text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
# Pad the tensor
pad = (0, 1, 0, 1)
x = F.pad(x, pad, mode="constant", value=0)
batch_size, channels, frames, height, width = x.shape
# (batch_size, channels, frames, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size * frames, channels, height, width)
... | 760 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
class GatedSelfAttentionDense(nn.Module):
r"""
A gated self-attention dense layer that combines visual features and object features.
Parameters:
query_dim (`int`): The number of channels in the query.
context_dim (`int`): The number of channels in the context.
n_heads (`int`): The n... | 761 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
self.register_parameter("alpha_attn", nn.Parameter(torch.tensor(0.0)))
self.register_parameter("alpha_dense", nn.Parameter(torch.tensor(0.0)))
self.enabled = True
def forward(self, x: torch.Tensor, objs: torch.Tensor) -> torch.Tensor:
if not self.enabled:
return x
n_vi... | 761 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
class JointTransformerBlock(nn.Module):
r"""
A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.
Reference: https://arxiv.org/abs/2403.03206
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): The num... | 762 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
self.use_dual_attention = use_dual_attention
self.context_pre_only = context_pre_only
context_norm_type = "ada_norm_continous" if context_pre_only else "ada_norm_zero"
if use_dual_attention:
self.norm1 = SD35AdaLayerNormZeroX(dim)
else:
self.norm1 = AdaLayerNormZ... | 762 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
if hasattr(F, "scaled_dot_product_attention"):
processor = JointAttnProcessor2_0()
else:
raise ValueError(
"The current PyTorch version does not support the `scaled_dot_product_attention` function."
)
self.attn = Attention(
query_dim=dim,
... | 762 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
if use_dual_attention:
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=None,
dim_head=attention_head_dim,
heads=num_attention_heads,
out_dim=dim,
bias=True,
processor=processor,
... | 762 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
# Copied from diffusers.models.attention.BasicTransformerBlock.set_chunk_feed_forward
def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
# Sets chunk feed-forward
self._chunk_size = chunk_size
self._chunk_dim = dim
def forward(
self,
hidden_states... | 762 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
if self.context_pre_only:
norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states, temb)
else:
norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
encoder_hidden_states, emb=temb
)
# Attenti... | 762 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
norm_hidden_states = self.norm2(hidden_states)
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
ff_output = _chunked_feed_forward(self.ff, norm_hidden_st... | 762 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
... | 762 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
class BasicTransformerBlock(nn.Module):
r"""
A basic Transformer block. | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): The number of heads to use for multi-head attention.
attention_head_dim (`int`): The number of channels in each head.
dropout (`float`, *optional*, defaults to 0.0): The dropout probabil... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
double_self_attention (`bool`, *optional*):
Whether to use two self-attention layers. In this case no cross attention layers are used.
upcast_attention (`bool`, *optional*):
Whether to upcast the attention computation to float32. This is useful for mixed precision training.
norm_... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
positional_embeddings (`str`, *optional*, defaults to `None`):
The type of positional embeddings to apply to.
num_positional_embeddings (`int`, *optional*, defaults to `None`):
The maximum number of positional embeddings to apply.
""" | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
dropout=0.0,
cross_attention_dim: Optional[int] = None,
activation_fn: str = "geglu",
num_embeds_ada_norm: Optional[int] = None,
attention_bias: bool = False,
... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
ff_inner_dim: Optional[int] = None,
ff_bias: bool = True,
attention_out_bias: bool = True,
):
super().__init__()
self.dim = dim
self.num_attention_heads = num_attention_heads
self.attention_head_dim = attention_head_dim
self.dropout = dropout
self.cros... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
# We keep these boolean flags for backward-compatibility.
self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
self.use_ada_layer_norm_single = norm_type == "ada_n... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
if positional_embeddings and (num_positional_embeddings is None):
raise ValueError(
"If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
)
if positional_embeddings == "sinusoidal":
self.pos_embed = SinusoidalPositiona... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if norm_type == "ada_norm":
self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
elif norm_type == "ada_norm_zero":
self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
elif norm_type ... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
self.attn1 = Attention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
cross_attention_dim=cross_attention_dim if only_cross_attention else None,
upcast_attention=upcast_atte... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim if not double_self_attention else None,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
# 3. Feed-forward
if norm_type == "ada_norm_continuous":
