOutput(BaseOutput):
|
||||
controlnet_block_samples: Tuple[torch.Tensor]
|
||||
class SD3ControlNetOutput(SD3ControlNetOutput):
|
||||
def __init__(self, *args, **kwargs):
|
||||
deprecation_message = "Importing `SD3ControlNetOutput` from `diffusers.models.controlnet_sd3` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet_sd3 import SD3ControlNetOutput`, instead."
|
||||
deprecate("SD3ControlNetOutput", "0.34", deprecation_message)
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
|
||||
class SD3ControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
|
||||
_supports_gradient_checkpointing = True
|
||||
class SD3ControlNetModel(SD3ControlNetModel):
|
||||
def __init__(self, *args, **kwargs):
|
||||
deprecation_message = "Importing `SD3ControlNetModel` from `diffusers.models.controlnet_sd3` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet_sd3 import SD3ControlNetModel`, instead."
|
||||
deprecate("SD3ControlNetModel", "0.34", deprecation_message)
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
@register_to_config
|
||||
def __init__(
|
||||
self,
|
||||
sample_size: int = 128,
|
||||
patch_size: int = 2,
|
||||
in_channels: int = 16,
|
||||
num_layers: int = 18,
|
||||
attention_head_dim: int = 64,
|
||||
num_attention_heads: int = 18,
|
||||
joint_attention_dim: int = 4096,
|
||||
caption_projection_dim: int = 1152,
|
||||
pooled_projection_dim: int = 2048,
|
||||
out_channels: int = 16,
|
||||
pos_embed_max_size: int = 96,
|
||||
extra_conditioning_channels: int = 0,
|
||||
):
|
||||
super().__init__()
|
||||
default_out_channels = in_channels
|
||||
self.out_channels = out_channels if out_channels is not None else default_out_channels
|
||||
self.inner_dim = num_attention_heads * attention_head_dim
|
||||
|
||||
self.pos_embed = PatchEmbed(
|
||||
height=sample_size,
|
||||
width=sample_size,
|
||||
patch_size=patch_size,
|
||||
in_channels=in_channels,
|
||||
embed_dim=self.inner_dim,
|
||||
pos_embed_max_size=pos_embed_max_size,
|
||||
)
|
||||
self.time_text_embed = CombinedTimestepTextProjEmbeddings(
|
||||
embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
|
||||
)
|
||||
self.context_embedder = nn.Linear(joint_attention_dim, caption_projection_dim)
|
||||
|
||||
# `attention_head_dim` is doubled to account for the mixing.
|
||||
# It needs to crafted when we get the actual checkpoints.
|
||||
self.transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
JointTransformerBlock(
|
||||
dim=self.inner_dim,
|
||||
num_attention_heads=num_attention_heads,
|
||||
attention_head_dim=self.config.attention_head_dim,
|
||||
context_pre_only=False,
|
||||
)
|
||||
for i in range(num_layers)
|
||||
]
|
||||
)
|
||||
|
||||
# controlnet_blocks
|
||||
self.controlnet_blocks = nn.ModuleList([])
|
||||
for _ in range(len(self.transformer_blocks)):
|
||||
controlnet_block = nn.Linear(self.inner_dim, self.inner_dim)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_blocks.append(controlnet_block)
|
||||
pos_embed_input = PatchEmbed(
|
||||
height=sample_size,
|
||||
width=sample_size,
|
||||
patch_size=patch_size,
|
||||
in_channels=in_channels + extra_conditioning_channels,
|
||||
embed_dim=self.inner_dim,
|
||||
pos_embed_type=None,
|
||||
)
|
||||
self.pos_embed_input = zero_module(pos_embed_input)
|
||||
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
|
||||
def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
|
||||
"""
|
||||
Sets the attention processor to use [feed forward
|
||||
chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
|
||||
|
||||
Parameters:
|
||||
chunk_size (`int`, *optional*):
|
||||
The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
|
||||
over each tensor of dim=`dim`.
|
||||
dim (`int`, *optional*, defaults to `0`):
|
||||
The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
|
||||
or dim=1 (sequence length).
|
||||
"""
|
||||
if dim not in [0, 1]:
|
||||
raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
|
||||
|
||||
# By default chunk size is 1
|
||||
chunk_size = chunk_size or 1
|
||||
|
||||
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
|
||||
if hasattr(module, "set_chunk_feed_forward"):
|
||||
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
|
||||
|
||||
for child in module.children():
|
||||
fn_recursive_feed_forward(child, chunk_size, dim)
|
||||
|
||||
for module in self.children():
|
||||
fn_recursive_feed_forward(module, chunk_size, dim)
|
||||
|
||||
@property
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
|
||||
def attn_processors(self) -> Dict[str, AttentionProcessor]:
|
||||
r"""
|
||||
Returns:
|
||||
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
||||
indexed by its weight name.
|
||||
"""
|
||||
# set recursively
|
||||
processors = {}
|
||||
|
||||
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
||||
if hasattr(module, "get_processor"):
|
||||
processors[f"{name}.processor"] = module.get_processor()
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
||||
|
||||
return processors
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_add_processors(name, module, processors)
|
||||
|
||||
return processors
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
|
||||
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
|
||||
r"""
|
||||
Sets the attention processor to use to compute attention.
|
||||
|
||||
Parameters:
|
||||
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
|
||||
The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
||||
for **all** `Attention` layers.
|
||||
|
||||
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
|
||||
processor. This is strongly recommended when setting trainable attention processors.
|
||||
|
||||
"""
|
||||
count = len(self.attn_processors.keys())
|
||||
|
||||
if isinstance(processor, dict) and len(processor) != count:
|
||||
raise ValueError(
|
||||
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
||||
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
||||
)
|
||||
|
||||
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
||||
if hasattr(module, "set_processor"):
|
||||
if not isinstance(processor, dict):
|
||||
module.set_processor(processor)
|
||||
else:
|
||||
module.set_processor(processor.pop(f"{name}.processor"))
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_attn_processor(name, module, processor)
|
||||
|
||||
# Copied from diffusers.models.transformers.transformer_sd3.SD3Transformer2DModel.fuse_qkv_projections
|
||||
def fuse_qkv_projections(self):
|
||||
"""
|
||||
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
|
||||
are fused. For cross-attention modules, key and value projection matrices are fused.
|
||||
|
||||
<Tip warning={true}>
|
||||
|
||||
This API is 🧪 experimental.
|
||||
|
||||
</Tip>
|
||||
"""
|
||||
self.original_attn_processors = None
|
||||
|
||||
for _, attn_processor in self.attn_processors.items():
|
||||
if "Added" in str(attn_processor.__class__.__name__):
|
||||
raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
|
||||
|
||||
self.original_attn_processors = self.attn_processors
|
||||
|
||||
for module in self.modules():
|
||||
if isinstance(module, Attention):
|
||||
module.fuse_projections(fuse=True)
|
||||
|
||||
self.set_attn_processor(FusedJointAttnProcessor2_0())
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
|
||||
def unfuse_qkv_projections(self):
|
||||
"""Disables the fused QKV projection if enabled.
|
||||
|
||||
<Tip warning={true}>
|
||||
|
||||
This API is 🧪 experimental.
|
||||
|
||||
</Tip>
|
||||
|
||||
"""
|
||||
if self.original_attn_processors is not None:
|
||||
self.set_attn_processor(self.original_attn_processors)
|
||||
|
||||
def _set_gradient_checkpointing(self, module, value=False):
|
||||
if hasattr(module, "gradient_checkpointing"):
|
||||
module.gradient_checkpointing = value
|
||||
|
||||
@classmethod
|
||||
def from_transformer(
|
||||
cls, transformer, num_layers=12, num_extra_conditioning_channels=1, load_weights_from_transformer=True
|
||||
):
|
||||
config = transformer.config
|
||||
config["num_layers"] = num_layers or config.num_layers
|
||||
config["extra_conditioning_channels"] = num_extra_conditioning_channels
|
||||
controlnet = cls(**config)
|
||||
|
||||
if load_weights_from_transformer:
|
||||
controlnet.pos_embed.load_state_dict(transformer.pos_embed.state_dict())
|
||||
controlnet.time_text_embed.load_state_dict(transformer.time_text_embed.state_dict())
|
||||
controlnet.context_embedder.load_state_dict(transformer.context_embedder.state_dict())
|
||||
controlnet.transformer_blocks.load_state_dict(transformer.transformer_blocks.state_dict(), strict=False)
|
||||
|
||||
controlnet.pos_embed_input = zero_module(controlnet.pos_embed_input)
|
||||
|
||||
return controlnet
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.FloatTensor,
|
||||
controlnet_cond: torch.Tensor,
|
||||
conditioning_scale: float = 1.0,
|
||||
encoder_hidden_states: torch.FloatTensor = None,
|
||||
pooled_projections: torch.FloatTensor = None,
|
||||
timestep: torch.LongTensor = None,
|
||||
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
return_dict: bool = True,
|
||||
) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
|
||||
"""
|
||||
The [`SD3Transformer2DModel`] forward method.
|
||||
|
||||
Args:
|
||||
hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
|
||||
Input `hidden_states`.
|
||||
controlnet_cond (`torch.Tensor`):
|
||||
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
|
||||
conditioning_scale (`float`, defaults to `1.0`):
|
||||
The scale factor for ControlNet outputs.
|
||||
encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
|
||||
Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
|
||||
pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
|
||||
from the embeddings of input conditions.
|
||||
timestep ( `torch.LongTensor`):
|
||||
Used to indicate denoising step.
|
||||
joint_attention_kwargs (`dict`, *optional*):
|
||||
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
||||
`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.transformer_2d.Transformer2DModelOutput`] instead of a plain
|
||||
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 joint_attention_kwargs is not None:
|
||||
joint_attention_kwargs = joint_attention_kwargs.copy()
|
||||
lora_scale = joint_attention_kwargs.pop("scale", 1.0)
|
||||
else:
|
||||
lora_scale = 1.0
|
||||
|
||||
if USE_PEFT_BACKEND:
|
||||
# weight the lora layers by setting `lora_scale` for each PEFT layer
|
||||
scale_lora_layers(self, lora_scale)
|
||||
else:
|
||||
if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
|
||||
logger.warning(
|
||||
"Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
|
||||
)
|
||||
|
||||
hidden_states = self.pos_embed(hidden_states) # takes care of adding positional embeddings too.
|
||||
temb = self.time_text_embed(timestep, pooled_projections)
|
||||
encoder_hidden_states = self.context_embedder(encoder_hidden_states)
|
||||
|
||||
# add
|
||||
hidden_states = hidden_states + self.pos_embed_input(controlnet_cond)
|
||||
|
||||
block_res_samples = ()
|
||||
|
||||
for block in self.transformer_blocks:
|
||||
if self.training and self.gradient_checkpointing:
|
||||
|
||||
def create_custom_forward(module, return_dict=None):
|
||||
def custom_forward(*inputs):
|
||||
if return_dict is not None:
|
||||
return module(*inputs, return_dict=return_dict)
|
||||
else:
|
||||
return module(*inputs)
|
||||
|
||||
return custom_forward
|
||||
|
||||
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
|
||||
encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
hidden_states,
|
||||
encoder_hidden_states,
|
||||
temb,
|
||||
**ckpt_kwargs,
|
||||
)
|
||||
|
||||
else:
|
||||
encoder_hidden_states, hidden_states = block(
|
||||
hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb
|
||||
)
|
||||
|
||||
block_res_samples = block_res_samples + (hidden_states,)
|
||||
|
||||
controlnet_block_res_samples = ()
|
||||
for block_res_sample, controlnet_block in zip(block_res_samples, self.controlnet_blocks):
|
||||
block_res_sample = controlnet_block(block_res_sample)
|
||||
controlnet_block_res_samples = controlnet_block_res_samples + (block_res_sample,)
|
||||
|
||||
# 6. scaling
|
||||
controlnet_block_res_samples = [sample * conditioning_scale for sample in controlnet_block_res_samples]
|
||||
|
||||
if USE_PEFT_BACKEND:
|
||||
# remove `lora_scale` from each PEFT layer
|
||||
unscale_lora_layers(self, lora_scale)
|
||||
|
||||
if not return_dict:
|
||||
return (controlnet_block_res_samples,)
|
||||
|
||||
return SD3ControlNetOutput(controlnet_block_samples=controlnet_block_res_samples)
|
||||
|
||||
|
||||
class SD3MultiControlNetModel(ModelMixin):
|
||||
r"""
|
||||
`SD3ControlNetModel` wrapper class for Multi-SD3ControlNet
|
||||
|
||||
This module is a wrapper for multiple instances of the `SD3ControlNetModel`. The `forward()` API is designed to be
|
||||
compatible with `SD3ControlNetModel`.
|
||||
|
||||
Args:
|
||||
controlnets (`List[SD3ControlNetModel]`):
|
||||
Provides additional conditioning to the unet during the denoising process. You must set multiple
|
||||
`SD3ControlNetModel` as a list.
|
||||
"""
|
||||
|
||||
def __init__(self, controlnets):
|
||||
super().__init__()
|
||||
self.nets = nn.ModuleList(controlnets)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.FloatTensor,
|
||||
controlnet_cond: List[torch.tensor],
|
||||
conditioning_scale: List[float],
|
||||
pooled_projections: torch.FloatTensor,
|
||||
encoder_hidden_states: torch.FloatTensor = None,
|
||||
timestep: torch.LongTensor = None,
|
||||
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
return_dict: bool = True,
|
||||
) -> Union[SD3ControlNetOutput, Tuple]:
|
||||
for i, (image, scale, controlnet) in enumerate(zip(controlnet_cond, conditioning_scale, self.nets)):
|
||||
block_samples = controlnet(
|
||||
hidden_states=hidden_states,
|
||||
timestep=timestep,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
pooled_projections=pooled_projections,
|
||||
controlnet_cond=image,
|
||||
conditioning_scale=scale,
|
||||
joint_attention_kwargs=joint_attention_kwargs,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
|
||||
# merge samples
|
||||
if i == 0:
|
||||
control_block_samples = block_samples
|
||||
else:
|
||||
control_block_samples = [
|
||||
control_block_sample + block_sample
|
||||
for control_block_sample, block_sample in zip(control_block_samples[0], block_samples[0])
|
||||
]
|
||||
control_block_samples = (tuple(control_block_samples),)
|
||||
|
||||
return control_block_samples
|
||||
class SD3MultiControlNetModel(SD3MultiControlNetModel):
|
||||
def __init__(self, *args, **kwargs):
|
||||
deprecation_message = "Importing `SD3MultiControlNetModel` from `diffusers.models.controlnet_sd3` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet_sd3 import SD3MultiControlNetModel`, instead."
|
||||
deprecate("SD3MultiControlNetModel", "0.34", deprecation_message)
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
@@ -12,777 +12,35 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from ..configuration_utils import ConfigMixin, register_to_config
|
||||
from ..loaders import FromOriginalModelMixin
|
||||
from ..utils import BaseOutput, logging
|
||||
from .attention_processor import (
|
||||
ADDED_KV_ATTENTION_PROCESSORS,
|
||||
CROSS_ATTENTION_PROCESSORS,
|
||||
AttentionProcessor,
|
||||
AttnAddedKVProcessor,
|
||||
AttnProcessor,
|
||||
from ..utils import deprecate, logging
|
||||
from .controlnets.controlnet_sparsectrl import ( # noqa
|
||||
SparseControlNetConditioningEmbedding,
|
||||
SparseControlNetModel,
|
||||
SparseControlNetOutput,
|
||||
zero_module,
|
||||
)
|
||||
from .embeddings import TimestepEmbedding, Timesteps
|
||||
from .modeling_utils import ModelMixin
|
||||
from .unets.unet_2d_blocks import UNetMidBlock2DCrossAttn
|
||||
from .unets.unet_2d_condition import UNet2DConditionModel
|
||||
from .unets.unet_motion_model import CrossAttnDownBlockMotion, DownBlockMotion
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
||||
|
||||
|
||||
@dataclass
|
||||
class SparseControlNetOutput(BaseOutput):
|
||||
"""
|
||||
The output of [`SparseControlNetModel`].
|
||||
class SparseControlNetOutput(SparseControlNetOutput):
|
||||
def __init__(self, *args, **kwargs):
|
||||
deprecation_message = "Importing `SparseControlNetOutput` from `diffusers.models.controlnet_sparsectrl` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet_sparsectrl import SparseControlNetOutput`, instead."
|
||||
deprecate("SparseControlNetOutput", "0.34", deprecation_message)
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
Args:
|
||||
down_block_res_samples (`tuple[torch.Tensor]`):
|
||||
A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
|
||||
be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
|
||||
used to condition the original UNet's downsampling activations.
|
||||
mid_down_block_re_sample (`torch.Tensor`):
|
||||
The activation of the middle block (the lowest sample resolution). Each tensor should be of shape
|
||||
`(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`.
|
||||
Output can be used to condition the original UNet's middle block activation.
|
||||
"""
|
||||
|
||||
down_block_res_samples: Tuple[torch.Tensor]
|
||||
mid_block_res_sample: torch.Tensor
|
||||
class SparseControlNetConditioningEmbedding(SparseControlNetConditioningEmbedding):
|
||||
def __init__(self, *args, **kwargs):
|
||||
deprecation_message = "Importing `SparseControlNetConditioningEmbedding` from `diffusers.models.controlnet_sparsectrl` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet_sparsectrl import SparseControlNetConditioningEmbedding`, instead."
|
||||
deprecate("SparseControlNetConditioningEmbedding", "0.34", deprecation_message)
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
|
||||
class SparseControlNetConditioningEmbedding(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
conditioning_embedding_channels: int,
|
||||
conditioning_channels: int = 3,
|
||||
block_out_channels: Tuple[int, ...] = (16, 32, 96, 256),
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
|
||||
self.blocks = nn.ModuleList([])
|
||||
|
||||
for i in range(len(block_out_channels) - 1):
|
||||
channel_in = block_out_channels[i]
|
||||
channel_out = block_out_channels[i + 1]
|
||||
self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
|
||||
self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
|
||||
|
||||
self.conv_out = zero_module(
|
||||
nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
|
||||
)
|
||||
|
||||
def forward(self, conditioning: torch.Tensor) -> torch.Tensor:
|
||||
embedding = self.conv_in(conditioning)
|
||||
embedding = F.silu(embedding)
|
||||
|
||||
for block in self.blocks:
|
||||
embedding = block(embedding)
|
||||
embedding = F.silu(embedding)
|
||||
|
||||
embedding = self.conv_out(embedding)
|
||||
return embedding
|
||||
|
||||
|
||||
class SparseControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
|
||||
"""
|
||||
A SparseControlNet model as described in [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion
|
||||
Models](https://arxiv.org/abs/2311.16933).
|
||||
|
||||
Args:
|
||||
in_channels (`int`, defaults to 4):
|
||||
The number of channels in the input sample.
|
||||
conditioning_channels (`int`, defaults to 4):
|
||||
The number of input channels in the controlnet conditional embedding module. If
|
||||
`concat_condition_embedding` is True, the value provided here is incremented by 1.
|
||||
flip_sin_to_cos (`bool`, defaults to `True`):
|
||||
Whether to flip the sin to cos in the time embedding.
|
||||
freq_shift (`int`, defaults to 0):
|
||||
The frequency shift to apply to the time embedding.
|
||||
down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
|
||||
The tuple of downsample blocks to use.
|
||||
only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
|
||||
block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
|
||||
The tuple of output channels for each block.
|
||||
layers_per_block (`int`, defaults to 2):
|
||||
The number of layers per block.
|
||||
downsample_padding (`int`, defaults to 1):
|
||||
The padding to use for the downsampling convolution.
|
||||
mid_block_scale_factor (`float`, defaults to 1):
|
||||
The scale factor to use for the mid block.
|
||||
act_fn (`str`, defaults to "silu"):
|
||||
The activation function to use.
|
||||
norm_num_groups (`int`, *optional*, defaults to 32):
|
||||
The number of groups to use for the normalization. If None, normalization and activation layers is skipped
|
||||
in post-processing.
|
||||
norm_eps (`float`, defaults to 1e-5):
|
||||
The epsilon to use for the normalization.
|
||||
cross_attention_dim (`int`, defaults to 1280):
|
||||
The dimension of the cross attention features.
|
||||
transformer_layers_per_block (`int` or `Tuple[int]`, *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`].
|
||||
transformer_layers_per_mid_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
|
||||
The number of transformer layers to use in each layer in the middle block.
