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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 in slices to compute attention in
several steps. This is useful for saving some memory in exchange for a small decrease in speed.
Args:
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
must be a multiple of `slice_size`.
"""
sliceable_head_dims = [] | 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 number of attention layers
for module in self.children():
fn_recursive_retrieve_sliceable_dims(module)
num_sliceable_layers = len(sliceable_head_dims)
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = [dim // 2 for dim in sliceable_head_dims]
elif slice_size == "max":
# make smallest slice possible
slice_size = num_sliceable_layers * [1]
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size | 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)}."
)
for i in range(len(slice_size)):
size = slice_size[i]
dim = sliceable_head_dims[i]
if size is not None and size > dim:
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
# Recursively walk through all the children.
# Any children which exposes the set_attention_slice method
# gets the message
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
if hasattr(module, "set_attention_slice"):
module.set_attention_slice(slice_size.pop()) | 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, value: bool = False) -> None:
if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):
module.gradient_checkpointing = value | 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[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guess_mode: bool = False,
return_dict: bool = True,
) -> Union[ControlNetOutput, Tuple[Tuple[torch.Tensor, ...], torch.Tensor]]:
"""
The [`ControlNetModel`] forward method. | 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_cond (`torch.Tensor`):
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
conditioning_scale (`float`, defaults to `1.0`):
The scale factor for ControlNet outputs.
class_labels (`torch.Tensor`, *optional*, defaults to `None`):
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the | 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
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
negative values to the attention scores corresponding to "discard" tokens.
added_cond_kwargs (`dict`):
Additional conditions for the Stable Diffusion XL UNet.
cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
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 | 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 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:
raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
# prepare attention_mask
if attention_mask is not None:
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
attention_mask = attention_mask.unsqueeze(1) | 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"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.time_proj(timesteps) | 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_cond)
aug_emb = None
if self.class_embedding is not None:
if class_labels is None:
raise ValueError("class_labels should be provided when num_class_embeds > 0")
if self.config.class_embed_type == "timestep":
class_labels = self.time_proj(class_labels)
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
emb = emb + class_emb
if self.config.addition_embed_type is not None:
if self.config.addition_embed_type == "text":
aug_emb = self.add_embedding(encoder_hidden_states) | 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 `added_cond_kwargs`"
)
text_embeds = added_cond_kwargs.get("text_embeds")
if "time_ids" 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 `time_ids` to be passed in `added_cond_kwargs`"
)
time_ids = added_cond_kwargs.get("time_ids")
time_embeds = self.add_time_proj(time_ids.flatten())
time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) | 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_cond = self.controlnet_cond_embedding(controlnet_cond)
sample = sample + controlnet_cond | 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,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
down_block_res_samples += res_samples | 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,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
)
else:
sample = self.mid_block(sample, emb)
# 5. Control net 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 = controlnet_down_block_res_samples + (down_block_res_sample,)
down_block_res_samples = controlnet_down_block_res_samples | 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
down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
mid_block_res_sample = mid_block_res_sample * scales[-1] # last one
else:
down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample = mid_block_res_sample * conditioning_scale
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) | 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_dim: int = 1152,
pooled_projection_dim: int = 2048,
out_channels: int = 16,
pos_embed_max_size: int = 96,
extra_conditioning_channels: int = 0,
dual_attention_layers: Tuple[int, ...] = (),
qk_norm: Optional[str] = None,
pos_embed_type: Optional[str] = "sincos",
use_pos_embed: bool = True,
force_zeros_for_pooled_projection: bool = True,
):
super().__init__()
default_out_channels = in_channels
self.out_channels = out_channels if out_channels is not None else default_out_channels
self.inner_dim = num_attention_heads * attention_head_dim | 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,
pos_embed_type=pos_embed_type,
)
else:
self.pos_embed = None
self.time_text_embed = CombinedTimestepTextProjEmbeddings(
embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
)
if joint_attention_dim is not None:
self.context_embedder = nn.Linear(joint_attention_dim, caption_projection_dim) | 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_attention_heads=num_attention_heads,
attention_head_dim=self.config.attention_head_dim,
context_pre_only=False,
qk_norm=qk_norm,
use_dual_attention=True if i in dual_attention_layers else False,
)
for i in range(num_layers)
]
)
else:
self.context_embedder = None
self.transformer_blocks = nn.ModuleList(
[
SD3SingleTransformerBlock(
dim=self.inner_dim,
num_attention_heads=num_attention_heads, | 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)
pos_embed_input = PatchEmbed(
height=sample_size,
width=sample_size,
patch_size=patch_size,
in_channels=in_channels + extra_conditioning_channels,
embed_dim=self.inner_dim,
pos_embed_type=None,
)
self.pos_embed_input = zero_module(pos_embed_input)
self.gradient_checkpointing = False | 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#2-chunked-feed-forward-layers).
