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merge=lfs -text +images/recon/r256_p896x1152_144138_mdiffae_v2_(1_step).png filter=lfs diff=lfs merge=lfs -text +images/recon/r256_p896x1152_144138_mdiffae_v2_(2_steps).png filter=lfs diff=lfs merge=lfs -text +images/recon/r256_p896x1152_144138_original.png filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..505f0f9ad7e9b9d809682e3ce6cbfd93327fa621 --- /dev/null +++ b/README.md @@ -0,0 +1,135 @@ +--- +license: apache-2.0 +tags: + - diffusion + - autoencoder + - image-reconstruction + - pytorch + - masked-autoencoder +library_name: mdiffae +--- + +# mdiffae-v2 + +**mDiffAE v2** — **M**asked **Diff**usion **A**uto**E**ncoder v2. +A fast, single-GPU-trainable diffusion autoencoder with a **96-channel** +spatial bottleneck and a skip-concat decoder with optional PDG sharpening. +**This is the recommended version** — it offers substantially better +reconstruction than [v1](https://huggingface.co/data-archetype/mdiffae-v1) +(+1.7 dB mean PSNR) while maintaining the same or better convergence for +downstream latent diffusion models. + +This variant (mdiffae-v2): 120.9M parameters, 461.2 MB. +Bottleneck: **96 channels** at patch size 16 +(compression ratio 8x). + +## Documentation + +- [Technical Report](technical_report_mdiffae_v2.md) — architecture, training changes from v1, and results +- [mDiffAE v1](https://huggingface.co/data-archetype/mdiffae-v1) — previous version +- [iRDiffAE Technical Report](https://huggingface.co/data-archetype/irdiffae-v1/blob/main/technical_report.md) — full background on VP diffusion, DiCo blocks, patchify encoder, AdaLN + +## Quick Start + +```python +import torch +from m_diffae_v2 import MDiffAEV2 + +# Load from HuggingFace Hub (or a local path) +model = MDiffAEV2.from_pretrained("data-archetype/mdiffae-v2", device="cuda") + +# Encode +images = ... # [B, 3, H, W] in [-1, 1], H and W divisible by 16 +latents = model.encode(images) + +# Decode (2 steps by default — PSNR-optimal) +recon = model.decode(latents, height=H, width=W) + +# Reconstruct (encode + 2-step decode) +recon = model.reconstruct(images) +``` + +> **Note:** Requires `pip install huggingface_hub safetensors` for Hub downloads. +> You can also pass a local directory path to `from_pretrained()`. + +## Architecture + +| Property | Value | +|---|---| +| Parameters | 120,893,792 | +| File size | 461.2 MB | +| Patch size | 16 | +| Model dim | 896 | +| Encoder depth | 4 | +| Decoder depth | 8 (2+4+2 skip-concat) | +| Bottleneck dim | 96 | +| Compression ratio | 8x | +| MLP ratio | 4.0 | +| Depthwise kernel | 7 | +| AdaLN rank | 128 | +| PDG | Conditioning degradation for CFG-style sharpening at inference | +| Training regularizer | Token masking (25-75% ratio, 90% apply prob) + Path drop (10% drop prob) | + +**Encoder**: Deterministic. Patchify (PixelUnshuffle + 1x1 conv) followed by +DiCo blocks with learned residual gates. No input RMSNorm. Post-bottleneck +RMSNorm (affine=False) normalizes the latent tokens. + +**Decoder**: VP diffusion conditioned on encoder latents and timestep via +shared-base + per-layer low-rank AdaLN-Zero. Skip-concat topology +(2 start + 4 middle + 2 end blocks) +with skip connections from start to end blocks. No outer RMSNorms +(input, latent conditioning, and output norms all removed). + +### Changes from v1 + +| Aspect | mDiffAE v1 | mDiffAE v2 | +|---|---|---| +| Bottleneck dim | 64 (12x compression) | **96** (8x compression) | +| Decoder topology | 4 flat sequential blocks | **8 blocks (2+4+2 skip-concat)** | +| Token mask apply prob | 50% | **90%** | +| Token mask ratio | Fixed 75% | **Uniform(25%, 75%)** | +| PDG training regularizer | Token masking (50%) | **Token masking (90%) + path drop (10%)** | +| Latent noise prob | 10% | **50%** | +| Encoder input norm | RMSNorm (affine) | **Removed** | +| Decoder input norm | RMSNorm (affine) | **Removed** | +| Decoder latent norm | RMSNorm (affine) | **Removed** | +| Decoder output norm | RMSNorm (affine) | **Removed** | + +## Recommended Settings + +| Mode | Steps | PDG | Strength | +|---|---|---|---| +| **Default** (best PSNR) | 2 | off | — | +| **Sharp** (perceptual) | 10 | on | 2.0 | + +```python +from m_diffae_v2 import MDiffAEV2InferenceConfig + +# Default — best PSNR, fast (2 steps, no PDG) +recon = model.decode(latents, height=H, width=W) + +# Sharp mode — perceptual sharpening (10 steps + PDG) +cfg = MDiffAEV2InferenceConfig(num_steps=10, pdg=True, pdg_strength=2.0) +recon = model.decode(latents, height=H, width=W, inference_config=cfg) +``` + +## Citation + +```bibtex +@misc{mdiffae_v2, + title = {mDiffAE v2: A Fast Masked Diffusion Autoencoder with Skip-Concat Decoder}, + author = {data-archetype}, + year = {2026}, + month = mar, + url = {https://huggingface.co/data-archetype/mdiffae-v2}, +} +``` + +## Dependencies + +- PyTorch >= 2.0 +- safetensors (for loading weights) + +## License + +Apache 2.0 diff --git a/config.json b/config.json new file mode 100644 index 0000000000000000000000000000000000000000..4b194640f7b1ac05d5574e4a23d74ac79661771d --- /dev/null +++ b/config.json @@ -0,0 +1,17 @@ +{ + "in_channels": 3, + "patch_size": 16, + "model_dim": 896, + "encoder_depth": 4, + "decoder_depth": 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sha256:4bfcd3d72d69e26f5e3bb6e2cd87ec8d0d15e751416a6aaef77b0b10ee0f27de +size 267313 diff --git a/images/recon/r256_p896x1152_144138_original.png b/images/recon/r256_p896x1152_144138_original.png new file mode 100644 index 0000000000000000000000000000000000000000..c08c48843f6055772b3853dc0e2e1ba1f8b7fcf9 --- /dev/null +++ b/images/recon/r256_p896x1152_144138_original.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:12d34552b11dcb277f8ddc1a9e4e4b0e912cda9235bfb4060d6e04eb0b0801d8 +size 166169 diff --git a/m_diffae_v2/__init__.py b/m_diffae_v2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..efe8222cc7be55570b3948f0b711af521929f30e --- /dev/null +++ b/m_diffae_v2/__init__.py @@ -0,0 +1,26 @@ +"""mDiffAE v2: Standalone masked diffusion autoencoder for HuggingFace distribution. + +mDiffAE v2 — a fast, single-GPU-trainable diffusion autoencoder with a +96-channel spatial bottleneck and a skip-concat decoder with dual PDG guidance. + +Usage:: + + from m_diffae_v2 import MDiffAEV2, MDiffAEV2InferenceConfig + + model = MDiffAEV2.from_pretrained("path/to/weights", device="cuda") + + # Encode + latents = model.encode(images) # images: [B,3,H,W] in [-1,1] + + # Decode — PSNR-optimal (1 step, default) + recon = model.decode(latents, height=H, width=W) + + # Decode — perceptual sharpness (10 steps + path-drop PDG) + cfg = MDiffAEV2InferenceConfig(num_steps=10, sampler="ddim", pdg_mode="path_drop") + recon = model.decode(latents, height=H, width=W, inference_config=cfg) +""" + +from .config import MDiffAEV2Config, MDiffAEV2InferenceConfig +from .model import MDiffAEV2 + +__all__ = ["MDiffAEV2", "MDiffAEV2Config", "MDiffAEV2InferenceConfig"] diff --git a/m_diffae_v2/adaln.py b/m_diffae_v2/adaln.py new file mode 100644 index 0000000000000000000000000000000000000000..c6f6bbde5033d14b370dd64d90c3b21cf5529eef --- /dev/null +++ b/m_diffae_v2/adaln.py @@ -0,0 +1,51 @@ +"""AdaLN-Zero modules for shared-base + low-rank-delta conditioning.""" + +from __future__ import annotations + +from torch import Tensor, nn + + +class AdaLNZeroProjector(nn.Module): + """Shared base AdaLN projection: SiLU -> Linear(d_cond -> 4*d_model). + + Returns packed modulation tensor [B, 4*d_model]. Zero-initialized. + """ + + def __init__(self, d_model: int, d_cond: int) -> None: + super().