|
||||
attention_head_dim (`int` or `Tuple[int]`, defaults to 8):
|
||||
The dimension of the attention heads.
|
||||
num_attention_heads (`int` or `Tuple[int]`, *optional*):
|
||||
The number of heads to use for multi-head attention.
|
||||
use_linear_projection (`bool`, defaults to `False`):
|
||||
upcast_attention (`bool`, defaults to `False`):
|
||||
resnet_time_scale_shift (`str`, defaults to `"default"`):
|
||||
Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
|
||||
conditioning_embedding_out_channels (`Tuple[int]`, defaults to `(16, 32, 96, 256)`):
|
||||
The tuple of output channel for each block in the `conditioning_embedding` layer.
|
||||
global_pool_conditions (`bool`, defaults to `False`):
|
||||
TODO(Patrick) - unused parameter
|
||||
controlnet_conditioning_channel_order (`str`, defaults to `rgb`):
|
||||
motion_max_seq_length (`int`, defaults to `32`):
|
||||
The maximum sequence length to use in the motion module.
|
||||
motion_num_attention_heads (`int` or `Tuple[int]`, defaults to `8`):
|
||||
The number of heads to use in each attention layer of the motion module.
|
||||
concat_conditioning_mask (`bool`, defaults to `True`):
|
||||
use_simplified_condition_embedding (`bool`, defaults to `True`):
|
||||
"""
|
||||
|
||||
_supports_gradient_checkpointing = True
|
||||
|
||||
@register_to_config
|
||||
def __init__(
|
||||
self,
|
||||
in_channels: int = 4,
|
||||
conditioning_channels: int = 4,
|
||||
flip_sin_to_cos: bool = True,
|
||||
freq_shift: int = 0,
|
||||
down_block_types: Tuple[str, ...] = (
|
||||
"CrossAttnDownBlockMotion",
|
||||
"CrossAttnDownBlockMotion",
|
||||
"CrossAttnDownBlockMotion",
|
||||
"DownBlockMotion",
|
||||
),
|
||||
only_cross_attention: Union[bool, Tuple[bool]] = False,
|
||||
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
|
||||
layers_per_block: int = 2,
|
||||
downsample_padding: int = 1,
|
||||
mid_block_scale_factor: float = 1,
|
||||
act_fn: str = "silu",
|
||||
norm_num_groups: Optional[int] = 32,
|
||||
norm_eps: float = 1e-5,
|
||||
cross_attention_dim: int = 768,
|
||||
transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
|
||||
transformer_layers_per_mid_block: Optional[Union[int, Tuple[int]]] = None,
|
||||
temporal_transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
|
||||
attention_head_dim: Union[int, Tuple[int, ...]] = 8,
|
||||
num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,
|
||||
use_linear_projection: bool = False,
|
||||
upcast_attention: bool = False,
|
||||
resnet_time_scale_shift: str = "default",
|
||||
conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
|
||||
global_pool_conditions: bool = False,
|
||||
controlnet_conditioning_channel_order: str = "rgb",
|
||||
motion_max_seq_length: int = 32,
|
||||
motion_num_attention_heads: int = 8,
|
||||
concat_conditioning_mask: bool = True,
|
||||
use_simplified_condition_embedding: bool = True,
|
||||
):
|
||||
super().__init__()
|
||||
self.use_simplified_condition_embedding = use_simplified_condition_embedding
|
||||
|
||||
# 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 = num_attention_heads or attention_head_dim
|
||||
|
||||
# Check inputs
|
||||
if len(block_out_channels) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if isinstance(transformer_layers_per_block, int):
|
||||
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
|
||||
if isinstance(temporal_transformer_layers_per_block, int):
|
||||
temporal_transformer_layers_per_block = [temporal_transformer_layers_per_block] * len(down_block_types)
|
||||
|
||||
# input
|
||||
conv_in_kernel = 3
|
||||
conv_in_padding = (conv_in_kernel - 1) // 2
|
||||
self.conv_in = nn.Conv2d(
|
||||
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
|
||||
)
|
||||
|
||||
if concat_conditioning_mask:
|
||||
conditioning_channels = conditioning_channels + 1
|
||||
|
||||
self.concat_conditioning_mask = concat_conditioning_mask
|
||||
|
||||
# control net conditioning embedding
|
||||
if use_simplified_condition_embedding:
|
||||
self.controlnet_cond_embedding = zero_module(
|
||||
nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
|
||||
)
|
||||
else:
|
||||
self.controlnet_cond_embedding = SparseControlNetConditioningEmbedding(
|
||||
conditioning_embedding_channels=block_out_channels[0],
|
||||
block_out_channels=conditioning_embedding_out_channels,
|
||||
conditioning_channels=conditioning_channels,
|
||||
)
|
||||
|
||||
# time
|
||||
time_embed_dim = block_out_channels[0] * 4
|
||||
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
|
||||
timestep_input_dim = block_out_channels[0]
|
||||
|
||||
self.time_embedding = TimestepEmbedding(
|
||||
timestep_input_dim,
|
||||
time_embed_dim,
|
||||
act_fn=act_fn,
|
||||
)
|
||||
|
||||
self.down_blocks = nn.ModuleList([])
|
||||
self.controlnet_down_blocks = nn.ModuleList([])
|
||||
|
||||
if isinstance(cross_attention_dim, int):
|
||||
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
|
||||
|
||||
if isinstance(only_cross_attention, bool):
|
||||
only_cross_attention = [only_cross_attention] * len(down_block_types)
|
||||
|
||||
if isinstance(attention_head_dim, int):
|
||||
attention_head_dim = (attention_head_dim,) * len(down_block_types)
|
||||
|
||||
if isinstance(num_attention_heads, int):
|
||||
num_attention_heads = (num_attention_heads,) * len(down_block_types)
|
||||
|
||||
if isinstance(motion_num_attention_heads, int):
|
||||
motion_num_attention_heads = (motion_num_attention_heads,) * len(down_block_types)
|
||||
|
||||
# down
|
||||
output_channel = block_out_channels[0]
|
||||
|
||||
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_down_blocks.append(controlnet_block)
|
||||
|
||||
for i, down_block_type in enumerate(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 == "CrossAttnDownBlockMotion":
|
||||
down_block = CrossAttnDownBlockMotion(
|
||||
in_channels=input_channel,
|
||||
out_channels=output_channel,
|
||||
temb_channels=time_embed_dim,
|
||||
dropout=0,
|
||||
num_layers=layers_per_block,
|
||||
transformer_layers_per_block=transformer_layers_per_block[i],
|
||||
resnet_eps=norm_eps,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
resnet_act_fn=act_fn,
|
||||
resnet_groups=norm_num_groups,
|
||||
resnet_pre_norm=True,
|
||||
num_attention_heads=num_attention_heads[i],
|
||||
cross_attention_dim=cross_attention_dim[i],
|
||||
add_downsample=not is_final_block,
|
||||
dual_cross_attention=False,
|
||||
use_linear_projection=use_linear_projection,
|
||||
only_cross_attention=only_cross_attention[i],
|
||||
upcast_attention=upcast_attention,
|
||||
temporal_num_attention_heads=motion_num_attention_heads[i],
|
||||
temporal_max_seq_length=motion_max_seq_length,
|
||||
temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],
|
||||
temporal_double_self_attention=False,
|
||||
)
|
||||
elif down_block_type == "DownBlockMotion":
|
||||
down_block = DownBlockMotion(
|
||||
in_channels=input_channel,
|
||||
out_channels=output_channel,
|
||||
temb_channels=time_embed_dim,
|
||||
dropout=0,
|
||||
num_layers=layers_per_block,
|
||||
resnet_eps=norm_eps,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
resnet_act_fn=act_fn,
|
||||
resnet_groups=norm_num_groups,
|
||||
resnet_pre_norm=True,
|
||||
add_downsample=not is_final_block,
|
||||
temporal_num_attention_heads=motion_num_attention_heads[i],
|
||||
temporal_max_seq_length=motion_max_seq_length,
|
||||
temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],
|
||||
temporal_double_self_attention=False,
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
"Invalid `block_type` encountered. Must be one of `CrossAttnDownBlockMotion` or `DownBlockMotion`"
|
||||
)
|
||||
|
||||
self.down_blocks.append(down_block)
|
||||
|
||||
for _ in range(layers_per_block):
|
||||
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_down_blocks.append(controlnet_block)
|
||||
|
||||
if not is_final_block:
|
||||
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_down_blocks.append(controlnet_block)
|
||||
|
||||
# mid
|
||||
mid_block_channels = block_out_channels[-1]
|
||||
|
||||
controlnet_block = nn.Conv2d(mid_block_channels, mid_block_channels, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_mid_block = controlnet_block
|
||||
|
||||
if transformer_layers_per_mid_block is None:
|
||||
transformer_layers_per_mid_block = (
|
||||
transformer_layers_per_block[-1] if isinstance(transformer_layers_per_block[-1], int) else 1
|
||||
)
|
||||
|
||||
self.mid_block = UNetMidBlock2DCrossAttn(
|
||||
in_channels=mid_block_channels,
|
||||
temb_channels=time_embed_dim,
|
||||
dropout=0,
|
||||
num_layers=1,
|
||||
transformer_layers_per_block=transformer_layers_per_mid_block,
|
||||
resnet_eps=norm_eps,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
resnet_act_fn=act_fn,
|
||||
resnet_groups=norm_num_groups,
|
||||
resnet_pre_norm=True,
|
||||
num_attention_heads=num_attention_heads[-1],
|
||||
output_scale_factor=mid_block_scale_factor,
|
||||
cross_attention_dim=cross_attention_dim[-1],
|
||||
dual_cross_attention=False,
|
||||
use_linear_projection=use_linear_projection,
|
||||
upcast_attention=upcast_attention,
|
||||
attention_type="default",
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_unet(
|
||||
cls,
|
||||
unet: UNet2DConditionModel,
|
||||
controlnet_conditioning_channel_order: str = "rgb",
|
||||
conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
|
||||
load_weights_from_unet: bool = True,
|
||||
conditioning_channels: int = 3,
|
||||
) -> "SparseControlNetModel":
|
||||
r"""
|
||||
Instantiate a [`SparseControlNetModel`] from [`UNet2DConditionModel`].
|
||||
|
||||
Parameters:
|
||||
unet (`UNet2DConditionModel`):
|
||||
The UNet model weights to copy to the [`SparseControlNetModel`]. All configuration options are also
|
||||
copied where applicable.
|
||||
"""
|
||||
transformer_layers_per_block = (
|
||||
unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1
|
||||
)
|
||||
down_block_types = unet.config.down_block_types
|
||||
|
||||
for i in range(len(down_block_types)):
|
||||
if "CrossAttn" in down_block_types[i]:
|
||||
down_block_types[i] = "CrossAttnDownBlockMotion"
|
||||
elif "Down" in down_block_types[i]:
|
||||
down_block_types[i] = "DownBlockMotion"
|
||||
else:
|
||||
raise ValueError("Invalid `block_type` encountered. Must be a cross-attention or down block")
|
||||
|
||||
controlnet = cls(
|
||||
in_channels=unet.config.in_channels,
|
||||
conditioning_channels=conditioning_channels,
|
||||
flip_sin_to_cos=unet.config.flip_sin_to_cos,
|
||||
freq_shift=unet.config.freq_shift,
|
||||
down_block_types=unet.config.down_block_types,
|
||||
only_cross_attention=unet.config.only_cross_attention,
|
||||
block_out_channels=unet.config.block_out_channels,
|
||||
layers_per_block=unet.config.layers_per_block,
|
||||
downsample_padding=unet.config.downsample_padding,
|
||||
mid_block_scale_factor=unet.config.mid_block_scale_factor,
|
||||
act_fn=unet.config.act_fn,
|
||||
norm_num_groups=unet.config.norm_num_groups,
|
||||
norm_eps=unet.config.norm_eps,
|
||||
cross_attention_dim=unet.config.cross_attention_dim,
|
||||
transformer_layers_per_block=transformer_layers_per_block,
|
||||
attention_head_dim=unet.config.attention_head_dim,
|
||||
num_attention_heads=unet.config.num_attention_heads,
|
||||
use_linear_projection=unet.config.use_linear_projection,
|
||||
upcast_attention=unet.config.upcast_attention,
|
||||
resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
|
||||
conditioning_embedding_out_channels=conditioning_embedding_out_channels,
|
||||
controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
|
||||
)
|
||||
|
||||
if load_weights_from_unet:
|
||||
controlnet.conv_in.load_state_dict(unet.conv_in.state_dict(), strict=False)
|
||||
controlnet.time_proj.load_state_dict(unet.time_proj.state_dict(), strict=False)
|
||||
controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict(), strict=False)
|
||||
controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict(), strict=False)
|
||||
controlnet.mid_block.load_state_dict(unet.mid_block.state_dict(), strict=False)
|
||||
|
||||
return controlnet
|
||||
|
||||
@property
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
|
||||
def attn_processors(self) -> Dict[str, AttentionProcessor]:
|
||||
r"""
|
||||
Returns:
|
||||
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
||||
indexed by its weight name.
|
||||
"""
|
||||
# set recursively
|
||||
processors = {}
|
||||
|
||||
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
||||
if hasattr(module, "get_processor"):
|
||||
processors[f"{name}.processor"] = module.get_processor()
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
||||
|
||||
return processors
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_add_processors(name, module, processors)
|
||||
|
||||
return processors
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
|
||||
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
|
||||
r"""
|
||||
Sets the attention processor to use to compute attention.
|
||||
|
||||
Parameters:
|
||||
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
|
||||
The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
||||
for **all** `Attention` layers.
|
||||
|
||||
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
|
||||
processor. This is strongly recommended when setting trainable attention processors.
|
||||
|
||||
"""
|
||||
count = len(self.attn_processors.keys())
|
||||
|
||||
if isinstance(processor, dict) and len(processor) != count:
|
||||
raise ValueError(
|
||||
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
||||
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
||||
)
|
||||
|
||||
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
||||
if hasattr(module, "set_processor"):
|
||||
if not isinstance(processor, dict):
|
||||
module.set_processor(processor)
|
||||
else:
|
||||
module.set_processor(processor.pop(f"{name}.processor"))
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_attn_processor(name, module, processor)
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
|
||||
def set_default_attn_processor(self):
|
||||
"""
|
||||
Disables custom attention processors and sets the default attention implementation.
|
||||
"""
|
||||
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
||||
processor = AttnAddedKVProcessor()
|
||||
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
||||
processor = AttnProcessor()
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
|
||||
)
|
||||
|
||||
self.set_attn_processor(processor)
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice
|
||||
def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:
|
||||
r"""
|
||||
Enable sliced attention computation.
|
||||
|
||||
When this option is enabled, the attention module splits the input tensor in slices to compute attention in
|
||||
several steps. This is useful for saving some memory in exchange for a small decrease in speed.
|
||||
|
||||
Args:
|
||||
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
|
||||
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
|
||||
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
|
||||
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
|
||||
must be a multiple of `slice_size`.
|
||||
"""
|
||||
sliceable_head_dims = []
|
||||
|
||||
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
|
||||
if hasattr(module, "set_attention_slice"):
|
||||
sliceable_head_dims.append(module.sliceable_head_dim)
|
||||
|
||||
for child in module.children():
|
||||
fn_recursive_retrieve_sliceable_dims(child)
|
||||
|
||||
# retrieve number of attention layers
|
||||
for module in self.children():
|
||||
fn_recursive_retrieve_sliceable_dims(module)
|
||||
|
||||
num_sliceable_layers = len(sliceable_head_dims)
|
||||
|
||||
if slice_size == "auto":
|
||||
# half the attention head size is usually a good trade-off between
|
||||
# speed and memory
|
||||
slice_size = [dim // 2 for dim in sliceable_head_dims]
|
||||
elif slice_size == "max":
|
||||
# make smallest slice possible
|
||||
slice_size = num_sliceable_layers * [1]
|
||||
|
||||
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
|
||||
|
||||
if len(slice_size) != len(sliceable_head_dims):
|
||||
raise ValueError(
|
||||
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
|
||||
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
|
||||
)
|
||||
|
||||
for i in range(len(slice_size)):
|
||||
size = slice_size[i]
|
||||
dim = sliceable_head_dims[i]
|
||||
if size is not None and size > dim:
|
||||
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
|
||||
|
||||
# Recursively walk through all the children.
|
||||
# Any children which exposes the set_attention_slice method
|
||||
# gets the message
|
||||
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
|
||||
if hasattr(module, "set_attention_slice"):
|
||||
module.set_attention_slice(slice_size.pop())
|
||||
|
||||
for child in module.children():
|
||||
fn_recursive_set_attention_slice(child, slice_size)
|
||||
|
||||
reversed_slice_size = list(reversed(slice_size))
|
||||
for module in self.children():
|
||||
fn_recursive_set_attention_slice(module, reversed_slice_size)
|
||||
|
||||
def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
|
||||
if isinstance(module, (CrossAttnDownBlockMotion, DownBlockMotion, UNetMidBlock2DCrossAttn)):
|
||||
module.gradient_checkpointing = value
|
||||
|
||||
def forward(
|
||||
self,
|
||||
sample: torch.Tensor,
|
||||
timestep: Union[torch.Tensor, float, int],
|
||||
encoder_hidden_states: torch.Tensor,
|
||||
controlnet_cond: torch.Tensor,
|
||||
conditioning_scale: float = 1.0,
|
||||
timestep_cond: Optional[torch.Tensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
conditioning_mask: Optional[torch.Tensor] = None,
|
||||
guess_mode: bool = False,
|
||||
return_dict: bool = True,
|
||||
) -> Union[SparseControlNetOutput, Tuple[Tuple[torch.Tensor, ...], torch.Tensor]]:
|
||||
"""
|
||||
The [`SparseControlNetModel`] forward method.
|
||||
|
||||
Args:
|
||||
sample (`torch.Tensor`):
|
||||
The noisy input tensor.
|
||||
timestep (`Union[torch.Tensor, float, int]`):
|
||||
The number of timesteps to denoise an input.
|
||||
encoder_hidden_states (`torch.Tensor`):
|
||||
The encoder hidden states.
|
||||
controlnet_cond (`torch.Tensor`):
|
||||
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
|
||||
conditioning_scale (`float`, defaults to `1.0`):
|
||||
The scale factor for ControlNet outputs.
|
||||
class_labels (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
|
||||
timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
|
||||
timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
|
||||
embeddings.
|
||||
attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
|
||||
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
|
||||
negative values to the attention scores corresponding to "discard" tokens.
|
||||
added_cond_kwargs (`dict`):
|
||||
Additional conditions for the Stable Diffusion XL UNet.
|
||||
cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
|
||||
A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
|
||||
guess_mode (`bool`, defaults to `False`):
|
||||
In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
|
||||
you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
|
||||
return_dict (`bool`, defaults to `True`):
|
||||
Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
|
||||
Returns:
|
||||
[`~models.controlnet.ControlNetOutput`] **or** `tuple`:
|
||||
If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
|
||||
returned where the first element is the sample tensor.
|
||||
"""
|
||||
sample_batch_size, sample_channels, sample_num_frames, sample_height, sample_width = sample.shape
|
||||
sample = torch.zeros_like(sample)
|
||||
|
||||
# check channel order
|
||||
channel_order = self.config.controlnet_conditioning_channel_order
|
||||
|
||||
if channel_order == "rgb":
|
||||
# in rgb order by default
|
||||
...
