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if slice_size is not None and self.added_kv_proj_dim is not None:
processor = SlicedAttnAddedKVProcessor(slice_size)
elif slice_size is not None:
processor = SlicedAttnProcessor(slice_size)
elif self.added_kv_proj_dim is not None:
processor = AttnAddedKVProcessor()
else:
# set attention processor
# We use the AttnProcessor2_0 by default when torch 2.x is used which uses
# torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
# but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
processor = (
AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
)
self.set_processor(processor)
def set_processor(self, processor: "AttnProcessor") -> None:
r"""
Set the attention processor to use. | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
Args:
processor (`AttnProcessor`):
The attention processor to use.
"""
# if current processor is in `self._modules` and if passed `processor` is not, we need to
# pop `processor` from `self._modules`
if (
hasattr(self, "processor")
and isinstance(self.processor, torch.nn.Module)
and not isinstance(processor, torch.nn.Module)
):
logger.info(f"You are removing possibly trained weights of {self.processor} with {processor}")
self._modules.pop("processor")
self.processor = processor
def get_processor(self, return_deprecated_lora: bool = False) -> "AttentionProcessor":
r"""
Get the attention processor in use.
Args:
return_deprecated_lora (`bool`, *optional*, defaults to `False`):
Set to `True` to return the deprecated LoRA attention processor. | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
Returns:
"AttentionProcessor": The attention processor in use.
"""
if not return_deprecated_lora:
return self.processor
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
**cross_attention_kwargs,
) -> torch.Tensor:
r"""
The forward method of the `Attention` class.
Args:
hidden_states (`torch.Tensor`):
The hidden states of the query.
encoder_hidden_states (`torch.Tensor`, *optional*):
The hidden states of the encoder.
attention_mask (`torch.Tensor`, *optional*):
The attention mask to use. If `None`, no mask is applied.
**cross_attention_kwargs:
Additional keyword arguments to pass along to the cross attention. | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
Returns:
`torch.Tensor`: The output of the attention layer.
"""
# The `Attention` class can call different attention processors / attention functions
# here we simply pass along all tensors to the selected processor class
# For standard processors that are defined here, `**cross_attention_kwargs` is empty
attn_parameters = set(inspect.signature(self.processor.__call__).parameters.keys())
quiet_attn_parameters = {"ip_adapter_masks", "ip_hidden_states"}
unused_kwargs = [
k for k, _ in cross_attention_kwargs.items() if k not in attn_parameters and k not in quiet_attn_parameters
]
if len(unused_kwargs) > 0:
logger.warning(
f"cross_attention_kwargs {unused_kwargs} are not expected by {self.processor.__class__.__name__} and will be ignored."
)
cross_attention_kwargs = {k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters} | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
return self.processor(
self,
hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
**cross_attention_kwargs,
)
def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor:
r"""
Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads`
is the number of heads initialized while constructing the `Attention` class.
Args:
tensor (`torch.Tensor`): The tensor to reshape.
Returns:
`torch.Tensor`: The reshaped tensor.
"""
head_size = self.heads
batch_size, seq_len, dim = tensor.shape
tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
return tensor | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor:
r"""
Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is
the number of heads initialized while constructing the `Attention` class.
Args:
tensor (`torch.Tensor`): The tensor to reshape.
out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is
reshaped to `[batch_size * heads, seq_len, dim // heads]`.
Returns:
`torch.Tensor`: The reshaped tensor.
"""
head_size = self.heads
if tensor.ndim == 3:
batch_size, seq_len, dim = tensor.shape
extra_dim = 1
else:
batch_size, extra_dim, seq_len, dim = tensor.shape
tensor = tensor.reshape(batch_size, seq_len * extra_dim, head_size, dim // head_size)
tensor = tensor.permute(0, 2, 1, 3) | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if out_dim == 3:
tensor = tensor.reshape(batch_size * head_size, seq_len * extra_dim, dim // head_size)
return tensor
def get_attention_scores(
self, query: torch.Tensor, key: torch.Tensor, attention_mask: Optional[torch.Tensor] = None
) -> torch.Tensor:
r"""
Compute the attention scores.
Args:
query (`torch.Tensor`): The query tensor.
key (`torch.Tensor`): The key tensor.
attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.
Returns:
`torch.Tensor`: The attention probabilities/scores.
"""
dtype = query.dtype
if self.upcast_attention:
query = query.float()
key = key.float() | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if attention_mask is None:
baddbmm_input = torch.empty(
query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device
)
beta = 0
else:
baddbmm_input = attention_mask
beta = 1
attention_scores = torch.baddbmm(
baddbmm_input,
query,
key.transpose(-1, -2),
beta=beta,
alpha=self.scale,
)
del baddbmm_input
if self.upcast_softmax:
attention_scores = attention_scores.float()
attention_probs = attention_scores.softmax(dim=-1)
del attention_scores
attention_probs = attention_probs.to(dtype)
return attention_probs
def prepare_attention_mask(
self, attention_mask: torch.Tensor, target_length: int, batch_size: int, out_dim: int = 3
) -> torch.Tensor:
r"""
Prepare the attention mask for the attention computation. | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
Args:
attention_mask (`torch.Tensor`):
The attention mask to prepare.
target_length (`int`):
The target length of the attention mask. This is the length of the attention mask after padding.
batch_size (`int`):
The batch size, which is used to repeat the attention mask.
out_dim (`int`, *optional*, defaults to `3`):
The output dimension of the attention mask. Can be either `3` or `4`.
