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2 * self.config.latent_channels,
kernel_size=(1, 1),
strides=(1, 1),
padding="VALID",
dtype=self.dtype,
)
self.post_quant_conv = nn.Conv(
self.config.latent_channels,
kernel_size=(1, 1),
strides=(1, 1),
paddi... | 927 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/vae_flax.py |
def init_weights(self, rng: jax.Array) -> FrozenDict:
# init input tensors
sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
sample = jnp.zeros(sample_shape, dtype=jnp.float32)
params_rng, dropout_rng, gaussian_rng = jax.random.split(rng, 3)
rngs = {"param... | 927 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/vae_flax.py |
def decode(self, latents, deterministic: bool = True, return_dict: bool = True):
if latents.shape[-1] != self.config.latent_channels:
latents = jnp.transpose(latents, (0, 2, 3, 1))
hidden_states = self.post_quant_conv(latents)
hidden_states = self.decoder(hidden_states, deterministi... | 927 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/vae_flax.py |
if not return_dict:
return (sample,)
return FlaxDecoderOutput(sample=sample) | 927 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/vae_flax.py |
class AdaLayerNorm(nn.Module):
r"""
Norm layer modified to incorporate timestep embeddings.
Parameters:
embedding_dim (`int`): The size of each embedding vector.
num_embeddings (`int`, *optional*): The size of the embeddings dictionary.
output_dim (`int`, *optional*):
norm_e... | 928 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
self.silu = nn.SiLU()
self.linear = nn.Linear(embedding_dim, output_dim)
self.norm = nn.LayerNorm(output_dim // 2, norm_eps, norm_elementwise_affine)
def forward(
self, x: torch.Tensor, timestep: Optional[torch.Tensor] = None, temb: Optional[torch.Tensor] = None
) -> torch.Tensor:
... | 928 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class FP32LayerNorm(nn.LayerNorm):
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
origin_dtype = inputs.dtype
return F.layer_norm(
inputs.float(),
self.normalized_shape,
self.weight.float() if self.weight is not None else None,
self.bias.floa... | 929 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class SD35AdaLayerNormZeroX(nn.Module):
r"""
Norm layer adaptive layer norm zero (AdaLN-Zero).
Parameters:
embedding_dim (`int`): The size of each embedding vector.
num_embeddings (`int`): The size of the embeddings dictionary.
"""
def __init__(self, embedding_dim: int, norm_type: ... | 930 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
def forward(
self,
hidden_states: torch.Tensor,
emb: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, ...]:
emb = self.linear(self.silu(emb))
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, shift_msa2, scale_msa2, gate_msa2 = emb.chunk(
9, d... | 930 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class AdaLayerNormZero(nn.Module):
r"""
Norm layer adaptive layer norm zero (adaLN-Zero).
Parameters:
embedding_dim (`int`): The size of each embedding vector.
num_embeddings (`int`): The size of the embeddings dictionary.
"""
def __init__(self, embedding_dim: int, num_embeddings: ... | 931 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
self.silu = nn.SiLU()
self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=bias)
if norm_type == "layer_norm":
self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)
elif norm_type == "fp32_layer_norm":
self.norm = FP32LayerNorm(embedding_di... | 931 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
def forward(
self,
x: torch.Tensor,
timestep: Optional[torch.Tensor] = None,
class_labels: Optional[torch.LongTensor] = None,
hidden_dtype: Optional[torch.dtype] = None,
emb: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Ten... | 931 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class AdaLayerNormZeroSingle(nn.Module):
r"""
Norm layer adaptive layer norm zero (adaLN-Zero).
Parameters:
embedding_dim (`int`): The size of each embedding vector.
num_embeddings (`int`): The size of the embeddings dictionary.
