text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
ff_output = self.ff(norm_hidden_states)
ff_output = gate_mlp * ff_output
hidden_states = ff_output + hidden_states
# TODO(aryan): maybe following line is not required
if hidden_states.ndim == 4:
hidden_states = hidden_states.squeeze(1)
return hidden_states | 1,104 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
class AllegroTransformer3DModel(ModelMixin, ConfigMixin):
_supports_gradient_checkpointing = True
"""
A 3D Transformer model for video-like data. | 1,105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
Args:
patch_size (`int`, defaults to `2`):
The size of spatial patches to use in the patch embedding layer.
patch_size_t (`int`, defaults to `1`):
The size of temporal patches to use in the patch embedding layer.
num_attention_heads (`int`, defaults to `24`):
... | 1,105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
attention_bias (`bool`, defaults to `True`):
Whether or not to use bias in the attention projection layers.
sample_height (`int`, defaults to `90`):
The height of the input latents.
sample_width (`int`, defaults to `160`):
The width of the input latents.
sampl... | 1,105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
Scaling factor to apply in 3D positional embeddings across height dimension.
interpolation_scale_w (`float`, defaults to `2.0`):
Scaling factor to apply in 3D positional embeddings across width dimension.
interpolation_scale_t (`float`, defaults to `2.2`):
Scaling factor to apply... | 1,105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
patch_size: int = 2,
patch_size_t: int = 1,
num_attention_heads: int = 24,
attention_head_dim: int = 96,
in_channels: int = 4,
out_channels: int = 4,
num_layers: int =... | 1,105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
interpolation_scale_t = (
interpolation_scale_t
if interpolation_scale_t is not None
else ((sample_frames - 1) // 16 + 1)
if sample_frames % 2 == 1
else sample_frames // 16
)
interpolation_scale_h = interpolation_scale_h if interpolation_scale_... | 1,105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
# 2. Transformer blocks
self.transformer_blocks = nn.ModuleList(
[
AllegroTransformerBlock(
self.inner_dim,
num_attention_heads,
attention_head_dim,
dropout=dropout,
cross_attention_di... | 1,105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
# 4. Timestep embeddings
self.adaln_single = AdaLayerNormSingle(self.inner_dim, use_additional_conditions=False)
# 5. Caption projection
self.caption_projection = PixArtAlphaTextProjection(in_features=caption_channels, hidden_size=self.inner_dim)
self.gradient_checkpointing = False
... | 1,105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
post_patch_num_frames = num_frames // p_t
post_patch_height = height // p
post_patch_width = width // p | 1,105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.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:
... | 1,105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
# convert mask into a bias that can be added to attention scores:
# (keep = +0, discard = -10000.0)
# b, frame+use_image_num, h, w -> a video with images
# b, 1, h, w -> only images
attention_mask = attention_mask.to(hidden_states.dtype)
attention_mask =... | 1,105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
if attention_mask.numel() > 0:
attention_mask = attention_mask.unsqueeze(1) # [batch_size, 1, num_frames, height, width]
attention_mask = F.max_pool3d(attention_mask, kernel_size=(p_t, p, p), stride=(p_t, p, p))
attention_mask = attention_mask.flatten(1).view(batch_size,... | 1,105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
# 2. Patch embeddings
hidden_states = hidden_states.permute(0, 2, 1, 3, 4).flatten(0, 1)
hidden_states = self.pos_embed(hidden_states)
hidden_states = hidden_states.unflatten(0, (batch_size, -1)).flatten(1, 2)
encoder_hidden_states = self.caption_projection(encoder_hidden_states)
... | 1,105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.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,
encoder_hidden_states,
timest... | 1,105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
# 4. Output normalization & projection
shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1)
hidden_states = self.norm_out(hidden_states)
# Modulation
hidden_states = hidden_states * (1 + scale) + shift
hidden_states = self.proj_out(hidden_st... | 1,105 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py |
class AdaLayerNormShift(nn.Module):
r"""
Norm layer modified to incorporate timestep embeddings.
