ComfyUI v0.15.0

* feat(api-nodes): add ByteDance Seedream-5 model

* made error message more correct

* rename seedream 5.0 model

comfy/ldm/lightricks/embeddings_connector.py

@@ -157,11 +157,9 @@ class Embeddings1DConnector(nn.Module):
         self.num_learnable_registers = num_learnable_registers
         if self.num_learnable_registers:
             self.learnable_registers = nn.Parameter(
-                torch.rand(
+                torch.empty(
                     self.num_learnable_registers, inner_dim, dtype=dtype, device=device
                 )
-                * 2.0
-                - 1.0
             )
 
     def get_fractional_positions(self, indices_grid):

comfy/ops.py

@@ -827,6 +827,10 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 else:
                     sd = {}
 
+                if not hasattr(self, 'weight'):
+                    logging.warning("Warning: state dict on uninitialized op {}".format(prefix))
+                    return sd
+
                 if self.bias is not None:
                     sd["{}bias".format(prefix)] = self.bias
 

comfy/text_encoders/lt.py

@@ -101,6 +101,7 @@ class LTXAVTEModel(torch.nn.Module):
         super().__init__()
         self.dtypes = set()
         self.dtypes.add(dtype)
+        self.compat_mode = False
 
         self.gemma3_12b = Gemma3_12BModel(device=device, dtype=dtype_llama, model_options=model_options, layer="all", layer_idx=None)
         self.dtypes.add(dtype_llama)
@@ -108,6 +109,28 @@ class LTXAVTEModel(torch.nn.Module):
         operations = self.gemma3_12b.operations # TODO
         self.text_embedding_projection = operations.Linear(3840 * 49, 3840, bias=False, dtype=dtype, device=device)
 
+    def enable_compat_mode(self):  # TODO: remove
+        from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector
+        operations = self.gemma3_12b.operations
+        dtype = self.text_embedding_projection.weight.dtype
+        device = self.text_embedding_projection.weight.device
+        self.audio_embeddings_connector = Embeddings1DConnector(
+            split_rope=True,
+            double_precision_rope=True,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        self.video_embeddings_connector = Embeddings1DConnector(
+            split_rope=True,
+            double_precision_rope=True,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+        self.compat_mode = True
+
     def set_clip_options(self, options):
         self.execution_device = options.get("execution_device", self.execution_device)
         self.gemma3_12b.set_clip_options(options)
@@ -129,6 +152,12 @@ class LTXAVTEModel(torch.nn.Module):
         out = out.reshape((out.shape[0], out.shape[1], -1))
         out = self.text_embedding_projection(out)
         out = out.float()
+
+        if self.compat_mode:
+            out_vid = self.video_embeddings_connector(out)[0]
+            out_audio = self.audio_embeddings_connector(out)[0]
+            out = torch.concat((out_vid, out_audio), dim=-1)
+
         return out.to(out_device), pooled
 
     def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
@@ -152,6 +181,16 @@ class LTXAVTEModel(torch.nn.Module):
                     missing_all.extend([f"{prefix}{k}" for k in missing])
                     unexpected_all.extend([f"{prefix}{k}" for k in unexpected])
 
+            if "model.diffusion_model.audio_embeddings_connector.transformer_1d_blocks.2.attn1.to_q.bias" not in sd:  # TODO: remove
+                ww = sd.get("model.diffusion_model.audio_embeddings_connector.transformer_1d_blocks.0.attn1.to_q.bias", None)
+                if ww is not None:
+                    if ww.shape[0] == 3840:
+                        self.enable_compat_mode()
+                        sdv = comfy.utils.state_dict_prefix_replace(sd, {"model.diffusion_model.video_embeddings_connector.": ""}, filter_keys=True)
+                        self.video_embeddings_connector.load_state_dict(sdv, strict=False, assign=getattr(self, "can_assign_sd", False))
+                        sda = comfy.utils.state_dict_prefix_replace(sd, {"model.diffusion_model.audio_embeddings_connector.": ""}, filter_keys=True)
+                        self.audio_embeddings_connector.load_state_dict(sda, strict=False, assign=getattr(self, "can_assign_sd", False))
+
             return (missing_all, unexpected_all)
 
     def memory_estimation_function(self, token_weight_pairs, device=None):

