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Model: deepseek-reasoner

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Messages: 132

UserStep 0

Task: cv-vae-loss

VAE Loss Function Design for Image Reconstruction

Objective

Design a training loss function for a Variational Autoencoder (VAE) that achieves the best reconstruction quality on CIFAR-10.

Background

Variational Autoencoders encode images into a compressed latent representation and decode them back. The quality of reconstruction depends critically on the training loss function. Standard approaches use combinations of:

Reconstruction loss: L1 or L2 pixel-level error
KL divergence: Regularizes the latent space toward a standard normal prior
Perceptual loss: LPIPS or VGG-based feature matching for perceptual quality
Adversarial loss: Discriminator-based training for sharpness
Frequency-domain loss: FFT-based weighting to preserve fine detail

Recent work on the Prism Hypothesis (UAE, Fan et al.) demonstrates that explicitly handling different frequency bands in the training objective can significantly improve reconstruction quality. The key insight is that semantic information concentrates at low frequencies while fine perceptual detail lives in higher bands.

Task

Implement the VAELoss class in custom_train.py (lines 32–76). Your loss function will be used to train an AutoencoderKL model from the diffusers library on CIFAR-10 32×32 images.

Editable Region (lines 32–76)

class VAELoss(nn.Module):
    def __init__(self, device):
        super().__init__()
        # Initialize your loss components here

    def forward(self, recon, target, posterior, step):
        # recon:     [B, 3, 32, 32] reconstructed images in [-1, 1]
        # target:    [B, 3, 32, 32] original images in [-1, 1]
        # posterior:  DiagonalGaussianDistribution
        #             - posterior.kl() -> KL divergence per sample
        #             - posterior.mean, posterior.logvar
        # step:       current training step (int)
        #
        # Return: (loss_tensor, metrics_dict)
        ...

Available Libraries

torch, torch.nn, torch.nn.functional — standard PyTorch
torch.fft — frequency-domain operations (fft2, ifft2, fftshift, etc.)
lpips — learned perceptual loss: lpips.LPIPS(net='vgg').to(device)
numpy, math

Architecture (Fixed)

The model is AutoencoderKL from diffusers with 3 blocks and 2 downsample stages, giving latent resolution 8×8 (f=4 compression) suited for 32×32 input:

latent_channels=4, layers_per_block=2
GroupNorm (32 groups) + SiLU activation

Channel widths scale via BLOCK_OUT_CHANNELS environment variable:

Small: (64, 128, 256) — lightweight
Medium: (128, 256, 512) — standard
Large: (256, 512, 512) — wide

Training (Fixed)

Optimizer: AdamW, lr=4e-4, weight_decay=1e-4
LR schedule: 5% warmup + cosine decay
Mixed precision (autocast + GradScaler)
Gradient clipping at 1.0
EMA with rate 0.999

Evaluation

Reconstruction quality is measured on the full CIFAR-10 test set (10,000 images):

