Merge c20a04fef0 into b08debceca

Signed-off-by: bigcat88 <bigcat88@icloud.com>

.coderabbit.yaml

@@ -4,12 +4,12 @@ early_access: false
 tone_instructions: "Only comment on issues introduced by this PR's changes. Do not flag pre-existing problems in moved, re-indented, or reformatted code."
 
 reviews:
-  profile: "chill"
-  request_changes_workflow: false
+  profile: "assertive"
+  request_changes_workflow: true
   high_level_summary: false
   poem: false
   review_status: false
-  review_details: false
+  review_details: true
   commit_status: true
   collapse_walkthrough: true
   changed_files_summary: false
@@ -39,6 +39,14 @@ reviews:
     - path: "**"
       instructions: |
         IMPORTANT: Only comment on issues directly introduced by this PR's code changes.
+        Treat AGENTS.md as mandatory repository policy, not optional style guidance.
+        Flag PR changes that violate AGENTS.md even when the code is otherwise functional.
+        In particular, enforce architecture boundaries, dtype/device/memory rules,
+        interface contracts, import style, no unnecessary try/except blocks, no inline
+        imports, no outbound internet paths in core ComfyUI, and narrow scoped fixes.
+        Prefer direct findings over suggestions when a rule is violated. Only ignore
+        AGENTS.md when it clearly conflicts with a newer explicit maintainer instruction
+        in the PR.
         Do NOT flag pre-existing issues in code that was merely moved, re-indented,
         de-indented, or reformatted without logic changes. If code appears in the diff
         only due to whitespace or structural reformatting (e.g., removing a `with:` block),
@@ -123,5 +131,10 @@ chat:
 
 knowledge_base:
   opt_out: false
+  code_guidelines:
+    enabled: true
+    filePatterns:
+      - files: "AGENTS.md"
+        applyTo: "**"
   learnings:
     scope: "auto"

comfy/sd1_clip.py

@@ -543,18 +543,24 @@ class SDTokenizer:
     def _try_get_embedding(self, embedding_name:str):
         '''
         Takes a potential embedding name and tries to retrieve it.
-        Returns a Tuple consisting of the embedding and any leftover string, embedding can be None.
+        Returns a Tuple consisting of the embedding, the cleaned embedding name, and any leftover string, embedding can be None.
         '''
         split_embed = embedding_name.split()
         embedding_name = split_embed[0]
         leftover = ' '.join(split_embed[1:])
+
+        match = re.search(r'[<\[]', embedding_name)
+        if match is not None:
+            leftover = embedding_name[match.start():] + (" " + leftover if leftover else "")
+            embedding_name = embedding_name[:match.start()]
+
         embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key)
         if embed is None:
             stripped = embedding_name.strip(',')
             if len(stripped) < len(embedding_name):
                 embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key)
-                return (embed, "{} {}".format(embedding_name[len(stripped):], leftover))
-        return (embed, leftover)
+                return (embed, embedding_name, "{} {}".format(embedding_name[len(stripped):], leftover))
+        return (embed, embedding_name, leftover)
 
     def pad_tokens(self, tokens, amount):
         if self.pad_left:
@@ -585,7 +591,7 @@ class SDTokenizer:
         tokens = []
         for weighted_segment, weight in parsed_weights:
             to_tokenize = unescape_important(weighted_segment)
-            split = re.split(' {0}|\n{0}'.format(self.embedding_identifier), to_tokenize)
+            split = re.split(r'(?<=\s){}'.format(re.escape(self.embedding_identifier)), to_tokenize)
             to_tokenize = [split[0]]
             for i in range(1, len(split)):
                 to_tokenize.append("{}{}".format(self.embedding_identifier, split[i]))
@@ -595,7 +601,7 @@ class SDTokenizer:
                 # if we find an embedding, deal with the embedding
                 if word.startswith(self.embedding_identifier) and self.embedding_directory is not None:
                     embedding_name = word[len(self.embedding_identifier):].strip('\n')
-                    embed, leftover = self._try_get_embedding(embedding_name)
+                    embed, embedding_name, leftover = self._try_get_embedding(embedding_name)
                     if embed is None:
                         logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring")
                     else:

comfy/text_encoders/llama.py

@@ -937,22 +937,41 @@ class BaseGenerate:
             return torch.argmax(logits, dim=-1, keepdim=True)
 
         # Sampling mode
-        if repetition_penalty != 1.0:
-            for i in range(logits.shape[0]):
-                for token_id in set(token_history):
-                    logits[i, token_id] *= repetition_penalty if logits[i, token_id] < 0 else 1/repetition_penalty
-
-        if presence_penalty is not None and presence_penalty != 0.0:
-            for i in range(logits.shape[0]):
-                for token_id in set(token_history):
-                    logits[i, token_id] -= presence_penalty
+        if len(token_history) > 0 and (repetition_penalty != 1.0 or (presence_penalty is not None and presence_penalty != 0.0)):
+            token_ids = torch.tensor(list(set(token_history)), device=logits.device)
+            token_logits = logits[:, token_ids]
+            if repetition_penalty != 1.0:
+                token_logits = torch.where(token_logits < 0, token_logits * repetition_penalty, token_logits / repetition_penalty)
+            if presence_penalty is not None and presence_penalty != 0.0:
+                token_logits = token_logits - presence_penalty
+            logits[:, token_ids] = token_logits
 
         if temperature != 1.0:
             logits = logits / temperature
 
         if top_k > 0:
-            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
-            logits[indices_to_remove] = torch.finfo(logits.dtype).min
+            top_k = min(top_k, logits.shape[-1])
+            logits, top_indices = torch.topk(logits, top_k)
+
+            if min_p > 0.0:
+                probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)
+                top_probs, _ = probs_before_filter.max(dim=-1, keepdim=True)
+                min_threshold = min_p * top_probs
+                indices_to_remove = probs_before_filter < min_threshold
+                logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+            if top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+                cumulative_probs = torch.cumsum(torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[..., 0] = False
+                indices_to_remove = torch.zeros_like(logits, dtype=torch.bool)
+                indices_to_remove.scatter_(1, sorted_indices, sorted_indices_to_remove)
+                logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+            probs = torch.nn.functional.softmax(logits, dim=-1)
+            next_token = torch.multinomial(probs, num_samples=1, generator=generator)
+            return top_indices.gather(1, next_token)
 
         if min_p > 0.0:
             probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)

comfy_api/latest/_io.py

@@ -1110,7 +1110,7 @@ class Autogrow(ComfyTypeI):
             self.template.validate()
 
     @staticmethod
-    def _expand_schema_for_dynamic(out_dict: dict[str, Any], live_inputs: dict[str, Any], value: tuple[str, dict[str, Any]], input_type: str, curr_prefix: list[str] | None):
+    def _expand_schema_for_dynamic(out_dict: dict[str, Any], live_inputs: dict[str, Any], value: tuple[str, dict[str, Any]], input_type: str, curr_prefix: list[str] | None, live_input_types: dict[str, str] | None = None):
         # NOTE: purposely do not include self in out_dict; instead use only the template inputs
         # need to figure out names based on template type
         is_names = ("names" in value[1]["template"])
@@ -1159,7 +1159,7 @@ class Autogrow(ComfyTypeI):
             finalized_prefix = finalize_prefix(curr_prefix)
             out_dict["dynamic_paths"][finalized_prefix] = finalized_prefix
             out_dict["dynamic_paths_default_value"][finalized_prefix] = DynamicPathsDefaultValue.EMPTY_DICT
-        parse_class_inputs(out_dict, live_inputs, new_dict, curr_prefix)
+        parse_class_inputs(out_dict, live_inputs, new_dict, curr_prefix, live_input_types)
 
 @comfytype(io_type="COMFY_DYNAMICCOMBO_V3")
 class DynamicCombo(ComfyTypeI):
@@ -1197,7 +1197,7 @@ class DynamicCombo(ComfyTypeI):
                     input.validate()
 
     @staticmethod
-    def _expand_schema_for_dynamic(out_dict: dict[str, Any], live_inputs: dict[str, Any], value: tuple[str, dict[str, Any]], input_type: str, curr_prefix: list[str] | None):
+    def _expand_schema_for_dynamic(out_dict: dict[str, Any], live_inputs: dict[str, Any], value: tuple[str, dict[str, Any]], input_type: str, curr_prefix: list[str] | None, live_input_types: dict[str, str] | None = None):
         finalized_id = finalize_prefix(curr_prefix)
         if finalized_id in live_inputs:
             key = live_inputs[finalized_id]
@@ -1209,57 +1209,206 @@ class DynamicCombo(ComfyTypeI):
                     selected_option = option
                     break
             if selected_option is not None:
-                parse_class_inputs(out_dict, live_inputs, selected_option["inputs"], curr_prefix)
+                parse_class_inputs(out_dict, live_inputs, selected_option["inputs"], curr_prefix, live_input_types)
                 # add self to inputs
                 out_dict[input_type][finalized_id] = value
                 out_dict["dynamic_paths"][finalized_id] = finalize_prefix(curr_prefix, curr_prefix[-1])
 
 @comfytype(io_type="COMFY_DYNAMICSLOT_V3")
 class DynamicSlot(ComfyTypeI):
+    """A slot whose revealed inputs depend on the type connected upstream.
+
+    Each ``Option`` declares a ``when`` condition; the first option whose
+    condition matches the slot's resolved upstream type (or whose
+    ``when=None`` matches an empty slot) decides which child inputs are
+    exposed.
+
+    Each concrete type may appear in at most one option's ``when``, so the
+    matching branch is unambiguous. The unconnected case (``when=None``) is
+    its own bucket and may also appear at most once.
+
+    The AnyType limitation documented in
+    :py:mod:`comfy_execution.type_resolver` applies: an upstream output
+    declared as ``AnyType`` resolves to ``"*"`` and will only match a
+    ``when=io.AnyType`` option, never a concrete-type one.
+    """
     Type = dict[str, Any]
 
