Implement Mamba MeanFlow x-prediction training

as_mamba.py

@@ -1,11 +1,14 @@
 from __future__ import annotations
 
 import math
-from dataclasses import asdict, dataclass
 import os
+from dataclasses import asdict, dataclass
 from pathlib import Path
 from typing import Iterator, Optional
 
+import lpips
+
+os.environ.setdefault("MPLCONFIGDIR", "/tmp/mamba_diffusion_mplconfig")
 import matplotlib
 
 matplotlib.use("Agg")
@@ -16,12 +19,17 @@ import torch.nn.functional as F
 from datasets import load_dataset
 from matplotlib import pyplot as plt
 from torch import Tensor, nn
+from torch.func import jvp
 from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
 
 from mamba2_minimal import InferenceCache, Mamba2, Mamba2Config, RMSNorm
 
 
+FIXED_VAL_SAMPLING_STEPS = 5
+FIXED_VAL_TIME_GRID = (1.0, 0.8, 0.6, 0.4, 0.2, 0.0)
+
+
 @dataclass
 class TrainConfig:
     seed: int = 42
@@ -29,13 +37,11 @@ class TrainConfig:
     epochs: int = 50
     steps_per_epoch: int = 200
     batch_size: int = 128
-    seq_len: int = 20
+    seq_len: int = 5
     lr: float = 2e-4
     weight_decay: float = 1e-2
-    dt_min: float = 1e-3
-    dt_max: float = 0.06
-    dt_alpha: float = 8.0
     lambda_flow: float = 1.0
+    lambda_perceptual: float = 0.4
     num_classes: int = 10
     image_size: int = 28
     channels: int = 1
@@ -52,10 +58,12 @@ class TrainConfig:
     use_residual: bool = False
     output_dir: str = "outputs"
     project: str = "as-mamba-mnist"
-    run_name: str = "mnist-flow"
+    run_name: str = "mnist-meanflow"
     val_every: int = 200
     val_samples_per_class: int = 8
     val_grid_rows: int = 4
+    val_sampling_steps: int = FIXED_VAL_SAMPLING_STEPS
+    time_grid_size: int = 256
     use_ddp: bool = False
 
 
@@ -127,6 +135,11 @@ def sinusoidal_embedding(t: Tensor, dim: int) -> Tensor:
     return emb.to(t.dtype)
 
 
+def safe_time_divisor(t: Tensor) -> Tensor:
+    eps = torch.finfo(t.dtype).eps
+    return torch.where(t > 0, t, torch.full_like(t, eps))
+
+
 class ASMamba(nn.Module):
     def __init__(self, cfg: TrainConfig) -> None:
         super().__init__()
@@ -150,32 +163,42 @@ class ASMamba(nn.Module):
         )
         self.backbone = Mamba2Backbone(args, use_residual=cfg.use_residual)
         self.cond_emb = nn.Embedding(cfg.num_classes, cfg.d_model)
-        self.delta_head = nn.Linear(cfg.d_model, input_dim)
+        self.clean_head = nn.Linear(cfg.d_model, input_dim)
 
     def forward(
         self,
-        x: Tensor,
-        dt: Tensor,
+        z_t: Tensor,
+        r: Tensor,
+        t: Tensor,
         cond: Tensor,
         h: Optional[list[InferenceCache]] = None,
     ) -> tuple[Tensor, list[InferenceCache]]:
-        if dt.dim() == 1:
-            dt = dt.unsqueeze(1)
-        elif dt.dim() == 3 and dt.shape[-1] == 1:
-            dt = dt.squeeze(-1)
-        dt = dt.to(dtype=x.dtype)
-        dt_emb = sinusoidal_embedding(dt, x.shape[-1])
-        x = x + dt_emb
+        if r.dim() == 1:
+            r = r.unsqueeze(1)
+        elif r.dim() == 3 and r.shape[-1] == 1:
+            r = r.squeeze(-1)
+        if t.dim() == 1:
+            t = t.unsqueeze(1)
+        elif t.dim() == 3 and t.shape[-1] == 1:
+            t = t.squeeze(-1)
+
+        r = r.to(dtype=z_t.dtype)
+        t = t.to(dtype=z_t.dtype)
+        z_t = z_t + sinusoidal_embedding(r, z_t.shape[-1]) + sinusoidal_embedding(
+            t, z_t.shape[-1]
+        )
         cond_vec = self.cond_emb(cond)
-        feats, h = self.backbone(x, cond_vec, h)
-        delta = self.delta_head(feats)
-        return delta, h
+        feats, h = self.backbone(z_t, cond_vec, h)
+        x_pred = self.clean_head(feats)
+        return x_pred, h
 
