Agent Conversation: deepseek-reasoner

Model: deepseek-reasoner

Total steps: 6

Messages: 13

UserStep 0

Task: cv-dbm-scheduler

Task: Time Scheduler for Diffusion Bridge Models (NFE=5)

Background

In diffusion bridge sampling, the time schedule controls the discretization step sizes. Currently, the uniform (linear) and karras schedules are the most widely used baselines in the field.

Objective

Design a novel time schedule optimized specifically for extremely low-step sampling (NFE = 5). Your goal is to achieve a better generation quality (lower FID) than the standard baselines.

⚠️ CRITICAL CONSTRAINT: To maintain compatibility with our evaluation interface, your code MUST be written inside the function named get_sigmas_uniform. Please ignore the function name—do NOT implement a basic linear/uniform schedule. Use this exact function slot to implement your new, advanced mathematical curve.

Implementation

import torch

def get_sigmas_uniform(n, t_min, t_max, device="cpu"):
    """
    Requirements:
    1. Length: Must return a 1D PyTorch tensor of exactly length `n + 1`.
    2. Monotonic: The sequence must strictly decrease from `t_max` to `t_min`.
    3. Terminal Value: The final element (index `n`) must exactly equal `t_min`.
    4. Device: Move the tensor to the requested `device`.
    """
    # For this task, n will typically be 5 (NFE=5).
    # Implement your novel schedule formulation here...
    
    # Example return:
    # return sigmas.to(device)

Action Budget (HARD LIMIT)

You have 20 total actions (every edit/test/submit/undo counts as 1) and 3 total test() calls. After 20 actions the run is force-stopped immediately. You MUST call submit(n=...) before the budget runs out — if no submit happens, your run records ZERO and all your work is lost. Plan accordingly: a typical good run uses ~15 edits, 3 tests, and 1 submit. Save submit budget for the end.

dbim-codebase/ddbm/karras_diffusion.py [EDITABLE — lines 301–311 only]

Lines 301-311:
   301: def get_sigmas_uniform(n, t_min, t_max, device="cpu"):
   302:     """
   303:     Requirements:
   304:     1. Length: Must return a 1D PyTorch tensor of exactly length `n + 1`.
   305:     2. Monotonic: The sequence must strictly decrease from `t_max` to `t_min`.
   306:     3. Terminal Value: The final element (index `n`) must exactly equal `t_min`.
   307:     4. Device: Move the tensor to the requested `device`.
   308:     """
   309:     # For this task, n will typically be 5 (NFE=5).
   310:     # Implement your novel schedule formulation here...
   311:     raise NotImplementedError("Custom scheduler not implemented yet.")

karras baseline — editable region [READ-ONLY — reference implementation]

Lines 301–311:
   298:     )
   299: 
   300: 
   301: def get_sigmas_uniform(n, t_min, t_max, device="cpu"):
   302:     rho = 7.0
   303:     ramp = torch.linspace(0, 1, n + 1)
   304:     min_inv_rho = t_min ** (1 / rho)
   305:     max_inv_rho = t_max ** (1 / rho)
   306:     sigmas = (max_inv_rho

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "dbim-codebase/ddbm/karras_diffusion.py",
4  "start_line": 301,
5  "end_line": 311,
6  "content": "def get_sigmas_uniform(n, t_min, t_max, device=\"cpu\"):\n    \"\"\"\n    Requirements:\n    1. Length: Must return a D PyTorch tensor of exactly length `n + 1`.\n    2. Monotonic: The sequence must strictly decrease from `t_max` to `t_min`.\n    3. Terminal Value: The final element (index `n`) must exactly equal `t_min`.\n    4. Device: Move the tensor to the requested `device`.\n    \"\"\"\n    # For this task, n will typically be 5 (NFE=5).\n    # Implement your novel schedule formulation here...\n    import math\n    # Uniform in log-SNR space\n    log_snr_max = -2.0 * math.log(t_max)\n    log_snr_min = -2.0 * math.log(t_min)\n    log_snr = torch.linspace(log_snr_max, log_snr_min, n + 1)\n    sigmas = torch.exp(-0.5 * log_snr)\n    sigmas[-1] = t_min  # ensure exact terminal value\n    return sigmas.to(device)"
7}

Tool ResultStep 1

OK: Replaced lines 301..311 with 18 line(s) in dbim-codebase/ddbm/karras_diffusion.py. Editable range: 301–318.

