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may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.
README.md
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# MiniMax H3 Spatial Guide
Native-size image/video guides and a drag-to-position editor for ComfyUI's native MiniMax H3 model. No additional Python dependencies.
![Drag the guide rectangle to choose its position](docs/position-editor.png)
## Install
Place this repository in `ComfyUI/custom_nodes/ComfyUI-MiniMaxH3-SpatialGuide`, restart ComfyUI, and refresh the browser. Requires a ComfyUI version with native MiniMax H3 support. If installing by URL through Manager, use this repository's Git URL.
```bash
cd ComfyUI/custom_nodes
git clone https://github.com/meareabc/ComfyUI-MiniMaxH3-SpatialGuide.git
```
## Nodes
**Add Spatial Guide for MiniMax H3**
- Connect H3 `model`, `positive`, video `vae`, target AV `latent`, and guide frames `image`.
- The guide keeps its input dimensions. `x` and `y` locate its **top-left corner** in target pixels: positive x moves right, positive y moves down.
- Connect **both output MODEL and positive** to the sampler/guider. Connect the target latent directly to the sampler.
- For audio, chain a native Add Guide after this node with only its audio inputs connected.
**MiniMax H3 Guide Position**
- Set `guide_width`, `guide_height`, `canvas_width`, and `canvas_height`, or connect their INT inputs.
- Drag the blue guide rectangle; its size stays fixed. Hold Shift to snap movement to 32 pixels. Center and Origin buttons provide quick alignment.
- Connect its `x` and `y` outputs to Add Spatial Guide's corresponding inputs. Coordinates save with the workflow and can also be edited numerically.
- Linked Primitive values update the preview immediately. Dimensions computed by other backend nodes synchronize after execution; before the first run the preview uses the saved dimensions. Enter sizes directly for an exact preview before running.
For a 640×480 guide on a 960×480 canvas, `x=0,y=0` leaves 320 pixels to the right; `x=160,y=0` centers it. For a 480×640 guide on a 480×960 canvas, use `x=0,y=160` to center it vertically.
## Behavior and validation
Video guides follow native Add Guide's valid lengths: 5, 22, 39… frames; batches below 5 frames use the first image. Nonmultiples of 32 are padded at the right/bottom with replicated edges, without scaling. This is positional conditioning, so generated pixels can change inside the guide region.
CPU tests cover native-size coordinates, mixed guides/audio/references, clone isolation, registration, and a small native H3 forward. Full pretrained outpainting quality has not been validated.
```bash
python -m unittest discover -s custom_nodes/ComfyUI-MiniMaxH3-SpatialGuide/tests -v
```
## 中文使用说明
重启并刷新后搜索 **MiniMax H3 Guide Position**，输入参考宽高和目标画布宽高，在预览里拖动蓝框。输出的 `x/y` 接到 **Add Spatial Guide for MiniMax H3**；表示参考区域左上角坐标，原点在目标画布左上角。按住 Shift 拖动可按 32 像素对齐，也可点击居中或左上归零。
Spatial Guide 的 **MODEL 和 positive 两个输出都要连接到采样器**，目标 latent 直接接采样器。音频可用原生 Add Guide 单独添加。
__init__.py
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import copy
import math
import torch
import torch.nn.functional as F
import comfy.conds
import comfy.patcher_extension
import nodes
from comfy.ldm.minimax.model import FRAME_PER_TOKEN
from comfy_api.latest import ComfyExtension, io
GUIDE_POSITION = "minimax_h3_spatial_guide"
WEB_DIRECTORY = "./web"
def spatial_guide_layout(native, keyframes):
_, _, height, width, _ = native.signature
frame_rows = (height // 2) * (width // 2)
spacing = 64.0 / math.sqrt(height * width)
guides = iter(kf for kf in keyframes if kf.get("latent") is not None)
position_parts, segments = [], []
img_pos, img_update, audio_pos, audio_update = [], [], [], []
cursor = 0
for start, stop, kind in native.segments:
positions = native.position_ids[start:stop]
if kind == "cond":
guide = next(guides)
if GUIDE_POSITION in guide:
x, y = guide[GUIDE_POSITION]
_, _, time, h, w = guide["latent"].shape
