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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 sample.
out_channels (`int`, *optional*, defaults to 3): Number of channels in the output.
center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
time_embedding_type (`str`, *optional*, defaults to `"positional"`): Type of time embedding to use.
freq_shift (`int`, *optional*, defaults to 0): Frequency shift for Fourier time embedding.
flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
Whether to flip sin to cos for Fourier time embedding.
down_block_types (`Tuple[str]`, *optional*, defaults to `("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`): | 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", "UpBlock2D")`):
Tuple of upsample block types.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(224, 448, 672, 896)`):
Tuple of block output channels.
layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block.
mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block.
downsample_padding (`int`, *optional*, defaults to `1`): The padding for the downsample convolution.
downsample_type (`str`, *optional*, defaults to `conv`):
The downsample type for downsampling layers. Choose between "conv" and "resnet" | 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.
attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension.
norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for normalization.
attn_norm_num_groups (`int`, *optional*, defaults to `None`):
If set to an integer, a group norm layer will be created in the mid block's [`Attention`] layer with the
given number of groups. If left as `None`, the group norm layer will only be created if
`resnet_time_scale_shift` is set to `default`, and if created will have `norm_num_groups` groups.
norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for normalization. | 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 which is ultimately summed with the time embeddings. Choose from `None`,
`"timestep"`, or `"identity"`.
num_class_embeds (`int`, *optional*, defaults to `None`):
Input dimension of the learnable embedding matrix to be projected to `time_embed_dim` when performing class
conditioning with `class_embed_type` equal to `None`.
""" | 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,
freq_shift: int = 0,
flip_sin_to_cos: bool = True,
down_block_types: Tuple[str, ...] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"),
mid_block_type: Optional[str] = "UNetMidBlock2D",
up_block_types: Tuple[str, ...] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"),
block_out_channels: Tuple[int, ...] = (224, 448, 672, 896),
layers_per_block: int = 2,
mid_block_scale_factor: float = 1,
downsample_padding: int = 1,
downsample_type: str = "conv",
upsample_type: str = "conv",
dropout: float = 0.0,
act_fn: str = "silu", | 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: Optional[int] = None,
num_train_timesteps: Optional[int] = None,
):
super().__init__() | 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_types`: {down_block_types}. `up_block_types`: {up_block_types}."
)
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}."
)
# input
self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)) | 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_channels[0], flip_sin_to_cos, freq_shift)
timestep_input_dim = block_out_channels[0]
elif time_embedding_type == "learned":
self.time_proj = nn.Embedding(num_train_timesteps, block_out_channels[0])
timestep_input_dim = block_out_channels[0]
self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) | 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 class_embed_type == "identity":
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
else:
self.class_embedding = None
self.down_blocks = nn.ModuleList([])
self.mid_block = None
self.up_blocks = nn.ModuleList([])
# 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 | 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=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,
downsample_padding=downsample_padding,
resnet_time_scale_shift=resnet_time_scale_shift,
downsample_type=downsample_type,
dropout=dropout,
)
self.down_blocks.append(down_block) | 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,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
resnet_time_scale_shift=resnet_time_scale_shift,
attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1],
resnet_groups=norm_num_groups,
attn_groups=attn_norm_num_groups,
add_attention=add_attention,
) | 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_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
is_final_block = i == len(block_out_channels) - 1 | 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_upsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,
resnet_time_scale_shift=resnet_time_scale_shift,
upsample_type=upsample_type,
dropout=dropout,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel | 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_channels[0], out_channels, kernel_size=3, padding=1)
def _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value
def forward(
self,
sample: torch.Tensor,
timestep: Union[torch.Tensor, float, int],
class_labels: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[UNet2DOutput, Tuple]:
r"""
The [`UNet2DModel`] forward method. | 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`):
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unets.unet_2d.UNet2DOutput`] instead of a plain tuple. | 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
if self.config.center_input_sample:
sample = 2 * sample - 1.0
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps * torch.ones(sample.shape[0], dtype=timesteps.dtype, device=timesteps.device)
t_emb = self.time_proj(timesteps) | 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 self.class_embedding is not None:
if class_labels is None:
raise ValueError("class_labels should be provided when doing class conditioning")
