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feat: Add include mode to layer filters

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silveroxides
2026-09-11 03:50:49 +02:00
parent 864ed422d8
commit f691ddc584
24 changed files with 727 additions and 117 deletions
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README.md
+12
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@@ -11,6 +11,18 @@ A collection of ComfyUI custom nodes for inspecting, modifying, merging, and cre
- **LoRA Extraction** – Extract LoRA adapters from model pairs using various SVD rank selection methods (Fixed, Ratio, Quantile, Knee-detection, Frobenius-norm)
- **Diffusion Model Dtype Conversion** – Stream models to fp32, fp16, or bf16 while preserving excluded tensor dtypes
## Layer filters
Nodes with `exclude_patterns` or `skip_patterns` have an appended `include_mode` toggle
(off by default). Turn it on to process only layers matching that same field,
using the node's existing regex or glob syntax. An empty include filter selects
nothing. Nonmatches follow the node's usual exclusion behavior: preserve the
source/anchor layer for pattern-exclusion mergers, resize, or conversion; omit
it from analysis or extraction. LoRA Merge to Model retains its existing skip
semantics: nonmatching base tensors are omitted from the saved model, while
guarded low-bit base tensors are always preserved. Its filter uses regex only.
`discard_patterns`, where available, still takes precedence.
## Example Workflows
<p align="center">
docs/model_analysis.md
+6
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@@ -4,6 +4,12 @@ These nodes compare two safetensors models without merging them or writing an
output file. Separate nodes are provided for checkpoints, diffusion models,
text encoders, LoRAs, and embeddings.
`exclude_patterns` accepts one regex per line, or glob patterns when
`glob_patterns` is enabled. By default, matches are excluded from metrics and
topology counts. Enable `include_mode` to analyze only matches instead; an empty
include filter selects no tensors. The same field and matching syntax are used
in both modes.
## Outputs
`comparison_report` contains conventional numerical differences and topology
nodes/consensus_merger.py
+17 -3
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@@ -883,6 +883,7 @@ def _cwb_merge_metadata(
"force_clear_cache",
"override_dtype",
"include_1d_diffs",
"include_mode",
"counterfactual_weight_sweep",
"embedding_coalesce",
"target_vector_count",
@@ -897,9 +898,10 @@ def _cwb_merge_metadata(
metadata["merge_options"][key] = params[key]
exclude_patterns = params["exclude_patterns"].split()
discard_patterns = params["discard_patterns"].split()
if exclude_patterns or discard_patterns:
if exclude_patterns or discard_patterns or params.get("include_mode", False):
metadata["filters"] = {
"exclude_patterns": exclude_patterns,
"include_mode": params.get("include_mode", False),
"discard_patterns": discard_patterns,
"glob_patterns": params["glob_patterns"],
}
@@ -1794,6 +1796,7 @@ class ConsensusMergerLogic:
requested_dtype = _requested_dtype(params["save_dtype"])
mismatch_mode = params["mismatch_mode"]
glob_mode = params["glob_patterns"]
include_mode = params.get("include_mode", False)
exclude = _compile_patterns(params["exclude_patterns"], glob_mode=glob_mode)
discard = _compile_patterns(params["discard_patterns"], glob_mode=glob_mode)
pbar = comfy.utils.ProgressBar(len(keys))
@@ -1834,7 +1837,8 @@ class ConsensusMergerLogic:
secondary_only = not primary_owned
preserve_index = 0 if primary_owned else source_indices[0]
guarded = any(key in low_bit_sets[i] for i in source_indices)
excluded = _matches_any_pattern(key, exclude, glob_mode=glob_mode)
matched = _matches_any_pattern(key, exclude, glob_mode=glob_mode)
excluded = not matched if include_mode else matched
if guarded or excluded:
write_preserved_tensor(
writer,
@@ -2058,6 +2062,7 @@ class ConsensusMergerLogic:
mismatch_mode = params["mismatch_mode"]
include_1d = params.get("include_1d_diffs", False)
glob_mode = params["glob_patterns"]
include_mode = params.get("include_mode", False)
exclude = _compile_patterns(params["exclude_patterns"], glob_mode=glob_mode)
discard = _compile_patterns(params["discard_patterns"], glob_mode=glob_mode)
written = set()
@@ -2266,10 +2271,11 @@ class ConsensusMergerLogic:
keys is not None and has_non_alpha_low_bit(keys, low_bit_sets[index])
for index, _, keys in matches
)
excluded = any(
matched = any(
_matches_any_pattern(key, exclude, glob_mode=glob_mode)
for key in layer_keys
)
excluded = not matched if include_mode else matched
companion_bearing = any(
keys is not None and layer_has_companions(keys)
for _, _, keys in matches
@@ -2647,6 +2653,11 @@ def _common_inputs(
default=False,
tooltip="Force generated tensors to save_dtype; guarded tensors and enabled 1D direct diffs are exempt.",
),
io.Boolean.Input(
"include_mode",
default=False,
tooltip="Use Exclude Patterns as a whitelist instead. Only matching layers are merged; nonmatching layers are preserved from the anchor.",
),
])
return inputs
@@ -2705,11 +2716,13 @@ class _CWBMergerNode(io.ComfyNode):
cls.DEFAULT_PRESET,
)
if cls.LORA_MODE:
include_mode = inputs.pop()
inputs.append(io.Boolean.Input(
"include_1d_diffs",
default=False,
tooltip="CWB-merge 1D direct diffs as FP32. Disabled preserves Model A.",
))
inputs.append(include_mode)
return io.Schema(
node_id=cls.NODE_ID,
display_name=cls.DISPLAY_NAME,
@@ -2838,6 +2851,7 @@ class CWBLoRAMultiMerger(io.ComfyNode):
io.Boolean.Input("override_dtype", default=False, tooltip="Force generated floating factors to the requested save dtype."),
io.Boolean.Input("include_1d_diffs", default=False, tooltip="CWB-merge 1D direct differences as FP32 instead of preserving the anchor."),
io.Boolean.Input("counterfactual_weight_sweep", default=False, tooltip="Evaluate alpha, beta, similarity-threshold, DSC, and comfort-bandpass weight combinations from each already-computed consensus similarity vector and append them to the report without additional model loads or saved outputs."),
io.Boolean.Input("include_mode", default=False, tooltip="Use Exclude Patterns as a whitelist instead. Only matching layers are merged; nonmatching layers are preserved from the anchor."),
],
outputs=[
io.AnyType.Output(display_name="output_filename"),
nodes/cwb_delta_lora_merger.py
+11 -1
View File
@@ -202,6 +202,7 @@ def _build_layer_units(
exclude_patterns,
discard_patterns,
glob_patterns,
include_mode=False,
):
units = []
stream_keys = [[] for _ in handlers]
@@ -213,7 +214,8 @@ def _build_layer_units(
if _matches_pattern(output_key, discard_patterns, glob_patterns):
rejected += 1
continue
preserve = _matches_pattern(output_key, exclude_patterns, glob_patterns)
matched = _matches_pattern(output_key, exclude_patterns, glob_patterns)
preserve = not matched if include_mode else matched
candidates = []
shapes = {}
for index, block in sorted(mapped):
@@ -414,6 +416,7 @@ class DeltaCWBLoRAMergerLogic:
discard_patterns,
glob_patterns,
force_clear_cache,
include_mode=False,
):
prepare_for_large_operation(
sum(estimate_model_size(path) for path in lora_paths) * 2.5,
@@ -439,6 +442,7 @@ class DeltaCWBLoRAMergerLogic:
compiled_excludes,
compiled_discards,
glob_patterns,
include_mode,
)
report.rejected_unsupported_layers = rejected
cursors = [
@@ -547,6 +551,7 @@ class DeltaCWBLoRAMergerLogic:
kwargs["discard_patterns"],
kwargs["glob_patterns"],
kwargs["force_clear_cache"],
kwargs.get("include_mode", False),
)
return io.NodeOutput(filename, report)
@@ -638,6 +643,11 @@ def _fixed_inputs(count, default_filename):
default=True,
tooltip="Clear Python and CUDA caches before each layer to reduce retained memory.",
),
io.Boolean.Input(
"include_mode",
default=False,
tooltip="Use Exclude Patterns as a whitelist instead. Only matching layers are merged; nonmatching layers are preserved from LoRA 1.",
),
])
return inputs
nodes/dora_extract_wd.py
+14 -12
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@@ -497,6 +497,7 @@ def extract_dora_from_files(
force_clear_cache: bool = True,
glob_skip_patterns: bool = False,
knee_probe_offset: int = 32,
include_mode: bool = False,
) -> None:
"""
Extract LoRA from difference between two models, writing incrementally to disk.
@@ -543,7 +544,7 @@ def extract_dora_from_files(
work_units = [
(key, key, key if key in keys_b else None)
for key in weight_keys
if not _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns)
if (not _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns) if not include_mode else _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns))
and not (key not in keys_b and mismatch_mode == "skip")
]
pbar = comfy.utils.ProgressBar(len(work_units))
@@ -554,7 +555,7 @@ def extract_dora_from_files(
weight_diff = None
layer_device = device
if _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns):
if (_matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns) if not include_mode else not _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns)):
return "skipped", None
# Load tensors with pinned memory for CUDA
@@ -773,6 +774,7 @@ def _get_common_inputs():
io.Boolean.Input("glob_skip_patterns", default=False,
tooltip="When True, skip_patterns use glob syntax (* = any sequence, ? = any char, dots are literal). "
"When False (default), patterns are Python regex matched as substrings."),
io.Boolean.Input("include_mode", default=False, tooltip="Use Skip Patterns as a whitelist instead. Only matching layers are extracted; an empty whitelist extracts nothing."),
]
@@ -803,7 +805,7 @@ class DoRAExtractFixed(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_dim, conv_dim, svd_niter, chunk_large_layers,
clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
@@ -813,7 +815,7 @@ class DoRAExtractFixed(io.ComfyNode):
model_a_path, model_b_path, "fixed", linear_dim, conv_dim,
device, save_dtype, output_path, linear_dim, conv_dim,
clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers, svd_niter,
lazy_load, force_clear_cache, glob_skip_patterns
lazy_load, force_clear_cache, glob_skip_patterns, include_mode=include_mode
)
return io.NodeOutput(output_name)
@@ -850,7 +852,7 @@ class DoRAExtractRatio(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_ratio, conv_ratio, probe_offset, linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
@@ -861,7 +863,7 @@ class DoRAExtractRatio(io.ComfyNode):
device, save_dtype, output_path, linear_max_rank, conv_max_rank,
clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache,
glob_skip_patterns=glob_skip_patterns, knee_probe_offset=probe_offset
glob_skip_patterns=glob_skip_patterns, knee_probe_offset=probe_offset, include_mode=include_mode
)
return io.NodeOutput(output_name)
@@ -898,7 +900,7 @@ class DoRAExtractQuantile(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_quantile, conv_quantile, probe_offset, linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
@@ -909,7 +911,7 @@ class DoRAExtractQuantile(io.ComfyNode):
device, save_dtype, output_path, linear_max_rank, conv_max_rank,
clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache,
glob_skip_patterns=glob_skip_patterns, knee_probe_offset=probe_offset
glob_skip_patterns=glob_skip_patterns, knee_probe_offset=probe_offset, include_mode=include_mode
)
return io.NodeOutput(output_name)
@@ -945,7 +947,7 @@ class DoRAExtractKnee(io.ComfyNode):
def execute(cls, model_a, model_b, knee_method, knee_probe_offset,
linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
@@ -956,7 +958,7 @@ class DoRAExtractKnee(io.ComfyNode):
device, save_dtype, output_path, linear_max_rank, conv_max_rank,
clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache,
glob_skip_patterns=glob_skip_patterns,
glob_skip_patterns=glob_skip_patterns, include_mode=include_mode,
knee_probe_offset=knee_probe_offset,
)
@@ -994,7 +996,7 @@ class DoRAExtractFrobenius(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_target, conv_target, probe_offset, linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
@@ -1005,7 +1007,7 @@ class DoRAExtractFrobenius(io.ComfyNode):
device, save_dtype, output_path, linear_max_rank, conv_max_rank,
clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache,
glob_skip_patterns=glob_skip_patterns, knee_probe_offset=probe_offset
glob_skip_patterns=glob_skip_patterns, knee_probe_offset=probe_offset, include_mode=include_mode
)
return io.NodeOutput(output_name)
nodes/dora_learned_wd.py
+13 -12
View File
@@ -354,6 +354,7 @@ def extract_dora_learned_from_files(
force_clear_cache: bool = True,
glob_skip_patterns: bool = False,
knee_probe_offset: int = 32,
include_mode: bool = False,
) -> None:
"""
Extract Learned DoRA from difference between two models.
