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def _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value
def forward(
self,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
timestep: torch.L... | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
# Apply patch embedding, timestep embedding, and project the caption embeddings.
hidden_states = self.pos_embed(hidden_states) # takes care of adding positional embeddings too.
temb = self.time_step_embed(timestep).to(dtype=next(self.parameters()).dtype)
temb = self.time_step_proj(temb)
... | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.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)
... | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
# Single DiT blocks that combine the `hidden_states` (image) and `encoder_hidden_states` (text)
if len(self.single_transformer_blocks) > 0:
encoder_seq_len = encoder_hidden_states.size(1)
combined_hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
for i... | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
combined_hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
combined_hidden_states,
temb,
... | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
hidden_states = hidden_states.reshape(
shape=(hidden_states.shape[0], height, width, patch_size, patch_size, out_channels)
)
hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
output = hidden_states.reshape(
shape=(hidden_states.shape[0], out_channels, height *... | 1,129 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py |
class T5FilmDecoder(ModelMixin, ConfigMixin):
r"""
T5 style decoder with FiLM conditioning.
Args:
input_dims (`int`, *optional*, defaults to `128`):
The number of input dimensions.
targets_length (`int`, *optional*, defaults to `256`):
The length of the targets.
... | 1,130 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
@register_to_config
def __init__(
self,
input_dims: int = 128,
targets_length: int = 256,
max_decoder_noise_time: float = 2000.0,
d_model: int = 768,
num_layers: int = 12,
num_heads: int = 12,
d_kv: int = 64,
d_ff: int = 2048,
dropout_r... | 1,130 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
self.decoders = nn.ModuleList()
for lyr_num in range(num_layers):
# FiLM conditional T5 decoder
lyr = DecoderLayer(d_model=d_model, d_kv=d_kv, num_heads=num_heads, d_ff=d_ff, dropout_rate=dropout_rate)
self.decoders.append(lyr)
self.decoder_norm = T5LayerNorm(d_model... | 1,130 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
time_steps = get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time,
embedding_dim=self.config.d_model,
max_period=self.config.max_decoder_noise_time,
).to(dtype=self.dtyp... | 1,130 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
inputs = self.continuous_inputs_projection(decoder_input_tokens)
inputs += position_encodings
y = self.dropout(inputs)
# decoder: No padding present.
decoder_mask = torch.ones(
decoder_input_tokens.shape[:2], device=decoder_input_tokens.device, dtype=inputs.dtype
)
... | 1,130 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
y = self.decoder_norm(y)
y = self.post_dropout(y)
spec_out = self.spec_out(y)
return spec_out | 1,130 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
class DecoderLayer(nn.Module):
r"""
T5 decoder layer.
Args:
d_model (`int`):
Size of the input hidden states.
d_kv (`int`):
Size of the key-value projection vectors.
num_heads (`int`):
Number of attention heads.
d_ff (`int`):
S... | 1,131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
# cross attention: layer 1
self.layer.append(
T5LayerCrossAttention(
d_model=d_model,
d_kv=d_kv,
num_heads=num_heads,
dropout_rate=dropout_rate,
layer_norm_epsilon=layer_norm_epsilon,
)
)
# F... | 1,131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
def forward(
self,
hidden_states: torch.Tensor,
conditioning_emb: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
encoder_dec... | 1,131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
# Apply Film Conditional Feed Forward layer
hidden_states = self.layer[-1](hidden_states, conditioning_emb)
return (hidden_states,) | 1,131 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
class T5LayerSelfAttentionCond(nn.Module):
r"""
T5 style self-attention layer with conditioning.
Args:
d_model (`int`):
Size of the input hidden states.
d_kv (`int`):
Size of the key-value projection vectors.
num_heads (`int`):
Number of attention... | 1,132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
def forward(
self,
hidden_states: torch.Tensor,
conditioning_emb: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
# pre_self_attention_layer_norm
normed_hidden_states = self.layer_norm(hidden_states)
if conditi... | 1,132 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
class T5LayerCrossAttention(nn.Module):
r"""
T5 style cross-attention layer.
Args:
d_model (`int`):
Size of the input hidden states.
d_kv (`int`):
Size of the key-value projection vectors.
num_heads (`int`):
Number of attention heads.
drop... | 1,133 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.attention(
norm... | 1,133 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
class T5LayerFFCond(nn.Module):
r"""
T5 style feed-forward conditional layer.
