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|---|---|---|
for _, attn_processor in self.attn_processors.items():
if "Added" in str(attn_processor.__class__.__name__):
raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
self.original_attn_processors = self.attn_processors
for module... | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
def forward(
self,
sample: torch.Tensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
timestep_cond: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] =... | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.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`):
... | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
negative values to the attention scores corresponding to "discard" tokens.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.att... | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
Returns:
[`~models.unets.unet_motion_model.UNetMotionOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unets.unet_motion_model.UNetMotionOutput`] is returned,
otherwise a `tuple` is returned where the first element is the sample tensor.
"""
# By defa... | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
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
# prepare attention_mask
if attention_mask is not None:
attention_mask = (1 - attention_mask.t... | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.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"
... | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.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, timestep_con... | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
text_embeds = added_cond_kwargs.get("text_embeds")
if "time_ids" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`... | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword arg... | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# 3. down
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
sample, res_samples = downsample_block(
hidden_states=sample,
... | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
for down_block_res_sample, down_block_additional_residual in zip(
down_block_res_samples, down_block_additional_residuals
):
down_block_res_sample = down_block_res_sample + down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
... | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
# 4. mid
if self.mid_block is not None:
# To support older versions of motion modules that don't have a mid_block
if hasattr(self.mid_block, "motion_modules"):
sample = self.mid_block(
sample,
emb,
encoder_hidden... | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.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... | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.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... | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
sample = self.conv_out(sample)
# reshape to (batch, channel, framerate, width, height)
sample = sample[None, :].reshape((-1, num_frames) + sample.shape[1:]).permute(0, 2, 1, 3, 4)
if not return_dict:
return (sample,)
return UNetMotionOutput(sample=sample) | 1,008 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py |
class FlaxUNet2DConditionOutput(BaseOutput):
"""
The output of [`FlaxUNet2DConditionModel`].
Args:
sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
"""
... | 1,009 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
class FlaxUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin):
r"""
A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
shaped output.
This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for it's generic meth... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
Inherent JAX features such as the following are supported:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readt... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
Parameters:
sample_size (`int`, *optional*):
The size of the input 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.
... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
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.
attention_head_dim (`int` or `Tuple[int]`, *optional*, defaults t... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
Enable memory efficient attention as described [here](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-at... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
sample_size: int = 32
in_channels: int = 4
out_channels: int = 4
down_block_types: Tuple[str, ...] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
up_block_types: Tuple[str, ...] = ("UpBlock2D", "CrossAttnUpBlock2D", "Cross... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1
addition_embed_type: Optional[str] = None
addition_time_embed_dim: Optional[int] = None
addition_embed_type_num_heads: int = 64
projection_class_embeddings_input_dim: Optional[int] = None | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
def init_weights(self, rng: jax.Array) -> FrozenDict:
# init input tensors
sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
sample = jnp.zeros(sample_shape, dtype=jnp.float32)
timesteps = jnp.ones((1,), dtype=jnp.int32)
encoder_hidden_states = jnp.zeros((1... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
added_cond_kwargs = None
if self.addition_embed_type == "text_time":
# we retrieve the expected `text_embeds_dim` by first checking if the architecture is a refiner
# or non-refiner architecture and then by "reverse-computing" from `projection_class_embeddings_input_dim`
is_r... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
time_ids_channels = self.projection_class_embeddings_input_dim - text_embeds_dim
time_ids_dims = time_ids_channels // self.addition_time_embed_dim
added_cond_kwargs = {
"text_embeds": jnp.zeros((1, text_embeds_dim), dtype=jnp.float32),
"time_ids": jnp.zeros((1, ti... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.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... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
