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class ControlNetUnionModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
"""
A ControlNetUnion model. | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
Args:
in_channels (`int`, defaults to 4):
The number of channels in the input sample.
flip_sin_to_cos (`bool`, defaults to `True`):
Whether to flip the sin to cos in the time embedding.
freq_shift (`int`, defaults to 0):
The frequency shift to apply to the tim... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
The scale factor to use for the mid block.
act_fn (`str`, defaults to "silu"):
The activation function to use.
norm_num_groups (`int`, *optional*, defaults to 32):
The number of groups to use for the normalization. If None, normalization and activation layers is skipped
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
dimension to `cross_attention_dim`.
encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
If given, the `encoder_hidden_states` and potentially other embeddings are down-project... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
"text". "text" will use the `TextTimeEmbedding` layer.
num_class_embeds (`int`, *optional*, defaults to 0):
Input dimension of the learnable embedding matrix to be projected to `time_emb... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(48, 96, 192, 384)`):
The tuple of output channel for each block in the `conditioning_embedding` layer.
global_pool_conditions (`bool`, def... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
_supports_gradient_checkpointing = True | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
@register_to_config
def __init__(
self,
in_channels: int = 4,
conditioning_channels: int = 3,
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
down_block_types: Tuple[str, ...] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,
use_linear_projection: bool = False,
class_embed_type: Optional[str] = None,
addition_embed_type: Optional[str] = None,
addition_time_embed_dim: Optional[int] = None,
num_class_embeds: Optional[int] = None,
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was cre... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
# input
conv_in_kernel = 3
conv_in_padding = (conv_in_kernel - 1) // 2
self.conv_in = nn.Conv2d(
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
)
# time
time_embed_dim = block_out_channels[0] * 4
self.time_proj... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
# class embedding
if class_embed_type is None and num_class_embeds is not None:
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
elif class_embed_type == "timestep":
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
elif cla... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
self.class_embeddi... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
if addition_embed_type == "text":
if encoder_hid_dim is not None:
text_time_embedding_from_dim = encoder_hid_dim
else:
text_time_embedding_from_dim = cross_attention_dim | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
self.add_embedding = TextTimeEmbedding(
text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
)
elif addition_embed_type == "text_image":
# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
elif addition_embed_type is not None:
raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
# control net conditioning embedding
self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
conditioning_embedding_channels=bloc... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
task_scale_factor = num_trans_channel**0.5
self.task_embedding = nn.Parameter(task_scale_factor * torch.randn(num_control_type, num_trans_channel))
self.transformer_layes = nn.ModuleList(
[ResidualAttentionBlock(num_trans_channel, num_trans_head) for _ in range(num_trans_layer)]
)
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(down_block_types)
# down
output_channel = block_out_channels[0]
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
controlnet_block = zero_module(controlnet_... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block,
transformer_layers_per_block=transformer_layers_per_block[i],
in_channels=input_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
resnet_time_scale_shift=resnet_time_scale_shift,
)
self.down_blocks.append(down_block) | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
for _ in range(layers_per_block):
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
controlnet_block = zero_module(controlnet_block)
self.controlnet_down_blocks.append(controlnet_block)
if not is_final_block:
controln... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
self.mid_block = UNetMidBlock2DCrossAttn(
transformer_layers_per_block=transformer_layers_per_block[-1],
in_channels=mid_block_channel,
temb_channels=time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_fac... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
@classmethod
def from_unet(
cls,
unet: UNet2DConditionModel,
controlnet_conditioning_channel_order: str = "rgb",
conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
load_weights_from_unet: bool = True,
):
r"""
Instantiate a ... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
Parameters:
unet (`UNet2DConditionModel`):
The UNet model weights to copy to the [`ControlNetUnionModel`]. All configuration options are also
copied where applicable.
"""
transformer_layers_per_block = (
unet.config.transformer_layers_per_block if ... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
controlnet = cls(
encoder_hid_dim=encoder_hid_dim,
encoder_hid_dim_type=encoder_hid_dim_type,
addition_embed_type=addition_embed_type,
addition_time_embed_dim=addition_time_embed_dim,
transformer_layers_per_block=transformer_layers_per_block,
in_ch... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
cross_attention_dim=unet.config.cross_attention_dim,
attention_head_dim=unet.config.attention_head_dim,
num_attention_heads=unet.config.num_attention_heads,
use_linear_projection=unet.config.use_linear_projection,
class_embed_type=unet.config.class_embed_type,
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
if load_weights_from_unet:
controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())
controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())
controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())
if controlnet.class_embedding:
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.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
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.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 ... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.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."
