ComfyUI v0.26.2

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

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/latest/_io.py

@@ -891,14 +891,6 @@ class Tracks(ComfyTypeIO):
         track_visibility: torch.Tensor
     Type = TrackDict
 
-@comfytype(io_type="DICT")
-class Dict(ComfyTypeIO):
-    Type = dict
-
-@comfytype(io_type="ARRAY")
-class Array(ComfyTypeIO):
-    Type = list
-
 @comfytype(io_type="COMFY_MULTITYPED_V3")
 class MultiType:
     Type = Any
@@ -1287,19 +1279,6 @@ class Color(ComfyTypeIO):
       def as_dict(self):
           return super().as_dict()
 
-
-@comfytype(io_type="COLORS")
-class Colors(ComfyTypeIO):
-    Type = list[Color.Type]
-
-    class Input(WidgetInput):
-        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
-                     socketless: bool=True, default: list[str]=None, advanced: bool=None):
-            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
-            if default is None:
-                self.default = []
-
-
 @comfytype(io_type="BOUNDING_BOX")
 class BoundingBox(ComfyTypeIO):
     class BoundingBoxDict(TypedDict):
@@ -1347,20 +1326,6 @@ class Curve(ComfyTypeIO):
             return d
 
 
-@comfytype(io_type="BOUNDING_BOXES")
-class BoundingBoxes(ComfyTypeIO):
-    class BoundingBoxWithMetadata(BoundingBox.BoundingBoxDict):
-        metadata: dict
-    Type = list[BoundingBoxWithMetadata]
-
-    class Input(WidgetInput):
-        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
-                     socketless: bool=True, default: list[dict]=None, advanced: bool=None):
-            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
-            if default is None:
-                self.default = []
-
-
 @comfytype(io_type="HISTOGRAM")
 class Histogram(ComfyTypeIO):
     """A histogram represented as a list of bin counts."""
@@ -2411,8 +2376,6 @@ __all__ = [
     "AnyType",
     "MultiType",
     "Tracks",
-    "Dict",
-    "Array",
     "Color",
     # Dynamic Types
     "MatchType",
@@ -2431,8 +2394,6 @@ __all__ = [
     "PriceBadgeDepends",
     "PriceBadge",
     "BoundingBox",
-    "BoundingBoxes",
-    "Colors",
     "Curve",
     "Histogram",
     "Range",

comfy_extras/color_util.py

@@ -1,23 +0,0 @@
-def hex_to_rgb(value: str) -> tuple[int, int, int]:
-    h = value.lstrip("#")
-    if len(h) != 6:
-        return (255, 255, 255)
-    try:
-        return (int(h[0:2], 16), int(h[2:4], 16), int(h[4:6], 16))
-    except ValueError:
-        return (255, 255, 255)
-
-
-def readable_color(rgb: tuple[int, int, int]) -> tuple[int, int, int]:
-    r, g, b = rgb
-    lum = 0.299 * r + 0.587 * g + 0.114 * b
-    if lum >= 130:
-        return (r, g, b)
-    t = (130 - lum) / (255 - lum)
-    return (round(r + (255 - r) * t), round(g + (255 - g) * t), round(b + (255 - b) * t))
-
-
-def normalize_palette(colors) -> list[str]:
-    if isinstance(colors, dict):
-        colors = colors.values()
-    return [c.upper() for c in colors if isinstance(c, str) and c]