self.norm3 = AdaLayerNormContinuous(
dim,
ada_norm_continous_conditioning_embedding_dim,
norm_elementwise_affine,
norm_eps,
ada_norm_bias,
"layer_n... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
# 5. Scale-shift for PixArt-Alpha.
if norm_type == "ada_norm_single":
self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
# let chunk size default to None
self._chunk_size = None
self._chunk_dim = 0
def set_chunk_feed_forward(self, chunk_size: Optional... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
timestep: Optional[torch.LongTensor] = None,
cross_attention... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
if self.norm_type == "ada_norm":
norm_hidden_states = self.norm1(hidden_states, timestep)
elif self.norm_type == "ada_norm_zero":
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
hidden_states, timestep, class_labels, hidden_dtype=hidden_states.d... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
raise ValueError("Incorrect norm used") | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
if self.pos_embed is not None:
norm_hidden_states = self.pos_embed(norm_hidden_states)
# 1. Prepare GLIGEN inputs
cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
# 1.2 GLIGEN Control
if gligen_kwargs is not None:
hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
# 3. Cross-Attention
if self.attn2 is not None:
if self.norm_type == "ada_norm":
norm_hidden_states = self.norm2(hidden_states, timestep)
... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
if self.pos_embed is not None and self.norm_type != "ada_norm_single":
norm_hidden_states = self.pos_embed(norm_hidden_states)
attn_output = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=encoder_at... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
if self.norm_type == "ada_norm_single":
norm_hidden_states = self.norm2(hidden_states)
norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
ff_output = _chunk... | 763 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
class LuminaFeedForward(nn.Module):
r"""
A feed-forward layer.
Parameters:
hidden_size (`int`):
The dimensionality of the hidden layers in the model. This parameter determines the width of the model's
hidden representations.
intermediate_size (`int`): The intermediat... | 764 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
def __init__(
self,
dim: int,
inner_dim: int,
multiple_of: Optional[int] = 256,
ffn_dim_multiplier: Optional[float] = None,
):
super().__init__()
inner_dim = int(2 * inner_dim / 3)
# custom hidden_size factor multiplier
if ffn_dim_multiplier is... | 764 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
class TemporalBasicTransformerBlock(nn.Module):
r"""
A basic Transformer block for video like data.
Parameters:
dim (`int`): The number of channels in the input and output.
time_mix_inner_dim (`int`): The number of channels for temporal attention.
num_attention_heads (`int`): The nu... | 765 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
self.ff_in = FeedForward(
dim,
dim_out=time_mix_inner_dim,
activation_fn="geglu",
)
self.norm1 = nn.LayerNorm(time_mix_inner_dim)
self.attn1 = Attention(
... | 765 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
# 2. Cross-Attn
if cross_attention_dim is not None:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second... | 765 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
# let chunk size default to None
self._chunk_size = None
self._chunk_dim = None
def set_chunk_feed_forward(self, chunk_size: Optional[int], **kwargs):
# Sets chunk feed-forward
self._chunk_size = chunk_size
# chunk dim should be hardcoded to 1 to have better speed vs. memory... | 765 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, seq_length, channels)
hidden_states = hidden_states.permute(0, 2, 1, 3)
hidden_states = hidden_states.reshape(batch_size * seq_length, num_frames, channels)
residual = hidden_states
hidden_states = self.norm_in(hidde... | 765 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
# 3. Cross-Attention
if self.attn2 is not None:
norm_hidden_states = self.norm2(hidden_states)
attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states)
hidden_states = attn_output + hidden_states
# 4. Feed-forward
norm_hidden_... | 765 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
class SkipFFTransformerBlock(nn.Module):
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
kv_input_dim: int,
kv_input_dim_proj_use_bias: bool,
dropout=0.0,
cross_attention_dim: Optional[int] = None,
attention_bia... | 766 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
out_bias=attention_out_bias,
)
def forward(self, ... | 766 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
attn_output = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
**cross_attention_kwargs,
)
hidden_states = attn_output + hidden_states
return hidden_states | 766 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
class FreeNoiseTransformerBlock(nn.Module):
r"""
A FreeNoise Transformer block. | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
Parameters:
dim (`int`):
The number of channels in the input and output.
