text
stringlengths 1
1.02k
| class_index
int64 0
1.38k
| source
stringclasses 431
values |
|---|---|---|
return custom_forward
if is_torch_version(">=", "1.11.0"):
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet),
hidden_states,
temb,
use_reentrant=False,
)
else:
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet), hidden_states, temb
)
else:
hidden_states = resnet(input_tensor=hidden_states, temb=temb)
hidden_states = motion_module(hidden_states, num_frames=num_frames)
output_states = output_states + (hidden_states,)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states=hidden_states)
output_states = output_states + (hidden_states,) | 1,001 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
return hidden_states, output_states | 1,001 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
class CrossAttnDownBlockMotion(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
num_attention_heads: int = 1,
cross_attention_dim: int = 1280,
output_scale_factor: float = 1.0,
downsample_padding: int = 1,
add_downsample: bool = True,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
only_cross_attention: bool = False,
upcast_attention: bool = False,
attention_type: str = "default",
temporal_cross_attention_dim: Optional[int] = None,
temporal_num_attention_heads: int = 8, | 1,002 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
temporal_max_seq_length: int = 32,
temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
temporal_double_self_attention: bool = True,
):
super().__init__()
resnets = []
attentions = []
motion_modules = [] | 1,002 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
self.has_cross_attention = True
self.num_attention_heads = num_attention_heads
# support for variable transformer layers per block
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = (transformer_layers_per_block,) * num_layers
elif len(transformer_layers_per_block) != num_layers:
raise ValueError(
f"transformer_layers_per_block must be an integer or a list of integers of length {num_layers}"
)
# support for variable transformer layers per temporal block
if isinstance(temporal_transformer_layers_per_block, int):
temporal_transformer_layers_per_block = (temporal_transformer_layers_per_block,) * num_layers
elif len(temporal_transformer_layers_per_block) != num_layers:
raise ValueError(
f"temporal_transformer_layers_per_block must be an integer or a list of integers of length {num_layers}"
) | 1,002 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
) | 1,002 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if not dual_cross_attention:
attentions.append(
Transformer2DModel(
num_attention_heads,
out_channels // num_attention_heads,
in_channels=out_channels,
num_layers=transformer_layers_per_block[i],
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
attention_type=attention_type,
)
)
else:
attentions.append(
DualTransformer2DModel(
num_attention_heads,
out_channels // num_attention_heads,
in_channels=out_channels, | 1,002 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
)
) | 1,002 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
motion_modules.append(
AnimateDiffTransformer3D(
num_attention_heads=temporal_num_attention_heads,
in_channels=out_channels,
num_layers=temporal_transformer_layers_per_block[i],
norm_num_groups=resnet_groups,
cross_attention_dim=temporal_cross_attention_dim,
attention_bias=False,
activation_fn="geglu",
positional_embeddings="sinusoidal",
num_positional_embeddings=temporal_max_seq_length,
attention_head_dim=out_channels // temporal_num_attention_heads,
double_self_attention=temporal_double_self_attention,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
self.motion_modules = nn.ModuleList(motion_modules) | 1,002 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if add_downsample:
self.downsamplers = nn.ModuleList(
[
Downsample2D(
out_channels,
use_conv=True,
out_channels=out_channels,
padding=downsample_padding,
name="op",
)
]
)
else:
self.downsamplers = None
self.gradient_checkpointing = False | 1,002 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
def forward(
self,
hidden_states: torch.Tensor,
temb: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
num_frames: int = 1,
encoder_attention_mask: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
additional_residuals: Optional[torch.Tensor] = None,
):
if cross_attention_kwargs is not None:
if cross_attention_kwargs.get("scale", None) is not None:
logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
output_states = ()
blocks = list(zip(self.resnets, self.attentions, self.motion_modules))
for i, (resnet, attn, motion_module) in enumerate(blocks):
if torch.is_grad_enabled() and self.gradient_checkpointing: | 1,002 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
return module(*inputs)
return custom_forward
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(resnet),
hidden_states,
temb,
**ckpt_kwargs,
)
else:
hidden_states = resnet(input_tensor=hidden_states, temb=temb) | 1,002 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
hidden_states = attn(
hidden_states=hidden_states,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
return_dict=False,
)[0]
hidden_states = motion_module(
hidden_states,
num_frames=num_frames,
)
# apply additional residuals to the output of the last pair of resnet and attention blocks
if i == len(blocks) - 1 and additional_residuals is not None:
hidden_states = hidden_states + additional_residuals
output_states = output_states + (hidden_states,)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states=hidden_states) | 1,002 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
output_states = output_states + (hidden_states,)
return hidden_states, output_states | 1,002 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
class CrossAttnUpBlockMotion(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
prev_output_channel: int,
temb_channels: int,
resolution_idx: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
num_attention_heads: int = 1,
cross_attention_dim: int = 1280,
output_scale_factor: float = 1.0,
add_upsample: bool = True,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
only_cross_attention: bool = False,
upcast_attention: bool = False,
attention_type: str = "default",
temporal_cross_attention_dim: Optional[int] = None, | 1,003 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
temporal_num_attention_heads: int = 8,
temporal_max_seq_length: int = 32,
temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
):
super().__init__()
resnets = []
attentions = []
motion_modules = [] | 1,003 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
self.has_cross_attention = True
self.num_attention_heads = num_attention_heads
# support for variable transformer layers per block
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = (transformer_layers_per_block,) * num_layers
elif len(transformer_layers_per_block) != num_layers:
raise ValueError(
f"transformer_layers_per_block must be an integer or a list of integers of length {num_layers}, got {len(transformer_layers_per_block)}"