comfy_extras/nodes_bounding_boxes.py

@@ -1,253 +0,0 @@
-import numpy as np
-import torch
-from PIL import Image, ImageDraw, ImageEnhance, ImageFont
-from typing_extensions import override
-
-from comfy_api.latest import ComfyExtension, io
-from comfy_extras.color_util import hex_to_rgb, normalize_palette, readable_color
-
-_PREVIEW_LONG_EDGE = 1024
-_PREVIEW_DIM = 0.25
-
-
-def pixels_to_fractions(box: dict, width: int, height: int) -> dict:
-    w = width or 1
-    h = height or 1
-    return {
-        "x": box.get("x", 0) / w,
-        "y": box.get("y", 0) / h,
-        "w": box.get("width", 0) / w,
-        "h": box.get("height", 0) / h,
-    }
-
-
-def fractions_to_pixels(box: dict, width: int, height: int) -> dict:
-    x, y = box.get("x", 0.0), box.get("y", 0.0)
-    w, h = box.get("w", 0.0), box.get("h", 0.0)
-    if w < 0:
-        x, w = x + w, -w
-    if h < 0:
-        y, h = y + h, -h
-    return {
-        "x": round(x * width),
-        "y": round(y * height),
-        "width": round(w * width),
-        "height": round(h * height),
-    }
-
-
-def fractions_to_bbox_frame(boxes: list, width: int, height: int) -> list:
-    pixels = [
-        fractions_to_pixels(box, width, height)
-        for box in boxes
-        if isinstance(box, dict)
-    ]
-    return [pixels] if pixels else []
-
-
-def _font(size: int):
-    try:
-        return ImageFont.load_default(size)
-    except Exception:
-        return ImageFont.load_default()
-
-
-def _wrap(draw, text: str, font, max_w: float) -> list[str]:
-    lines = []
-    for para in text.split("\n"):
-        line = ""
-        for word in para.split():
-            test = word if not line else line + " " + word
-            if line and draw.textlength(test, font=font) > max_w:
-                lines.append(line)
-                line = word
-            else:
-                line = test
-        lines.append(line)
-    return lines
-
-
-def _bg_from_image(image) -> Image.Image | None:
-    if image is None:
-        return None
-    try:
-        arr = (image[0].detach().cpu().numpy() * 255).clip(0, 255).astype(np.uint8)
-        return Image.fromarray(arr)
-    except Exception:
-        return None
-
-
-def render_preview(regions, width, height, bg=None):
-    if bg is not None:
-        iw, ih = bg.size
-        long_edge = max(iw, ih) or 1
-        scale = min(1.0, _PREVIEW_LONG_EDGE / long_edge)
-        rw, rh = max(1, round(iw * scale)), max(1, round(ih * scale))
-        base = bg.convert("RGB").resize((rw, rh), Image.LANCZOS)
-        base = ImageEnhance.Brightness(base).enhance(_PREVIEW_DIM)
-        img = base.convert("RGBA")
-    else:
-        long_edge = max(width, height) or 1
-        scale = min(1.0, _PREVIEW_LONG_EDGE / long_edge)
-        rw, rh = max(1, round(width * scale)), max(1, round(height * scale))
-        grey = round(_PREVIEW_DIM * 128)
-        img = Image.new("RGBA", (rw, rh), (grey, grey, grey, 255))
-
-    overlay = Image.new("RGBA", (rw, rh), (0, 0, 0, 0))
-    draw = ImageDraw.Draw(overlay)
-    fs = max(10, round(rh / 64))
-    font = _font(fs)
-    tag_font = _font(max(9, fs - 2))
-    line_h = fs + 2
-
-    for i, region in enumerate(regions):
-        if not isinstance(region, dict):
-            continue
-        palette = [c for c in (region.get("palette") or []) if c]
-        r, g, b = hex_to_rgb(palette[0]) if palette else (140, 140, 140)
-        x1 = max(0, min(rw, round(region.get("x", 0) * rw)))
-        y1 = max(0, min(rh, round(region.get("y", 0) * rh)))
-        x2 = max(0, min(rw, round((region.get("x", 0) + region.get("w", 0)) * rw)))
-        y2 = max(0, min(rh, round((region.get("y", 0) + region.get("h", 0)) * rh)))
-        if x2 < x1:
-            x1, x2 = x2, x1
-        if y2 < y1:
-            y1, y2 = y2, y1
-
-        draw.rectangle([x1, y1, x2, y2], outline=(r, g, b, 255), width=2)
-
-        swatches = palette[:5]
-        if swatches and (x2 - x1) > 2:
-            sh = max(5, fs // 2)
-            seg = (x2 - x1) / len(swatches)
-            for p, hexc in enumerate(swatches):
-                sx = x1 + round(p * seg)
-                draw.rectangle([sx, y1, x1 + round((p + 1) * seg), y1 + sh], fill=hex_to_rgb(hexc))
-
-        etype = "text" if region.get("type") == "text" else "obj"
-        tag = str(i + 1).zfill(2)
-        tw = draw.textlength(tag, font=tag_font)
-        draw.rectangle([x1, y1, x1 + tw + 6, y1 + fs + 2], fill=(r, g, b, 255))
-        tag_fill = (0, 0, 0, 255) if (0.299 * r + 0.587 * g + 0.114 * b) > 140 else (255, 255, 255, 255)
-        draw.text((x1 + 3, y1 + 1), tag, fill=tag_fill, font=tag_font)
-
