text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
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def setup(self):
# self attention (or cross_attention if only_cross_attention is True)
self.attn1 = FlaxAttention(
self.dim,
self.n_heads,
self.d_head,
self.dropout,
self.use_memory_efficient_attention,
self.split_head_dim,
... | 853 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py |
def __call__(self, hidden_states, context, deterministic=True):
# self attention
residual = hidden_states
if self.only_cross_attention:
hidden_states = self.attn1(self.norm1(hidden_states), context, deterministic=deterministic)
else:
hidden_states = self.attn1(sel... | 853 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py |
class FlaxTransformer2DModel(nn.Module):
r"""
A Spatial Transformer layer with Gated Linear Unit (GLU) activation function as described in:
https://arxiv.org/pdf/1506.02025.pdf | 854 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py |
Parameters:
in_channels (:obj:`int`):
Input number of channels
n_heads (:obj:`int`):
Number of heads
d_head (:obj:`int`):
Hidden states dimension inside each head
depth (:obj:`int`, *optional*, defaults to 1):
Number of transformers block
... | 854 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py |
enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.
""" | 854 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py |
in_channels: int
n_heads: int
d_head: int
depth: int = 1
dropout: float = 0.0
use_linear_projection: bool = False
only_cross_attention: bool = False
dtype: jnp.dtype = jnp.float32
use_memory_efficient_attention: bool = False
split_head_dim: bool = False
def setup(self):
... | 854 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py |
self.transformer_blocks = [
FlaxBasicTransformerBlock(
inner_dim,
self.n_heads,
self.d_head,
dropout=self.dropout,
only_cross_attention=self.only_cross_attention,
dtype=self.dtype,
use_memory_effi... | 854 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py |
def __call__(self, hidden_states, context, deterministic=True):
batch, height, width, channels = hidden_states.shape
residual = hidden_states
hidden_states = self.norm(hidden_states)
if self.use_linear_projection:
hidden_states = hidden_states.reshape(batch, height * width, c... | 854 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py |
if self.use_linear_projection:
hidden_states = self.proj_out(hidden_states)
hidden_states = hidden_states.reshape(batch, height, width, channels)
else:
hidden_states = hidden_states.reshape(batch, height, width, channels)
hidden_states = self.proj_out(hidden_state... | 854 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py |
class FlaxFeedForward(nn.Module):
r"""
Flax module that encapsulates two Linear layers separated by a non-linearity. It is the counterpart of PyTorch's
[`FeedForward`] class, with the following simplifications:
- The activation function is currently hardcoded to a gated linear unit from:
https://arx... | 855 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py |
def setup(self):
# The second linear layer needs to be called
# net_2 for now to match the index of the Sequential layer
self.net_0 = FlaxGEGLU(self.dim, self.dropout, self.dtype)
self.net_2 = nn.Dense(self.dim, dtype=self.dtype)
def __call__(self, hidden_states, deterministic=True)... | 855 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py |
class FlaxGEGLU(nn.Module):
r"""
Flax implementation of a Linear layer followed by the variant of the gated linear unit activation function from
https://arxiv.org/abs/2002.05202.
Parameters:
dim (:obj:`int`):
Input hidden states dimension
dropout (:obj:`float`, *optional*, d... | 856 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py |
class FlaxUpsample2D(nn.Module):
out_channels: int
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv = nn.Conv(
self.out_channels,
kernel_size=(3, 3),
strides=(1, 1),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
def _... | 857 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet_flax.py |
class FlaxDownsample2D(nn.Module):
out_channels: int
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv = nn.Conv(
self.out_channels,
kernel_size=(3, 3),
strides=(2, 2),
padding=((1, 1), (1, 1)), # padding="VALID",
dtype=self.dtype... | 858 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet_flax.py |
class FlaxResnetBlock2D(nn.Module):
in_channels: int
out_channels: int = None
dropout_prob: float = 0.0
use_nin_shortcut: bool = None
dtype: jnp.dtype = jnp.float32
def setup(self):
out_channels = self.in_channels if self.out_channels is None else self.out_channels
self.norm1 =... | 859 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet_flax.py |
use_nin_shortcut = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
self.conv_shortcut = None
if use_nin_shortcut:
self.conv_shortcut = nn.Conv(
out_channels,
kernel_size=(1, 1),
strides=(1, 1),
... | 859 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet_flax.py |
hidden_states = self.norm2(hidden_states)
hidden_states = nn.swish(hidden_states)
hidden_states = self.dropout(hidden_states, deterministic)
hidden_states = self.conv2(hidden_states)
if self.conv_shortcut is not None:
residual = self.conv_shortcut(residual)
return h... | 859 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet_flax.py |
class PatchEmbed(nn.Module):
"""
2D Image to Patch Embedding with support for SD3 cropping.
