dima/ddpmx.py

# how to use 
# ddpm = DDPM(Z_train, T=100, n_iter=20_000, key=random.PRNGKey(0))
# 
# Fast unconditional samples (try 15–30 first)
# z = ddpm.sample_dpmpp(N=4096, num_steps=20)
#
# Fast “refine latents” starting from an intermediate noise level
#z_ref = ddpm.refine_latents_dpmpp(z0, t_start=30, num_steps=20, add_noise=True)



# src/dima/ddpmx.py
from __future__ import annotations

from typing import Any, Optional, Dict
import os
import json

import numpy as np
import jax
import jax.numpy as jnp
from jax import random

from flax import linen as nn
from flax.training import train_state
from flax import struct, serialization as flax_ser
import optax


# ---------------------------------------------------------------------
# Helpers (as in ddpmx.py)
# ---------------------------------------------------------------------
def _sigma_to_alpha_sigma_t(sigma: jnp.ndarray) -> tuple[jnp.ndarray, jnp.ndarray]:
    """
    EDM-style sigma parameterization:
      alpha_t = 1 / sqrt(1 + sigma^2)
      sigma_t = sigma * alpha_t
    so that x = alpha_t * x0 + sigma_t * eps
    """
    alpha_t = 1.0 / jnp.sqrt(1.0 + sigma**2)
    sigma_t = sigma * alpha_t
    return alpha_t, sigma_t


def _make_lu_sigma_schedule(sigma_start: float, sigma_end: float, num_steps: int) -> np.ndarray:
    """
    "Lu" schedule uniform in lambda = -log(sigma).
    """
    sigma_start = float(max(sigma_start, 1e-12))
    sigma_end = float(max(sigma_end, 1e-12))
    lam_start = -np.log(sigma_start)
    lam_end = -np.log(sigma_end)
    lambdas = np.linspace(lam_start, lam_end, int(num_steps), dtype=np.float32)
    sigmas = np.exp(-lambdas).astype(np.float32)
    return sigmas


def cosine_schedule(T: int, s: float = 0.008):
    """
    Nichol & Dhariwal cosine schedule.
    Returns alpha, beta, alpha_bar with shape (T,).
    """
    steps = jnp.arange(T + 1, dtype=jnp.float32)
    f = jnp.cos(((steps / T + s) / (1.0 + s)) * jnp.pi / 2.0) ** 2
    alpha_bar_all = f / f[0]
    alpha_bar = alpha_bar_all[1:]  # (T,)
    alpha = alpha_bar / jnp.concatenate([jnp.array([1.0], dtype=jnp.float32), alpha_bar[:-1]])
    beta = 1.0 - alpha
    return alpha, beta, alpha_bar


def sinusoidal_embedding(t_idx: jnp.ndarray, dim: int) -> jnp.ndarray:
    """
    t_idx: (B,1) int32 or float32
    returns: (B,dim)
    """
    if t_idx.ndim != 2 or t_idx.shape[1] != 1:
        raise ValueError("t_idx must have shape (B,1)")
    t = t_idx.astype(jnp.float32)
    half = dim // 2
    denom = float(max(half - 1, 1))
    freqs = jnp.exp(-jnp.log(10_000.0) * jnp.arange(half, dtype=jnp.float32) / denom)
    args = t * freqs
    emb = jnp.concatenate([jnp.sin(args), jnp.cos(args)], axis=-1)
    if dim % 2 == 1:
        emb = jnp.pad(emb, ((0, 0), (0, 1)))
    return emb


class EpsMLP(nn.Module):
    """Simple MLP epsilon-predictor for DDPM in R^D."""
    hidden: int
    t_dim: int
    data_dim: int

