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# 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 ... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.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... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.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)}."
)
... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.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... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
def forward(
self,
sample: torch.Tensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
controlnet_cond: torch.Tensor,
conditioning_scale: float = 1.0,
class_labels: Optional[torch.Tensor] = None,
timestep_cond: Optional[to... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.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_... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
embeddings.
attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
return_dict (`bool`, defaults to `True`):
Whether or not to return a [`~models.controlnets.controlnet.ControlNetOutput`] instead of a plain
tuple. | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
Returns:
[`~models.controlnets.controlnet.ControlNetOutput`] **or** `tuple`:
If `return_dict` is `True`, a [`~models.controlnets.controlnet.ControlNetOutput`] is returned,
otherwise a tuple is returned where the first element is the sample tensor.
"""
# check ... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.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"
... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.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... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.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... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.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)
emb = emb + aug_emb if aug_emb is not None else emb
# 2. pre-process
sample = self.conv_in(sample)
controlnet_co... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.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,
... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
# 4. mid
if self.mid_block is not None:
if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=encoder_hidden_states,
... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
mid_block_res_sample = self.controlnet_mid_block(sample)
# 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
do... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return ControlNetOutput(
down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
) | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
class SD3ControlNetOutput(BaseOutput):
controlnet_block_samples: Tuple[torch.Tensor] | 1,070 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
class SD3ControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
_supports_gradient_checkpointing = True | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
@register_to_config
def __init__(
self,
sample_size: int = 128,
patch_size: int = 2,
in_channels: int = 16,
num_layers: int = 18,
attention_head_dim: int = 64,
num_attention_heads: int = 18,
joint_attention_dim: int = 4096,
caption_projection_d... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
if use_pos_embed:
self.pos_embed = PatchEmbed(
height=sample_size,
width=sample_size,
patch_size=patch_size,
in_channels=in_channels,
embed_dim=self.inner_dim,
pos_embed_max_size=pos_embed_max_size,
... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
# `attention_head_dim` is doubled to account for the mixing.
# It needs to crafted when we get the actual checkpoints.
self.transformer_blocks = nn.ModuleList(
[
JointTransformerBlock(
dim=self.inner_dim,
num_att... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
attention_head_dim=self.config.attention_head_dim,
)
for _ in range(num_layers)
]
) | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
# controlnet_blocks
self.controlnet_blocks = nn.ModuleList([])
for _ in range(len(self.transformer_blocks)):
controlnet_block = nn.Linear(self.inner_dim, self.inner_dim)
controlnet_block = zero_module(controlnet_block)
self.controlnet_blocks.append(controlnet_block)
... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
"""
Sets the attention processor to use [feed forward
chunking](https://huggingface.co/blog/reformer#... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
if hasattr(module, "set_chunk_feed_forward"):
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
for child in module.children():
fn_recursive_feed_forward(child, chunk_s... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.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... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.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... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.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... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
This API is 🧪 experimental.
</Tip>
"""
self.original_attn_processors = None
for _, attn_processor in self.attn_processors.items():
if "Added" in str(attn_processor.__class__.__name__):
raise ValueError("`fuse_qkv_projections()` is not supported for models h... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
"""
if self.original_attn_processors is not None:
self.set_attn_processor(self.original_attn_processors)
def _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value
# Notes: This is for ... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
@classmethod
def from_transformer(
cls, transformer, num_layers=12, num_extra_conditioning_channels=1, load_weights_from_transformer=True
):
config = transformer.config
config["num_layers"] = num_layers or config.num_layers
config["extra_conditioning_channels"] = num_extra_condit... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
def forward(
self,
hidden_states: torch.FloatTensor,
controlnet_cond: torch.Tensor,
conditioning_scale: float = 1.0,
encoder_hidden_states: torch.FloatTensor = None,
pooled_projections: torch.FloatTensor = None,
timestep: torch.LongTensor = None,
joint_att... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.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)`.
conditioning_scale (... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.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).
return_dict (`bool`, ... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
Returns:
If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
`tuple` where the first element is the sample tensor.
"""
if joint_attention_kwargs is not None:
joint_attention_kwargs = joint_attention_kwargs.copy()
... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
if self.pos_embed is not None and hidden_states.ndim != 4:
raise ValueError("hidden_states must be 4D when pos_embed is used")
# SD3.5 8b controlnet does not have a `pos_embed`,
# it use the `pos_embed` from the transformer to process input before passing to controlnet
elif self.pos... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
if self.pos_embed is not None:
hidden_states = self.pos_embed(hidden_states) # takes care of adding positional embeddings too.
temb = self.time_text_embed(timestep, pooled_projections)
if self.context_embedder is not None:
encoder_hidden_states = self.context_embedder(encoder_... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
if self.context_embedder is not None:
encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
else:
if self.context_embedder is not None:
encoder_hidden_states, hidden_states = block(
hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb
)
else:
# SD3.5 8b controlnet ... | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
if USE_PEFT_BACKEND:
# remove `lora_scale` from each PEFT layer
unscale_lora_layers(self, lora_scale)
if not return_dict:
return (controlnet_block_res_samples,)
return SD3ControlNetOutput(controlnet_block_samples=controlnet_block_res_samples) | 1,071 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
class SD3MultiControlNetModel(ModelMixin):
r"""
`SD3ControlNetModel` wrapper class for Multi-SD3ControlNet
This module is a wrapper for multiple instances of the `SD3ControlNetModel`. The `forward()` API is designed to be
compatible with `SD3ControlNetModel`.