) | 1,003 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# support for variable transformer layers per temporal block
if isinstance(temporal_transformer_layers_per_block, int):
temporal_transformer_layers_per_block = (temporal_transformer_layers_per_block,) * num_layers
elif len(temporal_transformer_layers_per_block) != num_layers:
raise ValueError(
f"temporal_transformer_layers_per_block must be an integer or a list of integers of length {num_layers}, got {len(temporal_transformer_layers_per_block)}"
)
for i in range(num_layers):
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
resnet_in_channels = prev_output_channel if i == 0 else out_channels | 1,003 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
resnets.append(
ResnetBlock2D(
in_channels=resnet_in_channels + res_skip_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
) | 1,003 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if not dual_cross_attention:
attentions.append(
Transformer2DModel(
num_attention_heads,
out_channels // num_attention_heads,
in_channels=out_channels,
num_layers=transformer_layers_per_block[i],
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
attention_type=attention_type,
)
)
else:
attentions.append(
DualTransformer2DModel(
num_attention_heads,
out_channels // num_attention_heads,
in_channels=out_channels, | 1,003 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
)
)
motion_modules.append(
AnimateDiffTransformer3D(
num_attention_heads=temporal_num_attention_heads,
in_channels=out_channels,
num_layers=temporal_transformer_layers_per_block[i],
norm_num_groups=resnet_groups,
cross_attention_dim=temporal_cross_attention_dim,
attention_bias=False,
activation_fn="geglu",
positional_embeddings="sinusoidal",
num_positional_embeddings=temporal_max_seq_length,
attention_head_dim=out_channels // temporal_num_attention_heads,
)
) | 1,003 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
self.motion_modules = nn.ModuleList(motion_modules)
if add_upsample:
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
else:
self.upsamplers = None
self.gradient_checkpointing = False
self.resolution_idx = resolution_idx | 1,003 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
def forward(
self,
hidden_states: torch.Tensor,
res_hidden_states_tuple: Tuple[torch.Tensor, ...],
temb: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
upsample_size: Optional[int] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
num_frames: int = 1,
) -> torch.Tensor:
if cross_attention_kwargs is not None:
if cross_attention_kwargs.get("scale", None) is not None:
logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
is_freeu_enabled = (
getattr(self, "s1", None)
and getattr(self, "s2", None)
and getattr(self, "b1", None)
and getattr(self, "b2", None)
) | 1,003 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
blocks = zip(self.resnets, self.attentions, self.motion_modules)
for resnet, attn, motion_module in blocks:
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
# FreeU: Only operate on the first two stages
if is_freeu_enabled:
hidden_states, res_hidden_states = apply_freeu(
self.resolution_idx,
hidden_states,
res_hidden_states,
s1=self.s1,
s2=self.s2,
b1=self.b1,
b2=self.b2,
)
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
if torch.is_grad_enabled() and self.gradient_checkpointing: | 1,003 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
return module(*inputs)
return custom_forward
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(resnet),
hidden_states,
temb,
**ckpt_kwargs,
)
else:
hidden_states = resnet(input_tensor=hidden_states, temb=temb) | 1,003 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
hidden_states = attn(
hidden_states=hidden_states,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
return_dict=False,
)[0]
hidden_states = motion_module(
hidden_states,
num_frames=num_frames,
)
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states=hidden_states, output_size=upsample_size)
return hidden_states | 1,003 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
class UpBlockMotion(nn.Module):
def __init__(
self,
in_channels: int,
prev_output_channel: int,
out_channels: int,
temb_channels: int,
resolution_idx: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
output_scale_factor: float = 1.0,
add_upsample: bool = True,
temporal_cross_attention_dim: Optional[int] = None,
temporal_num_attention_heads: int = 8,
temporal_max_seq_length: int = 32,
temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
):
super().__init__()
resnets = []
motion_modules = [] | 1,004 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# support for variable transformer layers per temporal block
if isinstance(temporal_transformer_layers_per_block, int):
temporal_transformer_layers_per_block = (temporal_transformer_layers_per_block,) * num_layers
elif len(temporal_transformer_layers_per_block) != num_layers:
raise ValueError(
f"temporal_transformer_layers_per_block must be an integer or a list of integers of length {num_layers}"
)
for i in range(num_layers):
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
resnet_in_channels = prev_output_channel if i == 0 else out_channels | 1,004 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
resnets.append(
ResnetBlock2D(
in_channels=resnet_in_channels + res_skip_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
) | 1,004 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
motion_modules.append(
AnimateDiffTransformer3D(
num_attention_heads=temporal_num_attention_heads,
in_channels=out_channels,
num_layers=temporal_transformer_layers_per_block[i],
norm_num_groups=resnet_groups,
cross_attention_dim=temporal_cross_attention_dim,
attention_bias=False,
activation_fn="geglu",
positional_embeddings="sinusoidal",
num_positional_embeddings=temporal_max_seq_length,
attention_head_dim=out_channels // temporal_num_attention_heads,
)
)
self.resnets = nn.ModuleList(resnets)
self.motion_modules = nn.ModuleList(motion_modules)
if add_upsample:
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
else:
self.upsamplers = None | 1,004 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
self.gradient_checkpointing = False
self.resolution_idx = resolution_idx
def forward(
self,
hidden_states: torch.Tensor,
res_hidden_states_tuple: Tuple[torch.Tensor, ...],
temb: Optional[torch.Tensor] = None,
upsample_size=None,
num_frames: int = 1,
*args,
**kwargs,
) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
is_freeu_enabled = (
getattr(self, "s1", None)
and getattr(self, "s2", None)
and getattr(self, "b1", None)
and getattr(self, "b2", None)
) | 1,004 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
blocks = zip(self.resnets, self.motion_modules)
for resnet, motion_module in blocks:
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
# FreeU: Only operate on the first two stages
if is_freeu_enabled:
hidden_states, res_hidden_states = apply_freeu(
self.resolution_idx,
hidden_states,
res_hidden_states,
s1=self.s1,
s2=self.s2,
b1=self.b1,
b2=self.b2,
)
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
