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class IPAdapterFaceIDPlusImageProjection(nn.Module):
"""FacePerceiverResampler 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`. Defaults to 1024.
hidden_dims (int):
The number of hidden channels. Defaults to 1280. depth (int): The number of blocks. Defaults
to 8. dim_head (int): The number of head channels. Defaults to 64. heads (int): Parallel attention heads.
Defaults to 16. num_tokens (int): Number of tokens num_queries (int): The number of queries. Defaults to 8.
ffn_ratio (float): The expansion ratio of feedforward network hidden
layer channels. Defaults to 4.
ffproj_ratio (float): The expansion ratio of feedforward network hidden
layer channels (for ID embeddings). Defaults to 4.
""" | 894 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def __init__(
self,
embed_dims: int = 768,
output_dims: int = 768,
hidden_dims: int = 1280,
id_embeddings_dim: int = 512,
depth: int = 4,
dim_head: int = 64,
heads: int = 16,
num_tokens: int = 4,
num_queries: int = 8,
ffn_ratio: float = 4,
ffproj_ratio: int = 2,
) -> None:
super().__init__()
from .attention import FeedForward
self.num_tokens = num_tokens
self.embed_dim = embed_dims
self.clip_embeds = None
self.shortcut = False
self.shortcut_scale = 1.0
self.proj = FeedForward(id_embeddings_dim, embed_dims * num_tokens, activation_fn="gelu", mult=ffproj_ratio)
self.norm = nn.LayerNorm(embed_dims)
self.proj_in = nn.Linear(hidden_dims, embed_dims)
self.proj_out = nn.Linear(embed_dims, output_dims)
self.norm_out = nn.LayerNorm(output_dims) | 894 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
self.layers = nn.ModuleList(
[IPAdapterPlusImageProjectionBlock(embed_dims, dim_head, heads, ffn_ratio) for _ in range(depth)]
)
def forward(self, id_embeds: torch.Tensor) -> torch.Tensor:
"""Forward pass.
Args:
id_embeds (torch.Tensor): Input Tensor (ID embeds).
Returns:
torch.Tensor: Output Tensor.
"""
id_embeds = id_embeds.to(self.clip_embeds.dtype)
id_embeds = self.proj(id_embeds)
id_embeds = id_embeds.reshape(-1, self.num_tokens, self.embed_dim)
id_embeds = self.norm(id_embeds)
latents = id_embeds
clip_embeds = self.proj_in(self.clip_embeds)
x = clip_embeds.reshape(-1, clip_embeds.shape[2], clip_embeds.shape[3])
for block in self.layers:
residual = latents
latents = block(x, latents, residual) | 894 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
latents = self.proj_out(latents)
out = self.norm_out(latents)
if self.shortcut:
out = id_embeds + self.shortcut_scale * out
return out | 894 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class IPAdapterTimeImageProjectionBlock(nn.Module):
"""Block for IPAdapterTimeImageProjection.
Args:
hidden_dim (`int`, defaults to 1280):
The number of hidden channels.
dim_head (`int`, defaults to 64):
The number of head channels.
heads (`int`, defaults to 20):
Parallel attention heads.
ffn_ratio (`int`, defaults to 4):
The expansion ratio of feedforward network hidden layer channels.
"""
def __init__(
self,
hidden_dim: int = 1280,
dim_head: int = 64,
heads: int = 20,
ffn_ratio: int = 4,
) -> None:
super().__init__()
from .attention import FeedForward | 895 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
self.ln0 = nn.LayerNorm(hidden_dim)
self.ln1 = nn.LayerNorm(hidden_dim)
self.attn = Attention(
query_dim=hidden_dim,
cross_attention_dim=hidden_dim,
dim_head=dim_head,
heads=heads,
bias=False,
out_bias=False,
)
self.ff = FeedForward(hidden_dim, hidden_dim, activation_fn="gelu", mult=ffn_ratio, bias=False)
# AdaLayerNorm
self.adaln_silu = nn.SiLU()
self.adaln_proj = nn.Linear(hidden_dim, 4 * hidden_dim)
self.adaln_norm = nn.LayerNorm(hidden_dim)
# Set attention scale and fuse KV
self.attn.scale = 1 / math.sqrt(math.sqrt(dim_head))
self.attn.fuse_projections()
self.attn.to_k = None
self.attn.to_v = None
def forward(self, x: torch.Tensor, latents: torch.Tensor, timestep_emb: torch.Tensor) -> torch.Tensor:
"""Forward pass. | 895 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
Args:
x (`torch.Tensor`):
Image features.
latents (`torch.Tensor`):
Latent features.
timestep_emb (`torch.Tensor`):
Timestep embedding.
Returns:
`torch.Tensor`: Output latent features.
"""
# Shift and scale for AdaLayerNorm
emb = self.adaln_proj(self.adaln_silu(timestep_emb))
shift_msa, scale_msa, shift_mlp, scale_mlp = emb.chunk(4, dim=1)
# Fused Attention
residual = latents
x = self.ln0(x)
latents = self.ln1(latents) * (1 + scale_msa[:, None]) + shift_msa[:, None]
batch_size = latents.shape[0]
query = self.attn.to_q(latents)
kv_input = torch.cat((x, latents), dim=-2)
key, value = self.attn.to_kv(kv_input).chunk(2, dim=-1)
inner_dim = key.shape[-1]
head_dim = inner_dim // self.attn.heads | 895 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
query = query.view(batch_size, -1, self.attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, self.attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, self.attn.heads, head_dim).transpose(1, 2)
weight = (query * self.attn.scale) @ (key * self.attn.scale).transpose(-2, -1)
weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
latents = weight @ value
latents = latents.transpose(1, 2).reshape(batch_size, -1, self.attn.heads * head_dim)
latents = self.attn.to_out[0](latents)
latents = self.attn.to_out[1](latents)
latents = latents + residual
## FeedForward
residual = latents
latents = self.adaln_norm(latents) * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
return self.ff(latents) + residual | 895 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class IPAdapterTimeImageProjection(nn.Module):
"""Resampler of SD3 IP-Adapter with timestep embedding. | 896 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
Args:
embed_dim (`int`, defaults to 1152):
The feature dimension.
output_dim (`int`, defaults to 2432):
The number of output channels.
hidden_dim (`int`, defaults to 1280):
The number of hidden channels.
depth (`int`, defaults to 4):
The number of blocks.
dim_head (`int`, defaults to 64):
The number of head channels.
heads (`int`, defaults to 20):
Parallel attention heads.
num_queries (`int`, defaults to 64):
The number of queries.
ffn_ratio (`int`, defaults to 4):
The expansion ratio of feedforward network hidden layer channels.
timestep_in_dim (`int`, defaults to 320):
The number of input channels for timestep embedding.
timestep_flip_sin_to_cos (`bool`, defaults to True):
Flip the timestep embedding order to `cos, sin` (if True) or `sin, cos` (if False). | 896 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
timestep_freq_shift (`int`, defaults to 0):
Controls the timestep delta between frequencies between dimensions.