Parameters:
chunk_size (`int`, *optional*):
The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
over each tensor of dim=`dim`.
dim (`int`, *optional*, defaults to `0`):
The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
or dim=1 (sequence length).
"""
if dim not in [0, 1]:
raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
# By default chunk size is 1
chunk_size = chunk_size or 1 | 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_size, dim)
for module in self.children():
fn_recursive_feed_forward(module, chunk_size, dim)
@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
indexed by its weight name.
"""
# set recursively
processors = {} | 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_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
# 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. | 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 needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.
"""
count = len(self.attn_processors.keys())
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."
) | 1,071 | /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}.processor"))
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor)
# Copied from diffusers.models.transformers.transformer_sd3.SD3Transformer2DModel.fuse_qkv_projections
def fuse_qkv_projections(self):
"""
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
are fused. For cross-attention modules, key and value projection matrices are fused.
<Tip warning={true}> | 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 having added KV projections.")
self.original_attn_processors = self.attn_processors
for module in self.modules():
if isinstance(module, Attention):
module.fuse_projections(fuse=True)
self.set_attn_processor(FusedJointAttnProcessor2_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.
</Tip> | 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 SD3.5 8b controlnet, which shares the pos_embed with the transformer
# we should have handled this in conversion script
def _get_pos_embed_from_transformer(self, transformer):
pos_embed = PatchEmbed(
height=transformer.config.sample_size,
width=transformer.config.sample_size,
patch_size=transformer.config.patch_size,
in_channels=transformer.config.in_channels,
embed_dim=transformer.inner_dim,
pos_embed_max_size=transformer.config.pos_embed_max_size,
)
pos_embed.load_state_dict(transformer.pos_embed.state_dict(), strict=True)
return pos_embed | 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_conditioning_channels
controlnet = cls.from_config(config)
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())
controlnet.transformer_blocks.load_state_dict(transformer.transformer_blocks.state_dict(), strict=False)
controlnet.pos_embed_input = zero_module(controlnet.pos_embed_input)
return controlnet | 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_attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
"""
The [`SD3Transformer2DModel`] forward method. | 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 (`float`, defaults to `1.0`):
The scale factor for ControlNet outputs.
encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
from the embeddings of input conditions.
timestep ( `torch.LongTensor`):
Used to indicate denoising step.
