Agent Conversation: google/gemini-3.1-pro-preview

Model: google/gemini-3.1-pro-preview

Total steps: 7

Messages: 15

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  "content": "class VAELoss(nn.Module):\n    def __init__(self, device):\n        super().__init__()\n        import lpips\n        import math\n        self.device = device\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        self.kl_weight = 1e-6\n        self.perceptual_weight = 0.5\n        self.freq_weight = 1.0\n\n        H, W = 32, 32\n        Y, X = torch.meshgrid(torch.linspace(-1, 1, H), torch.linspace(-1, 1, W), indexing='ij')\n        R = torch.sqrt(X**2 + Y**2)\n        self.R_norm = (R / math.sqrt(2.0)).to(device).unsqueeze(0).unsqueeze(0)\n        \n        self.lf_weight = torch.exp(- (self.R_norm**2) / 0.1)\n        self.hf_weight = 1.0 - self.lf_weight\n        self.hf_emphasis = 1.0 + self.R_norm\n\n    def forward(self, recon, target, posterior, step):\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        recon_fft = torch.fft.fftshift(torch.fft.fft2(recon.float(), norm=\"ortho\"))\n        target_fft = torch.fft.fftshift(torch.fft.fft2(target.float(), norm=\"ortho\"))\n        \n        complex_diff = torch.abs(recon_fft - target_fft)\n        lf_loss = (complex_diff * self.lf_weight).mean()\n        \n        recon_amp = torch.abs(recon_fft)\n        target_amp = torch.abs(target_fft)\n        amp_diff = torch.abs(recon_amp - target_amp)\n        hf_loss = (amp_diff * self.hf_weight * self.hf_emphasis).mean()\n        \n        freq_loss = lf_loss + hf_loss\n        \n        loss = rec_loss + self.perceptual_weight * p_loss + self.kl_weight * kl_loss + self.freq_weight * freq_loss\n        \n        return loss, {\n            \"rec_loss\": rec_loss.item(),\n            \"p_loss\": p_loss.item(),\n            \"kl_loss\": kl_loss.item(),\n            \"freq_loss\": freq_loss.item(),\n            \"lf_loss\": lf_loss.item(),\n            \"hf_loss\": hf_loss.item()\n        }",
3  "start_line": 32,
4  "op": "replace",
5  "filename": "custom_train.py",
6  "end_line": 76
7}

Tool ResultStep 1

ERROR: Package 'custom_train.py' is not in allowed packages

AssistantStep 2edit

Tool Input

1{
2  "start_line": 32,
3  "content": "class VAELoss(nn.Module):\n    def __init__(self, device):\n        super().__init__()\n        import lpips\n        import math\n        self.device = device\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        self.kl_weight = 1e-6\n        self.perceptual_weight = 0.5\n        self.freq_weight = 1.0\n\n        H, W = 32, 32\n        Y, X = torch.meshgrid(torch.linspace(-1, 1, H), torch.linspace(-1, 1, W), indexing='ij')\n        R = torch.sqrt(X**2 + Y**2)\n        self.R_norm = (R / math.sqrt(2.0)).to(device).unsqueeze(0).unsqueeze(0)\n        \n        self.lf_weight = torch.exp(- (self.R_norm**2) / 0.1)\n        self.hf_weight = 1.0 - self.lf_weight\n        self.hf_emphasis = 1.0 + self.R_norm\n\n    def forward(self, recon, target, posterior, step):\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        recon_fft = torch.fft.fftshift(torch.fft.fft2(recon.float(), norm=\"ortho\"))\n        target_fft = torch.fft.fftshift(torch.fft.fft2(target.float(), norm=\"ortho\"))\n        \n        complex_diff = torch.abs(recon_fft - target_fft)\n        lf_loss = (complex_diff * self.lf_weight).mean()\n        \n        recon_amp = torch.abs(recon_fft)\n        target_amp = torch.abs(target_fft)\n        amp_diff = torch.abs(recon_amp - target_amp)\n        hf_loss = (amp_diff * self.hf_weight * self.hf_emphasis).mean()\n        \n        freq_loss = lf_loss + hf_loss\n        \n        loss = rec_loss + self.perceptual_weight * p_loss + self.kl_weight * kl_loss + self.freq_weight * freq_loss\n        \n        return loss, {\n            \"rec_loss\": rec_loss.item(),\n            \"p_loss\": p_loss.item(),\n            \"kl_loss\": kl_loss.item(),\n            \"freq_loss\": freq_loss.item(),\n            \"lf_loss\": lf_loss.item(),\n            \"hf_loss\": hf_loss.item()\n        }",
4  "end_line": 76,
5  "filename": "diffusers-main/custom_train.py",
6  "op": "replace"
7}

Tool ResultStep 2

OK: Replaced lines 32..76 with 52 line(s) in diffusers-main/custom_train.py. Editable range: 32–83.

