Agent Conversation: qwen/qwen3.6-plus

Model: qwen/qwen3.6-plus
Total steps: 5
Messages: 11
UserStep 0

Task: cv-diffusion-prediction

Diffusion Prediction Parameterization

Background

In DDPM training, the model predicts a target from noisy input x_t. The noisy sample is constructed as:

x_t = sqrt(alpha_t) * x_0 + sqrt(1 - alpha_t) * epsilon

There are three standard parameterizations for what the model predicts:

  1. Epsilon prediction (Ho et al., 2020): predict the noise epsilon
  2. X0 prediction: directly predict the clean image x_0
  3. V-prediction (Salimans & Ho, 2022): predict the velocity v = sqrt(alpha_t) * epsilon - sqrt(1 - alpha_t) * x_0

These are mathematically equivalent (one can be converted to any other), but they result in different loss landscapes and training dynamics, leading to different FID scores under the same training budget.

Research Question

Can we design a prediction parameterization that achieves better FID than the standard epsilon, v-prediction, and x0-prediction baselines?

Task

You are given custom_train.py, a self-contained training script that trains an unconditional UNet2DModel (google/ddpm-cifar10-32 architecture) on CIFAR-10.

The editable region contains two functions:

  1. compute_training_target(x_0, noise, timesteps, schedule) — defines what the model should predict during training.

  2. predict_x0(model_output, x_t, timesteps, schedule) — recovers the predicted clean image from the model's output (used during DDIM sampling).

These two functions must be consistent: the sampling procedure must correctly invert the training parameterization.

The schedule dict provides precomputed noise schedule tensors:

  • alphas_cumprod: cumulative product of (1 - beta)
  • sqrt_alpha: sqrt(alphas_cumprod)
  • sqrt_one_minus_alpha: sqrt(1 - alphas_cumprod)

Evaluation

  • Dataset: CIFAR-10 (32x32)
  • Model: UNet2DModel (diffusers backbone) at three 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: 35000 steps per scale, AdamW lr=2e-4, EMA rate 0.9995, 8-GPU DDP
  • Inference: 50-step DDIM sampler
  • Metric: FID (lower is better), computed with clean-fid against CIFAR-10 train set (50k samples)

Baselines

Your method is evaluated on train_small and train_medium (train_large is skipped during agent runs). FID (lower is better) for the 3 baselines:

NameTargetfid (small)fid (medium)
epsilonpredict noise ε (standard DDPM parameterization)18.588.46
vpredpredict velocity v = α_t * ε - σ_t * x_0 (v-prediction, Salimans & Ho 2022)21.7011.59
x0predpredict clean data x_0 directly25.2513.56

The strongest baseline is epsilon at fid 18.58 (small) / 8.46 (medium) — standard noise prediction remains the best single-target parameterization at this scale. Your method must achieve **lower fid than `epsi

