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class SkipBlock(nn.Module):
def __init__(self, dim: int):
super().__init__()
self.skip_linear = nn.Linear(2 * dim, dim)
# Use torch.nn.LayerNorm for now, following the original code
self.norm = nn.LayerNorm(dim)
def forward(self, x, skip):
x = self.skip_linear(torch.ca... | 125 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
class UTransformerBlock(nn.Module):
r"""
A modification of BasicTransformerBlock which supports pre-LayerNorm and post-LayerNorm configurations. | 126 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): The number of heads to use for multi-head attention.
attention_head_dim (`int`): The number of channels in each head.
dropout (`float`, *optional*, defaults to 0.0): The dropout probabil... | 126 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
Whether to use only cross-attention layers. In this case two cross attention layers are used.
double_self_attention (`bool`, *optional*):
Whether to use two self-attention layers. In this case no cross attention layers are used.
upcast_attention (`bool`, *optional*):
Whether to u... | 126 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
Whether to use a final Dropout layer after the feedforward network.
""" | 126 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
dropout=0.0,
cross_attention_dim: Optional[int] = None,
activation_fn: str = "geglu",
num_embeds_ada_norm: Optional[int] = None,
attention_bias: bool = False,
... | 126 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
... | 126 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim if not double_self_attention else None,
heads=num_attention_heads,
dim_head=atte... | 126 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the secon... | 126 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
def forward(
self,
hidden_states,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
timestep=None,
cross_attention_kwargs=None,
class_labels=None,
):
# Pre-LayerNorm
if self.pre_layer_norm:
if sel... | 126 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# Post-LayerNorm
if not self.pre_layer_norm:
if self.use_ada_layer_norm:
attn_output = self.norm1(attn_output, timestep)
else:
attn_output = self.norm1(attn_output)
hidden_states = attn_output + hidden_states
if self.attn2 is not None:
... | 126 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 2. Cross-Attention
attn_output = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
**cross_attention_kwargs,
)
# Post-LayerNorm
if not self... | 126 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
class UniDiffuserBlock(nn.Module):
r"""
A modification of BasicTransformerBlock which supports pre-LayerNorm and post-LayerNorm configurations and puts the
LayerNorms on the residual backbone of the block. This matches the transformer block in the [original UniDiffuser
implementation](https://github.com... | 127 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): The number of heads to use for multi-head attention.
attention_head_dim (`int`): The number of channels in each head.
dropout (`float`, *optional*, defaults to 0.0): The dropout probabil... | 127 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
Whether to use only cross-attention layers. In this case two cross attention layers are used.
double_self_attention (`bool`, *optional*):
Whether to use two self-attention layers. In this case no cross attention layers are used.
upcast_attention (`bool`, *optional*):
Whether to u... | 127 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
Whether to use a final Dropout layer after the feedforward network.
""" | 127 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
dropout=0.0,
cross_attention_dim: Optional[int] = None,
activation_fn: str = "geglu",
num_embeds_ada_norm: Optional[int] = None,
attention_bias: bool = False,
... | 127 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
... | 127 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim if not double_self_attention else None,
heads=num_attention_heads,
dim_head=atte... | 127 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the secon... | 127 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
def forward(
self,
hidden_states,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
timestep=None,
cross_attention_kwargs=None,
class_labels=None,
):
# Following the diffusers transformer block implementation, put th... | 127 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
hidden_states = attn_output + hidden_states
# Following the diffusers transformer block implementation, put the LayerNorm on the
# residual backbone
# Post-LayerNorm
if not self.pre_layer_norm:
if self.use_ada_layer_norm:
hidden_states = self.norm1(hidden_sta... | 127 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 2. Cross-Attention
attn_output = self.attn2(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
**cross_attention_kwargs,
)
hidden_states = attn_output + hidden_states
... | 127 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
class UTransformer2DModel(ModelMixin, ConfigMixin):
"""
Transformer model based on the [U-ViT](https://github.com/baofff/U-ViT) architecture for image-like data. Compared
to [`Transformer2DModel`], this model has skip connections between transformer blocks in a "U"-shaped fashion,
similar to a U-Net. Su... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
Parameters:
num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
in_channels (`int`, *optional*):
Pass if the input is continuous. The... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
Configure if the TransformerBlocks' attention should contain a bias parameter.
sample_size (`int`, *optional*): Pass if the input is discrete. The width of the latent images.