|
||||
elif channel_order == "bgr":
|
||||
controlnet_cond = torch.flip(controlnet_cond, dims=[1])
|
||||
else:
|
||||
raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
|
||||
|
||||
# prepare attention_mask
|
||||
if attention_mask is not None:
|
||||
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
||||
attention_mask = attention_mask.unsqueeze(1)
|
||||
|
||||
# 1. time
|
||||
timesteps = timestep
|
||||
if not torch.is_tensor(timesteps):
|
||||
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
|
||||
# This would be a good case for the `match` statement (Python 3.10+)
|
||||
is_mps = sample.device.type == "mps"
|
||||
if isinstance(timestep, float):
|
||||
dtype = torch.float32 if is_mps else torch.float64
|
||||
else:
|
||||
dtype = torch.int32 if is_mps else torch.int64
|
||||
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
||||
elif len(timesteps.shape) == 0:
|
||||
timesteps = timesteps[None].to(sample.device)
|
||||
|
||||
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
||||
timesteps = timesteps.expand(sample.shape[0])
|
||||
|
||||
t_emb = self.time_proj(timesteps)
|
||||
|
||||
# timesteps does not contain any weights and will always return f32 tensors
|
||||
# but time_embedding might actually be running in fp16. so we need to cast here.
|
||||
# there might be better ways to encapsulate this.
|
||||
t_emb = t_emb.to(dtype=sample.dtype)
|
||||
|
||||
emb = self.time_embedding(t_emb, timestep_cond)
|
||||
emb = emb.repeat_interleave(sample_num_frames, dim=0)
|
||||
|
||||
# 2. pre-process
|
||||
batch_size, channels, num_frames, height, width = sample.shape
|
||||
|
||||
sample = sample.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
|
||||
sample = self.conv_in(sample)
|
||||
|
||||
batch_frames, channels, height, width = sample.shape
|
||||
sample = sample[:, None].reshape(sample_batch_size, sample_num_frames, channels, height, width)
|
||||
|
||||
if self.concat_conditioning_mask:
|
||||
controlnet_cond = torch.cat([controlnet_cond, conditioning_mask], dim=1)
|
||||
|
||||
batch_size, channels, num_frames, height, width = controlnet_cond.shape
|
||||
controlnet_cond = controlnet_cond.permute(0, 2, 1, 3, 4).reshape(
|
||||
batch_size * num_frames, channels, height, width
|
||||
)
|
||||
controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
|
||||
batch_frames, channels, height, width = controlnet_cond.shape
|
||||
controlnet_cond = controlnet_cond[:, None].reshape(batch_size, num_frames, channels, height, width)
|
||||
|
||||
sample = sample + controlnet_cond
|
||||
|
||||
batch_size, num_frames, channels, height, width = sample.shape
|
||||
sample = sample.reshape(sample_batch_size * sample_num_frames, channels, height, width)
|
||||
|
||||
# 3. down
|
||||
down_block_res_samples = (sample,)
|
||||
for downsample_block in self.down_blocks:
|
||||
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
||||
sample, res_samples = downsample_block(
|
||||
hidden_states=sample,
|
||||
temb=emb,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
num_frames=num_frames,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
)
|
||||
else:
|
||||
sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames)
|
||||
|
||||
down_block_res_samples += res_samples
|
||||
|
||||
# 4. mid
|
||||
if self.mid_block is not None:
|
||||
if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
|
||||
sample = self.mid_block(
|
||||
sample,
|
||||
emb,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
)
|
||||
else:
|
||||
sample = self.mid_block(sample, emb)
|
||||
|
||||
# 5. Control net 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 = 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
|
||||
if guess_mode and not self.config.global_pool_conditions:
|
||||
scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0
|
||||
scales = scales * conditioning_scale
|
||||
down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
|
||||
mid_block_res_sample = mid_block_res_sample * scales[-1] # last one
|
||||
else:
|
||||
down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
|
||||
mid_block_res_sample = mid_block_res_sample * conditioning_scale
|
||||
|
||||
if self.config.global_pool_conditions:
|
||||
down_block_res_samples = [
|
||||
torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
|
||||
]
|
||||
mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)
|
||||
|
||||
if not return_dict:
|
||||
return (down_block_res_samples, mid_block_res_sample)
|
||||
|
||||
return SparseControlNetOutput(
|
||||
down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
|
||||
)
|
||||
|
||||
|
||||
# Copied from diffusers.models.controlnet.zero_module
|
||||
def zero_module(module: nn.Module) -> nn.Module:
|
||||
for p in module.parameters():
|
||||
nn.init.zeros_(p)
|
||||
return module
|
||||
class SparseControlNetModel(SparseControlNetModel):
|
||||
def __init__(self, *args, **kwargs):
|
||||
deprecation_message = "Importing `SparseControlNetModel` from `diffusers.models.controlnet_sparsectrl` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet_sparsectrl import SparseControlNetModel`, instead."
|
||||
deprecate("SparseControlNetModel", "0.34", deprecation_message)
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
22
src/diffusers/models/controlnets/__init__.py
Normal file
22
src/diffusers/models/controlnets/__init__.py
Normal file
@@ -0,0 +1,22 @@
|
||||
from ...utils import is_flax_available, is_torch_available
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
from .controlnet import ControlNetModel, ControlNetOutput
|
||||
from .controlnet_flux import FluxControlNetModel, FluxControlNetOutput, FluxMultiControlNetModel
|
||||
from .controlnet_hunyuan import (
|
||||
HunyuanControlNetOutput,
|
||||
HunyuanDiT2DControlNetModel,
|
||||
HunyuanDiT2DMultiControlNetModel,
|
||||
)
|
||||
from .controlnet_sd3 import SD3ControlNetModel, SD3ControlNetOutput, SD3MultiControlNetModel
|
||||
from .controlnet_sparsectrl import (
|
||||
SparseControlNetConditioningEmbedding,
|
||||
SparseControlNetModel,
|
||||
SparseControlNetOutput,
|
||||
)
|
||||
from .controlnet_xs import ControlNetXSAdapter, ControlNetXSOutput, UNetControlNetXSModel
|
||||
from .multicontrolnet import MultiControlNetModel
|
||||
|
||||
if is_flax_available():
|
||||
from .controlnet_flax import FlaxControlNetModel
|
||||
872
src/diffusers/models/controlnets/controlnet.py
Normal file
872
src/diffusers/models/controlnets/controlnet.py
Normal file
@@ -0,0 +1,872 @@
|
||||
# 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 dataclasses import dataclass
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from ...configuration_utils import ConfigMixin, register_to_config
|
||||
from ...loaders.single_file_model import FromOriginalModelMixin
|
||||
from ...utils import BaseOutput, logging
|
||||
from ..attention_processor import (
|
||||
ADDED_KV_ATTENTION_PROCESSORS,
|
||||
CROSS_ATTENTION_PROCESSORS,
|
||||
AttentionProcessor,
|
||||
AttnAddedKVProcessor,
|
||||
AttnProcessor,
|
||||
)
|
||||
from ..embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps
|
||||
from ..modeling_utils import ModelMixin
|
||||
from ..unets.unet_2d_blocks import (
|
||||
CrossAttnDownBlock2D,
|
||||
DownBlock2D,
|
||||
UNetMidBlock2D,
|
||||
UNetMidBlock2DCrossAttn,
|
||||
get_down_block,
|
||||
)
|
||||
from ..unets.unet_2d_condition import UNet2DConditionModel
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
||||
|
||||
|
||||
@dataclass
|
||||
class ControlNetOutput(BaseOutput):
|
||||
"""
|
||||
The output of [`ControlNetModel`].
|
||||
|
||||
Args:
|
||||
down_block_res_samples (`tuple[torch.Tensor]`):
|
||||
A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
|
||||
be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
|
||||
used to condition the original UNet's downsampling activations.
|
||||
mid_down_block_re_sample (`torch.Tensor`):
|
||||
The activation of the middle block (the lowest sample resolution). Each tensor should be of shape
|
||||
`(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`.
|
||||
Output can be used to condition the original UNet's middle block activation.
|
||||
"""
|
||||
|
||||
down_block_res_samples: Tuple[torch.Tensor]
|
||||
mid_block_res_sample: torch.Tensor
|
||||
|
||||
|
||||
class ControlNetConditioningEmbedding(nn.Module):
|
||||
"""
|
||||
Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN
|
||||
[11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized
|
||||
training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the
|
||||
convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides
|
||||
(activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full
|
||||
model) to encode image-space conditions ... into feature maps ..."
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
conditioning_embedding_channels: int,
|
||||
conditioning_channels: int = 3,
|
||||
block_out_channels: Tuple[int, ...] = (16, 32, 96, 256),
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
|
||||
|
||||
self.blocks = nn.ModuleList([])
|
||||
|
||||
for i in range(len(block_out_channels) - 1):
|
||||
channel_in = block_out_channels[i]
|
||||
channel_out = block_out_channels[i + 1]
|
||||
self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
|
||||
self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
|
||||
|
||||
self.conv_out = zero_module(
|
||||
nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
|
||||
)
|
||||
|
||||
def forward(self, conditioning):
|
||||
embedding = self.conv_in(conditioning)
|
||||
embedding = F.silu(embedding)
|
||||
|
||||
for block in self.blocks:
|
||||
embedding = block(embedding)
|
||||
embedding = F.silu(embedding)
|
||||
|
||||
embedding = self.conv_out(embedding)
|
||||
|
||||
return embedding
|
||||
|
||||
|
||||
class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
|
||||
"""
|
||||
A ControlNet model.
|
||||
|
||||
Args:
|
||||
in_channels (`int`, defaults to 4):
|
||||
The number of channels in the input sample.
|
||||
flip_sin_to_cos (`bool`, defaults to `True`):
|
||||
Whether to flip the sin to cos in the time embedding.
|
||||
freq_shift (`int`, defaults to 0):
|
||||
The frequency shift to apply to the time embedding.
|
||||
down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
|
||||
The tuple of downsample blocks to use.
|
||||
only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
|
||||
block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
|
||||
The tuple of output channels for each block.
|
||||
layers_per_block (`int`, defaults to 2):
|
||||
The number of layers per block.
|
||||
downsample_padding (`int`, defaults to 1):
|
||||
The padding to use for the downsampling convolution.
|
||||
mid_block_scale_factor (`float`, defaults to 1):
|
||||
The scale factor to use for the mid block.
|
||||
act_fn (`str`, defaults to "silu"):
|
||||
The activation function to use.
|
||||
norm_num_groups (`int`, *optional*, defaults to 32):
|
||||
The number of groups to use for the normalization. If None, normalization and activation layers is skipped
|
||||
in post-processing.
|
||||
norm_eps (`float`, defaults to 1e-5):
|
||||
The epsilon to use for the normalization.
|
||||
cross_attention_dim (`int`, defaults to 1280):
|
||||
The dimension of the cross attention features.
|
||||
transformer_layers_per_block (`int` or `Tuple[int]`, *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`].
|
||||
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`.
|
||||
encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
|
||||
If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
|
||||
embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
|
||||
attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8):
|
||||
The dimension of the attention heads.
|
||||
use_linear_projection (`bool`, defaults to `False`):
|
||||
class_embed_type (`str`, *optional*, defaults to `None`):
|
||||
The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None,
|
||||
`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
|
||||
addition_embed_type (`str`, *optional*, defaults to `None`):
|
||||
Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
|
||||
"text". "text" will use the `TextTimeEmbedding` layer.
|
||||
num_class_embeds (`int`, *optional*, defaults to 0):
|
||||
Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
|
||||
class conditioning with `class_embed_type` equal to `None`.
|
||||
upcast_attention (`bool`, defaults to `False`):
|
||||
resnet_time_scale_shift (`str`, defaults to `"default"`):
|
||||
Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
|
||||
projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):
|
||||
The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
|
||||
`class_embed_type="projection"`.
|
||||
controlnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
|
||||
The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
|
||||
conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`):
|
||||
The tuple of output channel for each block in the `conditioning_embedding` layer.
|
||||
global_pool_conditions (`bool`, defaults to `False`):
|
||||
TODO(Patrick) - unused parameter.
|
||||
addition_embed_type_num_heads (`int`, defaults to 64):
|
||||
The number of heads to use for the `TextTimeEmbedding` layer.
|
||||
"""
|
||||
|
||||
_supports_gradient_checkpointing = True
|
||||
|
||||
@register_to_config
|
||||
def __init__(
|
||||
self,
|
||||
in_channels: int = 4,
|
||||
conditioning_channels: int = 3,
|
||||
flip_sin_to_cos: bool = True,
|
||||
freq_shift: int = 0,
|
||||
down_block_types: Tuple[str, ...] = (
|
||||
"CrossAttnDownBlock2D",
|
||||
"CrossAttnDownBlock2D",
|
||||
"CrossAttnDownBlock2D",
|
||||
"DownBlock2D",
|
||||
),
|
||||
mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
|
||||
only_cross_attention: Union[bool, Tuple[bool]] = False,
|
||||
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
|
||||
layers_per_block: int = 2,
|
||||
downsample_padding: int = 1,
|
||||
mid_block_scale_factor: float = 1,
|
||||
act_fn: str = "silu",
|
||||
norm_num_groups: Optional[int] = 32,
|
||||
norm_eps: float = 1e-5,
|
||||
cross_attention_dim: int = 1280,
|
||||
transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
|
||||
encoder_hid_dim: Optional[int] = None,
|
||||
encoder_hid_dim_type: Optional[str] = None,
|
||||
attention_head_dim: Union[int, Tuple[int, ...]] = 8,
|
||||
num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,
|
||||
use_linear_projection: bool = False,
|
||||
class_embed_type: Optional[str] = None,
|
||||
addition_embed_type: Optional[str] = None,
|
||||
addition_time_embed_dim: Optional[int] = None,
|
||||
num_class_embeds: Optional[int] = None,
|
||||
upcast_attention: bool = False,
|
||||
resnet_time_scale_shift: str = "default",
|
||||
projection_class_embeddings_input_dim: Optional[int] = None,
|
||||
controlnet_conditioning_channel_order: str = "rgb",
|
||||
conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
|
||||
global_pool_conditions: bool = False,
|
||||
addition_embed_type_num_heads: int = 64,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
# 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 = num_attention_heads or attention_head_dim
|
||||
|
||||
# Check inputs
|
||||
if len(block_out_channels) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if isinstance(transformer_layers_per_block, int):
|
||||
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
|
||||
|
||||
# input
|
||||
conv_in_kernel = 3
|
||||
conv_in_padding = (conv_in_kernel - 1) // 2
|
||||
self.conv_in = nn.Conv2d(
|
||||
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
|
||||
)
|
||||
|
||||
# time
|
||||
time_embed_dim = block_out_channels[0] * 4
|
||||
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
|
||||
timestep_input_dim = block_out_channels[0]
|
||||
self.time_embedding = TimestepEmbedding(
|
||||
timestep_input_dim,
|
||||
time_embed_dim,
|
||||
act_fn=act_fn,
|
||||
)
|
||||
|
||||
if encoder_hid_dim_type is None and encoder_hid_dim is not None:
|
||||
encoder_hid_dim_type = "text_proj"
|
||||
self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
|
||||
logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
|
||||
|
||||
if encoder_hid_dim is None and encoder_hid_dim_type is not None:
|
||||
raise ValueError(
|
||||
f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
|
||||
)
|
||||
|
||||
if encoder_hid_dim_type == "text_proj":
|
||||
self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
|
||||
elif encoder_hid_dim_type == "text_image_proj":
|
||||
# image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
|
||||
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
|
||||
# case when `addition_embed_type == "text_image_proj"` (Kandinsky 2.1)`
|
||||
self.encoder_hid_proj = TextImageProjection(
|
||||
text_embed_dim=encoder_hid_dim,
|
||||
image_embed_dim=cross_attention_dim,
|
||||
cross_attention_dim=cross_attention_dim,
|
||||
)
|
||||
|
||||
elif encoder_hid_dim_type is not None:
|
||||
raise ValueError(
|
||||
f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
|
||||
)
|
||||
else:
|
||||
self.encoder_hid_proj = None
|
||||
|
||||
# class embedding
|
||||
if class_embed_type is None and num_class_embeds is not None:
|
||||
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
|
||||
elif class_embed_type == "timestep":
|
||||
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
|
||||
elif class_embed_type == "identity":
|
||||
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
|
||||
elif class_embed_type == "projection":
|
||||
if projection_class_embeddings_input_dim is None:
|
||||
raise ValueError(
|
||||
"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
|
||||
)
|
||||
# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
|
||||
# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
|
||||
# 2. it projects from an arbitrary input dimension.
|
||||
#
|
||||
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
|
||||
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
|
||||
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
|
||||
self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
||||
else:
|
||||
self.class_embedding = None
|
||||
|
||||
if addition_embed_type == "text":
|
||||
if encoder_hid_dim is not None:
|
||||
text_time_embedding_from_dim = encoder_hid_dim
|
||||
else:
|
||||
text_time_embedding_from_dim = cross_attention_dim
|
||||
|
||||
self.add_embedding = TextTimeEmbedding(
|
||||
text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
|
||||
)
|
||||
elif addition_embed_type == "text_image":
|
||||
# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
|
||||
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
|
||||
# case when `addition_embed_type == "text_image"` (Kandinsky 2.1)`
|
||||
self.add_embedding = TextImageTimeEmbedding(
|
||||
text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
|
||||
)
|
||||
elif addition_embed_type == "text_time":
|
||||
self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
|
||||
self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
||||
|
||||
elif addition_embed_type is not None:
|
||||
raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
|
||||
|
||||
# control net conditioning embedding
|
||||
self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
|
||||
conditioning_embedding_channels=block_out_channels[0],
|
||||
block_out_channels=conditioning_embedding_out_channels,
|
||||
conditioning_channels=conditioning_channels,
|
||||
)
|
||||
|
||||
self.down_blocks = nn.ModuleList([])
|
||||
self.controlnet_down_blocks = nn.ModuleList([])
|
||||
|
||||
if isinstance(only_cross_attention, bool):
|
||||
only_cross_attention = [only_cross_attention] * len(down_block_types)
|
||||
|
||||
if isinstance(attention_head_dim, int):
|
||||
attention_head_dim = (attention_head_dim,) * len(down_block_types)
|
||||
|
||||
if isinstance(num_attention_heads, int):
|
||||
num_attention_heads = (num_attention_heads,) * len(down_block_types)
|
||||
|
||||
# down
|
||||
output_channel = block_out_channels[0]
|
||||
|
||||
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_down_blocks.append(controlnet_block)
|
||||
|
||||
for i, down_block_type in enumerate(down_block_types):
|
||||
input_channel = output_channel
|
||||
output_channel = block_out_channels[i]
|
||||
is_final_block = i == len(block_out_channels) - 1
|
||||
|
||||
down_block = get_down_block(
|
||||
down_block_type,
|
||||
num_layers=layers_per_block,
|
||||
transformer_layers_per_block=transformer_layers_per_block[i],
|
||||
in_channels=input_channel,
|
||||
out_channels=output_channel,
|
||||
temb_channels=time_embed_dim,
|
||||
add_downsample=not is_final_block,
|
||||
resnet_eps=norm_eps,
|
||||
resnet_act_fn=act_fn,
|
||||
resnet_groups=norm_num_groups,
|
||||
cross_attention_dim=cross_attention_dim,
|
||||
num_attention_heads=num_attention_heads[i],
|
||||
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
|
||||
downsample_padding=downsample_padding,
|
||||
use_linear_projection=use_linear_projection,
|
||||
only_cross_attention=only_cross_attention[i],
|
||||
upcast_attention=upcast_attention,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
)
|
||||
self.down_blocks.append(down_block)
|
||||
|
||||
for _ in range(layers_per_block):
|
||||
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_down_blocks.append(controlnet_block)
|
||||
|
||||
if not is_final_block:
|
||||
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_down_blocks.append(controlnet_block)
|
||||
|
||||
# mid
|
||||
mid_block_channel = block_out_channels[-1]
|
||||
|
||||
controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_mid_block = controlnet_block
|
||||
|
||||
if mid_block_type == "UNetMidBlock2DCrossAttn":
|
||||
self.mid_block = UNetMidBlock2DCrossAttn(
|
||||
transformer_layers_per_block=transformer_layers_per_block[-1],
|
||||
in_channels=mid_block_channel,
|
||||
temb_channels=time_embed_dim,
|
||||
resnet_eps=norm_eps,
|
||||
resnet_act_fn=act_fn,
|
||||
output_scale_factor=mid_block_scale_factor,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
cross_attention_dim=cross_attention_dim,
|
||||
num_attention_heads=num_attention_heads[-1],
|
||||
resnet_groups=norm_num_groups,
|
||||
use_linear_projection=use_linear_projection,
|
||||
upcast_attention=upcast_attention,
|
||||
)
|
||||
elif mid_block_type == "UNetMidBlock2D":
|
||||
self.mid_block = UNetMidBlock2D(
|
||||
in_channels=block_out_channels[-1],
|
||||
temb_channels=time_embed_dim,
|
||||
num_layers=0,
|
||||
resnet_eps=norm_eps,
|
||||
resnet_act_fn=act_fn,
|
||||
output_scale_factor=mid_block_scale_factor,
|
||||
resnet_groups=norm_num_groups,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
add_attention=False,
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"unknown mid_block_type : {mid_block_type}")
|
||||
|
||||
@classmethod
|
||||
def from_unet(
|
||||
cls,
|
||||
unet: UNet2DConditionModel,
|
||||
controlnet_conditioning_channel_order: str = "rgb",
|
||||
conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
|
||||
load_weights_from_unet: bool = True,
|
||||
conditioning_channels: int = 3,
|
||||
):
|
||||
r"""
|
||||
Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`].
