text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
# Compute query, key, and value matrices
query = self.to_q(latents)
kv_input = torch.cat((image_embeds, latents), dim=-2)
key, value = self.to_kv(kv_input).chunk(2, dim=-1)
# Reshape the tensors for multi-head attention
query = query.reshape(query.size(0), -1, self.heads, self.d... | 1,095 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
class LocalFacialExtractor(nn.Module):
def __init__(
self,
id_dim: int = 1280,
vit_dim: int = 1024,
depth: int = 10,
dim_head: int = 64,
heads: int = 16,
num_id_token: int = 5,
num_queries: int = 32,
output_dim: int = 2048,
ff_mult: int... | 1,096 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
# Attention and ConsisIDFeedForward layer stack
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(
nn.ModuleList(
[
PerceiverAttention(dim=vit_dim, dim_head=dim_head, heads=heads), # Perceiver Attention laye... | 1,096 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
# Mappings for each of the 5 different ViT features
for i in range(num_scale):
setattr(
self,
f"mapping_{i}",
nn.Sequential(
nn.Linear(vit_dim, vit_dim),
nn.LayerNorm(vit_dim),
nn.LeakyReLU(),... | 1,096 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
def forward(self, id_embeds: torch.Tensor, vit_hidden_states: List[torch.Tensor]) -> torch.Tensor:
# Repeat latent queries for the batch size
latents = self.latents.repeat(id_embeds.size(0), 1, 1)
# Map the identity embedding to tokens
id_embeds = self.id_embedding_mapping(id_embeds)
... | 1,096 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
# Pass through the PerceiverAttention and ConsisIDFeedForward layers
for attn, ff in self.layers[i * self.depth : (i + 1) * self.depth]:
latents = attn(ctx_feature, latents) + latents
latents = ff(latents) + latents
# Retain only the query latents
latents = l... | 1,096 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
class PerceiverCrossAttention(nn.Module):
def __init__(self, dim: int = 3072, dim_head: int = 128, heads: int = 16, kv_dim: int = 2048):
super().__init__()
self.scale = dim_head**-0.5
self.dim_head = dim_head
self.heads = heads
inner_dim = dim_head * heads
# Layer n... | 1,097 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
batch_size, seq_len, _ = hidden_states.shape
# Compute queries, keys, and values
query = self.to_q(hidden_states)
key, value = self.to_kv(image_embeds).chunk(2, dim=-1)
# Reshape tensors to split into attention heads
query = query.reshape(query.size(0), -1, self.heads, self.dim... | 1,097 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
# Reshape and permute to prepare for final linear transformation
out = out.permute(0, 2, 1, 3).reshape(batch_size, seq_len, -1)
return self.to_out(out) | 1,097 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
class ConsisIDBlock(nn.Module):
r"""
Transformer block used in [ConsisID](https://github.com/PKU-YuanGroup/ConsisID) model. | 1,098 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.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.
time_embed_dim (`int`):
... | 1,098 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
norm_eps (`float`, defaults to `1e-5`):
Epsilon value for normalization layers.
final_dropout (`bool` defaults to `False`):
Whether to apply a final dropout after the last feed-forward layer.
ff_inner_dim (`int`, *optional*, defaults to `None`):
Custom hidden dimensio... | 1,098 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
time_embed_dim: int,
dropout: float = 0.0,
activation_fn: str = "gelu-approximate",
attention_bias: bool = False,
qk_norm: bool = True,
norm_elementwise_affine... | 1,098 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
self.attn1 = Attention(
query_dim=dim,
dim_head=attention_head_dim,
heads=num_attention_heads,
qk_norm="layer_norm" if qk_norm else None,
eps=1e-6,
bias=attention_bias,
out_bias=attention_out_bias,
processor=CogVideoXAttnPro... | 1,098 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
# norm & modulate
norm_hidden_states, norm_encoder_hidden_states, gate_msa, enc_gate_msa = self.norm1(
hidden_states, encoder_hidden_states, temb
)
# attention
attn_hidden_states, attn_encoder_hidden_states = self.attn1(
hidden_states=norm_hidden_states,
... | 1,098 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
hidden_states = hidden_states + gate_ff * ff_output[:, text_seq_length:]
encoder_hidden_states = encoder_hidden_states + enc_gate_ff * ff_output[:, :text_seq_length]
return hidden_states, encoder_hidden_states | 1,098 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
class ConsisIDTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
"""
A Transformer model for video-like data in [ConsisID](https://github.com/PKU-YuanGroup/ConsisID). | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
Parameters:
num_attention_heads (`int`, defaults to `30`):
The number of heads to use for multi-head attention.
