diffusers/pr_13769/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", torch_dtype=torch.bfloat16)
# Intermediate Weight
transformer = HeliosTransformer3DModel.from_pretrained("BestWishYsh/Helios-Mid", subfolder="transformer", torch_dtype=torch.bfloat16)
# Best Efficiency
transformer = HeliosTransformer3DModel.from_pretrained("BestWishYsh/Helios-Distilled", subfolder="transformer", torch_dtype=torch.bfloat16)

HeliosTransformer3DModel[[diffusers.HeliosTransformer3DModel]]

diffusers.HeliosTransformer3DModel[[diffusers.HeliosTransformer3DModel]]

Source

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

forwarddiffusers.HeliosTransformer3DModel.forwardhttps://github.com/huggingface/diffusers/blob/vr_13769/src/diffusers/models/transformers/transformer_helios.py#L657[{"name": "hidden_states", "val": ": Tensor"}, {"name": "timestep", "val": ": LongTensor"}, {"name": "encoder_hidden_states", "val": ": Tensor"}, {"name": "indices_hidden_states", "val": " = None"}, {"name": "indices_latents_history_short", "val": " = None"}, {"name": "indices_latents_history_mid", "val": " = None"}, {"name": "indices_latents_history_long", "val": " = None"}, {"name": "latents_history_short", "val": " = None"}, {"name": "latents_history_mid", "val": " = None"}, {"name": "latents_history_long", "val": " = None"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}]- 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.0If 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.

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.

Returns:

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

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

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

Source

The output of Transformer2DModel.

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.