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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),
padding="VALID",
dtype=self.dtype,
) | 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 = {"params": params_rng, "dropout": dropout_rng, "gaussian": gaussian_rng}
return self.init(rngs, sample)["params"]
def encode(self, sample, deterministic: bool = True, return_dict: bool = True):
sample = jnp.transpose(sample, (0, 2, 3, 1))
hidden_states = self.encoder(sample, deterministic=deterministic)
moments = self.quant_conv(hidden_states)
posterior = FlaxDiagonalGaussianDistribution(moments)
if not return_dict:
return (posterior,)
return FlaxAutoencoderKLOutput(latent_dist=posterior) | 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, deterministic=deterministic)
hidden_states = jnp.transpose(hidden_states, (0, 3, 1, 2))
if not return_dict:
return (hidden_states,)
return FlaxDecoderOutput(sample=hidden_states)
def __call__(self, sample, sample_posterior=False, deterministic: bool = True, return_dict: bool = True):
posterior = self.encode(sample, deterministic=deterministic, return_dict=return_dict)
if sample_posterior:
rng = self.make_rng("gaussian")
hidden_states = posterior.latent_dist.sample(rng)
else:
hidden_states = posterior.latent_dist.mode()
sample = self.decode(hidden_states, return_dict=return_dict).sample | 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_elementwise_affine (`bool`, defaults to `False):
norm_eps (`bool`, defaults to `False`):
chunk_dim (`int`, defaults to `0`):
"""
def __init__(
self,
embedding_dim: int,
num_embeddings: Optional[int] = None,
output_dim: Optional[int] = None,
norm_elementwise_affine: bool = False,
norm_eps: float = 1e-5,
chunk_dim: int = 0,
):
super().__init__()
self.chunk_dim = chunk_dim
output_dim = output_dim or embedding_dim * 2
if num_embeddings is not None:
self.emb = nn.Embedding(num_embeddings, embedding_dim)
else:
self.emb = None | 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:
if self.emb is not None:
temb = self.emb(timestep)
temb = self.linear(self.silu(temb))
if self.chunk_dim == 1:
# This is a bit weird why we have the order of "shift, scale" here and "scale, shift" in the
# other if-branch. This branch is specific to CogVideoX for now.
shift, scale = temb.chunk(2, dim=1)
shift = shift[:, None, :]
scale = scale[:, None, :]
else:
scale, shift = temb.chunk(2, dim=0)
x = self.norm(x) * (1 + scale) + shift
return x | 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.float() if self.bias is not None else None,
self.eps,
).to(origin_dtype) | 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: str = "layer_norm", bias: bool = True) -> None:
super().__init__()
self.silu = nn.SiLU()
self.linear = nn.Linear(embedding_dim, 9 * embedding_dim, bias=bias)
if norm_type == "layer_norm":
self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)
else:
raise ValueError(f"Unsupported `norm_type` ({norm_type}) provided. Supported ones are: 'layer_norm'.") | 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, dim=1
)
norm_hidden_states = self.norm(hidden_states)
hidden_states = norm_hidden_states * (1 + scale_msa[:, None]) + shift_msa[:, None]
norm_hidden_states2 = norm_hidden_states * (1 + scale_msa2[:, None]) + shift_msa2[:, None]
return hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_hidden_states2, gate_msa2 | 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: Optional[int] = None, norm_type="layer_norm", bias=True):
super().__init__()
if num_embeddings is not None:
self.emb = CombinedTimestepLabelEmbeddings(num_embeddings, embedding_dim)
else:
self.emb = None | 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_dim, elementwise_affine=False, bias=False)
else:
raise ValueError(
f"Unsupported `norm_type` ({norm_type}) provided. Supported ones are: 'layer_norm', 'fp32_layer_norm'."
) | 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.Tensor, torch.Tensor]:
if self.emb is not None:
emb = self.emb(timestep, class_labels, hidden_dtype=hidden_dtype)
emb = self.linear(self.silu(emb))
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk(6, dim=1)
x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp | 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="layer_norm", bias=True):
super().__init__()
self.silu = nn.SiLU()
self.linear = nn.Linear(embedding_dim, 3 * embedding_dim, bias=bias)
if norm_type == "layer_norm":
self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)
else:
raise ValueError(
f"Unsupported `norm_type` ({norm_type}) provided. Supported ones are: 'layer_norm', 'fp32_layer_norm'."
