text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
time_embedding_input_dim = base_block_out_channels[0]
time_embedding_dim = base_block_out_channels[0] * 4
# Check inputs
if conditioning_channel_order not in ["rgb", "bgr"]:
raise ValueError(f"unknown `conditioning_channel_order`: {conditioning_channel_order}")
if len(block... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
if not isinstance(transformer_layers_per_block, (list, tuple)):
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
if not isinstance(cross_attention_dim, (list, tuple)):
cross_attention_dim = [cross_attention_dim] * len(down_block_types)
# see h... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# 5 - Create conditioning hint embedding
self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0],
block_out_channels=conditioning_embedding_out_channels,
conditioning_channels=conditioning_channels,
)
... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# down
base_out_channels = base_block_out_channels[0]
ctrl_out_channels = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
base_in_channels = base_out_channels
base_out_channels = base_block_out_channels[i]
ctrl_in_channels = ctrl_o... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
self.down_blocks.append(
get_down_block_adapter(
base_in_channels=base_in_channels,
base_out_channels=base_out_channels,
ctrl_in_channels=ctrl_in_channels,
ctrl_out_channels=ctrl_out_channels,
temb_channe... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# mid
self.mid_block = get_mid_block_adapter(
base_channels=base_block_out_channels[-1],
ctrl_channels=block_out_channels[-1],
temb_channels=time_embedding_dim,
transformer_layers_per_block=transformer_layers_per_block[-1],
num_attention_heads=num_atte... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
reversed_base_block_out_channels = list(reversed(base_block_out_channels))
base_out_channels = reversed_base_block_out_channels[0]
for i in range(len(down_block_types)):
prev_base_output_channel = base_out_channels
base_out_channels = reversed_base_block_out_channels[i]
... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
@classmethod
def from_unet(
cls,
unet: UNet2DConditionModel,
size_ratio: Optional[float] = None,
block_out_channels: Optional[List[int]] = None,
num_attention_heads: Optional[List[int]] = None,
learn_time_embedding: bool = False,
time_embedding_mix: int = 1.0,... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
Parameters:
unet (`UNet2DConditionModel`):
The UNet model we want to control. The dimensions of the ControlNetXSAdapter will be adapted to it.
size_ratio (float, *optional*, defaults to `None`):
When given, block_out_channels is set to a fraction of the base model... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
Whether the `ControlNetXSAdapter` should learn a time embedding.
time_embedding_mix (`float`, defaults to 1.0):
If 0, then only the control adapter's time embedding is used. If 1, then only the base unet's time
embedding is used. Otherwise, both are combined.
cond... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# Check input
fixed_size = block_out_channels is not None
relative_size = size_ratio is not None
if not (fixed_size ^ relative_size):
raise ValueError(
"Pass exactly one of `block_out_channels` (for absolute sizing) or `size_ratio` (for relative sizing)."
... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
model = cls(
conditioning_channels=conditioning_channels,
conditioning_channel_order=conditioning_channel_order,
conditioning_embedding_out_channels=conditioning_embedding_out_channels,
time_embedding_mix=time_embedding_mix,
learn_time_embedding=learn_time_emb... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# ensure that the ControlNetXSAdapter is the same dtype as the UNet2DConditionModel
model.to(unet.dtype)
return model
def forward(self, *args, **kwargs):
raise ValueError(
"A ControlNetXSAdapter cannot be run by itself. Use it together with a UNet2DConditionModel to instantiate... | 1,080 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
class UNetControlNetXSModel(ModelMixin, ConfigMixin):
r"""
A UNet fused with a ControlNet-XS adapter 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).
