text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
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for _ in range(num_layers):
attentions.append(
Transformer2DModel(
in_channels // num_attention_heads,
num_attention_heads,
in_channels=in_channels,
num_layers=1,
cross_attention_dim=cross_att... | 982 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
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,
... | 982 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
self.resnets = nn.ModuleList(resnets)
self.temp_convs = nn.ModuleList(temp_convs)
self.attentions = nn.ModuleList(attentions)
self.temp_attentions = nn.ModuleList(temp_attentions) | 982 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.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,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
... | 982 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
hidden_states = resnet(hidden_states, temb)
hidden_states = temp_conv(hidden_states, num_frames=num_frames) | 982 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
return hidden_states | 982 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
class CrossAttnDownBlock3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: st... | 983 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.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=re... | 983 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
out_channels // num_attention_heads,
num_attention_heads,
in_channels=out_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_line... | 983 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
self.attentions = nn.ModuleList(attentions)
self.temp_attentions = nn.ModuleList(temp_attentions) | 983 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
if add_downsample:
self.downsamplers = nn.ModuleList(
[
Downsample2D(
out_channels,
use_conv=True,
out_channels=out_channels,
padding=downsample_padding,
... | 983 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
for resnet, temp_conv, attn, temp_attn in zip(
self.resnets, self.temp_convs, self.attentions, self.temp_attentions
):
hidden_states = resnet(hidden_states, temb)
hidden_states = temp_conv(hidden_states, num_frames=num_frames)
hidden_states = attn(
... | 983 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
return hidden_states, output_states | 983 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
class DownBlock3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swis... | 984 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.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=re... | 984 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
if add_downsample:
self.downsamplers = nn.ModuleList(
[
Downsample2D(
out_channels,
use_conv=True,
out_channels=out_channels,
padding=downsample_padding,
... | 984 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
output_states += (hidden_states,)
return hidden_states, output_states | 984 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
class CrossAttnUpBlock3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
prev_output_channel: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "def... | 985 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
self.has_cross_attention = True
self.num_attention_heads = num_attention_heads
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 | 985 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.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... | 985 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
in_channels=out_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
... | 985 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
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
def forward(
self,
hidden_sta... | 985 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
# TODO(Patrick, William) - attention mask is not used
for resnet, temp_conv, attn, temp_attn in zip(
self.resnets, self.temp_convs, self.attentions, self.temp_attentions
):
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_... | 985 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
hidden_states = resnet(hidden_states, temb)
hidden_states = temp_conv(hidden_states, num_frames=num_frames)
hidden_states = attn(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
... | 985 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
class UpBlock3D(nn.Module):
def __init__(
self,
in_channels: int,
prev_output_channel: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
... | 986 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.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... | 986 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
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
def forward(
self,
hidden_sta... | 986 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
# 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... | 986 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
class MidBlockTemporalDecoder(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
attention_head_dim: int = 512,
num_layers: int = 1,
upcast_attention: bool = False,
):
super().__init__()
resnets = []
attentions = []
... | 987 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
attentions.append(
Attention(
query_dim=in_channels,
heads=in_channels // attention_head_dim,
dim_head=attention_head_dim,
eps=1e-6,
upcast_attention=upcast_attention,
norm_num_groups=32,
bias=Tru... | 987 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
return hidden_states | 987 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
class UpBlockTemporalDecoder(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
num_layers: int = 1,
add_upsample: bool = True,
):
super().__init__()
resnets = []
for i in range(num_layers):
input_channels = in_channel... | 988 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
if add_upsample:
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
else:
self.upsamplers = None
def forward(
self,
hidden_states: torch.Tensor,
image_only_indicator: torch.Tensor,
) -> torch.Tensor:
... | 988 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
class UNetMidBlockSpatioTemporal(nn.Module):
def __init__(
self,
in_channels: int,
temb_channels: int,
num_layers: int = 1,
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
num_attention_heads: int = 1,
cross_attention_dim: int = 1280,
):
... | 989 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
for i in range(num_layers):
attentions.append(
TransformerSpatioTemporalModel(
num_attention_heads,
in_channels // num_attention_heads,
in_channels=in_channels,
num_layers=transformer_layers_per_block[i],
... | 989 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
def forward(
self,
hidden_states: torch.Tensor,
temb: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
image_only_indicator: Optional[torch.Tensor] = None,
) -> torch.Tensor:
hidden_states = self.resnets[0](
hidden_state... | 989 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states = attn(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
image_only_indicator=image_only_indicator,
... | 989 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