-        body = region.get("desc", "") or ""
-        if etype == "text" and region.get("text"):
-            body = '"%s"%s' % (region["text"], " — " + body if body else "")
-        if body and (x2 - x1) > 8:
-            ty = y1 + fs + 5
-            for line in _wrap(draw, body, font, x2 - x1 - 8):
-                if ty > y2:
-                    break
-                draw.text((x1 + 4, ty), line, fill=readable_color((r, g, b)) + (255,), font=font)
-                ty += line_h
-
-    composed = Image.alpha_composite(img, overlay).convert("RGB")
-    arr = np.asarray(composed, dtype=np.float32) / 255.0
-    return torch.from_numpy(arr).unsqueeze(0)
-
-
-def boxes_to_regions(boxes, width: int, height: int) -> list:
-    regions: list = []
-    if not isinstance(boxes, list):
-        return regions
-    for box in boxes:
-        if not isinstance(box, dict):
-            continue
-        meta = box.get("metadata")
-        meta = meta if isinstance(meta, dict) else {}
-        regions.append({
-            **pixels_to_fractions(box, width, height),
-            "type": meta.get("type", "obj"),
-            "text": meta.get("text", ""),
-            "desc": meta.get("desc", ""),
-            "palette": meta.get("palette", []),
-        })
-    return regions
-
-
-def _norm_bbox(region: dict) -> list[int]:
-    def grid(value: float) -> int:
-        return max(0, min(1000, round(value * 1000)))
-
-    x, y = region.get("x", 0.0), region.get("y", 0.0)
-    w, h = region.get("w", 0.0), region.get("h", 0.0)
-    ymin, xmin, ymax, xmax = grid(y), grid(x), grid(y + h), grid(x + w)
-    if ymin > ymax:
-        ymin, ymax = ymax, ymin
-    if xmin > xmax:
-        xmin, xmax = xmax, xmin
-    return [ymin, xmin, ymax, xmax]
-
-
-def build_elements(regions: list) -> list:
-    elements = []
-    for region in regions:
-        if not isinstance(region, dict):
-            continue
-        etype = "text" if region.get("type") == "text" else "obj"
-        element = {"type": etype}
-        element["bbox"] = _norm_bbox(region)
-        if etype == "text":
-            element["text"] = region.get("text", "")
-        element["desc"] = region.get("desc", "")
-        palette = normalize_palette(region.get("palette", []))
-        if palette:
-            element["color_palette"] = palette[:5]
-        elements.append(element)
-    return elements
-
-
-class CreateBoundingBoxes(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        editor_state = io.BoundingBoxes.Input(
-            "editor_state",
-            socketless=False,
-            tooltip="Draw bounding boxes and set each box type, text, description, color palette. Start with background element first and foreground last.",
-        )
-        return io.Schema(
-            node_id="CreateBoundingBoxes",
-            display_name="Create Bounding Boxes",
-            category="utilities",
-            description="Draw bounding boxes in a canvas. Outputs Ideogram prompt elements, pixel-space bounding boxes, and a preview image.",
-            inputs=[
-                io.Image.Input(
-                    "background",
-                    optional=True,
-                    tooltip="Optional image used as background in the canvas and preview.",
-                ),
-                io.Int.Input("width", default=1024, min=64, max=16384, step=16,
-                             tooltip="Width of the canvas and the pixel grid for the bounding boxes."),
-                io.Int.Input("height", default=1024, min=64, max=16384, step=16,
-                             tooltip="Height of the canvas and the pixel grid for the bounding boxes."),
-                editor_state,
-            ],
-            outputs=[
-                io.Image.Output(display_name="preview"),
-                io.BoundingBox.Output(display_name="bboxes"),
-                io.Array.Output(display_name="elements"),
-            ],
-            is_experimental=True,
-        )
-
-    @classmethod
-    def execute(cls, width, height, editor_state=None, background=None) -> io.NodeOutput:
-        regions = boxes_to_regions(editor_state, width, height)
-        preview = render_preview(regions, width, height, _bg_from_image(background))
-        return io.NodeOutput(
-            preview,
-            fractions_to_bbox_frame(regions, width, height),
-            build_elements(regions),
-            ui={"dims": [width, height]},
-        )
-
-
-class BoundingBoxesExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-        return [CreateBoundingBoxes]
-
-
-async def comfy_entrypoint() -> BoundingBoxesExtension:
-    return BoundingBoxesExtension()