# time
self.time_proj = FlaxTimesteps(
block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift
)
self.time_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype)
only_cross_attention = self.only_cross_attention
if i... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
# addition embed types
if self.addition_embed_type is None:
self.add_embedding = None
elif self.addition_embed_type == "text_time":
if self.addition_time_embed_dim is None:
raise ValueError(
f"addition_embed_type {self.addition_embed_type} requ... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
# down
down_blocks = []
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1 | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
if down_block_type == "CrossAttnDownBlock2D":
down_block = FlaxCrossAttnDownBlock2D(
in_channels=input_channel,
out_channels=output_channel,
dropout=self.dropout,
num_layers=self.layers_per_block,
transfo... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
dropout=self.dropout,
num_layers=self.layers_per_block,
add_downsample=not is_final_block,
dtype=self.dtype,
) | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
down_blocks.append(down_block)
self.down_blocks = down_blocks
# mid
if self.config.mid_block_type == "UNetMidBlock2DCrossAttn":
self.mid_block = FlaxUNetMidBlock2DCrossAttn(
in_channels=block_out_channels[-1],
dropout=self.dropout,
num... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
# up
up_blocks = []
reversed_block_out_channels = list(reversed(block_out_channels))
reversed_num_attention_heads = list(reversed(num_attention_heads))
only_cross_attention = list(reversed(only_cross_attention))
output_channel = reversed_block_out_channels[0]
reversed_tra... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
if up_block_type == "CrossAttnUpBlock2D":
up_block = FlaxCrossAttnUpBlock2D(
in_channels=input_channel,
out_channels=output_channel,
prev_output_channel=prev_output_channel,
num_layers=self.layers_per_block + 1,
... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
out_channels=output_channel,
prev_output_channel=prev_output_channel,
num_layers=self.layers_per_block + 1,
add_upsample=not is_final_block,
dropout=self.dropout,
dtype=self.dtype,
) | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
up_blocks.append(up_block)
prev_output_channel = output_channel
self.up_blocks = up_blocks
# out
self.conv_norm_out = nn.GroupNorm(num_groups=32, epsilon=1e-5)
self.conv_out = nn.Conv(
self.out_channels,
kernel_size=(3, 3),
strides=(1, 1),... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
def __call__(
self,
sample: jnp.ndarray,
timesteps: Union[jnp.ndarray, float, int],
encoder_hidden_states: jnp.ndarray,
added_cond_kwargs: Optional[Union[Dict, FrozenDict]] = None,
down_block_additional_residuals: Optional[Tuple[jnp.ndarray, ...]] = None,
mid_bloc... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
are passed along to the UNet blocks.
down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
A tuple of tensors that if specified are added to the residuals of down unet blocks.
mid_block_additional_residual: (`torch.Tensor`, *optional*):
A tenso... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
Returns:
[`~models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] or `tuple`:
[`~models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is the sample tensor.
"""
... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
# additional embeddings
aug_emb = None
if self.addition_embed_type == "text_time":
if added_cond_kwargs is None:
raise ValueError(
f"Need to provide argument `added_cond_kwargs` for {self.__class__} when using `addition_embed_type={self.addition_embed_type... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
# compute time embeds
time_embeds = self.add_time_proj(jnp.ravel(time_ids)) # (1, 6) => (6,) => (6, 256)
time_embeds = jnp.reshape(time_embeds, (text_embeds.shape[0], -1))
add_embeds = jnp.concatenate([text_embeds, time_embeds], axis=-1)
aug_emb = self.add_embedding(add_... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
t_emb = t_emb + aug_emb if aug_emb is not None else t_emb
# 2. pre-process
sample = jnp.transpose(sample, (0, 2, 3, 1))
sample = self.conv_in(sample)
# 3. down
down_block_res_samples = (sample,)
for down_block in self.down_blocks:
if isinstance(down_block, F... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
for down_block_res_sample, down_block_additional_residual in zip(
down_block_res_samples, down_block_additional_residuals
):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
down_block_... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
# 5. up
for up_block in self.up_blocks:
res_samples = down_block_res_samples[-(self.layers_per_block + 1) :]
down_block_res_samples = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(up_block, FlaxCrossAttnUpBlock2D):
sample = up_block(
... | 1,010 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py |
class I2VGenXLTransformerTemporalEncoder(nn.Module):
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
activation_fn: str = "geglu",
upcast_attention: bool = False,
ff_inner_dim: Optional[int] = None,
dropout: int = 0.0,
... | 1,011 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
def forward(
self,
hidden_states: torch.Tensor,
) -> torch.Tensor:
norm_hidden_states = self.norm1(hidden_states)
attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None)
hidden_states = attn_output + hidden_states
if hidden_states.ndim == 4:
... | 1,011 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
class I2VGenXLUNet(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
r"""
I2VGenXL UNet. It is 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 fo... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.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... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
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.
cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features.
atte... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
_supports_gradient_checkpointing = False
@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",
... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
# When we first integrated the UNet into the library, we didn't have `attention_head_dim`. As a consequence
# of that, we used `num_attention_heads` for arguments that actually denote attention head dimension. This
# is why we ignore `num_attention_heads` and calculate it from `attention_head_dims` belo... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.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... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
# image embedding
self.image_latents_proj_in = nn.Sequential(
nn.Conv2d(4, in_channels * 4, 3, padding=1),
nn.SiLU(),
nn.Conv2d(in_channels * 4, in_channels * 4, 3, stride=1, padding=1),
nn.SiLU(),
nn.Conv2d(in_channels * 4, in_channels, 3, stride=1, p... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
# other embeddings -- time, context, fps, etc.
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="silu")
... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.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(
... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
# mid
self.mid_block = UNetMidBlock3DCrossAttn(
in_channels=block_out_channels[-1],
temb_channels=time_embed_dim,
resnet_eps=1e-05,
resnet_act_fn="silu",
output_scale_factor=1,
cross_attention_dim=cross_attention_dim,
num_attent... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.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... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.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... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
# out
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-05)
self.conv_act = get_activation("silu")
self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
@property
# Copied from diffusers.models.une... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
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
# Copi... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.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) !=... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.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)
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DCondi... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.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 ... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
for module in self.children():
fn_recursive_feed_forward(module, chunk_size, dim)
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking
def disable_forward_chunking(self):
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, ... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESS... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel._set_gradient_checkpointing
def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):
module.gradient_checkpoin... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
Args:
s1 (`float`):
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
mitigate the "oversmoothing effect" in the enhanced denoising process.
s2 (`float`):
Scaling factor for stage 2 to attenuate the con... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.disable_freeu
def disable_freeu(self):
"""Disables the FreeU mechanism."""
freeu_keys = {"s1", "s2", "b1", "b2"}
for i, upsample_block in enumerate(self.up_blocks):
for k in freeu_keys:
if... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
for _, attn_processor in self.attn_processors.items():
if "Added" in str(attn_processor.__class__.__name__):
raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
self.original_attn_processors = self.attn_processors
for module... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
def forward(
self,
sample: torch.Tensor,
timestep: Union[torch.Tensor, float, int],
fps: torch.Tensor,
image_latents: torch.Tensor,
image_embeddings: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
timestep_cond: Option... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.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.
fps (`torch.Tensor`): Frames per second for the vid... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] instead of a plai... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
Returns:
[`~models.unets.unet_3d_condition.UNet3DConditionOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] is returned,
otherwise a `tuple` is returned where the first element is the sample tensor.
"""
... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
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. time
timesteps = timestep
if not torch.is_tensor(timesteps):
# TODO: this require... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.time_proj(timesteps)
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16.... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