`UNetContr... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
@register_to_config
def __init__(
self,
# unet configs
sample_size: Optional[int] = 96,
down_block_types: Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
),
up_block_... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
projection_class_embeddings_input_dim: Optional[int] = None,
# additional controlnet configs
time_embedding_mix: float = 1.0,
ctrl_conditioning_channels: int = 3,
ctrl_conditioning_embedding_out_channels: Tuple[int] = (16, 32, 96, 256),
ctrl_conditioning_channel_order: str = "rgb... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
if time_embedding_mix < 0 or time_embedding_mix > 1:
raise ValueError("`time_embedding_mix` needs to be between 0 and 1.")
if time_embedding_mix < 1 and not ctrl_learn_time_embedding:
raise ValueError("To use `time_embedding_mix` < 1, `ctrl_learn_time_embedding` must be `True`")
... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
if not isinstance(transformer_layers_per_block, (list, tuple)):
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
if not isinstance(cross_attention_dim, (list, tuple)):
cross_attention_dim = [cross_attention_dim] * len(down_block_types)
if not ... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# # Input
self.base_conv_in = nn.Conv2d(4, block_out_channels[0], kernel_size=3, padding=1)
self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
conditioning_embedding_channels=ctrl_block_out_channels[0],
block_out_channels=ctrl_conditioning_embedding_out_channels,
... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
self.base_time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos=True, downscale_freq_shift=0)
self.base_time_embedding = TimestepEmbedding(
time_embed_input_dim,
time_embed_dim,
cond_proj_dim=time_cond_proj_dim,
)
if ctrl_learn_time_embedding:
... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# # Create down blocks
down_blocks = []
base_out_channels = block_out_channels[0]
ctrl_out_channels = ctrl_block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
base_in_channels = base_out_channels
base_out_channels = block_out_channels[i]
... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
down_blocks.append(
ControlNetXSCrossAttnDownBlock2D(
base_in_channels=base_in_channels,
base_out_channels=base_out_channels,
ctrl_in_channels=ctrl_in_channels,
ctrl_out_channels=ctrl_out_channels,
temb_c... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
) | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# # Create mid block
self.mid_block = ControlNetXSCrossAttnMidBlock2D(
base_channels=block_out_channels[-1],
ctrl_channels=ctrl_block_out_channels[-1],
temb_channels=time_embed_dim,
norm_num_groups=norm_num_groups,
ctrl_max_norm_num_groups=ctrl_max_nor... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# The skip connection channels are the output of the conv_in and of all the down subblocks
ctrl_skip_channels = [ctrl_block_out_channels[0]]
for i, out_channels in enumerate(ctrl_block_out_channels):
number_of_subblocks = (
3 if i < len(ctrl_block_out_channels) - 1 else 2
... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
has_crossattn = "CrossAttn" in up_block_type
is_final_block = i == len(block_out_channels) - 1 | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
up_blocks.append(
ControlNetXSCrossAttnUpBlock2D(
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
ctrl_skip_channels=ctrl_skip_channels_,
temb_channels=time_em... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
self.base_conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups)
self.base_conv_act = nn.SiLU()
self.base_conv_out = nn.Conv2d(block_out_channels[0], 4, kernel_size=3, padding=1)
@classmethod
def from_unet(
cls,
unet: UNet2DConditionModel,
... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
Parameters:
unet (`UNet2DConditionModel`):
The UNet model we want to control.
controlnet (`ControlNetXSAdapter`):
The ConntrolNet-XS adapter with which the UNet will be fused. If none is given, a new ConntrolNet-XS
adapter will be created.
... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
ctrl_optional_kwargs (`Dict`, *optional*, defaults to `None`):
Passed to the `init` of the new controlent if no controlent was given.
