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class UNet2DModel(ModelMixin, ConfigMixin):
r"""
A 2D UNet model that takes a noisy sample and a timestep and returns a sample shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving). | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
Parameters:
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
Height and width of input/output sample. Dimensions must be a multiple of `2 ** (len(block_out_channels) -
1)`.
in_channels (`int`, *optional*, defaults to 3): Number of channels in the input sa... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
Tuple of downsample block types.
mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2D"`):
Block type for middle of UNet, it can be either `UNetMidBlock2D` or `None`.
up_block_types (`Tuple[str]`, *optional*, defaults to `("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
upsample_type (`str`, *optional*, defaults to `conv`):
The upsample type for upsampling layers. Choose between "conv" and "resnet"
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
class_embed_type (`str`, *optional*, defaults to `None`):
The type of class embedding to use whi... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
_supports_gradient_checkpointing = True | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
@register_to_config
def __init__(
self,
sample_size: Optional[Union[int, Tuple[int, int]]] = None,
in_channels: int = 3,
out_channels: int = 3,
center_input_sample: bool = False,
time_embedding_type: str = "positional",
time_embedding_dim: Optional[int] = None... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
attention_head_dim: Optional[int] = 8,
norm_num_groups: int = 32,
attn_norm_num_groups: Optional[int] = None,
norm_eps: float = 1e-5,
resnet_time_scale_shift: str = "default",
add_attention: bool = True,
class_embed_type: Optional[str] = None,
num_class_embeds: Op... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
self.sample_size = sample_size
time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
# Check inputs
if len(down_block_types) != len(up_block_types):
raise ValueError(
f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_typ... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
# time
if time_embedding_type == "fourier":
self.time_proj = GaussianFourierProjection(embedding_size=block_out_channels[0], scale=16)
timestep_input_dim = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
self.time_proj = Timesteps(block_out_channel... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.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... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
add_downsample=not is_final_block,
resnet_eps=n... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
# mid
if mid_block_type is None:
self.mid_block = None
else:
self.mid_block = UNetMidBlock2D(
in_channels=block_out_channels[-1],
temb_channels=time_embed_dim,
dropout=dropout,
resnet_eps=norm_eps,
re... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
# up
reversed_block_out_channels = list(reversed(block_out_channels))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
input_... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
up_block = get_up_block(
up_block_type,
num_layers=layers_per_block + 1,
in_channels=input_channel,
out_channels=output_channel,
prev_output_channel=prev_output_channel,
temb_channels=time_embed_dim,
add_upsa... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
# out
num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32)
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=norm_eps)
self.conv_act = nn.SiLU()
self.conv_out = nn.Conv2d(block_out_chan... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
Args:
sample (`torch.Tensor`):
The noisy input tensor with the following shape `(batch, channel, height, width)`.
timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.
class_labels (`torch.Tensor`, *optional*, defaults to `None`):... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
Returns:
[`~models.unets.unet_2d.UNet2DOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unets.unet_2d.UNet2DOutput`] is returned, otherwise a `tuple` is
returned where the first element is the sample tensor.
"""
# 0. center input if necessary
... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.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=self.dtype)
emb = self.time_embedding(t_emb)
if s... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
# 3. down
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "skip_conv"):
sample, res_samples, skip_sample = downsample_block(
hidden_states=sample, temb=emb, skip_sample=skip_sample
)... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
if hasattr(upsample_block, "skip_conv"):
sample, skip_sample = upsample_block(sample, res_samples, emb, skip_sample)
else:
sample = upsample_block(sample, res_samples, emb)
# 6. post-process
sample = self.conv_norm_out(sample)
sample = self.conv_act(s... | 1,016 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
class FlaxCrossAttnDownBlock2D(nn.Module):
r"""
Cross Attention 2D Downsizing block - original architecture from Unet transformers:
https://arxiv.org/abs/2103.06104 | 1,017 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
Parameters:
in_channels (:obj:`int`):
Input channels
out_channels (:obj:`int`):
Output channels
dropout (:obj:`float`, *optional*, defaults to 0.0):
Dropout rate
num_layers (:obj:`int`, *optional*, defaults to 1):
Number of attention blocks... | 1,017 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.
dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
Parameters `dtype`
""" | 1,017 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
in_channels: int
out_channels: int
dropout: float = 0.0
num_layers: int = 1
num_attention_heads: int = 1
add_downsample: bool = True
use_linear_projection: bool = False
only_cross_attention: bool = False
use_memory_efficient_attention: bool = False
split_head_dim: bool = False
dt... | 1,017 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
attn_block = FlaxTransformer2DModel(
in_channels=self.out_channels,
n_heads=self.num_attention_heads,
d_head=self.out_channels // self.num_attention_heads,
depth=self.transformer_layers_per_block,
use_linear_projection=self.use_linear_proje... | 1,017 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
for resnet, attn in zip(self.resnets, self.attentions):
hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
output_states += (hidden_states,)
if self.add_downsample:
... | 1,017 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
class FlaxDownBlock2D(nn.Module):
r"""
Flax 2D downsizing block
Parameters:
in_channels (:obj:`int`):
Input channels
out_channels (:obj:`int`):
Output channels
dropout (:obj:`float`, *optional*, defaults to 0.0):
Dropout rate
num_layers (:... | 1,018 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
res_block = FlaxResnetBlock2D(
in_channels=in_channels,
out_channels=self.out_channels,
dropout_prob=self.dropout,
dtype=self.dtype,
)
resnets.append(res_block)
self.resnets = resnets
if self.add_downsample:
... | 1,018 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
class FlaxCrossAttnUpBlock2D(nn.Module):
r"""
Cross Attention 2D Upsampling block - original architecture from Unet transformers:
https://arxiv.org/abs/2103.06104 | 1,019 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
Parameters:
in_channels (:obj:`int`):
Input channels
out_channels (:obj:`int`):
Output channels
dropout (:obj:`float`, *optional*, defaults to 0.0):
Dropout rate
num_layers (:obj:`int`, *optional*, defaults to 1):
Number of attention blocks... | 1,019 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.
dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
Parameters `dtype`
""" | 1,019 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
in_channels: int
out_channels: int
prev_output_channel: int
dropout: float = 0.0
num_layers: int = 1
num_attention_heads: int = 1
add_upsample: bool = True
use_linear_projection: bool = False
only_cross_attention: bool = False
use_memory_efficient_attention: bool = False
split_he... | 1,019 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
attn_block = FlaxTransformer2DModel(
in_channels=self.out_channels,
n_heads=self.num_attention_heads,
d_head=self.out_channels // self.num_attention_heads,
depth=self.transformer_layers_per_block,
use_linear_projection=self.use_linear_proje... | 1,019 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
def __call__(self, hidden_states, res_hidden_states_tuple, temb, encoder_hidden_states, deterministic=True):
for resnet, attn in zip(self.resnets, self.attentions):
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_st... | 1,019 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
class FlaxUpBlock2D(nn.Module):
r"""
Flax 2D upsampling block
Parameters:
in_channels (:obj:`int`):
Input channels
out_channels (:obj:`int`):
Output channels
prev_output_channel (:obj:`int`):
Output channels from the previous block
dropout... | 1,020 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
for i in range(self.num_layers):
res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels
resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels
res_block = FlaxResnetBlock2D(
in_channels=resnet_in_channels + r... | 1,020 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
def __call__(self, hidden_states, res_hidden_states_tuple, temb, deterministic=True):
for resnet in self.resnets:
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
hidden_states = jnp.con... | 1,020 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
class FlaxUNetMidBlock2DCrossAttn(nn.Module):
r"""
Cross Attention 2D Mid-level block - original architecture from Unet transformers: https://arxiv.org/abs/2103.06104 | 1,021 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
Parameters:
in_channels (:obj:`int`):
Input channels
dropout (:obj:`float`, *optional*, defaults to 0.0):
Dropout rate
num_layers (:obj:`int`, *optional*, defaults to 1):
Number of attention blocks layers
num_attention_heads (:obj:`int`, *optional*, de... | 1,021 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
in_channels: int
dropout: float = 0.0
num_layers: int = 1
num_attention_heads: int = 1
use_linear_projection: bool = False
use_memory_efficient_attention: bool = False
split_head_dim: bool = False