attention_head_dim (`int`, defaults to `64`):
The number of channels in each head.
in_channels (`int`, defaults to `16`):
The number of channels in ... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
attention_bias (`bool`, defaults to `True`):
Whether to use bias in the attention projection layers.
sample_width (`int`, defaults to `90`):
The width of the input latents.
sample_height (`int`, defaults to `60`):
The height of the input latents.
sample_frames... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
The compression ratio across the temporal dimension. See documentation for `sample_frames`.
max_text_seq_length (`int`, defaults to `226`):
The maximum sequence length of the input text embeddings.
activation_fn (`str`, defaults to `"gelu-approximate"`):
Activation function to us... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
Scaling factor to apply in 3D positional embeddings across temporal dimensions.
is_train_face (`bool`, defaults to `False`):
Whether to use enable the identity-preserving module during the training process. When set to `True`, the
model will focus on identity-preserving tasks.
is... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
larger value increases the capacity to attend to more complex patterns, but also increases memory and
computation costs.
cross_attn_num_heads (`int`, optional, defaults to `16`):
The number of attention heads in the cross-attention layers. More heads allow for more parallel attention
... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
dictates the size of the transformer-generated feature vectors that will be processed for facial feature
extraction.
LFE_depth (`int`, optional, defaults to `10`):
The number of layers in the Local Facial Extractor (LFE). Increasing the depth allows the model to capture
more ... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
LFE_num_id_token (`int`, optional, defaults to `5`):
The number of identity tokens used in the Local Facial Extractor (LFE). This defines how many
identity-related tokens the model will process to ensure face identity preservation during feature
extraction.
LFE_num_querie (`i... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
The multiplication factor applied to the feed-forward network's hidden layer size in the Local Facial
Extractor (LFE). A higher value increases the model's capacity to learn more complex facial feature
transformations, but also increases the computation and memory requirements.
LFE_num_s... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
_supports_gradient_checkpointing = True | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
@register_to_config
def __init__(
self,
num_attention_heads: int = 30,
attention_head_dim: int = 64,
in_channels: int = 16,
out_channels: Optional[int] = 16,
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
time_embed_dim: int = 512,
text_emb... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
use_learned_positional_embeddings: bool = False,
is_train_face: bool = False,
is_kps: bool = False,
cross_attn_interval: int = 2,
cross_attn_dim_head: int = 128,
cross_attn_num_heads: int = 16,
LFE_id_dim: int = 1280,
LFE_vit_dim: int = 1024,
LFE_depth: in... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
if not use_rotary_positional_embeddings and use_learned_positional_embeddings:
raise ValueError(
"There are no ConsisID checkpoints available with disable rotary embeddings and learned positional "
"embeddings. If you're using a custom model and/or believe this should be supp... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
# 1. Patch embedding
self.patch_embed = CogVideoXPatchEmbed(
patch_size=patch_size,
in_channels=in_channels,
embed_dim=inner_dim,
text_embed_dim=text_embed_dim,
bias=True,
sample_width=sample_width,
sample_height=sample_height,
... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
# 2. Time embeddings
self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn)
# 3. Define spatio-temporal transformers blocks
self.transformer_blocks = nn.ModuleList(
[
... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
# 4. Output blocks
self.norm_out = AdaLayerNorm(
embedding_dim=time_embed_dim,
output_dim=2 * inner_dim,
norm_elementwise_affine=norm_elementwise_affine,
