m_diffae/model.py

"""MDiffAE: standalone HuggingFace-compatible mDiffAE model."""

from __future__ import annotations

from pathlib import Path

import torch
from torch import Tensor, nn

from .config import MDiffAEConfig, MDiffAEInferenceConfig
from .decoder import Decoder
from .encoder import Encoder
from .samplers import run_ddim, run_dpmpp_2m
from .vp_diffusion import get_schedule, make_initial_state, sample_noise


def _resolve_model_dir(
    path_or_repo_id: str | Path,
    *,
    revision: str | None,
    cache_dir: str | Path | None,
) -> Path:
    """Resolve a local path or HuggingFace Hub repo ID to a local directory."""

    local = Path(path_or_repo_id)
    if local.is_dir():
        return local
    # Not a local directory — try HuggingFace Hub
    repo_id = str(path_or_repo_id)
    try:
        from huggingface_hub import snapshot_download
    except ImportError:
        raise ImportError(
            f"'{repo_id}' is not an existing local directory. "
            "To download from HuggingFace Hub, install huggingface_hub: "
            "pip install huggingface_hub"
        )
    cache_dir_str = str(cache_dir) if cache_dir is not None else None
    local_dir = snapshot_download(
        repo_id,
        revision=revision,
        cache_dir=cache_dir_str,
    )
    return Path(local_dir)


class MDiffAE(nn.Module):
    """Standalone mDiffAE model for HuggingFace distribution.

    A masked diffusion autoencoder that encodes images to compact latents and
    decodes them back via iterative VP diffusion. Uses a flat 4-block decoder
    with token-level masking for PDG instead of the skip-concat + block-drop
    approach of iRDiffAE.

    Usage::

        model = MDiffAE.from_pretrained("data-archetype/mdiffae-v1")
        model = model.to("cuda", dtype=torch.bfloat16)

        # Encode
        latents = model.encode(images)  # images: [B,3,H,W] in [-1,1]

        # Decode (1 step by default — PSNR-optimal)
        recon = model.decode(latents, height=H, width=W)

        # Reconstruct (encode + 1-step decode)
        recon = model.reconstruct(images)
    """

    def __init__(self, config: MDiffAEConfig) -> None:
        super().__init__()
        self.config = config

        self.encoder = Encoder(
            in_channels=config.in_channels,
            patch_size=config.patch_size,
            model_dim=config.model_dim,
            depth=config.encoder_depth,
            bottleneck_dim=config.bottleneck_dim,
            mlp_ratio=config.mlp_ratio,
            depthwise_kernel_size=config.depthwise_kernel_size,
        )

        self.decoder = Decoder(
            in_channels=config.in_channels,
            patch_size=config.patch_size,
            model_dim=config.model_dim,
            depth=config.decoder_depth,
            bottleneck_dim=config.bottleneck_dim,
            mlp_ratio=config.mlp_ratio,
            depthwise_kernel_size=config.depthwise_kernel_size,
            adaln_low_rank_rank=config.adaln_low_rank_rank,
            pdg_mask_ratio=config.pdg_mask_ratio,
        )

    @classmethod
    def from_pretrained(
        cls,
        path_or_repo_id: str | Path,
        *,
        dtype: torch.dtype = torch.bfloat16,
        device: str | torch.device = "cpu",
        revision: str | None = None,
        cache_dir: str | Path | None = None,
    ) -> MDiffAE:
        """Load a pretrained model from a local directory or HuggingFace Hub.

        The directory (or repo) should contain:
        - config.json: Model architecture config.
        - model.safetensors (preferred) or model.pt: Model weights.

        Args:
            path_or_repo_id: Local directory path or HuggingFace Hub repo ID
                (e.g. ``"data-archetype/mdiffae-v1"``).
            dtype: Load weights in this dtype (float32 or bfloat16).
            device: Target device.
            revision: Git revision (branch, tag, or commit) for Hub downloads.
            cache_dir: Where to cache Hub downloads. Uses HF default if None.

