File size: 11,111 Bytes
92cd965 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 |
##################
# Stable Diffusion Dynamic Thresholding (CFG Scale Fix)
#
# Author: Alex 'mcmonkey' Goodwin
# GitHub URL: https://github.com/mcmonkeyprojects/sd-dynamic-thresholding
# Created: 2022/01/26
# Last updated: 2023/01/30
#
# For usage help, view the README.md file in the extension root, or via the GitHub page.
#
##################
import gradio as gr
import torch, traceback
import dynthres_core
from modules import scripts, script_callbacks, sd_samplers, sd_samplers_compvis, sd_samplers_kdiffusion, sd_samplers_common
try:
import dynthres_unipc
except Exception as e:
print(f"\n\n======\nError! UniPC sampler support failed to load! Is your WebUI up to date?\n(Error: {e})\n======")
######################### Data values #########################
VALID_MODES = ["Constant", "Linear Down", "Cosine Down", "Half Cosine Down", "Linear Up", "Cosine Up", "Half Cosine Up", "Power Up", "Power Down"]
######################### Script class entrypoint #########################
class Script(scripts.Script):
def title(self):
return "Dynamic Thresholding (CFG Scale Fix)"
def show(self, is_img2img):
return scripts.AlwaysVisible
def ui(self, is_img2img):
enabled = gr.Checkbox(value=False, label="Enable Dynamic Thresholding (CFG Scale Fix)")
# "Dynamic Thresholding (CFG Scale Fix)"
accordion = gr.Group(visible=False)
with accordion:
gr.HTML(value=f"<br>View <a style=\"border-bottom: 1px #00ffff dotted;\" href=\"https://github.com/mcmonkeyprojects/sd-dynamic-thresholding/wiki/Usage-Tips\">the wiki for usage tips.</a><br><br>")
mimic_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='Mimic CFG Scale', value=7.0)
with gr.Accordion("Dynamic Thresholding Advanced Options", open=False):
threshold_percentile = gr.Slider(minimum=90.0, value=100.0, maximum=100.0, step=0.05, label='Top percentile of latents to clamp')
mimic_mode = gr.Dropdown(VALID_MODES, value="Constant", label="Mimic Scale Scheduler")
mimic_scale_min = gr.Slider(minimum=0.0, maximum=30.0, step=0.5, label="Minimum value of the Mimic Scale Scheduler")
cfg_mode = gr.Dropdown(VALID_MODES, value="Constant", label="CFG Scale Scheduler")
cfg_scale_min = gr.Slider(minimum=0.0, maximum=30.0, step=0.5, label="Minimum value of the CFG Scale Scheduler")
power_val = gr.Slider(minimum=0.0, maximum=15.0, step=0.5, value=4.0, visible=False, label="Power Scheduler Value")
def shouldShowPowerScheduler(cfgMode, mimicMode):
if cfgMode in ["Power Up", "Power Down"] or mimicMode in ["Power Up", "Power Down"]:
return {"visible": True, "__type__": "update"}
return {"visible": False, "__type__": "update"}
cfg_mode.change(shouldShowPowerScheduler, inputs=[cfg_mode, mimic_mode], outputs=power_val)
mimic_mode.change(shouldShowPowerScheduler, inputs=[cfg_mode, mimic_mode], outputs=power_val)
enabled.change(
fn=lambda x: {"visible": x, "__type__": "update"},
inputs=[enabled],
outputs=[accordion],
show_progress = False)
self.infotext_fields = (
(enabled, lambda d: gr.Checkbox.update(value="Dynamic thresholding enabled" in d)),
(accordion, lambda d: gr.Accordion.update(visible="Dynamic thresholding enabled" in d)),
(mimic_scale, "Mimic scale"),
(threshold_percentile, "Threshold percentile"),
(mimic_scale_min, "Mimic scale minimum"),
(mimic_mode, lambda d: gr.Dropdown.update(value=d.get("Mimic mode", "Constant"))),
(cfg_mode, lambda d: gr.Dropdown.update(value=d.get("CFG mode", "Constant"))),
(cfg_scale_min, "CFG scale minimum"),
(power_val, "Power scheduler value"))
return [enabled, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, power_val]
last_id = 0
def process_batch(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, powerscale_power, batch_number, prompts, seeds, subseeds):
enabled = getattr(p, 'dynthres_enabled', enabled)
if not enabled:
return
orig_sampler_name = p.sampler_name
if orig_sampler_name in ["DDIM", "PLMS"]:
raise RuntimeError(f"Cannot use sampler {orig_sampler_name} with Dynamic Thresholding")
if orig_sampler_name == 'UniPC' and p.enable_hr:
raise RuntimeError(f"UniPC does not support Hires Fix. Auto WebUI silently swaps to DDIM for this, which DynThresh does not support. Please swap to a sampler capable of img2img processing for HR Fix to work.")