AssistantStep 1edit
Tool Input
1{
2  "content": "def compute_training_target(x_0, noise, timesteps, schedule):\n    \"\"\"Adaptive noise-v blending parameterization.\n    \n    Blends between noise prediction (best at high noise) and v-prediction \n    (better at low noise) using alpha as the interpolation weight.\n    \n    target = (1 - alpha) * noise + alpha * v\n           = noise - alpha * sqrt(1-alpha) * x_0\n    \n    At alpha=0: predicts noise (epsilon baseline)\n    At alpha=1: predicts v (vpred baseline)\n    \"\"\"\n    sqrt_alpha = schedule[\"sqrt_alpha\"][timesteps].view(-1, 1, 1, 1)\n    sqrt_one_minus_alpha = schedule[\"sqrt_one_minus_alpha\"][timesteps].view(-1, 1, 1, 1)\n    alpha = schedule[\"alphas_cumprod\"][timesteps].view(-1, 1, 1, 1)\n    \n    # Adaptive weight: w = alpha\n    # High noise (alpha~0): w~0, predict noise\n    # Low noise (alpha~1): w~1, predict v\n    w = alpha\n    \n    # v = sqrt_alpha * noise - sqrt_one_minus_alpha * x_0\n    v_target = sqrt_alpha * noise - sqrt_one_minus_alpha * x_0\n    \n    # Blended target\n    return (1.0 - w) * noise + w * v_target\n\n\ndef predict_x0(model_output, x_t, timesteps, schedule):\n    \"\"\"Recover x_0 from adaptive noise-v blended prediction.\n    \n    Given: model_output = (1-w)*noise + w*v\n           x_t = sqrt_alpha * x_0 + sqrt_one_minus_alpha * noise\n    \n    Derivation:\n    model_output = (1-w)*noise + w*(sqrt_alpha*noise - sqrt_one_minus_alpha*x_0)\n    model_output = A*noise - B*x_0  where A=(1-w)+w*sqrt_alpha, B=w*sqrt_one_minus_alpha\n    \n    From x_t: noise = (x_t - sqrt_alpha*x_0) / sqrt_one_minus_alpha\n    \n    Substitute:\n    model_output = A*(x_t - sqrt_alpha*x_0)/sqrt_one_minus_alpha - B*x_0\n    model_output = (A/sqrt_one_minus_alpha)*x_t - (A*sqrt_alpha/sqrt_one_minus_alpha + B)*x_0\n    \n    Solving for x_0:\n    x_0 = [(A/sqrt_one_minus_alpha)*x_t - model_output] / [A*sqrt_alpha/sqrt_one_minus_alpha + B]\n    \n    With w = alpha, this simplifies to:\n    A = 1 - alpha + alpha*sqrt_alpha\n    B = alpha * sqrt_one_minus_alpha\n    \"\"\"\n    sqrt_alpha = schedule[\"sqrt_alpha\"][timesteps].view(-1, 1, 1, 1)\n    sqrt_one_minus_alpha = schedule[\"sqrt_one_minus_alpha\"][timesteps].view(-1, 1, 1, 1)\n    alpha = schedule[\"alphas_cumprod\"][timesteps].view(-1, 1, 1, 1)\n    \n    w = alpha\n    A = (1.0 - w) + w * sqrt_alpha\n    B = w * sqrt_one_minus_alpha\n    \n    coeff_xt = A / sqrt_one_minus_alpha.clamp(min=1e-8)\n    denom = A * sqrt_alpha / sqrt_one_minus_alpha.clamp(min=1e-8) + B\n    \n    return (coeff_xt * x_t - model_output) / denom.clamp(min=1e-5)",
3  "start_line": 84,
4  "end_line": 118,
5  "filename": "custom_train.py",
6  "op": "replace"
7}
Tool ResultStep 1
ERROR: Package 'custom_train.py' is not in allowed packages
AssistantStep 2edit
Tool Input
1{
2  "content": "def compute_training_target(x_0, noise, timesteps, schedule):\n    \"\"\"Adaptive noise-v blending parameterization.\n    \n    Blends between noise prediction (best at high noise) and v-prediction \n    (better at low noise) using alpha as the interpolation weight.\n    \n    target = (1 - alpha) * noise + alpha * v\n           = noise - alpha * sqrt(1-alpha) * x_0\n    \n    At alpha=0: predicts noise (epsilon baseline)\n    At alpha=1: predicts v (vpred baseline)\n    \"\"\"\n    sqrt_alpha = schedule[\"sqrt_alpha\"][timesteps].view(-1, 1, 1, 1)\n    sqrt_one_minus_alpha = schedule[\"sqrt_one_minus_alpha\"][timesteps].view(-1, 1, 1, 1)\n    alpha = schedule[\"alphas_cumprod\"][timesteps].view(-1, 1, 1, 1)\n    \n    # Adaptive weight: w = alpha\n    # High noise (alpha~0): w~0, predict noise\n    # Low noise (alpha~1): w~1, predict v\n    w = alpha\n    \n    # v = sqrt_alpha * noise - sqrt_one_minus_alpha * x_0\n    v_target = sqrt_alpha * noise - sqrt_one_minus_alpha * x_0\n    \n    # Blended target\n    return (1.0 - w) * noise + w * v_target\n\n\ndef predict_x0(model_output, x_t, timesteps, schedule):\n    \"\"\"Recover x_0 from adaptive noise-v blended prediction.\n    \n    Given: model_output = (1-w)*noise + w*v\n           x_t = sqrt_alpha * x_0 + sqrt_one_minus_alpha * noise\n    \n    Derivation:\n    model_output = (1-w)*noise + w*(sqrt_alpha*noise - sqrt_one_minus_alpha*x_0)\n    model_output = A*noise - B*x_0  where A=(1-w)+w*sqrt_alpha, B=w*sqrt_one_minus_alpha\n    \n    From x_t: noise = (x_t - sqrt_alpha*x_0) / sqrt_one_minus_alpha\n    \n    Substitute:\n    model_output = A*(x_t - sqrt_alpha*x_0)/sqrt_one_minus_alpha - B*x_0\n    model_output = (A/sqrt_one_minus_alpha)*x_t - (A*sqrt_alpha/sqrt_one_minus_alpha + B)*x_0\n    \n    Solving for x_0:\n    x_0 = [(A/sqrt_one_minus_alpha)*x_t - model_output] / [A*sqrt_alpha/sqrt_one_minus_alpha + B]\n    \n    With w = alpha, this simplifies to:\n    A = 1 - alpha + alpha*sqrt_alpha\n    B = alpha * sqrt_one_minus_alpha\n    \"\"\"\n    sqrt_alpha = schedule[\"sqrt_alpha\"][timesteps].view(-1, 1, 1, 1)\n    sqrt_one_minus_alpha = schedule[\"sqrt_one_minus_alpha\"][timesteps].view(-1, 1, 1, 1)\n    alpha = schedule[\"alphas_cumprod\"][timesteps].view(-1, 1, 1, 1)\n    \n    w = alpha\n    A = (1.0 - w) + w * sqrt_alpha\n    B = w * sqrt_one_minus_alpha\n    \n    coeff_xt = A / sqrt_one_minus_alpha.clamp(min=1e-8)\n    denom = A * sqrt_alpha / sqrt_one_minus_alpha.clamp(min=1e-8) + B\n    \n    return (coeff_xt * x_t - model_output) / denom.clamp(min=1e-5)",
3  "start_line": 84,
4  "end_line": 118,
5  "filename": "diffusers-main/custom_train.py",
6  "op": "replace"
7}
Tool ResultStep 2
OK: Replaced lines 84..118 with 63 line(s) in diffusers-main/custom_train.py. Editable range: 83–146.