Returns:
`torch.Tensor`: The prepared attention mask.
"""
head_size = self.heads
if attention_mask is None:
return attention_mask | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
current_length: int = attention_mask.shape[-1]
if current_length != target_length:
if attention_mask.device.type == "mps":
# HACK: MPS: Does not support padding by greater than dimension of input tensor.
# Instead, we can manually construct the padding tensor.
padding_shape = (attention_mask.shape[0], attention_mask.shape[1], target_length)
padding = torch.zeros(padding_shape, dtype=attention_mask.dtype, device=attention_mask.device)
attention_mask = torch.cat([attention_mask, padding], dim=2)
else:
# TODO: for pipelines such as stable-diffusion, padding cross-attn mask:
# we want to instead pad by (0, remaining_length), where remaining_length is:
# remaining_length: int = target_length - current_length
# TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
attention_mask = F.pad(attention_mask, (0, target_length), value=0.0) | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if out_dim == 3:
if attention_mask.shape[0] < batch_size * head_size:
attention_mask = attention_mask.repeat_interleave(head_size, dim=0)
elif out_dim == 4:
attention_mask = attention_mask.unsqueeze(1)
attention_mask = attention_mask.repeat_interleave(head_size, dim=1)
return attention_mask
def norm_encoder_hidden_states(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
r"""
Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the
`Attention` class.
Args:
encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder.
Returns:
`torch.Tensor`: The normalized encoder hidden states.
"""
assert self.norm_cross is not None, "self.norm_cross must be defined to call self.norm_encoder_hidden_states" | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if isinstance(self.norm_cross, nn.LayerNorm):
encoder_hidden_states = self.norm_cross(encoder_hidden_states)
elif isinstance(self.norm_cross, nn.GroupNorm):
# Group norm norms along the channels dimension and expects
# input to be in the shape of (N, C, *). In this case, we want
# to norm along the hidden dimension, so we need to move
# (batch_size, sequence_length, hidden_size) ->
# (batch_size, hidden_size, sequence_length)
encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
encoder_hidden_states = self.norm_cross(encoder_hidden_states)
encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
else:
assert False
return encoder_hidden_states
@torch.no_grad()
def fuse_projections(self, fuse=True):
device = self.to_q.weight.data.device
dtype = self.to_q.weight.data.dtype | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if not self.is_cross_attention:
# fetch weight matrices.
concatenated_weights = torch.cat([self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data])
in_features = concatenated_weights.shape[1]
out_features = concatenated_weights.shape[0]
# create a new single projection layer and copy over the weights.
self.to_qkv = nn.Linear(in_features, out_features, bias=self.use_bias, device=device, dtype=dtype)
self.to_qkv.weight.copy_(concatenated_weights)
if self.use_bias:
concatenated_bias = torch.cat([self.to_q.bias.data, self.to_k.bias.data, self.to_v.bias.data])
self.to_qkv.bias.copy_(concatenated_bias)
else:
concatenated_weights = torch.cat([self.to_k.weight.data, self.to_v.weight.data])
in_features = concatenated_weights.shape[1]
out_features = concatenated_weights.shape[0] | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
self.to_kv = nn.Linear(in_features, out_features, bias=self.use_bias, device=device, dtype=dtype)
self.to_kv.weight.copy_(concatenated_weights)
if self.use_bias:
concatenated_bias = torch.cat([self.to_k.bias.data, self.to_v.bias.data])
self.to_kv.bias.copy_(concatenated_bias)
# handle added projections for SD3 and others.
if (
getattr(self, "add_q_proj", None) is not None
and getattr(self, "add_k_proj", None) is not None
and getattr(self, "add_v_proj", None) is not None
):
concatenated_weights = torch.cat(
[self.add_q_proj.weight.data, self.add_k_proj.weight.data, self.add_v_proj.weight.data]
)
in_features = concatenated_weights.shape[1]
out_features = concatenated_weights.shape[0] | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
self.to_added_qkv = nn.Linear(
in_features, out_features, bias=self.added_proj_bias, device=device, dtype=dtype
)
self.to_added_qkv.weight.copy_(concatenated_weights)
if self.added_proj_bias:
concatenated_bias = torch.cat(
[self.add_q_proj.bias.data, self.add_k_proj.bias.data, self.add_v_proj.bias.data]
)
self.to_added_qkv.bias.copy_(concatenated_bias)
self.fused_projections = fuse | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class SanaMultiscaleAttentionProjection(nn.Module):
def __init__(
self,
in_channels: int,
num_attention_heads: int,
kernel_size: int,
) -> None:
super().__init__()
channels = 3 * in_channels
self.proj_in = nn.Conv2d(
channels,
channels,
kernel_size,
padding=kernel_size // 2,
