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# 2. Blocks
for block in self.transformer_blocks:
hidden_states = block(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
timestep=timestep,
cross_attention_kwargs=cross_attention_kwargs,
class_labels=class_la... | 1,114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
class TransformerSpatioTemporalModel(nn.Module):
"""
A Transformer model for video-like data.
Parameters:
num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*, defaults to 88): The number of chann... | 1,115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: int = 320,
out_channels: Optional[int] = None,
num_layers: int = 1,
cross_attention_dim: Optional[int] = None,
):
super().__init__()
self.num_attentio... | 1,115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
# 3. Define transformers blocks
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(
inner_dim,
num_attention_heads,
attention_head_dim,
cross_attention_dim=cross_attention_dim,
... | 1,115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
time_embed_dim = in_channels * 4
self.time_pos_embed = TimestepEmbedding(in_channels, time_embed_dim, out_dim=in_channels)
self.time_proj = Timesteps(in_channels, True, 0)
self.time_mixer = AlphaBlender(alpha=0.5, merge_strategy="learned_with_images")
# 4. Define output layers
s... | 1,115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
image_only_indicator: Optional[torch.Tensor] = None,
return_dict: bool = True,
):
"""
Args:
hidden_states (`torch.Tensor` of shape `(batch size, c... | 1,115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
images, 0 indicates that the input contains video frames.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`]
instead of a plain tuple. | 1,115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
Returns:
[`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
If `return_dict` is True, an
[`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`] is returned, otherwise a
`tuple` where the first element... | 1,115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
residual = hidden_states
hidden_states = self.norm(hidden_states)
inner_dim = hidden_states.shape[1]
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_frames, height * width, inner_dim)
hidden_states = self.proj_in(hidden_states)
num_frames_emb = torch.arange(num_... | 1,115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
# 2. Blocks
for block, temporal_block in zip(self.transformer_blocks, self.temporal_transformer_blocks):
if torch.is_grad_enabled() and self.gradient_checkpointing:
hidden_states = torch.utils.checkpoint.checkpoint(
block,
hidden_states,
... | 1,115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
hidden_states_mix = temporal_block(
hidden_states_mix,
num_frames=num_frames,
encoder_hidden_states=time_context,
)
hidden_states = self.time_mixer(
x_spatial=hidden_states,
x_temporal=hidden_states_mix,
... | 1,115 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
class LTXVideoAttentionProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the LTX model. It applies a normalization layer and rotary embedding on the query and key vector.
"""
def __init__(self):
if no... | 1,116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if encoder_hidden_states is None:
encoder_hidden_states = hidde... | 1,116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
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).flatten(2, 3)
hidden_states = hidden_states.to(query.dtype)
hidden_states = attn.to_out[0](hidden_state... | 1,116 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
class LTXVideoRotaryPosEmbed(nn.Module):
def __init__(
self,
dim: int,
base_num_frames: int = 20,
base_height: int = 2048,
base_width: int = 2048,
patch_size: int = 1,
patch_size_t: int = 1,
theta: float = 10000.0,
) -> None:
super().__init... | 1,117 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
# Always compute rope in fp32
grid_h = torch.arange(height, dtype=torch.float32, device=hidden_states.device)
grid_w = torch.arange(width, dtype=torch.float32, device=hidden_states.device)
grid_f = torch.arange(num_frames, dtype=torch.float32, device=hidden_states.device)
grid = torch.me... | 1,117 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
start = 1.0
end = self.theta
freqs = self.theta ** torch.linspace(
math.log(start, self.theta),
math.log(end, self.theta),
self.dim // 6,
device=hidden_states.device,
dtype=torch.float32,
)
freqs = freqs * math.pi / 2.0
... | 1,117 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
class LTXVideoTransformerBlock(nn.Module):
r"""
Transformer block used in [LTX](https://huggingface.co/Lightricks/LTX-Video).
