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import torch
import torch.nn.functional as F
from torch import nn
from diffusers.models.attention_processor import Attention
class LoRALinearLayer(nn.Module):
def __init__(
self,
in_features: int,
out_features: int,
rank: int = 4,
network_alpha: Optional[float] = None,
device: Optional[Union[torch.device, str]] = None,
dtype: Optional[torch.dtype] = None,
number=0,
n_loras=1,
):
super().__init__()
self.down = nn.Linear(in_features, rank, bias=False, device=device, dtype=dtype)
self.up = nn.Linear(rank, out_features, bias=False, device=device, dtype=dtype)
# This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.
# See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
self.network_alpha = network_alpha
self.rank = rank
self.out_features = out_features
self.in_features = in_features
nn.init.normal_(self.down.weight, std=1 / rank)
nn.init.zeros_(self.up.weight)
self.number = number
self.n_loras = n_loras
def forward(self, hidden_states: torch.Tensor, cond_seq_len: int = None) -> torch.Tensor:
orig_dtype = hidden_states.dtype
dtype = self.down.weight.dtype
batch_size = hidden_states.shape[0]
cond_size = cond_seq_len
block_size = hidden_states.shape[1] - cond_size * self.n_loras
shape = (batch_size, hidden_states.shape[1], 3072)
mask = torch.ones(shape, device=hidden_states.device, dtype=dtype)
mask[:, : block_size + self.number * cond_size, :] = 0
mask[:, block_size + (self.number + 1) * cond_size :, :] = 0
hidden_states = mask * hidden_states
down_hidden_states = self.down(hidden_states.to(dtype))
up_hidden_states = self.up(down_hidden_states)
if self.network_alpha is not None:
up_hidden_states *= self.network_alpha / self.rank
return up_hidden_states.to(orig_dtype)
class TextLoRALinearLayer(nn.Module):
def __init__(
self,
in_features: int,
out_features: int,
rank: int = 4,
network_alpha: Optional[float] = None,
device: Optional[Union[torch.device, str]] = None,
dtype: Optional[torch.dtype] = None,
token_length=512,
):
super().__init__()
self.down = nn.Linear(in_features, rank, bias=False, device=device, dtype=dtype)
self.up = nn.Linear(rank, out_features, bias=False, device=device, dtype=dtype)
# This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.
# See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
self.network_alpha = network_alpha
self.rank = rank
self.out_features = out_features
self.in_features = in_features
nn.init.normal_(self.down.weight, std=1 / rank)
nn.init.zeros_(self.up.weight)
self.token_length = token_length
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
orig_dtype = hidden_states.dtype
dtype = self.down.weight.dtype
batch_size, seq_len, feature_dim = hidden_states.shape
if seq_len > self.token_length:
mask = torch.ones((batch_size, seq_len, feature_dim), device=hidden_states.device, dtype=dtype)
mask[:, self.token_length :, :] = 0
hidden_states = mask * hidden_states
down_hidden_states = self.down(hidden_states.to(dtype))
up_hidden_states = self.up(down_hidden_states)
if self.network_alpha is not None:
up_hidden_states *= self.network_alpha / self.rank
return up_hidden_states.to(orig_dtype)
class MultiSingleStreamBlockLoraProcessor(nn.Module):
def __init__(
self,
dim: int,
ranks=[],
lora_weights=[],
network_alphas=[],
device=None,
dtype=None,
n_loras=1,
text_lora_config=None,
):
super().__init__()
self.n_loras = n_loras
if text_lora_config is not None:
self.text_len = text_lora_config.get("token_length", 512)
else:
self.text_len = 512
self.q_loras = nn.ModuleList(
[
LoRALinearLayer(
dim, dim, ranks[i], network_alphas[i], device=device, dtype=dtype, number=i, n_loras=n_loras
)
for i in range(n_loras)
]
)
self.k_loras = nn.ModuleList(
[
LoRALinearLayer(
dim, dim, ranks[i], network_alphas[i], device=device, dtype=dtype, number=i, n_loras=n_loras
)
for i in range(n_loras)
]
)
self.v_loras = nn.ModuleList(
[
LoRALinearLayer(
dim, dim, ranks[i], network_alphas[i], device=device, dtype=dtype, number=i, n_loras=n_loras
)
for i in range(n_loras)
]
)
self.lora_weights = lora_weights
if text_lora_config is not None:
t_rank = text_lora_config.get("rank", 4)
t_alpha = text_lora_config.get("alpha", None)
self.text_q_lora = TextLoRALinearLayer(
dim, dim, t_rank, t_alpha, device=device, dtype=dtype, token_length=self.text_len
)
self.text_k_lora = TextLoRALinearLayer(
dim, dim, t_rank, t_alpha, device=device, dtype=dtype, token_length=self.text_len
)
self.text_v_lora = TextLoRALinearLayer(
dim, dim, t_rank, t_alpha, device=device, dtype=dtype, token_length=self.text_len
)
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
use_cond=False,
) -> torch.FloatTensor:
batch_size, seq_len, _ = hidden_states.shape
total_img_seq_len = seq_len - self.text_len
assert total_img_seq_len % (1 + self.n_loras) == 0, (
f"total_img_seq_len:{total_img_seq_len}, n_loras:{self.n_loras}, "
f"seq_len:{seq_len}, text_len:{self.text_len}"
)
cond_seq_len = total_img_seq_len // (1 + self.n_loras)
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
for i in range(self.n_loras):
query = query + self.lora_weights[i] * self.q_loras[i](hidden_states, cond_seq_len=cond_seq_len)
key = key + self.lora_weights[i] * self.k_loras[i](hidden_states, cond_seq_len=cond_seq_len)
value = value + self.lora_weights[i] * self.v_loras[i](hidden_states, cond_seq_len=cond_seq_len)