Args:
d_model (`int`):
Size of the input hidden states.
d_ff (`int`):
Size of the intermediate feed-forward layer.
dropout_rate (`float`):
Dropout probability.
l... | 1,134 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
def forward(self, hidden_states: torch.Tensor, conditioning_emb: Optional[torch.Tensor] = None) -> torch.Tensor:
forwarded_states = self.layer_norm(hidden_states)
if conditioning_emb is not None:
forwarded_states = self.film(forwarded_states, conditioning_emb)
forwarded_states = sel... | 1,134 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
class T5DenseGatedActDense(nn.Module):
r"""
T5 style feed-forward layer with gated activations and dropout.
Args:
d_model (`int`):
Size of the input hidden states.
d_ff (`int`):
Size of the intermediate feed-forward layer.
dropout_rate (`float`):
... | 1,135 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
hidden_states = self.wo(hidden_states)
return hidden_states | 1,135 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
class T5LayerNorm(nn.Module):
r"""
T5 style layer normalization module.
Args:
hidden_size (`int`):
Size of the input hidden states.
eps (`float`, `optional`, defaults to `1e-6`):
A small value used for numerical stability to avoid dividing by zero.
"""
def _... | 1,136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
# w/o mean and there is no bias. Additionally we ... | 1,136 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
class NewGELUActivation(nn.Module):
"""
Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see
the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
"""
def forward(self, input: torch.Tensor) -> torch.Tensor:
return... | 1,137 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
class T5FiLMLayer(nn.Module):
"""
T5 style FiLM Layer.
Args:
in_features (`int`):
Number of input features.
out_features (`int`):
Number of output features.
"""
def __init__(self, in_features: int, out_features: int):
super().__init__()
self.... | 1,138 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py |
class FluxSingleTransformerBlock(nn.Module):
r"""
A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.
Reference: https://arxiv.org/abs/2403.03206
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): Th... | 1,139 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
if is_torch_npu_available():
processor = FluxAttnProcessor2_0_NPU()
else:
processor = FluxAttnProcessor2_0()
self.attn = Attention(
query_dim=dim,
cross_attention_dim=None,
dim_head=attention_head_dim,
heads=num_attention_heads,
... | 1,139 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
def forward(
self,
hidden_states: torch.Tensor,
temb: torch.Tensor,
image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
) -> torch.Tensor:
residual = hidden_states
norm_hidden_states, g... | 1,139 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
return hidden_states | 1,139 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
class FluxTransformerBlock(nn.Module):
r"""
A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.
Reference: https://arxiv.org/abs/2403.03206
Args:
dim (`int`):
The embedding dimension of the block.
num_attention_heads (`int`):
... | 1,140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
if hasattr(F, "scaled_dot_product_attention"):
processor = FluxAttnProcessor2_0()
else:
raise ValueError(
"The current PyTorch version does not support the `scaled_dot_product_attention` function."
)
self.attn = Attention(
query_dim=dim,
... | 1,140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
# let chunk size default to None
self._chunk_size = None
self._chunk_dim = 0
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,
... | 1,140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
encoder_hidden_states, emb=temb
)
joint_attention_kwargs = joint_attention_kwargs or {}
# Attention.
attention_outputs = self.attn(
hidden_states=norm_hidden_states,... | 1,140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.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
if len(attentio... | 1,140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
context_ff_output = self.ff_context(norm_encoder_hidden_states)
encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
if encoder_hidden_states.dtype == torch.float16:
encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)
return encod... | 1,140 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
class FluxTransformer2DModel(
ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, FluxTransformer2DLoadersMixin
):
"""
The Transformer model introduced in Flux.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/ | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
Args:
patch_size (`int`, defaults to `1`):
Patch size to turn the input data into small patches.
in_channels (`int`, defaults to `64`):
The number of channels in the input.
out_channels (`int`, *optional*, defaults to `None`):
The number of channels in the out... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
`encoder_hidden_states`).
pooled_projection_dim (`int`, defaults to `768`):
The number of dimensions to use for the pooled projection.
guidance_embeds (`bool`, defaults to `False`):
Whether to use guidance embeddings for guidance-distilled variant of the model.