[Current file: dbim-codebase/ddbm/karras_diffusion.py | editable: 301–318 | total: 658 lines]
   298|     )
   299| 
   300| 
   301  def get_sigmas_uniform(n, t_min, t_max, device="cpu"):
   302      """
   303      Requirements:
   304      1. Length: Must return a D PyTorch tensor of exactly length `n + 1`.
   305      2. Monotonic: The sequence must strictly decrease from `t_max` to `t_min`.
   306      3. Terminal Value: The final element (index `n`) must exactly equal `t_min`.
   307      4. Device: Move the tensor to the requested `device`.
   308      """
   309      # For this task, n will typically be 5 (NFE=5).
   310      # Implement your novel schedule formulation here...
   311      import math
   312      # Uniform in log-SNR space
   313      log_snr_max = -2.0 * math.log(t_max)
   314      log_snr_min = -2.0 * math.log(t_min)
   315      log_snr = torch.lins

AssistantStep 2test

Tool ResultStep 2

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

### edges2handbags (scripts/run_e2h.sh)
Optimization Feedback: edges2handbags yielded an FID of 5.634.

### Imagenet (scripts/run_Imagenet.sh)
Optimization Feedback: Imagenet yielded an FID of 13.748.

[Leaderboard] Results saved: {'fid_edges2handbags': 5.634, 'elapsed_edges2handbags': 97, 'fid_Imagenet': 13.748, 'elapsed_Imagenet': 303}

AssistantStep 3edit

Tool Input

1{
2  "op": "replace",
3  "filename": "dbim-codebase/ddbm/karras_diffusion.py",
4  "start_line": 301,
5  "end_line": 318,
6  "content": "def get_sigmas_uniform(n, t_min, t_max, device=\"cpu\"):\n    \"\"\"\n    Requirements:\n    1. Length: Must return a 1D PyTorch tensor of exactly length `n + 1`.\n    2. Monotonic: The sequence must strictly decrease from `t_max` to `t_min`.\n    3. Terminal Value: The final element (index `n`) must exactly equal `t_min`.\n    4. Device: Move the tensor to the requested `device`.\n    \"\"\"\n    # For this task, n will typically be 5 (NFE=5).\n    # Implement your novel schedule formulation here...\n    import math\n    ramp = torch.linspace(0, 1, n + 1)\n    # Beta(0.5, 0.5) quantile function: sin^2(pi/2 * ramp)\n    transformed = torch.sin(ramp * (math.pi / 2)) ** 2\n    sigmas = t_max + (t_min - t_max) * transformed\n    sigmas[-1] = t_min  # ensure exact terminal value\n    return sigmas.to(device)"
7}

Tool ResultStep 3

OK: Replaced lines 301..318 with 17 line(s) in dbim-codebase/ddbm/karras_diffusion.py. Editable range: 301–317.

[Current file: dbim-codebase/ddbm/karras_diffusion.py | editable: 301–317 | total: 657 lines]
   298|     )
   299| 
   300| 
   301  def get_sigmas_uniform(n, t_min, t_max, device="cpu"):
   302      """
   303      Requirements:
   304      1. Length: Must return a 1D PyTorch tensor of exactly length `n + 1`.
   305      2. Monotonic: The sequence must strictly decrease from `t_max` to `t_min`.
   306      3. Terminal Value: The final element (index `n`) must exactly equal `t_min`.
   307      4. Device: Move the tensor to the requested `device`.
   308      """
   309      # For this task, n will typically be 5 (NFE=5).
   310      # Implement your novel schedule formulation here...
   311      import math
   312      ramp = torch.linspace(0, 1, n + 1)
   313      # Beta(0.5, 0.5) quantile function: sin^2(pi/2 * ramp)
   314      transformed = torch.sin(ramp * (math.pi /