comfy_extras/nodes_color.py

@@ -1,6 +1,5 @@
 from typing_extensions import override
 from comfy_api.latest import ComfyExtension, io
-from comfy_extras.color_util import hex_to_rgb
 
 
 class ColorToRGBInt(io.ComfyNode):
@@ -25,11 +24,9 @@ class ColorToRGBInt(io.ComfyNode):
         # expect format #RRGGBB
         if len(color) != 7 or color[0] != "#":
             raise ValueError("Color must be in format #RRGGBB")
-        try:
-            int(color[1:], 16)
-        except ValueError:
-            raise ValueError("Color must be in format #RRGGBB") from None
-        r, g, b = hex_to_rgb(color)
+        r = int(color[1:3], 16)
+        g = int(color[3:5], 16)
+        b = int(color[5:7], 16)
 
         rgb_int = r * 256 * 256 + g * 256 + b
         return io.NodeOutput(rgb_int, color)

comfy_extras/nodes_json_prompt.py

@@ -1,77 +0,0 @@
-from typing_extensions import override
-
-from comfy_api.latest import ComfyExtension, io
-from comfy_extras.color_util import normalize_palette
-
-
-class BuildJsonPromptIdeogram(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        color_palette = io.Colors.Input(
-            "color_palette",
-            socketless=False,
-            tooltip="Hex color codes that steer the image's dominant colors. Up to 16 entries.",
-        )
-        return io.Schema(
-            node_id="BuildJsonPromptIdeogram",
-            display_name="Build JSON Prompt (Ideogram)",
-            category="text",
-            description="Build a JSON prompt for the Ideogram 4 model.",
-            inputs=[
-                io.Array.Input("element", tooltip="Prompt elements from the node Create Bounding Boxes."),
-                io.String.Input("high_level_description", multiline=True, default="",
-                                tooltip="Optional description of the image in one or two sentences. Strongly recommended."),
-                io.String.Input("background", multiline=True, default="",
-                                tooltip="Mandatory description of the image background or environment."),
-                io.DynamicCombo.Input("style", options=[
-                    io.DynamicCombo.Option("none", []),
-                    io.DynamicCombo.Option("photo", [io.String.Input("photo", default="", tooltip="Camera or lens details for photographic outputs (e.g. 35mm, f/1.4, bokeh).")]),
-                    io.DynamicCombo.Option("art_style", [io.String.Input("art_style", default="", tooltip="Art style description (e.g. flat vector illustration, bold outlines).")]),
-                ]),
-                io.String.Input("aesthetics", default="", tooltip="Mandatory aesthetic keywords (e.g. moody, cinematic, desaturated)."),
-                io.String.Input("lighting", default="", tooltip="Mandatory lighting description (e.g. golden hour, rim light, dramatic shadows)."),
-                io.String.Input("medium", default="", tooltip="Mandatory medium type (e.g. photograph, illustration, 3d_render, painting, graphic_design). When style = photo, set to photograph."),
-                color_palette,
-            ],
-            outputs=[io.Dict.Output(display_name="prompt")],
-            is_experimental=True,
-        )
-
-    @classmethod
-    def execute(cls, element, style, high_level_description="", background="",
-                aesthetics="", lighting="", medium="", color_palette=None) -> io.NodeOutput:
-        elements = element if isinstance(element, list) else []
-        kind = style.get("style", "none") if isinstance(style, dict) else "none"
-        photo = style.get("photo", "") if isinstance(style, dict) else ""
-        art_style = style.get("art_style", "") if isinstance(style, dict) else ""
-        palette = normalize_palette(color_palette or [])
-
-        caption: dict = {}
-        if high_level_description.strip():
-            caption["high_level_description"] = high_level_description
-        if kind != "none":
-            style_desc: dict = {"aesthetics": aesthetics, "lighting": lighting}
-            if kind == "photo":
-                style_desc["photo"] = photo
-                style_desc["medium"] = medium
-            else:
-                style_desc["medium"] = medium
-                style_desc["art_style"] = art_style
-            if palette:
-                style_desc["color_palette"] = palette
-            caption["style_description"] = style_desc
-        caption["compositional_deconstruction"] = {
-            "background": background,
-            "elements": elements,
-        }
-        return io.NodeOutput(caption)
-
-
-class JsonPromptExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-        return [BuildJsonPromptIdeogram]
-
-
-async def comfy_entrypoint() -> JsonPromptExtension:
-    return JsonPromptExtension()