-    class Input(DynamicInput):
-        def __init__(self, slot: Input, inputs: list[Input],
-                    display_name: str=None, tooltip: str=None, lazy: bool=None, extra_dict=None):
-            assert(not isinstance(slot, DynamicInput))
-            self.slot = copy.copy(slot)
-            self.slot.display_name = slot.display_name if slot.display_name is not None else display_name
-            optional = True
-            self.slot.tooltip = slot.tooltip if slot.tooltip is not None else tooltip
-            self.slot.lazy = slot.lazy if slot.lazy is not None else lazy
-            self.slot.extra_dict = slot.extra_dict if slot.extra_dict is not None else extra_dict
-            super().__init__(slot.id, self.slot.display_name, optional, self.slot.tooltip, self.slot.lazy, self.slot.extra_dict)
+    class Option:
+        """One branch of inputs revealed when the slot's resolved type matches ``when``.
+
+        ``when`` accepts:
+          * ``None`` — no link present
+          * ``io.AnyType`` — upstream resolved type is literally ``"*"``
+          * a single ComfyType class (e.g. ``io.Image``)
+          * a list of ComfyType classes (shared branch across multiple types)
+          * a ``MultiType.Input`` instance (parsed via its ``.io_types``)
+        """
+        def __init__(self, when: Any, inputs: list[Input]):
+            self.when = when
             self.inputs = inputs
-            self.force_input = None
-            # force widget inputs to have no widgets, otherwise this would be awkward
-            if isinstance(self.slot, WidgetInput):
-                self.force_input = True
-                self.slot.force_input = True
+            # ``_when_types`` is the ordered tuple of io_types (deterministic);
+            # ``_when_set`` is the same content as a set for fast matching.
+            self._when_types = self._normalize_when(when)
+            self._when_set: frozenset[str] | None = (
+                None if self._when_types is None else frozenset(self._when_types)
+            )
+
+        @staticmethod
+        def _normalize_when(when: Any) -> tuple[str, ...] | None:
+            """Normalize ``when`` to an ordered, deduplicated tuple of io_types, or ``None`` for the unconnected case."""
+            if when is None:
+                return None
+            if isinstance(when, type) and issubclass(when, _ComfyType):
+                return (when.io_type,)
+            if isinstance(when, MultiType.Input):
+                result = tuple(dict.fromkeys(t.io_type for t in when.io_types))
+                if "*" in result and len(result) > 1:
+                    raise ValueError(
+                        "DynamicSlot.Option: AnyType cannot be grouped with concrete types; "
+                        "use a separate Option(when=io.AnyType, ...) instead"
+                    )
+                return result
+            if isinstance(when, Iterable) and not isinstance(when, str):
+                types: list[str] = []
+                for t in when:
+                    if not (isinstance(t, type) and issubclass(t, _ComfyType)):
+                        raise ValueError(
+                            f"DynamicSlot.Option: list entries must be ComfyType classes, got {t!r}"
+                        )
+                    if t.io_type not in types:
+                        types.append(t.io_type)
+                if not types:
+                    raise ValueError("DynamicSlot.Option: when=[] is not allowed; use when=None instead")
+                if "*" in types and len(types) > 1:
+                    raise ValueError(
+                        "DynamicSlot.Option: AnyType cannot be grouped with concrete types; "
+                        "use a separate Option(when=io.AnyType, ...) instead"
+                    )
+                return tuple(types)
+            raise ValueError(
+                "DynamicSlot.Option: when must be None, a ComfyType class, a list of ComfyType classes, "
+                f"or a MultiType.Input; got {when!r}"
+            )
+
+        def as_dict(self):
+            return {
+                "when": None if self._when_types is None else list(self._when_types),
+                "inputs": create_input_dict_v1(self.inputs),
+            }
+
+    class Input(DynamicInput):
+        def __init__(self, id: str, options: list[DynamicSlot.Option],
+                    display_name: str=None, optional: bool=True, tooltip: str=None, lazy: bool=None, extra_dict=None):
+            if not options:
+                raise ValueError("DynamicSlot.Input: at least one Option is required")
+            for opt in options:
+                if not isinstance(opt, DynamicSlot.Option):
+                    raise ValueError(
+                        f"DynamicSlot.Input: options must be DynamicSlot.Option instances, got {opt!r}"
+                    )
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+            self.options = options
+            # Enforce uniqueness: each io_type (and the unconnected case) may
+            # appear in at most one option's ``when``. Also derive the slot's
+            # declared connection type as the ordered union of every non-None
+            # option's ``when`` set so authors control displayed precedence.
+            seen_types: set[str] = set()
+            seen_none = False
+            connected_types: list[str] = []
+            for opt in options:
+                if opt._when_types is None:
+                    if seen_none:
+                        raise ValueError("DynamicSlot.Input: only one Option may declare when=None")
+                    seen_none = True
+                    continue
+                for t in opt._when_types:
+                    if t in seen_types:
+                        raise ValueError(
+                            f"DynamicSlot.Input: type {t!r} appears in more than one Option's `when`; "
+                            "each type must be claimed by exactly one option"
+                        )
+                    seen_types.add(t)
+                    connected_types.append(t)
+            if not connected_types:
+                raise ValueError(
+                    "DynamicSlot.Input: at least one Option must have a non-None `when`; "
+                    "a slot with only a `when=None` option can never be connected"
+                )
+            # A required slot demands a link, so the when=None branch is unreachable.
+            if not optional and seen_none:
+                raise ValueError(
+                    "DynamicSlot.Input: optional=False forbids when=None options; "
+                    "the unconnected branch is unreachable when a link is required"
+                )
+            self._slot_io_type = ",".join(connected_types)
+
+        # parse_class_inputs dispatches on the class io_type (COMFY_DYNAMICSLOT_V3),
+        # so get_all/get_io_type must not be overridden; slotType is published via as_dict.
 
         def get_all(self) -> list[Input]:
-            return [self.slot] + self.inputs
+            seen_ids: set[str] = set()
+            children: list[Input] = []
+            for opt in self.options:
+                for inp in opt.inputs:
+                    if inp.id in seen_ids:
+                        continue
+                    seen_ids.add(inp.id)
+                    children.append(inp)
+            return [self] + children
 
         def as_dict(self):
             return super().as_dict() | prune_dict({
-                "slotType": str(self.slot.get_io_type()),
-                "inputs": create_input_dict_v1(self.inputs),
-                "forceInput": self.force_input,
+                "slotType": self._slot_io_type,
+                "options": [o.as_dict() for o in self.options],
+                # Always render as a connector — slotType may include widget-capable
+                # types (INT/STRING/etc.) but a DynamicSlot is a connection point.
+                "forceInput": True,
             })
 
         def validate(self):
-            self.slot.validate()
-            for input in self.inputs:
-                input.validate()
+            for opt in self.options:
+                for inp in opt.inputs:
+                    inp.validate()
 
     @staticmethod
-    def _expand_schema_for_dynamic(out_dict: dict[str, Any], live_inputs: dict[str, Any], value: tuple[str, dict[str, Any]], input_type: str, curr_prefix: list[str] | None):
+    def _select_option(options: list[dict[str, Any]], live_input_types: dict[str, str] | None,
+                       finalized_id: str, has_link: bool) -> dict[str, Any] | None:
+        """Pick the first option whose ``when`` matches the slot's state.
+
+        Connected: pick the first option whose ``when`` set intersects the
+        comma-split resolved type. Unconnected: pick the first ``when=None``.
+        With per-option type uniqueness, at most one connected option can match
+        any single concrete type; ordering only matters when upstream declares
+        a multi-type union (e.g. ``"IMAGE,MASK"``).
+        """
+        if not has_link:
+            for opt in options:
+                if opt["when"] is None:
+                    return opt
+            return None
+        resolved = (live_input_types or {}).get(finalized_id, "*")
+        resolved_set = {t.strip() for t in resolved.split(",")}
+        for opt in options:
+            when = opt["when"]
+            if when is None:
+                continue
+            if resolved_set & set(when):
+                return opt
+        return None
+
+    @staticmethod
+    def _expand_schema_for_dynamic(out_dict: dict[str, Any], live_inputs: dict[str, Any], value: tuple[str, dict[str, Any]], input_type: str, curr_prefix: list[str] | None, live_input_types: dict[str, str] | None = None):
         finalized_id = finalize_prefix(curr_prefix)
-        if finalized_id in live_inputs:
-            inputs = value[1]["inputs"]
-            parse_class_inputs(out_dict, live_inputs, inputs, curr_prefix)
-            # add self to inputs
-            out_dict[input_type][finalized_id] = value
-            out_dict["dynamic_paths"][finalized_id] = finalize_prefix(curr_prefix, curr_prefix[-1])
+        options: list[dict[str, Any]] = value[1].get("options", [])
+        has_link = finalized_id in live_inputs and live_inputs[finalized_id] is not None
+        selected = DynamicSlot._select_option(options, live_input_types, finalized_id, has_link)
+        if selected is not None:
+            parse_class_inputs(out_dict, live_inputs, selected["inputs"], curr_prefix, live_input_types)
+        # Always advertise the slot itself so the connector renders even when no
+        # option matched (unmatched concrete + no AnyType option).
+        out_dict[input_type][finalized_id] = value
+        out_dict["dynamic_paths"][finalized_id] = finalize_prefix(curr_prefix, curr_prefix[-1])
 
 @comfytype(io_type="IMAGECOMPARE")
 class ImageCompare(ComfyTypeI):
@@ -1404,11 +1553,18 @@ class Range(ComfyTypeIO):
             })
 
 
-DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {}
-def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]):
+# Signature: (out_dict, live_inputs, value, input_type, curr_prefix, live_input_types).
+# live_input_types is {input_id: resolved_io_type} from TypeResolver; existing
+# expanders ignore it, future type-discriminated ones use it as discriminator.
+_DynamicInputFunc = Callable[
+    [dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None, dict[str, str] | None],
+    None,
+]
+DYNAMIC_INPUT_LOOKUP: dict[str, _DynamicInputFunc] = {}
+def register_dynamic_input_func(io_type: str, func: _DynamicInputFunc):
     DYNAMIC_INPUT_LOOKUP[io_type] = func
 
-def get_dynamic_input_func(io_type: str) -> Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]:
+def get_dynamic_input_func(io_type: str) -> _DynamicInputFunc:
     return DYNAMIC_INPUT_LOOKUP[io_type]
 
 def setup_dynamic_input_funcs():
@@ -1764,7 +1920,12 @@ class Schema:
         )
         return info
 
