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Messages: 34

UserStep 0

Task: cv-diffusion-architecture

Diffusion Model Architecture Design

Background

The UNet backbone is the standard architecture for denoising diffusion models. Key design choices include:

Block types: Whether to use pure convolutional blocks (DownBlock2D / UpBlock2D) or blocks with self-attention (AttnDownBlock2D / AttnUpBlock2D), and at which resolution levels.
Attention placement: Self-attention is expensive at high resolutions (32x32) but may improve global coherence. The original DDPM places attention only at 16x16.
Depth and normalization: layers_per_block, norm_num_groups, attention_head_dim, and other structural hyperparameters.
Custom modules: Entirely new backbone designs (e.g., hybrid convolution-transformer, gated blocks, multi-scale fusion) as long as they satisfy the input/output interface.

Research Question

What UNet architecture achieves the best FID on unconditional CIFAR-10 diffusion, given a fixed training procedure (epsilon prediction, DDIM sampling, same optimizer and schedule)?

Task

You are given custom_train.py, a self-contained unconditional DDPM training script on CIFAR-10. Everything is fixed except the build_model(device) function.

Your goal is to design a model architecture that achieves lower FID than the baselines. The model must satisfy:

Input: (x, timestep) where x is [B, 3, 32, 32], timestep is [B]
Output: an object with .sample attribute of shape [B, 3, 32, 32]
UNet2DModel from diffusers satisfies this interface, but you may also build a fully custom nn.Module.

Channel widths are provided via the BLOCK_OUT_CHANNELS environment variable (e.g. "128,256,256,256") so the same architecture scales across evaluation tiers. LAYERS_PER_BLOCK (default 2) is also available.

Evaluation

Dataset: CIFAR-10 (32x32, unconditional)
Training: fixed epsilon prediction, MSE loss, AdamW lr=2e-4, EMA
Model scales:
- Small: block_out_channels=(64,128,128,128), ~9M params, batch 128
- Medium: block_out_channels=(128,256,256,256), ~36M params, batch 128
- Large: block_out_channels=(256,512,512,512), ~140M params, batch 64
Training: 35,000 steps per scale, EMA rate 0.9995
Metric: FID (lower is better), computed with clean-fid against CIFAR-10 train set (50,000 samples)
Inference: 50-step DDIM sampling

Baselines

standard: Original DDPM architecture (Ho et al., 2020). Self-attention placed only at the 16x16 resolution level. This is the default google/ddpm-cifar10-32 configuration.
full-attn: Self-attention at every resolution level (32x32, 16x16, 8x8, 4x4). More expressive but significantly more compute and memory per step.
no-attn: Pure convolutional UNet with no per-resolution self-attention. Only the mid-block retains its default self-attention layer. Fastest and fewest parameters.

diffusers-main/custom_train.py [EDITABLE — lines 31–58 only]

undefined

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "diffusers-main/custom_train.py",
4  "start_line": 31,
5  "end_line": 58,
6  "content": "def build_model(device):\n    \"\"\"Hybrid attention: self-attention at 16x16 and 8x8 resolutions only.\"\"\"\n    channels = (128, 256, 256, 256)\n    if os.environ.get('BLOCK_OUT_CHANNELS'):\n        channels = tuple(int(x) for x in os.environ['BLOCK_OUT_CHANNELS'].split(','))\n    layers = int(os.environ.get('LAYERS_PER_BLOCK', 2))\n\n    return UNet2DModel(\n        sample_size=32,\n        in_channels=3,\n        out_channels=3,\n        block_out_channels=channels,\n        down_block_types=(\"DownBlock2D\", \"AttnDownBlock2D\", \"AttnDownBlock2D\", \"DownBlock2D\"),\n        up_block_types=(\"UpBlock2D\", \"AttnUpBlock2D\", \"AttnUpBlock2D\", \"UpBlock2D\"),\n        layers_per_block=layers,\n        norm_num_groups=16,  # reduced groups for better normalization\n        norm_eps=1e-6,\n        act_fn=\"silu\",\n        time_embedding_type=\"positional\",\n        flip_sin_to_cos=False,\n        freq_shift=1,\n        downsample_padding=0,\n        resnet_time_scale_shift=\"scale_shift\",  # improved time conditioning\n        attention_head_dim=8,  # explicit head dimension\n    ).to(device)"
7}

Tool ResultStep 1

OK: Replaced lines 31..58 with 25 line(s) in diffusers-main/custom_train.py. Editable range: 31–55.