| Me

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "diffusers-main/custom_train.py",
4  "start_line": 32,
5  "end_line": 76,
6  "content": "class VAELoss(nn.Module):\n    \"\"\"Novel VAE loss with adaptive frequency band weighting and adversarial training.\n    \n    Combines:\n    1. L1 reconstruction loss\n    2. Perceptual LPIPS loss\n    3. Adaptive frequency band loss (FFT-based, weighted by target amplitude)\n    4. KL divergence\n    5. Adversarial GAN loss with discriminator\n    6. Feature matching loss from discriminator intermediate layers\n    \"\"\"\n    def __init__(self, device):\n        super().__init__()\n        import lpips\n        # Perceptual loss\n        self.lpips_fn = lpips.LPIPS(net='vgg').to(device)\n        self.lpips_fn.eval()\n        for p in self.lpips_fn.parameters():\n            p.requires_grad_(False)\n        \n        # Discriminator (PatchGAN)\n        self.disc = NLayerDiscriminator().to(device)\n        self.disc_opt = torch.optim.Adam(self.disc.parameters(), lr=1e-4, betas=(0.5, 0.9))\n        self.disc_start = 5000  # steps before starting discriminator\n        \n        # Loss weights\n        self.kl_weight = 1e-6\n        self.perceptual_weight = 0.5\n        self.freq_weight = 0.2\n        self.fm_weight = 1.0  # feature matching weight\n        \n        # Frequency band parameters\n        self.num_bands = 5\n        self.band_boundaries = [0, 4, 8, 16, 32]  # radial frequency bounds (for 32x32)\n        \n    def compute_freq_band_loss(self, recon, target):\n        \"\"\"Compute adaptive frequency band loss using FFT.\n        \n        Split frequency domain into radial bands, weight each band by target amplitude.\n        \"\"\"\n        B, C, H, W = recon.shape\n        # Compute FFT\n        recon_fft = torch.fft.fft2(recon, dim=(-2, -1))\n        target_fft = torch.fft.fft2(target, dim=(-2, -1))\n        \n        # Shift zero frequency to center\n        recon_fft = torch.fft.fftshift(recon_fft, dim=(-2, -1))\n        target_fft = torch.fft.fftshift(target_fft, dim=(-2, -1))\n        \n        # Create radial frequency map\n        y = torch.linspace(-H//2, H//2-1, H, device=recon.device)\n        x = torch.linspace(-W//2, W//2-1, W, device=recon.device)\n        Y, X = torch.meshgrid(y, x, indexing='ij')\n        R = torch.sqrt(X**2 + Y**2)\n        \n        total_loss = 0.0\n        for i in range(self.num_bands):\n            low = self.band_boundaries[i]\n            high = self.band_boundaries[i+1] if i+1 < len(self.band_boundaries) else max(H, W)//2\n            \n            # Create mask for this band\n            mask = (R >= low) & (R < high)\n            mask = mask.float().unsqueeze(0).unsqueeze(0)  # 1x1xHxW\n            \n            # Compute amplitude of target in this band\n            target_amp = torch.abs(target_fft * mask).sum(dim=(-2,-1))  # BxC\n            # Normalize to get weight: bands with more energy get higher weight\n            band_weight = target_amp.detach() / (target_amp.sum(dim=(-1), keepdim=True) + 1e-8)\n            band_weight = band_weight.mean(dim=1, keepdim=True)  # Bx1\n            \n            # Compute L1 loss in frequency domain for this band\n            diff = torch.abs(recon_fft - target_fft) * mask\n            band_loss = diff.sum(dim=(-2,-1)).mean()  # scalar\n            \n            # Weight by target amplitude\n            weighted_loss = band_loss * band_weight.mean()\n            total_loss += weighted_loss\n        \n        return total_loss / self.num_bands\n    \n    def forward(self, recon, target, posterior, step):\n        # Reconstruction losses\n        rec_loss = F.l1_loss(recon, target)\n        p_loss = self.lpips_fn(recon.float(), target.float()).mean()\n        kl_loss = posterior.kl().mean()\n        \n        # Frequency band loss\n        freq_loss = self.compute_freq_band_loss(recon, target)\n        \n        # Total VAE loss (without adversarial)\n        vae_loss = rec_loss + self.perceptual_weight * p_loss + \\\n                   self.freq_weight * freq_loss + self.kl_weight * kl_loss\n        \n        # Store perceptual loss for GAN weighting (used in training loop)\n        self._perceptual_loss = p_loss\n        \n        # Metrics\n        metrics = {\n            \"rec_loss\": rec_loss.item(),\n            \"p_loss\": p_loss.item(),\n            \"freq_loss\": freq_loss.item(),\n            \"kl_loss\": kl_loss.item(),\n        }\n        \n        return vae_loss, metrics\n\n\nclass NLayerDiscriminator(nn.Module):\n    \"\"\"PatchGAN discriminator from taming-transformers/VQGAN.\"\"\"\n    def __init__(self, input_nc=3, ndf=64, n_layers=3):\n        super().__init__()\n        layers = [nn.Conv2d(input_nc, ndf, 4, 2, 1), nn.LeakyReLU(0.2, True)]\n        nf_mult = 1\n        for n in range(1, n_layers):\n            nf_mult_prev = nf_mult\n            nf_mult = min(2 ** n, 8)\n            layers += [\n                nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, 4, 2, 1, bias=False),\n                nn.BatchNorm2d(ndf * nf_mult),\n                nn.LeakyReLU(0.2, True),\n            ]\n        nf_mult_prev = nf_mult\n        nf_mult = min(2 ** n_layers, 8)\n        layers += [\n            nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, 4, 1, 1, bias=False),\n            nn.BatchNorm2d(ndf * nf_mult),\n            nn.LeakyReLU(0.2, True),\n            nn.Conv2d(ndf * nf_mult, 1, 4, 1, 1),\n        ]\n        self.net = nn.Sequential(*layers)\n        self.apply(self._init)\n    \n    @staticmethod\n    def _init(m):\n        if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):\n            nn.init.normal_(m.weight, 0.0, 0.02)\n    \n    def forward(self, x):\n        return self.net(x)"
7}

Tool ResultStep 1

OK: Replaced lines 32..76 with 139 line(s) in diffusers-main/custom_train.py. Editable range: 32–170.