    @nn.compact
    def __call__(self, x: jnp.ndarray, t_idx: jnp.ndarray) -> jnp.ndarray:
        t_emb = sinusoidal_embedding(t_idx, self.t_dim)
        t_h = nn.Dense(self.hidden)(t_emb)
        t_h = nn.gelu(t_h)

        h = nn.Dense(self.hidden)(x)
        h = nn.gelu(h + t_h)

        t_h2 = nn.Dense(self.hidden)(t_h)
        h = nn.Dense(self.hidden)(h)
        h = nn.gelu(h + t_h2)

        out = nn.Dense(self.data_dim)(h)
        return out


@struct.dataclass
class TrainStateEMA(train_state.TrainState):
    """Flax TrainState extended with EMA params."""
    ema_params: Any = struct.field(pytree_node=True)

    def apply_gradients(self, *, grads, ema_decay: float):
        updates, new_opt_state = self.tx.update(grads, self.opt_state, self.params)
        new_params = optax.apply_updates(self.params, updates)
        new_ema = optax.incremental_update(new_params, self.ema_params, step_size=1.0 - ema_decay)
        return self.replace(
            step=self.step + 1,
            params=new_params,
            opt_state=new_opt_state,
            ema_params=new_ema,
        )


# ---------------------------------------------------------------------
# DDPMX with HF upload/download integrated
# ---------------------------------------------------------------------
class DDPM:
    """
    DDPM (+ fast DPM-Solver++(2M) sampler utilities) for D-dimensional latents.

    Added persistence utilities:
      - save_local / load_local
      - upload_to_huggingface / download_from_huggingface

    Serialization is done via flax.serialization.to_state_dict / from_state_dict
    to avoid msgpack failures with non-serializable Python objects (e.g., tuples).
    """

    def __init__(
        self,
        Z_iX: jnp.ndarray,
        *,
        T: int = 100,
        hidden_dim: int = 128,
        t_embed_dim: int = 64,
        learning_rate: float = 1e-3,
        n_iter: int = 20_000,
        ema_decay: float = 0.999,
        beta_max: float = 0.02,
        batch_size: Optional[int] = None,
        key: jax.Array = random.PRNGKey(0),
        verbose_every: int = 0,
        eps: float = 1e-5,
    ):
        Z_iX = jnp.asarray(Z_iX, dtype=jnp.float32)
        if Z_iX.ndim != 2:
            raise ValueError("Z_iX must be 2D (N,D).")

        self.D = int(Z_iX.shape[1])
        self.T = int(T)

        # store config for checkpointing
        self.hidden_dim = int(hidden_dim)
        self.t_embed_dim = int(t_embed_dim)
        self.learning_rate = float(learning_rate)
        self.ema_decay = float(ema_decay)
        self.beta_max = float(beta_max)
        self.batch_size = batch_size
        self.verbose_every = int(verbose_every)
        self.eps = float(eps)

        self.key = key

        # VP schedule (cosine + clip)
        alpha, beta, alpha_bar = cosine_schedule(self.T)
        beta = jnp.minimum(beta, self.beta_max)
        alpha = 1.0 - beta
        alpha_bar = jnp.cumprod(alpha)

        self.alpha_s = alpha.astype(jnp.float32)
        self.beta_s = beta.astype(jnp.float32)
        self.alpha_bar_s = alpha_bar.astype(jnp.float32)

        # Precompute EDM-style "sigma_in" for DPM++ schedule interpolation
        # sigma_in = sqrt((1 - a_bar) / a_bar)
        self.sigma_in_train = jnp.sqrt(
            jnp.clip(
                (1.0 - self.alpha_bar_s) / jnp.clip(self.alpha_bar_s, self.eps, 1.0),
                self.eps,
                1e12,
            )
        ).astype(jnp.float32)

        # model + optimizer + EMA state
        self.model = EpsMLP(hidden=self.hidden_dim, t_dim=self.t_embed_dim, data_dim=self.D)
        params = self.model.init(
            self.key,
            jnp.zeros((1, self.D), dtype=jnp.float32),
            jnp.zeros((1, 1), dtype=jnp.int32),
        )["params"]

        tx = optax.adam(self.learning_rate)
        self.state = TrainStateEMA.create(
            apply_fn=self.model.apply,
            params=params,
            tx=tx,
            ema_params=params,
        )

        if int(n_iter) > 0:
            self._train(Z_iX, int(n_iter))

