feat(assets): register output files as assets after prompt execution

Add ingest_existing_file() to services/ingest.py as a public wrapper for
registering on-disk files (stat, BLAKE3 hash, MIME detection, path-based
tag derivation).

After each prompt execution in the main loop, iterate
history_result['outputs'] and register files with type 'output' as
assets. Runs while the asset seeder is paused, gated behind
asset_seeder.is_disabled(). Stores prompt_id in user_metadata for
provenance tracking.

Ingest uses a two-phase approach: insert a stub record (hash=NULL) first
for instant visibility, then defer hashing to the background seeder
enrich phase to avoid blocking the prompt worker thread.

When multiple enrich scans are enqueued while the seeder is busy, roots
are now unioned and compute_hashes uses sticky-true (OR) logic so no
queued work is silently dropped.

Extract _reset_to_idle helper in the asset seeder to deduplicate the
state reset pattern shared by _run_scan and mark_missing_outside_prefixes.

Separate history parsing from output file registration: move generic
file registration logic into register_output_files() in
app/assets/services/ingest.py, keeping only the ComfyUI history format
parsing (_collect_output_absolute_paths) in main.py.

app/assets/seeder.py

@@ -92,6 +92,7 @@ class _AssetSeeder:
         self._prune_first: bool = False
         self._progress_callback: ProgressCallback | None = None
         self._disabled: bool = False
+        self._pending_enrich: dict | None = None
 
     def disable(self) -> None:
         """Disable the asset seeder, preventing any scans from starting."""
@@ -196,6 +197,42 @@ class _AssetSeeder:
             compute_hashes=compute_hashes,
         )
 
+    def enqueue_enrich(
+        self,
+        roots: tuple[RootType, ...] = ("models", "input", "output"),
+        compute_hashes: bool = False,
+    ) -> bool:
+        """Start an enrichment scan now, or queue it for after the current scan.
+
+        If the seeder is idle, starts immediately. Otherwise, the enrich
+        request is stored and will run automatically when the current scan
+        finishes.
+
+        Args:
+            roots: Tuple of root types to scan
+            compute_hashes: If True, compute blake3 hashes
+
+        Returns:
+            True if started immediately, False if queued for later
+        """
+        if self.start_enrich(roots=roots, compute_hashes=compute_hashes):
+            return True
+        with self._lock:
+            if self._pending_enrich is not None:
+                existing_roots = set(self._pending_enrich["roots"])
+                existing_roots.update(roots)
+                self._pending_enrich["roots"] = tuple(existing_roots)
+                self._pending_enrich["compute_hashes"] = (
+                    self._pending_enrich["compute_hashes"] or compute_hashes
+                )
+            else:
+                self._pending_enrich = {
+                    "roots": roots,
+                    "compute_hashes": compute_hashes,
+                }
+            logging.info("Enrich scan queued (roots=%s)", self._pending_enrich["roots"])
+        return False
+
     def cancel(self) -> bool:
         """Request cancellation of the current scan.
 
@@ -381,9 +418,13 @@ class _AssetSeeder:
             return marked
         finally:
             with self._lock:
-                self._last_progress = self._progress
-                self._state = State.IDLE
-                self._progress = None
+                self._reset_to_idle()
+
+    def _reset_to_idle(self) -> None:
+        """Reset state to IDLE, preserving last progress. Caller must hold _lock."""
+        self._last_progress = self._progress
+        self._state = State.IDLE
+        self._progress = None
 
     def _is_cancelled(self) -> bool:
         """Check if cancellation has been requested."""
@@ -594,9 +635,14 @@ class _AssetSeeder:
                     },
                 )
             with self._lock:
-                self._last_progress = self._progress
-                self._state = State.IDLE
-                self._progress = None
+                self._reset_to_idle()
+                pending = self._pending_enrich
+                self._pending_enrich = None
+            if pending is not None:
+                self.start_enrich(
+                    roots=pending["roots"],
+                    compute_hashes=pending["compute_hashes"],
+                )
 
     def _run_fast_phase(self, roots: tuple[RootType, ...]) -> tuple[int, int, int]:
         """Run phase 1: fast scan to create stub records.

app/assets/services/__init__.py

@@ -23,6 +23,8 @@ from app.assets.services.ingest import (
     DependencyMissingError,
     HashMismatchError,
     create_from_hash,
+    ingest_existing_file,
+    register_output_files,
     upload_from_temp_path,
 )
 from app.assets.database.queries import (
@@ -72,6 +74,8 @@ __all__ = [
     "delete_asset_reference",
     "get_asset_by_hash",
     "get_asset_detail",
+    "ingest_existing_file",
+    "register_output_files",
     "get_mtime_ns",
     "get_size_and_mtime_ns",
     "list_assets_page",

app/assets/services/ingest.py

@@ -23,9 +23,11 @@ from app.assets.database.queries import (
     validate_tags_exist,
 )
 from app.assets.helpers import normalize_tags
+from app.assets.services.bulk_ingest import batch_insert_seed_assets
 from app.assets.services.file_utils import get_size_and_mtime_ns
 from app.assets.services.path_utils import (
     compute_relative_filename,
+    get_name_and_tags_from_asset_path,
     resolve_destination_from_tags,
     validate_path_within_base,
 )
@@ -128,6 +130,59 @@ def _ingest_file_from_path(
     )
 
 
+def register_output_files(
+    file_paths: Sequence[str],
+    user_metadata: UserMetadata = None,
+) -> int:
+    """Register a batch of output file paths as assets.
+
+    Returns the number of files successfully registered.
+    """
+    registered = 0
+    for abs_path in file_paths:
+        if not os.path.isfile(abs_path):
+            continue
+        try:
+            ingest_existing_file(abs_path, user_metadata=user_metadata)
+            registered += 1
+        except Exception:
+            logging.exception("Failed to register output: %s", abs_path)
+    return registered
+
+
+def ingest_existing_file(
+    abs_path: str,
+    user_metadata: UserMetadata = None,
+    extra_tags: Sequence[str] = (),
+    owner_id: str = "",
+) -> None:
+    """Register an existing on-disk file as an asset stub.
+
+    Inserts a stub record (hash=NULL) for immediate UX visibility.
+    The caller is responsible for triggering background enrichment
+    (hash computation, metadata extraction) via the asset seeder.
+    """
+    size_bytes, mtime_ns = get_size_and_mtime_ns(abs_path)
+    mime_type = mimetypes.guess_type(abs_path, strict=False)[0]
+    name, path_tags = get_name_and_tags_from_asset_path(abs_path)
+    tags = list(dict.fromkeys(path_tags + list(extra_tags)))
+
+    spec = {
+        "abs_path": abs_path,
+        "size_bytes": size_bytes,
+        "mtime_ns": mtime_ns,
+        "info_name": name,
+        "tags": tags,
+        "fname": os.path.basename(abs_path),
+        "metadata": None,
+        "hash": None,
+        "mime_type": mime_type,
+    }
+    with create_session() as session:
+        batch_insert_seed_assets(session, [spec], owner_id=owner_id)
+        session.commit()
+
+
 def _register_existing_asset(
     asset_hash: str,
     name: str,

comfy/comfy_types/node_typing.py

@@ -176,8 +176,8 @@ class InputTypeOptions(TypedDict):
     """COMBO type only. Specifies the configuration for a multi-select widget.
     Available after ComfyUI frontend v1.13.4
     https://github.com/Comfy-Org/ComfyUI_frontend/pull/2987"""
-    gradient_stops: NotRequired[list[dict]]
-    """Gradient color stops for gradientslider display mode. Each stop is {"offset": float, "color": [r, g, b]}."""
+    gradient_stops: NotRequired[list[list[float]]]
+    """Gradient color stops for gradientslider display mode. Each stop is [offset, r, g, b] (``FLOAT``)."""
 
 
 class HiddenInputTypeDict(TypedDict):

comfy/ldm/flux/layers.py

@@ -144,9 +144,9 @@ def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None):
             return tensor * m_mult
     else:
         for d in modulation_dims:
-            tensor[:, d[0]:d[1]] *= m_mult[:, d[2]:d[2] + 1]
+            tensor[:, d[0]:d[1]] *= m_mult[:, d[2]]
             if m_add is not None:
-                tensor[:, d[0]:d[1]] += m_add[:, d[2]:d[2] + 1]
+                tensor[:, d[0]:d[1]] += m_add[:, d[2]]
         return tensor
 