grid = positions.new_empty(time, h // 2, w // 2, 3)
grid[..., 0] = positions[::frame_rows, 0][:, None, None]
hh = torch.arange(h // 2, dtype=positions.dtype, device=positions.device)
ww = torch.arange(w // 2, dtype=positions.dtype, device=positions.device)
grid[..., 1] = positions[0, 1] + (hh[:, None] + y / 32.0) * spacing
grid[..., 2] = positions[0, 2] + (ww[None, :] + x / 32.0) * spacing
positions = grid.reshape(-1, 3)
count = positions.shape[0]
position_parts.append(positions)
segments.append((cursor, cursor + count, kind))
if kind in ("cond", "ref_img", "video"):
img_pos.append(torch.arange(cursor, cursor + count))
img_update.append(torch.full((count,), kind == "video", dtype=torch.bool))
elif kind in ("cond_audio", "ref_audio", "audio"):
audio_pos.append(torch.arange(cursor, cursor + count))
audio_update.append(torch.full((count,), kind == "audio", dtype=torch.bool))
cursor += count
layout = copy.copy(native)
layout.position_ids = torch.cat(position_parts)
layout.segments = segments
layout.seq_len = cursor
layout.img_pos = torch.cat(img_pos)
layout.img_update = torch.cat(img_update)
layout.audio_pos = torch.cat(audio_pos)
layout.audio_update = torch.cat(audio_update)
return layout
def spatial_guide_sample(executor, model_wrap, *args, **kwargs):
for conditions in model_wrap.conds.values():
for condition in conditions:
model_conds = condition["model_conds"]
payload = model_conds["minimax_payload"].cond
keyframes = payload.get("keyframes", [])
if any(GUIDE_POSITION in kf for kf in keyframes):
payload = dict(payload, layout=spatial_guide_layout(payload["layout"], keyframes))
model_conds["minimax_payload"] = comfy.conds.CONDConstant(payload)
return executor(model_wrap, *args, **kwargs)
def patch_spatial_guides(model):
patched = model.clone()
wrapper_type = comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE
if not patched.get_wrappers(wrapper_type, GUIDE_POSITION):
patched.add_wrapper_with_key(wrapper_type, GUIDE_POSITION, spatial_guide_sample)
return patched
class MiniMaxH3AddSpatialGuide(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="MiniMaxH3AddSpatialGuide",
display_name="Add Spatial Guide for MiniMax H3",
category="model/conditioning/minimax",
description="Place an image or video guide at its original size within the target canvas. Connect both MODEL and positive outputs to the sampler.",
inputs=[
io.Model.Input("model"),
io.Conditioning.Input("positive"),
io.Vae.Input("vae"),
io.Latent.Input("latent"),
io.Image.Input("image", tooltip="Guide frames at their original size. Video uses 5, 22, 39... frames, matching the native Add Guide node."),
io.Int.Input("frame_idx", default=0, min=-9999, max=9999),
io.Int.Input("x", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION,
tooltip="Guide's left edge in target pixels. Use half the width difference to center it."),
io.Int.Input("y", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION,
tooltip="Guide's top edge in target pixels. Use half the height difference to center it."),
],
outputs=[io.Model.Output(), io.Conditioning.Output(display_name="positive")],
)
@classmethod
def execute(cls, model, positive, vae, latent, image, frame_idx=0, x=0, y=0) -> io.NodeOutput:
samples = latent["samples"]
if not samples.is_nested or len(samples.tensors) != 2 or samples.tensors[0].ndim != 5 or samples.tensors[0].shape[1] != 24:
raise ValueError("MiniMaxH3AddSpatialGuide expects a MiniMax H3 AV latent")
frame_count = sum(FRAME_PER_TOKEN[k % 5] for k in range(samples.tensors[0].shape[2]))
guide_frames = image.shape[0]
guide_frames = 1 if guide_frames < 5 else guide_frames - (guide_frames - 5) % 17
resolved = frame_idx if frame_idx >= 0 else frame_count + frame_idx
if resolved < 0 or resolved + guide_frames > frame_count:
raise ValueError("a {} frame guide at frame_idx {} does not fit in the video's {} frames".format(guide_frames, frame_idx, frame_count))
frames = image[:guide_frames, ..., :3]
height, width = frames.shape[1:3]