[Current file: diffusers-main/custom_train.py | editable: 31–55 | total: 408 lines]
    28| # Model Architecture (EDITABLE REGION)
    29| # ============================================================================
    30| 
    31  def build_model(device):
    32      """Hybrid attention: self-attention at 16x16 and 8x8 resolutions only."""
    33      channels = (128, 256, 256, 256)
    34      if os.environ.get('BLOCK_OUT_CHANNELS'):
    35          channels = tuple(int(x) for x in os.environ['BLOCK_OUT_CHANNELS'].split(','))
    36      layers = int(os.environ.get('LAYERS_PER_BLOCK', 2))
    37  
    38      return UNet2DModel(
    39          sample_size=32,
    40          in_channels=3,
    41          out_channels=3,
    42          block_out_channels=channels,
    43          down_block_types=("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "DownBlock2D"),
    44

AssistantStep 2test

Tool ResultStep 2

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

### train_small (scripts/train_small.sh)
Training results:
Model parameters: 9.9M | GPUs: 8
step 200/35000 | loss 0.0367 | 20.7s
step 400/35000 | loss 0.0331 | 19.6s
step 600/35000 | loss 0.0392 | 19.7s
step 800/35000 | loss 0.0276 | 19.6s
step 1000/35000 | loss 0.0412 | 19.8s
step 1200/35000 | loss 0.0335 | 19.5s
step 1400/35000 | loss 0.0318 | 19.9s
step 1600/35000 | loss 0.0229 | 19.2s
step 1800/35000 | loss 0.0321 | 19.4s
step 2000/35000 | loss 0.0438 | 19.5s
step 2200/35000 | loss 0.0431 | 19.5s
step 2400/35000 | loss 0.0277 | 19.2s
step 2600/35000 | loss 0.0471 | 19.8s
step 2800/35000 | loss 0.0285 | 19.5s
step 3000/35000 | loss 0.0346 | 19.4s
step 3200/35000 | loss 0.0348 | 19.6s
step 3400/35000 | loss 0.0326 | 19.5s
step 3600/35000 | loss 0.0299 | 19.0s
step 3800/35000 | loss 0.0353 | 19.7s
step 4000/35000 | loss 0.0231 | 19.6s
step 4200/35000 | loss 0.0348 | 19.4s
step 4400/35000 | loss 0.0206 | 19.6s
step

AssistantStep 3edit

Tool Input

1{
2  "op": "replace",
3  "filename": "diffusers-main/custom_train.py",
4  "start_line": 31,
5  "end_line": 55,
6  "content": "def build_model(device):\n    \"\"\"Attention at 16x16, 8x8, and 4x4 resolutions (skip 32x32).\"\"\"\n    channels = (128, 256, 256, 256)\n    if os.environ.get('BLOCK_OUT_CHANNELS'):\n        channels = tuple(int(x) for x in os.environ['BLOCK_OUT_CHANNELS'].split(','))\n    layers = int(os.environ.get('LAYERS_PER_BLOCK', 2))\n\n    return UNet2DModel(\n        sample_size=32,\n        in_channels=3,\n        out_channels=3,\n        block_out_channels=channels,\n        down_block_types=(\"DownBlock2D\", \"AttnDownBlock2D\", \"AttnDownBlock2D\", \"AttnDownBlock2D\"),\n        up_block_types=(\"AttnUpBlock2D\", \"AttnUpBlock2D\", \"AttnUpBlock2D\", \"UpBlock2D\"),\n        layers_per_block=layers,\n        norm_num_groups=32,  # revert to default\n        norm_eps=1e-6,\n        act_fn=\"silu\",\n        time_embedding_type=\"positional\",\n        flip_sin_to_cos=False,\n        freq_shift=1,\n        downsample_padding=0,\n        resnet_time_scale_shift=\"scale_shift\",\n        attention_head_dim=8,\n    ).to(device)"
7}

Tool ResultStep 3

OK: Replaced lines 31..55 with 25 line(s) in diffusers-main/custom_train.py. Editable range: 31–55.

[Current file: diffusers-main/custom_train.py | editable: 31–55 | total: 408 lines]
    28| # Model Architecture (EDITABLE REGION)
    29| # ============================================================================
    30| 
    31  def build_model(device):
    32      """Attention at 16x16, 8x8, and 4x4 resolutions (skip 32x32)."""
    33      channels = (128, 256, 256, 256)
    34      if os.environ.get('BLOCK_OUT_CHANNELS'):
    35          channels = tuple(int(x) for x in os.environ['BLOCK_OUT_CHANNELS'].split(','))
    36      layers = int(os.environ.get('LAYERS_PER_BLOCK', 2))
    37  
    38      return UNet2DModel(
    39          sample_size=32,
    40          in_channels=3,
    41          out_channels=3,
    42          block_out_channels=channels,
    43          down_block_types=("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"),
    44          up_