 
@@ -223,19 +223,12 @@ class DoubleStreamBlock(nn.Module):
         del txt_k, img_k
         v = torch.cat((txt_v, img_v), dim=2)
         del txt_v, img_v
-
-        extra_options["img_slice"] = [txt.shape[1], q.shape[2]]
-        if "attn1_patch" in transformer_patches:
-            patch = transformer_patches["attn1_patch"]
-            for p in patch:
-                out = p(q, k, v, pe=pe, attn_mask=attn_mask, extra_options=extra_options)
-                q, k, v, pe, attn_mask = out.get("q", q), out.get("k", k), out.get("v", v), out.get("pe", pe), out.get("attn_mask", attn_mask)
-
         # run actual attention
         attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
         del q, k, v
 
         if "attn1_output_patch" in transformer_patches:
+            extra_options["img_slice"] = [txt.shape[1], attn.shape[1]]
             patch = transformer_patches["attn1_output_patch"]
             for p in patch:
                 attn = p(attn, extra_options)
@@ -328,12 +321,6 @@ class SingleStreamBlock(nn.Module):
         del qkv
         q, k = self.norm(q, k, v)
 
-        if "attn1_patch" in transformer_patches:
-            patch = transformer_patches["attn1_patch"]
-            for p in patch:
-                out = p(q, k, v, pe=pe, attn_mask=attn_mask, extra_options=extra_options)
-                q, k, v, pe, attn_mask = out.get("q", q), out.get("k", k), out.get("v", v), out.get("pe", pe), out.get("attn_mask", attn_mask)
-
         # compute attention
         attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
         del q, k, v

comfy/ldm/flux/math.py

@@ -31,8 +31,6 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
 
 def _apply_rope1(x: Tensor, freqs_cis: Tensor):
     x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)
-    if x_.shape[2] != 1 and freqs_cis.shape[2] != 1 and x_.shape[2] != freqs_cis.shape[2]:
-        freqs_cis = freqs_cis[:, :, :x_.shape[2]]
 
     x_out = freqs_cis[..., 0] * x_[..., 0]
     x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])

comfy/ldm/flux/model.py

@@ -44,22 +44,6 @@ class FluxParams:
     txt_norm: bool = False
 
 
-def invert_slices(slices, length):
-    sorted_slices = sorted(slices)
-    result = []
-    current = 0
-
-    for start, end in sorted_slices:
-        if current < start:
-            result.append((current, start))
-        current = max(current, end)
-
-    if current < length:
-        result.append((current, length))
-
-    return result
-
-
 class Flux(nn.Module):
     """
     Transformer model for flow matching on sequences.
@@ -154,7 +138,6 @@ class Flux(nn.Module):
         y: Tensor,
         guidance: Tensor = None,
         control = None,
-        timestep_zero_index=None,
         transformer_options={},
         attn_mask: Tensor = None,
     ) -> Tensor:
@@ -181,9 +164,13 @@ class Flux(nn.Module):
             txt = self.txt_norm(txt)
         txt = self.txt_in(txt)
 
+        vec_orig = vec
+        if self.params.global_modulation:
+            vec = (self.double_stream_modulation_img(vec_orig), self.double_stream_modulation_txt(vec_orig))
+
         if "post_input" in patches:
             for p in patches["post_input"]:
-                out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids, "transformer_options": transformer_options})
+                out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids})
                 img = out["img"]
                 txt = out["txt"]
                 img_ids = out["img_ids"]
@@ -195,24 +182,6 @@ class Flux(nn.Module):
         else:
             pe = None
 
-        vec_orig = vec
-        txt_vec = vec
-        extra_kwargs = {}
-        if timestep_zero_index is not None:
-            modulation_dims = []
-            batch = vec.shape[0] // 2
-            vec_orig = vec_orig.reshape(2, batch, vec.shape[1]).movedim(0, 1)
-            invert = invert_slices(timestep_zero_index, img.shape[1])
-            for s in invert:
-                modulation_dims.append((s[0], s[1], 0))
-            for s in timestep_zero_index:
-                modulation_dims.append((s[0], s[1], 1))
-            extra_kwargs["modulation_dims_img"] = modulation_dims
-            txt_vec = vec[:batch]
-
-        if self.params.global_modulation:
-            vec = (self.double_stream_modulation_img(vec_orig), self.double_stream_modulation_txt(txt_vec))
-
         blocks_replace = patches_replace.get("dit", {})
         transformer_options["total_blocks"] = len(self.double_blocks)
         transformer_options["block_type"] = "double"
@@ -226,8 +195,7 @@ class Flux(nn.Module):
                                                    vec=args["vec"],
                                                    pe=args["pe"],
                                                    attn_mask=args.get("attn_mask"),
-                                                   transformer_options=args.get("transformer_options"),
-                                                   **extra_kwargs)
+                                                   transformer_options=args.get("transformer_options"))
                     return out
 
                 out = blocks_replace[("double_block", i)]({"img": img,
@@ -245,8 +213,7 @@ class Flux(nn.Module):
                                  vec=vec,
                                  pe=pe,
                                  attn_mask=attn_mask,
-                                 transformer_options=transformer_options,
-                                 **extra_kwargs)
+                                 transformer_options=transformer_options)
 
             if control is not None: # Controlnet
                 control_i = control.get("input")
@@ -263,12 +230,6 @@ class Flux(nn.Module):
         if self.params.global_modulation:
             vec, _ = self.single_stream_modulation(vec_orig)
 
-        extra_kwargs = {}
-        if timestep_zero_index is not None:
-            lambda a: 0 if a == 0 else a + txt.shape[1]
-            modulation_dims_combined = list(map(lambda x: (0 if x[0] == 0 else x[0] + txt.shape[1], x[1] + txt.shape[1], x[2]), modulation_dims))
-            extra_kwargs["modulation_dims"] = modulation_dims_combined
-
         transformer_options["total_blocks"] = len(self.single_blocks)
         transformer_options["block_type"] = "single"
         transformer_options["img_slice"] = [txt.shape[1], img.shape[1]]
@@ -281,8 +242,7 @@ class Flux(nn.Module):
                                        vec=args["vec"],
                                        pe=args["pe"],
                                        attn_mask=args.get("attn_mask"),
-                                       transformer_options=args.get("transformer_options"),
-                                       **extra_kwargs)
+                                       transformer_options=args.get("transformer_options"))
                     return out
 
                 out = blocks_replace[("single_block", i)]({"img": img,
@@ -293,7 +253,7 @@ class Flux(nn.Module):
                                                           {"original_block": block_wrap})
                 img = out["img"]
             else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options, **extra_kwargs)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
 
             if control is not None: # Controlnet
                 control_o = control.get("output")
@@ -304,11 +264,7 @@ class Flux(nn.Module):
 
         img = img[:, txt.shape[1] :, ...]
 
-        extra_kwargs = {}
-        if timestep_zero_index is not None:
-            extra_kwargs["modulation_dims"] = modulation_dims
-
-        img = self.final_layer(img, vec_orig, **extra_kwargs)  # (N, T, patch_size ** 2 * out_channels)
+        img = self.final_layer(img, vec_orig)  # (N, T, patch_size ** 2 * out_channels)
         return img
 
     def process_img(self, x, index=0, h_offset=0, w_offset=0, transformer_options={}):
@@ -356,16 +312,13 @@ class Flux(nn.Module):
         w_len = ((w_orig + (patch_size // 2)) // patch_size)
         img, img_ids = self.process_img(x, transformer_options=transformer_options)
         img_tokens = img.shape[1]
-        timestep_zero_index = None
         if ref_latents is not None:
-            ref_num_tokens = []
             h = 0
             w = 0
             index = 0
             ref_latents_method = kwargs.get("ref_latents_method", self.params.default_ref_method)
-            timestep_zero = ref_latents_method == "index_timestep_zero"
             for ref in ref_latents:
-                if ref_latents_method in ("index", "index_timestep_zero"):
+                if ref_latents_method == "index":
                     index += self.params.ref_index_scale
                     h_offset = 0
                     w_offset = 0
@@ -389,13 +342,6 @@ class Flux(nn.Module):
                 kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)
                 img = torch.cat([img, kontext], dim=1)
                 img_ids = torch.cat([img_ids, kontext_ids], dim=1)
-                ref_num_tokens.append(kontext.shape[1])
-            if timestep_zero:
-                if index > 0:
-                    timestep = torch.cat([timestep, timestep * 0], dim=0)
-                    timestep_zero_index = [[img_tokens, img_ids.shape[1]]]
-            transformer_options = transformer_options.copy()
-            transformer_options["reference_image_num_tokens"] = ref_num_tokens
 
         txt_ids = torch.zeros((bs, context.shape[1], len(self.params.axes_dim)), device=x.device, dtype=torch.float32)
 