if getattr(self, "text_q_lora", None) is not None:
query = query + self.text_q_lora(hidden_states)
if getattr(self, "text_k_lora", None) is not None:
key = key + self.text_k_lora(hidden_states)
if getattr(self, "text_v_lora", None) is not None:
value = value + self.text_v_lora(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)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
if image_rotary_emb is not None:
from diffusers.models.embeddings import apply_rotary_emb
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
cond_size = cond_seq_len
block_size = hidden_states.shape[1] - cond_size * self.n_loras
hidden_states = F.scaled_dot_product_attention(query, key, value, 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)
cond_hidden_states = hidden_states[:, block_size:, :]
hidden_states = hidden_states[:, :block_size, :]
return hidden_states if not use_cond else (hidden_states, cond_hidden_states)
class MultiDoubleStreamBlockLoraProcessor(nn.Module):
def __init__(
self,
dim: int,
ranks=[],
lora_weights=[],
network_alphas=[],
device=None,
dtype=None,
n_loras=1,
text_lora_config=None,
):
super().__init__()
self.n_loras = n_loras
self.q_loras = nn.ModuleList(
[
LoRALinearLayer(
dim, dim, ranks[i], network_alphas[i], device=device, dtype=dtype, number=i, n_loras=n_loras
)
for i in range(n_loras)
]
)
self.k_loras = nn.ModuleList(
[
LoRALinearLayer(
dim, dim, ranks[i], network_alphas[i], device=device, dtype=dtype, number=i, n_loras=n_loras
)
for i in range(n_loras)
]
)
self.v_loras = nn.ModuleList(
[
LoRALinearLayer(
dim, dim, ranks[i], network_alphas[i], device=device, dtype=dtype, number=i, n_loras=n_loras
)
for i in range(n_loras)
]
)
self.proj_loras = nn.ModuleList(
[
LoRALinearLayer(
dim, dim, ranks[i], network_alphas[i], device=device, dtype=dtype, number=i, n_loras=n_loras
)
for i in range(n_loras)
]
)
self.lora_weights = lora_weights
if text_lora_config is not None:
t_rank = text_lora_config.get("rank", 4)
t_alpha = text_lora_config.get("alpha", None)
t_len = text_lora_config.get("token_length", 512)
self.text_q_lora = TextLoRALinearLayer(
dim, dim, t_rank, t_alpha, device=device, dtype=dtype, token_length=t_len
)
self.text_k_lora = TextLoRALinearLayer(
dim, dim, t_rank, t_alpha, device=device, dtype=dtype, token_length=t_len
)
self.text_v_lora = TextLoRALinearLayer(
dim, dim, t_rank, t_alpha, device=device, dtype=dtype, token_length=t_len
)
self.text_proj_lora = TextLoRALinearLayer(
dim, dim, t_rank, t_alpha, device=device, dtype=dtype, token_length=t_len
)
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
use_cond=False,
) -> torch.FloatTensor:
batch_size, total_img_seq_len, _ = hidden_states.shape
# `context` projections.
inner_dim = 3072
head_dim = inner_dim // attn.heads
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
if getattr(self, "text_q_lora", None) is not None:
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj + self.text_q_lora(
encoder_hidden_states
)
if getattr(self, "text_k_lora", None) is not None:
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj + self.text_k_lora(encoder_hidden_states)
if getattr(self, "text_v_lora", None) is not None:
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj + self.text_v_lora(
encoder_hidden_states
)
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
assert total_img_seq_len % (1 + self.n_loras) == 0, (
f"total_img_seq_len:{total_img_seq_len}, n_loras:{self.n_loras}"
)
cond_seq_len = total_img_seq_len // (1 + self.n_loras)
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
for i in range(self.n_loras):
query = query + self.lora_weights[i] * self.q_loras[i](hidden_states, cond_seq_len=cond_seq_len)
key = key + self.lora_weights[i] * self.k_loras[i](hidden_states, cond_seq_len=cond_seq_len)
value = value + self.lora_weights[i] * self.v_loras[i](hidden_states, cond_seq_len=cond_seq_len)
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)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# attention
query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
if image_rotary_emb is not None:
from diffusers.models.embeddings import apply_rotary_emb
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
cond_size = cond_seq_len
block_size = hidden_states.shape[1] - cond_size * self.n_loras
hidden_states = F.scaled_dot_product_attention(query, key, value, 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)
encoder_hidden_states, hidden_states = (
hidden_states[:, : encoder_hidden_states.shape[1]],
hidden_states[:, encoder_hidden_states.shape[1] :],
)
hidden_states_input = hidden_states
hidden_states = attn.to_out[0](hidden_states)
for i in range(self.n_loras):
hidden_states = hidden_states + self.lora_weights[i] * self.proj_loras[i](
hidden_states_input, cond_seq_len=cond_seq_len
)
hidden_states = attn.to_out[1](hidden_states)
encoder_input = encoder_hidden_states
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
if getattr(self, "text_proj_lora", None) is not None:
encoder_hidden_states = encoder_hidden_states + self.text_proj_lora(encoder_input)
cond_hidden_states = hidden_states[:, block_size:, :]
hidden_states = hidden_states[:, :block_size, :]
return (
(hidden_states, encoder_hidden_states, cond_hidden_states)
if use_cond
else (encoder_hidden_states, hidden_states)
)
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