Args:
height (`int`, defaults to `224`): The height of the image.
width (`int`, defaults to `224`): The width of the image.
patch_size (`int`, defaults to `16`): The size of the patches.
... | 860 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def __init__(
self,
height=224,
width=224,
patch_size=16,
in_channels=3,
embed_dim=768,
layer_norm=False,
flatten=True,
bias=True,
interpolation_scale=1,
pos_embed_type="sincos",
pos_embed_max_size=None, # For SD3 cropping
... | 860 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
self.patch_size = patch_size
self.height, self.width = height // patch_size, width // patch_size
self.base_size = height // patch_size
self.interpolation_scale = interpolation_scale
# Calculate positional embeddings based on max size or default
if pos_embed_max_size:
... | 860 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
if pos_embed_type is None:
self.pos_embed = None
elif pos_embed_type == "sincos":
pos_embed = get_2d_sincos_pos_embed(
embed_dim,
grid_size,
base_size=self.base_size,
interpolation_scale=self.interpolation_scale,
... | 860 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
height = height // self.patch_size
width = width // self.patch_size
if height > self.pos_embed_max_size:
raise ValueError(
f"Height ({height}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}."
)
if width > self.pos_embed_max_size:
... | 860 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def forward(self, latent):
if self.pos_embed_max_size is not None:
height, width = latent.shape[-2:]
else:
height, width = latent.shape[-2] // self.patch_size, latent.shape[-1] // self.patch_size
latent = self.proj(latent)
if self.flatten:
latent = lat... | 860 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
device=latent.device,
output_type="pt",
)
pos_embed = pos_embed.float().unsqueeze(0)
else:
pos_embed = self.pos_embed | 860 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
return (latent + pos_embed).to(latent.dtype) | 860 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class LuminaPatchEmbed(nn.Module):
"""
2D Image to Patch Embedding with support for Lumina-T2X
Args:
patch_size (`int`, defaults to `2`): The size of the patches.
in_channels (`int`, defaults to `4`): The number of input channels.
embed_dim (`int`, defaults to `768`): The output dim... | 861 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
Returns:
Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], torch.Tensor]: A tuple containing the patchified
and embedded tensor(s), the mask indicating the valid patches, the original image size(s), and the
frequency tensor(s).
"""
freqs_cis = freqs_cis.to(x[0]... | 861 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class CogVideoXPatchEmbed(nn.Module):
def __init__(
self,
patch_size: int = 2,
patch_size_t: Optional[int] = None,
in_channels: int = 16,
embed_dim: int = 1920,
text_embed_dim: int = 4096,
bias: bool = True,
sample_width: int = 90,
sample_heigh... | 862 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
self.patch_size = patch_size
self.patch_size_t = patch_size_t
self.embed_dim = embed_dim
self.sample_height = sample_height
self.sample_width = sample_width
self.sample_frames = sample_frames
self.temporal_compression_ratio = temporal_compression_ratio
self.max_te... | 862 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
if patch_size_t is None:
# CogVideoX 1.0 checkpoints
self.proj = nn.Conv2d(
in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
)
else:
# CogVideoX 1.5 checkpoints
self.proj = nn.Linear(in_channels... | 862 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def _get_positional_embeddings(
self, sample_height: int, sample_width: int, sample_frames: int, device: Optional[torch.device] = None
) -> torch.Tensor:
post_patch_height = sample_height // self.patch_size
post_patch_width = sample_width // self.patch_size
post_time_compression_fram... | 862 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
pos_embedding = get_3d_sincos_pos_embed(
self.embed_dim,
(post_patch_width, post_patch_height),
post_time_compression_frames,
self.spatial_interpolation_scale,
self.temporal_interpolation_scale,
device=device,
output_type="pt",
... | 862 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor):
r"""
Args:
text_embeds (`torch.Tensor`):
Input text embeddings. Expected shape: (batch_size, seq_length, embedding_dim).