     def step(
-        self, x: Tensor, dt: Tensor, cond: Tensor, h: list[InferenceCache]
+        self, z_t: Tensor, r: Tensor, t: Tensor, cond: Tensor, h: list[InferenceCache]
     ) -> tuple[Tensor, list[InferenceCache]]:
-        delta, h = self.forward(x.unsqueeze(1), dt.unsqueeze(1), cond, h)
-        return delta[:, 0, :], h
+        x_pred, h = self.forward(
+            z_t.unsqueeze(1), r.unsqueeze(1), t.unsqueeze(1), cond, h
+        )
+        return x_pred[:, 0, :], h
 
     def init_cache(self, batch_size: int, device: torch.device) -> list[InferenceCache]:
         return [
@@ -242,6 +265,34 @@ class SwanLogger:
             finish()
 
 
+class LPIPSPerceptualLoss(nn.Module):
+    def __init__(self, cfg: TrainConfig) -> None:
+        super().__init__()
+        torch_home = Path(cfg.output_dir) / ".torch"
+        torch_home.mkdir(parents=True, exist_ok=True)
+        os.environ["TORCH_HOME"] = str(torch_home)
+        self.channels = cfg.channels
+        self.loss_fn = lpips.LPIPS(net="vgg", verbose=False)
+        self.loss_fn.eval()
+        for param in self.loss_fn.parameters():
+            param.requires_grad_(False)
+
+    def _prepare_images(self, images: Tensor) -> Tensor:
+        if images.shape[1] == 1:
+            return images.repeat(1, 3, 1, 1)
+        if images.shape[1] != 3:
+            raise ValueError(
+                "LPIPS perceptual loss expects 1-channel or 3-channel images. "
+                f"Got {images.shape[1]} channels."
+            )
+        return images
+
+    def forward(self, pred: Tensor, target: Tensor) -> Tensor:
+        pred_rgb = self._prepare_images(pred)
+        target_rgb = self._prepare_images(target)
+        return self.loss_fn(pred_rgb, target_rgb).mean()
+
+
 def set_seed(seed: int) -> None:
     torch.manual_seed(seed)
     if torch.cuda.is_available():
@@ -266,40 +317,42 @@ def unwrap_model(model: nn.Module) -> nn.Module:
     return model.module if hasattr(model, "module") else model
 
 
-def validate_time_config(cfg: TrainConfig) -> None:
-    if cfg.seq_len <= 0:
-        raise ValueError("seq_len must be > 0")
-    base = 1.0 / cfg.seq_len
-    if cfg.dt_max <= base:
+def validate_config(cfg: TrainConfig) -> None:
+    if cfg.seq_len != 5:
         raise ValueError(
-            "dt_max must be > 1/seq_len to allow non-uniform dt_seq. "
-            f"Got dt_max={cfg.dt_max}, seq_len={cfg.seq_len}, 1/seq_len={base}."
+            f"seq_len must be 5 for the required 5-block training setup (got {cfg.seq_len})."
         )
-    if cfg.dt_min >= cfg.dt_max:
+    if cfg.time_grid_size < 2:
+        raise ValueError("time_grid_size must be >= 2.")
+    if cfg.lambda_perceptual < 0:
+        raise ValueError("lambda_perceptual must be >= 0.")
+    if cfg.val_sampling_steps != FIXED_VAL_SAMPLING_STEPS:
         raise ValueError(
-            f"dt_min must be < dt_max (got dt_min={cfg.dt_min}, dt_max={cfg.dt_max})."
+            f"val_sampling_steps is fixed to {FIXED_VAL_SAMPLING_STEPS} for validation sampling."
         )
 