__init__() + self.d_model = int(d_model) + self.d_cond = int(d_cond) + self.act = nn.SiLU() + self.proj = nn.Linear(self.d_cond, 4 * self.d_model) + nn.init.zeros_(self.proj.weight) + nn.init.zeros_(self.proj.bias) + + def forward(self, cond: Tensor) -> Tensor: + """Return packed modulation [B, 4*d_model] from conditioning [B, d_cond].""" + act = self.act(cond) + return self.proj(act) + + def forward_activated(self, act_cond: Tensor) -> Tensor: + """Return packed modulation from pre-activated conditioning.""" + return self.proj(act_cond) + + +class AdaLNZeroLowRankDelta(nn.Module): + """Per-layer low-rank delta: down(d_cond -> rank) -> up(rank -> 4*d_model). + + Zero-initialized up-projection preserves AdaLN "zero output" at init. + """ + + def __init__(self, *, d_model: int, d_cond: int, rank: int) -> None: + super().__init__() + self.d_model = int(d_model) + self.d_cond = int(d_cond) + self.rank = int(rank) + self.down = nn.Linear(self.d_cond, self.rank, bias=False) + self.up = nn.Linear(self.rank, 4 * self.d_model, bias=False) + nn.init.normal_(self.down.weight, mean=0.0, std=0.02) + nn.init.zeros_(self.up.weight) + + def forward(self, act_cond: Tensor) -> Tensor: + """Return packed delta modulation [B, 4*d_model] from activated cond.""" + return self.up(self.down(act_cond)) diff --git a/m_diffae_v2/compact_channel_attention.py b/m_diffae_v2/compact_channel_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..fcca6981bcbd4f8e9a35bd30147f33b6b8ed53a3 --- /dev/null +++ b/m_diffae_v2/compact_channel_attention.py @@ -0,0 +1,21 @@ +"""Compact Channel Attention (CCA) module.""" + +from __future__ import annotations + +from torch import Tensor, nn + + +class CompactChannelAttention(nn.Module): + """Global average pool -> 1x1 Conv2d -> Sigmoid channel gate.""" + + def __init__(self, channels: int) -> None: + super().__init__() + c = int(channels) + self.pool = nn.AdaptiveAvgPool2d(1) + self.proj = nn.Conv2d(c, c, kernel_size=1, padding=0, stride=1, bias=True) + self.act = nn.Sigmoid() + + def forward(self, x: Tensor) -> Tensor: + w = self.pool(x) + w = self.proj(w) + return self.act(w) diff --git a/m_diffae_v2/config.py b/m_diffae_v2/config.py new file mode 100644 index 0000000000000000000000000000000000000000..64ce5d6276aa8373a882f6fff275f210f8cdeace --- /dev/null +++ b/m_diffae_v2/config.py @@ -0,0 +1,60 @@ +"""Frozen model architecture and user-tunable inference configuration.""" + +from __future__ import annotations + +import json +from dataclasses import asdict, dataclass +from pathlib import Path + + +@dataclass(frozen=True) +class MDiffAEV2Config: + """Frozen model architecture config. Stored alongside weights as config.json.""" + + in_channels: int = 3 + patch_size: int = 16 + model_dim: int = 896 + encoder_depth: int = 4 + decoder_depth: int = 8 + decoder_start_blocks: int = 2 + decoder_end_blocks: int = 2 + bottleneck_dim: int = 96 + mlp_ratio: float = 4.0 + depthwise_kernel_size: int = 7 + adaln_low_rank_rank: int = 128 + # VP diffusion schedule endpoints + logsnr_min: float = -10.0 + logsnr_max: float = 10.0 + # Pixel-space noise std for VP diffusion initialization + pixel_noise_std: float = 0.558 + # Token mask ratio for inference-time PDG (fraction of 2x2 groups masked) + pdg_mask_ratio: float = 0.75 + + def save(self, path: str | Path) -> None: + """Save config as JSON.""" + p = Path(path) + p.parent.mkdir(parents=True, exist_ok=True) + p.write_text(json.dumps(asdict(self), indent=2) + "\n") + + @classmethod + def load(cls, path: str | Path) -> MDiffAEV2Config: + """Load config from JSON.""" + data = json.loads(Path(path).read_text()) + return cls(**data) + + +@dataclass +class MDiffAEV2InferenceConfig: + """User-tunable inference parameters with sensible defaults. + + PDG (Path-Drop Guidance) sharpens reconstructions by degrading conditioning + in one pass and amplifying the difference. When enabled, uses 2 NFE per step. + Recommended: ``pdg=True, pdg_strength=2.0, num_steps=10``. + """ + + num_steps: int = 2 # number of denoising steps (NFE) + sampler: str = "ddim" # "ddim" or "dpmpp_2m" + schedule: str = "linear" # "linear" or "cosine" + pdg: bool = False # enable PDG for perceptual sharpening + pdg_strength: float = 2.0 # CFG-like strength when pdg=True + seed: int | None = None diff --git a/m_diffae_v2/conv_mlp.py b/m_diffae_v2/conv_mlp.py new file mode 100644 index 0000000000000000000000000000000000000000..2b304b221c68f2b98c5d3036620237a9f9f5607b --- /dev/null +++ b/m_diffae_v2/conv_mlp.py @@ -0,0 +1,26 @@ +"""Conv-based MLP with GELU activation for DiCo blocks.""" + +from __future__ import annotations + +import torch.nn.functional as F +from torch import Tensor, nn + +from .norms import ChannelWiseRMSNorm + + +class ConvMLP(nn.Module): + """1x1 Conv-based MLP: RMSNorm -> Conv1x1 -> GELU -> Conv1x1.""" + + def __init__( + self, channels: int, hidden_channels: int, norm_eps: float = 1e-6 + ) -> None: + super().__init__() + self.norm = ChannelWiseRMSNorm(channels, eps=norm_eps, affine=False) + self.conv_in = nn.Conv2d(channels, hidden_channels, kernel_size=1, bias=True) + self.conv_out = nn.Conv2d(hidden_channels, channels, kernel_size=1, bias=True) + + def forward(self, x: Tensor) -> Tensor: + y = self.norm(x) + y = self.conv_in(y) + y = F.gelu(y) + return self.conv_out(y) diff --git a/m_diffae_v2/decoder.py b/m_diffae_v2/decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..1ed71211cd11e827c3ba794aa47be370aaefb164 --- /dev/null +++ b/m_diffae_v2/decoder.py @@ -0,0 +1,243 @@ +"""mDiffAE v2 decoder: skip-concat topology with dual PDG (token masking + path drop). + +No outer RMSNorms (use_other_outer_rms_norms=False during training): +norm_in, latent_norm, and norm_out are all absent. +""" + +from __future__ import annotations + +import math + +import torch +from torch import Tensor, nn + +from .adaln import AdaLNZeroLowRankDelta, AdaLNZeroProjector +from .dico_block import DiCoBlock +from .straight_through_encoder import Patchify +from .time_embed import SinusoidalTimeEmbeddingMLP + + +class Decoder(nn.Module): + """VP diffusion decoder conditioned on encoder latents and timestep. + + Architecture (skip-concat, 2+4+2 default): + Patchify x_t -> Fuse with upsampled z + -> Start blocks (2) -> Middle blocks (4) -> Skip fuse -> End blocks (2) + -> Conv1x1 -> PixelShuffle + + Dual PDG at inference: + - Path drop: replace middle block output with ``path_drop_mask_feature``. + - Token mask: replace a fraction of upsampled latent tokens with + ``latent_mask_feature`` before fusion. + """ + + def __init__( + self, + in_channels: int, + patch_size: int, + model_dim: int, + depth: int, + start_block_count: int, + end_block_count: int, + bottleneck_dim: int, + mlp_ratio: float, + depthwise_kernel_size: int, + adaln_low_rank_rank: int, + pdg_mask_ratio: float = 0.75, + ) -> None: + super().__init__() + self.patch_size = int(patch_size) + self.model_dim = int(model_dim) + self.pdg_mask_ratio = float(pdg_mask_ratio) + + # Input processing (no norm_in — use_other_outer_rms_norms=False) + self.patchify = Patchify(in_channels, patch_size, model_dim) + + # Latent conditioning path (no latent_norm) + self.latent_up = nn.Conv2d(bottleneck_dim, model_dim, kernel_size=1, bias=True) + self.fuse_in = nn.Conv2d(2 * model_dim, model_dim, kernel_size=1, bias=True) + + # Time embedding + self.time_embed = SinusoidalTimeEmbeddingMLP(model_dim) + + # AdaLN: shared base projector + per-block low-rank deltas + self.adaln_base = AdaLNZeroProjector(d_model=model_dim, d_cond=model_dim) + self.adaln_deltas = nn.ModuleList( + [ + AdaLNZeroLowRankDelta( + d_model=model_dim, d_cond=model_dim, rank=adaln_low_rank_rank + ) + for _ in range(depth) + ] + ) + + # Block layout: start + middle + end + middle_count = depth - start_block_count - end_block_count + self._middle_start_idx = start_block_count + self._end_start_idx = start_block_count + middle_count + + def _make_blocks(count: int) -> nn.ModuleList: + return nn.ModuleList( + [ + DiCoBlock( + model_dim, + mlp_ratio, + depthwise_kernel_size=depthwise_kernel_size, + use_external_adaln=True, + ) + for _ in range(count) + ] + ) + + self.start_blocks = _make_blocks(start_block_count) + self.middle_blocks = _make_blocks(middle_count) + self.fuse_skip = nn.Conv2d(2 * model_dim, model_dim, kernel_size=1, bias=True) + self.end_blocks = _make_blocks(end_block_count) + + # Learned mask features for dual PDG + self.latent_mask_feature = nn.Parameter(torch.zeros((1, model_dim, 1, 1))) + self.path_drop_mask_feature = nn.Parameter(torch.zeros((1, model_dim, 1, 1))) + + # Output head (no norm_out — use_other_outer_rms_norms=False) + self.out_proj = nn.Conv2d( + model_dim, in_channels * (patch_size**2), kernel_size=1, bias=True + ) + self.unpatchify = nn.PixelShuffle(patch_size) + + def _adaln_m_for_layer(self, cond: Tensor, layer_idx: int) -> Tensor: + """Compute packed AdaLN modulation = shared_base + per-layer delta.""" + act = self.adaln_base.act(cond) + base_m = self.adaln_base.forward_activated(act) + delta_m = self.adaln_deltas[layer_idx](act) + return base_m + delta_m + + def _run_blocks( + self, blocks: nn.ModuleList, x: Tensor, cond: Tensor, start_index: int + ) -> Tensor: + """Run a group of decoder blocks with per-block AdaLN modulation.""" + for local_idx, block in enumerate(blocks): + adaln_m = self._adaln_m_for_layer(cond, layer_idx=start_index + local_idx) + x = block(x, adaln_m=adaln_m) + return x + + def _apply_latent_token_mask(self, z_up: Tensor) -> Tensor: + """Replace a fraction of upsampled latent tokens with latent_mask_feature. + + Uses 2x2 groupwise masking: divides the spatial grid into 2x2 groups + and masks floor(ratio * 4) tokens per group (lowest random scores). + + Args: + z_up: [B, C, H, W] upsampled latent conditioning. + + Returns: + Masked tensor with same shape. + """ + b, c, h, w = z_up.shape + # Pad to even dims if needed + h_pad = (2 - h % 2) % 2 + w_pad = (2 - w % 2) % 2 + if h_pad > 0 or w_pad > 0: + z_up = torch.nn.functional.pad(z_up, (0, w_pad, 0, h_pad)) + _, _, h, w = z_up.shape + + # Reshape into 2x2 groups: [B, C, H/2, 2, W/2, 2] -> [B, C, H/2, W/2, 4] + x = z_up.reshape(b, c, h // 2, 2, w // 2, 2) + x = x.permute(0, 1, 2, 4, 3, 5).reshape(b, c, h // 2, w // 2, 4) + + # Random scores for each token in each group + scores = torch.rand(b, 1, h // 2, w // 2, 4, device=z_up.device) + + # Mask the floor(ratio * 4) lowest-scoring tokens per group + num_mask = math.floor(self.pdg_mask_ratio * 4) + if num_mask > 0: + _, indices = scores.sort(dim=-1) + mask = torch.zeros_like(scores, dtype=torch.bool) + mask.scatter_(-1, indices[..., :num_mask], True) + else: + mask = torch.zeros_like(scores, dtype=torch.bool) + + # Apply mask: replace masked tokens with latent_mask_feature + mask_feat = self.latent_mask_feature.to(device=z_up.device, dtype=z_up.dtype) + mask_feat = mask_feat.squeeze(-1).squeeze(-1) # [1, C] + mask_feat = mask_feat.view(1, c, 1, 1, 1).expand_as(x) + mask_expanded = mask.expand_as(x) + x = torch.where(mask_expanded, mask_feat, x) + + # Reshape back to [B, C, H, W] + x = x.reshape(b, c, h // 2, w // 2, 2, 2) + x = x.permute(0, 1, 2, 4, 3, 5).reshape(b, c, h, w) + + # Remove padding + if h_pad > 0 or w_pad > 0: + x = x[:, :, : h - h_pad, : w - w_pad] + + return x + + def forward( + self, + x_t: Tensor, + t: Tensor, + latents: Tensor, + *, + drop_middle_blocks: bool = False, + mask_latent_tokens: bool = False, + ) -> Tensor: + """Single decoder forward pass. + + Args: + x_t: Noised image [B, C, H, W]. + t: Timestep [B] in [0, 1]. + latents: Encoder latents [B, bottleneck_dim, h, w]. + drop_middle_blocks: If True, replace middle block output with + path_drop_mask_feature (for path-drop PDG). + mask_latent_tokens: If True, mask a fraction of upsampled latent + tokens with latent_mask_feature (for token-mask PDG). + + Returns: + x0 prediction [B, C, H, W]. + """ + # Patchify x_t (no norm_in) + x_feat = self.patchify(x_t) + + # Upsample latents (no latent_norm) + z_up = self.latent_up(latents) + + # Token masking for PDG (replaces latent tokens with latent_mask_feature) + if mask_latent_tokens: + z_up = self._apply_latent_token_mask(z_up) + + # Fuse x_feat and z_up + fused = torch.cat([x_feat, z_up], dim=1) + fused = self.fuse_in(fused) + + # Time conditioning + cond = self.time_embed(t.to(torch.float32).to(device=x_t.device)) + + # Start blocks + start_out = self._run_blocks(self.start_blocks, fused, cond, start_index=0) + + # Middle blocks (or path_drop_mask_feature for PDG) + if drop_middle_blocks: + middle_out = self.path_drop_mask_feature.to( + device=x_t.device, dtype=x_t.dtype + ).expand_as(start_out) + else: + middle_out = self._run_blocks( + self.middle_blocks, + start_out, + cond, + start_index=self._middle_start_idx, + ) + + # Skip fusion + skip_fused = torch.cat([start_out, middle_out], dim=1) + skip_fused = self.fuse_skip(skip_fused) + + # End blocks + end_out = self._run_blocks( + self.end_blocks, skip_fused, cond, start_index=self._end_start_idx + ) + + # Output head (no norm_out) + patches = self.out_proj(end_out) + return self.unpatchify(patches) diff --git a/m_diffae_v2/dico_block.py b/m_diffae_v2/dico_block.py new file mode 100644 index 0000000000000000000000000000000000000000..6ce6ecfc77342670345a05773182d8e52d4e41aa --- /dev/null +++ b/m_diffae_v2/dico_block.py @@ -0,0 +1,111 @@ +"""DiCo block: conv path (1x1 -> depthwise -> SiLU -> CCA -> 1x1) + GELU MLP.""" + +from __future__ import annotations + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from .compact_channel_attention import CompactChannelAttention +from .conv_mlp import ConvMLP +from .norms import ChannelWiseRMSNorm + + +class DiCoBlock(nn.Module): + """DiCo-style conv block with optional external AdaLN conditioning. + + Two modes: + - Unconditioned (encoder): uses learned per-channel residual gates. + - External AdaLN (decoder): receives packed modulation [B, 4*C] via adaln_m. + """ + + def __init__( + self, + channels: int, + mlp_ratio: float, + *, + depthwise_kernel_size: int = 7, + use_external_adaln: bool = False, + norm_eps: float = 1e-6, + ) -> None: + super().__init__() + self.channels = int(channels) + self.use_external_adaln = bool(use_external_adaln) + + # Pre-norm for conv and MLP paths (no affine) + self.norm1 = ChannelWiseRMSNorm(self.channels, eps=norm_eps, affine=False) + self.norm2 = ChannelWiseRMSNorm(self.channels, eps=norm_eps, affine=False) + + # Conv path: 1x1 -> depthwise kxk -> SiLU -> CCA -> 1x1 + self.conv1 = nn.Conv2d(self.channels, self.channels, kernel_size=1, bias=True) + self.conv2 = nn.Conv2d( + self.channels, + self.channels, + kernel_size=depthwise_kernel_size, + padding=depthwise_kernel_size // 2, + groups=self.channels, + bias=True, + ) + self.conv3 = nn.Conv2d(self.channels, self.channels, kernel_size=1, bias=True) + self.cca = CompactChannelAttention(self.channels) + + # MLP path: GELU activation + hidden_channels = max(int(round(float(self.channels) * mlp_ratio)), 1) + self.mlp = ConvMLP(self.channels, hidden_channels, norm_eps=norm_eps) + + # Conditioning: learned gates (encoder) or external adaln_m (decoder) + if not self.use_external_adaln: + self.gate_attn = nn.Parameter(torch.zeros(self.channels)) + self.gate_mlp = nn.Parameter(torch.zeros(self.channels)) + + def forward(self, x: Tensor, *, adaln_m: Tensor | None = None) -> Tensor: + b, c = x.shape[:2] + + if self.use_external_adaln: + if adaln_m is None: + raise ValueError( + "adaln_m required for externally-conditioned DiCoBlock" + ) + adaln_m_cast = adaln_m.to(device=x.device, dtype=x.dtype) + scale_a, gate_a, scale_m, gate_m = adaln_m_cast.chunk(4, dim=-1) + elif adaln_m is not None: + raise ValueError("adaln_m must be None for unconditioned DiCoBlock") + + residual = x + + # Conv path + x_att = self.norm1(x) + if self.use_external_adaln: + x_att = x_att * (1.0 + scale_a.view(b, c, 1, 1)) # type: ignore[possibly-undefined] + y = self.conv1(x_att) + y = self.conv2(y) + y = F.silu(y) + y = y * self.cca(y) + y = self.conv3(y) + + if self.use_external_adaln: + gate_a_view = torch.tanh(gate_a).view(b, c, 1, 1) # type: ignore[possibly-undefined] + x = residual + gate_a_view * y + else: + gate = self.gate_attn.view(1, self.channels, 1, 1).to( + dtype=y.dtype, device=y.device + ) + x = residual + gate * y + + # MLP path + residual_mlp = x + x_mlp = self.norm2(x) + if self.use_external_adaln: + x_mlp = x_mlp * (1.0 + scale_m.view(b, c, 1, 1)) # type: ignore[possibly-undefined] + y_mlp = self.mlp(x_mlp) + + if self.use_external_adaln: + gate_m_view = torch.tanh(gate_m).view(b, c, 1, 1) # type: ignore[possibly-undefined] + x = residual_mlp + gate_m_view * y_mlp + else: + gate = self.gate_mlp.view(1, self.channels, 1, 1).to( + dtype=y_mlp.dtype, device=y_mlp.device + ) + x = residual_mlp + gate * y_mlp + + return x diff --git a/m_diffae_v2/encoder.py b/m_diffae_v2/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..201dbc99f64e3ab306880aadf0d014a83bc662f0 --- /dev/null +++ b/m_diffae_v2/encoder.py @@ -0,0 +1,57 @@ +"""mDiffAE v2 encoder: patchify -> DiCoBlocks -> bottleneck projection. + +No input RMSNorm (use_other_outer_rms_norms=False during training). +Post-bottleneck RMSNorm (affine=False) is applied. +""" + +from __future__ import annotations + +from torch import Tensor, nn + +from .dico_block import DiCoBlock +from .norms import ChannelWiseRMSNorm +from .straight_through_encoder import Patchify + + +class Encoder(nn.Module): + """Deterministic encoder: Image [B,3,H,W] -> latents [B,bottleneck_dim,h,w]. + + Pipeline: Patchify -> DiCoBlocks (unconditioned) -> Conv1x1 -> RMSNorm(no affine) + """ + + def __init__( + self, + in_channels: int, + patch_size: int, + model_dim: int, + depth: int, + bottleneck_dim: int, + mlp_ratio: float, + depthwise_kernel_size: int, + ) -> None: + super().__init__() + self.patchify = Patchify(in_channels, patch_size, model_dim) + self.blocks = nn.ModuleList( + [ + DiCoBlock( + model_dim, + mlp_ratio, + depthwise_kernel_size=depthwise_kernel_size, + use_external_adaln=False, + ) + for _ in range(depth) + ] + ) + self.to_bottleneck = nn.Conv2d( + model_dim, bottleneck_dim, kernel_size=1, bias=True + ) + self.norm_out = ChannelWiseRMSNorm(bottleneck_dim, eps=1e-6, affine=False) + + def forward(self, images: Tensor) -> Tensor: + """Encode images [B,3,H,W] in [-1,1] to latents [B,bottleneck_dim,h,w].""" + z = self.patchify(images) + for block in self.blocks: + z = block(z) + z = self.to_bottleneck(z) + z = self.norm_out(z) + return z diff --git a/m_diffae_v2/model.py b/m_diffae_v2/model.py new file mode 100644 index 0000000000000000000000000000000000000000..d04bff5106d87fa8e6a035e42590ff4e69e0fdd1 --- /dev/null +++ b/m_diffae_v2/model.py @@ -0,0 +1,300 @@ +"""MDiffAEV2: standalone HuggingFace-compatible mDiffAE v2 model.""" + +from __future__ import annotations + +from pathlib import Path + +import torch +from torch import Tensor, nn + +from .config import MDiffAEV2Config, MDiffAEV2InferenceConfig +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 MDiffAEV2(nn.Module): + """Standalone mDiffAE v2 model for HuggingFace distribution. + + A diffusion autoencoder that encodes images to compact 96-channel + spatial latents and decodes them back via iterative VP diffusion. + Uses a skip-concat decoder (2+4+2 blocks) with dual PDG guidance. + + Usage:: + + model = MDiffAEV2.from_pretrained("path/to/weights") + model = model.to("cuda", dtype=torch.bfloat16) + + # Encode + latents = model.encode(images) # images: [B,3,H,W] in [-1,1] + + # Decode (2 steps by default — PSNR-optimal) + recon = model.decode(latents, height=H, width=W) + + # Reconstruct (encode + 2-step decode) + recon = model.reconstruct(images) + """ + + def __init__(self, config: MDiffAEV2Config) -> 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, + 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, + 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, + ) -> MDiffAEV2: + """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-v2"``). + 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 = MDiffAEV2Config.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: MDiffAEV2InferenceConfig | 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 MDiffAEV2InferenceConfig() + 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, + *, + 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, + mask_latent_tokens=mask_latent_tokens, + ) + + # Map pdg bool → internal mode string + pdg_mode = "token_mask" if cfg.pdg else "disabled" + + # 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_mode=pdg_mode, + pdg_strength=cfg.pdg_strength, + device=device, + ) + + return result + + @torch.no_grad() + def reconstruct( + self, + images: Tensor, + *, + inference_config: MDiffAEV2InferenceConfig | 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 + ) diff --git a/m_diffae_v2/norms.py b/m_diffae_v2/norms.py new file mode 100644 index 0000000000000000000000000000000000000000..8186c5e856d59e028c5d3bc4c3210f03ad088976 --- /dev/null +++ b/m_diffae_v2/norms.py @@ -0,0 +1,39 @@ +"""Channel-wise RMSNorm for NCHW tensors.""" + +from __future__ import annotations + +import torch +from torch import Tensor, nn + + +class ChannelWiseRMSNorm(nn.Module): + """Channel-wise RMSNorm with float32 reduction for numerical stability. + + Normalizes across channels per spatial position. Supports optional + per-channel affine weight and bias. + """ + + def __init__(self, channels: int, eps: float = 1e-6, affine: bool = True) -> None: + super().__init__() + self.channels: int = int(channels) + self._eps: float = float(eps) + if affine: + self.weight = nn.Parameter(torch.ones(self.channels)) + self.bias = nn.Parameter(torch.zeros(self.channels)) + else: + self.register_parameter("weight", None) + self.register_parameter("bias", None) + + def forward(self, x: Tensor) -> Tensor: + if x.dim() < 2: + return x + # Float32 accumulation for stability + ms = torch.mean(torch.square(x), dim=1, keepdim=True, dtype=torch.float32) + inv_rms = torch.rsqrt(ms + self._eps) + y = x * inv_rms + if self.weight is not None: + shape = (1, -1) + (1,) * (x.dim() - 2) + y = y * self.weight.view(shape).to(dtype=y.dtype) + if self.bias is not None: + y = y + self.bias.view(shape).to(dtype=y.dtype) + return y.to(dtype=x.dtype) diff --git a/m_diffae_v2/samplers.py b/m_diffae_v2/samplers.py new file mode 100644 index 0000000000000000000000000000000000000000..b6b6eba8c9019a965e0cf92fba5b795ef434d43c --- /dev/null +++ b/m_diffae_v2/samplers.py @@ -0,0 +1,255 @@ +"""DDIM and DPM++2M samplers for VP diffusion with dual PDG support.""" + +from __future__ import annotations + +from typing import Protocol + +import torch +from torch import Tensor + +from .vp_diffusion import ( + alpha_sigma_from_logsnr, + broadcast_time_like, + shifted_cosine_interpolated_logsnr_from_t, +) + + +class DecoderForwardFn(Protocol): + """Callable that predicts x0 from (x_t, t, latents) with dual PDG flags.""" + + def __call__( + self, + x_t: Tensor, + t: Tensor, + latents: Tensor, + *, + drop_middle_blocks: bool = False, + mask_latent_tokens: bool = False, + ) -> Tensor: ... + + +def _reconstruct_eps_from_x0( + *, x_t: Tensor, x0_hat: Tensor, alpha: Tensor, sigma: Tensor +) -> Tensor: + """Reconstruct eps_hat from (x_t, x0_hat) under VP parameterization. + + eps_hat = (x_t - alpha * x0_hat) / sigma. All float32. + """ + alpha_view = broadcast_time_like(alpha, x_t).to(dtype=torch.float32) + sigma_view = broadcast_time_like(sigma, x_t).to(dtype=torch.float32) + x_t_f32 = x_t.to(torch.float32) + x0_f32 = x0_hat.to(torch.float32) + return (x_t_f32 - alpha_view * x0_f32) / sigma_view + + +def _ddim_step( + *, + x0_hat: Tensor, + eps_hat: Tensor, + alpha_next: Tensor, + sigma_next: Tensor, + ref: Tensor, +) -> Tensor: + """DDIM step: x_next = alpha_next * x0_hat + sigma_next * eps_hat.""" + a = broadcast_time_like(alpha_next, ref).to(dtype=torch.float32) + s = broadcast_time_like(sigma_next, ref).to(dtype=torch.float32) + return a * x0_hat + s * eps_hat + + +def _predict_with_pdg( + forward_fn: DecoderForwardFn, + state: Tensor, + t_vec: Tensor, + latents: Tensor, + *, + pdg_mode: str, + pdg_strength: float, +) -> Tensor: + """Run decoder forward with optional PDG guidance. + + Args: + forward_fn: Decoder forward function. + state: Current noised state [B, C, H, W]. + t_vec: Timestep vector [B]. + latents: Encoder latents. + pdg_mode: "disabled", "path_drop", or "token_mask". + pdg_strength: CFG-like strength for PDG. + + Returns: + x0_hat prediction in float32. + """ + if pdg_mode == "path_drop": + x0_uncond = forward_fn( + state, t_vec, latents, drop_middle_blocks=True + ).to(torch.float32) + x0_cond = forward_fn( + state, t_vec, latents, drop_middle_blocks=False + ).to(torch.float32) + return x0_uncond + pdg_strength * (x0_cond - x0_uncond) + elif pdg_mode == "token_mask": + x0_uncond = forward_fn( + state, t_vec, latents, mask_latent_tokens=True + ).to(torch.float32) + x0_cond = forward_fn( + state, t_vec, latents, mask_latent_tokens=False + ).to(torch.float32) + return x0_uncond + pdg_strength * (x0_cond - x0_uncond) + else: + return forward_fn( + state, t_vec, latents, drop_middle_blocks=False + ).to(torch.float32) + + +def run_ddim( + *, + forward_fn: DecoderForwardFn, + initial_state: Tensor, + schedule: Tensor, + latents: Tensor, + logsnr_min: float, + logsnr_max: float, + log_change_high: float = 0.0, + log_change_low: float = 0.0, + pdg_mode: str = "disabled", + pdg_strength: float = 1.5, + device: torch.device | None = None, +) -> Tensor: + """Run DDIM sampling loop with dual PDG support. + + Args: + forward_fn: Decoder forward function (x_t, t, latents) -> x0_hat. + initial_state: Starting noised state [B, C, H, W] in float32. + schedule: Descending t-schedule [num_steps] in [0, 1]. + latents: Encoder latents [B, bottleneck_dim, h, w]. + logsnr_min, logsnr_max: VP schedule endpoints. + log_change_high, log_change_low: Shifted-cosine schedule parameters. + pdg_mode: "disabled", "path_drop", or "token_mask". + pdg_strength: CFG-like strength for PDG. + device: Target device. + + Returns: + Denoised samples [B, C, H, W] in float32. + """ + run_device = device or initial_state.device + batch_size = int(initial_state.shape[0]) + state = initial_state.to(device=run_device, dtype=torch.float32) + + # Precompute logSNR, alpha, sigma for all schedule points + lmb = shifted_cosine_interpolated_logsnr_from_t( + schedule.to(device=run_device), + logsnr_min=logsnr_min, + logsnr_max=logsnr_max, + log_change_high=log_change_high, + log_change_low=log_change_low, + ) + alpha_sched, sigma_sched = alpha_sigma_from_logsnr(lmb) + + for i in range(int(schedule.numel()) - 1): + t_i = schedule[i] + a_t = alpha_sched[i].expand(batch_size) + s_t = sigma_sched[i].expand(batch_size) + a_next = alpha_sched[i + 1].expand(batch_size) + s_next = sigma_sched[i + 1].expand(batch_size) + + # Model prediction with optional PDG + t_vec = t_i.expand(batch_size).to(device=run_device, dtype=torch.float32) + x0_hat = _predict_with_pdg( + forward_fn, state, t_vec, latents, + pdg_mode=pdg_mode, pdg_strength=pdg_strength, + ) + + eps_hat = _reconstruct_eps_from_x0( + x_t=state, x0_hat=x0_hat, alpha=a_t, sigma=s_t + ) + state = _ddim_step( + x0_hat=x0_hat, + eps_hat=eps_hat, + alpha_next=a_next, + sigma_next=s_next, + ref=state, + ) + + return state + + +def run_dpmpp_2m( + *, + forward_fn: DecoderForwardFn, + initial_state: Tensor, + schedule: Tensor, + latents: Tensor, + logsnr_min: float, + logsnr_max: float, + log_change_high: float = 0.0, + log_change_low: float = 0.0, + pdg_mode: str = "disabled", + pdg_strength: float = 1.5, + device: torch.device | None = None, +) -> Tensor: + """Run DPM++2M sampling loop with dual PDG support. + + Multi-step solver using exponential integrator formulation in half-lambda space. + """ + run_device = device or initial_state.device + batch_size = int(initial_state.shape[0]) + state = initial_state.to(device=run_device, dtype=torch.float32) + + # Precompute logSNR, alpha, sigma, half-lambda for all schedule points + lmb = shifted_cosine_interpolated_logsnr_from_t( + schedule.to(device=run_device), + logsnr_min=logsnr_min, + logsnr_max=logsnr_max, + log_change_high=log_change_high, + log_change_low=log_change_low, + ) + alpha_sched, sigma_sched = alpha_sigma_from_logsnr(lmb) + half_lambda = 0.5 * lmb.to(torch.float32) + + x0_prev: Tensor | None = None + + for i in range(int(schedule.numel()) - 1): + t_i = schedule[i] + s_t = sigma_sched[i].expand(batch_size) + a_next = alpha_sched[i + 1].expand(batch_size) + s_next = sigma_sched[i + 1].expand(batch_size) + + # Model prediction with optional PDG + t_vec = t_i.expand(batch_size).to(device=run_device, dtype=torch.float32) + x0_hat = _predict_with_pdg( + forward_fn, state, t_vec, latents, + pdg_mode=pdg_mode, pdg_strength=pdg_strength, + ) + + lam_t = half_lambda[i].expand(batch_size) + lam_next = half_lambda[i + 1].expand(batch_size) + h = (lam_next - lam_t).to(torch.float32) + phi_1 = torch.expm1(-h) + + sigma_ratio = (s_next / s_t).to(torch.float32) + + if i == 0 or x0_prev is None: + # First-order step + state = ( + sigma_ratio.view(-1, *([1] * (state.dim() - 1))) * state + - broadcast_time_like(a_next, state).to(torch.float32) + * broadcast_time_like(phi_1, state).to(torch.float32) + * x0_hat + ) + else: + # Second-order step + lam_prev = half_lambda[i - 1].expand(batch_size) + h_0 = (lam_t - lam_prev).to(torch.float32) + r0 = h_0 / h + d1_0 = (x0_hat - x0_prev) / broadcast_time_like(r0, x0_hat) + common = broadcast_time_like(a_next, state).to( + torch.float32 + ) * broadcast_time_like(phi_1, state).to(torch.float32) + state = ( + sigma_ratio.view(-1, *([1] * (state.dim() - 1))) * state + - common * x0_hat + - 0.5 * common * d1_0 + ) + + x0_prev = x0_hat + + return state diff --git a/m_diffae_v2/straight_through_encoder.py b/m_diffae_v2/straight_through_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..4f504543eeb5e5bab92f3b3a7795219616f1a08b --- /dev/null +++ b/m_diffae_v2/straight_through_encoder.py @@ -0,0 +1,27 @@ +"""PixelUnshuffle-based patchifier (no residual conv path).""" + +from __future__ import annotations + +from torch import Tensor, nn + + +class Patchify(nn.Module): + """PixelUnshuffle(patch) -> Conv2d 1x1 projection. + + Converts [B, C, H, W] images into [B, out_channels, H/patch, W/patch] features. + """ + + def __init__(self, in_channels: int, patch: int, out_channels: int) -> None: + super().