image_embeds (`torch.Tensor`):
Input image embeddings... | 862 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
if self.patch_size_t is None:
image_embeds = image_embeds.reshape(-1, channels, height, width)
image_embeds = self.proj(image_embeds)
image_embeds = image_embeds.view(batch_size, num_frames, *image_embeds.shape[1:])
image_embeds = image_embeds.flatten(3).transpose(2, 3) ... | 862 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
embeds = torch.cat(
[text_embeds, image_embeds], dim=1
).contiguous() # [batch, seq_length + num_frames x height x width, channels]
if self.use_positional_embeddings or self.use_learned_positional_embeddings:
if self.use_learned_positional_embeddings and (self.sample_width != w... | 862 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
if (
self.sample_height != height
or self.sample_width != width
or self.sample_frames != pre_time_compression_frames
):
pos_embedding = self._get_positional_embeddings(
height, width, pre_time_compression_frames, device=embe... | 862 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class CogView3PlusPatchEmbed(nn.Module):
def __init__(
self,
in_channels: int = 16,
hidden_size: int = 2560,
patch_size: int = 2,
text_hidden_size: int = 4096,
pos_embed_max_size: int = 128,
):
super().__init__()
self.in_channels = in_channels
... | 863 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
pos_embed = get_2d_sincos_pos_embed(
hidden_size, pos_embed_max_size, base_size=pos_embed_max_size, output_type="pt"
)
pos_embed = pos_embed.reshape(pos_embed_max_size, pos_embed_max_size, hidden_size)
self.register_buffer("pos_embed", pos_embed.float(), persistent=False)
def fo... | 863 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
height = height // self.patch_size
width = width // self.patch_size
hidden_states = hidden_states.view(batch_size, channel, height, self.patch_size, width, self.patch_size)
hidden_states = hidden_states.permute(0, 2, 4, 1, 3, 5).contiguous()
hidden_states = hidden_states.view(batch_size,... | 863 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
image_pos_embed = self.pos_embed[:height, :width].reshape(height * width, -1)
text_pos_embed = torch.zeros(
(text_length, self.hidden_size), dtype=image_pos_embed.dtype, device=image_pos_embed.device
)
pos_embed = torch.cat([text_pos_embed, image_pos_embed], dim=0)[None, ...]
... | 863 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class FluxPosEmbed(nn.Module):
# modified from https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/modules/layers.py#L11
def __init__(self, theta: int, axes_dim: List[int]):
super().__init__()
self.theta = theta
self.axes_dim = axes_dim | 864 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def forward(self, ids: torch.Tensor) -> torch.Tensor:
n_axes = ids.shape[-1]
cos_out = []
sin_out = []
pos = ids.float()
is_mps = ids.device.type == "mps"
is_npu = ids.device.type == "npu"
freqs_dtype = torch.float32 if (is_mps or is_npu) else torch.float64
... | 864 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class TimestepEmbedding(nn.Module):
def __init__(
self,
in_channels: int,
time_embed_dim: int,
act_fn: str = "silu",
out_dim: int = None,
post_act_fn: Optional[str] = None,
cond_proj_dim=None,
sample_proj_bias=True,
):
super().__init__()
... | 865 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def forward(self, sample, condition=None):
if condition is not None:
sample = sample + self.cond_proj(condition)
sample = self.linear_1(sample)
if self.act is not None:
sample = self.act(sample)
sample = self.linear_2(sample)
if self.post_act is not Non... | 865 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class Timesteps(nn.Module):
def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1):
super().__init__()
self.num_channels = num_channels
self.flip_sin_to_cos = flip_sin_to_cos
self.downscale_freq_shift = downscale_freq_shift
s... | 866 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class GaussianFourierProjection(nn.Module):
"""Gaussian Fourier embeddings for noise levels."""
