Merge branch 'master' into feat/ideogram-prompt-nodes

comfy/ops.py

@@ -1089,19 +1089,6 @@ def _load_quantized_module(module, super_load, state_dict, prefix, local_metadat
             if ts is None or bs is None:
                 raise ValueError(f"Missing NVFP4 scales for layer {layer_name}")
             scales = {"scale": ts, "block_scale": bs}
-        elif module.quant_format == "int8_tensorwise":
-            scale = pop_scale("weight_scale")
-            if scale is None:
-                raise ValueError(f"Missing INT8 weight scale for layer {layer_name}")
-            scales = {"scale": scale}
-            params_conf = layer_conf.get("params", {})
-            if not isinstance(params_conf, dict):
-                params_conf = {}
-            if layer_conf.get("convrot", params_conf.get("convrot", False)):
-                scales["convrot"] = True
-                scales["convrot_groupsize"] = int(
-                    layer_conf.get("convrot_groupsize", params_conf.get("convrot_groupsize", 256))
-                )
         else:
             raise ValueError(f"Unsupported quantization format: {module.quant_format}")
 
@@ -1144,10 +1131,6 @@ def _quantized_weight_state_dict(module, sd, prefix, extra_quant_conf=None, extr
         quant_conf = {"format": module.quant_format}
         if getattr(module, '_full_precision_mm_config', False):
             quant_conf["full_precision_matrix_mult"] = True
-        params = getattr(module.weight, "_params", None)
-        if module.quant_format == "int8_tensorwise" and getattr(params, "convrot", False):
-            quant_conf["convrot"] = True
-            quant_conf["convrot_groupsize"] = getattr(params, "convrot_groupsize", 256)
         if extra_quant_conf:
             quant_conf.update(extra_quant_conf)
         sd[f"{prefix}comfy_quant"] = torch.tensor(list(json.dumps(quant_conf).encode("utf-8")), dtype=torch.uint8)
@@ -1200,33 +1183,8 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
             def _forward(self, input, weight, bias):
                 return torch.nn.functional.linear(input, weight, bias)
 
-            def forward_comfy_cast_weights(
-                self,
-                input,
-                compute_dtype=None,
-                want_requant=False,
-                weight_only_quant=False,
-            ):
-                if weight_only_quant:
-                    weight, bias, offload_stream = cast_bias_weight(
-                        self,
-                        input=None,
-                        dtype=self.weight.dtype,
-                        device=input.device,
-                        bias_dtype=input.dtype,
-                        offloadable=True,
-                        compute_dtype=compute_dtype,
-                        want_requant=want_requant,
-                    )
-                    weight = weight.to(dtype=input.dtype)
-                else:
-                    weight, bias, offload_stream = cast_bias_weight(
-                        self,
-                        input,
-                        offloadable=True,
-                        compute_dtype=compute_dtype,
-                        want_requant=want_requant,
-                    )
+            def forward_comfy_cast_weights(self, input, compute_dtype=None, want_requant=False):
+                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True, compute_dtype=compute_dtype, want_requant=want_requant)
                 x = self._forward(input, weight, bias)
                 uncast_bias_weight(self, weight, bias, offload_stream)
                 return x
@@ -1245,10 +1203,9 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                     not getattr(self, 'comfy_force_cast_weights', False) and
                     len(self.weight_function) == 0 and len(self.bias_function) == 0
                 )
-                quantize_input = QUANT_ALGOS.get(getattr(self, 'quant_format', None), {}).get("quantize_input", True)
 
                 # Training path: quantized forward with compute_dtype backward via autograd function
-                if (input.requires_grad and _use_quantized and quantize_input):
+                if (input.requires_grad and _use_quantized):
 
                     weight, bias, offload_stream = cast_bias_weight(
                         self,
@@ -1270,7 +1227,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                     return output
 
                 # Inference path (unchanged)
-                if _use_quantized and quantize_input:
+                if _use_quantized:
 
                     # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others)
                     input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input
@@ -1284,13 +1241,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                             scale = comfy.model_management.cast_to_device(scale, input.device, None)
                         input = QuantizedTensor.from_float(input_reshaped, self.layout_type, scale=scale)
 
-                weight_only_quant = _use_quantized and not quantize_input and isinstance(self.weight, QuantizedTensor)
-                output = self.forward_comfy_cast_weights(
-                    input,
-                    compute_dtype,
-                    want_requant=isinstance(input, QuantizedTensor),
-                    weight_only_quant=weight_only_quant,
-                )
+                output = self.forward_comfy_cast_weights(input, compute_dtype, want_requant=isinstance(input, QuantizedTensor))
 