num_attention_heads (`int`):
The number of heads to use for multi-head attention.
attention_head_dim (`int`):
The number of channels in each head.
dropout (`float`, *optional*, de... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
Whether to use only cross-attention layers. In this case two cross attention layers are used.
double_self_attention (`bool`, defaults to `False`):
Whether to use two self-attention layers. In this case no cross attention layers are used.
upcast_attention (`bool`, defaults to `False`):
... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
positional_embeddings (`str`, *optional*):
The type of positional embeddings to apply to.
num_positional_embeddings (`int`, *optional*, defaults to `None`):
The maximum number of positional emb... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
weighting_scheme (`str`, defaults to `"pyramid"`):
The weighting scheme to use for weighting averaging of processed latent frames. As described in the
Equation 9. of the [FreeNoise](https://arxiv.org/abs/2310.15169) paper, "pyramid" is the default setting
used.
""" | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
dropout: float = 0.0,
cross_attention_dim: Optional[int] = None,
activation_fn: str = "geglu",
num_embeds_ada_norm: Optional[int] = None,
attention_bias: bool = False,... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
self.num_attention_heads = num_attention_heads
self.attention_head_dim = attention_head_dim
self.dropout = dropout
self.cross_attention_dim = cross_attention_dim
self.activation_fn = activation_fn
self.attention_bias = attention_bias
self.double_self_attention = double_se... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
self.set_free_noise_properties(context_length, context_stride, weighting_scheme)
# We keep these boolean flags for backward-compatibility.
self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
self.use_ada_layer_norm = (num_embeds_ada_norm is not None... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
if positional_embeddings and (num_positional_embeddings is None):
raise ValueError(
"If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
)
if positional_embeddings == "sinusoidal":
self.pos_embed = SinusoidalPositiona... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
self.norm2 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim if not double_self_attention else... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
# let chunk size default to None
self._chunk_size = None
self._chunk_dim = 0
def _get_frame_indices(self, num_frames: int) -> List[Tuple[int, int]]:
frame_indices = []
for i in range(0, num_frames - self.context_length + 1, self.context_stride):
window_start = i
... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
elif weighting_scheme == "pyramid":
if num_frames % 2 == 0:
# num_frames = 4 => [1, 2, 2, 1]
mid = num_frames // 2
weights = list(range(1, mid + 1))
weights = weights + weights[::-1]
else:
# num_frames = 5 => [1, 2, ... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
elif weighting_scheme == "delayed_reverse_sawtooth":
if num_frames % 2 == 0:
# num_frames = 4 => [0.01, 2, 2, 1]
mid = num_frames // 2
weights = [0.01] * (mid - 1) + [mid]
weights = weights + list(range(mid, 0, -1))
else:
... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0) -> None:
# Sets chunk feed-forward
self._chunk_size = chunk_size
self._chunk_dim = dim
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
enc... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
num_frames = hidden_states.size(1)
frame_indices = self._get_frame_indices(num_frames)
frame_weights = self._get_frame_weights(self.context_length, self.weighting_scheme)
frame_weights = torch.tensor(frame_weights, device=device, dtype=dtype).unsqueeze(0).unsqueeze(-1)
is_last_frame_batc... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
num_times_accumulated = torch.zeros((1, num_frames, 1), device=device)
accumulated_values = torch.zeros_like(hidden_states)
for i, (frame_start, frame_end) in enumerate(frame_indices):
# The reason for slicing here is to ensure that if (frame_end - frame_start) is to handle
# ca... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
attn_output = self.attn1(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
attention_mask=attention_mask,
**cross_attention_kwargs,
)
hidden_states_chunk = attn_output + hidden... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
attn_output = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
**cross_attention_kwargs,
)
hidden_states_chunk = attn_output + hidden_states_... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
# TODO(aryan): Maybe this could be done in a better way.
#
# Previously, this was:
# hidden_states = torch.where(
# num_times_accumulated > 0, accumulated_values / num_times_accumulated, accumulated_values
# )
#
# The reasoning for the change here is `torch.whe... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
num_times_accumulated.split(self.context_length, dim=1),
)
],
dim=1,
).to(dtype) | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
# 3. Feed-forward
norm_hidden_states = self.norm3(hidden_states)
if self._chunk_size is not None:
ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
else:
ff_output = self.ff(norm_hidden_states)
hidden_states = ff_o... | 767 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
class FeedForward(nn.Module):
r"""
A feed-forward layer.