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESS... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice
def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:
r"""
Enable sliced attention computation.
When this option is enabled, the attention module splits the input tensor ... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
if hasattr(module, "set_attention_slice"):
sliceable_head_dims.append(module.sliceable_head_dim)
for child in module.children():
fn_recursive_retrieve_sliceable_dims(child)
# retrieve num... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
if len(slice_size) != len(sliceable_head_dims):
raise ValueError(
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
)
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
for child in module.children():
fn_recursive_set_attention_slice(child, slice_size)
reversed_slice_size = list(reversed(slice_size))
for module in self.children():
fn_recursive_set_attention_slice(module, reversed_slice_size)
def _set_gradient_checkpointing(self, module... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
def forward(
self,
sample: torch.Tensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
controlnet_cond: List[torch.Tensor],
control_type: torch.Tensor,
control_type_idx: List[int],
conditioning_scale: float = 1.0,
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
Args:
sample (`torch.Tensor`):
The noisy input tensor.
timestep (`Union[torch.Tensor, float, int]`):
The number of timesteps to denoise an input.
encoder_hidden_states (`torch.Tensor`):
The encoder hidden states.
controlnet_... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
embeddings.
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
guess_mode (`bool`, defaults to `False`):
In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
return_dict (`bool`, defaults to `True`):
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
Returns:
[`~models.controlnet.ControlNetOutput`] **or** `tuple`:
If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
returned where the first element is the sample tensor.
"""
# check channel order
ch... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
t_emb = t_emb.to(dtype=sample.dtype)
emb = self.time_embedding(t_emb, timestep_c... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
elif self.config.addition_embed_type == "text_time":
if "text_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
add_embeds = add_embeds.to(emb.dtype)
aug_emb = self.add_embedding(add_embeds)
control_embeds = self.control_type_proj(control_type.flatten())
control_embeds = control_embeds.reshape((t_emb.shape[0], -1))
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
condition = sample
feat_seq = torch.mean(condition, dim=(2, 3))
inputs.append(feat_seq.unsqueeze(1))
condition_list.append(condition)
x = torch.cat(inputs, dim=1)
for layer in self.transformer_layes:
x = layer(x)
controlnet_cond_fuser = sample * 0.0
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
# 3. down
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
sample, res_samples = downsample_block(
hidden_states=sample,
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
# 4. mid
if self.mid_block is not None:
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
)
... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
# 6. scaling
if guess_mode and not self.config.global_pool_conditions:
scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0
scales = scales * conditioning_scale
down_block_res_samples = [sample * scale for sample, scale in zip(do... | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
return ControlNetOutput(
down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
) | 1,088 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
class FluxControlNetOutput(BaseOutput):
controlnet_block_samples: Tuple[torch.Tensor]
controlnet_single_block_samples: Tuple[torch.Tensor] | 1,089 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
class FluxControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
patch_size: int = 1,
in_channels: int = 64,
num_layers: int = 19,
num_single_layers: int = 38,
attention_head... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope)
text_time_guidance_cls = (
CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings
)
self.time_text_embed = text_time_guidance_cls(
embedding_dim=self.inner_d... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_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 i in range(num_s... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
if conditioning_embedding_channels is not None:
self.input_hint_block = ControlNetConditioningEmbedding(
conditioning_embedding_channels=conditioning_embedding_channels, block_out_channels=(16, 16, 16, 16)
)
self.controlnet_x_embedder = torch.nn.Linear(in_channels, se... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.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... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.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... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.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... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
@classmethod
def from_transformer(
cls,
transformer,
num_layers: int = 4,
num_single_layers: int = 10,
attention_head_dim: int = 128,
num_attention_heads: int = 24,
load_weights_from_transformer=True,
):
config = dict(transformer.config)
co... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
if load_weights_from_transformer:
controlnet.pos_embed.load_state_dict(transformer.pos_embed.state_dict())
controlnet.time_text_embed.load_state_dict(transformer.time_text_embed.state_dict())
controlnet.context_embedder.load_state_dict(transformer.context_embedder.state_dict())
... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
def forward(
self,
hidden_states: torch.Tensor,
controlnet_cond: torch.Tensor,
controlnet_mode: torch.Tensor = None,
conditioning_scale: float = 1.0,
encoder_hidden_states: torch.Tensor = None,
pooled_projections: torch.Tensor = None,
timestep: torch.LongT... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
Args:
hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
Input `hidden_states`.