 
-def sample_time_sequence(
-    cfg: TrainConfig, batch_size: int, device: torch.device
-) -> Tensor:
-    alpha = float(cfg.dt_alpha)
-    if alpha <= 0:
-        raise ValueError("dt_alpha must be > 0")
-    dist = torch.distributions.Gamma(alpha, 1.0)
-    raw = dist.sample((batch_size, cfg.seq_len)).to(device)
-    dt_seq = raw / raw.sum(dim=-1, keepdim=True)
-    base = 1.0 / cfg.seq_len
-    max_dt = float(cfg.dt_max)
-    if max_dt <= base:
-        return torch.full_like(dt_seq, base)
-    max_current = dt_seq.max(dim=-1, keepdim=True).values
-    if (max_current > max_dt).any():
-        gamma = (max_dt - base) / (max_current - base)
-        gamma = gamma.clamp(0.0, 1.0)
-        dt_seq = gamma * dt_seq + (1.0 - gamma) * base
-    return dt_seq
+def sample_block_times(
+    cfg: TrainConfig, batch_size: int, device: torch.device, dtype: torch.dtype
+) -> tuple[Tensor, Tensor]:
+    # Sampling sorted discrete cut points allows repeated boundaries, so zero-length
+    # interior blocks occur with non-zero probability while keeping t > 0.
+    cuts = torch.randint(
+        1,
+        cfg.time_grid_size,
+        (batch_size, cfg.seq_len - 1),
+        device=device,
+    )
+    cuts, _ = torch.sort(cuts, dim=-1)
+    boundaries = torch.cat(
+        [
+            torch.zeros(batch_size, 1, device=device, dtype=dtype),
+            cuts.to(dtype=dtype) / float(cfg.time_grid_size),
+            torch.ones(batch_size, 1, device=device, dtype=dtype),
+        ],
+        dim=-1,
+    )
+    return boundaries[:, :-1], boundaries[:, 1:]
 
 
 def build_dataloader(
@@ -346,15 +399,66 @@ def infinite_loader(loader: DataLoader) -> Iterator[dict]:
             yield batch
 
 
+def build_noisy_sequence(
+    x0: Tensor,
+    eps: Tensor,
+    t_seq: Tensor,
+) -> tuple[Tensor, Tensor]:
+    z_t = (1.0 - t_seq.unsqueeze(-1)) * x0[:, None, :] + t_seq.unsqueeze(-1) * eps[:, None, :]
+    v_gt = eps - x0
+    return z_t, v_gt
+
+
 def compute_losses(
-    delta: Tensor,
+    model: nn.Module,
+    perceptual_loss_fn: LPIPSPerceptualLoss,
+    x0: Tensor,
+    z_t: Tensor,
     v_gt: Tensor,
-    dt_seq: Tensor,
-) -> dict[str, Tensor]:
-    losses: dict[str, Tensor] = {}
-    target_disp = v_gt[:, None, :] * dt_seq.unsqueeze(-1)
-    losses["flow"] = F.mse_loss(delta, target_disp)
-    return losses
+    r_seq: Tensor,
+    t_seq: Tensor,
+    cond: Tensor,
+    cfg: TrainConfig,
+) -> tuple[dict[str, Tensor], Tensor]:
+    seq_len = z_t.shape[1]
+    safe_t = safe_time_divisor(t_seq).unsqueeze(-1)
+
+    x_pred, _ = model(z_t, r_seq, t_seq, cond)
+    u = (z_t - x_pred) / safe_t
+
+    x_pred_inst, _ = model(z_t, t_seq, t_seq, cond)
+    v_inst = ((z_t - x_pred_inst) / safe_t).detach()
+
+    def u_fn(z_in: Tensor, r_in: Tensor, t_in: Tensor) -> Tensor:
+        x_pred_local, _ = model(z_in, r_in, t_in, cond)
+        return (z_in - x_pred_local) / safe_time_divisor(t_in).unsqueeze(-1)
+
+    _, dudt = jvp(
+        u_fn,
+        (z_t, r_seq, t_seq),
+        (v_inst, torch.zeros_like(r_seq), torch.ones_like(t_seq)),
+    )
+    corrected_velocity = u + (t_seq - r_seq).unsqueeze(-1) * dudt.detach()
+    target_velocity = v_gt[:, None, :].expand(-1, seq_len, -1)
+
+    pred_images = x_pred.reshape(
+        x0.shape[0] * seq_len, cfg.channels, cfg.image_size, cfg.image_size
+    )
+    target_images = (
+        x0.reshape(x0.shape[0], cfg.channels, cfg.image_size, cfg.image_size)
+        .unsqueeze(1)
+        .expand(-1, seq_len, -1, -1, -1)
+        .reshape(x0.shape[0] * seq_len, cfg.channels, cfg.image_size, cfg.image_size)
+    )
+
+    losses = {
+        "flow": F.mse_loss(corrected_velocity, target_velocity),
+        "perceptual": perceptual_loss_fn(pred_images, target_images),
+    }
+    losses["total"] = cfg.lambda_flow * losses["flow"] + cfg.lambda_perceptual * losses[
+        "perceptual"
+    ]
+    return losses, x_pred
 
 
 def make_grid(images: Tensor, nrow: int) -> np.ndarray:
@@ -389,30 +493,36 @@ def save_image_grid(
         plt.close(fig)
 