Note that this is fixed at training time as it is used for learning a number of position embeddings. See
`ImagePo... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
The number of diffusion steps used during training. Note that this is fixed at training time as it is used
to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for
up to but not more than steps than `num_embeds_ada_norm`.
use_linear_proje... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
The transformer block implementation to use. If `"unidiffuser"`, has the LayerNorms on the residual
backbone of each transformer block; otherwise has them in the attention/feedforward branches (the standard
behavior in `diffusers`.)
pre_layer_norm (`bool`, *optional*):
Whethe... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
@register_to_config
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: Optional[int] = None,
out_channels: Optional[int] = None,
num_layers: int = 1,
dropout: float = 0.0,
norm_num_groups: int = 32,
cr... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
super().__init__()
self.use_linear_projection = use_linear_projection
self.num_attention_heads = num_attention_heads
self.attention_head_dim = attention_head_dim
inner_dim = num_attention_heads * attention_head_dim | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 1. Input
# Only support patch input of shape (batch_size, num_channels, height, width) for now
assert in_channels is not None and patch_size is not None, "Patch input requires in_channels and patch_size."
assert sample_size is not None, "UTransformer2DModel over patched input must provide sam... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 3. Define transformers blocks
# Modify this to have in_blocks ("downsample" blocks, even though we don't actually downsample), a mid_block,
# and out_blocks ("upsample" blocks). Like a U-Net, there are skip connections from in_blocks to out_blocks in
# a "U"-shaped fashion (e.g. first in_block... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
attention_bias=attention_bias,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
norm_type=norm_type,
pre_layer_norm=pre_layer_norm,
norm_elementwise_affine=norm_elementwise_affine,
... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
self.transformer_mid_block = block_cls(
inner_dim,
num_attention_heads,
attention_head_dim,
dropout=dropout,
cross_attention_dim=cross_attention_dim,
activation_fn=activation_fn,
num_embeds_ada_norm=num_embeds_ada_norm,
atte... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# For each skip connection, we use a SkipBlock (concatenation + Linear + LayerNorm) to process the inputs
# before each transformer out_block.
self.transformer_out_blocks = nn.ModuleList(
[
nn.ModuleDict(
{
"skip": SkipBlock(
... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
norm_type=norm_type,
pre_layer_norm=pre_layer_norm,
norm_elementwise_affine=norm_elementwise_affine,
final_dropout=ff_final_dropout,
),
}
)
for d in range(num_l... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 4. Define output layers
self.out_channels = in_channels if out_channels is None else out_channels
# Following the UniDiffuser U-ViT implementation, we process the transformer output with
# a LayerNorm layer with per-element affine params
self.norm_out = nn.LayerNorm(inner_dim) | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
def forward(
self,
hidden_states,
encoder_hidden_states=None,
timestep=None,
class_labels=None,
cross_attention_kwargs=None,
return_dict: bool = True,
hidden_states_is_embedding: bool = False,
unpatchify: bool = True,
):
"""
Arg... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
Optional class labels to be applied as an embedding in AdaLayerZeroNorm. Used to indicate class labels
conditioning.
cross_attention_kwargs (*optional*):
Keyword arguments to ... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
Whether to unpatchify the transformer output. | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
Returns:
[`~models.transformer_2d.Transformer2DModelOutput`] or `tuple`:
[`~models.transformer_2d.Transformer2DModelOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is the sample tensor.