) | 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 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa | 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 = nn.SiLU()
self.linear = nn.Linear(
min(embedding_dim, 1024),
4 * embedding_dim,
bias=True,
)
self.norm = RMSNorm(embedding_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine)
def forward(
self,
x: torch.Tensor,
emb: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
# emb = self.emb(timestep, encoder_hidden_states, encoder_mask)
emb = self.linear(self.silu(emb))
scale_msa, gate_msa, scale_mlp, gate_mlp = emb.chunk(4, dim=1)
x = self.norm(x) * (1 + scale_msa[:, None]) | 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 use additional conditions for normalization or not.
"""
def __init__(self, embedding_dim: int, use_additional_conditions: bool = False):
super().__init__()
self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(
embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions
)
self.silu = nn.SiLU()
self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True) | 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 modulation happening here.
added_cond_kwargs = added_cond_kwargs or {"resolution": None, "aspect_ratio": None}
embedded_timestep = self.emb(timestep, **added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_dtype)
return self.linear(self.silu(embedded_timestep)), embedded_timestep | 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 the channels into.
act_fn (`str`, *optional*, defaults to `None`): The activation function to use.
eps (`float`, *optional*, defaults to `1e-5`): The epsilon value to use for numerical stability.
"""
def __init__(
self, embedding_dim: int, out_dim: int, num_groups: int, act_fn: Optional[str] = None, eps: float = 1e-5
):
super().__init__()
self.num_groups = num_groups
self.eps = eps
if act_fn is None:
self.act = None
else:
self.act = get_activation(act_fn)
self.linear = nn.Linear(embedding_dim, out_dim * 2) | 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) + shift
return x | 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 projected conditioning embeddings.
# Note that AdaLayerNorm does not let the norm layer have scale and shift parameters.
# However, this is how it was implemented in the original code, and it's rather likely you should
# set `elementwise_affine` to False.
elementwise_affine=True,
eps=1e-5,
bias=True,
norm_type="layer_norm",
):
super().__init__()
self.silu = nn.SiLU()
self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias)
if norm_type == "layer_norm":
self.norm = LayerNorm(embedding_dim, eps, elementwise_affine, bias)
elif norm_type == "rms_norm": | 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.chunk(emb, 2, dim=1)
x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
return x | 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 the projected conditioning embeddings.
# Note that AdaLayerNorm does not let the norm layer have scale and shift parameters.
# However, this is how it was implemented in the original code, and it's rather likely you should
# set `elementwise_affine` to False.
elementwise_affine=True,
eps=1e-5,
bias=True,
norm_type="layer_norm",
out_dim: Optional[int] = None,
):
super().__init__()
# AdaLN
self.silu = nn.SiLU()
self.linear_1 = nn.Linear(conditioning_embedding_dim, embedding_dim, bias=bias) | 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}")
self.linear_2 = None
if out_dim is not None:
self.linear_2 = nn.Linear(embedding_dim, out_dim, bias=bias)
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_1(self.silu(conditioning_embedding).to(x.dtype))
scale = emb
x = self.norm(x) * (1 + scale)[:, None, :]
if self.linear_2 is not None:
x = self.linear_2(x)
return x | 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: int, dim: int):
super().__init__()
self.silu = nn.SiLU()
self.linear = nn.Linear(embedding_dim, 12 * dim, bias=True)
self.norm_x = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-5)
self.norm_c = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-5) | 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,
gate_msa,
shift_mlp,
scale_mlp,
gate_mlp,
c_shift_msa,
c_scale_msa,
c_gate_msa,
c_shift_mlp,
c_scale_mlp,
c_gate_mlp,
) = emb.chunk(12, dim=1)
normed_x = self.norm_x(x)
normed_context = self.norm_c(context)
x = normed_x * (1 + scale_msa[:, None]) + shift_msa[:, None]
context = normed_context * (1 + c_scale_msa[:, None]) + c_shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp, context, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp | 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 = nn.Linear(conditioning_dim, 6 * embedding_dim, bias=bias)
self.norm = nn.LayerNorm(embedding_dim, eps=eps, elementwise_affine=elementwise_affine) | 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 + scale)[:, None, :] + shift[:, None, :]
encoder_hidden_states = self.norm(encoder_hidden_states) * (1 + enc_scale)[:, None, :] + enc_shift[:, None, :]
return hidden_states, encoder_hidden_states, gate[:, None, :], enc_gate[:, None, :] | 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)
if elementwise_affine:
self.weight = nn.Parameter(torch.ones(dim))
self.bias = nn.Parameter(torch.zeros(dim)) if bias else None
else:
self.weight = None
self.bias = None
def forward(self, input):
return F.layer_norm(input, self.dim, self.weight, self.bias, self.eps) | 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 = torch.Size(dim)