# Complete VAE/DiT patches without scaling or cropping the source.
if height % 32 or width % 32:
frames = F.pad(frames.movedim(-1, 1), (0, -width % 32, 0, -height % 32), mode="replicate").movedim(1, -1)
keyframe = {"resolved_frame_index": resolved, "latent": vae.encode(frames), GUIDE_POSITION: (x, y)}
positive = [[cond, {**options, "minimax_keyframes": list(options.get("minimax_keyframes", [])) + [keyframe]}]
for cond, options in positive]
return io.NodeOutput(patch_spatial_guides(model), positive)
class MiniMaxH3GuidePosition(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="MiniMaxH3GuidePosition",
display_name="MiniMax H3 Guide Position",
category="model/conditioning/minimax",
description="Drag the guide rectangle inside the target canvas. Outputs the guide's top-left x/y in target pixels.",
inputs=[
io.Int.Input("guide_width", default=640, min=1, max=nodes.MAX_RESOLUTION),
io.Int.Input("guide_height", default=480, min=1, max=nodes.MAX_RESOLUTION),
io.Int.Input("canvas_width", default=960, min=1, max=nodes.MAX_RESOLUTION),
io.Int.Input("canvas_height", default=480, min=1, max=nodes.MAX_RESOLUTION),
io.Int.Input("x", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, socketless=True),
io.Int.Input("y", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, socketless=True),
],
outputs=[io.Int.Output(display_name="x"), io.Int.Output(display_name="y")],
)
@classmethod
def execute(cls, guide_width, guide_height, canvas_width, canvas_height, x, y) -> io.NodeOutput:
return io.NodeOutput(x, y, ui={"guide_size": [guide_width, guide_height], "canvas_size": [canvas_width, canvas_height]})
class MiniMaxH3SpatialGuideExtension(ComfyExtension):
async def get_node_list(self):
return [MiniMaxH3AddSpatialGuide, MiniMaxH3GuidePosition]
async def comfy_entrypoint():
return MiniMaxH3SpatialGuideExtension()
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pyproject.toml
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[project]
name = "minimax-h3-spatial-guide"
version = "0.1.0"
description = "Native-size spatial guides and drag positioning for MiniMax H3 in ComfyUI"
readme = "README.md"
license = { file = "LICENSE" }
requires-python = ">=3.10"
dependencies = []
[project.urls]
Repository = "https://github.com/meareabc/ComfyUI-MiniMaxH3-SpatialGuide"
[tool.comfy]
DisplayName = "MiniMax H3 Spatial Guide"
tests/test_spatial_guide.py
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import asyncio
import importlib.util
import math
from pathlib import Path
from types import SimpleNamespace
import unittest
from unittest.mock import patch
import torch
import comfy.cli_args
comfy.cli_args.args.cpu = True
import comfy.conds
from comfy.ldm.minimax.model import MiniMaxH3Model, PackedLayout, patchify_video
from comfy.model_patcher import ModelPatcher
from comfy.nested_tensor import NestedTensor
spec = importlib.util.spec_from_file_location("h3_spatial_guide", Path(__file__).resolve().parents[1] / "__init__.py")
guide = importlib.util.module_from_spec(spec)
spec.loader.exec_module(guide)
def segment_positions(layout, kind):
return [layout.position_ids[a:b] for a, b, k in layout.segments if k == kind]
class SpatialGuideTests(unittest.TestCase):
def test_native_size_and_offsets_match_target_region(self):
for x in (0, 160, 320):