"""
# 0. Check inputs
i... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# In ("downsample") blocks
skips = []
for in_block in self.transformer_in_blocks:
hidden_states = in_block(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
timestep=timestep,
cross_attention_kwargs=cross_attention_kw... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 3. Output
# Don't support AdaLayerNorm for now, so no conditioning/scale/shift logic
hidden_states = self.norm_out(hidden_states)
# hidden_states = self.proj_out(hidden_states)
if unpatchify:
# unpatchify
height = width = int(hidden_states.shape[1] ** 0.5)
... | 128 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
class UniDiffuserModel(ModelMixin, ConfigMixin):
"""
Transformer model for a image-text [UniDiffuser](https://arxiv.org/pdf/2303.06555.pdf) model. This is a
modification of [`UTransformer2DModel`] with input and output heads for the VAE-embedded latent image, the
CLIP-embedded image, and the CLIP-embedd... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
Parameters:
text_dim (`int`): The hidden dimension of the CLIP text model used to embed images.
clip_img_dim (`int`): The hidden dimension of the CLIP vision model used to embed prompts.
num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
cross_attention_dim (`int`, *optional*): The number of encoder_hidden_states dimensions to use.
attention_bias (`bool`, *optional*):
Configure if the TransformerBlocks' attention should contain a bias parameter.
sample_size (`int`, *optional*): Pass if the input is discrete. The width of the... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
num_embeds_ada_norm ( `int`, *optional*): Pass if at least one of the norm_layers is `AdaLayerNorm`.
The number of diffusion steps used during training. Note that this is fixed at training time as it is used
to learn a number of embeddings that are added to the hidden states. During inference, y... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
block_type (`str`, *optional*, defaults to `"unidiffuser"`):
The transformer block implementation to use. If `"unidiffuser"`, has the LayerNorms on the residual
backbone of each transformer block; otherwise has them in the attention/feedforward branches (the standard
behavior in `dif... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
Whether to use a final Dropout layer after the feedforward network.
use_data_type_embedding (`bool`, *optional*):
Whether to use a data type embedding. This is only relevant for UniDiffuser-v1 style models; UniDiffuser-v1
is continue-trained from UniDiffuser-v0 on non-publically-availabl... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
@register_to_config
def __init__(
self,
text_dim: int = 768,
clip_img_dim: int = 512,
num_text_tokens: int = 77,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: Optional[int] = None,
out_channels: Optional[int] = None,
... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
norm_elementwise_affine: bool = True,
use_patch_pos_embed=False,
ff_final_dropout: bool = True,
use_data_type_embedding: bool = False,
):
super().__init__() | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 0. Handle dimensions
self.inner_dim = num_attention_heads * attention_head_dim
assert sample_size is not None, "UniDiffuserModel over patched input must provide sample_size"
self.sample_size = sample_size
self.in_channels = in_channels
self.out_channels = in_channels if out_ch... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 1. Define input layers
# 1.1 Input layers for text and image input
# For now, only support patch input for VAE latent image input
self.vae_img_in = PatchEmbed(
height=sample_size,
width=sample_size,
patch_size=patch_size,
in_channels=in_channels,... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 1.2. Timestep embeddings for t_img, t_text
self.timestep_img_proj = Timesteps(
self.inner_dim,
flip_sin_to_cos=True,
downscale_freq_shift=0,
)
self.timestep_img_embed = (
TimestepEmbedding(
self.inner_dim,
4 * self... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 1.3. Positional embedding
self.num_text_tokens = num_text_tokens
self.num_tokens = 1 + 1 + num_text_tokens + 1 + self.num_patches
self.pos_embed = nn.Parameter(torch.zeros(1, self.num_tokens, self.inner_dim))
self.pos_embed_drop = nn.Dropout(p=dropout)
trunc_normal_(self.pos_em... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 2. Define transformer blocks
self.transformer = UTransformer2DModel(
num_attention_heads=num_attention_heads,
attention_head_dim=attention_head_dim,
in_channels=in_channels,
out_channels=out_channels,
num_layers=num_layers,
dropout=dropou... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
use_patch_pos_embed=use_patch_pos_embed,
ff_final_dropout=ff_final_dropout,
) | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 3. Define output layers
patch_dim = (patch_size**2) * out_channels
self.vae_img_out = nn.Linear(self.inner_dim, patch_dim)
self.clip_img_out = nn.Linear(self.inner_dim, clip_img_dim)
self.text_out = nn.Linear(self.inner_dim, text_dim)
@torch.jit.ignore
def no_weight_decay(self... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
def forward(
self,
latent_image_embeds: torch.Tensor,
image_embeds: torch.Tensor,
prompt_embeds: torch.Tensor,
timestep_img: Union[torch.Tensor, float, int],
timestep_text: Union[torch.Tensor, float, int],
data_type: Optional[Union[torch.Tensor, float, int]] = 1,
... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
Current denoising step for the image.