"""
def __init__(self, embedding_dim: int, norm_type=... | 932 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
def forward(
self,
x: torch.Tensor,
emb: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
emb = self.linear(self.silu(emb))
shift_msa, scale_msa, gate_msa = emb.chunk(3, dim=1)
x = self.norm(x) * (1 + sca... | 932 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class LuminaRMSNormZero(nn.Module):
"""
Norm layer adaptive RMS normalization zero.
Parameters:
embedding_dim (`int`): The size of each embedding vector.
"""
def __init__(self, embedding_dim: int, norm_eps: float, norm_elementwise_affine: bool):
super().__init__()
self.silu... | 933 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
return x, gate_msa, scale_mlp, gate_mlp | 933 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class AdaLayerNormSingle(nn.Module):
r"""
Norm layer adaptive layer norm single (adaLN-single).
As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).
Parameters:
embedding_dim (`int`): The size of each embedding vector.
use_additional_conditions (`bool`): To... | 934 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
def forward(
self,
timestep: torch.Tensor,
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
batch_size: Optional[int] = None,
hidden_dtype: Optional[torch.dtype] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
# No... | 934 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class AdaGroupNorm(nn.Module):
r"""
GroupNorm layer modified to incorporate timestep embeddings.
Parameters:
embedding_dim (`int`): The size of each embedding vector.
num_embeddings (`int`): The size of the embeddings dictionary.
num_groups (`int`): The number of groups to separate ... | 935 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor:
if self.act:
emb = self.act(emb)
emb = self.linear(emb)
emb = emb[:, :, None, None]
scale, shift = emb.chunk(2, dim=1)
x = F.group_norm(x, self.num_groups, eps=self.eps)
x = x * (1 + scale... | 935 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class AdaLayerNormContinuous(nn.Module):
def __init__(
self,
embedding_dim: int,
conditioning_embedding_dim: int,
# NOTE: It is a bit weird that the norm layer can be configured to have scale and shift parameters
# because the output is immediately scaled and shifted by the p... | 936 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
self.norm = RMSNorm(embedding_dim, eps, elementwise_affine)
else:
raise ValueError(f"unknown norm_type {norm_type}") | 936 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
# convert back to the original dtype in case `conditioning_embedding`` is upcasted to float32 (needed for hunyuanDiT)
emb = self.linear(self.silu(conditioning_embedding).to(x.dtype))
scale, shift = torch.ch... | 936 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class LuminaLayerNormContinuous(nn.Module):
def __init__(
self,
embedding_dim: int,
conditioning_embedding_dim: int,
# NOTE: It is a bit weird that the norm layer can be configured to have scale and shift parameters
# because the output is immediately scaled and shifted by th... | 937 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
if norm_type == "layer_norm":
self.norm = LayerNorm(embedding_dim, eps, elementwise_affine, bias)
elif norm_type == "rms_norm":
self.norm = RMSNorm(embedding_dim, eps=eps, elementwise_affine=elementwise_affine)
else:
raise ValueError(f"unknown norm_type {norm_type}")
... | 937 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class CogView3PlusAdaLayerNormZeroTextImage(nn.Module):
r"""
Norm layer adaptive layer norm zero (adaLN-Zero).
Parameters:
embedding_dim (`int`): The size of each embedding vector.
num_embeddings (`int`): The size of the embeddings dictionary.
