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class PixArtTransformer2DModel(ModelMixin, ConfigMixin):
r"""
A 2D Transformer model as introduced in PixArt family of models (https://arxiv.org/abs/2310.00426,
https://arxiv.org/abs/2403.04692). | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.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 72): The number of channels in each head.
in_channels (int, defaults to 4): The number of channels in the input.
out_channe... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
attention_bias (bool, optional, defaults to True):
Configure if the Transformer blocks' attention should contain a bias parameter.
sample_size (int, defaults to 128):
The width of the latent images. This parameter is fixed during training.
patch_size (int, defaults to 2):
... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
norm_type (str, optional, defaults to "ada_norm_zero"):
Specifies the type of normalization used, can be 'ada_norm_zero'.
norm_elementwise_affine (bool, optional, defaults to False):
If true, enables element-wise affine parameters in the normalization layers.
norm_eps (float, opt... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
Deprecated argument. Will be removed in a future version.
num_vector_embeds (bool, optional, defaults to False):
Deprecated argument. Will be removed in a future version.
""" | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
_supports_gradient_checkpointing = True
_no_split_modules = ["BasicTransformerBlock", "PatchEmbed"] | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
@register_to_config
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 72,
in_channels: int = 4,
out_channels: Optional[int] = 8,
num_layers: int = 28,
dropout: float = 0.0,
norm_num_groups: int = 32,
cross_attention_d... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
# Validate inputs.
if norm_type != "ada_norm_single":
raise NotImplementedError(
f"Forward pass is not implemented when `patch_size` is not None and `norm_type` is '{norm_type}'."
)
elif norm_type == "ada_norm_single" and num_embeds_ada_norm is None:
r... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
# Set some common variables used across the board.
self.attention_head_dim = attention_head_dim
self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
self.out_channels = in_channels if out_channels is None else out_channels
if use_additional_conditions is None... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
interpolation_scale = (
self.config.interpolation_scale
if self.config.interpolation_scale is not None
else max(self.config.sample_size // 64, 1)
)
self.pos_embed = PatchEmbed(
height=self.config.sample_size,
width=self.config.sample_size,
... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(
self.inner_dim,
self.config.num_attention_heads,
self.config.attention_head_dim,
dropout=self.config.dropout,
cross_attention_... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
# 3. Output blocks.
self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)
self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim**0.5)
self.proj_out = nn.Linear(self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_ch... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.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
... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.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 ... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.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."
... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
Safe to just use `AttnProcessor()` as PixArt doesn't have any exotic attention processors in default model.
"""
self.set_attn_processor(AttnProcessor())
... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
for _, attn_processor in self.attn_processors.items():
if "Added" in str(attn_processor.__class__.__name__):
raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
self.original_attn_processors = self.attn_processors
for module... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
timestep: Optional[torch.LongTensor] = None,
added_cond_kwargs: Dict[str, torch.Tensor] = None,
cross_attention_kwargs: Dict[str, Any] = None,
attention_mask: Opt... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.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)`, *optional*):
Conditional embeddings for cross attention ... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
attention_mask ( `torch.Tensor`, *optional*):
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
* Mask `(batch, sequence_length)` True = keep, False = discard.
* Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.
If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
above. This bias will be added to the... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
# we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
# we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
# expects mask of shape:
... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
# (keep = +0, discard = -10000.0)
attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
attention_mask = attention_mask.unsqueeze(1) | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
# convert encoder_attention_mask to a bias the same way we do for attention_mask
if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
encoder_attention_mask = encoder_attention... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
if self.caption_projection is not None:
encoder_hidden_states = self.caption_projection(encoder_hidden_states)
encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1])
# 2. Blocks
for block in self.transformer_blocks:
if torch.is_gr... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
attention_mask,
encoder_hidde... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
# 3. Output
shift, scale = (
self.scale_shift_table[None] + embedded_timestep[:, None].to(self.scale_shift_table.device)
).chunk(2, dim=1)
hidden_states = self.norm_out(hidden_states)
# Modulation
hidden_states = hidden_states * (1 + scale.to(hidden_states.device)) + ... | 1,144 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py |
class HunyuanVideoAttnProcessor2_0:
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"HunyuanVideoAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0."
