Correct PIL contract in ImageBlend channel-cap rationale

PIL.Image.fromarray accepts 2-channel (LA mode) arrays as well, not just 1/3/4-channel. Reword the inline comments and test docstrings to say 'rejects > 4-channel arrays', which is the actual constraint driving the cap. Also drop a too-narrow 'mode in (L, RGB, RGBA)' assertion in test_save_compatible_output_passes_through_pil so a future 2-channel result would not be flagged as a failure.
Cap ImageBlend channel-mismatch output at 4 channels (RGBA)
2026-06-01 05:27:54 +08:00 · 2026-04-27 07:58:20 +00:00 · 2026-04-27 07:18:16 +00:00 · 2026-04-27 07:08:14 +00:00 · 2026-04-27 06:36:44 +00:00 · 2026-04-27 06:31:18 +00:00
31 changed files with 33546 additions and 1096 deletions
--- a/.gitignore
+++ b/.gitignore
@ -21,6 +21,5 @@ venv*/
 *.log
 web_custom_versions/
 .DS_Store
-openapi.yaml
 filtered-openapi.yaml
 uv.lock
--- a/blueprints/Crop
+++ b/blueprints/Crop
--- a/blueprints/Crop
+++ b/blueprints/Crop
--- a/(Z-Image-Turbo).json
+++ b/(Z-Image-Turbo).json
@ -160,7 +160,7 @@
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@ -2482,4 +2482,4 @@
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--- a/blueprints/Depth
+++ b/blueprints/Depth
@ -261,7 +261,7 @@
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@ -5208,4 +5208,4 @@
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--- a/blueprints/First-Last-Frame
+++ b/blueprints/First-Last-Frame
--- a/blueprints/Image
+++ b/blueprints/Image
--- a/blueprints/Image
+++ b/blueprints/Image
@ -128,7 +128,7 @@
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@ -1837,4 +1837,4 @@
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--- a/blueprints/Image
+++ b/blueprints/Image
--- a/blueprints/Image
+++ b/blueprints/Image
--- a/(Qwen-image).json
+++ b/(Qwen-image).json
@ -124,7 +124,7 @@
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@ -1923,4 +1923,4 @@
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--- a/(Qwen-Image).json
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@ -204,7 +204,7 @@
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@ -2749,4 +2749,4 @@
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@ -1,15 +1,14 @@
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@ -56,6 +55,38 @@
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@ -66,28 +97,41 @@
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+              },
+              "Node name for S&R": "UNETLoader",
              "models": [
                {
                  "name": "qwen_image_layered_bf16.safetensors",
@ -1191,8 +1349,8 @@
            "bounding": [
              -330,
              110,
-              366.7470703125,
-              421.6
+              370,
+              610
            ],
            "color": "#3f789e",
            "font_size": 24,
@ -1391,6 +1549,38 @@
            "target_id": 83,
            "target_slot": 2,
            "type": "INT"
+          },
+          {
+            "id": 377,
+            "origin_id": -10,
+            "origin_slot": 5,
+            "target_id": 3,
+            "target_slot": 4,
+            "type": "INT"
+          },
+          {
+            "id": 378,
+            "origin_id": -10,
+            "origin_slot": 6,
+            "target_id": 37,
+            "target_slot": 0,
+            "type": "COMBO"
+          },
+          {
+            "id": 379,
+            "origin_id": -10,
+            "origin_slot": 7,
+            "target_id": 38,
+            "target_slot": 0,
+            "type": "COMBO"
+          },
+          {
+            "id": 380,
+            "origin_id": -10,
+            "origin_slot": 8,
+            "target_id": 39,
+            "target_slot": 0,
+            "type": "COMBO"
          }
        ],
        "extra": {
@ -1400,7 +1590,6 @@
      }
    ]
  },
-  "config": {},
  "extra": {
    "ds": {
      "scale": 1.14,
@ -1409,7 +1598,6 @@
        6.855893974423647
      ]
    },