"""
if controlnet is None:
controlnet = ControlNetXSAdapter.from_unet(
unet, size_ratio, ctrl_block_out_channels, **ctrl_optio... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# # get params
params_for_unet = [
"sample_size",
"down_block_types",
"up_block_types",
"block_out_channels",
"norm_num_groups",
"cross_attention_dim",
"transformer_layers_per_block",
"addition_embed_type",
... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
params_for_controlnet = [
"conditioning_channels",
"conditioning_embedding_out_channels",
"conditioning_channel_order",
"learn_time_embedding",
"block_out_channels",
"num_attention_heads",
"max_norm_num_groups",
]
params... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
optional_modules_from_unet = [
"add_time_proj",
"add_embedding",
]
for m in optional_modules_from_unet:
if hasattr(unet, m) and getattr(unet, m) is not None:
getattr(model, "base_" + m).load_state_dict(getattr(unet, m).state_dict())
# from con... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# from both
model.down_blocks = nn.ModuleList(
ControlNetXSCrossAttnDownBlock2D.from_modules(b, c)
for b, c in zip(unet.down_blocks, controlnet.down_blocks)
)
model.mid_block = ControlNetXSCrossAttnMidBlock2D.from_modules(unet.mid_block, controlnet.mid_block)
mode... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# Unfreeze ControlNetXSAdapter
base_parts = [
"base_time_proj",
"base_time_embedding",
"base_add_time_proj",
"base_add_embedding",
"base_conv_in",
"base_conv_norm_out",
"base_conv_act",
"base_conv_out",
]
... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor ... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.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... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.enable_freeu
def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
The suffixes after the scaling factors represent the stage blocks where... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
Args:
s1 (`float`):
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
mitigate the "oversmoothing effect" in the enhanced denoising process.
s2 (`float`):
Scaling factor for stage 2 to attenuate the con... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.disable_freeu
def disable_freeu(self):
"""Disables the FreeU mechanism."""
freeu_keys = {"s1", "s2", "b1", "b2"}
for i, upsample_block in enumerate(self.up_blocks):
for k in freeu_keys:
if... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
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... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
def forward(
self,
sample: Tensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
controlnet_cond: Optional[torch.Tensor] = None,
conditioning_scale: Optional[float] = 1.0,
class_labels: Optional[torch.Tensor] = None,
times... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
Args:
sample (`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 (... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.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
... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
apply_control (`bool`, defaults to `True`):
If `False`, the input is run only through the base model. | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
Returns:
[`~models.controlnetxs.ControlNetXSOutput`] **or** `tuple`:
If `return_dict` is `True`, a [`~models.controlnetxs.ControlNetXSOutput`] is returned, otherwise a
tuple is returned where the first element is the sample tensor.
"""
# check channel order
... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.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"
... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.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)
if self.config.ctrl_learn_time_embedding an... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
if self.config.addition_embed_type is None:
pass
elif self.config.addition_embed_type == "text_time":
# SDXL - style
if "text_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` ... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
add_embeds = add_embeds.to(temb.dtype)
aug_emb = self.base_add_embedding(add_embeds)
else:
raise ValueError(
f"ControlNet-XS currently only supports StableDiffusion and StableDiffusion-XL, so additi... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
temb = temb + aug_emb if aug_emb is not None else temb
# text embeddings
cemb = encoder_hidden_states