keyframes = [{"resolved_frame_index": 0, "latent": torch.empty(1, 24, 7, 30, 40), guide.GUIDE_POSITION: (x, 0)}]
native = PackedLayout(8, 12, 30, 60, 65, keyframes=keyframes)
result = guide.spatial_guide_layout(native, keyframes)
target = segment_positions(native, "video")[0].reshape(12, 15, 30, 3)
expected = target[:7, :, x // 32:x // 32 + 20].reshape(-1, 3)
torch.testing.assert_close(segment_positions(result, "cond")[0], expected, rtol=0, atol=1e-13)
self.assertEqual(segment_positions(result, "cond")[0].shape[0], patchify_video(keyframes[0]["latent"]).shape[0])
for kind in ("text", "audio", "video"):
torch.testing.assert_close(segment_positions(result, kind)[0], segment_positions(native, kind)[0], rtol=0, atol=0)
def test_same_size_is_native_equivalent(self):
keyframes = [{"resolved_frame_index": 3, "latent": torch.empty(1, 24, 2, 6, 10), guide.GUIDE_POSITION: (0, 0)}]
native = PackedLayout(4, 7, 6, 10, 37, keyframes=keyframes)
result = guide.spatial_guide_layout(native, keyframes)
self.assertEqual(result.segments, native.segments)
self.assertEqual(result.signature, native.signature)
for name in ("position_ids", "img_pos", "img_update", "audio_pos", "audio_update"):
torch.testing.assert_close(getattr(result, name), getattr(native, name), rtol=0, atol=1e-13)
def test_mixed_guides_audio_references_and_fractional_offsets(self):
keyframes = [
{"resolved_frame_index": 0, "latent": torch.empty(1, 24, 2, 4, 6), guide.GUIDE_POSITION: (17, 3), "audio_latent": torch.empty(1, 32, 2, 8)},
{"resolved_frame_index": 5, "latent": torch.empty(1, 24, 1, 6, 10)},
{"resolved_frame_index": 7, "latent": torch.empty(1, 24, 1, 8, 12), guide.GUIDE_POSITION: (-4, 9)},
]
refs = [{"kind": "image", "latent_h": 4, "latent_w": 4}]
native = PackedLayout(4, 7, 6, 10, 37, keyframes=keyframes, refs=refs)
before = native.position_ids.clone()
result = guide.spatial_guide_layout(native, keyframes)
for original, changed in zip(native.segments, result.segments):
if original[2] != "cond":
torch.testing.assert_close(native.position_ids[original[0]:original[1]], result.position_ids[changed[0]:changed[1]], rtol=0, atol=0)
original_guides = segment_positions(native, "cond")
new_guides = segment_positions(result, "cond")
torch.testing.assert_close(new_guides[1], original_guides[1], rtol=0, atol=0)
expected_offset = torch.tensor([3, 17], dtype=torch.float64) * 2 / math.sqrt(6 * 10)
torch.testing.assert_close(new_guides[0][0, 1:] - original_guides[0][0, 1:], expected_offset)
self.assertEqual([p.shape[0] for p in new_guides], [12, 15, 24])
self.assertEqual(result.img_update.sum().item(), 7 * 15)
self.assertEqual(result.img_pos.numel() + result.audio_pos.numel() + 4, result.seq_len)
torch.testing.assert_close(native.position_ids, before, rtol=0, atol=0)
def test_node_preserves_pixels_and_model_patches_are_scoped(self):
model = ModelPatcher(torch.nn.Module(), load_device=torch.device("cpu"), offload_device=torch.device("cpu"))
images = torch.rand(24, 48, 65, 3)
encoded = []
def encode(frames):
encoded.append(frames)
return torch.zeros(1, 24, 7, frames.shape[1] // 16, frames.shape[2] // 16)
positive = [[torch.zeros(1, 2, 8), {}], [torch.zeros(1, 3, 8), {"minimax_keyframes": [{"resolved_frame_index": 0}]}]]
latent = {"samples": NestedTensor([torch.zeros(1, 24, 12, 6, 10), torch.zeros(1, 32, 2, 65)])}