                 # Reshape output back to 3D if input was 3D
                 if reshaped_3d:

comfy/quant_ops.py

@@ -10,7 +10,6 @@ try:
         QuantizedLayout,
         TensorCoreFP8Layout as _CKFp8Layout,
         TensorCoreNVFP4Layout as _CKNvfp4Layout,
-        TensorWiseINT8Layout as _CKTensorWiseINT8Layout,
         register_layout_op,
         register_layout_class,
         get_layout_class,
@@ -48,9 +47,6 @@ except ImportError as e:
     class _CKNvfp4Layout:
         pass
 
-    class _CKTensorWiseINT8Layout:
-        pass
-
     def register_layout_class(name, cls):
         pass
 
@@ -178,7 +174,6 @@ class TensorCoreFP8E5M2Layout(_TensorCoreFP8LayoutBase):
 
 # Backward compatibility alias - default to E4M3
 TensorCoreFP8Layout = TensorCoreFP8E4M3Layout
-TensorWiseINT8Layout = _CKTensorWiseINT8Layout
 
 
 # ==============================================================================
@@ -189,7 +184,6 @@ register_layout_class("TensorCoreFP8Layout", TensorCoreFP8Layout)
 register_layout_class("TensorCoreFP8E4M3Layout", TensorCoreFP8E4M3Layout)
 register_layout_class("TensorCoreFP8E5M2Layout", TensorCoreFP8E5M2Layout)
 register_layout_class("TensorCoreNVFP4Layout", TensorCoreNVFP4Layout)
-register_layout_class("TensorWiseINT8Layout", _CKTensorWiseINT8Layout)
 if _CK_MXFP8_AVAILABLE:
     register_layout_class("TensorCoreMXFP8Layout", TensorCoreMXFP8Layout)
 
@@ -220,13 +214,6 @@ if _CK_MXFP8_AVAILABLE:
         "group_size": 32,
     }
 
-QUANT_ALGOS["int8_tensorwise"] = {
-    "storage_t": torch.int8,
-    "parameters": {"weight_scale"},
-    "comfy_tensor_layout": "TensorWiseINT8Layout",
-    "quantize_input": False,
-}
-
 
 # ==============================================================================
 # Re-exports for backward compatibility
@@ -239,7 +226,6 @@ __all__ = [
     "TensorCoreFP8E4M3Layout",
     "TensorCoreFP8E5M2Layout",
     "TensorCoreNVFP4Layout",
-    "TensorWiseINT8Layout",
     "QUANT_ALGOS",
     "register_layout_op",
 ]

comfy_api_nodes/apis/bytedance.py

@@ -177,10 +177,6 @@ SEEDANCE2_PRICE_PER_1K_TOKENS = {
     ("dreamina-seedance-2-0-fast-260128", True, "480p"): 0.0033,
     ("dreamina-seedance-2-0-fast-260128", False, "720p"): 0.0056,
     ("dreamina-seedance-2-0-fast-260128", True, "720p"): 0.0033,
-    ("dreamina-seedance-2-0-mini", False, "480p"): 0.0035,
-    ("dreamina-seedance-2-0-mini", True, "480p"): 0.0021,
-    ("dreamina-seedance-2-0-mini", False, "720p"): 0.0035,
-    ("dreamina-seedance-2-0-mini", True, "720p"): 0.0021,
 }
 
 
@@ -282,10 +278,6 @@ SEEDANCE2_REF_VIDEO_PIXEL_LIMITS = {
         "480p": {"min": 409_600, "max": 927_408},
         "720p": {"min": 409_600, "max": 927_408},
     },
-    "dreamina-seedance-2-0-mini": {
-        "480p": {"min": 409_600, "max": 927_408},
-        "720p": {"min": 409_600, "max": 927_408},
-    },
 }
 
 # The time in this dictionary are given for 10 seconds duration.

comfy_api_nodes/nodes_bytedance.py

@@ -89,7 +89,6 @@ BYTEPLUS_SEEDANCE2_TASK_STATUS_ENDPOINT = "/proxy/byteplus-seedance2/api/v3/cont
 SEEDANCE_MODELS = {
     "Seedance 2.0": "dreamina-seedance-2-0-260128",
     "Seedance 2.0 Fast": "dreamina-seedance-2-0-fast-260128",
-    "Seedance 2.0 Mini": "dreamina-seedance-2-0-mini",
 }
 