 
-def rollout_trajectory(
+def sample_class_images(
     model: ASMamba,
-    x0: Tensor,
+    cfg: TrainConfig,
+    device: torch.device,
     cond: Tensor,
-    dt_seq: Tensor,
 ) -> Tensor:
-    device = x0.device
     model.eval()
-    h = model.init_cache(batch_size=x0.shape[0], device=device)
-    x = x0
-    if dt_seq.dim() == 1:
-        dt_seq = dt_seq.unsqueeze(0).expand(x0.shape[0], -1)
-    elif dt_seq.shape[0] == 1 and x0.shape[0] > 1:
-        dt_seq = dt_seq.expand(x0.shape[0], -1)
-    traj = [x0.detach().cpu()]
+    input_dim = cfg.channels * cfg.image_size * cfg.image_size
+    z_t = torch.randn(cond.shape[0], input_dim, device=device)
+    time_grid = torch.tensor(FIXED_VAL_TIME_GRID, device=device)
 
     with torch.no_grad():
-        for step_idx in range(dt_seq.shape[1]):
-            dt = dt_seq[:, step_idx]
-            delta, h = model.step(x, dt, cond, h)
-            x = x + delta
-            traj.append(x.detach().cpu())
+        for step_idx in range(FIXED_VAL_SAMPLING_STEPS):
+            t_cur = torch.full(
+                (cond.shape[0],),
+                float(time_grid[step_idx].item()),
+                device=device,
+            )
+            t_next = time_grid[step_idx + 1]
+            x_pred, _ = model(
+                z_t.unsqueeze(1),
+                t_cur.unsqueeze(1),
+                t_cur.unsqueeze(1),
+                cond,
+            )
+            x_pred = x_pred[:, 0, :]
+            u_inst = (z_t - x_pred) / safe_time_divisor(t_cur).unsqueeze(-1)
+            z_t = z_t + (t_next - t_cur).unsqueeze(-1) * u_inst
 
-    return torch.stack(traj, dim=1)
+    return z_t.view(cond.shape[0], cfg.channels, cfg.image_size, cfg.image_size)
 
 
 def log_class_samples(
@@ -424,24 +534,13 @@ def log_class_samples(
 ) -> None:
     if cfg.val_samples_per_class <= 0:
         return
+    training_mode = model.training
     model.eval()
-    max_steps = cfg.seq_len
-    input_dim = cfg.channels * cfg.image_size * cfg.image_size
-    dt_seq = torch.full(
-        (cfg.val_samples_per_class, max_steps),
-        1.0 / max_steps,
-        device=device,
-    )
-
     for cls in range(cfg.num_classes):
         cond = torch.full(
             (cfg.val_samples_per_class,), cls, device=device, dtype=torch.long
         )
-        x0 = torch.randn(cfg.val_samples_per_class, input_dim, device=device)
-        traj = rollout_trajectory(model, x0, cond, dt_seq=dt_seq)
-        x_final = traj[:, -1, :].view(
-            cfg.val_samples_per_class, cfg.channels, cfg.image_size, cfg.image_size
-        )
+        x_final = sample_class_images(model, cfg, device, cond)
         save_path = Path(cfg.output_dir) / f"val_class_{cls}_step_{step:06d}.png"
         save_image_grid(x_final, save_path, nrow=cfg.val_grid_rows)
         logger.log_image(
@@ -450,12 +549,25 @@ def log_class_samples(
             caption=f"class {cls} step {step}",
             step=step,
         )
+    if training_mode:
         model.train()
 