-    "workflowRendererVersion": "LG"
-  },
-  "version": 0.4
-}
+    "ue_links": []
+  }
+}
--- a/blueprints/Image
+++ b/blueprints/Image
--- a/blueprints/Pose
+++ b/blueprints/Pose
--- a/blueprints/Text
+++ b/blueprints/Text
--- a/blueprints/Text
+++ b/blueprints/Text
--- a/blueprints/Text
+++ b/blueprints/Text
--- a/blueprints/Text
+++ b/blueprints/Text
--- a/(Qwen-Image).json
+++ b/(Qwen-Image).json
--- a/blueprints/Text
+++ b/blueprints/Text
--- a/comfy_api/latest/_input_impl/video_types.py
+++ b/comfy_api/latest/_input_impl/video_types.py
@ -240,19 +240,34 @@ class VideoFromFile(VideoInput):
            start_time = self.__start_time
        # Get video frames
        frames = []
+        alphas = None
        start_pts = int(start_time / video_stream.time_base)
        end_pts = int((start_time + self.__duration) / video_stream.time_base)
        container.seek(start_pts, stream=video_stream)
+        image_format = 'gbrpf32le'
        for frame in container.decode(video_stream):
+            if alphas is None:
+                for comp in frame.format.components:
+                    if comp.is_alpha:
+                        alphas = []
+                        image_format = 'gbrapf32le'
+                        break
+
            if frame.pts < start_pts:
                continue
            if self.__duration and frame.pts >= end_pts:
                break
-            img = frame.to_ndarray(format='rgb24')  # shape: (H, W, 3)
-            img = torch.from_numpy(img) / 255.0  # shape: (H, W, 3)
-            frames.append(img)

-        images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 3, 0, 0)
+            img = frame.to_ndarray(format=image_format)  # shape: (H, W, 4)
+            if alphas is None:
+                frames.append(torch.from_numpy(img))
+            else:
+                frames.append(torch.from_numpy(img[..., :-1]))
+                alphas.append(torch.from_numpy(img[..., -1:]))
+
+        images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 0, 0, 3)
+        if alphas is not None:
+            alphas = torch.stack(alphas) if len(alphas) > 0 else torch.zeros(0, 0, 0, 1)

        # Get frame rate
        frame_rate = Fraction(video_stream.average_rate) if video_stream.average_rate else Fraction(1)
@ -295,7 +310,7 @@ class VideoFromFile(VideoInput):
                })

        metadata = container.metadata
-        return VideoComponents(images=images, audio=audio, frame_rate=frame_rate, metadata=metadata)
+        return VideoComponents(images=images, alpha=alphas, audio=audio, frame_rate=frame_rate, metadata=metadata)

    def get_components(self) -> VideoComponents:
        if isinstance(self.__file, io.BytesIO):
--- a/comfy_api/latest/_util/video_types.py
+++ b/comfy_api/latest/_util/video_types.py
@ -3,7 +3,7 @@ from dataclasses import dataclass
 from enum import Enum
 from fractions import Fraction
 from typing import Optional
-from .._input import ImageInput, AudioInput
+from .._input import ImageInput, AudioInput, MaskInput

 class VideoCodec(str, Enum):
    AUTO = "auto"
@ -48,5 +48,4 @@ class VideoComponents:
    frame_rate: Fraction
    audio: Optional[AudioInput] = None
    metadata: Optional[dict] = None
-
-
+    alpha: Optional[MaskInput] = None
--- a/comfy_api_nodes/nodes_sora.py
+++ b/comfy_api_nodes/nodes_sora.py
@ -33,9 +33,13 @@ class OpenAIVideoSora2(IO.ComfyNode):
    def define_schema(cls):
        return IO.Schema(
            node_id="OpenAIVideoSora2",
-            display_name="OpenAI Sora - Video",
+            display_name="OpenAI Sora - Video (Deprecated)",
            category="api node/video/Sora",
-            description="OpenAI video and audio generation.",
+            description=(
+                "OpenAI video and audio generation.\n\n"
+                "DEPRECATION NOTICE: OpenAI will stop serving the Sora v2 API in September 2026. "
+                "This node will be removed from ComfyUI at that time."