@@ -403,6 +349,6 @@ class Flux(nn.Module):
             for i in self.params.txt_ids_dims:
                 txt_ids[:, :, i] = torch.linspace(0, context.shape[1] - 1, steps=context.shape[1], device=x.device, dtype=torch.float32)
 
-        out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options, attn_mask=kwargs.get("attention_mask", None))
+        out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
         out = out[:, :img_tokens]
         return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h_orig,:w_orig]

comfy/ldm/modules/attention.py

@@ -372,8 +372,7 @@ def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
                 r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
                 del s2
             break
-        except Exception as e:
-            model_management.raise_non_oom(e)
+        except model_management.OOM_EXCEPTION as e:
             if first_op_done == False:
                 model_management.soft_empty_cache(True)
                 if cleared_cache == False:

comfy/ldm/modules/diffusionmodules/model.py

@@ -258,8 +258,7 @@ def slice_attention(q, k, v):
                 r1[:, :, i:end] = torch.bmm(v, s2)
                 del s2
             break
-        except Exception as e:
-            model_management.raise_non_oom(e)
+        except model_management.OOM_EXCEPTION as e:
             model_management.soft_empty_cache(True)
             steps *= 2
             if steps > 128:
@@ -315,8 +314,7 @@ def pytorch_attention(q, k, v):
     try:
         out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
         out = out.transpose(2, 3).reshape(orig_shape)
-    except Exception as e:
-        model_management.raise_non_oom(e)
+    except model_management.OOM_EXCEPTION:
         logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
         oom_fallback = True
     if oom_fallback:

comfy/ldm/modules/sub_quadratic_attention.py

@@ -169,8 +169,7 @@ def _get_attention_scores_no_kv_chunking(
     try:
         attn_probs = attn_scores.softmax(dim=-1)
         del attn_scores
-    except Exception as e:
-        model_management.raise_non_oom(e)
+    except model_management.OOM_EXCEPTION:
         logging.warning("ran out of memory while running softmax in  _get_attention_scores_no_kv_chunking, trying slower in place softmax instead")
         attn_scores -= attn_scores.max(dim=-1, keepdim=True).values # noqa: F821 attn_scores is not defined
         torch.exp(attn_scores, out=attn_scores)

comfy/ldm/qwen_image/model.py

@@ -149,9 +149,6 @@ class Attention(nn.Module):
         seq_img = hidden_states.shape[1]
         seq_txt = encoder_hidden_states.shape[1]
 
-        transformer_patches = transformer_options.get("patches", {})
-        extra_options = transformer_options.copy()
-
         # Project and reshape to BHND format (batch, heads, seq, dim)
         img_query = self.to_q(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous()
         img_key = self.to_k(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous()
@@ -170,22 +167,15 @@ class Attention(nn.Module):
         joint_key = torch.cat([txt_key, img_key], dim=2)
         joint_value = torch.cat([txt_value, img_value], dim=2)
 
+        joint_query = apply_rope1(joint_query, image_rotary_emb)
+        joint_key = apply_rope1(joint_key, image_rotary_emb)
+
         if encoder_hidden_states_mask is not None:
             attn_mask = torch.zeros((batch_size, 1, seq_txt + seq_img), dtype=hidden_states.dtype, device=hidden_states.device)
             attn_mask[:, 0, :seq_txt] = encoder_hidden_states_mask
         else:
             attn_mask = None
 
-        extra_options["img_slice"] = [txt_query.shape[2], joint_query.shape[2]]
-        if "attn1_patch" in transformer_patches:
-            patch = transformer_patches["attn1_patch"]
-            for p in patch:
-                out = p(joint_query, joint_key, joint_value, pe=image_rotary_emb, attn_mask=encoder_hidden_states_mask, extra_options=extra_options)
-                joint_query, joint_key, joint_value, image_rotary_emb, encoder_hidden_states_mask = out.get("q", joint_query), out.get("k", joint_key), out.get("v", joint_value), out.get("pe", image_rotary_emb), out.get("attn_mask", encoder_hidden_states_mask)
-
-        joint_query = apply_rope1(joint_query, image_rotary_emb)
-        joint_key = apply_rope1(joint_key, image_rotary_emb)
-
         joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads,
                                                          attn_mask, transformer_options=transformer_options,
                                                          skip_reshape=True)
@@ -454,7 +444,6 @@ class QwenImageTransformer2DModel(nn.Module):
 
         timestep_zero_index = None
         if ref_latents is not None:
-            ref_num_tokens = []
             h = 0
             w = 0
             index = 0
@@ -485,16 +474,16 @@ class QwenImageTransformer2DModel(nn.Module):
                 kontext, kontext_ids, _ = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)
                 hidden_states = torch.cat([hidden_states, kontext], dim=1)
                 img_ids = torch.cat([img_ids, kontext_ids], dim=1)
-                ref_num_tokens.append(kontext.shape[1])
             if timestep_zero:
                 if index > 0:
                     timestep = torch.cat([timestep, timestep * 0], dim=0)
                     timestep_zero_index = num_embeds
-            transformer_options = transformer_options.copy()
-            transformer_options["reference_image_num_tokens"] = ref_num_tokens
 
         txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))
         txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous()
+        del ids, txt_ids, img_ids
 
         hidden_states = self.img_in(hidden_states)
         encoder_hidden_states = self.txt_norm(encoder_hidden_states)
@@ -506,18 +495,6 @@ class QwenImageTransformer2DModel(nn.Module):
         patches = transformer_options.get("patches", {})
         blocks_replace = patches_replace.get("dit", {})
 
-        if "post_input" in patches:
-            for p in patches["post_input"]:
-                out = p({"img": hidden_states, "txt": encoder_hidden_states, "img_ids": img_ids, "txt_ids": txt_ids, "transformer_options": transformer_options})
-                hidden_states = out["img"]
-                encoder_hidden_states = out["txt"]
-                img_ids = out["img_ids"]
-                txt_ids = out["txt_ids"]
-
-        ids = torch.cat((txt_ids, img_ids), dim=1)
-        image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous()
-        del ids, txt_ids, img_ids
-
         transformer_options["total_blocks"] = len(self.transformer_blocks)
         transformer_options["block_type"] = "double"
         for i, block in enumerate(self.transformer_blocks):

comfy/model_detection.py

@@ -1,5 +1,4 @@
 import json
-import comfy.memory_management
 import comfy.supported_models
 import comfy.supported_models_base
 import comfy.utils
@@ -1119,13 +1118,8 @@ def convert_diffusers_mmdit(state_dict, output_prefix=""):
                         new[:old_weight.shape[0]] = old_weight
                         old_weight = new
 
-                    if old_weight is out_sd.get(t[0], None) and comfy.memory_management.aimdo_enabled:
-                        old_weight = old_weight.clone()
-
                     w = old_weight.narrow(offset[0], offset[1], offset[2])
                 else:
-                    if comfy.memory_management.aimdo_enabled:
-                        weight = weight.clone()
                     old_weight = weight
                     w = weight
                 w[:] = fun(weight)

comfy/model_management.py

@@ -270,23 +270,6 @@ try:
 except:
     OOM_EXCEPTION = Exception
 
-try:
-    ACCELERATOR_ERROR = torch.AcceleratorError
-except AttributeError:
-    ACCELERATOR_ERROR = RuntimeError
-
-def is_oom(e):
-    if isinstance(e, OOM_EXCEPTION):
-        return True
-    if isinstance(e, ACCELERATOR_ERROR) and (getattr(e, 'error_code', None) == 2 or "out of memory" in str(e).lower()):
-        discard_cuda_async_error()
-        return True
-    return False
-
-def raise_non_oom(e):
-    if not is_oom(e):
-        raise e
-
 XFORMERS_VERSION = ""
 XFORMERS_ENABLED_VAE = True
 if args.disable_xformers:
@@ -1280,7 +1263,7 @@ def discard_cuda_async_error():
         b = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
         _ = a + b
         synchronize()
-    except RuntimeError:
+    except torch.AcceleratorError:
         #Dump it! We already know about it from the synchronous return
         pass
 

comfy/model_patcher.py

@@ -599,27 +599,6 @@ class ModelPatcher:
 