comfy_api/latest/_io.py

@@ -1237,82 +1237,6 @@ class BoundingBox(ComfyTypeIO):
             return d
 
 
-@comfytype(io_type="CURVE")
-class Curve(ComfyTypeIO):
-    Type = list
-
-    class Input(WidgetInput):
-        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
-                     socketless: bool=True, default: list=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 = [[0, 0], [1, 1]]
-
-        def as_dict(self):
-            return super().as_dict()
-
-
-@comfytype(io_type="RANGE")
-class Range(ComfyTypeIO):
-    Type = dict  # {"min": float, "max": float, "midpoint"?: float}
-
-    class Input(WidgetInput):
-        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
-                     socketless: bool=True, default: dict=None,
-                     display: str=None,
-                     gradient_stops: list=None,
-                     show_midpoint: bool=None,
-                     midpoint_scale: str=None,
-                     value_min: float=None,
-                     value_max: float=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 = {"min": 0.0, "max": 1.0}
-            self.display = display
-            self.gradient_stops = gradient_stops
-            self.show_midpoint = show_midpoint
-            self.midpoint_scale = midpoint_scale
-            self.value_min = value_min
-            self.value_max = value_max
-
-        def as_dict(self):
-            return super().as_dict() | prune_dict({
-                "display": self.display,
-                "gradient_stops": self.gradient_stops,
-                "show_midpoint": self.show_midpoint,
-                "midpoint_scale": self.midpoint_scale,
-                "value_min": self.value_min,
-                "value_max": self.value_max,
-            })
-
-
-@comfytype(io_type="COLOR_CURVES")
-class ColorCurves(ComfyTypeIO):
-    class ColorCurvesDict(TypedDict):
-        rgb: list[list[float]]
-        red: list[list[float]]
-        green: list[list[float]]
-        blue: list[list[float]]
-
-    Type = ColorCurvesDict
-
-    class Input(WidgetInput):
-        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
-                     socketless: bool=True, default: 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 = {
-                    "rgb": [[0, 0], [1, 1]],
-                    "red": [[0, 0], [1, 1]],
-                    "green": [[0, 0], [1, 1]],
-                    "blue": [[0, 0], [1, 1]]
-                }
-
-        def as_dict(self):
-            return super().as_dict()
-
-
 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]):
     DYNAMIC_INPUT_LOOKUP[io_type] = func
@@ -2299,7 +2223,5 @@ __all__ = [
     "PriceBadgeDepends",
     "PriceBadge",
     "BoundingBox",
-    "Curve",
-    "ColorCurves",
     "NodeReplace",
 ]

comfy_api_nodes/apis/bytedance.py

@@ -27,6 +27,7 @@ class Seedream4TaskCreationRequest(BaseModel):
     sequential_image_generation: str = Field("disabled")
     sequential_image_generation_options: Seedream4Options = Field(Seedream4Options(max_images=15))
     watermark: bool = Field(False)
+    output_format: str | None = None
 
 
 class ImageTaskCreationResponse(BaseModel):
@@ -106,6 +107,7 @@ RECOMMENDED_PRESETS_SEEDREAM_4 = [
     ("2496x1664 (3:2)", 2496, 1664),
     ("1664x2496 (2:3)", 1664, 2496),
     ("3024x1296 (21:9)", 3024, 1296),
+    ("3072x3072 (1:1)", 3072, 3072),
     ("4096x4096 (1:1)", 4096, 4096),
     ("Custom", None, None),
 ]

comfy_api_nodes/apis/kling.py

@@ -134,6 +134,13 @@ class ImageToVideoWithAudioRequest(BaseModel):
     shot_type: str | None = Field(None)
 
 
+class KlingAvatarRequest(BaseModel):
+    image: str = Field(...)
+    sound_file: str = Field(...)
+    prompt: str | None = Field(None)
+    mode: str = Field(...)
+
+
 class MotionControlRequest(BaseModel):
     prompt: str = Field(...)
     image_url: str = Field(...)

comfy_api_nodes/nodes_bytedance.py

@@ -37,6 +37,12 @@ from comfy_api_nodes.util import (
 
 BYTEPLUS_IMAGE_ENDPOINT = "/proxy/byteplus/api/v3/images/generations"
 