Parameters:
dim (`int`): The number of channels in the input.
dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
mult (`int`, *optional*, defaults to 4): The multiplier to use f... | 768 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
def __init__(
self,
dim: int,
dim_out: Optional[int] = None,
mult: int = 4,
dropout: float = 0.0,
activation_fn: str = "geglu",
final_dropout: bool = False,
inner_dim=None,
bias: bool = True,
):
super().__init__()
if inner_dim i... | 768 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
if activation_fn == "gelu":
act_fn = GELU(dim, inner_dim, bias=bias)
if activation_fn == "gelu-approximate":
act_fn = GELU(dim, inner_dim, approximate="tanh", bias=bias)
elif activation_fn == "geglu":
act_fn = GEGLU(dim, inner_dim, bias=bias)
elif activation_f... | 768 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
self.net = nn.ModuleList([])
# project in
self.net.append(act_fn)
# project dropout
self.net.append(nn.Dropout(dropout))
# project out
self.net.append(nn.Linear(inner_dim, dim_out, bias=bias))
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropou... | 768 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py |
class Attention(nn.Module):
r"""
A cross attention layer. | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
Parameters:
query_dim (`int`):
The number of channels in the query.
cross_attention_dim (`int`, *optional*):
The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
heads (`int`, *optional*, defaults to 8):
The number of he... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
Set to `True` for the query, key, and value linear layers to contain a bias parameter.
upcast_attention (`bool`, *optional*, defaults to False):
Set to `True` to upcast the attention computation to `float32`.
upcast_softmax (`bool`, *optional*, defaults to False):
Set to `True` t... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
The number of groups to use for the group norm in the attention.
spatial_norm_dim (`int`, *optional*, defaults to `None`):
The number of channels to use for the spatial normalization.
out_bias (`bool`, *optional*, defaults to `True`):
Set to `True` to use a bias in the output lin... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
A factor to rescale the output by dividing it with this value.
residual_connection (`bool`, *optional*, defaults to `False`):
Set to `True` to add the residual connection to the output.
_from_deprecated_attn_block (`bool`, *optional*, defaults to `False`):
Set to `True` if the at... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
def __init__(
self,
query_dim: int,
cross_attention_dim: Optional[int] = None,
heads: int = 8,
kv_heads: Optional[int] = None,
dim_head: int = 64,
dropout: float = 0.0,
bias: bool = False,
upcast_attention: bool = False,
upcast_softmax: boo... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
out_context_dim: int = None,
context_pre_only=None,
pre_only=False,
elementwise_affine: bool = True,
is_causal: bool = False,
):
super().__init__() | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# To prevent circular import.
from .normalization import FP32LayerNorm, LpNorm, RMSNorm | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
self.inner_dim = out_dim if out_dim is not None else dim_head * heads
self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads
self.query_dim = query_dim
self.use_bias = bias
self.is_cross_attention = cross_attention_dim is not None
self.cross_attention_dim... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# we make use of this private variable to know whether this class is loaded
# with an deprecated state dict so that we can convert it on the fly
self._from_deprecated_attn_block = _from_deprecated_attn_block
self.scale_qk = scale_qk
self.scale = dim_head**-0.5 if self.scale_qk else 1.0
... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if self.added_kv_proj_dim is None and self.only_cross_attention:
raise ValueError(
"`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`."
