text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
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class SimpleCrossAttnUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
prev_output_channel: int,
temb_channels: int,
resolution_idx: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float... | 1,059 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.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
resnets.append(
ResnetBlock2D(
in_channels=resnet_in_channels + res_skip_ch... | 1,059 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
attentions.append(
Attention(
query_dim=out_channels,
cross_attention_dim=out_channels,
heads=self.num_heads,
dim_head=self.attention_head_dim,
added_kv_proj_dim=cross_attention_dim,
n... | 1,059 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
if add_upsample:
self.upsamplers = nn.ModuleList(
[
ResnetBlock2D(
in_channels=out_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
... | 1,059 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
def forward(
self,
hidden_states: torch.Tensor,
res_hidden_states_tuple: Tuple[torch.Tensor, ...],
temb: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
upsample_size: Optional[int] = None,
attention_mask: Optional[torch.Tensor... | 1,059 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
if attention_mask is None:
# if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask.
mask = None if encoder_hidden_states is None else encoder_attention_mask
else:
# when attention_mask is defined: we don't even check for encoder_attent... | 1,059 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
for resnet, attn in zip(self.resnets, self.attentions):
# resnet
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)... | 1,059 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
hidden_states = attn(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=mask,
**cross_attention_k... | 1,059 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
class KUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
resolution_idx: int,
dropout: float = 0.0,
num_layers: int = 5,
resnet_eps: float = 1e-5,
resnet_act_fn: str = "gelu",
resnet_grou... | 1,060 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
resnets.append(
ResnetBlockCondNorm2D(
in_channels=in_channels,
out_channels=k_out_channels if (i == num_layers - 1) else out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=groups,
... | 1,060 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
def forward(
self,
hidden_states: torch.Tensor,
res_hidden_states_tuple: Tuple[torch.Tensor, ...],
temb: Optional[torch.Tensor] = None,
upsample_size: Optional[int] = None,
*args,
**kwargs,
) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", Non... | 1,060 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
return custom_forward
if is_torch_version(">=", "1.11.0"):
hidden_states = torch.utils.checkpoint.checkpoint(
... | 1,060 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
class KCrossAttnUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
resolution_idx: int,
dropout: float = 0.0,
num_layers: int = 4,
resnet_eps: float = 1e-5,
resnet_act_fn: str = "gelu",
re... | 1,061 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
# in_channels, and out_channels for the block (k-unet)
k_in_channels = out_channels if is_first_block else 2 * out_channels
k_out_channels = in_channels
num_layers = num_layers - 1
for i in range(num_layers):
in_channels = k_in_channels if i == 0 else out_channels
... | 1,061 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
resnets.append(
ResnetBlockCondNorm2D(
in_channels=in_channels,
out_channels=out_channels,
conv_2d_out_channels=conv_2d_out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
group... | 1,061 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
temb_channels=temb_channels,
attention_bias=True,
add_self_attention=add_self_attention,
cross_attention_norm="layer_norm",
upcast_attention=upcast_attention,
)
) | 1,061 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
self.resnets = nn.ModuleList(resnets)
self.attentions = nn.ModuleList(attentions)
if add_upsample:
self.upsamplers = nn.ModuleList([KUpsample2D()])
else:
self.upsamplers = None
self.gradient_checkpointing = False
self.resolution_idx = resolution_idx
... | 1,061 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
for resnet, attn in zip(self.resnets, self.attentions):
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:
... | 1,061 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet),
hidden_states,
temb,
**ckpt_kwargs,
... | 1,061 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
cross_attention_kwargs=cross_attention_kwargs,
encoder_attention_mask=encoder_attention_mask,
) | 1,061 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states)
return hidden_states | 1,061 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
class KAttentionBlock(nn.Module):
r"""
A basic Transformer block. | 1,062 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): The number of heads to use for multi-head attention.
attention_head_dim (`int`): The number of channels in each head.
dropout (`float`, *optional*, defaults to 0.0): The dropout probabil... | 1,062 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
cross_attention_norm (`str`, *optional*, defaults to `None`):
The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`.