[Current file: diffusers-main/custom_train.py | editable: 32–170 | total: 570 lines]
    29| # Loss Function (EDITABLE REGION)
    30| # ============================================================================
    31| 
    32  class VAELoss(nn.Module):
    33      """Novel VAE loss with adaptive frequency band weighting and adversarial training.
    34      
    35      Combines:
    36      1. L1 reconstruction loss
    37      2. Perceptual LPIPS loss
    38      3. Adaptive frequency band loss (FFT-based, weighted by target amplitude)
    39      4. KL divergence
    40      5. Adversarial GAN loss with discriminator
    41      6. Feature matching loss from discriminator intermediate layers
    42      """
    43      def __init__(self, device):
    44          super().__init__()
    45          import lpips
    46          # Perceptual loss
    47          self.lpips_fn = lpi

AssistantStep 2undo

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 2

Undo complete:
Restored: diffusers-main/custom_train.py

AssistantStep 3edit

Tool Input

1{
2  "op": "replace",
3  "filename": "diffusers-main/custom_train.py",
4  "start_line": 32,
5  "end_line": 76,
6  "content": "class NLayerDiscriminator(nn.Module):\n    \"\"\"PatchGAN discriminator with feature extraction for feature matching loss.\"\"\"\n    def __init__(self, input_nc=3, ndf=64, n_layers=3):\n        super().__init__()\n        self.n_layers = n_layers\n        \n        # Build sequential layers while storing intermediate layers\n        layers = []\n        # Layer 0\n        layers.append(nn.Conv2d(input_nc, ndf, 4, 2, 1))\n        layers.append(nn.LeakyReLU(0.2, True))\n        \n        nf_mult = 1\n        for n in range(1, n_layers):\n            nf_mult_prev = nf_mult\n            nf_mult = min(2 ** n, 8)\n            layers.append(nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, 4, 2, 1, bias=False))\n            layers.append(nn.BatchNorm2d(ndf * nf_mult))\n            layers.append(nn.LeakyReLU(0.2, True))\n        \n        nf_mult_prev = nf_mult\n        nf_mult = min(2 ** n_layers, 8)\n        layers.append(nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, 4, 1, 1, bias=False))\n        layers.append(nn.BatchNorm2d(ndf * nf_mult))\n        layers.append(nn.LeakyReLU(0.2, True))\n        layers.append(nn.Conv2d(ndf * nf_mult, 1, 4, 1, 1))\n        \n        self.net = nn.Sequential(*layers)\n        self.apply(self._init)\n    \n    @staticmethod\n    def _init(m):\n        if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):\n            nn.init.normal_(m.weight, 0.0, 0.02)\n    \n    def forward(self, x):\n        return self.net(x)\n    \n    def forward_features(self, x):\n        \"\"\"Forward pass returning intermediate features for feature matching.\"\"\"\n        features = []\n        for i, layer in enumerate(self.net):\n            x = layer(x)\n            if isinstance(layer, nn.LeakyReLU):\n                features.append(x)\n        return features\n\n\nclass VAELoss(nn.Module):\n    \"\"\"Novel VAE loss with adaptive frequency band weighting, adversarial training, and feature matching.\n    \n    Key contributions:\n    1. Adaptive frequency band loss: FFT-based loss with content-aware weighting\n    2. Feature matching loss from discriminator intermediate features\n    3. Combined adversarial training with adaptive weighting\n    \"\"\"\n    def __init__(self, device):\n        super().__init__()\n        import lpips\n        # Perceptual loss\n        self.lpips_fn = lpips.LPIPS(net='vgg').to(device)\n        self.lpips_fn.eval()\n        for p in self.lpips_fn.parameters():\n            p.requires_grad_(False)\n        \n        # Discriminator (PatchGAN)\n        self.disc = NLayerDiscriminator().to(device)\n        self.disc_opt = torch.optim.Adam(self.disc.parameters(), lr=1e-4, betas=(0.5, 0.9))\n        self.disc_start = 5000  # steps before starting discriminator\n        \n        # Loss weights\n        self.kl_weight = 1e-6\n        self.perceptual_weight = 0.5\n        self.freq_weight = 0.2\n        self.fm_weight = 0.1  # feature matching weight\n        \n        # Frequency band parameters\n        self.num_bands = 5\n        self.band_boundaries = [0, 4, 8, 16, 32]  # radial frequency bounds (for 32x32)\n        \n    def compute_freq_band_loss(self, recon, target):\n        \"\"\"Compute adaptive frequency band loss using FFT.