From 7e2ea9994cb95ccf4ce61ed43c2c8f8e8a73b909 Mon Sep 17 00:00:00 2001 From: "Alex \"mcmonkey\" Goodwin" Date: Mon, 30 Jan 2023 09:42:00 -0800 Subject: [PATCH] add minimum value of the scale scheduler variables --- scripts/dynamic_thresholding.py | 23 ++++++++++++++++------- 1 file changed, 16 insertions(+), 7 deletions(-) diff --git a/scripts/dynamic_thresholding.py b/scripts/dynamic_thresholding.py index 699cd2f..606385f 100644 --- a/scripts/dynamic_thresholding.py +++ b/scripts/dynamic_thresholding.py @@ -36,20 +36,22 @@ class Script(scripts.Script): 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. \n... \n") 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): - gr.Markdown("You can configure the **scale scheduler** for either the CFG Scale or the Mimic Scale here. \n'**Constant**' is default. \nIn testing, setting both to '**Linear Down**' or '**Constant**' seems to produce best results. \nOther setting combos produce interesting results as well. \nSet '**Top percentile**' to how much clamping you want. 90% is slightly underclamped, 100% clamps completely and tries to stop any/all burn. The effect tends to scale as it approaches 100%, (eg 90% and 95% are much more similar than 98% and 99%). \n... \n") + gr.Markdown("You can configure the **scale scheduler** for either the CFG Scale or the Mimic Scale here. \n'**Constant**' is default. \nIn testing, setting both to '**Linear Down**' or '**Constant**' seems to produce best results. \nOther setting combos produce interesting results as well. \nSet '**Top percentile**' to how much clamping you want. 90% is slightly underclamped, 100% clamps completely and tries to stop any/all burn. The effect tends to scale as it approaches 100%, (eg 90% and 95% are much more similar than 98% and 99%). \nSet '**Minimum value of the Scale Scheduler**' only if you've set the scale scheduler to something other than '**Constant**', to set the lowest value it will go to (default 0, but higher values are likely better). \n... \n") 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") enabled.change( fn=lambda x: {"visible": x, "__type__": "update"}, inputs=[enabled], outputs=[accordion], show_progress = False) - return [enabled, mimic_scale, threshold_percentile, mimic_mode, cfg_mode] + return [enabled, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min] last_id = 0 - def process_batch(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, cfg_mode, batch_number, prompts, seeds, subseeds): + def process_batch(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, batch_number, prompts, seeds, subseeds): enabled = p.dynthres_enabled if hasattr(p, 'dynthres_enabled') else enabled if not enabled: return @@ -58,7 +60,9 @@ class Script(scripts.Script): 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 + mimic_scale_min = p.dynthres_mimic_scale_min if hasattr(p, 'dynthres_mimic_scale_min') else mimic_scale_min cfg_mode = p.dynthres_cfg_mode if hasattr(p, 'dynthres_cfg_mode') else cfg_mode + cfg_scale_min = p.dynthres_cfg_scale_min if hasattr(p, 'dynthres_cfg_scale_min') else cfg_scale_min # 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"{p.sampler_name}_dynthres{Script.last_id}" @@ -68,7 +72,7 @@ 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, mimic_mode, cfg_mode, p.steps) + cfg = CustomCFGDenoiser(result.model_wrap_cfg.inner_model, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, p.steps) result.model_wrap_cfg = cfg return result newSampler = sd_samplers_common.SamplerData(fixed_sampler_name, newConstructor, sampler.aliases, sampler.options) @@ -78,7 +82,7 @@ class Script(scripts.Script): p.fixed_sampler_name = fixed_sampler_name sd_samplers.all_samplers_map[fixed_sampler_name] = newSampler - def postprocess_batch(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, cfg_mode, batch_number, images): + def postprocess_batch(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, batch_number, images): if not enabled or not hasattr(p, 'orig_sampler_name'): return p.sampler_name = p.orig_sampler_name @@ -89,20 +93,22 @@ class Script(scripts.Script): ######################### Implementation logic ######################### class CustomCFGDenoiser(sd_samplers_kdiffusion.CFGDenoiser): - def __init__(self, model, mimic_scale, threshold_percentile, mimic_mode, cfg_mode, maxSteps): + def __init__(self, model, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, 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 + self.cfg_scale_min = cfg_scale_min + self.mimic_scale_min = mimic_scale_min 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, cfgScale, conds_list): - mimicScale = self.mimic_scale + mimicScale = self.mimic_scale - self.mimic_scale_min if self.mimic_mode == "Constant": pass elif self.mimic_mode == "Linear Down": @@ -113,6 +119,8 @@ class CustomCFGDenoiser(sd_samplers_kdiffusion.CFGDenoiser): mimicScale *= self.step / self.maxSteps elif self.mimic_mode == "Cosine Up": mimicScale *= math.cos(self.step / self.maxSteps) + mimicScale += self.mimic_scale_min + cfgScale -= self.cfg_scale_min if self.cfg_mode == "Constant": pass elif self.cfg_mode == "Linear Down": @@ -123,6 +131,7 @@ class CustomCFGDenoiser(sd_samplers_kdiffusion.CFGDenoiser): cfgScale *= self.step / self.maxSteps elif self.cfg_mode == "Cosine Up": cfgScale *= math.cos(self.step / self.maxSteps) + cfgScale += self.cfg_scale_min # 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"