if torch.is_grad_enabled() and self.gradient_checkpointing:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs) | 1,004 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
return custom_forward
if is_torch_version(">=", "1.11.0"):
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet),
hidden_states,
temb,
use_reentrant=False,
)
else:
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet), hidden_states, temb
)
else:
hidden_states = resnet(input_tensor=hidden_states, temb=temb)
hidden_states = motion_module(hidden_states, num_frames=num_frames)
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states=hidden_states, output_size=upsample_size)
return hidden_states | 1,004 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
class UNetMidBlockCrossAttnMotion(nn.Module):
def __init__(
self,
in_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
num_attention_heads: int = 1,
output_scale_factor: float = 1.0,
cross_attention_dim: int = 1280,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
upcast_attention: bool = False,
attention_type: str = "default",
temporal_num_attention_heads: int = 1,
temporal_cross_attention_dim: Optional[int] = None,
temporal_max_seq_length: int = 32,
temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
):
super().__init__() | 1,005 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
self.has_cross_attention = True
self.num_attention_heads = num_attention_heads
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
# support for variable transformer layers per block
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = (transformer_layers_per_block,) * num_layers
elif len(transformer_layers_per_block) != num_layers:
raise ValueError(
f"`transformer_layers_per_block` should be an integer or a list of integers of length {num_layers}."
) | 1,005 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# support for variable transformer layers per temporal block
if isinstance(temporal_transformer_layers_per_block, int):
temporal_transformer_layers_per_block = (temporal_transformer_layers_per_block,) * num_layers
elif len(temporal_transformer_layers_per_block) != num_layers:
raise ValueError(
f"`temporal_transformer_layers_per_block` should be an integer or a list of integers of length {num_layers}."
) | 1,005 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# there is always at least one resnet
resnets = [
ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
]
attentions = []
motion_modules = [] | 1,005 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
for i in range(num_layers):
if not dual_cross_attention:
attentions.append(
Transformer2DModel(
num_attention_heads,
in_channels // num_attention_heads,
in_channels=in_channels,
num_layers=transformer_layers_per_block[i],
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
upcast_attention=upcast_attention,
attention_type=attention_type,
)
)
else:
attentions.append(
DualTransformer2DModel(
num_attention_heads,
in_channels // num_attention_heads,
in_channels=in_channels,
num_layers=1, | 1,005 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
)
)
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
motion_modules.append(
AnimateDiffTransformer3D(
num_attention_heads=temporal_num_attention_heads,
attention_head_dim=in_channels // temporal_num_attention_heads,
in_channels=in_channels, | 1,005 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
num_layers=temporal_transformer_layers_per_block[i],
norm_num_groups=resnet_groups,
cross_attention_dim=temporal_cross_attention_dim,
attention_bias=False,
positional_embeddings="sinusoidal",
num_positional_embeddings=temporal_max_seq_length,
activation_fn="geglu",
)
) | 1,005 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
self.motion_modules = nn.ModuleList(motion_modules)
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
temb: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
num_frames: int = 1,
) -> torch.Tensor:
if cross_attention_kwargs is not None:
if cross_attention_kwargs.get("scale", None) is not None:
logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
hidden_states = self.resnets[0](input_tensor=hidden_states, temb=temb) | 1,005 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
blocks = zip(self.attentions, self.resnets[1:], self.motion_modules)
for attn, resnet, motion_module in blocks:
hidden_states = attn(
hidden_states=hidden_states,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
return_dict=False,
)[0]
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_dict)
else:
return module(*inputs)
return custom_forward | 1,005 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.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(motion_module),
hidden_states,
temb,
**ckpt_kwargs,
)
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet),
hidden_states,
temb,
**ckpt_kwargs,
)
else:
hidden_states = motion_module(
hidden_states,
num_frames=num_frames,
)
hidden_states = resnet(input_tensor=hidden_states, temb=temb)
return hidden_states | 1,005 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
class MotionModules(nn.Module):
def __init__(
self,
in_channels: int,
layers_per_block: int = 2,
transformer_layers_per_block: Union[int, Tuple[int]] = 8,
num_attention_heads: Union[int, Tuple[int]] = 8,
attention_bias: bool = False,
cross_attention_dim: Optional[int] = None,
activation_fn: str = "geglu",
norm_num_groups: int = 32,
max_seq_length: int = 32,
):
super().__init__()
self.motion_modules = nn.ModuleList([])
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = (transformer_layers_per_block,) * layers_per_block
elif len(transformer_layers_per_block) != layers_per_block:
raise ValueError(
f"The number of transformer layers per block must match the number of layers per block, "
f"got {layers_per_block} and {len(transformer_layers_per_block)}"
) | 1,006 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
for i in range(layers_per_block):
self.motion_modules.append(
AnimateDiffTransformer3D(
in_channels=in_channels,
num_layers=transformer_layers_per_block[i],
norm_num_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
activation_fn=activation_fn,
attention_bias=attention_bias,
num_attention_heads=num_attention_heads,
attention_head_dim=in_channels // num_attention_heads,
positional_embeddings="sinusoidal",
num_positional_embeddings=max_seq_length,
)
) | 1,006 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
class MotionAdapter(ModelMixin, ConfigMixin, FromOriginalModelMixin):
@register_to_config
def __init__(
self,
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
motion_layers_per_block: Union[int, Tuple[int]] = 2,
motion_transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]] = 1,
motion_mid_block_layers_per_block: int = 1,
motion_transformer_layers_per_mid_block: Union[int, Tuple[int]] = 1,
motion_num_attention_heads: Union[int, Tuple[int]] = 8,
motion_norm_num_groups: int = 32,
motion_max_seq_length: int = 32,
use_motion_mid_block: bool = True,
conv_in_channels: Optional[int] = None,
):
"""Container to store AnimateDiff Motion Modules | 1,007 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
Args:
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each UNet block.