Args:
controlnets (`List[SD3Co... | 1,072 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
def forward(
self,
hidden_states: torch.FloatTensor,
controlnet_cond: List[torch.tensor],
conditioning_scale: List[float],
pooled_projections: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
timestep: torch.LongTensor = None,
joint_atte... | 1,072 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
# merge samples
if i == 0:
control_block_samples = block_samples
else:
control_block_samples = [
control_block_sample + block_sample
for control_block_sample, block_sample in zip(control_block_samples[0], block_samples[0])
... | 1,072 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py |
class SparseControlNetOutput(BaseOutput):
"""
The output of [`SparseControlNetModel`].
Args:
down_block_res_samples (`tuple[torch.Tensor]`):
A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
be of shape `(batch_size, c... | 1,073 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
class SparseControlNetConditioningEmbedding(nn.Module):
def __init__(
self,
conditioning_embedding_channels: int,
conditioning_channels: int = 3,
block_out_channels: Tuple[int, ...] = (16, 32, 96, 256),
):
super().__init__()
self.conv_in = nn.Conv2d(conditioning_... | 1,074 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
def forward(self, conditioning: torch.Tensor) -> torch.Tensor:
embedding = self.conv_in(conditioning)
embedding = F.silu(embedding)
for block in self.blocks:
embedding = block(embedding)
embedding = F.silu(embedding)
embedding = self.conv_out(embedding)
... | 1,074 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
class SparseControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
"""
A SparseControlNet model as described in [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion
Models](https://arxiv.org/abs/2311.16933). | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
Args:
in_channels (`int`, defaults to 4):
The number of channels in the input sample.
conditioning_channels (`int`, defaults to 4):
The number of input channels in the controlnet conditional embedding module. If
`concat_condition_embedding` is True, the value provided... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
layers_per_block (`int`, defaults to 2):
The number of layers per block.
downsample_padding (`int`, defaults to 1):
The padding to use for the downsampling convolution.
mid_block_scale_factor (`float`, defaults to 1):
The scale factor to use for the mid block.
... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
[`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
transformer_layers_per_mid_block (`i... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
conditioning_embedding_out_channels (`Tuple[int]`, defaults to `(16, 32, 96, 256)`):
The tuple of output channel for each block in the `conditioning_embedding` layer.
global_pool_conditions (`bool`, defaults to `False`):
TODO(Patrick) - unused parameter
controlnet_conditioning_ch... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
_supports_gradient_checkpointing = True | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
@register_to_config
def __init__(
self,
in_channels: int = 4,
conditioning_channels: int = 4,
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
down_block_types: Tuple[str, ...] = (
"CrossAttnDownBlockMotion",
"CrossAttnDownBlockMotion",
... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
temporal_transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
attention_head_dim: Union[int, Tuple[int, ...]] = 8,
num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,
use_linear_projection: bool = False,
upcast_attention: bool = False,
resnet_time_scale_s... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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}."
... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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
)
if concat_conditioning_mask:
conditioning_channels = conditioning_ch... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
# time
time_embed_dim = block_out_channels[0] * 4
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
timestep_input_dim = block_out_channels[0]
self.time_embedding = TimestepEmbedding(
timestep_input_dim,
time_embed_dim,
ac... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
if isinstance(motion_num_attention_heads, int):
motion_num_attention_heads = (motion_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 = ze... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
if down_block_type == "CrossAttnDownBlockMotion":
down_block = CrossAttnDownBlockMotion(
in_channels=input_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
dropout=0,
num_layers=layers_... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
only_cross_attention=only_cross_attention[i],
upcast_attention=upcast_attention,
temporal_num_attention_heads=motion_num_attention_heads[i],
temporal_max_seq_length=motion_max_seq_length,
temporal_transformer_layers_per_block=temporal_trans... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
add_downsample=not is_final_block,
temporal_num_attention_heads=motion_num_attention_heads[i],
temporal_max_seq_length=motion_max_seq_length,
temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],
temporal_double_se... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
self.down_blocks.append(down_block)
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)
... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
if transformer_layers_per_mid_block is None:
transformer_layers_per_mid_block = (
transformer_layers_per_block[-1] if isinstance(transformer_layers_per_block[-1], int) else 1
) | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
self.mid_block = UNetMidBlock2DCrossAttn(
in_channels=mid_block_channels,
temb_channels=time_embed_dim,
dropout=0,
num_layers=1,
transformer_layers_per_block=transformer_layers_per_mid_block,
resnet_eps=norm_eps,
resnet_time_scale_shift... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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,
conditioning_channels: int = 3,
... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
for i in range(len(down_block_types)):
if "CrossAttn" in down_block_types[i]:
down_block_types[i] = "CrossAttnDownBlockMotion"
elif "Down" in down_block_types[i]:
down_block_types[i] = "DownBlockMotion"
else:
raise ValueError("Invalid `... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
controlnet = cls(
in_channels=unet.config.in_channels,
conditioning_channels=conditioning_channels,
flip_sin_to_cos=unet.config.flip_sin_to_cos,
freq_shift=unet.config.freq_shift,
down_block_types=unet.config.down_block_types,
only_cross_attention=... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
use_linear_projection=unet.config.use_linear_projection,
upcast_attention=unet.config.upcast_attention,
resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
conditioning_embedding_out_channels=conditioning_embedding_out_channels,
controlnet_conditioning_channel_or... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
if load_weights_from_unet:
controlnet.conv_in.load_state_dict(unet.conv_in.state_dict(), strict=False)
controlnet.time_proj.load_state_dict(unet.time_proj.state_dict(), strict=False)
controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict(), strict=False)
... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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 ... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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)}."