groups=channels,
bias=False,
)
self.proj_out = nn.Conv2d(channels, channels, 1, 1, 0, groups=3 * num_attention_heads, bias=False)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.proj_in(hidden_states)
hidden_states = self.proj_out(hidden_states)
return hidden_states | 770 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class SanaMultiscaleLinearAttention(nn.Module):
r"""Lightweight multi-scale linear attention"""
def __init__(
self,
in_channels: int,
out_channels: int,
num_attention_heads: Optional[int] = None,
attention_head_dim: int = 8,
mult: float = 1.0,
norm_type: str = "batch_norm",
kernel_sizes: Tuple[int, ...] = (5,),
eps: float = 1e-15,
residual_connection: bool = False,
):
super().__init__()
# To prevent circular import
from .normalization import get_normalization
self.eps = eps
self.attention_head_dim = attention_head_dim
self.norm_type = norm_type
self.residual_connection = residual_connection
num_attention_heads = (
int(in_channels // attention_head_dim * mult) if num_attention_heads is None else num_attention_heads
)
inner_dim = num_attention_heads * attention_head_dim | 771 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
self.to_q = nn.Linear(in_channels, inner_dim, bias=False)
self.to_k = nn.Linear(in_channels, inner_dim, bias=False)
self.to_v = nn.Linear(in_channels, inner_dim, bias=False)
self.to_qkv_multiscale = nn.ModuleList()
for kernel_size in kernel_sizes:
self.to_qkv_multiscale.append(
SanaMultiscaleAttentionProjection(inner_dim, num_attention_heads, kernel_size)
)
self.nonlinearity = nn.ReLU()
self.to_out = nn.Linear(inner_dim * (1 + len(kernel_sizes)), out_channels, bias=False)
self.norm_out = get_normalization(norm_type, num_features=out_channels)
self.processor = SanaMultiscaleAttnProcessor2_0() | 771 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
def apply_linear_attention(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor) -> torch.Tensor:
value = F.pad(value, (0, 0, 0, 1), mode="constant", value=1) # Adds padding
scores = torch.matmul(value, key.transpose(-1, -2))
hidden_states = torch.matmul(scores, query)
hidden_states = hidden_states.to(dtype=torch.float32)
hidden_states = hidden_states[:, :, :-1] / (hidden_states[:, :, -1:] + self.eps)
return hidden_states
def apply_quadratic_attention(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor) -> torch.Tensor:
scores = torch.matmul(key.transpose(-1, -2), query)
scores = scores.to(dtype=torch.float32)
scores = scores / (torch.sum(scores, dim=2, keepdim=True) + self.eps)
hidden_states = torch.matmul(value, scores.to(value.dtype))
return hidden_states
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return self.processor(self, hidden_states) | 771 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class MochiAttention(nn.Module):
def __init__(
self,
query_dim: int,
added_kv_proj_dim: int,
processor: "MochiAttnProcessor2_0",
heads: int = 8,
dim_head: int = 64,
dropout: float = 0.0,
bias: bool = False,
added_proj_bias: bool = True,
out_dim: Optional[int] = None,
out_context_dim: Optional[int] = None,
out_bias: bool = True,
context_pre_only: bool = False,
eps: float = 1e-5,
):
super().__init__()
from .normalization import MochiRMSNorm
self.inner_dim = out_dim if out_dim is not None else dim_head * heads
self.out_dim = out_dim if out_dim is not None else query_dim
self.out_context_dim = out_context_dim if out_context_dim else query_dim
self.context_pre_only = context_pre_only
self.heads = out_dim // dim_head if out_dim is not None else heads | 772 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
self.norm_q = MochiRMSNorm(dim_head, eps, True)
self.norm_k = MochiRMSNorm(dim_head, eps, True)
self.norm_added_q = MochiRMSNorm(dim_head, eps, True)
self.norm_added_k = MochiRMSNorm(dim_head, eps, True)
self.to_q = nn.Linear(query_dim, self.inner_dim, bias=bias)
self.to_k = nn.Linear(query_dim, self.inner_dim, bias=bias)
self.to_v = nn.Linear(query_dim, self.inner_dim, bias=bias)
self.add_k_proj = nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
self.add_v_proj = nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
if self.context_pre_only is not None:
self.add_q_proj = nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
self.to_out = nn.ModuleList([])
self.to_out.append(nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
self.to_out.append(nn.Dropout(dropout)) | 772 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if not self.context_pre_only:
self.to_add_out = nn.Linear(self.inner_dim, self.out_context_dim, bias=out_bias)
self.processor = processor
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
**kwargs,
):
return self.processor(
self,
hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
**kwargs,
) | 772 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class MochiAttnProcessor2_0:
"""Attention processor used in Mochi."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("MochiAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0.")