 
+def build_perceptual_loss(
+    cfg: TrainConfig, device: torch.device, rank: int, use_ddp: bool
+) -> LPIPSPerceptualLoss:
+    if use_ddp and rank != 0:
+        torch.distributed.barrier()
+    perceptual_loss_fn = LPIPSPerceptualLoss(cfg).to(device)
+    if use_ddp and rank == 0:
+        torch.distributed.barrier()
+    return perceptual_loss_fn
+
+
 def train(cfg: TrainConfig) -> ASMamba:
-    validate_time_config(cfg)
+    validate_config(cfg)
     use_ddp, rank, world_size, device = setup_distributed(cfg)
+    del world_size
     set_seed(cfg.seed + rank)
     output_dir = Path(cfg.output_dir)
     if rank == 0:
@@ -467,48 +579,53 @@ def train(cfg: TrainConfig) -> ASMamba:
     optimizer = torch.optim.AdamW(
         model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay
     )
+    perceptual_loss_fn = build_perceptual_loss(cfg, device, rank, use_ddp)
     logger = SwanLogger(cfg, enabled=(rank == 0))
 
     loader, sampler = build_dataloader(cfg, distributed=use_ddp)
     loader_iter = infinite_loader(loader)
 
     global_step = 0
-    for _ in range(cfg.epochs):
+    for epoch_idx in range(cfg.epochs):
         if sampler is not None:
-            sampler.set_epoch(global_step)
+            sampler.set_epoch(epoch_idx)
         model.train()
         for _ in range(cfg.steps_per_epoch):
             batch = next(loader_iter)
-            x1 = batch["pixel_values"].to(device)
+            x0 = batch["pixel_values"].to(device)
             cond = batch["labels"].to(device)
-            b = x1.shape[0]
-            x1 = x1.view(b, -1)
-            x0 = torch.randn_like(x1)
-            v_gt = x1 - x0
-            dt_seq = sample_time_sequence(cfg, b, device)
-            t_seq = torch.cumsum(dt_seq, dim=-1)
-            t_seq = torch.cat([torch.zeros(b, 1, device=device), t_seq[:, :-1]], dim=-1)
-            x_seq = x0[:, None, :] + t_seq[:, :, None] * v_gt[:, None, :]
+            b = x0.shape[0]
+            x0 = x0.view(b, -1)
+            eps = torch.randn_like(x0)
 
-            delta, _ = model(x_seq, dt_seq, cond)
+            r_seq, t_seq = sample_block_times(cfg, b, device, x0.dtype)
+            z_t, v_gt = build_noisy_sequence(x0, eps, t_seq)
 
-            losses = compute_losses(
-                delta=delta,
+            losses, _ = compute_losses(
+                model=model,
+                perceptual_loss_fn=perceptual_loss_fn,
+                x0=x0,
+                z_t=z_t,
                 v_gt=v_gt,
-                dt_seq=dt_seq,
+                r_seq=r_seq,
+                t_seq=t_seq,
+                cond=cond,
+                cfg=cfg,
             )
 
-            loss = cfg.lambda_flow * losses["flow"]
-
             optimizer.zero_grad(set_to_none=True)
-            loss.backward()
+            losses["total"].backward()
             optimizer.step()
 
-            if global_step % 10 == 0:
+            if global_step % 10 == 0 and rank == 0:
                 logger.log(
                     {
-                        "loss/total": float(loss.item()),
+                        "loss/total": float(losses["total"].item()),
                         "loss/flow": float(losses["flow"].item()),
+                        "loss/perceptual": float(losses["perceptual"].item()),
+                        "time/r_mean": float(r_seq.mean().item()),
+                        "time/t_mean": float(t_seq.mean().item()),
+                        "time/zero_block_frac": float((t_seq == r_seq).float().mean().item()),
                     },
                     step=global_step,
                 )

main.py

@@ -4,7 +4,7 @@ from as_mamba import TrainConfig, run_training_and_plot
 
 
 def build_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser(description="Train AS-Mamba on MNIST flow matching.")
+    parser = argparse.ArgumentParser(description="Train AS-Mamba on MNIST MeanFlow x-prediction.")
     parser.add_argument("--epochs", type=int, default=None)
     parser.add_argument("--steps-per-epoch", type=int, default=None)
     parser.add_argument("--batch-size", type=int, default=None)
@@ -15,10 +15,8 @@ def build_parser() -> argparse.ArgumentParser:
     parser.add_argument("--output-dir", type=str, default=None)
     parser.add_argument("--project", type=str, default=None)
     parser.add_argument("--run-name", type=str, default=None)
-    parser.add_argument("--dt-alpha", type=float, default=None)
-    parser.add_argument("--dt-min", type=float, default=None)
-    parser.add_argument("--dt-max", type=float, default=None)
     parser.add_argument("--lambda-flow", type=float, default=None)
+    parser.add_argument("--lambda-perceptual", type=float, default=None)
     parser.add_argument("--num-classes", type=int, default=None)
     parser.add_argument("--image-size", type=int, default=None)
     parser.add_argument("--channels", type=int, default=None)
@@ -36,6 +34,8 @@ def build_parser() -> argparse.ArgumentParser:
     parser.add_argument("--val-every", type=int, default=None)
     parser.add_argument("--val-samples-per-class", type=int, default=None)
     parser.add_argument("--val-grid-rows", type=int, default=None)
+    parser.add_argument("--val-sampling-steps", type=int, default=None)
+    parser.add_argument("--time-grid-size", type=int, default=None)
     parser.add_argument("--use-ddp", action=argparse.BooleanOptionalAction, default=None)
     return parser
 

