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"""CPU regressions for general MiniMax H3 masked-target nodes."""
import importlib.util
import json
import os
import sys
import types
import torch
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
PACKAGE = "h3_general_mask_test_pkg"
class NestedTensor:
def __init__(self, parts):
self.parts = tuple(parts)
self.is_nested = True
def unbind(self):
return list(self.parts)
def _load(name):
path = os.path.join(ROOT, "%s.py" % name)
spec = importlib.util.spec_from_file_location(
"%s.%s" % (PACKAGE, name), path)
module = importlib.util.module_from_spec(spec)
sys.modules[spec.name] = module
spec.loader.exec_module(module)
return module
def main():
package = types.ModuleType(PACKAGE)
package.__path__ = [ROOT]
sys.modules[PACKAGE] = package
comfy = types.ModuleType("comfy")
comfy.__path__ = []
nested = types.ModuleType("comfy.nested_tensor")
nested.NestedTensor = NestedTensor
comfy.nested_tensor = nested
sys.modules["comfy"] = comfy
sys.modules["comfy.nested_tensor"] = nested
ops = _load("masking_ops")
_load("masking_support")
nodes = _load("masking_nodes")
support_calls = []
nodes.require_h3_mask_support = lambda operation: support_calls.append(
operation)
assert ops.floor_h3_frame_count(140) == 124
assert ops.h3_pixel_frames_for_video_latents(1) == 1
assert ops.h3_pixel_frames_for_video_latents(2) == 5
assert ops.h3_pixel_frames_for_video_latents(7) == 22
assert ops.h3_pixel_frames_for_video_latents(12) == 39
assert ops.h3_pixel_frames_for_video_latents(102) == 345
assert ops.h3_video_latents_for_pixel_frames(1) == 1
assert ops.h3_video_latents_for_pixel_frames(5) == 2
assert ops.h3_video_latents_for_pixel_frames(22) == 7
assert ops.h3_video_latent_frame_groups(7) == (
(0, 1), (1, 5), (5, 9), (9, 13),
(13, 17), (17, 18), (18, 22),
)
frames = torch.zeros((140, 32, 64, 3))
audio = {
"waveform": torch.zeros((1, 2, 300_000)),
"sample_rate": 48_000,
}
trimmed_frames, trimmed_audio, length, info = (
nodes.MiniMaxH3ContexTrimSourceAV().trim(frames, audio))
assert trimmed_frames.shape[0] == 124
assert trimmed_audio["waveform"].shape[-1] == 248_000
assert length == 124 and "dropped 16" in info
pixel_mask = torch.zeros((1, 64, 64))
pixel_mask[:, :16, :16] = 1.0
_raw, snapped, cells = ops.quantize_h3_pixel_mask(
pixel_mask, 1, 64, 64)
assert cells[0, 0].tolist() == [[1.0, 0.0], [0.0, 0.0]]
assert torch.all(snapped[:, :32, :32] == 1.0)
moving_preview_mask = torch.zeros((5, 64, 64))
moving_preview_mask[4, 0, 0] = 1.0
_raw, causal_preview, _cells = ops.quantize_h3_pixel_mask(
moving_preview_mask, 5, 64, 64)
assert not torch.count_nonzero(causal_preview[0])
assert torch.all(causal_preview[1:, :32, :32] == 1.0)
assert not torch.count_nonzero(causal_preview[1:, 32:, :])
video = torch.zeros((1, 16, 2, 4, 4))
source_audio = torch.zeros((1, 32, 2, 6))
target = {"samples": NestedTensor((video, source_audio))}
edit_mask = torch.zeros((1, 64, 64))
edit_mask[:, :32, :32] = 1.0
masked, mask_info = nodes.MiniMaxH3ContexMaskedTarget().apply(
target,
edit_mask,
"white = generate",
"preserve source audio",
)
video_mask, audio_mask = masked["noise_mask"].unbind()
assert video_mask.shape == (1, 1, 2, 4, 4)
assert audio_mask.shape == (1, 1, 2, 6)
assert torch.all(video_mask[..., :2, :2] == 1.0)
assert torch.all(video_mask[..., 2:, :] == 0.0)
assert not torch.count_nonzero(audio_mask)
assert "existing mask none" in mask_info
assert support_calls == ["general masked-target generation"]
assert "H3 exact causal/token max" in mask_info
tracked = torch.zeros((22, 64, 64))