def __call__(
self,
attn: "MochiAttention",
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.Tensor:
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
query = query.unflatten(2, (attn.heads, -1))
key = key.unflatten(2, (attn.heads, -1))
value = value.unflatten(2, (attn.heads, -1))
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key) | 773 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
encoder_query = attn.add_q_proj(encoder_hidden_states)
encoder_key = attn.add_k_proj(encoder_hidden_states)
encoder_value = attn.add_v_proj(encoder_hidden_states)
encoder_query = encoder_query.unflatten(2, (attn.heads, -1))
encoder_key = encoder_key.unflatten(2, (attn.heads, -1))
encoder_value = encoder_value.unflatten(2, (attn.heads, -1))
if attn.norm_added_q is not None:
encoder_query = attn.norm_added_q(encoder_query)
if attn.norm_added_k is not None:
encoder_key = attn.norm_added_k(encoder_key)
if image_rotary_emb is not None:
def apply_rotary_emb(x, freqs_cos, freqs_sin):
x_even = x[..., 0::2].float()
x_odd = x[..., 1::2].float()
cos = (x_even * freqs_cos - x_odd * freqs_sin).to(x.dtype)
sin = (x_even * freqs_sin + x_odd * freqs_cos).to(x.dtype)
return torch.stack([cos, sin], dim=-1).flatten(-2) | 773 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
query = apply_rotary_emb(query, *image_rotary_emb)
key = apply_rotary_emb(key, *image_rotary_emb)
query, key, value = query.transpose(1, 2), key.transpose(1, 2), value.transpose(1, 2)
encoder_query, encoder_key, encoder_value = (
encoder_query.transpose(1, 2),
encoder_key.transpose(1, 2),
encoder_value.transpose(1, 2),
)
sequence_length = query.size(2)
encoder_sequence_length = encoder_query.size(2)
total_length = sequence_length + encoder_sequence_length
batch_size, heads, _, dim = query.shape
attn_outputs = []
for idx in range(batch_size):
mask = attention_mask[idx][None, :]
valid_prompt_token_indices = torch.nonzero(mask.flatten(), as_tuple=False).flatten() | 773 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
valid_encoder_query = encoder_query[idx : idx + 1, :, valid_prompt_token_indices, :]
valid_encoder_key = encoder_key[idx : idx + 1, :, valid_prompt_token_indices, :]
valid_encoder_value = encoder_value[idx : idx + 1, :, valid_prompt_token_indices, :]
valid_query = torch.cat([query[idx : idx + 1], valid_encoder_query], dim=2)
valid_key = torch.cat([key[idx : idx + 1], valid_encoder_key], dim=2)
valid_value = torch.cat([value[idx : idx + 1], valid_encoder_value], dim=2)
attn_output = F.scaled_dot_product_attention(
valid_query, valid_key, valid_value, dropout_p=0.0, is_causal=False
)
valid_sequence_length = attn_output.size(2)
attn_output = F.pad(attn_output, (0, 0, 0, total_length - valid_sequence_length))
attn_outputs.append(attn_output)
hidden_states = torch.cat(attn_outputs, dim=0)
hidden_states = hidden_states.transpose(1, 2).flatten(2, 3) | 773 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
hidden_states, encoder_hidden_states = hidden_states.split_with_sizes(
(sequence_length, encoder_sequence_length), dim=1
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if hasattr(attn, "to_add_out"):
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
return hidden_states, encoder_hidden_states | 773 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class AttnProcessor:
r"""
Default processor for performing attention-related computations.
"""
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
*args,
**kwargs,
) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim | 774 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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
)
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
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) | 774 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(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 | 774 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class CustomDiffusionAttnProcessor(nn.Module):
r"""
Processor for implementing attention for the Custom Diffusion method.
Args:
train_kv (`bool`, defaults to `True`):
Whether to newly train the key and value matrices corresponding to the text features.
train_q_out (`bool`, defaults to `True`):
Whether to newly train query matrices corresponding to the latent image features.
hidden_size (`int`, *optional*, defaults to `None`):
The hidden size of the attention layer.
cross_attention_dim (`int`, *optional*, defaults to `None`):
The number of channels in the `encoder_hidden_states`.
out_bias (`bool`, defaults to `True`):
Whether to include the bias parameter in `train_q_out`.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability to use.
""" | 775 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
def __init__(
self,
train_kv: bool = True,
train_q_out: bool = True,
hidden_size: Optional[int] = None,
cross_attention_dim: Optional[int] = None,
out_bias: bool = True,
dropout: float = 0.0,
):
super().__init__()
self.train_kv = train_kv
self.train_q_out = train_q_out
self.hidden_size = hidden_size
self.cross_attention_dim = cross_attention_dim | 775 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# `_custom_diffusion` id for easy serialization and loading.
if self.train_kv:
self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
if self.train_q_out:
self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)
self.to_out_custom_diffusion = nn.ModuleList([])
self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))
self.to_out_custom_diffusion.append(nn.Dropout(dropout)) | 775 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
if self.train_q_out:
query = self.to_q_custom_diffusion(hidden_states).to(attn.to_q.weight.dtype)
else:
query = attn.to_q(hidden_states.to(attn.to_q.weight.dtype))
if encoder_hidden_states is None:
crossattn = False
encoder_hidden_states = hidden_states
else:
crossattn = True
if attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) | 775 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if self.train_kv:
key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype))
value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype))
key = key.to(attn.to_q.weight.dtype)
value = value.to(attn.to_q.weight.dtype)
else:
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
if crossattn:
detach = torch.ones_like(key)
detach[:, :1, :] = detach[:, :1, :] * 0.0
key = detach * key + (1 - detach) * key.detach()
value = detach * value + (1 - detach) * value.detach()
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value) | 775 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
if self.train_q_out:
# linear proj
hidden_states = self.to_out_custom_diffusion[0](hidden_states)
# dropout
hidden_states = self.to_out_custom_diffusion[1](hidden_states)
else:
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
return hidden_states | 775 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class AttnAddedKVProcessor:
r"""
Processor for performing attention-related computations with extra learnable key and value matrices for the text
encoder.
"""
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
*args,
**kwargs,
) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
residual = hidden_states | 776 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
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)
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
query = attn.head_to_batch_dim(query)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj) | 776 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if not attn.only_cross_attention:
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
else:
key = encoder_hidden_states_key_proj
value = encoder_hidden_states_value_proj
attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
hidden_states = hidden_states + residual
return hidden_states | 776 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class AttnAddedKVProcessor2_0:
r"""
Processor for performing scaled dot-product attention (enabled by default if you're using PyTorch 2.0), with extra
learnable key and value matrices for the text encoder.