patched, output = guide.MiniMaxH3AddSpatialGuide.execute(model, positive, SimpleNamespace(encode=encode), latent, images, frame_idx=-39, x=17, y=3)
self.assertEqual(encoded[0].shape, (22, 64, 96, 3))
torch.testing.assert_close(encoded[0][:, :48, :65], images[:22], rtol=0, atol=0)
torch.testing.assert_close(encoded[0][:, :48, 65:], images[:22, :, -1:].expand(-1, -1, 31, -1), rtol=0, atol=0)
self.assertNotIn("minimax_keyframes", positive[0][1])
self.assertEqual(len(positive[1][1]["minimax_keyframes"]), 1)
self.assertEqual([len(c[1]["minimax_keyframes"]) for c in output], [1, 2])
self.assertNotIn("noise_mask", latent)
self.assertEqual(model.wrappers, {})
chained = guide.patch_spatial_guides(patched)
wrapper_type = comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE
self.assertEqual(len(chained.get_wrappers(wrapper_type, guide.GUIDE_POSITION)), 1)
keyframes = output[0][1]["minimax_keyframes"]
native = PackedLayout(2, 12, 6, 10, 65, keyframes=keyframes)
payload = {"keyframes": keyframes, "layout": native}
negative = {"layout": native}
sampler_model = SimpleNamespace(conds={
"positive": [{"model_conds": {"minimax_payload": comfy.conds.CONDConstant(payload)}}],
"negative": [{"model_conds": {"minimax_payload": comfy.conds.CONDConstant(negative)}}],
})
executor = comfy.patcher_extension.WrapperExecutor.new_executor(
lambda wrapped, value: value, chained.get_wrappers(wrapper_type, guide.GUIDE_POSITION))
self.assertEqual(executor.execute(sampler_model, 123), 123)
result = sampler_model.conds["positive"][0]["model_conds"]["minimax_payload"].cond["layout"]
self.assertEqual(segment_positions(result, "cond")[0].shape[0], 7 * 2 * 3)
self.assertIs(payload["layout"], native)
self.assertIs(sampler_model.conds["negative"][0]["model_conds"]["minimax_payload"].cond, negative)
def test_small_native_model_accepts_local_guide(self):
torch.manual_seed(3)
model = MiniMaxH3Model(hidden_size=8, num_layers=1, token_refiner_num_layers=0,
num_attention_heads=1, attention_head_dim=8, ffn_hidden_size=16,
timestep_input_dim=4, time_embed_hidden_size=8, time_embed_dim=4,
text_dim=8, rope_inv_freq_len=1, dtype=torch.float32,
device=torch.device("cpu"), operations=torch.nn)
model.rope.inv_freq.fill_(1.0)
video = torch.randn(1, 24, 2, 4, 6)
audio = torch.randn(1, 32, 2, 5)
context = torch.randn(1, 3, 8)
keyframes = [{"resolved_frame_index": 0, "latent": torch.randn(1, 24, 2, 2, 4), guide.GUIDE_POSITION: (32, 16)}]
native = PackedLayout(3, 2, 4, 6, 5, keyframes=keyframes)
payload = {"layout": guide.spatial_guide_layout(native, keyframes), "cond_video_latents": [keyframes[0]["latent"]]}
with patch("comfy.model_management.in_training", True):
result = model._forward([video, audio], torch.tensor([500.0]), context, minimax_payload=payload)
for actual, original in zip(result, (video, audio)):
self.assertEqual(actual.shape, original.shape)
self.assertTrue(torch.isfinite(actual).all())
def test_extension_registers_v3_node(self):
extension = asyncio.run(guide.comfy_entrypoint())
self.assertEqual(asyncio.run(extension.get_node_list()), [guide.MiniMaxH3AddSpatialGuide, guide.MiniMaxH3GuidePosition])
self.assertEqual(guide.MiniMaxH3AddSpatialGuide.define_schema().node_id, "MiniMaxH3AddSpatialGuide")
def test_position_outputs_pixel_coordinates_unchanged(self):
result = guide.MiniMaxH3GuidePosition.execute(640, 480, 960, 480, 160, -17)
