add minimum value of the scale scheduler variables
Alex "mcmonkey" Goodwin
parent
b5a7983454
commit
7e2ea9994c
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@ -36,20 +36,22 @@ class Script(scripts.Script):
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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")
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mimic_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='Mimic CFG Scale', value=7.0)
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with gr.Accordion("Dynamic Thresholding Advanced Options", open=False):
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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")
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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")
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threshold_percentile = gr.Slider(minimum=90.0, value=100.0, maximum=100.0, step=0.05, label='Top percentile of latents to clamp')
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mimic_mode = gr.Dropdown(VALID_MODES, value="Constant", label="Mimic Scale Scheduler")
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mimic_scale_min = gr.Slider(minimum=0.0, maximum=30.0, step=0.5, label="Minimum value of the Mimic Scale Scheduler")
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cfg_mode = gr.Dropdown(VALID_MODES, value="Constant", label="CFG Scale Scheduler")
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cfg_scale_min = gr.Slider(minimum=0.0, maximum=30.0, step=0.5, label="Minimum value of the CFG Scale Scheduler")
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enabled.change(
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fn=lambda x: {"visible": x, "__type__": "update"},
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inputs=[enabled],
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outputs=[accordion],
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show_progress = False)
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return [enabled, mimic_scale, threshold_percentile, mimic_mode, cfg_mode]
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return [enabled, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min]
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last_id = 0
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def process_batch(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, cfg_mode, batch_number, prompts, seeds, subseeds):
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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):
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enabled = p.dynthres_enabled if hasattr(p, 'dynthres_enabled') else enabled
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if not enabled:
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return
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@ -58,7 +60,9 @@ class Script(scripts.Script):
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mimic_scale = p.dynthres_mimic_scale if hasattr(p, 'dynthres_mimic_scale') else mimic_scale
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threshold_percentile = p.dynthres_threshold_percentile if hasattr(p, 'dynthres_threshold_percentile') else threshold_percentile
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mimic_mode = p.dynthres_mimic_mode if hasattr(p, 'dynthres_mimic_mode') else mimic_mode
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mimic_scale_min = p.dynthres_mimic_scale_min if hasattr(p, 'dynthres_mimic_scale_min') else mimic_scale_min
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cfg_mode = p.dynthres_cfg_mode if hasattr(p, 'dynthres_cfg_mode') else cfg_mode
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cfg_scale_min = p.dynthres_cfg_scale_min if hasattr(p, 'dynthres_cfg_scale_min') else cfg_scale_min
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# Note: the ID number is to protect the edge case of multiple simultaneous runs with different settings
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Script.last_id += 1
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fixed_sampler_name = f"{p.sampler_name}_dynthres{Script.last_id}"
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@ -68,7 +72,7 @@ class Script(scripts.Script):
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sampler = sd_samplers.all_samplers_map[p.sampler_name]
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def newConstructor(model):
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result = sampler.constructor(model)
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cfg = CustomCFGDenoiser(result.model_wrap_cfg.inner_model, mimic_scale, threshold_percentile, mimic_mode, cfg_mode, p.steps)
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cfg = CustomCFGDenoiser(result.model_wrap_cfg.inner_model, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, p.steps)
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result.model_wrap_cfg = cfg
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return result
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newSampler = sd_samplers_common.SamplerData(fixed_sampler_name, newConstructor, sampler.aliases, sampler.options)
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@ -78,7 +82,7 @@ class Script(scripts.Script):
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p.fixed_sampler_name = fixed_sampler_name
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sd_samplers.all_samplers_map[fixed_sampler_name] = newSampler
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def postprocess_batch(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, cfg_mode, batch_number, images):
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def postprocess_batch(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, batch_number, images):
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if not enabled or not hasattr(p, 'orig_sampler_name'):
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return
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p.sampler_name = p.orig_sampler_name
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@ -89,20 +93,22 @@ class Script(scripts.Script):
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######################### Implementation logic #########################
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class CustomCFGDenoiser(sd_samplers_kdiffusion.CFGDenoiser):
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def __init__(self, model, mimic_scale, threshold_percentile, mimic_mode, cfg_mode, maxSteps):
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def __init__(self, model, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, maxSteps):
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super().__init__(model)
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self.mimic_scale = mimic_scale
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self.threshold_percentile = threshold_percentile
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self.mimic_mode = mimic_mode
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self.cfg_mode = cfg_mode
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self.maxSteps = maxSteps
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self.cfg_scale_min = cfg_scale_min
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self.mimic_scale_min = mimic_scale_min
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def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
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denoised_uncond = x_out[-uncond.shape[0]:]
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return self.dynthresh(x_out[:-uncond.shape[0]], denoised_uncond, cond_scale, conds_list)
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def dynthresh(self, cond, uncond, cfgScale, conds_list):
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mimicScale = self.mimic_scale
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mimicScale = self.mimic_scale - self.mimic_scale_min
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if self.mimic_mode == "Constant":
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pass
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elif self.mimic_mode == "Linear Down":
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@ -113,6 +119,8 @@ class CustomCFGDenoiser(sd_samplers_kdiffusion.CFGDenoiser):
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mimicScale *= self.step / self.maxSteps
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elif self.mimic_mode == "Cosine Up":
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mimicScale *= math.cos(self.step / self.maxSteps)
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mimicScale += self.mimic_scale_min
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cfgScale -= self.cfg_scale_min
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if self.cfg_mode == "Constant":
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pass
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elif self.cfg_mode == "Linear Down":
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@ -123,6 +131,7 @@ class CustomCFGDenoiser(sd_samplers_kdiffusion.CFGDenoiser):
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cfgScale *= self.step / self.maxSteps
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elif self.cfg_mode == "Cosine Up":
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cfgScale *= math.cos(self.step / self.maxSteps)
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cfgScale += self.cfg_scale_min
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# uncond shape is (batch, 4, height, width)
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conds_per_batch = cond.shape[0] / uncond.shape[0]
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assert conds_per_batch == int(conds_per_batch), "Expected # of conds per batch to be constant across batches"
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