CreatiLayout / src /models /transformer_sd3_SiamLayout.py
HuiZhang
Update src/models/transformer_sd3_SiamLayout.py
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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 typing import Any, Dict, List, Optional, Union
import torch
import torch.nn as nn
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
from src.models.attention_SiamLayout import SiamLayoutJointTransformerBlock
from diffusers.models.attention_processor import Attention, AttentionProcessor, FusedJointAttnProcessor2_0
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import AdaLayerNormContinuous
from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
from diffusers.models.embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from diffusers.models.activations import FP32SiLU, get_activation
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def get_fourier_embeds_from_boundingbox(embed_dim, box):
"""
Args:
embed_dim: int
box: a 3-D tensor [B x N x 4] representing the bounding boxes for GLIGEN pipeline
Returns:
[B x N x embed_dim] tensor of positional embeddings
"""
batch_size, num_boxes = box.shape[:2]
emb = 100 ** (torch.arange(embed_dim) / embed_dim)
emb = emb[None, None, None].to(device=box.device, dtype=box.dtype)
emb = emb * box.unsqueeze(-1)
emb = torch.stack((emb.sin(), emb.cos()), dim=-1)
emb = emb.permute(0, 1, 3, 4, 2).reshape(batch_size, num_boxes, embed_dim * 2 * 4)
return emb
class PixArtAlphaTextProjection(nn.Module):
"""
Projects caption embeddings. Also handles dropout for classifier-free guidance.
Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
"""
def __init__(self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh"):
super().__init__()
if out_features is None:
out_features = hidden_size
self.linear_1 = nn.Linear(in_features=in_features, out_features=hidden_size, bias=True)
if act_fn == "gelu_tanh":
self.act_1 = nn.GELU(approximate="tanh")
elif act_fn == "silu":
self.act_1 = nn.SiLU()
elif act_fn == "silu_fp32":
self.act_1 = FP32SiLU()
else:
raise ValueError(f"Unknown activation function: {act_fn}")
self.linear_2 = nn.Linear(in_features=hidden_size, out_features=out_features, bias=True)
def forward(self, caption):
hidden_states = self.linear_1(caption)
hidden_states = self.act_1(hidden_states)
hidden_states = self.linear_2(hidden_states)
return hidden_states
class TextBoundingboxProjection(nn.Module):
def __init__(self, pooled_projection_dim,positive_len, out_dim, fourier_freqs=8):
super().__init__()
self.positive_len = positive_len
self.out_dim = out_dim
self.fourier_embedder_dim = fourier_freqs
self.position_dim = fourier_freqs * 2 * 4 # 2: sin/cos, 4: xyxy #64
if isinstance(out_dim, tuple):
out_dim = out_dim[0]
self.text_embedder = PixArtAlphaTextProjection(pooled_projection_dim, positive_len, act_fn="silu")
self.linears = PixArtAlphaTextProjection(in_features=self.positive_len + self.position_dim,hidden_size=out_dim//2,out_features=out_dim, act_fn="silu")
self.null_positive_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))
self.null_position_feature = torch.nn.Parameter(torch.zeros([self.position_dim]))
def forward(
self,
boxes,
masks,
positive_embeddings,
phrases_masks=None,
image_masks=None,
phrases_embeddings=None,
image_embeddings=None,
):
masks = masks.unsqueeze(-1)
# embedding position (it may includes padding as placeholder)
xyxy_embedding = get_fourier_embeds_from_boundingbox(self.fourier_embedder_dim, boxes)
# learnable null embedding
xyxy_null = self.null_position_feature.view(1, 1, -1)
# replace padding with learnable null embedding
xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null
# learnable null embedding
positive_null = self.null_positive_feature.view(1, 1, -1)
positive_embeddings = self.text_embedder(positive_embeddings)
# replace padding with learnable null embedding
positive_embeddings = positive_embeddings * masks + (1 - masks) * positive_null
objs = self.linears(torch.cat([positive_embeddings, xyxy_embedding], dim=-1))
return objs
class SiamLayoutSD3Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
"""
The Transformer model introduced in Stable Diffusion 3.
Reference: https://arxiv.org/abs/2403.03206
Parameters:
sample_size (`int`): The width of the latent images. This is fixed during training since
it is used to learn a number of position embeddings.
patch_size (`int`): Patch size to turn the input data into small patches.
in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
num_layers (`int`, *optional*, defaults to 18): The number of layers of Transformer blocks to use.
attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
caption_projection_dim (`int`): Number of dimensions to use when projecting the `encoder_hidden_states`.
pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
out_channels (`int`, defaults to 16): Number of output channels.