         return models
 
-    def model_patches_call_function(self, function_name="cleanup", arguments={}):
-        to = self.model_options["transformer_options"]
-        if "patches" in to:
-            patches = to["patches"]
-            for name in patches:
-                patch_list = patches[name]
-                for i in range(len(patch_list)):
-                    if hasattr(patch_list[i], function_name):
-                        getattr(patch_list[i], function_name)(**arguments)
-        if "patches_replace" in to:
-            patches = to["patches_replace"]
-            for name in patches:
-                patch_list = patches[name]
-                for k in patch_list:
-                    if hasattr(patch_list[k], function_name):
-                        getattr(patch_list[k], function_name)(**arguments)
-        if "model_function_wrapper" in self.model_options:
-            wrap_func = self.model_options["model_function_wrapper"]
-            if hasattr(wrap_func, function_name):
-                getattr(wrap_func, function_name)(**arguments)
-
     def model_dtype(self):
         if hasattr(self.model, "get_dtype"):
             return self.model.get_dtype()
@@ -1083,7 +1062,6 @@ class ModelPatcher:
         return comfy.lora.calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype)
 
     def cleanup(self):
-        self.model_patches_call_function(function_name="cleanup")
         self.clean_hooks()
         if hasattr(self.model, "current_patcher"):
             self.model.current_patcher = None

comfy/sd.py

@@ -954,8 +954,7 @@ class VAE:
                 if pixel_samples is None:
                     pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
                 pixel_samples[x:x+batch_number] = out
-        except Exception as e:
-            model_management.raise_non_oom(e)
+        except model_management.OOM_EXCEPTION:
             logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
             #NOTE: We don't know what tensors were allocated to stack variables at the time of the
             #exception and the exception itself refs them all until we get out of this except block.
@@ -1030,8 +1029,7 @@ class VAE:
                     samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
                 samples[x:x + batch_number] = out
 
-        except Exception as e:
-            model_management.raise_non_oom(e)
+        except model_management.OOM_EXCEPTION:
             logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
             #NOTE: We don't know what tensors were allocated to stack variables at the time of the
             #exception and the exception itself refs them all until we get out of this except block.

comfy_api/latest/_io.py

@@ -297,7 +297,7 @@ class Float(ComfyTypeIO):
         '''Float input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: float=None, min: float=None, max: float=None, step: float=None, round: float=None,
-                    display_mode: NumberDisplay=None, gradient_stops: list[dict]=None,
+                    display_mode: NumberDisplay=None, gradient_stops: list[list[float]]=None,
                     socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
             super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link, advanced)
             self.min = min

comfy_api_nodes/apis/reve.py

@@ -1,68 +0,0 @@
-from pydantic import BaseModel, Field
-
-
-class RevePostprocessingOperation(BaseModel):
-    process: str = Field(..., description="The postprocessing operation: upscale or remove_background.")
-    upscale_factor: int | None = Field(
-        None,
-        description="Upscale factor (2, 3, or 4). Only used when process is upscale.",
-        ge=2,
-        le=4,
-    )
-
-
-class ReveImageCreateRequest(BaseModel):
-    prompt: str = Field(...)
-    aspect_ratio: str | None = Field(...)
-    version: str = Field(...)
-    test_time_scaling: int = Field(
-        ...,
-        description="If included, the model will spend more effort making better images. Values between 1 and 15.",
-        ge=1,
-        le=15,
-    )
-    postprocessing: list[RevePostprocessingOperation] | None = Field(
-        None, description="Optional postprocessing operations to apply after generation."
-    )
-
-
-class ReveImageEditRequest(BaseModel):
-    edit_instruction: str = Field(...)
-    reference_image: str = Field(..., description="A base64 encoded image to use as reference for the edit.")
-    aspect_ratio: str | None = Field(...)
-    version: str = Field(...)
-    test_time_scaling: int | None = Field(
-        ...,
-        description="If included, the model will spend more effort making better images. Values between 1 and 15.",
-        ge=1,
-        le=15,
-    )
-    postprocessing: list[RevePostprocessingOperation] | None = Field(
-        None, description="Optional postprocessing operations to apply after generation."
-    )
-
-
-class ReveImageRemixRequest(BaseModel):
-    prompt: str = Field(...)
-    reference_images: list[str] = Field(..., description="A list of 1-6 base64 encoded reference images.")
-    aspect_ratio: str | None = Field(...)
-    version: str = Field(...)
-    test_time_scaling: int | None = Field(
-        ...,
-        description="If included, the model will spend more effort making better images. Values between 1 and 15.",
-        ge=1,
-        le=15,
-    )
-    postprocessing: list[RevePostprocessingOperation] | None = Field(
-        None, description="Optional postprocessing operations to apply after generation."
-    )
-
-
-class ReveImageResponse(BaseModel):
-    image: str | None = Field(None, description="The base64 encoded image data.")
-    request_id: str | None = Field(None, description="A unique id for the request.")
-    credits_used: float | None = Field(None, description="The number of credits used for this request.")
-    version: str | None = Field(None, description="The specific model version used.")
-    content_violation: bool | None = Field(
-        None, description="Indicates whether the generated image violates the content policy."
-    )

