Plain short hints for the model loader and other long settings; details moved to the docs; a test fails on hints over 200 characters
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
This commit is contained in:
digital-garbage
co-authored by
Claude Sonnet 5.5
Claude Sonnet 5.5
parent
12bf6c6308
commit
0a8e16ab2b
@@ -41,3 +41,29 @@ def test_no_hint_points_at_the_inspector_for_a_control_it_does_not_have():
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def test_no_setting_hint_points_at_the_inspector(shipped):
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assert not [f"{m}.{n}" for m, n, s in _settings(shipped) if "inspector" in str(s.get("hint", "")).lower()]
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def test_no_hint_or_tooltip_is_an_essay():
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"""Hints are one or two plain sentences. Details belong in the docs."""
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import ast
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import pathlib
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root = pathlib.Path(__file__).resolve().parents[2]
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limit, long = 200, []
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for path in sorted(root.glob("modules/**/*.py")) + sorted(root.glob("core/*.py")):
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if "tests" in path.parts:
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continue
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tree = ast.parse(path.read_text())
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for n in ast.walk(tree):
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pairs = []
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if isinstance(n, ast.keyword) and n.arg in ("tooltip", "hint"):
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pairs = [n.value]
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elif isinstance(n, ast.Dict):
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pairs = [v for k, v in zip(n.keys, n.values) if isinstance(k, ast.Constant) and k.value in ("tooltip", "hint")]
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for v in pairs:
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try:
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text = ast.literal_eval(v)
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except Exception: # noqa: BLE001 -- built at run time, not checked here
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continue
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if isinstance(text, str) and len(text) > limit:
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long.append(f"{path.relative_to(root)}:{v.lineno} ({len(text)})")
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assert not long, f"hints over {limit} characters: {long}"
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@@ -202,3 +202,13 @@ Most used:
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| `/fun/api/modules` | the manifest (`?traits=a,b` narrows to a model) |
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| `/fun/api/m/<id>/...` | a module's own routes |
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| `/fun/app/...` | the app. Allowed types: `.js .css .html .woff2 .svg`. |
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## Diffusion Model Loader: speed settings in detail
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The in-app hints are short; the details are here.
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- **mxfp8**: MiniMax H3 only. Stores attention and MLP weights of the middle blocks as MXFP8 (one scale per 32 weights); the first 2 and last 3 blocks stay bf16. Blackwell GPUs multiply it at about twice the bf16 rate. Needs torch 2.10+ with CUDA 13 and a current comfy-kitchen. A LoRA applied to these layers slows them back down.
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- **int8_convrot**: MiniMax H3 from a bf16/fp16 file. Weights are rotated, then stored as int8 (about half the memory). Speed-up is lost on layers a LoRA touches or when compute dtype is forced.
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- **fp16_accumulation**: sets torch's fp16 matmul accumulation flag; needs a torch build that has it.
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- **sla / sla_method**: block-sparse attention for H3, run alongside the chosen attention backend. `sla` keeps a fixed share (`sla_sparsity`, 0.90 validated, break-even near 0.60), `sol-attn` uses a per-block threshold (`sla_tau`), `vsa` works on video cubes and needs a model trained for it, `veda` uses a learned predictor file from `models/veda` (Veda, arXiv 2605.30325). `sla_sparsity_start` ramps the share up over the steps (arXiv 2610.21129).
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- **sla_block_size / sla_protect_audio**: H3 packs audio at 80 rows per second, so large blocks force seconds of speech through one pattern; protecting audio keeps words and sound at full attention.