joint_attention_kwargs (`dict`, *optional*): | 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`, *optional*, defaults to `True`):
Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
tuple. | 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()
lora_scale = joint_attention_kwargs.pop("scale", 1.0)
else:
lora_scale = 1.0
if USE_PEFT_BACKEND:
# weight the lora layers by setting `lora_scale` for each PEFT layer
scale_lora_layers(self, lora_scale)
else:
if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
logger.warning(
"Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
) | 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_embed is None and hidden_states.ndim != 3:
raise ValueError("hidden_states must be 3D when pos_embed is not used")
if self.context_embedder is not None and encoder_hidden_states is None:
raise ValueError("encoder_hidden_states must be provided when context_embedder is used")
# SD3.5 8b controlnet does not have a `context_embedder`, it does not use `encoder_hidden_states`
elif self.context_embedder is None and encoder_hidden_states is not None:
raise ValueError("encoder_hidden_states should not be provided when context_embedder is not used") | 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_hidden_states)
# add
hidden_states = hidden_states + self.pos_embed_input(controlnet_cond)
block_res_samples = ()
for block in self.transformer_blocks:
if torch.is_grad_enabled() and self.gradient_checkpointing:
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)
return custom_forward | 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),
hidden_states,
encoder_hidden_states,
temb,
**ckpt_kwargs,
)
else:
# SD3.5 8b controlnet use single transformer block, which does not use `encoder_hidden_states`
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block), hidden_states, temb, **ckpt_kwargs
) | 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 use single transformer block, which does not use `encoder_hidden_states`
hidden_states = block(hidden_states, temb)
block_res_samples = block_res_samples + (hidden_states,)
controlnet_block_res_samples = ()
for block_res_sample, controlnet_block in zip(block_res_samples, self.controlnet_blocks):
block_res_sample = controlnet_block(block_res_sample)
controlnet_block_res_samples = controlnet_block_res_samples + (block_res_sample,)
# 6. scaling
controlnet_block_res_samples = [sample * conditioning_scale for sample in controlnet_block_res_samples] | 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[SD3ControlNetModel]`):
Provides additional conditioning to the unet during the denoising process. You must set multiple
`SD3ControlNetModel` as a list.
"""
def __init__(self, controlnets):
super().__init__()
self.nets = nn.ModuleList(controlnets) | 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_attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
) -> Union[SD3ControlNetOutput, Tuple]:
for i, (image, scale, controlnet) in enumerate(zip(controlnet_cond, conditioning_scale, self.nets)):
block_samples = controlnet(
hidden_states=hidden_states,
timestep=timestep,
encoder_hidden_states=encoder_hidden_states,
pooled_projections=pooled_projections,
controlnet_cond=image,
conditioning_scale=scale,
joint_attention_kwargs=joint_attention_kwargs,
return_dict=return_dict,
) | 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])
]
control_block_samples = (tuple(control_block_samples),)
return control_block_samples | 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, channel * resolution, height //resolution, width // resolution)`. Output can be
used to condition the original UNet's downsampling activations.
mid_down_block_re_sample (`torch.Tensor`):
The activation of the middle block (the lowest sample resolution). Each tensor should be of shape
`(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`.
Output can be used to condition the original UNet's middle block activation.
"""
down_block_res_samples: Tuple[torch.Tensor]
mid_block_res_sample: torch.Tensor | 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_channels, block_out_channels[0], kernel_size=3, padding=1)
self.blocks = nn.ModuleList([])
for i in range(len(block_out_channels) - 1):
channel_in = block_out_channels[i]
channel_out = block_out_channels[i + 1]
self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
self.conv_out = zero_module(
nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
) | 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)
return 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 here is incremented by 1.
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 time embedding.
down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
The tuple of downsample blocks to use.
only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block. | 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.
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
in post-processing.
norm_eps (`float`, defaults to 1e-5):
The epsilon to use for the normalization.
cross_attention_dim (`int`, defaults to 1280):
The dimension of the cross attention features.
transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1): | 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 (`int` or `Tuple[int]`, *optional*, defaults to 1):
The number of transformer layers to use in each layer in the middle block.
attention_head_dim (`int` or `Tuple[int]`, defaults to 8):
The dimension of the attention heads.
num_attention_heads (`int` or `Tuple[int]`, *optional*):
The number of heads to use for multi-head attention.
use_linear_projection (`bool`, defaults to `False`):
upcast_attention (`bool`, defaults to `False`):
resnet_time_scale_shift (`str`, defaults to `"default"`):
Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`. | 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_channel_order (`str`, defaults to `rgb`):
motion_max_seq_length (`int`, defaults to `32`):
The maximum sequence length to use in the motion module.