|
||||
|
||||
Parameters:
|
||||
unet (`UNet2DConditionModel`):
|
||||
The UNet model weights to copy to the [`ControlNetModel`]. All configuration options are also copied
|
||||
where applicable.
|
||||
"""
|
||||
transformer_layers_per_block = (
|
||||
unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1
|
||||
)
|
||||
encoder_hid_dim = unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None
|
||||
encoder_hid_dim_type = unet.config.encoder_hid_dim_type if "encoder_hid_dim_type" in unet.config else None
|
||||
addition_embed_type = unet.config.addition_embed_type if "addition_embed_type" in unet.config else None
|
||||
addition_time_embed_dim = (
|
||||
unet.config.addition_time_embed_dim if "addition_time_embed_dim" in unet.config else None
|
||||
)
|
||||
|
||||
controlnet = cls(
|
||||
encoder_hid_dim=encoder_hid_dim,
|
||||
encoder_hid_dim_type=encoder_hid_dim_type,
|
||||
addition_embed_type=addition_embed_type,
|
||||
addition_time_embed_dim=addition_time_embed_dim,
|
||||
transformer_layers_per_block=transformer_layers_per_block,
|
||||
in_channels=unet.config.in_channels,
|
||||
flip_sin_to_cos=unet.config.flip_sin_to_cos,
|
||||
freq_shift=unet.config.freq_shift,
|
||||
down_block_types=unet.config.down_block_types,
|
||||
only_cross_attention=unet.config.only_cross_attention,
|
||||
block_out_channels=unet.config.block_out_channels,
|
||||
layers_per_block=unet.config.layers_per_block,
|
||||
downsample_padding=unet.config.downsample_padding,
|
||||
mid_block_scale_factor=unet.config.mid_block_scale_factor,
|
||||
act_fn=unet.config.act_fn,
|
||||
norm_num_groups=unet.config.norm_num_groups,
|
||||
norm_eps=unet.config.norm_eps,
|
||||
cross_attention_dim=unet.config.cross_attention_dim,
|
||||
attention_head_dim=unet.config.attention_head_dim,
|
||||
num_attention_heads=unet.config.num_attention_heads,
|
||||
use_linear_projection=unet.config.use_linear_projection,
|
||||
class_embed_type=unet.config.class_embed_type,
|
||||
num_class_embeds=unet.config.num_class_embeds,
|
||||
upcast_attention=unet.config.upcast_attention,
|
||||
resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
|
||||
projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
|
||||
mid_block_type=unet.config.mid_block_type,
|
||||
controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
|
||||
conditioning_embedding_out_channels=conditioning_embedding_out_channels,
|
||||
conditioning_channels=conditioning_channels,
|
||||
)
|
||||
|
||||
if load_weights_from_unet:
|
||||
controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())
|
||||
controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())
|
||||
controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())
|
||||
|
||||
if controlnet.class_embedding:
|
||||
controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())
|
||||
|
||||
if hasattr(controlnet, "add_embedding"):
|
||||
controlnet.add_embedding.load_state_dict(unet.add_embedding.state_dict())
|
||||
|
||||
controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())
|
||||
controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())
|
||||
|
||||
return controlnet
|
||||
|
||||
@property
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
|
||||
def attn_processors(self) -> Dict[str, AttentionProcessor]:
|
||||
r"""
|
||||
Returns:
|
||||
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
||||
indexed by its weight name.
|
||||
"""
|
||||
# set recursively
|
||||
processors = {}
|
||||
|
||||
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
||||
if hasattr(module, "get_processor"):
|
||||
processors[f"{name}.processor"] = module.get_processor()
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
||||
|
||||
return processors
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_add_processors(name, module, processors)
|
||||
|
||||
return processors
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
|
||||
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
|
||||
r"""
|
||||
Sets the attention processor to use to compute attention.
|
||||
|
||||
Parameters:
|
||||
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
|
||||
The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
||||
for **all** `Attention` layers.
|
||||
|
||||
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
|
||||
processor. This is strongly recommended when setting trainable attention processors.
|
||||
|
||||
"""
|
||||
count = len(self.attn_processors.keys())
|
||||
|
||||
if isinstance(processor, dict) and len(processor) != count:
|
||||
raise ValueError(
|
||||
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
||||
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
||||
)
|
||||
|
||||
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
||||
if hasattr(module, "set_processor"):
|
||||
if not isinstance(processor, dict):
|
||||
module.set_processor(processor)
|
||||
else:
|
||||
module.set_processor(processor.pop(f"{name}.processor"))
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_attn_processor(name, module, processor)
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
|
||||
def set_default_attn_processor(self):
|
||||
"""
|
||||
Disables custom attention processors and sets the default attention implementation.
|
||||
"""
|
||||
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
||||
processor = AttnAddedKVProcessor()
|
||||
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
||||
processor = AttnProcessor()
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
|
||||
)
|
||||
|
||||
self.set_attn_processor(processor)
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice
|
||||
def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:
|
||||
r"""
|
||||
Enable sliced attention computation.
|
||||
|
||||
When this option is enabled, the attention module splits the input tensor in slices to compute attention in
|
||||
several steps. This is useful for saving some memory in exchange for a small decrease in speed.
|
||||
|
||||
Args:
|
||||
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
|
||||
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
|
||||
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
|
||||
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
|
||||
must be a multiple of `slice_size`.
|
||||
"""
|
||||
sliceable_head_dims = []
|
||||
|
||||
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
|
||||
if hasattr(module, "set_attention_slice"):
|
||||
sliceable_head_dims.append(module.sliceable_head_dim)
|
||||
|
||||
for child in module.children():
|
||||
fn_recursive_retrieve_sliceable_dims(child)
|
||||
|
||||
# retrieve number of attention layers
|
||||
for module in self.children():
|
||||
fn_recursive_retrieve_sliceable_dims(module)
|
||||
|
||||
num_sliceable_layers = len(sliceable_head_dims)
|
||||
|
||||
if slice_size == "auto":
|
||||
# half the attention head size is usually a good trade-off between
|
||||
# speed and memory
|
||||
slice_size = [dim // 2 for dim in sliceable_head_dims]
|
||||
elif slice_size == "max":
|
||||
# make smallest slice possible
|
||||
slice_size = num_sliceable_layers * [1]
|
||||
|
||||
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
|
||||
|
||||
if len(slice_size) != len(sliceable_head_dims):
|
||||
raise ValueError(
|
||||
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
|
||||
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
|
||||
)
|
||||
|
||||
for i in range(len(slice_size)):
|
||||
size = slice_size[i]
|
||||
dim = sliceable_head_dims[i]
|
||||
if size is not None and size > dim:
|
||||
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
|
||||
|
||||
# Recursively walk through all the children.
|
||||
# Any children which exposes the set_attention_slice method
|
||||
# gets the message
|
||||
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
|
||||
if hasattr(module, "set_attention_slice"):
|
||||
module.set_attention_slice(slice_size.pop())
|
||||
|
||||
for child in module.children():
|
||||
fn_recursive_set_attention_slice(child, slice_size)
|
||||
|
||||
reversed_slice_size = list(reversed(slice_size))
|
||||
for module in self.children():
|
||||
fn_recursive_set_attention_slice(module, reversed_slice_size)
|
||||
|
||||
def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
|
||||
if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):
|
||||
module.gradient_checkpointing = value
|
||||
|
||||
def forward(
|
||||
self,
|
||||
sample: torch.Tensor,
|
||||
timestep: Union[torch.Tensor, float, int],
|
||||
encoder_hidden_states: torch.Tensor,
|
||||
controlnet_cond: torch.Tensor,
|
||||
conditioning_scale: float = 1.0,
|
||||
class_labels: Optional[torch.Tensor] = None,
|
||||
timestep_cond: Optional[torch.Tensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
|
||||
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
guess_mode: bool = False,
|
||||
return_dict: bool = True,
|
||||
) -> Union[ControlNetOutput, Tuple[Tuple[torch.Tensor, ...], torch.Tensor]]:
|
||||
"""
|
||||
The [`ControlNetModel`] forward method.
|
||||
|
||||
Args:
|
||||
sample (`torch.Tensor`):
|
||||
The noisy input tensor.
|
||||
timestep (`Union[torch.Tensor, float, int]`):
|
||||
The number of timesteps to denoise an input.
|
||||
encoder_hidden_states (`torch.Tensor`):
|
||||
The encoder hidden states.
|
||||
controlnet_cond (`torch.Tensor`):
|
||||
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
|
||||
conditioning_scale (`float`, defaults to `1.0`):
|
||||
The scale factor for ControlNet outputs.
|
||||
class_labels (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
|
||||
timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
|
||||
timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
|
||||
embeddings.
|
||||
attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
|
||||
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
|
||||
negative values to the attention scores corresponding to "discard" tokens.
|
||||
added_cond_kwargs (`dict`):
|
||||
Additional conditions for the Stable Diffusion XL UNet.
|
||||
cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
|
||||
A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
|
||||
guess_mode (`bool`, defaults to `False`):
|
||||
In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
|
||||
you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
|
||||
return_dict (`bool`, defaults to `True`):
|
||||
Whether or not to return a [`~models.controlnets.controlnet.ControlNetOutput`] instead of a plain
|
||||
tuple.
|
||||
|
||||
Returns:
|
||||
[`~models.controlnets.controlnet.ControlNetOutput`] **or** `tuple`:
|
||||
If `return_dict` is `True`, a [`~models.controlnets.controlnet.ControlNetOutput`] is returned,
|
||||
otherwise a tuple is returned where the first element is the sample tensor.
|
||||
"""
|
||||
# check channel order
|
||||
channel_order = self.config.controlnet_conditioning_channel_order
|
||||
|
||||
if channel_order == "rgb":
|
||||
# in rgb order by default
|
||||
...
|
||||
elif channel_order == "bgr":
|
||||
controlnet_cond = torch.flip(controlnet_cond, dims=[1])
|
||||
else:
|
||||
raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
|
||||
|
||||
# prepare attention_mask
|
||||
if attention_mask is not None:
|
||||
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
||||
attention_mask = attention_mask.unsqueeze(1)
|
||||
|
||||
# 1. time
|
||||
timesteps = timestep
|
||||
if not torch.is_tensor(timesteps):
|
||||
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
|
||||
# This would be a good case for the `match` statement (Python 3.10+)
|
||||
is_mps = sample.device.type == "mps"
|
||||
if isinstance(timestep, float):
|
||||
dtype = torch.float32 if is_mps else torch.float64
|
||||
else:
|
||||
dtype = torch.int32 if is_mps else torch.int64
|
||||
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
||||
elif len(timesteps.shape) == 0:
|
||||
timesteps = timesteps[None].to(sample.device)
|
||||
|
||||
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
||||
timesteps = timesteps.expand(sample.shape[0])
|
||||
|
||||
t_emb = self.time_proj(timesteps)
|
||||
|
||||
# timesteps does not contain any weights and will always return f32 tensors
|
||||
# but time_embedding might actually be running in fp16. so we need to cast here.
|
||||
# there might be better ways to encapsulate this.
|
||||
t_emb = t_emb.to(dtype=sample.dtype)
|
||||
|
||||
emb = self.time_embedding(t_emb, timestep_cond)
|
||||
aug_emb = None
|
||||
|
||||
if self.class_embedding is not None:
|
||||
if class_labels is None:
|
||||
raise ValueError("class_labels should be provided when num_class_embeds > 0")
|
||||
|
||||
if self.config.class_embed_type == "timestep":
|
||||
class_labels = self.time_proj(class_labels)
|
||||
|
||||
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
|
||||
emb = emb + class_emb
|
||||
|
||||
if self.config.addition_embed_type is not None:
|
||||
if self.config.addition_embed_type == "text":
|
||||
aug_emb = self.add_embedding(encoder_hidden_states)
|
||||
|
||||
elif self.config.addition_embed_type == "text_time":
|
||||
if "text_embeds" not in added_cond_kwargs:
|
||||
raise ValueError(
|
||||
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
|
||||
)
|
||||
text_embeds = added_cond_kwargs.get("text_embeds")
|
||||
if "time_ids" not in added_cond_kwargs:
|
||||
raise ValueError(
|
||||
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
|
||||
)
|
||||
time_ids = added_cond_kwargs.get("time_ids")
|
||||
time_embeds = self.add_time_proj(time_ids.flatten())
|
||||
time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
|
||||
|
||||
add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
|
||||
add_embeds = add_embeds.to(emb.dtype)
|
||||
aug_emb = self.add_embedding(add_embeds)
|
||||
|
||||
emb = emb + aug_emb if aug_emb is not None else emb
|
||||
|
||||
# 2. pre-process
|
||||
sample = self.conv_in(sample)
|
||||
|
||||
controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
|
||||
sample = sample + controlnet_cond
|
||||
|
||||
# 3. down
|
||||
down_block_res_samples = (sample,)
|
||||
for downsample_block in self.down_blocks:
|
||||
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
||||
sample, res_samples = downsample_block(
|
||||
hidden_states=sample,
|
||||
temb=emb,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
)
|
||||
else:
|
||||
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
|
||||
|
||||
down_block_res_samples += res_samples
|
||||
|
||||
# 4. mid
|
||||
if self.mid_block is not None:
|
||||
if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
|
||||
sample = self.mid_block(
|
||||
sample,
|
||||
emb,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
)
|
||||
else:
|
||||
sample = self.mid_block(sample, emb)
|
||||
|
||||
# 5. Control net 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 = 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
|
||||
if guess_mode and not self.config.global_pool_conditions:
|
||||
scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0
|
||||
scales = scales * conditioning_scale
|
||||
down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
|
||||
mid_block_res_sample = mid_block_res_sample * scales[-1] # last one
|
||||
else:
|
||||
down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
|
||||
mid_block_res_sample = mid_block_res_sample * conditioning_scale
|
||||
|
||||
if self.config.global_pool_conditions:
|
||||
down_block_res_samples = [
|
||||
torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
|
||||
]
|
||||
mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)
|
||||
|
||||
if not return_dict:
|
||||
return (down_block_res_samples, mid_block_res_sample)
|
||||
|
||||
return ControlNetOutput(
|
||||
down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
|
||||
)
|
||||
|
||||
|
||||
def zero_module(module):
|
||||
for p in module.parameters():
|
||||
nn.init.zeros_(p)
|
||||
return module
|
||||
@@ -19,11 +19,11 @@ 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 (
|
||||
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,
|
||||
536
src/diffusers/models/controlnets/controlnet_flux.py
Normal file
536
src/diffusers/models/controlnets/controlnet_flux.py
Normal file
@@ -0,0 +1,536 @@
|
||||
# Copyright 2024 Black Forest Labs, The HuggingFace Team and The InstantX 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 dataclasses import dataclass
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from ...configuration_utils import ConfigMixin, register_to_config
|
||||
from ...loaders import PeftAdapterMixin
|
||||
from ...models.attention_processor import AttentionProcessor
|
||||
from ...models.modeling_utils import ModelMixin
|
||||
from ...utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
|
||||
from ..controlnet import BaseOutput, ControlNetConditioningEmbedding, zero_module
|
||||
from ..embeddings import CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings, FluxPosEmbed
|
||||
from ..modeling_outputs import Transformer2DModelOutput
|
||||
from ..transformers.transformer_flux import FluxSingleTransformerBlock, FluxTransformerBlock
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
||||
|
||||
|
||||
@dataclass
|
||||
class FluxControlNetOutput(BaseOutput):
|
||||
controlnet_block_samples: Tuple[torch.Tensor]
|
||||
controlnet_single_block_samples: Tuple[torch.Tensor]
|
||||
|
||||
|
||||
class FluxControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
|
||||
_supports_gradient_checkpointing = True
|
||||
|
||||
@register_to_config
|
||||
def __init__(
|
||||
self,
|
||||
patch_size: int = 1,
|
||||
in_channels: int = 64,
|
||||
num_layers: int = 19,
|
||||
num_single_layers: int = 38,
|
||||
attention_head_dim: int = 128,
|
||||
num_attention_heads: int = 24,
|
||||
joint_attention_dim: int = 4096,
|
||||
pooled_projection_dim: int = 768,
|
||||
guidance_embeds: bool = False,
|
||||
axes_dims_rope: List[int] = [16, 56, 56],
|
||||
num_mode: int = None,
|
||||
conditioning_embedding_channels: int = None,
|
||||
):
|
||||
super().__init__()
|
||||
self.out_channels = in_channels
|
||||
self.inner_dim = num_attention_heads * attention_head_dim
|
||||
|
||||
self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope)
|
||||
text_time_guidance_cls = (
|
||||
CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings
|
||||
)
|
||||
self.time_text_embed = text_time_guidance_cls(
|
||||
embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
|
||||
)
|
||||
|
||||
self.context_embedder = nn.Linear(joint_attention_dim, self.inner_dim)
|
||||
self.x_embedder = torch.nn.Linear(in_channels, self.inner_dim)
|
||||
|
||||
self.transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
FluxTransformerBlock(
|
||||
dim=self.inner_dim,
|
||||
num_attention_heads=num_attention_heads,
|
||||
attention_head_dim=attention_head_dim,
|
||||
)
|
||||
for i in range(num_layers)
|
||||
]
|
||||
)
|
||||
|
||||
self.single_transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
FluxSingleTransformerBlock(
|
||||
dim=self.inner_dim,
|
||||
num_attention_heads=num_attention_heads,
|
||||
attention_head_dim=attention_head_dim,
|
||||
)
|
||||
for i in range(num_single_layers)
|
||||
]
|
||||
)
|
||||
|
||||
# controlnet_blocks
|
||||
self.controlnet_blocks = nn.ModuleList([])
|
||||
for _ in range(len(self.transformer_blocks)):
|
||||
self.controlnet_blocks.append(zero_module(nn.Linear(self.inner_dim, self.inner_dim)))
|
||||
|
||||
self.controlnet_single_blocks = nn.ModuleList([])
|
||||
for _ in range(len(self.single_transformer_blocks)):
|
||||
self.controlnet_single_blocks.append(zero_module(nn.Linear(self.inner_dim, self.inner_dim)))
|
||||
|
||||
self.union = num_mode is not None
|
||||
if self.union:
|
||||
self.controlnet_mode_embedder = nn.Embedding(num_mode, self.inner_dim)
|
||||
|
||||
if conditioning_embedding_channels is not None:
|
||||
self.input_hint_block = ControlNetConditioningEmbedding(
|
||||
conditioning_embedding_channels=conditioning_embedding_channels, block_out_channels=(16, 16, 16, 16)
|
||||
)
|
||||
self.controlnet_x_embedder = torch.nn.Linear(in_channels, self.inner_dim)
|
||||
else:
|
||||
self.input_hint_block = None
|
||||
self.controlnet_x_embedder = zero_module(torch.nn.Linear(in_channels, self.inner_dim))
|
||||
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
@property
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
|
||||
def attn_processors(self):
|
||||
r"""
|
||||
Returns:
|
||||
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
||||
indexed by its weight name.
|
||||
"""
|
||||
# set recursively
|
||||
processors = {}
|
||||
|
||||
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
||||
if hasattr(module, "get_processor"):
|
||||
processors[f"{name}.processor"] = module.get_processor()
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
||||
|
||||
return processors
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_add_processors(name, module, processors)
|
||||
|
||||
return processors
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
|
||||
def set_attn_processor(self, processor):
|
||||
r"""
|
||||
Sets the attention processor to use to compute attention.
|
||||
|
||||
Parameters:
|
||||
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
|
||||
The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
||||
for **all** `Attention` layers.
|
||||
|
||||
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
|
||||
processor. This is strongly recommended when setting trainable attention processors.