-def get_finalized_class_inputs(d: dict[str, Any], live_inputs: dict[str, Any], include_hidden=False) -> tuple[dict[str, Any], V3Data]:
+def get_finalized_class_inputs(d: dict[str, Any], live_inputs: dict[str, Any], include_hidden=False, live_input_types: dict[str, str] | None = None) -> tuple[dict[str, Any], V3Data]:
+    """Expand a node's V3 schema against a concrete prompt.
+
+    ``live_input_types`` is an optional ``{input_id: resolved_io_type}`` map
+    (from ``TypeResolver``) used by future type-discriminated dynamic inputs.
+    """
     out_dict = {
         "required": {},
         "optional": {},
@@ -1774,7 +1935,7 @@ def get_finalized_class_inputs(d: dict[str, Any], live_inputs: dict[str, Any], i
     d = d.copy()
     # ignore hidden for parsing
     hidden = d.pop("hidden", None)
-    parse_class_inputs(out_dict, live_inputs, d)
+    parse_class_inputs(out_dict, live_inputs, d, None, live_input_types)
     if hidden is not None and include_hidden:
         out_dict["hidden"] = hidden
     v3_data = {}
@@ -1787,7 +1948,7 @@ def get_finalized_class_inputs(d: dict[str, Any], live_inputs: dict[str, Any], i
         v3_data["dynamic_paths_default_value"] = dynamic_paths_default_value
     return out_dict, hidden, v3_data
 
-def parse_class_inputs(out_dict: dict[str, Any], live_inputs: dict[str, Any], curr_dict: dict[str, Any], curr_prefix: list[str] | None=None) -> None:
+def parse_class_inputs(out_dict: dict[str, Any], live_inputs: dict[str, Any], curr_dict: dict[str, Any], curr_prefix: list[str] | None=None, live_input_types: dict[str, str] | None = None) -> None:
     for input_type, inner_d in curr_dict.items():
         for id, value in inner_d.items():
             io_type = value[0]
@@ -1795,7 +1956,7 @@ def parse_class_inputs(out_dict: dict[str, Any], live_inputs: dict[str, Any], cu
                 # dynamic inputs need to be handled with lookup functions
                 dynamic_input_func = get_dynamic_input_func(io_type)
                 new_prefix = handle_prefix(curr_prefix, id)
-                dynamic_input_func(out_dict, live_inputs, value, input_type, new_prefix)
+                dynamic_input_func(out_dict, live_inputs, value, input_type, new_prefix, live_input_types)
             else:
                 # non-dynamic inputs get directly transferred
                 finalized_id = finalize_prefix(curr_prefix, id)

comfy_api_nodes/util/request_logger.py

@@ -9,6 +9,7 @@ from typing import Any
 import folder_paths
 
 logger = logging.getLogger(__name__)
+_SENSITIVE_HEADERS = {"authorization", "x-api-key"}
 
 
 def get_log_directory():
@@ -73,6 +74,10 @@ def _format_data_for_logging(data: Any) -> str:
     return str(data)
 
 
+def _redact_headers(headers: dict) -> dict:
+    return {k: ("***" if k.lower() in _SENSITIVE_HEADERS else v) for k, v in headers.items()}
+
+
 def log_request_response(
     operation_id: str,
     request_method: str,
@@ -101,7 +106,7 @@ def log_request_response(
         log_content.append(f"Method: {request_method}")
         log_content.append(f"URL: {request_url}")
         if request_headers:
-            log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}")
+            log_content.append(f"Headers:\n{_format_data_for_logging(_redact_headers(request_headers))}")
         if request_params:
             log_content.append(f"Params:\n{_format_data_for_logging(request_params)}")
         if request_data is not None:

comfy_execution/graph.py

@@ -26,6 +26,7 @@ class DynamicPrompt:
         self.ephemeral_prompt = {}
         self.ephemeral_parents = {}
         self.ephemeral_display = {}
+        self._type_resolver = None  # lazy; invalidated by add_ephemeral_node
 
     def get_node(self, node_id):
         if node_id in self.ephemeral_prompt:
@@ -41,6 +42,17 @@ class DynamicPrompt:
         self.ephemeral_prompt[node_id] = node_info
         self.ephemeral_parents[node_id] = parent_id
         self.ephemeral_display[node_id] = display_id
+        # Selective downstream invalidation would need topological info; the
+        # cache is small, so just wipe it.
+        if self._type_resolver is not None:
+            self._type_resolver.invalidate()
+
+    def get_type_resolver(self):
+        """Lazily build and return the per-prompt TypeResolver."""
+        if self._type_resolver is None:
+            from comfy_execution.type_resolver import TypeResolver
+            self._type_resolver = TypeResolver(self)
+        return self._type_resolver
 
     def get_real_node_id(self, node_id):
         while node_id in self.ephemeral_parents:

comfy_execution/type_resolver.py

@@ -0,0 +1,369 @@
+"""Server-side type resolver for prompt graphs.
+
+Resolves the concrete io_type of any output/input slot by walking the prompt
+graph. Handles V1/V3 ``RETURN_TYPES``, V3 ``MatchType`` template chains, and
+falls back to ``AnyType`` (with a one-shot warning) on cycles, depth overflow,
+or unresolvable wildcards.
+
+Works against either a raw prompt dict or a ``DynamicPrompt``. All resolved
+values are strings, so resolver state is cross-process serializable.
+
+Known limitation: when an upstream node declares its output as ``AnyType``
+(``"*"``) — Reroute, generic If/Else, many V1 utility nodes — the resolver
+returns ``"*"``. It has no way to introspect the runtime value to recover a
+more specific type. Downstream consumers (e.g. :py:class:`DynamicSlot`) will
+treat such links as AnyType and select their ``AnyType`` branch (or none),
+not a concrete-type branch.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+from comfy_api.latest import _io as io
+from comfy_api.internal import _ComfyNodeInternal
+
+
+def _parse_link(val: Any) -> tuple[str, int] | None:
+    """Return ``(src_node_id, src_slot_idx)`` if ``val`` is a well-formed link, else ``None``.
+
+    A link is ``[node_id: str, slot_idx: int]``. Malformed shapes return ``None``
+    so callers can fall back to AnyType rather than raise.
+    """
+    if not isinstance(val, (list, tuple)) or len(val) != 2:
+        return None
+    src_node, src_slot = val[0], val[1]
+    if not isinstance(src_node, str):
+        return None
+    # bool is a subclass of int — reject so True/False aren't read as slot 1/0.
+    if isinstance(src_slot, bool) or not isinstance(src_slot, int):
+        return None
+    return src_node, src_slot
+
+ANY_TYPE: str = io.AnyType.io_type
+MAX_RESOLVE_DEPTH: int = 64  # belt-and-suspenders cap; real MatchType chains stay tiny
+
+
+class TypeResolver:
+    """Resolves concrete io_types for a prompt graph.
+
+    Instantiate once per prompt (or per ``DynamicPrompt``) and reuse; results
+    are cached. Call :py:meth:`invalidate` (or :py:meth:`invalidate_node`) when
+    the underlying graph mutates (e.g. when an ephemeral node is added).
+    """
+
+    def __init__(self, prompt_source: Any):
+        """Args:
+            prompt_source: Either a ``DynamicPrompt`` (anything with
+                ``get_node(node_id)`` / ``has_node(node_id)``) or a plain
+                ``dict[node_id, {"class_type", "inputs"}]``.
+        """
+        self._source = prompt_source
+        self._output_cache: dict[tuple[str, int], str] = {}
+        self._is_output_list_cache: dict[tuple[str, int], bool] = {}
+        self._warned: set[tuple[str, Any, str]] = set()
+
+    # ---- prompt access ----------------------------------------------------
+    def _has_node(self, node_id: str) -> bool:
+        if hasattr(self._source, "has_node"):
+            return self._source.has_node(node_id)
+        return node_id in self._source
+
+    def _get_node(self, node_id: str) -> dict[str, Any] | None:
+        try:
+            if hasattr(self._source, "get_node"):
+                return self._source.get_node(node_id)
+            return self._source[node_id]
+        except Exception:
+            return None
+
+    @staticmethod
+    def _get_class_def(class_type: str):
+        # Local import: nodes <-> comfy_execution would cycle at import time.
+        import nodes
+        return nodes.NODE_CLASS_MAPPINGS.get(class_type)
+
+    def _get_class_def_for_node(self, node_id: str):
+        """Return (node_dict, class_def) for ``node_id``, or ``(None, None)``."""
+        if not self._has_node(node_id):
+            return None, None
+        node = self._get_node(node_id)
+        if node is None:
+            return None, None
+        class_type = node.get("class_type")
+        if not isinstance(class_type, str):
+            return node, None
+        return node, self._get_class_def(class_type)
+
+    # ---- cache management -------------------------------------------------
+    def invalidate(self) -> None:
+        """Clear all cached resolutions. Cheap; call after any graph mutation."""
+        self._output_cache.clear()
+        self._is_output_list_cache.clear()
+        # Keep self._warned: re-emitting already-logged warnings would just spam.
+
+    def invalidate_node(self, node_id: str) -> None:
+        """Clear cached entries for a single node (e.g. after node-level expand)."""
+        for key in [k for k in self._output_cache if k[0] == node_id]:
+            del self._output_cache[key]
+        for key in [k for k in self._is_output_list_cache if k[0] == node_id]:
+            del self._is_output_list_cache[key]
+
+    # ---- output resolution -----------------------------------------------
+    def resolve_output_type(self, node_id: str, slot_idx: int,
+                            _stack: frozenset[tuple[str, int]] | None = None) -> str:
+        """Return the resolved io_type string of ``node_id``'s output slot.
+
+        Falls back to ``ANY_TYPE`` on cycle, depth-overflow, unknown class,
+        out-of-range slot, missing node, malformed link, or unresolved
+        MatchType template.
+        """
+        # Degrade gracefully on non-int slot_idx (e.g. malformed API JSON).
+        if isinstance(slot_idx, bool) or not isinstance(slot_idx, int):
+            return ANY_TYPE
+
+        cache_key = (node_id, slot_idx)
+        if cache_key in self._output_cache:
+            return self._output_cache[cache_key]
+
+        if _stack is None:
+            _stack = frozenset()
+        if cache_key in _stack:
+            self._warn(node_id, slot_idx, "cycle detected during type resolution; defaulting to AnyType")
+            return ANY_TYPE
+        if len(_stack) >= MAX_RESOLVE_DEPTH:
+            self._warn(node_id, slot_idx, f"exceeded MAX_RESOLVE_DEPTH={MAX_RESOLVE_DEPTH}; defaulting to AnyType")
+            return ANY_TYPE
+        next_stack = _stack | {cache_key}
+
+        node, class_def = self._get_class_def_for_node(node_id)
+        if class_def is None:
+            return ANY_TYPE
+        class_type = node.get("class_type")
+
+        try:
+            return_types = class_def.RETURN_TYPES
+        except Exception:
+            return ANY_TYPE
+        if return_types is None or slot_idx < 0 or slot_idx >= len(return_types):
+            return ANY_TYPE
+
+        declared = return_types[slot_idx]
+
+        # Only V3 schemas carry MatchType template info; V1 RETURN_TYPES are
+        # always concrete strings.
+        resolved = declared
+        if isinstance(class_def, type) and issubclass(class_def, _ComfyNodeInternal):
+            schema = getattr(class_def, "SCHEMA", None)
+            if schema is None:
+                # RETURN_TYPES access above usually populates SCHEMA — be defensive.
+                try:
+                    schema = class_def.GET_SCHEMA()
+                except Exception:
+                    schema = None
+            if schema is not None and slot_idx < len(schema.outputs):
+                out = schema.outputs[slot_idx]
+                if isinstance(out, io.MatchType.Output):
+                    resolved = self._resolve_match_template(
+                        node_id, schema, out.template.template_id, next_stack
+                    )
+
+        # Warn only for V1 wildcards declared as "*"; unresolved MatchType
+        # templates warn separately in _resolve_match_template, avoiding double-warns.
+        if isinstance(resolved, str) and resolved == ANY_TYPE and declared == ANY_TYPE:
+            self._warn(
+                node_id, slot_idx,
+                f"node '{class_type}' output slot {slot_idx} is wildcard; defaulting to AnyType",
+            )
+
+        if not isinstance(resolved, str):
+            # e.g. legacy combo declared as a list of options.
+            self._warn(node_id, slot_idx,
+                       f"node '{class_type}' output slot {slot_idx} has non-string return type {type(resolved).__name__}; defaulting to AnyType")
+            resolved = ANY_TYPE
+
+        self._output_cache[cache_key] = resolved
+        return resolved
+
+    def _resolve_match_template(self, node_id: str, schema, template_id: str,
+                                stack: frozenset[tuple[str, int]]) -> str:
+        """Walk MatchType.Inputs sharing ``template_id``; return first concrete resolution or ``ANY_TYPE``."""
+        node = self._get_node(node_id)
+        inputs_dict = (node or {}).get("inputs", {}) or {}
+        any_input_seen = False
+        for inp in schema.inputs:
+            if not isinstance(inp, io.MatchType.Input):
+                continue
+            if inp.template.template_id != template_id:
+                continue
+            any_input_seen = True
+            val = inputs_dict.get(inp.id)
+            if val is None:
+                continue
+            link = _parse_link(val)
+            if link is not None:
+                t = self.resolve_output_type(link[0], link[1], stack)
+                if t != ANY_TYPE:
+                    return t
+            # Literal or malformed link: MatchType slots have no declared concrete type.
+        if not any_input_seen:
+            # Node-author bug: output template has no matching Input.
+            self._warn(node_id, None,
+                       f"MatchType output template '{template_id}' has no matching Input on the node; defaulting to AnyType")
+        else:
+            self._warn(node_id, None,
+                       f"MatchType template '{template_id}' has no bound concrete upstream input; defaulting to AnyType")
+        return ANY_TYPE
+
+    def is_output_list(self, node_id: str, slot_idx: int) -> bool:
+        """Whether the source slot is declared as a list output (``OUTPUT_IS_LIST[idx]``)."""
+        if isinstance(slot_idx, bool) or not isinstance(slot_idx, int):
+            return False
+        cache_key = (node_id, slot_idx)
+        if cache_key in self._is_output_list_cache:
+            return self._is_output_list_cache[cache_key]
+        result = False
+        _, class_def = self._get_class_def_for_node(node_id)
+        if class_def is not None:
+            lst = getattr(class_def, "OUTPUT_IS_LIST", None)
+            if lst is not None and 0 <= slot_idx < len(lst):
+                result = bool(lst[slot_idx])
+        self._is_output_list_cache[cache_key] = result
+        return result
+
+    # ---- input resolution ------------------------------------------------
+    def resolve_input_type(self, node_id: str, input_id: str) -> str:
+        """Resolve the io_type of the value currently bound to a node's input.
+
+        * If the value is a link, return the resolved type of the source slot.
+        * If the value is a literal, return the declared slot's effective
+          io_type (peeling dynamic-input wrappers — e.g. an Autogrow-of-Image
+          slot resolves to ``IMAGE``, not ``COMFY_AUTOGROW_V3``).
+        * If the value is missing, malformed, or the slot is unknown, return
+          ``ANY_TYPE``.
+        """
+        node = self._get_node(node_id)
+        if node is None:
+            return ANY_TYPE
+        inputs = node.get("inputs", {}) or {}
+        if input_id not in inputs:
+            return ANY_TYPE
+        link = _parse_link(inputs[input_id])
+        if link is not None:
+            return self.resolve_output_type(link[0], link[1])
+        return self.get_declared_slot_io_type(node_id, input_id)
+
+    def is_input_list(self, node_id: str, input_id: str) -> bool:
+        """Whether the value bound to ``input_id`` originates from a list output."""
+        node = self._get_node(node_id)
+        if node is None:
+            return False
+        link = _parse_link((node.get("inputs", {}) or {}).get(input_id))
+        if link is None:
+            return False
+        return self.is_output_list(link[0], link[1])
+
+    def get_declared_slot_io_type(self, node_id: str, input_id: str) -> str:
+        """Return the effective declared io_type of a node's input slot.
+
+        Peels dynamic-input wrappers so that the user-facing element type is
+        returned:
+
+        * Autogrow → wrapped template input's io_type
+        * DynamicSlot → underlying slot's io_type
+        * Anything else → the slot's own io_type
+        * DynamicCombo / unsupported → ``ANY_TYPE`` (the combo key is itself
+          dynamic, not a meaningful type for consumers)
+        """
+        _, class_def = self._get_class_def_for_node(node_id)
+        if class_def is None:
+            return ANY_TYPE
+
+        # Prefer V3 schema (carries dynamic-input wrapper info).
+        if isinstance(class_def, type) and issubclass(class_def, _ComfyNodeInternal):
+            schema = getattr(class_def, "SCHEMA", None)
+            if schema is None:
+                try:
+                    class_def.GET_SCHEMA()
+                    schema = getattr(class_def, "SCHEMA", None)
+                except Exception:
+                    schema = None
+            if schema is not None:
+                # Top-level input id.
+                for inp in schema.inputs:
+                    if inp.id == input_id:
+                        return self._effective_io_type(inp)
+                # Nested (DynamicSlot / DynamicCombo `parent.child`).
+                if "." in input_id:
+                    top, _, _ = input_id.partition(".")
+                    for inp in schema.inputs:
+                        if inp.id != top:
+                            continue
+                        for child in inp.get_all():
+                            if child is inp:
+                                continue
+                            if child.id == input_id.split(".", 1)[1]:
+                                return self._effective_io_type(child)
+                # Fall through to V1 dict (hidden inputs, etc.).
+
+        try:
+            inputs = class_def.INPUT_TYPES()
+        except Exception:
+            return ANY_TYPE
+        for section in ("required", "optional"):
+            section_d = inputs.get(section, {})
+            if input_id in section_d:
+                entry = section_d[input_id]
+                if not entry:
+                    return ANY_TYPE
+                t = entry[0]
+                if isinstance(t, str):
+                    return t
+                if isinstance(t, list):  # legacy combo declared as list of options
+                    return io.Combo.io_type
+                return ANY_TYPE
+        return ANY_TYPE
+
+    @staticmethod
+    def _effective_io_type(inp) -> str:
+        """Return the consumer-facing io_type of a (possibly dynamic) input."""
+        # Autogrow / DynamicSlot wrap a real element type; that's what consumers care about.
+        if isinstance(inp, io.Autogrow.Input):
+            try:
+                return inp.template.input.get_io_type()
+            except Exception:
+                return ANY_TYPE
+        if isinstance(inp, io.DynamicSlot.Input):
+            # Auto-derived slot type — comma-joined union of all option `when` types.
+            return getattr(inp, "_slot_io_type", ANY_TYPE)
+        # DynamicCombo's "type" is a key selector, not a connection type.
+        if isinstance(inp, io.DynamicCombo.Input):
+            return ANY_TYPE
+        try:
+            return inp.get_io_type()
+        except Exception:
+            return ANY_TYPE
+
+    # ---- bulk helpers ----------------------------------------------------
+    def compute_live_input_types(self, node_id: str) -> dict[str, str]:
+        """Build the ``{input_id: resolved_io_type}`` map for a node.
+
+        Consumed by ``_io.get_finalized_class_inputs`` so future per-type
+        dynamic-input expansion can branch on what was actually connected.
+        """
+        node = self._get_node(node_id)
+        if node is None:
+            return {}
+        out: dict[str, str] = {}
+        for input_id in (node.get("inputs", {}) or {}).keys():
+            out[input_id] = self.resolve_input_type(node_id, input_id)
+        return out
+
+    # ---- diagnostics -----------------------------------------------------
+    def _warn(self, node_id: str, slot_idx: Any, msg: str) -> None:
+        key = (node_id, slot_idx, msg)
+        if key in self._warned:
+            return
+        self._warned.add(key)
+        logging.warning("TypeResolver: node=%s slot=%s %s", node_id, slot_idx, msg)

comfy_extras/nodes_color.py

@@ -16,23 +16,30 @@ class ColorToRGBInt(io.ComfyNode):
             ],
             outputs=[
                 io.Int.Output(display_name="rgb_int"),
-                io.Color.Output(display_name="hex")
+                io.Color.Output(display_name="hex"),
+                io.Float.Output(display_name="alpha"),
             ],
         )
 
     @classmethod
     def execute(cls, color: str) -> io.NodeOutput:
-        # expect format #RRGGBB
-        if len(color) != 7 or color[0] != "#":
-            raise ValueError("Color must be in format #RRGGBB")
+        # expect format #RRGGBB or #RRGGBBAA
+        if len(color) not in (7, 9) or color[0] != "#":
+            raise ValueError("Color must be in format #RRGGBB or #RRGGBBAA")
         try:
             int(color[1:], 16)
         except ValueError:
-            raise ValueError("Color must be in format #RRGGBB") from None
+            raise ValueError("Color must be in format #RRGGBB or #RRGGBBAA") from None
+
+        alpha = 1.0
+        if len(color) == 9:
+            alpha = int(color[7:9], 16) / 255.0
+            color = color[:7]
+
         r, g, b = hex_to_rgb(color)
 
         rgb_int = r * 256 * 256 + g * 256 + b
-        return io.NodeOutput(rgb_int, color)
+        return io.NodeOutput(rgb_int, color, alpha)
 
 
 class ColorExtension(ComfyExtension):

execution.py

@@ -84,8 +84,8 @@ class IsChangedCache:
             self.is_changed[node_id] = node["is_changed"]
             return self.is_changed[node_id]
 