motion_num_attention_heads (`int` or `Tuple[int]`, defaults to `8`):
The number of heads to use in each attention layer of the motion module.
concat_conditioning_mask (`bool`, defaults to `True`):
use_simplified_condition_embedding (`bool`, defaults to `True`):
""" | 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",
"CrossAttnDownBlockMotion",
"DownBlockMotion",
),
only_cross_attention: Union[bool, Tuple[bool]] = False,
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
layers_per_block: int = 2,
downsample_padding: int = 1,
mid_block_scale_factor: float = 1,
act_fn: str = "silu",
norm_num_groups: Optional[int] = 32,
norm_eps: float = 1e-5,
cross_attention_dim: int = 768,
transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
transformer_layers_per_mid_block: Optional[Union[int, Tuple[int]]] = None, | 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_shift: str = "default",
conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
global_pool_conditions: bool = False,
controlnet_conditioning_channel_order: str = "rgb",
motion_max_seq_length: int = 32,
motion_num_attention_heads: int = 8,
concat_conditioning_mask: bool = True,
use_simplified_condition_embedding: bool = True,
):
super().__init__()
self.use_simplified_condition_embedding = use_simplified_condition_embedding | 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 created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
num_attention_heads = num_attention_heads or attention_head_dim
# Check inputs
if len(block_out_channels) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
) | 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}."
)
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
)
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
if isinstance(temporal_transformer_layers_per_block, int):
temporal_transformer_layers_per_block = [temporal_transformer_layers_per_block] * len(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_channels + 1
self.concat_conditioning_mask = concat_conditioning_mask
# control net conditioning embedding
if use_simplified_condition_embedding:
self.controlnet_cond_embedding = zero_module(
nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
)
else:
self.controlnet_cond_embedding = SparseControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0],
block_out_channels=conditioning_embedding_out_channels,
conditioning_channels=conditioning_channels,
) | 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,
act_fn=act_fn,
)
self.down_blocks = nn.ModuleList([])
self.controlnet_down_blocks = nn.ModuleList([])
if isinstance(cross_attention_dim, int):
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
if isinstance(only_cross_attention, bool):
only_cross_attention = [only_cross_attention] * len(down_block_types)
if isinstance(attention_head_dim, int):
attention_head_dim = (attention_head_dim,) * len(down_block_types)
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(down_block_types) | 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 = zero_module(controlnet_block)
self.controlnet_down_blocks.append(controlnet_block)
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1 | 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_per_block,
transformer_layers_per_block=transformer_layers_per_block[i],
resnet_eps=norm_eps,
resnet_time_scale_shift=resnet_time_scale_shift,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
resnet_pre_norm=True,
num_attention_heads=num_attention_heads[i],
cross_attention_dim=cross_attention_dim[i],
add_downsample=not is_final_block,
dual_cross_attention=False,
use_linear_projection=use_linear_projection, | 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_transformer_layers_per_block[i],
temporal_double_self_attention=False,
)
elif down_block_type == "DownBlockMotion":
down_block = DownBlockMotion(
in_channels=input_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
dropout=0,
num_layers=layers_per_block,
resnet_eps=norm_eps,
resnet_time_scale_shift=resnet_time_scale_shift,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
resnet_pre_norm=True, | 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_self_attention=False,
)
else:
raise ValueError(
"Invalid `block_type` encountered. Must be one of `CrossAttnDownBlockMotion` or `DownBlockMotion`"
) | 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)
if not is_final_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)
# mid
mid_block_channels = block_out_channels[-1]
controlnet_block = nn.Conv2d(mid_block_channels, mid_block_channels, kernel_size=1)
controlnet_block = zero_module(controlnet_block)
self.controlnet_mid_block = 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=resnet_time_scale_shift,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
resnet_pre_norm=True,
num_attention_heads=num_attention_heads[-1],
output_scale_factor=mid_block_scale_factor,
cross_attention_dim=cross_attention_dim[-1],
dual_cross_attention=False,
use_linear_projection=use_linear_projection,
upcast_attention=upcast_attention,
attention_type="default",
) | 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,
) -> "SparseControlNetModel":
r"""
Instantiate a [`SparseControlNetModel`] from [`UNet2DConditionModel`].