+SEEDREAM_MODELS = {
+    "seedream 5.0 lite": "seedream-5-0-260128",
+    "seedream-4-5-251128": "seedream-4-5-251128",
+    "seedream-4-0-250828": "seedream-4-0-250828",
+}
+
 # Long-running tasks endpoints(e.g., video)
 BYTEPLUS_TASK_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks"
 BYTEPLUS_TASK_STATUS_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks"  # + /{task_id}
@@ -180,14 +186,13 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ByteDanceSeedreamNode",
-            display_name="ByteDance Seedream 4.5",
+            display_name="ByteDance Seedream 5.0",
             category="api node/image/ByteDance",
             description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
             inputs=[
                 IO.Combo.Input(
                     "model",
-                    options=["seedream-4-5-251128", "seedream-4-0-250828"],
-                    tooltip="Model name",
+                    options=list(SEEDREAM_MODELS.keys()),
                 ),
                 IO.String.Input(
                     "prompt",
@@ -198,7 +203,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                 IO.Image.Input(
                     "image",
                     tooltip="Input image(s) for image-to-image generation. "
-                    "List of 1-10 images for single or multi-reference generation.",
+                    "Reference image(s) for single or multi-reference generation.",
                     optional=True,
                 ),
                 IO.Combo.Input(
@@ -210,8 +215,8 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                     "width",
                     default=2048,
                     min=1024,
-                    max=4096,
-                    step=8,
+                    max=6240,
+                    step=2,
                     tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`",
                     optional=True,
                 ),
@@ -219,8 +224,8 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                     "height",
                     default=2048,
                     min=1024,
-                    max=4096,
-                    step=8,
+                    max=4992,
+                    step=2,
                     tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`",
                     optional=True,
                 ),
@@ -283,7 +288,8 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                 depends_on=IO.PriceBadgeDepends(widgets=["model"]),
                 expr="""
                 (
-                  $price := $contains(widgets.model, "seedream-4-5-251128") ? 0.04 : 0.03;
+                  $price := $contains(widgets.model, "5.0 lite") ? 0.035 :
+                            $contains(widgets.model, "4-5") ? 0.04 : 0.03;
                   {
                     "type":"usd",
                     "usd": $price,
@@ -309,6 +315,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
         watermark: bool = False,
         fail_on_partial: bool = True,
     ) -> IO.NodeOutput:
+        model = SEEDREAM_MODELS[model]
         validate_string(prompt, strip_whitespace=True, min_length=1)
         w = h = None
         for label, tw, th in RECOMMENDED_PRESETS_SEEDREAM_4:
@@ -318,15 +325,12 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
 
         if w is None or h is None:
             w, h = width, height
-            if not (1024 <= w <= 4096) or not (1024 <= h <= 4096):
-                raise ValueError(
-                    f"Custom size out of range: {w}x{h}. " "Both width and height must be between 1024 and 4096 pixels."
-                )
+
         out_num_pixels = w * h
         mp_provided = out_num_pixels / 1_000_000.0
-        if "seedream-4-5" in model and out_num_pixels < 3686400:
+        if ("seedream-4-5" in model or "seedream-5-0" in model) and out_num_pixels < 3686400:
             raise ValueError(
-                f"Minimum image resolution that Seedream 4.5 can generate is 3.68MP, "
+                f"Minimum image resolution for the selected model is 3.68MP, "
                 f"but {mp_provided:.2f}MP provided."
             )
         if "seedream-4-0" in model and out_num_pixels < 921600:
@@ -334,9 +338,18 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                 f"Minimum image resolution that the selected model can generate is 0.92MP, "
                 f"but {mp_provided:.2f}MP provided."
             )
+        max_pixels = 10_404_496 if "seedream-5-0" in model else 16_777_216
+        if out_num_pixels > max_pixels:
+            raise ValueError(
+                f"Maximum image resolution for the selected model is {max_pixels / 1_000_000:.2f}MP, "
+                f"but {mp_provided:.2f}MP provided."
+            )
         n_input_images = get_number_of_images(image) if image is not None else 0
-        if n_input_images > 10:
-            raise ValueError(f"Maximum of 10 reference images are supported, but {n_input_images} received.")
+        max_num_of_images = 14 if model == "seedream-5-0-260128" else 10
+        if n_input_images > max_num_of_images:
+            raise ValueError(
+                f"Maximum of {max_num_of_images} reference images are supported, but {n_input_images} received."
+            )
         if sequential_image_generation == "auto" and n_input_images + max_images > 15:
             raise ValueError(
                 "The maximum number of generated images plus the number of reference images cannot exceed 15."
@@ -364,6 +377,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                 sequential_image_generation=sequential_image_generation,
                 sequential_image_generation_options=Seedream4Options(max_images=max_images),
                 watermark=watermark,
+                output_format="png" if model == "seedream-5-0-260128" else None,
             ),
         )
         if len(response.data) == 1:

comfy_api_nodes/nodes_kling.py

@@ -50,6 +50,7 @@ from comfy_api_nodes.apis import (
 )
 from comfy_api_nodes.apis.kling import (
     ImageToVideoWithAudioRequest,
+    KlingAvatarRequest,
     MotionControlRequest,
     MultiPromptEntry,
     OmniImageParamImage,
@@ -74,6 +75,7 @@ from comfy_api_nodes.util import (
     upload_image_to_comfyapi,
     upload_images_to_comfyapi,
     upload_video_to_comfyapi,
+    validate_audio_duration,
     validate_image_aspect_ratio,
     validate_image_dimensions,
     validate_string,
@@ -3139,6 +3141,103 @@ class KlingFirstLastFrameNode(IO.ComfyNode):
         return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
 
 
+class KlingAvatarNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingAvatarNode",
+            display_name="Kling Avatar 2.0",
+            category="api node/video/Kling",
+            description="Generate broadcast-style digital human videos from a single photo and an audio file.",
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="Avatar reference image. "
+                    "Width and height must be at least 300px. Aspect ratio must be between 1:2.5 and 2.5:1.",
+                ),
+                IO.Audio.Input(
+                    "sound_file",
+                    tooltip="Audio input. Must be between 2 and 300 seconds in duration.",
+                ),
+                IO.Combo.Input("mode", options=["std", "pro"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    optional=True,
+                    tooltip="Optional prompt to define avatar actions, emotions, and camera movements.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["mode"]),
+                expr="""
+                (
+                  $prices := {"std": 0.056, "pro": 0.112};
+                  {"type":"usd","usd": $lookup($prices, widgets.mode), "format":{"suffix":"/second"}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        sound_file: Input.Audio,
+        mode: str,
+        seed: int,
+        prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_image_dimensions(image, min_width=300, min_height=300)
+        validate_image_aspect_ratio(image, (1, 2.5), (2.5, 1))
+        validate_audio_duration(sound_file, min_duration=2, max_duration=300)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/avatar/image2video", method="POST"),
+            response_model=TaskStatusResponse,
+            data=KlingAvatarRequest(
+                image=await upload_image_to_comfyapi(cls, image),
+                sound_file=await upload_audio_to_comfyapi(
+                    cls, sound_file, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg"
+                ),
+                prompt=prompt or None,
+                mode=mode,
+            ),
+        )
+        if response.code:
+            raise RuntimeError(
+                f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+            )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/kling/v1/videos/avatar/image2video/{response.data.task_id}"),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: (r.data.task_status if r.data else None),
+            max_poll_attempts=800,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
 class KlingExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -3167,6 +3266,7 @@ class KlingExtension(ComfyExtension):
             MotionControl,
             KlingVideoNode,
             KlingFirstLastFrameNode,
+            KlingAvatarNode,
         ]
 
 