\n        \n        Split frequency domain into radial bands, weight each band by target amplitude.\n        Higher weight for bands with more energy (content-aware).\n        \"\"\"\n        B, C, H, W = recon.shape\n        # Compute FFT\n        recon_fft = torch.fft.fft2(recon, dim=(-2, -1))\n        target_fft = torch.fft.fft2(target, dim=(-2, -1))\n        \n        # Shift zero frequency to center\n        recon_fft = torch.fft.fftshift(recon_fft, dim=(-2, -1))\n        target_fft = torch.fft.fftshift(target_fft, dim=(-2, -1))\n        \n        # Create radial frequency map\n        y = torch.linspace(-H//2, H//2-1, H, device=recon.device)\n        x = torch.linspace(-W//2, W//2-1, W, device=recon.device)\n        Y, X = torch.meshgrid(y, x, indexing='ij')\n        R = torch.sqrt(X**2 + Y**2)\n        \n        total_loss = 0.0\n        for i in range(self.num_bands):\n            low = self.band_boundaries[i]\n            high = self.band_boundaries[i+1] if i+1 < len(self.band_boundaries) else max(H, W)//2\n            \n            # Create mask for this band\n            mask = (R >= low) & (R < high)\n            mask = mask.float().unsqueeze(0).unsqueeze(0)  # 1x1xHxW\n            \n            # Compute amplitude of target in this band\n            target_amp = torch.abs(target_fft * mask).sum(dim=(-2,-1))  # BxC\n            # Normalize to get weight: bands with more energy get higher weight\n            band_weight = target_amp.detach() / (target_amp.sum(dim=(-1), keepdim=True) + 1e-8)\n            band_weight = band_weight.mean(dim=1, keepdim=True)  # Bx1\n            \n            # Compute L1 loss in frequency domain for this band\n            diff = torch.abs(recon_fft - target_fft) * mask\n            band_loss = diff.sum(dim=(-2,-1)).mean()  # scalar\n            \n            # Weight by target amplitude\n            weighted_loss = band_loss * band_weight.mean()\n            total_loss += weighted_loss\n        \n        return total_loss / self.num_bands\n    \n    def compute_feature_matching_loss(self, recon, target):\n        \"\"\"Compute feature matching loss using discriminator intermediate features.\"\"\"\n        fake_features = self.disc.forward_features(recon)\n        real_features = self.disc.forward_features(target)\n        loss = 0.0\n        for fake, real in zip(fake_features, real_features):\n            loss += F.l1_loss(fake, real.detach())\n        return loss / len(fake_features)\n    \n    def forward(self, recon, target, posterior, step):\n        # Reconstruction losses\n        rec_loss = F.l1_loss(recon, target)\n        p_loss = self.lpips_fn(recon.float(), target.float()).mean()\n        kl_loss = posterior.kl().mean()\n        \n        # Frequency band loss\n        freq_loss = self.compute_freq_band_loss(recon, target)\n        \n        # Total VAE loss (without adversarial)\n        vae_loss = rec_loss + self.perceptual_weight * p_loss + \\\n                   self.freq_weight * freq_loss + self.kl_weight * kl_loss\n        \n        # Feature matching loss (only after discriminator start)\n        if step >= self.disc_start:\n            fm_loss = self.compute_feature_matching_loss(recon, target)\n            vae_loss = vae_loss + self.fm_weight * fm_loss\n        else:\n            fm_loss = torch.tensor(0.0, device=recon.device)\n        \n        # Store perceptual loss for GAN weighting (used in training loop)\n        self._perceptual_loss = p_loss\n        \n        # Metrics\n        metrics = {\n            \"rec_loss\": rec_loss.item(),\n            \"p_loss\": p_loss.item(),\n            \"freq_loss\": freq_loss.item(),\n            \"kl_loss\": kl_loss.item(),\n            \"fm_loss\": fm_loss.item() if step >= self.disc_start else 0.0,\n        }\n        \n        return vae_loss, metrics"
7}

Tool ResultStep 3

OK: Replaced lines 32..76 with 168 line(s) in diffusers-main/custom_train.py. Editable range: 32–199.