 DEPRECATED_MODELS = {"seedance-1-0-lite-t2v-250428", "seedance-1-0-lite-i2v-250428"}
@@ -1624,10 +1623,8 @@ class ByteDance2TextToVideoNode(IO.ComfyNode):
                     options=[
                         IO.DynamicCombo.Option("Seedance 2.0", _seedance2_text_inputs(["480p", "720p", "1080p", "4k"])),
                         IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_text_inputs(["480p", "720p"])),
-                        IO.DynamicCombo.Option("Seedance 2.0 Mini", _seedance2_text_inputs(["480p", "720p"])),
                     ],
-                    tooltip="Seedance 2.0 for maximum quality; Fast for speed optimization; "
-                    "Mini for the fastest, lowest-cost generation.",
+                    tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.",
                 ),
                 IO.Int.Input(
                     "seed",
@@ -1669,7 +1666,6 @@ class ByteDance2TextToVideoNode(IO.ComfyNode):
                   $dur := $lookup(widgets, "model.duration");
                   $pricePer1K := $res = "4k"    ? 0.00572 :
                                  $res = "1080p" ? 0.011011 :
-                                 $contains($m, "mini") ? 0.005005 :
                                  $contains($m, "fast") ? 0.008008 : 0.01001;
                   $rate := $res = "4k"    ? $rate4k :
                            $res = "1080p" ? $rate1080 :
@@ -1738,13 +1734,8 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
                             "Seedance 2.0 Fast",
                             _seedance2_text_inputs(["480p", "720p"], default_ratio="adaptive"),
                         ),
-                        IO.DynamicCombo.Option(
-                            "Seedance 2.0 Mini",
-                            _seedance2_text_inputs(["480p", "720p"], default_ratio="adaptive"),
-                        ),
                     ],
-                    tooltip="Seedance 2.0 for maximum quality; Fast for speed optimization; "
-                    "Mini for the fastest, lowest-cost generation.",
+                    tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.",
                 ),
                 IO.Image.Input(
                     "first_frame",
@@ -1810,7 +1801,6 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
                   $dur := $lookup(widgets, "model.duration");
                   $pricePer1K := $res = "4k"    ? 0.00572 :
                                  $res = "1080p" ? 0.011011 :
-                                 $contains($m, "mini") ? 0.005005 :
                                  $contains($m, "fast") ? 0.008008 : 0.01001;
                   $rate := $res = "4k"    ? $rate4k :
                            $res = "1080p" ? $rate1080 :
@@ -2034,13 +2024,8 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
                             "Seedance 2.0 Fast",
                             _seedance2_reference_inputs(["480p", "720p"], default_ratio="adaptive"),
                         ),
-                        IO.DynamicCombo.Option(
-                            "Seedance 2.0 Mini",
-                            _seedance2_reference_inputs(["480p", "720p"], default_ratio="adaptive"),
-                        ),
                     ],
-                    tooltip="Seedance 2.0 for maximum quality; Fast for speed optimization; "
-                    "Mini for the fastest, lowest-cost generation.",
+                    tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.",
                 ),
                 IO.Int.Input(
                     "seed",
@@ -2086,11 +2071,9 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
                   $dur := $lookup(widgets, "model.duration");
                   $noVideoPricePer1K := $res = "4k"    ? 0.00572 :
                                         $res = "1080p" ? 0.011011 :
-                                        $contains($m, "mini") ? 0.005005 :
                                         $contains($m, "fast") ? 0.008008 : 0.01001;
                   $videoPricePer1K := $res = "4k"    ? 0.003432 :
                                       $res = "1080p" ? 0.006721 :
-                                      $contains($m, "mini") ? 0.003003 :
                                       $contains($m, "fast") ? 0.004719 : 0.006149;
                   $rate := $res = "4k"    ? $rate4k :
                            $res = "1080p" ? $rate1080 :

comfy_execution/graph.py

@@ -3,7 +3,6 @@ from typing import Type, Literal
 import nodes
 import asyncio
 import inspect
-import traceback
 from comfy_execution.graph_utils import is_link, ExecutionBlocker
 from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, InputTypeOptions
 
@@ -264,25 +263,7 @@ class ExecutionList(TopologicalSort):
             }
             return None, error_details, ex
 