def __init__(
self, embedding_size: int = 256, scale: float = 1.0, set_W_to_weight=True, log=True, flip_sin_to_cos=False
):
super().__init__()
self.weight = nn.Parameter(torch.randn(embed... | 867 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
if self.flip_sin_to_cos:
out = torch.cat([torch.cos(x_proj), torch.sin(x_proj)], dim=-1)
else:
out = torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
return out | 867 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class SinusoidalPositionalEmbedding(nn.Module):
"""Apply positional information to a sequence of embeddings.
Takes in a sequence of embeddings with shape (batch_size, seq_length, embed_dim) and adds positional embeddings to
them
Args:
embed_dim: (int): Dimension of the positional embedding.
... | 868 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class ImagePositionalEmbeddings(nn.Module):
"""
Converts latent image classes into vector embeddings. Sums the vector embeddings with positional embeddings for the
height and width of the latent space.
For more details, see figure 10 of the dall-e paper: https://arxiv.org/abs/2102.12092
For VQ-dif... | 869 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def __init__(
self,
num_embed: int,
height: int,
width: int,
embed_dim: int,
):
super().__init__()
self.height = height
self.width = width
self.num_embed = num_embed
self.embed_dim = embed_dim
self.emb = nn.Embedding(self.num_... | 869 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
# 1 x H x W x D -> 1 x L xD
pos_emb = pos_emb.view(1, self.height * self.width, -1)
emb = emb + pos_emb[:, : emb.shape[1], :]
return emb | 869 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class LabelEmbedding(nn.Module):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
Args:
num_classes (`int`): The number of classes.
hidden_size (`int`): The size of the vector embeddings.
dropout_prob (`float`): The probab... | 870 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def token_drop(self, labels, force_drop_ids=None):
"""
Drops labels to enable classifier-free guidance.
"""
if force_drop_ids is None:
drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
else:
drop_ids = torch.tensor(force_drop... | 870 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class TextImageProjection(nn.Module):
def __init__(
self,
text_embed_dim: int = 1024,
image_embed_dim: int = 768,
cross_attention_dim: int = 768,
num_image_text_embeds: int = 10,
):
super().__init__()
self.num_image_text_embeds = num_image_text_embeds
... | 871 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class ImageProjection(nn.Module):
def __init__(
self,
image_embed_dim: int = 768,
cross_attention_dim: int = 768,
num_image_text_embeds: int = 32,
):
super().__init__()
self.num_image_text_embeds = num_image_text_embeds
self.image_embeds = nn.Linear(image... | 872 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class IPAdapterFullImageProjection(nn.Module):
def __init__(self, image_embed_dim=1024, cross_attention_dim=1024):
super().__init__()
from .attention import FeedForward
self.ff = FeedForward(image_embed_dim, cross_attention_dim, mult=1, activation_fn="gelu")
self.norm = nn.LayerNorm... | 873 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class IPAdapterFaceIDImageProjection(nn.Module):
def __init__(self, image_embed_dim=1024, cross_attention_dim=1024, mult=1, num_tokens=1):
super().__init__()
from .attention import FeedForward
self.num_tokens = num_tokens
self.cross_attention_dim = cross_attention_dim
self.f... | 874 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class CombinedTimestepLabelEmbeddings(nn.Module):
def __init__(self, num_classes, embedding_dim, class_dropout_prob=0.1):
super().__init__()
self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=1)
self.timestep_embedder = TimestepEmbedding(in_channels=256,... | 875 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class CombinedTimestepTextProjEmbeddings(nn.Module):