__init__() + self.patch = int(patch) + self.unshuffle = nn.PixelUnshuffle(self.patch) + in_after = in_channels * (self.patch * self.patch) + self.proj = nn.Conv2d(in_after, out_channels, kernel_size=1, bias=True) + + def forward(self, x: Tensor) -> Tensor: + if x.shape[2] % self.patch != 0 or x.shape[3] % self.patch != 0: + raise ValueError( + f"Input H={x.shape[2]} and W={x.shape[3]} must be divisible by patch={self.patch}" + ) + y = self.unshuffle(x) + return self.proj(y) diff --git a/m_diffae_v2/time_embed.py b/m_diffae_v2/time_embed.py new file mode 100644 index 0000000000000000000000000000000000000000..2ba8f2f1713cae701318747155027455e0fcbf60 --- /dev/null +++ b/m_diffae_v2/time_embed.py @@ -0,0 +1,83 @@ +"""Sinusoidal timestep embedding with MLP projection.""" + +from __future__ import annotations + +import math + +import torch +from torch import Tensor, nn + + +def _log_spaced_frequencies( + half: int, max_period: float, *, device: torch.device | None = None +) -> Tensor: + """Log-spaced frequencies for sinusoidal embedding.""" + return torch.exp( + -math.log(max_period) + * torch.arange(half, device=device, dtype=torch.float32) + / max(float(half - 1), 1.0) + ) + + +def sinusoidal_time_embedding( + t: Tensor, + dim: int, + *, + max_period: float = 10000.0, + scale: float | None = None, + freqs: Tensor | None = None, +) -> Tensor: + """Sinusoidal timestep embedding (DDPM/DiT-style). Always float32.""" + t32 = t.to(torch.float32) + if scale is not None: + t32 = t32 * float(scale) + half = dim // 2 + if freqs is not None: + freqs = freqs.to(device=t32.device, dtype=torch.float32) + else: + freqs = _log_spaced_frequencies(half, max_period, device=t32.device) + angles = t32[:, None] * freqs[None, :] + return torch.cat([torch.sin(angles), torch.cos(angles)], dim=-1) + + +class SinusoidalTimeEmbeddingMLP(nn.Module): + """Sinusoidal time embedding followed by Linear -> SiLU -> Linear.""" + + def __init__( + self, + dim: int, + *, + freq_dim: int = 256, + hidden_mult: float = 1.0, + time_scale: float = 1000.0, + max_period: float = 10000.0, + ) -> None: + super().__init__() + self.dim = int(dim) + self.freq_dim = int(freq_dim) + self.time_scale = float(time_scale) + self.max_period = float(max_period) + hidden_dim = max(int(round(int(dim) * float(hidden_mult))), 1) + + freqs = _log_spaced_frequencies(self.freq_dim // 2, self.max_period) + self.register_buffer("freqs", freqs, persistent=True) + + self.proj_in = nn.Linear(self.freq_dim, hidden_dim) + self.act = nn.SiLU() + self.proj_out = nn.Linear(hidden_dim, self.dim) + + def forward(self, t: Tensor) -> Tensor: + freqs: Tensor = self.freqs # type: ignore[assignment] + emb_freq = sinusoidal_time_embedding( + t.to(torch.float32), + self.freq_dim, + max_period=self.max_period, + scale=self.time_scale, + freqs=freqs, + ) + dtype_in = self.proj_in.weight.dtype + hidden = self.proj_in(emb_freq.to(dtype_in)) + hidden = self.act(hidden) + if hidden.dtype != self.proj_out.weight.dtype: + hidden = hidden.to(self.proj_out.weight.dtype) + return self.proj_out(hidden) diff --git a/m_diffae_v2/vp_diffusion.py b/m_diffae_v2/vp_diffusion.py new file mode 100644 index 0000000000000000000000000000000000000000..88cc54faf93ac83d676800fd90c694271c86370f --- /dev/null +++ b/m_diffae_v2/vp_diffusion.py @@ -0,0 +1,151 @@ +"""VP diffusion math: logSNR schedules, alpha/sigma computation, noise construction.""" + +from __future__ import annotations + +import math + +import torch +from torch import Tensor + + +def alpha_sigma_from_logsnr(lmb: Tensor) -> tuple[Tensor, Tensor]: + """Compute (alpha, sigma) from logSNR in float32. + + VP constraint: alpha^2 + sigma^2 = 1. + """ + lmb32 = lmb.to(dtype=torch.float32) + alpha = torch.sqrt(torch.sigmoid(lmb32)) + sigma = torch.sqrt(torch.sigmoid(-lmb32)) + return alpha, sigma + + +def broadcast_time_like(coeff: Tensor, x: Tensor) -> Tensor: + """Broadcast [B] coefficient to match x for per-sample scaling.""" + view_shape = (int(x.shape[0]),) + (1,) * (x.dim() - 1) + return coeff.view(view_shape) + + +def _cosine_interpolated_params( + logsnr_min: float, logsnr_max: float +) -> tuple[float, float]: + """Compute (a, b) for cosine-interpolated logSNR schedule. + + logsnr(t) = -2 * log(tan(a*t + b)) + logsnr(0) = logsnr_max, logsnr(1) = logsnr_min + """ + b = math.atan(math.exp(-0.5 * logsnr_max)) + a = math.atan(math.exp(-0.5 * logsnr_min)) - b + return a, b + + +def cosine_interpolated_logsnr_from_t( + t: Tensor, *, logsnr_min: float, logsnr_max: float +) -> Tensor: + """Map t in [0,1] to logSNR via cosine-interpolated schedule. Always float32.""" + a, b = _cosine_interpolated_params(logsnr_min, logsnr_max) + t32 = t.to(dtype=torch.float32) + a_t = torch.tensor(a, device=t32.device, dtype=torch.float32) + b_t = torch.tensor(b, device=t32.device, dtype=torch.float32) + u = a_t * t32 + b_t + return -2.0 * torch.log(torch.tan(u)) + + +def shifted_cosine_interpolated_logsnr_from_t( + t: Tensor, + *, + logsnr_min: float, + logsnr_max: float, + log_change_high: float = 0.0, + log_change_low: float = 0.0, +) -> Tensor: + """SiD2 "shifted cosine" schedule: logSNR with resolution-dependent shifts. + + lambda(t) = (1-t) * (base(t) + log_change_high) + t * (base(t) + log_change_low) + """ + base = cosine_interpolated_logsnr_from_t( + t, logsnr_min=logsnr_min, logsnr_max=logsnr_max + ) + t32 = t.to(dtype=torch.float32) + high = base + float(log_change_high) + low = base + float(log_change_low) + return (1.0 - t32) * high + t32 * low + + +def get_schedule(schedule_type: str, num_steps: int) -> Tensor: + """Generate a descending t-schedule in [0, 1] for VP diffusion sampling. + + ``num_steps`` is the number of function evaluations (NFE = decoder forward + passes). Internally the schedule has ``num_steps + 1`` time points + (including both endpoints). + + Args: + schedule_type: "linear" or "cosine". + num_steps: Number of decoder forward passes (NFE), >= 1. + + Returns: + Descending 1D tensor with ``num_steps + 1`` elements from ~1.0 to ~0.0. + """ + # NOTE: the upstream training code (src/ode/time_schedules.py) uses a + # different convention where num_steps counts schedule *points* (so NFE = + # num_steps - 1). This export package corrects the off-by-one so that + # num_steps means NFE directly. TODO: align the upstream convention. + n = max(int(num_steps) + 1, 2) + if schedule_type == "linear": + base = torch.linspace(0.0, 1.0, n) + elif schedule_type == "cosine": + i = torch.arange(n, dtype=torch.float32) + base = 0.5 * (1.0 - torch.cos(math.pi * (i / (n - 1)))) + else: + raise ValueError( + f"Unsupported schedule type: {schedule_type!r}. Use 'linear' or 'cosine'." + ) + # Descending: high t (noisy) -> low t (clean) + return torch.flip(base, dims=[0]) + + +def make_initial_state( + *, + noise: Tensor, + t_start: Tensor, + logsnr_min: float, + logsnr_max: float, + log_change_high: float = 0.0, + log_change_low: float = 0.0, +) -> Tensor: + """Construct VP initial state x_t0 = sigma_start * noise (since x0=0). + + All math in float32. + """ + batch = int(noise.shape[0]) + lmb_start = shifted_cosine_interpolated_logsnr_from_t( + t_start.expand(batch).to(dtype=torch.float32), + logsnr_min=logsnr_min, + logsnr_max=logsnr_max, + log_change_high=log_change_high, + log_change_low=log_change_low, + ) + _alpha_start, sigma_start = alpha_sigma_from_logsnr(lmb_start) + sigma_view = broadcast_time_like(sigma_start, noise) + return sigma_view * noise.to(dtype=torch.float32) + + +def sample_noise( + shape: tuple[int, ...], + *, + noise_std: float = 1.0, + seed: int | None = None, + device: torch.device | None = None, + dtype: torch.dtype = torch.float32, +) -> Tensor: + """Sample Gaussian noise with optional seeding. CPU-seeded for reproducibility.""" + if seed is None: + noise = torch.randn( + shape, device=device or torch.device("cpu"), dtype=torch.float32 + ) + else: + gen = torch.Generator(device="cpu") + gen.manual_seed(int(seed)) + noise = torch.randn(shape, generator=gen, device="cpu", dtype=torch.float32) + noise = noise.mul(float(noise_std)) + target_device = device if device is not None else torch.device("cpu") + return noise.to(device=target_device, dtype=dtype) diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..2f8a471064bdc18a599143bb34b2642c0e506408 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a635179cbd4afee2b2f2b75bf1a0eb31ec0dfb42d996c09fef14ece6d85954c4 +size 483595528 diff --git a/results.html b/results.html new file mode 100644 index 0000000000000000000000000000000000000000..8c10a53facc5d62ded2ccfc6fbe1478a59da019a --- /dev/null +++ b/results.html @@ -0,0 +1,662 @@ + + + + +iRDiffAE — Reconstruction Results + + + +

iRDiffAE — Reconstruction Results

+

Comparison of iRDiffAE model variants and Flux VAE references.