mimic_scale = getattr(p, 'dynthres_mimic_scale', mimic_scale)
threshold_percentile = getattr(p, 'dynthres_threshold_percentile', threshold_percentile)
mimic_mode = getattr(p, 'dynthres_mimic_mode', mimic_mode)
mimic_scale_min = getattr(p, 'dynthres_mimic_scale_min', mimic_scale_min)
cfg_mode = getattr(p, 'dynthres_cfg_mode', cfg_mode)
cfg_scale_min = getattr(p, 'dynthres_cfg_scale_min', cfg_scale_min)
experiment_mode = getattr(p, 'dynthres_experiment_mode', 0)
power_val = getattr(p, 'dynthres_power_val', powerscale_power)
p.extra_generation_params["Dynamic thresholding enabled"] = True
p.extra_generation_params["Mimic scale"] = mimic_scale
p.extra_generation_params["Threshold percentile"] = threshold_percentile
p.extra_generation_params["Sampler"] = orig_sampler_name
if mimic_mode != "Constant":
p.extra_generation_params["Mimic mode"] = mimic_mode
p.extra_generation_params["Mimic scale minimum"] = mimic_scale_min
if cfg_mode != "Constant":
p.extra_generation_params["CFG mode"] = cfg_mode
p.extra_generation_params["CFG scale minimum"] = cfg_scale_min
if cfg_mode in ["Power Up", "Power Down"] or mimic_mode in ["Power Up", "Power Down"]:
p.extra_generation_params["Power scheduler value"] = power_val
# Note: the ID number is to protect the edge case of multiple simultaneous runs with different settings
Script.last_id += 1
fixed_sampler_name = f"{orig_sampler_name}_dynthres{Script.last_id}"
# Percentage to portion
threshold_percentile *= 0.01
# Make a placeholder sampler
sampler = sd_samplers.all_samplers_map[orig_sampler_name]
dtData = dynthres_core.DynThresh(mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, power_val, experiment_mode, p.steps)
if orig_sampler_name == "UniPC":
def uniPCConstructor(model):
return CustomVanillaSDSampler(dynthres_unipc.CustomUniPCSampler, model, dtData)
newSampler = sd_samplers_common.SamplerData(fixed_sampler_name, uniPCConstructor, sampler.aliases, sampler.options)
else:
def newConstructor(model):
result = sampler.constructor(model)
cfg = CustomCFGDenoiser(result.model_wrap_cfg.inner_model, dtData)
result.model_wrap_cfg = cfg
return result
newSampler = sd_samplers_common.SamplerData(fixed_sampler_name, newConstructor, sampler.aliases, sampler.options)
# Apply for usage
p.orig_sampler_name = orig_sampler_name
p.sampler_name = fixed_sampler_name
p.fixed_sampler_name = fixed_sampler_name
sd_samplers.all_samplers_map[fixed_sampler_name] = newSampler
if p.sampler is not None:
p.sampler = sd_samplers.create_sampler(fixed_sampler_name, p.sd_model)
def postprocess_batch(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, powerscale_power, batch_number, images):
if not enabled or not hasattr(p, 'orig_sampler_name'):
return
p.sampler_name = p.orig_sampler_name
del sd_samplers.all_samplers_map[p.fixed_sampler_name]
del p.orig_sampler_name
del p.fixed_sampler_name
######################### CompVis Implementation logic #########################
class CustomVanillaSDSampler(sd_samplers_compvis.VanillaStableDiffusionSampler):
def __init__(self, constructor, sd_model, dtData):
super().__init__(constructor, sd_model)
self.sampler.main_class = dtData
######################### K-Diffusion Implementation logic #########################
class CustomCFGDenoiser(sd_samplers_kdiffusion.CFGDenoiser):
def __init__(self, model, dtData):
super().__init__(model)
self.main_class = dtData
def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
denoised_uncond = x_out[-uncond.shape[0]:]
# conds_list shape is (batch, cond, 2)
weights = torch.tensor(conds_list, device=uncond.device).select(2, 1)
weights = weights.reshape(*weights.shape, 1, 1, 1)
self.main_class.step = self.step
return self.main_class.dynthresh(x_out[:-uncond.shape[0]], denoised_uncond, cond_scale, weights)
######################### XYZ Plot Script Support logic #########################
def make_axis_options():
xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module
def apply_mimic_scale(p, x, xs):
if x != 0:
setattr(p, "dynthres_enabled", True)
setattr(p, "dynthres_mimic_scale", x)
else:
setattr(p, "dynthres_enabled", False)
def confirm_scheduler(p, xs):
for x in xs:
if x not in VALID_MODES:
raise RuntimeError(f"Unknown Scheduler: {x}")
extra_axis_options = [
xyz_grid.AxisOption("[DynThres] Mimic Scale", float, apply_mimic_scale),
xyz_grid.AxisOption("[DynThres] Threshold Percentile", float, xyz_grid.apply_field("dynthres_threshold_percentile")),
xyz_grid.AxisOption("[DynThres] Mimic Scheduler", str, xyz_grid.apply_field("dynthres_mimic_mode"), confirm=confirm_scheduler, choices=lambda: VALID_MODES),
xyz_grid.AxisOption("[DynThres] Mimic minimum", float, xyz_grid.apply_field("dynthres_mimic_scale_min")),
xyz_grid.AxisOption("[DynThres] CFG Scheduler", str, xyz_grid.apply_field("dynthres_cfg_mode"), confirm=confirm_scheduler, choices=lambda: VALID_MODES),
xyz_grid.AxisOption("[DynThres] CFG minimum", float, xyz_grid.apply_field("dynthres_cfg_scale_min")),
xyz_grid.AxisOption("[DynThres] Power scheduler value", float, xyz_grid.apply_field("dynthres_power_val"))
]
if not any("[DynThres]" in x.label for x in xyz_grid.axis_options):
xyz_grid.axis_options.extend(extra_axis_options)
def callbackBeforeUi():
try:
make_axis_options()
except Exception as e:
traceback.print_exc()
print(f"Failed to add support for X/Y/Z Plot Script because: {e}")
script_callbacks.on_before_ui(callbackBeforeUi)
|