[Current file: diffusers-main/custom_train.py | editable: 32–199 | total: 599 lines]
    29| # Loss Function (EDITABLE REGION)
    30| # ============================================================================
    31| 
    32  class NLayerDiscriminator(nn.Module):
    33      """PatchGAN discriminator with feature extraction for feature matching loss."""
    34      def __init__(self, input_nc=3, ndf=64, n_layers=3):
    35          super().__init__()
    36          self.n_layers = n_layers
    37          
    38          # Build sequential layers while storing intermediate layers
    39          layers = []
    40          # Layer 0
    41          layers.append(nn.Conv2d(input_nc, ndf, 4, 2, 1))
    42          layers.append(nn.LeakyReLU(0.2, True))
    43          
    44          nf_mult = 1
    45          for n in range(1, n_layers):
    46              nf_mult_prev = nf_m

AssistantStep 4test

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "diffusers-main/custom_train.py",
4  "start_line": 32,
5  "end_line": 76,
6  "content": "class VAELoss(nn.Module):\n    \"\"\"Frequency-Adaptive Multi-Scale Perceptual VAE Loss.\n    \n    Novel contributions:\n    1. Frequency band decomposition (low/mid/high) with tailored loss functions.\n    2. Adaptive KL annealing (warm-up then decay).\n    3. Gradient matching loss for edge preservation.\n    4. Perceptual loss focused on mid-frequency semantic content.\n    \"\"\"\n\n    def __init__(self, device):\n        super().__init__()\n        import lpips\n        # Perceptual loss for mid-frequency band\n        self.lpips_fn = lpips.LPIPS(net='vgg').to(device)\n        self.lpips_fn.eval()\n        for p in self.lpips_fn.parameters():\n            p.requires_grad_(False)\n        \n        # Frequency masks for 32x32 images\n        self.register_buffer('freq_mask_low', self._create_freq_mask(device, 'low'))\n        self.register_buffer('freq_mask_mid', self._create_freq_mask(device, 'mid'))\n        self.register_buffer('freq_mask_high', self._create_freq_mask(device, 'high'))\n        \n        # Loss weights (will be adjusted during training via step)\n        self.base_kl_weight = 1e-6\n        self.kl_warmup_steps = 2000\n        self.kl_max_weight = 1e-4\n        self.kl_decay_steps = 3000\n        \n        self.low_weight = 1.0\n        self.mid_weight = 0.5\n        self.high_weight = 0.2\n        self.grad_weight = 0.1\n        \n        self.perceptual_weight = 0.5\n        \n    def _create_freq_mask(self, device, band):\n        \"\"\"Create frequency band mask for 32x32 images.\"\"\"\n        h, w = 32, 32\n        # Frequency coordinates (0 to 0.5 Nyquist)\n        fy = torch.fft.fftfreq(h, device=device).reshape(-1, 1)\n        fx = torch.fft.fftfreq(w, device=device).reshape(1, -1)\n        freq = torch.sqrt(fx**2 + fy**2)  # radial frequency\n        \n        if band == 'low':\n            mask = (freq <= 1/8).float()\n        elif band == 'mid':\n            mask = ((freq > 1/8) & (freq <= 1/4)).float()\n        else:  # high\n            mask = (freq > 1/4).float()\n        return mask\n\n    def _band_reconstruction(self, img, mask):\n        \"\"\"Reconstruct image band via FFT filtering.\"\"\"\n        # img: [B, C, H, W] in [-1, 1]\n        fft = torch.fft.fft2(img, dim=(-2, -1))\n        fft_shifted = torch.fft.fftshift(fft, dim=(-2, -1))\n        fft_band = fft_shifted * mask\n        fft_unshifted = torch.fft.ifftshift(fft_band, dim=(-2, -1))\n        band = torch.fft.ifft2(fft_unshifted, dim=(-2, -1)).real\n        return band\n\n    def _gradient_loss(self, recon, target):\n        \"\"\"Sobel gradient matching loss.