ddpm cfg algo requirements fix

pdf/paper.tex

@@ -710,10 +710,10 @@ Formellement, l'algorithme d'entraînement par \gls{cfg} est décrit dans l'algo
   \begin{algorithmic}[1]
     \Require $T \in \mathbb{N}^\star$: number of diffusion steps
     \Require $\alpha_t \in \mathbb{R}^T$: variance schedule
+    \Require $p_\text{uncond} \in [0, 1]$: probability of unconditional training
     \Require $\boldsymbol{x}_0$: data distribution to be learned
-    \Require $\boldsymbol{c}$: embeddings distribution to be learned
+    \Require $\boldsymbol{c}$: embedding distribution to be learned
     \Require $\epsilon_\theta$: neural network
-    \Require $p_\text{uncond}$: probability of unconditional training
     \Repeat
     \State $(x_0, c) \sim (\boldsymbol{x_0}, \boldsymbol{c})$
     \State $c \leftarrow \varnothing$ with probability $p_\text{uncond}$