)
def __call__(
self,
attn... | 1,145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2)
key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2)
value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2)
# 2. QK normalization
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.nor... | 1,145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
if attn.add_q_proj is None and encoder_hidden_states is not None:
query = torch.cat(
[
apply_rotary_emb(query[:, :, : -encoder_hidden_states.shape[1]], image_rotary_emb),
query[:, :, -encoder_hidden_states.shape[1] :],
... | 1,145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
# 4. Encoder condition QKV projection and normalization
if attn.add_q_proj is not None and encoder_hidden_states is not None:
encoder_query = attn.add_q_proj(encoder_hidden_states)
encoder_key = attn.add_k_proj(encoder_hidden_states)
encoder_value = attn.add_v_proj(encoder_hi... | 1,145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
# 5. Attention
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
hidden_states = hidden_states.to(query.dtype)
# 6. Output projecti... | 1,145 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
class HunyuanVideoPatchEmbed(nn.Module):
def __init__(
self,
patch_size: Union[int, Tuple[int, int, int]] = 16,
in_chans: int = 3,
embed_dim: int = 768,
) -> None:
super().__init__()
patch_size = (patch_size, patch_size, patch_size) if isinstance(patch_size, int)... | 1,146 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
class HunyuanVideoAdaNorm(nn.Module):
def __init__(self, in_features: int, out_features: Optional[int] = None) -> None:
super().__init__()
out_features = out_features or 2 * in_features
self.linear = nn.Linear(in_features, out_features)
self.nonlinearity = nn.SiLU()
def forward... | 1,147 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
class HunyuanVideoIndividualTokenRefinerBlock(nn.Module):
def __init__(
self,
num_attention_heads: int,
attention_head_dim: int,
mlp_width_ratio: str = 4.0,
mlp_drop_rate: float = 0.0,
attention_bias: bool = True,
) -> None:
super().__init__()
hid... | 1,148 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
def forward(
self,
hidden_states: torch.Tensor,
temb: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
norm_hidden_states = self.norm1(hidden_states)
attn_output = self.attn(
hidden_states=norm_hidden_states,
enc... | 1,148 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
class HunyuanVideoIndividualTokenRefiner(nn.Module):
def __init__(
self,
num_attention_heads: int,
attention_head_dim: int,
num_layers: int,
mlp_width_ratio: float = 4.0,
mlp_drop_rate: float = 0.0,
attention_bias: bool = True,
) -> None:
super()._... | 1,149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
def forward(
self,
hidden_states: torch.Tensor,
temb: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> None:
self_attn_mask = None
if attention_mask is not None:
batch_size = attention_mask.shape[0]
seq_len = attention_mask.sha... | 1,149 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
class HunyuanVideoTokenRefiner(nn.Module):
def __init__(
self,
in_channels: int,
num_attention_heads: int,
attention_head_dim: int,
num_layers: int,
mlp_ratio: float = 4.0,
mlp_drop_rate: float = 0.0,
attention_bias: bool = True,
) -> None:
... | 1,150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
def forward(
self,
hidden_states: torch.Tensor,
timestep: torch.LongTensor,
attention_mask: Optional[torch.LongTensor] = None,
) -> torch.Tensor:
if attention_mask is None:
pooled_projections = hidden_states.mean(dim=1)
else:
original_dtype = h... | 1,150 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
class HunyuanVideoRotaryPosEmbed(nn.Module):
def __init__(self, patch_size: int, patch_size_t: int, rope_dim: List[int], theta: float = 256.0) -> None:
super().__init__()
self.patch_size = patch_size
self.patch_size_t = patch_size_t
self.rope_dim = rope_dim
self.theta = thet... | 1,151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
axes_grids = []
for i in range(3):
# Note: The following line diverges from original behaviour. We create the grid on the device, whereas
# original implementation creates it on CPU and then moves it to device. This results in numerical
# differences in layerwise debugging ou... | 1,151 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
class HunyuanVideoSingleTransformerBlock(nn.Module):
def __init__(
self,
num_attention_heads: int,
attention_head_dim: int,
mlp_ratio: float = 4.0,
qk_norm: str = "rms_norm",
) -> None:
super().__init__()
hidden_size = num_attention_heads * attention_head... | 1,152 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
temb: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
) -> torch.Tensor:
text_seq_length = e... | 1,152 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
# 2. Attention
attn_output, context_attn_output = self.attn(
hidden_states=norm_hidden_states,
encoder_hidden_states=norm_encoder_hidden_states,
attention_mask=attention_mask,
image_rotary_emb=image_rotary_emb,
)
attn_output = torch.cat([attn_outpu... | 1,152 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
class HunyuanVideoTransformerBlock(nn.Module):
def __init__(
self,
num_attention_heads: int,
attention_head_dim: int,
mlp_ratio: float,
qk_norm: str = "rms_norm",
) -> None:
super().__init__()
hidden_size = num_attention_heads * attention_head_dim
... | 1,153 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.ff = FeedForward(hidden_size, mult=mlp_ratio, activation_fn="gelu-approximate")
self.norm2_context = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.ff_context = FeedForward(hidden_size, mult=mlp_... | 1,153 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
# 2. Joint attention
attn_output, context_attn_output = self.attn(
hidden_states=norm_hidden_states,
encoder_hidden_states=norm_encoder_hidden_states,
attention_mask=attention_mask,
image_rotary_emb=freqs_cis,
)
# 3. Modulation and residual connec... | 1,153 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
hidden_states = hidden_states + gate_mlp.unsqueeze(1) * ff_output
encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
return hidden_states, encoder_hidden_states | 1,153 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
r"""
A Transformer model for video-like data used in [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo). | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
Args:
in_channels (`int`, defaults to `16`):
The number of channels in the input.