def __init__(self, embedding_dim, pooled_projection_dim):
super().__init__()
self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed... | 876 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class CombinedTimestepGuidanceTextProjEmbeddings(nn.Module):
def __init__(self, embedding_dim, pooled_projection_dim):
super().__init__()
self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
self.timestep_embedder = TimestepEmbedding(in_channels=256, ti... | 877 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
pooled_projections = self.text_embedder(pooled_projection)
conditioning = time_guidance_emb + pooled_projections
return conditioning | 877 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class CogView3CombinedTimestepSizeEmbeddings(nn.Module):
def __init__(self, embedding_dim: int, condition_dim: int, pooled_projection_dim: int, timesteps_dim: int = 256):
super().__init__()
self.time_proj = Timesteps(num_channels=timesteps_dim, flip_sin_to_cos=True, downscale_freq_shift=0)
... | 878 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
original_size_proj = self.condition_proj(original_size.flatten()).view(original_size.size(0), -1)
crop_coords_proj = self.condition_proj(crop_coords.flatten()).view(crop_coords.size(0), -1)
target_size_proj = self.condition_proj(target_size.flatten()).view(target_size.size(0), -1)
# (B, 3 * con... | 878 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class HunyuanDiTAttentionPool(nn.Module):
# Copied from https://github.com/Tencent/HunyuanDiT/blob/cb709308d92e6c7e8d59d0dff41b74d35088db6a/hydit/modules/poolers.py#L6
def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
super().__init__()
self.positiona... | 879 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def forward(self, x):
x = x.permute(1, 0, 2) # NLC -> LNC
x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (L+1)NC
x = x + self.positional_embedding[:, None, :].to(x.dtype) # (L+1)NC
x, _ = F.multi_head_attention_forward(
query=x[:1],
key=x,
... | 879 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
return x.squeeze(0) | 879 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class HunyuanCombinedTimestepTextSizeStyleEmbedding(nn.Module):
def __init__(
self,
embedding_dim,
pooled_projection_dim=1024,
seq_len=256,
cross_attention_dim=2048,
use_style_cond_and_image_meta_size=True,
):
super().__init__()
self.time_proj = T... | 880 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
# Here we use a default learned embedder layer for future extension.
self.use_style_cond_and_image_meta_size = use_style_cond_and_image_meta_size
if use_style_cond_and_image_meta_size:
self.style_embedder = nn.Embedding(1, embedding_dim)
extra_in_dim = 256 * 6 + embedding_dim + p... | 880 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
if self.use_style_cond_and_image_meta_size:
# extra condition2: image meta size embedding
image_meta_size = self.size_proj(image_meta_size.view(-1))
image_meta_size = image_meta_size.to(dtype=hidden_dtype)
image_meta_size = image_meta_size.view(-1, 6 * 256) # (N, 1536)
... | 880 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class LuminaCombinedTimestepCaptionEmbedding(nn.Module):
def __init__(self, hidden_size=4096, cross_attention_dim=2048, frequency_embedding_size=256):
super().__init__()
self.time_proj = Timesteps(
num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0
... | 881 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
# caption condition embedding:
caption_mask_float = caption_mask.float().unsqueeze(-1)
caption_feats_pool = (caption_feat * caption_mask_float).sum(dim=1) / caption_mask_float.sum(dim=1)
caption_feats_pool = caption_feats_pool.to(caption_feat)
caption_embed = self.caption_embedder(captio... | 881 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class MochiCombinedTimestepCaptionEmbedding(nn.Module):
def __init__(
self,
embedding_dim: int,
pooled_projection_dim: int,
text_embed_dim: int,
time_embed_dim: int = 256,