@@ -380,7 +381,7 @@ def extract_dora_learned_from_files(
work_units = [
(key, key, key if key in keys_b else None)
for key in weight_keys
if not _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns)
if (not _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns) if not include_mode else _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns))
and not (key not in keys_b and mismatch_mode == "skip")
]
pbar = comfy.utils.ProgressBar(len(work_units))
@@ -393,7 +394,7 @@ def extract_dora_learned_from_files(
tensor_ft = None
layer_device = device
if _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns):
if (_matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns) if not include_mode else not _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns)):
return "skipped", None
if key not in keys_b:
@@ -597,7 +598,7 @@ class DoRALearnedExtractFixed(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_dim, conv_dim, optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, svd_niter, chunk_large_layers,
clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
@@ -607,7 +608,7 @@ class DoRALearnedExtractFixed(io.ComfyNode):
model_a_path, model_b_path, "fixed", linear_dim, conv_dim,
device, save_dtype, output_path, optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, linear_dim, conv_dim,
clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers, svd_niter,
lazy_load, force_clear_cache, glob_skip_patterns
lazy_load, force_clear_cache, glob_skip_patterns, include_mode=include_mode
)
return io.NodeOutput(output_name)
@@ -641,7 +642,7 @@ class DoRALearnedExtractRatio(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_ratio, conv_ratio, probe_offset, linear_max_rank, conv_max_rank,
optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, chunk_large_layers, clamp_quantile, min_diff, mismatch_mode,
output_filename, save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
output_filename, save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
@@ -651,7 +652,7 @@ class DoRALearnedExtractRatio(io.ComfyNode):
model_a_path, model_b_path, "ratio", linear_ratio, conv_ratio,
device, save_dtype, output_path, optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, linear_max_rank, conv_max_rank,
clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns, include_mode=include_mode,
knee_probe_offset=probe_offset
)
@@ -686,7 +687,7 @@ class DoRALearnedExtractQuantile(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_quantile, conv_quantile, probe_offset, linear_max_rank, conv_max_rank,
optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, chunk_large_layers, clamp_quantile, min_diff, mismatch_mode,
output_filename, save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
output_filename, save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
@@ -696,7 +697,7 @@ class DoRALearnedExtractQuantile(io.ComfyNode):
model_a_path, model_b_path, "quantile", linear_quantile, conv_quantile,
device, save_dtype, output_path, optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, linear_max_rank, conv_max_rank,
clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns, include_mode=include_mode,
knee_probe_offset=probe_offset
)
@@ -731,7 +732,7 @@ class DoRALearnedExtractKnee(io.ComfyNode):
def execute(cls, model_a, model_b, knee_method, knee_probe_offset,
linear_max_rank, conv_max_rank,
optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, chunk_large_layers, clamp_quantile, min_diff, mismatch_mode,
output_filename, save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
output_filename, save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
@@ -742,7 +743,7 @@ class DoRALearnedExtractKnee(io.ComfyNode):
device, save_dtype, output_path, optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, linear_max_rank, conv_max_rank,
clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache,
glob_skip_patterns=glob_skip_patterns, knee_probe_offset=knee_probe_offset
glob_skip_patterns=glob_skip_patterns, knee_probe_offset=knee_probe_offset, include_mode=include_mode
)
return io.NodeOutput(output_name)
@@ -776,7 +777,7 @@ class DoRALearnedExtractFrobenius(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_target, conv_target, probe_offset, linear_max_rank, conv_max_rank,
optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, chunk_large_layers, clamp_quantile, min_diff, mismatch_mode,
output_filename, save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
output_filename, save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
@@ -786,7 +787,7 @@ class DoRALearnedExtractFrobenius(io.ComfyNode):
model_a_path, model_b_path, "sv_fro", linear_target, conv_target,
device, save_dtype, output_path, optimize_iters, learning_rate, optimizer, lr_schedule, lr_patience, lr_factor, lr_cooldown, early_stop_loss, early_stop_stall, early_stop_lr, linear_max_rank, conv_max_rank,
clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns, include_mode=include_mode,
knee_probe_offset=probe_offset
)
nodes/dtype_conversion.py
+13 -4
View File
@@ -55,6 +55,7 @@ def convert_diffusion_model_dtype(
exclude_patterns: str,
output_filename: str,
reference_model: str | None = None,
include_mode: bool = False,
) -> tuple[str, str]:
try:
destination_dtype = TARGET_DTYPES[target_dtype]
@@ -104,7 +105,7 @@ def convert_diffusion_model_dtype(
key, source = batch[0]
original_dtypes[str(source.dtype).removeprefix("torch.")] += 1
output = source
if _is_excluded(key, exclusions):
if _is_excluded(key, exclusions) != include_mode:
counts["excluded"] += 1
elif not source.is_floating_point():
counts["non_floating"] += 1
@@ -175,7 +176,8 @@ class DiffusionModelDtypeConversion(io.ComfyNode):
description=(
"Streams a diffusion-model safetensors file into fp32, fp16, "
"or bf16. Floating tensors matching an exclusion regex retain "
"their original dtype; non-floating tensors are always preserved."
"their original dtype, or enable Include Mode to convert only matches; "
"non-floating tensors are always preserved."
),
inputs=[
io.Combo.Input(
@@ -185,7 +187,7 @@ class DiffusionModelDtypeConversion(io.ComfyNode):
),
io.Combo.Input(
"target_dtype", options=["fp32", "fp16", "bf16"],
tooltip="Target dtype for floating tensors not matched by an exclusion pattern.",
tooltip="Target dtype for floating tensors selected by the layer filter.",
),
io.Combo.Input(
"reference_model",
@@ -199,13 +201,18 @@ class DiffusionModelDtypeConversion(io.ComfyNode):
multiline=True,
tooltip=(
"Optional Python regex patterns, one per line. Matching "
"tensor keys retain their original dtype."
"tensor keys retain their original dtype, or are the only keys "
"converted when Include Mode is enabled."
),
),
io.String.Input(
"output_filename", default="converted_model",
tooltip="Output filename without extension, written under ComfyUI's diffusion-model directory.",
),
io.Boolean.Input(
"include_mode", default=False,
tooltip="Use Exclude Patterns as an include-only filter. Only matching tensors are converted; an empty filter converts nothing. Nonmatches retain their original dtype.",
),
],
outputs=[
io.AnyType.Output(display_name="output_path"),
@@ -222,6 +229,7 @@ class DiffusionModelDtypeConversion(io.ComfyNode):
reference_model: str,
exclude_patterns: str,
output_filename: str,
include_mode: bool = False,
) -> io.NodeOutput:
output_path, report = convert_diffusion_model_dtype(
model_name,
@@ -229,5 +237,6 @@ class DiffusionModelDtypeConversion(io.ComfyNode):
exclude_patterns,
output_filename,
reference_model,
include_mode,
)
return io.NodeOutput(output_path, report)
nodes/lodestone_merger.py
+7 -5
View File
@@ -92,7 +92,7 @@ def _is_cuda_oom(error):
)
def _build_units(layer_map, infos, handlers, mismatch_mode, exclude_patterns, discard_patterns, glob_patterns):
def _build_units(layer_map, infos, handlers, mismatch_mode, exclude_patterns, discard_patterns, glob_patterns, include_mode=False):
units = []
stream_keys = [[] for _ in handlers]
for core, mapped in layer_map.items():
@@ -101,7 +101,8 @@ def _build_units(layer_map, infos, handlers, mismatch_mode, exclude_patterns, di
output_key = canonical_lora_key(core, "diff")
if _matches(output_key, discard_patterns, glob_patterns):
continue
preserve = _matches(output_key, exclude_patterns, glob_patterns)
matched = _matches(output_key, exclude_patterns, glob_patterns)
preserve = not matched if include_mode else matched
sources = []
for index, block in sorted(mapped):
roles = infos[index]["pairs"][block]
@@ -178,7 +179,7 @@ def _process(unit, loaded, method, save_dtype, requested_device):
def merge_lodestone_loras(lora_paths, method, device, save_dtype, output_filename,
mismatch_mode="skip", exclude_patterns="", discard_patterns="",
glob_patterns=False, verbose=True):
glob_patterns=False, verbose=True, include_mode=False):
operation = "Lodestone LoRA Merge"
prepare_for_large_operation(sum(estimate_model_size(path) for path in lora_paths) * 2.5, torch.device(device))
handlers = [MemoryEfficientSafeOpen(path, low_memory=True) for path in lora_paths]
@@ -192,7 +193,7 @@ def merge_lodestone_loras(lora_paths, method, device, save_dtype, output_filenam
layer_map = build_lora_layer_map(infos, None)
validate_canonical_blocks(layer_map, operation)
units, stream_keys = _build_units(
layer_map, infos, handlers, mismatch_mode, exclude_patterns, discard_patterns, glob_patterns
layer_map, infos, handlers, mismatch_mode, exclude_patterns, discard_patterns, glob_patterns, include_mode
)
cursors = [
AsyncTensorCursor(handler, keys, pin_memory=torch.device(device).type == "cuda")
@@ -244,6 +245,7 @@ def _controls(default_filename):
io.String.Input("exclude_patterns", default="", multiline=True, tooltip="Optional layer-name patterns to copy from LoRA 1 instead of merging. Enter one pattern per line; useful for protecting specific blocks."),
io.String.Input("discard_patterns", default="", multiline=True, tooltip="Optional layer-name patterns to leave out of the saved LoRA completely. Enter one pattern per line; discarded layers cannot affect the model."),
io.Boolean.Input("glob_patterns", default=False, tooltip="Choose filter syntax. Off uses regular expressions; on uses shell-style globs where * matches any text and ? matches one character."),
io.Boolean.Input("include_mode", default=False, tooltip="Use Exclude Patterns as a whitelist instead. Only matching layers are merged; nonmatching layers are preserved from LoRA 1."),
]
@@ -252,7 +254,7 @@ def _execute(kwargs, count):
dtype = {"fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16}[kwargs["save_dtype"]]
merge_lodestone_loras(paths, kwargs["calc_mode"], kwargs["process_device"], dtype,
kwargs["output_filename"], kwargs["mismatch_mode"],
kwargs["exclude_patterns"], kwargs["discard_patterns"], kwargs["glob_patterns"])
kwargs["exclude_patterns"], kwargs["discard_patterns"], kwargs["glob_patterns"], include_mode=kwargs.get("include_mode", False))
return canonical_model_artifact_path("loras", kwargs["output_filename"])[1]
nodes/lora_extract_svd.py
+14 -12
View File
@@ -499,6 +499,7 @@ def extract_lora_from_files(
glob_skip_patterns: bool = False,
include_1d_diffs: bool = False,
knee_probe_offset: int = 32,
include_mode: bool = False,
) -> None:
"""
Extract LoRA from difference between two models, writing incrementally to disk.
