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# 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 ..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
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 {}
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
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