""" | 896 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def __init__(
self,
embed_dim: int = 1152,
output_dim: int = 2432,
hidden_dim: int = 1280,
depth: int = 4,
dim_head: int = 64,
heads: int = 20,
num_queries: int = 64,
ffn_ratio: int = 4,
timestep_in_dim: int = 320,
timestep_flip_sin_to_cos: bool = True,
timestep_freq_shift: int = 0,
) -> None:
super().__init__()
self.latents = nn.Parameter(torch.randn(1, num_queries, hidden_dim) / hidden_dim**0.5)
self.proj_in = nn.Linear(embed_dim, hidden_dim)
self.proj_out = nn.Linear(hidden_dim, output_dim)
self.norm_out = nn.LayerNorm(output_dim)
self.layers = nn.ModuleList(
[IPAdapterTimeImageProjectionBlock(hidden_dim, dim_head, heads, ffn_ratio) for _ in range(depth)]
)
self.time_proj = Timesteps(timestep_in_dim, timestep_flip_sin_to_cos, timestep_freq_shift) | 896 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
self.time_embedding = TimestepEmbedding(timestep_in_dim, hidden_dim, act_fn="silu") | 896 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
def forward(self, x: torch.Tensor, timestep: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""Forward pass.
Args:
x (`torch.Tensor`):
Image features.
timestep (`torch.Tensor`):
Timestep in denoising process.
Returns:
`Tuple`[`torch.Tensor`, `torch.Tensor`]: The pair (latents, timestep_emb).
"""
timestep_emb = self.time_proj(timestep).to(dtype=x.dtype)
timestep_emb = self.time_embedding(timestep_emb)
latents = self.latents.repeat(x.size(0), 1, 1)
x = self.proj_in(x)
x = x + timestep_emb[:, None]
for block in self.layers:
latents = block(x, latents, timestep_emb)
latents = self.proj_out(latents)
latents = self.norm_out(latents)
return latents, timestep_emb | 896 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class MultiIPAdapterImageProjection(nn.Module):
def __init__(self, IPAdapterImageProjectionLayers: Union[List[nn.Module], Tuple[nn.Module]]):
super().__init__()
self.image_projection_layers = nn.ModuleList(IPAdapterImageProjectionLayers)
def forward(self, image_embeds: List[torch.Tensor]):
projected_image_embeds = [] | 897 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
# currently, we accept `image_embeds` as
# 1. a tensor (deprecated) with shape [batch_size, embed_dim] or [batch_size, sequence_length, embed_dim]
# 2. list of `n` tensors where `n` is number of ip-adapters, each tensor can hae shape [batch_size, num_images, embed_dim] or [batch_size, num_images, sequence_length, embed_dim]
if not isinstance(image_embeds, list):
deprecation_message = (
"You have passed a tensor as `image_embeds`.This is deprecated and will be removed in a future release."
" Please make sure to update your script to pass `image_embeds` as a list of tensors to suppress this warning."
)
deprecate("image_embeds not a list", "1.0.0", deprecation_message, standard_warn=False)
image_embeds = [image_embeds.unsqueeze(1)] | 897 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
if len(image_embeds) != len(self.image_projection_layers):
raise ValueError(
f"image_embeds must have the same length as image_projection_layers, got {len(image_embeds)} and {len(self.image_projection_layers)}"
)
for image_embed, image_projection_layer in zip(image_embeds, self.image_projection_layers):
batch_size, num_images = image_embed.shape[0], image_embed.shape[1]
image_embed = image_embed.reshape((batch_size * num_images,) + image_embed.shape[2:])
image_embed = image_projection_layer(image_embed)
image_embed = image_embed.reshape((batch_size, num_images) + image_embed.shape[1:])
projected_image_embeds.append(image_embed)
return projected_image_embeds | 897 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py |
class ResnetBlockCondNorm2D(nn.Module):
r"""
A Resnet block that use normalization layer that incorporate conditioning information. | 898 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
Parameters:
in_channels (`int`): The number of channels in the input.
out_channels (`int`, *optional*, default to be `None`):
The number of output channels for the first conv2d layer. If None, same as `in_channels`.
dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
groups_out (`int`, *optional*, default to None):
The number of groups to use for the second normalization layer. if set to None, same as `groups`.
eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use.
time_embedding_norm (`str`, *optional*, default to `"ada_group"` ): | 898 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
The normalization layer for time embedding `temb`. Currently only support "ada_group" or "spatial".
kernel (`torch.Tensor`, optional, default to None): FIR filter, see
[`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].
output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.
use_in_shortcut (`bool`, *optional*, default to `True`):
If `True`, add a 1x1 nn.conv2d layer for skip-connection.
up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer.
down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer.
conv_shortcut_bias (`bool`, *optional*, default to `True`): If `True`, adds a learnable bias to the
`conv_shortcut` output.
conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output.
If None, same as `out_channels`.