@@ -560,7 +561,7 @@ def extract_lora_from_files(
work_units = [
(key, key, key if key in keys_b else None)
for key in extraction_keys
if not _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns)
if (not _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns) if not include_mode else _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns))
and not (key not in keys_b and mismatch_mode == "skip")
]
pbar = comfy.utils.ProgressBar(len(work_units))
@@ -571,7 +572,7 @@ def extract_lora_from_files(
weight_diff = None
layer_device = device
if _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns):
if (_matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns) if not include_mode else not _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns)):
return "skipped", None
source_dtypes = [handler_a.get_dtype(key)]
@@ -808,6 +809,7 @@ def _get_common_inputs():
"When False (default), patterns are Python regex matched as substrings."),
io.Boolean.Input("include_1d_diffs", default=False,
tooltip="Include 1D tensors as FP32 and keys ending in .scale or .lin as full .diff patches"),
io.Boolean.Input("include_mode", default=False, tooltip="Use Skip Patterns as a whitelist instead. Only matching layers are extracted; an empty whitelist extracts nothing."),
]
@@ -839,7 +841,7 @@ class LoRAExtractFixed(io.ComfyNode):
def execute(cls, model_a, model_b, linear_dim, conv_dim, svd_niter, chunk_large_layers,
clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache,
include_1d_diffs=False) -> io.NodeOutput:
include_1d_diffs=False, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
@@ -849,7 +851,7 @@ class LoRAExtractFixed(io.ComfyNode):
model_a_path, model_b_path, "fixed", linear_dim, conv_dim,
device, save_dtype, output_path, linear_dim, conv_dim,
clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers, svd_niter,
lazy_load, force_clear_cache, glob_skip_patterns, include_1d_diffs
lazy_load, force_clear_cache, glob_skip_patterns, include_1d_diffs, include_mode=include_mode
)
return io.NodeOutput(output_name)
@@ -887,7 +889,7 @@ class LoRAExtractRatio(io.ComfyNode):
def execute(cls, model_a, model_b, linear_ratio, conv_ratio, probe_offset, linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache,
include_1d_diffs=False) -> io.NodeOutput:
include_1d_diffs=False, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
@@ -898,7 +900,7 @@ class LoRAExtractRatio(io.ComfyNode):
device, save_dtype, output_path, linear_max_rank, conv_max_rank,
clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache,
glob_skip_patterns=glob_skip_patterns, include_1d_diffs=include_1d_diffs,
glob_skip_patterns=glob_skip_patterns, include_1d_diffs=include_1d_diffs, include_mode=include_mode,
knee_probe_offset=probe_offset,
)
@@ -937,7 +939,7 @@ class LoRAExtractQuantile(io.ComfyNode):
def execute(cls, model_a, model_b, linear_quantile, conv_quantile, probe_offset, linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache,
include_1d_diffs=False) -> io.NodeOutput:
include_1d_diffs=False, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
@@ -948,7 +950,7 @@ class LoRAExtractQuantile(io.ComfyNode):
device, save_dtype, output_path, linear_max_rank, conv_max_rank,
clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache,
glob_skip_patterns=glob_skip_patterns, include_1d_diffs=include_1d_diffs,
glob_skip_patterns=glob_skip_patterns, include_1d_diffs=include_1d_diffs, include_mode=include_mode,
knee_probe_offset=probe_offset,
)
@@ -986,7 +988,7 @@ class LoRAExtractKnee(io.ComfyNode):
linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache,
include_1d_diffs=False) -> io.NodeOutput:
include_1d_diffs=False, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
@@ -997,7 +999,7 @@ class LoRAExtractKnee(io.ComfyNode):
device, save_dtype, output_path, linear_max_rank, conv_max_rank,
clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache,
glob_skip_patterns=glob_skip_patterns, include_1d_diffs=include_1d_diffs,
glob_skip_patterns=glob_skip_patterns, include_1d_diffs=include_1d_diffs, include_mode=include_mode,
knee_probe_offset=knee_probe_offset,
)
@@ -1036,7 +1038,7 @@ class LoRAExtractFrobenius(io.ComfyNode):
def execute(cls, model_a, model_b, linear_target, conv_target, probe_offset, linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache,
include_1d_diffs=False) -> io.NodeOutput:
include_1d_diffs=False, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("diffusion_models", model_a)
model_b_path = folder_paths.get_full_path_or_raise("diffusion_models", model_b)
@@ -1047,7 +1049,7 @@ class LoRAExtractFrobenius(io.ComfyNode):
device, save_dtype, output_path, linear_max_rank, conv_max_rank,
clamp_quantile, min_diff, skip_patterns, mismatch_mode, chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache,
glob_skip_patterns=glob_skip_patterns, include_1d_diffs=include_1d_diffs,
glob_skip_patterns=glob_skip_patterns, include_1d_diffs=include_1d_diffs, include_mode=include_mode,
knee_probe_offset=probe_offset,
)
nodes/lora_resize.py
+33 -15
View File
@@ -648,12 +648,14 @@ def _resize_filter_inputs():
io.String.Input("exclude_patterns", default="", multiline=True, tooltip="One pattern per line. Matching layer names or source/canonical tensor keys keep the whole layer at its original rank and precision, including alpha."),
io.String.Input("discard_patterns", default="", multiline=True, tooltip="One pattern per line. Remove the whole matching layer, including factors and alpha. Discard takes precedence over exclude."),
io.Boolean.Input("glob_patterns", default=False, tooltip="Use shell-style glob patterns instead of regular expressions for exclude and discard filters."),
io.Boolean.Input("include_mode", default=False, tooltip="Treat exclude patterns as an include list. Matching layers are resized; nonmatching layers are preserved unchanged. Discard patterns still remove matching layers."),
]
def _build_resize_work_units(
handler, pairs, passthrough_keys, low_bit_keys,
exclude_patterns="", discard_patterns="", glob_patterns=False,
include_mode=False,
):
"""Plan raw-copy and streamed work without loading tensor payloads."""
raw_units = []
@@ -691,7 +693,8 @@ def _build_resize_work_units(
if matches(discards, names):
claimed.update(key for _, key, _ in group_entries)
continue
excluded = matches(excludes, names)
matched = matches(excludes, names)
preserve = matched != include_mode
if layer_has_low_bit(block_keys, low_bit_keys):
raw_entries = entries + related_passthrough
@@ -699,7 +702,7 @@ def _build_resize_work_units(
claimed.update(key for _, key, _ in raw_entries)
continue
if excluded:
if preserve:
stream_units.append({"kind": "preserve", "entries": group_entries})
claimed.update(key for _, key, _ in group_entries)
elif layer_has_companions(block_keys):
@@ -725,11 +728,12 @@ def _build_resize_work_units(
if matches(discards, [key]):
continue
entry = (None, key, key)
matched = matches(excludes, [key])
if key in low_bit_keys:
raw_units.append({"entries": [entry]})
else:
stream_units.append({
"kind": "preserve" if matches(excludes, [key]) else "copy",
"kind": "preserve" if matched != include_mode else "copy",
"entries": [entry],
})
claimed.add(key)
@@ -902,6 +906,7 @@ def resize_lora_file(
exclude_patterns: str = "",
discard_patterns: str = "",
glob_patterns: bool = False,
include_mode: bool = False,
) -> str:
"""Resize a LoRA through one bounded UEL stream and factor-space SVD."""
if min_rank < 1:
@@ -925,7 +930,7 @@ def resize_lora_file(
raw_units, stream_units, rank_counts, preserved_companion_groups = (
_build_resize_work_units(
handler, pairs, passthrough_keys, low_bit_keys,
exclude_patterns, discard_patterns, glob_patterns,
exclude_patterns, discard_patterns, glob_patterns, include_mode,
)
)
rank_text = _rank_summary(rank_counts)
@@ -948,6 +953,9 @@ def resize_lora_file(
)
metadata["ss_network_dim"] = "Dynamic"
metadata["ss_network_alpha"] = "Dynamic"
metadata["modelutils_resize_filter_mode"] = (
"include" if include_mode else "exclude"
)
output_path, _ = canonical_model_artifact_path("loras", output_filename)
@@ -1230,7 +1238,7 @@ class LoRAResizeFixed(io.ComfyNode):
)
@classmethod
def execute(cls, lora_name, new_rank, output_filename, save_dtype, device, force_clear_cache, exclude_patterns="", discard_patterns="", glob_patterns=False) -> io.NodeOutput:
def execute(cls, lora_name, new_rank, output_filename, save_dtype, device, force_clear_cache, exclude_patterns="", discard_patterns="", glob_patterns=False, include_mode=False) -> io.NodeOutput:
lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
dtype = {"fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}[save_dtype]
@@ -1240,6 +1248,7 @@ class LoRAResizeFixed(io.ComfyNode):
exclude_patterns=exclude_patterns,
discard_patterns=discard_patterns,
glob_patterns=glob_patterns,
include_mode=include_mode,
)
_, output_name = canonical_model_artifact_path("loras", output_filename)
return io.NodeOutput(output_name)
@@ -1273,7 +1282,7 @@ class LoRAResizeRatio(io.ComfyNode):
)
@classmethod
def execute(cls, lora_name, max_rank, ratio, output_filename, save_dtype, device, force_clear_cache, exclude_patterns="", discard_patterns="", glob_patterns=False) -> io.NodeOutput:
def execute(cls, lora_name, max_rank, ratio, output_filename, save_dtype, device, force_clear_cache, exclude_patterns="", discard_patterns="", glob_patterns=False, include_mode=False) -> io.NodeOutput:
lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
dtype = {"fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}[save_dtype]
@@ -1283,6 +1292,7 @@ class LoRAResizeRatio(io.ComfyNode):
exclude_patterns=exclude_patterns,
discard_patterns=discard_patterns,
glob_patterns=glob_patterns,
include_mode=include_mode,
)
_, output_name = canonical_model_artifact_path("loras", output_filename)
return io.NodeOutput(output_name)
@@ -1318,7 +1328,7 @@ class LoRAResizeFrobenius(io.ComfyNode):
)
@classmethod
def execute(cls, lora_name, max_rank, min_rank, target, output_filename, save_dtype, device, force_clear_cache, exclude_patterns="", discard_patterns="", glob_patterns=False) -> io.NodeOutput:
def execute(cls, lora_name, max_rank, min_rank, target, output_filename, save_dtype, device, force_clear_cache, exclude_patterns="", discard_patterns="", glob_patterns=False, include_mode=False) -> io.NodeOutput:
lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
dtype = {"fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}[save_dtype]
@@ -1328,6 +1338,7 @@ class LoRAResizeFrobenius(io.ComfyNode):
exclude_patterns=exclude_patterns,
discard_patterns=discard_patterns,
glob_patterns=glob_patterns,
include_mode=include_mode,
min_rank=min_rank,
)
_, output_name = canonical_model_artifact_path("loras", output_filename)
@@ -1362,7 +1373,7 @@ class LoRAResizeCumulative(io.ComfyNode):
)
@classmethod
def execute(cls, lora_name, max_rank, target, output_filename, save_dtype, device, force_clear_cache, exclude_patterns="", discard_patterns="", glob_patterns=False) -> io.NodeOutput:
def execute(cls, lora_name, max_rank, target, output_filename, save_dtype, device, force_clear_cache, exclude_patterns="", discard_patterns="", glob_patterns=False, include_mode=False) -> io.NodeOutput:
lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
dtype = {"fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}[save_dtype]
@@ -1372,6 +1383,7 @@ class LoRAResizeCumulative(io.ComfyNode):
exclude_patterns=exclude_patterns,
discard_patterns=discard_patterns,
glob_patterns=glob_patterns,
include_mode=include_mode,
)
_, output_name = canonical_model_artifact_path("loras", output_filename)
return io.NodeOutput(output_name)
@@ -1396,6 +1408,7 @@ def merge_loras_to_model(
force_clear_cache: bool = False,
include_1d_diffs: bool = False,
return_report: bool = False,
include_mode: bool = False,
) -> str | tuple[str, str]:
"""
Merge multiple LoRAs into a base model and save the result directly.