AssistantStep 4test

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "diffusers-main/custom_train.py",
4  "start_line": 31,
5  "end_line": 58,
6  "content": "def build_model(device):\n    \"\"\"Asymmetric attention UNet: attention at 16x16,8x8 on down path, attention at 4x4,8x8 on up path.\n    Uses GELU activation and smaller norm groups for potential better performance.\"\"\"\n    channels = (128, 256, 256, 256)\n    if os.environ.get('BLOCK_OUT_CHANNELS'):\n        channels = tuple(int(x) for x in os.environ['BLOCK_OUT_CHANNELS'].split(','))\n    layers = int(os.environ.get('LAYERS_PER_BLOCK', 2))\n\n    return UNet2DModel(\n        sample_size=32,\n        in_channels=3,\n        out_channels=3,\n        block_out_channels=channels,\n        # Down: attention at 16x16 (index1) and 8x8 (index2), conv at 32x32 and 4x4\n        down_block_types=(\"DownBlock2D\", \"AttnDownBlock2D\", \"AttnDownBlock2D\", \"DownBlock2D\"),\n        # Up: attention at 4x4->8x8 (index0) and 8x8->16x16 (index1), conv at 16x16->32x32 and output\n        up_block_types=(\"AttnUpBlock2D\", \"AttnUpBlock2D\", \"UpBlock2D\", \"UpBlock2D\"),\n        layers_per_block=layers,\n        norm_num_groups=16,  # smaller groups for potentially better normalization\n        norm_eps=1e-6,\n        act_fn=\"gelu\",  # GELU activation as used in transformers\n        time_embedding_type=\"positional\",\n        flip_sin_to_cos=True,\n        freq_shift=0,\n        downsample_padding=1,\n        attention_head_dim=8,\n        resnet_time_scale_shift=\"scale_shift\",\n    ).to(device)"
7}

Tool ResultStep 1

OK: Replaced lines 31..58 with 28 line(s) in diffusers-main/custom_train.py. Editable range: 31–58.

[Current file: diffusers-main/custom_train.py | editable: 31–58 | total: 411 lines]
    28| # Model Architecture (EDITABLE REGION)
    29| # ============================================================================
    30| 
    31  def build_model(device):
    32      """Asymmetric attention UNet: attention at 16x16,8x8 on down path, attention at 4x4,8x8 on up path.
    33      Uses GELU activation and smaller norm groups for potential better performance."""
    34      channels = (128, 256, 256, 256)
    35      if os.environ.get('BLOCK_OUT_CHANNELS'):
    36          channels = tuple(int(x) for x in os.environ['BLOCK_OUT_CHANNELS'].split(','))
    37      layers = int(os.environ.get('LAYERS_PER_BLOCK', 2))
    38  
    39      return UNet2DModel(
    40          sample_size=32,
    41          in_channels=3,
    42          out_channels=3,
    43          block_out_channels=channel

AssistantStep 2test

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "diffusers-main/custom_train.py",
4  "start_line": 31,
5  "end_line": 58,
6  "content": "def build_model(device):\n    \"\"\"Progressive Attention UNet: attention at all resolutions with increasing head dimensions.\n    \n    Attention head dimensions grow as resolution decreases: more capacity for global structure.\n    Uses smaller head dimension at high-resolution (32x32) to limit compute.\n    \"\"\"\n    channels = (128, 256, 256, 256)\n    if os.environ.get('BLOCK_OUT_CHANNELS'):\n        channels = tuple(int(x) for x in os.environ['BLOCK_OUT_CHANNELS'].split(','))\n    layers = int(os.environ.get('LAYERS_PER_BLOCK', 2))\n    \n    # Progressive attention head dimensions: (32x32, 16x16, 8x8, 4x4)\n    # Head dimension doubles each level, starting with 4 at highest resolution.\n    head_dims = (4, 8, 16, 32)\n    \n    return UNet2DModel(\n        sample_size=32,\n        in_channels=3,\n        out_channels=3,\n        block_out_channels=channels,\n        down_block_types=(\"AttnDownBlock2D\", \"AttnDownBlock2D\", \"AttnDownBlock2D\", \"AttnDownBlock2D\"),\n        up_block_types=(\"AttnUpBlock2D\", \"AttnUpBlock2D\", \"AttnUpBlock2D\", \"AttnUpBlock2D\"),\n        layers_per_block=layers,\n        norm_num_groups=8,  # smaller groups for better gradient flow\n        norm_eps=1e-6,\n        act_fn=\"silu\",\n        time_embedding_type=\"positional\",\n        flip_sin_to_cos=True,\n        freq_shift=0,\n        downsample_padding=1,\n        attention_head_dim=head_dims,  # tuple per block\n        resnet_time_scale_shift=\"scale_shift\",\n    ).to(device)"
7}

Tool ResultStep 1

OK: Replaced lines 31..58 with 33 line(s) in diffusers-main/custom_train.py. Editable range: 31–63.

