replace script with AlwaysVisible to allow combining with scripts
Alex "mcmonkey" Goodwin
parent
1cb63313cd
commit
2d3e5da9d4
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@ -42,7 +42,7 @@ The core functionality of this PR was mainly developed by [Birch-san](https://gi
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- Install the extension and restart.
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- Go to txt2img or img2img
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- Select `Script` at the bottom and select `Dynamic Thresholding (CFG Scale Fix)`
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- Check the `Enable Dynamic Thresholding (CFG Scale Fix)` box
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- Read the info on-page and set the sliders where you want em.
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- Click generate.
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@ -26,44 +26,50 @@ class Script(scripts.Script):
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return "Dynamic Thresholding (CFG Scale Fix)"
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def show(self, is_img2img):
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return True
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return scripts.AlwaysVisible
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def ui(self, is_img2img):
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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. \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")
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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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threshold_percentile = gr.Slider(minimum=90.0, value=90.0, maximum=100.0, step=0.05, label='Top percentile of latents to clamp')
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with gr.Accordion("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 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")
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mimic_mode = gr.Dropdown(["Constant", "Linear Down", "Cosine Down", "Linear Up", "Cosine Up"], value="Constant", label="Mimic Scale Scheduler")
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cfg_mode = gr.Dropdown(["Constant", "Linear Down", "Cosine Down", "Linear Up", "Cosine Up"], value="Constant", label="CFG Scale Scheduler")
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return [mimic_scale, threshold_percentile, mimic_mode, cfg_mode]
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enabled = gr.Checkbox(value=False, label="Enable Dynamic Thresholding (CFG Scale Fix)")
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# "Dynamic Thresholding (CFG Scale Fix)"
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accordion = gr.Group(visible=False)
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with accordion:
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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. \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")
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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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threshold_percentile = gr.Slider(minimum=90.0, value=90.0, maximum=100.0, step=0.05, label='Top percentile of latents to clamp')
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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 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")
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mimic_mode = gr.Dropdown(["Constant", "Linear Down", "Cosine Down", "Linear Up", "Cosine Up"], value="Constant", label="Mimic Scale Scheduler")
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cfg_mode = gr.Dropdown(["Constant", "Linear Down", "Cosine Down", "Linear Up", "Cosine Up"], value="Constant", label="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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def run(self, p, mimic_scale, threshold_percentile, mimic_mode, cfg_mode):
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def process(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, cfg_mode):
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if not enabled:
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return
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# Note: the random number is to protect the edge case of multiple simultaneous runs with different settings
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fixed_sampler_name = f"{p.sampler_name}_dynthres{random.randrange(100)}"
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try:
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# Percentage to portion
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threshold_percentile *= 0.01
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# Make a placeholder sampler
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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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result.model_wrap_cfg = cfg
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return result
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newSampler = sd_samplers.SamplerData(fixed_sampler_name, newConstructor, sampler.aliases, sampler.options)
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sd_samplers.all_samplers_map[fixed_sampler_name] = newSampler
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# Prep data
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p = copy(p)
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p.sampler_name = fixed_sampler_name
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# Run
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proc = process_images(p)
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# Cleanup
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del sd_samplers.all_samplers_map[fixed_sampler_name]
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return proc
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except Exception as e:
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del sd_samplers.all_samplers_map[fixed_sampler_name]
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raise e
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p.fixed_sampler_name = fixed_sampler_name
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# Percentage to portion
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threshold_percentile *= 0.01
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# Make a placeholder sampler
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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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result.model_wrap_cfg = cfg
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return result
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newSampler = sd_samplers.SamplerData(fixed_sampler_name, newConstructor, sampler.aliases, sampler.options)
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p.sampler_name = fixed_sampler_name
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sd_samplers.all_samplers_map[fixed_sampler_name] = newSampler
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def postprocess(self, p, enabled, mimic_scale, threshold_percentile, mimic_mode, cfg_mode):
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if not enabled:
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return
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del sd_samplers.all_samplers_map[p.fixed_sampler_name]
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######################### Implementation logic #########################
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