diffusers/pr_14333/en/api/models/helios_transformer3d.md

HeliosTransformer3DModel

A 14B Real-Time Autogressive Diffusion Transformer model (support T2V, I2V and V2V) for 3D video-like data from Helios was introduced in Helios: Real Real-Time Long Video Generation Model by Peking University & ByteDance & etc.

The model can be loaded with the following code snippet.

from diffusers import HeliosTransformer3DModel

# Best Quality
transformer = HeliosTransformer3DModel.from_pretrained("BestWishYsh/Helios-Base", subfolder="transformer", dtype=torch.bfloat16)
# Intermediate Weight
transformer = HeliosTransformer3DModel.from_pretrained("BestWishYsh/Helios-Mid", subfolder="transformer", dtype=torch.bfloat16)
# Best Efficiency
transformer = HeliosTransformer3DModel.from_pretrained("BestWishYsh/Helios-Distilled", subfolder="transformer", dtype=torch.bfloat16)

HeliosTransformer3DModel[[diffusers.HeliosTransformer3DModel]]

diffusers.HeliosTransformer3DModel[[diffusers.HeliosTransformer3DModel]]

diffusers.HeliosTransformer3DModel(patch_size: tuple = (1, 2, 2), num_attention_heads: int = 40, attention_head_dim: int = 128, in_channels: int = 16, out_channels: int = 16, text_dim: int = 4096, freq_dim: int = 256, ffn_dim: int = 13824, num_layers: int = 40, cross_attn_norm: bool = True, qk_norm: str | None = 'rms_norm_across_heads', eps: float = 1e-06, added_kv_proj_dim: int | None = None, rope_dim: tuple = (44, 42, 42), rope_theta: float = 10000.0, guidance_cross_attn: bool = True, zero_history_timestep: bool = True, has_multi_term_memory_patch: bool = True, is_amplify_history: bool = False, history_scale_mode: str = 'per_head')

Source

Parameters:

patch_size (tuple[int], defaults to (1, 2, 2)) : 3D patch dimensions for video embedding (t_patch, h_patch, w_patch).

num_attention_heads (int, defaults to 40) : Fixed length for text embeddings.

attention_head_dim (int, defaults to 128) : The number of channels in each head.

in_channels (int, defaults to 16) : The number of channels in the input.

out_channels (int, defaults to 16) : The number of channels in the output.

text_dim (int, defaults to 512) : Input dimension for text embeddings.

freq_dim (int, defaults to 256) : Dimension for sinusoidal time embeddings.

ffn_dim (int, defaults to 13824) : Intermediate dimension in feed-forward network.

num_layers (int, defaults to 40) : The number of layers of transformer blocks to use.

window_size (tuple[int], defaults to (-1, -1)) : Window size for local attention (-1 indicates global attention).

cross_attn_norm (bool, defaults to True) : Enable cross-attention normalization.

qk_norm (bool, defaults to True) : Enable query/key normalization.

eps (float, defaults to 1e-6) : Epsilon value for normalization layers.

add_img_emb (bool, defaults to False) : Whether to use img_emb.

added_kv_proj_dim (int, optional, defaults to None) : The number of channels to use for the added key and value projections. If None, no projection is used.

A Transformer model for video-like data used in the Helios model.

forward[[diffusers.HeliosTransformer3DModel.forward]]

forward(hidden_states: Tensor, timestep: LongTensor, encoder_hidden_states: Tensor, indices_hidden_states = None, indices_latents_history_short = None, indices_latents_history_mid = None, indices_latents_history_long = None, latents_history_short = None, latents_history_mid = None, latents_history_long = None, return_dict: bool = True, attention_kwargs: dict[str, typing.Any] | None = None)

Source

Parameters:

hidden_states (torch.Tensor of shape (batch_size, num_channels, num_frames, height, width)) : Input hidden_states.

timestep (torch.LongTensor) : Used to indicate denoising step.

encoder_hidden_states (torch.Tensor of shape (batch_size, sequence_len, embed_dims)) : Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.

indices_hidden_states (torch.Tensor, optional) : Frame indices for hidden_states used to compute the rotary positional embeddings.

indices_latents_history_short (torch.Tensor, optional) : Frame indices for the short history latents.

indices_latents_history_mid (torch.Tensor, optional) : Frame indices for the mid history latents.

indices_latents_history_long (torch.Tensor, optional) : Frame indices for the long history latents.

latents_history_short (torch.Tensor, optional) : Short history latents conditioning.

latents_history_mid (torch.Tensor, optional) : Mid history latents conditioning.

latents_history_long (torch.Tensor, optional) : Long history latents conditioning.

return_dict (bool, optional, defaults to True) : Whether or not to return a ~models.transformer_2d.Transformer2DModelOutput instead of a plain tuple.

attention_kwargs (dict, optional) : A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor.

Returns:

If return_dict is True, an ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a tuple where the first element is the sample tensor.

The HeliosTransformer3DModel forward method.

Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

diffusers.models.modeling_outputs.Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

diffusers.models.modeling_outputs.Transformer2DModelOutput(sample: torch.Tensor)

Source

Parameters:

sample (torch.Tensor of shape (batch_size, num_channels, height, width) or (batch size, num_vector_embeds - 1, num_latent_pixels) if Transformer2DModel is discrete) : The hidden states output conditioned on the encoder_hidden_states input. If discrete, returns probability distributions for the unnoised latent pixels.

The output of Transformer2DModel.