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296 | 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
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.
out_channels (`int`, *optional*):
The number of channels in the output.
num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
attention_bias (`bool`, *optional*):
Configure if the `TransformerBlocks` attention should contain a bias parameter.
sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
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.
num_embeds_ada_norm ( `int`, *optional*):
The number of diffusion steps used during training. Pass if at least one of the norm_layers is
`AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
added to the hidden states. During inference, you can denoise for up to but not more steps than
`num_embeds_ada_norm`.
norm_type (`str`, *optional*, defaults to `"layer_norm"`):
The type of normalization to use. Options are `"layer_norm"` or `"ada_layer_norm"`.
norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
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*):
The number of frames in the video-like data.
"""
@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] = None,
attention_bias: bool = False,
sample_size: int = 64,
patch_size: Optional[int] = None,
activation_fn: str = "geglu",
num_embeds_ada_norm: Optional[int] = None,
norm_type: str = "layer_norm",
norm_elementwise_affine: bool = True,
norm_eps: float = 1e-5,
caption_channels: int = None,
video_length: int = 16,
):
super().__init__()
inner_dim = num_attention_heads * attention_head_dim
# 1. Define input layers
self.height = sample_size
self.width = sample_size
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,
interpolation_scale=interpolation_scale,
)
self.patch_size = self.config.patch_size
# 2. Define spatial transformers blocks
self.transformer_blocks = nn.CellList(
[
BasicTransformerBlock(
inner_dim,
num_attention_heads,
attention_head_dim,
dropout=dropout,
cross_attention_dim=cross_attention_dim,
activation_fn=activation_fn,
num_embeds_ada_norm=num_embeds_ada_norm,
attention_bias=attention_bias,
norm_type=norm_type,
norm_elementwise_affine=norm_elementwise_affine,
norm_eps=norm_eps,
)
for d in range(num_layers)
]
)
# 3. Define temporal transformers blocks
self.temporal_transformer_blocks = nn.CellList(
[
BasicTransformerBlock(
inner_dim,
num_attention_heads,
attention_head_dim,
dropout=dropout,
cross_attention_dim=None,
activation_fn=activation_fn,
num_embeds_ada_norm=num_embeds_ada_norm,
attention_bias=attention_bias,
norm_type=norm_type,
norm_elementwise_affine=norm_elementwise_affine,
norm_eps=norm_eps,
)
for d in range(num_layers)
]
)
# 4. Define output layers
self.out_channels = in_channels if out_channels is None else out_channels
self.norm_out = LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
self.scale_shift_table = ms.Parameter(ops.randn((2, inner_dim)) / inner_dim**0.5)
self.proj_out = nn.Dense(inner_dim, patch_size * patch_size * self.out_channels)
# 5. Latte other blocks.
self.adaln_single = AdaLayerNormSingle(inner_dim, use_additional_conditions=False)
self.caption_projection = PixArtAlphaTextProjection(in_features=caption_channels, hidden_size=inner_dim)
# define temporal positional embedding
temp_pos_embed = get_1d_sincos_pos_embed_from_grid(
inner_dim, ops.arange(0, video_length).unsqueeze(1).numpy()
) # 1152 hidden size
self.temp_pos_embed = ms.Tensor.from_numpy(temp_pos_embed).float().unsqueeze(0)
self.gradient_checkpointing = False
def _set_gradient_checkpointing(self, module, value=False):
self.gradient_checkpointing = value
def construct(
self,
hidden_states: ms.Tensor,
timestep: Optional[ms.Tensor] = None,
encoder_hidden_states: Optional[ms.Tensor] = None,
encoder_attention_mask: Optional[ms.Tensor] = None,
enable_temporal_attentions: bool = True,
return_dict: bool = False,
):
"""
The [`LatteTransformer3DModel`] forward method.
Args:
hidden_states shape `(batch size, channel, num_frame, height, width)`:
Input `hidden_states`.
timestep ( `ms.Tensor`, *optional*):
Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
self-attention.
encoder_attention_mask ( `ms.Tensor`, *optional*):
Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:
* Mask `(batcheight, sequence_length)` True = keep, False = discard.
* Bias `(batcheight, 1, sequence_length)` 0 = keep, -10000 = discard.
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.
return_dict (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
tuple.
Returns:
If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
`tuple` where the first element is the sample tensor.
"""
# 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
height, width = (
hidden_states.shape[-2] // self.patch_size,
hidden_states.shape[-1] // self.patch_size,
)
num_patches = height * width
hidden_states = self.pos_embed(hidden_states) # alrady add positional embeddings
added_cond_kwargs = {"resolution": None, "aspect_ratio": None}
timestep, embedded_timestep = self.adaln_single(
timestep, added_cond_kwargs=added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype
)
# 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_frame, dim=0).view(
-1, encoder_hidden_states.shape[-2], encoder_hidden_states.shape[-1]
)
# Prepare timesteps for spatial and temporal block
timestep_spatial = timestep.repeat_interleave(num_frame, dim=0).view(-1, timestep.shape[-1])
timestep_temp = timestep.repeat_interleave(num_patches, dim=0).view(-1, timestep.shape[-1])
# Spatial and temporal transformer blocks
for i, (spatial_block, temp_block) in enumerate(zip(self.transformer_blocks, self.temporal_transformer_blocks)):
hidden_states = spatial_block(
hidden_states,
None, # attention_mask
encoder_hidden_states_spatial,
encoder_attention_mask,
timestep_spatial,
None, # cross_attention_kwargs
None, # class_labels
)
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, 3)
hidden_states = hidden_states.reshape(-1, hidden_states.shape[-2], hidden_states.shape[-1])
if i == 0 and num_frame > 1:
hidden_states = (hidden_states + self.temp_pos_embed).to(hidden_states.dtype)
hidden_states = temp_block(
hidden_states,
None, # attention_mask
None, # encoder_hidden_states
None, # encoder_attention_mask
timestep_temp,
None, # cross_attention_kwargs
None, # class_labels
)
# (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_states.reshape(-1, hidden_states.shape[-2], hidden_states.shape[-1])
embedded_timestep = embedded_timestep.repeat_interleave(num_frame, dim=0).view(-1, embedded_timestep.shape[-1])
shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, axis=1)
hidden_states = self.norm_out(hidden_states)
# Modulation
hidden_states = hidden_states * (1 + scale) + shift
hidden_states = self.proj_out(hidden_states)
# unpatchify
if self.adaln_single is None:
height = width = int(hidden_states.shape[1] ** 0.5)
hidden_states = hidden_states.reshape((-1, height, width, self.patch_size, self.patch_size, self.out_channels))
hidden_states = hidden_states.transpose(0, 5, 1, 3, 2, 4)
output = hidden_states.reshape((-1, self.out_channels, height * self.patch_size, width * self.patch_size))
output = output.reshape(batch_size, -1, output.shape[-3], output.shape[-2], output.shape[-1]).permute(
0, 2, 1, 3, 4
)
if not return_dict:
return (output,)
return Transformer2DModelOutput(sample=output)
|