if self.config.class_embed_type == "timestep":
class_labels = self.time_proj(class_labels)
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
emb = emb + class_emb
elif self.class_embedding is None and class_labels is not None:
raise ValueError("class_embedding needs to be initialized in order to use class conditioning")
# 2. pre-process
skip_sample = sample
sample = self.conv_in(sample) | 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
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
down_block_res_samples += res_samples
# 4. mid
if self.mid_block is not None:
sample = self.mid_block(sample, emb)
# 5. up
skip_sample = None
for upsample_block in self.up_blocks:
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] | 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(sample)
sample = self.conv_out(sample)
if skip_sample is not None:
sample += skip_sample
if self.config.time_embedding_type == "fourier":
timesteps = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]))))
sample = sample / timesteps
if not return_dict:
return (sample,)
return UNet2DOutput(sample=sample) | 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 layers
num_attention_heads (:obj:`int`, *optional*, defaults to 1):
Number of attention heads of each spatial transformer block
add_downsample (:obj:`bool`, *optional*, defaults to `True`):
Whether to add downsampling layer before each final output
use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
enable memory efficient attention https://arxiv.org/abs/2112.05682
split_head_dim (`bool`, *optional*, defaults to `False`):
Whether to split the head dimension into a new axis for the self-attention computation. In most cases, | 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
dtype: jnp.dtype = jnp.float32
transformer_layers_per_block: int = 1
def setup(self):
resnets = []
attentions = []
for i in range(self.num_layers):
in_channels = self.in_channels if i == 0 else self.out_channels
res_block = FlaxResnetBlock2D(
in_channels=in_channels,
out_channels=self.out_channels,
dropout_prob=self.dropout,
dtype=self.dtype,
)
resnets.append(res_block) | 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_projection,
only_cross_attention=self.only_cross_attention,
use_memory_efficient_attention=self.use_memory_efficient_attention,
split_head_dim=self.split_head_dim,
dtype=self.dtype,
)
attentions.append(attn_block)
self.resnets = resnets
self.attentions = attentions
if self.add_downsample:
self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)
def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True):
output_states = () | 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:
hidden_states = self.downsamplers_0(hidden_states)
output_states += (hidden_states,)
return hidden_states, output_states | 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 (:obj:`int`, *optional*, defaults to 1):
Number of attention blocks layers
add_downsample (:obj:`bool`, *optional*, defaults to `True`):
Whether to add downsampling layer before each final output
dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
Parameters `dtype`
"""
in_channels: int
out_channels: int
dropout: float = 0.0
num_layers: int = 1
add_downsample: bool = True
dtype: jnp.dtype = jnp.float32
def setup(self):
resnets = []
for i in range(self.num_layers):
in_channels = self.in_channels if i == 0 else self.out_channels | 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:
self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)
def __call__(self, hidden_states, temb, deterministic=True):
output_states = ()
for resnet in self.resnets:
hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
output_states += (hidden_states,)
if self.add_downsample:
hidden_states = self.downsamplers_0(hidden_states)
output_states += (hidden_states,)
return hidden_states, output_states | 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 layers
num_attention_heads (:obj:`int`, *optional*, defaults to 1):
Number of attention heads of each spatial transformer block
add_upsample (:obj:`bool`, *optional*, defaults to `True`):
Whether to add upsampling layer before each final output
use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
enable memory efficient attention https://arxiv.org/abs/2112.05682
split_head_dim (`bool`, *optional*, defaults to `False`):
Whether to split the head dimension into a new axis for the self-attention computation. In most cases, | 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_head_dim: bool = False
dtype: jnp.dtype = jnp.float32
transformer_layers_per_block: int = 1
def setup(self):
resnets = []
attentions = []
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 + res_skip_channels,
out_channels=self.out_channels,
dropout_prob=self.dropout,
dtype=self.dtype,
)
resnets.append(res_block) | 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_projection,
only_cross_attention=self.only_cross_attention,
use_memory_efficient_attention=self.use_memory_efficient_attention,
split_head_dim=self.split_head_dim,
dtype=self.dtype,
)
attentions.append(attn_block)
self.resnets = resnets
self.attentions = attentions
if self.add_upsample:
self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype) | 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_states_tuple[:-1]
hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1)
hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
if self.add_upsample:
hidden_states = self.upsamplers_0(hidden_states)
return hidden_states | 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 (:obj:`float`, *optional*, defaults to 0.0):
Dropout rate
num_layers (:obj:`int`, *optional*, defaults to 1):
Number of attention blocks layers
add_downsample (:obj:`bool`, *optional*, defaults to `True`):
Whether to add downsampling layer before each final output
dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
Parameters `dtype`
"""
in_channels: int
out_channels: int
prev_output_channel: int
dropout: float = 0.0
num_layers: int = 1
add_upsample: bool = True
