# HunyuanVideoTransformer3DModel A Diffusion Transformer model for 3D video-like data was introduced in [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://huggingface.co/papers/2412.03603) by Tencent. The model can be loaded with the following code snippet. ```python from diffusers import HunyuanVideoTransformer3DModel transformer = HunyuanVideoTransformer3DModel.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder="transformer", torch_dtype=torch.bfloat16) ``` ## HunyuanVideoTransformer3DModel[[diffusers.HunyuanVideoTransformer3DModel]] - **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. - **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 number of channels in each head. - **num_layers** (`int`, defaults to `20`) -- The number of layers of dual-stream blocks to use. - **num_single_layers** (`int`, defaults to `40`) -- The number of layers of single-stream blocks to use. - **num_refiner_layers** (`int`, defaults to `2`) -- The number of layers of refiner blocks to use. - **mlp_ratio** (`float`, defaults to `4.0`) -- The ratio of the hidden layer size to the input size in the feedforward network. - **patch_size** (`int`, defaults to `2`) -- The size of the spatial patches to use in the patch embedding layer. - **patch_size_t** (`int`, defaults to `1`) -- The size of the tmeporal patches to use in the patch embedding layer. - **qk_norm** (`str`, defaults to `rms_norm`) -- The normalization to use for the query and key projections in the attention layers. - **guidance_embeds** (`bool`, defaults to `True`) -- Whether to use guidance embeddings in the model. - **text_embed_dim** (`int`, defaults to `4096`) -- Input dimension of text embeddings from the text encoder. - **pooled_projection_dim** (`int`, defaults to `768`) -- The dimension of the pooled projection of the text embeddings. - **rope_theta** (`float`, defaults to `256.0`) -- The value of theta to use in the RoPE layer. - **rope_axes_dim** (`tuple[int]`, defaults to `(16, 56, 56)`) -- The dimensions of the axes to use in the RoPE layer. - **image_condition_type** (`str`, *optional*, defaults to `None`) -- The type of image conditioning to use. If `None`, no image conditioning is used. If `latent_concat`, the image is concatenated to the latent stream. If `token_replace`, the image is used to replace first-frame tokens in the latent stream and apply conditioning. A Transformer model for video-like data used in [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo). - **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. - **encoder_attention_mask** (`torch.Tensor`) -- Mask applied to `encoder_hidden_states` during attention. - **pooled_projections** (`torch.Tensor` of shape `(batch_size, projection_dim)`) -- Embeddings projected from the embeddings of input conditions. - **guidance** (`torch.Tensor`, *optional*) -- Guidance scale embedding used for guidance-distilled variants of the model. - **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](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.transformer_2d.Transformer2DModelOutput` instead of a plain tuple.If `return_dict` is True, an `~models.transformer_2d.Transformer2DModelOutput` is returned, otherwise a `tuple` where the first element is the sample tensor. The [HunyuanVideoTransformer3DModel](/docs/diffusers/main/en/api/models/hunyuan_video_transformer_3d#diffusers.HunyuanVideoTransformer3DModel) forward method. ## Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]] - **sample** (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [Transformer2DModel](/docs/diffusers/main/en/api/models/transformer2d#diffusers.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](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel).