From e43f6e401b4c4486c16451a908249cf940a030ee Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin" <git_commits@alexgoodwin.dev>
Date: Thu, 26 Jan 2023 18:22:49 -0800
Subject: [PATCH] initial commit: script

---
 scripts/dynamic_thresholding.py | 105 ++++++++++++++++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 scripts/dynamic_thresholding.py

diff --git a/scripts/dynamic_thresholding.py b/scripts/dynamic_thresholding.py
new file mode 100644
index 0000000..e18b12d
--- /dev/null
+++ b/scripts/dynamic_thresholding.py
@@ -0,0 +1,105 @@
+##################
+# 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: 2023/01/26
+#
+# For usage help, view the README.md file in the extension root, or via the GitHub page.
+#
+##################
+
+import gradio as gr
+import random
+import torch
+from copy import copy
+from modules import sd_samplers, scripts
+from modules.processing import process_images, Processed
+from modules.shared import opts
+
+######################### Script class entrypoint #########################
+class Script(scripts.Script):
+
+    def title(self):
+        return "Dynamic Thresholding (CFG Scale Fix)"
+
+    def show(self, is_img2img):
+        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")
+        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]
+
+    def run(self, p, help_info, mimic_scale, threshold_percentile):
+        # 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:
+            # Percentage to portion
+            threshold_percentile *= 0.01
+            # Make a placeholder sampler
+            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)
+                result.model_wrap_cfg = cfg
+                return result
+            newSampler = sd_samplers.SamplerData(fixed_sampler_name, newConstructor, sampler.aliases, sampler.options)
+            sd_samplers.all_samplers_map[fixed_sampler_name] = newSampler
+            # Prep data
+            p = copy(p)
+            p.sampler_name = fixed_sampler_name
+            # Run
+            proc = process_images(p)
+            # Cleanup
+            del sd_samplers.all_samplers_map[fixed_sampler_name]
+            return proc
+        except Exception as e:
+            del sd_samplers.all_samplers_map[fixed_sampler_name]
+            raise e
+
+######################### Implementation logic #########################
+
+class CustomCFGDenoiser(sd_samplers.CFGDenoiser):
+    def __init__(self, model, mimic_scale, threshold_percentile):
+        super().__init__(model)
+        self.mimic_scale = mimic_scale
+        self.threshold_percentile = threshold_percentile
+        
+    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+        denoised_uncond = x_out[-uncond.shape[0]:]
+        return dynthresh(x_out[:-uncond.shape[0]], denoised_uncond, cond_scale, conds_list, self.mimic_scale, self.threshold_percentile)
+
+def dynthresh(cond, uncond, cond_scale, conds_list, mimic_scale, threshold_percentile):
+    # 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"
+    cond_stacked = cond.reshape((-1, int(conds_per_batch)) + uncond.shape[1:])
+    diff = cond_stacked - uncond.unsqueeze(1)
+    # conds_list shape is (batch, cond, 2)
+    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 * mimic_scale
+
+    dt_flattened = dynthresh_target.flatten(2)
+    dt_means = dt_flattened.mean(dim=2).unsqueeze(2)
+    dt_recentered = dt_flattened - dt_means
+    dt_max = dt_recentered.abs().max(dim=2).values.unsqueeze(2)
+
+    ut = uncond + diff_weighted * cond_scale
+    ut_flattened = ut.flatten(2)
+    ut_means = ut_flattened.mean(dim=2).unsqueeze(2)
+    ut_centered = ut_flattened - ut_means
+
+    ut_q = torch.quantile(ut_centered.abs(), threshold_percentile, dim=2).unsqueeze(2)
+    s = torch.maximum(ut_q, dt_max)
+    t_clamped = ut_centered.clamp(-s, s)
+    t_normalized = t_clamped / s
+    t_renormalized = t_normalized * dt_max
+
+    uncentered = t_renormalized + ut_means
+    unflattened = uncentered.unflatten(2, dynthresh_target.shape[2:])
+    return unflattened