|
||||
|
||||
"""
|
||||
count = len(self.attn_processors.keys())
|
||||
|
||||
if isinstance(processor, dict) and len(processor) != count:
|
||||
raise ValueError(
|
||||
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
||||
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
||||
)
|
||||
|
||||
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
||||
if hasattr(module, "set_processor"):
|
||||
if not isinstance(processor, dict):
|
||||
module.set_processor(processor)
|
||||
else:
|
||||
module.set_processor(processor.pop(f"{name}.processor"))
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_attn_processor(name, module, processor)
|
||||
|
||||
def _set_gradient_checkpointing(self, module, value=False):
|
||||
if hasattr(module, "gradient_checkpointing"):
|
||||
module.gradient_checkpointing = value
|
||||
|
||||
@classmethod
|
||||
def from_transformer(
|
||||
cls,
|
||||
transformer,
|
||||
num_layers: int = 4,
|
||||
num_single_layers: int = 10,
|
||||
attention_head_dim: int = 128,
|
||||
num_attention_heads: int = 24,
|
||||
load_weights_from_transformer=True,
|
||||
):
|
||||
config = transformer.config
|
||||
config["num_layers"] = num_layers
|
||||
config["num_single_layers"] = num_single_layers
|
||||
config["attention_head_dim"] = attention_head_dim
|
||||
config["num_attention_heads"] = num_attention_heads
|
||||
|
||||
controlnet = cls(**config)
|
||||
|
||||
if load_weights_from_transformer:
|
||||
controlnet.pos_embed.load_state_dict(transformer.pos_embed.state_dict())
|
||||
controlnet.time_text_embed.load_state_dict(transformer.time_text_embed.state_dict())
|
||||
controlnet.context_embedder.load_state_dict(transformer.context_embedder.state_dict())
|
||||
controlnet.x_embedder.load_state_dict(transformer.x_embedder.state_dict())
|
||||
controlnet.transformer_blocks.load_state_dict(transformer.transformer_blocks.state_dict(), strict=False)
|
||||
controlnet.single_transformer_blocks.load_state_dict(
|
||||
transformer.single_transformer_blocks.state_dict(), strict=False
|
||||
)
|
||||
|
||||
controlnet.controlnet_x_embedder = zero_module(controlnet.controlnet_x_embedder)
|
||||
|
||||
return controlnet
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
controlnet_cond: torch.Tensor,
|
||||
controlnet_mode: torch.Tensor = None,
|
||||
conditioning_scale: float = 1.0,
|
||||
encoder_hidden_states: torch.Tensor = None,
|
||||
pooled_projections: torch.Tensor = None,
|
||||
timestep: torch.LongTensor = None,
|
||||
img_ids: torch.Tensor = None,
|
||||
txt_ids: torch.Tensor = None,
|
||||
guidance: torch.Tensor = None,
|
||||
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
return_dict: bool = True,
|
||||
) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
|
||||
"""
|
||||
The [`FluxTransformer2DModel`] forward method.
|
||||
|
||||
Args:
|
||||
hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
|
||||
Input `hidden_states`.
|
||||
controlnet_cond (`torch.Tensor`):
|
||||
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
|
||||
controlnet_mode (`torch.Tensor`):
|
||||
The mode tensor of shape `(batch_size, 1)`.
|
||||
conditioning_scale (`float`, defaults to `1.0`):
|
||||
The scale factor for ControlNet outputs.
|
||||
encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
|
||||
Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
|
||||
pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
|
||||
from the embeddings of input conditions.
|
||||
timestep ( `torch.LongTensor`):
|
||||
Used to indicate denoising step.
|
||||
block_controlnet_hidden_states: (`list` of `torch.Tensor`):
|
||||
A list of tensors that if specified are added to the residuals of transformer blocks.
|
||||
joint_attention_kwargs (`dict`, *optional*):
|
||||
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
||||
`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.transformer_2d.Transformer2DModelOutput`] instead of a plain
|
||||
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 joint_attention_kwargs is not None:
|
||||
joint_attention_kwargs = joint_attention_kwargs.copy()
|
||||
lora_scale = joint_attention_kwargs.pop("scale", 1.0)
|
||||
else:
|
||||
lora_scale = 1.0
|
||||
|
||||
if USE_PEFT_BACKEND:
|
||||
# weight the lora layers by setting `lora_scale` for each PEFT layer
|
||||
scale_lora_layers(self, lora_scale)
|
||||
else:
|
||||
if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
|
||||
logger.warning(
|
||||
"Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
|
||||
)
|
||||
hidden_states = self.x_embedder(hidden_states)
|
||||
|
||||
if self.input_hint_block is not None:
|
||||
controlnet_cond = self.input_hint_block(controlnet_cond)
|
||||
batch_size, channels, height_pw, width_pw = controlnet_cond.shape
|
||||
height = height_pw // self.config.patch_size
|
||||
width = width_pw // self.config.patch_size
|
||||
controlnet_cond = controlnet_cond.reshape(
|
||||
batch_size, channels, height, self.config.patch_size, width, self.config.patch_size
|
||||
)
|
||||
controlnet_cond = controlnet_cond.permute(0, 2, 4, 1, 3, 5)
|
||||
controlnet_cond = controlnet_cond.reshape(batch_size, height * width, -1)
|
||||
# add
|
||||
hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_cond)
|
||||
|
||||
timestep = timestep.to(hidden_states.dtype) * 1000
|
||||
if guidance is not None:
|
||||
guidance = guidance.to(hidden_states.dtype) * 1000
|
||||
else:
|
||||
guidance = None
|
||||
temb = (
|
||||
self.time_text_embed(timestep, pooled_projections)
|
||||
if guidance is None
|
||||
else self.time_text_embed(timestep, guidance, pooled_projections)
|
||||
)
|
||||
encoder_hidden_states = self.context_embedder(encoder_hidden_states)
|
||||
|
||||
if self.union:
|
||||
# union mode
|
||||
if controlnet_mode is None:
|
||||
raise ValueError("`controlnet_mode` cannot be `None` when applying ControlNet-Union")
|
||||
# union mode emb
|
||||
controlnet_mode_emb = self.controlnet_mode_embedder(controlnet_mode)
|
||||
encoder_hidden_states = torch.cat([controlnet_mode_emb, encoder_hidden_states], dim=1)
|
||||
txt_ids = torch.cat([txt_ids[:1], txt_ids], dim=0)
|
||||
|
||||
if txt_ids.ndim == 3:
|
||||
logger.warning(
|
||||
"Passing `txt_ids` 3d torch.Tensor is deprecated."
|
||||
"Please remove the batch dimension and pass it as a 2d torch Tensor"
|
||||
)
|
||||
txt_ids = txt_ids[0]
|
||||
if img_ids.ndim == 3:
|
||||
logger.warning(
|
||||
"Passing `img_ids` 3d torch.Tensor is deprecated."
|
||||
"Please remove the batch dimension and pass it as a 2d torch Tensor"
|
||||
)
|
||||
img_ids = img_ids[0]
|
||||
|
||||
ids = torch.cat((txt_ids, img_ids), dim=0)
|
||||
image_rotary_emb = self.pos_embed(ids)
|
||||
|
||||
block_samples = ()
|
||||
for index_block, block in enumerate(self.transformer_blocks):
|
||||
if self.training and self.gradient_checkpointing:
|
||||
|
||||
def create_custom_forward(module, return_dict=None):
|
||||
def custom_forward(*inputs):
|
||||
if return_dict is not None:
|
||||
return module(*inputs, return_dict=return_dict)
|
||||
else:
|
||||
return module(*inputs)
|
||||
|
||||
return custom_forward
|
||||
|
||||
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
|
||||
encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
hidden_states,
|
||||
encoder_hidden_states,
|
||||
temb,
|
||||
image_rotary_emb,
|
||||
**ckpt_kwargs,
|
||||
)
|
||||
|
||||
else:
|
||||
encoder_hidden_states, hidden_states = block(
|
||||
hidden_states=hidden_states,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
temb=temb,
|
||||
image_rotary_emb=image_rotary_emb,
|
||||
)
|
||||
block_samples = block_samples + (hidden_states,)
|
||||
|
||||
hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
|
||||
|
||||
single_block_samples = ()
|
||||
for index_block, block in enumerate(self.single_transformer_blocks):
|
||||
if self.training and self.gradient_checkpointing:
|
||||
|
||||
def create_custom_forward(module, return_dict=None):
|
||||
def custom_forward(*inputs):
|
||||
if return_dict is not None:
|
||||
return module(*inputs, return_dict=return_dict)
|
||||
else:
|
||||
return module(*inputs)
|
||||
|
||||
return custom_forward
|
||||
|
||||
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
|
||||
hidden_states = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
hidden_states,
|
||||
temb,
|
||||
image_rotary_emb,
|
||||
**ckpt_kwargs,
|
||||
)
|
||||
|
||||
else:
|
||||
hidden_states = block(
|
||||
hidden_states=hidden_states,
|
||||
temb=temb,
|
||||
image_rotary_emb=image_rotary_emb,
|
||||
)
|
||||
single_block_samples = single_block_samples + (hidden_states[:, encoder_hidden_states.shape[1] :],)
|
||||
|
||||
# controlnet block
|
||||
controlnet_block_samples = ()
|
||||
for block_sample, controlnet_block in zip(block_samples, self.controlnet_blocks):
|
||||
block_sample = controlnet_block(block_sample)
|
||||
controlnet_block_samples = controlnet_block_samples + (block_sample,)
|
||||
|
||||
controlnet_single_block_samples = ()
|
||||
for single_block_sample, controlnet_block in zip(single_block_samples, self.controlnet_single_blocks):
|
||||
single_block_sample = controlnet_block(single_block_sample)
|
||||
controlnet_single_block_samples = controlnet_single_block_samples + (single_block_sample,)
|
||||
|
||||
# scaling
|
||||
controlnet_block_samples = [sample * conditioning_scale for sample in controlnet_block_samples]
|
||||
controlnet_single_block_samples = [sample * conditioning_scale for sample in controlnet_single_block_samples]
|
||||
|
||||
controlnet_block_samples = None if len(controlnet_block_samples) == 0 else controlnet_block_samples
|
||||
controlnet_single_block_samples = (
|
||||
None if len(controlnet_single_block_samples) == 0 else controlnet_single_block_samples
|
||||
)
|
||||
|
||||
if USE_PEFT_BACKEND:
|
||||
# remove `lora_scale` from each PEFT layer
|
||||
unscale_lora_layers(self, lora_scale)
|
||||
|
||||
if not return_dict:
|
||||
return (controlnet_block_samples, controlnet_single_block_samples)
|
||||
|
||||
return FluxControlNetOutput(
|
||||
controlnet_block_samples=controlnet_block_samples,
|
||||
controlnet_single_block_samples=controlnet_single_block_samples,
|
||||
)
|
||||
|
||||
|
||||
class FluxMultiControlNetModel(ModelMixin):
|
||||
r"""
|
||||
`FluxMultiControlNetModel` wrapper class for Multi-FluxControlNetModel
|
||||
|
||||
This module is a wrapper for multiple instances of the `FluxControlNetModel`. The `forward()` API is designed to be
|
||||
compatible with `FluxControlNetModel`.
|
||||
|
||||
Args:
|
||||
controlnets (`List[FluxControlNetModel]`):
|
||||
Provides additional conditioning to the unet during the denoising process. You must set multiple
|
||||
`FluxControlNetModel` as a list.
|
||||
"""
|
||||
|
||||
def __init__(self, controlnets):
|
||||
super().__init__()
|
||||
self.nets = nn.ModuleList(controlnets)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.FloatTensor,
|
||||
controlnet_cond: List[torch.tensor],
|
||||
controlnet_mode: List[torch.tensor],
|
||||
conditioning_scale: List[float],
|
||||
encoder_hidden_states: torch.Tensor = None,
|
||||
pooled_projections: torch.Tensor = None,
|
||||
timestep: torch.LongTensor = None,
|
||||
img_ids: torch.Tensor = None,
|
||||
txt_ids: torch.Tensor = None,
|
||||
guidance: torch.Tensor = None,
|
||||
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
return_dict: bool = True,
|
||||
) -> Union[FluxControlNetOutput, Tuple]:
|
||||
# ControlNet-Union with multiple conditions
|
||||
# only load one ControlNet for saving memories
|
||||
if len(self.nets) == 1 and self.nets[0].union:
|
||||
controlnet = self.nets[0]
|
||||
|
||||
for i, (image, mode, scale) in enumerate(zip(controlnet_cond, controlnet_mode, conditioning_scale)):
|
||||
block_samples, single_block_samples = controlnet(
|
||||
hidden_states=hidden_states,
|
||||
controlnet_cond=image,
|
||||
controlnet_mode=mode[:, None],
|
||||
conditioning_scale=scale,
|
||||
timestep=timestep,
|
||||
guidance=guidance,
|
||||
pooled_projections=pooled_projections,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
txt_ids=txt_ids,
|
||||
img_ids=img_ids,
|
||||
joint_attention_kwargs=joint_attention_kwargs,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
|
||||
# merge samples
|
||||
if i == 0:
|
||||
control_block_samples = block_samples
|
||||
control_single_block_samples = single_block_samples
|
||||
else:
|
||||
control_block_samples = [
|
||||
control_block_sample + block_sample
|
||||
for control_block_sample, block_sample in zip(control_block_samples, block_samples)
|
||||
]
|
||||
|
||||
control_single_block_samples = [
|
||||
control_single_block_sample + block_sample
|
||||
for control_single_block_sample, block_sample in zip(
|
||||
control_single_block_samples, single_block_samples
|
||||
)
|
||||
]
|
||||
|
||||
# Regular Multi-ControlNets
|
||||
# load all ControlNets into memories
|
||||
else:
|
||||
for i, (image, mode, scale, controlnet) in enumerate(
|
||||
zip(controlnet_cond, controlnet_mode, conditioning_scale, self.nets)
|
||||
):
|
||||
block_samples, single_block_samples = controlnet(
|
||||
hidden_states=hidden_states,
|
||||
controlnet_cond=image,
|
||||
controlnet_mode=mode[:, None],
|
||||
conditioning_scale=scale,
|
||||
timestep=timestep,
|
||||
guidance=guidance,
|
||||
pooled_projections=pooled_projections,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
txt_ids=txt_ids,
|
||||
img_ids=img_ids,
|
||||
joint_attention_kwargs=joint_attention_kwargs,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
|
||||
# merge samples
|
||||
if i == 0:
|
||||
control_block_samples = block_samples
|
||||
control_single_block_samples = single_block_samples
|
||||
else:
|
||||
if block_samples is not None and control_block_samples is not None:
|
||||
control_block_samples = [
|
||||
control_block_sample + block_sample
|
||||
for control_block_sample, block_sample in zip(control_block_samples, block_samples)
|
||||
]
|
||||
if single_block_samples is not None and control_single_block_samples is not None:
|
||||
control_single_block_samples = [
|
||||
control_single_block_sample + block_sample
|
||||
for control_single_block_sample, block_sample in zip(
|
||||
control_single_block_samples, single_block_samples
|
||||
)
|
||||
]
|
||||
|
||||
return control_block_samples, control_single_block_samples
|
||||
@@ -17,17 +17,17 @@ from typing import Dict, Optional, Union
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from ..configuration_utils import ConfigMixin, register_to_config
|
||||
from ..utils import logging
|
||||
from .attention_processor import AttentionProcessor
|
||||
from .controlnet import BaseOutput, Tuple, zero_module
|
||||
from .embeddings import (
|
||||
from ...configuration_utils import ConfigMixin, register_to_config
|
||||
from ...utils import logging
|
||||
from ..attention_processor import AttentionProcessor
|
||||
from ..embeddings import (
|
||||
HunyuanCombinedTimestepTextSizeStyleEmbedding,
|
||||
PatchEmbed,
|
||||
PixArtAlphaTextProjection,
|
||||
)
|
||||
from .modeling_utils import ModelMixin
|
||||
from .transformers.hunyuan_transformer_2d import HunyuanDiTBlock
|
||||
from ..modeling_utils import ModelMixin
|
||||
from ..transformers.hunyuan_transformer_2d import HunyuanDiTBlock
|
||||
from .controlnet import BaseOutput, Tuple, zero_module
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
||||
422
src/diffusers/models/controlnets/controlnet_sd3.py
Normal file
422
src/diffusers/models/controlnets/controlnet_sd3.py
Normal file
@@ -0,0 +1,422 @@
|
||||
# Copyright 2024 Stability AI, The HuggingFace Team and The InstantX 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 dataclasses import dataclass
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from ...configuration_utils import ConfigMixin, register_to_config
|
||||
from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
|
||||
from ...utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
|
||||
from ..attention import JointTransformerBlock
|
||||
from ..attention_processor import Attention, AttentionProcessor, FusedJointAttnProcessor2_0
|
||||
from ..embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed
|
||||
from ..modeling_outputs import Transformer2DModelOutput
|
||||
from ..modeling_utils import ModelMixin
|
||||
from .controlnet import BaseOutput, zero_module
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
||||
|
||||
|
||||
@dataclass
|
||||
class SD3ControlNetOutput(BaseOutput):
|
||||
controlnet_block_samples: Tuple[torch.Tensor]
|
||||
|
||||
|
||||
class SD3ControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
|
||||
_supports_gradient_checkpointing = True
|
||||
|
||||
@register_to_config
|
||||
def __init__(
|
||||
self,
|
||||
sample_size: int = 128,
|
||||
patch_size: int = 2,
|
||||
in_channels: int = 16,
|
||||
num_layers: int = 18,
|
||||
attention_head_dim: int = 64,
|
||||
num_attention_heads: int = 18,
|
||||
joint_attention_dim: int = 4096,
|
||||
caption_projection_dim: int = 1152,
|
||||
pooled_projection_dim: int = 2048,
|
||||
out_channels: int = 16,
|
||||
pos_embed_max_size: int = 96,
|
||||
extra_conditioning_channels: int = 0,
|
||||
):
|
||||
super().__init__()
|
||||
default_out_channels = in_channels
|
||||
self.out_channels = out_channels if out_channels is not None else default_out_channels
|
||||
self.inner_dim = num_attention_heads * attention_head_dim
|
||||
|
||||
self.pos_embed = PatchEmbed(
|
||||
height=sample_size,
|
||||
width=sample_size,
|
||||
patch_size=patch_size,
|
||||
in_channels=in_channels,
|
||||
embed_dim=self.inner_dim,
|
||||
pos_embed_max_size=pos_embed_max_size,
|
||||
)
|
||||
self.time_text_embed = CombinedTimestepTextProjEmbeddings(
|
||||
embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
|
||||
)
|
||||
self.context_embedder = nn.Linear(joint_attention_dim, caption_projection_dim)
|
||||
|
||||
# `attention_head_dim` is doubled to account for the mixing.
|
||||
# It needs to crafted when we get the actual checkpoints.
|
||||
self.transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
JointTransformerBlock(
|
||||
dim=self.inner_dim,
|
||||
num_attention_heads=num_attention_heads,
|
||||
attention_head_dim=self.config.attention_head_dim,
|
||||
context_pre_only=False,
|
||||
)
|
||||
for i in range(num_layers)
|
||||
]
|
||||
)
|
||||
|
||||
# controlnet_blocks
|
||||
self.controlnet_blocks = nn.ModuleList([])
|
||||
for _ in range(len(self.transformer_blocks)):
|
||||
controlnet_block = nn.Linear(self.inner_dim, self.inner_dim)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_blocks.append(controlnet_block)
|
||||
pos_embed_input = PatchEmbed(
|
||||
height=sample_size,
|
||||
width=sample_size,
|
||||
patch_size=patch_size,
|
||||
in_channels=in_channels + extra_conditioning_channels,
|
||||
embed_dim=self.inner_dim,
|
||||
pos_embed_type=None,
|
||||
)
|
||||
self.pos_embed_input = zero_module(pos_embed_input)
|
||||
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
|
||||
def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
|
||||
"""
|
||||
Sets the attention processor to use [feed forward
|
||||
chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
|
||||
|
||||
Parameters:
|
||||
chunk_size (`int`, *optional*):
|
||||
The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
|
||||
over each tensor of dim=`dim`.
|
||||
dim (`int`, *optional*, defaults to `0`):
|
||||
The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
|
||||
or dim=1 (sequence length).
|
||||
"""
|
||||
if dim not in [0, 1]:
|
||||
raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
|
||||
|
||||
# By default chunk size is 1
|
||||
chunk_size = chunk_size or 1
|
||||
|
||||
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
|
||||
if hasattr(module, "set_chunk_feed_forward"):
|
||||
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
|
||||
|
||||
for child in module.children():
|
||||
fn_recursive_feed_forward(child, chunk_size, dim)
|
||||
|
||||
for module in self.children():
|
||||
fn_recursive_feed_forward(module, chunk_size, dim)
|
||||
|
||||
@property
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
|
||||
def attn_processors(self) -> Dict[str, AttentionProcessor]:
|
||||
r"""
|
||||
Returns:
|
||||
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
||||
indexed by its weight name.
