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import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
import weakref
from typing import Union, TYPE_CHECKING
from diffusers import Transformer2DModel
from transformers import T5EncoderModel, CLIPTextModel, CLIPTokenizer, T5Tokenizer, CLIPVisionModelWithProjection
from toolkit.paths import REPOS_ROOT
sys.path.append(REPOS_ROOT)
if TYPE_CHECKING:
from toolkit.stable_diffusion_model import StableDiffusion
from toolkit.custom_adapter import CustomAdapter
class AttnProcessor2_0(torch.nn.Module):
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
"""
def __init__(
self,
hidden_size=None,
cross_attention_dim=None,
):
super().__init__()
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
def __call__(
self,
attn,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
):
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
class SingleValueAdapterAttnProcessor(nn.Module):
r"""
Attention processor for Custom TE for PyTorch 2.0.
Args:
hidden_size (`int`):
The hidden size of the attention layer.
cross_attention_dim (`int`):
The number of channels in the `encoder_hidden_states`.
scale (`float`, defaults to 1.0):
the weight scale of image prompt.
adapter
"""
def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, adapter=None,
adapter_hidden_size=None, has_bias=False, **kwargs):
super().__init__()
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
self.adapter_ref: weakref.ref = weakref.ref(adapter)
self.hidden_size = hidden_size
self.adapter_hidden_size = adapter_hidden_size
self.cross_attention_dim = cross_attention_dim
self.scale = scale
self.to_k_adapter = nn.Linear(adapter_hidden_size, hidden_size, bias=has_bias)
self.to_v_adapter = nn.Linear(adapter_hidden_size, hidden_size, bias=has_bias)
@property
def is_active(self):
return self.adapter_ref().is_active
# return False
@property
def unconditional_embeds(self):
return self.adapter_ref().adapter_ref().unconditional_embeds
@property
def conditional_embeds(self):
return self.adapter_ref().adapter_ref().conditional_embeds
def __call__(
self,
attn,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
):
is_active = self.adapter_ref().is_active
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
# will be none if disabled
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# only use one TE or the other. If our adapter is active only use ours
if self.is_active and self.conditional_embeds is not None:
adapter_hidden_states = self.conditional_embeds
if adapter_hidden_states.shape[0] < batch_size:
# doing cfg
adapter_hidden_states = torch.cat([
self.unconditional_embeds,
adapter_hidden_states
], dim=0)
# needs to be shape (batch, 1, 1)
if len(adapter_hidden_states.shape) == 2:
adapter_hidden_states = adapter_hidden_states.unsqueeze(1)
# conditional_batch_size = adapter_hidden_states.shape[0]
# conditional_query = query
# for ip-adapter
vd_key = self.to_k_adapter(adapter_hidden_states)
vd_value = self.to_v_adapter(adapter_hidden_states)
vd_key = vd_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
vd_value = vd_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
vd_hidden_states = F.scaled_dot_product_attention(
query, vd_key, vd_value, attn_mask=None, dropout_p=0.0, is_causal=False
)
vd_hidden_states = vd_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
vd_hidden_states = vd_hidden_states.to(query.dtype)
hidden_states = hidden_states + self.scale * vd_hidden_states
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
class SingleValueAdapter(torch.nn.Module):
def __init__(
self,
adapter: 'CustomAdapter',
sd: 'StableDiffusion',
num_values: int = 1,
):
super(SingleValueAdapter, self).__init__()
is_pixart = sd.is_pixart
self.adapter_ref: weakref.ref = weakref.ref(adapter)
self.sd_ref: weakref.ref = weakref.ref(sd)
self.token_size = num_values
# init adapter modules
attn_procs = {}
