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Add LTX Multiple Controls node: guide+mask+identity in one call

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Chaining multiple LTXIdentityOverlapConditioning nodes silently drops all
but the last one's reference -- each apply() overwrites transformer_options
["_id_ref_specs"] instead of merging with what the upstream node set. This
node builds every active slot's ref_specs in a single call so guide, mask,
and identity references all combine correctly. Each slot (all optional) has
its own source_id/phase_scale/layout/ref_resize_mode, defaulted (not
hardcoded) to match the trained recipe.
This commit is contained in:
alisson-anjos
2026-07-22 07:08:41 +00:00
parent 7112d6e640
commit e0c4c94661
2 changed files with 199 additions and 0 deletions
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__init__.py
+4
View File
@@ -10,6 +10,8 @@ from .amv_guide_node import NODE_CLASS_MAPPINGS as AMV_NODE_CLASS_MAPPINGS
from .amv_guide_node import NODE_DISPLAY_NAME_MAPPINGS as AMV_NODE_DISPLAY_NAME_MAPPINGS
from .ltx_identity_overlap import NODE_CLASS_MAPPINGS as IDT_NODE_CLASS_MAPPINGS
from .ltx_identity_overlap import NODE_DISPLAY_NAME_MAPPINGS as IDT_NODE_DISPLAY_NAME_MAPPINGS
from .ltx_multiple_controls import NODE_CLASS_MAPPINGS as MC_NODE_CLASS_MAPPINGS
from .ltx_multiple_controls import NODE_DISPLAY_NAME_MAPPINGS as MC_NODE_DISPLAY_NAME_MAPPINGS
try:
from .ltx_identity_multiangle import NODE_CLASS_MAPPINGS as MA_NODE_CLASS_MAPPINGS
from .ltx_identity_multiangle import NODE_DISPLAY_NAME_MAPPINGS as MA_NODE_DISPLAY_NAME_MAPPINGS
@@ -41,6 +43,7 @@ NODE_CLASS_MAPPINGS = {
**A2R_NODE_CLASS_MAPPINGS,
**AMV_NODE_CLASS_MAPPINGS,
**IDT_NODE_CLASS_MAPPINGS,
**MC_NODE_CLASS_MAPPINGS,
**MA_NODE_CLASS_MAPPINGS,
**MRS_NODE_CLASS_MAPPINGS,
}
@@ -54,6 +57,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
**A2R_NODE_DISPLAY_NAME_MAPPINGS,
**AMV_NODE_DISPLAY_NAME_MAPPINGS,
**IDT_NODE_DISPLAY_NAME_MAPPINGS,
**MC_NODE_DISPLAY_NAME_MAPPINGS,
**MA_NODE_DISPLAY_NAME_MAPPINGS,
**MRS_NODE_DISPLAY_NAME_MAPPINGS,
}
ltx_multiple_controls.py
+195
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@@ -0,0 +1,195 @@
"""LTX Multiple Controls — single-node version of chaining several LTXIdentityOverlapConditioning
(LTX Identity Transfer) calls for a guide (motion/structure) + mask (replacement region) +
identity (appearance) recipe, e.g. character-replace-in-driving-video training.
WHY THIS NODE EXISTS: LTXIdentityOverlapConditioning stores its reference specs in
model.model_options["transformer_options"]["_id_ref_specs"], and each call OVERWRITES that key
with its own specs list rather than merging with whatever the upstream node already put there.
Chaining N of those nodes therefore only keeps the LAST node's reference active at inference —
the earlier ones (e.g. the guide) silently have ZERO effect despite the graph looking correct.
This node builds every slot's ref_specs in ONE apply() call so they all end up in the same list.
Each slot (guide/mask/identity) is fully independent: its own source_id, phase_scale, layout,
ref_resize_mode -- no slot is hardcoded to a fixed layout/phase, only given sane defaults that
match the trained recipe (guide/mask: source_id=0 => no RoPE phase tag, same as source_phase=
false at train time; identity: source_id=2 => tagged, same as source_phase=true). All three
slots are optional -- leave any input unconnected to skip that slot entirely.
