ComfyUI/comfy_extras/nodes_tcfg.py

72 lines
2.4 KiB
Python
Raw Blame History

This file contains ambiguous Unicode characters!

This file contains ambiguous Unicode characters that may be confused with others in your current locale. If your use case is intentional and legitimate, you can safely ignore this warning. Use the Escape button to highlight these characters.

# TCFG: Tangential Damping Classifier-free Guidance - (arXiv: https://arxiv.org/abs/2503.18137)
import torch
from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
def score_tangential_damping(cond_score: torch.Tensor, uncond_score: torch.Tensor) -> torch.Tensor:
"""Drop tangential components from uncond score to align with cond score."""
# (B, 1, ...)
batch_num = cond_score.shape[0]
cond_score_flat = cond_score.reshape(batch_num, 1, -1).float()
uncond_score_flat = uncond_score.reshape(batch_num, 1, -1).float()
# Score matrix A (B, 2, ...)
score_matrix = torch.cat((uncond_score_flat, cond_score_flat), dim=1)
try:
_, _, Vh = torch.linalg.svd(score_matrix, full_matrices=False)
except RuntimeError:
# Fallback to CPU
_, _, Vh = torch.linalg.svd(score_matrix.cpu(), full_matrices=False)
# Drop the tangential components
v1 = Vh[:, 0:1, :].to(uncond_score_flat.device) # (B, 1, ...)
uncond_score_td = (uncond_score_flat @ v1.transpose(-2, -1)) * v1
return uncond_score_td.reshape_as(uncond_score).to(uncond_score.dtype)
class TCFG(ComfyNodeABC):
@classmethod
def INPUT_TYPES(cls) -> InputTypeDict:
return {
"required": {
"model": (IO.MODEL, {}),
}
}
RETURN_TYPES = (IO.MODEL,)
RETURN_NAMES = ("patched_model",)
FUNCTION = "patch"
CATEGORY = "advanced/guidance"
DESCRIPTION = "TCFG Tangential Damping CFG (2503.18137)\n\nRefine the uncond (negative) to align with the cond (positive) for improving quality."
def patch(self, model):
m = model.clone()
def tangential_damping_cfg(args):
# Assume [cond, uncond, ...]
x = args["input"]
conds_out = args["conds_out"]
if len(conds_out) <= 1 or None in args["conds"][:2]:
# Skip when either cond or uncond is None
return conds_out
cond_pred = conds_out[0]
uncond_pred = conds_out[1]
uncond_td = score_tangential_damping(x - cond_pred, x - uncond_pred)
uncond_pred_td = x - uncond_td
return [cond_pred, uncond_pred_td] + conds_out[2:]
m.set_model_sampler_pre_cfg_function(tangential_damping_cfg)
return (m,)
NODE_CLASS_MAPPINGS = {
"TCFG": TCFG,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"TCFG": "Tangential Damping CFG",
}