capacitor_diffae/model.py

"""CapacitorDiffAE: standalone HuggingFace-compatible diffusion autoencoder."""

from __future__ import annotations

from pathlib import Path

import torch
from torch import Tensor, nn

from .config import CapacitorDiffAEConfig, CapacitorDiffAEInferenceConfig
from .decoder import Decoder
from .encoder import Encoder, EncoderPosterior
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
    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 CapacitorDiffAE(nn.Module):
    """Standalone Capacitor DiffAE model for HuggingFace distribution.

    A diffusion autoencoder built on FCDM (Fully Convolutional Diffusion Model)
    blocks. Encodes images to compact 128-channel spatial latents via a
    VP-parameterized diagonal Gaussian posterior, and decodes them back via
    iterative VP diffusion with a skip-concat decoder.

    Usage::

        model = CapacitorDiffAE.from_pretrained("path/to/weights")
        model = model.to("cuda", dtype=torch.bfloat16)

        # Encode (returns posterior mode by default)
        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: CapacitorDiffAEConfig) -> 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,
            bottleneck_posterior_kind=config.bottleneck_posterior_kind,
            bottleneck_norm_mode=config.bottleneck_norm_mode,
        )

        self.decoder = Decoder(
            in_channels=config.in_channels,
            patch_size=config.patch_size,
            model_dim=config.model_dim,
            depth=config.decoder_depth,
            start_block_count=config.decoder_start_blocks,
            end_block_count=config.decoder_end_blocks,
            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,
        )

    @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,
    ) -> CapacitorDiffAE:
        """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.
            dtype: Load weights in this dtype (float32 or bfloat16).
            device: Target device.
            revision: Git revision for Hub downloads.
            cache_dir: Where to cache Hub downloads.

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

        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 (posterior mode).

        Args:
            images: [B, 3, H, W] in [-1, 1], H and W 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))

    def encode_posterior(self, images: Tensor) -> EncoderPosterior:
        """Encode images and return the full posterior (mean + logsnr).

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

        Returns:
            EncoderPosterior with mean and logsnr tensors.
        """
        try:
            model_dtype = next(self.parameters()).dtype
        except StopIteration:
            model_dtype = torch.float32
        return self.encoder.encode_posterior(images.to(dtype=model_dtype))

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

        Args:
            latents: [B, bottleneck_dim, h, w] encoder latents.
            height: Output image height (divisible by patch_size).
            width: Output image width (divisible by patch_size).
            inference_config: Optional inference parameters.

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

        try:
            model_dtype = next(self.parameters()).dtype
        except StopIteration:
            model_dtype = torch.float32

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

        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,
        )

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

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

            pdg_mode = "path_drop" if cfg.pdg else "disabled"

            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_mode=pdg_mode,
                pdg_strength=cfg.pdg_strength,
                device=device,
            )

        return result

    @torch.no_grad()
    def reconstruct(
        self,
        images: Tensor,
        *,
        inference_config: CapacitorDiffAEInferenceConfig | 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
        )