@@ -1416,6 +1429,7 @@ def merge_loras_to_model(
lazy_load: Low memory mode: load tensors from disk on demand
force_clear_cache: Clear CUDA cache after each layer
return_report: Return the path and merge report instead of only the path
include_mode: Use skip_patterns_str to select matching base keys instead
Returns:
Path to saved merged model, optionally paired with the merge report
@@ -1664,7 +1678,7 @@ def merge_loras_to_model(
if planned_base_key.endswith(".weight"):
core = extract_core_layer_base(planned_base_key)
planned_low_rank = lora_lookup.get(core.replace(".", "_"), [])
skipped = any(pattern.search(planned_base_key) for pattern in skip_patterns)
skipped = any(pattern.search(planned_base_key) for pattern in skip_patterns) != include_mode
entries = {}
if planned_base_key not in base_low_bit_keys and not skipped:
entries[-1] = [planned_base_key]
@@ -1745,20 +1759,21 @@ def merge_loras_to_model(
(pattern.pattern for pattern in skip_patterns if pattern.search(base_key)),
None,
)
if skip_pattern is not None:
if (skip_pattern is not None) != include_mode:
skip_reason = "no include pattern matched" if include_mode else f"pattern={skip_pattern}"
stats["skipped"] += 1
base_outcomes["OMITTED BY SKIP PATTERN"].append(
f"{base_key}: pattern={skip_pattern}"
f"{base_key}: {skip_reason}"
)
for info, block_name, _, direct_key, _ in direct_contributions:
record_group_event(
info, block_name, "skipped", base_key,
f"target omitted by pattern {skip_pattern}; source={direct_key}",
f"target omitted: {skip_reason}; source={direct_key}",
)
for info, block_name, block_keys in low_rank_contributions:
record_group_event(
info, block_name, "skipped", base_key,
f"target omitted by pattern {skip_pattern}; sources="
f"target omitted: {skip_reason}; sources="
+ ", ".join(sorted(layer_tensor_keys(block_keys).values())),
)
pbar.update(1)
@@ -2220,7 +2235,7 @@ class LoRAMergeToModel(io.ComfyNode):
tooltip="Weight strength for LoRA 8"),
# Settings
io.String.Input("skip_patterns", default="", multiline=True,
tooltip="Regex patterns for layers to skip"),
tooltip="Regex patterns for base tensor keys to omit, or to include when Include Mode is enabled. Guarded low-bit base tensors are always preserved."),
io.String.Input("output_filename", default="merged_model", tooltip="Output filename without extension, written under ComfyUI's diffusion-model directory."),
io.Combo.Input("save_dtype", options=["fp16", "bf16", "fp32"], default="fp16", tooltip="Data type used to save the model after applying the LoRA."),
io.Combo.Input("device", options=["cuda", "cpu"], default="cuda", tooltip="Device used for per-layer LoRA application; CUDA out-of-memory retries the affected layer on CPU."),
@@ -2228,6 +2243,8 @@ class LoRAMergeToModel(io.ComfyNode):
io.Boolean.Input("force_clear_cache", default=False, tooltip="Clear CUDA cache after each layer (slower but saves VRAM)"),
io.Boolean.Input("include_1d_diffs", default=False,
tooltip="Apply 1D direct-diff tensors as FP32. Disabled preserves prior behavior."),
io.Boolean.Input("include_mode", default=False,
tooltip="Use Skip Patterns as an include-only filter. Only matching base tensors are processed and saved; an empty filter selects nothing. Guarded low-bit base tensors remain preserved."),
],
outputs=[
io.AnyType.Output(display_name="output_path"),
@@ -2241,7 +2258,7 @@ class LoRAMergeToModel(io.ComfyNode):
lora_1, weight_1, lora_2, weight_2, lora_3, weight_3, lora_4, weight_4,
lora_5, weight_5, lora_6, weight_6, lora_7, weight_7, lora_8, weight_8,
skip_patterns, output_filename, save_dtype, device, lazy_load, force_clear_cache,
include_1d_diffs) -> io.NodeOutput:
include_1d_diffs, include_mode=False) -> io.NodeOutput:
# Build LoRA list based on count
count = int(lora_count)
@@ -2270,6 +2287,7 @@ class LoRAMergeToModel(io.ComfyNode):
force_clear_cache=force_clear_cache,
include_1d_diffs=include_1d_diffs,
return_report=True,
include_mode=include_mode,
)
_, output_name = canonical_model_artifact_path("diffusion_models", output_filename)
return io.NodeOutput(output_name, report)
nodes/merger.py
+41 -12
View File
@@ -128,6 +128,7 @@ class MergerLogic:
# Compile filter patterns
glob_mode = recipe_params.get('glob_patterns', False)
include_mode = recipe_params.get('include_mode', False)
exclude_patterns = _compile_patterns(recipe_params.get('exclude_patterns', ''), glob_mode=glob_mode)
discard_patterns = _compile_patterns(recipe_params.get('discard_patterns', ''), glob_mode=glob_mode)
@@ -216,7 +217,11 @@ class MergerLogic:
model_type == "loras"
and len(primary_handler.get_shape(key)) == 1
and not include_1d_diffs
) or _matches_any_pattern(key, exclude_patterns, glob_mode=glob_mode)
) or (
not _matches_any_pattern(key, exclude_patterns, glob_mode=glob_mode)
if include_mode else
_matches_any_pattern(key, exclude_patterns, glob_mode=glob_mode)
)
requested_names = [primary_model_name]
if not preserve_a:
requested_names.extend(
@@ -297,7 +302,11 @@ class MergerLogic:
del cpu_tensor
# Check exclude patterns - use Model A only, no merge
if preserve_model_a_1d or _matches_any_pattern(key, exclude_patterns, glob_mode=glob_mode):
if preserve_model_a_1d or (
not _matches_any_pattern(key, exclude_patterns, glob_mode=glob_mode)
if include_mode else
_matches_any_pattern(key, exclude_patterns, glob_mode=glob_mode)
):
t = tensor_a.detach().to(target_dtype).cpu()
writer.write_batch([(key, t)])
@@ -418,6 +427,7 @@ class CheckpointTwoMerger(io.ComfyNode):
io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
io.Boolean.Input("override_dtype", default=False, tooltip="Force the entire model to be saved as the selected save_dtype. If False (default), higher precision dtypes are preserved."),
io.Boolean.Input("include_mode", default=False, tooltip="Use Exclude Patterns as a whitelist instead. Only matching tensors are merged; nonmatching tensors are preserved from Model A."),
],
outputs=[
io.AnyType.Output(display_name="output_filename"),
@@ -430,7 +440,7 @@ class CheckpointTwoMerger(io.ComfyNode):
calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
gamma: float, delta: float, epsilon: float, zeta: float, seed: int, output_filename: str, save_dtype: str,
process_device: str, exclude_patterns: str, discard_patterns: str,
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool) -> io.NodeOutput:
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool, include_mode: bool = False) -> io.NodeOutput:
doc = load_documentation_from_file('merger_2_model_modes.md')
if execution_mode == "DOCUMENTATION ONLY":
return io.NodeOutput("Documentation mode active. No merge performed.", doc)
@@ -443,6 +453,7 @@ class CheckpointTwoMerger(io.ComfyNode):
"device": process_device, "dtype": torch.float32,
"exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
"glob_patterns": glob_patterns,
"include_mode": include_mode,
"lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
}
model_names = {"model_a": model_a, "model_b": model_b}
@@ -484,6 +495,7 @@ class ModelTwoMerger(io.ComfyNode):
io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
io.Boolean.Input("override_dtype", default=False, tooltip="Force the entire model to be saved as the selected save_dtype. If False (default), higher precision dtypes are preserved."),
io.Boolean.Input("include_mode", default=False, tooltip="Use Exclude Patterns as a whitelist instead. Only matching tensors are merged; nonmatching tensors are preserved from Model A."),
],
outputs=[
io.AnyType.Output(display_name="output_filename"),
@@ -496,7 +508,7 @@ class ModelTwoMerger(io.ComfyNode):
calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
gamma: float, delta: float, epsilon: float, zeta: float, seed: int, output_filename: str, save_dtype: str,
process_device: str, exclude_patterns: str, discard_patterns: str,
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool) -> io.NodeOutput:
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool, include_mode: bool = False) -> io.NodeOutput:
doc = load_documentation_from_file('merger_2_model_modes.md')
if execution_mode == "DOCUMENTATION ONLY":
return io.NodeOutput("Documentation mode active. No merge performed.", doc)
@@ -509,6 +521,7 @@ class ModelTwoMerger(io.ComfyNode):
"device": process_device, "dtype": torch.float32,
"exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
"glob_patterns": glob_patterns,
"include_mode": include_mode,
"lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
}
model_names = {"model_a": model_a, "model_b": model_b}
@@ -550,6 +563,7 @@ class TextEncoderTwoMerger(io.ComfyNode):
io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
io.Boolean.Input("override_dtype", default=False, tooltip="Force the entire model to be saved as the selected save_dtype. If False (default), higher precision dtypes are preserved."),
io.Boolean.Input("include_mode", default=False, tooltip="Use Exclude Patterns as a whitelist instead. Only matching tensors are merged; nonmatching tensors are preserved from Model A."),
],
outputs=[
io.AnyType.Output(display_name="output_filename"),
@@ -562,7 +576,7 @@ class TextEncoderTwoMerger(io.ComfyNode):
calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
gamma: float, delta: float, epsilon: float, zeta: float, seed: int, output_filename: str, save_dtype: str,
process_device: str, exclude_patterns: str, discard_patterns: str,
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool) -> io.NodeOutput:
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool, include_mode: bool = False) -> io.NodeOutput:
doc = load_documentation_from_file('merger_2_model_modes.md')
if execution_mode == "DOCUMENTATION ONLY":
return io.NodeOutput("Documentation mode active. No merge performed.", doc)
@@ -575,6 +589,7 @@ class TextEncoderTwoMerger(io.ComfyNode):
"device": process_device, "dtype": torch.float32,
"exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
"glob_patterns": glob_patterns,
"include_mode": include_mode,
"lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
}
model_names = {"model_a": model_a, "model_b": model_b}
@@ -618,6 +633,7 @@ class LoRATwoMerger(io.ComfyNode):
io.Boolean.Input("override_dtype", default=False, tooltip="Force merged non-1D tensors to save_dtype. Enabled 1D direct diffs remain FP32."),
io.Boolean.Input("include_1d_diffs", default=False,
tooltip="Merge 1D tensors. When disabled, preserve Model A's 1D tensors unchanged."),
io.Boolean.Input("include_mode", default=False, tooltip="Use Exclude Patterns as a whitelist instead. Only matching tensors are merged; nonmatching tensors are preserved from Model A."),
],
outputs=[
io.AnyType.Output(display_name="output_filename"),
@@ -631,7 +647,7 @@ class LoRATwoMerger(io.ComfyNode):
gamma: float, delta: float, epsilon: float, zeta: float, seed: int, output_filename: str, save_dtype: str,
process_device: str, exclude_patterns: str, discard_patterns: str,
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool,
include_1d_diffs: bool) -> io.NodeOutput:
include_1d_diffs: bool, include_mode: bool = False) -> io.NodeOutput:
doc = load_documentation_from_file('merger_2_model_modes.md')
if execution_mode == "DOCUMENTATION ONLY":
return io.NodeOutput("Documentation mode active. No merge performed.", doc)
@@ -644,6 +660,7 @@ class LoRATwoMerger(io.ComfyNode):
"device": process_device, "dtype": torch.float32,
"exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
"glob_patterns": glob_patterns,
"include_mode": include_mode,
"lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
"include_1d_diffs": include_1d_diffs,
}
@@ -686,6 +703,7 @@ class EmbeddingTwoMerger(io.ComfyNode):
io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
io.Boolean.Input("override_dtype", default=False, tooltip="Force the entire model to be saved as the selected save_dtype. If False (default), higher precision dtypes are preserved."),
io.Boolean.Input("include_mode", default=False, tooltip="Use Exclude Patterns as a whitelist instead. Only matching tensors are merged; nonmatching tensors are preserved from Model A."),
],
outputs=[
io.AnyType.Output(display_name="output_filename"),
@@ -698,7 +716,7 @@ class EmbeddingTwoMerger(io.ComfyNode):
calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
gamma: float, delta: float, epsilon: float, zeta: float, seed: int, output_filename: str, save_dtype: str,
process_device: str, exclude_patterns: str, discard_patterns: str,
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool) -> io.NodeOutput:
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool, include_mode: bool = False) -> io.NodeOutput:
doc = load_documentation_from_file('merger_2_model_modes.md')
if execution_mode == "DOCUMENTATION ONLY":
return io.NodeOutput("Documentation mode active. No merge performed.", doc)
@@ -711,6 +729,7 @@ class EmbeddingTwoMerger(io.ComfyNode):
"device": process_device, "dtype": torch.float32,
"exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
"glob_patterns": glob_patterns,
"include_mode": include_mode,
"lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
}
model_names = {"model_a": model_a, "model_b": model_b}
@@ -755,6 +774,7 @@ class CheckpointThreeMerger(io.ComfyNode):
io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
io.Boolean.Input("override_dtype", default=False, tooltip="Force the entire model to be saved as the selected save_dtype. If False (default), higher precision dtypes are preserved."),
io.Boolean.Input("include_mode", default=False, tooltip="Use Exclude Patterns as a whitelist instead. Only matching tensors are merged; nonmatching tensors are preserved from Model A."),
],
outputs=[
io.AnyType.Output(display_name="output_filename"),
@@ -767,7 +787,7 @@ class CheckpointThreeMerger(io.ComfyNode):
calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
gamma: float, delta: float, epsilon: float, zeta: float, seed: int, output_filename: str, save_dtype: str,
process_device: str, exclude_patterns: str, discard_patterns: str,
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool) -> io.NodeOutput:
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool, include_mode: bool = False) -> io.NodeOutput:
doc = load_documentation_from_file('merger_3_model_modes.md')
if execution_mode == "DOCUMENTATION ONLY":
return io.NodeOutput("Documentation mode active. No merge performed.", doc)
@@ -780,6 +800,7 @@ class CheckpointThreeMerger(io.ComfyNode):
"device": process_device, "dtype": torch.float32,
"exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
"glob_patterns": glob_patterns,
"include_mode": include_mode,
"lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
}
model_names = {"model_a": model_a, "model_b": model_b, "model_c": model_c}
@@ -822,6 +843,7 @@ class ModelThreeMerger(io.ComfyNode):
io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
io.Boolean.Input("override_dtype", default=False, tooltip="Force the entire model to be saved as the selected save_dtype. If False (default), higher precision dtypes are preserved."),
io.Boolean.Input("include_mode", default=False, tooltip="Use Exclude Patterns as a whitelist instead. Only matching tensors are merged; nonmatching tensors are preserved from Model A."),
],
outputs=[
io.AnyType.Output(display_name="output_filename"),
@@ -834,7 +856,7 @@ class ModelThreeMerger(io.ComfyNode):
calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
gamma: float, delta: float, epsilon: float, zeta: float, seed: int, output_filename: str, save_dtype: str,
process_device: str, exclude_patterns: str, discard_patterns: str,
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool) -> io.NodeOutput:
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool, include_mode: bool = False) -> io.NodeOutput:
doc = load_documentation_from_file('merger_3_model_modes.md')
if execution_mode == "DOCUMENTATION ONLY":
return io.NodeOutput("Documentation mode active. No merge performed.", doc)
@@ -847,6 +869,7 @@ class ModelThreeMerger(io.ComfyNode):
"device": process_device, "dtype": torch.float32,
"exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
"glob_patterns": glob_patterns,
"include_mode": include_mode,
"lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
}
model_names = {"model_a": model_a, "model_b": model_b, "model_c": model_c}
@@ -889,6 +912,7 @@ class TextEncoderThreeMerger(io.ComfyNode):
io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
io.Boolean.Input("override_dtype", default=False, tooltip="Force the entire model to be saved as the selected save_dtype. If False (default), higher precision dtypes are preserved."),
io.Boolean.Input("include_mode", default=False, tooltip="Use Exclude Patterns as a whitelist instead. Only matching tensors are merged; nonmatching tensors are preserved from Model A."),
],
outputs=[
io.AnyType.Output(display_name="output_filename"),
@@ -901,7 +925,7 @@ class TextEncoderThreeMerger(io.ComfyNode):
calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
gamma: float, delta: float, epsilon: float, zeta: float, seed: int, output_filename: str, save_dtype: str,
process_device: str, exclude_patterns: str, discard_patterns: str,
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool) -> io.NodeOutput:
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool, include_mode: bool = False) -> io.NodeOutput:
doc = load_documentation_from_file('merger_3_model_modes.md')
if execution_mode == "DOCUMENTATION ONLY":
return io.NodeOutput("Documentation mode active. No merge performed.", doc)
@@ -914,6 +938,7 @@ class TextEncoderThreeMerger(io.ComfyNode):
"device": process_device, "dtype": torch.float32,
"exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
"glob_patterns": glob_patterns,
"include_mode": include_mode,
"lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
}
model_names = {"model_a": model_a, "model_b": model_b, "model_c": model_c}
@@ -958,6 +983,7 @@ class LoRAThreeMerger(io.ComfyNode):
io.Boolean.Input("override_dtype", default=False, tooltip="Force merged non-1D tensors to save_dtype. Enabled 1D direct diffs remain FP32."),
io.Boolean.Input("include_1d_diffs", default=False,
tooltip="Merge 1D tensors. When disabled, preserve Model A's 1D tensors unchanged."),
io.Boolean.Input("include_mode", default=False, tooltip="Use Exclude Patterns as a whitelist instead. Only matching tensors are merged; nonmatching tensors are preserved from Model A."),
],
outputs=[
io.AnyType.Output(display_name="output_filename"),
@@ -971,7 +997,7 @@ class LoRAThreeMerger(io.ComfyNode):
gamma: float, delta: float, epsilon: float, zeta: float, seed: int, output_filename: str, save_dtype: str,
process_device: str, exclude_patterns: str, discard_patterns: str,
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool,
include_1d_diffs: bool) -> io.NodeOutput:
include_1d_diffs: bool, include_mode: bool = False) -> io.NodeOutput:
doc = load_documentation_from_file('merger_3_model_modes.md')
if execution_mode == "DOCUMENTATION ONLY":
return io.NodeOutput("Documentation mode active. No merge performed.", doc)
@@ -984,6 +1010,7 @@ class LoRAThreeMerger(io.ComfyNode):
"device": process_device, "dtype": torch.float32,
"exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
"glob_patterns": glob_patterns,
"include_mode": include_mode,
"lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
"include_1d_diffs": include_1d_diffs,
}
@@ -1027,6 +1054,7 @@ class EmbeddingThreeMerger(io.ComfyNode):
io.Boolean.Input("lazy_load", default=True, tooltip="Low memory mode: load tensors from disk on demand"),
io.Boolean.Input("force_clear_cache", default=True, tooltip="Clear CUDA cache after each layer"),
io.Boolean.Input("override_dtype", default=False, tooltip="Force the entire model to be saved as the selected save_dtype. If False (default), higher precision dtypes are preserved."),
io.Boolean.Input("include_mode", default=False, tooltip="Use Exclude Patterns as a whitelist instead. Only matching tensors are merged; nonmatching tensors are preserved from Model A."),
],
outputs=[
io.AnyType.Output(display_name="output_filename"),
@@ -1039,7 +1067,7 @@ class EmbeddingThreeMerger(io.ComfyNode):
calc_mode: str, mismatch_mode: str, alignment_mode: str, alpha: float, beta: float,
gamma: float, delta: float, epsilon: float, zeta: float, seed: int, output_filename: str, save_dtype: str,
process_device: str, exclude_patterns: str, discard_patterns: str,
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool) -> io.NodeOutput:
glob_patterns: bool, lazy_load: bool, force_clear_cache: bool, override_dtype: bool, include_mode: bool = False) -> io.NodeOutput:
doc = load_documentation_from_file('merger_3_model_modes.md')
if execution_mode == "DOCUMENTATION ONLY":
return io.NodeOutput("Documentation mode active. No merge performed.", doc)
@@ -1052,6 +1080,7 @@ class EmbeddingThreeMerger(io.ComfyNode):
"device": process_device, "dtype": torch.float32,
"exclude_patterns": exclude_patterns, "discard_patterns": discard_patterns,
"glob_patterns": glob_patterns,
"include_mode": include_mode,
"lazy_load": lazy_load, "force_clear_cache": force_clear_cache,
}
model_names = {"model_a": model_a, "model_b": model_b, "model_c": model_c}
nodes/model_analysis.py
+10 -4
View File
@@ -781,13 +781,14 @@ class ModelAnalysisLogic:
params.get("exclude_patterns", ""),
glob_mode=glob_mode,
)
include_mode = bool(params.get("include_mode", False))
excluded_a = {
key for key in keys_a
if _matches_any_pattern(key, excluded_patterns, glob_mode=glob_mode)
if _matches_any_pattern(key, excluded_patterns, glob_mode=glob_mode) != include_mode
}
excluded_b = {
key for key in keys_b
if _matches_any_pattern(key, excluded_patterns, glob_mode=glob_mode)
if _matches_any_pattern(key, excluded_patterns, glob_mode=glob_mode) != include_mode
}
keys_a.difference_update(excluded_a)
keys_b.difference_update(excluded_b)
@@ -1092,12 +1093,17 @@ def _common_inputs(model_type: str, *, alignment_control: bool):
"exclude_patterns",
default="",
multiline=True,
tooltip="One pattern per line. Matching tensors are excluded from all comparison metrics and topology counts. Uses regex unless Glob Patterns is enabled.",
tooltip="One pattern per line. Matching tensors are excluded from all comparison metrics and topology counts, or are the only tensors analyzed when Include Mode is enabled. Uses regex unless Glob Patterns is enabled.",
),
io.Boolean.Input(
"glob_patterns",
default=False,
tooltip="Interpret exclusion entries as shell-style glob patterns instead of regular expressions.",
tooltip="Interpret layer-filter entries as shell-style glob patterns instead of regular expressions.",
),
io.Boolean.Input(
"include_mode",
default=False,
tooltip="Use Exclude Patterns as an include-only filter. Analyze only matching tensors; an empty filter selects nothing.",
),
])
return inputs
nodes/text_encoder_extract.py
+24 -22
View File
@@ -81,6 +81,7 @@ def extract_te_from_files(
force_clear_cache: bool = True,
glob_skip_patterns: bool = False,
knee_probe_offset: int = 32,
include_mode: bool = False,
) -> None:
"""
Extract LoRA/DoRA from difference between two Text Encoder models, writing incrementally to disk.
@@ -108,7 +109,7 @@ def extract_te_from_files(
work_units = [
(key, key, key if key in keys_b else None)
for key in weight_keys
if not _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns)
if (not _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns) if not include_mode else _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns))
and not (key not in keys_b and mismatch_mode == "skip")
]
pbar = comfy.utils.ProgressBar(len(work_units))
@@ -119,7 +120,7 @@ def extract_te_from_files(
weight_diff = None
layer_device = device
if _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns):
if (_matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns) if not include_mode else not _matches_any_pattern(key, skip_patterns, glob_mode=glob_skip_patterns)):
return "skipped", None
# Load tensors with pinned memory for CUDA
@@ -300,6 +301,7 @@ def _get_common_inputs():
io.Boolean.Input("glob_skip_patterns", default=False,
tooltip="When True, skip_patterns use glob syntax (* = any sequence, ? = any char, dots are literal). "
"When False (default), patterns are Python regex matched as substrings."),
io.Boolean.Input("include_mode", default=False, tooltip="Use Skip Patterns as a whitelist instead. Only matching layers are extracted; an empty whitelist extracts nothing."),
]
@@ -329,7 +331,7 @@ class TextEncoderLoRAExtractFixed(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_dim, conv_dim, svd_niter, chunk_large_layers,
clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("text_encoders", model_a)
model_b_path = folder_paths.get_full_path_or_raise("text_encoders", model_b)
@@ -341,7 +343,7 @@ class TextEncoderLoRAExtractFixed(io.ComfyNode):
linear_max_rank=linear_dim, conv_max_rank=conv_dim,
clamp_quantile=clamp_quantile, min_diff=min_diff, skip_patterns_str=skip_patterns,