num_attention_heads: int = 8,
) -> None:
super().__init__()
self.time_proj ... | 882 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def forward(
self,
timestep: torch.LongTensor,
encoder_hidden_states: torch.Tensor,
encoder_attention_mask: torch.Tensor,
hidden_dtype: Optional[torch.dtype] = None,
):
time_proj = self.time_proj(timestep)
time_emb = self.timestep_embedder(time_proj.to(dtype=h... | 882 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class TextTimeEmbedding(nn.Module):
def __init__(self, encoder_dim: int, time_embed_dim: int, num_heads: int = 64):
super().__init__()
self.norm1 = nn.LayerNorm(encoder_dim)
self.pool = AttentionPooling(num_heads, encoder_dim)
self.proj = nn.Linear(encoder_dim, time_embed_dim)
... | 883 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class TextImageTimeEmbedding(nn.Module):
def __init__(self, text_embed_dim: int = 768, image_embed_dim: int = 768, time_embed_dim: int = 1536):
super().__init__()
self.text_proj = nn.Linear(text_embed_dim, time_embed_dim)
self.text_norm = nn.LayerNorm(time_embed_dim)
self.image_proj ... | 884 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class ImageTimeEmbedding(nn.Module):
def __init__(self, image_embed_dim: int = 768, time_embed_dim: int = 1536):
super().__init__()
self.image_proj = nn.Linear(image_embed_dim, time_embed_dim)
self.image_norm = nn.LayerNorm(time_embed_dim)
def forward(self, image_embeds: torch.Tensor):
... | 885 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class ImageHintTimeEmbedding(nn.Module):
def __init__(self, image_embed_dim: int = 768, time_embed_dim: int = 1536):
super().__init__()
self.image_proj = nn.Linear(image_embed_dim, time_embed_dim)
self.image_norm = nn.LayerNorm(time_embed_dim)
self.input_hint_block = nn.Sequential(
... | 886 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def forward(self, image_embeds: torch.Tensor, hint: torch.Tensor):
# image
time_image_embeds = self.image_proj(image_embeds)
time_image_embeds = self.image_norm(time_image_embeds)
hint = self.input_hint_block(hint)
return time_image_embeds, hint | 886 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class AttentionPooling(nn.Module):
# Copied from https://github.com/deep-floyd/IF/blob/2f91391f27dd3c468bf174be5805b4cc92980c0b/deepfloyd_if/model/nn.py#L54
def __init__(self, num_heads, embed_dim, dtype=None):
super().__init__()
self.dtype = dtype
self.positional_embedding = nn.Paramet... | 887 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def shape(x):
# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
x = x.view(bs, -1, self.num_heads, self.dim_per_head)
# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
x = x.transpose(1, 2)
# (bs, n_heads, length, dim_pe... | 887 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
# (bs*n_heads, class_token_length, length+class_token_length):
scale = 1 / math.sqrt(math.sqrt(self.dim_per_head))
weight = torch.einsum("bct,bcs->bts", q * scale, k * scale) # More stable with f16 than dividing afterwards
weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
... | 887 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class MochiAttentionPool(nn.Module):
def __init__(
self,
num_attention_heads: int,
embed_dim: int,
output_dim: Optional[int] = None,
) -> None:
super().__init__()
self.output_dim = output_dim or embed_dim
self.num_attention_heads = num_attention_heads
... | 888 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
Returns:
pooled: (B, D) tensor of pooled tokens.
"""
assert x.size(1) == mask.size(1) # Expected mask to have same length as tokens.
assert x.size(0) == mask.size(0) # Expected mask to have same batch size as tokens.
mask = mask[:, :, None].to(dtype=x.dtype)
mask = ... | 888 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
# Construct attention mask, shape: (B, 1, num_queries=1, num_keys=1+L).
attn_mask = mask[:, None, None, :].bool() # (B, 1, 1, L).
attn_mask = F.pad(attn_mask, (1, 0), value=True) # (B, 1, 1, 1+L).