"""
_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,
attention_type = "layout",
max_boxes_per_image =10
):
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 = self.config.num_attention_heads * self.config.attention_head_dim
self.pos_embed = PatchEmbed(
height=self.config.sample_size,
width=self.config.sample_size,
patch_size=self.config.patch_size,
in_channels=self.config.in_channels,
embed_dim=self.inner_dim,
pos_embed_max_size=pos_embed_max_size, # hard-code for now.
)
self.time_text_embed = CombinedTimestepTextProjEmbeddings(
embedding_dim=self.inner_dim, pooled_projection_dim=self.config.pooled_projection_dim
)
self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.config.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(
[
SiamLayoutJointTransformerBlock(
dim=self.inner_dim,
num_attention_heads=self.config.num_attention_heads,
attention_head_dim=self.config.attention_head_dim,
context_pre_only=i == num_layers - 1,
attention_type=attention_type,
bbox_pre_only= i == num_layers - 1,
bbox_with_temb= True,
)
for i in range(self.config.num_layers)
]
)
self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)
self.gradient_checkpointing = False
self.attention_type = attention_type
self.max_boxes_per_image = max_boxes_per_image
if self.attention_type == "layout":
self.position_net = TextBoundingboxProjection(
pooled_projection_dim=self.config.pooled_projection_dim,positive_len=self.inner_dim, out_dim=self.inner_dim
)
# 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)
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking
def disable_forward_chunking(self):
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, None, 0)
@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.fuse_qkv_projections with FusedAttnProcessor2_0->FusedJointAttnProcessor2_0
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
def forward(
self,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
pooled_projections: torch.FloatTensor = None,
timestep: torch.LongTensor = None,
block_controlnet_hidden_states: List = None,
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
layout_kwargs = None,
bbox_scale=1.0,
) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
"""
The [`SD3Transformer2DModel`] forward method.
Args:
hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
Input `hidden_states`.
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."
)
height, width = hidden_states.shape[-2:]
hidden_states = self.pos_embed(hidden_states)
temb = self.time_text_embed(timestep, pooled_projections)
encoder_hidden_states = self.context_embedder(encoder_hidden_states)
if self.attention_type=="layout" and layout_kwargs is not None and layout_kwargs.get("layout", None) is not None:
layout_args = layout_kwargs["layout"]
bbox_raw = layout_args["boxes"]
bbox_text_embeddings = layout_args["positive_embeddings"].to(dtype=hidden_states.dtype,device=hidden_states.device)
bbox_masks = layout_args["masks"]
bbox_hidden_states = self.position_net(boxes=bbox_raw,masks=bbox_masks,positive_embeddings=bbox_text_embeddings)
else:
N = hidden_states.shape[0]
bbox_hidden_states = torch.zeros(N, self.max_boxes_per_image,self.inner_dim, dtype=hidden_states.dtype, device=hidden_states.device)
bbox_masks = torch.zeros(N, self.max_boxes_per_image, dtype=hidden_states.dtype, device=hidden_states.device)
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,bbox_hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
encoder_hidden_states,
temb,
bbox_hidden_states,
**ckpt_kwargs,
)
else:
encoder_hidden_states, hidden_states,bbox_hidden_states = block(
hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb,bbox_hidden_states= bbox_hidden_states,bbox_scale=bbox_scale
)
# controlnet residual
if block_controlnet_hidden_states is not None and block.context_pre_only is False:
interval_control = len(self.transformer_blocks) // len(block_controlnet_hidden_states)
hidden_states = hidden_states + block_controlnet_hidden_states[index_block // interval_control]
hidden_states = self.norm_out(hidden_states, temb)
hidden_states = self.proj_out(hidden_states)
# unpatchify
patch_size = self.config.patch_size
height = height // patch_size
width = width // patch_size
hidden_states = hidden_states.reshape(
shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels)
)
hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
output = hidden_states.reshape(
shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size)
)
if USE_PEFT_BACKEND:
# remove `lora_scale` from each PEFT layer
unscale_lora_layers(self, lora_scale)
if not return_dict:
return (output,)
return Transformer2DModelOutput(sample=output)