norm_eps=norm_eps,
chunk_dim=1,
)
self.proj_out = nn.Linear(inner_dim, patch_size ... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
# 5. Define identity-preserving config
if is_train_face:
# LFE configs
self.LFE_id_dim = LFE_id_dim
self.LFE_vit_dim = LFE_vit_dim
self.LFE_depth = LFE_depth
self.LFE_dim_head = LFE_dim_head
self.LFE_num_heads = LFE_num_heads
se... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
self._init_face_inputs() | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
self.gradient_checkpointing = False
def _set_gradient_checkpointing(self, module, value=False):
self.gradient_checkpointing = value | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
def _init_face_inputs(self):
self.local_facial_extractor = LocalFacialExtractor(
id_dim=self.LFE_id_dim,
vit_dim=self.LFE_vit_dim,
depth=self.LFE_depth,
dim_head=self.LFE_dim_head,
heads=self.LFE_num_heads,
num_id_token=self.LFE_num_id_toke... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor ... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
timestep: Union[int, float, torch.LongTensor],
timestep_cond: Optional[torch.Tensor] = None,
image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
attention_kwargs... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
if USE_PEFT_BACKEND:
# weight the lora layers by setting `lora_scale` for each PEFT layer
scale_lora_layers(self, lora_scale)
else:
if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
logger.warning(
"Passin... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
batch_size, num_frames, channels, height, width = hidden_states.shape
# 1. Time embedding
timesteps = timestep
t_emb = self.time_proj(timesteps)
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16. s... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
text_seq_length = encoder_hidden_states.shape[1]
encoder_hidden_states = hidden_states[:, :text_seq_length] # torch.Size([1, 226, 3072])
hidden_states = hidden_states[:, text_seq_length:] # torch.Size([1, 17550, 3072])
# 3. Transformer blocks
ca_idx = 0
for i, block in enumera... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
encoder_hidden_states,
... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
if self.is_train_face:
if i % self.cross_attn_interval == 0 and valid_face_emb is not None:
hidden_states = hidden_states + self.local_face_scale * self.perceiver_cross_attention[ca_idx](
valid_face_emb, hidden_states
) # torch.Size([2, 32... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
# 5. Unpatchify
# Note: we use `-1` instead of `channels`:
# - It is okay to `channels` use for ConsisID (number of input channels is equal to output channels)
p = self.config.patch_size
output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p)
o... | 1,099 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
class PriorTransformerOutput(BaseOutput):
"""
The output of [`PriorTransformer`].
Args:
predicted_image_embedding (`torch.Tensor` of shape `(batch_size, embedding_dim)`):
The predicted CLIP image embedding conditioned on the CLIP text embedding input.
"""
predicted_image_embedd... | 1,100 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
class PriorTransformer(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin):
"""
A Prior Transformer model. | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
Parameters:
num_attention_heads (`int`, *optional*, defaults to 32): The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
num_layers (`int`, *optional*, defaults to 20): The number of layers of Transform... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
The activation function to use to create timestep embeddings.
norm_in_type (`str`, *optional*, defaults to None): The normalization layer to apply on hidden states before
passing to Transformer blocks. Set it to `None` if normalization is not needed.
embedding_proj_norm_type (`str`, *optiona... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
product between the text embedding and image embedding as proposed in the unclip paper
https://arxiv.org/abs/2204.06125 If it is `None`, no additional embeddings will be prepended.
time_embed_dim (`int, *optional*, defaults to None): The dimension of timestep embeddings.