temb,
image_only_indicator=image_only_indicator,
) | 989 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
return hidden_states | 989 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
class DownBlockSpatioTemporal(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
num_layers: int = 1,
add_downsample: bool = True,
):
super().__init__()
resnets = []
for i in range(num_layers):
... | 990 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
if add_downsample:
self.downsamplers = nn.ModuleList(
[
Downsample2D(
out_channels,
use_conv=True,
out_channels=out_channels,
name="op",
)
]... | 990 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
if is_torch_version(">=", "1.11.0"):
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet),
hidden_states,
temb,
image_only_indicator,
use_reentrant=Fals... | 990 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
output_states = output_states + (hidden_states,)
return hidden_states, output_states | 990 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
class CrossAttnDownBlockSpatioTemporal(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
num_layers: int = 1,
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
num_attention_heads: int = 1,
cross_attention... | 991 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
SpatioTemporalResBlock(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
... | 991 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
if add_downsample:
self.downsamplers = nn.ModuleList(
[
Downsample2D(
out_channels,
use_conv=True,
out_channels=out_channels,
padding=1,
name="op",
... | 991 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.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)
... | 991 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
hidden_states = attn(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
image_only_indicator=image_only_indicator,
return_dict=False,
)[0]
else:
hidden_states = resnet(
... | 991 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
return hidden_states, output_states | 991 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
class UpBlockSpatioTemporal(nn.Module):
def __init__(
self,
in_channels: int,
prev_output_channel: int,
out_channels: int,
temb_channels: int,
resolution_idx: Optional[int] = None,
num_layers: int = 1,
resnet_eps: float = 1e-6,
add_upsample: bo... | 992 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
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
def forward(
self,
hidden_sta... | 992 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
return custom_forward | 992 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
if is_torch_version(">=", "1.11.0"):
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet),
hidden_states,
temb,
image_only_indicator,
use_reentrant=Fals... | 992 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
return hidden_states | 992 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
class CrossAttnUpBlockSpatioTemporal(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
prev_output_channel: int,
temb_channels: int,
resolution_idx: Optional[int] = None,
num_layers: int = 1,
transformer_layers_per_block: Union[int, ... | 993 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
resnets.append(
SpatioTemporalResBlock(
in_channels=resnet_in_channels + res_skip_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
)
)
attentions.appen... | 993 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.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,
encoder_hidden_states: Optional[torch.Tensor] = None,
... | 993 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.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)
... | 993 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.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(resnet),
hidden_states,
temb,
image_only_indicator,
... | 993 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
image_only_indicator=image_only_indicator,
return_dict=False,
)[0] | 993 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states, upsample_size)
return hidden_states | 993 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_blocks.py |
class UVit2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
_supports_gradient_checkpointing = True | 994 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
@register_to_config
def __init__(
self,
# global config
hidden_size: int = 1024,
use_bias: bool = False,
hidden_dropout: float = 0.0,
# conditioning dimensions
cond_embed_dim: int = 768,
micro_cond_encode_dim: int = 256,
micro_cond_embed_dim: i... | 994 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
layer_norm_eps: float = 1e-6,
ln_elementwise_affine: bool = True,
sample_size: int = 64,
):
super().__init__() | 994 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
self.encoder_proj = nn.Linear(encoder_hidden_size, hidden_size, bias=use_bias)
self.encoder_proj_layer_norm = RMSNorm(hidden_size, layer_norm_eps, ln_elementwise_affine)
self.embed = UVit2DConvEmbed(
in_channels, block_out_channels, vocab_size, ln_elementwise_affine, layer_norm_eps, use_bia... | 994 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
self.project_to_hidden_norm = RMSNorm(block_out_channels, layer_norm_eps, ln_elementwise_affine)
self.project_to_hidden = nn.Linear(block_out_channels, hidden_size, bias=use_bias) | 994 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
self.transformer_layers = nn.ModuleList(
[
BasicTransformerBlock(
dim=hidden_size,
num_attention_heads=num_attention_heads,
attention_head_dim=hidden_size // num_attention_heads,
dropout=hidden_dropout,
... | 994 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
self.project_from_hidden_norm = RMSNorm(hidden_size, layer_norm_eps, ln_elementwise_affine)
self.project_from_hidden = nn.Linear(hidden_size, block_out_channels, bias=use_bias)
self.up_block = UVitBlock(
block_out_channels,
num_res_blocks,
hidden_size,
hi... | 994 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
def forward(self, input_ids, encoder_hidden_states, pooled_text_emb, micro_conds, cross_attention_kwargs=None):
encoder_hidden_states = self.encoder_proj(encoder_hidden_states)
encoder_hidden_states = self.encoder_proj_layer_norm(encoder_hidden_states)
micro_cond_embeds = get_timestep_embedding... | 994 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
hidden_states = self.down_block(
hidden_states,
pooled_text_emb=pooled_text_emb,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
)
batch_size, channels, height, width = hidden_states.shape
hidden_states ... | 994 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
hidden_states = layer_(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs={"pooled_text_emb": pooled_text_emb},
)
hidden_states = self.project_from_hidden_norm(... | 994 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.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
... | 994 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.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 ... | 994 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.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."