-        # Intentionally do not use cached outputs here. We only want constants in IS_CHANGED
-        input_data_all, _, v3_data = get_input_data(node["inputs"], class_def, node_id, None)
+        # Intentionally do not use cached outputs here. We only want constants in IS_CHANGED.
+        input_data_all, _, v3_data = get_input_data(node["inputs"], class_def, node_id, None, self.dynprompt)
         try:
             is_changed = await _async_map_node_over_list(self.prompt_id, node_id, class_def, input_data_all, is_changed_name, v3_data=v3_data)
             is_changed = await resolve_map_node_over_list_results(is_changed)
@@ -153,13 +153,16 @@ class CacheSet:
 
 SENSITIVE_EXTRA_DATA_KEYS = ("auth_token_comfy_org", "api_key_comfy_org")
 
-def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=None, extra_data={}):
+def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=None, extra_data={}, live_input_types=None):
     is_v3 = issubclass(class_def, _ComfyNodeInternal)
     v3_data: io.V3Data = {}
     hidden_inputs_v3 = {}
     valid_inputs = class_def.INPUT_TYPES()
     if is_v3:
-        valid_inputs, hidden, v3_data = _io.get_finalized_class_inputs(valid_inputs, inputs)
+        # Let dynamic schemas branch on resolved upstream types, not just literal values.
+        if live_input_types is None and dynprompt is not None and hasattr(dynprompt, "get_type_resolver"):
+            live_input_types = dynprompt.get_type_resolver().compute_live_input_types(unique_id)
+        valid_inputs, hidden, v3_data = _io.get_finalized_class_inputs(valid_inputs, inputs, live_input_types=live_input_types)
     input_data_all = {}
     missing_keys = {}
     for x in inputs:
@@ -831,9 +834,17 @@ class PromptExecutor:
             self._notify_prompt_lifecycle("end", prompt_id)
 
 
-async def validate_inputs(prompt_id, prompt, item, validated, visiting=None):
+async def validate_inputs(prompt_id, prompt, item, validated, visiting=None, type_resolver=None):
+    """Validate inputs for a single node, recursing into upstream nodes.
+
+    ``type_resolver`` is built once at the top of recursion and shared so
+    MatchType chains are only walked once per prompt.
+    """
     if visiting is None:
         visiting = []
+    if type_resolver is None:
+        from comfy_execution.type_resolver import TypeResolver
+        type_resolver = TypeResolver(prompt)
 
     unique_id = item
     if unique_id in validated:
@@ -865,10 +876,12 @@ async def validate_inputs(prompt_id, prompt, item, validated, visiting=None):
     v3_data = None
     validate_function_inputs = []
     validate_has_kwargs = False
+    live_input_types = None
     if issubclass(obj_class, _ComfyNodeInternal):
         obj_class: _io._ComfyNodeBaseInternal
         class_inputs = obj_class.INPUT_TYPES()
-        class_inputs, _, v3_data = _io.get_finalized_class_inputs(class_inputs, inputs)
+        live_input_types = type_resolver.compute_live_input_types(unique_id)
+        class_inputs, _, v3_data = _io.get_finalized_class_inputs(class_inputs, inputs, live_input_types=live_input_types)
         validate_function_name = "validate_inputs"
         validate_function = first_real_override(obj_class, validate_function_name)
     else:
@@ -918,11 +931,20 @@ async def validate_inputs(prompt_id, prompt, item, validated, visiting=None):
                 continue
 
             o_id = val[0]
-            o_class_type = prompt[o_id]['class_type']
-            r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES
-            received_type = r[val[1]]
+            # Walks MatchType/template chains so API workflows without
+            # frontend-injected type metadata get the same answer as the UI.
+            received_type = type_resolver.resolve_output_type(o_id, val[1])
             received_types[x] = received_type
-            if 'input_types' not in validate_function_inputs and not validate_node_input(received_type, input_type):
+            # DynamicSlot's declared input_type is just the dispatch tag
+            # (COMFY_DYNAMICSLOT_V3); a link is valid iff some Option would
+            # actually claim the resolved upstream type.
+            if input_type == _io.DynamicSlot.io_type and isinstance(extra_info, dict):
+                link_valid = _io.DynamicSlot._select_option(
+                    extra_info.get("options", []), {x: received_type}, x, has_link=True
+                ) is not None
+            else:
+                link_valid = validate_node_input(received_type, input_type)
+            if 'input_types' not in validate_function_inputs and not link_valid:
                 details = f"{x}, received_type({received_type}) mismatch input_type({input_type})"
                 error = {
                     "type": "return_type_mismatch",
@@ -940,7 +962,7 @@ async def validate_inputs(prompt_id, prompt, item, validated, visiting=None):
             try:
                 visiting.append(unique_id)
                 try:
-                    r = await validate_inputs(prompt_id, prompt, o_id, validated, visiting)
+                    r = await validate_inputs(prompt_id, prompt, o_id, validated, visiting, type_resolver)
                 finally:
                     visiting.pop()
                 if r[0] is False:
@@ -1068,7 +1090,11 @@ async def validate_inputs(prompt_id, prompt, item, validated, visiting=None):
                         continue
 
     if len(validate_function_inputs) > 0 or validate_has_kwargs:
-        input_data_all, _, v3_data = get_input_data(inputs, obj_class, unique_id)
+        # Reuse the precomputed live_input_types so a custom validate_inputs()
+        # sees the same DynamicSlot branch that finalization picked above.
+        input_data_all, _, v3_data = get_input_data(
+            inputs, obj_class, unique_id, live_input_types=live_input_types
+        )
         input_filtered = {}
         for x in input_data_all:
             if x in validate_function_inputs or validate_has_kwargs:
@@ -1165,11 +1191,14 @@ async def validate_prompt(prompt_id, prompt, partial_execution_list: Union[list[
     errors = []
     node_errors = {}
     validated = {}
+    # Shared across output validations so MatchType chains walk only once.
+    from comfy_execution.type_resolver import TypeResolver
+    type_resolver = TypeResolver(prompt)
     for o in outputs:
         valid = False
         reasons = []
         try:
-            m = await validate_inputs(prompt_id, prompt, o, validated)
+            m = await validate_inputs(prompt_id, prompt, o, validated, None, type_resolver)
             valid = m[0]
             reasons = m[1]
         except Exception as ex:

requirements.txt

@@ -1,6 +1,6 @@
 comfyui-frontend-package==1.45.20
 comfyui-workflow-templates==0.11.2
-comfyui-embedded-docs==0.5.6
+comfyui-embedded-docs==0.5.7
 torch
 torchsde
 torchvision