"""
import logging
import torch
from .ltx_identity_overlap import (
_anchored_crop_resize,
_draw_crop_overlay,
_find_ltxv,
_install_patches,
_letterbox_resize,
)
from . import ltx_identity_overlap as _ido
log = logging.getLogger("LTXMultipleControls")
_LAYOUT_CHOICES = ["overlap", "st_drc", "strata"]
_RESIZE_CHOICES = ["match_target", "match_target_letterbox", "native_resolution"]
def _encode_ref(vae, latent, img, ref_resize_mode, crop_anchor, w_sf, h_sf):
"""Resize (per ref_resize_mode/crop_anchor) + VAE-encode ONE reference IMAGE batch
([N,H,W,C], typically a whole video's frames). Mirrors LTXIdentityOverlapConditioning's
own _encode_one so behavior is byte-identical per slot."""
import comfy.utils
if ref_resize_mode == "native_resolution":
_, src_h, src_w, _ = img.shape
tgt_w = max(w_sf, round(src_w / w_sf) * w_sf)
tgt_h = max(h_sf, round(src_h / h_sf) * h_sf)
else:
_, _, _, lat_h, lat_w = latent["samples"].shape
tgt_w, tgt_h = lat_w * w_sf, lat_h * h_sf
_, src_h0, src_w0, _ = img.shape
crop_box = (0, 0, src_w0, src_h0)
if ref_resize_mode == "match_target_letterbox":
ref_px = _letterbox_resize(img, tgt_w, tgt_h)[:, :, :, :3]
elif ref_resize_mode == "match_target" and crop_anchor != "center":
ref_px, crop_box = _anchored_crop_resize(img, tgt_w, tgt_h, anchor=crop_anchor)
ref_px = ref_px[:, :, :, :3]
else:
ref_px = comfy.utils.common_upscale(img.movedim(-1, 1), tgt_w, tgt_h, "bilinear", "center").movedim(1, -1)[:, :, :, :3]
if ref_resize_mode == "match_target":
_, crop_box = _anchored_crop_resize(img[:1], tgt_w, tgt_h, anchor="center")
ref_lat = vae.encode(ref_px)
overlay = _draw_crop_overlay(img[:1], crop_box)
return ref_lat, ref_px[:1].clone(), overlay, crop_box, src_w0, src_h0
class LTXMultipleControls:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"model": ("MODEL",),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"vae": ("VAE",),
"latent": ("LATENT",),
}, "optional": {
"guide_video": ("IMAGE", {"tooltip": "Motion/structure driving video frames (IMAGE batch, e.g. from "
"GetVideoComponents). Leave unconnected to skip the guide slot."}),
"guide_source_id": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 8.0, "step": 1.0,
"tooltip": "0 = no RoPE phase tag (matches source_phase=false at train time -- needed "
"for the guide's positions to line up with the target frame-by-frame)."}),
"guide_phase_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 4.0, "step": 0.1}),
"guide_layout": (_LAYOUT_CHOICES, {"default": "overlap"}),
"guide_ref_resize_mode": (_RESIZE_CHOICES, {"default": "match_target"}),
"mask_video": ("IMAGE", {"tooltip": "Per-frame replacement-region mask (e.g. SAM2 silhouette), same "
"frame count/alignment as guide_video. Leave unconnected to skip."}),
"mask_source_id": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 8.0, "step": 1.0,
"tooltip": "0 = no RoPE phase tag (matches source_phase=false), same reasoning as guide."}),
"mask_phase_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 4.0, "step": 0.1}),
"mask_layout": (_LAYOUT_CHOICES, {"default": "overlap"}),
"mask_ref_resize_mode": (_RESIZE_CHOICES, {"default": "match_target"}),
"identity_image": ("IMAGE", {"tooltip": "Appearance reference (face/character), no positional "
"correspondence with the target needed. Leave unconnected to skip."}),
"identity_source_id": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 8.0, "step": 1.0,
"tooltip": "Nonzero = tagged with its own RoPE phase (matches source_phase=true)."}),
"identity_phase_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 4.0, "step": 0.1}),
"identity_layout": (_LAYOUT_CHOICES, {"default": "overlap"}),
"identity_ref_resize_mode": (_RESIZE_CHOICES, {"default": "native_resolution"}),
"crop_anchor": (["center", "top", "bottom", "left", "right"], {"default": "center",
"tooltip": "Shared by all slots using ref_resize_mode=match_target with a mismatched "
"aspect ratio -- which part of the source survives the crop."}),
"reference_guidance_scale": ("FLOAT", {"default": 1.0, "min": 1.0, "max": 10.0, "step": 0.1,
"tooltip": "ST-DRC-style reference-CFG applied to the WHOLE combined reference set "
"(all active slots together). 1.0 = off."}),
"debug_log": ("BOOLEAN", {"default": False}),
}}
RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "STRING")
RETURN_NAMES = ("model", "positive", "negative", "latent", "debug")
FUNCTION = "apply"
CATEGORY = "LTX/identity"
DESCRIPTION = ("Guide + mask + identity reference conditioning in ONE node call, so all active slots' "
"RoPE reference specs actually combine (chaining LTXIdentityOverlapConditioning nodes does "
"NOT -- each call overwrites the previous one's specs). Every slot is optional and its "
"own source_id/phase_scale/layout/ref_resize_mode -- nothing is hardcoded, only defaulted "
"to match the trained recipe (guide/mask: source_id=0 i.e. no phase; identity: source_id=2).")