mismatch_mode=mismatch_mode, chunk_large_layers=chunk_large_layers, svd_niter=svd_niter,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns, include_mode=include_mode
)
return io.NodeOutput(output_name)
@@ -370,7 +372,7 @@ class TextEncoderLoRAExtractRatio(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_ratio, conv_ratio, probe_offset, linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("text_encoders", model_a)
model_b_path = folder_paths.get_full_path_or_raise("text_encoders", model_b)
@@ -382,7 +384,7 @@ class TextEncoderLoRAExtractRatio(io.ComfyNode):
linear_max_rank=linear_max_rank, conv_max_rank=conv_max_rank,
clamp_quantile=clamp_quantile, min_diff=min_diff, skip_patterns_str=skip_patterns,
mismatch_mode=mismatch_mode, chunk_large_layers=chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns, include_mode=include_mode,
knee_probe_offset=probe_offset
)
return io.NodeOutput(output_name)
@@ -412,7 +414,7 @@ class TextEncoderLoRAExtractQuantile(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_quantile, conv_quantile, probe_offset, linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("text_encoders", model_a)
model_b_path = folder_paths.get_full_path_or_raise("text_encoders", model_b)
@@ -424,7 +426,7 @@ class TextEncoderLoRAExtractQuantile(io.ComfyNode):
linear_max_rank=linear_max_rank, conv_max_rank=conv_max_rank,
clamp_quantile=clamp_quantile, min_diff=min_diff, skip_patterns_str=skip_patterns,
mismatch_mode=mismatch_mode, chunk_large_layers=chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns, include_mode=include_mode,
knee_probe_offset=probe_offset
)
return io.NodeOutput(output_name)
@@ -454,7 +456,7 @@ class TextEncoderLoRAExtractKnee(io.ComfyNode):
def execute(cls, model_a, model_b, knee_method, knee_probe_offset,
linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("text_encoders", model_a)
model_b_path = folder_paths.get_full_path_or_raise("text_encoders", model_b)
@@ -467,7 +469,7 @@ class TextEncoderLoRAExtractKnee(io.ComfyNode):
clamp_quantile=clamp_quantile, min_diff=min_diff, skip_patterns_str=skip_patterns,
mismatch_mode=mismatch_mode, chunk_large_layers=chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache,
glob_skip_patterns=glob_skip_patterns, knee_probe_offset=knee_probe_offset
glob_skip_patterns=glob_skip_patterns, knee_probe_offset=knee_probe_offset, include_mode=include_mode
)
return io.NodeOutput(output_name)
@@ -496,7 +498,7 @@ class TextEncoderLoRAExtractFrobenius(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_target, conv_target, probe_offset, linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("text_encoders", model_a)
model_b_path = folder_paths.get_full_path_or_raise("text_encoders", model_b)
@@ -508,7 +510,7 @@ class TextEncoderLoRAExtractFrobenius(io.ComfyNode):
linear_max_rank=linear_max_rank, conv_max_rank=conv_max_rank,
clamp_quantile=clamp_quantile, min_diff=min_diff, skip_patterns_str=skip_patterns,
mismatch_mode=mismatch_mode, chunk_large_layers=chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns, include_mode=include_mode,
knee_probe_offset=probe_offset
)
return io.NodeOutput(output_name)
@@ -540,7 +542,7 @@ class TextEncoderDoRAExtractFixed(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_dim, conv_dim, svd_niter, chunk_large_layers,
clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("text_encoders", model_a)
model_b_path = folder_paths.get_full_path_or_raise("text_encoders", model_b)
@@ -552,7 +554,7 @@ class TextEncoderDoRAExtractFixed(io.ComfyNode):
linear_max_rank=linear_dim, conv_max_rank=conv_dim,
clamp_quantile=clamp_quantile, min_diff=min_diff, skip_patterns_str=skip_patterns,
mismatch_mode=mismatch_mode, chunk_large_layers=chunk_large_layers, svd_niter=svd_niter,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns, include_mode=include_mode
)
return io.NodeOutput(output_name)
@@ -581,7 +583,7 @@ class TextEncoderDoRAExtractRatio(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_ratio, conv_ratio, probe_offset, linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("text_encoders", model_a)
model_b_path = folder_paths.get_full_path_or_raise("text_encoders", model_b)
@@ -593,7 +595,7 @@ class TextEncoderDoRAExtractRatio(io.ComfyNode):
linear_max_rank=linear_max_rank, conv_max_rank=conv_max_rank,
clamp_quantile=clamp_quantile, min_diff=min_diff, skip_patterns_str=skip_patterns,
mismatch_mode=mismatch_mode, chunk_large_layers=chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns, include_mode=include_mode,
knee_probe_offset=probe_offset
)
return io.NodeOutput(output_name)
@@ -623,7 +625,7 @@ class TextEncoderDoRAExtractQuantile(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_quantile, conv_quantile, probe_offset, linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("text_encoders", model_a)
model_b_path = folder_paths.get_full_path_or_raise("text_encoders", model_b)
@@ -635,7 +637,7 @@ class TextEncoderDoRAExtractQuantile(io.ComfyNode):
linear_max_rank=linear_max_rank, conv_max_rank=conv_max_rank,
clamp_quantile=clamp_quantile, min_diff=min_diff, skip_patterns_str=skip_patterns,
mismatch_mode=mismatch_mode, chunk_large_layers=chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns, include_mode=include_mode,
knee_probe_offset=probe_offset
)
return io.NodeOutput(output_name)
@@ -665,7 +667,7 @@ class TextEncoderDoRAExtractKnee(io.ComfyNode):
def execute(cls, model_a, model_b, knee_method, knee_probe_offset,
linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("text_encoders", model_a)
model_b_path = folder_paths.get_full_path_or_raise("text_encoders", model_b)
@@ -678,7 +680,7 @@ class TextEncoderDoRAExtractKnee(io.ComfyNode):
clamp_quantile=clamp_quantile, min_diff=min_diff, skip_patterns_str=skip_patterns,
mismatch_mode=mismatch_mode, chunk_large_layers=chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache,
glob_skip_patterns=glob_skip_patterns, knee_probe_offset=knee_probe_offset
glob_skip_patterns=glob_skip_patterns, knee_probe_offset=knee_probe_offset, include_mode=include_mode
)
return io.NodeOutput(output_name)
@@ -707,7 +709,7 @@ class TextEncoderDoRAExtractFrobenius(io.ComfyNode):
@classmethod
def execute(cls, model_a, model_b, linear_target, conv_target, probe_offset, linear_max_rank, conv_max_rank,
chunk_large_layers, clamp_quantile, min_diff, mismatch_mode, output_filename,
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache) -> io.NodeOutput:
save_dtype, device, skip_patterns, glob_skip_patterns, lazy_load, force_clear_cache, include_mode=False) -> io.NodeOutput:
model_a_path = folder_paths.get_full_path_or_raise("text_encoders", model_a)
model_b_path = folder_paths.get_full_path_or_raise("text_encoders", model_b)
@@ -719,7 +721,7 @@ class TextEncoderDoRAExtractFrobenius(io.ComfyNode):
linear_max_rank=linear_max_rank, conv_max_rank=conv_max_rank,
clamp_quantile=clamp_quantile, min_diff=min_diff, skip_patterns_str=skip_patterns,
mismatch_mode=mismatch_mode, chunk_large_layers=chunk_large_layers,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns,
lazy_load=lazy_load, force_clear_cache=force_clear_cache, glob_skip_patterns=glob_skip_patterns, include_mode=include_mode,
knee_probe_offset=probe_offset
)
return io.NodeOutput(output_name)
tests/test_adaptive_extraction_pipeline.py
+35
View File
@@ -217,3 +217,38 @@ def test_lora_extraction_uses_only_async_uel_and_releases_sources(monkeypatch, t
"diffusion_model.layer.lora_A.weight",
"diffusion_model.layer.lora_B.weight",
]
@pytest.mark.parametrize("module_name, function_name, extra", [
("nodes.lora_extract_svd", "extract_lora_from_files", {"include_1d_diffs": False}),
("nodes.dora_extract_wd", "extract_dora_from_files", {}),
("nodes.dora_learned_wd", "extract_dora_learned_from_files", {"optimize_iters": 0}),
("nodes.text_encoder_extract", "extract_te_from_files", {"is_dora": False}),
("nodes.text_encoder_extract", "extract_te_from_files", {"is_dora": True}),
])
def test_extraction_include_mode_selects_only_matching_layers(monkeypatch, tmp_path, module_name, function_name, extra):
module = _load_module(module_name)
first, second = tmp_path / "first.safetensors", tmp_path / "second.safetensors"
match, other = "model.encoder.match.weight", "model.encoder.other.weight"
_write_uel(first, {match: torch.eye(2), other: torch.eye(2)})
_write_uel(second, {match: torch.zeros(2, 2), other: torch.zeros(2, 2)})
monkeypatch.setattr(module, "prepare_for_large_operation", lambda *args: None)
monkeypatch.setattr(module, "cleanup_after_operation", lambda: None)
common = dict(linear_max_rank=1, conv_max_rank=1, force_clear_cache=False,
skip_patterns_str="match", include_mode=True, **extra)
selected = tmp_path / "selected.safetensors"
getattr(module, function_name)(str(first), str(second), "fixed", 1, 1, "cpu", "fp32", str(selected), **common)
with MemoryEfficientSafeOpen(str(selected), low_memory=True) as result:
keys = list(result.keys())
assert keys
assert all("match" in key for key in keys)
empty = tmp_path / "empty.safetensors"
common["skip_patterns_str"] = ""
getattr(module, function_name)(str(first), str(second), "fixed", 1, 1, "cpu", "fp32", str(empty), **common)
with MemoryEfficientSafeOpen(str(empty), low_memory=True) as result:
assert list(result.keys()) == []
globbed = tmp_path / "globbed.safetensors"
common.update(skip_patterns_str="*.match.weight", glob_skip_patterns=True)
getattr(module, function_name)(str(first), str(second), "fixed", 1, 1, "cpu", "fp32", str(globbed), **common)
with MemoryEfficientSafeOpen(str(globbed), low_memory=True) as result:
assert list(result.keys()) == keys
tests/test_consensus_merger.py
+36 -3
View File
@@ -651,9 +651,10 @@ def test_all_cwb_schemas_and_compact_control_contract(cwb):
if node is cwb.CWBLoRAMultiMerger:
assert ids[:4] == ["execution_mode", "lora_count", "lora_1", "lora_2"]
assert schema.inputs[1].default == "2"
assert ids[-2:] == [
assert ids[-3:] == [
"include_1d_diffs",
"counterfactual_weight_sweep",
"include_mode",
]
continue
if node in (cwb.CWBEmbeddingSelfCoalesce, cwb.CWBEmbeddingMultiMerger):
@@ -667,10 +668,11 @@ def test_all_cwb_schemas_and_compact_control_contract(cwb):
if node.INPUT_COUNT == 3:
assert ids[3] == "model_c"
if node.LORA_MODE:
assert schema.inputs[-1].id == "include_1d_diffs"
assert schema.inputs[-1].default is False
assert schema.inputs[-2].id == "include_1d_diffs"
else:
assert "include_1d_diffs" not in ids
assert schema.inputs[-1].id == "include_mode"
assert schema.inputs[-1].default is False
def test_use_case_preset_names_match_complete_settings(cwb):
@@ -1261,6 +1263,37 @@ def test_secondary_shape_conflict_skip_preserves_earliest_source(monkeypatch, tm
torch.testing.assert_close(tensor, torch.tensor([2.0, 3.0]))
@pytest.mark.parametrize("lora_mode", [False, True])
@pytest.mark.parametrize("patterns, glob_patterns, selected", [
(r"\.selected", False, True),
("*.selected*", True, True),
("", False, False),
("no_match", False, False),
])
def test_include_filter_preserves_nonmatches_and_discards_first(
monkeypatch, tmp_path, cwb, lora_mode, patterns, glob_patterns, selected,
):
paths = {name: str(tmp_path / f"{name}.safetensors") for name in ("a", "b")}
for name, value in (("a", 2.0), ("b", 6.0)):
save_file({
f"diffusion_model.{layer}.diff": torch.full((2, 2), value)
for layer in ("selected", "other", "selected_discard")
}, paths[name])
_patch_io(monkeypatch, cwb, tmp_path, paths)
category = "loras" if lora_mode else "diffusion_models"
result = cwb.ConsensusMergerLogic.execute(
["a", "b"], category,
_params("include_filter", exclude_patterns=patterns, include_mode=True,
glob_patterns=glob_patterns, discard_patterns="*discard*" if glob_patterns else "discard",
power_alpha=0.0),
lora_mode=lora_mode,
)
tensors = load_file(str(_result_path(tmp_path, category, result)))
assert set(tensors) == {"diffusion_model.selected.diff", "diffusion_model.other.diff"}
torch.testing.assert_close(tensors["diffusion_model.selected.diff"], torch.full((2, 2), 4.0 if selected else 2.0))
torch.testing.assert_close(tensors["diffusion_model.other.diff"], torch.full((2, 2), 2.0))
def test_secondary_only_filters_and_low_bit_preservation(monkeypatch, tmp_path, cwb):
a = tmp_path / "filter_a.safetensors"
b = tmp_path / "filter_b.safetensors"
tests/test_cwb_delta_lora_merger.py
+33 -3
View File
@@ -186,6 +186,36 @@ def test_streamed_merge_normalizes_alpha_once_and_supports_mixed_rank(