comfy_extras/nodes_model_merging_model_specific.py

@@ -337,36 +337,6 @@ class ModelMergeQwenImage(comfy_extras.nodes_model_merging.ModelMergeBlocks):
 
         return {"required": arg_dict}
 
-class ModelMergeKrea2(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "model/merging/model specific"
-
-    @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
-
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
-
-        arg_dict["first."] = argument
-        arg_dict["tmlp."] = argument
-        arg_dict["txtmlp."] = argument
-        arg_dict["tproj."] = argument
-
-        for i in range(2):
-            arg_dict["txtfusion.layerwise_blocks.{}.".format(i)] = argument
-
-        arg_dict["txtfusion.projector."] = argument
-
-        for i in range(2):
-            arg_dict["txtfusion.refiner_blocks.{}.".format(i)] = argument
-
-        for i in range(28):
-            arg_dict["blocks.{}.".format(i)] = argument
-
-        arg_dict["last."] = argument
-
-        return {"required": arg_dict}
-
 NODE_CLASS_MAPPINGS = {
     "ModelMergeSD1": ModelMergeSD1,
     "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks
@@ -383,5 +353,4 @@ NODE_CLASS_MAPPINGS = {
     "ModelMergeCosmosPredict2_2B": ModelMergeCosmosPredict2_2B,
     "ModelMergeCosmosPredict2_14B": ModelMergeCosmosPredict2_14B,
     "ModelMergeQwenImage": ModelMergeQwenImage,
-    "ModelMergeKrea2": ModelMergeKrea2,
 }

comfy_extras/nodes_seed.py

@@ -1,33 +0,0 @@
-import sys
-from typing_extensions import override
-
-from comfy_api.latest import ComfyExtension, io
-
-
-class SeedNode(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="SeedNode",
-            display_name="Seed",
-            search_aliases=["seed", "random"],
-            category="utilities",
-            inputs=[
-                io.Int.Input("seed", min=0, max=sys.maxsize, control_after_generate=io.ControlAfterGenerate.fixed),
-            ],
-            outputs=[io.Int.Output(display_name="seed")],
-        )
-
-    @classmethod
-    def execute(cls, seed: int) -> io.NodeOutput:
-        return io.NodeOutput(seed)
-
-
-class SeedExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-        return [SeedNode]
-
-
-async def comfy_entrypoint() -> SeedExtension:
-    return SeedExtension()

comfy_extras/nodes_string.py

@@ -440,57 +440,6 @@ class JsonExtractString(io.ComfyNode):
         except (json.JSONDecodeError, TypeError):
             return io.NodeOutput("")
 