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@@ -56,151 +56,71 @@ class FunUIDiffusionModelLoader(io.ComfyNode):
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inputs=[
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io.Combo.Input("model_name", options=_model_file_choices()),
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io.Combo.Input("weight_dtype", options=WEIGHT_DTYPES, default="default",
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tooltip="How the weights are stored. 'default' keeps whatever "
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"the file already is."),
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tooltip="How the model is stored in memory. 'default' keeps the file as it is. A smaller type uses less memory, with a small quality cost."),
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io.Combo.Input("compute_dtype", options=COMPUTE_DTYPES, default="default",
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tooltip="What the maths runs in, independently of storage."),
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tooltip="The precision used for the maths while generating. 'default' is safe; a lower one is faster, with a small quality cost."),
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io.Combo.Input("attention", options=attention_choices(), default="default",
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tooltip="Attention backend. Only what this machine can run is "
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"listed; 'default' keeps ComfyUI's launch choice."),
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tooltip="The attention method. Faster ones need extra packages and only the ones this machine can run are listed. 'default' keeps ComfyUI's own choice."),
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io.Boolean.Input("fp16_accumulation", default=False, optional=True,
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tooltip="Faster fp16 matmuls where the torch build supports it."),
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tooltip="Speeds up fp16 maths slightly, with a small quality trade-off. Needs a recent PyTorch and a GPU that supports it."),
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io.Boolean.Input("sla", default=False, optional=True, extra_dict={"funui_requires": ["sparse_attention"]},
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tooltip="Block-sparse attention for MiniMax H3 -- the "
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"inference path lightx2v's SLA turbo LoRA was "
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"distilled against, which is why that LoRA gives no "
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"speedup on its own. Roughly 3.7x the attention "
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"throughput at 768p/15s. Runs ALONGSIDE the attention "
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"backend above rather than replacing it: SLA takes "
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"H3's long packed self-attention, the chosen backend "
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"takes the text refiner, masked calls and any trailing "
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"dense steps. Skipped, with the reason, on anything "
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"that is not MiniMax H3 or on a machine without Triton."),
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tooltip="Faster attention for MiniMax H3, with a quality trade-off. Needed for the SLA turbo LoRA to give any speedup. Requires Triton and an H3 model; otherwise it is skipped and the status says why."),
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# SLA settings. Every one is validated at its default (see sla_attention);
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# they are here because a knob whose value was measured is still a knob.
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io.Float.Input("sla_sparsity", extra_dict=shows(("sla", [True]), ("sla_method", ["sla", "vsa"])), default=sla_attention.SLA_DEFAULTS["sparsity_ratio"],
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min=0.0, max=0.95, step=0.05, optional=True,
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tooltip="Fraction of key blocks skipped while SLA is on (of video "
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"cubes for method vsa). 0.90 is validated; 0.85 is lightx2v's own value "
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"and ~15% slower. Break-even is around 0.60 -- below that "
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"the kernel is SLOWER than dense, so a low value is a "
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"loss, not a safe fallback."),
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tooltip="How much of the attention is skipped. Higher is faster but softer. 0.90 is a good start; below 0.60 it is slower than normal attention."),
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io.Combo.Input("sla_block_size", extra_dict=shows(("sla", [True]), ("sla_method", ["sla"]), ("sla_engine", ["triton"])),
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options=["32", "64", "128"],
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default=str(sla_attention.SLA_DEFAULTS["block_size"]),
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optional=True,
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tooltip="How many sequence tokens share one key selection. H3 "
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"packs audio at 80 rows per second, so a 128-row block "
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"forces 1.6s of speech down one attention pattern while "
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"the same rows are 3% of a video frame. Use 128 only "
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"when the audio does not matter; 32 routes finer still for "
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"about the same time. Triton engine only."),
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tooltip="Smaller blocks follow the audio more closely at about the same speed. Use 128 only if sound does not matter. Triton engine only."),
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io.Boolean.Input("sla_protect_audio", extra_dict=shows(("sla", [True]), ("sla_method", ["sla", "sol-attn"])),
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default=sla_attention.SLA_DEFAULTS["protect_audio"], optional=True,
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tooltip="Always attend the prompt's words and every audio "
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"stream, whatever top-k picks. Audio is ~1% of the packed "
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"sequence, so plain top-k regularly drops all of it "
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"and the soundtrack degrades while the video still "
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"looks fine. Reference images stay sparse: see sla_references."),
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tooltip="Keeps the prompt and the sound at full attention, so the soundtrack does not degrade while the picture still looks fine. Recommended on."),
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io.Int.Input("sla_min_seq_len", extra_dict=shows(("sla", [True])),
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default=sla_attention.SLA_DEFAULTS["min_seq_len"],
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min=0, max=1000000, step=1024, optional=True,
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tooltip="Sequences shorter than this stay dense. Guards the short "
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"text refiner, which must never be sparsified, and "
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"low-resolution runs where block selection costs more "
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"than it saves."),
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tooltip="Short inputs below this length skip the speed-up, because it would not pay off there. Leave at the default."),
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io.Int.Input("sla_dense_last_steps", extra_dict=shows(("sla", [True]), ("sla_method", ["sla", "sol-attn", "veda"])),
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default=sla_attention.SLA_DEFAULTS["dense_last_steps"],
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min=0, max=8, optional=True,
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tooltip="Run the last N sampling steps at full attention. 0 "
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"matches lightx2v; 1 was tested and did not help, for "
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"+20% time."),
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tooltip="Runs the last N steps with normal attention for a cleaner finish. Costs extra time; 0 is fine for most runs."),
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io.Boolean.Input("int8_convrot", default=False, optional=True,
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tooltip="MiniMax H3 from an ordinary (bf16/fp16) file: store the "
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"attention and MLP weights as int8 (rotated first, so no lone "
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"outlier spoils the rounding), the way Comfy-Org's own int8_convrot "
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"files are. About half the memory and int8 matmuls on GPUs that "
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"have them; slightly different pictures. Does nothing to a file "
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"that is already quantized or is not H3 (the status says so), and "
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"is skipped when a weight dtype is forced above. The memory saving "
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"always holds; the speed-up is lost on layers a LoRA is applied "
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"to and when compute dtype is forced."),
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tooltip="MiniMax H3 only: stores the big layers as int8 for about half the memory, with slightly different pictures. Skipped for files that are already quantized; the status says so."),
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# Added later, so after everything: saved workflows can store widget values by position.