comfy_api_nodes/nodes_reve.py

@@ -1,395 +0,0 @@
-from io import BytesIO
-
-from typing_extensions import override
-
-from comfy_api.latest import IO, ComfyExtension, Input
-from comfy_api_nodes.apis.reve import (
-    ReveImageCreateRequest,
-    ReveImageEditRequest,
-    ReveImageRemixRequest,
-    RevePostprocessingOperation,
-)
-from comfy_api_nodes.util import (
-    ApiEndpoint,
-    bytesio_to_image_tensor,
-    sync_op_raw,
-    tensor_to_base64_string,
-    validate_string,
-)
-
-
-def _build_postprocessing(upscale: dict, remove_background: bool) -> list[RevePostprocessingOperation] | None:
-    ops = []
-    if upscale["upscale"] == "enabled":
-        ops.append(
-            RevePostprocessingOperation(
-                process="upscale",
-                upscale_factor=upscale["upscale_factor"],
-            )
-        )
-    if remove_background:
-        ops.append(RevePostprocessingOperation(process="remove_background"))
-    return ops or None
-
-
-def _postprocessing_inputs():
-    return [
-        IO.DynamicCombo.Input(
-            "upscale",
-            options=[
-                IO.DynamicCombo.Option("disabled", []),
-                IO.DynamicCombo.Option(
-                    "enabled",
-                    [
-                        IO.Int.Input(
-                            "upscale_factor",
-                            default=2,
-                            min=2,
-                            max=4,
-                            step=1,
-                            tooltip="Upscale factor (2x, 3x, or 4x).",
-                        ),
-                    ],
-                ),
-            ],
-            tooltip="Upscale the generated image. May add additional cost.",
-        ),
-        IO.Boolean.Input(
-            "remove_background",
-            default=False,
-            tooltip="Remove the background from the generated image. May add additional cost.",
-        ),
-    ]
-
-
-def _reve_price_extractor(headers: dict) -> float | None:
-    credits_used = headers.get("x-reve-credits-used")
-    if credits_used is not None:
-        return float(credits_used) / 524.48
-    return None
-
-
-def _reve_response_header_validator(headers: dict) -> None:
-    error_code = headers.get("x-reve-error-code")
-    if error_code:
-        raise ValueError(f"Reve API error: {error_code}")
-    if headers.get("x-reve-content-violation", "").lower() == "true":
-        raise ValueError("The generated image was flagged for content policy violation.")
-
-
-def _model_inputs(versions: list[str], aspect_ratios: list[str]):
-    return [
-        IO.DynamicCombo.Option(
-            version,
-            [
-                IO.Combo.Input(
-                    "aspect_ratio",
-                    options=aspect_ratios,
-                    tooltip="Aspect ratio of the output image.",
-                ),
-                IO.Int.Input(
-                    "test_time_scaling",
-                    default=1,
-                    min=1,
-                    max=5,
-                    step=1,
-                    tooltip="Higher values produce better images but cost more credits.",
-                    advanced=True,
-                ),
-            ],
-        )
-        for version in versions
-    ]
-
-
-class ReveImageCreateNode(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="ReveImageCreateNode",
-            display_name="Reve Image Create",
-            category="api node/image/Reve",
-            description="Generate images from text descriptions using Reve.",
-            inputs=[
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Text description of the desired image. Maximum 2560 characters.",
-                ),
-                IO.DynamicCombo.Input(
-                    "model",
-                    options=_model_inputs(
-                        ["reve-create@20250915"],
-                        aspect_ratios=["3:2", "16:9", "9:16", "2:3", "4:3", "3:4", "1:1"],
-                    ),
-                    tooltip="Model version to use for generation.",
-                ),
-                *_postprocessing_inputs(),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    control_after_generate=True,
-                    tooltip="Seed controls whether the node should re-run; "
-                    "results are non-deterministic regardless of seed.",
-                ),
-            ],
-            outputs=[IO.Image.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd":0.03432,"format":{"approximate":true,"note":"(base)"}}""",
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt: str,
-        model: dict,
-        upscale: dict,
-        remove_background: bool,
-        seed: int,
-    ) -> IO.NodeOutput:
-        validate_string(prompt, min_length=1, max_length=2560)
-        response = await sync_op_raw(
-            cls,
-            ApiEndpoint(
-                path="/proxy/reve/v1/image/create",
-                method="POST",
-                headers={"Accept": "image/webp"},
-            ),
-            as_binary=True,
-            price_extractor=_reve_price_extractor,
-            response_header_validator=_reve_response_header_validator,
-            data=ReveImageCreateRequest(
-                prompt=prompt,
-                aspect_ratio=model["aspect_ratio"],
-                version=model["model"],
-                test_time_scaling=model["test_time_scaling"],
-                postprocessing=_build_postprocessing(upscale, remove_background),
-            ),
-        )
-        return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(response)))
-
-
-class ReveImageEditNode(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="ReveImageEditNode",
-            display_name="Reve Image Edit",
-            category="api node/image/Reve",
-            description="Edit images using natural language instructions with Reve.",
-            inputs=[
-                IO.Image.Input("image", tooltip="The image to edit."),
-                IO.String.Input(
-                    "edit_instruction",
-                    multiline=True,
-                    default="",
-                    tooltip="Text description of how to edit the image. Maximum 2560 characters.",
-                ),
-                IO.DynamicCombo.Input(
-                    "model",
-                    options=_model_inputs(
-                        ["reve-edit@20250915", "reve-edit-fast@20251030"],
-                        aspect_ratios=["auto", "16:9", "9:16", "3:2", "2:3", "4:3", "3:4", "1:1"],
-                    ),
-                    tooltip="Model version to use for editing.",
-                ),
-                *_postprocessing_inputs(),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    control_after_generate=True,
-                    tooltip="Seed controls whether the node should re-run; "
-                    "results are non-deterministic regardless of seed.",
-                ),
-            ],
-            outputs=[IO.Image.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(
-                    widgets=["model"],
-                ),
-                expr="""
-                (
-                    $isFast := $contains(widgets.model, "fast");
-                    $base := $isFast ? 0.01001 : 0.0572;
-                    {"type": "usd", "usd": $base, "format": {"approximate": true, "note": "(base)"}}
-                )
-                """,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        image: Input.Image,
-        edit_instruction: str,
-        model: dict,
-        upscale: dict,
-        remove_background: bool,
-        seed: int,
-    ) -> IO.NodeOutput:
-        validate_string(edit_instruction, min_length=1, max_length=2560)
-        tts = model["test_time_scaling"]
-        ar = model["aspect_ratio"]
-        response = await sync_op_raw(
-            cls,
-            ApiEndpoint(
-                path="/proxy/reve/v1/image/edit",
-                method="POST",
-                headers={"Accept": "image/webp"},
-            ),
-            as_binary=True,
-            price_extractor=_reve_price_extractor,
-            response_header_validator=_reve_response_header_validator,
-            data=ReveImageEditRequest(
-                edit_instruction=edit_instruction,
-                reference_image=tensor_to_base64_string(image),
-                aspect_ratio=ar if ar != "auto" else None,
-                version=model["model"],
-                test_time_scaling=tts if tts and tts > 1 else None,
-                postprocessing=_build_postprocessing(upscale, remove_background),
-            ),
-        )
-        return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(response)))
-
-
-class ReveImageRemixNode(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="ReveImageRemixNode",
-            display_name="Reve Image Remix",
-            category="api node/image/Reve",
-            description="Combine reference images with text prompts to create new images using Reve.",
-            inputs=[
-                IO.Autogrow.Input(
-                    "reference_images",
-                    template=IO.Autogrow.TemplatePrefix(
-                        IO.Image.Input("image"),
-                        prefix="image_",
-                        min=1,
-                        max=6,
-                    ),
-                ),
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Text description of the desired image. "
-                    "May include XML img tags to reference specific images by index, "
-                    "e.g. <img>0</img>, <img>1</img>, etc.",
-                ),
-                IO.DynamicCombo.Input(
-                    "model",
-                    options=_model_inputs(
-                        ["reve-remix@20250915", "reve-remix-fast@20251030"],
-                        aspect_ratios=["auto", "16:9", "9:16", "3:2", "2:3", "4:3", "3:4", "1:1"],
-                    ),
-                    tooltip="Model version to use for remixing.",
-                ),
-                *_postprocessing_inputs(),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=2147483647,
-                    control_after_generate=True,
-                    tooltip="Seed controls whether the node should re-run; "
-                    "results are non-deterministic regardless of seed.",
-                ),
-            ],
-            outputs=[IO.Image.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(
-                    widgets=["model"],
-                ),
-                expr="""
-                (
-                    $isFast := $contains(widgets.model, "fast");
-                    $base := $isFast ? 0.01001 : 0.0572;
-                    {"type": "usd", "usd": $base, "format": {"approximate": true, "note": "(base)"}}
-                )
-                """,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        reference_images: IO.Autogrow.Type,
-        prompt: str,
-        model: dict,
-        upscale: dict,
-        remove_background: bool,
-        seed: int,
-    ) -> IO.NodeOutput:
-        validate_string(prompt, min_length=1, max_length=2560)
-        if not reference_images:
-            raise ValueError("At least one reference image is required.")
-        ref_base64_list = []
-        for key in reference_images:
-            ref_base64_list.append(tensor_to_base64_string(reference_images[key]))
-        if len(ref_base64_list) > 6:
-            raise ValueError("Maximum 6 reference images are allowed.")
-        tts = model["test_time_scaling"]
-        ar = model["aspect_ratio"]
-        response = await sync_op_raw(
-            cls,
-            ApiEndpoint(
-                path="/proxy/reve/v1/image/remix",
-                method="POST",
-                headers={"Accept": "image/webp"},
-            ),
-            as_binary=True,
-            price_extractor=_reve_price_extractor,
-            response_header_validator=_reve_response_header_validator,
-            data=ReveImageRemixRequest(
-                prompt=prompt,
-                reference_images=ref_base64_list,
-                aspect_ratio=ar if ar != "auto" else None,
-                version=model["model"],
-                test_time_scaling=tts if tts and tts > 1 else None,
-                postprocessing=_build_postprocessing(upscale, remove_background),
-            ),
-        )
-        return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(response)))
-
-
-class ReveExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
-        return [
-            ReveImageCreateNode,
-            ReveImageEditNode,
-            ReveImageRemixNode,
-        ]
-
-
-async def comfy_entrypoint() -> ReveExtension:
-    return ReveExtension()

comfy_api_nodes/util/client.py

@@ -67,7 +67,6 @@ class _RequestConfig:
     progress_origin_ts: float | None = None
     price_extractor: Callable[[dict[str, Any]], float | None] | None = None
     is_rate_limited: Callable[[int, Any], bool] | None = None
-    response_header_validator: Callable[[dict[str, str]], None] | None = None
 
 
 @dataclass
@@ -203,13 +202,11 @@ async def sync_op_raw(
     monitor_progress: bool = True,
     max_retries_on_rate_limit: int = 16,
     is_rate_limited: Callable[[int, Any], bool] | None = None,
-    response_header_validator: Callable[[dict[str, str]], None] | None = None,
 ) -> dict[str, Any] | bytes:
     """
     Make a single network request.
       - If as_binary=False (default): returns JSON dict (or {'_raw': '<text>'} if non-JSON).
       - If as_binary=True: returns bytes.
-      - response_header_validator: optional callback receiving response headers dict
     """
     if isinstance(data, BaseModel):
         data = data.model_dump(exclude_none=True)
@@ -235,7 +232,6 @@ async def sync_op_raw(
         price_extractor=price_extractor,
         max_retries_on_rate_limit=max_retries_on_rate_limit,
         is_rate_limited=is_rate_limited,
-        response_header_validator=response_header_validator,
     )
     return await _request_base(cfg, expect_binary=as_binary)
 