out_channels (`int`, defaults to `16`):
The number of channels in the output.
num_attention_heads (`int`, defaults to `24`):
The number of heads to use for multi-head attention.
... | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
The size of the spatial patches to use in the patch embedding layer.
patch_size_t (`int`, defaults to `1`):
The size of the tmeporal patches to use in the patch embedding layer.
qk_norm (`str`, defaults to `rms_norm`):
The normalization to use for the query and key projections in... | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
_supports_gradient_checkpointing = True
_no_split_modules = [
"HunyuanVideoTransformerBlock",
"HunyuanVideoSingleTransformerBlock",
"HunyuanVideoPatchEmbed",
"HunyuanVideoTokenRefiner",
]
@register_to_config
def __init__(
self,
in_channels: int = 16,
... | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
# 1. Latent and condition embedders
self.x_embedder = HunyuanVideoPatchEmbed((patch_size_t, patch_size, patch_size), in_channels, inner_dim)
self.context_embedder = HunyuanVideoTokenRefiner(
text_embed_dim, num_attention_heads, attention_head_dim, num_layers=num_refiner_layers
)
... | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
# 4. Single stream transformer blocks
self.single_transformer_blocks = nn.ModuleList(
[
HunyuanVideoSingleTransformerBlock(
num_attention_heads, attention_head_dim, mlp_ratio=mlp_ratio, qk_norm=qk_norm
)
for _ in range(num_single_la... | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.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
... | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.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 ... | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.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."
... | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
def _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value
def forward(
self,
hidden_states: torch.Tensor,
timestep: torch.LongTensor,
encoder_hidden_states: torch.Tensor,
... | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.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... | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
# 2. Conditional embeddings
temb = self.time_text_embed(timestep, guidance, pooled_projections)
hidden_states = self.x_embedder(hidden_states)
encoder_hidden_states = self.context_embedder(encoder_hidden_states, timestep, encoder_attention_mask)
# 3. Attention mask preparation
l... | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
for i in range(batch_size):
attention_mask[i, : effective_sequence_length[i]] = True
# [B, 1, 1, N], for broadcasting across attention heads
attention_mask = attention_mask.unsqueeze(1).unsqueeze(1)
# 4. Transformer blocks
if torch.is_grad_enabled() and self.gradient_checkpo... | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
for block in self.transformer_blocks:
hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
encoder_hidden_states,
temb,
attention_mask,
... | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
else:
for block in self.transformer_blocks:
hidden_states, encoder_hidden_states = block(
hidden_states, encoder_hidden_states, temb, attention_mask, image_rotary_emb
)
for block in self.single_transformer_blocks:
hidden_states... | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
if USE_PEFT_BACKEND:
# remove `lora_scale` from each PEFT layer
unscale_lora_layers(self, lora_scale)
if not return_dict:
return (hidden_states,)
return Transformer2DModelOutput(sample=hidden_states) | 1,154 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py |
class CogVideoXBlock(nn.Module):
r"""
Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model. | 1,155 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_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`):
... | 1,155 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_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... | 1,155 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_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... | 1,155 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_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... | 1,155 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
temb: torch.Tensor,
image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
attention_kwargs: Optional[Dict[str, Any]] = None,
) -> torch.Tensor:
text_seq_length... | 1,155 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
hidden_states = hidden_states + gate_msa * attn_hidden_states
encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_encoder_hidden_states
# norm & modulate
norm_hidden_states, norm_encoder_hidden_states, gate_ff, enc_gate_ff = self.norm2(
hidden_states, encoder_hidden_... | 1,155 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
"""
A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo). | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_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 ... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
The number of layers of Transformer blocks to use.