motion_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 2):
The number of motion layers per UNet block.
motion_transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple[int]]`, *optional*, defaults to 1):
The number of transformer layers to use in each motion layer in each block.
motion_mid_block_layers_per_block (`int`, *optional*, defaults to 1):
The number of motion layers in the middle UNet block.
motion_transformer_layers_per_mid_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
The number of transformer layers to use in each motion layer in the middle block.
motion_num_attention_heads (`int` or `Tuple[int]`, *optional*, defaults to 8): | 1,007 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
The number of heads to use in each attention layer of the motion module.
motion_norm_num_groups (`int`, *optional*, defaults to 32):
The number of groups to use in each group normalization layer of the motion module.
motion_max_seq_length (`int`, *optional*, defaults to 32):
The maximum sequence length to use in the motion module.
use_motion_mid_block (`bool`, *optional*, defaults to True):
Whether to use a motion module in the middle of the UNet.
""" | 1,007 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
super().__init__()
down_blocks = []
up_blocks = []
if isinstance(motion_layers_per_block, int):
motion_layers_per_block = (motion_layers_per_block,) * len(block_out_channels)
elif len(motion_layers_per_block) != len(block_out_channels):
raise ValueError(
f"The number of motion layers per block must match the number of blocks, "
f"got {len(block_out_channels)} and {len(motion_layers_per_block)}"
)
if isinstance(motion_transformer_layers_per_block, int):
motion_transformer_layers_per_block = (motion_transformer_layers_per_block,) * len(block_out_channels) | 1,007 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if isinstance(motion_transformer_layers_per_mid_block, int):
motion_transformer_layers_per_mid_block = (
motion_transformer_layers_per_mid_block,
) * motion_mid_block_layers_per_block
elif len(motion_transformer_layers_per_mid_block) != motion_mid_block_layers_per_block:
raise ValueError(
f"The number of layers per mid block ({motion_mid_block_layers_per_block}) "
f"must match the length of motion_transformer_layers_per_mid_block ({len(motion_transformer_layers_per_mid_block)})"
) | 1,007 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if isinstance(motion_num_attention_heads, int):
motion_num_attention_heads = (motion_num_attention_heads,) * len(block_out_channels)
elif len(motion_num_attention_heads) != len(block_out_channels):
raise ValueError(
f"The length of the attention head number tuple in the motion module must match the "
f"number of block, got {len(motion_num_attention_heads)} and {len(block_out_channels)}"
)
if conv_in_channels:
# input
self.conv_in = nn.Conv2d(conv_in_channels, block_out_channels[0], kernel_size=3, padding=1)
else:
self.conv_in = None | 1,007 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
for i, channel in enumerate(block_out_channels):
output_channel = block_out_channels[i]
down_blocks.append(
MotionModules(
in_channels=output_channel,
norm_num_groups=motion_norm_num_groups,
cross_attention_dim=None,
activation_fn="geglu",
attention_bias=False,
num_attention_heads=motion_num_attention_heads[i],
max_seq_length=motion_max_seq_length,
layers_per_block=motion_layers_per_block[i],
transformer_layers_per_block=motion_transformer_layers_per_block[i],
)
) | 1,007 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if use_motion_mid_block:
self.mid_block = MotionModules(
in_channels=block_out_channels[-1],
norm_num_groups=motion_norm_num_groups,
cross_attention_dim=None,
activation_fn="geglu",
attention_bias=False,
num_attention_heads=motion_num_attention_heads[-1],
max_seq_length=motion_max_seq_length,
layers_per_block=motion_mid_block_layers_per_block,
transformer_layers_per_block=motion_transformer_layers_per_mid_block,
)
else:
self.mid_block = None
reversed_block_out_channels = list(reversed(block_out_channels))
output_channel = reversed_block_out_channels[0] | 1,007 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
reversed_motion_layers_per_block = list(reversed(motion_layers_per_block))
reversed_motion_transformer_layers_per_block = list(reversed(motion_transformer_layers_per_block))
reversed_motion_num_attention_heads = list(reversed(motion_num_attention_heads))
for i, channel in enumerate(reversed_block_out_channels):
output_channel = reversed_block_out_channels[i]
up_blocks.append(
MotionModules(
in_channels=output_channel,
norm_num_groups=motion_norm_num_groups,
cross_attention_dim=None,
activation_fn="geglu",
attention_bias=False,
num_attention_heads=reversed_motion_num_attention_heads[i],
max_seq_length=motion_max_seq_length,
layers_per_block=reversed_motion_layers_per_block[i] + 1,
transformer_layers_per_block=reversed_motion_transformer_layers_per_block[i], | 1,007 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
)
) | 1,007 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
self.down_blocks = nn.ModuleList(down_blocks)
self.up_blocks = nn.ModuleList(up_blocks)
def forward(self, sample):
pass | 1,007 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin):
r"""
A modified conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a
sample shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
"""
_supports_gradient_checkpointing = True | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
@register_to_config
def __init__(
self,
sample_size: Optional[int] = None,
in_channels: int = 4,
out_channels: int = 4,
down_block_types: Tuple[str, ...] = (
"CrossAttnDownBlockMotion",
"CrossAttnDownBlockMotion",
"CrossAttnDownBlockMotion",