+            ),
            inputs=[
                IO.Combo.Input(
                    "model",
--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@ -11,7 +11,6 @@ import kornia
 import comfy.utils
 import comfy.model_management
 from comfy_extras.nodes_latent import reshape_latent_to
-import node_helpers
 from comfy_api.latest import ComfyExtension, io
 from nodes import MAX_RESOLUTION

@ -36,8 +35,23 @@ class Blend(io.ComfyNode):

    @classmethod
    def execute(cls, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str) -> io.NodeOutput:
-        image1, image2 = node_helpers.image_alpha_fix(image1, image2)
        image2 = image2.to(image1.device)
+        # Reconcile mismatched channel counts. Downstream nodes (SaveImage,
+        # PreviewImage) ultimately call PIL.Image.fromarray, which rejects
+        # arrays with more than 4 channels, so we cap the output at 4
+        # (RGBA): any image with > 4 channels is truncated, and any image
+        # with fewer channels than the (capped) target is padded with 1.0s
+        # so the extra slot behaves like an opaque alpha channel.
+        if image1.shape[-1] != image2.shape[-1] or image1.shape[-1] > 4 or image2.shape[-1] > 4:
+            target_channels = min(max(image1.shape[-1], image2.shape[-1]), 4)
+            if image1.shape[-1] > target_channels:
+                image1 = image1[..., :target_channels]
+            elif image1.shape[-1] < target_channels:
+                image1 = torch.cat([image1, torch.ones(*image1.shape[:-1], target_channels - image1.shape[-1], device=image1.device, dtype=image1.dtype)], dim=-1)
+            if image2.shape[-1] > target_channels:
+                image2 = image2[..., :target_channels]
+            elif image2.shape[-1] < target_channels:
+                image2 = torch.cat([image2, torch.ones(*image2.shape[:-1], target_channels - image2.shape[-1], device=image2.device, dtype=image2.dtype)], dim=-1)
        if image1.shape != image2.shape:
            image2 = image2.permute(0, 3, 1, 2)
            image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')
--- a/comfy_extras/nodes_sd3.py
+++ b/comfy_extras/nodes_sd3.py
@ -54,7 +54,7 @@ class EmptySD3LatentImage(io.ComfyNode):

    @classmethod
    def execute(cls, width, height, batch_size=1) -> io.NodeOutput:
-        latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
        return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 8})

    generate = execute  # TODO: remove
--- a/openapi.yaml
+++ b/openapi.yaml
--- a/requirements.txt
+++ b/requirements.txt
@ -1,4 +1,4 @@
-comfyui-frontend-package==1.42.14
+comfyui-frontend-package==1.42.15
 comfyui-workflow-templates==0.9.62
 comfyui-embedded-docs==0.4.4
 torch
--- a/tests-unit/comfy_extras_test/image_blend_test.py
+++ b/tests-unit/comfy_extras_test/image_blend_test.py
@ -0,0 +1,151 @@
+import sys
+from unittest.mock import patch, MagicMock
+
+# `comfy.model_management` initializes the GPU at module import time, which
+# fails in CPU-only environments. Stub it out before any `comfy.*` imports
+# load it transitively. We don't use it in these tests.
+sys.modules.setdefault("comfy.model_management", MagicMock())
+
+import torch  # noqa: E402
+
+# Mock nodes module to prevent CUDA initialization during import
+mock_nodes = MagicMock()
+mock_nodes.MAX_RESOLUTION = 16384
+
+# Mock server module for PromptServer
+mock_server = MagicMock()
+
+with patch.dict("sys.modules", {"nodes": mock_nodes, "server": mock_server}):
+    from comfy_extras.nodes_post_processing import Blend  # noqa: E402
+
+
+class TestImageBlend:
+    """Regression tests for the ImageBlend node, especially channel-count handling."""