axes_dims_... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
_supports_gradient_checkpointing = True
_no_split_modules = ["FluxTransformerBlock", "FluxSingleTransformerBlock"]
@register_to_config
def __init__(
self,
patch_size: int = 1,
in_channels: int = 64,
out_channels: Optional[int] = None,
num_layers: int = 19,
nu... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
text_time_guidance_cls = (
CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings
)
self.time_text_embed = text_time_guidance_cls(
embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
)
self.cont... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
self.single_transformer_blocks = nn.ModuleList(
[
FluxSingleTransformerBlock(
dim=self.inner_dim,
num_attention_heads=num_attention_heads,
attention_head_dim=attention_head_dim,
)
for _ in range(num_s... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.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
... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.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 ... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.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."
... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedFluxAttnProcessor2_0
def fuse_qkv_projections(self):
"""
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
self.set_attn_processor(FusedFluxAttnProcessor2_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.
... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor = None,
pooled_projections: torch.Tensor = None,
timestep: torch.LongTensor = None,
img_ids: torch.Tensor = None,
txt_ids: torch.Tensor = None,
guidance: torch.Tensor = Non... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
Args:
hidden_states (`torch.Tensor` of shape `(batch_size, image_sequence_length, in_channels)`):
Input `hidden_states`.
encoder_hidden_states (`torch.Tensor` of shape `(batch_size, text_sequence_length, joint_attention_dim)`):
Conditional embeddings (embeddings c... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
`self.processor` in
[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.Transformer2DM... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.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()
... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
timestep = timestep.to(hidden_states.dtype) * 1000
if guidance is not None:
guidance = guidance.to(hidden_states.dtype) * 1000
else:
guidance = None
temb = (
self.time_text_embed(timestep, pooled_projections)
if guidance is None
else s... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
ids = torch.cat((txt_ids, img_ids), dim=0)
image_rotary_emb = self.pos_embed(ids)
if joint_attention_kwargs is not None and "ip_adapter_image_embeds" in joint_attention_kwargs:
ip_adapter_image_embeds = joint_attention_kwargs.pop("ip_adapter_image_embeds")
ip_hidden_states = sel... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.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,
... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
# controlnet residual
if controlnet_block_samples is not None:
interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
interval_control = int(np.ceil(interval_control))
# For Xlabs ControlNet.
if controlnet_blocks_repeat... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.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)
... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
else:
hidden_states = block(
hidden_states=hidden_states,
temb=temb,
image_rotary_emb=image_rotary_emb,
joint_attention_kwargs=joint_attention_kwargs,
)
# controlnet residual
if contr... | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.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) | 1,141 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py |
class Transformer2DModelOutput(Transformer2DModelOutput):
def __init__(self, *args, **kwargs):
deprecation_message = "Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs imp... | 1,142 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
class Transformer2DModel(LegacyModelMixin, LegacyConfigMixin):
"""
A 2D Transformer model for image-like data. | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/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 of channels in the input ... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
Includes the class for the masked latent pixel.
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
num_embeds_ada_norm ( `int`, *opti... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
attention_bias (`bool`, *optional*):
Configure if the `TransformerBlocks` attention should contain a bias parameter.
"""
_supports_gradient_checkpointing = True
_no_split_modules = ["BasicTransfor... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.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... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
norm_eps: float = 1e-5,
attention_type: str = "default",
caption_channels: int = None,
interpolation_scale: float = None,
use_additional_conditions: Optional[bool] = None,
):
super().__init__() | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
# Validate inputs.
if patch_size is not None:
if norm_type not in ["ada_norm", "ada_norm_zero", "ada_norm_single"]:
raise NotImplementedError(
f"Forward pass is not implemented when `patch_size` is not None and `norm_type` is '{norm_type}'."
)
... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
# Define whether input is continuous or discrete depending on configuration
self.is_input_continuous =... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
if self.is_input_continuous and self.is_input_vectorized:
raise ValueError(
f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
" sure that either `in_channels` or `num_vector_embeds` is None."