unet_sd = sd.unet.state_dict()
attn_processor_keys = []
if is_pixart:
transformer: Transformer2DModel = sd.unet
for i, module in transformer.transformer_blocks.named_children():
attn_processor_keys.append(f"transformer_blocks.{i}.attn1")
# cross attention
attn_processor_keys.append(f"transformer_blocks.{i}.attn2")
else:
attn_processor_keys = list(sd.unet.attn_processors.keys())
for name in attn_processor_keys:
cross_attention_dim = None if name.endswith("attn1.processor") or name.endswith("attn.1") else sd.unet.config['cross_attention_dim']
if name.startswith("mid_block"):
hidden_size = sd.unet.config['block_out_channels'][-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(sd.unet.config['block_out_channels']))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = sd.unet.config['block_out_channels'][block_id]
elif name.startswith("transformer"):
hidden_size = sd.unet.config['cross_attention_dim']
else:
# they didnt have this, but would lead to undefined below
raise ValueError(f"unknown attn processor name: {name}")
if cross_attention_dim is None:
attn_procs[name] = AttnProcessor2_0()
else:
layer_name = name.split(".processor")[0]
to_k_adapter = unet_sd[layer_name + ".to_k.weight"]
to_v_adapter = unet_sd[layer_name + ".to_v.weight"]
# if is_pixart:
# to_k_bias = unet_sd[layer_name + ".to_k.bias"]
# to_v_bias = unet_sd[layer_name + ".to_v.bias"]
# else:
# to_k_bias = None
# to_v_bias = None
# add zero padding to the adapter
if to_k_adapter.shape[1] < self.token_size:
to_k_adapter = torch.cat([
to_k_adapter,
torch.randn(to_k_adapter.shape[0], self.token_size - to_k_adapter.shape[1]).to(
to_k_adapter.device, dtype=to_k_adapter.dtype) * 0.01
],
dim=1
)
to_v_adapter = torch.cat([
to_v_adapter,
torch.randn(to_v_adapter.shape[0], self.token_size - to_v_adapter.shape[1]).to(
to_k_adapter.device, dtype=to_k_adapter.dtype) * 0.01
],
dim=1
)
# if is_pixart:
# to_k_bias = torch.cat([
# to_k_bias,
# torch.zeros(self.token_size - to_k_adapter.shape[1]).to(
# to_k_adapter.device, dtype=to_k_adapter.dtype)
# ],
# dim=0
# )
# to_v_bias = torch.cat([
# to_v_bias,
# torch.zeros(self.token_size - to_v_adapter.shape[1]).to(
# to_k_adapter.device, dtype=to_k_adapter.dtype)
# ],
# dim=0
# )
elif to_k_adapter.shape[1] > self.token_size:
to_k_adapter = to_k_adapter[:, :self.token_size]
to_v_adapter = to_v_adapter[:, :self.token_size]
# if is_pixart:
# to_k_bias = to_k_bias[:self.token_size]
# to_v_bias = to_v_bias[:self.token_size]
else:
to_k_adapter = to_k_adapter
to_v_adapter = to_v_adapter
# if is_pixart:
# to_k_bias = to_k_bias
# to_v_bias = to_v_bias
weights = {
"to_k_adapter.weight": to_k_adapter * 0.01,
"to_v_adapter.weight": to_v_adapter * 0.01,
}
# if is_pixart:
# weights["to_k_adapter.bias"] = to_k_bias
# weights["to_v_adapter.bias"] = to_v_bias
attn_procs[name] = SingleValueAdapterAttnProcessor(
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
scale=1.0,
adapter=self,
adapter_hidden_size=self.token_size,
has_bias=False,
)
attn_procs[name].load_state_dict(weights)
if self.sd_ref().is_pixart:
# we have to set them ourselves
transformer: Transformer2DModel = sd.unet
for i, module in transformer.transformer_blocks.named_children():
module.attn1.processor = attn_procs[f"transformer_blocks.{i}.attn1"]
module.attn2.processor = attn_procs[f"transformer_blocks.{i}.attn2"]
self.adapter_modules = torch.nn.ModuleList([
transformer.transformer_blocks[i].attn1.processor for i in range(len(transformer.transformer_blocks))
] + [
transformer.transformer_blocks[i].attn2.processor for i in range(len(transformer.transformer_blocks))
])
else:
sd.unet.set_attn_processor(attn_procs)
self.adapter_modules = torch.nn.ModuleList(sd.unet.attn_processors.values())
# make a getter to see if is active
@property
def is_active(self):
return self.adapter_ref().is_active
def forward(self, input):
return input
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