"DownBlockMotion",
),
up_block_types: Tuple[str, ...] = (
"UpBlockMotion",
"CrossAttnUpBlockMotion",
"CrossAttnUpBlockMotion",
"CrossAttnUpBlockMotion",
),
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
layers_per_block: Union[int, Tuple[int]] = 2,
downsample_padding: int = 1,
mid_block_scale_factor: float = 1,
act_fn: str = "silu",
norm_num_groups: int = 32,
norm_eps: float = 1e-5,
cross_attention_dim: int = 1280,
transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
reverse_transformer_layers_per_block: Optional[Union[int, Tuple[int], Tuple[Tuple]]] = None,
temporal_transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
reverse_temporal_transformer_layers_per_block: Optional[Union[int, Tuple[int], Tuple[Tuple]]] = None,
transformer_layers_per_mid_block: Optional[Union[int, Tuple[int]]] = None,
temporal_transformer_layers_per_mid_block: Optional[Union[int, Tuple[int]]] = 1,
use_linear_projection: bool = False,
num_attention_heads: Union[int, Tuple[int, ...]] = 8,
motion_max_seq_length: int = 32,
motion_num_attention_heads: Union[int, Tuple[int, ...]] = 8,
reverse_motion_num_attention_heads: Optional[Union[int, Tuple[int, ...], Tuple[Tuple[int, ...], ...]]] = None,
use_motion_mid_block: bool = True,
mid_block_layers: int = 1,
encoder_hid_dim: Optional[int] = None,
encoder_hid_dim_type: Optional[str] = None, | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
addition_embed_type: Optional[str] = None,
addition_time_embed_dim: Optional[int] = None,
projection_class_embeddings_input_dim: Optional[int] = None,
time_cond_proj_dim: Optional[int] = None,
):
super().__init__() | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
self.sample_size = sample_size
# Check inputs
if len(down_block_types) != len(up_block_types):
raise ValueError(
f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
)
if len(block_out_channels) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
)
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
)
if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
)
if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:
for layer_number_per_block in transformer_layers_per_block:
if isinstance(layer_number_per_block, list):
raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.") | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if (
isinstance(temporal_transformer_layers_per_block, list)
and reverse_temporal_transformer_layers_per_block is None
):
for layer_number_per_block in temporal_transformer_layers_per_block:
if isinstance(layer_number_per_block, list):
raise ValueError(
"Must provide 'reverse_temporal_transformer_layers_per_block` if using asymmetrical motion module in UNet."
)
# input
conv_in_kernel = 3
conv_out_kernel = 3
conv_in_padding = (conv_in_kernel - 1) // 2
self.conv_in = nn.Conv2d(
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
)
# time
time_embed_dim = block_out_channels[0] * 4
self.time_proj = Timesteps(block_out_channels[0], True, 0)
timestep_input_dim = block_out_channels[0] | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
self.time_embedding = TimestepEmbedding(
timestep_input_dim, time_embed_dim, act_fn=act_fn, cond_proj_dim=time_cond_proj_dim
)
if encoder_hid_dim_type is None:
self.encoder_hid_proj = None
if addition_embed_type == "text_time":
self.add_time_proj = Timesteps(addition_time_embed_dim, True, 0)
self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
# class embedding
self.down_blocks = nn.ModuleList([])
self.up_blocks = nn.ModuleList([])
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(down_block_types)
if isinstance(cross_attention_dim, int):
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
if isinstance(layers_per_block, int):
layers_per_block = [layers_per_block] * len(down_block_types) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
if isinstance(reverse_transformer_layers_per_block, int):
reverse_transformer_layers_per_block = [reverse_transformer_layers_per_block] * len(down_block_types)
if isinstance(temporal_transformer_layers_per_block, int):
temporal_transformer_layers_per_block = [temporal_transformer_layers_per_block] * len(down_block_types)
if isinstance(reverse_temporal_transformer_layers_per_block, int):
reverse_temporal_transformer_layers_per_block = [reverse_temporal_transformer_layers_per_block] * len(
down_block_types
)
if isinstance(motion_num_attention_heads, int):
motion_num_attention_heads = (motion_num_attention_heads,) * len(down_block_types) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1 | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if down_block_type == "CrossAttnDownBlockMotion":
down_block = CrossAttnDownBlockMotion(
in_channels=input_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
num_layers=layers_per_block[i],
transformer_layers_per_block=transformer_layers_per_block[i],
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
num_attention_heads=num_attention_heads[i],
cross_attention_dim=cross_attention_dim[i],
downsample_padding=downsample_padding,
add_downsample=not is_final_block,
use_linear_projection=use_linear_projection,
temporal_num_attention_heads=motion_num_attention_heads[i],
temporal_max_seq_length=motion_max_seq_length, | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],
)
elif down_block_type == "DownBlockMotion":
down_block = DownBlockMotion(
in_channels=input_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
num_layers=layers_per_block[i],
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
add_downsample=not is_final_block,
downsample_padding=downsample_padding,
temporal_num_attention_heads=motion_num_attention_heads[i],
temporal_max_seq_length=motion_max_seq_length,
temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],
)
else:
raise ValueError( | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
"Invalid `down_block_type` encountered. Must be one of `CrossAttnDownBlockMotion` or `DownBlockMotion`"
) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
self.down_blocks.append(down_block)
# mid
if transformer_layers_per_mid_block is None:
transformer_layers_per_mid_block = (
transformer_layers_per_block[-1] if isinstance(transformer_layers_per_block[-1], int) else 1
) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if use_motion_mid_block:
self.mid_block = UNetMidBlockCrossAttnMotion(
in_channels=block_out_channels[-1],
temb_channels=time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
cross_attention_dim=cross_attention_dim[-1],
num_attention_heads=num_attention_heads[-1],
resnet_groups=norm_num_groups,
dual_cross_attention=False,
use_linear_projection=use_linear_projection,
num_layers=mid_block_layers,
temporal_num_attention_heads=motion_num_attention_heads[-1],
temporal_max_seq_length=motion_max_seq_length,
transformer_layers_per_block=transformer_layers_per_mid_block,
temporal_transformer_layers_per_block=temporal_transformer_layers_per_mid_block,
) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
else:
self.mid_block = UNetMidBlock2DCrossAttn(
in_channels=block_out_channels[-1],
temb_channels=time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
cross_attention_dim=cross_attention_dim[-1],
num_attention_heads=num_attention_heads[-1],
resnet_groups=norm_num_groups,
dual_cross_attention=False,
use_linear_projection=use_linear_projection,
num_layers=mid_block_layers,
transformer_layers_per_block=transformer_layers_per_mid_block,
)
# count how many layers upsample the images
self.num_upsamplers = 0 | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# up
reversed_block_out_channels = list(reversed(block_out_channels))
reversed_num_attention_heads = list(reversed(num_attention_heads))
reversed_layers_per_block = list(reversed(layers_per_block))
reversed_cross_attention_dim = list(reversed(cross_attention_dim))
reversed_motion_num_attention_heads = list(reversed(motion_num_attention_heads))
if reverse_transformer_layers_per_block is None:
reverse_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
if reverse_temporal_transformer_layers_per_block is None:
reverse_temporal_transformer_layers_per_block = list(reversed(temporal_transformer_layers_per_block))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
is_final_block = i == len(block_out_channels) - 1 | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
# add upsample block for all BUT final layer
if not is_final_block:
add_upsample = True
self.num_upsamplers += 1
else:
add_upsample = False | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if up_block_type == "CrossAttnUpBlockMotion":
up_block = CrossAttnUpBlockMotion(
in_channels=input_channel,
out_channels=output_channel,
prev_output_channel=prev_output_channel,
temb_channels=time_embed_dim,
resolution_idx=i,
num_layers=reversed_layers_per_block[i] + 1,
transformer_layers_per_block=reverse_transformer_layers_per_block[i],
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
num_attention_heads=reversed_num_attention_heads[i],
cross_attention_dim=reversed_cross_attention_dim[i],
add_upsample=add_upsample,
use_linear_projection=use_linear_projection,
temporal_num_attention_heads=reversed_motion_num_attention_heads[i], | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
temporal_max_seq_length=motion_max_seq_length,
temporal_transformer_layers_per_block=reverse_temporal_transformer_layers_per_block[i],
)
elif up_block_type == "UpBlockMotion":
up_block = UpBlockMotion(
in_channels=input_channel,
prev_output_channel=prev_output_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
resolution_idx=i,
num_layers=reversed_layers_per_block[i] + 1,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
add_upsample=add_upsample,
temporal_num_attention_heads=reversed_motion_num_attention_heads[i],
temporal_max_seq_length=motion_max_seq_length, | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
temporal_transformer_layers_per_block=reverse_temporal_transformer_layers_per_block[i],
)
else:
raise ValueError(
"Invalid `up_block_type` encountered. Must be one of `CrossAttnUpBlockMotion` or `UpBlockMotion`"
) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
if norm_num_groups is not None:
self.conv_norm_out = nn.GroupNorm(
num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
)
self.conv_act = nn.SiLU()
else:
self.conv_norm_out = None
self.conv_act = None
conv_out_padding = (conv_out_kernel - 1) // 2
self.conv_out = nn.Conv2d(
block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
)
@classmethod
def from_unet2d(
cls,
unet: UNet2DConditionModel,
motion_adapter: Optional[MotionAdapter] = None,
load_weights: bool = True,
):
has_motion_adapter = motion_adapter is not None
if has_motion_adapter:
motion_adapter.to(device=unet.device) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# check compatibility of number of blocks
if len(unet.config["down_block_types"]) != len(motion_adapter.config["block_out_channels"]):
raise ValueError("Incompatible Motion Adapter, got different number of blocks") | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# check layers compatibility for each block
if isinstance(unet.config["layers_per_block"], int):
expanded_layers_per_block = [unet.config["layers_per_block"]] * len(unet.config["down_block_types"])
else:
expanded_layers_per_block = list(unet.config["layers_per_block"])
if isinstance(motion_adapter.config["motion_layers_per_block"], int):
expanded_adapter_layers_per_block = [motion_adapter.config["motion_layers_per_block"]] * len(
motion_adapter.config["block_out_channels"]
)
else:
expanded_adapter_layers_per_block = list(motion_adapter.config["motion_layers_per_block"])
if expanded_layers_per_block != expanded_adapter_layers_per_block:
raise ValueError("Incompatible Motion Adapter, got different number of layers per block") | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# based on https://github.com/guoyww/AnimateDiff/blob/895f3220c06318ea0760131ec70408b466c49333/animatediff/models/unet.py#L459
config = dict(unet.config)
config["_class_name"] = cls.__name__
down_blocks = []
for down_blocks_type in config["down_block_types"]:
if "CrossAttn" in down_blocks_type:
down_blocks.append("CrossAttnDownBlockMotion")
else:
down_blocks.append("DownBlockMotion")
config["down_block_types"] = down_blocks
up_blocks = []
for down_blocks_type in config["up_block_types"]:
if "CrossAttn" in down_blocks_type:
up_blocks.append("CrossAttnUpBlockMotion")
else:
up_blocks.append("UpBlockMotion")
config["up_block_types"] = up_blocks | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if has_motion_adapter:
config["motion_num_attention_heads"] = motion_adapter.config["motion_num_attention_heads"]
config["motion_max_seq_length"] = motion_adapter.config["motion_max_seq_length"]
config["use_motion_mid_block"] = motion_adapter.config["use_motion_mid_block"]
config["layers_per_block"] = motion_adapter.config["motion_layers_per_block"]
config["temporal_transformer_layers_per_mid_block"] = motion_adapter.config[
"motion_transformer_layers_per_mid_block"
]
config["temporal_transformer_layers_per_block"] = motion_adapter.config[
"motion_transformer_layers_per_block"
]
config["motion_num_attention_heads"] = motion_adapter.config["motion_num_attention_heads"] | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# For PIA UNets we need to set the number input channels to 9
if motion_adapter.config["conv_in_channels"]:
config["in_channels"] = motion_adapter.config["conv_in_channels"]
# Need this for backwards compatibility with UNet2DConditionModel checkpoints
if not config.get("num_attention_heads"):
config["num_attention_heads"] = config["attention_head_dim"]
expected_kwargs, optional_kwargs = cls._get_signature_keys(cls)
config = FrozenDict({k: config.get(k) for k in config if k in expected_kwargs or k in optional_kwargs})
config["_class_name"] = cls.__name__
model = cls.from_config(config)
if not load_weights:
return model | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# Logic for loading PIA UNets which allow the first 4 channels to be any UNet2DConditionModel conv_in weight
# while the last 5 channels must be PIA conv_in weights.
if has_motion_adapter and motion_adapter.config["conv_in_channels"]:
model.conv_in = motion_adapter.conv_in
updated_conv_in_weight = torch.cat(
[unet.conv_in.weight, motion_adapter.conv_in.weight[:, 4:, :, :]], dim=1
)
model.conv_in.load_state_dict({"weight": updated_conv_in_weight, "bias": unet.conv_in.bias})
else:
model.conv_in.load_state_dict(unet.conv_in.state_dict())
model.time_proj.load_state_dict(unet.time_proj.state_dict())
model.time_embedding.load_state_dict(unet.time_embedding.state_dict()) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if any(
isinstance(proc, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0))
for proc in unet.attn_processors.values()
):
attn_procs = {}
for name, processor in unet.attn_processors.items():
if name.endswith("attn1.processor"):
attn_processor_class = (
AttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else AttnProcessor
)
attn_procs[name] = attn_processor_class()
else:
attn_processor_class = (
IPAdapterAttnProcessor2_0
if hasattr(F, "scaled_dot_product_attention")
else IPAdapterAttnProcessor
)
attn_procs[name] = attn_processor_class(
hidden_size=processor.hidden_size,
cross_attention_dim=processor.cross_attention_dim, | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
scale=processor.scale,
num_tokens=processor.num_tokens,
)
for name, processor in model.attn_processors.items():
if name not in attn_procs:
attn_procs[name] = processor.__class__()
model.set_attn_processor(attn_procs)
model.config.encoder_hid_dim_type = "ip_image_proj"
model.encoder_hid_proj = unet.encoder_hid_proj | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
for i, down_block in enumerate(unet.down_blocks):
model.down_blocks[i].resnets.load_state_dict(down_block.resnets.state_dict())
if hasattr(model.down_blocks[i], "attentions"):
model.down_blocks[i].attentions.load_state_dict(down_block.attentions.state_dict())
if model.down_blocks[i].downsamplers:
model.down_blocks[i].downsamplers.load_state_dict(down_block.downsamplers.state_dict())
for i, up_block in enumerate(unet.up_blocks):
model.up_blocks[i].resnets.load_state_dict(up_block.resnets.state_dict())
if hasattr(model.up_blocks[i], "attentions"):
model.up_blocks[i].attentions.load_state_dict(up_block.attentions.state_dict())
if model.up_blocks[i].upsamplers:
model.up_blocks[i].upsamplers.load_state_dict(up_block.upsamplers.state_dict()) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
model.mid_block.resnets.load_state_dict(unet.mid_block.resnets.state_dict())
model.mid_block.attentions.load_state_dict(unet.mid_block.attentions.state_dict())
if unet.conv_norm_out is not None:
model.conv_norm_out.load_state_dict(unet.conv_norm_out.state_dict())
if unet.conv_act is not None:
model.conv_act.load_state_dict(unet.conv_act.state_dict())
model.conv_out.load_state_dict(unet.conv_out.state_dict())
if has_motion_adapter:
model.load_motion_modules(motion_adapter)
# ensure that the Motion UNet is the same dtype as the UNet2DConditionModel
model.to(unet.dtype)
return model
def freeze_unet2d_params(self) -> None:
"""Freeze the weights of just the UNet2DConditionModel, and leave the motion modules
unfrozen for fine tuning.