AssistantStep 4test

Tool ResultStep 4

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

### edges2handbags (scripts/run_e2h.sh)
Optimization Feedback: edges2handbags yielded an FID of 4.905.

### Imagenet (scripts/run_Imagenet.sh)
Optimization Feedback: Imagenet yielded an FID of 5.438.

[Leaderboard] Results saved: {'fid_edges2handbags': 4.905, 'elapsed_edges2handbags': 94, 'fid_Imagenet': 5.438, 'elapsed_Imagenet': 299}

AssistantStep 5edit

Tool Input

1{
2  "op": "replace",
3  "filename": "dbim-codebase/ddbm/karras_diffusion.py",
4  "start_line": 301,
5  "end_line": 317,
6  "content": "def get_sigmas_uniform(n, t_min, t_max, device=\"cpu\"):\n    \"\"\"\n    Requirements:\n    1. Length: Must return a 1D PyTorch tensor of exactly length `n + 1`.\n    2. Monotonic: The sequence must strictly decrease from `t_max` to `t_min`.\n    3. Terminal Value: The final element (index `n`) must exactly equal `t_min`.\n    4. Device: Move the tensor to the requested `device`.\n    \"\"\"\n    # For this task, n will typically be 5 (NFE=5).\n    # Implement your novel schedule formulation here...\n    import math\n    ramp = torch.linspace(0, 1, n + 1)\n    # Power law transformation with exponent p < 1 to allocate more steps near t_min\n    p = 0.7\n    transformed = ramp ** p\n    sigmas = t_max + (t_min - t_max) * transformed\n    sigmas[-1] = t_min  # ensure exact terminal value\n    return sigmas.to(device)"
7}

Tool ResultStep 5

OK: Replaced lines 301..317 with 18 line(s) in dbim-codebase/ddbm/karras_diffusion.py. Editable range: 301–318.

[Current file: dbim-codebase/ddbm/karras_diffusion.py | editable: 301–318 | total: 658 lines]
   298|     )
   299| 
   300| 
   301  def get_sigmas_uniform(n, t_min, t_max, device="cpu"):
   302      """
   303      Requirements:
   304      1. Length: Must return a 1D PyTorch tensor of exactly length `n + 1`.
   305      2. Monotonic: The sequence must strictly decrease from `t_max` to `t_min`.
   306      3. Terminal Value: The final element (index `n`) must exactly equal `t_min`.
   307      4. Device: Move the tensor to the requested `device`.
   308      """
   309      # For this task, n will typically be 5 (NFE=5).
   310      # Implement your novel schedule formulation here...
   311      import math
   312      ramp = torch.linspace(0, 1, n + 1)
   313      # Power law transformation with exponent p < 1 to allocate more steps near t_min
   314      p = 0.7
   315

AssistantStep 6test

Tool ResultStep 6

[Test #3] (0 tests remaining)

### edges2handbags (scripts/run_e2h.sh)
Optimization Feedback: edges2handbags yielded an FID of 4.846.

### Imagenet (scripts/run_Imagenet.sh)
Optimization Feedback: Imagenet yielded an FID of 7.226.

[Leaderboard] Results saved: {'fid_edges2handbags': 4.846, 'elapsed_edges2handbags': 93, 'fid_Imagenet': 7.226, 'elapsed_Imagenet': 307}

[auto-submit] Test budget exhausted — auto-submitted BEST test #3 as final (metrics: {'fid_edges2handbags': 4.846, 'elapsed_edges2handbags': 93, 'fid_Imagenet': 7.226, 'elapsed_Imagenet': 307}).