group_size (`int`, *optional*, defaults to 32):
The number of groups to separate the channels into for group normalization... | 1,062 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
dropout: float = 0.0,
cross_attention_dim: Optional[int] = None,
attention_bias: bool = False,
upcast_attention: bool = False,
temb_channels: int = 768, # for ada_gro... | 1,062 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
# 2. Cross-Attn
self.norm2 = AdaGroupNorm(temb_channels, dim, max(1, dim // group_size))
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
... | 1,062 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
# TODO: mark emb as non-optional (self.norm2 requires it).
# requires assessing impact of change to positional param interface.
emb: Optional[torch.Tensor] = None,
... | 1,062 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
height, weight = norm_hidden_states.shape[2:]
norm_hidden_states = self._to_3d(norm_hidden_states, height, weight)
attn_output = self.attn1(
norm_hidden_states,
encoder_hidden_states=None,
attention_mask=attention_mask,
**cross_att... | 1,062 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
height, weight = norm_hidden_states.shape[2:]
norm_hidden_states = self._to_3d(norm_hidden_states, height, weight)
attn_output = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask if encoder_hidden_states is None... | 1,062 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py |
class MultiControlNetModel(ModelMixin):
r"""
Multiple `ControlNetModel` wrapper class for Multi-ControlNet
This module is a wrapper for multiple instances of the `ControlNetModel`. The `forward()` API is designed to be
compatible with `ControlNetModel`.
Args:
controlnets (`List[ControlNetM... | 1,063 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/multicontrolnet.py |
def forward(
self,
sample: torch.Tensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
controlnet_cond: List[torch.tensor],
conditioning_scale: List[float],
class_labels: Optional[torch.Tensor] = None,
timestep_cond: Optio... | 1,063 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/multicontrolnet.py |
class_labels=class_labels,
timestep_cond=timestep_cond,
attention_mask=attention_mask,
added_cond_kwargs=added_cond_kwargs,
cross_attention_kwargs=cross_attention_kwargs,
guess_mode=guess_mode,
return_dict=return_dict,
... | 1,063 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/multicontrolnet.py |
# merge samples
if i == 0:
down_block_res_samples, mid_block_res_sample = down_samples, mid_sample
else:
down_block_res_samples = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(down_block_res_samples, do... | 1,063 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/multicontrolnet.py |
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to which to save. Will be created if it doesn't exist.
is_main_process (`bool`, *optional*, defaults to `True`):
Whether the process calling this is the main process or not. Useful when in distributed t... | 1,063 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/multicontrolnet.py |
variant (`str`, *optional*):
If specified, weights are saved in the format pytorch_model.<variant>.bin.
"""
for idx, controlnet in enumerate(self.nets):
suffix = "" if idx == 0 else f"_{idx}"
controlnet.save_pretrained(
save_directory + suffix,
... | 1,063 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/multicontrolnet.py |
@classmethod
def from_pretrained(cls, pretrained_model_path: Optional[Union[str, os.PathLike]], **kwargs):
r"""
Instantiate a pretrained MultiControlNet model from multiple pre-trained controlnet models.
The model is set in evaluation mode by default using `model.eval()` (Dropout modules ar... | 1,063 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/multicontrolnet.py |
Parameters:
pretrained_model_path (`os.PathLike`):
A path to a *directory* containing model weights saved using
[`~models.controlnets.multicontrolnet.MultiControlNetModel.save_pretrained`], e.g.,
`./my_model_directory/controlnet`.
torch_dtype (`str... | 1,063 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/multicontrolnet.py |
parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
same device. | 1,063 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/multicontrolnet.py |
To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
more information about each option see [designing a device
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
max_memory (`Dict`, ... | 1,063 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/multicontrolnet.py |
setting this argument to `True` will raise an error.
variant (`str`, *optional*):
If specified load weights from `variant` filename, *e.g.* pytorch_model.<variant>.bin. `variant` is
ignored when using `from_flax`.
use_safetensors (`bool`, *optional*, defaults to `... | 1,063 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/multicontrolnet.py |
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirect... | 1,063 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/multicontrolnet.py |
class HunyuanControlNetOutput(BaseOutput):
controlnet_block_samples: Tuple[torch.Tensor] | 1,064 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
class HunyuanDiT2DControlNetModel(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
conditioning_channels: int = 3,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: Optional[int] = None,
patch_size: Optional[int] = None,
... | 1,065 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
self.text_embedder = PixArtAlphaTextProjection(
in_features=cross_attention_dim_t5,
hidden_size=cross_attention_dim_t5 * 4,
out_features=cross_attention_dim,
act_fn="silu_fp32",
)
self.text_embedding_padding = nn.Parameter(
torch.randn(text_le... | 1,065 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
# controlnet_blocks
self.controlnet_blocks = nn.ModuleList([]) | 1,065 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
# HunyuanDiT Blocks
self.blocks = nn.ModuleList(
[
HunyuanDiTBlock(
dim=self.inner_dim,
num_attention_heads=self.config.num_attention_heads,
activation_fn=activation_fn,
ff_inner_dim=int(self.inner_dim * ... | 1,065 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
@property
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 = {... | 1,065 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated pro... | 1,065 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.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."