controlnet_cond (`torch.Tensor`):
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
controlnet_mode (`to... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
block_controlnet_hidden_states: (`list` of `torch.Tensor`):
A list of tensors that if specified are added to the residuals of transformer blocks.
joint_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as ... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_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()
... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
if self.input_hint_block is not None:
controlnet_cond = self.input_hint_block(controlnet_cond)
batch_size, channels, height_pw, width_pw = controlnet_cond.shape
height = height_pw // self.config.patch_size
width = width_pw // self.config.patch_size
controlnet_... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_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 se... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
if txt_ids.ndim == 3:
logger.warning(
"Passing `txt_ids` 3d torch.Tensor is deprecated."
"Please remove the batch dimension and pass it as a 2d torch Tensor"
)
txt_ids = txt_ids[0]
if img_ids.ndim == 3:
logger.warning(
... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_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)
... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
else:
encoder_hidden_states, hidden_states = block(
hidden_states=hidden_states,
encoder_hidden_states=encoder_hidden_states,
temb=temb,
image_rotary_emb=image_rotary_emb,
)
block_samples = block_... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.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,
temb,
image_rotary_emb,
... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
controlnet_single_block_samples = ()
for single_block_sample, controlnet_block in zip(single_block_samples, self.controlnet_single_blocks):
single_block_sample = controlnet_block(single_block_sample)
controlnet_single_block_samples = controlnet_single_block_samples + (single_block_sample... | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
if not return_dict:
return (controlnet_block_samples, controlnet_single_block_samples)
return FluxControlNetOutput(
controlnet_block_samples=controlnet_block_samples,
controlnet_single_block_samples=controlnet_single_block_samples,
) | 1,090 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
class FluxMultiControlNetModel(ModelMixin):
r"""
`FluxMultiControlNetModel` wrapper class for Multi-FluxControlNetModel
This module is a wrapper for multiple instances of the `FluxControlNetModel`. The `forward()` API is designed to be
compatible with `FluxControlNetModel`.
Args:
controlne... | 1,091 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
def forward(
self,
hidden_states: torch.FloatTensor,
controlnet_cond: List[torch.tensor],
controlnet_mode: List[torch.tensor],
conditioning_scale: List[float],
encoder_hidden_states: torch.Tensor = None,
pooled_projections: torch.Tensor = None,
timestep: t... | 1,091 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
for i, (image, mode, scale) in enumerate(zip(controlnet_cond, controlnet_mode, conditioning_scale)):
block_samples, single_block_samples = controlnet(
hidden_states=hidden_states,
controlnet_cond=image,
controlnet_mode=mode[:, None],
... | 1,091 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
# merge samples
if i == 0:
control_block_samples = block_samples
control_single_block_samples = single_block_samples
else:
control_block_samples = [
control_block_sample + block_sample
... | 1,091 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
# Regular Multi-ControlNets
# load all ControlNets into memories
else:
for i, (image, mode, scale, controlnet) in enumerate(
zip(controlnet_cond, controlnet_mode, conditioning_scale, self.nets)
):
block_samples, single_block_samples = controlnet(
... | 1,091 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
# merge samples
if i == 0:
control_block_samples = block_samples
control_single_block_samples = single_block_samples
else:
if block_samples is not None and control_block_samples is not None:
control_block... | 1,091 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
return control_block_samples, control_single_block_samples | 1,091 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py |
class FlaxControlNetOutput(BaseOutput):
"""
The output of [`FlaxControlNetModel`].
Args:
down_block_res_samples (`jnp.ndarray`):
mid_block_res_sample (`jnp.ndarray`):
"""
down_block_res_samples: jnp.ndarray
mid_block_res_sample: jnp.ndarray | 1,092 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
class FlaxControlNetConditioningEmbedding(nn.Module):
conditioning_embedding_channels: int
block_out_channels: Tuple[int, ...] = (16, 32, 96, 256)
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.conv_in = nn.Conv(
self.block_out_channels[0],
kernel_size=(3, ... | 1,093 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
blocks = []
for i in range(len(self.block_out_channels) - 1):
channel_in = self.block_out_channels[i]
channel_out = self.block_out_channels[i + 1]
conv1 = nn.Conv(
channel_in,
kernel_size=(3, 3),
padding=((1, 1), (1, 1)),
... | 1,093 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
def __call__(self, conditioning: jnp.ndarray) -> jnp.ndarray:
embedding = self.conv_in(conditioning)
embedding = nn.silu(embedding)
for block in self.blocks:
embedding = block(embedding)
embedding = nn.silu(embedding)
embedding = self.conv_out(embedding)
... | 1,093 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
class FlaxControlNetModel(nn.Module, FlaxModelMixin, ConfigMixin):
r"""
A ControlNet model.