# Average non-padding token features. These will be used as the query.
x_pool = self.poo... | 888 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
# Extract heads.
head_dim = D // self.num_attention_heads
kv = kv.unflatten(2, (2, self.num_attention_heads, head_dim)) # (B, 1+L, 2, H, head_dim)
kv = kv.transpose(1, 3) # (B, H, 2, 1+L, head_dim)
k, v = kv.unbind(2) # (B, H, 1+L, head_dim)
q = q.unflatten(1, (self.num_attent... | 888 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class GLIGENTextBoundingboxProjection(nn.Module):
def __init__(self, positive_len, out_dim, feature_type="text-only", fourier_freqs=8):
super().__init__()
self.positive_len = positive_len
self.out_dim = out_dim
self.fourier_embedder_dim = fourier_freqs
self.position_dim = fo... | 889 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
elif feature_type == "text-image":
self.linears_text = nn.Sequential(
nn.Linear(self.positive_len + self.position_dim, 512),
nn.SiLU(),
nn.Linear(512, 512),
nn.SiLU(),
nn.Linear(512, out_dim),
)
self.line... | 889 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def forward(
self,
boxes,
masks,
positive_embeddings=None,
phrases_masks=None,
image_masks=None,
phrases_embeddings=None,
image_embeddings=None,
):
masks = masks.unsqueeze(-1)
# embedding position (it may includes padding as placeholde... | 889 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
# replace padding with learnable null embedding
positive_embeddings = positive_embeddings * masks + (1 - masks) * positive_null
objs = self.linears(torch.cat([positive_embeddings, xyxy_embedding], dim=-1))
# positionet with text and image information
else:
phrases_m... | 889 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
objs_text = self.linears_text(torch.cat([phrases_embeddings, xyxy_embedding], dim=-1))
objs_image = self.linears_image(torch.cat([image_embeddings, xyxy_embedding], dim=-1))
objs = torch.cat([objs_text, objs_image], dim=1)
return objs | 889 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
"""
For PixArt-Alpha.
Reference:
https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29
"""
def __init__(self, embedding_dim, size_emb_dim, use_additi... | 890 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
self.use_additional_conditions = use_additional_conditions
if use_additional_conditions:
self.additional_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
self.resolution_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)
... | 890 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
if self.use_additional_conditions:
resolution_emb = self.additional_condition_proj(resolution.flatten()).to(hidden_dtype)
resolution_emb = self.resolution_embedder(resolution_emb).reshape(batch_size, -1)
aspect_ratio_emb = self.additional_condition_proj(aspect_ratio.flatten()).to(hid... | 890 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class PixArtAlphaTextProjection(nn.Module):
"""
Projects caption embeddings. Also handles dropout for classifier-free guidance.
Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
"""
def __init__(self, in_features, hidden_size, out_features=... | 891 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def forward(self, caption):
hidden_states = self.linear_1(caption)
hidden_states = self.act_1(hidden_states)
hidden_states = self.linear_2(hidden_states)
return hidden_states | 891 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class IPAdapterPlusImageProjectionBlock(nn.Module):
def __init__(
self,
embed_dims: int = 768,
dim_head: int = 64,
heads: int = 16,
ffn_ratio: float = 4,
) -> None:
super().__init__()
from .attention import FeedForward
self.ln0 = nn.LayerNorm(embe... | 892 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def forward(self, x, latents, residual):
encoder_hidden_states = self.ln0(x)
latents = self.ln1(latents)
encoder_hidden_states = torch.cat([encoder_hidden_states, latents], dim=-2)
latents = self.attn(latents, encoder_hidden_states) + residual
latents = self.ff(latents) + latents... | 892 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class IPAdapterPlusImageProjection(nn.Module):
"""Resampler of IP-Adapter Plus.
Args:
embed_dims (int): The feature dimension. Defaults to 768. output_dims (int): The number of output channels,
that is the same
number of the channels in the `unet.config.cross_attention_dim`. Default... | 893 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def __init__(
self,
embed_dims: int = 768,
output_dims: int = 1024,
hidden_dims: int = 1280,
depth: int = 4,
dim_head: int = 64,
heads: int = 16,
num_queries: int = 8,
ffn_ratio: float = 4,
) -> None:
super().__init__()
self.lat... | 893 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
x = self.proj_in(x)
for block in self.layers:
residual = latents
latents = block(x, latents, residual)
latents = self.proj_out(latents)
return self.norm_out(latents) | 893 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
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