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io.Combo.Input("sla_engine", extra_dict=shows(("sla", [True]), ("sla_method", ["sla"])), options=list(sla_attention.ENGINES),
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default=sla_attention.SLA_DEFAULTS["engine"], optional=True,
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tooltip="What runs the sparse calls. comfy_kitchen: ComfyUI's compiled "
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"int8 sol_attn (needs comfy-kitchen 0.2.32+; the Triton kernel "
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"runs instead where it is missing, and the status says so). "
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"triton: FunUI's own kernel, the only one with block size and "
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"stabilize motion."),
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tooltip="What runs the faster attention. comfy_kitchen is ComfyUI's own and needs comfy-kitchen 0.2.32+; triton is FunUI's and has more options. Falls back to triton if missing."),
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io.String.Input("sla_dense_steps", extra_dict=shows(("sla", [True]), ("sla_method", ["sla", "sol-attn", "veda"])), default=sla_attention.SLA_DEFAULTS["dense_steps"],
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optional=True,
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tooltip="Steps at full attention, counted from 1: '1' is the first "
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"step, '1-2' the first two, '1,6' the first and sixth; blank or '0', "
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"none. The first steps set the layout and how closely the "
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"prompt is followed, so the first is exact by default. Adds to "
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"dense last steps. The log names the steps that ran this way."),
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tooltip="Steps that use normal attention, counted from 1 ('1', '1-2', '1,6'). The first step sets the layout, so it is exact by default. Blank or 0 means none."),
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io.Combo.Input("sla_references", extra_dict=shows(("sla", [True]), ("sla_method", ["sla", "sol-attn"])), options=list(sla_attention.REFERENCES),
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default=sla_attention.SLA_DEFAULTS["references"], optional=True,
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tooltip="Reference images, conditioning frames and the prompt's vision "
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"tokens. off: sparse like the video. light: each keeps its best "
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"15% for every query block. heavy: all of them exact, slow with "
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"big references. On comfy_kitchen, light picks one 15% for all "
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"query blocks."),
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tooltip="How reference pictures are treated. off: skipped like the video. light: keeps the most relevant parts. heavy: kept exact, but slow with big references."),
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io.Boolean.Input("sla_tail", extra_dict=shows(("sla", [True]), ("sla_method", ["sla"]), ("sla_engine", ["comfy_kitchen"])), default=sla_attention.SLA_DEFAULTS["tail"], optional=True,
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tooltip="comfy_kitchen engine: the skipped blocks still count, as one "
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"averaged term per query block, instead of not at all. Off "
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"matches what the SLA turbo LoRA was trained against; untested "
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"on H3."),
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tooltip="comfy_kitchen engine: also keeps a rough summary of the skipped parts. Off matches the SLA turbo LoRA; untested on H3."),
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io.Boolean.Input("sla_stabilize_motion", extra_dict=shows(("sla", [True]), ("sla_method", ["sla"]), ("sla_engine", ["triton"])),
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default=sla_attention.SLA_DEFAULTS["stabilize_motion"], optional=True,
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tooltip="Triton engine: each layer leans toward the blocks it picked last "
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"step, so near ties stop flipping and showing as a faint double "
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"exposure on fast motion. A fix for that one symptom; a little "
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"more memory."),
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tooltip="Triton engine: removes a faint double image on fast motion. Uses a little more memory."),
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io.Combo.Input("sla_method", extra_dict=shows(("sla", [True])), options=list(sla_attention.METHODS),
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default=sla_attention.SLA_DEFAULTS["method"], optional=True,
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tooltip="How the blocks worth attending are chosen. sla: a fixed share "
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"(sla_sparsity), what SLA turbo LoRAs were trained against. "
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"sol-attn: a per-block threshold (sla_tau), no training needed; "
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"comfy_kitchen engine. vsa: video cubes, only for models trained "
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"for it (the status says when the model lacks VSA's layers); run "
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"by ComfyUI's own Model Sparse Attention, keeping 1 - sla_sparsity "
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"of the cubes. veda (experimental, tested on a GPU): "
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"a small learned predictor (Veda, arXiv 2605.30325, a file "