[Current file: diffusers-main/custom_train.py | editable: 32–83 | total: 483 lines]
    29| # Loss Function (EDITABLE REGION)
    30| # ============================================================================
    31| 
    32  class VAELoss(nn.Module):
    33      def __init__(self, device):
    34          super().__init__()
    35          import lpips
    36          import math
    37          self.device = device
    38          self.lpips_fn = lpips.LPIPS(net='vgg').to(device)
    39          self.lpips_fn.eval()
    40          for p in self.lpips_fn.parameters():
    41              p.requires_grad_(False)
    42              
    43          self.kl_weight = 1e-6
    44          self.perceptual_weight = 0.5
    45          self.freq_weight = 1.0
    46  
    47          H, W = 32, 32
    48          Y, X = torch.meshgrid(torch.linspace(-1, 1, H), torch.linspace(-1, 1, W), in

AssistantStep 3test

Tool ResultStep 3

[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=0.5642 rec_loss=0.2016 p_loss=0.4629 kl_loss=382.0620 freq_loss=0.1308 lf_loss=0.0520 hf_loss=0.0788 | 14.3s
step 400/20000 | loss=0.4518 rec_loss=0.1523 p_loss=0.3796 kl_loss=537.7694 freq_loss=0.1091 lf_loss=0.0391 hf_loss=0.0699 | 13.1s
step 600/20000 | loss=0.3726 rec_loss=0.1286 p_loss=0.2852 kl_loss=787.7623 freq_loss=0.1006 lf_loss=0.0319 hf_loss=0.0688 | 13.1s
step 800/20000 | loss=0.3174 rec_loss=0.1110 p_loss=0.2356 kl_loss=944.4575 freq_loss=0.0876 lf_loss=0.0265 hf_loss=0.0611 | 13.0s
step 1000/20000 | loss=0.2791 rec_loss=0.0939 p_loss=0.2085 kl_loss=1041.9685 freq_loss=0.0799 lf_loss=0.0228 hf_loss=0.0571 | 12.6s
step 1200/20000 | loss=0.2561 rec_loss=0.0894 p_loss=0.1688 kl_loss=1187.6589 freq_loss=0.0811 lf_loss=0.0204 hf_loss=0.0607 | 12.9s
step 1400/20000 | loss=0.2497 rec_loss=0.0895 p_loss=0.1548 kl_l

AssistantStep 4edit

Tool Input

1{
2  "content": "class VAELoss(nn.Module):\n    def __init__(self, device):\n        super().__init__()\n        import lpips\n        import math\n        self.device = device\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        self.kl_weight = 1e-6\n        self.perceptual_weight = 1.0\n\n        H, W = 32, 32\n        Y, X = torch.meshgrid(torch.linspace(-1, 1, H), torch.linspace(-1, 1, W), indexing='ij')\n        R = torch.sqrt(X**2 + Y**2)\n        R_norm = (R / math.sqrt(2.0)).to(device).unsqueeze(0).unsqueeze(0)\n        \n        self.lf_mask = torch.exp(- (R_norm**2) / 0.1)\n        self.hf_mask = 1.0 - self.lf_mask\n        self.hf_emphasis = 1.0 + R_norm\n\n    def forward(self, recon, target, posterior, step):\n        p_loss = self.lpips_fn(recon.float(), target.float()).mean()\n        kl_loss = posterior.kl().mean()\n        \n        recon_fft = torch.fft.fftshift(torch.fft.fft2(recon.float(), norm=\"ortho\"))\n        target_fft = torch.fft.fftshift(torch.fft.fft2(target.float(), norm=\"ortho\"))\n        \n        recon_lf = torch.fft.ifft2(torch.fft.ifftshift(recon_fft * self.lf_mask), norm=\"ortho\").real\n        target_lf = torch.fft.ifft2(torch.fft.ifftshift(target_fft * self.lf_mask), norm=\"ortho\").real\n        lf_loss = F.l1_loss(recon_lf, target_lf)\n        \n        recon_amp = torch.abs(recon_fft)\n        target_amp = torch.abs(target_fft)\n        amp_diff = torch.abs(recon_amp - target_amp)\n        hf_loss = (amp_diff * self.hf_mask * self.hf_emphasis).mean()\n        \n        loss = lf_loss + 0.5 * hf_loss + self.perceptual_weight * p_loss + self.kl_weight * kl_loss\n        \n        return loss, {\n            \"lf_loss\": lf_loss.item(),\n            \"hf_loss\": hf_loss.item(),\n            \"p_loss\": p_loss.item(),\n            \"kl_loss\": kl_loss.item()\n        }",
3  "filename": "diffusers-main/custom_train.py",
4  "start_line": 32,
5  "end_line": 83,
6  "op": "replace"
7}