dtype: jnp.dtype = jnp.float32
transformer_layers_per_block: int = 1
def setup(self):
... | 1,021 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
for _ in range(self.num_layers):
attn_block = FlaxTransformer2DModel(
in_channels=self.in_channels,
n_heads=self.num_attention_heads,
d_head=self.in_channels // self.num_attention_heads,
depth=self.transformer_layers_per_block,
... | 1,021 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True):
hidden_states = self.resnets[0](hidden_states, temb)
for attn, resnet in zip(self.attentions, self.resnets[1:]):
hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
... | 1,021 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py |
class UNetSpatioTemporalConditionOutput(BaseOutput):
"""
The output of [`UNetSpatioTemporalConditionModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_frames, num_channels, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input. Output of l... | 1,022 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
class UNetSpatioTemporalConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
r"""
A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state, and a timestep and
returns a sample shaped output.
This model inherits from [`ModelMixin`]. Check the superclas... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
Parameters:
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
Height and width of input/output sample.
in_channels (`int`, *optional*, defaults to 8): Number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 4): Number of channels i... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
addition_time_embed_dim: (`int`, defaults to 256):
Dimension to to encode the additional time ids.
projection_class_embeddings_input_dim (`int`, defaults to 768):
The dimension of the projection of encoded `added_time_ids`.
layers_per_block (`int`, *optional*, defaults to 2): The... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
num_attention_heads (`int`, `Tuple[int]`, defaults to `(5, 10, 10, 20)`):
The number of attention heads.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
""" | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
_supports_gradient_checkpointing = True | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
@register_to_config
def __init__(
self,
sample_size: Optional[int] = None,
in_channels: int = 8,
out_channels: int = 4,
down_block_types: Tuple[str] = (
"CrossAttnDownBlockSpatioTemporal",
"CrossAttnDownBlockSpatioTemporal",
"CrossAttnDownB... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
num_attention_heads: Union[int, Tuple[int]] = (5, 10, 20, 20),
num_frames: int = 25,
):
super().__init__() | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
self.sample_size = sample_size
# Check inputs
if len(down_block_types) != len(up_block_types):
raise ValueError(
f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
self.time_proj = Timesteps(block_out_channels[0], True, downscale_freq_shift=0)
timestep_input_dim = block_out_channels[0]
self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
self.add_time_proj = Timesteps(addition_time_embed_dim, True, downscale_freq_shift=0)
s... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
blocks_time_embed_dim = time_embed_dim
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types)... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block[i],
transformer_layers_per_block=transformer_layers_per_block[i],
in_channels=input_channel,
out_channels=output_channel,
temb_channels=blocks_time_em... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
# count how many layers upsample the images
self.num_upsamplers = 0
# up
reversed_block_out_channels = list(reversed(block_out_channels))
reversed_num_attention_heads = list(reversed(num_attention_heads))
reversed_layers_per_block = list(reversed(layers_per_block))
rever... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
# add upsample block for all BUT final layer
if not is_final_block:
add_upsample = True
self.num_upsamplers += 1
else:
add_upsample = False | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
up_block = get_up_block(
up_block_type,
num_layers=reversed_layers_per_block[i] + 1,
transformer_layers_per_block=reversed_transformer_layers_per_block[i],
in_channels=input_channel,
out_channels=output_channel,
prev_output_... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
self.conv_out = nn.Conv2d(
block_out_channels[0],
out_channels,
kernel_size=3,
padding=1,
)
@property
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary contai... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
r"""
Sets the attention processor to use to compute attention.
... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.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."
... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnProcessor()
else:
... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
"""
Sets the attention processor to use [feed forward
chunking](https://huggingface.co/blog/reformer#... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
if hasattr(module, "set_chunk_feed_forward"):
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
for child in module.children():
fn_recursive_feed_forward(child, chunk_s... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
Args:
sample (`torch.Tensor`):
The noisy input tensor with the following shape `(batch, num_frames, channel, height, width)`.
timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.
encoder_hidden_states (`torch.Tensor`):
... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
If `return_dict` is True, an [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] is
returned, otherwise a `tuple` is returned where the first element is the sample tensor.
"""
# By default samples have to be AT least a multiple of the overall upsampling factor.
# T... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
forward_upsample_size = False
upsample_size = None
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
logger.info("Forward upsample size to force interpolation output... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.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"
... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.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)
... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
# Flatten the batch and frames dimensions
# sample: [batch, frames, channels, height, width] -> [batch * frames, channels, height, width]
sample = sample.flatten(0, 1)
# Repeat the embeddings num_video_frames times
# emb: [batch, channels] -> [batch * frames, channels]
emb = emb.... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
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,
... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
# 5. up
for i, upsample_block in enumerate(self.up_blocks):
is_final_block = i == len(self.up_blocks) - 1
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
# if we... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
encoder_hidden_states=encoder_hidden_sta... | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
# 7. Reshape back to original shape
sample = sample.reshape(batch_size, num_frames, *sample.shape[1:])
if not return_dict:
return (sample,)
return UNetSpatioTemporalConditionOutput(sample=sample) | 1,023 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py |
class UNet3DConditionOutput(BaseOutput):
"""
The output of [`UNet3DConditionModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, num_frames, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
... | 1,024 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
r"""
A conditional 3D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generi... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
Parameters:
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
Height and width of input/output sample.
in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 4): The number of ch... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
The dimension of `cond_proj` layer in the timestep embedding.
""" | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
_supports_gradient_checkpointing = False | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
@register_to_config
def __init__(
self,
sample_size: Optional[int] = None,
in_channels: int = 4,
out_channels: int = 4,
down_block_types: Tuple[str, ...] = (
"CrossAttnDownBlock3D",
"CrossAttnDownBlock3D",
"CrossAttnDownBlock3D",
... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
time_cond_proj_dim: Optional[int] = None,
):
super().__init__() | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
self.sample_size = sample_size
if num_attention_heads is not None:
raise NotImplementedError(
"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.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... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
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}."
)
if not isinstance(num_attention_hea... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
# time
time_embed_dim = block_out_channels[0] * 4
self.time_proj = Timesteps(block_out_channels[0], True, 0)
timestep_input_dim = block_out_channels[0]
self.time_embedding = TimestepEmbedding(
timestep_input_dim,
time_embed_dim,
act_fn=act_fn,
... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
# down
output_channel = block_out_channels[0]
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
down_block = get_down_block(
... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
# mid
self.mid_block = UNetMidBlock3DCrossAttn(
in_channels=block_out_channels[-1],
temb_channels=time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
cross_attention_dim=cross_attention_dim... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
# add upsample block for all BUT final layer
if not is_final_block:
add_upsample = Tr... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
up_block = get_up_block(
up_block_type,
num_layers=layers_per_block + 1,
in_channels=input_channel,
out_channels=output_channel,
prev_output_channel=prev_output_channel,
temb_channels=time_embed_dim,
add_upsa... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
# out
if norm_num_groups is not None:
self.conv_norm_out = nn.GroupNorm(
num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
)
self.conv_act = get_activation("silu")
else:
self.conv_norm_out = None
self.... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
if hasattr(module, "get_processor"):
processors[f"{name}.processor"] = module.get_processor()
for sub_name, child in module.named_children():
fn_recurs... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
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"`):
... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
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 == "a... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
if len(slice_size) != len(sliceable_head_dims):
raise ValueError(
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
)
... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.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)
# Copied from diffusers.models.unets.unet_2d... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
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) !=... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
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)
def enable_forward_chunking(self, chunk_size: Optional[int] = None... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
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 ... | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
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