Parameters:
unet (`UNet2DConditionModel`):
The UNet model weights to copy to the [`SparseControlNetModel`]. All configuration options are also
copied where applicable.
"""
transformer_layers_per_block = (
unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1
)
down_block_types = unet.config.down_block_types | 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 `block_type` encountered. Must be a cross-attention or down block") | 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=unet.config.only_cross_attention,
block_out_channels=unet.config.block_out_channels,
layers_per_block=unet.config.layers_per_block,
downsample_padding=unet.config.downsample_padding,
mid_block_scale_factor=unet.config.mid_block_scale_factor,
act_fn=unet.config.act_fn,
norm_num_groups=unet.config.norm_num_groups,
norm_eps=unet.config.norm_eps,
cross_attention_dim=unet.config.cross_attention_dim,
transformer_layers_per_block=transformer_layers_per_block,
attention_head_dim=unet.config.attention_head_dim,
num_attention_heads=unet.config.num_attention_heads, | 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_order=controlnet_conditioning_channel_order,
) | 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)
controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict(), strict=False)
controlnet.mid_block.load_state_dict(unet.mid_block.state_dict(), strict=False)
return controlnet
@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
indexed by its weight name.
"""
# set recursively
processors = {} | 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_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
# 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. | 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 needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.
"""
count = len(self.attn_processors.keys())
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."
) | 1,075 | /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}.processor"))
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor) | 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_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnAddedKVProcessor()
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnProcessor()
else:
raise ValueError(
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
)
self.set_attn_processor(processor) | 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 in slices to compute attention in
several steps. This is useful for saving some memory in exchange for a small decrease in speed.
Args:
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
must be a multiple of `slice_size`.
"""
sliceable_head_dims = [] | 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 number of attention layers
for module in self.children():
fn_recursive_retrieve_sliceable_dims(module)
num_sliceable_layers = len(sliceable_head_dims)
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = [dim // 2 for dim in sliceable_head_dims]
elif slice_size == "max":
# make smallest slice possible
slice_size = num_sliceable_layers * [1]
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size | 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)}."
)
for i in range(len(slice_size)):
size = slice_size[i]
dim = sliceable_head_dims[i]
if size is not None and size > dim:
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
# Recursively walk through all the children.
# Any children which exposes the set_attention_slice method
# gets the message
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
if hasattr(module, "set_attention_slice"):
module.set_attention_slice(slice_size.pop()) | 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, value: bool = False) -> None:
if isinstance(module, (CrossAttnDownBlockMotion, DownBlockMotion, UNetMidBlock2DCrossAttn)):
module.gradient_checkpointing = value | 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[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
conditioning_mask: Optional[torch.Tensor] = None,
guess_mode: bool = False,
return_dict: bool = True,
) -> Union[SparseControlNetOutput, Tuple[Tuple[torch.Tensor, ...], torch.Tensor]]:
"""
The [`SparseControlNetModel`] forward method. | 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_cond (`torch.Tensor`):
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
conditioning_scale (`float`, defaults to `1.0`):
The scale factor for ControlNet outputs.
class_labels (`torch.Tensor`, *optional*, defaults to `None`):
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the | 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
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
negative values to the attention scores corresponding to "discard" tokens.
added_cond_kwargs (`dict`):
Additional conditions for the Stable Diffusion XL UNet.
cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
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 | 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** `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.