|
||||
"""
|
||||
# set recursively
|
||||
processors = {}
|
||||
|
||||
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
||||
if hasattr(module, "get_processor"):
|
||||
processors[f"{name}.processor"] = module.get_processor()
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
||||
|
||||
return processors
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_add_processors(name, module, processors)
|
||||
|
||||
return processors
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
|
||||
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
|
||||
r"""
|
||||
Sets the attention processor to use to compute attention.
|
||||
|
||||
Parameters:
|
||||
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
|
||||
The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
||||
for **all** `Attention` layers.
|
||||
|
||||
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
|
||||
processor. This is strongly recommended when setting trainable attention processors.
|
||||
|
||||
"""
|
||||
count = len(self.attn_processors.keys())
|
||||
|
||||
if isinstance(processor, dict) and len(processor) != count:
|
||||
raise ValueError(
|
||||
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
||||
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
||||
)
|
||||
|
||||
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
||||
if hasattr(module, "set_processor"):
|
||||
if not isinstance(processor, dict):
|
||||
module.set_processor(processor)
|
||||
else:
|
||||
module.set_processor(processor.pop(f"{name}.processor"))
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_attn_processor(name, module, processor)
|
||||
|
||||
# Copied from diffusers.models.transformers.transformer_sd3.SD3Transformer2DModel.fuse_qkv_projections
|
||||
def fuse_qkv_projections(self):
|
||||
"""
|
||||
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
|
||||
are fused. For cross-attention modules, key and value projection matrices are fused.
|
||||
|
||||
<Tip warning={true}>
|
||||
|
||||
This API is 🧪 experimental.
|
||||
|
||||
</Tip>
|
||||
"""
|
||||
self.original_attn_processors = None
|
||||
|
||||
for _, attn_processor in self.attn_processors.items():
|
||||
if "Added" in str(attn_processor.__class__.__name__):
|
||||
raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
|
||||
|
||||
self.original_attn_processors = self.attn_processors
|
||||
|
||||
for module in self.modules():
|
||||
if isinstance(module, Attention):
|
||||
module.fuse_projections(fuse=True)
|
||||
|
||||
self.set_attn_processor(FusedJointAttnProcessor2_0())
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
|
||||
def unfuse_qkv_projections(self):
|
||||
"""Disables the fused QKV projection if enabled.
|
||||
|
||||
<Tip warning={true}>
|
||||
|
||||
This API is 🧪 experimental.
|
||||
|
||||
</Tip>
|
||||
|
||||
"""
|
||||
if self.original_attn_processors is not None:
|
||||
self.set_attn_processor(self.original_attn_processors)
|
||||
|
||||
def _set_gradient_checkpointing(self, module, value=False):
|
||||
if hasattr(module, "gradient_checkpointing"):
|
||||
module.gradient_checkpointing = value
|
||||
|
||||
@classmethod
|
||||
def from_transformer(
|
||||
cls, transformer, num_layers=12, num_extra_conditioning_channels=1, load_weights_from_transformer=True
|
||||
):
|
||||
config = transformer.config
|
||||
config["num_layers"] = num_layers or config.num_layers
|
||||
config["extra_conditioning_channels"] = num_extra_conditioning_channels
|
||||
controlnet = cls(**config)
|
||||
|
||||
if load_weights_from_transformer:
|
||||
controlnet.pos_embed.load_state_dict(transformer.pos_embed.state_dict())
|
||||
controlnet.time_text_embed.load_state_dict(transformer.time_text_embed.state_dict())
|
||||
controlnet.context_embedder.load_state_dict(transformer.context_embedder.state_dict())
|
||||
controlnet.transformer_blocks.load_state_dict(transformer.transformer_blocks.state_dict(), strict=False)
|
||||
|
||||
controlnet.pos_embed_input = zero_module(controlnet.pos_embed_input)
|
||||
|
||||
return controlnet
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.FloatTensor,
|
||||
controlnet_cond: torch.Tensor,
|
||||
conditioning_scale: float = 1.0,
|
||||
encoder_hidden_states: torch.FloatTensor = None,
|
||||
pooled_projections: torch.FloatTensor = None,
|
||||
timestep: torch.LongTensor = None,
|
||||
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
return_dict: bool = True,
|
||||
) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
|
||||
"""
|
||||
The [`SD3Transformer2DModel`] forward method.
|
||||
|
||||
Args:
|
||||
hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
|
||||
Input `hidden_states`.
|
||||
controlnet_cond (`torch.Tensor`):
|
||||
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
|
||||
conditioning_scale (`float`, defaults to `1.0`):
|
||||
The scale factor for ControlNet outputs.
|
||||
encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
|
||||
Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
|
||||
pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
|
||||
from the embeddings of input conditions.
|
||||
timestep ( `torch.LongTensor`):
|
||||
Used to indicate denoising step.
|
||||
joint_attention_kwargs (`dict`, *optional*):
|
||||
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
||||
`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.transformer_2d.Transformer2DModelOutput`] instead of a plain
|
||||
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 joint_attention_kwargs is not None:
|
||||
joint_attention_kwargs = joint_attention_kwargs.copy()
|
||||
lora_scale = joint_attention_kwargs.pop("scale", 1.0)
|
||||
else:
|
||||
lora_scale = 1.0
|
||||
|
||||
if USE_PEFT_BACKEND:
|
||||
# weight the lora layers by setting `lora_scale` for each PEFT layer
|
||||
scale_lora_layers(self, lora_scale)
|
||||
else:
|
||||
if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
|
||||
logger.warning(
|
||||
"Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
|
||||
)
|
||||
|
||||
hidden_states = self.pos_embed(hidden_states) # takes care of adding positional embeddings too.
|
||||
temb = self.time_text_embed(timestep, pooled_projections)
|
||||
encoder_hidden_states = self.context_embedder(encoder_hidden_states)
|
||||
|
||||
# add
|
||||
hidden_states = hidden_states + self.pos_embed_input(controlnet_cond)
|
||||
|
||||
block_res_samples = ()
|
||||
|
||||
for block in self.transformer_blocks:
|
||||
if self.training and self.gradient_checkpointing:
|
||||
|
||||
def create_custom_forward(module, return_dict=None):
|
||||
def custom_forward(*inputs):
|
||||
if return_dict is not None:
|
||||
return module(*inputs, return_dict=return_dict)
|
||||
else:
|
||||
return module(*inputs)
|
||||
|
||||
return custom_forward
|
||||
|
||||
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
|
||||
encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
hidden_states,
|
||||
encoder_hidden_states,
|
||||
temb,
|
||||
**ckpt_kwargs,
|
||||
)
|
||||
|
||||
else:
|
||||
encoder_hidden_states, hidden_states = block(
|
||||
hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb
|
||||
)
|
||||
|
||||
block_res_samples = block_res_samples + (hidden_states,)
|
||||
|
||||
controlnet_block_res_samples = ()
|
||||
for block_res_sample, controlnet_block in zip(block_res_samples, self.controlnet_blocks):
|
||||
block_res_sample = controlnet_block(block_res_sample)
|
||||
controlnet_block_res_samples = controlnet_block_res_samples + (block_res_sample,)
|
||||
|
||||
# 6. scaling
|
||||
controlnet_block_res_samples = [sample * conditioning_scale for sample in controlnet_block_res_samples]
|
||||
|
||||
if USE_PEFT_BACKEND:
|
||||
# remove `lora_scale` from each PEFT layer
|
||||
unscale_lora_layers(self, lora_scale)
|
||||
|
||||
if not return_dict:
|
||||
return (controlnet_block_res_samples,)
|
||||
|
||||
return SD3ControlNetOutput(controlnet_block_samples=controlnet_block_res_samples)
|
||||
|
||||
|
||||
class SD3MultiControlNetModel(ModelMixin):
|
||||
r"""
|
||||
`SD3ControlNetModel` wrapper class for Multi-SD3ControlNet
|
||||
|
||||
This module is a wrapper for multiple instances of the `SD3ControlNetModel`. The `forward()` API is designed to be
|
||||
compatible with `SD3ControlNetModel`.
|
||||
|
||||
Args:
|
||||
controlnets (`List[SD3ControlNetModel]`):
|
||||
Provides additional conditioning to the unet during the denoising process. You must set multiple
|
||||
`SD3ControlNetModel` as a list.
|
||||
"""
|
||||
|
||||
def __init__(self, controlnets):
|
||||
super().__init__()
|
||||
self.nets = nn.ModuleList(controlnets)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.FloatTensor,
|
||||
controlnet_cond: List[torch.tensor],
|
||||
conditioning_scale: List[float],
|
||||
pooled_projections: torch.FloatTensor,
|
||||
encoder_hidden_states: torch.FloatTensor = None,
|
||||
timestep: torch.LongTensor = None,
|
||||
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
return_dict: bool = True,
|
||||
) -> Union[SD3ControlNetOutput, Tuple]:
|
||||
for i, (image, scale, controlnet) in enumerate(zip(controlnet_cond, conditioning_scale, self.nets)):
|
||||
block_samples = controlnet(
|
||||
hidden_states=hidden_states,
|
||||
timestep=timestep,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
pooled_projections=pooled_projections,
|
||||
controlnet_cond=image,
|
||||
conditioning_scale=scale,
|
||||
joint_attention_kwargs=joint_attention_kwargs,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
|
||||
# merge samples
|
||||
if i == 0:
|
||||
control_block_samples = block_samples
|
||||
else:
|
||||
control_block_samples = [
|
||||
control_block_sample + block_sample
|
||||
for control_block_sample, block_sample in zip(control_block_samples[0], block_samples[0])
|
||||
]
|
||||
control_block_samples = (tuple(control_block_samples),)
|
||||
|
||||
return control_block_samples
|
||||
788
src/diffusers/models/controlnets/controlnet_sparsectrl.py
Normal file
788
src/diffusers/models/controlnets/controlnet_sparsectrl.py
Normal file
@@ -0,0 +1,788 @@
|
||||
# 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 dataclasses import dataclass
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from ...configuration_utils import ConfigMixin, register_to_config
|
||||
from ...loaders import FromOriginalModelMixin
|
||||
from ...utils import BaseOutput, logging
|
||||
from ..attention_processor import (
|
||||
ADDED_KV_ATTENTION_PROCESSORS,
|
||||
CROSS_ATTENTION_PROCESSORS,
|
||||
AttentionProcessor,
|
||||
AttnAddedKVProcessor,
|
||||
AttnProcessor,
|
||||
)
|
||||
from ..embeddings import TimestepEmbedding, Timesteps
|
||||
from ..modeling_utils import ModelMixin
|
||||
from ..unets.unet_2d_blocks import UNetMidBlock2DCrossAttn
|
||||
from ..unets.unet_2d_condition import UNet2DConditionModel
|
||||
from ..unets.unet_motion_model import CrossAttnDownBlockMotion, DownBlockMotion
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
||||
|
||||
|
||||
@dataclass
|
||||
class SparseControlNetOutput(BaseOutput):
|
||||
"""
|
||||
The output of [`SparseControlNetModel`].
|
||||
|
||||
Args:
|
||||
down_block_res_samples (`tuple[torch.Tensor]`):
|
||||
A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
|
||||
be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
|
||||
used to condition the original UNet's downsampling activations.
|
||||
mid_down_block_re_sample (`torch.Tensor`):
|
||||
The activation of the middle block (the lowest sample resolution). Each tensor should be of shape
|
||||
`(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`.
|
||||
Output can be used to condition the original UNet's middle block activation.
|
||||
"""
|
||||
|
||||
down_block_res_samples: Tuple[torch.Tensor]
|
||||
mid_block_res_sample: torch.Tensor
|
||||
|
||||
|
||||
class SparseControlNetConditioningEmbedding(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
conditioning_embedding_channels: int,
|
||||
conditioning_channels: int = 3,
|
||||
block_out_channels: Tuple[int, ...] = (16, 32, 96, 256),
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
|
||||
self.blocks = nn.ModuleList([])
|
||||
|
||||
for i in range(len(block_out_channels) - 1):
|
||||
channel_in = block_out_channels[i]
|
||||
channel_out = block_out_channels[i + 1]
|
||||
self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
|
||||
self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
|
||||
|
||||
self.conv_out = zero_module(
|
||||
nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
|
||||
)
|
||||
|
||||
def forward(self, conditioning: torch.Tensor) -> torch.Tensor:
|
||||
embedding = self.conv_in(conditioning)
|
||||
embedding = F.silu(embedding)
|
||||
|
||||
for block in self.blocks:
|
||||
embedding = block(embedding)
|
||||
embedding = F.silu(embedding)
|
||||
|
||||
embedding = self.conv_out(embedding)
|
||||
return embedding
|
||||
|
||||
|
||||
class SparseControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
|
||||
"""
|
||||
A SparseControlNet model as described in [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion
|
||||
Models](https://arxiv.org/abs/2311.16933).
|
||||
|
||||
Args:
|
||||
in_channels (`int`, defaults to 4):
|
||||
The number of channels in the input sample.
|
||||
conditioning_channels (`int`, defaults to 4):
|
||||
The number of input channels in the controlnet conditional embedding module. If
|
||||
`concat_condition_embedding` is True, the value provided here is incremented by 1.
|
||||
flip_sin_to_cos (`bool`, defaults to `True`):
|
||||
Whether to flip the sin to cos in the time embedding.
|
||||
freq_shift (`int`, defaults to 0):
|
||||
The frequency shift to apply to the time embedding.
|
||||
down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
|
||||
The tuple of downsample blocks to use.
|
||||
only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
|
||||
block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
|
||||
The tuple of output channels for each block.
|
||||
layers_per_block (`int`, defaults to 2):
|
||||
The number of layers per block.
|
||||
downsample_padding (`int`, defaults to 1):
|
||||
The padding to use for the downsampling convolution.
|
||||
mid_block_scale_factor (`float`, defaults to 1):
|
||||
The scale factor to use for the mid block.
|
||||
act_fn (`str`, defaults to "silu"):
|
||||
The activation function to use.
|
||||
norm_num_groups (`int`, *optional*, defaults to 32):
|
||||
The number of groups to use for the normalization. If None, normalization and activation layers is skipped
|
||||
in post-processing.
|
||||
norm_eps (`float`, defaults to 1e-5):
|
||||
The epsilon to use for the normalization.
|
||||
cross_attention_dim (`int`, defaults to 1280):
|
||||
The dimension of the cross attention features.
|
||||
transformer_layers_per_block (`int` or `Tuple[int]`, *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`].
|
||||
transformer_layers_per_mid_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
|
||||
The number of transformer layers to use in each layer in the middle block.
|
||||
attention_head_dim (`int` or `Tuple[int]`, defaults to 8):
|
||||
The dimension of the attention heads.
|
||||
num_attention_heads (`int` or `Tuple[int]`, *optional*):
|
||||
The number of heads to use for multi-head attention.
|
||||
use_linear_projection (`bool`, defaults to `False`):
|
||||
upcast_attention (`bool`, defaults to `False`):
|
||||
resnet_time_scale_shift (`str`, defaults to `"default"`):
|
||||
Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
|
||||
conditioning_embedding_out_channels (`Tuple[int]`, defaults to `(16, 32, 96, 256)`):
|
||||
The tuple of output channel for each block in the `conditioning_embedding` layer.
|
||||
global_pool_conditions (`bool`, defaults to `False`):
|
||||
TODO(Patrick) - unused parameter
|
||||
controlnet_conditioning_channel_order (`str`, defaults to `rgb`):
|
||||
motion_max_seq_length (`int`, defaults to `32`):
|
||||
The maximum sequence length to use in the motion module.
|
||||
motion_num_attention_heads (`int` or `Tuple[int]`, defaults to `8`):
|
||||
The number of heads to use in each attention layer of the motion module.
|
||||
concat_conditioning_mask (`bool`, defaults to `True`):
|
||||
use_simplified_condition_embedding (`bool`, defaults to `True`):
|
||||
"""
|
||||
|
||||
_supports_gradient_checkpointing = True
|
||||
|
||||
@register_to_config
|
||||
def __init__(
|
||||
self,
|
||||
in_channels: int = 4,
|
||||
conditioning_channels: int = 4,
|
||||
flip_sin_to_cos: bool = True,
|
||||
freq_shift: int = 0,
|
||||
down_block_types: Tuple[str, ...] = (
|
||||
"CrossAttnDownBlockMotion",
|
||||
"CrossAttnDownBlockMotion",
|
||||
"CrossAttnDownBlockMotion",
|
||||
"DownBlockMotion",
|
||||
),
|
||||
only_cross_attention: Union[bool, Tuple[bool]] = False,
|
||||
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
|
||||
layers_per_block: int = 2,
|
||||
downsample_padding: int = 1,
|
||||
mid_block_scale_factor: float = 1,
|
||||
act_fn: str = "silu",
|
||||
norm_num_groups: Optional[int] = 32,
|
||||
norm_eps: float = 1e-5,
|
||||
cross_attention_dim: int = 768,
|
||||
transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
|
||||
transformer_layers_per_mid_block: Optional[Union[int, Tuple[int]]] = None,
|
||||
temporal_transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
|
||||
attention_head_dim: Union[int, Tuple[int, ...]] = 8,
|
||||
num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,
|
||||
use_linear_projection: bool = False,
|
||||
upcast_attention: bool = False,
|
||||
resnet_time_scale_shift: str = "default",
|
||||
conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
|
||||
global_pool_conditions: bool = False,
|
||||
controlnet_conditioning_channel_order: str = "rgb",
|
||||
motion_max_seq_length: int = 32,
|
||||
motion_num_attention_heads: int = 8,
|
||||
concat_conditioning_mask: bool = True,
|
||||
use_simplified_condition_embedding: bool = True,
|
||||
):
|
||||
super().__init__()
|
||||
self.use_simplified_condition_embedding = use_simplified_condition_embedding
|
||||
|
||||
# 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 = num_attention_heads or attention_head_dim
|
||||
|
||||
# Check inputs
|
||||
if len(block_out_channels) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if isinstance(transformer_layers_per_block, int):
|
||||
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
|
||||
if isinstance(temporal_transformer_layers_per_block, int):
|
||||
temporal_transformer_layers_per_block = [temporal_transformer_layers_per_block] * len(down_block_types)
|
||||
|
||||
# input
|
||||
conv_in_kernel = 3
|
||||
conv_in_padding = (conv_in_kernel - 1) // 2
|
||||
self.conv_in = nn.Conv2d(
|
||||
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
|
||||
)
|
||||
|
||||
if concat_conditioning_mask:
|
||||
conditioning_channels = conditioning_channels + 1
|
||||
|
||||
self.concat_conditioning_mask = concat_conditioning_mask
|
||||
|
||||
# control net conditioning embedding
|
||||
if use_simplified_condition_embedding:
|
||||
self.controlnet_cond_embedding = zero_module(
|
||||
nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
|
||||
)
|
||||
else:
|
||||
self.controlnet_cond_embedding = SparseControlNetConditioningEmbedding(
|
||||
conditioning_embedding_channels=block_out_channels[0],
|
||||
block_out_channels=conditioning_embedding_out_channels,
|
||||
conditioning_channels=conditioning_channels,
|
||||
)
|
||||
|
||||
# time
|
||||
time_embed_dim = block_out_channels[0] * 4
|
||||
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
|
||||
timestep_input_dim = block_out_channels[0]
|
||||
|
||||
self.time_embedding = TimestepEmbedding(
|
||||
timestep_input_dim,
|
||||
time_embed_dim,
|
||||
act_fn=act_fn,
|
||||
)
|
||||
|
||||
self.down_blocks = nn.ModuleList([])
|
||||
self.controlnet_down_blocks = nn.ModuleList([])
|
||||
|
||||
if isinstance(cross_attention_dim, int):
|
||||
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
|
||||
|
||||
if isinstance(only_cross_attention, bool):
|
||||
only_cross_attention = [only_cross_attention] * len(down_block_types)
|
||||
|
||||
if isinstance(attention_head_dim, int):
|
||||
attention_head_dim = (attention_head_dim,) * len(down_block_types)
|
||||
|
||||
if isinstance(num_attention_heads, int):
|
||||
num_attention_heads = (num_attention_heads,) * len(down_block_types)
|
||||
|
||||
if isinstance(motion_num_attention_heads, int):
|
||||
motion_num_attention_heads = (motion_num_attention_heads,) * len(down_block_types)
|
||||
|
||||
# down
|
||||
output_channel = block_out_channels[0]
|
||||
|
||||
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_down_blocks.append(controlnet_block)
|
||||
|
||||
for i, down_block_type in enumerate(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 == "CrossAttnDownBlockMotion":
|
||||
down_block = CrossAttnDownBlockMotion(
|
||||
in_channels=input_channel,
|
||||
out_channels=output_channel,
|
||||
temb_channels=time_embed_dim,
|
||||
dropout=0,
|
||||
num_layers=layers_per_block,
|
||||
transformer_layers_per_block=transformer_layers_per_block[i],
|
||||
resnet_eps=norm_eps,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
resnet_act_fn=act_fn,
|
||||
resnet_groups=norm_num_groups,
|
||||
resnet_pre_norm=True,
|
||||
num_attention_heads=num_attention_heads[i],
|
||||
cross_attention_dim=cross_attention_dim[i],
|
||||
add_downsample=not is_final_block,
|
||||
dual_cross_attention=False,
|
||||
use_linear_projection=use_linear_projection,
|
||||
only_cross_attention=only_cross_attention[i],
|
||||
upcast_attention=upcast_attention,
|
||||
temporal_num_attention_heads=motion_num_attention_heads[i],
|
||||
temporal_max_seq_length=motion_max_seq_length,
|
||||
temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],
|
||||
temporal_double_self_attention=False,
|
||||
)
|
||||
elif down_block_type == "DownBlockMotion":
|
||||
down_block = DownBlockMotion(
|
||||
in_channels=input_channel,
|
||||
out_channels=output_channel,
|
||||
temb_channels=time_embed_dim,
|
||||
dropout=0,
|
||||
num_layers=layers_per_block,
|
||||
resnet_eps=norm_eps,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
resnet_act_fn=act_fn,
|
||||
resnet_groups=norm_num_groups,
|
||||
resnet_pre_norm=True,
|
||||
add_downsample=not is_final_block,
|
||||
temporal_num_attention_heads=motion_num_attention_heads[i],
|
||||
temporal_max_seq_length=motion_max_seq_length,
|
||||
temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],
|
||||
temporal_double_self_attention=False,
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
"Invalid `block_type` encountered. Must be one of `CrossAttnDownBlockMotion` or `DownBlockMotion`"
|
||||
)
|
||||
|
||||
self.down_blocks.append(down_block)
|
||||
|
||||
for _ in range(layers_per_block):
|
||||
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_down_blocks.append(controlnet_block)
|
||||
|
||||
if not is_final_block:
|
||||
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_down_blocks.append(controlnet_block)
|
||||
|
||||
# mid
|
||||
mid_block_channels = block_out_channels[-1]
|
||||
|
||||
controlnet_block = nn.Conv2d(mid_block_channels, mid_block_channels, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_mid_block = controlnet_block
|
||||
|
||||
if transformer_layers_per_mid_block is None:
|
||||
transformer_layers_per_mid_block = (
|
||||
transformer_layers_per_block[-1] if isinstance(transformer_layers_per_block[-1], int) else 1
|
||||
)
|
||||
|
||||
self.mid_block = UNetMidBlock2DCrossAttn(
|
||||
in_channels=mid_block_channels,
|
||||
temb_channels=time_embed_dim,
|
||||
dropout=0,
|
||||
num_layers=1,
|
||||
transformer_layers_per_block=transformer_layers_per_mid_block,
|
||||
resnet_eps=norm_eps,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
resnet_act_fn=act_fn,
|
||||
resnet_groups=norm_num_groups,
|
||||
resnet_pre_norm=True,
|
||||
num_attention_heads=num_attention_heads[-1],
|
||||
output_scale_factor=mid_block_scale_factor,
|
||||
cross_attention_dim=cross_attention_dim[-1],
|
||||
dual_cross_attention=False,
|
||||
use_linear_projection=use_linear_projection,
|
||||
upcast_attention=upcast_attention,
|
||||
attention_type="default",
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_unet(
|
||||
cls,
|
||||
unet: UNet2DConditionModel,
|
||||
controlnet_conditioning_channel_order: str = "rgb",
|
||||
conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
|
||||
load_weights_from_unet: bool = True,
|
||||
conditioning_channels: int = 3,
|
||||
) -> "SparseControlNetModel":
|
||||
r"""
|
||||
Instantiate a [`SparseControlNetModel`] from [`UNet2DConditionModel`].