If None, will be... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
@register_to_config
def __init__(
self,
num_attention_heads: int = 32,
attention_head_dim: int = 64,
num_layers: int = 20,
embedding_dim: int = 768,
num_embeddings=77,
additional_embeddings=4,
dropout: float = 0.0,
time_embed_act_fn: str = "sil... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
time_embed_dim = time_embed_dim or inner_dim
embedding_proj_dim = embedding_proj_dim or embedding_dim
clip_embed_dim = clip_embed_dim or embedding_dim
self.time_proj = Timesteps(inner_dim, True, 0)
self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, out_dim=inner_dim, act... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
if encoder_hid_proj_type is None:
self.encoder_hidden_states_proj = None
elif encoder_hid_proj_type == "linear":
self.encoder_hidden_states_proj = nn.Linear(embedding_dim, inner_dim)
else:
raise ValueError(f"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}")... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(
inner_dim,
num_attention_heads,
attention_head_dim,
dropout=dropout,
activation_fn="gelu",
attention_bias=... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
causal_attention_mask = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings], -10000.0
)
causal_attention_mask.triu_(1)
causal_attention_mask = causal_attention_mask[None, ...]
self.register_buffer("causal_attention_mask", causal_attent... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
if hasattr(module, "get_processor"):
processors[f"{name}.processor"] = module.get_processor()
for sub_name, child in module.named_children():
fn_recurs... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers.
If `processor` is a dict, the key need... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor"):
if not isinstance(processor, dict):
module.set_processor(processor)
else:
module.set_processor(processor.pop(f"{name}.p... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESS... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
def forward(
self,
hidden_states,
timestep: Union[torch.Tensor, float, int],
proj_embedding: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.BoolTensor] = None,
return_dict: bool = True,
):
"""
... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
Args:
hidden_states (`torch.Tensor` of shape `(batch_size, embedding_dim)`):
The currently predicted image embeddings.
timestep (`torch.LongTensor`):
Current denoising step.
proj_embedding (`torch.Tensor` of shape `(batch_size, embedding_dim)`):
... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
Returns:
[`~models.transformers.prior_transformer.PriorTransformerOutput`] or `tuple`:
If return_dict is True, a [`~models.transformers.prior_transformer.PriorTransformerOutput`] is
returned, otherwise a tuple is returned where the first element is the sample tensor.
... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
timesteps_projected = timesteps_projected.to(dtype=self.dtype)
time_embeddings = self.time_embedding(timesteps_projected)
if self.embedding_proj_norm ... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
additional_embeds = []
additional_embeddings_len = 0
if encoder_hidden_states is not None:
additional_embeds.append(encoder_hidden_states)
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape) == 2:
proj_embeddings = proj_... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
additional_embeddings_len = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
positional_embeddi... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
if attention_mask is not None:
attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
attention_mask = F.pad(attention_mask, (0, self.additional_embeddings), value=0.0)
attention_mask = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype... | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=predicted_image_embedding)
def post_process_latents(self, prior_latents):
prior_latents = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents | 1,101 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py |
class SD3SingleTransformerBlock(nn.Module):
r"""
A Single Transformer block as part of the MMDiT architecture, used in Stable Diffusion 3 ControlNet.
Reference: https://arxiv.org/abs/2403.03206
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads... | 1,102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
self.attn = Attention(
query_dim=dim,
dim_head=attention_head_dim,
heads=num_attention_heads,
out_dim=dim,
bias=True,
processor=processor,
eps=1e-6,
)
self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
... | 1,102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
norm_hidden_states = self.norm2(hidden_states)
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
ff_output = self.ff(norm_hidden_states)
ff_output = gate_mlp.unsqueeze(1) * ff_output
hidden_states = hidden_states + ff_output
return hidden_... | 1,102 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
class SD3Transformer2DModel(
ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, SD3Transformer2DLoadersMixin
):
"""
The Transformer model introduced in Stable Diffusion 3.