"""
def __init__(self, embedding_dim: ... | 938 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
def forward(
self,
x: torch.Tensor,
context: torch.Tensor,
emb: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
emb = self.linear(self.silu(emb))
(
shift_msa,
scale_msa,
... | 938 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class CogVideoXLayerNormZero(nn.Module):
def __init__(
self,
conditioning_dim: int,
embedding_dim: int,
elementwise_affine: bool = True,
eps: float = 1e-5,
bias: bool = True,
) -> None:
super().__init__()
self.silu = nn.SiLU()
self.linear ... | 939 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
def forward(
self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, temb: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
shift, scale, gate, enc_shift, enc_scale, enc_gate = self.linear(self.silu(temb)).chunk(6, dim=1)
hidden_states = self.norm(hidden_states) * (1 + sc... | 939 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class LayerNorm(nn.Module):
def __init__(self, dim, eps: float = 1e-5, elementwise_affine: bool = True, bias: bool = True):
super().__init__()
self.eps = eps
if isinstance(dim, numbers.Integral):
dim = (dim,)
self.dim = torch.Size(dim)
... | 940 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class RMSNorm(nn.Module):
def __init__(self, dim, eps: float, elementwise_affine: bool = True, bias: bool = False):
super().__init__()
self.eps = eps
self.elementwise_affine = elementwise_affine
if isinstance(dim, numbers.Integral):
dim = (dim,)
self.dim = torc... | 941 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
if self.weight is not None:
# convert into half-precision if necessary
if self.weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(self.weight.dtype)
hidden_states = torch_npu.npu_rms_norm(hidden_states, self.weight, epsilon=s... | 941 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
if self.weight is not None:
# convert into half-precision if necessary
if self.weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(self.weight.dtype)
hidden_states = hidden_states * self.weight
if self.bias... | 941 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class MochiRMSNorm(nn.Module):
def __init__(self, dim, eps: float, elementwise_affine: bool = True):
super().__init__()
self.eps = eps
if isinstance(dim, numbers.Integral):
dim = (dim,)
self.dim = torch.Size(dim)
if elementwise_affine:
self.weight ... | 942 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class GlobalResponseNorm(nn.Module):
# Taken from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105
def __init__(self, dim):
super().__init__()
self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))
self.beta = nn.Parameter(t... | 943 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class LpNorm(nn.Module):
def __init__(self, p: int = 2, dim: int = -1, eps: float = 1e-12):
super().__init__()
self.p = p
self.dim = dim
self.eps = eps
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return F.normalize(hidden_states, p=self.p, dim=self.d... | 944 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/normalization.py |
class VQEncoderOutput(VQEncoderOutput):
def __init__(self, *args, **kwargs):
deprecation_message = "Importing `VQEncoderOutput` from `diffusers.models.vq_model` is deprecated and this will be removed in a future version. Please use `from diffusers.models.autoencoders.vq_model import VQEncoderOutput`, instea... | 945 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/vq_model.py |
class VQModel(VQModel):
def __init__(self, *args, **kwargs):
deprecation_message = "Importing `VQModel` from `diffusers.models.vq_model` is deprecated and this will be removed in a future version. Please use `from diffusers.models.autoencoders.vq_model import VQModel`, instead."
deprecate("VQModel",... | 946 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/vq_model.py |
class FluxControlNetOutput(FluxControlNetOutput):
def __init__(self, *args, **kwargs):
deprecation_message = "Importing `FluxControlNetOutput` from `diffusers.models.controlnet_flux` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet_flux imp... | 947 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnet_flux.py |
class FluxControlNetModel(FluxControlNetModel):
def __init__(
self,
patch_size: int = 1,
in_channels: int = 64,
num_layers: int = 19,
num_single_layers: int = 38,
attention_head_dim: int = 128,
num_attention_heads: int = 24,
joint_attention_dim: int = ... | 948 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnet_flux.py |
num_layers=num_layers,
num_single_layers=num_single_layers,
attention_head_dim=attention_head_dim,
num_attention_heads=num_attention_heads,
joint_attention_dim=joint_attention_dim,
pooled_projection_dim=pooled_projection_dim,
