nodes/mcmonkeyprojects-sd-dynamic-thresholding
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add option for external configuration via 'p' keys, add safety check for ddim/plms

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Alex "mcmonkey" Goodwin
2023-01-27 01:03:27 -08:00
parent 2d3e5da9d4
commit 2690b379af
1 changed files with 14 additions and 7 deletions
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scripts/dynamic_thresholding.py
+14 -7
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@@ -14,10 +14,10 @@ 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
from modules.shared import opts
######################### Data values #########################
VALID_MODES = ["Constant", "Linear Down", "Cosine Down", "Linear Up", "Cosine Up"]
######################### Script class entrypoint #########################
class Script(scripts.Script):
@@ -38,8 +38,8 @@ class Script(scripts.Script):
threshold_percentile = gr.Slider(minimum=90.0, value=90.0, maximum=100.0, step=0.05, label='Top percentile of latents to clamp')
with gr.Accordion("Dynamic Thresholding 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")
mimic_mode = gr.Dropdown(VALID_MODES, value="Constant", label="Mimic Scale Scheduler")
cfg_mode = gr.Dropdown(VALID_MODES, value="Constant", label="CFG Scale Scheduler")
enabled.change(
fn=lambda x: {"visible": x, "__type__": "update"},
inputs=[enabled],
@@ -47,9 +47,16 @@ class Script(scripts.Script):
show_progress = False)
return [enabled, mimic_scale, threshold_percentile, mimic_mode, cfg_mode]
def process(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, cfg_mode):
def process_batch(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, cfg_mode, batch_number, prompts, seeds, subseeds):
enabled = p.dynthres_enabled if hasattr(p, 'dynthres_enabled') else enabled
if not enabled:
return
if p.sampler_name in ["DDIM", "PLMS"]:
raise RuntimeError(f"Cannot use sampler {p.sampler_name} with Dynamic Thresholding")
mimic_scale = p.dynthres_mimic_scale if hasattr(p, 'dynthres_mimic_scale') else mimic_scale
threshold_percentile = p.dynthres_threshold_percentile if hasattr(p, 'dynthres_threshold_percentile') else threshold_percentile
mimic_mode = p.dynthres_mimic_mode if hasattr(p, 'dynthres_mimic_mode') else mimic_mode
cfg_mode = p.dynthres_cfg_mode if hasattr(p, 'dynthres_cfg_mode') else 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)}"
p.fixed_sampler_name = fixed_sampler_name
@@ -66,7 +73,7 @@ class Script(scripts.Script):
p.sampler_name = fixed_sampler_name
sd_samplers.all_samplers_map[fixed_sampler_name] = newSampler
def postprocess(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, cfg_mode):
def postprocess_batch(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, cfg_mode, batch_number, images):
if not enabled:
return
del sd_samplers.all_samplers_map[p.fixed_sampler_name]