\"\"\"\n        sobel_x = torch.tensor([[1, 0, -1], [2, 0, -2], [1, 0, -1]], dtype=torch.float32, device=recon.device).view(1, 1, 3, 3)\n        sobel_y = torch.tensor([[1, 2, 1], [0, 0, 0], [-1, -2, -1]], dtype=torch.float32, device=recon.device).view(1, 1, 3, 3)\n        \n        grad_x_recon = F.conv2d(recon, sobel_x.expand(3, 1, 3, 3), groups=3, padding=1)\n        grad_y_recon = F.conv2d(recon, sobel_y.expand(3, 1, 3, 3), groups=3, padding=1)\n        grad_x_target = F.conv2d(target, sobel_x.expand(3, 1, 3, 3), groups=3, padding=1)\n        grad_y_target = F.conv2d(target, sobel_y.expand(3, 1, 3, 3), groups=3, padding=1)\n        \n        loss = F.l1_loss(grad_x_recon, grad_x_target) + F.l1_loss(grad_y_recon, grad_y_target)\n        return loss\n\n    def forward(self, recon, target, posterior, step):\n        # Ensure images are in [-1, 1] (they are)\n        \n        # Frequency band decomposition\n        low_recon = self._band_reconstruction(recon, self.freq_mask_low)\n        low_target = self._band_reconstruction(target, self.freq_mask_low)\n        mid_recon = self._band_reconstruction(recon, self.freq_mask_mid)\n        mid_target = self._band_reconstruction(target, self.freq_mask_mid)\n        high_recon = self._band_reconstruction(recon, self.freq_mask_high)\n        high_target = self._band_reconstruction(target, self.freq_mask_high)\n        \n        # Band-specific losses\n        low_loss = F.l1_loss(low_recon, low_target)\n        mid_loss = self.lpips_fn(mid_recon.float(), mid_target.float()).mean()\n        high_loss = F.l1_loss(high_recon, high_target)\n        \n        # Gradient loss on full image\n        grad_loss = self._gradient_loss(recon, target)\n        \n        # KL annealing schedule\n        if step < self.kl_warmup_steps:\n            kl_weight = self.base_kl_weight * (step / self.kl_warmup_steps)\n        elif step < self.kl_warmup_steps + self.kl_decay_steps:\n            # After warmup, gradually increase to max then decay\n            progress = (step - self.kl_warmup_steps) / self.kl_decay_steps\n            kl_weight = self.kl_max_weight * (1 - 0.5 * (1 - torch.cos(torch.pi * progress)))\n        else:\n            kl_weight = self.kl_max_weight * 0.5\n        \n        kl_loss = posterior.kl().mean()\n        \n        # Total loss\n        total_loss = (self.low_weight * low_loss +\n                     self.mid_weight * mid_loss +\n                     self.high_weight * high_loss +\n                     self.grad_weight * grad_loss +\n                     kl_weight * kl_loss)\n        \n        metrics = {\n            \"loss\": total_loss.item(),\n            \"low\": low_loss.item(),\n            \"mid\": mid_loss.item(),\n            \"high\": high_loss.item(),\n            \"grad\": grad_loss.item(),\n            \"kl\": kl_loss.item(),\n            \"kl_weight\": kl_weight,\n        }\n        return total_loss, metrics"
7}

Tool ResultStep 1

OK: Replaced lines 32..76 with 124 line(s) in diffusers-main/custom_train.py. Editable range: 32–155.

[Current file: diffusers-main/custom_train.py | editable: 32–155 | total: 555 lines]
    29| # Loss Function (EDITABLE REGION)
    30| # ============================================================================
    31| 
    32  class VAELoss(nn.Module):
    33      """Frequency-Adaptive Multi-Scale Perceptual VAE Loss.
    34      
    35      Novel contributions:
    36      1. Frequency band decomposition (low/mid/high) with tailored loss functions.
    37      2. Adaptive KL annealing (warm-up then decay).
    38      3. Gradient matching loss for edge preservation.
    39      4. Perceptual loss focused on mid-frequency semantic content.
    40      """
    41  
    42      def __init__(self, device):
    43          super().__init__()
    44          import lpips
    45          # Perceptual loss for mid-frequency band
    46          self.lpips_fn = lpips.LPIPS(net='vgg').to(