Args:
dim (`int`):
The number of channels in the input and output.
num_attention_heads (`int`):
The number of heads to use for multi-head att... | 1,118 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
cross_attention_dim: int,
qk_norm: str = "rms_norm_across_heads",
activation_fn: str = "gelu-approximate",
attention_bias: bool = True,
attention_out_bias: bool = True... | 1,118 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
self.norm2 = RMSNorm(dim, eps=eps, elementwise_affine=elementwise_affine)
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim,
heads=num_attention_heads,
kv_heads=num_attention_heads,
dim_head=attention_head_dim,
... | 1,118 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
temb: torch.Tensor,
image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
batch_size ... | 1,118 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
attn_hidden_states = self.attn2(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
image_rotary_emb=None,
attention_mask=encoder_attention_mask,
)
hidden_states = hidden_states + attn_hidden_states
norm_hidden_states = self.norm2(hidden_s... | 1,118 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
class LTXVideoTransformer3DModel(ModelMixin, ConfigMixin, FromOriginalModelMixin, PeftAdapterMixin):
r"""
A Transformer model for video-like data used in [LTX](https://huggingface.co/Lightricks/LTX-Video). | 1,119 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
Args:
in_channels (`int`, defaults to `128`):
The number of channels in the input.
out_channels (`int`, defaults to `128`):
The number of channels in the output.
patch_size (`int`, defaults to `1`):
The size of the spatial patches to use in the patch embedding... | 1,119 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
Activation function to use in feed-forward.
qk_norm (`str`, defaults to `"rms_norm_across_heads"`):
The normalization layer to use.
""" | 1,119 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
in_channels: int = 128,
out_channels: int = 128,
patch_size: int = 1,
patch_size_t: int = 1,
num_attention_heads: int = 32,
attention_head_dim: int = 64,
cross_attenti... | 1,119 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim**0.5)
self.time_embed = AdaLayerNormSingle(inner_dim, use_additional_conditions=False)
self.caption_projection = PixArtAlphaTextProjection(in_features=caption_channels, hidden_size=inner_dim)
self.rope = LTXVideoRotary... | 1,119 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
self.transformer_blocks = nn.ModuleList(
[
LTXVideoTransformerBlock(
dim=inner_dim,
num_attention_heads=num_attention_heads,
attention_head_dim=attention_head_dim,
cross_attention_dim=cross_attention_dim,
... | 1,119 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
def _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
timestep: torch.LongTensor,
... | 1,119 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
if USE_PEFT_BACKEND:
# weight the lora layers by setting `lora_scale` for each PEFT layer
scale_lora_layers(self, lora_scale)
else:
if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
logger.warning(
"Passin... | 1,119 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
temb, embedded_timestep = self.time_embed(
timestep.flatten(),
batch_size=batch_size,
hidden_dtype=hidden_states.dtype,
)
temb = temb.view(batch_size, -1, temb.size(-1))
embedded_timestep = embedded_timestep.view(batch_size, -1, embedded_timestep.size(-1))
... | 1,119 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
encoder_hidden_states,
temb,
... | 1,119 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
hidden_states = self.norm_out(hidden_states)
hidden_states = hidden_states * (1 + scale) + shift
output = self.proj_out(hidden_states)
if USE_PEFT_BACKEND:
# remove `lora_scale` from each PEFT layer
unscale_lora_layers(self, lora_scale)
if not return_dict:
... | 1,119 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py |
class LatteTransformer3DModel(ModelMixin, ConfigMixin):
_supports_gradient_checkpointing = True
"""
A 3D Transformer model for video-like data, paper: https://arxiv.org/abs/2401.03048, offical code:
https://github.com/Vchitect/Latte | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
Parameters:
num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
in_channels (`int`, *optional*):
The number of channels in the input.... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
This is fixed during training since it is used to learn a number of position embeddings.
patch_size (`int`, *optional*):
The size of the patches to use in the patch embedding layer.
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
Whether or not to use elementwise affine in normalization layers.
norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon value to use in normalization layers.
caption_channels (`int`, *optional*):
The number of channels in the caption embeddings.
video_length (`int`, *optional... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
@register_to_config
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: Optional[int] = None,
out_channels: Optional[int] = None,
num_layers: int = 1,
dropout: float = 0.0,
cross_attention_dim: Optional[int] = ... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
interpolation_scale = self.config.sample_size // 64
interpolation_scale = max(interpolation_scale, 1)
self.pos_embed = PatchEmbed(
height=sample_size,
width=sample_size,
patch_size=patch_size,
in_channels=in_channels,
embed_dim=inner_dim,
... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
# 2. Define spatial transformers blocks
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(
inner_dim,
num_attention_heads,
attention_head_dim,
dropout=dropout,
cross_att... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
# 3. Define temporal transformers blocks
self.temporal_transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(
inner_dim,
num_attention_heads,
attention_head_dim,
dropout=dropout,
... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
# 4. Define output layers
self.out_channels = in_channels if out_channels is None else out_channels
self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim**0.5)
self.proj_out = nn.Linear(inne... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
def _set_gradient_checkpointing(self, module, value=False):
self.gradient_checkpointing = value
def forward(
self,
hidden_states: torch.Tensor,
timestep: Optional[torch.LongTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: ... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
Args:
hidden_states shape `(batch size, channel, num_frame, height, width)`:
Input `hidden_states`.
