From 519d7bda8decc81f71d7c2e38dbf4742257423bb Mon Sep 17 00:00:00 2001
From: meareabc <74286633+meareabc@users.noreply.github.com>
Date: Wed, 28 Oct 2026 09:03:12 +0800
Subject: [PATCH] Add MiniMax H3 spatial guides and position editor
---
.gitignore | 7 +
LICENSE | 674 ++++++++++++++++++++++++++++++++++++
README.md | 48 +++
__init__.py | 158 +++++++++
docs/position-editor.png | Bin 0 -> 3376 bytes
pyproject.toml | 14 +
tests/test_spatial_guide.py | 147 ++++++++
web/guide_position.js | 259 ++++++++++++++
8 files changed, 1307 insertions(+)
create mode 100644 .gitignore
create mode 100644 LICENSE
create mode 100644 README.md
create mode 100644 __init__.py
create mode 100644 docs/position-editor.png
create mode 100644 pyproject.toml
create mode 100644 tests/test_spatial_guide.py
create mode 100644 web/guide_position.js
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..c235f5c
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,7 @@
+__pycache__/
+*.py[cod]
+.pytest_cache/
+*.egg-info/
+dist/
+build/
+.env
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..f288702
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
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+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
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+IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+ 16. Limitation of Liability.
+
+ IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+
+ Copyright (C)
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+
+Also add information on how to contact you by electronic and paper mail.
+
+ If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+ Copyright (C)
+ This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+ This is free software, and you are welcome to redistribute it
+ under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
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+might be different; for a GUI interface, you would use an "about box".
+
+ You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+.
+
+ The GNU General Public License does not permit incorporating your program
+into proprietary programs. If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
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+Public License instead of this License. But first, please read
+.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..815714c
--- /dev/null
+++ b/README.md
@@ -0,0 +1,48 @@
+# 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.
+
+
+
+## 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。
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..a02adfe
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,158 @@
+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()
diff --git a/docs/position-editor.png b/docs/position-editor.png
new file mode 100644
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+dependencies = []
+
+[project.urls]
+Repository = "https://github.com/meareabc/ComfyUI-MiniMaxH3-SpatialGuide"
+
+[tool.comfy]
+DisplayName = "MiniMax H3 Spatial Guide"
diff --git a/tests/test_spatial_guide.py b/tests/test_spatial_guide.py
new file mode 100644
index 0000000..4ce36d4
--- /dev/null
+++ b/tests/test_spatial_guide.py
@@ -0,0 +1,147 @@
+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()
diff --git a/web/guide_position.js b/web/guide_position.js
new file mode 100644
index 0000000..b5588a0
--- /dev/null
+++ b/web/guide_position.js
@@ -0,0 +1,259 @@
+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);
+ };
+ },
+});