[Current file: diffusers-main/custom_train.py | editable: 31–63 | total: 416 lines]
    28| # Model Architecture (EDITABLE REGION)
    29| # ============================================================================
    30| 
    31  def build_model(device):
    32      """Progressive Attention UNet: attention at all resolutions with increasing head dimensions.
    33      
    34      Attention head dimensions grow as resolution decreases: more capacity for global structure.
    35      Uses smaller head dimension at high-resolution (32x32) to limit compute.
    36      """
    37      channels = (128, 256, 256, 256)
    38      if os.environ.get('BLOCK_OUT_CHANNELS'):
    39          channels = tuple(int(x) for x in os.environ['BLOCK_OUT_CHANNELS'].split(','))
    40      layers = int(os.environ.get('LAYERS_PER_BLOCK', 2))
    41      
    42      # Progressive attention head dimensi

AssistantStep 2test

Tool ResultStep 2

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

### train_small (scripts/train_small.sh)
Training results:
[rank0]: Traceback (most recent call last):
[rank0]: TypeError: unsupported operand type(s) for //: 'int' and 'tuple'
[rank6]: Traceback (most recent call last):
[rank6]: TypeError: unsupported operand type(s) for //: 'int' and 'tuple'
[rank5]: Traceback (most recent call last):
[rank5]: TypeError: unsupported operand type(s) for //: 'int' and 'tuple'
[rank7]: Traceback (most recent call last):
[rank7]: TypeError: unsupported operand type(s) for //: 'int' and 'tuple'
[rank2]: Traceback (most recent call last):
[rank2]: TypeError: unsupported operand type(s) for //: 'int' and 'tuple'
[rank4]: Traceback (most recent call last):
[rank4]: TypeError: unsupported operand type(s) for //: 'int' and 'tuple'
[rank1]: Traceback (most recent call last):
[rank1]: TypeError: unsupported operand type(s) for //: 'int' and 'tuple'
Traceback (most recent call last):
File "/ho

AssistantStep 3undo

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 3

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

AssistantStep 4edit

Tool Input

1{
2  "op": "replace",
3  "filename": "diffusers-main/custom_train.py",
4  "start_line": 31,
5  "end_line": 58,
6  "content": "def build_model(device):\n    \"\"\"Enhanced full-attention UNet with improved normalization and activation.\n    \n    Attention at all four resolutions (32x32, 16x16, 8x8, 4x4) for maximum global coupling.\n    Uses smaller norm_num_groups (8) for better gradient flow with small batch sizes.\n    GELU activation instead of SiLU for potentially smoother gradients.\n    \"\"\"\n    channels = (128, 256, 256, 256)\n    if os.environ.get('BLOCK_OUT_CHANNELS'):\n        channels = tuple(int(x) for x in os.environ['BLOCK_OUT_CHANNELS'].split(','))\n    layers = int(os.environ.get('LAYERS_PER_BLOCK', 2))\n    \n    return UNet2DModel(\n        sample_size=32,\n        in_channels=3,\n        out_channels=3,\n        block_out_channels=channels,\n        down_block_types=(\"AttnDownBlock2D\", \"AttnDownBlock2D\", \"AttnDownBlock2D\", \"AttnDownBlock2D\"),\n        up_block_types=(\"AttnUpBlock2D\", \"AttnUpBlock2D\", \"AttnUpBlock2D\", \"AttnUpBlock2D\"),\n        layers_per_block=layers,\n        norm_num_groups=8,  # reduced from 32 for finer normalization\n        norm_eps=1e-6,\n        act_fn=\"gelu\",  # GELU instead of SiLU\n        time_embedding_type=\"positional\",\n        flip_sin_to_cos=True,\n        freq_shift=0,\n        downsample_padding=1,\n        attention_head_dim=8,\n        resnet_time_scale_shift=\"scale_shift\",\n    ).to(device)"
7}

Tool ResultStep 4

OK: Replaced lines 31..58 with 30 line(s) in diffusers-main/custom_train.py. Editable range: 31–60.

[Current file: diffusers-main/custom_train.py | editable: 31–60 | total: 413 lines]
    28| # Model Architecture (EDITABLE REGION)
    29| # ============================================================================
    30| 
    31  def build_model(device):
    32      """Enhanced full-attention UNet with improved normalization and activation.
    33      
    34      Attention at all four resolutions (32x32, 16x16, 8x8, 4x4) for maximum global coupling.
    35      Uses smaller norm_num_groups (8) for better gradient flow with small batch sizes.
    36      GELU activation instead of SiLU for potentially smoother gradients.
    37      """
    38      channels = (128, 256, 256, 256)
    39      if os.environ.get('BLOCK_OUT_CHANNELS'):
    40          channels = tuple(int(x) for x in os.environ['BLOCK_OUT_CHANNELS'].split(','))
    41      layers = int(os.environ.get('LAYERS_PER_BLOCK

AssistantStep 5test