AssistantStep 2test

Tool ResultStep 2

[Test #1] (2 tests remaining; call submit(n=1) to submit this result)

### train_small (scripts/train_small.sh)
Model parameters: 13.9M | GPUs: 8
step 200/20000 | loss=0.2243 loss=0.2243 low=0.0034 mid=0.1116 high=0.2063 grad=1.2380 kl=324.7324 kl_weight=0.0000 | 14.2s
step 400/20000 | loss=0.1688 loss=0.1688 low=0.0032 mid=0.0943 high=0.1389 grad=0.9055 kl=607.5802 kl_weight=0.0000 | 13.5s
step 600/20000 | loss=0.1508 loss=0.1508 low=0.0034 mid=0.0834 high=0.1213 grad=0.8121 kl=853.3331 kl_weight=0.0000 | 13.7s
step 800/20000 | loss=0.1307 loss=0.1307 low=0.0030 mid=0.0750 high=0.1110 grad=0.6757 kl=998.4268 kl_weight=0.0000 | 13.0s
step 1000/20000 | loss=0.1171 loss=0.1171 low=0.0029 mid=0.0696 high=0.0914 grad=0.6051 kl=1116.2150 kl_weight=0.0000 | 13.2s
step 1200/20000 | loss=0.1216 loss=0.1216 low=0.0033 mid=0.0665 high=0.0990 grad=0.6450 kl=1193.4707 kl_weight=0.0000 | 13.2s
step 1400/20000 | loss=0.1159 loss=0.1159 low=0.0032 mid=0.0628 high=0.0940 grad=0.6160 kl=1182.6001 kl_we

AssistantStep 3edit

Tool Input

1{
2  "op": "replace",
3  "filename": "diffusers-main/custom_train.py",
4  "start_line": 108,
5  "end_line": 155,
6  "content": "    def forward(self, recon, target, posterior, step):\n        # Ensure images are in [-1, 1] (they are)\n        \n        # Frequency band decomposition\n        low_recon = self._band_reconstruction(recon, self.freq_mask_low)\n        low_target = self._band_reconstruction(target, self.freq_mask_low)\n        mid_recon = self._band_reconstruction(recon, self.freq_mask_mid)\n        mid_target = self._band_reconstruction(target, self.freq_mask_mid)\n        high_recon = self._band_reconstruction(recon, self.freq_mask_high)\n        high_target = self._band_reconstruction(target, self.freq_mask_high)\n        \n        # Band-specific losses\n        low_loss = F.l1_loss(low_recon, low_target)\n        mid_loss = self.lpips_fn(mid_recon.float(), mid_target.float()).mean()\n        high_loss = F.l1_loss(high_recon, high_target)\n        \n        # Gradient loss on full image\n        grad_loss = self._gradient_loss(recon, target)\n        \n        # KL annealing schedule\n        step_tensor = torch.tensor(step, dtype=torch.float32, device=recon.device)\n        if step < self.kl_warmup_steps:\n            kl_weight = self.base_kl_weight * (step_tensor / self.kl_warmup_steps)\n        elif step < self.kl_warmup_steps + self.kl_decay_steps:\n            # After warmup, gradually increase to max then decay\n            progress = (step_tensor - self.kl_warmup_steps) / self.kl_decay_steps\n            kl_weight = self.kl_max_weight * (1 - 0.5 * (1 - torch.cos(torch.pi * progress)))\n        else:\n            kl_weight = torch.tensor(self.kl_max_weight * 0.5, device=recon.device)\n        \n        kl_loss = posterior.kl().mean()\n        \n        # Total loss\n        total_loss = (self.low_weight * low_loss +\n                     self.mid_weight * mid_loss +\n                     self.high_weight * high_loss +\n                     self.grad_weight * grad_loss +\n                     kl_weight * kl_loss)\n        \n        metrics = {\n            \"loss\": total_loss.item(),\n            \"low\": low_loss.item(),\n            \"mid\": mid_loss.item(),\n            \"high\": high_loss.item(),\n            \"grad\": grad_loss.item(),\n            \"kl\": kl_loss.item(),\n            \"kl_weight\": kl_weight.item(),\n        }\n        return total_loss, metrics"
7}

Tool ResultStep 3

OK: Replaced lines 108..155 with 49 line(s) in diffusers-main/custom_train.py. Editable range: 32–156.