"""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"AttnAddedKVProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
) | 777 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
*args,
**kwargs,
) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
residual = hidden_states
hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size, out_dim=4) | 777 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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)
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
query = attn.head_to_batch_dim(query, out_dim=4)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj, out_dim=4)
encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj, out_dim=4) | 777 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if not attn.only_cross_attention:
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
key = attn.head_to_batch_dim(key, out_dim=4)
value = attn.head_to_batch_dim(value, out_dim=4)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
else:
key = encoder_hidden_states_key_proj
value = encoder_hidden_states_value_proj
# 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, residual.shape[1]) | 777 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
hidden_states = hidden_states + residual
return hidden_states | 777 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class JointAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
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: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
*args,
**kwargs,
) -> torch.FloatTensor:
residual = hidden_states
batch_size = hidden_states.shape[0]
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads | 778 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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)
# `context` projections.
if encoder_hidden_states is not None:
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) | 778 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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) | 778 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
query = torch.cat([query, encoder_hidden_states_query_proj], dim=2)
key = torch.cat([key, encoder_hidden_states_key_proj], dim=2)
value = torch.cat([value, encoder_hidden_states_value_proj], dim=2)
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)
if encoder_hidden_states is not None:
# Split the attention outputs.
hidden_states, encoder_hidden_states = (
hidden_states[:, : residual.shape[1]],
hidden_states[:, residual.shape[1] :],
)
if not attn.context_pre_only:
encoder_hidden_states = attn.to_add_out(encoder_hidden_states) | 778 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if encoder_hidden_states is not None:
return hidden_states, encoder_hidden_states
else:
return hidden_states | 778 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class PAGJointAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"PAGJointAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
) -> torch.FloatTensor:
residual = hidden_states | 779 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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)
context_input_ndim = encoder_hidden_states.ndim
if context_input_ndim == 4:
batch_size, channel, height, width = encoder_hidden_states.shape
encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
# store the length of image patch sequences to create a mask that prevents interaction between patches
# similar to making the self-attention map an identity matrix
identity_block_size = hidden_states.shape[1]
# chunk
hidden_states_org, hidden_states_ptb = hidden_states.chunk(2)
encoder_hidden_states_org, encoder_hidden_states_ptb = encoder_hidden_states.chunk(2) | 779 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
################## original path ##################
batch_size = encoder_hidden_states_org.shape[0]
# `sample` projections.
query_org = attn.to_q(hidden_states_org)
key_org = attn.to_k(hidden_states_org)
value_org = attn.to_v(hidden_states_org)
# `context` projections.
encoder_hidden_states_org_query_proj = attn.add_q_proj(encoder_hidden_states_org)
encoder_hidden_states_org_key_proj = attn.add_k_proj(encoder_hidden_states_org)
encoder_hidden_states_org_value_proj = attn.add_v_proj(encoder_hidden_states_org)
# attention
query_org = torch.cat([query_org, encoder_hidden_states_org_query_proj], dim=1)
key_org = torch.cat([key_org, encoder_hidden_states_org_key_proj], dim=1)
value_org = torch.cat([value_org, encoder_hidden_states_org_value_proj], dim=1) | 779 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
inner_dim = key_org.shape[-1]
head_dim = inner_dim // attn.heads
query_org = query_org.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key_org = key_org.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value_org = value_org.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
hidden_states_org = F.scaled_dot_product_attention(
query_org, key_org, value_org, dropout_p=0.0, is_causal=False
)
hidden_states_org = hidden_states_org.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states_org = hidden_states_org.to(query_org.dtype)
# Split the attention outputs.
hidden_states_org, encoder_hidden_states_org = (
hidden_states_org[:, : residual.shape[1]],
hidden_states_org[:, residual.shape[1] :],
) | 779 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# linear proj
hidden_states_org = attn.to_out[0](hidden_states_org)
# dropout
hidden_states_org = attn.to_out[1](hidden_states_org)
if not attn.context_pre_only:
encoder_hidden_states_org = attn.to_add_out(encoder_hidden_states_org)
if input_ndim == 4:
hidden_states_org = hidden_states_org.transpose(-1, -2).reshape(batch_size, channel, height, width)
if context_input_ndim == 4:
encoder_hidden_states_org = encoder_hidden_states_org.transpose(-1, -2).reshape(
batch_size, channel, height, width
)
################## perturbed path ##################
batch_size = encoder_hidden_states_ptb.shape[0]
# `sample` projections.
query_ptb = attn.to_q(hidden_states_ptb)
key_ptb = attn.to_k(hidden_states_ptb)
value_ptb = attn.to_v(hidden_states_ptb) | 779 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# `context` projections.
encoder_hidden_states_ptb_query_proj = attn.add_q_proj(encoder_hidden_states_ptb)
encoder_hidden_states_ptb_key_proj = attn.add_k_proj(encoder_hidden_states_ptb)
encoder_hidden_states_ptb_value_proj = attn.add_v_proj(encoder_hidden_states_ptb)
# attention
query_ptb = torch.cat([query_ptb, encoder_hidden_states_ptb_query_proj], dim=1)
key_ptb = torch.cat([key_ptb, encoder_hidden_states_ptb_key_proj], dim=1)
value_ptb = torch.cat([value_ptb, encoder_hidden_states_ptb_value_proj], dim=1)
inner_dim = key_ptb.shape[-1]
head_dim = inner_dim // attn.heads
query_ptb = query_ptb.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key_ptb = key_ptb.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value_ptb = value_ptb.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | 779 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# create a full mask with all entries set to 0
seq_len = query_ptb.size(2)
full_mask = torch.zeros((seq_len, seq_len), device=query_ptb.device, dtype=query_ptb.dtype)
# set the attention value between image patches to -inf
full_mask[:identity_block_size, :identity_block_size] = float("-inf")
# set the diagonal of the attention value between image patches to 0
full_mask[:identity_block_size, :identity_block_size].fill_diagonal_(0)
# expand the mask to match the attention weights shape
full_mask = full_mask.unsqueeze(0).unsqueeze(0) # Add batch and num_heads dimensions
hidden_states_ptb = F.scaled_dot_product_attention(
query_ptb, key_ptb, value_ptb, attn_mask=full_mask, dropout_p=0.0, is_causal=False
)
hidden_states_ptb = hidden_states_ptb.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states_ptb = hidden_states_ptb.to(query_ptb.dtype) | 779 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# split the attention outputs.