... | 994 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESS... | 994 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
class UVit2DConvEmbed(nn.Module):
def __init__(self, in_channels, block_out_channels, vocab_size, elementwise_affine, eps, bias):
super().__init__()
self.embeddings = nn.Embedding(vocab_size, in_channels)
self.layer_norm = RMSNorm(in_channels, eps, elementwise_affine)
self.conv = nn.... | 995 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
class UVitBlock(nn.Module):
def __init__(
self,
channels,
num_res_blocks: int,
hidden_size,
hidden_dropout,
ln_elementwise_affine,
layer_norm_eps,
use_bias,
block_num_heads,
attention_dropout,
downsample: bool,
upsample:... | 996 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
self.res_blocks = nn.ModuleList(
[
ConvNextBlock(
channels,
layer_norm_eps,
ln_elementwise_affine,
use_bias,
hidden_dropout,
hidden_size,
)
... | 996 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
if upsample:
self.upsample = Upsample2D(
channels,
use_conv_transpose=True,
kernel_size=2,
padding=0,
name="conv",
norm_type="rms_norm",
eps=layer_norm_eps,
elementwise_affine=ln_e... | 996 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
batch_size, channels, height, width = x.shape
x = x.view(batch_size, channels, height * width).permute(0, 2, 1)
x = attention_block(
x, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs
)
x = x.permute(0, 2, 1).view... | 996 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
class ConvNextBlock(nn.Module):
def __init__(
self, channels, layer_norm_eps, ln_elementwise_affine, use_bias, hidden_dropout, hidden_size, res_ffn_factor=4
):
super().__init__()
self.depthwise = nn.Conv2d(
channels,
channels,
kernel_size=3,
... | 997 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
x = self.depthwise(x)
x = x.permute(0, 2, 3, 1)
x = self.norm(x)
x = self.channelwise_linear_1(x)
x = self.channelwise_act(x)
x = self.channelwise_norm(x)
x = self.channelwise_linear_2(x)
x = self.channelwise_dropout(x)
x = x.permute(0, 3, 1, 2)
... | 997 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
class ConvMlmLayer(nn.Module):
def __init__(
self,
block_out_channels: int,
in_channels: int,
use_bias: bool,
ln_elementwise_affine: bool,
layer_norm_eps: float,
codebook_size: int,
):
super().__init__()
self.conv1 = nn.Conv2d(block_out_cha... | 998 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/uvit_2d.py |
class UNetMotionOutput(BaseOutput):
"""
The output of [`UNetMotionOutput`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, num_frames, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
"""
... | 999 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
class AnimateDiffTransformer3D(nn.Module):
"""
A Transformer model for video-like data. | 1,000 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.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 ... | 1,000 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
This is fixed during training since it is used to learn a number of position embeddings.
activation_fn (`str`, *optional*, defaults to `"geglu"`):
Activation function to use in feed-forward. See `diffusers.models.activations.get_activation` for supported
activation functions.
nor... | 1,000 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
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,
norm_num_groups: int = 32,
cross_attention_dim: Optio... | 1,000 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
self.norm = nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
self.proj_in = nn.Linear(in_channels, inner_dim)
# 3. Define transformers blocks
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(
... | 1,000 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
self.proj_out = nn.Linear(inner_dim, in_channels)
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.LongTensor] = None,
timestep: Optional[torch.LongTensor] = None,
class_labels: Optional[torch.LongTensor] = None,
num_frames: int =... | 1,000 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
Args:
hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.Tensor` of shape `(batch size, channel, height, width)` if continuous):
Input hidden_states.
encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_sta... | 1,000 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
The number of frames to be processed per batch. This is used to reshape the hidden states.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diff... | 1,000 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
Returns:
torch.Tensor:
The output tensor.
"""
# 1. Input
batch_frames, channel, height, width = hidden_states.shape
batch_size = batch_frames // num_frames
residual = hidden_states
hidden_states = hidden_states[None, :].reshape(batch_size, nu... | 1,000 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# 3. Output
hidden_states = self.proj_out(input=hidden_states)
hidden_states = (
hidden_states[None, None, :]
.reshape(batch_size, height, width, num_frames, channel)
.permute(0, 3, 4, 1, 2)
.contiguous()
)
hidden_states = hidden_states.res... | 1,000 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
class DownBlockMotion(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "... | 1,001 | /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:
rai... | 1,001 | /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=re... | 1,001 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
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 // tem... | 1,001 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
self.resnets = nn.ModuleList(resnets)
self.motion_modules = nn.ModuleList(motion_modules)
if add_downsample:
self.downsamplers = nn.ModuleList(
[
Downsample2D(
out_channels,
use_conv=True,
... | 1,001 | /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,
num_frames: int = 1,
*args,
**kwargs,
) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_me... | 1,001 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
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