+
+    def create_test_image(self, batch_size=1, height=64, width=64, channels=3):
+        return torch.rand(batch_size, height, width, channels)
+
+    def test_same_shape_rgb(self):
+        """Baseline: identical RGB inputs produce an RGB output."""
+        image1 = self.create_test_image(channels=3)
+        image2 = self.create_test_image(channels=3)
+        result = Blend.execute(image1, image2, 0.5, "normal")
+        assert result[0].shape == (1, 64, 64, 3)
+
+    def test_rgb_plus_rgba(self):
+        """RGB image1 + RGBA image2 should pad image1 to 4 channels."""
+        image1 = self.create_test_image(channels=3)
+        image2 = self.create_test_image(channels=4)
+        result = Blend.execute(image1, image2, 0.5, "normal")
+        assert result[0].shape == (1, 64, 64, 4)
+
+    def test_rgba_plus_rgb(self):
+        """RGBA image1 + RGB image2 should pad image2 to 4 channels."""
+        image1 = self.create_test_image(channels=4)
+        image2 = self.create_test_image(channels=3)
+        result = Blend.execute(image1, image2, 0.5, "normal")
+        assert result[0].shape == (1, 64, 64, 4)
+
+    def test_channel_gap_larger_than_one(self):
+        """Channel-count gap > 1 (e.g. 3 vs 5) should not raise.
+
+        This is the exact runtime error reported in CORE-103:
+        'The size of tensor a (5) must match the size of tensor b (3) at
+        non-singleton dimension 3'.
+
+        The output is capped at 4 channels (RGBA) because downstream
+        SaveImage/PreviewImage rely on PIL.Image.fromarray, which rejects
+        arrays with more than 4 channels. Without this cap, the failure
+        would just shift from blend-time to save-time.
+        """
+        image1 = self.create_test_image(channels=3)
+        image2 = self.create_test_image(channels=5)
+        result = Blend.execute(image1, image2, 0.5, "multiply")
+        assert result[0].shape == (1, 64, 64, 4)
+
+    def test_output_capped_at_four_channels(self):
+        """Both inputs having > 4 channels should still produce a 4-channel
+        output. PIL.Image.fromarray (used by SaveImage/PreviewImage)
+        rejects arrays with more than 4 channels."""
+        image1 = self.create_test_image(channels=6)
+        image2 = self.create_test_image(channels=5)
+        result = Blend.execute(image1, image2, 0.5, "normal")
+        assert result[0].shape == (1, 64, 64, 4)
+
+    def test_save_compatible_output_passes_through_pil(self):
+        """The blended result must be convertible by PIL.Image.fromarray,
+        which is what SaveImage/PreviewImage do downstream. Catches the
+        case where a >4-channel output would silently break save/preview."""
+        from PIL import Image
+        import numpy as np
+
+        image1 = self.create_test_image(channels=3)
+        image2 = self.create_test_image(channels=5)
+        result = Blend.execute(image1, image2, 0.5, "normal")
+        # Mirror SaveImage's exact conversion (nodes.py:1662). PIL accepts
+        # 1/2/3/4-channel arrays (L/LA/RGB/RGBA); a >4-channel output would
+        # raise "TypeError: Cannot handle this data type" here.
+        arr = np.clip(255.0 * result[0][0].cpu().numpy(), 0, 255).astype(np.uint8)
+        Image.fromarray(arr)
+
+    def test_different_size_and_channels(self):
+        """Different spatial size AND different channel counts should both be reconciled."""
+        image1 = self.create_test_image(height=64, width=64, channels=3)
+        image2 = self.create_test_image(height=32, width=32, channels=4)
+        result = Blend.execute(image1, image2, 0.5, "screen")
+        assert result[0].shape == (1, 64, 64, 4)
+
+    def test_all_blend_modes_with_channel_mismatch(self):
+        """Every blend mode should work with mismatched channel counts."""
