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class CogView3PlusTransformer2DModel(ModelMixin, ConfigMixin):
r"""
The Transformer model introduced in [CogView3: Finer and Faster Text-to-Image Generation via Relay
Diffusion](https://huggingface.co/papers/2403.05121). | 1,158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
Args:
patch_size (`int`, defaults to `2`):
The size of the patches to use in the patch embedding layer.
in_channels (`int`, defaults to `16`):
The number of channels in the input.
num_layers (`int`, defaults to `30`):
The number of layers of Transformer blocks... | 1,158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
The embedding dimension of the input SDXL-style resolution conditions (original_size, target_size,
crop_coords).
pos_embed_max_size (`int`, defaults to `128`):
The maximum resolution of the positional embeddings, from which slices of shape `H x W` are taken and added
to input... | 1,158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
_supports_gradient_checkpointing = True
_no_split_modules = ["CogView3PlusTransformerBlock", "CogView3PlusPatchEmbed"]
@register_to_config
def __init__(
self,
patch_size: int = 2,
in_channels: int = 16,
num_layers: int = 30,
attention_head_dim: int = 40,
num_... | 1,158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
self.patch_embed = CogView3PlusPatchEmbed(
in_channels=in_channels,
hidden_size=self.inner_dim,
patch_size=patch_size,
text_hidden_size=text_embed_dim,
pos_embed_max_size=pos_embed_max_size,
)
self.time_condition_embed = CogView3CombinedTimest... | 1,158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
self.norm_out = AdaLayerNormContinuous(
embedding_dim=self.inner_dim,
conditioning_embedding_dim=time_embed_dim,
elementwise_affine=False,
eps=1e-6,
)
self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)
... | 1,158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
if hasattr(module, "get_processor"):
processors[f"{name}.processor"] = module.get_processor()
for sub_name, child in module.named_children():
fn_recurs... | 1,158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
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 need... | 1,158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
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}.p... | 1,158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
timestep: torch.LongTensor,
original_size: torch.Tensor,
target_size: torch.Tensor,
crop_coords: torch.Tensor,
return_dict: bool = True,
) -> Union[torch.Tensor, Trans... | 1,158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
Args:
hidden_states (`torch.Tensor`):
Input `hidden_states` of shape `(batch size, channel, height, width)`.
encoder_hidden_states (`torch.Tensor`):
Conditional embeddings (embeddings computed from the input conditions such as prompts) of shape
`(b... | 1,158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
CogView3 uses SDXL-like micro-conditioning for crop coordinates as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.tran... | 1,158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
Returns:
`torch.Tensor` or [`~models.transformer_2d.Transformer2DModelOutput`]:
The denoised latents using provided inputs as conditioning.
"""
height, width = hidden_states.shape[-2:]
text_seq_length = encoder_hidden_states.shape[1]
hidden_states = self.patc... | 1,158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
encoder_hidden_states,
... | 1,158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
hidden_states = hidden_states.reshape(
shape=(hidden_states.shape[0], height, width, self.out_channels, patch_size, patch_size)
)
hidden_states = torch.einsum("nhwcpq->nchpwq", hidden_states)
output = hidden_states.reshape(
shape=(hidden_states.shape[0], self.out_channels... | 1,158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py |
class StableAudioGaussianFourierProjection(nn.Module):
"""Gaussian Fourier embeddings for noise levels."""
# Copied from diffusers.models.embeddings.GaussianFourierProjection.__init__
def __init__(
self, embedding_size: int = 256, scale: float = 1.0, set_W_to_weight=True, log=True, flip_sin_to_cos=... | 1,159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
if self.flip_sin_to_cos:
out = torch.cat([torch.cos(x_proj), torch.sin(x_proj)], dim=-1)
else:
out = torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
return out | 1,159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
class StableAudioDiTBlock(nn.Module):
r"""
Transformer block used in Stable Audio model (https://github.com/Stability-AI/stable-audio-tools). Allow skip
connection and QKNorm
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): The number... | 1,160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
def __init__(
self,
dim: int,
num_attention_heads: int,
num_key_value_attention_heads: int,
attention_head_dim: int,
dropout=0.0,
cross_attention_dim: Optional[int] = None,
upcast_attention: bool = False,
norm_eps: float = 1e-5,
ff_inner_di... | 1,160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
kv_heads=num_key_value_attention_heads,
dropout=dropout,
bias=False,
upcast_attention=up... | 1,160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
# Sets chunk feed-forward
self._chunk_size = chunk_size
self._chunk_dim = dim
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hid... | 1,160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
attn_output = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
)
hidden_states = attn_output + hidden_states
# 3. Feed-forward
norm_hidden_states = self.norm3(hidden_states)
ff... | 1,160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
class StableAudioDiTModel(ModelMixin, ConfigMixin):
"""
The Diffusion Transformer model introduced in Stable Audio.