# 4. context embeddings.
# The context embeddings consist of both text embeddings from the input prompt
# AND the image embeddings from the input image. For images, both VAE encodings
# and the CLIP image embeddings are incorporated.
# So the final `context_embeddings` becomes the query ... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
image_latents_for_context_embds = image_latents[:, :, :1, :]
image_latents_context_embs = image_latents_for_context_embds.permute(0, 2, 1, 3, 4).reshape(
image_latents_for_context_embds.shape[0] * image_latents_for_context_embds.shape[2],
image_latents_for_context_embds.shape[1],
... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
image_emb = self.context_embedding(image_embeddings)
image_emb = image_emb.view(-1, self.config.in_channels, self.config.cross_attention_dim)
context_emb = torch.cat([context_emb, image_emb], dim=1)
context_emb = context_emb.repeat_interleave(repeats=num_frames, dim=0) | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
image_latents = image_latents.permute(0, 2, 1, 3, 4).reshape(
image_latents.shape[0] * image_latents.shape[2],
image_latents.shape[1],
image_latents.shape[3],
image_latents.shape[4],
)
image_latents = self.image_latents_proj_in(image_latents)
image... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
# 5. pre-process
sample = torch.cat([sample, image_latents], dim=1)
sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:])
sample = self.conv_in(sample)
sample = self.transformer_in(
sample,
num_frames=num_frames,
... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
# 6. down
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
sample, res_samples = downsample_block(
hidden_states=sample,
... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
# 7. mid
if self.mid_block is not None:
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=context_emb,
num_frames=num_frames,
cross_attention_kwargs=cross_attention_kwargs,
)
# 8. up
... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.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=context_emb,
... | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
# reshape to (batch, channel, framerate, width, height)
sample = sample[None, :].reshape((-1, num_frames) + sample.shape[1:]).permute(0, 2, 1, 3, 4)
if not return_dict:
return (sample,)
return UNet3DConditionOutput(sample=sample) | 1,012 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py |
class UNet1DOutput(BaseOutput):
"""
The output of [`UNet1DModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, sample_size)`):
The hidden states output from the last layer of the model.
"""
sample: torch.Tensor | 1,013 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d.py |
class UNet1DModel(ModelMixin, ConfigMixin):
r"""
A 1D 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). | 1,014 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d.py |
Parameters:
sample_size (`int`, *optional*): Default length of sample. Should be adaptable at runtime.
in_channels (`int`, *optional*, defaults to 2): Number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 2): Number of channels in the output.
extra_in_chann... | 1,014 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d.py |
down_block_types (`Tuple[str]`, *optional*, defaults to `("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")`):
Tuple of downsample block types.
up_block_types (`Tuple[str]`, *optional*, defaults to `("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")`):
Tuple of upsample block types.
... | 1,014 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d.py |
downsample_each_block (`int`, *optional*, defaults to `False`):
Experimental feature for using a UNet without upsampling.
""" | 1,014 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d.py |
@register_to_config
def __init__(
self,
sample_size: int = 65536,
sample_rate: Optional[int] = None,
in_channels: int = 2,
out_channels: int = 2,
extra_in_channels: int = 0,
time_embedding_type: str = "fourier",
flip_sin_to_cos: bool = True,
us... | 1,014 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d.py |
# time
if time_embedding_type == "fourier":
self.time_proj = GaussianFourierProjection(
embedding_size=8, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
)
timestep_input_dim = 2 * block_out_channels[0]
elif time_embedding_type == "po... | 1,014 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d.py |
self.down_blocks = nn.ModuleList([])
self.mid_block = None
self.up_blocks = nn.ModuleList([])
self.out_block = None
# down
output_channel = in_channels
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_ch... | 1,014 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d.py |
# mid
self.mid_block = get_mid_block(
mid_block_type,
in_channels=block_out_channels[-1],
mid_channels=block_out_channels[-1],
out_channels=block_out_channels[-1],
embed_dim=block_out_channels[0],
num_layers=layers_per_block,
ad... | 1,014 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d.py |
up_block = get_up_block(
up_block_type,
num_layers=layers_per_block,
in_channels=prev_output_channel,
out_channels=output_channel,
temb_channels=block_out_channels[0],
add_upsample=not is_final_block,
)
... | 1,014 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d.py |
def forward(
self,
sample: torch.Tensor,
timestep: Union[torch.Tensor, float, int],
return_dict: bool = True,
) -> Union[UNet1DOutput, Tuple]:
r"""
The [`UNet1DModel`] forward method.
Args:
sample (`torch.Tensor`):
The noisy input ... | 1,014 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d.py |
# 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)
timestep_e... | 1,014 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d.py |
# 3. mid
if self.mid_block:
sample = self.mid_block(sample, timestep_embed)
# 4. up
for i, upsample_block in enumerate(self.up_blocks):
res_samples = down_block_res_samples[-1:]
down_block_res_samples = down_block_res_samples[:-1]
sample = upsampl... | 1,014 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d.py |
class UNet2DOutput(BaseOutput):
"""
The output of [`UNet2DModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The hidden states output from the last layer of the model.
"""
sample: torch.Tensor | 1,015 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py |
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