tests-unit/comfy_api_test/test_dynamic_slot.py

@@ -0,0 +1,350 @@
+"""Unit tests for the redesigned ``DynamicSlot`` with type-keyed options."""
+
+import pytest
+
+from comfy_api.latest import _io as io
+
+
+def _opt(when, ids=None):
+    """Build an Option whose inputs are placeholder String widgets named after ids."""
+    ids = ids or []
+    inputs = [io.String.Input(name) for name in ids]
+    return io.DynamicSlot.Option(when=when, inputs=inputs)
+
+
+# ---------------------------------------------------------------------------
+# Option.when normalization
+# ---------------------------------------------------------------------------
+
+def test_option_when_none():
+    o = _opt(None, ["a"])
+    assert o._when_types is None
+    assert o._when_set is None
+    assert o.as_dict()["when"] is None
+
+
+def test_option_when_single_type():
+    o = _opt(io.Image)
+    assert o._when_types == ("IMAGE",)
+    assert o._when_set == frozenset({"IMAGE"})
+    assert o.as_dict()["when"] == ["IMAGE"]
+
+
+def test_option_when_anytype():
+    o = _opt(io.AnyType)
+    assert o._when_types == ("*",)
+    assert o.as_dict()["when"] == ["*"]
+
+
+def test_option_when_list_preserves_order():
+    """Declaration order is preserved in both the tuple and the serialized form."""
+    o = _opt([io.Mask, io.Image])
+    assert o._when_types == ("MASK", "IMAGE")
+    assert o.as_dict()["when"] == ["MASK", "IMAGE"]
+
+
+def test_option_when_list_dedups_within_option():
+    o = _opt([io.Image, io.Image, io.Mask])
+    assert o._when_types == ("IMAGE", "MASK")
+
+
+def test_option_when_multitype_input():
+    mt = io.MultiType.Input("x", types=[io.Image, io.Latent])
+    o = _opt(mt)
+    assert o._when_types == ("IMAGE", "LATENT")
+
+
+def test_option_when_empty_list_rejected():
+    with pytest.raises(ValueError, match="when=\\[\\]"):
+        io.DynamicSlot.Option(when=[], inputs=[])
+
+
+def test_option_when_garbage_rejected():
+    with pytest.raises(ValueError, match="when must be"):
+        io.DynamicSlot.Option(when="IMAGE", inputs=[])
+
+
+def test_option_when_list_with_non_comfytype_rejected():
+    with pytest.raises(ValueError, match="list entries"):
+        io.DynamicSlot.Option(when=[io.Image, "MASK"], inputs=[])
+
+
+def test_option_when_list_with_anytype_rejected():
+    """AnyType must stand alone — it represents the unresolvable-wildcard
+    state, not a concrete type that can share a branch with concrete types."""
+    with pytest.raises(ValueError, match="AnyType cannot be grouped"):
+        io.DynamicSlot.Option(when=[io.Image, io.AnyType], inputs=[])
+
+
+def test_option_when_multitype_with_anytype_rejected():
+    mt = io.MultiType.Input("x", types=[io.Image, io.AnyType])
+    with pytest.raises(ValueError, match="AnyType cannot be grouped"):
+        io.DynamicSlot.Option(when=mt, inputs=[])
+
+
+# ---------------------------------------------------------------------------
+# DynamicSlot.Input construction and serialization
+# ---------------------------------------------------------------------------
+
+def test_input_requires_at_least_one_option():
+    with pytest.raises(ValueError, match="at least one Option"):
+        io.DynamicSlot.Input("x", options=[])
+
+
+def test_input_requires_non_none_option():
+    with pytest.raises(ValueError, match="non-None `when`"):
+        io.DynamicSlot.Input("x", options=[_opt(None, ["a"])])
+
+
+def test_input_auto_derives_slot_type():
+    inp = io.DynamicSlot.Input("x", options=[
+        _opt(io.Image, ["a"]),
+        _opt(io.Mask, ["b"]),
+        _opt(None, ["c"]),
+    ])
+    # Declared order preserved across non-None options; None contributes nothing.
+    assert inp._slot_io_type == "IMAGE,MASK"
+    d = inp.as_dict()
+    assert d["slotType"] == "IMAGE,MASK"
+    assert len(d["options"]) == 3
+
+
+def test_input_includes_anytype_in_slot_type():
+    inp = io.DynamicSlot.Input("x", options=[
+        _opt(io.Image, ["a"]),
+        _opt(io.AnyType, ["b"]),
+    ])
+    assert inp._slot_io_type == "IMAGE,*"
+
+
+def test_input_rejects_duplicate_type_across_options():
+    with pytest.raises(ValueError, match="appears in more than one"):
+        io.DynamicSlot.Input("x", options=[
+            _opt(io.Image, ["a"]),
+            _opt([io.Image, io.Mask], ["b"]),
+        ])
+
+
+def test_input_rejects_duplicate_anytype_across_options():
+    with pytest.raises(ValueError, match="appears in more than one"):
+        io.DynamicSlot.Input("x", options=[
+            _opt(io.AnyType, ["a"]),
+            _opt(io.AnyType, ["b"]),
+        ])
+
+
+def test_input_rejects_duplicate_when_none():
+    with pytest.raises(ValueError, match="only one Option may declare when=None"):
+        io.DynamicSlot.Input("x", options=[
+            _opt(io.Image, ["a"]),
+            _opt(None, ["b"]),
+            _opt(None, ["c"]),
+        ])
+
+
+def test_input_rejects_non_option_entry():
+    with pytest.raises(ValueError, match="must be DynamicSlot.Option instances"):
+        io.DynamicSlot.Input("x", options=[_opt(io.Image, ["a"]), "not an option"])
+
+
+def test_input_defaults_to_optional_and_always_force_input():
+    """The slot is always rendered as a connector, never as a widget, even
+    when slotType includes widget-capable types like INT/STRING."""
+    inp = io.DynamicSlot.Input("x", options=[_opt(io.Int, ["n"])])
+    d = inp.as_dict()
+    assert d["forceInput"] is True
+    # default optional=True → slot lives in optional bucket via DynamicInput
+    assert inp.optional is True
+
+
+def test_input_required_slot_allowed_without_when_none():
+    inp = io.DynamicSlot.Input("x", optional=False, options=[_opt(io.Image, ["a"])])
+    assert inp.optional is False
+
+
+def test_input_required_slot_rejects_when_none_option():
+    with pytest.raises(ValueError, match="optional=False forbids when=None"):
+        io.DynamicSlot.Input(
+            "x",
+            optional=False,
+            options=[_opt(io.Image, ["a"]), _opt(None, ["b"])],
+        )
+
+
+def test_input_get_all_prepends_self_and_dedups_children():
+    inp = io.DynamicSlot.Input("x", options=[
+        _opt(io.Image, ["shared", "image_only"]),
+        _opt(io.Mask, ["shared", "mask_only"]),
+    ])
+    items = inp.get_all()
+    # Convention shared with Autogrow / DynamicCombo: parent first, then children.
+    assert items[0] is inp
+    assert [i.id for i in items[1:]] == ["shared", "image_only", "mask_only"]
+
+
+# ---------------------------------------------------------------------------
+# Option selection
+# ---------------------------------------------------------------------------
+
+def _select(options, live_input_types, has_link, finalized_id="x"):
+    """Convenience wrapper that runs the dispatch through the dict form (post-as_dict)."""
+    serialized = [o.as_dict() for o in options]
+    return io.DynamicSlot._select_option(
+        serialized, live_input_types, finalized_id, has_link
+    )
+
+
+def test_select_unconnected_picks_none_option():
+    options = [_opt(io.Image, ["img_widgets"]), _opt(None, ["empty_widgets"])]
+    sel = _select(options, {}, has_link=False)
+    assert sel is not None
+    assert sel["when"] is None
+
+
+def test_select_unconnected_with_no_none_option_returns_none():
+    options = [_opt(io.Image, ["x"])]
+    assert _select(options, {}, has_link=False) is None
+
+
+def test_select_concrete_type_match():
+    options = [
+        _opt(io.Image, ["a"]),
+        _opt(io.Mask, ["b"]),
+        _opt(io.AnyType, ["c"]),
+    ]
+    sel = _select(options, {"x": "MASK"}, has_link=True)
+    assert sel["when"] == ["MASK"]
+
+
+def test_select_anytype_matches_wildcard_resolved():
+    options = [_opt(io.Image, ["a"]), _opt(io.AnyType, ["c"])]
+    sel = _select(options, {"x": "*"}, has_link=True)
+    assert sel["when"] == ["*"]
+
+
+def test_select_anytype_does_not_match_concrete():
+    options = [_opt(io.AnyType, ["c"])]
+    # MASK isn't in any option's set; AnyType only matches "*". No expansion.
+    assert _select(options, {"x": "MASK"}, has_link=True) is None
+
+
+def test_select_anytype_branch_does_not_swallow_unenumerated_concrete():
+    """Regression: a slot exposing IMAGE + AnyType must reject LATENT upstream
+    instead of expanding the AnyType branch. validate_inputs relies on this
+    to compute link validity (slotType="IMAGE,*" alone would over-accept)."""
+    options = [_opt(io.Image, ["image_widget"]), _opt(io.AnyType, ["any_widget"])]
+    assert _select(options, {"x": "LATENT"}, has_link=True) is None
+    # Sanity: IMAGE still matches the IMAGE branch and "*" still matches AnyType.
+    assert _select(options, {"x": "IMAGE"}, has_link=True)["when"] == ["IMAGE"]
+    assert _select(options, {"x": "*"}, has_link=True)["when"] == ["*"]
+
+
+def test_select_first_match_wins_on_union_upstream():
+    """Ordering only matters when upstream declares a multi-type union; with
+    per-option type uniqueness, single concrete types can never match two
+    options."""
+    options = [
+        _opt([io.Image, io.Mask], ["image_or_mask"]),
+        _opt(io.Latent, ["latent_only"]),
+    ]
+    # Upstream union "IMAGE,LATENT" intersects both options; first option wins.
+    sel = _select(options, {"x": "IMAGE,LATENT"}, has_link=True)
+    first_input_id = next(iter(sel["inputs"]["required"].keys()))
+    assert first_input_id == "image_or_mask"
+
+
+def test_select_multitype_upstream_intersects_option_set():
+    """When upstream declares MultiType like 'IMAGE,MASK', any option that
+    intersects with that set matches (first wins)."""
+    options = [
+        _opt(io.Latent, ["latent_only"]),
+        _opt(io.Mask, ["mask_only"]),
+    ]
+    sel = _select(options, {"x": "IMAGE,MASK"}, has_link=True)
+    assert sel["when"] == ["MASK"]
+
+
+def test_select_missing_resolved_falls_through_to_anytype():
+    """If live_input_types lacks an entry for this slot but a link exists,
+    we treat it as '*' (resolver default for unresolvable links)."""
+    options = [_opt(io.Image, ["a"]), _opt(io.AnyType, ["c"])]
+    sel = _select(options, {}, has_link=True)
+    assert sel["when"] == ["*"]
+
+
+# ---------------------------------------------------------------------------
+# End-to-end expansion via _expand_schema_for_dynamic
+# ---------------------------------------------------------------------------
+
+def test_expand_unconnected_path():
+    """An unconnected slot with a `when=None` option expands that option's children."""
+    inp = io.DynamicSlot.Input("x", options=[
+        _opt(io.Image, ["image_widget"]),
+        _opt(None, ["empty_widget"]),
+    ])
+    d = inp.as_dict()
+    value = (io.DynamicSlot.io_type, d)
+    out_dict = {
+        "required": {}, "optional": {}, "hidden": {},
+        "dynamic_paths": {}, "dynamic_paths_default_value": {},
+    }
+    io.DynamicSlot._expand_schema_for_dynamic(
+        out_dict=out_dict,
+        live_inputs={},  # no entry for "x" → unconnected
+        value=value,
+        input_type="optional",
+        curr_prefix=["x"],
+        live_input_types=None,
+    )
+    # The slot itself is always advertised in the caller's bucket.
+    assert "x" in out_dict["optional"]
+    # Children land in their own buckets (required by default) with
+    # parent-prefixed ids.
+    assert "x.empty_widget" in out_dict["required"]
+    assert "x.image_widget" not in out_dict["required"]
+
+
+def test_expand_typed_path():
+    """A connected slot expands the matching type's children."""
+    inp = io.DynamicSlot.Input("x", options=[
+        _opt(io.Image, ["image_widget"]),
+        _opt(io.Mask, ["mask_widget"]),
+    ])
+    d = inp.as_dict()
+    value = (io.DynamicSlot.io_type, d)
+    out_dict = {
+        "required": {}, "optional": {}, "hidden": {},
+        "dynamic_paths": {}, "dynamic_paths_default_value": {},
+    }
+    io.DynamicSlot._expand_schema_for_dynamic(
+        out_dict=out_dict,
+        live_inputs={"x": ["src_node", 0]},  # link present
+        value=value,
+        input_type="optional",
+        curr_prefix=["x"],
+        live_input_types={"x": "MASK"},
+    )
+    assert "x" in out_dict["optional"]
+    assert "x.mask_widget" in out_dict["required"]
+    assert "x.image_widget" not in out_dict["required"]
+
+
+def test_expand_unmatched_concrete_still_advertises_slot():
+    """Resolved type not in any option → no children, but the slot itself stays."""
+    inp = io.DynamicSlot.Input("x", options=[_opt(io.Image, ["image_widget"])])
+    d = inp.as_dict()
+    value = (io.DynamicSlot.io_type, d)
+    out_dict = {
+        "required": {}, "optional": {}, "hidden": {},
+        "dynamic_paths": {}, "dynamic_paths_default_value": {},
+    }
+    io.DynamicSlot._expand_schema_for_dynamic(
+        out_dict=out_dict,
+        live_inputs={"x": ["src_node", 0]},
+        value=value,
+        input_type="optional",
+        curr_prefix=["x"],
+        live_input_types={"x": "LATENT"},
+    )
+    assert "x" in out_dict["optional"]
+    assert "x.image_widget" not in out_dict["required"]