""" | 898 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
def __init__(
self,
*,
in_channels: int,
out_channels: Optional[int] = None,
conv_shortcut: bool = False,
dropout: float = 0.0,
temb_channels: int = 512,
groups: int = 32,
groups_out: Optional[int] = None,
eps: float = 1e-6,
non_linearity: str = "swish",
time_embedding_norm: str = "ada_group", # ada_group, spatial
output_scale_factor: float = 1.0,
use_in_shortcut: Optional[bool] = None,
up: bool = False,
down: bool = False,
conv_shortcut_bias: bool = True,
conv_2d_out_channels: Optional[int] = None,
):
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.up = up
self.down = down
self.output_scale_factor = output_scale_factor | 898 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
self.time_embedding_norm = time_embedding_norm | 898 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
if groups_out is None:
groups_out = groups
if self.time_embedding_norm == "ada_group": # ada_group
self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps)
elif self.time_embedding_norm == "spatial":
self.norm1 = SpatialNorm(in_channels, temb_channels)
else:
raise ValueError(f" unsupported time_embedding_norm: {self.time_embedding_norm}")
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
if self.time_embedding_norm == "ada_group": # ada_group
self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps)
elif self.time_embedding_norm == "spatial": # spatial
self.norm2 = SpatialNorm(out_channels, temb_channels)
else:
raise ValueError(f" unsupported time_embedding_norm: {self.time_embedding_norm}")
self.dropout = torch.nn.Dropout(dropout) | 898 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
conv_2d_out_channels = conv_2d_out_channels or out_channels
self.conv2 = nn.Conv2d(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)
self.nonlinearity = get_activation(non_linearity)
self.upsample = self.downsample = None
if self.up:
self.upsample = Upsample2D(in_channels, use_conv=False)
elif self.down:
self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut
self.conv_shortcut = None
if self.use_in_shortcut:
self.conv_shortcut = nn.Conv2d(
in_channels,
conv_2d_out_channels,
kernel_size=1,
stride=1,
padding=0,
bias=conv_shortcut_bias,
) | 898 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
def forward(self, input_tensor: torch.Tensor, temb: torch.Tensor, *args, **kwargs) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
hidden_states = input_tensor
hidden_states = self.norm1(hidden_states, temb)
hidden_states = self.nonlinearity(hidden_states) | 898 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
if self.upsample is not None:
# upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
if hidden_states.shape[0] >= 64:
input_tensor = input_tensor.contiguous()
hidden_states = hidden_states.contiguous()
input_tensor = self.upsample(input_tensor)
hidden_states = self.upsample(hidden_states)
elif self.downsample is not None:
input_tensor = self.downsample(input_tensor)
hidden_states = self.downsample(hidden_states)
hidden_states = self.conv1(hidden_states)
hidden_states = self.norm2(hidden_states, temb)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.conv2(hidden_states)
if self.conv_shortcut is not None:
input_tensor = self.conv_shortcut(input_tensor) | 898 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
return output_tensor | 898 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
class ResnetBlock2D(nn.Module):
r"""
A Resnet block. | 899 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
Parameters:
in_channels (`int`): The number of channels in the input.
out_channels (`int`, *optional*, default to be `None`):
The number of output channels for the first conv2d layer. If None, same as `in_channels`.
dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
groups_out (`int`, *optional*, default to None):
The number of groups to use for the second normalization layer. if set to None, same as `groups`.
eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use.
time_embedding_norm (`str`, *optional*, default to `"default"` ): Time scale shift config. | 899 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
By default, apply timestep embedding conditioning with a simple shift mechanism. Choose "scale_shift" for a
stronger conditioning with scale and shift.
kernel (`torch.Tensor`, optional, default to None): FIR filter, see
[`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].
output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.
use_in_shortcut (`bool`, *optional*, default to `True`):
If `True`, add a 1x1 nn.conv2d layer for skip-connection.
up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer.
down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer.
conv_shortcut_bias (`bool`, *optional*, default to `True`): If `True`, adds a learnable bias to the
`conv_shortcut` output.
conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output. | 899 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
If None, same as `out_channels`.
""" | 899 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
def __init__(
self,
*,
in_channels: int,
out_channels: Optional[int] = None,
conv_shortcut: bool = False,
dropout: float = 0.0,
temb_channels: int = 512,
groups: int = 32,
groups_out: Optional[int] = None,
pre_norm: bool = True,
eps: float = 1e-6,
non_linearity: str = "swish",
skip_time_act: bool = False,
time_embedding_norm: str = "default", # default, scale_shift,
kernel: Optional[torch.Tensor] = None,
output_scale_factor: float = 1.0,
use_in_shortcut: Optional[bool] = None,
up: bool = False,
down: bool = False,
conv_shortcut_bias: bool = True,
conv_2d_out_channels: Optional[int] = None,
):
super().__init__()
if time_embedding_norm == "ada_group":
raise ValueError(
"This class cannot be used with `time_embedding_norm==ada_group`, please use `ResnetBlockCondNorm2D` instead",
) | 899 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
if time_embedding_norm == "spatial":
raise ValueError(
"This class cannot be used with `time_embedding_norm==spatial`, please use `ResnetBlockCondNorm2D` instead",
) | 899 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
self.pre_norm = True
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.up = up
self.down = down
self.output_scale_factor = output_scale_factor
self.time_embedding_norm = time_embedding_norm
self.skip_time_act = skip_time_act
if groups_out is None:
groups_out = groups
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) | 899 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
if temb_channels is not None:
if self.time_embedding_norm == "default":
self.time_emb_proj = nn.Linear(temb_channels, out_channels)
elif self.time_embedding_norm == "scale_shift":
self.time_emb_proj = nn.Linear(temb_channels, 2 * out_channels)
else:
raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
else:
self.time_emb_proj = None
self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
self.dropout = torch.nn.Dropout(dropout)
conv_2d_out_channels = conv_2d_out_channels or out_channels
self.conv2 = nn.Conv2d(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)
self.nonlinearity = get_activation(non_linearity) | 899 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
self.upsample = self.downsample = None
if self.up:
if kernel == "fir":
fir_kernel = (1, 3, 3, 1)
self.upsample = lambda x: upsample_2d(x, kernel=fir_kernel)
elif kernel == "sde_vp":
self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest")
else:
self.upsample = Upsample2D(in_channels, use_conv=False)
elif self.down:
if kernel == "fir":
fir_kernel = (1, 3, 3, 1)
self.downsample = lambda x: downsample_2d(x, kernel=fir_kernel)
elif kernel == "sde_vp":
self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2)
else:
self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut | 899 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
self.conv_shortcut = None
if self.use_in_shortcut:
self.conv_shortcut = nn.Conv2d(
in_channels,
conv_2d_out_channels,
kernel_size=1,
stride=1,
padding=0,
bias=conv_shortcut_bias,
)
def forward(self, input_tensor: torch.Tensor, temb: torch.Tensor, *args, **kwargs) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
hidden_states = input_tensor
hidden_states = self.norm1(hidden_states)
hidden_states = self.nonlinearity(hidden_states) | 899 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
if self.upsample is not None:
# upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
if hidden_states.shape[0] >= 64:
input_tensor = input_tensor.contiguous()
hidden_states = hidden_states.contiguous()
input_tensor = self.upsample(input_tensor)
hidden_states = self.upsample(hidden_states)
elif self.downsample is not None:
input_tensor = self.downsample(input_tensor)
hidden_states = self.downsample(hidden_states)
hidden_states = self.conv1(hidden_states)
if self.time_emb_proj is not None:
if not self.skip_time_act:
temb = self.nonlinearity(temb)
temb = self.time_emb_proj(temb)[:, :, None, None] | 899 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
if self.time_embedding_norm == "default":
if temb is not None:
hidden_states = hidden_states + temb
hidden_states = self.norm2(hidden_states)
elif self.time_embedding_norm == "scale_shift":
if temb is None:
raise ValueError(
f" `temb` should not be None when `time_embedding_norm` is {self.time_embedding_norm}"
)
time_scale, time_shift = torch.chunk(temb, 2, dim=1)
hidden_states = self.norm2(hidden_states)
hidden_states = hidden_states * (1 + time_scale) + time_shift
else:
hidden_states = self.norm2(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.conv2(hidden_states)
if self.conv_shortcut is not None:
input_tensor = self.conv_shortcut(input_tensor) | 899 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
return output_tensor | 899 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
class Conv1dBlock(nn.Module):
"""
Conv1d --> GroupNorm --> Mish
Parameters:
inp_channels (`int`): Number of input channels.