Reference: https://github.com/Stability-AI/stable-audio-tools | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
Parameters:
sample_size ( `int`, *optional*, defaults to 1024): The size of the input sample.
in_channels (`int`, *optional*, defaults to 64): The number of channels in the input.
num_layers (`int`, *optional*, defaults to 24): The number of layers of Transformer blocks to use.
attention... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
global_states_input_dim ( `int`, *optional*, defaults to 1536):
Input dimension of the global hidden states projection.
cross_attention_input_dim ( `int`, *optional*, defaults to 768):
Input dimension of the cross-attention projection
""" | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: int = 1024,
in_channels: int = 64,
num_layers: int = 24,
attention_head_dim: int = 64,
num_attention_heads: int = 24,
num_key_value_attention_heads: int = 12,
... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
self.timestep_proj = nn.Sequential(
nn.Linear(time_proj_dim, self.inner_dim, bias=True),
nn.SiLU(),
nn.Linear(self.inner_dim, self.inner_dim, bias=True),
)
self.global_proj = nn.Sequential(
nn.Linear(global_states_input_dim, self.inner_dim, bias=False),
... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
self.transformer_blocks = nn.ModuleList(
[
StableAudioDiTBlock(
dim=self.inner_dim,
num_attention_heads=num_attention_heads,
num_key_value_attention_heads=num_key_value_attention_heads,
attention_head_dim=attenti... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor ... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
# Copied from diffusers.models.transformers.hunyuan_transformer_2d.HunyuanDiT2DModel.set_default_attn_processor with Hunyuan->StableAudio
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
self.set_attn_pr... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
def forward(
self,
hidden_states: torch.FloatTensor,
timestep: torch.LongTensor = None,
encoder_hidden_states: torch.FloatTensor = None,
global_hidden_states: torch.FloatTensor = None,
rotary_embedding: torch.FloatTensor = None,
return_dict: bool = True,
a... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
Args:
hidden_states (`torch.FloatTensor` of shape `(batch size, in_channels, sequence_len)`):
Input `hidden_states`.
timestep ( `torch.LongTensor`):
Used to indicate denoising step.
encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, enco... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
tuple.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_len)`, *optional*):
Mask to avoid performing attention on padding token indices, formed by concatenating the attention
masks
for the two text encoders together. Mask values selected in `... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
encoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_len)`, *optional*):
Mask to avoid performing attention on padding token cross-attention indices, formed by concatenating
... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
Returns:
If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
`tuple` where the first element is the sample tensor.
"""
cross_a... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
# prepend global states to hidden states
hidden_states = torch.cat([global_hidden_states, hidden_states], dim=-2)
if attention_mask is not None:
prepend_mask = torch.ones((hidden_states.shape[0], 1), device=hidden_states.device, dtype=torch.bool)
attention_mask = torch.cat([prepe... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
attention_mask,
cross_attenti... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
# (batch_size, sequence_length, dim) -> (batch_size, dim, sequence_length)
# remove prepend length that has been added by global hidden states
hidden_states = hidden_states.transpose(1, 2)[:, :, 1:]
hidden_states = self.postprocess_conv(hidden_states) + hidden_states
if not return_dict:... | 1,161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py |
class MochiModulatedRMSNorm(nn.Module):
def __init__(self, eps: float):
super().__init__()
self.eps = eps
self.norm = RMSNorm(0, eps, False)
def forward(self, hidden_states, scale=None):
hidden_states_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.fl... | 1,162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
class MochiLayerNormContinuous(nn.Module):
def __init__(
self,
embedding_dim: int,
conditioning_embedding_dim: int,
eps=1e-5,
bias=True,
):
super().__init__()
# AdaLN
self.silu = nn.SiLU()
self.linear_1 = nn.Linear(conditioning_embedding_d... | 1,163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
class MochiRMSNormZero(nn.Module):
r"""
Adaptive RMS Norm used in Mochi.
Parameters:
embedding_dim (`int`): The size of each embedding vector.