"""
sample_batch_size, sample_channels, sample_num_frames, sample_height, sample_width = sample.shape
sample = torch.zeros_like(sample) | 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:
raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
# prepare attention_mask
if attention_mask is not None:
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
attention_mask = attention_mask.unsqueeze(1) | 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"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.time_proj(timesteps) | 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_cond)
emb = emb.repeat_interleave(sample_num_frames, dim=0)
# 2. pre-process
batch_size, channels, num_frames, height, width = sample.shape
sample = sample.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
sample = self.conv_in(sample)
batch_frames, channels, height, width = sample.shape
sample = sample[:, None].reshape(sample_batch_size, sample_num_frames, channels, height, width)
if self.concat_conditioning_mask:
controlnet_cond = torch.cat([controlnet_cond, conditioning_mask], dim=1) | 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, channels, height, width = controlnet_cond.shape
controlnet_cond = controlnet_cond[:, None].reshape(batch_size, num_frames, channels, height, width)
sample = sample + controlnet_cond
batch_size, num_frames, channels, height, width = sample.shape
sample = sample.reshape(sample_batch_size * sample_num_frames, channels, height, width) | 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,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
num_frames=num_frames,
cross_attention_kwargs=cross_attention_kwargs,
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames)
down_block_res_samples += res_samples | 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,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
)
else:
sample = self.mid_block(sample, emb)
# 5. Control net 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 = controlnet_down_block_res_samples + (down_block_res_sample,) | 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 1.0
scales = scales * conditioning_scale
down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
mid_block_res_sample = mid_block_res_sample * scales[-1] # last one
else:
down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample = mid_block_res_sample * conditioning_scale | 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:
return (down_block_res_samples, mid_block_res_sample)
return SparseControlNetOutput(
down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
) | 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 output, but is already the final output.
"""
sample: Tensor = None | 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,
attentions: Optional[nn.ModuleList] = None,
downsampler: Optional[nn.Conv2d] = None,
):
super().__init__()
self.resnets = resnets
self.base_to_ctrl = base_to_ctrl
self.ctrl_to_base = ctrl_to_base
self.attentions = attentions
self.downsamplers = downsampler | 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().__init__()
self.midblock = midblock
self.base_to_ctrl = base_to_ctrl
self.ctrl_to_base = ctrl_to_base | 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
methods implemented for all models (such as downloading or saving).
Like `UNetControlNetXSModel`, `ControlNetXSAdapter` is compatible with StableDiffusion and StableDiffusion-XL. It's
default parameters are compatible with StableDiffusion. | 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_out_channels (`tuple[int]`, defaults to `(16, 32, 96, 256)`):
The tuple of output channels for each block in the `controlnet_cond_embedding` layer.
time_embedding_mix (`float`, defaults to 1.0):
If 0, then only the control adapters's time embedding is used. If 1, then only the base unet's time
embedding is used. Otherwise, both are combined.
learn_time_embedding (`bool`, defaults to `False`):
Whether a time embedding should be learned. If yes, `UNetControlNetXSModel` will combine the time
embeddings of the base model and the control adapter. If no, `UNetControlNetXSModel` will use the base | 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 `[320, 640, 1280, 1280]`):
The tuple of output channels for each block in the base unet.
cross_attention_dim (`int`, defaults to 1024):
The dimension of the cross attention features.
down_block_types (`list[str]`, defaults to `["CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"]`):
The tuple of downsample blocks to use.
sample_size (`int`, defaults to 96):
Height and width of input/output sample.
transformer_layers_per_block (`Union[int, Tuple[int]]`, defaults to 1): | 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 should always be upcasted.
max_norm_num_groups (`int`, defaults to 32):
Maximum number of groups in group normal. The actual number will be the largest divisor of the respective
channels, that is <= max_norm_num_groups.
""" | 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_attention_heads: Union[int, Tuple[int]] = 4,
block_out_channels: Tuple[int] = (4, 8, 16, 16),
base_block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
cross_attention_dim: int = 1024,
down_block_types: Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
),
sample_size: Optional[int] = 96,
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
upcast_attention: bool = True,
max_norm_num_groups: int = 32,
use_linear_projection: bool = True,
):
super().__init__() | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
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