    # -------------------------
    # Training
    # -------------------------
    @staticmethod
    def _loss(params, apply_fn, x_t, t_idx, eps_true):
        eps_pred = apply_fn({"params": params}, x_t, t_idx)
        return jnp.mean((eps_pred - eps_true) ** 2)

    @staticmethod
    @jax.jit
    def _train_step(
        state: TrainStateEMA,
        x0_batch: jnp.ndarray,
        key: jax.Array,
        alpha_bar_s: jnp.ndarray,
        ema_decay: float,
        eps: float,
    ):
        B = x0_batch.shape[0]
        key, k_eps, k_t = random.split(key, 3)
        eps_noise = random.normal(k_eps, shape=x0_batch.shape)
        t_idx = random.randint(k_t, shape=(B, 1), minval=0, maxval=alpha_bar_s.shape[0])

        a_bar_t = jnp.take(alpha_bar_s, t_idx.squeeze(-1))[:, None]
        a_bar_t = jnp.clip(a_bar_t, eps, 1.0)
        x_t = jnp.sqrt(a_bar_t) * x0_batch + jnp.sqrt(1.0 - a_bar_t) * eps_noise

        def loss_fn(p):
            return DDPM._loss(p, state.apply_fn, x_t, t_idx, eps_noise)

        loss, grads = jax.value_and_grad(loss_fn)(state.params)
        new_state = state.apply_gradients(grads=grads, ema_decay=ema_decay)
        return new_state, loss, key

    def _train(self, Z_iX: jnp.ndarray, n_iter: int):
        N = int(Z_iX.shape[0])
        bs = N if (self.batch_size is None) else min(int(self.batch_size), N)

        for it in range(n_iter):
            if bs >= N:
                batch = Z_iX
            else:
                self.key, k_perm = random.split(self.key)
                idx = random.permutation(k_perm, N)[:bs]
                batch = Z_iX[idx]

            self.state, loss, self.key = self._train_step(
                self.state,
                batch,
                self.key,
                self.alpha_bar_s,
                self.ema_decay,
                self.eps,
            )

            if self.verbose_every and (it % self.verbose_every == 0 or it == n_iter - 1):
                print(f"iter {it:6d} loss {float(loss):.6f}", end="\r")

        if self.verbose_every:
            print("\ntraining complete.")

    # -------------------------
    # Standard DDPM refine/sample
    # -------------------------
    @staticmethod
    def _posterior_variance(alpha_s, beta_s, alpha_bar_s, t):
        a_bar_t = alpha_bar_s[t]
        a_bar_prev = jnp.where(t > 0, alpha_bar_s[t - 1], jnp.array(1.0, dtype=alpha_bar_s.dtype))
        return ((1.0 - a_bar_prev) / (1.0 - a_bar_t)) * beta_s[t]

    @staticmethod
    def _make_sampler_step(params_ema, apply_fn, alpha_s, beta_s, alpha_bar_s, eps: float):
        @jax.jit
        def step(carry, _):
            key, t, x = carry
            key, k = random.split(key)

            alpha_t = jnp.clip(alpha_s[t], eps, 1.0)
            a_bar_t = jnp.clip(alpha_bar_s[t], eps, 1.0)

            sqrt_alpha = jnp.sqrt(alpha_t)
            sqrt_one_minus = jnp.sqrt(jnp.clip(1.0 - a_bar_t, eps, 1.0))