assert metadata["output_representation"] == "full_direct_difference"
@pytest.mark.parametrize("patterns, glob_patterns, selected", [
(r"\.selected", False, True),
("*.selected*", True, True),
("", False, False),
("no_match", False, False),
])
def test_include_filter_preserves_nonmatches_and_discards_first(
monkeypatch, tmp_path, delta_cwb, patterns, glob_patterns, selected,
):
paths = [tmp_path / f"{name}.safetensors" for name in ("a", "b")]
for path, value in zip(paths, (2.0, 6.0)):
_write(path, {
f"diffusion_model.{layer}.diff": torch.full((2, 2), value)
for layer in ("selected", "other", "selected_discard")
})
monkeypatch.setattr(delta_cwb.folder_paths, "models_dir", str(tmp_path))
monkeypatch.setattr(delta_cwb, "prepare_for_large_operation", lambda *args: None)
monkeypatch.setattr(delta_cwb, "cleanup_after_operation", lambda: None)
filename, _ = delta_cwb.DeltaCWBLoRAMergerLogic.execute(
[str(path) for path in paths], [path.name for path in paths], _settings(delta_cwb),
"balanced_mean", False, "skip", "include_filter", torch.float32,
"full_difference", 384, "cpu", patterns,
"*discard*" if glob_patterns else "discard", glob_patterns, False, include_mode=True,
)
tensors = _read(tmp_path / "loras" / filename)
assert set(tensors) == {"diffusion_model.selected.diff", "diffusion_model.other.diff"}
torch.testing.assert_close(tensors["diffusion_model.selected.diff"], torch.full((2, 2), 4.0 if selected else 2.0))
torch.testing.assert_close(tensors["diffusion_model.other.diff"], torch.full((2, 2), 2.0))
def test_existing_diff_and_1d_output_remain_fp32(monkeypatch, tmp_path, delta_cwb):
first = tmp_path / "diff_first.safetensors"
second = tmp_path / "diff_second.safetensors"
@@ -350,7 +380,7 @@ def test_exact_node_schema_contract(delta_cwb):
"lora_1", "lora_2", "cwb_preset", "cwb_config", "mismatch_mode",
"output_filename", "save_dtype", "output_representation", "output_rank",
"process_device", "exclude_patterns",
"discard_patterns", "glob_patterns", "force_clear_cache",
"discard_patterns", "glob_patterns", "force_clear_cache", "include_mode",
]
multi_ids = [value.id for value in schemas[2].inputs]
assert multi_ids[:9] == ["lora_count", *[f"lora_{index}" for index in range(1, 9)]]
@@ -360,8 +390,8 @@ def test_exact_node_schema_contract(delta_cwb):
assert schemas[0].inputs[7].default == "full_difference"
assert schemas[0].inputs[8].default == 384
assert schemas[0].inputs[9].default == "cuda"
assert schemas[0].inputs[-2].default is False
assert schemas[0].inputs[-1].default is True
assert schemas[0].inputs[-2].default is True
assert schemas[0].inputs[-1].default is False
assert "agreement" in schemas[0].inputs[2].tooltip
assert "Only a failed CUDA layer retries on CPU" in schemas[0].inputs[9].tooltip
tests/test_dtype_conversion.py
+40
View File
@@ -68,7 +68,9 @@ def test_schema_is_diffusion_model_scoped(conversion):
"reference_model",
"exclude_patterns",
"output_filename",
"include_mode",
]
assert schema.inputs[-1].default is False
assert schema.inputs[1].options == ["fp32", "fp16", "bf16"]
assert [item.display_name for item in schema.outputs] == [
"output_path",
@@ -152,6 +154,44 @@ def test_converts_floating_and_preserves_excluded_and_nonfloating(
assert "Non-floating tensors preserved: 1" in report
@pytest.mark.parametrize("patterns, selected", [
(r"^blocks\.0\.weight$", {"blocks.0.weight"}),
(r"^blocks\.0\.weight$" + "\n" + r"^blocks\.0\.norm\.weight$", {"blocks.0.weight", "blocks.0.norm.weight"}),
("", set()),
("no_such_layer", set()),
])
@pytest.mark.parametrize("use_reference", [False, True])
def test_include_filter_only_converts_matches(
monkeypatch, tmp_path, conversion, patterns, selected, use_reference,
):
source = tmp_path / "source.safetensors"
reference = tmp_path / "reference.safetensors"
tensors = {
"blocks.0.weight": torch.arange(4, dtype=torch.float32),
"blocks.0.norm.weight": torch.ones(3, dtype=torch.float32),
"position_ids": torch.tensor([1, 2], dtype=torch.int64),
}
_write_uel(source, tensors)
_write_uel(reference, {"blocks.0.weight": tensors["blocks.0.weight"].to(torch.bfloat16)})
_patch_runtime(monkeypatch, tmp_path, conversion, source)
monkeypatch.setattr(
conversion.folder_paths, "get_full_path_or_raise",
lambda _, name: str(reference if name == "reference" else source),
)
output_name, _ = conversion.DiffusionModelDtypeConversion.execute(
source.name, "fp16", "reference" if use_reference else "None",
patterns, "include_converted", include_mode=True,
)
result, _ = _read_uel(tmp_path / "diffusion_models" / output_name)
for key, tensor in tensors.items():
expected_dtype = tensor.dtype
if key in selected:
expected_dtype = torch.bfloat16 if use_reference and key == "blocks.0.weight" else torch.float16
assert result[key].dtype == expected_dtype
torch.testing.assert_close(result[key].to(tensor.dtype), tensor)
def test_uses_bounded_async_stream_and_releases_every_tensor(
monkeypatch, tmp_path, conversion
):
tests/test_groups.toml
+4
View File
@@ -48,6 +48,10 @@ python_tests = [
paths = ["nodes/lora_resize.py"]
python_tests = ["tests/test_lora_resize_factor_space.py", "tests/test_lora_resize_min_rank.py"]
[groups.merger_filters]
paths = ["nodes/merger.py"]
python_tests = ["tests/test_merger_filters.py"]
[groups.dtype_conversion]
paths = ["nodes/dtype_conversion.py"]
python_tests = ["tests/test_dtype_conversion.py"]
tests/test_lodestone_merger.py
+29
View File
@@ -129,6 +129,35 @@ def test_streamed_merge_writes_only_alpha_normalized_full_delta(monkeypatch, tmp
torch.testing.assert_close(tensors["diffusion_model.layer.diff"], torch.diag(torch.tensor([1.0, 4.0])))
@pytest.mark.parametrize("patterns, glob_patterns, selected", [
(r"\.selected", False, True),
("*.selected*", True, True),
("", False, False),
("no_match", False, False),
])
def test_include_filter_preserves_nonmatches_and_discards_first(
monkeypatch, tmp_path, lodestone, patterns, glob_patterns, selected,
):
paths = [tmp_path / f"{name}.safetensors" for name in ("a", "b")]
for path, value in zip(paths, (2.0, 6.0)):
_write(path, {
f"diffusion_model.{layer}.diff": torch.full((2, 2), value)
for layer in ("selected", "other", "selected_discard")
})
monkeypatch.setattr(lodestone.folder_paths, "models_dir", str(tmp_path))
monkeypatch.setattr(lodestone, "prepare_for_large_operation", lambda *args: None)
monkeypatch.setattr(lodestone, "cleanup_after_operation", lambda: None)
output = lodestone.merge_lodestone_loras(
[str(path) for path in paths], "sum", "cpu", torch.float32, "include_filter",
exclude_patterns=patterns, glob_patterns=glob_patterns, include_mode=True,
discard_patterns="*discard*" if glob_patterns else "discard", verbose=False,
)
tensors = _read(output)
assert set(tensors) == {"diffusion_model.selected.diff", "diffusion_model.other.diff"}
torch.testing.assert_close(tensors["diffusion_model.selected.diff"], torch.full((2, 2), 8.0 if selected else 2.0))
torch.testing.assert_close(tensors["diffusion_model.other.diff"], torch.full((2, 2), 2.0))
def test_dedicated_nodes_do_not_modify_factor_merger_schema(lodestone):
standard = _load_module("nodes.lora_merger")
assert [node.define_schema().node_id for node in lodestone.LODESTONE_MERGER_NODES] == [
tests/test_lora_diff_dtype_contract.py
+61 -6
View File
@@ -417,18 +417,68 @@ def test_merge_to_model_report_lists_skipped_tensor_and_pattern(
assert "[SKIPPED BY PATTERN] (1)" in report
@pytest.mark.parametrize("patterns, selected", [
(r"selected\.weight$", True),
("", False),
("no_match", False),
])
def test_merge_to_model_include_filter_limits_stream_and_output(
monkeypatch, tmp_path, modules, patterns, selected,
):
resize, _ = modules
_patch_output(monkeypatch, resize, tmp_path)
base = tmp_path / "include_base.safetensors"
adapter = tmp_path / "include_adapter.safetensors"
save_file({
f"model.diffusion_model.{name}.weight": torch.zeros((2, 2))
for name in ("selected", "other")
}, str(base))
save_file({
f"diffusion_model.{name}.{factor}": torch.ones(shape)
for name in ("selected", "other")
for factor, shape in (("lora_A.weight", (1, 2)), ("lora_B.weight", (2, 1)))
}, str(adapter))
planned_keys = []
real_stream = resize.stream_work_units
def capture_units(handlers, units, **kwargs):
units = list(units)
planned_keys.extend(key for _, entries in units for keys in entries.values() for key in keys)
return real_stream(handlers, units, **kwargs)
monkeypatch.setattr(resize, "stream_work_units", capture_units)
path, report = resize.merge_loras_to_model(
[str(adapter)], [1.0], str(base), "cpu", torch.float32,
"include_result", skip_patterns_str=patterns, verbose=False,
return_report=True, include_mode=True,
)
tensors = load_file(path)
assert set(tensors) == ({"model.diffusion_model.selected.weight"} if selected else set())
if selected:
torch.testing.assert_close(tensors["model.diffusion_model.selected.weight"], torch.ones((2, 2)))
assert len(planned_keys) == 3
assert all("selected" in key for key in planned_keys)
else:
assert planned_keys == []
assert "no include pattern matched" in report
@pytest.mark.parametrize("include_mode", [False, True])
def test_merge_to_model_node_returns_path_blank_line_report(
monkeypatch, modules
monkeypatch, modules, include_mode,
):
resize, _ = modules
monkeypatch.setattr(
resize.folder_paths, "get_full_path_or_raise",
lambda category, name: f"{category}/{name}",
)
monkeypatch.setattr(
resize, "merge_loras_to_model",
lambda **kwargs: ("saved/model.safetensors", "SUCCESS\nDETAILS"),
)
received = {}
def merge(**kwargs):
received.update(kwargs)
return "saved/model.safetensors", "SUCCESS\nDETAILS"
monkeypatch.setattr(resize, "merge_loras_to_model", merge)
result = resize.LoRAMergeToModel.execute(
"base.safetensors", "1",
@@ -436,9 +486,11 @@ def test_merge_to_model_node_returns_path_blank_line_report(
"None", 1.0, "None", 1.0, "None", 1.0,
"None", 1.0, "None", 1.0, "None", 1.0, "None", 1.0,
"", "merged", "fp32", "cpu", True, False, False,
include_mode=include_mode,
)
assert result.result == ("merged.safetensors", "SUCCESS\nDETAILS")
assert received["include_mode"] is include_mode
def test_mixed_parser_scans_every_key_and_retains_unpaired(modules):
@@ -486,7 +538,10 @@ def test_include_1d_switches_are_appended_and_default_off(modules, generic_modul
for node_class in node_classes:
final_input = node_class.define_schema().inputs[-1]
assert final_input.id == "include_1d_diffs"
expected = "include_mode" if node_class in {
generic.LoRATwoMerger, generic.LoRAThreeMerger, resize.LoRAMergeToModel
} else "include_1d_diffs"
assert final_input.id == expected
assert final_input.default is False
outputs = resize.LoRAMergeToModel.define_schema().outputs
tests/test_lora_resize_factor_space.py
+53 -1
View File
@@ -88,6 +88,55 @@ def test_resize_filters_preserve_and_discard_whole_layers(monkeypatch, tmp_path,
assert actual["diffusion_model.resize.lora_A.weight"].shape[0] == 1
@pytest.mark.parametrize("glob", [False, True])
def test_resize_include_mode_only_resizes_matching_layers(monkeypatch, tmp_path, resize, glob):
source = tmp_path / "include_filters.safetensors"
tensors = {}
for name in ("include", "discard", "skip"):
tensors[f"diffusion_model.{name}.lora_A.weight"] = torch.eye(3, dtype=torch.float16)
tensors[f"diffusion_model.{name}.lora_B.weight"] = torch.eye(3, dtype=torch.float16)
tensors[f"diffusion_model.{name}.alpha"] = torch.tensor(1.5)
tensors["standalone.include"] = torch.ones(1)
tensors["standalone.skip"] = torch.ones(1)
_write_uel(source, tensors)
monkeypatch.setattr(resize.folder_paths, "models_dir", str(tmp_path))
monkeypatch.setattr(resize, "prepare_for_large_operation", lambda *args: None)
monkeypatch.setattr(resize, "cleanup_after_operation", lambda: None)
output = resize.resize_lora_file(
str(source), 1, None, None, "cpu", torch.bfloat16, "included", verbose=False,
exclude_patterns="*include*\n*discard*\nstandalone.include" if glob else r"\.(include|discard)\." + "\n^standalone\\.include$",
discard_patterns="*discard*" if glob else r"\.discard\.",
glob_patterns=glob,
include_mode=True,
)
actual = _read_uel(output)
assert actual["diffusion_model.include.lora_A.weight"].shape[0] == 1
assert torch.equal(actual["standalone.include"], tensors["standalone.include"])
assert not any(".discard." in key for key in actual)
for key in tensors:
if ".skip." in key or key == "standalone.skip":
assert torch.equal(actual[key], tensors[key])
def test_resize_include_mode_empty_patterns_preserves_all_tensors(monkeypatch, tmp_path, resize):
source = tmp_path / "empty_include.safetensors"