Parameters:
embedding_dim (`int`): The size of each embedding vector.
num_embeddings (`int`): The size of the embeddings dictionary.
"""
def __init__(self, embedding_dim: int, elementwi... | 1,106 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
class HunyuanDiTBlock(nn.Module):
r"""
Transformer block used in Hunyuan-DiT model (https://github.com/Tencent/HunyuanDiT). Allow skip connection and
QKNorm | 1,107 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
Parameters:
dim (`int`):
The number of channels in the input and output.
num_attention_heads (`int`):
The number of headsto use for multi-head attention.
cross_attention_dim (`int`,*optional*):
The size of the encoder_hidden_states vector for cross attention.
... | 1,107 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
ff_inner_dim (`int`, *optional*):
The size of the hidden layer in the feed-forward block. Defaults to `None`.
ff_bias (`bool`, *optional*, defaults to `True`):
Whether to use bias in the feed-forward block.
skip (`bool`, *optional*, defaults to `False`):
Whether to us... | 1,107 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
def __init__(
self,
dim: int,
num_attention_heads: int,
cross_attention_dim: int = 1024,
dropout=0.0,
activation_fn: str = "geglu",
norm_elementwise_affine: bool = True,
norm_eps: float = 1e-6,
final_dropout: bool = False,
ff_inner_dim: Opt... | 1,107 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
self.attn1 = Attention(
query_dim=dim,
cross_attention_dim=None,
dim_head=dim // num_attention_heads,
heads=num_attention_heads,
qk_norm="layer_norm" if qk_norm else None,
eps=1e-6,
bias=True,
processor=HunyuanAttnProcessor2... | 1,107 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
self.ff = FeedForward(
dim,
dropout=dropout, ### 0.0
activation_fn=activation_fn, ### approx GeLU
final_dropout=final_dropout, ### 0.0
inner_dim=ff_inner_dim, ### int(dim * mlp_ratio)
bias=ff_bias,
)
# 4. Skip Connection
... | 1,107 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
image_rotary_emb=None,
skip=None,
) -> torch.Tensor:
# Notice that normalization is always applied before the real comput... | 1,107 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
# 2. Cross-Attention
hidden_states = hidden_states + self.attn2(
self.norm2(hidden_states),
encoder_hidden_states=encoder_hidden_states,
image_rotary_emb=image_rotary_emb,
)
# FFN Layer ### TODO: switch norm2 and norm3 in the state dict
mlp_inputs = s... | 1,107 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
class HunyuanDiT2DModel(ModelMixin, ConfigMixin):
"""
HunYuanDiT: Diffusion model with a Transformer backbone.
Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers. | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_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 88):
The number of channels in each head.
in_channels (`int`, *optional*):
The number o... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
hidden_size (`int`, *optional*):
The size of hidden layer in the conditioning embedding layers.
num_layers (`int`, *optional*, defaults to 1):
The number of layers of Transformer blocks to use.
mlp_ratio (`float`, *optional*, defaults to 4.0):
The ratio of the hidden ... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
Whether or not to use style condition and image meta size. True for version <=1.1, False for version >= 1.2
""" | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
@register_to_config
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: Optional[int] = None,
patch_size: Optional[int] = None,
activation_fn: str = "gelu-approximate",
sample_size=32,
hidden_size=1152,
... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
self.text_embedder = PixArtAlphaTextProjection(
in_features=cross_attention_dim_t5,
hidden_size=cross_attention_dim_t5 * 4,
out_features=cross_attention_dim,
act_fn="silu_fp32",
)
self.text_embedding_padding = nn.Parameter(
torch.randn(text_le... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
# HunyuanDiT Blocks
self.blocks = nn.ModuleList(
[
HunyuanDiTBlock(
dim=self.inner_dim,
num_attention_heads=self.config.num_attention_heads,
activation_fn=activation_fn,
ff_inner_dim=int(self.inner_dim * ... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedHunyuanAttnProcessor2_0
def fuse_qkv_projections(self):
"""
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
self.set_attn_processor(FusedHunyuanAttnProcessor2_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 🧪 experimenta... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.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... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.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... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.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... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
def forward(
self,
hidden_states,
timestep,
encoder_hidden_states=None,
text_embedding_mask=None,
encoder_hidden_states_t5=None,
text_embedding_mask_t5=None,
image_meta_size=None,
style=None,
image_rotary_emb=None,
controlnet_block_... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
Args:
hidden_states (`torch.Tensor` of shape `(batch size, dim, height, width)`):
The input tensor.