tests-unit/execution_test/test_type_resolver.py

@@ -0,0 +1,482 @@
+"""Unit tests for :mod:`comfy_execution.type_resolver`.
+
+These tests stand up a small in-memory ``NODE_CLASS_MAPPINGS`` for the test
+node classes (V1 and V3) and a fake DynamicPrompt-like dict, then verify the
+resolver's behaviour for:
+
+  * Static V1 ``RETURN_TYPES`` resolution.
+  * V1 wildcard outputs (must yield ``AnyType`` and warn once).
+  * V3 ``MatchType`` chains resolved via the downstream node's bound inputs.
+  * ``MatchType`` with no upstream bound (fall back to ``AnyType`` + warn).
+  * ``MatchType`` cycles (termination at ``AnyType`` + warn, no recursion blow-up).
+  * Deep chains capped by ``MAX_RESOLVE_DEPTH``.
+  * Input-type resolution for both literal values and links.
+  * Effective slot io_type peeling for ``Autogrow`` (returns the wrapped type).
+  * ``compute_live_input_types`` produces the right shape.
+  * Cache invalidation.
+
+The tests deliberately patch ``nodes.NODE_CLASS_MAPPINGS`` so they don't need
+the whole ComfyUI bootstrap.
+"""
+
+from __future__ import annotations
+
+import logging
+import sys
+import types as _pytypes
+
+import pytest
+
+
+# ---------------------------------------------------------------------------
+# Lightweight V1 test node factory
+# ---------------------------------------------------------------------------
+
+def _v1_node(return_types: tuple[str, ...], input_types_dict: dict | None = None,
+             output_is_list: tuple[bool, ...] | None = None):
+    """Build a V1 node class with the given RETURN_TYPES / INPUT_TYPES()."""
+    if input_types_dict is None:
+        input_types_dict = {"required": {}}
+
+    class _V1:
+        RETURN_TYPES = return_types
+        if output_is_list is not None:
+            OUTPUT_IS_LIST = output_is_list
+
+        @classmethod
+        def INPUT_TYPES(cls):
+            return input_types_dict
+
+    return _V1
+
+
+# ---------------------------------------------------------------------------
+# Fixture: install fake nodes module before importing the resolver
+# ---------------------------------------------------------------------------
+
+@pytest.fixture
+def fake_nodes_module():
+    """Install a synthetic ``nodes`` module with an empty mappings dict.
+
+    Yields the mappings dict so tests can populate it per case. Cleans up
+    afterwards. We also have to make sure comfy_execution.type_resolver picks
+    up our fake module on its local import.
+    """
+    real_nodes = sys.modules.get("nodes")
+    fake = _pytypes.ModuleType("nodes")
+    fake.NODE_CLASS_MAPPINGS = {}
+    sys.modules["nodes"] = fake
+    try:
+        yield fake.NODE_CLASS_MAPPINGS
+    finally:
+        if real_nodes is not None:
+            sys.modules["nodes"] = real_nodes
+        else:
+            del sys.modules["nodes"]
+
+
+@pytest.fixture
+def TypeResolver(fake_nodes_module):
+    # Late import so it picks up our fake `nodes` module.
+    from comfy_execution.type_resolver import TypeResolver as TR
+    return TR
+
+
+# ---------------------------------------------------------------------------
+# V1 resolution
+# ---------------------------------------------------------------------------
+
+def test_v1_static_return_types_resolves(fake_nodes_module, TypeResolver):
+    fake_nodes_module["AddNode"] = _v1_node(("INT",))
+    prompt = {"n1": {"class_type": "AddNode", "inputs": {}}}
+    r = TypeResolver(prompt)
+    assert r.resolve_output_type("n1", 0) == "INT"
+
+
+def test_v1_wildcard_warns_once_and_returns_any(fake_nodes_module, TypeResolver, caplog):
+    fake_nodes_module["WildNode"] = _v1_node(("*",))
+    prompt = {"n1": {"class_type": "WildNode", "inputs": {}}}
+    r = TypeResolver(prompt)
+    with caplog.at_level(logging.WARNING, logger="root"):
+        assert r.resolve_output_type("n1", 0) == "*"
+        # second call should still return * but not produce a second warning
+        assert r.resolve_output_type("n1", 0) == "*"
+    warnings = [rec for rec in caplog.records if "TypeResolver" in rec.message]
+    assert len(warnings) == 1, f"expected exactly one warning, got {warnings}"
+
+
+def test_unknown_node_returns_any(fake_nodes_module, TypeResolver):
+    prompt = {"n1": {"class_type": "NopeNode", "inputs": {}}}
+    r = TypeResolver(prompt)
+    assert r.resolve_output_type("n1", 0) == "*"
+
+
+def test_out_of_range_slot_returns_any(fake_nodes_module, TypeResolver):
+    fake_nodes_module["AddNode"] = _v1_node(("INT",))
+    prompt = {"n1": {"class_type": "AddNode", "inputs": {}}}
+    r = TypeResolver(prompt)
+    assert r.resolve_output_type("n1", 5) == "*"
+
+
+def test_missing_node_returns_any(fake_nodes_module, TypeResolver):
+    fake_nodes_module["AddNode"] = _v1_node(("INT",))
+    prompt = {"n1": {"class_type": "AddNode", "inputs": {}}}
+    r = TypeResolver(prompt)
+    assert r.resolve_output_type("nonexistent", 0) == "*"
+
+
+# ---------------------------------------------------------------------------
+# is_output_list / is_input_list
+# ---------------------------------------------------------------------------
+
+def test_is_output_list(fake_nodes_module, TypeResolver):
+    fake_nodes_module["ListNode"] = _v1_node(("IMAGE", "MASK"), output_is_list=(True, False))
+    prompt = {"n1": {"class_type": "ListNode", "inputs": {}}}
+    r = TypeResolver(prompt)
+    assert r.is_output_list("n1", 0) is True
+    assert r.is_output_list("n1", 1) is False
+
+
+def test_is_input_list_follows_link(fake_nodes_module, TypeResolver):
+    fake_nodes_module["ListNode"] = _v1_node(("IMAGE",), output_is_list=(True,))
+    fake_nodes_module["Consumer"] = _v1_node(
+        ("INT",),
+        {"required": {"img": ("IMAGE",)}},
+    )
+    prompt = {
+        "src": {"class_type": "ListNode", "inputs": {}},
+        "dst": {"class_type": "Consumer", "inputs": {"img": ["src", 0]}},
+    }
+    r = TypeResolver(prompt)
+    assert r.is_input_list("dst", "img") is True
+
+
+# ---------------------------------------------------------------------------
+# V3 MatchType resolution
+# ---------------------------------------------------------------------------
+
+def _make_switch_node_class():
+    """Build a V3 Switch-like node with MatchType inputs/outputs."""
+    from comfy_api.latest import io
+
+    class Switch(io.ComfyNode):
+        @classmethod
+        def define_schema(cls):
+            template = io.MatchType.Template("switch")
+            return io.Schema(
+                node_id="TestSwitch",
+                inputs=[
+                    io.Boolean.Input("switch"),
+                    io.MatchType.Input("on_false", template=template, optional=True),
+                    io.MatchType.Input("on_true", template=template, optional=True),
+                ],
+                outputs=[io.MatchType.Output(template=template)],
+            )
+
+        @classmethod
+        def execute(cls, switch, on_false=None, on_true=None):
+            return io.NodeOutput(on_true if switch else on_false)
+
+    # Force schema computation so SCHEMA / RETURN_TYPES are populated.
+    Switch.GET_SCHEMA()
+    return Switch
+
+
+def test_matchtype_resolves_to_upstream_concrete(fake_nodes_module, TypeResolver):
+    fake_nodes_module["TestSwitch"] = _make_switch_node_class()
+    fake_nodes_module["ImageSrc"] = _v1_node(("IMAGE",))
+    prompt = {
+        "img": {"class_type": "ImageSrc", "inputs": {}},
+        "sw": {
+            "class_type": "TestSwitch",
+            "inputs": {"switch": True, "on_true": ["img", 0]},
+        },
+    }
+    r = TypeResolver(prompt)
+    assert r.resolve_output_type("sw", 0) == "IMAGE"
+
+
+def test_matchtype_first_concrete_wins(fake_nodes_module, TypeResolver):
+    fake_nodes_module["TestSwitch"] = _make_switch_node_class()
+    fake_nodes_module["ImageSrc"] = _v1_node(("IMAGE",))
+    fake_nodes_module["LatentSrc"] = _v1_node(("LATENT",))
+    prompt = {
+        "img": {"class_type": "ImageSrc", "inputs": {}},
+        "lat": {"class_type": "LatentSrc", "inputs": {}},
+        "sw": {
+            "class_type": "TestSwitch",
+            "inputs": {
+                "switch": False,
+                "on_false": ["img", 0],   # listed first in schema → wins
+                "on_true":  ["lat", 0],
+            },
+        },
+    }
+    r = TypeResolver(prompt)
+    assert r.resolve_output_type("sw", 0) == "IMAGE"
+
+
+def test_matchtype_no_bound_input_returns_any(fake_nodes_module, TypeResolver, caplog):
+    fake_nodes_module["TestSwitch"] = _make_switch_node_class()
+    prompt = {"sw": {"class_type": "TestSwitch", "inputs": {"switch": True}}}
+    r = TypeResolver(prompt)
+    with caplog.at_level(logging.WARNING, logger="root"):
+        assert r.resolve_output_type("sw", 0) == "*"
+    assert any("MatchType" in rec.message for rec in caplog.records)
+
+
+def test_matchtype_skips_wildcard_input(fake_nodes_module, TypeResolver):
+    """If the first matched input resolves to AnyType, the resolver tries the next."""
+    fake_nodes_module["TestSwitch"] = _make_switch_node_class()
+    fake_nodes_module["WildNode"] = _v1_node(("*",))
+    fake_nodes_module["ImageSrc"] = _v1_node(("IMAGE",))
+    prompt = {
+        "wild": {"class_type": "WildNode", "inputs": {}},
+        "img": {"class_type": "ImageSrc", "inputs": {}},
+        "sw": {
+            "class_type": "TestSwitch",
+            "inputs": {
+                "switch": True,
+                "on_false": ["wild", 0],
+                "on_true":  ["img", 0],
+            },
+        },
+    }
+    r = TypeResolver(prompt)
+    assert r.resolve_output_type("sw", 0) == "IMAGE"
+
+
