Refactor MultiGPU scheduler for readability and termination safety (#14001)

Behaviour-equivalent cleanup of _calc_cond_batch_multigpu device
scheduling. No change to batching decisions or memory checks for any
valid input.

Changes:

* Replace re-summed batched_to_run_length with a per-device load
  dict (device_load), so capacity checks are O(1) and use a single
  source of truth.
* Extract device selection into next_available_device(), which scans
  at most len(devices) positions and raises if no device has
  remaining capacity. This makes the 'skip a full device' rule live
  in one place instead of two and guarantees the outer while loop
  cannot spin forever on a scheduling bug.
* Drop the unused current_device assignment before the outer loop
  and the index_device % len(devices) modulo dance (now handled
  inside next_available_device).
* Minor cleanups: list comprehensions for total_conds, conds_to_batch,
  and the devices list.

comfy/samplers.py

@@ -388,33 +388,40 @@ def _calc_cond_batch_multigpu(model: BaseModel, conds: list[list[dict]], x_in: t
 
     model.current_patcher.prepare_state(timestep, model_options)
 
-    devices = [dev_m for dev_m in model_options['multigpu_clones'].keys()]
+    devices = list(model_options['multigpu_clones'].keys())
     device_batched_hooked_to_run: dict[torch.device, list[tuple[comfy.hooks.HookGroup, tuple]]] = {}
+    # Track conds currently scheduled per device; single source of truth for capacity checks.
+    device_load: dict[torch.device, int] = {d: 0 for d in devices}
 
-    total_conds = 0
+    total_conds = sum(len(to_run) for to_run in hooked_to_run.values())
-    for to_run in hooked_to_run.values():
-        total_conds += len(to_run)
     conds_per_device = max(1, math.ceil(total_conds / len(devices)))
-    index_device = 0
+
-    current_device = devices[index_device]
+    def next_available_device(start: int) -> tuple[int, torch.device]:
+        """Return (index, device) for the next device with remaining capacity, starting at `start`.
+
+        Scans at most len(devices) positions, so this always terminates. Raises if no device
+        has remaining capacity, which would indicate a bug in conds_per_device accounting.
+        """
+        for offset in range(len(devices)):
+            i = (start + offset) % len(devices)
+            if device_load[devices[i]] < conds_per_device:
+                return i, devices[i]
+        raise RuntimeError(
+            f"MultiGPU scheduler: all {len(devices)} devices at capacity "
+            f"({conds_per_device}) but conds remain to schedule"
+        )
+
     # run every hooked_to_run separately
+    index_device = 0
     for hooks, to_run in hooked_to_run.items():
         while len(to_run) > 0:
-            current_device = devices[index_device % len(devices)]
+            index_device, current_device = next_available_device(index_device)
-            batched_to_run = device_batched_hooked_to_run.setdefault(current_device, [])
+            remaining_capacity = conds_per_device - device_load[current_device]
-            # keep track of conds currently scheduled onto this device
-            batched_to_run_length = 0
-            for btr in batched_to_run:
-                batched_to_run_length += len(btr[1])
-            remaining_capacity = conds_per_device - batched_to_run_length
-            if remaining_capacity <= 0:
-                index_device += 1
-                continue
 
             first = to_run[0]
             first_shape = first[0][0].shape
+            # collect candidate indices that can be concatenated with `first`, up to remaining capacity
             to_batch_temp = []
-            # make sure not over conds_per_device limit when creating temp batch
             for x in range(len(to_run)):
                 if can_concat_cond(to_run[x][0], first[0]) and len(to_batch_temp) < remaining_capacity:
                     to_batch_temp += [x]
@@ -429,13 +436,12 @@ def _calc_cond_batch_multigpu(model: BaseModel, conds: list[list[dict]], x_in: t
                 if model.memory_required(input_shape) * 1.5 < free_memory:
                     to_batch = batch_amount
                     break
-            conds_to_batch = []
-            for x in to_batch:
-                conds_to_batch.append(to_run.pop(x))
-            batched_to_run_length += len(conds_to_batch)
 
-            batched_to_run.append((hooks, conds_to_batch))
+            conds_to_batch = [to_run.pop(x) for x in to_batch]
-            if batched_to_run_length >= conds_per_device:
+            device_load[current_device] += len(conds_to_batch)
+            device_batched_hooked_to_run.setdefault(current_device, []).append((hooks, conds_to_batch))
+
+            if device_load[current_device] >= conds_per_device:
                 index_device += 1
 
     class thread_result(NamedTuple):