... | 1,065 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
@classmethod
def from_transformer(
cls, transformer, conditioning_channels=3, transformer_num_layers=None, load_weights_from_transformer=True
):
config = transformer.config
activation_fn = config.activation_fn
attention_head_dim = config.attention_head_dim
cross_attention... | 1,065 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
controlnet = cls(
conditioning_channels=conditioning_channels,
transformer_num_layers=transformer_num_layers,
activation_fn=activation_fn,
attention_head_dim=attention_head_dim,
cross_attention_dim=cross_attention_dim,
cross_attention_dim_t5=cross_... | 1,065 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
def forward(
self,
hidden_states,
timestep,
controlnet_cond: torch.Tensor,
conditioning_scale: float = 1.0,
encoder_hidden_states=None,
text_embedding_mask=None,
encoder_hidden_states_t5=None,
text_embedding_mask_t5=None,
image_meta_size=No... | 1,065 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
Args:
hidden_states (`torch.Tensor` of shape `(batch size, dim, height, width)`):
The input tensor.
timestep ( `torch.LongTensor`, *optional*):
Used to indicate denoising step.
controlnet_cond ( `torch.Tensor` ):
The conditioning input to ControlNet.
c... | 1,065 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
text_embedding_mask_t5: torch.Tensor
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output
of T5 Text Encoder.
image_meta_size (torch.Tensor):
Conditional embedding indicate the image sizes
style: torch.Tensor:
... | 1,065 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
height, width = hidden_states.shape[-2:]
hidden_states = self.pos_embed(hidden_states) # b,c,H,W -> b, N, C
# 2. pre-process
hidden_states = hidden_states + self.input_block(self.pos_embed(controlnet_cond))
temb = self.time_extra_emb(
timestep, encoder_hidden_states_t5, i... | 1,065 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
encoder_hidden_states = torch.where(text_embedding_mask, encoder_hidden_states, self.text_embedding_padding)
block_res_samples = ()
for layer, block in enumerate(self.blocks):
hidden_states = block(
hidden_states,
temb=temb,
encoder_hidden_sta... | 1,065 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
return HunyuanControlNetOutput(controlnet_block_samples=controlnet_block_res_samples) | 1,065 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
class HunyuanDiT2DMultiControlNetModel(ModelMixin):
r"""
`HunyuanDiT2DMultiControlNetModel` wrapper class for Multi-HunyuanDiT2DControlNetModel
This module is a wrapper for multiple instances of the `HunyuanDiT2DControlNetModel`. The `forward()` API is
designed to be compatible with `HunyuanDiT2DContro... | 1,066 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
def forward(
self,
hidden_states,
timestep,
controlnet_cond: torch.Tensor,
conditioning_scale: float = 1.0,
encoder_hidden_states=None,
text_embedding_mask=None,
encoder_hidden_states_t5=None,
text_embedding_mask_t5=None,
image_meta_size=No... | 1,066 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
Args:
hidden_states (`torch.Tensor` of shape `(batch size, dim, height, width)`):
The input tensor.
timestep ( `torch.LongTensor`, *optional*):
Used to indicate denoising step.
controlnet_cond ( `torch.Tensor` ):
The conditioning input to ControlNet.
c... | 1,066 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
text_embedding_mask_t5: torch.Tensor
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output
of T5 Text Encoder.
image_meta_size (torch.Tensor):
Conditional embedding indicate the image sizes
style: torch.Tensor:
... | 1,066 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
text_embedding_mask=text_embedding_mask,
encoder_hidden_states_t5=encoder_hidden_states_t5,
text_embedding_mask_t5=text_embedding_mask_t5,
image_meta_size=image_meta_size,
style=style,
image_rotary_emb=image_rotary_emb,
retu... | 1,066 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
# merge samples
if i == 0:
control_block_samples = block_samples
else:
control_block_samples = [
control_block_sample + block_sample
for control_block_sample, block_sample in zip(control_block_samples[0], block_samples[0])
... | 1,066 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py |
class ControlNetOutput(BaseOutput):
"""
The output of [`ControlNetModel`].