out_channels (`int`): Number of output channels.
kernel_size (`int` or `tuple`): Size of the convolving kernel.
n_groups (`int`, default `8`): Number of groups to separate the channels into.
activation (`str`, defaults to `mish`): Name of the activation function.
"""
def __init__(
self,
inp_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int]],
n_groups: int = 8,
activation: str = "mish",
):
super().__init__()
self.conv1d = nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2)
self.group_norm = nn.GroupNorm(n_groups, out_channels)
self.mish = get_activation(activation) | 900 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
intermediate_repr = self.conv1d(inputs)
intermediate_repr = rearrange_dims(intermediate_repr)
intermediate_repr = self.group_norm(intermediate_repr)
intermediate_repr = rearrange_dims(intermediate_repr)
output = self.mish(intermediate_repr)
return output | 900 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
class ResidualTemporalBlock1D(nn.Module):
"""
Residual 1D block with temporal convolutions.
Parameters:
inp_channels (`int`): Number of input channels.
out_channels (`int`): Number of output channels.
embed_dim (`int`): Embedding dimension.
kernel_size (`int` or `tuple`): Size of the convolving kernel.
activation (`str`, defaults `mish`): It is possible to choose the right activation function.
"""
def __init__(
self,
inp_channels: int,
out_channels: int,
embed_dim: int,
kernel_size: Union[int, Tuple[int, int]] = 5,
activation: str = "mish",
):
super().__init__()
self.conv_in = Conv1dBlock(inp_channels, out_channels, kernel_size)
self.conv_out = Conv1dBlock(out_channels, out_channels, kernel_size)
self.time_emb_act = get_activation(activation)
self.time_emb = nn.Linear(embed_dim, out_channels) | 901 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
self.residual_conv = (
nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity()
)
def forward(self, inputs: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
"""
Args:
inputs : [ batch_size x inp_channels x horizon ]
t : [ batch_size x embed_dim ]
returns:
out : [ batch_size x out_channels x horizon ]
"""
t = self.time_emb_act(t)
t = self.time_emb(t)
out = self.conv_in(inputs) + rearrange_dims(t)
out = self.conv_out(out)
return out + self.residual_conv(inputs) | 901 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
class TemporalConvLayer(nn.Module):
"""
Temporal convolutional layer that can be used for video (sequence of images) input Code mostly copied from:
https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/models/multi_modal/video_synthesis/unet_sd.py#L1016
Parameters:
in_dim (`int`): Number of input channels.
out_dim (`int`): Number of output channels.
dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
"""
def __init__(
self,
in_dim: int,
out_dim: Optional[int] = None,
dropout: float = 0.0,
norm_num_groups: int = 32,
):
super().__init__()
out_dim = out_dim or in_dim
self.in_dim = in_dim
self.out_dim = out_dim | 902 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
# conv layers
self.conv1 = nn.Sequential(
nn.GroupNorm(norm_num_groups, in_dim),
nn.SiLU(),
nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding=(1, 0, 0)),
)
self.conv2 = nn.Sequential(
nn.GroupNorm(norm_num_groups, out_dim),
nn.SiLU(),
nn.Dropout(dropout),
nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
)
self.conv3 = nn.Sequential(
nn.GroupNorm(norm_num_groups, out_dim),
nn.SiLU(),
nn.Dropout(dropout),
nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
)
self.conv4 = nn.Sequential(
nn.GroupNorm(norm_num_groups, out_dim),
nn.SiLU(),
nn.Dropout(dropout),
nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
) | 902 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
# zero out the last layer params,so the conv block is identity
nn.init.zeros_(self.conv4[-1].weight)
nn.init.zeros_(self.conv4[-1].bias)
def forward(self, hidden_states: torch.Tensor, num_frames: int = 1) -> torch.Tensor:
hidden_states = (
hidden_states[None, :].reshape((-1, num_frames) + hidden_states.shape[1:]).permute(0, 2, 1, 3, 4)
)
identity = hidden_states
hidden_states = self.conv1(hidden_states)
hidden_states = self.conv2(hidden_states)
hidden_states = self.conv3(hidden_states)
hidden_states = self.conv4(hidden_states)
hidden_states = identity + hidden_states
hidden_states = hidden_states.permute(0, 2, 1, 3, 4).reshape(
(hidden_states.shape[0] * hidden_states.shape[2], -1) + hidden_states.shape[3:]
)
return hidden_states | 902 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
class TemporalResnetBlock(nn.Module):
r"""
A Resnet block.
Parameters:
in_channels (`int`): The number of channels in the input.
out_channels (`int`, *optional*, default to be `None`):
The number of output channels for the first conv2d layer. If None, same as `in_channels`.
temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
"""
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
temb_channels: int = 512,
eps: float = 1e-6,
):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
kernel_size = (3, 1, 1)
padding = [k // 2 for k in kernel_size] | 903 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
self.norm1 = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=eps, affine=True)
self.conv1 = nn.Conv3d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=1,
padding=padding,
)
if temb_channels is not None:
self.time_emb_proj = nn.Linear(temb_channels, out_channels)
else:
self.time_emb_proj = None
self.norm2 = torch.nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=eps, affine=True)
self.dropout = torch.nn.Dropout(0.0)
self.conv2 = nn.Conv3d(
out_channels,
out_channels,
kernel_size=kernel_size,
stride=1,
padding=padding,
)
self.nonlinearity = get_activation("silu")
self.use_in_shortcut = self.in_channels != out_channels | 903 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
self.conv_shortcut = None
if self.use_in_shortcut:
self.conv_shortcut = nn.Conv3d(
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0,
)
def forward(self, input_tensor: torch.Tensor, temb: torch.Tensor) -> torch.Tensor:
hidden_states = input_tensor
hidden_states = self.norm1(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.conv1(hidden_states)
if self.time_emb_proj is not None:
temb = self.nonlinearity(temb)
temb = self.time_emb_proj(temb)[:, :, :, None, None]
temb = temb.permute(0, 2, 1, 3, 4)
hidden_states = hidden_states + temb
hidden_states = self.norm2(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.conv2(hidden_states) | 903 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
if self.conv_shortcut is not None:
input_tensor = self.conv_shortcut(input_tensor)
output_tensor = input_tensor + hidden_states
return output_tensor | 903 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
class SpatioTemporalResBlock(nn.Module):
r"""
A SpatioTemporal Resnet block. | 904 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
Parameters:
in_channels (`int`): The number of channels in the input.