        Returns:
            Loaded model in eval mode.
        """
        model_dir = _resolve_model_dir(
            path_or_repo_id, revision=revision, cache_dir=cache_dir
        )
        config = MDiffAEConfig.load(model_dir / "config.json")
        model = cls(config)

        # Try safetensors first, fall back to .pt
        safetensors_path = model_dir / "model.safetensors"
        pt_path = model_dir / "model.pt"

        if safetensors_path.exists():
            try:
                from safetensors.torch import load_file

                state_dict = load_file(str(safetensors_path), device=str(device))
            except ImportError:
                raise ImportError(
                    "safetensors package required to load .safetensors files. "
                    "Install with: pip install safetensors"
                )
        elif pt_path.exists():
            state_dict = torch.load(
                str(pt_path), map_location=device, weights_only=True
            )
        else:
            raise FileNotFoundError(
                f"No model weights found in {model_dir}. "
                "Expected model.safetensors or model.pt."
            )

        model.load_state_dict(state_dict)
        model = model.to(dtype=dtype, device=torch.device(device))
        model.eval()
        return model

    def encode(self, images: Tensor) -> Tensor:
        """Encode images to latents.

        Args:
            images: [B, 3, H, W] in [-1, 1], H and W must be divisible by patch_size.

        Returns:
            Latents [B, bottleneck_dim, H/patch, W/patch].
        """
        try:
            model_dtype = next(self.parameters()).dtype
        except StopIteration:
            model_dtype = torch.float32
        return self.encoder(images.to(dtype=model_dtype))

    @torch.no_grad()
    def decode(
        self,
        latents: Tensor,
        height: int,
        width: int,
        *,
        inference_config: MDiffAEInferenceConfig | None = None,
    ) -> Tensor:
        """Decode latents to images via VP diffusion.

        Args:
            latents: [B, bottleneck_dim, h, w] encoder latents.
            height: Output image height (must be divisible by patch_size).
            width: Output image width (must be divisible by patch_size).
            inference_config: Optional inference parameters. Uses defaults if None.

        Returns:
            Reconstructed images [B, 3, H, W] in float32.
        """
        cfg = inference_config or MDiffAEInferenceConfig()
        config = self.config
        batch = int(latents.shape[0])
        device = latents.device

        # Determine model dtype from parameters
        try:
            model_dtype = next(self.parameters()).dtype
        except StopIteration:
            model_dtype = torch.float32

        # Validate dimensions
        if height % config.patch_size != 0 or width % config.patch_size != 0:
            raise ValueError(
                f"height={height} and width={width} must be divisible by patch_size={config.patch_size}"
            )

        # Generate initial noise
        shape = (batch, config.in_channels, height, width)
        noise = sample_noise(
            shape,
            noise_std=config.pixel_noise_std,
            seed=cfg.seed,
            device=torch.device("cpu"),
            dtype=torch.float32,
        )

        # Build schedule
        schedule = get_schedule(cfg.schedule, cfg.num_steps).to(device=device)

        # Construct initial state: sigma_start * noise
        initial_state = make_initial_state(
            noise=noise.to(device=device),
            t_start=schedule[0:1],
            logsnr_min=config.logsnr_min,
            logsnr_max=config.logsnr_max,
        )

        # Disable autocast for numerical precision
        device_type = "cuda" if device.type == "cuda" else "cpu"
        with torch.autocast(device_type=device_type, enabled=False):
            latents_in = latents.to(device=device)

            def _forward_fn(
                x_t: Tensor,
                t: Tensor,
                latents: Tensor,
                *,
                mask_tokens: bool = False,
            ) -> Tensor:
                return self.decoder(
                    x_t.to(dtype=model_dtype),
                    t,
                    latents.to(dtype=model_dtype),
                    mask_tokens=mask_tokens,
                )

            # Select sampler
            if cfg.sampler == "ddim":
                sampler_fn = run_ddim
            elif cfg.sampler == "dpmpp_2m":
                sampler_fn = run_dpmpp_2m
            else:
                raise ValueError(
                    f"Unsupported sampler: {cfg.sampler!r}. Use 'ddim' or 'dpmpp_2m'."
                )

            result = sampler_fn(
                forward_fn=_forward_fn,
                initial_state=initial_state,
                schedule=schedule,
                latents=latents_in,
                logsnr_min=config.logsnr_min,
                logsnr_max=config.logsnr_max,
                pdg_enabled=cfg.pdg_enabled,
                pdg_strength=cfg.pdg_strength,
                device=device,
            )

        return result

    @torch.no_grad()
    def reconstruct(
        self,
        images: Tensor,
        *,
        inference_config: MDiffAEInferenceConfig | None = None,
    ) -> Tensor:
        """Encode then decode. Convenience wrapper.

        Args:
            images: [B, 3, H, W] in [-1, 1].
            inference_config: Optional inference parameters.

        Returns:
            Reconstructed images [B, 3, H, W] in float32.
        """
        latents = self.encode(images)
        _, _, h, w = images.shape
        return self.decode(
            latents, height=h, width=w, inference_config=inference_config
        )