-
-def _dump_json(value, indent):
-    return json.dumps(value, ensure_ascii=False, indent=indent or None)
-
-
-class ConvertDictionaryToString(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="ConvertDictionaryToString",
-            display_name="Convert Dictionary to String",
-            category="text",
-            search_aliases=["json", "dict to json", "stringify", "serialize", "dict to string"],
-            inputs=[
-                io.Dict.Input("dictionary"),
-                io.Int.Input("indent", default=2, min=0, max=8,
-                             tooltip="Spaces per indent level. 0 produces compact single-line string."),
-            ],
-            outputs=[
-                io.String.Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, dictionary, indent=2):
-        return io.NodeOutput(_dump_json(dictionary, indent))
-
-
-class ConvertArrayToString(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="ConvertArrayToString",
-            display_name="Convert Array to String",
-            category="text",
-            search_aliases=["json", "list to json", "stringify", "serialize", "list to string", "array to json"],
-            inputs=[
-                io.Array.Input("array"),
-                io.Int.Input("indent", default=2, min=0, max=8,
-                             tooltip="Spaces per indent level. 0 produces compact single-line string."),
-            ],
-            outputs=[
-                io.String.Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, array, indent=2):
-        return io.NodeOutput(_dump_json(array, indent))
-
-
 class StringExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
@@ -508,8 +457,6 @@ class StringExtension(ComfyExtension):
             RegexExtract,
             RegexReplace,
             JsonExtractString,
-            ConvertDictionaryToString,
-            ConvertArrayToString,
         ]
 
 async def comfy_entrypoint() -> StringExtension:

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.26.0"
+__version__ = "0.26.2"

nodes.py

@@ -2374,8 +2374,6 @@ async def init_builtin_extra_nodes():
         "nodes_images.py",
         "nodes_video_model.py",
         "nodes_ideogram4.py",
-        "nodes_bounding_boxes.py",
-        "nodes_json_prompt.py",
         "nodes_train.py",
         "nodes_dataset.py",
         "nodes_sag.py",
@@ -2475,7 +2473,6 @@ async def init_builtin_extra_nodes():
         "nodes_gaussian_splat.py",
         "nodes_triposplat.py",
         "nodes_depth_anything_3.py",
-        "nodes_seed.py",
     ]
 
     import_failed = []

openapi.yaml

@@ -1692,12 +1692,6 @@ paths:
                             schema:
                                 $ref: '#/components/schemas/ErrorResponse'
                     description: Unsupported media type
-                "422":
-                    content:
-                        application/json:
-                            schema:
-                                $ref: '#/components/schemas/ErrorResponse'
-                    description: Validation error (e.g., disallowed model_type tag)
                 "500":
                     content:
                         application/json:
@@ -2143,12 +2137,6 @@ paths:
                             schema:
                                 $ref: '#/components/schemas/ErrorResponse'
                     description: Source asset with given hash not found
-                "422":
-                    content:
-                        application/json:
-                            schema:
-                                $ref: '#/components/schemas/ErrorResponse'
-                    description: Validation error (e.g., disallowed model_type tag)
                 "500":
                     content:
                         application/json:
@@ -2369,10 +2357,6 @@ paths:
             description: |
                 Returns a list of model folders available in the system.
                 This is an experimental endpoint that replaces the legacy /models endpoint.
-                Each folder's name is the identifier to pass to /api/experiment/models/{folder}.
-                Once the model_type migration is active the names are model_type folder_names
-                (e.g. `ultralytics_bbox`); a folder with no folder_name mapping is returned by
-                its directory path.
             operationId: getModelFolders
             responses:
                 "200":
@@ -3004,7 +2988,7 @@ paths:
                     format: uuid
                     type: string
                 - description: |
-                    When present, each output item in the response receives a `short_url` field containing a short link for that asset. Omit this parameter (the default) to receive a response identical to the no-param baseline. The value selects the link's lifetime and auth model: use `ephemeral_tool_chain` for short-lived (≤5 minute) machine-to-machine handoffs — these are public bearer links where the link ID itself is the credential, so anyone holding the link can resolve it (intended for pasting into an agent/MCP tool chain); use `default` for durable (30 day) human-revisitable links, which are owner-gated and resolvable only by the authenticated owner. Links are always minted under the authenticated request owner's identity; the auth model is selected by the server and is never settable by the caller.
+                    When present, each output item in the response receives a `short_url` field containing an owner-gated durable link for that asset. Omit this parameter (the default) to receive a response identical to the no-param baseline. The value selects the link's lifetime: use `ephemeral_tool_chain` for short-lived machine-to-machine handoffs (~15 minutes); use `default` for durable human-revisitable links (30 days). Links are minted only for the authenticated request owner and are not resolvable by other users.
                   in: query
                   name: short_link
                   schema:

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.26.0"
+version = "0.26.2"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -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()
+