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 * res... | 1,067 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
class ControlNetConditioningEmbedding(nn.Module):
"""
Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN
[11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized
training. This requires Control... | 1,068 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
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.appen... | 1,068 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
"""
A ControlNet model. | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
Args:
in_channels (`int`, defaults to 4):
The number of channels in the input sample.
flip_sin_to_cos (`bool`, defaults to `True`):
Whether to flip the sin to cos in the time embedding.
freq_shift (`int`, defaults to 0):
The frequency shift to apply to the tim... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
The scale factor to use for the mid block.
act_fn (`str`, defaults to "silu"):
The activation function to use.
norm_num_groups (`int`, *optional*, defaults to 32):
The number of groups to use for the normalization. If None, normalization and activation layers is skipped
... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
dimension to `cross_attention_dim`.
encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
If given, the `encoder_hidden_states` and potentially other embeddings are down-project... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
"text". "text" will use the `TextTimeEmbedding` layer.
num_class_embeds (`int`, *optional*, defaults to 0):
Input dimension of the learnable embedding matrix to be projected to `time_emb... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`):
The tuple of output channel for each block in the `conditioning_embedding` layer.
global_pool_conditions (`bool`, defa... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
_supports_gradient_checkpointing = True | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
@register_to_config
def __init__(
self,
in_channels: int = 4,
conditioning_channels: int = 3,
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
down_block_types: Tuple[str, ...] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
attention_head_dim: Union[int, Tuple[int, ...]] = 8,
num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,
use_linear_projection: bool = False,
class_embed_type: Optional[str] = None,
addition_embed_type: Optional[str] = None,
addition_time_embed_dim: Optional[int] =... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was cre... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
# input
conv_in_kernel = 3
conv_in_padding = (conv_in_kernel - 1) // 2
self.conv_in = nn.Conv2d(
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
)
# time
time_embed_dim = block_out_channels[0] * 4
self.time_proj... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
if encoder_hid_dim is None and encoder_hid_dim_type is not None:
raise ValueError(
f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
)
if encoder_hid_dim_type == "text_proj":
self.encoder_hid_proj = nn.Linear... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
elif encoder_hid_dim_type is not None:
raise ValueError(
f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
)
else:
self.encoder_hid_proj = None | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
# class embedding
if class_embed_type is None and num_class_embeds is not None:
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
elif class_embed_type == "timestep":
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
elif cla... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
self.class_embeddi... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
if addition_embed_type == "text":
if encoder_hid_dim is not None:
text_time_embedding_from_dim = encoder_hid_dim
else:
text_time_embedding_from_dim = cross_attention_dim | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
self.add_embedding = TextTimeEmbedding(
text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
)
elif addition_embed_type == "text_image":
# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
elif addition_embed_type is not None:
raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
# control net conditioning embedding
self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
conditioning_embedding_channels=bloc... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
# 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):... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block,
transformer_layers_per_block=transformer_layers_per_block[i],
in_channels=input_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
resnet_time_scale_shift=resnet_time_scale_shift,
)
self.down_blocks.append(down_block) | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
for _ in range(layers_per_block):
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
controlnet_block = zero_module(controlnet_block)
self.controlnet_down_blocks.append(controlnet_block)
if not is_final_block:
controln... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
if mid_block_type == "UNetMidBlock2DCrossAttn":
self.mid_block = UNetMidBlock2DCrossAttn(
transformer_layers_per_block=transformer_layers_per_block[-1],
in_channels=mid_block_channel,
temb_channels=time_embed_dim,
resnet_eps=norm_eps,
... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
resnet_groups=norm_num_groups,
resnet_time_scale_shift=resnet_time_scale_shift,
add_attention=False,
)
else:
raise ValueE... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
@classmethod
def from_unet(
cls,
unet: UNet2DConditionModel,
controlnet_conditioning_channel_order: str = "rgb",
conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
load_weights_from_unet: bool = True,
conditioning_channels: int = 3,
... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
Parameters:
unet (`UNet2DConditionModel`):
The UNet model weights to copy to the [`ControlNetModel`]. All configuration options are also copied
where applicable.
"""
transformer_layers_per_block = (
unet.config.transformer_layers_per_block if "tran... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
controlnet = cls(
encoder_hid_dim=encoder_hid_dim,
encoder_hid_dim_type=encoder_hid_dim_type,
addition_embed_type=addition_embed_type,
addition_time_embed_dim=addition_time_embed_dim,
transformer_layers_per_block=transformer_layers_per_block,
in_ch... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
cross_attention_dim=unet.config.cross_attention_dim,
attention_head_dim=unet.config.attention_head_dim,
num_attention_heads=unet.config.num_attention_heads,
use_linear_projection=unet.config.use_linear_projection,
class_embed_type=unet.config.class_embed_type,
... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
if load_weights_from_unet:
controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())
controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())
controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())
if controlnet.class_embedding:
... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.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
... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.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 ... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.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."
... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.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... | 1,069 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py |
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