"""
def __init__(
self, embedding_dim: int, hidden_dim: int, eps: float = 1e-5, elementwise_affine: bool = False
) -> None:
sup... | 1,164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
class MochiTransformerBlock(nn.Module):
r"""
Transformer block used in [Mochi](https://huggingface.co/genmo/mochi-1-preview).
Args:
dim (`int`):
The number of channels in the input and output.
num_attention_heads (`int`):
The number of heads to use for multi-head att... | 1,165 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
pooled_projection_dim: int,
qk_norm: str = "rms_norm",
activation_fn: str = "swiglu",
context_pre_only: bool = False,
eps: float = 1e-6,
) -> None:
super()... | 1,165 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
self.attn1 = MochiAttention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
bias=False,
added_kv_proj_dim=pooled_projection_dim,
added_proj_bias=False,
out_dim=dim,
out_context_dim=pooled_projection_d... | 1,165 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
self.ff = FeedForward(dim, inner_dim=self.ff_inner_dim, activation_fn=activation_fn, bias=False)
self.ff_context = None
if not context_pre_only:
self.ff_context = FeedForward(
pooled_projection_dim,
inner_dim=self.ff_context_inner_dim,
activati... | 1,165 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
if not self.context_pre_only:
norm_encoder_hidden_states, enc_gate_msa, enc_scale_mlp, enc_gate_mlp = self.norm1_context(
encoder_hidden_states, temb
)
else:
norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states, temb)
attn_hidden_stat... | 1,165 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
if not self.context_pre_only:
encoder_hidden_states = encoder_hidden_states + self.norm2_context(
context_attn_hidden_states, torch.tanh(enc_gate_msa).unsqueeze(1)
)
norm_encoder_hidden_states = self.norm3_context(
encoder_hidden_states, (1 + enc_scale... | 1,165 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
class MochiRoPE(nn.Module):
r"""
RoPE implementation used in [Mochi](https://huggingface.co/genmo/mochi-1-preview).
Args:
base_height (`int`, defaults to `192`):
Base height used to compute interpolation scale for rotary positional embeddings.
base_width (`int`, defaults to `192... | 1,166 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
def _get_positions(
self,
num_frames: int,
height: int,
width: int,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
) -> torch.Tensor:
scale = (self.target_area / (height * width)) ** 0.5
t = torch.arange(num_frames, device... | 1,166 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
freqs_cos = torch.cos(freqs)
freqs_sin = torch.sin(freqs)
return freqs_cos, freqs_sin
def forward(
self,
pos_frequencies: torch.Tensor,
num_frames: int,
height: int,
width: int,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtyp... | 1,166 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
r"""
A Transformer model for video-like data introduced in [Mochi](https://huggingface.co/genmo/mochi-1-preview). | 1,167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
Args:
patch_size (`int`, defaults to `2`):
The size of the patches to use in the patch embedding layer.
num_attention_heads (`int`, defaults to `24`):
The number of heads to use for multi-head attention.
attention_head_dim (`int`, defaults to `128`):
The numbe... | 1,167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
activation_fn (`str`, defaults to `"swiglu"`):
Activation function to use in feed-forward.
max_sequence_length (`int`, defaults to `256`):
The maximum sequence length of text embeddings supported.