+        image1 = self.create_test_image(channels=3)
+        image2 = self.create_test_image(channels=4)
+        for mode in [
+            "normal",
+            "multiply",
+            "screen",
+            "overlay",
+            "soft_light",
+            "difference",
+        ]:
+            result = Blend.execute(image1, image2, 0.5, mode)
+            assert result[0].shape == (1, 64, 64, 4), (
+                f"blend mode {mode} produced wrong shape"
+            )
+
+    def test_output_clamped(self):
+        """Output values should be clamped to [0, 1] even when intermediate
+        results would go negative.
+
+        With `difference` mode, image1=0 and image2=1, the unclamped blend
+        produces image1*(1-bf) + (image1-image2)*bf = -bf, which is negative.
+        The output therefore exercises the clamp branch.
+        """
+        image1 = torch.zeros(1, 8, 8, 3)
+        image2 = torch.ones(1, 8, 8, 3)
+        result = Blend.execute(image1, image2, 0.5, "difference")
+        assert result[0].min() >= 0.0
+        assert result[0].max() <= 1.0
+        # All pixels would be -0.5 without the clamp; verify they were clipped to 0.
+        assert torch.all(result[0] == 0.0)
+
+    def test_padding_value_is_one(self):
+        """Verify the padded channel(s) are filled with 1.0, not 0.0 or some
+        other value. This is the semantic guarantee of the channel-alignment
+        logic (it acts like an opaque alpha channel).
+
+        Setup: image1 has 3 channels of zeros, image2 has 4 channels of ones.
+        After padding, image1 becomes [0, 0, 0, X] where X is the pad value.
+        With `multiply` blend_mode and blend_factor=1.0:
+            output = image1 * (1 - 1) + (image1 * image2) * 1
+                   = image1 * image2
+                   = [0, 0, 0, X * 1] = [0, 0, 0, X]
+        So output channel 4 reveals the pad value used for image1.
+        """
+        image1 = torch.zeros(1, 4, 4, 3)
+        image2 = torch.ones(1, 4, 4, 4)
+        result = Blend.execute(image1, image2, 1.0, "multiply")
+        assert result[0].shape == (1, 4, 4, 4)
+        # First three channels: 0 * 1 = 0
+        assert torch.all(result[0][..., :3] == 0.0)
+        # Fourth channel: pad_value * 1 = pad_value -> must be 1.0
+        assert torch.all(result[0][..., 3] == 1.0)
--- a/tests-unit/comfy_extras_test/image_blend_workflow_test.py
+++ b/tests-unit/comfy_extras_test/image_blend_workflow_test.py
@ -0,0 +1,56 @@
+import json
+import pathlib
+
+
+WORKFLOW_PATH = (
+    pathlib.Path(__file__).resolve().parents[2]
+    / "tests"
+    / "inference"
+    / "graphs"
+    / "image_blend_channel_mismatch.json"
+)
+
+
+def test_workflow_loads():
+    with open(WORKFLOW_PATH) as f:
+        graph = json.load(f)
+    assert isinstance(graph, dict) and graph, "workflow JSON is empty"
+
+
+def test_workflow_uses_expected_node_types():
+    """The workflow uses a fixed, minimal set of nodes. If any are renamed
+    or removed upstream, this test fails fast instead of letting the demo
+    bitrot silently."""
+    expected = {
+        "EmptyImage",
+        "SolidMask",
+        "JoinImageWithAlpha",
+        "ImageBlend",
+        "SaveImage",
+    }
+    with open(WORKFLOW_PATH) as f:
+        graph = json.load(f)
+    actual = {node["class_type"] for node in graph.values()}
+    assert expected.issubset(actual), (
+        f"workflow is missing required node types: {expected - actual}"
+    )
+
+
+def test_workflow_exercises_imageblend_with_mismatched_channels():
+    """Sanity-check that the workflow actually wires an RGB output and an
+    RGBA output into ImageBlend (the CORE-103 case). If someone edits the
+    JSON and accidentally breaks this guarantee, the demo loses its point."""