comfy_extras/nodes_color_curves.py

@@ -1,137 +0,0 @@
-from typing_extensions import override
-import torch
-import numpy as np
-
-from comfy_api.latest import ComfyExtension, io, ui
-
-
-def _monotone_cubic_hermite(xs, ys, x_query):
-    """Evaluate monotone cubic Hermite interpolation at x_query points."""
-    n = len(xs)
-    if n == 0:
-        return np.zeros_like(x_query)
-    if n == 1:
-        return np.full_like(x_query, ys[0])
-
-    # Compute slopes
-    deltas = np.diff(ys) / np.maximum(np.diff(xs), 1e-10)
-
-    # Compute tangents (Fritsch-Carlson)
-    slopes = np.zeros(n)
-    slopes[0] = deltas[0]
-    slopes[-1] = deltas[-1]
-    for i in range(1, n - 1):
-        if deltas[i - 1] * deltas[i] <= 0:
-            slopes[i] = 0
-        else:
-            slopes[i] = (deltas[i - 1] + deltas[i]) / 2
-
-    # Enforce monotonicity
-    for i in range(n - 1):
-        if deltas[i] == 0:
-            slopes[i] = 0
-            slopes[i + 1] = 0
-        else:
-            alpha = slopes[i] / deltas[i]
-            beta = slopes[i + 1] / deltas[i]
-            s = alpha ** 2 + beta ** 2
-            if s > 9:
-                t = 3 / np.sqrt(s)
-                slopes[i] = t * alpha * deltas[i]
-                slopes[i + 1] = t * beta * deltas[i]
-
-    # Evaluate
-    result = np.zeros_like(x_query, dtype=np.float64)
-    indices = np.searchsorted(xs, x_query, side='right') - 1
-    indices = np.clip(indices, 0, n - 2)
-
-    for i in range(n - 1):
-        mask = indices == i
-        if not np.any(mask):
-            continue
-        dx = xs[i + 1] - xs[i]
-        if dx == 0:
-            result[mask] = ys[i]
-            continue
-        t = (x_query[mask] - xs[i]) / dx
-        t2 = t * t
-        t3 = t2 * t
-        h00 = 2 * t3 - 3 * t2 + 1
-        h10 = t3 - 2 * t2 + t
-        h01 = -2 * t3 + 3 * t2
-        h11 = t3 - t2
-        result[mask] = h00 * ys[i] + h10 * dx * slopes[i] + h01 * ys[i + 1] + h11 * dx * slopes[i + 1]
-
-    # Clamp edges
-    result[x_query <= xs[0]] = ys[0]
-    result[x_query >= xs[-1]] = ys[-1]
-
-    return result
-
-
-def _build_lut(points):
-    """Build a 256-entry LUT from curve control points in [0,1] space."""
-    if not points or len(points) < 2:
-        return np.arange(256, dtype=np.float64) / 255.0
-
-    pts = sorted(points, key=lambda p: p[0])
-    xs = np.array([p[0] for p in pts], dtype=np.float64)
-    ys = np.array([p[1] for p in pts], dtype=np.float64)
-
-    x_query = np.linspace(0, 1, 256)
-    lut = _monotone_cubic_hermite(xs, ys, x_query)
-    return np.clip(lut, 0, 1)
-
-
-class ColorCurvesNode(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="ColorCurves",
-            display_name="Color Curves",
-            category="image/adjustment",
-            inputs=[
-                io.Image.Input("image"),
-                io.ColorCurves.Input("settings"),
-            ],
-            outputs=[
-                io.Image.Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, image: torch.Tensor, settings: dict) -> io.NodeOutput:
-        rgb_pts = settings.get("rgb", [[0, 0], [1, 1]])
-        red_pts = settings.get("red", [[0, 0], [1, 1]])
-        green_pts = settings.get("green", [[0, 0], [1, 1]])
-        blue_pts = settings.get("blue", [[0, 0], [1, 1]])
-
-        rgb_lut = _build_lut(rgb_pts)
-        red_lut = _build_lut(red_pts)
-        green_lut = _build_lut(green_pts)
-        blue_lut = _build_lut(blue_pts)
-
-        # Convert to numpy for LUT application
-        img_np = image.cpu().numpy().copy()
-
-        # Apply per-channel curves then RGB master curve.
-        # Index with floor(val * 256) clamped to [0, 255] to match GPU NEAREST
-        # texture sampling on a 256-wide LUT texture.
-        for ch, ch_lut in enumerate([red_lut, green_lut, blue_lut]):
-            indices = np.clip((img_np[..., ch] * 256).astype(np.int32), 0, 255)
-            img_np[..., ch] = ch_lut[indices]
-            indices = np.clip((img_np[..., ch] * 256).astype(np.int32), 0, 255)
-            img_np[..., ch] = rgb_lut[indices]
-
-        result = torch.from_numpy(np.clip(img_np, 0, 1)).to(image.device, dtype=image.dtype)
-        return io.NodeOutput(result, ui=ui.PreviewImage(result))
-
-
-class ColorCurvesExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-        return [ColorCurvesNode]
-
-
-async def comfy_entrypoint() -> ColorCurvesExtension:
-    return ColorCurvesExtension()

comfy_extras/nodes_images.py

@@ -6,6 +6,7 @@ import folder_paths
 import json
 import os
 import re
+import math
 import torch
 import comfy.utils
 