self.assertEqual(result.result, (160, -17))
self.assertEqual(result.ui, {"guide_size": [640, 480], "canvas_size": [960, 480]})
schema = guide.MiniMaxH3GuidePosition.define_schema()
self.assertEqual([output.display_name for output in schema.outputs], ["x", "y"])
if __name__ == "__main__":
unittest.main()
web/guide_position.js
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import { app } from "../../scripts/app.js";
const DIMENSIONS = ["guide_width", "guide_height", "canvas_width", "canvas_height"];
const HEIGHT = 290;
function widget(node, name) {
return node.widgets?.find((item) => item.name === name);
}
function inputValue(node, name, visited = new Set()) {
if (visited.has(node)) return undefined;
visited = new Set(visited).add(node);
const slot = node.inputs?.findIndex((input) => input.name === name) ?? -1;
if (slot < 0 || node.inputs[slot].link == null) return widget(node, name)?.value;
const source = node.getInputNode(slot);
const link = node.getInputLink(slot);
if (!source || !link) return undefined;
const type = source.comfyClass || source.type;
if (type === "ComfySwitchNode") {
const selected = inputValue(source, "switch", visited);
return selected == null ? undefined : inputValue(source, selected ? "on_true" : "on_false", visited);
}
if (type === "Reroute") return inputValue(source, source.inputs[0].name, visited);
if (["PrimitiveInt", "PrimitiveBoolean", "PrimitiveNode", "easy int"].includes(type)) {
return inputValue(source, "value", visited);
}
return undefined;
}
function createPositionEditor(node) {
const root = document.createElement("div");
root.className = "h3-guide-position";
root.style.cssText = "width:100%;height:290px;display:flex;flex-direction:column;gap:6px;box-sizing:border-box;padding:6px;color:#dce5ef;font:12px sans-serif;";
const canvas = document.createElement("canvas");
canvas.style.cssText = "width:100%;height:218px;flex:1;min-height:100px;border:1px solid #435066;border-radius:5px;box-sizing:border-box;touch-action:none;cursor:grab;";
canvas.setAttribute("aria-label", "Drag guide position / 拖动参考区域");
const row = document.createElement("div");
row.style.cssText = "display:flex;gap:6px;align-items:center;";
const status = document.createElement("div");
status.style.cssText = "white-space:nowrap;font-size:11px;color:#a9b8cb;min-height:14px;";
const coordinates = document.createElement("span");
coordinates.style.marginLeft = "auto";
const context = canvas.getContext("2d");
let view;
let drag;
let liveKey;
let frame;
let removed = false;
function dimensions() {
const values = DIMENSIONS.map((name, index) => {
const linked = inputValue(node, name);
const stored = node.properties?.h3GuideSize?.[index];
return Number(linked ?? stored ?? widget(node, name)?.value);
});
return values.every((value) => Number.isFinite(value) && value > 0) ? values : null;
}
function position() {
return [Number(widget(node, "x").value), Number(widget(node, "y").value)];
}
function setPosition(x, y) {
for (const [name, value] of [["x", x], ["y", y]]) {
const target = widget(node, name);
target.value = Math.max(target.options?.min ?? -16384, Math.min(target.options?.max ?? 16384, Math.round(value)));
target.callback?.(target.value);
}
node.graph?.setDirtyCanvas(true, true);
draw();
}
function draw() {
if (removed) return;
const bounds = canvas.getBoundingClientRect();