self.weight = None
self.bias = None
if elementwise_affine:
self.weight = nn.Parameter(torch.ones(dim))
if bias:
self.bias = nn.Parameter(torch.zeros(dim))
def forward(self, hidden_states):
if is_torch_npu_available():
import torch_npu | 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=self.eps)[0]
if self.bias is not None:
hidden_states = hidden_states + self.bias
else:
input_dtype = hidden_states.dtype
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.eps) | 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 is not None:
hidden_states = hidden_states + self.bias
else:
hidden_states = hidden_states.to(input_dtype)
return hidden_states | 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 = nn.Parameter(torch.ones(dim))
else:
self.weight = None
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
if self.weight is not None:
hidden_states = hidden_states * self.weight
hidden_states = hidden_states.to(input_dtype)
return hidden_states | 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(torch.zeros(1, 1, 1, dim))
def forward(self, x):
gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
nx = gx / (gx.mean(dim=-1, keepdim=True) + 1e-6)
return self.gamma * (x * nx) + self.beta + x | 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.dim, eps=self.eps) | 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`, instead."
deprecate("VQEncoderOutput", "0.31", deprecation_message)
super().__init__(*args, **kwargs) | 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", "0.31", deprecation_message)
super().__init__(*args, **kwargs) | 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 import FluxControlNetOutput`, instead."
deprecate("diffusers.models.controlnet_flux.FluxControlNetOutput", "0.34", deprecation_message)
super().__init__(*args, **kwargs) | 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 = 4096,
pooled_projection_dim: int = 768,
guidance_embeds: bool = False,
axes_dims_rope: List[int] = [16, 56, 56],
num_mode: int = None,
conditioning_embedding_channels: int = None,
):
deprecation_message = "Importing `FluxControlNetModel` from `diffusers.models.controlnet_flux` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet_flux import FluxControlNetModel`, instead."
deprecate("diffusers.models.controlnet_flux.FluxControlNetModel", "0.34", deprecation_message)
super().__init__(
patch_size=patch_size,
in_channels=in_channels, | 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=guidance_embeds,
axes_dims_rope=axes_dims_rope,
num_mode=num_mode,
conditioning_embedding_channels=conditioning_embedding_channels,
) | 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.controlnet_flux import FluxMultiControlNetModel`, instead."
deprecate("diffusers.models.controlnet_flux.FluxMultiControlNetModel", "0.34", deprecation_message)
super().__init__(*args, **kwargs) | 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_linearity: Optional[str] = None,
time_embedding_norm: str = "default",
output_scale_factor: float = 1.0,
add_downsample: bool = True,
):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.time_embedding_norm = time_embedding_norm
self.add_downsample = add_downsample
self.output_scale_factor = output_scale_factor
if groups_out is None:
groups_out = groups | 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(resnets)
if non_linearity is None:
self.nonlinearity = None
else:
self.nonlinearity = get_activation(non_linearity)
self.downsample = None
if add_downsample:
self.downsample = Downsample1D(out_channels, use_conv=True, padding=1)
def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
output_states = ()
hidden_states = self.resnets[0](hidden_states, temb)
for resnet in self.resnets[1:]:
hidden_states = resnet(hidden_states, temb)
output_states += (hidden_states,) | 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,
time_embedding_norm: str = "default",
output_scale_factor: float = 1.0,
add_upsample: bool = True,
):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.time_embedding_norm = time_embedding_norm
self.add_upsample = add_upsample
self.output_scale_factor = output_scale_factor
if groups_out is None:
groups_out = groups
# there will always be at least one resnet
resnets = [ResidualTemporalBlock1D(2 * in_channels, out_channels, embed_dim=temb_channels)] | 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_linearity)
self.upsample = None
if add_upsample:
self.upsample = Upsample1D(out_channels, use_conv_transpose=True)
def forward(
self,
hidden_states: torch.Tensor,
res_hidden_states_tuple: Optional[Tuple[torch.Tensor, ...]] = None,
temb: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if res_hidden_states_tuple is not None:
res_hidden_states = res_hidden_states_tuple[-1]
hidden_states = torch.cat((hidden_states, res_hidden_states), dim=1) | 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 = self.upsample(hidden_states)
return 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, in_channels // 2, embed_dim=embed_dim)
self.down1 = Downsample1D(out_channels // 2, use_conv=True)
self.res2 = ResidualTemporalBlock1D(in_channels // 2, in_channels // 4, embed_dim=embed_dim)
self.down2 = Downsample1D(out_channels // 4, use_conv=True)
def forward(self, x: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
x = self.res1(x, temb)
x = self.down1(x)
x = self.res2(x, temb)
x = self.down2(x)
return x | 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().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.add_downsample = add_downsample