dropout (`float`, defaults to `0.0`):
The dropout probability to use.
attention_bias (`bool`, defaults to `True`):
Whether to use bias in the attention projection layers.
sample_width (`int`, defaults to `90`):
... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
The size of the patches to use in the patch embedding layer.
temporal_compression_ratio (`int`, defaults to `4`):
The compression ratio across the temporal dimension. See documentation for `sample_frames`.
max_text_seq_length (`int`, defaults to `226`):
The maximum sequence lengt... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
Scaling factor to apply in 3D positional embeddings across spatial dimensions.
temporal_interpolation_scale (`float`, defaults to `1.0`):
Scaling factor to apply in 3D positional embeddings across temporal dimensions.
""" | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
_supports_gradient_checkpointing = True
_no_split_modules = ["CogVideoXBlock", "CogVideoXPatchEmbed"] | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_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,
ofs_embe... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
use_rotary_positional_embeddings: bool = False,
use_learned_positional_embeddings: bool = False,
patch_bias: bool = True,
):
super().__init__()
inner_dim = num_attention_heads * attention_head_dim | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
if not use_rotary_positional_embeddings and use_learned_positional_embeddings:
raise ValueError(
"There are no CogVideoX checkpoints available with disable rotary embeddings and learned positional "
"embeddings. If you're using a custom model and/or believe this should be sup... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
# 1. Patch embedding
self.patch_embed = CogVideoXPatchEmbed(
patch_size=patch_size,
patch_size_t=patch_size_t,
in_channels=in_channels,
embed_dim=inner_dim,
text_embed_dim=text_embed_dim,
bias=patch_bias,
sample_width=sample_wid... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn)
self.ofs_proj = None
self.ofs_embedding = None
if ofs_embed_dim:
self.ofs_proj = Timesteps(ofs_embed_dim, flip_sin_to... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
# 3. Define spatio-temporal transformers blocks
self.transformer_blocks = nn.ModuleList(
[
CogVideoXBlock(
dim=inner_dim,
num_attention_heads=num_attention_heads,
attention_head_dim=attention_head_dim,
ti... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_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,
)
if patch_size_t is None:
# For CogV... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_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
... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_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 ... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_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."
... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedCogVideoXAttnProcessor2_0
def fuse_qkv_projections(self):
"""
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
self.set_attn_processor(FusedCogVideoXAttnProcessor2_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 🧪 experimen... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_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,
ofs: Optional[Union[int, float, torch.LongTensor]] = None,
image_rotary_emb: Optional[... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_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... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
if self.ofs_embedding is not None:
ofs_emb = self.ofs_proj(ofs)
ofs_emb = ofs_emb.to(dtype=hidden_states.dtype)
ofs_emb = self.ofs_embedding(ofs_emb)
emb = emb + ofs_emb
# 2. Patch embedding
hidden_states = self.patch_embed(encoder_hidden_states, hidden_s... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_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,
... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
if not self.config.use_rotary_positional_embeddings:
# CogVideoX-2B
hidden_states = self.norm_final(hidden_states)
else:
# CogVideoX-5B
hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
hidden_states = self.norm_final(hidden_stat... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
if p_t is None:
output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p)
output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
else:
output = hidden_states.reshape(
batch_size, (num_frames + p_t - 1) // p_t, ... | 1,156 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py |
class CogView3PlusTransformerBlock(nn.Module):
r"""
Transformer block used in [CogView](https://github.com/THUDM/CogView3) model.
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... | 1,157 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
self.attn1 = Attention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
out_dim=dim,
bias=True,
qk_norm="layer_norm",
elementwise_affine=False,
eps=1e-6,
processor=CogVideoXAttnProcessor2_0... | 1,157 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
# norm & modulate
(
norm_hidden_states,
gate_msa,
shift_mlp,
scale_mlp,
gate_mlp,
norm_encoder_hidden_states,
c_gate_msa,
c_shift_mlp,
c_scale_mlp,
c_gate_mlp,
) = self.norm1(hidden_st... | 1,157 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
# feed-forward
norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
ff_ou... | 1,157 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
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