@@ -773,12 +769,6 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
                                     cfg.node_cls, cfg.wait_label, int(now - start_time), cfg.estimated_total
                                 )
                     bytes_payload = bytes(buff)
-                    resp_headers = {k.lower(): v for k, v in resp.headers.items()}
-                    if cfg.price_extractor:
-                        with contextlib.suppress(Exception):
-                            extracted_price = cfg.price_extractor(resp_headers)
-                    if cfg.response_header_validator:
-                        cfg.response_header_validator(resp_headers)
                     operation_succeeded = True
                     final_elapsed_seconds = int(time.monotonic() - start_time)
                     request_logger.log_request_response(
@@ -786,7 +776,7 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
                         request_method=method,
                         request_url=url,
                         response_status_code=resp.status,
-                        response_headers=resp_headers,
+                        response_headers=dict(resp.headers),
                         response_content=bytes_payload,
                     )
                     return bytes_payload

comfy_extras/nodes_flux.py

@@ -6,7 +6,6 @@ import comfy.model_management
 import torch
 import math
 import nodes
-import comfy.ldm.flux.math
 
 class CLIPTextEncodeFlux(io.ComfyNode):
     @classmethod
@@ -232,68 +231,6 @@ class Flux2Scheduler(io.ComfyNode):
         sigmas = get_schedule(steps, round(seq_len))
         return io.NodeOutput(sigmas)
 
-class KV_Attn_Input:
-    def __init__(self):
-        self.cache = {}
-
-    def __call__(self, q, k, v, extra_options, **kwargs):
-        reference_image_num_tokens = extra_options.get("reference_image_num_tokens", [])
-        if len(reference_image_num_tokens) == 0:
-            return {}
-
-        ref_toks = sum(reference_image_num_tokens)
-        cache_key = "{}_{}".format(extra_options["block_type"], extra_options["block_index"])
-        if cache_key in self.cache:
-            kk, vv = self.cache[cache_key]
-            self.set_cache = False
-            return {"q": q, "k": torch.cat((k, kk), dim=2), "v": torch.cat((v, vv), dim=2)}
-
-        self.cache[cache_key] = (k[:, :, -ref_toks:].clone(), v[:, :, -ref_toks:].clone())
-        self.set_cache = True
-        return {"q": q, "k": k, "v": v}
-
-    def cleanup(self):
-        self.cache = {}
-
-
-class FluxKVCache(io.ComfyNode):
-    @classmethod
-    def define_schema(cls) -> io.Schema:
-        return io.Schema(
-            node_id="FluxKVCache",
-            display_name="Flux KV Cache",
-            description="Enables KV Cache optimization for reference images on Flux family models.",
-            category="",
-            is_experimental=True,
-            inputs=[
-                io.Model.Input("model", tooltip="The model to use KV Cache on."),
-            ],
-            outputs=[
-                io.Model.Output(tooltip="The patched model with KV Cache enabled."),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, model: io.Model.Type) -> io.NodeOutput:
-        m = model.clone()
-        input_patch_obj = KV_Attn_Input()
-
-        def model_input_patch(inputs):
-            if len(input_patch_obj.cache) > 0:
-                ref_image_tokens = sum(inputs["transformer_options"].get("reference_image_num_tokens", []))
-                if ref_image_tokens > 0:
-                    img = inputs["img"]
-                    inputs["img"] = img[:, :-ref_image_tokens]
-            return inputs
-
-        m.set_model_attn1_patch(input_patch_obj)
-        m.set_model_post_input_patch(model_input_patch)
-        if hasattr(model.model.diffusion_model, "params"):
-            m.add_object_patch("diffusion_model.params.default_ref_method", "index_timestep_zero")
-        else:
-            m.add_object_patch("diffusion_model.default_ref_method", "index_timestep_zero")
-
-        return io.NodeOutput(m)
 
 class FluxExtension(ComfyExtension):
     @override
@@ -306,7 +243,6 @@ class FluxExtension(ComfyExtension):
             FluxKontextMultiReferenceLatentMethod,
             EmptyFlux2LatentImage,
             Flux2Scheduler,
-            FluxKVCache,
         ]
 
 

comfy_extras/nodes_painter.py

@@ -1,127 +0,0 @@
-from __future__ import annotations
-
-import hashlib
-import os
-
-import numpy as np
-import torch
-from PIL import Image
-
-import folder_paths
-import node_helpers
-from comfy_api.latest import ComfyExtension, io, UI
-from typing_extensions import override
-
-
-def hex_to_rgb(hex_color: str) -> tuple[float, float, float]:
-    hex_color = hex_color.lstrip("#")
-    if len(hex_color) != 6:
-        return (0.0, 0.0, 0.0)
-    r = int(hex_color[0:2], 16) / 255.0
-    g = int(hex_color[2:4], 16) / 255.0
-    b = int(hex_color[4:6], 16) / 255.0
-    return (r, g, b)
-
-
-class PainterNode(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="Painter",
-            display_name="Painter",
-            category="image",
-            inputs=[
-                io.Image.Input(
-                    "image",
-                    optional=True,
-                    tooltip="Optional base image to paint over",
-                ),
-                io.String.Input(
-                    "mask",
-                    default="",
-                    socketless=True,
-                    extra_dict={"widgetType": "PAINTER", "image_upload": True},
-                ),
-                io.Int.Input(
-                    "width",
-                    default=512,
-                    min=64,
-                    max=4096,
-                    step=64,
-                    socketless=True,
-                    extra_dict={"hidden": True},
-                ),
-                io.Int.Input(
-                    "height",
-                    default=512,
-                    min=64,
-                    max=4096,
-                    step=64,
-                    socketless=True,
-                    extra_dict={"hidden": True},
-                ),
-                io.Color.Input("bg_color", default="#000000"),
-            ],
-            outputs=[
-                io.Image.Output("IMAGE"),
-                io.Mask.Output("MASK"),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, mask, width, height, bg_color="#000000", image=None) -> io.NodeOutput:
-        if image is not None:
-            base_image = image[:1]
-            h, w = base_image.shape[1], base_image.shape[2]
-        else:
-            h, w = height, width
-            r, g, b = hex_to_rgb(bg_color)
-            base_image = torch.zeros((1, h, w, 3), dtype=torch.float32)
-            base_image[0, :, :, 0] = r
-            base_image[0, :, :, 1] = g
-            base_image[0, :, :, 2] = b
-
-        if mask and mask.strip():
-            mask_path = folder_paths.get_annotated_filepath(mask)
-            painter_img = node_helpers.pillow(Image.open, mask_path)
-            painter_img = painter_img.convert("RGBA")
-
-            if painter_img.size != (w, h):
-                painter_img = painter_img.resize((w, h), Image.LANCZOS)
-
-            painter_np = np.array(painter_img).astype(np.float32) / 255.0
-            painter_rgb = painter_np[:, :, :3]
-            painter_alpha = painter_np[:, :, 3:4]
-
-            mask_tensor = torch.from_numpy(painter_np[:, :, 3]).unsqueeze(0)
-
-            base_np = base_image[0].cpu().numpy()
-            composited = painter_rgb * painter_alpha + base_np * (1.0 - painter_alpha)
-            out_image = torch.from_numpy(composited).unsqueeze(0)
-        else:
-            mask_tensor = torch.zeros((1, h, w), dtype=torch.float32)
-            out_image = base_image
-
-        return io.NodeOutput(out_image, mask_tensor, ui=UI.PreviewImage(out_image))
-
-    @classmethod
-    def fingerprint_inputs(cls, mask, width, height, bg_color="#000000", image=None):
-        if mask and mask.strip():
-            mask_path = folder_paths.get_annotated_filepath(mask)
-            if os.path.exists(mask_path):
-                m = hashlib.sha256()
-                with open(mask_path, "rb") as f:
-                    m.update(f.read())
-                return m.digest().hex()
-        return ""
-
-
-
-class PainterExtension(ComfyExtension):
-    @override
-    async def get_node_list(self):
-        return [PainterNode]
-
-
-async def comfy_entrypoint():
-    return PainterExtension()

comfy_extras/nodes_upscale_model.py

@@ -86,8 +86,7 @@ class ImageUpscaleWithModel(io.ComfyNode):
                     pbar = comfy.utils.ProgressBar(steps)
                     s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
                     oom = False
-                except Exception as e:
-                    model_management.raise_non_oom(e)
+                except model_management.OOM_EXCEPTION as e:
                     tile //= 2
                     if tile < 128:
                         raise e

execution.py

@@ -612,7 +612,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
         logging.error(traceback.format_exc())
         tips = ""
 
-        if comfy.model_management.is_oom(ex):
+        if isinstance(ex, comfy.model_management.OOM_EXCEPTION):
             tips = "This error means you ran out of memory on your GPU.\n\nTIPS: If the workflow worked before you might have accidentally set the batch_size to a large number."
             logging.info("Memory summary: {}".format(comfy.model_management.debug_memory_summary()))
             logging.error("Got an OOM, unloading all loaded models.")