guidance_embeds=guidan... | 948 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnet_flux.py |
class FluxMultiControlNetModel(FluxMultiControlNetModel):
def __init__(self, *args, **kwargs):
deprecation_message = "Importing `FluxMultiControlNetModel` from `diffusers.models.controlnet_flux` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.control... | 949 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnet_flux.py |
class DownResnetBlock1D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
num_layers: int = 1,
conv_shortcut: bool = False,
temb_channels: int = 32,
groups: int = 32,
groups_out: Optional[int] = None,
non_lin... | 950 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
# there will always be at least one resnet
resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=temb_channels)]
for _ in range(num_layers):
resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels))
self.resnets = nn.ModuleList(r... | 950 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
if self.nonlinearity is not None:
hidden_states = self.nonlinearity(hidden_states)
if self.downsample is not None:
hidden_states = self.downsample(hidden_states)
return hidden_states, output_states | 950 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class UpResnetBlock1D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
num_layers: int = 1,
temb_channels: int = 32,
groups: int = 32,
groups_out: Optional[int] = None,
non_linearity: Optional[str] = None,
t... | 951 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
for _ in range(num_layers):
resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels))
self.resnets = nn.ModuleList(resnets)
if non_linearity is None:
self.nonlinearity = None
else:
self.nonlinearity = get_activation(non_lin... | 951 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
hidden_states = self.resnets[0](hidden_states, temb)
for resnet in self.resnets[1:]:
hidden_states = resnet(hidden_states, temb)
if self.nonlinearity is not None:
hidden_states = self.nonlinearity(hidden_states)
if self.upsample is not None:
hidden_states = ... | 951 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class ValueFunctionMidBlock1D(nn.Module):
def __init__(self, in_channels: int, out_channels: int, embed_dim: int):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.embed_dim = embed_dim
self.res1 = ResidualTemporalBlock1D(in_channels, i... | 952 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class MidResTemporalBlock1D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
embed_dim: int,
num_layers: int = 1,
add_downsample: bool = False,
add_upsample: bool = False,
non_linearity: Optional[str] = None,
):
super().... | 953 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
self.upsample = None
if add_upsample:
self.upsample = Upsample1D(out_channels, use_conv=True)
self.downsample = None
if add_downsample:
self.downsample = Downsample1D(out_channels, use_conv=True)
if self.upsample and self.downsample:
raise ValueError... | 953 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class OutConv1DBlock(nn.Module):
def __init__(self, num_groups_out: int, out_channels: int, embed_dim: int, act_fn: str):
super().__init__()
self.final_conv1d_1 = nn.Conv1d(embed_dim, embed_dim, 5, padding=2)
self.final_conv1d_gn = nn.GroupNorm(num_groups_out, embed_dim)
self.final_c... | 954 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class OutValueFunctionBlock(nn.Module):
def __init__(self, fc_dim: int, embed_dim: int, act_fn: str = "mish"):
super().__init__()
self.final_block = nn.ModuleList(
[
nn.Linear(fc_dim + embed_dim, fc_dim // 2),
get_activation(act_fn),
nn.Lin... | 955 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class Downsample1d(nn.Module):
def __init__(self, kernel: str = "linear", pad_mode: str = "reflect"):
super().__init__()
self.pad_mode = pad_mode
kernel_1d = torch.tensor(_kernels[kernel])
self.pad = kernel_1d.shape[0] // 2 - 1
self.register_buffer("kernel", kernel_1d)
d... | 956 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class Upsample1d(nn.Module):
def __init__(self, kernel: str = "linear", pad_mode: str = "reflect"):
super().__init__()
self.pad_mode = pad_mode
kernel_1d = torch.tensor(_kernels[kernel]) * 2
self.pad = kernel_1d.shape[0] // 2 - 1
self.register_buffer("kernel", kernel_1d)
... | 957 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class SelfAttention1d(nn.Module):
def __init__(self, in_channels: int, n_head: int = 1, dropout_rate: float = 0.0):
super().__init__()
self.channels = in_channels
self.group_norm = nn.GroupNorm(1, num_channels=in_channels)
self.num_heads = n_head
self.query = nn.Linear(self.... | 958 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
residual = hidden_states
batch, channel_dim, seq = hidden_states.shape
hidden_states = self.group_norm(hidden_states)
hidden_states = hidden_states.transpose(1, 2)