+    with open(WORKFLOW_PATH) as f:
+        graph = json.load(f)
+    blend_nodes = [n for n in graph.values() if n["class_type"] == "ImageBlend"]
+    assert len(blend_nodes) == 1, "expected exactly one ImageBlend node"
+    blend = blend_nodes[0]
+    src1_id, _ = blend["inputs"]["image1"]
+    src2_id, _ = blend["inputs"]["image2"]
+    types = {graph[src1_id]["class_type"], graph[src2_id]["class_type"]}
+    assert "JoinImageWithAlpha" in types, (
+        "workflow no longer feeds an RGBA image into ImageBlend"
+    )
+    assert "EmptyImage" in types, (
+        "workflow no longer feeds a plain RGB image into ImageBlend"
+    )
--- a/tests/inference/graphs/image_blend_channel_mismatch.json
+++ b/tests/inference/graphs/image_blend_channel_mismatch.json
@ -0,0 +1,69 @@
+{
+    "1": {
+        "inputs": {
+            "width": 256,
+            "height": 256,
+            "batch_size": 1,
+            "color": 16711680
+        },
+        "class_type": "EmptyImage",
+        "_meta": {
+            "title": "RGB image (3 channels, red)"
+        }
+    },
+    "2": {
+        "inputs": {
+            "width": 256,
+            "height": 256,
+            "batch_size": 1,
+            "color": 255
+        },
+        "class_type": "EmptyImage",
+        "_meta": {
+            "title": "Base image for RGBA (blue)"
+        }
+    },
+    "3": {
+        "inputs": {
+            "value": 0.5,
+            "width": 256,
+            "height": 256
+        },
+        "class_type": "SolidMask",
+        "_meta": {
+            "title": "Alpha mask (0.5)"
+        }
+    },
+    "4": {
+        "inputs": {
+            "image": ["2", 0],
+            "alpha": ["3", 0]
+        },
+        "class_type": "JoinImageWithAlpha",
+        "_meta": {
+            "title": "RGBA image (4 channels)"
+        }
+    },
+    "5": {
+        "inputs": {
+            "image1": ["1", 0],
+            "image2": ["4", 0],
+            "blend_factor": 0.5,
+            "blend_mode": "normal"
+        },
+        "class_type": "ImageBlend",
+        "_meta": {
+            "title": "Blend RGB (3ch) + RGBA (4ch) - exercises CORE-103 fix"
+        }
+    },
+    "6": {
+        "inputs": {
+            "filename_prefix": "image_blend_channel_test",
+            "images": ["5", 0]
+        },
+        "class_type": "SaveImage",
+        "_meta": {
+            "title": "Save blended output (will be RGBA)"
+        }
+    }
+}
Author	SHA1	Message	Date
Glary-Bot	7c0c70b608	Correct PIL contract in ImageBlend channel-cap rationale PIL.Image.fromarray accepts 2-channel (LA mode) arrays as well, not just 1/3/4-channel. Reword the inline comments and test docstrings to say 'rejects > 4-channel arrays', which is the actual constraint driving the cap. Also drop a too-narrow 'mode in (L, RGB, RGBA)' assertion in test_save_compatible_output_passes_through_pil so a future 2-channel result would not be flagged as a failure.	2026-04-27 07:58:20 +00:00
Glary-Bot	ae88cd1966	Cap ImageBlend channel-mismatch output at 4 channels (RGBA) Address review feedback: the previous fix allowed ImageBlend to return tensors with > 4 channels (e.g. blending a 3-channel and a 5-channel image produced a 5-channel tensor). This shifted the original failure from blend-time to save/preview-time, because SaveImage and PreviewImage both call PIL.Image.fromarray, which only supports 1/3/4-channel arrays. Fix: - In Blend.execute, the alignment target is now min(max(c1, c2), 4): any image with more than 4 channels is truncated, any image with fewer is padded with 1.0s up to the (capped) target. This makes the RGB/RGBA case work and also makes the >4-channel case work end-to-end rather than just deferring its failure. - Update the regression test that previously codified the wrong 5-channel-output behavior to assert the correct 