tracked[4, 0, 0] = 1.0
tracked[5, -1, -1] = 0.5
tracked[21, 0, -1] = 1.0
exact_video = torch.zeros((1, 16, 7, 4, 4))
exact = ops.reduce_h3_mask_to_video_latent(tracked, exact_video)
assert exact.shape == (1, 1, 7, 4, 4)
assert not torch.count_nonzero(exact[:, :, 0])
assert torch.all(exact[:, :, 1, :2, :2] == 1.0)
assert torch.all(exact[:, :, 2, 2:, 2:] == 0.5)
assert not torch.count_nonzero(exact[:, :, 3:6])
assert torch.all(exact[:, :, 6, :2, 2:] == 1.0)
static = ops.reduce_h3_mask_to_video_latent(
torch.ones((1, 64, 64)), exact_video)
assert torch.all(static == 1.0)
try:
ops.reduce_h3_mask_to_video_latent(
torch.ones((21, 64, 64)), exact_video)
except ValueError as exc:
assert "exactly 22 tracked masks" in str(exc)
assert "Loop Mask Slice" in str(exc)
else:
raise AssertionError("off-grid tracked mask batch was accepted")
legacy = ops.resize_mask_to_video_latent(
torch.stack((torch.zeros((8, 8)), torch.ones((8, 8)))),
exact_video,
)
assert legacy.shape == (1, 1, 7, 4, 4)
legacy_target, legacy_info = nodes.MiniMaxH3ContexMaskedTarget().apply(
target,
torch.stack((torch.zeros((8, 8)), torch.ones((8, 8)))),
"white = generate",
"preserve source audio",
mask_conversion="legacy trilinear",
)
assert legacy_target["noise_mask"].unbind()[0].shape == (
1, 1, 2, 4, 4)
assert "mask legacy trilinear" in legacy_info
old_video_mask = torch.ones((1, 1, 2, 4, 4))
old_video_mask[:, :, :1] = 0.0
old_audio_mask = torch.ones((1, 1, 2, 6))
old_audio_mask[..., :2] = 0.0
prefixed = {
"samples": target["samples"],
"noise_mask": NestedTensor((old_video_mask, old_audio_mask)),
}
combined, combined_info = nodes.MiniMaxH3ContexMaskedTarget().apply(
prefixed,
torch.ones((1, 64, 64)),
"white = generate",
"generate all audio",
)
combined_video, combined_audio = combined["noise_mask"].unbind()
assert not torch.count_nonzero(combined_video[:, :, :1])
assert torch.all(combined_video[:, :, 1:] == 1.0)
assert not torch.count_nonzero(combined_audio[..., :2])
assert torch.all(combined_audio[..., 2:] == 1.0)
assert "existing mask intersected" in combined_info
temporal = torch.cat((
torch.zeros((1, 8, 8)),
torch.ones((4, 8, 8)),
))
followed, _ = nodes.MiniMaxH3ContexMaskedTarget().apply(
target,
temporal,
"white = generate",
"follow video mask",
)
followed_audio = followed["noise_mask"].unbind()[1]
assert float(followed_audio[..., 0].max()) == 0.0
assert float(followed_audio[..., -1].min()) == 1.0
try:
nodes.MiniMaxH3ContexMaskedTarget().apply(
target,
edit_mask,
"white = generate",
"custom audio mask",
)
except ValueError as exc:
assert "requires the optional audio_mask" in str(exc)
else:
raise AssertionError("custom audio mode accepted a missing audio mask")
preview_mask, preview, preview_info = (
nodes.MiniMaxH3ContexMaskGridPreview().preview(
torch.zeros((5, 64, 64, 3)),
edit_mask,
"runtime exact (latent max)",
0,
2,
0.38,
True,
True,
))
assert preview_mask.shape == (5, 64, 64)
assert preview.shape == (1, 64, 64, 3)
assert "preview frame 2/4" in preview_info
preserve_input = torch.ones((5, 64, 64))
preserve_input[:, :16, :16] = 0.0
preserve_snapped, _preview, preserve_info = (
nodes.MiniMaxH3ContexMaskGridPreview().preview(
torch.zeros((5, 64, 64, 3)),
preserve_input,
"runtime exact (latent max)",
0,
0,
0.38,
False,
False,
"white = preserve",
))
assert torch.all(preserve_snapped[:, :32, :32] == 0.0)
assert torch.all(preserve_snapped[:, 32:, :] == 1.0)
assert "white = preserve" in preserve_info
mask_schema = nodes.MiniMaxH3ContexMaskedTarget.INPUT_TYPES()
conversion = mask_schema["optional"]["mask_conversion"]