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"in models/veda) picks the 128-token tiles at its own trained "
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"budget, 90% for text-to-video; sla_sparsity and the rising "
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"share are not used. Needs CUDA "
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"and Triton."),
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tooltip="How the parts to skip are chosen. sla: a fixed share, what turbo LoRAs expect. sol-attn: adapts per block. vsa: only for models trained for it. veda: experimental, needs CUDA."),
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io.Float.Input("sla_tau", extra_dict=shows(("sla", [True]), ("sla_method", ["sol-attn"])), default=sla_attention.SLA_DEFAULTS["tau"], min=0.0, max=4.0,
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step=0.05, optional=True,
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tooltip="sol-attn only: higher skips more. About 1.0 keeps ~16% of key "
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"blocks exact, 1.5 ~7%, 2.0 ~3%."),
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tooltip="sol-attn only: higher skips more attention. About 1.0 is gentle, 2.0 is aggressive."),
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# Added after everything: saved workflows store widget values by position.
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io.Float.Input("sla_sparsity_start", extra_dict=shows(("sla", [True]), ("sla_method", ["sla"])), default=sla_attention.SLA_DEFAULTS["sparsity_start"],
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min=0.0, max=0.95, step=0.05, optional=True,
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tooltip="Skip fewer blocks early and more late: the share rises in a straight "
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"line from this value on the first step to sla_sparsity on the last "
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"(attention concentrates as denoising goes, GSRS, arXiv 2610.21129). "
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"0 is off: one fixed share all run. One H3 render: quality held, perhaps a little better. Try 0.70 "
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"with sla_sparsity 0.90. Early steps cost more time than late ones."),
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tooltip="Skips less at the first steps and more at the last, up to the sparsity above. 0 turns it off. Try 0.70 with sparsity 0.90."),
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io.Boolean.Input("mxfp8", default=False, optional=True,
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tooltip="MiniMax H3 from an ordinary (bf16/fp16) file: store the attention and MLP "
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"weights of the middle blocks as MXFP8 (one scale per 32 weights), which "
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"Blackwell GPUs (RTX 50, RTX PRO 6000) multiply at about twice the bf16 "
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"rate. The first 2 and last 3 blocks stay bf16, as in NVIDIA's Sol-H3. "
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"Needs torch 2.10+ with CUDA 13 and a current comfy-kitchen, else it is "
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"skipped and the status says so. Slightly different pictures: listen to "
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"the sound first, it spoils before the picture does. A LoRA applied to "
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"these layers slows them back down."),
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tooltip="MiniMax H3 only: about twice the speed on RTX 50 and newer GPUs, with slightly different pictures. Requires torch 2.10+ with CUDA 13; otherwise skipped."),
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# Added last: saved workflows store widget values by position.
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io.Combo.Input("veda_predictor", extra_dict=shows(("sla", [True]), ("sla_method", ["veda"])), options=veda_attention.predictor_choices(),
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default="auto", optional=True,
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tooltip="veda: the predictor file in ComfyUI/models/veda. auto takes the text-to-video one "
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"when it is there, else the first. Use the R2VA file for reference-to-video runs."),
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tooltip="veda: which predictor file in models/veda to use. auto picks the text-to-video one. Use the R2VA file for reference-to-video."),
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io.Boolean.Input("veda_timing", extra_dict=shows(("sla", [True]), ("sla_method", ["veda"])), default=False, optional=True,
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tooltip="veda: also log how long each phase of its attention took this run (gather, score, select, "
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"kernel, scatter). Changes nothing in the picture; the clock waits for the GPU once per run."),
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tooltip="veda: logs how long each stage took. The picture is unchanged."),
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],
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outputs=[
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io.Model.Output(display_name="model"),
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@@ -45,10 +45,7 @@ class FunUIVAELoader(io.ComfyNode):
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tooltip="The precision the VAE works in. bfloat16 is the default; MiniMax H3 needs it."),
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io.Combo.Input("decode_vae_name", options=["None", *vae_files()], default="None", optional=True,
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display_name="Decode-only VAE (Optional)",
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tooltip="A second VAE used only for the final picture decode; this one still encodes "