query_proj = self.query(hidden_states)
key... | 958 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
hidden_states = hidden_states.permute(0, 2, 1, 3).contiguous()
new_hidden_states_shape = hidden_states.size()[:-2] + (self.channels,)
hidden_states = hidden_states.view(new_hidden_states_shape)
# compute next hidden_states
hidden_states = self.proj_attn(hidden_states)
hidden_sta... | 958 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class ResConvBlock(nn.Module):
def __init__(self, in_channels: int, mid_channels: int, out_channels: int, is_last: bool = False):
super().__init__()
self.is_last = is_last
self.has_conv_skip = in_channels != out_channels
if self.has_conv_skip:
self.conv_skip = nn.Conv1d(... | 959 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
hidden_states = self.conv_1(hidden_states)
hidden_states = self.group_norm_1(hidden_states)
hidden_states = self.gelu_1(hidden_states)
hidden_states = self.conv_2(hidden_states)
if not self.is_last:
hidden_states = self.group_norm_2(hidden_states)
hidden_states =... | 959 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class UNetMidBlock1D(nn.Module):
def __init__(self, mid_channels: int, in_channels: int, out_channels: Optional[int] = None):
super().__init__()
out_channels = in_channels if out_channels is None else out_channels | 960 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
# there is always at least one resnet
self.down = Downsample1d("cubic")
resnets = [
ResConvBlock(in_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResCo... | 960 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
hidden_states = self.down(hidden_states)
for attn, resnet in zip(self.attentions, self.resnets):
... | 960 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class AttnDownBlock1D(nn.Module):
def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):
super().__init__()
mid_channels = out_channels if mid_channels is None else mid_channels
self.down = Downsample1d("cubic")
resnets = [
ResConvBl... | 961 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
for resnet, attn in zip(self.resnets, self.attentions):
hidden_states = resnet(hidden_states)
hidden_states = attn(hidden_states)
return hidden_states, (hidden_states,) | 961 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class DownBlock1D(nn.Module):
def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):
super().__init__()
mid_channels = out_channels if mid_channels is None else mid_channels
self.down = Downsample1d("cubic")
resnets = [
ResConvBlock(... | 962 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class DownBlock1DNoSkip(nn.Module):
def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):
super().__init__()
mid_channels = out_channels if mid_channels is None else mid_channels
resnets = [
ResConvBlock(in_channels, mid_channels, mid_chann... | 963 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class AttnUpBlock1D(nn.Module):
def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):
super().__init__()
mid_channels = out_channels if mid_channels is None else mid_channels
resnets = [
ResConvBlock(2 * in_channels, mid_channels, mid_chann... | 964 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
def forward(
self,
hidden_states: torch.Tensor,
res_hidden_states_tuple: Tuple[torch.Tensor, ...],
temb: Optional[torch.Tensor] = None,
) -> torch.Tensor:
res_hidden_states = res_hidden_states_tuple[-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim... | 964 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class UpBlock1D(nn.Module):
def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):
super().__init__()
mid_channels = in_channels if mid_channels is None else mid_channels
resnets = [
ResConvBlock(2 * in_channels, mid_channels, mid_channels),... | 965 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
return hidden_states | 965 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class UpBlock1DNoSkip(nn.Module):
def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):
super().__init__()
mid_channels = in_channels if mid_channels is None else mid_channels
resnets = [
ResConvBlock(2 * in_channels, mid_channels, mid_chan... | 966 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py |
class UNet2DConditionOutput(BaseOutput):
"""
The output of [`UNet2DConditionModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
"""
sam... | 967 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
class UNet2DConditionModel(
ModelMixin, ConfigMixin, FromOriginalModelMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin
):
r"""
A 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`]. Ch... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
Parameters:
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
Height and width of input/output sample.