assert conversion[0] == list(ops.MASK_CONVERSION_MODES)
assert conversion[1]["default"] == ops.MASK_CONVERSION_MODES[0]
expected_ids = {
"MiniMaxH3ContexTrimSourceAV",
"MiniMaxH3ContexMaskedTarget",
"MiniMaxH3ContexMaskGridPreview",
}
assert expected_ids == set(nodes.NODE_CLASS_MAPPINGS)
old_pack_ids = {
"MiniMaxH3TrimSourceAV",
"MiniMaxH3SetGenerationMask",
"MiniMaxH3MaskGridPreview",
"MiniMaxH3PerRowMaskPatch",
}
assert not old_pack_ids.intersection(nodes.NODE_CLASS_MAPPINGS)
workflow_path = os.path.join(
ROOT, "example_workflows",
"Masked Video Inpaint - MiniMax H3 0.6.json")
with open(workflow_path, "r", encoding="utf-8") as handle:
workflow = json.load(handle)
workflow_nodes = {item["id"]: item for item in workflow["nodes"]}
workflow_types = {item["type"] for item in workflow["nodes"]}
assert {
"MiniMaxH3ContexMaskedTarget",
"MiniMaxH3ContexMaskGridPreview",
"MiniMaxH3ContexLoopSourceAVTarget",
"MiniMaxH3ContexLoopMaskSlice",
"MiniMaxH3ChainLoopStart",
"MiniMaxH3ChainLoopEnd",
} <= workflow_types
assert not workflow_types.intersection({
"LTXVConcatAVLatent", "LTXVSeparateAVLatent"})
assert not old_pack_ids.intersection(workflow_types)
seen_links = set()
for link in workflow["links"]:
link_id, source_id, source_slot, target_id, target_slot, link_type = link
assert link_id not in seen_links
seen_links.add(link_id)
source = workflow_nodes[source_id]["outputs"][source_slot]
target_input = workflow_nodes[target_id]["inputs"][target_slot]
assert link_id in source["links"]
assert target_input["link"] == link_id
assert isinstance(link_type, str) and link_type
assert workflow["last_link_id"] == max(seen_links)
assert workflow["last_node_id"] == max(workflow_nodes)
masked_node = next(
item for item in workflow["nodes"]
if item["type"] == "MiniMaxH3ContexMaskedTarget")
sampler = next(
item for item in workflow["nodes"]
if item["type"] == "SamplerCustomAdvanced")
mask_link = next(
link for link in workflow["links"]
if link[0] == sampler["inputs"][4]["link"])
assert mask_link[1:3] == [masked_node["id"], 0]
source_target = next(
item for item in workflow["nodes"]
if item["type"] == "MiniMaxH3ContexLoopSourceAVTarget")
chain_context = next(
item for item in workflow["nodes"]
if item["type"] == "MiniMaxH3ChainContext")
source_target_link = next(
link for link in workflow["links"]
if link[0] == chain_context["inputs"][3]["link"])
assert source_target_link[1:3] == [source_target["id"], 0]
masked_source_link = next(
link for link in workflow["links"]
if link[0] == masked_node["inputs"][0]["link"])
assert masked_source_link[1:3] == [chain_context["id"], 3]
sigma = next(
item for item in workflow["nodes"]
if item["type"] == "MiniMaxH3SigmaShift")
guider = next(
item for item in workflow["nodes"] if item["type"] == "BasicGuider")
scheduler = next(
item for item in workflow["nodes"] if item["type"] == "BasicScheduler")
links_by_id = {link[0]: link for link in workflow["links"]}
def model_origin(item):
link = links_by_id[item["inputs"][0]["link"]]
source = workflow_nodes[link[1]]
while source["type"] == "Reroute":
link = links_by_id[source["inputs"][0]["link"]]
source = workflow_nodes[link[1]]
return source["id"], link[2]
assert model_origin(guider) == (sigma["id"], 0)
assert model_origin(scheduler) == (sigma["id"], 0)
print(
"general H3 masking: AV trim, 32px grid, spatial/temporal masks, "
"custom audio control, prefix-safe intersection, and synchronized "
"Chain Loop example wiring pass")
if __name__ == "__main__":
main()
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