text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
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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()
... | 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 ... | 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: Optio... | 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, `**cr... | 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... | 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:
... | 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"""
Comput... | 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.badd... | 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 u... | 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.
... | 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_siz... | 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 w... | 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 ... | 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_(concat... | 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... | 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,
chan... | 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: ... | 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.appe... | 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)
hid... | 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,
... | 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.... | 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: Option... | 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,
at... | 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))
... | 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),
encode... | 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[i... | 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, "t... | 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,
... | 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_st... | 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_s... | 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`, defau... | 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
... | 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)
i... | 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... | 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.t... | 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_state... | 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.Tens... | 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:
en... | 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.ca... | 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"):
... | 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 ... | 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(... | 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)
... | 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 ... | 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 at... | 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... | 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_causa... | 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... | 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:... | 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.
... | 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).... | 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:
hi... | 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
... | 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("-... | 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
h... | 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 ... | 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:... | 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 ########... | 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]
hea... | 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
h... | 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_stat... | 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).... | 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... | 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
... | 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 PyTorc... | 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:... | 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, s... | 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, enco... | 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.Atte... | 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()
... | 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... | 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 ... | 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 ... | 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_hid... | 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... | 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_state... | 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... | 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_s... | 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_siz... | 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.tra... | 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:
... | 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(
"FusedAuraFlowAttnProcesso... | 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,
en... | 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_siz... | 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.tra... | 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:
... | 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 ... | 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 at... | 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... | 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 imp... | 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 hi... | 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 ... | 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... | 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... | 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 imp... | 787 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
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