@@ -682,6 +683,172 @@ class ImageScaleToMaxDimension(IO.ComfyNode):
     upscale = execute    # TODO: remove
 
 
+class SplitImageToTileList(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SplitImageToTileList",
+            category="image/batch",
+            search_aliases=["split image", "tile image", "slice image"],
+            display_name="Split Image into List of Tiles",
+            description="Splits an image into a batched list of tiles with a specified overlap.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Int.Input("tile_width", default=1024, min=64, max=MAX_RESOLUTION),
+                IO.Int.Input("tile_height", default=1024, min=64, max=MAX_RESOLUTION),
+                IO.Int.Input("overlap", default=128, min=0, max=4096),
+            ],
+            outputs=[
+                IO.Image.Output(is_output_list=True),
+            ],
+        )
+
+    @staticmethod
+    def get_grid_coords(width, height, tile_width, tile_height, overlap):
+        coords = []
+        stride_x = max(1, tile_width - overlap)
+        stride_y = max(1, tile_height - overlap)
+
+        y = 0
+        while y < height:
+            x = 0
+            y_end = min(y + tile_height, height)
+            y_start = max(0, y_end - tile_height)
+
+            while x < width:
+                x_end = min(x + tile_width, width)
+                x_start = max(0, x_end - tile_width)
+
+                coords.append((x_start, y_start, x_end, y_end))
+
+                if x_end >= width:
+                    break
+                x += stride_x
+
+            if y_end >= height:
+                break
+            y += stride_y
+
+        return coords
+
+    @classmethod
+    def execute(cls, image, tile_width, tile_height, overlap):
+        b, h, w, c = image.shape
+        coords = cls.get_grid_coords(w, h, tile_width, tile_height, overlap)
+
+        output_list = []
+        for (x_start, y_start, x_end, y_end) in coords:
+            tile = image[:, y_start:y_end, x_start:x_end, :]
+            output_list.append(tile)
+
+        return IO.NodeOutput(output_list)
+
+
+class ImageMergeTileList(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ImageMergeTileList",
+            display_name="Merge List of Tiles to Image",
+            category="image/batch",
+            search_aliases=["split image", "tile image", "slice image"],
+            is_input_list=True,
+            inputs=[
+                IO.Image.Input("image_list"),
+                IO.Int.Input("final_width", default=1024, min=64, max=32768),
+                IO.Int.Input("final_height", default=1024, min=64, max=32768),
+                IO.Int.Input("overlap", default=128, min=0, max=4096),
+            ],
+            outputs=[
+                IO.Image.Output(is_output_list=False),
+            ],
+        )
+
+    @staticmethod
+    def get_grid_coords(width, height, tile_width, tile_height, overlap):
+        coords = []
+        stride_x = max(1, tile_width - overlap)
+        stride_y = max(1, tile_height - overlap)
+
+        y = 0
+        while y < height:
+            x = 0
+            y_end = min(y + tile_height, height)
+            y_start = max(0, y_end - tile_height)
+
+            while x < width:
+                x_end = min(x + tile_width, width)
+                x_start = max(0, x_end - tile_width)
+
+                coords.append((x_start, y_start, x_end, y_end))
+
+                if x_end >= width:
+                    break
+                x += stride_x
+
+            if y_end >= height:
+                break
+            y += stride_y
+
+        return coords
+
+    @classmethod
+    def execute(cls, image_list, final_width, final_height, overlap):
+        w = final_width[0]
+        h = final_height[0]
+        ovlp = overlap[0]
+        feather_str = 1.0
+
+        first_tile = image_list[0]
+        b, t_h, t_w, c = first_tile.shape
+        device = first_tile.device
+        dtype = first_tile.dtype
+
+        coords = cls.get_grid_coords(w, h, t_w, t_h, ovlp)
+
+        canvas = torch.zeros((b, h, w, c), device=device, dtype=dtype)
+        weights = torch.zeros((b, h, w, 1), device=device, dtype=dtype)
+
+        if ovlp > 0:
+            y_w = torch.sin(math.pi * torch.linspace(0, 1, t_h, device=device, dtype=dtype))
+            x_w = torch.sin(math.pi * torch.linspace(0, 1, t_w, device=device, dtype=dtype))
+            y_w = torch.clamp(y_w, min=1e-5)
+            x_w = torch.clamp(x_w, min=1e-5)
+
+            sine_mask = (y_w.unsqueeze(1) * x_w.unsqueeze(0)).unsqueeze(0).unsqueeze(-1)
+            flat_mask = torch.ones_like(sine_mask)
+
+            weight_mask = torch.lerp(flat_mask, sine_mask, feather_str)
+        else:
+            weight_mask = torch.ones((1, t_h, t_w, 1), device=device, dtype=dtype)
+
+        for i, (x_start, y_start, x_end, y_end) in enumerate(coords):
+            if i >= len(image_list):
+                break
+
+            tile = image_list[i]
+
+            region_h = y_end - y_start
+            region_w = x_end - x_start
+
+            real_h = min(region_h, tile.shape[1])
+            real_w = min(region_w, tile.shape[2])
+
+            y_end_actual = y_start + real_h
+            x_end_actual = x_start + real_w
+
+            tile_crop = tile[:, :real_h, :real_w, :]
+            mask_crop = weight_mask[:, :real_h, :real_w, :]
+
+            canvas[:, y_start:y_end_actual, x_start:x_end_actual, :] += tile_crop * mask_crop
+            weights[:, y_start:y_end_actual, x_start:x_end_actual, :] += mask_crop
+
+        weights[weights == 0] = 1.0
+        merged_image = canvas / weights
+
+        return IO.NodeOutput(merged_image)
+
+
 class ImagesExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -701,6 +868,8 @@ class ImagesExtension(ComfyExtension):
             ImageRotate,
             ImageFlip,
             ImageScaleToMaxDimension,
+            SplitImageToTileList,
+            ImageMergeTileList,
         ]
 