)
... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
def forward(
self,
sample: torch.Tensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
controlnet_cond: torch.Tensor,
conditioning_scale: float = 1.0,
timestep_cond: Optional[torch.Tensor] = None,
attention_mask: Optional[... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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_... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
embeddings.
attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
return_dict (`bool`, defaults to `True`):
Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
Returns:
[`~models.controlnet.ControlNetOutput`] **or** `t... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
# check channel order
channel_order = self.config.controlnet_conditioning_channel_order
if channel_order == "rgb":
# in rgb order by default
...
elif channel_order == "bgr":
controlnet_cond = torch.flip(controlnet_cond, dims=[1])
else:
rai... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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"
... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
batch_size, channels, num_frames, height, width = controlnet_cond.shape
controlnet_cond = controlnet_cond.permute(0, 2, 1, 3, 4).reshape(
batch_size * num_frames, channels, height, width
)
controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
batch_frames, channel... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.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,
... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
# 4. mid
if self.mid_block is not None:
if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=encoder_hidden_states,
... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
down_block_res_samples = controlnet_down_block_res_samples
mid_block_res_sample = self.controlnet_mid_block(sample)
# 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 ... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
if self.config.global_pool_conditions:
down_block_res_samples = [
torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
]
mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)
if not return_dict:
... | 1,075 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py |
class ControlNetXSOutput(BaseOutput):
"""
The output of [`UNetControlNetXSModel`].
Args:
sample (`Tensor` of shape `(batch_size, num_channels, height, width)`):
The output of the `UNetControlNetXSModel`. Unlike `ControlNetOutput` this is NOT to be added to the base
model out... | 1,076 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
class DownBlockControlNetXSAdapter(nn.Module):
"""Components that together with corresponding components from the base model will form a
`ControlNetXSCrossAttnDownBlock2D`"""
def __init__(
self,
resnets: nn.ModuleList,
base_to_ctrl: nn.ModuleList,
ctrl_to_base: nn.ModuleList... | 1,077 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
class MidBlockControlNetXSAdapter(nn.Module):
"""Components that together with corresponding components from the base model will form a
`ControlNetXSCrossAttnMidBlock2D`"""
def __init__(self, midblock: UNetMidBlock2DCrossAttn, base_to_ctrl: nn.ModuleList, ctrl_to_base: nn.ModuleList):
super().__ini... | 1,078 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
class UpBlockControlNetXSAdapter(nn.Module):
"""Components that together with corresponding components from the base model will form a `ControlNetXSCrossAttnUpBlock2D`"""
def __init__(self, ctrl_to_base: nn.ModuleList):
super().__init__()
self.ctrl_to_base = ctrl_to_base | 1,079 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
class ControlNetXSAdapter(ModelMixin, ConfigMixin):
r"""
A `ControlNetXSAdapter` model. To use it, pass it into a `UNetControlNetXSModel` (together with a
`UNet2DConditionModel` base model).
This model inherits from [`ModelMixin`] and [`ConfigMixin`]. Check the superclass documentation for it's generic... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
Parameters:
conditioning_channels (`int`, defaults to 3):
Number of channels of conditioning input (e.g. an image)
conditioning_channel_order (`str`, defaults to `"rgb"`):
The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
conditioning_embedding_... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
model's time embedding.
num_attention_heads (`list[int]`, defaults to `[4]`):
The number of attention heads.
block_out_channels (`list[int]`, defaults to `[4, 8, 16, 16]`):
The tuple of output channels for each block.
base_block_out_channels (`list[int]`, defaults to `[32... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
upcast_attention (`bool`, defaults to `True`):
Whether the attention computation sh... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
@register_to_config
def __init__(
self,
conditioning_channels: int = 3,
conditioning_channel_order: str = "rgb",
conditioning_embedding_out_channels: Tuple[int] = (16, 32, 96, 256),
time_embedding_mix: float = 1.0,
learn_time_embedding: bool = False,
num_atten... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
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