[Current file: diffusers-main/custom_train.py | editable: 83–146 | total: 520 lines]
    80| # ============================================================================
    81| # Prediction parameterization (EDITABLE REGION)
    82| # ============================================================================
    83  
    84  def compute_training_target(x_0, noise, timesteps, schedule):
    85      """Adaptive noise-v blending parameterization.
    86      
    87      Blends between noise prediction (best at high noise) and v-prediction 
    88      (better at low noise) using alpha as the interpolation weight.
    89      
    90      target = (1 - alpha) * noise + alpha * v
    91             = noise - alpha * sqrt(1-alpha) * x_0
    92      
    93      At alpha=0: predicts noise (epsilon baseline)
    94      At alpha=1: predicts v (vpred baseline)
    95      """
    96
AssistantStep 3edit
Tool Input
1{
2  "content": "    return (coeff_xt * x_t - model_output) / denom.clamp(min=1e-5)\n\n\n# ============================================================================\n# Sampling — uses diffusers DDIMScheduler with predict_x0 bridge\n# ============================================================================",
3  "end_line": 149,
4  "filename": "diffusers-main/custom_train.py",
5  "op": "replace",
6  "start_line": 146
7}
Tool ResultStep 3
ERROR: Lines 146..149 exceed the editable range. You may only edit lines 83–146.