if (!bounds.width || !bounds.height) return;
const dpr = window.devicePixelRatio || 1;
const width = Math.round(bounds.width * dpr);
const height = Math.round(bounds.height * dpr);
if (canvas.width !== width || canvas.height !== height) {
canvas.width = width;
canvas.height = height;
}
context.setTransform(dpr, 0, 0, dpr, 0, 0);
context.fillStyle = "#111923";
context.fillRect(0, 0, bounds.width, bounds.height);
const sizes = dimensions();
if (!sizes) {
status.textContent = "请填写参考宽高和画布宽高 / Enter dimensions";
return;
}
const [gw, gh, cw, ch] = sizes;
const [x, y] = position();
if (!drag) {
const left = Math.min(0, x), top = Math.min(0, y);
const right = Math.max(cw, x + gw), bottom = Math.max(ch, y + gh);
const scale = Math.min((bounds.width - 36) / (right - left), (bounds.height - 36) / (bottom - top));
view = { scale, ox: (bounds.width - (right - left) * scale) / 2 - left * scale,
oy: (bounds.height - (bottom - top) * scale) / 2 - top * scale };
}
const { scale, ox, oy } = view;
context.fillStyle = "#233247";
context.fillRect(ox, oy, cw * scale, ch * scale);
context.strokeStyle = "#7d8fa8";
context.lineWidth = 1;
context.strokeRect(ox, oy, cw * scale, ch * scale);
context.save();
context.beginPath();
context.rect(ox, oy, cw * scale, ch * scale);
context.clip();
context.strokeStyle = "#384b62";
for (let i = 1; i < 3; i++) {
context.beginPath();
context.moveTo(ox + cw * scale * i / 3, oy);
context.lineTo(ox + cw * scale * i / 3, oy + ch * scale);
context.moveTo(ox, oy + ch * scale * i / 3);
context.lineTo(ox + cw * scale, oy + ch * scale * i / 3);
context.stroke();
}
context.restore();
context.fillStyle = "rgba(64, 164, 217, 0.35)";
context.fillRect(ox + x * scale, oy + y * scale, gw * scale, gh * scale);
context.strokeStyle = "#65d5ff";
context.lineWidth = 2;
context.strokeRect(ox + x * scale, oy + y * scale, gw * scale, gh * scale);
context.fillStyle = "#d2f5ff";
context.font = "12px sans-serif";
context.textAlign = "center";
context.fillText("Guide", ox + (x + gw / 2) * scale, oy + (y + gh / 2) * scale + 4);
coordinates.textContent = `x ${x} · y ${y}`;
const unresolved = DIMENSIONS.some((name) => inputValue(node, name) == null);
status.textContent = `Guide ${gw}×${gh} → ${cw}×${ch}${unresolved ? " · 连线尺寸运行后同步" : ""}`;
}
function button(label, action) {
const element = document.createElement("button");
element.type = "button";
element.textContent = label;
element.style.cssText = "background:#26364b;color:#e1efff;border:1px solid #506783;border-radius:4px;padding:4px 8px;cursor:pointer;";
element.addEventListener("click", () => {
const sizes = dimensions();
if (!sizes) return;
node.graph?.beforeChange();
action(sizes);
node.graph?.afterChange();
});
row.appendChild(element);
}
button("居中 / Center", ([gw, gh, cw, ch]) => setPosition((cw - gw) / 2, (ch - gh) / 2));
button("左上 / Origin", () => setPosition(0, 0));
row.appendChild(coordinates);
root.append(canvas, row, status);
root.title = "拖动蓝框调整左上角坐标；按住 Shift 以 32 像素对齐。";
for (const type of ["pointerdown", "pointerup", "mousedown", "mouseup", "click", "dblclick"]) {
root.addEventListener(type, (event) => event.stopPropagation());
}
canvas.addEventListener("pointerdown", (event) => {
if (event.button !== 0 || !view || !dimensions()) return;
const bounds = canvas.getBoundingClientRect();