# Preparation
h_ctrl = h_base = sample
hs_base, hs_ctrl = [], []
# Cross Control
guided_hint = self.controlnet_cond_embedding(controlnet_cond)
# 1 - c... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
for down in self.down_blocks:
h_base, h_ctrl, residual_hb, residual_hc = down(
hidden_states_base=h_base,
hidden_states_ctrl=h_ctrl,
temb=temb,
encoder_hidden_states=cemb,
conditioning_scale=conditioning_scale,
c... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# 3 - up
for up in self.up_blocks:
n_resnets = len(up.resnets)
skips_hb = hs_base[-n_resnets:]
skips_hc = hs_ctrl[-n_resnets:]
hs_base = hs_base[:-n_resnets]
hs_ctrl = hs_ctrl[:-n_resnets]
h_base = up(
hidden_states=h_base,
... | 1,081 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
class ControlNetXSCrossAttnDownBlock2D(nn.Module):
def __init__(
self,
base_in_channels: int,
base_out_channels: int,
ctrl_in_channels: int,
ctrl_out_channels: int,
temb_channels: int,
norm_num_groups: int = 32,
ctrl_max_norm_num_groups: int = 32,
... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = [transformer_layers_per_block] * num_layers
for i in range(num_layers):
base_in_channels = base_in_channels if i == 0 else base_out_channels
ctrl_in_channels = ctrl_in_channels if i == 0 else ct... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
base_resnets.append(
ResnetBlock2D(
in_channels=base_in_channels,
out_channels=base_out_channels,
temb_channels=temb_channels,
groups=norm_num_groups,
)
)
ctrl_resnets.append(
... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
if has_crossattn:
base_attentions.append(
Transformer2DModel(
base_num_attention_heads,
base_out_channels // base_num_attention_heads,
in_channels=base_out_channels,
num_layers=transformer... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
cross_attention_dim=cross_attention_dim,
use_linear_projection=use_linear_projection,
upcast_attention=upcast_attention,
norm_num_groups=find_largest_factor(ctrl_out_channels, max_factor=ctrl_max_norm_num_groups),
)
... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# After the resnet/attention application, information is added from control to base
# Addition requires change in number of channels
ctrl_to_base.append(make_zero_conv(ctrl_out_channels, base_out_channels))
if add_downsample:
# Before the downsampler application, information... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# After the downsampler application, information is added from control to base
# Addition requires change in number of channels
ctrl_to_base.append(make_zero_conv(ctrl_out_channels, base_out_channels))
else:
self.base_downsamplers = None
self.ctrl_downsamplers = N... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
@classmethod
def from_modules(cls, base_downblock: CrossAttnDownBlock2D, ctrl_downblock: DownBlockControlNetXSAdapter):
# get params
def get_first_cross_attention(block):
return block.attentions[0].transformer_blocks[0].attn2 | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
base_in_channels = base_downblock.resnets[0].in_channels
base_out_channels = base_downblock.resnets[0].out_channels
ctrl_in_channels = (
ctrl_downblock.resnets[0].in_channels - base_in_channels
) # base channels are concatted to ctrl channels in init
ctrl_out_channels = ctrl... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
upcast_attention = get_first_cross_attention(base_downblock).upcast_attention
use_linear_projection = base_downblock.attentions[0].use_linear_projection
else:
has_crossattn = False
transformer_layers_per_block = None
base_num_attention_heads = None
ctr... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# create model
model = cls(
base_in_channels=base_in_channels,
base_out_channels=base_out_channels,
ctrl_in_channels=ctrl_in_channels,
ctrl_out_channels=ctrl_out_channels,
temb_channels=temb_channels,
norm_num_groups=num_groups,
... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# # load weights
model.base_resnets.load_state_dict(base_downblock.resnets.state_dict())
model.ctrl_resnets.load_state_dict(ctrl_downblock.resnets.state_dict())
if has_crossattn:
model.base_attentions.load_state_dict(base_downblock.attentions.state_dict())
model.ctrl_atte... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
def freeze_base_params(self) -> None:
"""Freeze the weights of the parts belonging to the base UNet2DConditionModel, and leave everything else unfrozen for fine
tuning."""