-        try:
-            self.staged_node_id = self.ux_friendly_pick_node(available)
-        except Exception as ex:
-            # Backstop: the ordering heuristics in ux_friendly_pick_node are
-            # defensive, but should anything else there fail, surface it as an
-            # execution error instead of letting it kill the prompt worker
-            # thread. Blame an available node (best effort).
-            blamed_node = self.dynprompt.get_display_node_id(available[0])
-            exception_type = type(ex).__qualname__
-            if type(ex).__module__ != "builtins":
-                exception_type = type(ex).__module__ + "." + exception_type
-            error_details = {
-                "node_id": blamed_node,
-                "exception_message": str(ex),
-                "exception_type": exception_type,
-                "traceback": traceback.format_tb(ex.__traceback__),
-                "current_inputs": []
-            }
-            return None, error_details, ex
+        self.staged_node_id = self.ux_friendly_pick_node(available)
         return self.staged_node_id, None, None
 
     def ux_friendly_pick_node(self, node_list):
@@ -290,28 +271,19 @@ class ExecutionList(TopologicalSort):
         # Technically this has no effect on the overall length of execution, but it feels better as a user
         # for a PreviewImage to display a result as soon as it can
         # Some other heuristics could probably be used here to improve the UX further.
-        # These node-ordering heuristics only affect *order*, never correctness.
-        # A malformed node (e.g. a FUNCTION typo, or a node whose schema-derived
-        # attributes raise) must not crash scheduling: failing a heuristic just
-        # means "not prioritized". The node then proceeds to normal execution,
-        # where the real error is raised and reported against the correct node.
         def is_output(node_id):
             class_type = self.dynprompt.get_node(node_id)["class_type"]
             class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
-            try:
-                return hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True
-            except Exception:
-                return False
+            if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True:
+                return True
+            return False
 
         # If an available node is async, do that first.
         # This will execute the asynchronous function earlier, reducing the overall time.
         def is_async(node_id):
             class_type = self.dynprompt.get_node(node_id)["class_type"]
             class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
-            try:
-                return inspect.iscoroutinefunction(getattr(class_def, class_def.FUNCTION))
-            except Exception:
-                return False
+            return inspect.iscoroutinefunction(getattr(class_def, class_def.FUNCTION))
 
         for node_id in node_list:
             if is_output(node_id) or is_async(node_id):

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.45.19
-comfyui-workflow-templates==0.10.7
+comfyui-workflow-templates==0.10.2
 comfyui-embedded-docs==0.5.5
 torch
 torchsde
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy>=2.0.0
 filelock
 av>=16.0.0
-comfy-kitchen==0.2.12
+comfy-kitchen==0.2.10
 comfy-aimdo==0.4.10
 requests
 simpleeval>=1.0.0

tests-unit/comfy_quant/test_mixed_precision.py

@@ -228,62 +228,6 @@ class TestMixedPrecisionOps(unittest.TestCase):
         with self.assertRaises(KeyError):
             model.load_state_dict(state_dict, strict=False)
 
-    def test_int8_convrot_metadata_loads_into_params(self):
-        """ConvRot metadata must reach TensorWiseINT8Layout params."""
-        torch.manual_seed(123)
-        layer_quant_config = {
-            "layer": {
-                "format": "int8_tensorwise",
-                "convrot": True,
-                "convrot_groupsize": 256,
-            }
-        }
-        weight = torch.randn(16, 256, dtype=torch.bfloat16)
-        bias = torch.randn(16, dtype=torch.bfloat16)
-        q_weight = QuantizedTensor.from_float(
-            weight,
-            "TensorWiseINT8Layout",
-            per_channel=True,
-            convrot=True,
-            convrot_groupsize=256,
-        )
-        state_dict = {
-            "layer.weight": q_weight._qdata,
-            "layer.bias": bias,
-            "layer.weight_scale": q_weight._params.scale,
-        }
-
-        state_dict, _ = comfy.utils.convert_old_quants(
-            state_dict,
-            metadata={"_quantization_metadata": json.dumps({"layers": layer_quant_config})},
-        )
-        model = torch.nn.Module()
-        model.layer = ops.mixed_precision_ops({}).Linear(256, 16, device="cpu", dtype=torch.bfloat16)
-        model.load_state_dict(state_dict, strict=False)
-
-        self.assertIsInstance(model.layer.weight, QuantizedTensor)
-        self.assertEqual(model.layer.weight._layout_cls, "TensorWiseINT8Layout")
-        self.assertTrue(model.layer.weight._params.convrot)
-        self.assertEqual(model.layer.weight._params.convrot_groupsize, 256)
-
-        input_tensor = torch.randn(4, 256, dtype=torch.bfloat16)
-        loaded_out = model.layer(input_tensor)
-        ref_out = torch.nn.functional.linear(input_tensor, q_weight, bias)
-        self.assertTrue(torch.equal(loaded_out, ref_out))
-
-        fp16_input = input_tensor.to(torch.float16)
-        loaded_fp16_out = model.layer(fp16_input)
-        ref_fp16_out = torch.nn.functional.linear(
-            fp16_input,
-            q_weight.to(dtype=torch.float16),
-            bias.to(dtype=torch.float16),
-        )
-        self.assertTrue(torch.equal(loaded_fp16_out, ref_fp16_out))
-
-        saved = model.state_dict()
-        saved_conf = json.loads(saved["layer.comfy_quant"].numpy().tobytes())
-        self.assertTrue(saved_conf["convrot"])
-        self.assertEqual(saved_conf["convrot_groupsize"], 256)
-
 if __name__ == "__main__":
     unittest.main()
+