out_channels (`int`, *optional*, default to be `None`):
The number of output channels for the first conv2d layer. If None, same as `in_channels`.
temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the spatial resenet.
temporal_eps (`float`, *optional*, defaults to `eps`): The epsilon to use for the temporal resnet.
merge_factor (`float`, *optional*, defaults to `0.5`): The merge factor to use for the temporal mixing.
merge_strategy (`str`, *optional*, defaults to `learned_with_images`):
The merge strategy to use for the temporal mixing.
switch_spatial_to_temporal_mix (`bool`, *optional*, defaults to `False`):
If `True`, switch the spatial and temporal mixing.
""" | 904 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
temb_channels: int = 512,
eps: float = 1e-6,
temporal_eps: Optional[float] = None,
merge_factor: float = 0.5,
merge_strategy="learned_with_images",
switch_spatial_to_temporal_mix: bool = False,
):
super().__init__()
self.spatial_res_block = ResnetBlock2D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=eps,
)
self.temporal_res_block = TemporalResnetBlock(
in_channels=out_channels if out_channels is not None else in_channels,
out_channels=out_channels if out_channels is not None else in_channels,
temb_channels=temb_channels,
eps=temporal_eps if temporal_eps is not None else eps,
) | 904 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
self.time_mixer = AlphaBlender(
alpha=merge_factor,
merge_strategy=merge_strategy,
switch_spatial_to_temporal_mix=switch_spatial_to_temporal_mix,
)
def forward(
self,
hidden_states: torch.Tensor,
temb: Optional[torch.Tensor] = None,
image_only_indicator: Optional[torch.Tensor] = None,
):
num_frames = image_only_indicator.shape[-1]
hidden_states = self.spatial_res_block(hidden_states, temb)
batch_frames, channels, height, width = hidden_states.shape
batch_size = batch_frames // num_frames
hidden_states_mix = (
hidden_states[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)
)
hidden_states = (
hidden_states[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)
)
if temb is not None:
temb = temb.reshape(batch_size, num_frames, -1) | 904 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
hidden_states = self.temporal_res_block(hidden_states, temb)
hidden_states = self.time_mixer(
x_spatial=hidden_states_mix,
x_temporal=hidden_states,
image_only_indicator=image_only_indicator,
)
hidden_states = hidden_states.permute(0, 2, 1, 3, 4).reshape(batch_frames, channels, height, width)
return hidden_states | 904 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
class AlphaBlender(nn.Module):
r"""
A module to blend spatial and temporal features.
Parameters:
alpha (`float`): The initial value of the blending factor.
merge_strategy (`str`, *optional*, defaults to `learned_with_images`):
The merge strategy to use for the temporal mixing.
switch_spatial_to_temporal_mix (`bool`, *optional*, defaults to `False`):
If `True`, switch the spatial and temporal mixing.
"""
strategies = ["learned", "fixed", "learned_with_images"]
def __init__(
self,
alpha: float,
merge_strategy: str = "learned_with_images",
switch_spatial_to_temporal_mix: bool = False,
):
super().__init__()
self.merge_strategy = merge_strategy
self.switch_spatial_to_temporal_mix = switch_spatial_to_temporal_mix # For TemporalVAE
if merge_strategy not in self.strategies:
raise ValueError(f"merge_strategy needs to be in {self.strategies}") | 905 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
if self.merge_strategy == "fixed":
self.register_buffer("mix_factor", torch.Tensor([alpha]))
elif self.merge_strategy == "learned" or self.merge_strategy == "learned_with_images":
self.register_parameter("mix_factor", torch.nn.Parameter(torch.Tensor([alpha])))
else:
raise ValueError(f"Unknown merge strategy {self.merge_strategy}")
def get_alpha(self, image_only_indicator: torch.Tensor, ndims: int) -> torch.Tensor:
if self.merge_strategy == "fixed":
alpha = self.mix_factor
elif self.merge_strategy == "learned":
alpha = torch.sigmoid(self.mix_factor)
elif self.merge_strategy == "learned_with_images":
if image_only_indicator is None:
raise ValueError("Please provide image_only_indicator to use learned_with_images merge strategy") | 905 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
alpha = torch.where(
image_only_indicator.bool(),
torch.ones(1, 1, device=image_only_indicator.device),
torch.sigmoid(self.mix_factor)[..., None],
)
# (batch, channel, frames, height, width)
if ndims == 5:
alpha = alpha[:, None, :, None, None]
# (batch*frames, height*width, channels)
elif ndims == 3:
alpha = alpha.reshape(-1)[:, None, None]
else:
raise ValueError(f"Unexpected ndims {ndims}. Dimensions should be 3 or 5")
else:
raise NotImplementedError
return alpha
def forward(
self,
x_spatial: torch.Tensor,
x_temporal: torch.Tensor,
image_only_indicator: Optional[torch.Tensor] = None,
) -> torch.Tensor:
alpha = self.get_alpha(image_only_indicator, x_spatial.ndim)
alpha = alpha.to(x_spatial.dtype) | 905 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
if self.switch_spatial_to_temporal_mix:
alpha = 1.0 - alpha
x = alpha * x_spatial + (1.0 - alpha) * x_temporal
return x | 905 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py |
class ModelMixin(torch.nn.Module, PushToHubMixin):
r"""
Base class for all models.
[`ModelMixin`] takes care of storing the model configuration and provides methods for loading, downloading and
saving models.
- **config_name** ([`str`]) -- Filename to save a model to when calling [`~models.ModelMixin.save_pretrained`].