 

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.14.1"
+__version__ = "0.15.0"

nodes.py

@@ -2035,144 +2035,6 @@ class ImagePadForOutpaint:
         return (new_image, mask.unsqueeze(0))
 
 
-class TestCurveWidget:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "curve": ("CURVE", {"default": [[0, 0], [1, 1]]}),
-            }
-        }
-
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("points",)
-    FUNCTION = "execute"
-    OUTPUT_NODE = True
-    CATEGORY = "testing"
-
-    def execute(self, curve):
-        import json
-        result = json.dumps(curve, indent=2)
-        print("Curve points:", result)
-        return {"ui": {"text": [result]}, "result": (result,)}
-
-
-class TestRangePlain:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "range": ("RANGE", {"default": {"min": 0.0, "max": 1.0}}),
-                "range_midpoint": ("RANGE", {
-                    "default": {"min": 0.2, "max": 0.8, "midpoint": 0.5},
-                    "show_midpoint": True,
-                }),
-            }
-        }
-
-    RETURN_TYPES = ("STRING",)
-    FUNCTION = "execute"
-    OUTPUT_NODE = True
-    CATEGORY = "testing"
-
-    def execute(self, **kwargs):
-        import json
-        result = json.dumps(kwargs, indent=2)
-        return {"ui": {"text": [result]}, "result": (result,)}
-
-
-class TestRangeGradient:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "range": ("RANGE", {
-                    "default": {"min": 0.0, "max": 1.0},
-                    "display": "gradient",
-                    "gradient_stops": [
-                        {"offset": 0.0, "color": [0, 0, 0]},
-                        {"offset": 1.0, "color": [255, 255, 255]}
-                    ],
-                }),
-                "range_midpoint": ("RANGE", {
-                    "default": {"min": 0.0, "max": 1.0, "midpoint": 0.5},
-                    "display": "gradient",
-                    "gradient_stops": [
-                        {"offset": 0.0, "color": [0, 0, 0]},
-                        {"offset": 1.0, "color": [255, 255, 255]}
-                    ],
-                    "show_midpoint": True,
-                    "midpoint_scale": "gamma",
-                }),
-            }
-        }
-
-    RETURN_TYPES = ("STRING",)
-    FUNCTION = "execute"
-    OUTPUT_NODE = True
-    CATEGORY = "testing"
-
-    def execute(self, **kwargs):
-        import json
-        result = json.dumps(kwargs, indent=2)
-        return {"ui": {"text": [result]}, "result": (result,)}
-
-
-class TestRangeHistogram:
-    RANGE_OPTS = {
-        "display": "histogram",
-        "show_midpoint": True,
-        "midpoint_scale": "gamma",
-        "value_min": 0,
-        "value_max": 255,
-    }
-
-    @classmethod
-    def INPUT_TYPES(s):
-        default = {"min": 0, "max": 255, "midpoint": 0.5}
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "rgb":   ("RANGE", {"default": {**default}, **s.RANGE_OPTS}),
