feat: the model loader shows each SLA field only in the mode it belongs to (sparsity for sla/vsa, tau for sol-attn, block size and stabilize for Triton, tail for comfy_kitchen)
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
This commit is contained in:
digital-garbage
co-authored by
Claude Opus 5.5
Claude Opus 5.5
parent
8eaf5be5f4
commit
fc92e5869b
@@ -53,7 +53,7 @@ export const models = (app) => function mount() {
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if (JSON.stringify((live.inputs || {})[name]) === JSON.stringify(value)) return; // a blur that changed nothing saves nothing
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await ps.save({ inputs: { [slot.id]: { [name]: value } } });
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const spec = specs[slot.node];
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if (spec && (spec.widgets || []).some((w) => w.shows && w.shows.input === name)) { // a choice that brings fields: what they show is what runs
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if (spec && (spec.widgets || []).some((w) => (w.shows || []).some((c) => c.input === name))) { // a choice that brings fields: what they show is what runs
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const now = slotsNow().find((x) => x.id === slot.id);
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const fill = now && shownValues(now, spec);
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if (fill && Object.keys(fill).length) await ps.save({ inputs: { [slot.id]: fill } });
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@@ -19,18 +19,18 @@ export function sourcesFor(slot, type, slots, specs) {
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}
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// The dropdowns' own values as the form would fill them, so a new node's revealed fields follow its default choice.
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const shownParents = (slot, spec) => Object.fromEntries(((spec && spec.widgets) || []).filter((w) => w.type === "COMBO" && !(slot.inputs && slot.inputs[w.name] !== undefined))
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.map((w) => [w.name, w.default !== undefined ? w.default : (w.choices || [])[0]]));
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const shownParents = (slot, spec) => Object.fromEntries(((spec && spec.widgets) || []).filter((w) => !(slot.inputs && slot.inputs[w.name] !== undefined))
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.map((w) => [w.name, w.default !== undefined ? w.default : w.type === "BOOLEAN" ? false : (w.choices || [])[0]]));
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/** A node that is new, or whose input was just unwired, has nothing SET on it while its form shows defaults:
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* these are the values to send so that what runs is what is seen. */
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/** Whether a field a dynamic dropdown's choice brings is showing: its dropdown (as set, else its default) is on one of its choices. */
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export const showing = (w, slot, spec) => {
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if (!w.shows) return true;
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const parent = ((spec && spec.widgets) || []).find((x) => x.name === w.shows.input);
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const picked = slot.inputs && slot.inputs[w.shows.input] !== undefined ? slot.inputs[w.shows.input] : parent && (parent.default !== undefined ? parent.default : (parent.choices || [])[0]);
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return w.shows.when.includes(picked);
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};
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/** Whether a field is drawn: every input its `shows` names ([{input, when}]: a dynamic dropdown's choice, or a
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* node's own rule) holds one of the listed values -- as set, else as the form shows it by default. */
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export const showing = (w, slot, spec) => (w.shows || []).every(({ input, when }) => {
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const parent = ((spec && spec.widgets) || []).find((x) => x.name === input);
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const picked = slot.inputs && slot.inputs[input] !== undefined ? slot.inputs[input] : parent && (parent.default !== undefined ? parent.default : parent.type === "BOOLEAN" ? false : (parent.choices || [])[0]);
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return when.includes(picked);
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});
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export function shownValues(slot, spec, onlyInput) {
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const out = {};
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@@ -21,3 +21,18 @@ test("what the form shows is sent for a slot with nothing set, and nothing alrea
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assert.deepEqual(shownValues({ id: "c", inputs: { steps: 4 } }, specs.C), { mode: "x", on: false });
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assert.deepEqual(shownValues({ id: "c", inputs: {} }, specs.C, "steps"), { steps: 8 });
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});
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test("a field shows only in the mode it belongs to: every rule it names must hold, defaults count", async () => {
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const { showing, shownValues } = await import("./wiring.js");
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const spec = { widgets: [
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{ name: "sla", type: "BOOLEAN", default: false },
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{ name: "sla_method", type: "COMBO", choices: ["sla", "sol-attn", "vsa"], default: "sla", shows: [{ input: "sla", when: [true] }] },
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{ name: "sla_sparsity", type: "FLOAT", default: 0.9, shows: [{ input: "sla", when: [true] }, { input: "sla_method", when: ["sla", "vsa"] }] },
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{ name: "sla_tau", type: "FLOAT", default: 1.3, shows: [{ input: "sla", when: [true] }, { input: "sla_method", when: ["sol-attn"] }] },
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] };
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const drawn = (inputs) => spec.widgets.filter((w) => showing(w, { inputs }, spec)).map((w) => w.name);
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assert.deepEqual(drawn({}), ["sla"]);
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assert.deepEqual(drawn({ sla: true }), ["sla", "sla_method", "sla_sparsity"]);
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assert.deepEqual(drawn({ sla: true, sla_method: "sol-attn" }), ["sla", "sla_method", "sla_tau"]);
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assert.deepEqual(shownValues({ inputs: { sla: true, sla_method: "sol-attn" } }, spec), { sla_tau: 1.3 });
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});
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+4
-1
@@ -53,6 +53,9 @@ def _one(name: str, declared: Any, required: bool) -> Dict[str, Any]:
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for key in KEPT:
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if key in options:
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widget[key] = options[key]
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# [{input, when}]: the form draws this only while every named input holds one of its values.