timestep ( `torch.LongTensor`, *optional*):
Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
encoder_... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
above. This bias will be added to the cross-attention scores.
enable_temporal_attentions:
(`bool`, *optional*, defaults to `True`): Whether to enable temporal attentions.
... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
# Reshape hidden states
batch_size, channels, num_frame, height, width = hidden_states.shape
# batch_size channels num_frame height width -> (batch_size * num_frame) channels height width
hidden_states = hidden_states.permute(0, 2, 1, 3, 4).reshape(-1, channels, height, width)
# Input
... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
# Prepare text embeddings for spatial block
# batch_size num_tokens hidden_size -> (batch_size * num_frame) num_tokens hidden_size
encoder_hidden_states = self.caption_projection(encoder_hidden_states) # 3 120 1152
encoder_hidden_states_spatial = encoder_hidden_states.repeat_interleave(num_fram... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
# Spatial and temporal transformer blocks
for i, (spatial_block, temp_block) in enumerate(
zip(self.transformer_blocks, self.temporal_transformer_blocks)
):
if torch.is_grad_enabled() and self.gradient_checkpointing:
hidden_states = torch.utils.checkpoint.checkpoi... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
None, # cross_attention_kwargs
None, # class_labels
) | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
if enable_temporal_attentions:
# (batch_size * num_frame) num_tokens hidden_size -> (batch_size * num_tokens) num_frame hidden_size
hidden_states = hidden_states.reshape(
batch_size, -1, hidden_states.shape[-2], hidden_states.shape[-1]
).permute(0, 2, ... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
if torch.is_grad_enabled() and self.gradient_checkpointing:
hidden_states = torch.utils.checkpoint.checkpoint(
temp_block,
hidden_states,
None, # attention_mask
None, # encoder_hidden_states
... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
# (batch_size * num_tokens) num_frame hidden_size -> (batch_size * num_frame) num_tokens hidden_size
hidden_states = hidden_states.reshape(
batch_size, -1, hidden_states.shape[-2], hidden_states.shape[-1]
).permute(0, 2, 1, 3)
hidden_states = hidden_st... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
# unpatchify
if self.adaln_single is None:
height = width = int(hidden_states.shape[1] ** 0.5)
hidden_states = hidden_states.reshape(
shape=(-1, height, width, self.config.patch_size, self.config.patch_size, self.out_channels)
)
hidden_states = torch.einsum("nhwpq... | 1,120 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py |
class GLUMBConv(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
expand_ratio: float = 4,
norm_type: Optional[str] = None,
residual_connection: bool = True,
) -> None:
super().__init__()
hidden_channels = int(expand_ratio * in_... | 1,121 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
if self.residual_connection:
residual = hidden_states
hidden_states = self.conv_inverted(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.conv_depth(hidden_states)
hi... | 1,121 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
class SanaTransformerBlock(nn.Module):
r"""
Transformer block introduced in [Sana](https://huggingface.co/papers/2410.10629).
"""
def __init__(
self,
dim: int = 2240,
num_attention_heads: int = 70,
attention_head_dim: int = 32,
dropout: float = 0.0,
num_c... | 1,122 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
# 1. Self Attention
self.norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=norm_eps)
self.attn1 = Attention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
cross_at... | 1,122 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
# 3. Feed-forward
self.ff = GLUMBConv(dim, dim, mlp_ratio, norm_type=None, residual_connection=False)
self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
... | 1,122 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
# 2. Self Attention
norm_hidden_states = self.norm1(hidden_states)
norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
norm_hidden_states = norm_hidden_states.to(hidden_states.dtype)
attn_output = self.attn1(norm_hidden_states)
hidden_states = hidden_states + g... | 1,122 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
norm_hidden_states = norm_hidden_states.unflatten(1, (height, width)).permute(0, 3, 1, 2)
ff_output = self.ff(norm_hidden_states)
ff_output = ff_output.flatten(2, 3).permute(0, 2, 1)
hidden_states = hidden_states + gate_mlp * ff_output
return hidden_states | 1,122 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
class SanaTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
r"""
A 2D Transformer model introduced in [Sana](https://huggingface.co/papers/2410.10629) family of models. | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
Args:
in_channels (`int`, defaults to `32`):
The number of channels in the input.