            B = x.shape[0]
            t_batch = jnp.full((B, 1), t, dtype=jnp.int32)

            eps_pred = apply_fn({"params": params_ema}, x, t_batch)
            x0_hat = (x - sqrt_one_minus * eps_pred) / jnp.sqrt(a_bar_t)

            a_bar_prev = jnp.where(t > 0, alpha_bar_s[t - 1], jnp.array(1.0, dtype=alpha_bar_s.dtype))
            denom = jnp.clip(1.0 - a_bar_t, eps, 1.0)

            coef1 = jnp.sqrt(jnp.clip(a_bar_prev, eps, 1.0)) * beta_s[t] / denom
            coef2 = sqrt_alpha * (1.0 - a_bar_prev) / denom
            mean = coef1 * x0_hat + coef2 * x

            beta_tilde = DDPM._posterior_variance(alpha_s, beta_s, alpha_bar_s, t)
            sigma = jnp.sqrt(jnp.clip(beta_tilde, 0.0, 1.0))

            z = random.normal(k, x.shape)
            z = jnp.where(t == 0, 0.0, z)
            x_prev = mean + sigma * z

            return (key, t - 1, x_prev), x_prev

        return step

    def refine_latents(
        self,
        z0: jnp.ndarray,
        t_start: int = 10,
        key: Optional[jax.Array] = None,
        add_noise: bool = True,
    ) -> jnp.ndarray:
        z0 = jnp.asarray(z0, dtype=jnp.float32)
        if z0.ndim != 2 or z0.shape[1] != self.D:
            raise ValueError(f"z0 must have shape (B,{self.D}).")
        if not (0 <= int(t_start) < self.T):
            raise ValueError(f"t_start must be in [0, {self.T-1}]")
        t_start = int(t_start)

        if key is None:
            self.key, key = random.split(self.key)
        else:
            self.key, _ = random.split(key)

        key, k_eps = random.split(key)
        eps_noise = random.normal(k_eps, z0.shape)
        a_bar_t = jnp.clip(self.alpha_bar_s[t_start], self.eps, 1.0)

        if add_noise:
            z_t = jnp.sqrt(a_bar_t) * z0 + jnp.sqrt(1.0 - a_bar_t) * eps_noise
        else:
            z_t = z0

        step = self._make_sampler_step(
            self.state.ema_params,
            self.state.apply_fn,
            self.alpha_s,
            self.beta_s,
            self.alpha_bar_s,
            self.eps,
        )

        (final_key, _, _), trace = jax.lax.scan(
            step,
            (key, t_start, z_t),
            xs=None,
            length=t_start + 1,
        )
        self.key = final_key
        return trace[-1]

    def __call__(
        self,
        z0: jnp.ndarray,
        t_start: int = 10,
        key: Optional[jax.Array] = None,
        add_noise: bool = True,
    ) -> jnp.ndarray:
        return self.refine_latents(z0, t_start=t_start, key=key, add_noise=add_noise)

    def reverse_from_T(self, x_T: jnp.ndarray) -> jnp.ndarray:
        x_T = jnp.asarray(x_T, dtype=jnp.float32)
        if x_T.ndim != 2 or x_T.shape[1] != self.D:
            raise ValueError(f"x_T must have shape (B,{self.D}).")

        step = self._make_sampler_step(
            self.state.ema_params,
            self.state.apply_fn,
            self.alpha_s,
            self.beta_s,
            self.alpha_bar_s,
            self.eps,
        )

        self.key, k0 = random.split(self.key)
        (_, _, _), trace = jax.lax.scan(
            step,
            (k0, self.T - 1, x_T),
            xs=None,
            length=self.T,
        )
        return trace[-1]

    def sample(self, N: int = 10_000) -> jnp.ndarray:
        self.key, k = random.split(self.key)
        noise = random.normal(k, (int(N), self.D)).astype(jnp.float32)
        return self.reverse_from_T(noise)