|
||||
|
||||
Parameters:
|
||||
unet (`UNet2DConditionModel`):
|
||||
The UNet model weights to copy to the [`SparseControlNetModel`]. All configuration options are also
|
||||
copied where applicable.
|
||||
"""
|
||||
transformer_layers_per_block = (
|
||||
unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1
|
||||
)
|
||||
down_block_types = unet.config.down_block_types
|
||||
|
||||
for i in range(len(down_block_types)):
|
||||
if "CrossAttn" in down_block_types[i]:
|
||||
down_block_types[i] = "CrossAttnDownBlockMotion"
|
||||
elif "Down" in down_block_types[i]:
|
||||
down_block_types[i] = "DownBlockMotion"
|
||||
else:
|
||||
raise ValueError("Invalid `block_type` encountered. Must be a cross-attention or down block")
|
||||
|
||||
controlnet = cls(
|
||||
in_channels=unet.config.in_channels,
|
||||
conditioning_channels=conditioning_channels,
|
||||
flip_sin_to_cos=unet.config.flip_sin_to_cos,
|
||||
freq_shift=unet.config.freq_shift,
|
||||
down_block_types=unet.config.down_block_types,
|
||||
only_cross_attention=unet.config.only_cross_attention,
|
||||
block_out_channels=unet.config.block_out_channels,
|
||||
layers_per_block=unet.config.layers_per_block,
|
||||
downsample_padding=unet.config.downsample_padding,
|
||||
mid_block_scale_factor=unet.config.mid_block_scale_factor,
|
||||
act_fn=unet.config.act_fn,
|
||||
norm_num_groups=unet.config.norm_num_groups,
|
||||
norm_eps=unet.config.norm_eps,
|
||||
cross_attention_dim=unet.config.cross_attention_dim,
|
||||
transformer_layers_per_block=transformer_layers_per_block,
|
||||
attention_head_dim=unet.config.attention_head_dim,
|
||||
num_attention_heads=unet.config.num_attention_heads,
|
||||
use_linear_projection=unet.config.use_linear_projection,
|
||||
upcast_attention=unet.config.upcast_attention,
|
||||
resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
|
||||
conditioning_embedding_out_channels=conditioning_embedding_out_channels,
|
||||
controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
|
||||
)
|
||||
|
||||
if load_weights_from_unet:
|
||||
controlnet.conv_in.load_state_dict(unet.conv_in.state_dict(), strict=False)
|
||||
controlnet.time_proj.load_state_dict(unet.time_proj.state_dict(), strict=False)
|
||||
controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict(), strict=False)
|
||||
controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict(), strict=False)
|
||||
controlnet.mid_block.load_state_dict(unet.mid_block.state_dict(), strict=False)
|
||||
|
||||
return controlnet
|
||||
|
||||
@property
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
|
||||
def attn_processors(self) -> Dict[str, AttentionProcessor]:
|
||||
r"""
|
||||
Returns:
|
||||
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
||||
indexed by its weight name.
|
||||
"""
|
||||
# set recursively
|
||||
processors = {}
|
||||
|
||||
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
||||
if hasattr(module, "get_processor"):
|
||||
processors[f"{name}.processor"] = module.get_processor()
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
||||
|
||||
return processors
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_add_processors(name, module, processors)
|
||||
|
||||
return processors
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
|
||||
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
|
||||
r"""
|
||||
Sets the attention processor to use to compute attention.
|
||||
|
||||
Parameters:
|
||||
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
|
||||
The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
||||
for **all** `Attention` layers.
|
||||
|
||||
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
|
||||
processor. This is strongly recommended when setting trainable attention processors.
|
||||
|
||||
"""
|
||||
count = len(self.attn_processors.keys())
|
||||
|
||||
if isinstance(processor, dict) and len(processor) != count:
|
||||
raise ValueError(
|
||||
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
||||
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
||||
)
|
||||
|
||||
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
||||
if hasattr(module, "set_processor"):
|
||||
if not isinstance(processor, dict):
|
||||
module.set_processor(processor)
|
||||
else:
|
||||
module.set_processor(processor.pop(f"{name}.processor"))
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_attn_processor(name, module, processor)
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
|
||||
def set_default_attn_processor(self):
|
||||
"""
|
||||
Disables custom attention processors and sets the default attention implementation.
|
||||
"""
|
||||
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
||||
processor = AttnAddedKVProcessor()
|
||||
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
||||
processor = AttnProcessor()
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
|
||||
)
|
||||
|
||||
self.set_attn_processor(processor)
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice
|
||||
def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:
|
||||
r"""
|
||||
Enable sliced attention computation.
|
||||
|
||||
When this option is enabled, the attention module splits the input tensor in slices to compute attention in
|
||||
several steps. This is useful for saving some memory in exchange for a small decrease in speed.
|
||||
|
||||
Args:
|
||||
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
|
||||
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
|
||||
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
|
||||
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
|
||||
must be a multiple of `slice_size`.
|
||||
"""
|
||||
sliceable_head_dims = []
|
||||
|
||||
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
|
||||
if hasattr(module, "set_attention_slice"):
|
||||
sliceable_head_dims.append(module.sliceable_head_dim)
|
||||
|
||||
for child in module.children():
|
||||
fn_recursive_retrieve_sliceable_dims(child)
|
||||
|
||||
# retrieve number of attention layers
|
||||
for module in self.children():
|
||||
fn_recursive_retrieve_sliceable_dims(module)
|
||||
|
||||
num_sliceable_layers = len(sliceable_head_dims)
|
||||
|
||||
if slice_size == "auto":
|
||||
# half the attention head size is usually a good trade-off between
|
||||
# speed and memory
|
||||
slice_size = [dim // 2 for dim in sliceable_head_dims]
|
||||
elif slice_size == "max":
|
||||
# make smallest slice possible
|
||||
slice_size = num_sliceable_layers * [1]
|
||||
|
||||
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
|
||||
|
||||
if len(slice_size) != len(sliceable_head_dims):
|
||||
raise ValueError(
|
||||
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
|
||||
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
|
||||
)
|
||||
|
||||
for i in range(len(slice_size)):
|
||||
size = slice_size[i]
|
||||
dim = sliceable_head_dims[i]
|
||||
if size is not None and size > dim:
|
||||
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
|
||||
|
||||
# Recursively walk through all the children.
|
||||
# Any children which exposes the set_attention_slice method
|
||||
# gets the message
|
||||
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
|
||||
if hasattr(module, "set_attention_slice"):
|
||||
module.set_attention_slice(slice_size.pop())
|
||||
|
||||
for child in module.children():
|
||||
fn_recursive_set_attention_slice(child, slice_size)
|
||||
|
||||
reversed_slice_size = list(reversed(slice_size))
|
||||
for module in self.children():
|
||||
fn_recursive_set_attention_slice(module, reversed_slice_size)
|
||||
|
||||
def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
|
||||
if isinstance(module, (CrossAttnDownBlockMotion, DownBlockMotion, UNetMidBlock2DCrossAttn)):
|
||||
module.gradient_checkpointing = value
|
||||
|
||||
def forward(
|
||||
self,
|
||||
sample: torch.Tensor,
|
||||
timestep: Union[torch.Tensor, float, int],
|
||||
encoder_hidden_states: torch.Tensor,
|
||||
controlnet_cond: torch.Tensor,
|
||||
conditioning_scale: float = 1.0,
|
||||
timestep_cond: Optional[torch.Tensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
conditioning_mask: Optional[torch.Tensor] = None,
|
||||
guess_mode: bool = False,
|
||||
return_dict: bool = True,
|
||||
) -> Union[SparseControlNetOutput, Tuple[Tuple[torch.Tensor, ...], torch.Tensor]]:
|
||||
"""
|
||||
The [`SparseControlNetModel`] forward method.
|
||||
|
||||
Args:
|
||||
sample (`torch.Tensor`):
|
||||
The noisy input tensor.
|
||||
timestep (`Union[torch.Tensor, float, int]`):
|
||||
The number of timesteps to denoise an input.
|
||||
encoder_hidden_states (`torch.Tensor`):
|
||||
The encoder hidden states.
|
||||
controlnet_cond (`torch.Tensor`):
|
||||
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
|
||||
conditioning_scale (`float`, defaults to `1.0`):
|
||||
The scale factor for ControlNet outputs.
|
||||
class_labels (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
|
||||
timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
|
||||
timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
|
||||
embeddings.
|
||||
attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
|
||||
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
|
||||
negative values to the attention scores corresponding to "discard" tokens.
|
||||
added_cond_kwargs (`dict`):
|
||||
Additional conditions for the Stable Diffusion XL UNet.
|
||||
cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
|
||||
A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
|
||||
guess_mode (`bool`, defaults to `False`):
|
||||
In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
|
||||
you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
|
||||
return_dict (`bool`, defaults to `True`):
|
||||
Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
|
||||
Returns:
|
||||
[`~models.controlnet.ControlNetOutput`] **or** `tuple`:
|
||||
If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
|
||||
returned where the first element is the sample tensor.
|
||||
"""
|
||||
sample_batch_size, sample_channels, sample_num_frames, sample_height, sample_width = sample.shape
|
||||
sample = torch.zeros_like(sample)
|
||||
|
||||
# check channel order
|
||||
channel_order = self.config.controlnet_conditioning_channel_order
|
||||
|
||||
if channel_order == "rgb":
|
||||
# in rgb order by default
|
||||
...
|
||||
elif channel_order == "bgr":
|
||||
controlnet_cond = torch.flip(controlnet_cond, dims=[1])
|
||||
else:
|
||||
raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
|
||||
|
||||
# prepare attention_mask
|
||||
if attention_mask is not None:
|
||||
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
||||
attention_mask = attention_mask.unsqueeze(1)
|
||||
|
||||
# 1. time
|
||||
timesteps = timestep
|
||||
if not torch.is_tensor(timesteps):
|
||||
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
|
||||
# This would be a good case for the `match` statement (Python 3.10+)
|
||||
is_mps = sample.device.type == "mps"
|
||||
if isinstance(timestep, float):
|
||||
dtype = torch.float32 if is_mps else torch.float64
|
||||
else:
|
||||
dtype = torch.int32 if is_mps else torch.int64
|
||||
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
||||
elif len(timesteps.shape) == 0:
|
||||
timesteps = timesteps[None].to(sample.device)
|
||||
|
||||
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
||||
timesteps = timesteps.expand(sample.shape[0])
|
||||
|
||||
t_emb = self.time_proj(timesteps)
|
||||
|
||||
# timesteps does not contain any weights and will always return f32 tensors
|
||||
# but time_embedding might actually be running in fp16. so we need to cast here.
|
||||
# there might be better ways to encapsulate this.
|
||||
t_emb = t_emb.to(dtype=sample.dtype)
|
||||
|
||||
emb = self.time_embedding(t_emb, timestep_cond)
|
||||
emb = emb.repeat_interleave(sample_num_frames, dim=0)
|
||||
|
||||
# 2. pre-process
|
||||
batch_size, channels, num_frames, height, width = sample.shape
|
||||
|
||||
sample = sample.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
|
||||
sample = self.conv_in(sample)
|
||||
|
||||
batch_frames, channels, height, width = sample.shape
|
||||
sample = sample[:, None].reshape(sample_batch_size, sample_num_frames, channels, height, width)
|
||||
|
||||
if self.concat_conditioning_mask:
|
||||
controlnet_cond = torch.cat([controlnet_cond, conditioning_mask], dim=1)
|
||||
|
||||
batch_size, channels, num_frames, height, width = controlnet_cond.shape
|
||||
controlnet_cond = controlnet_cond.permute(0, 2, 1, 3, 4).reshape(
|
||||
batch_size * num_frames, channels, height, width
|
||||
)
|
||||
controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
|
||||
batch_frames, channels, height, width = controlnet_cond.shape
|
||||
controlnet_cond = controlnet_cond[:, None].reshape(batch_size, num_frames, channels, height, width)
|
||||
|
||||
sample = sample + controlnet_cond
|
||||
|
||||
batch_size, num_frames, channels, height, width = sample.shape
|
||||
sample = sample.reshape(sample_batch_size * sample_num_frames, channels, height, width)
|
||||
|
||||
# 3. down
|
||||
down_block_res_samples = (sample,)
|
||||
for downsample_block in self.down_blocks:
|
||||
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
||||
sample, res_samples = downsample_block(
|
||||
hidden_states=sample,
|
||||
temb=emb,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
num_frames=num_frames,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
)
|
||||
else:
|
||||
sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames)
|
||||
|
||||
down_block_res_samples += res_samples
|
||||
|
||||
# 4. mid
|
||||
if self.mid_block is not None:
|
||||
if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
|
||||
sample = self.mid_block(
|
||||
sample,
|
||||
emb,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
)
|
||||
else:
|
||||
sample = self.mid_block(sample, emb)
|
||||
|
||||
# 5. Control net 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 = 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
|
||||
if guess_mode and not self.config.global_pool_conditions:
|
||||
scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0
|
||||
scales = scales * conditioning_scale
|
||||
down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
|
||||
mid_block_res_sample = mid_block_res_sample * scales[-1] # last one
|
||||
else:
|
||||
down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
|
||||
mid_block_res_sample = mid_block_res_sample * conditioning_scale
|
||||
|
||||
if self.config.global_pool_conditions:
|
||||
down_block_res_samples = [
|
||||
torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
|
||||
]
|
||||
mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)
|
||||
|
||||
if not return_dict:
|
||||
return (down_block_res_samples, mid_block_res_sample)
|
||||
|
||||
return SparseControlNetOutput(
|
||||
down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
|
||||
)
|
||||
|
||||
|
||||
# Copied from diffusers.models.controlnets.controlnet.zero_module
|
||||
def zero_module(module: nn.Module) -> nn.Module:
|
||||
for p in module.parameters():
|
||||
nn.init.zeros_(p)
|
||||
return module
|
||||
@@ -19,10 +19,10 @@ import torch
|
||||
import torch.utils.checkpoint
|
||||
from torch import Tensor, nn
|
||||
|
||||
from ..configuration_utils import ConfigMixin, register_to_config
|
||||
from ..utils import BaseOutput, is_torch_version, logging
|
||||
from ..utils.torch_utils import apply_freeu
|
||||
from .attention_processor import (
|
||||
from ...configuration_utils import ConfigMixin, register_to_config
|
||||
from ...utils import BaseOutput, is_torch_version, logging
|
||||
from ...utils.torch_utils import apply_freeu
|
||||
from ..attention_processor import (
|
||||
ADDED_KV_ATTENTION_PROCESSORS,
|
||||
CROSS_ATTENTION_PROCESSORS,
|
||||
Attention,
|
||||
@@ -31,10 +31,9 @@ from .attention_processor import (
|
||||
AttnProcessor,
|
||||
FusedAttnProcessor2_0,
|
||||
)
|
||||
from .controlnet import ControlNetConditioningEmbedding
|
||||
from .embeddings import TimestepEmbedding, Timesteps
|
||||
from .modeling_utils import ModelMixin
|
||||
from .unets.unet_2d_blocks import (
|
||||
from ..embeddings import TimestepEmbedding, Timesteps
|
||||
from ..modeling_utils import ModelMixin
|
||||
from ..unets.unet_2d_blocks import (
|
||||
CrossAttnDownBlock2D,
|
||||
CrossAttnUpBlock2D,
|
||||
Downsample2D,
|
||||
@@ -43,7 +42,8 @@ from .unets.unet_2d_blocks import (
|
||||
UNetMidBlock2DCrossAttn,
|
||||
Upsample2D,
|
||||
)
|
||||
from .unets.unet_2d_condition import UNet2DConditionModel
|
||||
from ..unets.unet_2d_condition import UNet2DConditionModel
|
||||
from .controlnet import ControlNetConditioningEmbedding
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
||||
@@ -1062,7 +1062,8 @@ class UNetControlNetXSModel(ModelMixin, ConfigMixin):
|
||||
added_cond_kwargs (`dict`):
|
||||
Additional conditions for the Stable Diffusion XL UNet.
|
||||
return_dict (`bool`, defaults to `True`):
|
||||
Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
|
||||
Whether or not to return a [`~models.controlnets.controlnet.ControlNetOutput`] instead of a plain
|
||||
tuple.