)
elif self.... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
deprecation_message = (
f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
" incorrectly set to `'layer_norm'`. Make sure to set `norm_type` to `'ada_norm'` ... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
# Set some common variables used across the board.
self.use_linear_projection = use_linear_projection
self.interpolation_scale = interpolation_scale
self.caption_channels = caption_channels
self.num_attention_heads = num_attention_heads
self.attention_head_dim = attention_head_di... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
# 2. Initialize the right blocks.
# These functions follow a common structure:
# a. Initialize the input blocks. b. Initialize the transformer blocks.
# c. Initialize the output blocks and other projection blocks when necessary.
if self.is_input_continuous:
self._init_continu... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/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_... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
)
for _ in range(self.config.num_layers)
]
) | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
if self.use_linear_projection:
self.proj_out = torch.nn.Linear(self.inner_dim, self.out_channels)
else:
self.proj_out = torch.nn.Conv2d(self.inner_dim, self.out_channels, kernel_size=1, stride=1, padding=0)
def _init_vectorized_inputs(self, norm_type):
assert self.config.sam... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/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_... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
)
for _ in range(self.config.num_layers)
]
) | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
self.norm_out = nn.LayerNorm(self.inner_dim)
self.out = nn.Linear(self.inner_dim, self.config.num_vector_embeds - 1)
def _init_patched_inputs(self, norm_type):
assert self.config.sample_size is not None, "Transformer2DModel over patched input must provide sample_size"
self.height = self.co... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/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_... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
)
for _ in range(self.config.num_layers)
]
) | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
if self.config.norm_type != "ada_norm_single":
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.c... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
# PixArt-Alpha blocks.
self.adaln_single = None
if self.config.norm_type == "ada_norm_single":
# TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use
# additional conditions until we find better name
self.adaln_single = AdaLayerNormSingl... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/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,
class_labels: Optional[torch.LongTensor] = None,
cross_attention_k... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.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.Tensor` of shape `(batch size, sequence len, embed ... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
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/models/attention_processor.py).
attention_mask ( `tor... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/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... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
Returns:
If `return_dict` is True, an [`~models.transformers.transformer_2d.Transformer2DModelOutput`] is returned,
otherwise a `tuple` where the first element is the sample tensor.
"""
if cross_attention_kwargs is not None:
if cross_attention_kwargs.get("scale", None... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
if attention_mask is not None and attentio... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/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... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
# 1. Input
if self.is_input_continuous:
batch_size, _, height, width = hidden_states.shape
residual = hidden_states
hidden_states, inner_dim = self._operate_on_continuous_inputs(hidden_states)
elif self.is_input_vectorized:
hidden_states = self.latent_imag... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.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)
... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/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... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
# 3. Output
if self.is_input_continuous:
output = self._get_output_for_continuous_inputs(
hidden_states=hidden_states,
residual=residual,
batch_size=batch_size,
height=height,
width=width,
inner_dim=inner... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
def _operate_on_continuous_inputs(self, hidden_states):
batch, _, height, width = hidden_states.shape
hidden_states = self.norm(hidden_states)
if not self.use_linear_projection:
hidden_states = self.proj_in(hidden_states)
inner_dim = hidden_states.shape[1]
hi... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
if self.adaln_single is not None:
if self.use_additional_conditions and added_cond_kwargs is None:
raise ValueError(
"`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`."
)
timestep, embedded_timestep = self.... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
def _get_output_for_continuous_inputs(self, hidden_states, residual, batch_size, height, width, inner_dim):
if not self.use_linear_projection:
hidden_states = (
hidden_states.reshape(batch_size, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
)
hidd... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
def _get_output_for_vectorized_inputs(self, hidden_states):
hidden_states = self.norm_out(hidden_states)
logits = self.out(hidden_states)
# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
logits = logits.permute(0, 2, 1)
# log(p(x_0))
output = F.log_softmax(lo... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
def _get_output_for_patched_inputs(
self, hidden_states, timestep, class_labels, embedded_timestep, height=None, width=None
):
if self.config.norm_type != "ada_norm_single":
conditioning = self.transformer_blocks[0].norm1.emb(
timestep, class_labels, hidden_dtype=hidden_s... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
# unpatchify
if self.adaln_single is None:
height = width = int(hidden_states.shape[1] ** 0.5)
hidden_states = hidden_states.reshape(
shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)
)
hidden_states = torch.einsum("nhwpqc->nchpwq", hi... | 1,143 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py |
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