dtype: jnp.dtype = jnp.float32
def setup(self):
resnets = [] | 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 + res_skip_channels,
out_channels=self.out_channels,
dropout_prob=self.dropout,
dtype=self.dtype,
)
resnets.append(res_block)
self.resnets = resnets
if self.add_upsample:
self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype) | 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.concatenate((hidden_states, res_hidden_states), axis=-1)
hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
if self.add_upsample:
hidden_states = self.upsamplers_0(hidden_states)
return hidden_states | 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*, defaults to 1):
Number of attention heads of each spatial transformer block
use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
enable memory efficient attention https://arxiv.org/abs/2112.05682
split_head_dim (`bool`, *optional*, defaults to `False`):
Whether to split the head dimension into a new axis for the self-attention computation. In most cases,
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`
""" | 1,021 | /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):
# there is always at least one resnet
resnets = [
FlaxResnetBlock2D(
in_channels=self.in_channels,
out_channels=self.in_channels,
dropout_prob=self.dropout,
dtype=self.dtype,
)
]
attentions = [] | 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,
use_linear_projection=self.use_linear_projection,
use_memory_efficient_attention=self.use_memory_efficient_attention,
split_head_dim=self.split_head_dim,
dtype=self.dtype,
)
attentions.append(attn_block)
res_block = FlaxResnetBlock2D(
in_channels=self.in_channels,
out_channels=self.in_channels,
dropout_prob=self.dropout,
dtype=self.dtype,
)
resnets.append(res_block)
self.resnets = resnets
self.attentions = attentions | 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)
hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
return hidden_states | 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 last layer of model.
"""
sample: torch.Tensor = None | 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 superclass documentation for it's generic methods implemented
for all models (such as downloading or saving). | 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 in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "DownBlockSpatioTemporal")`):
The tuple of downsample blocks to use.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal")`):
The tuple of upsample blocks to use.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block. | 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 number of layers per block.
cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
The dimension of the cross attention features.
transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
[`~models.unets.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal`],
[`~models.unets.unet_3d_blocks.CrossAttnUpBlockSpatioTemporal`],
[`~models.unets.unet_3d_blocks.UNetMidBlockSpatioTemporal`]. | 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",
"CrossAttnDownBlockSpatioTemporal",
"DownBlockSpatioTemporal",
),
up_block_types: Tuple[str] = (
"UpBlockSpatioTemporal",
"CrossAttnUpBlockSpatioTemporal",
"CrossAttnUpBlockSpatioTemporal",
"CrossAttnUpBlockSpatioTemporal",
),
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
addition_time_embed_dim: int = 256,
projection_class_embeddings_input_dim: int = 768,
layers_per_block: Union[int, Tuple[int]] = 2,
cross_attention_dim: Union[int, Tuple[int]] = 1024,
transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, | 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}."
)
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_heads, int) and len(num_attention_heads) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
) | 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}."
)
if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
)
# input
self.conv_in = nn.Conv2d(
in_channels,
block_out_channels[0],
kernel_size=3,
padding=1,
)
# time
time_embed_dim = block_out_channels[0] * 4 | 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)
self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
self.down_blocks = nn.ModuleList([])
self.up_blocks = nn.ModuleList([])
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(down_block_types)
if isinstance(cross_attention_dim, int):
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
if isinstance(layers_per_block, int):
layers_per_block = [layers_per_block] * len(down_block_types) | 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):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1 | 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_embed_dim,
add_downsample=not is_final_block,
resnet_eps=1e-5,
cross_attention_dim=cross_attention_dim[i],
num_attention_heads=num_attention_heads[i],
resnet_act_fn="silu",
)
self.down_blocks.append(down_block)
# mid
self.mid_block = UNetMidBlockSpatioTemporal(
block_out_channels[-1],
temb_channels=blocks_time_embed_dim,
transformer_layers_per_block=transformer_layers_per_block[-1],
cross_attention_dim=cross_attention_dim[-1],
num_attention_heads=num_attention_heads[-1],
) | 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))
reversed_cross_attention_dim = list(reversed(cross_attention_dim))
reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
is_final_block = i == len(block_out_channels) - 1
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)] | 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_channel=prev_output_channel,
temb_channels=blocks_time_embed_dim,
add_upsample=add_upsample,
resnet_eps=1e-5,
resolution_idx=i,
cross_attention_dim=reversed_cross_attention_dim[i],
num_attention_heads=reversed_num_attention_heads[i],
resnet_act_fn="silu",
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-5)
self.conv_act = nn.SiLU() | 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 containing all attention processors used in the model with
indexed by its weight name.