def apply(self, model, positive, negative, vae, latent,
guide_video=None, guide_source_id=0.0, guide_phase_scale=1.0,
guide_layout="overlap", guide_ref_resize_mode="match_target",
mask_video=None, mask_source_id=0.0, mask_phase_scale=1.0,
mask_layout="overlap", mask_ref_resize_mode="match_target",
identity_image=None, identity_source_id=2.0, identity_phase_scale=1.0,
identity_layout="overlap", identity_ref_resize_mode="native_resolution",
crop_anchor="center", reference_guidance_scale=1.0, debug_log=False):
import comfy.samplers
_ido._DEBUG_ENABLED = bool(debug_log)
m = model.clone()
ltxv = _find_ltxv(m)
_, w_sf, h_sf = vae.downscale_index_formula
slots = [
("guide", guide_video, guide_source_id, guide_phase_scale, guide_layout, guide_ref_resize_mode),
("mask", mask_video, mask_source_id, mask_phase_scale, mask_layout, mask_ref_resize_mode),
("identity", identity_image, identity_source_id, identity_phase_scale, identity_layout, identity_ref_resize_mode),
]
ref_specs = []
summary = []
for name, img, source_id, phase_scale, layout, resize_mode in slots:
if img is None:
continue
ref_lat, _px, _overlay, crop_box, src_w0, src_h0 = _encode_ref(
vae, latent, img, resize_mode, crop_anchor, w_sf, h_sf)
seg_value = float(source_id) * float(phase_scale)
ref_specs.append({"latent": ref_lat, "seg_value": seg_value, "layout": layout, "strata_slot": len(ref_specs)})
summary.append(f"{name}: {img.shape[0]}f {src_w0}x{src_h0}px -> {layout}, seg={seg_value:g}, mode={resize_mode}")
if not ref_specs:
log.warning("LTXMultipleControls: no slots connected -- passing through unchanged.")
return (m, positive, negative, latent, "LTX Multiple Controls: no active slots (nothing connected).")
_install_patches(ltxv)
ltxv._id_rope_theta = 10000.0
m.model_options = dict(m.model_options)
to = dict(m.model_options.get("transformer_options", {}))
to["_id_ref_specs"] = ref_specs
m.model_options["transformer_options"] = to
if reference_guidance_scale != 1.0:
noref_to = dict(to)
noref_to.pop("_id_ref_specs", None)
ref_scale = float(reference_guidance_scale)
def _ref_cfg_function(args):
cond = args["cond"]
uncond = args["uncond"]
cond_scale = args["cond_scale"]
denoised = uncond + (cond - uncond) * cond_scale
noref_model_options = dict(args["model_options"])
noref_model_options["transformer_options"] = noref_to
(noref_pred,) = comfy.samplers.calc_cond_batch(
args["model"], [args["input_cond"]], args["input"], args["timestep"], noref_model_options,
)
noref_denoised = args["input"] - noref_pred
denoised = denoised + (ref_scale - 1.0) * (cond - noref_denoised)
return denoised
m.set_model_sampler_cfg_function(_ref_cfg_function, disable_cfg1_optimization=True)
dbg = (
"=== LTX Multiple Controls ===\n"
f"{len(ref_specs)} active slot(s):\n " + "\n ".join(summary) + "\n"
f"reference-CFG: {'off' if reference_guidance_scale == 1.0 else f'ON, scale={reference_guidance_scale}'}\n"
"Set LTX_IDOVERLAP_DEBUG=1 for per-step shape logs. Connect negative + CFG 3-5, no LightX2V."
)
log.info("\n" + dbg)
return (m, positive, negative, latent, dbg)
NODE_CLASS_MAPPINGS = {"LTXMultipleControls": LTXMultipleControls}
NODE_DISPLAY_NAME_MAPPINGS = {"LTXMultipleControls": "LTX Multiple Controls"}