hidden_states_ptb, encoder_hidden_states_ptb = (
hidden_states_ptb[:, : residual.shape[1]],
hidden_states_ptb[:, residual.shape[1] :],
)
# linear proj
hidden_states_ptb = attn.to_out[0](hidden_states_ptb)
# dropout
hidden_states_ptb = attn.to_out[1](hidden_states_ptb)
if not attn.context_pre_only:
encoder_hidden_states_ptb = attn.to_add_out(encoder_hidden_states_ptb)
if input_ndim == 4:
hidden_states_ptb = hidden_states_ptb.transpose(-1, -2).reshape(batch_size, channel, height, width)
if context_input_ndim == 4:
encoder_hidden_states_ptb = encoder_hidden_states_ptb.transpose(-1, -2).reshape(
batch_size, channel, height, width
) | 779 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
################ concat ###############
hidden_states = torch.cat([hidden_states_org, hidden_states_ptb])
encoder_hidden_states = torch.cat([encoder_hidden_states_org, encoder_hidden_states_ptb])
return hidden_states, encoder_hidden_states | 779 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class PAGCFGJointAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"PAGCFGJointAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
*args,
**kwargs,
) -> torch.FloatTensor:
residual = hidden_states | 780 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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)
context_input_ndim = encoder_hidden_states.ndim
if context_input_ndim == 4:
batch_size, channel, height, width = encoder_hidden_states.shape
encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
identity_block_size = hidden_states.shape[
1
] # patch embeddings width * height (correspond to self-attention map width or height)
# chunk
hidden_states_uncond, hidden_states_org, hidden_states_ptb = hidden_states.chunk(3)
hidden_states_org = torch.cat([hidden_states_uncond, hidden_states_org]) | 780 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
(
encoder_hidden_states_uncond,
encoder_hidden_states_org,
encoder_hidden_states_ptb,
) = encoder_hidden_states.chunk(3)
encoder_hidden_states_org = torch.cat([encoder_hidden_states_uncond, encoder_hidden_states_org])
################## original path ##################
batch_size = encoder_hidden_states_org.shape[0]
# `sample` projections.
query_org = attn.to_q(hidden_states_org)
key_org = attn.to_k(hidden_states_org)
value_org = attn.to_v(hidden_states_org)
# `context` projections.
encoder_hidden_states_org_query_proj = attn.add_q_proj(encoder_hidden_states_org)
encoder_hidden_states_org_key_proj = attn.add_k_proj(encoder_hidden_states_org)
encoder_hidden_states_org_value_proj = attn.add_v_proj(encoder_hidden_states_org) | 780 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# attention
query_org = torch.cat([query_org, encoder_hidden_states_org_query_proj], dim=1)
key_org = torch.cat([key_org, encoder_hidden_states_org_key_proj], dim=1)
value_org = torch.cat([value_org, encoder_hidden_states_org_value_proj], dim=1)
inner_dim = key_org.shape[-1]
head_dim = inner_dim // attn.heads
query_org = query_org.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key_org = key_org.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value_org = value_org.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
hidden_states_org = F.scaled_dot_product_attention(
query_org, key_org, value_org, dropout_p=0.0, is_causal=False
)
hidden_states_org = hidden_states_org.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states_org = hidden_states_org.to(query_org.dtype) | 780 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# Split the attention outputs.
hidden_states_org, encoder_hidden_states_org = (
hidden_states_org[:, : residual.shape[1]],
hidden_states_org[:, residual.shape[1] :],
)
# linear proj
hidden_states_org = attn.to_out[0](hidden_states_org)
# dropout
hidden_states_org = attn.to_out[1](hidden_states_org)
if not attn.context_pre_only:
encoder_hidden_states_org = attn.to_add_out(encoder_hidden_states_org)
if input_ndim == 4:
hidden_states_org = hidden_states_org.transpose(-1, -2).reshape(batch_size, channel, height, width)
if context_input_ndim == 4:
encoder_hidden_states_org = encoder_hidden_states_org.transpose(-1, -2).reshape(
batch_size, channel, height, width
)
################## perturbed path ##################
batch_size = encoder_hidden_states_ptb.shape[0] | 780 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# `sample` projections.