# there will always be at least one resnet
resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=embed_dim)]
for _ in range(num_layers):
resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=embed_dim))
self.resnets = nn.ModuleList(resnets)
if non_linearity is None:
self.nonlinearity = None
else:
self.nonlinearity = get_activation(non_linearity) | 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("Block cannot downsample and upsample")
def forward(self, hidden_states: torch.Tensor, temb: torch.Tensor) -> torch.Tensor:
hidden_states = self.resnets[0](hidden_states, temb)
for resnet in self.resnets[1:]:
hidden_states = resnet(hidden_states, temb)
if self.upsample:
hidden_states = self.upsample(hidden_states)
if self.downsample:
hidden_states = self.downsample(hidden_states)
return hidden_states | 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_conv1d_act = get_activation(act_fn)
self.final_conv1d_2 = nn.Conv1d(embed_dim, out_channels, 1)
def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
hidden_states = self.final_conv1d_1(hidden_states)
hidden_states = rearrange_dims(hidden_states)
hidden_states = self.final_conv1d_gn(hidden_states)
hidden_states = rearrange_dims(hidden_states)
hidden_states = self.final_conv1d_act(hidden_states)
hidden_states = self.final_conv1d_2(hidden_states)
return hidden_states | 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.Linear(fc_dim // 2, 1),
]
)
def forward(self, hidden_states: torch.Tensor, temb: torch.Tensor) -> torch.Tensor:
hidden_states = hidden_states.view(hidden_states.shape[0], -1)
hidden_states = torch.cat((hidden_states, temb), dim=-1)
for layer in self.final_block:
hidden_states = layer(hidden_states)
return hidden_states | 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)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = F.pad(hidden_states, (self.pad,) * 2, self.pad_mode)
weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]])
indices = torch.arange(hidden_states.shape[1], device=hidden_states.device)
kernel = self.kernel.to(weight)[None, :].expand(hidden_states.shape[1], -1)
weight[indices, indices] = kernel
return F.conv1d(hidden_states, weight, stride=2) | 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)
def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
hidden_states = F.pad(hidden_states, ((self.pad + 1) // 2,) * 2, self.pad_mode)
weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]])
indices = torch.arange(hidden_states.shape[1], device=hidden_states.device)
kernel = self.kernel.to(weight)[None, :].expand(hidden_states.shape[1], -1)
weight[indices, indices] = kernel
return F.conv_transpose1d(hidden_states, weight, stride=2, padding=self.pad * 2 + 1) | 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.channels, self.channels)
self.key = nn.Linear(self.channels, self.channels)
self.value = nn.Linear(self.channels, self.channels)
self.proj_attn = nn.Linear(self.channels, self.channels, bias=True)
self.dropout = nn.Dropout(dropout_rate, inplace=True)
def transpose_for_scores(self, projection: torch.Tensor) -> torch.Tensor:
new_projection_shape = projection.size()[:-1] + (self.num_heads, -1)
# move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D)
new_projection = projection.view(new_projection_shape).permute(0, 2, 1, 3)
return new_projection | 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_proj = self.key(hidden_states)
value_proj = self.value(hidden_states)
query_states = self.transpose_for_scores(query_proj)
key_states = self.transpose_for_scores(key_proj)
value_states = self.transpose_for_scores(value_proj)
scale = 1 / math.sqrt(math.sqrt(key_states.shape[-1]))
attention_scores = torch.matmul(query_states * scale, key_states.transpose(-1, -2) * scale)
attention_probs = torch.softmax(attention_scores, dim=-1)
# compute attention output
hidden_states = torch.matmul(attention_probs, value_states) | 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_states = hidden_states.transpose(1, 2)
hidden_states = self.dropout(hidden_states)
output = hidden_states + residual
return output | 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(in_channels, out_channels, 1, bias=False)
self.conv_1 = nn.Conv1d(in_channels, mid_channels, 5, padding=2)
self.group_norm_1 = nn.GroupNorm(1, mid_channels)
self.gelu_1 = nn.GELU()
self.conv_2 = nn.Conv1d(mid_channels, out_channels, 5, padding=2)
if not self.is_last:
self.group_norm_2 = nn.GroupNorm(1, out_channels)
self.gelu_2 = nn.GELU()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
residual = self.conv_skip(hidden_states) if self.has_conv_skip else hidden_states | 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 = self.gelu_2(hidden_states)
output = hidden_states + residual
return output | 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),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, out_channels),
]
attentions = [
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(out_channels, out_channels // 32),
]
self.up = Upsample1d(kernel="cubic") | 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):
hidden_states = resnet(hidden_states)
hidden_states = attn(hidden_states)
hidden_states = self.up(hidden_states)
return hidden_states | 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 = [
ResConvBlock(in_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, out_channels),
]
attentions = [