4-channel cap. - Add test_output_capped_at_four_channels (both inputs > 4 channels). - Add test_save_compatible_output_passes_through_pil that mirrors SaveImage's exact PIL.Image.fromarray conversion to catch regressions in the save/preview path. - Add a small workflow-validation test (image_blend_workflow_test.py) that loads tests/inference/graphs/image_blend_channel_mismatch.json and verifies its node types and wiring, so the demo workflow can't silently bitrot. Verified end-to-end against a local ComfyUI server: the workflow runs, output is RGBA, downstream SaveImage succeeds.	2026-04-27 07:18:16 +00:00
Glary-Bot	2b731a99fd	Add API workflow JSON demonstrating ImageBlend channel-count fix Adds a self-contained API workflow at tests/inference/graphs/image_blend_channel_mismatch.json that exercises the previously-broken RGB + RGBA blend case end-to-end: EmptyImage (RGB, red) -> image1 EmptyImage (RGB, blue) + SolidMask (0.5) -> JoinImageWithAlpha (RGBA) -> image2 -> ImageBlend (3ch + 4ch) -> SaveImage The workflow uses no model checkpoints and runs in seconds on CPU. With the fix it produces a 4-channel RGBA output (alpha preserved); the previous behavior would have silently dropped the alpha channel via node_helpers.image_alpha_fix. Verified end-to-end against a local ComfyUI instance: workflow executes successfully, output PNG is RGBA, center pixel is (127, 0, 127, 191) - red and blue blended at 0.5 with alpha 0.5*1.5=0.75 (191/255).	2026-04-27 07:08:14 +00:00
Glary-Bot	c390b88d8c	Strengthen ImageBlend regression tests with deterministic assertions - test_output_clamped now uses inputs that would produce -0.5 without clamping, so it actually exercises the torch.clamp(..., 0, 1) call. - test_padding_value_is_one verifies that the channel-alignment logic pads with 1.0 specifically (not 0.0 or some other value), which is the semantic guarantee of treating the extra channel as an opaque alpha.	2026-04-27 06:36:44 +00:00
Glary-Bot	618f1026fc	Address review feedback for ImageBlend channel-count fix - Add regression tests covering RGB+RGBA, RGBA+RGB, channel gap > 1 (the exact CORE-103 error case), all blend modes with mismatch, and output value clamping. - Soften the inline comment to reflect that channel padding is well- defined for alpha-like extra channels rather than claiming support for arbitrary channel layouts.	2026-04-27 06:31:18 +00:00
Glary-Bot	6a1284e20b	Fix ImageBlend node to handle mismatched channel counts Replace the limited node_helpers.image_alpha_fix call (which only handles RGB<->RGBA differences by padding by exactly one channel) with the same generalized channel-padding logic used by the ImageStitch node. This allows ImageBlend to work with any combination of channel counts (e.g. 3 vs 4, 3 vs 5, 4 vs 3, etc.) by padding the image with fewer channels using 1.0s up to the larger channel count. Behavior between ImageBlend and ImageStitch is now consistent. Fixes CORE-103.	2026-04-27 06:21:33 +00:00
comfyanonymous	115f418b64	Make EmptySD3LatentImage node use intermediate dtype. (#13577 )	2026-04-26 23:23:57 -04:00
Daxiong (Lin)	7385eb2800	Add new ComfyUI blueprints and fix subgraph naming (#13371 ) * Remove local tag from subgraph name * New Subgraph blueprints * Remove duplicate blueprint * Update Subgraph size * Update subgraph * Update Blueprint * Remove local tag from subgraph name * New Subgraph blueprints * Remove duplicate blueprint * Update Subgraph size * Update subgraph * Update Blueprint * Update LTX 2.0 Pose to Video * Fix crop blueprint split coverage Made-with: Cursor * Clean up image edit blueprint metadata Made-with: Cursor * Update subgraph blueprints --------- Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>	2026-04-26 22:59:16 +08:00
comfyanonymous	df22bcd5e1	Support loading the alpha channel of videos. (#13564 ) Not exposed in nodes yet.	2026-04-25 21:02:58 -04:00