[Current file: diffusers-main/custom_train.py | editable: 83–146 | total: 520 lines]
    80| # ============================================================================
    81| # Prediction parameterization (EDITABLE REGION)
    82| # ============================================================================
    83  
    84  def compute_training_target(x_0, noise, timesteps, schedule):
    85      """Adaptive noise-v blending parameterization.
    86      
    87      Blends between noise prediction (best at high noise) and v-prediction 
    88      (better at low noise) using alpha as the interpolation weight.
    89      
    90      target = (1 - alpha) * noise + alpha * v
    91             = noise - alpha * sqrt(1-alpha) * x_0
    92      
    93      At alpha=0: predicts noise (epsilon baseline)
    94      At alpha=1: predicts v (vpred baseline)
    95      """
    96      sqrt_alpha = schedul
AssistantStep 4edit
Tool Input
1{
2  "content": "    return (coeff_xt * x_t - model_output) / denom.clamp(min=1e-5)\n\n\n# ============================================================================",
3  "end_line": 149,
4  "filename": "diffusers-main/custom_train.py",
5  "op": "replace",
6  "start_line": 146
7}
Tool ResultStep 4
ERROR: Lines 146..149 exceed the editable range. You may only edit lines 83–146.

[Current file: diffusers-main/custom_train.py | editable: 83–146 | total: 520 lines]
    80| # ============================================================================
    81| # Prediction parameterization (EDITABLE REGION)
    82| # ============================================================================
    83  
    84  def compute_training_target(x_0, noise, timesteps, schedule):
    85      """Adaptive noise-v blending parameterization.
    86      
    87      Blends between noise prediction (best at high noise) and v-prediction 
    88      (better at low noise) using alpha as the interpolation weight.
    89      
    90      target = (1 - alpha) * noise + alpha * v
    91             = noise - alpha * sqrt(1-alpha) * x_0
    92      
    93      At alpha=0: predicts noise (epsilon baseline)
    94      At alpha=1: predicts v (vpred baseline)
    95      """
    96      sqrt_alpha = schedul
AssistantStep 5test
Tool ResultStep 5
[Test #1] (2 tests remaining; call submit(n=1) to submit this result)

### train_small (scripts/train_small.sh)
Training results:
Model parameters: 9.0M | GPUs: 8
step 200/35000 | loss 0.0419 | 16.8s
step 400/35000 | loss 0.0374 | 16.0s
step 600/35000 | loss 0.0440 | 16.4s
step 800/35000 | loss 0.0320 | 16.1s
step 1000/35000 | loss 0.0462 | 16.1s
step 1200/35000 | loss 0.0394 | 16.2s
step 1400/35000 | loss 0.0376 | 16.5s
step 1600/35000 | loss 0.0271 | 16.5s
step 1800/35000 | loss 0.0371 | 16.8s
step 2000/35000 | loss 0.0492 | 16.7s
step 2200/35000 | loss 0.0488 | 16.2s
step 2400/35000 | loss 0.0331 | 16.5s
step 2600/35000 | loss 0.0529 | 16.6s
step 2800/35000 | loss 0.0336 | 16.3s
step 3000/35000 | loss 0.0404 | 16.3s
step 3200/35000 | loss 0.0404 | 16.3s
step 3400/35000 | loss 0.0371 | 16.5s
step 3600/35000 | loss 0.0353 | 16.3s
step 3800/35000 | loss 0.0405 | 17.0s
step 4000/35000 | loss 0.0282 | 16.5s
step 4200/35000 | loss 0.0416 | 16.4s
step 4400/35000 | loss 0.0260 | 16.4s
step