nodes/mcmonkeyprojects-sd-dynamic-thresholding
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add experimental advanced options under an accordion

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Alex "mcmonkey" Goodwin
2023-01-27 00:03:20 -08:00
parent b8c4dfa55b
commit 1cb63313cd
1 changed files with 42 additions and 7 deletions
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scripts/dynamic_thresholding.py
+42 -7
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@@ -13,6 +13,7 @@
import gradio as gr
import random
import torch
import math
from copy import copy
from modules import sd_samplers, scripts
from modules.processing import process_images, Processed
@@ -28,12 +29,16 @@ class Script(scripts.Script):
return True
def ui(self, is_img2img):
help_info = gr.Markdown("### Dynamic Thresholding (CFG Scale Fix) \nThresholds high CFG scales to make them work better. \nSet your actual **CFG Scale** to the high value you want above (eg: 20). \nThen set '**Mimic CFG Scale**' below to a (lower) CFG scale to mimic the effects of (eg: 10). Make sure it's not *too* different from your actual scale, it can only compensate so far. \nSet '**Top percentile**' to how much clamping you want. 90% is good is normal, 100% clamps so hard it's like the mimic scale is the real scale. This scales as it approaches 100%, (eg 90% and 95% are much more similar than 98% and 99%). \n... \n")
gr.Markdown("Thresholds high CFG scales to make them work better. \nSet your actual **CFG Scale** to the high value you want above (eg: 20). \nThen set '**Mimic CFG Scale**' below to a (lower) CFG scale to mimic the effects of (eg: 10). Make sure it's not *too* different from your actual scale, it can only compensate so far. \nSet '**Top percentile**' to how much clamping you want. 90% is good is normal, 100% clamps so hard it's like the mimic scale is the real scale. This scales as it approaches 100%, (eg 90% and 95% are much more similar than 98% and 99%). \n... \n")
mimic_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='Mimic CFG Scale', value=7.0)
threshold_percentile = gr.Slider(minimum=90.0, value=90.0, maximum=100.0, step=0.05, label='Top percentile of latents to clamp')
return [help_info, mimic_scale, threshold_percentile]
with gr.Accordion("Advanced Options", open=False):
gr.Markdown("You can configure the **scale scheduler** for either the CFG Scale or the Mimic Scale here. \n'**Constant**' is normal. \nSetting **Mimic** to '**Cosine Down**' seems to produce better results. Needs more testing. \nSetting **CFG** to '**Linear Down**' produces results that are just like the raw high scale CFG but with better quality fine details. \nOther setting combos produce interesting results as well. \n... \n")
mimic_mode = gr.Dropdown(["Constant", "Linear Down", "Cosine Down", "Linear Up", "Cosine Up"], value="Constant", label="Mimic Scale Scheduler")
cfg_mode = gr.Dropdown(["Constant", "Linear Down", "Cosine Down", "Linear Up", "Cosine Up"], value="Constant", label="CFG Scale Scheduler")
return [mimic_scale, threshold_percentile, mimic_mode, cfg_mode]
def run(self, p, help_info, mimic_scale, threshold_percentile):
def run(self, p, mimic_scale, threshold_percentile, mimic_mode, cfg_mode):
# Note: the random number is to protect the edge case of multiple simultaneous runs with different settings
fixed_sampler_name = f"{p.sampler_name}_dynthres{random.randrange(100)}"
try:
@@ -43,7 +48,7 @@ class Script(scripts.Script):
sampler = sd_samplers.all_samplers_map[p.sampler_name]
def newConstructor(model):
result = sampler.constructor(model)
cfg = CustomCFGDenoiser(result.model_wrap_cfg.inner_model, mimic_scale, threshold_percentile)
cfg = CustomCFGDenoiser(result.model_wrap_cfg.inner_model, mimic_scale, threshold_percentile, mimic_mode, cfg_mode, p.steps)
result.model_wrap_cfg = cfg
return result
newSampler = sd_samplers.SamplerData(fixed_sampler_name, newConstructor, sampler.aliases, sampler.options)
@@ -63,16 +68,46 @@ class Script(scripts.Script):
######################### Implementation logic #########################
class CustomCFGDenoiser(sd_samplers.CFGDenoiser):
def __init__(self, model, mimic_scale, threshold_percentile):
def __init__(self, model, mimic_scale, threshold_percentile, mimic_mode, cfg_mode, maxSteps):
super().__init__(model)
self.mimic_scale = mimic_scale
self.threshold_percentile = threshold_percentile
self.mimic_mode = mimic_mode
self.cfg_mode = cfg_mode
self.maxSteps = maxSteps
def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
denoised_uncond = x_out[-uncond.shape[0]:]
return self.dynthresh(x_out[:-uncond.shape[0]], denoised_uncond, cond_scale, conds_list)
def dynthresh(self, cond, uncond, cond_scale, conds_list):
mimicScale = self.mimic_scale
match self.mimic_mode:
case "Constant":
pass
case "Linear Down":
mimicScale *= 1.0 - (self.step / self.maxSteps)
case "Cosine Down":
mimicScale *= 1.0 - math.cos(self.step / self.maxSteps)
case "Linear Up":
mimicScale *= self.step / self.maxSteps
pass
case "Cosine Up":
mimicScale *= math.cos(self.step / self.maxSteps)
pass
match self.cfg_mode:
case "Constant":
pass
case "Linear Down":
cond_scale *= 1.0 - (self.step / self.maxSteps)
case "Cosine Down":
cond_scale *= 1.0 - math.cos(self.step / self.maxSteps)
case "Linear Up":
cond_scale *= self.step / self.maxSteps
pass
case "Cosine Up":
cond_scale *= math.cos(self.step / self.maxSteps)
pass
# uncond shape is (batch, 4, height, width)
conds_per_batch = cond.shape[0] / uncond.shape[0]
assert conds_per_batch == int(conds_per_batch), "Expected # of conds per batch to be constant across batches"
@@ -82,7 +117,7 @@ class CustomCFGDenoiser(sd_samplers.CFGDenoiser):
weights = torch.tensor(conds_list).select(2, 1)
weights = weights.reshape(*weights.shape, 1, 1, 1).to(diff.device)
diff_weighted = (diff * weights).sum(1)
dynthresh_target = uncond + diff_weighted * self.mimic_scale
dynthresh_target = uncond + diff_weighted * mimicScale
dt_flattened = dynthresh_target.flatten(2)
dt_means = dt_flattened.mean(dim=2).unsqueeze(2)