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"(references, start pictures). For a decoder that has no encoder: MiniMax H3's "
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"Sol-H3-VAE (sol_h3_vae.safetensors, whole clip at once, about 2.5 times faster; "
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"needs a ComfyUI that supports it) or an X2 Detail VAE. None decodes with this VAE."),
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tooltip="A separate VAE used only to turn the result into pictures; this one still encodes. Pick MiniMax H3's Sol-H3-VAE for about 2.5 times faster decoding, or an X2 Detail VAE. None uses this VAE for both."),
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],
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outputs=[
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io.Vae.Output(display_name="vae"),
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@@ -104,9 +104,7 @@ class FunUILoadMedia(io.ComfyNode):
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tooltip="Read the whole video and its sound. Off: only the first frame, which is all a picture input needs."),
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io.String.Input(
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"prepare", default="", optional=True,
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tooltip="With full on: how to get the video ready, as JSON. Keys: fps, skip_first, cut_last, every_nth, frame_cap, "
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"width, height, fit (crop|pad|stretch), crop_left/right/top/bottom (percent), multiple_of. "
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"Empty: all frames at 24 fps, size untouched."),
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tooltip="With full on: how to get the video ready, as JSON (fps, skip_first, cut_last, every_nth, frame_cap, width, height, fit, crop_*, multiple_of). Empty keeps all frames at 24 fps."),
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],
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outputs=[io.Image.Output(display_name="image"), io.Image.Output(display_name="frames"),
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io.Audio.Output(display_name="audio")],
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@@ -45,10 +45,7 @@ class FunUIDecode(io.ComfyNode):
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io.Vae.Input("audio_vae", optional=True,
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tooltip="Only for models that generate sound alongside the video."),
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io.Int.Input("tile_size", default=0, min=0, max=4096, step=64, optional=True,
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tooltip="Decode in tiles of this many pixels, to fit less VRAM. 0 = the "
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"model's own way. MiniMax H3: 0 is ComfyUI's 256px tiles, the size "
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"its decoder was trained on; bigger tiles show a grid. An X2 Detail "
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"VAE is always tiled (256 at least)."),
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tooltip="Decodes in tiles of this many pixels to fit less VRAM. 0 uses the model's own way. For MiniMax H3 keep 0, since bigger tiles show a grid."),
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],
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outputs=[
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io.Image.Output(display_name="images"),
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@@ -239,7 +239,7 @@ class FunUISaveVideo(io.ComfyNode):
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io.Combo.Input("codec", options=list(CODECS), default="h264", optional=True,
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tooltip="For the GPU encoder. H.264: plays in every browser. H.265: smaller files, but Firefox may not play it."),
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io.Combo.Input("quality", options=list(QUALITIES), default="high", optional=True,
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tooltip="How closely the file keeps the picture. standard: small files, but fine texture smears and blocks show on a sharp high-resolution picture. high: visually clean for a bigger file. maximum: near lossless, large."),
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tooltip="How closely the file keeps the picture. standard: small files, but fine texture can smear. high: clean, bigger file. maximum: near lossless, large."),
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io.Custom("FUNUI_SETTINGS").Input("settings", optional=True),
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||||
],
|
||||
hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
|
||||
|
||||
@@ -51,7 +51,7 @@ SETTINGS = {
|
||||
{"value": "noise", "label": "Starting noise"},
|
||||
],
|
||||
"label": "Merge", "ui": "segmented",
|
||||
"hint": "Step answers: the model answers twice at chosen steps and the pictures are blended. Second run: a whole second video from the second seed is blended in at chosen steps. Starting noise: mixes the second seed's noise into the start, at no extra cost.",
|
||||
"hint": "Blends in a second seed. Step answers: asks the model twice at chosen steps. Second run: blends a whole second video (slower). Starting noise: mixes the second seed's noise in at no extra cost.",
|
||||
"when": {"enabled": True},
|
||||
},
|
||||
"steps": {
|
||||
|
||||
@@ -27,7 +27,7 @@ SETTINGS = {
|
||||
"style": {
|
||||
"type": "enum", "default": "natural", "label": "Temporal style",
|
||||
"options": [{"value": v, "label": v.replace("_", " ")} for v in STYLES],
|
||||
"hint": "How motion feels. natural: unchanged. auto: picks a style per shot from its words. accelerate, freeze: smoother, held motion. decelerate: punchier. pulse: punch then ease. rapid start/end: punchy at the start and/or end. loop: the end joins the start.",
|
||||
"hint": "Changes how motion feels, such as smoother, punchier or looping. natural leaves it unchanged; auto picks a style per shot from its words.",
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user