out_channels (`int`, *optional*, defaults to `32`):
The number of channels in the output.
num_attention_heads (`int`, defaults to `70`):
The number of heads to use for multi-head... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
The number of channels in the caption embeddings.
mlp_ratio (`float`, defaults to `2.5`):
The expansion ratio to use in the GLUMBConv layer.
dropout (`float`, defaults to `0.0`):
The dropout probability.
attention_bias (`bool`, defaults to `False`):
Whether to... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
_supports_gradient_checkpointing = True
_no_split_modules = ["SanaTransformerBlock", "PatchEmbed"]
@register_to_config
def __init__(
self,
in_channels: int = 32,
out_channels: Optional[int] = 32,
num_attention_heads: int = 70,
attention_head_dim: int = 32,
nu... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
# 1. Patch Embedding
self.patch_embed = PatchEmbed(
height=sample_size,
width=sample_size,
patch_size=patch_size,
in_channels=in_channels,
embed_dim=inner_dim,
interpolation_scale=interpolation_scale,
pos_embed_type="sincos" if ... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
# 3. Transformer blocks
self.transformer_blocks = nn.ModuleList(
[
SanaTransformerBlock(
inner_dim,
num_attention_heads,
attention_head_dim,
dropout=dropout,
num_cross_attention_heads=... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels)
self.gradient_checkpointing = False
def _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointin... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
if hasattr(module, "get_processor"):
processors[f"{name}.processor"] = module.get_processor()
for sub_name, child in module.named_children():
fn_recurs... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers.
If `processor` is a dict, the key need... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor"):
if not isinstance(processor, dict):
module.set_processor(processor)
else:
module.set_processor(processor.pop(f"{name}.p... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
timestep: torch.LongTensor,
encoder_attention_mask: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
attention_kwargs: Optional[Dict[str, Any]] = None... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
if USE_PEFT_BACKEND:
# weight the lora layers by setting `lora_scale` for each PEFT layer
scale_lora_layers(self, lora_scale)
else:
if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
logger.warning(
"Passin... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
# we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
# we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
# expects mask of shape:
... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
# (keep = +0, discard = -10000.0)
attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
attention_mask = attention_mask.unsqueeze(1) | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
# convert encoder_attention_mask to a bias the same way we do for attention_mask
if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
encoder_attention_mask = encoder_attention... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
# 2. Transformer blocks
if torch.is_grad_enabled() and self.gradient_checkpointing:
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dic... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
for block in self.transformer_blocks:
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
# 3. Normalization
shift, scale = (
self.scale_shift_table[None] + embedded_timestep[:, None].to(self.scale_shift_table.device)
).chunk(2, dim=1)
hidden_states = self.norm_out(hidden_states)
# 4. Modulation
hidden_states = hidden_states * (1 + scale) + shift
... | 1,123 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py |
class AuraFlowPatchEmbed(nn.Module):
def __init__(
self,
height=224,
width=224,
patch_size=16,
in_channels=3,
embed_dim=768,
pos_embed_max_size=None,
):
super().__init__()
self.num_patches = (height // patch_size) * (width // patch_size)
... | 1,124 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
def pe_selection_index_based_on_dim(self, h, w):
# select subset of positional embedding based on H, W, where H, W is size of latent
# PE will be viewed as 2d-grid, and H/p x W/p of the PE will be selected
# because original input are in flattened format, we have to flatten this 2d grid as well.... | 1,124 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
def forward(self, latent):
batch_size, num_channels, height, width = latent.size()
latent = latent.view(
batch_size,
num_channels,
height // self.patch_size,
self.patch_size,
width // self.patch_size,
self.patch_size,
)
... | 1,124 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
class AuraFlowFeedForward(nn.Module):
def __init__(self, dim, hidden_dim=None) -> None:
super().__init__()
if hidden_dim is None:
hidden_dim = 4 * dim
final_hidden_dim = int(2 * hidden_dim / 3)
final_hidden_dim = find_multiple(final_hidden_dim, 256)
self.linear_... | 1,125 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
class AuraFlowPreFinalBlock(nn.Module):
def __init__(self, embedding_dim: int, conditioning_embedding_dim: int):
super().__init__()
self.silu = nn.SiLU()
self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=False)
def forward(self, x: torch.Tensor, conditioning_e... | 1,126 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
class AuraFlowSingleTransformerBlock(nn.Module):
"""Similar to `AuraFlowJointTransformerBlock` with a single DiT instead of an MMDiT."""