[Current file: diffusers-main/custom_train.py | editable: 32–156 | total: 556 lines]
    29| # Loss Function (EDITABLE REGION)
    30| # ============================================================================
    31| 
    32  class VAELoss(nn.Module):
    33      """Frequency-Adaptive Multi-Scale Perceptual VAE Loss.
    34      
    35      Novel contributions:
    36      1. Frequency band decomposition (low/mid/high) with tailored loss functions.
    37      2. Adaptive KL annealing (warm-up then decay).
    38      3. Gradient matching loss for edge preservation.
    39      4. Perceptual loss focused on mid-frequency semantic content.
    40      """
    41  
    42      def __init__(self, device):
    43          super().__init__()
    44          import lpips
    45          # Perceptual loss for mid-frequency band
    46          self.lpips_fn = lpips.LPIPS(net='vgg').to

AssistantStep 4edit

Tool Input

1{
2  "op": "replace",
3  "filename": "diffusers-main/custom_train.py",
4  "start_line": 62,
5  "end_line": 67,
6  "content": "          self.low_weight = 0.1\n          self.mid_weight = 1.0\n          self.high_weight = 0.5\n          self.grad_weight = 0.01\n          \n          self.perceptual_weight = 0.5"
7}

Tool ResultStep 4

OK: Replaced lines 62..67 with 6 line(s) in diffusers-main/custom_train.py. Editable range: 32–156.

[Current file: diffusers-main/custom_train.py | editable: 32–156 | total: 556 lines]
    29| # Loss Function (EDITABLE REGION)
    30| # ============================================================================
    31| 
    32  class VAELoss(nn.Module):
    33      """Frequency-Adaptive Multi-Scale Perceptual VAE Loss.
    34      
    35      Novel contributions:
    36      1. Frequency band decomposition (low/mid/high) with tailored loss functions.
    37      2. Adaptive KL annealing (warm-up then decay).
    38      3. Gradient matching loss for edge preservation.
    39      4. Perceptual loss focused on mid-frequency semantic content.
    40      """
    41  
    42      def __init__(self, device):
    43          super().__init__()
    44          import lpips
    45          # Perceptual loss for mid-frequency band
    46          self.lpips_fn = lpips.LPIPS(net='vgg').to(de

AssistantStep 5edit

Tool Input

1{
2  "op": "replace",
3  "filename": "diffusers-main/custom_train.py",
4  "start_line": 62,
5  "end_line": 67,
6  "content": "          self.low_weight = 0.1\n          self.mid_weight = 1.0\n          self.high_weight = 0.5\n          self.grad_weight = 0.01\n          \n          self.perceptual_weight = 0.5"
7}

Tool ResultStep 5

OK: Replaced lines 62..67 with 6 line(s) in diffusers-main/custom_train.py. Editable range: 32–156.

[Current file: diffusers-main/custom_train.py | editable: 32–156 | total: 556 lines]
    29| # Loss Function (EDITABLE REGION)
    30| # ============================================================================
    31| 
    32  class VAELoss(nn.Module):
    33      """Frequency-Adaptive Multi-Scale Perceptual VAE Loss.
    34      
    35      Novel contributions:
    36      1. Frequency band decomposition (low/mid/high) with tailored loss functions.
    37      2. Adaptive KL annealing (warm-up then decay).
    38      3. Gradient matching loss for edge preservation.
    39      4. Perceptual loss focused on mid-frequency semantic content.
    40      """
    41  
    42      def __init__(self, device):
    43          super().__init__()
    44          import lpips
    45          # Perceptual loss for mid-frequency band
    46          self.lpips_fn = lpips.LPIPS(net='vgg').to(de

AssistantStep 6edit

Tool Input

1{
2  "op": "replace",
3  "filename": "diffusers-main/custom_train.py",
4  "start_line": 56,
5  "end_line": 67,
6  "content": "        # Loss weights (will be adjusted during training via step)\n        self.base_kl_weight = 1e-6\n        self.kl_warmup_steps = 2000\n        self.kl_max_weight = 1e-4\n        self.kl_decay_steps = 3000\n        \n        self.low_weight = 0.1\n        self.mid_weight = 1.0\n        self.high_weight = 0.5\n        self.grad_weight = 0.01\n        \n        self.perceptual_weight = 0.5"
7}