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+ Image width: + + + + + + +
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Top 3 PCA components of encoder latents mapped to RGB. Colors are aligned across models +via spatial correlation matching. Reruns (same encoder) are omitted.

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+ + + + diff --git a/technical_report_mdiffae_v2.md b/technical_report_mdiffae_v2.md new file mode 100644 index 0000000000000000000000000000000000000000..b87476ab248eec13545b0d7dc20b925731235da8 --- /dev/null +++ b/technical_report_mdiffae_v2.md @@ -0,0 +1,298 @@ +# mDiffAE v2 — Technical Report + +## 1. Overview + +**mDiffAE v2** (Masked Diffusion AutoEncoder v2) is a diffusion autoencoder that +compresses images into compact spatial latent representations and reconstructs +them via iterative VP diffusion decoding. It is the successor to +[mDiffAE v1](https://huggingface.co/data-archetype/mdiffae-v1), featuring a +larger decoder with skip connections, optional PDG sharpening, more aggressive +token masking, and removed outer normalization layers. + +Compared to v1, mDiffAE v2 offers substantially better image reconstruction +(+1.7 dB mean PSNR on a 2k-image benchmark) while maintaining the same or +better convergence speed when used as a latent space for downstream diffusion +models. + +**Key improvements over v1:** + +| Aspect | v1 | v2 | +|---|---|---| +| Bottleneck | 64ch (12x compression) | 96ch (8x compression) | +| Decoder | 4 flat sequential blocks | 8 blocks (2+4+2 skip-concat) | +| Token mask apply prob | 50% | 90% | +| Token mask ratio | Fixed 75% | Uniform(25%, 75%) | +| PDG training regularizer | Token masking (50%) | Token masking (90%) + path drop (10%) | +| Latent noise prob | 10% | 50% | +| Outer RMSNorms | 4 norms (encoder in, decoder in/latent/out) | All 4 removed (post-bottleneck latent norm retained) | + +## 2. Architecture + +### 2.1 Encoder + +The encoder is deterministic and maps pixel-space images to spatial latent tokens: + +``` +Image [B, 3, H, W] + → Patchify: PixelUnshuffle(16) + Conv1x1 → [B, 896, H/16, W/16] + → 4 × DiCoBlock (unconditioned, learned gates) + → Conv1x1 → [B, 96, H/16, W/16] + → RMSNorm (affine=False) + → Latents [B, 96, H/16, W/16] +``` + +**v2 change:** The input RMSNorm after patchify has been removed +(`use_other_outer_rms_norms=False`). The post-bottleneck RMSNorm (affine=False) +is retained to normalize the latent tokens. + +### 2.2 Decoder + +The decoder uses VP diffusion conditioned on encoder latents and timestep. It +follows a **START-MIDDLE-END skip-concat** topology: + +``` +x_t [B, 3, H, W] (noised image) + → Patchify: PixelUnshuffle(16) + Conv1x1 → [B, 896, h, w] (no norm) + +z [B, 96, h, w] (encoder latents) + → Conv1x1 → [B, 896, h, w] (no norm) + → (optional: token masking for PDG) + + → Concat[x_feat, z_up] → Conv1x1 (fuse) → [B, 896, h, w] + + → 2 × Start blocks (DiCoBlock with AdaLN-Zero) → start_out + → 4 × Middle blocks (DiCoBlock with AdaLN-Zero) → middle_out + (or path_drop_mask_feature if drop_middle_blocks=True) + → Concat[start_out, middle_out] → Conv1x1 (skip fuse) → [B, 896, h, w] + → 2 × End blocks (DiCoBlock with AdaLN-Zero) + + → Conv1x1 → [B, 3×16², h, w] (no norm) + → PixelShuffle(16) → [B, 3, H, W] +``` + +**v2 changes from v1:** +- **Skip-concat topology** (2 start + 4 middle + 2 end) replaces 4 flat blocks. + Start block outputs skip-connect (concatenate) with middle block outputs before + the end blocks, preserving low-level features. +- **All outer RMSNorms removed:** `decoder_norm_in`, `decoder_latent_norm`, and + `decoder_norm_out` are all absent. The only normalization is within DiCoBlocks + (pre-activation RMSNorm inside each block). +- **Two learned mask features** for training regularization: `latent_mask_feature` + (token masking) and `path_drop_mask_feature` (path drop). The token mask feature + is also used for PDG at inference (see Section 4). + +### 2.3 DiCoBlock + +Each DiCoBlock follows the same design as v1 and iRDiffAE: + +``` +Residual path: + x → RMSNorm → (AdaLN scale) → Conv1x1 → DepthwiseConv7x7 → SiLU → CCA → Conv1x1 + → (AdaLN gate: tanh) → + residual + + x → RMSNorm → (AdaLN scale) → ConvMLP(GELU) → (AdaLN gate: tanh) → + residual +``` + +**Encoder blocks** use learned per-channel residual gates (no AdaLN). +**Decoder blocks** use external AdaLN-Zero conditioning via packed modulation +`[scale_attn, gate_attn, scale_mlp, gate_mlp]`. + +### 2.4 AdaLN-Zero Conditioning + +The decoder uses **shared-base + per-layer low-rank delta** AdaLN: + +``` +Time t → SinusoidalEmbedding(256) → Linear(896) → SiLU → Linear(896) → cond [B, 896] + +For each layer i: + base_m = SharedProjector(SiLU(cond)) [B, 4×896] + delta_m = LowRankDelta_i(SiLU(cond)) [B, 4×896] (rank 128) + adaln_m = base_m + delta_m +``` + +One shared projector serves all 8 decoder blocks; per-layer deltas (rank-128) +are zero-initialized at training start, preserving the identity-init property. + +## 3. Training + +### 3.1 Token Masking + +During training, **90% of batches** apply token masking to the upsampled decoder +conditioning latents (z_up) before fusion with the patchified noisy input. + +The mask ratio is sampled uniformly from **[0.25, 0.75]** per sample (v1 used a +fixed 0.75 ratio at 50% apply probability). Masking uses **2×2 groupwise** +selection: the spatial grid is divided into 2×2 groups, and within each group, +`floor(ratio × 4)` tokens are replaced with a learned `latent_mask_feature`. + +This progressive masking forces the decoder to reconstruct from partial +conditioning, acting as an implicit regularizer that prevents decoder +over-reliance on any single latent token. + +### 3.2 Path Drop + +During training, **10% of samples** have their middle decoder blocks entirely +dropped — the middle block output is replaced with a learned +`path_drop_mask_feature` that broadcasts across all spatial positions. The skip +connection still fuses `start_out` with this constant mask, so the end blocks +must reconstruct from the start block features and the mask alone. + +This regularizes the middle blocks and enables **path-drop guidance** at +inference (see Section 4). + +### 3.3 Latent Noise + +With **50% probability** (up from 10% in v1), noise is injected into the +encoder latents during training. The noise timestep tau is sampled +**independently** from the decoder timestep t (independent uniform stratified +sampling), with a logSNR shift of 1.0. The higher noise probability +compensates for the wider bottleneck (96 vs 64 channels) — more latent +capacity benefits from stronger regularization to prevent the decoder from +memorizing individual token values. + +### 3.4 VP Diffusion + +The decoder predicts x0 from noised input x_t under the VP (variance-preserving) +diffusion framework: + +- **Noise schedule:** Cosine-interpolated logSNR with endpoints [-10, 10] +- **Parameterization:** x-prediction (direct x0 output) +- **Loss:** SiD2 x-pred sigmoid weighting +- **Pixel noise std:** 0.558 + +## 4. Inference: PDG + +mDiffAE v2 supports optional **PDG** — a CFG-style sharpening mechanism. +When enabled, each denoising step runs the decoder twice: once with degraded +conditioning (75% of latent tokens masked) and once with full conditioning. +The guidance formula amplifies the detail that full conditioning recovers: + +``` +x0_degraded = decoder(x_t, t, latents, mask_latent_tokens=True) +x0_full = decoder(x_t, t, latents, mask_latent_tokens=False) +x0_guided = x0_degraded + strength × (x0_full - x0_degraded) +``` + +### 4.1 Recommended Settings + +| Mode | Steps | PDG | Strength | +|---|---|---|---| +| **Default** (best PSNR) | 2 | off | — | +| **Sharp** (perceptual) | 10 | on | 2.0 | + +## 5. Removed Components + +Compared to the internal training model, the standalone export removes: + +- **Encoder input mask feature** — training-only component +- **Running latent statistics** — mean/var/count buffers for downstream models +- **Outer RMSNorms** — encoder_norm_in, decoder_norm_in, decoder_latent_norm, + decoder_norm_out (exist in checkpoint but unused during forward pass) +- **REPA projector** — not trained in v2 +- **Residual 3×3 conv** — in patchify; only the 1×1 projection is used +- **Latent FM regularizer** — training-only auxiliary model + +## 6. Compression Ratio + +| Metric | Value | +|---|---| +| Input | 3 channels × 16² = 768 values per patch | +| Latent | 96 channels per patch | +| Compression | 768 / 96 = **8×** | + +Compared to v1's 64-channel bottleneck (12× compression), v2 uses more latent +capacity for higher reconstruction fidelity. + +## 7. Results + +Reconstruction quality evaluated on a curated set of test images covering photographs, book covers, and documents. Flux.1 and Flux.2 VAEs are included as references. + +### 7.1 Interactive Viewer + +**[Open full-resolution comparison viewer](https://huggingface.co/spaces/data-archetype/mdiffae-v2-results)** — side-by-side reconstructions, RGB deltas, and latent PCA with adjustable image size. + +### 7.2 Inference Settings + +| Setting | Value | +|---------|-------| +| Sampler | ddim | +| Steps | 2 | +| Schedule | linear | +| Seed | 42 | +| PDG | off | +| Batch size (timing) | 4 | + +> All models run in bfloat16. Timings measured on an NVIDIA RTX Pro 6000 (Blackwell). + +### 7.3 Reconstruction Quality (2k images) + +Evaluated on 2000 images (2/3 photographs + 1/3 book covers) from the validation pool. + +| Model | Mean PSNR (dB) | Std (dB) | Median (dB) | +|-------|---------------|----------|-------------| +| mDiffAE v2 (2 steps) | 35.81 | 4.62 | 35.86 | +| Flux.1 VAE | 34.62 | 4.32 | 35.16 | +| Flux.2 VAE | 36.30 | 4.58 | 36.14 | + +| Percentile | mDiffAE v2 (2 steps) | Flux.1 VAE | Flux.2 VAE | +|------------|-----------|------------|------------| +| p5 | 28.14 | 27.05 | 28.99 | +| p10 | 29.59 | 28.45 | 30.38 | +| p25 | 32.31 | 31.58 | 32.87 | +| p50 | 35.86 | 35.16 | 36.14 | +| p75 | 39.60 | 38.00 | 39.85 | +| p90 | 41.89 | 39.77 | 42.51 | +| p95 | 42.81 | 40.56 | 43.65 | + +### 7.4 Speed and Per-Image Detail (39 images) + +| Metric | mdiffae_v2 (2 steps) | Flux.1 VAE | Flux.2 VAE | +|--------|--------|--------|--------| +| Avg PSNR (dB) | 33.61 | 32.76 | 34.16 | +| Avg encode (ms/image) | 2.4 | 64.7 | 46.5 | +| Avg decode (ms/image) | 10.9 | 138.9 | 93.2 | + +### 7.5 Per-Image PSNR (dB) + +| Image | mdiffae_v2 (2 steps) | Flux.1 VAE | Flux.2 VAE | +|-------|--------|--------|--------| +| p640x1536:94623 | 33.38 | 31.29 | 33.50 | +| p640x1536:94624 | 29.12 | 27.62 | 30.03 | +| p640x1536:94625 | 32.88 | 31.65 | 33.98 | +| p640x1536:94626 | 31.12 | 29.44 | 31.53 | +| p640x1536:94627 | 31.01 | 28.70 | 30.53 | +| p640x1536:94628 | 27.51 | 26.38 | 28.88 | +| p960x1024:216264 | 44.75 | 40.87 | 45.39 | +| p960x1024:216265 | 27.80 | 25.82 | 27.80 | +| p960x1024:216266 | 40.57 | 47.77 | 46.20 | +| p960x1024:216267 | 39.31 | 37.65 | 39.23 | +| p960x1024:216268 | 37.44 | 35.27 | 36.13 | +| p960x1024:216269 | 30.88 | 28.45 | 30.24 | +| p960x1024:216270 | 33.91 | 31.92 | 34.18 | +| p960x1024:216271 | 42.16 | 38.92 | 42.18 | +| p704x1472:94699 | 41.88 | 40.43 | 41.79 | +| p704x1472:94700 | 30.73 | 29.52 | 32.08 | +| p704x1472:94701 | 36.59 | 35.44 | 37.90 | +| p704x1472:94702 | 32.75 | 30.74 | 32.50 | +| p704x1472:94703 | 30.68 | 29.07 | 31.35 | +| p704x1472:94704 | 30.94 | 29.22 | 31.84 | +| p704x1472:94705 | 38.02 | 36.38 | 37.44 | +| p704x1472:94706 | 33.10 | 31.50 | 33.66 | +| r256_p1344x704:15577 | 29.24 | 28.32 | 29.98 | +| r256_p1344x704:15578 | 30.45 | 29.35 | 30.79 | +| r256_p1344x704:15579 | 31.57 | 30.44 | 31.83 | +| r256_p1344x704:15580 | 35.65 | 36.12 | 36.03 | +| r256_p1344x704:15581 | 35.43 | 37.42 | 36.94 | +| r256_p1344x704:15582 | 31.25 | 30.64 | 32.10 | +| r256_p1344x704:15583 | 34.70 | 34.67 | 34.54 | +| r256_p1344x704:15584 | 31.11 | 30.34 | 31.76 | +| r256_p896x1152:144131 | 32.79 | 33.10 | 33.60 | +| r256_p896x1152:144132 | 35.08 | 34.23 | 35.32 | +| r256_p896x1152:144133 | 37.83 | 37.85 | 37.33 | +| r256_p896x1152:144134 | 34.04 | 34.25 | 34.47 | +| r256_p896x1152:144135 | 29.24 | 28.17 | 29.87 | +| r256_p896x1152:144136 | 35.28 | 35.24 | 35.68 | +| r256_p896x1152:144137 | 31.59 | 32.70 | 32.86 | +| r256_p896x1152:144138 | 25.62 | 24.15 | 25.63 | +| VAE_accuracy_test_image | 33.32 | 36.69 | 35.25 | +