Tool ResultStep 4

OK: Replaced lines 32..83 with 47 line(s) in diffusers-main/custom_train.py. Editable range: 32–78.

[Current file: diffusers-main/custom_train.py | editable: 32–78 | total: 478 lines]
    29| # Loss Function (EDITABLE REGION)
    30| # ============================================================================
    31| 
    32  class VAELoss(nn.Module):
    33      def __init__(self, device):
    34          super().__init__()
    35          import lpips
    36          import math
    37          self.device = device
    38          self.lpips_fn = lpips.LPIPS(net='vgg').to(device)
    39          self.lpips_fn.eval()
    40          for p in self.lpips_fn.parameters():
    41              p.requires_grad_(False)
    42              
    43          self.kl_weight = 1e-6
    44          self.perceptual_weight = 1.0
    45  
    46          H, W = 32, 32
    47          Y, X = torch.meshgrid(torch.linspace(-1, 1, H), torch.linspace(-1, 1, W), indexing='ij')
    48          R = torch.

AssistantStep 5test

Tool ResultStep 5

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

### train_small (scripts/train_small.sh)
Model parameters: 13.9M | GPUs: 8
step 200/20000 | loss=0.6547 lf_loss=0.1620 hf_loss=0.0829 p_loss=0.4509 kl_loss=382.1886 | 14.1s
step 400/20000 | loss=0.4825 lf_loss=0.0978 hf_loss=0.0744 p_loss=0.3469 kl_loss=565.1967 | 13.1s
step 600/20000 | loss=0.4129 lf_loss=0.0976 hf_loss=0.0759 p_loss=0.2766 kl_loss=721.9986 | 13.1s
step 800/20000 | loss=0.3421 lf_loss=0.0652 hf_loss=0.0671 p_loss=0.2424 kl_loss=926.6144 | 13.0s
step 1000/20000 | loss=0.3214 lf_loss=0.0764 hf_loss=0.0626 p_loss=0.2126 kl_loss=1058.3762 | 12.9s
step 1200/20000 | loss=0.2727 lf_loss=0.0595 hf_loss=0.0661 p_loss=0.1791 kl_loss=1156.2877 | 13.0s
step 1400/20000 | loss=0.2306 lf_loss=0.0379 hf_loss=0.0644 p_loss=0.1592 kl_loss=1256.1853 | 13.0s
step 1600/20000 | loss=0.2114 lf_loss=0.0441 hf_loss=0.0575 p_loss=0.1372 kl_loss=1385.3876 | 12.8s
step 1800/20000 | loss=0.2032 lf_loss=0.0399 hf_loss=0.0597 p_l