+def test_matchtype_cycle_terminates_at_any(fake_nodes_module, TypeResolver):
+    """Two switches that feed each other must not recurse forever."""
+    fake_nodes_module["TestSwitch"] = _make_switch_node_class()
+    prompt = {
+        "a": {"class_type": "TestSwitch", "inputs": {"switch": True, "on_true": ["b", 0]}},
+        "b": {"class_type": "TestSwitch", "inputs": {"switch": True, "on_true": ["a", 0]}},
+    }
+    r = TypeResolver(prompt)
+    # Must not raise / recurse forever; both resolve to AnyType.
+    assert r.resolve_output_type("a", 0) == "*"
+    assert r.resolve_output_type("b", 0) == "*"
+
+
+def test_matchtype_chain_resolves_through(fake_nodes_module, TypeResolver):
+    """A → B → C → IMAGE: chain must walk all the way."""
+    fake_nodes_module["TestSwitch"] = _make_switch_node_class()
+    fake_nodes_module["ImageSrc"] = _v1_node(("IMAGE",))
+    prompt = {
+        "src": {"class_type": "ImageSrc", "inputs": {}},
+        "a": {"class_type": "TestSwitch", "inputs": {"switch": True, "on_true": ["src", 0]}},
+        "b": {"class_type": "TestSwitch", "inputs": {"switch": True, "on_true": ["a", 0]}},
+        "c": {"class_type": "TestSwitch", "inputs": {"switch": True, "on_true": ["b", 0]}},
+    }
+    r = TypeResolver(prompt)
+    assert r.resolve_output_type("c", 0) == "IMAGE"
+
+
+# ---------------------------------------------------------------------------
+# Input resolution and effective io_type peeling
+# ---------------------------------------------------------------------------
+
+def test_resolve_input_type_literal_uses_declared(fake_nodes_module, TypeResolver):
+    fake_nodes_module["Sink"] = _v1_node(("INT",), {"required": {"steps": ("INT",)}})
+    prompt = {"n1": {"class_type": "Sink", "inputs": {"steps": 20}}}
+    r = TypeResolver(prompt)
+    assert r.resolve_input_type("n1", "steps") == "INT"
+
+
+def test_resolve_input_type_link(fake_nodes_module, TypeResolver):
+    fake_nodes_module["Src"] = _v1_node(("LATENT",))
+    fake_nodes_module["Sink"] = _v1_node(("INT",), {"required": {"x": ("*",)}})
+    prompt = {
+        "src": {"class_type": "Src", "inputs": {}},
+        "sink": {"class_type": "Sink", "inputs": {"x": ["src", 0]}},
+    }
+    r = TypeResolver(prompt)
+    assert r.resolve_input_type("sink", "x") == "LATENT"
+
+
+def test_effective_slot_type_peels_autogrow(fake_nodes_module, TypeResolver):
+    from comfy_api.latest import io
+
+    class AutogrowImg(io.ComfyNode):
+        @classmethod
+        def define_schema(cls):
+            template = io.Autogrow.TemplatePrefix(
+                input=io.Image.Input("img"),
+                prefix="img",
+                min=1,
+            )
+            return io.Schema(
+                node_id="AutogrowImg",
+                inputs=[io.Autogrow.Input("imgs", template=template)],
+                outputs=[io.Image.Output()],
+            )
+
+        @classmethod
+        def execute(cls, imgs):
+            return io.NodeOutput(None)
+
+    AutogrowImg.GET_SCHEMA()
+    fake_nodes_module["AutogrowImg"] = AutogrowImg
+    prompt = {"n1": {"class_type": "AutogrowImg", "inputs": {}}}
+    r = TypeResolver(prompt)
+    # The user-facing element type, not the autogrow wrapper.
+    assert r.get_declared_slot_io_type("n1", "imgs") == "IMAGE"
+
+
+# ---------------------------------------------------------------------------
+# compute_live_input_types
+# ---------------------------------------------------------------------------
+
+def test_effective_slot_type_on_v3_plain_input(fake_nodes_module, TypeResolver):
+    """V3 input that is neither Autogrow/DynamicSlot/DynamicCombo must still resolve.
+
+    Regression test: importing ``io`` from the public re-export skipped
+    ``DynamicSlot``, so an ``isinstance`` chain in ``_effective_io_type`` raised
+    ``AttributeError`` the first time it ran against a plain V3 input.
+    """
+    from comfy_api.latest import io
+
+    class BoolSink(io.ComfyNode):
+        @classmethod
+        def define_schema(cls):
+            return io.Schema(
+                node_id="BoolSink",
+                inputs=[io.Boolean.Input("flag")],
+                outputs=[io.Boolean.Output()],
+            )
+
+        @classmethod
+        def execute(cls, flag):
+            return io.NodeOutput(flag)
+
+    BoolSink.GET_SCHEMA()
+    fake_nodes_module["BoolSink"] = BoolSink
+    prompt = {"n": {"class_type": "BoolSink", "inputs": {"flag": True}}}
+    r = TypeResolver(prompt)
+    assert r.get_declared_slot_io_type("n", "flag") == "BOOLEAN"
+    assert r.compute_live_input_types("n") == {"flag": "BOOLEAN"}
+
+
+def test_effective_slot_type_peels_dynamic_slot(fake_nodes_module, TypeResolver):
+    """A DynamicSlot input reports its auto-derived slotType (union of `when` types)."""
+    from comfy_api.latest import _io as io
+
+    class DSNode(io.ComfyNode):
+        @classmethod
+        def define_schema(cls):
+            return io.Schema(
+                node_id="DSNode",
+                inputs=[
+                    io.DynamicSlot.Input("slot", options=[
+                        io.DynamicSlot.Option(when=io.Image, inputs=[]),
+                        io.DynamicSlot.Option(when=io.Latent, inputs=[]),
+                    ]),
+                ],
+                outputs=[io.String.Output()],
+            )
+
+        @classmethod
+        def execute(cls, **kwargs):
+            return io.NodeOutput("")
+
+    DSNode.GET_SCHEMA()
+    fake_nodes_module["DSNode"] = DSNode
+    prompt = {"n": {"class_type": "DSNode", "inputs": {}}}
+    r = TypeResolver(prompt)
+    assert r.get_declared_slot_io_type("n", "slot") == "IMAGE,LATENT"
+
+
+def test_compute_live_input_types_mixes_links_and_literals(fake_nodes_module, TypeResolver):
+    fake_nodes_module["Src"] = _v1_node(("MODEL",))
+    fake_nodes_module["Sink"] = _v1_node(
+        ("INT",),
+        {"required": {"model": ("MODEL",), "steps": ("INT",)}},
+    )
+    prompt = {
+        "src": {"class_type": "Src", "inputs": {}},
+        "sink": {
+            "class_type": "Sink",
+            "inputs": {"model": ["src", 0], "steps": 20},
+        },
+    }
+    r = TypeResolver(prompt)
+    assert r.compute_live_input_types("sink") == {"model": "MODEL", "steps": "INT"}
+
+
+# ---------------------------------------------------------------------------
+# Cache invalidation
+# ---------------------------------------------------------------------------
+
+def test_invalidate_clears_cache(fake_nodes_module, TypeResolver):
+    fake_nodes_module["Src"] = _v1_node(("IMAGE",))
+    prompt = {"n1": {"class_type": "Src", "inputs": {}}}
+    r = TypeResolver(prompt)
+    assert r.resolve_output_type("n1", 0) == "IMAGE"
+    # Mutate the underlying class and invalidate; the resolver must re-read.
+    fake_nodes_module["Src"] = _v1_node(("LATENT",))
+    r.invalidate()
+    assert r.resolve_output_type("n1", 0) == "LATENT"
+
+
+# ---------------------------------------------------------------------------
+# Malformed input robustness
+# ---------------------------------------------------------------------------
+
+def test_malformed_link_does_not_crash(fake_nodes_module, TypeResolver):
+    """A link with a non-int slot index must not raise; resolver returns AnyType."""
+    fake_nodes_module["Src"] = _v1_node(("IMAGE",))
+    fake_nodes_module["Sink"] = _v1_node(("INT",), {"required": {"x": ("*",)}})
+    prompt = {
+        "src": {"class_type": "Src", "inputs": {}},
+        # slot index sent as a string (common API JSON mistake)
+        "sink": {"class_type": "Sink", "inputs": {"x": ["src", "0"]}},
+    }
+    r = TypeResolver(prompt)
+    # Falls back to declared slot type (still "*"), no exception.
+    assert r.resolve_input_type("sink", "x") == "*"
+
+
+def test_malformed_link_wrong_arity_does_not_crash(fake_nodes_module, TypeResolver):
+    fake_nodes_module["Src"] = _v1_node(("IMAGE",))
+    fake_nodes_module["Sink"] = _v1_node(("INT",), {"required": {"x": ("*",)}})
+    prompt = {
+        "src": {"class_type": "Src", "inputs": {}},
+        "sink": {"class_type": "Sink", "inputs": {"x": ["src"]}},  # arity 1
+    }
+    r = TypeResolver(prompt)
+    assert r.resolve_input_type("sink", "x") == "*"
+
+
+def test_direct_resolve_output_type_with_bad_slot_idx_returns_any(fake_nodes_module, TypeResolver):
+    fake_nodes_module["Src"] = _v1_node(("IMAGE",))
+    prompt = {"src": {"class_type": "Src", "inputs": {}}}
+    r = TypeResolver(prompt)
+    # type-wise these should be unreachable through normal validation but the
+    # resolver must still degrade gracefully.
+    assert r.resolve_output_type("src", "0") == "*"
+    assert r.resolve_output_type("src", True) == "*"  # bool is a subclass of int
+    assert r.is_output_list("src", "0") is False
+
+
+def test_non_string_class_type_returns_any(fake_nodes_module, TypeResolver):
+    prompt = {"n1": {"class_type": 42, "inputs": {}}}
+    r = TypeResolver(prompt)
+    assert r.resolve_output_type("n1", 0) == "*"
+
+
+def test_invalidate_node_only_clears_that_node(fake_nodes_module, TypeResolver):
+    fake_nodes_module["SrcA"] = _v1_node(("IMAGE",))
+    fake_nodes_module["SrcB"] = _v1_node(("LATENT",))
+    prompt = {
+        "a": {"class_type": "SrcA", "inputs": {}},
+        "b": {"class_type": "SrcB", "inputs": {}},
+    }
+    r = TypeResolver(prompt)
+    r.resolve_output_type("a", 0)
+    r.resolve_output_type("b", 0)
+    fake_nodes_module["SrcA"] = _v1_node(("MASK",))
+    r.invalidate_node("a")
+    assert r.resolve_output_type("a", 0) == "MASK"
+    # b's cached result survives even though SrcB was unchanged
+    assert ("b", 0) in r._output_cache