main.py

@@ -3,16 +3,16 @@ comfy.options.enable_args_parsing()
 
 import os
 import importlib.util
-import shutil
 import importlib.metadata
 import folder_paths
 import time
 from comfy.cli_args import args, enables_dynamic_vram
 from app.logger import setup_logger
+from app.assets.seeder import asset_seeder
+from app.assets.services import register_output_files
 import itertools
 import utils.extra_config
 from utils.mime_types import init_mime_types
-import faulthandler
 import logging
 import sys
 from comfy_execution.progress import get_progress_state
@@ -27,8 +27,6 @@ if __name__ == "__main__":
 
 setup_logger(log_level=args.verbose, use_stdout=args.log_stdout)
 
-faulthandler.enable(file=sys.stderr, all_threads=False)
-
 import comfy_aimdo.control
 
 if enables_dynamic_vram():
@@ -68,15 +66,8 @@ if __name__ == "__main__":
 
 
 def handle_comfyui_manager_unavailable():
-    manager_req_path = os.path.join(os.path.dirname(os.path.abspath(folder_paths.__file__)), "manager_requirements.txt")
-    uv_available = shutil.which("uv") is not None
-
-    pip_cmd = f"{sys.executable} -m pip install -r {manager_req_path}"
-    msg = f"\n\nTo use the `--enable-manager` feature, the `comfyui-manager` package must be installed first.\ncommand:\n\t{pip_cmd}"
-    if uv_available:
-        msg += f"\nor using uv:\n\tuv pip install -r {manager_req_path}"
-    msg += "\n"
-    logging.warning(msg)
+    if not args.windows_standalone_build:
+        logging.warning(f"\n\nYou appear to be running comfyui-manager from source, this is not recommended. Please install comfyui-manager using the following command:\ncommand:\n\t{sys.executable} -m pip install --pre comfyui_manager\n")
     args.enable_manager = False
 
 
@@ -184,6 +175,7 @@ execute_prestartup_script()
 
 # Main code
 import asyncio
+import shutil
 import threading
 import gc
 
@@ -192,7 +184,6 @@ if 'torch' in sys.modules:
 
 
 import comfy.utils
-from app.assets.seeder import asset_seeder
 
 import execution
 import server
@@ -240,6 +231,24 @@ def cuda_malloc_warning():
             logging.warning("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n")
 
 
+def _collect_output_absolute_paths(history_result: dict) -> list[str]:
+    """Extract absolute file paths for output items from a history result."""
+    paths = []
+    base_dir = folder_paths.get_directory_by_type("output")
+    for node_output in history_result.get("outputs", {}).values():
+        for items in node_output.values():
+            if not isinstance(items, list):
+                continue
+            for item in items:
+                if not isinstance(item, dict) or item.get("type") != "output":
+                    continue
+                filename = item.get("filename")
+                if not filename:
+                    continue
+                paths.append(os.path.join(base_dir, item.get("subfolder", ""), filename))
+    return paths
+
+
 def prompt_worker(q, server_instance):
     current_time: float = 0.0
     cache_type = execution.CacheType.CLASSIC
@@ -274,6 +283,7 @@ def prompt_worker(q, server_instance):
 
             asset_seeder.pause()
             e.execute(item[2], prompt_id, extra_data, item[4])
+
             need_gc = True
 
             remove_sensitive = lambda prompt: prompt[:5] + prompt[6:]
@@ -317,6 +327,11 @@ def prompt_worker(q, server_instance):
                 last_gc_collect = current_time
                 need_gc = False
                 hook_breaker_ac10a0.restore_functions()
+
+                if not asset_seeder.is_disabled():
+                    paths = _collect_output_absolute_paths(e.history_result)
+                    if register_output_files(paths, user_metadata={"prompt_id": prompt_id}) > 0:
+                        asset_seeder.enqueue_enrich(roots=("output",), compute_hashes=True)
                 asset_seeder.resume()
 
 

manager_requirements.txt

@@ -1 +1 @@
-comfyui_manager==4.1b2
+comfyui_manager==4.1b1

middleware/cache_middleware.py

@@ -32,7 +32,7 @@ async def cache_control(
     )
 
     if request.path.endswith(".js") or request.path.endswith(".css") or is_entry_point:
-        response.headers.setdefault("Cache-Control", "no-store")
+        response.headers.setdefault("Cache-Control", "no-cache")
         return response
 
     # Early return for non-image files - no cache headers needed

nodes.py

@@ -2450,7 +2450,6 @@ async def init_builtin_extra_nodes():
         "nodes_nag.py",
         "nodes_sdpose.py",
         "nodes_math.py",
-        "nodes_painter.py",
     ]
 
     import_failed = []

requirements.txt

@@ -1,5 +1,5 @@
-comfyui-frontend-package==1.41.18
-comfyui-workflow-templates==0.9.21
+comfyui-frontend-package==1.39.19
+comfyui-workflow-templates==0.9.11
 comfyui-embedded-docs==0.4.3
 torch
 torchsde
@@ -22,8 +22,8 @@ alembic
 SQLAlchemy
 filelock
 av>=14.2.0
-comfy-kitchen>=0.2.8
-comfy-aimdo>=0.2.10
+comfy-kitchen>=0.2.7
+comfy-aimdo>=0.2.9
 requests
 simpleeval>=1.0.0
 blake3

server.py

@@ -310,7 +310,7 @@ class PromptServer():
         @routes.get("/")
         async def get_root(request):
             response = web.FileResponse(os.path.join(self.web_root, "index.html"))
-            response.headers['Cache-Control'] = 'no-store, must-revalidate'
+            response.headers['Cache-Control'] = 'no-cache'
             response.headers["Pragma"] = "no-cache"
             response.headers["Expires"] = "0"
             return response

tests-unit/server_test/test_cache_control.py

@@ -28,31 +28,31 @@ CACHE_SCENARIOS = [
     },
     # JavaScript/CSS scenarios
     {
-        "name": "js_no_store",
+        "name": "js_no_cache",
         "path": "/script.js",
         "status": 200,
-        "expected_cache": "no-store",
+        "expected_cache": "no-cache",
         "should_have_header": True,
     },
     {
-        "name": "css_no_store",
+        "name": "css_no_cache",
         "path": "/styles.css",
         "status": 200,
-        "expected_cache": "no-store",
+        "expected_cache": "no-cache",
         "should_have_header": True,
     },
     {
-        "name": "index_json_no_store",
+        "name": "index_json_no_cache",
         "path": "/api/index.json",
         "status": 200,
-        "expected_cache": "no-store",
+        "expected_cache": "no-cache",
         "should_have_header": True,
     },
     {
-        "name": "localized_index_json_no_store",
+        "name": "localized_index_json_no_cache",
         "path": "/templates/index.zh.json",
         "status": 200,
-        "expected_cache": "no-store",
+        "expected_cache": "no-cache",
         "should_have_header": True,
     },
     # Non-matching files