)
if norm_num_group... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if qk_norm is None:
self.norm_q = None
self.norm_k = None
elif qk_norm == "layer_norm":
self.norm_q = nn.LayerNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
self.norm_k = nn.LayerNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# LTX applies qk norm across all heads
self.norm_q = RMSNorm(dim_head * heads, eps=eps)
self.norm_k = RMSNorm(dim_head * kv_heads, eps=eps)
elif qk_norm == "l2":
self.norm_q = LpNorm(p=2, dim=-1, eps=eps)
self.norm_k = LpNorm(p=2, dim=-1, eps=eps)
else:
... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if cross_attention_norm is None:
self.norm_cross = None
elif cross_attention_norm == "layer_norm":
self.norm_cross = nn.LayerNorm(self.cross_attention_dim)
elif cross_attention_norm == "group_norm":
if self.added_kv_proj_dim is not None:
# The given `e... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
self.norm_cross = nn.GroupNorm(
num_channels=norm_cross_num_channels, num_groups=cross_attention_norm_num_groups, eps=1e-5, affine=True
)
else:
raise ValueError(
f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
self.added_proj_bias = added_proj_bias
if self.added_kv_proj_dim is not None:
self.add_k_proj = nn.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias)
self.add_v_proj = nn.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias)
if self.context_pre_on... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if self.context_pre_only is not None and not self.context_pre_only:
self.to_add_out = nn.Linear(self.inner_dim, self.out_context_dim, bias=out_bias)
else:
self.to_add_out = None
if qk_norm is not None and added_kv_proj_dim is not None:
if qk_norm == "fp32_layer_norm"... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# 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 ... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
Args:
use_xla_flash_attention (`bool`):
Whether to use pallas flash attention kernel from `torch_xla` or not.
partition_spec (`Tuple[]`, *optional*):
Specify the partition specification if using SPMD. Otherwise None.
"""
if use_xla_flash_attention:... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
)
self.set_processor(processor) | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
def set_use_npu_flash_attention(self, use_npu_flash_attention: bool) -> None:
r"""
Set whether to use npu flash attention from `torch_npu` or not.
"""
if use_npu_flash_attention:
processor = AttnProcessorNPU()
else:
# set attention processor
#... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
def set_use_memory_efficient_attention_xformers(
self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None
) -> None:
r"""
Set whether to use memory efficient attention from `xformers` or not. | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
Args:
use_memory_efficient_attention_xformers (`bool`):
Whether to use memory efficient attention from `xformers` or not.
attention_op (`Callable`, *optional*):
The attention operation to use. Defaults to `None` which uses the default attention operation from
... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
(IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, IPAdapterXFormersAttnProcessor),
)
is_joint_processor = hasattr(self, "processor") and isinstance(
self.processor,
(
JointAttnProcessor2_0,
XFormersJointAttnProcessor,
),
) | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if use_memory_efficient_attention_xformers:
if is_added_kv_processor and is_custom_diffusion:
raise NotImplementedError(
f"Memory efficient attention is currently not supported for custom diffusion for attention processor type {self.processor}"
)
... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# Make sure we can run the memory efficient attention
_ = xformers.ops.memory_efficient_attention(
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
if is_custom_diffusion:
processor = CustomDiffusionXFormersAttnProcessor(
train_kv=self.processor.train_kv,
train_q_out=self.processor.train_q_out,
hidden_size=self.processor.hidden_size,
cross_attention_dim=self.processor.c... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
"Memory efficient attention with `xformers` might currently not work correctly if an attention mask is required for the attention operation."
)
processor = XFormersAttnAddedKVProcessor(attention_op=attention_op)
elif is_ip_adapter:
processor = IPAdapterXFormer... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
processor = XFormersJointAttnProcessor(attention_op=attention_op)
else:
processor = XFormersAttnProcessor(attention_op=attention_op)
else:
if is_custom_diffusion:
attn_processor_class = (
CustomDiffusionAttnProcessor2_0
... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
elif is_ip_adapter:
processor = IPAdapterAttnProcessor2_0(
hidden_size=self.processor.hidden_size,
cross_attention_dim=self.processor.cross_attention_dim,
num_tokens=self.processor.num_tokens,
scale=self.processor.scale,
... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
# 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()
) | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
self.set_processor(processor)
def set_attention_slice(self, slice_size: int) -> None:
r"""
Set the slice size for attention computation.
Args:
slice_size (`int`):
The slice size for attention computation.
"""
if slice_size is not None and slice_s... | 769 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.