const [x, y] = position();
const [gw, gh] = dimensions();
const px = (event.clientX - bounds.left - view.ox) / view.scale;
const py = (event.clientY - bounds.top - view.oy) / view.scale;
if (px < x || py < y || px > x + gw || py > y + gh) return;
event.preventDefault();
node.graph?.beforeChange();
drag = { id: event.pointerId, x, y, px: event.clientX, py: event.clientY };
canvas.setPointerCapture(event.pointerId);
canvas.style.cursor = "grabbing";
});
canvas.addEventListener("pointermove", (event) => {
if (!drag || event.pointerId !== drag.id) return;
const step = event.shiftKey ? 32 : 1;
setPosition(Math.round((drag.x + (event.clientX - drag.px) / view.scale) / step) * step,
Math.round((drag.y + (event.clientY - drag.py) / view.scale) / step) * step);
});
function endDrag() {
if (!drag) return;
drag = null;
canvas.style.cursor = "grab";
node.graph?.afterChange();
draw();
}
canvas.addEventListener("lostpointercapture", endDrag);
canvas.addEventListener("pointerup", endDrag);
canvas.addEventListener("pointercancel", endDrag);
const domWidget = node.addDOMWidget("guide_position_preview", "div", root, {
serialize: false, hideOnZoom: false,
getMinHeight: () => HEIGHT, getMaxHeight: () => HEIGHT, getHeight: () => HEIGHT,
});
domWidget.computeSize = (width) => [width, HEIGHT];
const observer = new ResizeObserver(draw);
observer.observe(canvas);
for (const name of [...DIMENSIONS, "x", "y"]) {
const target = widget(node, name);
const original = target.callback;
target.callback = function (...args) {
const result = original?.apply(this, args);
draw();
return result;
};
}
// Linked primitives may change without calling this node's widget callbacks.
function refresh() {
if (removed) return;
const bounds = canvas.getBoundingClientRect();
const key = JSON.stringify([dimensions(), position(), bounds.width, bounds.height]);
if (key !== liveKey) {
liveKey = key;
draw();
}
frame = requestAnimationFrame(refresh);
}
frame = requestAnimationFrame(refresh);
node.setSize([Math.max(node.size[0], 390), Math.max(node.size[1], node.computeSize()[1])]);
return {
draw,
remove() {
removed = true;
cancelAnimationFrame(frame);
observer.disconnect();
},
};
}
app.registerExtension({
name: "MiniMaxH3.SpatialGuidePosition",
async beforeRegisterNodeDef(nodeType, nodeData) {
if (nodeData.name !== "MiniMaxH3GuidePosition") return;
const created = nodeType.prototype.onNodeCreated;
nodeType.prototype.onNodeCreated = function (...args) {
const result = created?.apply(this, args);
this.h3PositionEditor = createPositionEditor(this);
return result;
};
const configured = nodeType.prototype.onConfigure;
nodeType.prototype.onConfigure = function (...args) {
const result = configured?.apply(this, args);
requestAnimationFrame(() => this.h3PositionEditor?.draw());
return result;
};
const executed = nodeType.prototype.onExecuted;
nodeType.prototype.onExecuted = function (message, ...args) {
const result = executed?.call(this, message, ...args);
if (message.guide_size && message.canvas_size) {
this.properties ??= {};
this.properties.h3GuideSize = [...message.guide_size, ...message.canvas_size];
this.h3PositionEditor?.draw();
}
return result;
};
const removed = nodeType.prototype.onRemoved;
nodeType.prototype.onRemoved = function (...args) {
this.h3PositionEditor?.remove();
return removed?.apply(this, args);
};
},
});