timestep ( `torch.LongTensor`, *optional*):
Used to indicate denoising step.
encoder_hidden_states ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *opti... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
of T5 Text Encoder.
image_meta_size (torch.Tensor):
Conditional embedding indicate the image sizes
style: torch.Tensor:
Conditional embedding indicate the style
image_rotary_emb (`torch.Tensor`):
The image rotary embeddings to apply on query and key tensors du... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
height, width = hidden_states.shape[-2:]
hidden_states = self.pos_embed(hidden_states)
temb = self.time_extra_emb(
timestep, encoder_hidden_states_t5, image_meta_size, style, hidden_dtype=timestep.dtype
) # [B, D]
# text projection
batch_size, sequence_length, _ =... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
skips = []
for layer, block in enumerate(self.blocks):
if layer > self.config.num_layers // 2:
if controlnet_block_samples is not None:
skip = skips.pop() + controlnet_block_samples.pop()
else:
skip = skips.pop()
... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
if controlnet_block_samples is not None and len(controlnet_block_samples) != 0:
raise ValueError("The number of controls is not equal to the number of skip connections.")
# final layer
hidden_states = self.norm_out(hidden_states, temb.to(torch.float32))
hidden_states = self.proj_out... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
hidden_states = hidden_states.reshape(
shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels)
)
hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
output = hidden_states.reshape(
shape=(hidden_states.shape[0], self.out_channels... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
Parameters:
chunk_size (`int`, *optional*):
The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
over each tensor of dim=`dim`.
dim (`int`, *optional*, defaults to `0`):
The dimension over which the ... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
for module in self.children():
fn_recursive_feed_forward(module, chunk_size, dim)
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking
def disable_forward_chunking(self):
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, ... | 1,108 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py |
class LuminaNextDiTBlock(nn.Module):
"""
A LuminaNextDiTBlock for LuminaNextDiT2DModel.
Parameters:
dim (`int`): Embedding dimension of the input features.
num_attention_heads (`int`): Number of attention heads.
num_kv_heads (`int`):
Number of attention heads in key and ... | 1,109 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
def __init__(
self,
dim: int,
num_attention_heads: int,
num_kv_heads: int,
multiple_of: int,
ffn_dim_multiplier: float,
norm_eps: float,
qk_norm: bool,
cross_attention_dim: int,
norm_elementwise_affine: bool = True,
) -> None:
s... | 1,109 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
# Cross-attention
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim,
dim_head=dim // num_attention_heads,
qk_norm="layer_norm_across_heads" if qk_norm else None,
heads=num_attention_heads,
kv_heads=num_kv_heads,... | 1,109 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
self.norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine)
self.ffn_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine)
self.norm1_context = RMSNorm(cross_attention_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine)
def forward(
... | 1,109 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
Parameters:
hidden_states (`torch.Tensor`): The input of hidden_states for LuminaNextDiTBlock.
attention_mask (`torch.Tensor): The input of hidden_states corresponse attention mask.
image_rotary_emb (`torch.Tensor`): Precomputed cosine and sine frequencies.
encoder_hidden... | 1,109 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
# Self-attention
norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb)
self_attn_output = self.attn1(
hidden_states=norm_hidden_states,
encoder_hidden_states=norm_hidden_states,
attention_mask=attention_mask,
query_rotary_emb=... | 1,109 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
# Cross-attention
norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states)
cross_attn_output = self.attn2(
hidden_states=norm_hidden_states,
encoder_hidden_states=norm_encoder_hidden_states,
attention_mask=encoder_mask,
query_rotary_emb=image... | 1,109 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
return hidden_states | 1,109 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
class LuminaNextDiT2DModel(ModelMixin, ConfigMixin):
"""
LuminaNextDiT: Diffusion model with a Transformer backbone.
Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers. | 1,110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.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`, *optional*, (`int`, *optional*, defaults to 2):
The size of each patch in the image. This parameter defines... | 1,110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
The number of attention heads in each attention layer. This parameter specifies how many separate attention
mechanisms are used.
num_kv_heads (`int`, *optional*, defaults to 8):
The number of key-value heads in the attention mechanism, if different from the number of attention heads.
... | 1,110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
Whether the model should learn the sigma parameter, which might be related to uncertainty or variance in
predictions.
qk_norm (`bool`, *optional*, defaults to True):
Indicates if the queries and keys in the attention mechanism should be normalized.
cross_attention_dim (`int`, *op... | 1,110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
@register_to_config
def __init__(
self,
sample_size: int = 128,
patch_size: Optional[int] = 2,
in_channels: Optional[int] = 4,
hidden_size: Optional[int] = 2304,
num_layers: Optional[int] = 32,
num_attention_heads: Optional[int] = 32,
num_kv_heads: Opt... | 1,110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
self.head_dim = hidden_size // num_attention_heads
self.scaling_factor = scaling_factor | 1,110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
self.patch_embedder = LuminaPatchEmbed(
patch_size=patch_size, in_channels=in_channels, embed_dim=hidden_size, bias=True
)
self.pad_token = nn.Parameter(torch.empty(hidden_size))
self.time_caption_embed = LuminaCombinedTimestepCaptionEmbedding(
hidden_size=min(hidden_si... | 1,110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
self.layers = nn.ModuleList(
[
LuminaNextDiTBlock(
hidden_size,
num_attention_heads,
num_kv_heads,
multiple_of,
ffn_dim_multiplier,
norm_eps,
qk_norm,
... | 1,110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
def forward(
self,
hidden_states: torch.Tensor,
timestep: torch.Tensor,
encoder_hidden_states: torch.Tensor,
encoder_mask: torch.Tensor,
image_rotary_emb: torch.Tensor,
cross_attention_kwargs: Dict[str, Any] = None,
return_dict=True,
) -> torch.Tensor:... | 1,110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
encoder_mask = encoder_mask.bool()
for layer in self.layers:
hidden_states = layer(
hidden_states,
mask,
image_rotary_emb,
encoder_hidden_states,
encoder_mask,
temb=temb,
cross_attention_k... | 1,110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
return Transformer2DModelOutput(sample=output) | 1,110 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py |
class DiTTransformer2DModel(ModelMixin, ConfigMixin):
r"""
A 2D Transformer model as introduced in DiT (https://arxiv.org/abs/2212.09748). | 1,111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dit_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... | 1,111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dit_transformer_2d.py |
sample_size (int, defaults to 32):
The width of the latent images. This parameter is fixed during training.
patch_size (int, defaults to 2):
Size of the patches the model processes, relevant for architectures working on non-sequential data.
activation_fn (str, optional, defaults ... | 1,111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dit_transformer_2d.py |
If true, enables element-wise affine parameters in the normalization layers.
norm_eps (float, optional, defaults to 1e-5):
A small constant added to the denominator in normalization layers to prevent division by zero.
""" | 1,111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dit_transformer_2d.py |
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 72,
in_channels: int = 4,
out_channels: Optional[int] = None,
num_layers: int = 28,
dropout: float = 0.0,
norm... | 1,111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dit_transformer_2d.py |
# Validate inputs.
if norm_type != "ada_norm_zero":
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_zero" and num_embeds_ada_norm is None:
raise... | 1,111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dit_transformer_2d.py |
self.patch_size = self.config.patch_size
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,
) | 1,111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dit_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,
activation_fn=se... | 1,111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dit_transformer_2d.py |
# 3. Output blocks.
self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)
self.proj_out_1 = nn.Linear(self.inner_dim, 2 * self.inner_dim)
self.proj_out_2 = nn.Linear(
self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_channels
... | 1,111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dit_transformer_2d.py |
Args:
hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
Input `hidden_states`.
timestep ( `torch.LongTensor`, *optional*):
Used to indicate ... | 1,111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dit_transformer_2d.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.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plai... | 1,111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dit_transformer_2d.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.