""" | 1,167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
_supports_gradient_checkpointing = True
_no_split_modules = ["MochiTransformerBlock"]
@register_to_config
def __init__(
self,
patch_size: int = 2,
num_attention_heads: int = 24,
attention_head_dim: int = 128,
num_layers: int = 48,
pooled_projection_dim: int =... | 1,167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
self.time_embed = MochiCombinedTimestepCaptionEmbedding(
embedding_dim=inner_dim,
pooled_projection_dim=pooled_projection_dim,
text_embed_dim=text_embed_dim,
time_embed_dim=time_embed_dim,
num_attention_heads=8,
)
self.pos_frequencies = nn.Par... | 1,167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
self.norm_out = AdaLayerNormContinuous(
inner_dim,
inner_dim,
elementwise_affine=False,
eps=1e-6,
norm_type="layer_norm",
)
self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels)
self.gradient_checkpointing = Fals... | 1,167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
if USE_PEFT_BACKEND:
# weight the lora layers by setting `lora_scale` for each PEFT layer
scale_lora_layers(self, lora_scale)
else:
if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
logger.warning(
"Passin... | 1,167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
hidden_states = hidden_states.permute(0, 2, 1, 3, 4).flatten(0, 1)
hidden_states = self.patch_embed(hidden_states)
hidden_states = hidden_states.unflatten(0, (batch_size, -1)).flatten(1, 2)
image_rotary_emb = self.rope(
self.pos_frequencies,
num_frames,
post_... | 1,167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
encoder_hidden_states,
... | 1,167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
hidden_states = hidden_states.reshape(batch_size, num_frames, post_patch_height, post_patch_width, p, p, -1)
hidden_states = hidden_states.permute(0, 6, 1, 2, 4, 3, 5)
output = hidden_states.reshape(batch_size, -1, num_frames, height, width)
if USE_PEFT_BACKEND:
# remove `lora_scale... | 1,167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py |
class TemporalDecoder(nn.Module):
def __init__(
self,
in_channels: int = 4,
out_channels: int = 3,
block_out_channels: Tuple[int] = (128, 256, 512, 512),
layers_per_block: int = 2,
):
super().__init__()
self.layers_per_block = layers_per_block
sel... | 1,168 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
is_final_block = i == len(block_out_channels) - 1
up_block = UpBlockTemporalDecoder(
num_layers=self.layers_per_block + 1,
in_channels=prev_output_channel,
out_channels=output_channel,
add_upsample=not is_final_block,
)
... | 1,168 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
conv_out_kernel_size = (3, 1, 1)
padding = [int(k // 2) for k in conv_out_kernel_size]
self.time_conv_out = torch.nn.Conv3d(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=conv_out_kernel_size,
padding=padding,
)
self.gra... | 1,168 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
if is_torch_version(">=", "1.11.0"):
# middle
sample = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block),
sample,
image_only_indicator,
use_reentrant=False,
)
... | 1,168 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
# up
for up_block in self.up_blocks:
sample = torch.utils.checkpoint.checkpoint(
create_custom_forward(up_block),
sample,
image_only_indicator,
)
else:
# middle
... | 1,168 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
sample = sample.permute(0, 2, 1, 3, 4).reshape(batch_frames, channels, height, width)
return sample | 1,168 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
class AutoencoderKLTemporalDecoder(ModelMixin, ConfigMixin):
r"""
A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (s... | 1,169 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
Parameters:
in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
out_channels (int, *optional*, defaults to 3): Number of channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
Tuple of downsample b... | 1,169 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
/ scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Reso... | 1,169 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
block_out_channels: Tuple[int] = (64,),
layers_per_block: int = 1,
latent_... | 1,169 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
# pass init params to Decoder
self.decoder = TemporalDecoder(
in_channels=latent_channels,
out_channels=out_channels,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
)
self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * ... | 1,169 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
if hasattr(module, "get_processor"):
processors[f"{name}.processor"] = module.get_processor()
for sub_name, child in module.named_children():
fn_recurs... | 1,169 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
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 need... | 1,169 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
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}.p... | 1,169 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnProcessor()
else:
... | 1,169 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
Args:
x (`torch.Tensor`): Input batch of images.
return_dict (`bool`, *optional*, defaults to `True`):
Whether to return a [`~models.autoencoders.autoencoder_kl.AutoencoderKLOutput`] instead of a plain
tuple.
Returns:
The latent representa... | 1,169 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
Args:
z (`torch.Tensor`): Input batch of latent vectors.
return_dict (`bool`, *optional*, defaults to `True`):
Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
Returns:
[`~models.vae.DecoderOutput`] or `tuple`:
If re... | 1,169 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
def forward(
self,
sample: torch.Tensor,
sample_posterior: bool = False,
return_dict: bool = True,
generator: Optional[torch.Generator] = None,
num_frames: int = 1,
) -> Union[DecoderOutput, torch.Tensor]:
r"""
Args:
sample (`torch.Tensor`)... | 1,169 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py |
class CogVideoXSafeConv3d(nn.Conv3d):
r"""
A 3D convolution layer that splits the input tensor into smaller parts to avoid OOM in CogVideoX Model.
"""
def forward(self, input: torch.Tensor) -> torch.Tensor:
memory_count = (
(input.shape[0] * input.shape[1] * input.shape[2] * input.s... | 1,170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
output_chunks = []
for input_chunk in input_chunks:
output_chunks.append(super().forward(input_chunk))
output = torch.cat(output_chunks, dim=2)
return output
else:
return super().forward(input) | 1,170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
class CogVideoXCausalConv3d(nn.Module):
r"""A 3D causal convolution layer that pads the input tensor to ensure causality in CogVideoX Model.