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(out_channels, out_channels // 32),
]
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) | 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(in_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, out_channels),
]
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 resnet in self.resnets:
hidden_states = resnet(hidden_states)
return hidden_states, (hidden_states,) | 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_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, out_channels),
]
self.resnets = nn.ModuleList(resnets)
def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
hidden_states = torch.cat([hidden_states, temb], dim=1)
for resnet in self.resnets:
hidden_states = resnet(hidden_states)
return hidden_states, (hidden_states,) | 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_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, out_channels),
]
attentions = [
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(out_channels, out_channels // 32),
]
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
self.up = Upsample1d(kernel="cubic") | 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=1)
for resnet, attn in zip(self.resnets, self.attentions):
hidden_states = resnet(hidden_states)
hidden_states = attn(hidden_states)
hidden_states = self.up(hidden_states)
return hidden_states | 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),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, out_channels),
]
self.resnets = nn.ModuleList(resnets)
self.up = Upsample1d(kernel="cubic")
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=1)
for resnet in self.resnets:
hidden_states = resnet(hidden_states)
hidden_states = self.up(hidden_states) | 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_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, out_channels, is_last=True),
]
self.resnets = nn.ModuleList(resnets)
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=1)
for resnet in self.resnets:
hidden_states = resnet(hidden_states)
return hidden_states | 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.
"""
sample: torch.Tensor = None | 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`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving). | 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 in the output.
center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
Whether to flip the sin to cos in the time embedding.
freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
The tuple of downsample blocks to use.
mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`): | 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", "CrossAttnUpBlock2D")`):
The tuple of upsample blocks to use.
only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
Whether to include self-attention in the basic transformer blocks, see
[`~models.attention.BasicTransformerBlock`].
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block.
layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. | 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`, *optional*, defaults to 32): The number of groups to use for the normalization.
If `None`, normalization and activation layers is skipped in post-processing.
norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
The dimension of the cross attention features.
transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for | 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 type [`~models.attention.BasicTransformerBlock`], in the upsampling
blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for
[`~models.unets.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unets.unet_2d_blocks.CrossAttnUpBlock2D`],
[`~models.unets.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
encoder_hid_dim (`int`, *optional*, defaults to None):
If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
dimension to `cross_attention_dim`.
encoder_hid_dim_type (`str`, *optional*, defaults to `None`): | 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 (`int`, *optional*):
The number of attention heads. If not defined, defaults to `attention_head_dim`
resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
class_embed_type (`str`, *optional*, defaults to `None`):
The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
addition_embed_type (`str`, *optional*, defaults to `None`): | 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 (`int`, *optional*, defaults to `None`):
Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
class conditioning with `class_embed_type` equal to `None`.
time_embedding_type (`str`, *optional*, defaults to `positional`):
The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
time_embedding_dim (`int`, *optional*, defaults to `None`):
An optional override for the dimension of the projected time embedding.
time_embedding_act_fn (`str`, *optional*, defaults to `None`): | 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. Choose from `silu`, `mish` and `gelu`.
time_cond_proj_dim (`int`, *optional*, defaults to `None`):
The dimension of `cond_proj` layer in the timestep embedding.
conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
`class_embed_type="projection"`. Required when `class_embed_type="projection"`.
class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time | 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_cross_attention` is `None`, the
`only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
otherwise.