AssistantStep 6edit

Tool Input

1{
2  "filename": "diffusers-main/custom_train.py",
3  "end_line": 78,
4  "content": "class VAELoss(nn.Module):\n    def __init__(self, device):\n        super().__init__()\n        import lpips\n        import math\n        self.device = device\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        self.kl_weight = 1e-6\n        self.perceptual_weight = 1.0\n\n        H, W = 32, 32\n        Y, X = torch.meshgrid(torch.linspace(-1, 1, H), torch.linspace(-1, 1, W), indexing='ij')\n        R = torch.sqrt(X**2 + Y**2)\n        R_norm = (R / math.sqrt(2.0)).to(device).unsqueeze(0).unsqueeze(0)\n        \n        self.lf_mask = torch.exp(- (R_norm**2) / 0.1)\n        \n        self.num_rings = 16\n        rings = torch.floor(R_norm * self.num_rings)\n        rings = torch.clamp(rings, 0, self.num_rings - 1)\n        self.ring_masks = []\n        for i in range(self.num_rings):\n            weight = 1.0 + (i / self.num_rings)\n            self.ring_masks.append(( (rings == i).float(), weight ))\n\n    def forward(self, recon, target, posterior, step):\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        recon_fft = torch.fft.fftshift(torch.fft.fft2(recon.float(), norm=\"ortho\"))\n        target_fft = torch.fft.fftshift(torch.fft.fft2(target.float(), norm=\"ortho\"))\n        \n        recon_lf = torch.fft.ifft2(torch.fft.ifftshift(recon_fft * self.lf_mask), norm=\"ortho\").real\n        target_lf = torch.fft.ifft2(torch.fft.ifftshift(target_fft * self.lf_mask), norm=\"ortho\").real\n        lf_loss = F.l1_loss(recon_lf, target_lf)\n        \n        recon_amp = torch.abs(recon_fft)\n        target_amp = torch.abs(target_fft)\n        radial_loss = 0.0\n        for mask, weight in self.ring_masks:\n            r_recon = (recon_amp * mask).sum(dim=(-2, -1)) / (mask.sum() + 1e-8)\n            r_target = (target_amp * mask).sum(dim=(-2, -1)) / (mask.sum() + 1e-8)\n            radial_loss += weight * F.l1_loss(r_recon, r_target)\n        radial_loss = radial_loss / self.num_rings\n        \n        loss = 0.5 * rec_loss + 1.0 * lf_loss + 2.0 * radial_loss + self.perceptual_weight * p_loss + self.kl_weight * kl_loss\n        \n        return loss, {\n            \"rec_loss\": rec_loss.item(),\n            \"lf_loss\": lf_loss.item(),\n            \"radial_loss\": radial_loss.item(),\n            \"p_loss\": p_loss.item(),\n            \"kl_loss\": kl_loss.item()\n        }",
5  "start_line": 32,
6  "op": "replace"
7}

Tool ResultStep 6

OK: Replaced lines 32..78 with 59 line(s) in diffusers-main/custom_train.py. Editable range: 32–90.

[Current file: diffusers-main/custom_train.py | editable: 32–90 | total: 490 lines]
    29| # Loss Function (EDITABLE REGION)
    30| # ============================================================================
    31| 
    32  class VAELoss(nn.Module):
    33      def __init__(self, device):
    34          super().__init__()
    35          import lpips
    36          import math
    37          self.device = device
    38          self.lpips_fn = lpips.LPIPS(net='vgg').to(device)
    39          self.lpips_fn.eval()
    40          for p in self.lpips_fn.parameters():
    41              p.requires_grad_(False)
    42              
    43          self.kl_weight = 1e-6
    44          self.perceptual_weight = 1.0
    45  
    46          H, W = 32, 32
    47          Y, X = torch.meshgrid(torch.linspace(-1, 1, H), torch.linspace(-1, 1, W), indexing='ij')
    48          R = torch.

AssistantStep 7test

Tool ResultStep 7

[Test #3] (0 tests remaining)

### train_small (scripts/train_small.sh)
Model parameters: 13.9M | GPUs: 8
step 200/20000 | loss=0.9495 rec_loss=0.2403 lf_loss=0.1598 radial_loss=0.0892 p_loss=0.4907 kl_loss=292.7350 | 14.8s
step 400/20000 | loss=0.8517 rec_loss=0.1987 lf_loss=0.1342 radial_loss=0.0945 p_loss=0.4287 kl_loss=479.1118 | 13.9s
step 600/20000 | loss=0.6935 rec_loss=0.1751 lf_loss=0.1066 radial_loss=0.0801 p_loss=0.3384 kl_loss=646.7761 | 14.3s
step 800/20000 | loss=0.5260 rec_loss=0.1318 lf_loss=0.0686 radial_loss=0.0548 p_loss=0.2812 kl_loss=741.4052 | 13.8s
step 1000/20000 | loss=0.4630 rec_loss=0.1230 lf_loss=0.0611 radial_loss=0.0443 p_loss=0.2510 kl_loss=880.7092 | 13.8s
step 1200/20000 | loss=0.4059 rec_loss=0.1201 lf_loss=0.0508 radial_loss=0.0390 p_loss=0.2159 kl_loss=965.4910 | 13.7s
step 1400/20000 | loss=0.4042 rec_loss=0.1217 lf_loss=0.0519 radial_loss=0.0414 p_loss=0.2074 kl_loss=1114.4631 | 13.7s
step 1600/20000 | loss=0.4100 rec_loss=0.1181 lf_loss=0.0665 rad