    # -------------------------
    # DPM-Solver++(2M) schedule + sampler
    # -------------------------
    def _make_dpmpp_schedule(self, *, num_steps: int, t_start: int) -> tuple[jnp.ndarray, jnp.ndarray]:
        """
        Returns:
          sigmas_in: (K+1,) float32 decreasing, last one is 0
          t_cont:    (K,)   float32 continuous "time" indices for model calls
        """
        t_start = int(t_start)
        if not (0 <= t_start < self.T):
            raise ValueError(f"t_start must be in [0, {self.T-1}]")
        if int(num_steps) < 1:
            raise ValueError("num_steps must be >= 1")

        sigma_start = float(self.sigma_in_train[t_start])
        sigma_end = float(self.sigma_in_train[0])

        sigmas_k = _make_lu_sigma_schedule(sigma_start, sigma_end, int(num_steps))
        sigmas = np.concatenate([sigmas_k, np.array([0.0], np.float32)], axis=0)

        sigma_train = np.array(self.sigma_in_train).astype(np.float32)  # (T,)
        log_sig_train = np.log(np.maximum(sigma_train, 1e-12))
        t_train = np.arange(self.T, dtype=np.float32)

        log_sig = np.log(np.maximum(sigmas[:-1], 1e-12))
        t_cont = np.interp(log_sig, log_sig_train, t_train).astype(np.float32)

        return jnp.array(sigmas, dtype=jnp.float32), jnp.array(t_cont, dtype=jnp.float32)

    @staticmethod
    @jax.jit
    def _dpmpp_2m_midpoint_sample(
        params_ema: Any,
        apply_fn: Any,
        x_start: jnp.ndarray,   # (B,D)
        sigmas_in: jnp.ndarray, # (K+1,)
        t_cont: jnp.ndarray,    # (K,)
        eps: float,
    ) -> jnp.ndarray:
        """
        DPM-Solver++ (2M, midpoint) sampler.
        """
        sigma_s = sigmas_in[:-1]   # (K,)
        sigma_t = sigmas_in[1:]    # (K,)

        alpha_s, sigma_s_t = _sigma_to_alpha_sigma_t(sigma_s)
        alpha_t, sigma_t_t = _sigma_to_alpha_sigma_t(sigma_t)

        lambda_s = jnp.log(alpha_s) - jnp.log(sigma_s_t)
        lambda_t = jnp.log(alpha_t) - jnp.log(sigma_t_t)

        K = t_cont.shape[0]
        is_first = jnp.arange(K) == 0
        is_last = jnp.arange(K) == (K - 1)

        def step(carry, inp):
            x, m_prev, lam_prev = carry
            (a_s, s_s, a_t, s_t, lam_s_i, lam_t_i, t_i, first_i, last_i) = inp

            B = x.shape[0]
            t_batch = jnp.full((B, 1), t_i, dtype=jnp.float32)

            eps_pred = apply_fn({"params": params_ema}, x, t_batch)

            a_s_b = jnp.clip(a_s, eps, 1.0)
            x0 = (x - s_s * eps_pred) / a_s_b

            h = lam_t_i - lam_s_i
            exp_neg_h = jnp.exp(-h)

            # 1st-order
            x_first = (s_t / s_s) * x - (a_t * (exp_neg_h - 1.0)) * x0

            def do_second(_):
                h0 = lam_s_i - lam_prev
                r0 = h0 / jnp.clip(h, 1e-12)
                D1 = (x0 - m_prev) / jnp.clip(r0, 1e-12)
                x_second = (s_t / s_s) * x - (a_t * (exp_neg_h - 1.0)) * (x0 + 0.5 * D1)
                return x_second

            x_next = jax.lax.cond(first_i | last_i, lambda _: x_first, do_second, operand=None)
            return (x_next, x0, lam_s_i), x_next

        xs = (
            alpha_s, sigma_s_t,
            alpha_t, sigma_t_t,
            lambda_s, lambda_t,
            t_cont, is_first, is_last
        )

        x0_init = jnp.zeros_like(x_start)
        lam_init = jnp.array(0.0, dtype=jnp.float32)

        (x_final, _, _), _ = jax.lax.scan(step, (x_start, x0_init, lam_init), xs)
        return x_final

    def refine_latents_dpmpp(
        self,
        z0: jnp.ndarray,
        *,
        t_start: int = 10,
        num_steps: int = 20,
        key: Optional[jax.Array] = None,
        add_noise: bool = True,
    ) -> jnp.ndarray:
        z0 = jnp.asarray(z0, dtype=jnp.float32)
        if z0.ndim != 2 or z0.shape[1] != self.D:
            raise ValueError(f"z0 must have shape (B,{self.D}).")
        if not (0 <= int(t_start) < self.T):
            raise ValueError(f"t_start must be in [0, {self.T-1}]")

        if key is None:
            self.key, key = random.split(self.key)
        else:
            self.key, _ = random.split(key)