tests/test_asset_seeder.py

@@ -0,0 +1,250 @@
+"""Tests for app.assets.seeder – enqueue_enrich and pending-queue behaviour."""
+
+import threading
+from unittest.mock import patch
+
+import pytest
+
+from app.assets.seeder import Progress, _AssetSeeder, State
+
+
+@pytest.fixture()
+def seeder():
+    """Fresh seeder instance for each test."""
+    return _AssetSeeder()
+
+
+# ---------------------------------------------------------------------------
+# _reset_to_idle
+# ---------------------------------------------------------------------------
+
+
+class TestResetToIdle:
+    def test_sets_idle_and_clears_progress(self, seeder):
+        """_reset_to_idle should move state to IDLE and snapshot progress."""
+        progress = Progress(scanned=10, total=20, created=5, skipped=3)
+        seeder._state = State.RUNNING
+        seeder._progress = progress
+
+        with seeder._lock:
+            seeder._reset_to_idle()
+
+        assert seeder._state is State.IDLE
+        assert seeder._progress is None
+        assert seeder._last_progress is progress
+
+    def test_noop_when_progress_already_none(self, seeder):
+        """_reset_to_idle should handle None progress gracefully."""
+        seeder._state = State.CANCELLING
+        seeder._progress = None
+
+        with seeder._lock:
+            seeder._reset_to_idle()
+
+        assert seeder._state is State.IDLE
+        assert seeder._progress is None
+        assert seeder._last_progress is None
+
+
+# ---------------------------------------------------------------------------
+# enqueue_enrich – immediate start when idle
+# ---------------------------------------------------------------------------
+
+
+class TestEnqueueEnrichStartsImmediately:
+    def test_starts_when_idle(self, seeder):
+        """enqueue_enrich should delegate to start_enrich and return True when idle."""
+        with patch.object(seeder, "start_enrich", return_value=True) as mock:
+            assert seeder.enqueue_enrich(roots=("output",), compute_hashes=True) is True
+            mock.assert_called_once_with(roots=("output",), compute_hashes=True)
+
+    def test_no_pending_when_started_immediately(self, seeder):
+        """No pending request should be stored when start_enrich succeeds."""
+        with patch.object(seeder, "start_enrich", return_value=True):
+            seeder.enqueue_enrich(roots=("output",))
+        assert seeder._pending_enrich is None
+
+
+# ---------------------------------------------------------------------------
+# enqueue_enrich – queuing when busy
+# ---------------------------------------------------------------------------
+
+
+class TestEnqueueEnrichQueuesWhenBusy:
+    def test_queues_when_busy(self, seeder):
+        """enqueue_enrich should store a pending request when seeder is busy."""
+        with patch.object(seeder, "start_enrich", return_value=False):
+            result = seeder.enqueue_enrich(roots=("models",), compute_hashes=False)
+
+        assert result is False
+        assert seeder._pending_enrich == {
+            "roots": ("models",),
+            "compute_hashes": False,
+        }
+
+    def test_queues_preserves_compute_hashes_true(self, seeder):
+        with patch.object(seeder, "start_enrich", return_value=False):
+            seeder.enqueue_enrich(roots=("input",), compute_hashes=True)
+
+        assert seeder._pending_enrich["compute_hashes"] is True
+
+
+# ---------------------------------------------------------------------------
+# enqueue_enrich – merging when a pending request already exists
+# ---------------------------------------------------------------------------
+
+
+class TestEnqueueEnrichMergesPending:
+    def _make_busy(self, seeder):
+        """Patch start_enrich to always return False (seeder busy)."""
+        return patch.object(seeder, "start_enrich", return_value=False)
+
+    def test_merges_roots(self, seeder):
+        """A second enqueue should merge roots with the existing pending request."""
+        with self._make_busy(seeder):
+            seeder.enqueue_enrich(roots=("models",))
+            seeder.enqueue_enrich(roots=("output",))
+
+        merged = set(seeder._pending_enrich["roots"])
+        assert merged == {"models", "output"}
+
+    def test_merges_overlapping_roots(self, seeder):
+        """Duplicate roots should be deduplicated."""
+        with self._make_busy(seeder):
+            seeder.enqueue_enrich(roots=("models", "input"))
+            seeder.enqueue_enrich(roots=("input", "output"))
+
+        merged = set(seeder._pending_enrich["roots"])
+        assert merged == {"models", "input", "output"}
+
+    def test_compute_hashes_sticky_true(self, seeder):
+        """Once compute_hashes is True it should stay True after merging."""
+        with self._make_busy(seeder):
+            seeder.enqueue_enrich(roots=("models",), compute_hashes=True)
+            seeder.enqueue_enrich(roots=("output",), compute_hashes=False)
+
+        assert seeder._pending_enrich["compute_hashes"] is True
+
+    def test_compute_hashes_upgrades_to_true(self, seeder):
+        """A later enqueue with compute_hashes=True should upgrade the pending request."""
+        with self._make_busy(seeder):
+            seeder.enqueue_enrich(roots=("models",), compute_hashes=False)
+            seeder.enqueue_enrich(roots=("output",), compute_hashes=True)
+
+        assert seeder._pending_enrich["compute_hashes"] is True
+
+    def test_compute_hashes_stays_false(self, seeder):
+        """If both enqueues have compute_hashes=False it stays False."""
+        with self._make_busy(seeder):
+            seeder.enqueue_enrich(roots=("models",), compute_hashes=False)
+            seeder.enqueue_enrich(roots=("output",), compute_hashes=False)
+
+        assert seeder._pending_enrich["compute_hashes"] is False
+
+    def test_triple_merge(self, seeder):
+        """Three successive enqueues should all merge correctly."""
+        with self._make_busy(seeder):
+            seeder.enqueue_enrich(roots=("models",), compute_hashes=False)
+            seeder.enqueue_enrich(roots=("input",), compute_hashes=False)
+            seeder.enqueue_enrich(roots=("output",), compute_hashes=True)
+
+        merged = set(seeder._pending_enrich["roots"])
+        assert merged == {"models", "input", "output"}
+        assert seeder._pending_enrich["compute_hashes"] is True
+
+
+# ---------------------------------------------------------------------------
+# Pending enrich drains after scan completes
+# ---------------------------------------------------------------------------
+
+
+class TestPendingEnrichDrain:
+    """Verify that _run_scan drains _pending_enrich via start_enrich."""
+
+    @patch("app.assets.seeder.dependencies_available", return_value=True)
+    @patch("app.assets.seeder.get_all_known_prefixes", return_value=[])
+    @patch("app.assets.seeder.sync_root_safely", return_value=set())
+    @patch("app.assets.seeder.collect_paths_for_roots", return_value=[])
+    @patch("app.assets.seeder.build_asset_specs", return_value=([], {}, 0))
+    def test_pending_enrich_starts_after_scan(self, *_mocks):
+        """After a fast scan finishes, the pending enrich should be started."""
+        seeder = _AssetSeeder()
+
+        seeder._pending_enrich = {
+            "roots": ("output",),
+            "compute_hashes": True,
+        }
+
+        with patch.object(seeder, "start_enrich", return_value=True) as mock_start:
+            seeder.start_fast(roots=("models",))
+            seeder.wait(timeout=5)
+
+            mock_start.assert_called_once_with(
+                roots=("output",),
+                compute_hashes=True,
+            )
+
+        assert seeder._pending_enrich is None
+
+    @patch("app.assets.seeder.dependencies_available", return_value=True)
+    @patch("app.assets.seeder.get_all_known_prefixes", return_value=[])
+    @patch("app.assets.seeder.sync_root_safely", return_value=set())
+    @patch("app.assets.seeder.collect_paths_for_roots", return_value=[])
+    @patch("app.assets.seeder.build_asset_specs", return_value=([], {}, 0))
+    def test_pending_cleared_even_when_start_fails(self, *_mocks):
+        """_pending_enrich should be cleared even if start_enrich returns False."""
+        seeder = _AssetSeeder()
+        seeder._pending_enrich = {
+            "roots": ("output",),
+            "compute_hashes": False,
+        }
+
+        with patch.object(seeder, "start_enrich", return_value=False):
+            seeder.start_fast(roots=("models",))
+            seeder.wait(timeout=5)
+
+        assert seeder._pending_enrich is None
+
+    @patch("app.assets.seeder.dependencies_available", return_value=True)
+    @patch("app.assets.seeder.get_all_known_prefixes", return_value=[])
+    @patch("app.assets.seeder.sync_root_safely", return_value=set())
+    @patch("app.assets.seeder.collect_paths_for_roots", return_value=[])
+    @patch("app.assets.seeder.build_asset_specs", return_value=([], {}, 0))
+    def test_no_drain_when_no_pending(self, *_mocks):
+        """start_enrich should not be called when there is no pending request."""
+        seeder = _AssetSeeder()
+        assert seeder._pending_enrich is None
+
+        with patch.object(seeder, "start_enrich", return_value=True) as mock_start:
+            seeder.start_fast(roots=("models",))
+            seeder.wait(timeout=5)
+
+            mock_start.assert_not_called()
+
+
+# ---------------------------------------------------------------------------
+# Thread-safety of enqueue_enrich
+# ---------------------------------------------------------------------------
+
+
+class TestEnqueueEnrichThreadSafety:
+    def test_concurrent_enqueues(self, seeder):
+        """Multiple threads enqueuing should not lose roots."""
+        with patch.object(seeder, "start_enrich", return_value=False):
+            barrier = threading.Barrier(3)
+
+            def enqueue(root):
+                barrier.wait()
+                seeder.enqueue_enrich(roots=(root,), compute_hashes=False)
+
+            threads = [
+                threading.Thread(target=enqueue, args=(r,))
+                for r in ("models", "input", "output")
+            ]
+            for t in threads:
+                t.start()
+            for t in threads:
+                t.join(timeout=5)
+
+        merged = set(seeder._pending_enrich["roots"])
+        assert merged == {"models", "input", "output"}