tests-unit/execution_test/scheduler_malformed_node_test.py

@@ -1,97 +0,0 @@
-"""Regression tests for scheduler resilience to malformed nodes.
-
-A node whose FUNCTION points at a method that does not exist (e.g. a typo in a
-custom node) used to raise inside the scheduling heuristic, escaping the prompt
-worker's error handling and silently killing the worker thread. Scheduling must
-instead either proceed (so the error surfaces through normal execution) or report
-the failure as an execution error.
-"""
-import asyncio
-
-import nodes
-from comfy_execution.graph import DynamicPrompt, ExecutionList
-
-
-class _MalformedV1Node:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {"required": {}}
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "invert"  # the actual method below is misspelled
-    OUTPUT_NODE = True
-    CATEGORY = "Test"
-
-    def invvert(self):
-        return (None,)
-
-
-class _RaisingDescriptor:
-    def __get__(self, obj, owner):
-        raise RuntimeError("schema error")
-
-
-class _SchemaRaisesNode:
-    """A node whose schema-derived attribute access raises, as a broken V3 node would."""
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {"required": {}}
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "run"
-    OUTPUT_NODE = _RaisingDescriptor()
-    CATEGORY = "Test"
-
-    def run(self):
-        return (None,)
-
-
-class _FakeOutputCache:
-    def all_node_ids(self):
-        return set()
-
-    async def get(self, node_id):
-        return None
-
-
-def _make_execution_list(class_type, class_def):
-    nodes.NODE_CLASS_MAPPINGS[class_type] = class_def
-    prompt = {"1": {"class_type": class_type, "inputs": {}}}
-    execution_list = ExecutionList(DynamicPrompt(prompt), _FakeOutputCache())
-    execution_list.add_node("1")
-    return execution_list
-
-
-def test_malformed_function_does_not_crash_scheduler():
-    """A FUNCTION-typo node schedules without raising; the error surfaces later."""
-    execution_list = _make_execution_list("MalformedV1Node", _MalformedV1Node)
-    node_id, error, ex = asyncio.run(execution_list.stage_node_execution())
-    assert ex is None
-    assert error is None
-    assert node_id == "1"
-
-
-def test_schema_attribute_error_does_not_crash_scheduler():
-    """A node whose attribute access raises during heuristics still schedules."""
-    execution_list = _make_execution_list("SchemaRaisesNode", _SchemaRaisesNode)
-    node_id, error, ex = asyncio.run(execution_list.stage_node_execution())
-    assert ex is None
-    assert error is None
-    assert node_id == "1"
-
-
-def test_pick_node_failure_is_reported_not_raised():
-    """An unexpected scheduling error is returned as an error, not raised."""
-    execution_list = _make_execution_list("MalformedV1Node", _MalformedV1Node)
-
-    def raise_on_pick(_available):
-        raise RuntimeError("boom")
-
-    execution_list.ux_friendly_pick_node = raise_on_pick
-    node_id, error, ex = asyncio.run(execution_list.stage_node_execution())
-    assert node_id is None
-    assert isinstance(ex, RuntimeError)
-    assert error["node_id"] == "1"
-    assert error["exception_type"] == "RuntimeError"
-    assert error["exception_message"] == "boom"
-    assert error["traceback"]