        # forward-noise to t_start
        key, k_eps = random.split(key)
        eps_noise = random.normal(k_eps, z0.shape)
        a_bar = jnp.clip(self.alpha_bar_s[int(t_start)], self.eps, 1.0)

        if add_noise:
            x_start = jnp.sqrt(a_bar) * z0 + jnp.sqrt(1.0 - a_bar) * eps_noise
        else:
            x_start = z0

        sigmas_in, t_cont = self._make_dpmpp_schedule(num_steps=int(num_steps), t_start=int(t_start))
        x_final = self._dpmpp_2m_midpoint_sample(
            self.state.ema_params,
            self.state.apply_fn,
            x_start,
            sigmas_in,
            t_cont,
            self.eps,
        )
        return x_final

    def reverse_from_T_dpmpp(self, x_T: jnp.ndarray, *, num_steps: int = 20) -> jnp.ndarray:
        x_T = jnp.asarray(x_T, dtype=jnp.float32)
        if x_T.ndim != 2 or x_T.shape[1] != self.D:
            raise ValueError(f"x_T must have shape (B,{self.D}).")

        sigmas_in, t_cont = self._make_dpmpp_schedule(num_steps=int(num_steps), t_start=self.T - 1)
        return self._dpmpp_2m_midpoint_sample(
            self.state.ema_params,
            self.state.apply_fn,
            x_T,
            sigmas_in,
            t_cont,
            self.eps,
        )

    def sample_dpmpp(self, N: int = 10_000, *, num_steps: int = 20) -> jnp.ndarray:
        self.key, k = random.split(self.key)
        x_T = random.normal(k, (int(N), self.D)).astype(jnp.float32)
        return self.reverse_from_T_dpmpp(x_T, num_steps=int(num_steps))

    # -----------------------------------------------------------------
    # Persistence: state_dict / save_local / load_local
    # -----------------------------------------------------------------
    def _config_dict(self) -> Dict[str, Any]:
        return {
            "class_name": "DDPMX",
            "T": int(self.T),
            "D": int(self.D),
            "hidden_dim": int(self.hidden_dim),
            "t_embed_dim": int(self.t_embed_dim),
            "learning_rate": float(self.learning_rate),
            "ema_decay": float(self.ema_decay),
            "beta_max": float(self.beta_max),
            "batch_size": None if self.batch_size is None else int(self.batch_size),
            "eps": float(self.eps),
            "key": np.array(self.key).tolist(),
        }

    def save_local(self, weights_file: str = "ddpmx_weights.msgpack", config_file: str = "ddpmx_config.json") -> None:
        """
        Saves:
          - config_file: JSON with hyperparams + PRNG key
          - weights_file: msgpack with flax state_dict of TrainStateEMA
        """
        cfg = self._config_dict()
        with open(config_file, "w", encoding="utf-8") as f:
            json.dump(cfg, f, indent=2, ensure_ascii=False)

        # Robust serialization (avoid msgpack tuple errors)
        state_sd = flax_ser.to_state_dict(self.state)
        blob = flax_ser.msgpack_serialize(state_sd)
        with open(weights_file, "wb") as f:
            f.write(blob)

    @classmethod
    def load_local(
        cls,
        weights_file: str,
        config_file: str,
        *,
        Z_iX: Optional[jnp.ndarray] = None,
    ) -> "DDPM":
        """
        Reconstructs a DDPMX instance from local files.