query_ptb = attn.to_q(hidden_states_ptb)
key_ptb = attn.to_k(hidden_states_ptb)
value_ptb = attn.to_v(hidden_states_ptb)
# `context` projections.
encoder_hidden_states_ptb_query_proj = attn.add_q_proj(encoder_hidden_states_ptb)
encoder_hidden_states_ptb_key_proj = attn.add_k_proj(encoder_hidden_states_ptb)
encoder_hidden_states_ptb_value_proj = attn.add_v_proj(encoder_hidden_states_ptb)
# attention
query_ptb = torch.cat([query_ptb, encoder_hidden_states_ptb_query_proj], dim=1)
key_ptb = torch.cat([key_ptb, encoder_hidden_states_ptb_key_proj], dim=1)
value_ptb = torch.cat([value_ptb, encoder_hidden_states_ptb_value_proj], dim=1) | 780 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
inner_dim = key_ptb.shape[-1]
head_dim = inner_dim // attn.heads
query_ptb = query_ptb.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key_ptb = key_ptb.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value_ptb = value_ptb.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# create a full mask with all entries set to 0
seq_len = query_ptb.size(2)
full_mask = torch.zeros((seq_len, seq_len), device=query_ptb.device, dtype=query_ptb.dtype)
# set the attention value between image patches to -inf
full_mask[:identity_block_size, :identity_block_size] = float("-inf")
# set the diagonal of the attention value between image patches to 0
full_mask[:identity_block_size, :identity_block_size].fill_diagonal_(0)
# expand the mask to match the attention weights shape
full_mask = full_mask.unsqueeze(0).unsqueeze(0) # Add batch and num_heads dimensions | 780 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
hidden_states_ptb = F.scaled_dot_product_attention(
query_ptb, key_ptb, value_ptb, attn_mask=full_mask, dropout_p=0.0, is_causal=False
)
hidden_states_ptb = hidden_states_ptb.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states_ptb = hidden_states_ptb.to(query_ptb.dtype)
# split the attention outputs.
hidden_states_ptb, encoder_hidden_states_ptb = (
hidden_states_ptb[:, : residual.shape[1]],
hidden_states_ptb[:, residual.shape[1] :],
)
# linear proj
hidden_states_ptb = attn.to_out[0](hidden_states_ptb)
# dropout
hidden_states_ptb = attn.to_out[1](hidden_states_ptb)
if not attn.context_pre_only:
encoder_hidden_states_ptb = attn.to_add_out(encoder_hidden_states_ptb) | 780 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if input_ndim == 4:
hidden_states_ptb = hidden_states_ptb.transpose(-1, -2).reshape(batch_size, channel, height, width)
if context_input_ndim == 4:
encoder_hidden_states_ptb = encoder_hidden_states_ptb.transpose(-1, -2).reshape(
batch_size, channel, height, width
)
################ concat ###############
hidden_states = torch.cat([hidden_states_org, hidden_states_ptb])
encoder_hidden_states = torch.cat([encoder_hidden_states_org, encoder_hidden_states_ptb])
return hidden_states, encoder_hidden_states | 780 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class FusedJointAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
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: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
*args,
**kwargs,
) -> torch.FloatTensor:
residual = hidden_states | 781 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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)
context_input_ndim = encoder_hidden_states.ndim
if context_input_ndim == 4:
batch_size, channel, height, width = encoder_hidden_states.shape
encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size = encoder_hidden_states.shape[0]
# `sample` projections.
qkv = attn.to_qkv(hidden_states)
split_size = qkv.shape[-1] // 3
query, key, value = torch.split(qkv, split_size, dim=-1) | 781 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# `context` projections.
encoder_qkv = attn.to_added_qkv(encoder_hidden_states)
split_size = encoder_qkv.shape[-1] // 3
(
encoder_hidden_states_query_proj,
encoder_hidden_states_key_proj,
encoder_hidden_states_value_proj,
) = torch.split(encoder_qkv, split_size, dim=-1)
# attention
query = torch.cat([query, encoder_hidden_states_query_proj], dim=1)
key = torch.cat([key, encoder_hidden_states_key_proj], dim=1)
value = torch.cat([value, encoder_hidden_states_value_proj], dim=1)
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) | 781 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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)
# Split the attention outputs.
hidden_states, encoder_hidden_states = (
hidden_states[:, : residual.shape[1]],
hidden_states[:, residual.shape[1] :],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if not attn.context_pre_only:
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if context_input_ndim == 4:
encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) | 781 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
return hidden_states, encoder_hidden_states | 781 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class XFormersJointAttnProcessor:
r"""
Processor for implementing memory efficient attention using xFormers.
Args:
attention_op (`Callable`, *optional*, defaults to `None`):
The base
[operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
operator.
"""
def __init__(self, attention_op: Optional[Callable] = None):
self.attention_op = attention_op
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
*args,
**kwargs,
) -> torch.FloatTensor:
residual = hidden_states | 782 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
query = attn.head_to_batch_dim(query).contiguous()
key = attn.head_to_batch_dim(key).contiguous()
value = attn.head_to_batch_dim(value).contiguous()
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# `context` projections.
if encoder_hidden_states is not None:
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) | 782 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
encoder_hidden_states_query_proj = attn.head_to_batch_dim(encoder_hidden_states_query_proj).contiguous()
encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj).contiguous()
encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj).contiguous()
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)
query = torch.cat([query, encoder_hidden_states_query_proj], dim=1)
key = torch.cat([key, encoder_hidden_states_key_proj], dim=1)
value = torch.cat([value, encoder_hidden_states_value_proj], dim=1) | 782 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
hidden_states = xformers.ops.memory_efficient_attention(
query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
)
hidden_states = hidden_states.to(query.dtype)
hidden_states = attn.batch_to_head_dim(hidden_states)
if encoder_hidden_states is not None:
# Split the attention outputs.
hidden_states, encoder_hidden_states = (
hidden_states[:, : residual.shape[1]],
hidden_states[:, residual.shape[1] :],
)
if not attn.context_pre_only:
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if encoder_hidden_states is not None:
return hidden_states, encoder_hidden_states
else:
return hidden_states | 782 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class AllegroAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the Allegro model. It applies a normalization layer and rotary embedding on the query and key vector.