|
||||
apply_control (`bool`, defaults to `True`):
|
||||
If `False`, the input is run only through the base model.
|
||||
|
||||
183
src/diffusers/models/controlnets/multicontrolnet.py
Normal file
183
src/diffusers/models/controlnets/multicontrolnet.py
Normal file
@@ -0,0 +1,183 @@
|
||||
import os
|
||||
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from ...models.controlnets.controlnet import ControlNetModel, ControlNetOutput
|
||||
from ...models.modeling_utils import ModelMixin
|
||||
from ...utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class MultiControlNetModel(ModelMixin):
|
||||
r"""
|
||||
Multiple `ControlNetModel` wrapper class for Multi-ControlNet
|
||||
|
||||
This module is a wrapper for multiple instances of the `ControlNetModel`. The `forward()` API is designed to be
|
||||
compatible with `ControlNetModel`.
|
||||
|
||||
Args:
|
||||
controlnets (`List[ControlNetModel]`):
|
||||
Provides additional conditioning to the unet during the denoising process. You must set multiple
|
||||
`ControlNetModel` as a list.
|
||||
"""
|
||||
|
||||
def __init__(self, controlnets: Union[List[ControlNetModel], Tuple[ControlNetModel]]):
|
||||
super().__init__()
|
||||
self.nets = nn.ModuleList(controlnets)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
sample: torch.Tensor,
|
||||
timestep: Union[torch.Tensor, float, int],
|
||||
encoder_hidden_states: torch.Tensor,
|
||||
controlnet_cond: List[torch.tensor],
|
||||
conditioning_scale: List[float],
|
||||
class_labels: Optional[torch.Tensor] = None,
|
||||
timestep_cond: Optional[torch.Tensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
|
||||
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
guess_mode: bool = False,
|
||||
return_dict: bool = True,
|
||||
) -> Union[ControlNetOutput, Tuple]:
|
||||
for i, (image, scale, controlnet) in enumerate(zip(controlnet_cond, conditioning_scale, self.nets)):
|
||||
down_samples, mid_sample = controlnet(
|
||||
sample=sample,
|
||||
timestep=timestep,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
controlnet_cond=image,
|
||||
conditioning_scale=scale,
|
||||
class_labels=class_labels,
|
||||
timestep_cond=timestep_cond,
|
||||
attention_mask=attention_mask,
|
||||
added_cond_kwargs=added_cond_kwargs,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
guess_mode=guess_mode,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
|
||||
# merge samples
|
||||
if i == 0:
|
||||
down_block_res_samples, mid_block_res_sample = down_samples, mid_sample
|
||||
else:
|
||||
down_block_res_samples = [
|
||||
samples_prev + samples_curr
|
||||
for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)
|
||||
]
|
||||
mid_block_res_sample += mid_sample
|
||||
|
||||
return down_block_res_samples, mid_block_res_sample
|
||||
|
||||
def save_pretrained(
|
||||
self,
|
||||
save_directory: Union[str, os.PathLike],
|
||||
is_main_process: bool = True,
|
||||
save_function: Callable = None,
|
||||
safe_serialization: bool = True,
|
||||
variant: Optional[str] = None,
|
||||
):
|
||||
"""
|
||||
Save a model and its configuration file to a directory, so that it can be re-loaded using the
|
||||
`[`~pipelines.controlnet.MultiControlNetModel.from_pretrained`]` class method.
|
||||
|
||||
Arguments:
|
||||
save_directory (`str` or `os.PathLike`):
|
||||
Directory to which to save. Will be created if it doesn't exist.
|
||||
is_main_process (`bool`, *optional*, defaults to `True`):
|
||||
Whether the process calling this is the main process or not. Useful when in distributed training like
|
||||
TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on
|
||||
the main process to avoid race conditions.
|
||||
save_function (`Callable`):
|
||||
The function to use to save the state dictionary. Useful on distributed training like TPUs when one
|
||||
need to replace `torch.save` by another method. Can be configured with the environment variable
|
||||
`DIFFUSERS_SAVE_MODE`.
|
||||
safe_serialization (`bool`, *optional*, defaults to `True`):
|
||||
Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
|
||||
variant (`str`, *optional*):
|
||||
If specified, weights are saved in the format pytorch_model.<variant>.bin.
|
||||
"""
|
||||
for idx, controlnet in enumerate(self.nets):
|
||||
suffix = "" if idx == 0 else f"_{idx}"
|
||||
controlnet.save_pretrained(
|
||||
save_directory + suffix,
|
||||
is_main_process=is_main_process,
|
||||
save_function=save_function,
|
||||
safe_serialization=safe_serialization,
|
||||
variant=variant,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_path: Optional[Union[str, os.PathLike]], **kwargs):
|
||||
r"""
|
||||
Instantiate a pretrained MultiControlNet model from multiple pre-trained controlnet models.
|
||||
|
||||
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
|
||||
the model, you should first set it back in training mode with `model.train()`.
|
||||
|
||||
The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
|
||||
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
|
||||
task.
|
||||
|
||||
The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
|
||||
weights are discarded.
|
||||
|
||||
Parameters:
|
||||
pretrained_model_path (`os.PathLike`):
|
||||
A path to a *directory* containing model weights saved using
|
||||
[`~diffusers.pipelines.controlnet.MultiControlNetModel.save_pretrained`], e.g.,
|
||||
`./my_model_directory/controlnet`.
|
||||
torch_dtype (`str` or `torch.dtype`, *optional*):
|
||||
Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
|
||||
will be automatically derived from the model's weights.
|
||||
output_loading_info(`bool`, *optional*, defaults to `False`):
|
||||
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
|
||||
device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
|
||||
A map that specifies where each submodule should go. It doesn't need to be refined to each
|
||||
parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
|
||||
same device.
|
||||
|
||||
To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
|
||||
more information about each option see [designing a device
|
||||
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
|
||||
max_memory (`Dict`, *optional*):
|
||||
A dictionary device identifier to maximum memory. Will default to the maximum memory available for each
|
||||
GPU and the available CPU RAM if unset.
|
||||
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
|
||||
Speed up model loading by not initializing the weights and only loading the pre-trained weights. This
|
||||
also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the
|
||||
model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,
|
||||
setting this argument to `True` will raise an error.
|
||||
variant (`str`, *optional*):
|
||||
If specified load weights from `variant` filename, *e.g.* pytorch_model.<variant>.bin. `variant` is
|
||||
ignored when using `from_flax`.
|
||||
use_safetensors (`bool`, *optional*, defaults to `None`):
|
||||
If set to `None`, the `safetensors` weights will be downloaded if they're available **and** if the
|
||||
`safetensors` library is installed. If set to `True`, the model will be forcibly loaded from
|
||||
`safetensors` weights. If set to `False`, loading will *not* use `safetensors`.
|
||||
"""
|
||||
idx = 0
|
||||
controlnets = []
|
||||
|
||||
# load controlnet and append to list until no controlnet directory exists anymore
|
||||
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
|
||||
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
|
||||
model_path_to_load = pretrained_model_path
|
||||
while os.path.isdir(model_path_to_load):
|
||||
controlnet = ControlNetModel.from_pretrained(model_path_to_load, **kwargs)
|
||||
controlnets.append(controlnet)
|
||||
|
||||
idx += 1
|
||||
model_path_to_load = pretrained_model_path + f"_{idx}"
|
||||
|
||||
logger.info(f"{len(controlnets)} controlnets loaded from {pretrained_model_path}.")
|
||||
|
||||
if len(controlnets) == 0:
|
||||
raise ValueError(
|
||||
f"No ControlNets found under {os.path.dirname(pretrained_model_path)}. Expected at least {pretrained_model_path + '_0'}."
|
||||
)
|
||||
|
||||
return cls(controlnets)
|
||||
@@ -24,7 +24,7 @@ from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPV
|
||||
from ...image_processor import PipelineImageInput, VaeImageProcessor
|
||||
from ...loaders import IPAdapterMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
|
||||
from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel
|
||||
from ...models.controlnet_sparsectrl import SparseControlNetModel
|
||||
from ...models.controlnets.controlnet_sparsectrl import SparseControlNetModel
|
||||
from ...models.lora import adjust_lora_scale_text_encoder
|
||||
from ...models.unets.unet_motion_model import MotionAdapter
|
||||
from ...schedulers import KarrasDiffusionSchedulers
|
||||
|
||||
@@ -1,183 +1,12 @@
|
||||
import os
|
||||
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from ...models.controlnet import ControlNetModel, ControlNetOutput
|
||||
from ...models.modeling_utils import ModelMixin
|
||||
from ...utils import logging
|
||||
from ...models.controlnets.multicontrolnet import MultiControlNetModel
|
||||
from ...utils import deprecate, logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class MultiControlNetModel(ModelMixin):
|
||||
r"""
|
||||
Multiple `ControlNetModel` wrapper class for Multi-ControlNet
|
||||
|
||||
This module is a wrapper for multiple instances of the `ControlNetModel`. The `forward()` API is designed to be
|
||||
compatible with `ControlNetModel`.
|
||||
|
||||
Args:
|
||||
controlnets (`List[ControlNetModel]`):
|
||||
Provides additional conditioning to the unet during the denoising process. You must set multiple
|
||||
`ControlNetModel` as a list.
|
||||
"""
|
||||
|
||||
def __init__(self, controlnets: Union[List[ControlNetModel], Tuple[ControlNetModel]]):
|
||||
super().__init__()
|
||||
self.nets = nn.ModuleList(controlnets)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
sample: torch.Tensor,
|
||||
timestep: Union[torch.Tensor, float, int],
|
||||
encoder_hidden_states: torch.Tensor,
|
||||
controlnet_cond: List[torch.tensor],
|
||||
conditioning_scale: List[float],
|
||||
class_labels: Optional[torch.Tensor] = None,
|
||||
timestep_cond: Optional[torch.Tensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
|
||||
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
guess_mode: bool = False,
|
||||
return_dict: bool = True,
|
||||
) -> Union[ControlNetOutput, Tuple]:
|
||||
for i, (image, scale, controlnet) in enumerate(zip(controlnet_cond, conditioning_scale, self.nets)):
|
||||
down_samples, mid_sample = controlnet(
|
||||
sample=sample,
|
||||
timestep=timestep,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
controlnet_cond=image,
|
||||
conditioning_scale=scale,
|
||||
class_labels=class_labels,
|
||||
timestep_cond=timestep_cond,
|
||||
attention_mask=attention_mask,
|
||||
added_cond_kwargs=added_cond_kwargs,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
guess_mode=guess_mode,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
|
||||
# merge samples
|
||||
if i == 0:
|
||||
down_block_res_samples, mid_block_res_sample = down_samples, mid_sample
|
||||
else:
|
||||
down_block_res_samples = [
|
||||
samples_prev + samples_curr
|
||||
for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)
|
||||
]
|
||||
mid_block_res_sample += mid_sample
|
||||
|
||||
return down_block_res_samples, mid_block_res_sample
|
||||
|
||||
def save_pretrained(
|
||||
self,
|
||||
save_directory: Union[str, os.PathLike],
|
||||
is_main_process: bool = True,
|
||||
save_function: Callable = None,
|
||||
safe_serialization: bool = True,
|
||||
variant: Optional[str] = None,
|
||||
):
|
||||
"""
|
||||
Save a model and its configuration file to a directory, so that it can be re-loaded using the
|
||||
`[`~pipelines.controlnet.MultiControlNetModel.from_pretrained`]` class method.
|
||||
|
||||
Arguments:
|
||||
save_directory (`str` or `os.PathLike`):
|
||||
Directory to which to save. Will be created if it doesn't exist.
|
||||
is_main_process (`bool`, *optional*, defaults to `True`):
|
||||
Whether the process calling this is the main process or not. Useful when in distributed training like
|
||||
TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on
|
||||
the main process to avoid race conditions.
|
||||
save_function (`Callable`):
|
||||
The function to use to save the state dictionary. Useful on distributed training like TPUs when one
|
||||
need to replace `torch.save` by another method. Can be configured with the environment variable
|
||||
`DIFFUSERS_SAVE_MODE`.
|
||||
safe_serialization (`bool`, *optional*, defaults to `True`):
|
||||
Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
|
||||
variant (`str`, *optional*):
|
||||
If specified, weights are saved in the format pytorch_model.<variant>.bin.
|
||||
"""
|
||||
for idx, controlnet in enumerate(self.nets):
|
||||
suffix = "" if idx == 0 else f"_{idx}"
|
||||
controlnet.save_pretrained(
|
||||
save_directory + suffix,
|
||||
is_main_process=is_main_process,
|
||||
save_function=save_function,
|
||||
safe_serialization=safe_serialization,
|
||||
variant=variant,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_path: Optional[Union[str, os.PathLike]], **kwargs):
|
||||
r"""
|
||||
Instantiate a pretrained MultiControlNet model from multiple pre-trained controlnet models.
|
||||
|
||||
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
|
||||
the model, you should first set it back in training mode with `model.train()`.
|
||||
|
||||
The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
|
||||
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
|
||||
task.
|
||||
|
||||
The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
|
||||
weights are discarded.
|
||||
|
||||
Parameters:
|
||||
pretrained_model_path (`os.PathLike`):
|
||||
A path to a *directory* containing model weights saved using
|
||||
[`~diffusers.pipelines.controlnet.MultiControlNetModel.save_pretrained`], e.g.,
|
||||
`./my_model_directory/controlnet`.
|
||||
torch_dtype (`str` or `torch.dtype`, *optional*):
|
||||
Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
|
||||
will be automatically derived from the model's weights.
|
||||
output_loading_info(`bool`, *optional*, defaults to `False`):
|
||||
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
|
||||
device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
|
||||
A map that specifies where each submodule should go. It doesn't need to be refined to each
|
||||
parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
|
||||
same device.
|
||||
|
||||
To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
|
||||
more information about each option see [designing a device
|
||||
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
|
||||
max_memory (`Dict`, *optional*):
|
||||
A dictionary device identifier to maximum memory. Will default to the maximum memory available for each
|
||||
GPU and the available CPU RAM if unset.
|
||||
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
|
||||
Speed up model loading by not initializing the weights and only loading the pre-trained weights. This
|
||||
also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the
|
||||
model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,
|
||||
setting this argument to `True` will raise an error.
|
||||
variant (`str`, *optional*):
|
||||
If specified load weights from `variant` filename, *e.g.* pytorch_model.<variant>.bin. `variant` is
|
||||
ignored when using `from_flax`.
|
||||
use_safetensors (`bool`, *optional*, defaults to `None`):
|
||||
If set to `None`, the `safetensors` weights will be downloaded if they're available **and** if the
|
||||
`safetensors` library is installed. If set to `True`, the model will be forcibly loaded from
|
||||
`safetensors` weights. If set to `False`, loading will *not* use `safetensors`.
|
||||
"""
|
||||
idx = 0
|
||||
controlnets = []
|
||||
|
||||
# load controlnet and append to list until no controlnet directory exists anymore
|
||||
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
|
||||
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
|
||||
model_path_to_load = pretrained_model_path
|
||||
while os.path.isdir(model_path_to_load):
|
||||
controlnet = ControlNetModel.from_pretrained(model_path_to_load, **kwargs)
|
||||
controlnets.append(controlnet)
|
||||
|
||||
idx += 1
|
||||
model_path_to_load = pretrained_model_path + f"_{idx}"
|
||||
|
||||
logger.info(f"{len(controlnets)} controlnets loaded from {pretrained_model_path}.")
|
||||
|
||||
if len(controlnets) == 0:
|
||||
raise ValueError(
|
||||
f"No ControlNets found under {os.path.dirname(pretrained_model_path)}. Expected at least {pretrained_model_path + '_0'}."
|
||||
)
|
||||
|
||||
return cls(controlnets)
|
||||
class MultiControlNetModel(MultiControlNetModel):
|
||||
def __init__(self, *args, **kwargs):
|
||||
deprecation_message = "Importing `MultiControlNetModel` from `diffusers.pipelines.controlnet.multicontrolnet` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.multicontrolnet import MultiControlNetModel`, instead."
|
||||
deprecate("MultiControlNetModel", "0.34", deprecation_message)
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
@@ -26,7 +26,7 @@ from transformers import (
|
||||
from ...image_processor import PipelineImageInput, VaeImageProcessor
|
||||
from ...loaders import FromSingleFileMixin, SD3LoraLoaderMixin
|
||||
from ...models.autoencoders import AutoencoderKL
|
||||
from ...models.controlnet_sd3 import SD3ControlNetModel, SD3MultiControlNetModel
|
||||
from ...models.controlnets.controlnet_sd3 import SD3ControlNetModel, SD3MultiControlNetModel
|
||||
from ...models.transformers import SD3Transformer2DModel
|
||||
from ...schedulers import FlowMatchEulerDiscreteScheduler
|
||||
from ...utils import (
|
||||
|
||||
@@ -26,7 +26,7 @@ from transformers import (
|
||||
from ...image_processor import PipelineImageInput, VaeImageProcessor
|
||||
from ...loaders import FromSingleFileMixin, SD3LoraLoaderMixin
|
||||
from ...models.autoencoders import AutoencoderKL
|
||||
from ...models.controlnet_sd3 import SD3ControlNetModel, SD3MultiControlNetModel
|
||||
from ...models.controlnets.controlnet_sd3 import SD3ControlNetModel, SD3MultiControlNetModel
|
||||
from ...models.transformers import SD3Transformer2DModel
|
||||
from ...schedulers import FlowMatchEulerDiscreteScheduler
|
||||
from ...utils import (
|
||||
|
||||
@@ -27,7 +27,7 @@ from transformers import (
|
||||
from ...image_processor import PipelineImageInput, VaeImageProcessor
|
||||
from ...loaders import FluxLoraLoaderMixin, FromSingleFileMixin, TextualInversionLoaderMixin
|
||||
from ...models.autoencoders import AutoencoderKL
|
||||
from ...models.controlnet_flux import FluxControlNetModel, FluxMultiControlNetModel
|
||||
from ...models.controlnets.controlnet_flux import FluxControlNetModel, FluxMultiControlNetModel
|
||||
from ...models.transformers import FluxTransformer2DModel
|
||||
from ...schedulers import FlowMatchEulerDiscreteScheduler
|
||||
from ...utils import (
|
||||
|
||||
@@ -13,7 +13,7 @@ from transformers import (
|
||||
from ...image_processor import PipelineImageInput, VaeImageProcessor
|
||||
from ...loaders import FluxLoraLoaderMixin, FromSingleFileMixin, TextualInversionLoaderMixin
|
||||
from ...models.autoencoders import AutoencoderKL
|
||||
from ...models.controlnet_flux import FluxControlNetModel, FluxMultiControlNetModel
|
||||
from ...models.controlnets.controlnet_flux import FluxControlNetModel, FluxMultiControlNetModel
|
||||
from ...models.transformers import FluxTransformer2DModel
|
||||
from ...schedulers import FlowMatchEulerDiscreteScheduler
|
||||
from ...utils import (
|
||||
|
||||
@@ -14,7 +14,7 @@ from transformers import (
|
||||
from ...image_processor import PipelineImageInput, VaeImageProcessor
|
||||
from ...loaders import FluxLoraLoaderMixin, FromSingleFileMixin, TextualInversionLoaderMixin
|
||||
from ...models.autoencoders import AutoencoderKL
|
||||
from ...models.controlnet_flux import FluxControlNetModel, FluxMultiControlNetModel
|
||||
from ...models.controlnets.controlnet_flux import FluxControlNetModel, FluxMultiControlNetModel
|
||||
from ...models.transformers import FluxTransformer2DModel
|
||||
from ...schedulers import FlowMatchEulerDiscreteScheduler
|
||||
from ...utils import (
|
||||
|
||||
@@ -31,7 +31,7 @@ from diffusers import (
|
||||
from diffusers.image_processor import VaeImageProcessor
|
||||
from diffusers.loaders import IPAdapterMixin
|
||||
from diffusers.models.attention_processor import AttnProcessor
|
||||
from diffusers.models.controlnet_xs import UNetControlNetXSModel
|
||||
from diffusers.models.controlnets.controlnet_xs import UNetControlNetXSModel
|
||||
from diffusers.models.unets.unet_3d_condition import UNet3DConditionModel
|
||||
from diffusers.models.unets.unet_i2vgen_xl import I2VGenXLUNet
|
||||
from diffusers.models.unets.unet_motion_model import UNetMotionModel
|
||||
|
||||
Reference in New Issue
Block a user