# Unfreeze everything
for param in self.parameters():
param.requires_grad = True
# Freeze base ... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
def forward(
self,
hidden_states_base: Tensor,
temb: Tensor,
encoder_hidden_states: Optional[Tensor] = None,
hidden_states_ctrl: Optional[Tensor] = None,
conditioning_scale: Optional[float] = 1.0,
attention_mask: Optional[Tensor] = None,
cross_attention_kw... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
base_blocks = list(zip(self.base_resnets, self.base_attentions))
ctrl_blocks = list(zip(self.ctrl_resnets, self.ctrl_attentions))
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# apply base subblock
if torch.is_grad_enabled() and self.gradient_checkpointing:
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
h_base = torch.utils.checkpoint.checkpoint(
create_custom_forward(b_res),
... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# apply ctrl subblock
if apply_control:
if torch.is_grad_enabled() and self.gradient_checkpointing:
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
h_ctrl = torch.utils.checkpoint.checkpoint(
... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# add ctrl -> base
if apply_control:
h_base = h_base + c2b(h_ctrl) * conditioning_scale
base_output_states = base_output_states + (h_base,)
ctrl_output_states = ctrl_output_states + (h_ctrl,)
if self.base_downsamplers is not None: # if we have a base_downsa... | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
base_output_states = base_output_states + (h_base,)
ctrl_output_states = ctrl_output_states + (h_ctrl,)
return h_base, h_ctrl, base_output_states, ctrl_output_states | 1,082 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
class ControlNetXSCrossAttnMidBlock2D(nn.Module):
def __init__(
self,
base_channels: int,
ctrl_channels: int,
temb_channels: Optional[int] = None,
norm_num_groups: int = 32,
ctrl_max_norm_num_groups: int = 32,
transformer_layers_per_block: int = 1,
bas... | 1,083 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
self.base_midblock = UNetMidBlock2DCrossAttn(
transformer_layers_per_block=transformer_layers_per_block,
in_channels=base_channels,
temb_channels=temb_channels,
resnet_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
num_attention_h... | 1,083 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
self.ctrl_midblock = UNetMidBlock2DCrossAttn(
transformer_layers_per_block=transformer_layers_per_block,
in_channels=ctrl_channels + base_channels,
out_channels=ctrl_channels,
temb_channels=temb_channels,
# number or norm groups must divide both in_channels an... | 1,083 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
@classmethod
def from_modules(
cls,
base_midblock: UNetMidBlock2DCrossAttn,
ctrl_midblock: MidBlockControlNetXSAdapter,
):
base_to_ctrl = ctrl_midblock.base_to_ctrl
ctrl_to_base = ctrl_midblock.ctrl_to_base
ctrl_midblock = ctrl_midblock.midblock
# get par... | 1,083 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
base_channels = ctrl_to_base.out_channels
ctrl_channels = ctrl_to_base.in_channels
transformer_layers_per_block = len(base_midblock.attentions[0].transformer_blocks)
temb_channels = base_midblock.resnets[0].time_emb_proj.in_features
num_groups = base_midblock.resnets[0].norm1.num_groups
... | 1,083 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# create model
model = cls(
base_channels=base_channels,
ctrl_channels=ctrl_channels,
temb_channels=temb_channels,
norm_num_groups=num_groups,
ctrl_max_norm_num_groups=ctrl_num_groups,
transformer_layers_per_block=transformer_layers_per_blo... | 1,083 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
def freeze_base_params(self) -> None:
"""Freeze the weights of the parts belonging to the base UNet2DConditionModel, and leave everything else unfrozen for fine
tuning."""