tensors = {"standalone": torch.ones(2)}
_write_uel(source, tensors)
monkeypatch.setattr(resize.folder_paths, "models_dir", str(tmp_path))
monkeypatch.setattr(resize, "prepare_for_large_operation", lambda *args: None)
monkeypatch.setattr(resize, "cleanup_after_operation", lambda: None)
output = resize.resize_lora_file(
str(source), 1, None, None, "cpu", torch.float32, "empty", verbose=False,
include_mode=True,
)
actual = _read_uel(output)
assert torch.equal(actual["standalone"], tensors["standalone"])
@pytest.mark.parametrize("node_name,extra", [
("LoRAResizeFixed", {"new_rank": 1}),
("LoRAResizeRatio", {"max_rank": 1, "ratio": 2.0}),
@@ -100,16 +149,19 @@ def test_all_resize_nodes_forward_filters(monkeypatch, tmp_path, resize, node_na
assert controls["exclude_patterns"].default == ""
assert controls["discard_patterns"].default == ""
assert controls["glob_patterns"].default is False
assert controls["include_mode"].default is False
assert node.define_schema().inputs[-1].id == "include_mode"
calls = []
monkeypatch.setattr(resize.folder_paths, "models_dir", str(tmp_path))
monkeypatch.setattr(resize.folder_paths, "get_full_path_or_raise", lambda *args: "input")
monkeypatch.setattr(resize, "resize_lora_file", lambda *args, **kwargs: calls.append(kwargs))
node.execute(lora_name="input", output_filename="out", save_dtype="fp16", device="cpu",
force_clear_cache=False, exclude_patterns="keep*", discard_patterns="drop*",
glob_patterns=True, **extra)
glob_patterns=True, include_mode=True, **extra)
assert calls[0]["exclude_patterns"] == "keep*"
assert calls[0]["discard_patterns"] == "drop*"
assert calls[0]["glob_patterns"] is True
assert calls[0]["include_mode"] is True
def test_resize_invalid_filter_fails_before_writer(monkeypatch, tmp_path, resize):
tests/test_merger_filters.py
+70
View File
@@ -0,0 +1,70 @@
import importlib
import sys
import types
from pathlib import Path
import pytest
import torch
from safetensors.torch import load_file, save_file
REPO_ROOT = Path(__file__).resolve().parents[1]
PACKAGE_NAME = "modelutils_merger_filter_tests"
def _module(name):
if PACKAGE_NAME not in sys.modules:
package = types.ModuleType(PACKAGE_NAME)
package.__path__ = [str(REPO_ROOT)]
sys.modules[PACKAGE_NAME] = package
return importlib.import_module(f"{PACKAGE_NAME}.{name}")
@pytest.fixture(scope="module")
def modules():
return _module("nodes.merger"), _module("nodes.merger_ops")
def _params(name, *, patterns="", include=False, discard="", glob=False):
return {
"mismatch_mode": "skip", "alignment_mode": "pad/crop", "alpha": 0.5,
"beta": 0.0, "gamma": 0.5, "delta": 2.0, "epsilon": 0.01,
"zeta": 0.0, "seed": 0, "output_filename": name, "save_dtype": "fp32",
"override_dtype": True, "device": "cpu", "dtype": torch.float32,
"exclude_patterns": patterns, "include_mode": include,
"discard_patterns": discard, "glob_patterns": glob, "lazy_load": True,
"force_clear_cache": False,
}
def _run(monkeypatch, tmp_path, merger, operations, params):
paths = {name: str(tmp_path / f"{name}.safetensors") for name in ("a", "b")}
save_file({"match.weight": torch.tensor([2.0]), "other.weight": torch.tensor([4.0])}, paths["a"])
save_file({"match.weight": torch.tensor([6.0]), "other.weight": torch.tensor([8.0])}, paths["b"])
monkeypatch.setattr(merger.folder_paths, "get_full_path", lambda _kind, name: paths.get(name))
monkeypatch.setattr(merger.folder_paths, "get_folder_paths", lambda _kind: [str(tmp_path)])
monkeypatch.setattr(merger.folder_paths, "models_dir", str(tmp_path))
monkeypatch.setattr(merger, "prepare_for_large_operation", lambda *args: None)
monkeypatch.setattr(merger, "cleanup_after_operation", lambda: None)
params.update({"model_a": "a", "model_b": "b"})
merger.MergerLogic.execute_merge({"model_a": "a", "model_b": "b"}, "Weight-Sum", operations.TWO_MODEL_MODES, params, "checkpoints")
return load_file(str(tmp_path / "checkpoints" / f"{params['output_filename']}.safetensors"))
def test_include_filter_controls_preflight_and_streaming(monkeypatch, tmp_path, modules):
merger, operations = modules
selected = _run(monkeypatch, tmp_path, merger, operations, _params("selected", patterns="match", include=True))
assert selected["match.weight"].item() == 4.0
assert selected["other.weight"].item() == 4.0
empty = _run(monkeypatch, tmp_path, merger, operations, _params("empty", include=True))
assert empty["match.weight"].item() == 2.0
assert empty["other.weight"].item() == 4.0
discarded = _run(monkeypatch, tmp_path, merger, operations, _params("discarded", patterns="match", include=True, discard="match"))
assert "match.weight" not in discarded
assert discarded["other.weight"].item() == 4.0
globbed = _run(monkeypatch, tmp_path, merger, operations, _params("globbed", patterns="*.weight", include=True, glob=True))
assert globbed["match.weight"].item() == 4.0
assert globbed["other.weight"].item() == 6.0
tests/test_model_analysis.py
+45 -2
View File
@@ -53,6 +53,8 @@ def test_five_node_schema_contract(analysis):
assert schema.inputs[0].options == ["ANALYZE", "DOCUMENTATION ONLY"]
assert "lazy_load" not in [item.id for item in schema.inputs]
input_ids = [item.id for item in schema.inputs]
assert input_ids[-3:] == ["exclude_patterns", "glob_patterns", "include_mode"]
assert schema.inputs[-1].default is False
if node in (analysis.LoRAModelAnalysis, analysis.EmbeddingModelAnalysis):
assert input_ids[3] == "cwb_similarity_alignment"
else:
@@ -227,7 +229,14 @@ def test_lora_cross_format_keys_compare_as_one_logical_pair(monkeypatch, tmp_pat
assert all(len(call[1]) == 2 for call in async_calls)
def test_excluded_lora_blocks_do_not_enter_metrics(monkeypatch, tmp_path, analysis):
@pytest.mark.parametrize("patterns, glob_patterns, include_mode", [
(r"^diffusion_model\.blocks\.4\.", False, False),
(r"^diffusion_model\.blocks\.3\.", False, True),
("diffusion_model.blocks.3.*", True, True),
])
def test_excluded_lora_blocks_do_not_enter_metrics(
monkeypatch, tmp_path, analysis, patterns, glob_patterns, include_mode,
):
path_a = tmp_path / "lora_a.safetensors"
path_b = tmp_path / "lora_b.safetensors"
tensors_a = {}
@@ -247,7 +256,7 @@ def test_excluded_lora_blocks_do_not_enter_metrics(monkeypatch, tmp_path, analys
comparison, cwb_report = analysis.ModelAnalysisLogic.execute(
"a", "b", "loras",
_params(exclude_patterns=r"^diffusion_model\.blocks\.4\."),
_params(exclude_patterns=patterns, glob_patterns=glob_patterns, include_mode=include_mode),
lora_mode=True,
)
@@ -262,6 +271,40 @@ def test_excluded_lora_blocks_do_not_enter_metrics(monkeypatch, tmp_path, analys
assert "blocks_4_" not in cwb_report
@pytest.mark.parametrize("patterns, glob_patterns, expected_keys", [
(r"^blocks\.0\.", False, ["blocks.0.weight"]),
("blocks.0.*", True, ["blocks.0.weight"]),
("", False, []),
("no_such_layer", True, []),
])
def test_include_filter_limits_analysis_inventory(
monkeypatch, tmp_path, analysis, patterns, glob_patterns, expected_keys,
):
paths = {name: str(tmp_path / f"{name}.safetensors") for name in ("a", "b")}
for name, value in (("a", 1.0), ("b", 2.0)):
save_file({
"blocks.0.weight": torch.full((2, 2), value),
"blocks.1.weight": torch.full((2, 2), value),
f"only_{name}.weight": torch.full((2, 2), value),
}, paths[name])
monkeypatch.setattr(analysis.folder_paths, "get_full_path", lambda _, name: paths[name])
monkeypatch.setattr(analysis, "prepare_for_large_operation", lambda *args, **kwargs: None)
monkeypatch.setattr(analysis, "cleanup_after_operation", lambda: None)
streamed = []
original_stream = analysis.MemoryEfficientSafeOpen.async_stream
def stream(handler, keys, *args, **kwargs):
streamed.extend(keys)
return original_stream(handler, keys, *args, **kwargs)
monkeypatch.setattr(analysis.MemoryEfficientSafeOpen, "async_stream", stream)
analysis.ModelAnalysisLogic.execute(
"a", "b", "diffusion_models",
_params(exclude_patterns=patterns, glob_patterns=glob_patterns, include_mode=True),
)
assert sorted(streamed) == sorted(expected_keys * 2)
def test_embedding_similarity_alignment_is_explicit_opt_in(analysis):
a = torch.tensor([[1.0, 0.0], [0.0, 1.0]])
b = torch.tensor([[0.0, 1.0], [1.0, 0.0]])
tests/test_schema_tooltips.py
+106
View File
@@ -1,6 +1,13 @@
import ast
import importlib
import inspect
import sys
import types
from pathlib import Path
import pytest
from comfy_api.latest import io
NODE_DIR = Path(__file__).resolve().parents[1] / "nodes"
@@ -40,3 +47,102 @@ def test_every_schema_input_has_a_direct_static_tooltip():
assert not missing, "Inputs without tooltips:\n" + "\n".join(missing)
assert not non_static, "Tooltips must be direct non-empty string literals:\n" + "\n".join(non_static)
FILTER_MODULES = [
"merger", "consensus_merger", "cwb_delta_lora_merger", "lodestone_merger",
"lora_resize", "dtype_conversion", "model_analysis",
"lora_extract_svd", "dora_extract_wd", "dora_learned_wd", "text_encoder_extract",
]
def test_filter_schema_audit_covers_all_direct_filter_definitions():
discovered = set()
for path in NODE_DIR.glob("*.py"):
tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path))
for call in ast.walk(tree):
if (
isinstance(call, ast.Call)
and isinstance(call.func, ast.Attribute)
and call.func.attr == "Input"
and call.args
and isinstance(call.args[0], ast.Constant)
and call.args[0].value in {"exclude_patterns", "skip_patterns"}
):
discovered.add(path.stem)
assert discovered <= set(FILTER_MODULES), f"Unaudited filter modules: {discovered - set(FILTER_MODULES)}"
def _load_filter_module(module_name):
package_name = "modelutils_filter_schema_tests"
if package_name not in sys.modules:
package = types.ModuleType(package_name)
package.__path__ = [str(NODE_DIR.parent)]
sys.modules[package_name] = package
return importlib.import_module(f"{package_name}.nodes.{module_name}")
@pytest.mark.parametrize("module_name", FILTER_MODULES)
def test_layer_filter_schemas_append_disabled_include_toggle(module_name):
module = _load_filter_module(module_name)
checked = []
for name, node in vars(module).items():
if name.startswith("_") or not isinstance(node, type):
continue
if node.__module__ != module.__name__ or not issubclass(node, io.ComfyNode):
continue
schema = node.define_schema()
ids = [item.id for item in schema.inputs]
if not {"exclude_patterns", "skip_patterns"}.intersection(ids):
continue
assert ids.count("include_mode") == 1, schema.node_id
assert ids[-1] == "include_mode", schema.node_id
assert isinstance(schema.inputs[-1], io.Boolean.Input), schema.node_id
assert schema.inputs[-1].default is False, schema.node_id
checked.append(schema.node_id)
assert checked, f"No filter nodes checked in {module_name}"
@pytest.mark.parametrize("module_name, helper_name, expected_count", [
("lora_extract_svd", "extract_lora_from_files", 5),
("dora_extract_wd", "extract_dora_from_files", 5),
("dora_learned_wd", "extract_dora_learned_from_files", 5),
("text_encoder_extract", "extract_te_from_files", 10),
])
@pytest.mark.parametrize("include_mode", [False, True])
def test_all_extraction_nodes_forward_include_mode_without_shifting_parameters(
monkeypatch, module_name, helper_name, expected_count, include_mode,
):
module = _load_filter_module(module_name)
signature = inspect.signature(getattr(module, helper_name))
assert list(signature.parameters)[-1] == "include_mode"
captured = []
def extract(*args, **kwargs):
bound = signature.bind(*args, **kwargs)
bound.apply_defaults()
captured.append(bound.arguments)
monkeypatch.setattr(module, helper_name, extract)
monkeypatch.setattr(module.folder_paths, "get_full_path_or_raise", lambda category, name: name)
monkeypatch.setattr(module, "_build_lora_output_path", lambda name: (name, name))
for name, node in vars(module).items():
if name.startswith("_") or not isinstance(node, type):
continue
if node.__module__ != module.__name__ or not issubclass(node, io.ComfyNode):
continue
schema = node.define_schema()
values = {}
for widget in schema.inputs:
value = getattr(widget, "default", None)
if value is None:
options = getattr(widget, "options", [])
value = options[0] if options else widget.id
values[widget.id] = value
values["include_mode"] = include_mode
values["skip_patterns"] = "selected_layer"
node.execute(**values)
assert captured[-1]["include_mode"] is include_mode, schema.node_id
assert captured[-1]["skip_patterns_str"] == "selected_layer", schema.node_id
assert captured[-1]["knee_probe_offset"] == values.get("knee_probe_offset", 32), schema.node_id
assert len(captured) == expected_count