Reference: https://arxiv.org/abs/2403.03206 | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
Parameters:
sample_size (`int`): The width of the latent images. This is fixed during training since
it is used to learn a number of position embeddings.
patch_size (`int`): Patch size to turn the input data into small patches.
in_channels (`int`, *optional*, defaults to 16): The num... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
out_channels (`int`, defaults to 16): Number of output channels. | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
"""
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: int = 128,
patch_size: int = 2,
in_channels: int = 16,
num_layers: int = 18,
attention_head_dim: int = 64,
num_attention_heads: int = 18,
joint_a... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
self.pos_embed = PatchEmbed(
height=self.config.sample_size,
width=self.config.sample_size,
patch_size=self.config.patch_size,
in_channels=self.config.in_channels,
embed_dim=self.inner_dim,
pos_embed_max_size=pos_embed_max_size, # hard-code for no... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
# `attention_head_dim` is doubled to account for the mixing.
# It needs to crafted when we get the actual checkpoints.
self.transformer_blocks = nn.ModuleList(
[
JointTransformerBlock(
dim=self.inner_dim,
num_attention_heads=self.config... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
"""
Sets the attention processor to use [feed forward
chunking](https://huggingface.co/blog/reformer#... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
if hasattr(module, "set_chunk_feed_forward"):
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
for child in module.children():
fn_recursive_feed_forward(child, chunk_s... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor ... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedJointAttnProcessor2_0
def fuse_qkv_projections(self):
"""
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
self.set_attn_processor(FusedJointAttnProcessor2_0())
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
def unfuse_qkv_projections(self):
"""Disables the fused QKV projection if enabled.
<Tip warning={true}>
This API is 🧪 experimental.... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
def forward(
self,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
pooled_projections: torch.FloatTensor = None,
timestep: torch.LongTensor = None,
block_controlnet_hidden_states: List = None,
joint_attention_kwargs: Optional[Dic... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
Args:
hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
Input `hidden_states`.
encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
Conditional embeddings (embeddings computed from the in... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
Returns:
If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
`tuple` where the first element is the sample tensor.
"""
if joint_attention_kwargs is not None:
joint_attention_kwargs = joint_attention_kwargs.copy()
... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
hidden_states = self.pos_embed(hidden_states) # takes care of adding positional embeddings too.
temb = self.time_text_embed(timestep, pooled_projections)
encoder_hidden_states = self.context_embedder(encoder_hidden_states)
if joint_attention_kwargs is not None and "ip_adapter_image_embeds" in ... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
return module(*inputs)
... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
encoder_hidden_states,
... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
# controlnet residual
if block_controlnet_hidden_states is not None and block.context_pre_only is False:
interval_control = len(self.transformer_blocks) / len(block_controlnet_hidden_states)
hidden_states = hidden_states + block_controlnet_hidden_states[int(index_block / inte... | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
if USE_PEFT_BACKEND:
# remove `lora_scale` from each PEFT layer
unscale_lora_layers(self, lora_scale)
if not return_dict:
return (output,)
return Transformer2DModelOutput(sample=output) | 1,103 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py |
class AllegroTransformerBlock(nn.Module):
r"""
Transformer block used in [Allegro](https://github.com/rhymes-ai/Allegro) model. | 1,104 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
Args:
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`, defaults to `0.0`):
... | 1,104 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
Epsilon value for normalization layers.
final_dropout (`bool` defaults to `False`):
Whether to apply a final dropout after the last feed-forward layer.
""" | 1,104 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.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",
attention_bias: bool = False,
norm_elementwise_affine: bool = True,
norm_... | 1,104 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
# 2. Cross Attention
self.norm2 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
... | 1,104 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
temb: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
image_rotary_emb=No... | 1,104 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
attn_output = self.attn1(
norm_hidden_states,
encoder_hidden_states=None,
attention_mask=attention_mask,
image_rotary_emb=image_rotary_emb,
)
attn_output = gate_msa * attn_output
hidden_states = attn_output + hidden_states
if hidden_states... | 1,104 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
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