# Unfreeze everything
for param in self.parameters():
param.requires_grad = True
# Freeze base ... | 1,083 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
def forward(
self,
hidden_states_base: Tensor,
temb: Tensor,
encoder_hidden_states: Tensor,
hidden_states_ctrl: Optional[Tensor] = None,
conditioning_scale: Optional[float] = 1.0,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
attention_mask: Opt... | 1,083 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
joint_args = {
"temb": temb,
"encoder_hidden_states": encoder_hidden_states,
"attention_mask": attention_mask,
"cross_attention_kwargs": cross_attention_kwargs,
"encoder_attention_mask": encoder_attention_mask,
}
if apply_control:
... | 1,083 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
class ControlNetXSCrossAttnUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
prev_output_channel: int,
ctrl_skip_channels: List[int],
temb_channels: int,
norm_num_groups: int = 32,
resolution_idx: Optional[int] = None,
... | 1,084 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
for i in range(num_layers):
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
resnet_in_channels = prev_output_channel if i == 0 else out_channels
ctrl_to_base.append(make_zero_conv(ctrl_skip_channels[i], resnet_in_channels))
resnets.append(
... | 1,084 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
if has_crossattn:
attentions.append(
Transformer2DModel(
num_attention_heads,
out_channels // num_attention_heads,
in_channels=out_channels,
num_layers=transformer_layers_per_block[i],
... | 1,084 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
self.gradient_checkpointing = False
self.resolution_idx = resolution_idx
@classmethod
def from_modules(cls, base_upblock: CrossAttnUpBlock2D, ctrl_upblock: UpBlockControlNetXSAdapter):
ctrl_to_base_skip_connections = ctrl_upblock.ctrl_to_base
# get params
def get_first_cross_at... | 1,084 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
out_channels = base_upblock.resnets[0].out_channels
in_channels = base_upblock.resnets[-1].in_channels - out_channels
prev_output_channels = base_upblock.resnets[0].in_channels - out_channels
ctrl_skip_channelss = [c.in_channels for c in ctrl_to_base_skip_connections]
temb_channels = bas... | 1,084 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
else:
has_crossattn = False
transformer_layers_per_block = None
num_attention_heads = None
cross_attention_dim = None
upcast_attention = None
use_linear_projection = None
add_upsample = base_upblock.upsamplers is not None | 1,084 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# create model
model = cls(
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channels,
ctrl_skip_channels=ctrl_skip_channelss,
temb_channels=temb_channels,
norm_num_groups=num_groups,
resolutio... | 1,084 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# load weights
model.resnets.load_state_dict(base_upblock.resnets.state_dict())
if has_crossattn:
model.attentions.load_state_dict(base_upblock.attentions.state_dict())
if add_upsample:
model.upsamplers.load_state_dict(base_upblock.upsamplers[0].state_dict())
mode... | 1,084 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
# Freeze base part
base_parts = [self.resnets]
if isinstance(self.attentions, nn.ModuleList): # attentions can be a list of Nones
base_parts.append(self.attentions)
if self.upsamplers is not None:
base_parts.append(self.upsamplers)
for part in base_parts:
... | 1,084 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
def forward(
self,
hidden_states: Tensor,
res_hidden_states_tuple_base: Tuple[Tensor, ...],
res_hidden_states_tuple_ctrl: Tuple[Tensor, ...],
temb: Tensor,
encoder_hidden_states: Optional[Tensor] = None,
conditioning_scale: Optional[float] = 1.0,
cross_att... | 1,084 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
return module(*inputs)
return custom_forward
def m... | 1,084 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
for resnet, attn, c2b, res_h_base, res_h_ctrl in zip(
self.resnets,
self.attentions,
self.ctrl_to_base,
reversed(res_hidden_states_tuple_base),
reversed(res_hidden_states_tuple_ctrl),
):
if apply_control:
hidden_states += c2... | 1,084 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
if torch.is_grad_enabled() and self.gradient_checkpointing:
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet),
hidden_... | 1,084 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
if self.upsamplers is not None:
hidden_states = self.upsamplers(hidden_states, upsample_size)
return hidden_states | 1,084 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py |
class QuickGELU(nn.Module):
"""
Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs
"""
def forward(self, input: torch.Tensor) -> torch.Tensor:
return input * torch.sigmoid(1.702 * input) | 1,085 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
class ResidualAttentionMlp(nn.Module):
def __init__(self, d_model: int):
super().__init__()
self.c_fc = nn.Linear(d_model, d_model * 4)
self.gelu = QuickGELU()
self.c_proj = nn.Linear(d_model * 4, d_model)
def forward(self, x: torch.Tensor):
x = self.c_fc(x)
x = ... | 1,086 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
class ResidualAttentionBlock(nn.Module):
def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
super().__init__()
self.attn = nn.MultiheadAttention(d_model, n_head)
self.ln_1 = nn.LayerNorm(d_model)
self.mlp = ResidualAttentionMlp(d_model)
self.ln_2 =... | 1,087 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.