timestep_text (`torch.long` or `float` or `int`):
Current denoising step for the text.
data_type: (`torch.int` or `float` or `int`, *optional*, defaults to `1`):
Only used in UniDiffuser-v1-style models. Can be either `1`, to ... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
Returns:
`tuple`: Returns relevant parts of the model's noise prediction: the first element of the tuple is tbe VAE
image embedding, the second element is the CLIP image embedding, and the third element is the CLIP text
embedding.
"""
batch_size = latent_image_embeds.... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep_img = timestep_img * torch.ones(batch_size, dtype=timestep_img.dtype, device=timestep_img.device)
timestep_img_token = self.timestep_img_proj(timestep_img)
# t_img_token does not contain any weights and will al... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep_text = timestep_text * torch.ones(batch_size, dtype=timestep_text.dtype, device=timestep_text.device)
timestep_text_token = self.timestep_text_proj(timestep_text)
# t_text_token does not contain any weights and... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
data_type = data_type * torch.ones(batch_size, dtype=data_type.dtype, device=data_type.device)
data_type_token = self.data_type_token_embedding(data_type).unsqueeze(dim=1)
hidden_states = torch.cat(
... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 1.5. Prepare the positional embeddings and add to hidden states
# Note: I think img_vae should always have the proper shape, so there's no need to interpolate
# the position embeddings.
if self.use_data_type_embedding:
pos_embed = torch.cat(
[self.pos_embed[:, : 1 +... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# 3. Output
# Split out the predicted noise representation.
if self.use_data_type_embedding:
(
t_img_token_out,
t_text_token_out,
data_type_token_out,
text_out,
img_clip_out,
img_vae_out,
... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
# unpatchify
height = width = int(img_vae_out.shape[1] ** 0.5)
img_vae_out = img_vae_out.reshape(
shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)
)
img_vae_out = torch.einsum("nhwpqc->nchpwq", img_vae_out)
img_vae_out = img_vae_out.resha... | 129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py |
class CrossAttnStoreProcessor:
def __init__(self):
self.attention_probs = None
def __call__(
self,
attn,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
):
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = a... | 130 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
self.attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(self.attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states =... | 130 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
class StableDiffusionSAGPipeline(DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, IPAdapterMixin):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implement... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14))... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
more details about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as input... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
_exclude_from_cpu_offload = ["safety_checker"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
)
... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torc... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
prompt_embeds_tuple = self.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`b... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
if prompt is not None and isinstance(prompt, str):
batch_size = 1... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = sel... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
if clip_skip is None:
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
prompt_embeds = prompt_embeds[0]
else:
prompt_embeds = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
if self.text_encoder is not None:
prompt_embeds_dtype = self.text_encoder.dtype
elif self.unet is not None:
prompt_embeds_dtype = self.unet.dtype
else:
prompt_embeds_dtype = prompt_embeds.dtype
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, devic... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is no... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
negative... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
image = image.to(device=device, dtype=dtype)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_imag... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
def prepare_ip_adapter_image_embeds(
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
):
if ip_adapter_image_embeds is None:
if not isinstance(ip_adapter_image, list):
ip_adapter_image = [ip_adapter_image]
... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
image_embeds = []
for single_ip_adapter_image, image_proj_layer in zip(
ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
):
output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
single_image_embeds, single_nega... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
image_embeds.append(single_image_embeds)
else:
image_embeds = ip_adapter_image_embeds
return image_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
d... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used w... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
# Copied from diffusers.pipelines.stable_diffusion_k_diffusion.pipeline_stable_diffusion_k_diffusion.StableDiffusionKDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if callback_on_step_end_tensor_... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `pr... | 131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py |
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