[pull] master from Comfy-Org:master

app/assets/database/bulk_ops.py

@@ -92,14 +92,23 @@ def seed_from_paths_batch(
         session.execute(ins_asset, chunk)
 
     # try to claim AssetCacheState (file_path)
-    winners_by_path: set[str] = set()
+    # Insert with ON CONFLICT DO NOTHING, then query to find which paths were actually inserted
     ins_state = (
         sqlite.insert(AssetCacheState)
         .on_conflict_do_nothing(index_elements=[AssetCacheState.file_path])
-        .returning(AssetCacheState.file_path)
     )
     for chunk in _iter_chunks(state_rows, _rows_per_stmt(3)):
-        winners_by_path.update((session.execute(ins_state, chunk)).scalars().all())
+        session.execute(ins_state, chunk)
+
+    # Query to find which of our paths won (were actually inserted)
+    winners_by_path: set[str] = set()
+    for chunk in _iter_chunks(path_list, MAX_BIND_PARAMS):
+        result = session.execute(
+            sqlalchemy.select(AssetCacheState.file_path)
+            .where(AssetCacheState.file_path.in_(chunk))
+            .where(AssetCacheState.asset_id.in_([path_to_asset[p] for p in chunk]))
+        )
+        winners_by_path.update(result.scalars().all())
 
     all_paths_set = set(path_list)
     losers_by_path = all_paths_set - winners_by_path
@@ -112,16 +121,23 @@ def seed_from_paths_batch(
         return {"inserted_infos": 0, "won_states": 0, "lost_states": len(losers_by_path)}
 
     # insert AssetInfo only for winners
+    # Insert with ON CONFLICT DO NOTHING, then query to find which were actually inserted
     winner_info_rows = [asset_to_info[path_to_asset[p]] for p in winners_by_path]
     ins_info = (
         sqlite.insert(AssetInfo)
         .on_conflict_do_nothing(index_elements=[AssetInfo.asset_id, AssetInfo.owner_id, AssetInfo.name])
-        .returning(AssetInfo.id)
     )
+    for chunk in _iter_chunks(winner_info_rows, _rows_per_stmt(9)):
+        session.execute(ins_info, chunk)
 
+    # Query to find which info rows were actually inserted (by matching our generated IDs)
+    all_info_ids = [row["id"] for row in winner_info_rows]
     inserted_info_ids: set[str] = set()
-    for chunk in _iter_chunks(winner_info_rows, _rows_per_stmt(9)):
-        inserted_info_ids.update((session.execute(ins_info, chunk)).scalars().all())
+    for chunk in _iter_chunks(all_info_ids, MAX_BIND_PARAMS):
+        result = session.execute(
+            sqlalchemy.select(AssetInfo.id).where(AssetInfo.id.in_(chunk))
+        )
+        inserted_info_ids.update(result.scalars().all())
 
     # build and insert tag + meta rows for the AssetInfo
     tag_rows: list[dict] = []

comfy/float.py

@@ -69,26 +69,31 @@ def stochastic_rounding(value, dtype, seed=0):
 
 # TODO: improve this?
 def stochastic_float_to_fp4_e2m1(x, generator):
+    orig_shape = x.shape
     sign = torch.signbit(x).to(torch.uint8)
-    x_abs = x.abs()
 
-    exp = torch.floor(torch.log2(x_abs) + 1.0).clamp(0, 3)
+    exp = torch.floor(torch.log2(x.abs()) + 1.0).clamp(0, 3)
     x += (torch.rand(x.size(), dtype=x.dtype, layout=x.layout, device=x.device, generator=generator) - 0.5) * (2 ** (exp - 2.0)) * 1.25
 
-    x_abs = x.abs()
-    exp = torch.floor(torch.log2(x_abs) + 1.1925).clamp(0, 3)
+    x = x.abs()
+    exp = torch.floor(torch.log2(x) + 1.1925).clamp(0, 3)
 
     mantissa = torch.where(
         exp > 0,
-        (x_abs / (2.0 ** (exp - 1)) - 1.0) * 2.0,
-        (x_abs * 2.0)
+        (x / (2.0 ** (exp - 1)) - 1.0) * 2.0,
+        (x * 2.0),
+        out=x
     ).round().to(torch.uint8)
+    del x
+
+    exp = exp.to(torch.uint8)
 
-    fp4 = (sign << 3) | (exp.to(torch.uint8) << 1) | mantissa
+    fp4 = (sign << 3) | (exp << 1) | mantissa
+    del sign, exp, mantissa
 
     fp4_flat = fp4.view(-1)
     packed = (fp4_flat[0::2] << 4) | fp4_flat[1::2]
-    return packed.reshape(list(x.shape)[:-1] + [-1])
+    return packed.reshape(list(orig_shape)[:-1] + [-1])
 
 
 def to_blocked(input_matrix, flatten: bool = True) -> torch.Tensor:

comfy/supported_models.py

@@ -845,7 +845,7 @@ class LTXAV(LTXV):
 
     def __init__(self, unet_config):
         super().__init__(unet_config)
-        self.memory_usage_factor = 0.061  # TODO
+        self.memory_usage_factor = 0.077  # TODO
 
     def get_model(self, state_dict, prefix="", device=None):
         out = model_base.LTXAV(self, device=device)

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.8.2"
+__version__ = "0.9.1"

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.8.2"
+version = "0.9.1"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.36.13
+comfyui-frontend-package==1.36.14
 comfyui-workflow-templates==0.8.4
 comfyui-embedded-docs==0.4.0
 torch