"""
# 1. Input
height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // s... | 1,111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dit_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,
None,
None,
... | 1,111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dit_transformer_2d.py |
# 3. Output
conditioning = self.transformer_blocks[0].norm1.emb(timestep, class_labels, hidden_dtype=hidden_states.dtype)
shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
hidden_state... | 1,111 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dit_transformer_2d.py |
class DualTransformer2DModel(nn.Module):
"""
Dual transformer wrapper that combines two `Transformer2DModel`s for mixed inference. | 1,112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dual_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 88): The number of channels in each head.
in_channels (`int`, *optional*):
Pass if the input is continuous. The... | 1,112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dual_transformer_2d.py |
Pass if the input is discrete. The number of classes of the vector embeddings of the latent pixels.
Includes the class for the masked latent pixel.
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
num_embeds_ada_norm ( `int`, *optional... | 1,112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dual_transformer_2d.py |
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: Optional[int] = None,
num_layers: int = 1,
dropout: float = 0.0,
norm_num_groups: int = 32,
cross_attention_dim: Optional[int] = None,
attention_bias: boo... | 1,112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dual_transformer_2d.py |
attention_bias=attention_bias,
sample_size=sample_size,
num_vector_embeds=num_vector_embeds,
activation_fn=activation_fn,
num_embeds_ada_norm=num_embeds_ada_norm,
)
for _ in range(2)
]
) | 1,112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dual_transformer_2d.py |
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
self.mix_ratio = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`... | 1,112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dual_transformer_2d.py |
def forward(
self,
hidden_states,
encoder_hidden_states,
timestep=None,
attention_mask=None,
cross_attention_kwargs=None,
return_dict: bool = True,
):
"""
Args:
hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size... | 1,112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dual_transformer_2d.py |
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/mode... | 1,112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dual_transformer_2d.py |
Returns:
[`~models.transformers.transformer_2d.Transformer2DModelOutput`] or `tuple`:
[`~models.transformers.transformer_2d.Transformer2DModelOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is the sample tensor.
"""
in... | 1,112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dual_transformer_2d.py |
encoded_states = []
tokens_start = 0
# attention_mask is not used yet
for i in range(2):
# for each of the two transformers, pass the corresponding condition tokens
condition_state = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
... | 1,112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dual_transformer_2d.py |
if not return_dict:
return (output_states,)
return Transformer2DModelOutput(sample=output_states) | 1,112 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dual_transformer_2d.py |
class TransformerTemporalModelOutput(BaseOutput):
"""
The output of [`TransformerTemporalModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size x num_frames, num_channels, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input.
"""
sample: ... | 1,113 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
class TransformerTemporalModel(ModelMixin, ConfigMixin):
"""
A Transformer model for video-like data. | 1,114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.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*):
The number of channels in the input ... | 1,114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
This is fixed during training since it is used to learn a number of position embeddings.
activation_fn (`str`, *optional*, defaults to `"geglu"`):
Activation function to use in feed-forward. See `diffusers.models.activations.get_activation` for supported
activation functions.
nor... | 1,114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.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... | 1,114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
self.proj_in = nn.Linear(in_channels, inner_dim)
# 3. Define transformers blocks
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(
... | 1,114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
self.proj_out = nn.Linear(inner_dim, in_channels)
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.LongTensor] = None,
timestep: Optional[torch.LongTensor] = None,
class_labels: torch.LongTensor = None,
num_frames: int = 1,
... | 1,114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
Args:
hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.Tensor` of shape `(batch size, channel, height, width)` if continuous):
Input hidden_states.
encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_sta... | 1,114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
The number of frames to be processed per batch. This is used to reshape the hidden states.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diff... | 1,114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
Returns:
[`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
If `return_dict` is True, an
[`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`] is returned, otherwise a
`tuple` where the first element... | 1,114 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py |
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