"""
# set recursively
processors = {}
def fn_recursive_add_processors(
name: str,
module: torch.nn.Module,
processors: Dict[str, AttentionProcessor],
):
if hasattr(module, "get_processor"):
processors[f"{name}.processor"] = module.get_processor()
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors | 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.
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers.
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.
"""
count = len(self.attn_processors.keys()) | 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."
)
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor"):
if not isinstance(processor, dict):
module.set_processor(processor)
else:
module.set_processor(processor.pop(f"{name}.processor"))
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor) | 1,023 | /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:
raise ValueError(
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
)
self.set_attn_processor(processor)
def _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value | 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#2-chunked-feed-forward-layers).
Parameters:
chunk_size (`int`, *optional*):
The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
over each tensor of dim=`dim`.
dim (`int`, *optional*, defaults to `0`):
The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
or dim=1 (sequence length).
"""
if dim not in [0, 1]:
raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
# By default chunk size is 1
chunk_size = chunk_size or 1 | 1,023 | /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_size, dim)
for module in self.children():
fn_recursive_feed_forward(module, chunk_size, dim)
def forward(
self,
sample: torch.Tensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
added_time_ids: torch.Tensor,
return_dict: bool = True,
) -> Union[UNetSpatioTemporalConditionOutput, Tuple]:
r"""
The [`UNetSpatioTemporalConditionModel`] forward method. | 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`):
The encoder hidden states with shape `(batch, sequence_length, cross_attention_dim)`.
added_time_ids: (`torch.Tensor`):
The additional time ids with shape `(batch, num_additional_ids)`. These are encoded with sinusoidal
embeddings and added to the time embeddings.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] instead
of a plain tuple.
Returns:
[`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] or `tuple`: | 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.
# The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
# However, the upsampling interpolation output size can be forced to fit any upsampling size
# on the fly if necessary.
default_overall_up_factor = 2**self.num_upsamplers | 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 size.")
forward_upsample_size = True | 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"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
batch_size, num_frames = sample.shape[:2]
timesteps = timesteps.expand(batch_size)
t_emb = self.time_proj(timesteps) | 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)
time_embeds = self.add_time_proj(added_time_ids.flatten())
time_embeds = time_embeds.reshape((batch_size, -1))
time_embeds = time_embeds.to(emb.dtype)
aug_emb = self.add_embedding(time_embeds)
emb = emb + aug_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.repeat_interleave(num_frames, dim=0)
# encoder_hidden_states: [batch, 1, channels] -> [batch * frames, 1, channels]
encoder_hidden_states = encoder_hidden_states.repeat_interleave(num_frames, dim=0)
# 2. pre-process
sample = self.conv_in(sample)
image_only_indicator = torch.zeros(batch_size, num_frames, dtype=sample.dtype, device=sample.device) | 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,
encoder_hidden_states=encoder_hidden_states,
image_only_indicator=image_only_indicator,
)
else:
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
image_only_indicator=image_only_indicator,
)
down_block_res_samples += res_samples
# 4. mid
sample = self.mid_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
image_only_indicator=image_only_indicator,
) | 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 have not reached the final block and need to forward the
# upsample size, we do it here
if not is_final_block and forward_upsample_size:
upsample_size = down_block_res_samples[-1].shape[2:] | 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_states,
upsample_size=upsample_size,
image_only_indicator=image_only_indicator,
)
else:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
upsample_size=upsample_size,
image_only_indicator=image_only_indicator,
)
# 6. post-process
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample) | 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.
