merge changes from upstream#550
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- Takes c = (B, d) conditioning → projects to 6×d parameters via SiLU + Linear - Zero-initialized output linear → all gates start at 0, so the model is a pure identity at init (training stability) - Reuses the same self.norm (no learnable affine) for both SA and FFN pre-norms TransformerLayer: - Removed norm1, norm2 — replaced by self.adaLN - forward now takes cond and uses the adaptive shift/scale/gate for both sub-layers - SA residual: x = x + gate_sa * attn_out — gate is learned per-feature, per-layer - FFN pre-norm: adaLN.norm(x) * (1 + scale_ff) + shift_ff — same shared norm instance VectorFieldTransformer: - Removed norm_task from __init__ (task no longer a sequence token) - forward computes cond = timestep_emb + task_token.squeeze(1) — both are (B, d) and summed - Prefix is now just [img_tokens, state_tokens] — shorter sequence, cheaper attention - cond passed to every layer so each layer independently adapts its normalization
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merge changes from upstream