""" | 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: Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
),
mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
only_cross_attention: Union[bool, Tuple[bool]] = False,
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,
dropout: float = 0.0,
act_fn: str = "silu", | 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: Optional[int] = None,
encoder_hid_dim_type: Optional[str] = None,
attention_head_dim: Union[int, Tuple[int]] = 8,
num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
class_embed_type: Optional[str] = None,
addition_embed_type: Optional[str] = None,
addition_time_embed_dim: Optional[int] = None,
num_class_embeds: Optional[int] = None,
upcast_attention: bool = False,
resnet_time_scale_shift: str = "default",
resnet_skip_time_act: bool = False,
resnet_out_scale_factor: float = 1.0, | 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,
projection_class_embeddings_input_dim: Optional[int] = None,
attention_type: str = "default",
class_embeddings_concat: bool = False,
mid_block_only_cross_attention: Optional[bool] = None,
cross_attention_norm: Optional[str] = None,
addition_embed_type_num_heads: int = 64,
):
super().__init__() | 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#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
) | 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 created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
num_attention_heads = num_attention_heads or attention_head_dim | 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=cross_attention_dim,
transformer_layers_per_block=transformer_layers_per_block,
reverse_transformer_layers_per_block=reverse_transformer_layers_per_block,
attention_head_dim=attention_head_dim,
num_attention_heads=num_attention_heads,
)
# input
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
) | 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_embedding = TimestepEmbedding(
timestep_input_dim,
time_embed_dim,
act_fn=act_fn,
post_act_fn=timestep_post_act,
cond_proj_dim=time_cond_proj_dim,
)
self._set_encoder_hid_proj(
encoder_hid_dim_type,
cross_attention_dim=cross_attention_dim,
encoder_hid_dim=encoder_hid_dim,
) | 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=timestep_input_dim,
)
self._set_add_embedding(
addition_embed_type,
addition_embed_type_num_heads=addition_embed_type_num_heads,
addition_time_embed_dim=addition_time_embed_dim,
cross_attention_dim=cross_attention_dim,
encoder_hid_dim=encoder_hid_dim,
flip_sin_to_cos=flip_sin_to_cos,
freq_shift=freq_shift,
projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
time_embed_dim=time_embed_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_block_only_cross_attention is None:
mid_block_only_cross_attention = only_cross_attention
only_cross_attention = [only_cross_attention] * len(down_block_types)
if mid_block_only_cross_attention is None:
mid_block_only_cross_attention = False
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(down_block_types)
if isinstance(attention_head_dim, int):
attention_head_dim = (attention_head_dim,) * len(down_block_types)
if isinstance(cross_attention_dim, int):
cross_attention_dim = (cross_attention_dim,) * len(down_block_types) | 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:
# The time embeddings are concatenated with the class embeddings. The dimension of the
# time embeddings passed to the down, middle, and up blocks is twice the dimension of the
# regular time embeddings
blocks_time_embed_dim = time_embed_dim * 2
else:
blocks_time_embed_dim = time_embed_dim
# 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 | 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_embed_dim,
add_downsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim[i],
num_attention_heads=num_attention_heads[i],
downsample_padding=downsample_padding,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention[i],
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift, | 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_channel,
dropout=dropout,
)
self.down_blocks.append(down_block) | 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_block_scale_factor,
transformer_layers_per_block=transformer_layers_per_block[-1],
num_attention_heads=num_attention_heads[-1],
cross_attention_dim=cross_attention_dim[-1],
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
mid_block_only_cross_attention=mid_block_only_cross_attention,
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
attention_type=attention_type,
resnet_skip_time_act=resnet_skip_time_act,
cross_attention_norm=cross_attention_norm, | 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))
reversed_cross_attention_dim = list(reversed(cross_attention_dim))
reversed_transformer_layers_per_block = (
list(reversed(transformer_layers_per_block))
if reverse_transformer_layers_per_block is None
else reverse_transformer_layers_per_block
)
only_cross_attention = list(reversed(only_cross_attention))
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 | 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 = True
self.num_upsamplers += 1
else:
add_upsample = False | 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_channel=prev_output_channel,
temb_channels=blocks_time_embed_dim,
add_upsample=add_upsample,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resolution_idx=i,
resnet_groups=norm_num_groups,
cross_attention_dim=reversed_cross_attention_dim[i],
num_attention_heads=reversed_num_attention_heads[i],
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention[i],
upcast_attention=upcast_attention, | 968 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition.py |
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