"""
sample: torch.Tensor | 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 generic methods implemented
for all models (such as downloading or saving). | 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 channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D")`):
The tuple of downsample blocks to use.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D")`):
The tuple of upsample blocks to use.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block.
layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. | 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.
norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
If `None`, normalization and activation layers is skipped in post-processing.
norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
cross_attention_dim (`int`, *optional*, defaults to 1024): The dimension of the cross attention features.
attention_head_dim (`int`, *optional*, defaults to 64): The dimension of the attention heads.
num_attention_heads (`int`, *optional*): The number of attention heads.
time_cond_proj_dim (`int`, *optional*, defaults to `None`): | 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",
"DownBlock3D",
),
up_block_types: Tuple[str, ...] = (
"UpBlock3D",
"CrossAttnUpBlock3D",
"CrossAttnUpBlock3D",
"CrossAttnUpBlock3D",
),
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
layers_per_block: int = 2,
downsample_padding: int = 1,
mid_block_scale_factor: float = 1,
act_fn: str = "silu",
norm_num_groups: Optional[int] = 32,
norm_eps: float = 1e-5,
cross_attention_dim: int = 1024,
attention_head_dim: Union[int, Tuple[int]] = 64,
num_attention_heads: Optional[Union[int, Tuple[int]]] = None, | 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/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
) | 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 created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
num_attention_heads = num_attention_heads or attention_head_dim
# Check inputs
if len(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}."
) | 1,025 | /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_heads, int) and len(num_attention_heads) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
)
# input
conv_in_kernel = 3
conv_out_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
) | 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,
cond_proj_dim=time_cond_proj_dim,
)
self.transformer_in = TransformerTemporalModel(
num_attention_heads=8,
attention_head_dim=attention_head_dim,
in_channels=block_out_channels[0],
num_layers=1,
norm_num_groups=norm_num_groups,
)
# class embedding
self.down_blocks = nn.ModuleList([])
self.up_blocks = nn.ModuleList([])
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(down_block_types) | 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(
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=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads[i],
downsample_padding=downsample_padding,
dual_cross_attention=False,
)
self.down_blocks.append(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,
num_attention_heads=num_attention_heads[-1],
resnet_groups=norm_num_groups,
dual_cross_attention=False,
)
# 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))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
is_final_block = i == len(block_out_channels) - 1 | 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 = True
self.num_upsamplers += 1
else:
add_upsample = False | 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_upsample=add_upsample,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
num_attention_heads=reversed_num_attention_heads[i],
dual_cross_attention=False,
resolution_idx=i,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel | 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.conv_act = None
conv_out_padding = (conv_out_kernel - 1) // 2
self.conv_out = nn.Conv2d(
block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
)
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
indexed by its weight name.
"""
# set recursively
processors = {} | 1,025 | /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_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice
def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:
r"""
Enable sliced attention computation. | 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"`):
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
must be a multiple of `slice_size`.
"""
sliceable_head_dims = []
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
if hasattr(module, "set_attention_slice"):
sliceable_head_dims.append(module.sliceable_head_dim) | 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 == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = [dim // 2 for dim in sliceable_head_dims]
elif slice_size == "max":
# make smallest slice possible
slice_size = num_sliceable_layers * [1]
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size | 1,025 | /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)}."
)
for i in range(len(slice_size)):
size = slice_size[i]
dim = sliceable_head_dims[i]
if size is not None and size > dim:
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
# Recursively walk through all the children.
# Any children which exposes the set_attention_slice method
# gets the message
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
if hasattr(module, "set_attention_slice"):
module.set_attention_slice(slice_size.pop()) | 1,025 | /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_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 (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers. | 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) != 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."
)
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor"):
if not isinstance(processor, dict):
module.set_processor(processor)
else:
module.set_processor(processor.pop(f"{name}.processor")) | 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, dim: int = 0) -> None:
"""
Sets the attention processor to use [feed forward
chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers). | 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 feed-forward computation should be chunked. Choose between dim=0 (batch)
or dim=1 (sequence length).
"""
if dim not in [0, 1]:
raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
# By default chunk size is 1
chunk_size = chunk_size or 1
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
if hasattr(module, "set_chunk_feed_forward"):
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
for child in module.children():
fn_recursive_feed_forward(child, chunk_size, dim) | 1,025 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py |
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