Comfy Org PR Bot	5e3f15a830	Bump comfyui-frontend-package to 1.42.15 (#13556 )	2026-04-24 17:21:39 -07:00
comfyanonymous	4304c15e9b	Properly load higher bit depth videos. (#13542 )	2026-04-24 16:46:10 -04:00
Alexander Piskun	7636599389	chore(api-nodes): add upcoming-deprecation notice to Sora nodes (#13549 )	2026-04-24 06:54:10 -07:00
Matt Miller	443074eee9	Add OpenAPI 3.1 specification for ComfyUI API (#13397 ) * Add OpenAPI 3.1 specification for ComfyUI API Adds a comprehensive OpenAPI 3.1 spec documenting all HTTP endpoints exposed by ComfyUI's server, including prompt execution, queue management, file uploads, userdata, settings, system stats, object info, assets, and internal routes. The spec was validated against the source code with adversarial review from multiple models, and passes Spectral linting with zero errors. Also removes openapi.yaml from .gitignore so the spec is tracked. * Mark /api/history endpoints as deprecated Address Jacob's review feedback on PR #13397 by explicitly marking the three /api/history operations as deprecated in the OpenAPI spec: * GET /api/history -> superseded by GET /api/jobs * POST /api/history -> superseded by /api/jobs management * GET /api/history/{prompt_id} -> superseded by GET /api/jobs/{job_id} Each operation gains deprecated: true plus a description that names the replacement. A formal sunset timeline (RFC 8594 Deprecation and RFC 8553 Sunset headers, minimum-runway policy) is being defined separately and will be applied as a follow-up. * Address Spectral lint findings in openapi.yaml - Add operation descriptions to 52 endpoints (prompt, queue, upload, view, models, userdata, settings, assets, internal, etc.) - Add schema descriptions to 22 component schemas - Add parameter descriptions to 8 path parameters that were missing them - Remove 6 unused component schemas: TaskOutput, EmbeddingsResponse, ExtensionsResponse, LogRawResponse, UserInfo, UserDataFullInfo No wire/shape changes. Reduces Spectral findings from 92 to 4. The remaining 4 are real issues (WebSocket 101 on /ws, loose error schema, and two snake_case warnings on real wire field names) and are worth addressing separately. * fix(openapi): address jtreminio oneOf review on /api/userdata Restructure the UserData response schemas to address the review feedback on the `oneOf` without a discriminator, and fix two accuracy bugs found while doing it. Changes - GET /api/userdata response: extract the inline `oneOf` to a named schema (`ListUserdataResponse`) and add the missing third variant returned when `split=true` and `full_info=false` (array of `[relative_path, ...path_components]`). Previously only two of the three actual server response shapes were described. - UserDataResponse (POST endpoints): correct the description — this schema is a single item, not a list — and point at the canonical `GetUserDataResponseFullFile` schema instead of the duplicate `UserDataResponseFull`. Also removes the malformed blank line in `UserDataResponseShort`. - Delete the now-unused `UserDataResponseFull` and `UserDataResponseShort` schemas (replaced by reuse of `GetUserDataResponseFullFile` and an inline string variant). - Add an `x-variant-selector` vendor extension to both `oneOf` sites documenting which query-parameter combination selects which branch, since a true OpenAPI `discriminator` is not applicable (the variants are type-disjoint and the selector lives in the request, not the response body). This keeps the shapes the server actually emits (no wire-breaking change) while making the selection rule explicit for SDK generators and readers. --------- Co-authored-by: guill <jacob.e.segal@gmail.com>	2026-04-23 21:00:25 -07:00
Terry Jia	2e0503780d	range type (#13322 ) Co-authored-by: guill <jacob.e.segal@gmail.com>	2026-04-23 20:51:34 -07:00