in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 4): Number of channels i... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or
`UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlo... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
norm_num_groups (`int`, *opt... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
[`~models.unets.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unets.unet_2d_blocks.CrossAttnUpBlock2D`],
[`~models.unets.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):
The number of transformer blocks of... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
num_attention_heads (`... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
"text". "text" will use the `TextTimeEmbedding` layer.
addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
Dimension for the timestep embeddings.
num_class_embeds (... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
Optional activation function to use only once on the time embeddings before they are passed to the rest of
the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
timestep_post_act (`str`, *optional*, defaults to `None`):
The second activation function to use in timestep embedding. Ch... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
embeddings with the class embeddings.
mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
`only_cross_attention` is given as a single boolean and `mid_block_only_cros... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
_supports_gradient_checkpointing = True
_no_split_modules = ["BasicTransformerBlock", "ResnetBlock2D", "CrossAttnUpBlock2D"] | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
@register_to_config
def __init__(
self,
sample_size: Optional[Union[int, Tuple[int, int]]] = None,
in_channels: int = 4,
out_channels: int = 4,
center_input_sample: bool = False,
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
down_block_types: Tupl... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
norm_num_groups: Optional[int] = 32,
norm_eps: float = 1e-5,
cross_attention_dim: Union[int, Tuple[int]] = 1280,
transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
encoder_hid_dim: Opti... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
time_embedding_type: str = "positional",
time_embedding_dim: Optional[int] = None,
time_embedding_act_fn: Optional[str] = None,
timestep_post_act: Optional[str] = None,
time_cond_proj_dim: Optional[int] = None,
conv_in_kernel: int = 3,
conv_out_kernel: int = 3,
pr... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
self.sample_size = sample_size
if num_attention_heads is not None:
raise ValueError(
"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#iss... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was cre... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
# Check inputs
self._check_config(
down_block_types=down_block_types,
up_block_types=up_block_types,
only_cross_attention=only_cross_attention,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
cross_attention_dim=cr... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
# time
time_embed_dim, timestep_input_dim = self._set_time_proj(
time_embedding_type,
block_out_channels=block_out_channels,
flip_sin_to_cos=flip_sin_to_cos,
freq_shift=freq_shift,
time_embedding_dim=time_embedding_dim,
)
self.time_emb... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
# class embedding
self._set_class_embedding(
class_embed_type,
act_fn=act_fn,
num_class_embeds=num_class_embeds,
projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
time_embed_dim=time_embed_dim,
timestep_input_dim=... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
if time_embedding_act_fn is None:
self.time_embed_act = None
else:
self.time_embed_act = get_activation(time_embedding_act_fn)
self.down_blocks = nn.ModuleList([])
self.up_blocks = nn.ModuleList([])
if isinstance(only_cross_attention, bool):
if mid_b... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
if isinstance(layers_per_block, int):
layers_per_block = [layers_per_block] * len(down_block_types)
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
if class_embeddings_concat:
# ... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block[i],
transformer_layers_per_block=transformer_layers_per_block[i],
in_channels=input_channel,
out_channels=output_channel,
temb_channels=blocks_time_em... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
attention_type=attention_type,
resnet_skip_time_act=resnet_skip_time_act,
resnet_out_scale_factor=resnet_out_scale_factor,
cross_attention_norm=cross_attention_norm,
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
# mid
self.mid_block = get_mid_block(
mid_block_type,
temb_channels=blocks_time_embed_dim,
in_channels=block_out_channels[-1],
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
output_scale_factor=mid_blo... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
attention_head_dim=attention_head_dim[-1],
dropout=dropout,
) | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
# count how many layers upsample the images
self.num_upsamplers = 0
# 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))
rever... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.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 = Tr... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
up_block = get_up_block(
up_block_type,
num_layers=reversed_layers_per_block[i] + 1,
transformer_layers_per_block=reversed_transformer_layers_per_block[i],
in_channels=input_channel,
out_channels=output_channel,
prev_output_... | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
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