"""
config_name = CONFIG_NAME
_automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"]
_supports_gradient_checkpointing = False
_keys_to_ignore_on_load_unexpected = None
_no_split_modules = None
_keep_in_fp32_modules = None
def __init__(self):
super().__init__() | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
def __getattr__(self, name: str) -> Any:
"""The only reason we overwrite `getattr` here is to gracefully deprecate accessing
config attributes directly. See https://github.com/huggingface/diffusers/pull/3129 We need to overwrite
__getattr__ here in addition so that we don't trigger `torch.nn.Module`'s __getattr__':
https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module
"""
is_in_config = "_internal_dict" in self.__dict__ and hasattr(self.__dict__["_internal_dict"], name)
is_attribute = name in self.__dict__ | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
if is_in_config and not is_attribute:
deprecation_message = f"Accessing config attribute `{name}` directly via '{type(self).__name__}' object attribute is deprecated. Please access '{name}' over '{type(self).__name__}'s config object instead, e.g. 'unet.config.{name}'."
deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False, stacklevel=3)
return self._internal_dict[name]
# call PyTorch's https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module
return super().__getattr__(name)
@property
def is_gradient_checkpointing(self) -> bool:
"""
Whether gradient checkpointing is activated for this model or not.
"""
return any(hasattr(m, "gradient_checkpointing") and m.gradient_checkpointing for m in self.modules()) | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
def enable_gradient_checkpointing(self) -> None:
"""
Activates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or
*checkpoint activations* in other frameworks).
"""
if not self._supports_gradient_checkpointing:
raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
self.apply(partial(self._set_gradient_checkpointing, value=True))
def disable_gradient_checkpointing(self) -> None:
"""
Deactivates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or
*checkpoint activations* in other frameworks).
"""
if self._supports_gradient_checkpointing:
self.apply(partial(self._set_gradient_checkpointing, value=False))
def set_use_npu_flash_attention(self, valid: bool) -> None:
r"""
Set the switch for the npu flash attention.
""" | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
def fn_recursive_set_npu_flash_attention(module: torch.nn.Module):
if hasattr(module, "set_use_npu_flash_attention"):
module.set_use_npu_flash_attention(valid)
for child in module.children():
fn_recursive_set_npu_flash_attention(child)
for module in self.children():
if isinstance(module, torch.nn.Module):
fn_recursive_set_npu_flash_attention(module)
def enable_npu_flash_attention(self) -> None:
r"""
Enable npu flash attention from torch_npu
"""
self.set_use_npu_flash_attention(True)
def disable_npu_flash_attention(self) -> None:
r"""
disable npu flash attention from torch_npu
"""
self.set_use_npu_flash_attention(False) | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
def set_use_xla_flash_attention(
self, use_xla_flash_attention: bool, partition_spec: Optional[Callable] = None, **kwargs
) -> None:
# Recursively walk through all the children.
# Any children which exposes the set_use_xla_flash_attention method
# gets the message
def fn_recursive_set_flash_attention(module: torch.nn.Module):
if hasattr(module, "set_use_xla_flash_attention"):
module.set_use_xla_flash_attention(use_xla_flash_attention, partition_spec, **kwargs)
for child in module.children():
fn_recursive_set_flash_attention(child)
for module in self.children():
if isinstance(module, torch.nn.Module):
fn_recursive_set_flash_attention(module) | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
def enable_xla_flash_attention(self, partition_spec: Optional[Callable] = None, **kwargs):
r"""
Enable the flash attention pallals kernel for torch_xla.
"""
self.set_use_xla_flash_attention(True, partition_spec, **kwargs)
def disable_xla_flash_attention(self):
r"""
Disable the flash attention pallals kernel for torch_xla.
"""
self.set_use_xla_flash_attention(False)
def set_use_memory_efficient_attention_xformers(
self, valid: bool, attention_op: Optional[Callable] = None
) -> None:
# Recursively walk through all the children.
# Any children which exposes the set_use_memory_efficient_attention_xformers method
# gets the message
def fn_recursive_set_mem_eff(module: torch.nn.Module):
if hasattr(module, "set_use_memory_efficient_attention_xformers"):
module.set_use_memory_efficient_attention_xformers(valid, attention_op) | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
for child in module.children():
fn_recursive_set_mem_eff(child)
for module in self.children():
if isinstance(module, torch.nn.Module):
fn_recursive_set_mem_eff(module)
def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None) -> None:
r"""
Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).
When this option is enabled, you should observe lower GPU memory usage and a potential speed up during
inference. Speed up during training is not guaranteed.
<Tip warning={true}>
⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.
</Tip> | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
Parameters:
attention_op (`Callable`, *optional*):
Override the default `None` operator for use as `op` argument to the
[`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)
function of xFormers.
Examples:
```py
>>> import torch
>>> from diffusers import UNet2DConditionModel
>>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp
>>> model = UNet2DConditionModel.from_pretrained(
... "stabilityai/stable-diffusion-2-1", subfolder="unet", torch_dtype=torch.float16
... )
>>> model = model.to("cuda")
>>> model.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
```
"""
self.set_use_memory_efficient_attention_xformers(True, attention_op) | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
def disable_xformers_memory_efficient_attention(self) -> None:
r"""
Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).
"""
self.set_use_memory_efficient_attention_xformers(False)
def save_pretrained(
self,
save_directory: Union[str, os.PathLike],
is_main_process: bool = True,
save_function: Optional[Callable] = None,
safe_serialization: bool = True,
variant: Optional[str] = None,
max_shard_size: Union[int, str] = "10GB",
push_to_hub: bool = False,
**kwargs,
):
"""
Save a model and its configuration file to a directory so that it can be reloaded using the
[`~models.ModelMixin.from_pretrained`] class method. | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to save a model and its configuration file to. Will be created if it doesn't exist.
is_main_process (`bool`, *optional*, defaults to `True`):
Whether the process calling this is the main process or not. Useful during distributed training and you
need to call this function on all processes. In this case, set `is_main_process=True` only on the main
process to avoid race conditions.
save_function (`Callable`):
The function to use to save the state dictionary. Useful during distributed training when you need to
replace `torch.save` with another method. Can be configured with the environment variable
`DIFFUSERS_SAVE_MODE`.
safe_serialization (`bool`, *optional*, defaults to `True`): | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
variant (`str`, *optional*):
If specified, weights are saved in the format `pytorch_model.<variant>.bin`.
max_shard_size (`int` or `str`, defaults to `"10GB"`):
The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size
lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5GB"`).
If expressed as an integer, the unit is bytes. Note that this limit will be decreased after a certain
period of time (starting from Oct 2024) to allow users to upgrade to the latest version of `diffusers`.
