nodes/Extraltodeus-ComfyUI-AutomaticCFG
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Extraltodeus
2024-05-01 23:19:28 +02:00
committed by GitHub
parent e6791eb99f
commit 8651451dd2
1 changed files with 12 additions and 7 deletions
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nodes.py
+12 -7
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@@ -4,6 +4,7 @@ import numpy as np
import torch
import math
import torch.nn.functional as F
from colorama import Fore, Style
original_sampling_function = deepcopy(comfy.samplers.sampling_function)
minimum_sigma_to_disable_uncond = 0
@@ -102,7 +103,7 @@ def blur_tensor(input_tensor, sigma=2, kernel_size=7):
blurred_batch.append(torch.stack(blurred_channels))
return torch.stack(blurred_batch).to(device)
def square_and_norm(cond_input, method, exp_value, exp_normalize, pcp, psi, sigma, args, eval_string = ""):
def square_and_norm(cond_input, method, exp_value, exp_normalize, pcp, psi, sigma, sigmax, args, eval_string = ""):
"""
There may or may not be an actual reasoning behind each of these methods.
Some like the sine value have interesting properties. Enabled for both cond and uncond preds it somehow make them stronger.
@@ -191,7 +192,7 @@ def square_and_norm(cond_input, method, exp_value, exp_normalize, pcp, psi, sigm
elif method == "zero":
cond = torch.zeros_like(cond)
elif method == "previous_average":
if sigma > 14:
if sigma > (sigmax - 1):
cond = torch.zeros_like(cond)
else:
cond = (pcp / psi * sigma + cond) / 2
@@ -333,11 +334,11 @@ class advancedDynamicCFG:
uncond_pred = cond_pred.clone() * fake_uncond_multiplier
if cond_exp and sigma <= cond_exp_sigma_start and sigma >= cond_exp_sigma_end:
cond_pred = square_and_norm(cond_pred, cond_exp_method, cond_exp_value, cond_exp_normalize, previous_cond_pred, previous_sigma, sigma.item(), args)
cond_pred = square_and_norm(cond_pred, cond_exp_method, cond_exp_value, cond_exp_normalize, previous_cond_pred, previous_sigma, sigma.item(), sigmax, args)
if uncond_exp and sigma <= uncond_exp_sigma_start and sigma >= uncond_exp_sigma_end and not fake_uncond_step():
uncond_pred = square_and_norm(uncond_pred, uncond_exp_method, uncond_exp_value, uncond_exp_normalize, previous_cond_pred, previous_sigma, sigma.item(), args)
uncond_pred = square_and_norm(uncond_pred, uncond_exp_method, uncond_exp_value, uncond_exp_normalize, previous_cond_pred, previous_sigma, sigma.item(), sigmax, args)
if fake_uncond_step() and fake_uncond_exp:
uncond_pred = square_and_norm(uncond_pred, fake_uncond_exp_method, fake_uncond_exp_value, fake_uncond_exp_normalize, previous_cond_pred, previous_sigma, sigma.item(), args, eval_string)
uncond_pred = square_and_norm(uncond_pred, fake_uncond_exp_method, fake_uncond_exp_value, fake_uncond_exp_normalize, previous_cond_pred, previous_sigma, sigma.item(), sigmax, args, eval_string)
previous_cond_pred = deepcopy(cond_pred)
if sigma >= sigmax or cond_scale > 1:
@@ -496,7 +497,7 @@ class simpleDynamicCFGwarpDrive:
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
"uncond_sigma_start": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
"uncond_sigma_start": ("FLOAT", {"default": 5.5, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
"uncond_sigma_end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
"fake_uncond_sigma_end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
}}
@@ -507,6 +508,10 @@ class simpleDynamicCFGwarpDrive:
def patch(self, model, uncond_sigma_start, uncond_sigma_end, fake_uncond_sigma_end):
advcfg = advancedDynamicCFG()
print(f"{Fore.CYAN}WARP DRIVE MODE ENGAGED!{Style.RESET_ALL}\nSettings suggestions:\n"
f" {Fore.GREEN}1/1/1: {Fore.YELLOW}Maaaxxxiiimum speeeeeed.{Style.RESET_ALL} {Fore.RED}Uncond disabled.{Style.RESET_ALL} {Fore.MAGENTA}Fasten your seatbelt!{Style.RESET_ALL}\n"
f" {Fore.GREEN}3/1/1: {Fore.YELLOW}Risky space-time continuum distortion,{Style.RESET_ALL} {Fore.MAGENTA}awesome for prompts with a clear subject!{Style.RESET_ALL}\n"
f" {Fore.GREEN}5.5/1/1: {Fore.GREEN}Frameshift Drive Autopilot: Engaged.{Style.RESET_ALL} {Fore.MAGENTA}Should work with anything but do it better and faster!{Style.RESET_ALL}")
m = advcfg.patch(model=model, automatic_cfg = "hard",
skip_uncond = True, uncond_sigma_start = uncond_sigma_start, uncond_sigma_end = uncond_sigma_end,
fake_uncond_sigma_end = fake_uncond_sigma_end, fake_uncond_sigma_start = 1000, fake_uncond_start=True,
@@ -531,4 +536,4 @@ class simpleDynamicCFGunpatch:
comfy.samplers.sampling_function = original_sampling_function
maximum_sigma_to_enable_uncond, minimum_sigma_to_disable_uncond = 1000000, 0
print(f"Sampling function unpatched.")
return (model, )
return (model, )