This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for it’s generic methods
implemented for all models (such as downloading or saving).
This model is also a Flax Linen [`flax.li... | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
Parameters:
sample_size (`int`, *optional*):
The size of the input sample.
in_channels (`int`, *optional*, defaults to 4):
The number of channels in the input sample.
down_block_types (`Tuple[str]`, *optional*, defaults to `("FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownB... | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
The dimension of the cross attention features.
dropout (`float`, *optional*, defaults to 0):
Dropout probability for down, up and bottleneck blocks.
flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
Whether to flip the sin to cos in the time embedding.
freq_shift ... | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
sample_size: int = 32
in_channels: int = 4
down_block_types: Tuple[str, ...] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
only_cross_attention: Union[bool, Tuple[bool, ...]] = False
block_out_channels: Tuple[int, ...] = ... | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
def init_weights(self, rng: jax.Array) -> FrozenDict:
# init input tensors
sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
sample = jnp.zeros(sample_shape, dtype=jnp.float32)
timesteps = jnp.ones((1,), dtype=jnp.int32)
encoder_hidden_states = jnp.zeros((1... | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was cre... | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
# time
self.time_proj = FlaxTimesteps(
block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift
)
self.time_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype)
self.controlnet_cond_embedding = FlaxControlNetConditioningEm... | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
controlnet_block = nn.Conv(
output_channel,
kernel_size=(1, 1),
padding="VALID",
kernel_init=nn.initializers.zeros_init(),
bias_init=nn.initializers.zeros_init(),
dtype=self.dtype,
)
controlnet_down_blocks.append(controlnet_block)
... | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
if down_block_type == "CrossAttnDownBlock2D":
down_block = FlaxCrossAttnDownBlock2D(
in_channels=input_channel,
out_channels=output_channel,
dropout=self.dropout,
num_layers=self.layers_per_block,
num_att... | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
down_blocks.append(down_block)
for _ in range(self.layers_per_block):
controlnet_block = nn.Conv(
output_channel,
kernel_size=(1, 1),
padding="VALID",
kernel_init=nn.initializers.zeros_init(),
... | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
self.down_blocks = down_blocks
self.controlnet_down_blocks = controlnet_down_blocks
# mid
mid_block_channel = block_out_channels[-1]
self.mid_block = FlaxUNetMidBlock2DCrossAttn(
in_channels=mid_block_channel,
dropout=self.dropout,
num_attention_heads... | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
def __call__(
self,
sample: jnp.ndarray,
timesteps: Union[jnp.ndarray, float, int],
encoder_hidden_states: jnp.ndarray,
controlnet_cond: jnp.ndarray,
conditioning_scale: float = 1.0,
return_dict: bool = True,
train: bool = False,
) -> Union[FlaxControl... | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
Whether or not to return a [`models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] instead of
a plain tuple.
train (`bool`, *optional*, defaults to `False`):
Use deterministic functions and disable dropout when not training. | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
Returns:
[`~models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] or `tuple`:
[`~models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] if `return_dict` is True, otherwise
a `tuple`. When returning a tuple, the first element is the sample tensor.
""... | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
# 2. pre-process
sample = jnp.transpose(sample, (0, 2, 3, 1))
sample = self.conv_in(sample)
controlnet_cond = jnp.transpose(controlnet_cond, (0, 2, 3, 1))
controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
sample += controlnet_cond
# 3. down
down_... | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
# 5. contronet blocks
controlnet_down_block_res_samples = ()
for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
down_block_res_sample = controlnet_block(down_block_res_sample)
controlnet_down_block_res_samples += (down_block_r... | 1,094 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py |
class PerceiverAttention(nn.Module):
def __init__(self, dim: int, dim_head: int = 64, heads: int = 8, kv_dim: Optional[int] = None):
super().__init__()
self.scale = dim_head**-0.5
self.dim_head = dim_head
self.heads = heads
inner_dim = dim_head * heads
self.norm1 = ... | 1,095 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py |
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