        Z_iX is only used to provide shape (N,D) for initialization; training is skipped.
        If Z_iX is None, a dummy array of shape (1,D) is created.
        """
        with open(config_file, "r", encoding="utf-8") as f:
            cfg = json.load(f)

        D = int(cfg["D"])
        if Z_iX is None:
            Z_iX = jnp.zeros((1, D), dtype=jnp.float32)

        # Build a fresh instance with the same architecture, skip training
        obj = cls(
            Z_iX,
            T=int(cfg["T"]),
            hidden_dim=int(cfg["hidden_dim"]),
            t_embed_dim=int(cfg["t_embed_dim"]),
            learning_rate=float(cfg["learning_rate"]),
            n_iter=0,
            ema_decay=float(cfg["ema_decay"]),
            beta_max=float(cfg["beta_max"]),
            batch_size=cfg["batch_size"],
            key=random.PRNGKey(0),
            verbose_every=0,
            eps=float(cfg["eps"]),
        )

        with open(weights_file, "rb") as f:
            state_sd = flax_ser.msgpack_restore(f.read())

        obj.state = flax_ser.from_state_dict(obj.state, state_sd)

        key_list = cfg.get("key", None)
        if key_list is not None:
            obj.key = jnp.array(key_list, dtype=jnp.uint32)

        return obj

    # -----------------------------------------------------------------
    # Hugging Face Hub: upload / download
    # -----------------------------------------------------------------
    def upload_to_huggingface(
        self,
        repo_id: str,
        *,
        token: Optional[str] = None,
        weights_file: str = "ddpmx_weights.msgpack",
        config_file: str = "ddpmx_config.json",
        repo_type: str = "model",
        revision: Optional[str] = None,
    ) -> Dict[str, str]:
        """
        Saves locally and uploads (weights_file, config_file) to Hugging Face Hub.
        """
        try:
            from huggingface_hub import create_repo, upload_file
        except Exception as e:
            raise RuntimeError(
                "huggingface_hub not installed. Install it (e.g., `pip install huggingface_hub`)."
            ) from e

        self.save_local(weights_file=weights_file, config_file=config_file)

        create_repo(repo_id, token=token, repo_type=repo_type, exist_ok=True)

        w_name = os.path.basename(weights_file)
        c_name = os.path.basename(config_file)

        upload_file(
            path_or_fileobj=weights_file,
            path_in_repo=w_name,
            repo_id=repo_id,
            repo_type=repo_type,
            token=token,
            revision=revision,
        )
        upload_file(
            path_or_fileobj=config_file,
            path_in_repo=c_name,
            repo_id=repo_id,
            repo_type=repo_type,
            token=token,
            revision=revision,
        )

        return {"repo_id": repo_id, "weights": w_name, "config": c_name}

    @classmethod
    def download_from_huggingface(
        cls,
        repo_id: str,
        *,
        token: Optional[str] = None,
        weights_file: str = "ddpmx_weights.msgpack",
        config_file: str = "ddpmx_config.json",
        repo_type: str = "model",
        revision: Optional[str] = None,
        cache_dir: Optional[str] = None,
        Z_iX: Optional[jnp.ndarray] = None,
    ) -> "DDPM":
        """
        Downloads (weights_file, config_file) from Hugging Face Hub and reconstructs the class.
        """
        try:
            from huggingface_hub import hf_hub_download
        except Exception as e:
            raise RuntimeError(
                "huggingface_hub not installed. Install it (e.g., `pip install huggingface_hub`)."
            ) from e

        w_name = os.path.basename(weights_file)
        c_name = os.path.basename(config_file)

        w_path = hf_hub_download(
            repo_id=repo_id,
            filename=w_name,
            repo_type=repo_type,
            token=token,
            revision=revision,
            cache_dir=cache_dir,
        )
        c_path = hf_hub_download(
            repo_id=repo_id,
            filename=c_name,
            repo_type=repo_type,
            token=token,
            revision=revision,
            cache_dir=cache_dir,
        )

        return cls.load_local(w_path, c_path, Z_iX=Z_iX)



__all__ = ["DDPM", "EpsMLP", "cosine_schedule", "sinusoidal_embedding"]