This is to establish a common default size for this argument across different libraries in the Hugging
Face ecosystem (`transformers`, and `accelerate`, for example). | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the
repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
namespace).
kwargs (`Dict[str, Any]`, *optional*):
Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
"""
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
hf_quantizer = getattr(self, "hf_quantizer", None)
if hf_quantizer is not None:
quantization_serializable = (
hf_quantizer is not None
and isinstance(hf_quantizer, DiffusersQuantizer)
and hf_quantizer.is_serializable
)
if not quantization_serializable:
raise ValueError(
f"The model is quantized with {hf_quantizer.quantization_config.quant_method} and is not serializable - check out the warnings from"
" the logger on the traceback to understand the reason why the quantized model is not serializable."
)
weights_name = SAFETENSORS_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME
weights_name = _add_variant(weights_name, variant)
weights_name_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(
".safetensors", "{suffix}.safetensors"
) | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
os.makedirs(save_directory, exist_ok=True)
if push_to_hub:
commit_message = kwargs.pop("commit_message", None)
private = kwargs.pop("private", None)
create_pr = kwargs.pop("create_pr", False)
token = kwargs.pop("token", None)
repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id
# Only save the model itself if we are using distributed training
model_to_save = self
# Attach architecture to the config
# Save the config
if is_main_process:
model_to_save.save_config(save_directory)
# Save the model
state_dict = model_to_save.state_dict()
# Save the model
state_dict_split = split_torch_state_dict_into_shards(
state_dict, max_shard_size=max_shard_size, filename_pattern=weights_name_pattern
) | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
# Clean the folder from a previous save
if is_main_process:
for filename in os.listdir(save_directory):
if filename in state_dict_split.filename_to_tensors.keys():
continue
full_filename = os.path.join(save_directory, filename)
if not os.path.isfile(full_filename):
continue
weights_without_ext = weights_name_pattern.replace(".bin", "").replace(".safetensors", "")
weights_without_ext = weights_without_ext.replace("{suffix}", "")
filename_without_ext = filename.replace(".bin", "").replace(".safetensors", "")
# make sure that file to be deleted matches format of sharded file, e.g. pytorch_model-00001-of-00005
if (
filename.startswith(weights_without_ext)
and _REGEX_SHARD.fullmatch(filename_without_ext) is not None
):
os.remove(full_filename) | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
for filename, tensors in state_dict_split.filename_to_tensors.items():
shard = {tensor: state_dict[tensor] for tensor in tensors}
filepath = os.path.join(save_directory, filename)
if safe_serialization:
# At some point we will need to deal better with save_function (used for TPU and other distributed
# joyfulness), but for now this enough.
safetensors.torch.save_file(shard, filepath, metadata={"format": "pt"})
else:
torch.save(shard, filepath) | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
if state_dict_split.is_sharded:
index = {
"metadata": state_dict_split.metadata,
"weight_map": state_dict_split.tensor_to_filename,
}
save_index_file = SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME
save_index_file = os.path.join(save_directory, _add_variant(save_index_file, variant))
# Save the index as well
with open(save_index_file, "w", encoding="utf-8") as f:
content = json.dumps(index, indent=2, sort_keys=True) + "\n"
f.write(content)
logger.info(
f"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be "
f"split in {len(state_dict_split.filename_to_tensors)} checkpoint shards. You can find where each parameters has been saved in the "
f"index located at {save_index_file}."
)
else: | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
path_to_weights = os.path.join(save_directory, weights_name)
logger.info(f"Model weights saved in {path_to_weights}") | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
if push_to_hub:
# Create a new empty model card and eventually tag it
model_card = load_or_create_model_card(repo_id, token=token)
model_card = populate_model_card(model_card)
model_card.save(Path(save_directory, "README.md").as_posix())
self._upload_folder(
save_directory,
repo_id,
token=token,
commit_message=commit_message,
create_pr=create_pr,
)
def dequantize(self):
"""
Potentially dequantize the model in case it has been quantized by a quantization method that support
dequantization.
"""
hf_quantizer = getattr(self, "hf_quantizer", None)
if hf_quantizer is None:
raise ValueError("You need to first quantize your model in order to dequantize it")
return hf_quantizer.dequantize(self) | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
@classmethod
@validate_hf_hub_args
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
r"""
Instantiate a pretrained PyTorch model from a pretrained model configuration.
The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To
train the model, set it back in training mode with `model.train()`.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
the Hub.
- A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
with [`~ModelMixin.save_pretrained`]. | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.
torch_dtype (`str` or `torch.dtype`, *optional*):
Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the
dtype is automatically derived from the model's weights.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
output_loading_info (`bool`, *optional*, defaults to `False`):
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether to only load local model weights and configuration files or not. If set to `True`, the model
won't be downloaded from the Hub.
token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
`diffusers-cli login` (stored in `~/.huggingface`) is used.
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.
from_flax (`bool`, *optional*, defaults to `False`): | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
Load the model weights from a Flax checkpoint save file.
subfolder (`str`, *optional*, defaults to `""`):
The subfolder location of a model file within a larger model repository on the Hub or locally.
mirror (`str`, *optional*):
Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
information.
device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
A map that specifies where each submodule should go. It doesn't need to be defined for each
parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
same device. Defaults to `None`, meaning that the model will be loaded on CPU. | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
more information about each option see [designing a device
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
max_memory (`Dict`, *optional*):
A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
each GPU and the available CPU RAM if unset.
offload_folder (`str` or `os.PathLike`, *optional*):
The path to offload weights if `device_map` contains the value `"disk"`.
offload_state_dict (`bool`, *optional*):
If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True` | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
when there is some disk offload.
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Speed up model loading only loading the pretrained weights and not initializing the weights. This also
tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
argument to `True` will raise an error.
variant (`str`, *optional*):
Load weights from a specified `variant` filename such as `"fp16"` or `"ema"`. This is ignored when
loading `from_flax`.
use_safetensors (`bool`, *optional*, defaults to `None`):
If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
`safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors`
weights. If set to `False`, `safetensors` weights are not loaded.
disable_mmap ('bool', *optional*, defaults to 'False'):
Whether to disable mmap when loading a Safetensors model. This option can perform better when the model
is on a network mount or hard drive, which may not handle the seeky-ness of mmap very well. | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
<Tip>
To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with
`huggingface-cli login`. You can also activate the special
["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a
firewalled environment.
