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import comfy.samplers
import comfy.utils
import torch
from comfy.model_patcher import ModelPatcher
from ltx_video.utils.skip_layer_strategy import SkipLayerStrategy
from .nodes_registry import comfy_node
def stg(
noise_pred_pos,
noise_pred_neg,
noise_pred_pertubed,
cfg_scale,
stg_scale,
rescale_scale,
):
noise_pred = (
noise_pred_neg
+ cfg_scale * (noise_pred_pos - noise_pred_neg)
+ stg_scale * (noise_pred_pos - noise_pred_pertubed)
)
if rescale_scale != 0:
factor = noise_pred_pos.std() / noise_pred.std()
factor = rescale_scale * factor + (1 - rescale_scale)
noise_pred = noise_pred * factor
return noise_pred
@comfy_node(name="LTXVApplySTG")
class LTXVApplySTG:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": (
"MODEL",
{"tooltip": "The model to apply the STG to."},
),
"stg_mode": (["attention", "residual"],),
"block_indices": (
"STRING",
{
"default": "14, 19",
"tooltip": "Comma-separated indices of the blocks to apply the STG to.",
},
),
}
}
FUNCTION = "apply_stg"
RETURN_TYPES = ("MODEL",)
RETURN_NAMES = ("model",)
CATEGORY = "lightricks/LTXV"
def apply_stg(self, model: ModelPatcher, stg_mode: str, block_indices: str):
skip_block_list = [int(i.strip()) for i in block_indices.split(",")]
stg_mode = (
SkipLayerStrategy.Attention
if stg_mode == "attention"
else SkipLayerStrategy.Residual
)
new_model = model.clone()
new_model.model_options["transformer_options"]["skip_layer_strategy"] = stg_mode
if "skip_block_list" in new_model.model_options["transformer_options"]:
skip_block_list.extend(
new_model.model_options["transformer_options"]["skip_block_list"]
)
new_model.model_options["transformer_options"][
"skip_block_list"
] = skip_block_list
return (new_model,)
class STGGuider(comfy.samplers.CFGGuider):
def set_conds(self, positive, negative):
self.inner_set_conds(
{"positive": positive, "negative": negative, "perturbed": positive}
)
def set_cfg(self, cfg, stg_scale, rescale_scale: float = None):
self.cfg = cfg
self.stg_scale = stg_scale
self.rescale_scale = rescale_scale
def predict_noise(
self,
x: torch.Tensor,
timestep: torch.Tensor,
model_options: dict = {},
seed=None,
):
# in CFGGuider.predict_noise, we call sampling_function(), which uses cfg_function() to compute pos & neg
# but we'd rather do a single batch of sampling pos, neg, and perturbed, so we call calc_cond_batch([perturbed,pos,neg]) directly
perturbed_cond = self.conds.get("perturbed", None)
positive_cond = self.conds.get("positive", None)
negative_cond = self.conds.get("negative", None)
noise_pred_neg = 0
# no similar optimization for stg=0, use CFG guider instead.
if self.cfg > 1:
model_options["transformer_options"]["ptb_index"] = 2
(noise_pred_perturbed, noise_pred_pos, noise_pred_neg) = (
comfy.samplers.calc_cond_batch(
self.inner_model,
[perturbed_cond, positive_cond, negative_cond],
x,
timestep,
model_options,
)
)
else:
model_options["transformer_options"]["ptb_index"] = 1
(noise_pred_perturbed, noise_pred_pos) = comfy.samplers.calc_cond_batch(
self.inner_model,
[perturbed_cond, positive_cond],
x,
timestep,
model_options,
)
stg_result = stg(
noise_pred_pos,
noise_pred_neg,
noise_pred_perturbed,
self.cfg,
self.stg_scale,
self.rescale_scale,
)
# normally this would be done in cfg_function, but we skipped
# that for efficiency: we can compute the noise predictions in
# a single call to calc_cond_batch() (rather than two)
# so we replicate the hook here
for fn in model_options.get("sampler_post_cfg_function", []):
args = {
"denoised": stg_result,
"cond": positive_cond,
"uncond": negative_cond,
"model": self.inner_model,
"uncond_denoised": noise_pred_neg,
"cond_denoised": noise_pred_pos,
"sigma": timestep,
"model_options": model_options,
"input": x,
# not in the original call in samplers.py:cfg_function, but made available for future hooks
"perturbed_cond": positive_cond,
"perturbed_cond_denoised": noise_pred_perturbed,
}
stg_result = fn(args)
return stg_result
@comfy_node(name="STGGuider")
class STGGuiderNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"cfg": (
"FLOAT",
{
"default": 1.0,
"min": 0.0,
"max": 100.0,
"step": 0.1,
"round": 0.01,
},
),
"stg": (
"FLOAT",
{"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01},
),
"rescale": (
"FLOAT",
{"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01},
),
}
}
RETURN_TYPES = ("GUIDER",)
FUNCTION = "get_guider"
CATEGORY = "lightricks/LTXV"
def get_guider(self, model, positive, negative, cfg, stg, rescale):
guider = STGGuider(model)
guider.set_conds(positive, negative)
guider.set_cfg(cfg, stg, rescale)
return (guider,)
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