def __init__(self, dim, num_attention_heads, attention_head_dim):
super().__init__()
self.norm1 = AdaLayerNormZero(dim, bias=False, norm_type="fp32_layer_nor... | 1,127 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
# Norm + Projection.
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
# Attention.
attn_output = self.attn(hidden_states=norm_hidden_states)
# Process attention outputs for the `hidden_states`.
hidden_states = self.norm2(residua... | 1,127 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
class AuraFlowJointTransformerBlock(nn.Module):
r"""
Transformer block for Aura Flow. Similar to SD3 MMDiT. Differences (non-exhaustive):
* QK Norm in the attention blocks
* No bias in the attention blocks
* Most LayerNorms are in FP32
Parameters:
dim (`int`): The number of... | 1,128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
processor = AuraFlowAttnProcessor2_0()
self.attn = Attention(
query_dim=dim,
cross_attention_dim=None,
added_kv_proj_dim=dim,
added_proj_bias=False,
dim_head=attention_head_dim,
heads=num_attention_heads,
qk_norm="fp32_layer_nor... | 1,128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
# Norm + Projection.
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
encoder_hidden_states, emb=temb
)
# Attention.
a... | 1,128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
# Process attention outputs for the `encoder_hidden_states`.
encoder_hidden_states = self.norm2_context(residual_context + c_gate_msa.unsqueeze(1) * context_attn_output)
encoder_hidden_states = encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
encoder_hidden_states = c_ga... | 1,128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
class AuraFlowTransformer2DModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
r"""
A 2D Transformer model as introduced in AuraFlow (https://blog.fal.ai/auraflow/). | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
Parameters:
sample_size (`int`): The width of the latent images. This is fixed during training since
it is used to learn a number of position embeddings.
patch_size (`int`): Patch size to turn the input data into small patches.
in_channels (`int`, *optional*, defaults to 16): The num... | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
caption_projection_dim (`int`): Number of dimensions to use when projecting the `encoder_hidden_states`.
out_channels (`int`, defaults to 16): Number of output channels.
pos_embed_max_size (`int`, defaults to 4096): Maximum positions to embed from the image latents.
""" | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
_no_split_modules = ["AuraFlowJointTransformerBlock", "AuraFlowSingleTransformerBlock", "AuraFlowPatchEmbed"]
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: int = 64,
patch_size: int = 2,
in_channels: int = 4,
num_mmdit_l... | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
self.pos_embed = AuraFlowPatchEmbed(
height=self.config.sample_size,
width=self.config.sample_size,
patch_size=self.config.patch_size,
in_channels=self.config.in_channels,
embed_dim=self.inner_dim,
pos_embed_max_size=pos_embed_max_size,
)
... | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
self.joint_transformer_blocks = nn.ModuleList(
[
AuraFlowJointTransformerBlock(
dim=self.inner_dim,
num_attention_heads=self.config.num_attention_heads,
attention_head_dim=self.config.attention_head_dim,
)
... | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
# https://arxiv.org/abs/2309.16588
# prevents artifacts in the attention maps
self.register_tokens = nn.Parameter(torch.randn(1, 8, self.inner_dim) * 0.02)
self.gradient_checkpointing = False
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_pro... | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
# Copi... | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.
"""
count = len(self.attn_processors.keys())
if isinstance(processor, dict) and len(processor) !=... | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor)
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DCondi... | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
for _, attn_processor in self.attn_processors.items():
if "Added" in str(attn_processor.__class__.__name__):
raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
self.original_attn_processors = self.attn_processors
for module... | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
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