</Tip>
Example:
```py
from diffusers import UNet2DConditionModel
unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")
```
If you get the error message below, you need to finetune the weights for your downstream task: | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
```bash
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
- conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```
"""
cache_dir = kwargs.pop("cache_dir", None)
ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False)
force_download = kwargs.pop("force_download", False)
from_flax = kwargs.pop("from_flax", False)
proxies = kwargs.pop("proxies", None)
output_loading_info = kwargs.pop("output_loading_info", False)
local_files_only = kwargs.pop("local_files_only", None)
token = kwargs.pop("token", None)
revision = kwargs.pop("revision", None) | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
torch_dtype = kwargs.pop("torch_dtype", None)
subfolder = kwargs.pop("subfolder", None)
device_map = kwargs.pop("device_map", None)
max_memory = kwargs.pop("max_memory", None)
offload_folder = kwargs.pop("offload_folder", None)
offload_state_dict = kwargs.pop("offload_state_dict", False)
low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
variant = kwargs.pop("variant", None)
use_safetensors = kwargs.pop("use_safetensors", None)
quantization_config = kwargs.pop("quantization_config", None)
dduf_entries: Optional[Dict[str, DDUFEntry]] = kwargs.pop("dduf_entries", None)
disable_mmap = kwargs.pop("disable_mmap", False) | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
allow_pickle = False
if use_safetensors is None:
use_safetensors = True
allow_pickle = True
if low_cpu_mem_usage and not is_accelerate_available():
low_cpu_mem_usage = False
logger.warning(
"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
" `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
" install accelerate\n```\n."
)
if device_map is not None and not is_accelerate_available():
raise NotImplementedError(
"Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set"
" `device_map=None`. You can install accelerate with `pip install accelerate`."
) | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
# Check if we can handle device_map and dispatching the weights
if device_map is not None and not is_torch_version(">=", "1.9.0"):
raise NotImplementedError(
"Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
" `device_map=None`."
)
if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
raise NotImplementedError(
"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
" `low_cpu_mem_usage=False`."
)
if low_cpu_mem_usage is False and device_map is not None:
raise ValueError(
f"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and"
" dispatching. Please make sure to set `low_cpu_mem_usage=True`."
) | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
# change device_map into a map if we passed an int, a str or a torch.device
if isinstance(device_map, torch.device):
device_map = {"": device_map}
elif isinstance(device_map, str) and device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
try:
device_map = {"": torch.device(device_map)}
except RuntimeError:
raise ValueError(
"When passing device_map as a string, the value needs to be a device name (e.g. cpu, cuda:0) or "
f"'auto', 'balanced', 'balanced_low_0', 'sequential' but found {device_map}."
)
elif isinstance(device_map, int):
if device_map < 0:
raise ValueError(
"You can't pass device_map as a negative int. If you want to put the model on the cpu, pass device_map = 'cpu' "
)
else:
device_map = {"": device_map} | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
if device_map is not None:
if low_cpu_mem_usage is None:
low_cpu_mem_usage = True
elif not low_cpu_mem_usage:
raise ValueError("Passing along a `device_map` requires `low_cpu_mem_usage=True`")
if low_cpu_mem_usage:
if device_map is not None and not is_torch_version(">=", "1.10"):
# The max memory utils require PyTorch >= 1.10 to have torch.cuda.mem_get_info.
raise ValueError("`low_cpu_mem_usage` and `device_map` require PyTorch >= 1.10.")
# Load config if we don't provide a configuration
config_path = pretrained_model_name_or_path
user_agent = {
"diffusers": __version__,
"file_type": "model",
"framework": "pytorch",
} | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
# load config
config, unused_kwargs, commit_hash = cls.load_config(
config_path,
cache_dir=cache_dir,
return_unused_kwargs=True,
return_commit_hash=True,
force_download=force_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
dduf_entries=dduf_entries,
**kwargs,
)
# no in-place modification of the original config.
config = copy.deepcopy(config) | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
# determine initial quantization config.
#######################################
pre_quantized = "quantization_config" in config and config["quantization_config"] is not None
if pre_quantized or quantization_config is not None:
if pre_quantized:
config["quantization_config"] = DiffusersAutoQuantizer.merge_quantization_configs(
config["quantization_config"], quantization_config
)
else:
config["quantization_config"] = quantization_config
hf_quantizer = DiffusersAutoQuantizer.from_config(
config["quantization_config"], pre_quantized=pre_quantized
)
else:
hf_quantizer = None | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
if hf_quantizer is not None:
if device_map is not None:
raise NotImplementedError(
"Currently, providing `device_map` is not supported for quantized models. Providing `device_map` as an input will be added in the future."
)
hf_quantizer.validate_environment(torch_dtype=torch_dtype, from_flax=from_flax, device_map=device_map)
torch_dtype = hf_quantizer.update_torch_dtype(torch_dtype)
# In order to ensure popular quantization methods are supported. Can be disable with `disable_telemetry`
user_agent["quant"] = hf_quantizer.quantization_config.quant_method.value | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
# Force-set to `True` for more mem efficiency
if low_cpu_mem_usage is None:
low_cpu_mem_usage = True
logger.info("Set `low_cpu_mem_usage` to True as `hf_quantizer` is not None.")
elif not low_cpu_mem_usage:
raise ValueError("`low_cpu_mem_usage` cannot be False or None when using quantization.")
# Check if `_keep_in_fp32_modules` is not None
use_keep_in_fp32_modules = (cls._keep_in_fp32_modules is not None) and (
(torch_dtype == torch.float16) or hasattr(hf_quantizer, "use_keep_in_fp32_modules")
)
if use_keep_in_fp32_modules:
keep_in_fp32_modules = cls._keep_in_fp32_modules
if not isinstance(keep_in_fp32_modules, list):
keep_in_fp32_modules = [keep_in_fp32_modules] | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
if low_cpu_mem_usage is None:
low_cpu_mem_usage = True
logger.info("Set `low_cpu_mem_usage` to True as `_keep_in_fp32_modules` is not None.")
elif not low_cpu_mem_usage:
raise ValueError("`low_cpu_mem_usage` cannot be False when `keep_in_fp32_modules` is True.")
else:
keep_in_fp32_modules = []
####################################### | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
# Determine if we're loading from a directory of sharded checkpoints.
is_sharded = False
index_file = None
is_local = os.path.isdir(pretrained_model_name_or_path)
index_file_kwargs = {
"is_local": is_local,
"pretrained_model_name_or_path": pretrained_model_name_or_path,
"subfolder": subfolder or "",
"use_safetensors": use_safetensors,
"cache_dir": cache_dir,
"variant": variant,
"force_download": force_download,
"proxies": proxies,
"local_files_only": local_files_only,
"token": token,
"revision": revision,
"user_agent": user_agent,
"commit_hash": commit_hash,
"dduf_entries": dduf_entries,
}
index_file = _fetch_index_file(**index_file_kwargs)
# In case the index file was not found we still have to consider the legacy format. | 906 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_utils.py |
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