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if isinstance(options.get("funpack_shows"), list):
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widget["shows"] = options["funpack_shows"]
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return widget
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@@ -96,7 +99,7 @@ def describe(class_type: str) -> Optional[dict]:
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kind, options = comfy_types.declared(declared)
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if comfy_types.is_combo(kind) and comfy_types.reveals(options):
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for dotted, decl, req, parent, keys in comfy_types.revealed(name, options):
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sub = dict(_one(dotted, decl, req), shows={"input": parent, "when": keys})
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sub = dict(_one(dotted, decl, req), shows=[{"input": parent, "when": keys}])
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(widgets if sub["type"] in PRIMITIVE and not sub.get("forceInput") else sockets).append(sub)
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outputs = list(getattr(node, "RETURN_TYPES", ()) or ())
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@@ -31,6 +31,12 @@ def _model_file_choices():
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gguf_support.gguf_names("diffusion_models")
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def shows(*conditions):
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"""Draw an input only while each (other input, values) holds: SLA's fields belong to one method
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or engine each, and a field that does nothing in the mode picked is noise. Read by core/widgets."""
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return {"funpack_shows": [{"input": name, "when": list(values)} for name, values in conditions]}
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class FunPackDiffusionModelLoader(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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@@ -64,14 +70,14 @@ class FunPackDiffusionModelLoader(io.ComfyNode):
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"that is not MiniMax H3 or on a machine without Triton."),
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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", default=sla_attention.SLA_DEFAULTS["sparsity_ratio"],
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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, when attention is "
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"sla_h3. 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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io.Combo.Input("sla_block_size",
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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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@@ -81,21 +87,21 @@ class FunPackDiffusionModelLoader(io.ComfyNode):
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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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io.Boolean.Input("sla_protect_audio",
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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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io.Int.Input("sla_min_seq_len",
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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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io.Int.Input("sla_dense_last_steps",
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io.Int.Input("sla_dense_last_steps", extra_dict=shows(("sla", [True]), ("sla_method", ["sla", "sol-attn"])),
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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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@@ -112,38 +118,38 @@ class FunPackDiffusionModelLoader(io.ComfyNode):
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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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# Added later, so after everything: saved workflows can store widget values by position.
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io.Combo.Input("sla_engine", options=list(sla_attention.ENGINES),
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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: FunPack's own kernel, the only one with block size and "
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"stabilize motion."),
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io.String.Input("sla_dense_steps", default=sla_attention.SLA_DEFAULTS["dense_steps"],
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io.String.Input("sla_dense_steps", extra_dict=shows(("sla", [True]), ("sla_method", ["sla", "sol-attn"])), default=sla_attention.SLA_DEFAULTS["dense_steps"],
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optional=True,
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tooltip="Steps at full attention, counted from 0: '0' or '0,1' or "
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"'0-2'. The first steps set the layout and how closely the "
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"prompt is followed, so the first one is kept exact by "
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"default. Blank: none. Adds to dense last steps. Not used by vsa."),
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io.Combo.Input("sla_references", options=list(sla_attention.REFERENCES),
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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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io.Boolean.Input("sla_tail", default=sla_attention.SLA_DEFAULTS["tail"], optional=True,
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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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io.Boolean.Input("sla_stabilize_motion",
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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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io.Combo.Input("sla_method", options=list(sla_attention.METHODS),
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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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@@ -152,7 +158,7 @@ class FunPackDiffusionModelLoader(io.ComfyNode):
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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."),
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io.Float.Input("sla_tau", default=sla_attention.SLA_DEFAULTS["tau"], min=0.0, max=4.0,
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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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@@ -490,3 +490,15 @@ def test_sol_attn_routes_by_threshold_and_vsa_hands_over_to_comfyui(monkeypatch)
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assert installed and got["vsa"] is True and abs(got["topk_ratio"] - 0.1) < 1e-9
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assert got["override"] is backend # dense calls still go to the chosen backend
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assert "no VSA layers" in note
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def test_the_loader_draws_each_sla_field_only_in_its_mode():
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"""'All values are fucked up': every SLA field was drawn whatever the method, sparsity beside
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sol-attn's own threshold."""
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from modules.loaders.diffusion_model.nodes import FunPackDiffusionModelLoader as L
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rules = {i.id: {c["input"]: c["when"] for c in (i.extra_dict or {}).get("funpack_shows", [])}
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for i in L.define_schema().inputs}
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assert rules["sla_sparsity"]["sla_method"] == ["sla", "vsa"] and rules["sla_tau"]["sla_method"] == ["sol-attn"]
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assert rules["sla_block_size"]["sla_engine"] == ["triton"] and rules["sla_tail"]["sla_engine"] == ["comfy_kitchen"]
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assert all(r.get("sla") == [True] for name, r in rules.items() if name.startswith("sla_"))
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assert rules["sla"] == {} and rules["int8_convrot"] == {}
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