diffusers/pr_13751/en/api/models/prior_transformer.md

PriorTransformer

The Prior Transformer was originally introduced in Hierarchical Text-Conditional Image Generation with CLIP Latents by Ramesh et al. It is used to predict CLIP image embeddings from CLIP text embeddings; image embeddings are predicted through a denoising diffusion process.

The abstract from the paper is:

Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples.

PriorTransformer[[diffusers.PriorTransformer]]

diffusers.PriorTransformer[[diffusers.PriorTransformer]]

Source

A Prior Transformer model.

forwarddiffusers.PriorTransformer.forwardhttps://github.com/huggingface/diffusers/blob/vr_13751/src/diffusers/models/transformers/prior_transformer.py#L183[{"name": "hidden_states", "val": ""}, {"name": "timestep", "val": ": torch.Tensor | float | int"}, {"name": "proj_embedding", "val": ": Tensor"}, {"name": "encoder_hidden_states", "val": ": torch.Tensor | None = None"}, {"name": "attention_mask", "val": ": torch.BoolTensor | None = None"}, {"name": "return_dict", "val": ": bool = True"}]- hidden_states (torch.Tensor of shape (batch_size, embedding_dim)) -- The currently predicted image embeddings.

• timestep (torch.LongTensor) -- Current denoising step.
• proj_embedding (torch.Tensor of shape (batch_size, embedding_dim)) -- Projected embedding vector the denoising process is conditioned on.
• encoder_hidden_states (torch.Tensor of shape (batch_size, num_embeddings, embedding_dim)) -- Hidden states of the text embeddings the denoising process is conditioned on.
• attention_mask (torch.BoolTensor of shape (batch_size, num_embeddings)) -- Text mask for the text embeddings.
• return_dict (bool, optional, defaults to True) -- Whether or not to return a PriorTransformerOutput instead of a plain tuple.0PriorTransformerOutput or tupleIf return_dict is True, a PriorTransformerOutput is returned, otherwise a tuple is returned where the first element is the sample tensor.

The PriorTransformer forward method.

Parameters:

num_attention_heads (int, optional, defaults to 32) : The number of heads to use for multi-head attention.

attention_head_dim (int, optional, defaults to 64) : The number of channels in each head.

num_layers (int, optional, defaults to 20) : The number of layers of Transformer blocks to use.

embedding_dim (int, optional, defaults to 768) : The dimension of the model input hidden_states

num_embeddings (int, optional, defaults to 77) : The number of embeddings of the model input hidden_states

additional_embeddings (int, optional, defaults to 4) : The number of additional tokens appended to the projected hidden_states. The actual length of the used hidden_states is num_embeddings + additional_embeddings.

dropout (float, optional, defaults to 0.0) : The dropout probability to use.

time_embed_act_fn (str, optional, defaults to 'silu') : The activation function to use to create timestep embeddings.

norm_in_type (str, optional, defaults to None) : The normalization layer to apply on hidden states before passing to Transformer blocks. Set it to None if normalization is not needed.

embedding_proj_norm_type (str, optional, defaults to None) : The normalization layer to apply on the input proj_embedding. Set it to None if normalization is not needed.

encoder_hid_proj_type (str, optional, defaults to linear) : The projection layer to apply on the input encoder_hidden_states. Set it to None if encoder_hidden_states is None.

added_emb_type (str, optional, defaults to prd) : Additional embeddings to condition the model. Choose from prd or None. if choose prd, it will prepend a token indicating the (quantized) dot product between the text embedding and image embedding as proposed in the unclip paper https://huggingface.co/papers/2204.06125 If it is None, no additional embeddings will be prepended.

time_embed_dim (int, *optional*, defaults to None) : The dimension of timestep embeddings. If None, will be set to num_attention_heads * attention_head_dim`

embedding_proj_dim (int, optional, default to None) : The dimension of proj_embedding. If None, will be set to embedding_dim.

clip_embed_dim (int, optional, default to None) : The dimension of the output. If None, will be set to embedding_dim.

Returns:

[PriorTransformerOutput](/docs/diffusers/pr_13751/en/api/models/prior_transformer#diffusers.models.transformers.prior_transformer.PriorTransformerOutput) or tuple``

If return_dict is True, a PriorTransformerOutput is returned, otherwise a tuple is returned where the first element is the sample tensor.

set_default_attn_processor[[diffusers.PriorTransformer.set_default_attn_processor]]

Source

Disables custom attention processors and sets the default attention implementation.

PriorTransformerOutput[[diffusers.models.transformers.prior_transformer.PriorTransformerOutput]]

diffusers.models.transformers.prior_transformer.PriorTransformerOutput[[diffusers.models.transformers.prior_transformer.PriorTransformerOutput]]

Source

The output of PriorTransformer.

Parameters:

predicted_image_embedding (torch.Tensor of shape (batch_size, embedding_dim)) : The predicted CLIP image embedding conditioned on the CLIP text embedding input.