pyproject.toml

@@ -7,8 +7,10 @@ requires-python = ">=3.12"
 dependencies = [
     "datasets>=2.19.0",
     "einops>=0.7.0",
+    "lpips>=0.1.4",
     "matplotlib>=3.8.0",
     "numpy>=1.26.0",
     "swanlab>=0.5.0",
     "torch>=2.2.0",
+    "torchvision>=0.24.1",
 ]

train_as_mamba.sh

@@ -5,13 +5,11 @@ DEVICE="cuda"
 EPOCHS=2000
 STEPS_PER_EPOCH=200
 BATCH_SIZE=512
-SEQ_LEN=1
+SEQ_LEN=5
 LR=1e-3
 WEIGHT_DECAY=1e-2
-DT_MIN=5e-4
-DT_MAX=1.1
-DT_ALPHA=9.0
 LAMBDA_FLOW=1.0
+LAMBDA_PERCEPTUAL=0.4
 NUM_CLASSES=10
 IMAGE_SIZE=28
 CHANNELS=1
@@ -30,8 +28,10 @@ USE_DDP=true
 VAL_EVERY=1000
 VAL_SAMPLES_PER_CLASS=8
 VAL_GRID_ROWS=4
+VAL_SAMPLING_STEPS=5
+TIME_GRID_SIZE=256
 PROJECT="as-mamba-mnist"
-RUN_NAME="mnist-flow-res-5seq"
+RUN_NAME="mnist-meanflow-xpred"
 OUTPUT_DIR="outputs"
 
 USE_RESIDUAL_FLAG="--use-residual"
@@ -47,10 +47,8 @@ uv run torchrun --nproc_per_node=2 main.py \
   --seq-len "${SEQ_LEN}" \
   --lr "${LR}" \
   --weight-decay "${WEIGHT_DECAY}" \
-  --dt-min "${DT_MIN}" \
-  --dt-max "${DT_MAX}" \
-  --dt-alpha "${DT_ALPHA}" \
   --lambda-flow "${LAMBDA_FLOW}" \
+  --lambda-perceptual "${LAMBDA_PERCEPTUAL}" \
   --num-classes "${NUM_CLASSES}" \
   --image-size "${IMAGE_SIZE}" \
   --channels "${CHANNELS}" \
@@ -69,6 +67,8 @@ uv run torchrun --nproc_per_node=2 main.py \
   --val-every "${VAL_EVERY}" \
   --val-samples-per-class "${VAL_SAMPLES_PER_CLASS}" \
   --val-grid-rows "${VAL_GRID_ROWS}" \
+  --val-sampling-steps "${VAL_SAMPLING_STEPS}" \
+  --time-grid-size "${TIME_GRID_SIZE}" \
   --project "${PROJECT}" \
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 version = "80.9.0"
@@ -1792,20 +1869,24 @@ source = { virtual = "." }
 dependencies = [
     { name = "datasets" },
     { name = "einops" },
+    { name = "lpips" },
     { name = "matplotlib" },
     { name = "numpy" },
     { name = "swanlab" },
     { name = "torch" },
+    { name = "torchvision" },
 ]
 
 [package.metadata]
 requires-dist = [
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     { name = "einops", specifier = ">=0.7.0" },
+    { name = "lpips", specifier = ">=0.1.4" },
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     { name = "numpy", specifier = ">=1.26.0" },
     { name = "swanlab", specifier = ">=0.5.0" },
     { name = "torch", specifier = ">=2.2.0" },
+    { name = "torchvision", specifier = ">=0.24.1" },
 ]
 
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 ]
 
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