-                "red":   ("RANGE", {"default": {**default}, **s.RANGE_OPTS}),
-                "green": ("RANGE", {"default": {**default}, **s.RANGE_OPTS}),
-                "blue":  ("RANGE", {"default": {**default}, **s.RANGE_OPTS}),
-            }
-        }
-
-    RETURN_TYPES = ("STRING",)
-    FUNCTION = "execute"
-    OUTPUT_NODE = True
-    CATEGORY = "testing"
-
-    def execute(self, image, rgb, red, green, blue):
-        import json
-        import numpy as np
-
-        img = image[0].cpu().numpy()  # (H, W, C)
-
-        # Per-channel histograms
-        hist_r, _ = np.histogram(img[:, :, 0].flatten(), bins=256, range=(0.0, 1.0))
-        hist_g, _ = np.histogram(img[:, :, 1].flatten(), bins=256, range=(0.0, 1.0))
-        hist_b, _ = np.histogram(img[:, :, 2].flatten(), bins=256, range=(0.0, 1.0))
-
-        # Luminance histogram (BT.709)
-        luminance = 0.2126 * img[:, :, 0] + 0.7152 * img[:, :, 1] + 0.0722 * img[:, :, 2]
-        hist_rgb, _ = np.histogram(luminance.flatten(), bins=256, range=(0.0, 1.0))
-
-        result = json.dumps({"rgb": rgb, "red": red, "green": green, "blue": blue}, indent=2)
-        return {
-            "ui": {
-                "text": [result],
-                "range_histogram_rgb":   hist_rgb.astype(np.uint32).tolist(),
-                "range_histogram_red":   hist_r.astype(np.uint32).tolist(),
-                "range_histogram_green": hist_g.astype(np.uint32).tolist(),
-                "range_histogram_blue":  hist_b.astype(np.uint32).tolist(),
-            },
-            "result": (result,)
-        }
-
-
 NODE_CLASS_MAPPINGS = {
     "KSampler": KSampler,
     "CheckpointLoaderSimple": CheckpointLoaderSimple,
@@ -2241,10 +2103,6 @@ NODE_CLASS_MAPPINGS = {
     "ConditioningZeroOut": ConditioningZeroOut,
     "ConditioningSetTimestepRange": ConditioningSetTimestepRange,
     "LoraLoaderModelOnly": LoraLoaderModelOnly,
-    "TestCurveWidget": TestCurveWidget,
-    "TestRangePlain": TestRangePlain,
-    "TestRangeGradient": TestRangeGradient,
-    "TestRangeHistogram": TestRangeHistogram,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -2313,10 +2171,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     # _for_testing
     "VAEDecodeTiled": "VAE Decode (Tiled)",
     "VAEEncodeTiled": "VAE Encode (Tiled)",
-    "TestCurveWidget": "Test Curve Widget",
-    "TestRangePlain": "Test Range (Plain)",
-    "TestRangeGradient": "Test Range (Gradient)",
-    "TestRangeHistogram": "Test Range (Histogram)",
 }
 
 EXTENSION_WEB_DIRS = {}

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.14.1"
+version = "0.15.0"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,6 +1,6 @@
-comfyui-frontend-package==1.39.14
-comfyui-workflow-templates==0.8.43
-comfyui-embedded-docs==0.4.1
+comfyui-frontend-package==1.39.16
+comfyui-workflow-templates==0.9.3
+comfyui-embedded-docs==0.4.3
 torch
 torchsde
 torchvision