Args:
in_channels (`int`): Number of channels in the input tensor.
out_channels (`int`): Number of output channels produced by the convolution.
ke... | 1,171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
time_kernel_size, height_kernel_size, width_kernel_size = kernel_size
# TODO(aryan): configure calculation based on stride and dilation in the future.
# Since CogVideoX does not use it, it is currently tailored to "just work" with Mochi
time_pad = time_kernel_size - 1
height_pad = (heig... | 1,171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
stride = stride if isinstance(stride, tuple) else (stride, 1, 1)
dilation = (dilation, 1, 1)
self.conv = CogVideoXSafeConv3d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
dilation=dilation,
... | 1,171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
def forward(self, inputs: torch.Tensor, conv_cache: Optional[torch.Tensor] = None) -> torch.Tensor:
inputs = self.fake_context_parallel_forward(inputs, conv_cache)
if self.pad_mode == "replicate":
conv_cache = None
else:
padding_2d = (self.width_pad, self.width_pad, self... | 1,171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
class CogVideoXSpatialNorm3D(nn.Module):
r"""
Spatially conditioned normalization as defined in https://arxiv.org/abs/2209.09002. This implementation is specific
to 3D-video like data.
CogVideoXSafeConv3d is used instead of nn.Conv3d to avoid OOM in CogVideoX Model.
Args:
f_channels (`int`... | 1,172 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
def __init__(
self,
f_channels: int,
zq_channels: int,
groups: int = 32,
):
super().__init__()
self.norm_layer = nn.GroupNorm(num_channels=f_channels, num_groups=groups, eps=1e-6, affine=True)
self.conv_y = CogVideoXCausalConv3d(zq_channels, f_channels, kernel... | 1,172 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
if f.shape[2] > 1 and f.shape[2] % 2 == 1:
f_first, f_rest = f[:, :, :1], f[:, :, 1:]
f_first_size, f_rest_size = f_first.shape[-3:], f_rest.shape[-3:]
z_first, z_rest = zq[:, :, :1], zq[:, :, 1:]
z_first = F.interpolate(z_first, size=f_first_size)
z_rest = F.... | 1,172 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
class CogVideoXResnetBlock3D(nn.Module):
r"""
A 3D ResNet block used in the CogVideoX model. | 1,173 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
dropout (`float`, defaults to `0.0`):
Dropout rate.
temb_channels (`int`, defaults to `512`):
... | 1,173 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
dropout: float = 0.0,
temb_channels: int = 512,
groups: int = 32,
eps: float = 1e-6,
non_linearity: str = "swish",
conv_shortcut: bool = False,
spatial_norm_dim: Opti... | 1,173 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
if spatial_norm_dim is None:
self.norm1 = nn.GroupNorm(num_channels=in_channels, num_groups=groups, eps=eps)
self.norm2 = nn.GroupNorm(num_channels=out_channels, num_groups=groups, eps=eps)
else:
self.norm1 = CogVideoXSpatialNorm3D(
f_channels=in_channels,
... | 1,173 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
self.dropout = nn.Dropout(dropout)
self.conv2 = CogVideoXCausalConv3d(
in_channels=out_channels, out_channels=out_channels, kernel_size=3, pad_mode=pad_mode
)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
self.conv_shortcut = CogVid... | 1,173 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
if zq is not None:
hidden_states, new_conv_cache["norm1"] = self.norm1(hidden_states, zq, conv_cache=conv_cache.get("norm1"))
else:
hidden_states = self.norm1(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states, new_conv_cache["conv1"] = self.co... | 1,173 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
inputs, new_conv_cache["conv_shortcut"] = self.conv_shortcut(
inputs, conv_cache=conv_cache.get("conv_shortcut")
)
else:
inputs = self.conv_shortcut(inputs)
... | 1,173 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
class CogVideoXDownBlock3D(nn.Module):
r"""
A downsampling block used in the CogVideoX model. | 1,174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
temb_channels (`int`, defaults to `512`):
Number of time embedding channels.
num_layers (`int`, defaul... | 1,174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
Whether or not to downsample across temporal dimension.
pad_mode (str, defaults to `"first"`):
Padding mode.
""" | 1,174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
_supports_gradient_checkpointing = True
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
... | 1,174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
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