"""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"AllegroAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.Tensor:
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb) | 783 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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) | 783 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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)
# Apply RoPE if needed
if image_rotary_emb is not None and not attn.is_cross_attention:
from .embeddings import apply_rotary_emb_allegro
query = apply_rotary_emb_allegro(query, image_rotary_emb[0], image_rotary_emb[1])
key = apply_rotary_emb_allegro(key, image_rotary_emb[0], image_rotary_emb[1]) | 783 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# 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 | 783 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class AuraFlowAttnProcessor2_0:
"""Attention processor used typically in processing Aura Flow."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention") and is_torch_version("<", "2.1"):
raise ImportError(
"AuraFlowAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to at least 2.1 or above as we use `scale` in `F.scaled_dot_product_attention()`. "
)
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
*args,
**kwargs,
) -> torch.FloatTensor:
batch_size = hidden_states.shape[0]
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states) | 784 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# `context` projections.
if encoder_hidden_states is not None:
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)
# Reshape.
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim)
key = key.view(batch_size, -1, attn.heads, head_dim)
value = value.view(batch_size, -1, attn.heads, head_dim)
# Apply QK norm.
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key) | 784 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# Concatenate the projections.
if encoder_hidden_states is not None:
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(batch_size, -1, attn.heads, head_dim)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
)
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_q(encoder_hidden_states_key_proj) | 784 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
query = torch.cat([encoder_hidden_states_query_proj, query], dim=1)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
query = query.transpose(1, 2)
key = key.transpose(1, 2)
value = value.transpose(1, 2)
# Attention.
hidden_states = F.scaled_dot_product_attention(
query, key, value, dropout_p=0.0, scale=attn.scale, 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)
# Split the attention outputs.
if encoder_hidden_states is not None:
hidden_states, encoder_hidden_states = (
hidden_states[:, encoder_hidden_states.shape[1] :],
hidden_states[:, : encoder_hidden_states.shape[1]],
) | 784 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if encoder_hidden_states is not None:
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
if encoder_hidden_states is not None:
return hidden_states, encoder_hidden_states
else:
return hidden_states | 784 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class FusedAuraFlowAttnProcessor2_0:
"""Attention processor used typically in processing Aura Flow with fused projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention") and is_torch_version("<", "2.1"):
raise ImportError(
"FusedAuraFlowAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to at least 2.1 or above as we use `scale` in `F.scaled_dot_product_attention()`. "
)
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
*args,
**kwargs,
) -> torch.FloatTensor:
batch_size = hidden_states.shape[0]
# `sample` projections.
qkv = attn.to_qkv(hidden_states)
split_size = qkv.shape[-1] // 3
query, key, value = torch.split(qkv, split_size, dim=-1) | 785 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# `context` projections.
if encoder_hidden_states is not None:
encoder_qkv = attn.to_added_qkv(encoder_hidden_states)
split_size = encoder_qkv.shape[-1] // 3
(
encoder_hidden_states_query_proj,
encoder_hidden_states_key_proj,
encoder_hidden_states_value_proj,
) = torch.split(encoder_qkv, split_size, dim=-1)
# Reshape.
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim)
key = key.view(batch_size, -1, attn.heads, head_dim)
value = value.view(batch_size, -1, attn.heads, head_dim)
# Apply QK norm.
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key) | 785 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# Concatenate the projections.
if encoder_hidden_states is not None:
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(batch_size, -1, attn.heads, head_dim)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
)
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_q(encoder_hidden_states_key_proj) | 785 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
query = torch.cat([encoder_hidden_states_query_proj, query], dim=1)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
query = query.transpose(1, 2)
key = key.transpose(1, 2)
value = value.transpose(1, 2)
# Attention.
hidden_states = F.scaled_dot_product_attention(
query, key, value, dropout_p=0.0, scale=attn.scale, 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)
# Split the attention outputs.
if encoder_hidden_states is not None:
hidden_states, encoder_hidden_states = (
hidden_states[:, encoder_hidden_states.shape[1] :],
hidden_states[:, : encoder_hidden_states.shape[1]],
) | 785 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if encoder_hidden_states is not None:
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
if encoder_hidden_states is not None:
return hidden_states, encoder_hidden_states
else:
return hidden_states | 785 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class FluxAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("FluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
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,
) -> torch.FloatTensor:
batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads | 786 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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)
# the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
if encoder_hidden_states is not None:
# `context` projections.
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) | 786 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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) | 786 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# 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 .embeddings import apply_rotary_emb
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
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)
if encoder_hidden_states is not None:
encoder_hidden_states, hidden_states = (
hidden_states[:, : encoder_hidden_states.shape[1]],
hidden_states[:, encoder_hidden_states.shape[1] :],
) | 786 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
return hidden_states, encoder_hidden_states
else:
return hidden_states | 786 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
class FluxAttnProcessor2_0_NPU:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"FluxAttnProcessor2_0_NPU requires PyTorch 2.0 and torch NPU, to use it, please upgrade PyTorch to 2.0 and install torch NPU"
)
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,
) -> torch.FloatTensor:
batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states) | 787 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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)
# the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
if encoder_hidden_states is not None:
# `context` projections.
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) | 787 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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) | 787 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# 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 .embeddings import apply_rotary_emb
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb) | 787 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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