"""
# Freeze everything
for param in self.parameters():
param.requires_grad = False | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# Unfreeze Motion Modules
for down_block in self.down_blocks:
motion_modules = down_block.motion_modules
for param in motion_modules.parameters():
param.requires_grad = True
for up_block in self.up_blocks:
motion_modules = up_block.motion_modules
for param in motion_modules.parameters():
param.requires_grad = True
if hasattr(self.mid_block, "motion_modules"):
motion_modules = self.mid_block.motion_modules
for param in motion_modules.parameters():
param.requires_grad = True | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
def load_motion_modules(self, motion_adapter: Optional[MotionAdapter]) -> None:
for i, down_block in enumerate(motion_adapter.down_blocks):
self.down_blocks[i].motion_modules.load_state_dict(down_block.motion_modules.state_dict())
for i, up_block in enumerate(motion_adapter.up_blocks):
self.up_blocks[i].motion_modules.load_state_dict(up_block.motion_modules.state_dict())
# to support older motion modules that don't have a mid_block
if hasattr(self.mid_block, "motion_modules"):
self.mid_block.motion_modules.load_state_dict(motion_adapter.mid_block.motion_modules.state_dict())
def save_motion_modules(
self,
save_directory: str,
is_main_process: bool = True,
safe_serialization: bool = True,
variant: Optional[str] = None,
push_to_hub: bool = False,
**kwargs,
) -> None:
state_dict = self.state_dict() | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# Extract all motion modules
motion_state_dict = {}
for k, v in state_dict.items():
if "motion_modules" in k:
motion_state_dict[k] = v
adapter = MotionAdapter(
block_out_channels=self.config["block_out_channels"],
motion_layers_per_block=self.config["layers_per_block"],
motion_norm_num_groups=self.config["norm_num_groups"],
motion_num_attention_heads=self.config["motion_num_attention_heads"],
motion_max_seq_length=self.config["motion_max_seq_length"],
use_motion_mid_block=self.config["use_motion_mid_block"],
)
adapter.load_state_dict(motion_state_dict)
adapter.save_pretrained(
save_directory=save_directory,
is_main_process=is_main_process,
safe_serialization=safe_serialization,
variant=variant,
push_to_hub=push_to_hub,
**kwargs,
) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
indexed by its weight name.
"""
# set recursively
processors = {}
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_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 | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
r"""
Sets the attention processor to use to compute attention.
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 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()) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
)
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}.processor"))
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) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
"""
Sets the attention processor to use [feed forward
chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
Parameters:
chunk_size (`int`, *optional*):
The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
over each tensor of dim=`dim`.
dim (`int`, *optional*, defaults to `0`):
The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
or dim=1 (sequence length).
"""
if dim not in [0, 1]:
raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
# By default chunk size is 1
chunk_size = chunk_size or 1 | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
if hasattr(module, "set_chunk_feed_forward"):
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
for child in module.children():
fn_recursive_feed_forward(child, chunk_size, dim)
for module in self.children():
fn_recursive_feed_forward(module, chunk_size, dim)
def disable_forward_chunking(self) -> None:
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
if hasattr(module, "set_chunk_feed_forward"):
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
for child in module.children():
fn_recursive_feed_forward(child, chunk_size, dim)
for module in self.children():
fn_recursive_feed_forward(module, None, 0) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
def set_default_attn_processor(self) -> None:
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnAddedKVProcessor()
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnProcessor()
else:
raise ValueError(
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
)
self.set_attn_processor(processor) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
if isinstance(module, (CrossAttnDownBlockMotion, DownBlockMotion, CrossAttnUpBlockMotion, UpBlockMotion)):
module.gradient_checkpointing = value
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.enable_freeu
def enable_freeu(self, s1: float, s2: float, b1: float, b2: float) -> None:
r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
The suffixes after the scaling factors represent the stage blocks where they are being applied.
Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that
are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
Args:
s1 (`float`):
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
mitigate the "oversmoothing effect" in the enhanced denoising process.
s2 (`float`):
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
mitigate the "oversmoothing effect" in the enhanced denoising process.
b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
"""
for i, upsample_block in enumerate(self.up_blocks):
setattr(upsample_block, "s1", s1)
setattr(upsample_block, "s2", s2)
setattr(upsample_block, "b1", b1)
setattr(upsample_block, "b2", b2) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.disable_freeu
def disable_freeu(self) -> None:
"""Disables the FreeU mechanism."""
freeu_keys = {"s1", "s2", "b1", "b2"}
for i, upsample_block in enumerate(self.up_blocks):
for k in freeu_keys:
if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
setattr(upsample_block, k, None)
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
def fuse_qkv_projections(self):
"""
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
are fused. For cross-attention modules, key and value projection matrices are fused.
<Tip warning={true}>
This API is 🧪 experimental.
</Tip>
"""
self.original_attn_processors = None | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.