# `Edifice.Blocks.AdaptiveNorm`
[🔗](https://github.com/blasphemetheus/edifice/blob/main/lib/edifice/blocks/adaptive_norm.ex#L1)

Adaptive Layer Normalization (AdaLN / AdaLN-Zero).

Conditional normalization where scale and shift parameters are predicted
from a conditioning signal (e.g., timestep embedding, class label). Used
in Diffusion Transformers (DiT) and class-conditional generation.

## Variants

- **AdaLN**: Replace fixed gamma/beta with condition-predicted parameters
- **AdaLN-Zero**: Also predict a gating factor alpha, initialized to zero

## Formula

    AdaLN(x, c) = gamma(c) * LayerNorm(x) + beta(c)
    AdaLN-Zero(x, c) = alpha(c) * (gamma(c) * LayerNorm(x) + beta(c))

## Usage

    # AdaLN conditioning on timestep embedding
    output = AdaptiveNorm.layer(input, condition,
      hidden_size: 256,
      mode: :adaln_zero
    )

## References
- "Scalable Diffusion Models with Transformers" (Peebles & Xie, 2023)
- https://arxiv.org/abs/2212.09748

# `layer`

```elixir
@spec layer(Axon.t(), Axon.t(), keyword()) :: Axon.t()
```

Build an AdaLN / AdaLN-Zero layer.

## Parameters
  - `input` - Input tensor Axon node [batch, ..., hidden_size]
  - `condition` - Conditioning signal Axon node [batch, cond_dim]

## Options
  - `:hidden_size` - Feature dimension (required)
  - `:mode` - :adaln or :adaln_zero (default: :adaln_zero)
  - `:name` - Layer name prefix (default: "adaptive_norm")

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*Consult [api-reference.md](api-reference.md) for complete listing*