add description to VAE setting page
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36ca80d004
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@ -259,6 +259,7 @@ class OptionInfo:
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self.onchange = onchange
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self.section = section
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self.refresh = refresh
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self.do_not_save = False
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self.comment_before = comment_before
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"""HTML text that will be added after label in UI"""
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@ -291,6 +292,13 @@ class OptionInfo:
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return self
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class OptionHTML(OptionInfo):
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def __init__(self, text):
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super().__init__(str(text).strip(), label='', component=lambda **kwargs: gr.HTML(elem_classes="settings-info", **kwargs))
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self.do_not_save = True
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def options_section(section_identifier, options_dict):
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for v in options_dict.values():
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v.section = section_identifier
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@ -443,6 +451,12 @@ options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
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}))
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options_templates.update(options_section(('vae', "VAE"), {
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"sd_vae_explanation": OptionHTML("""
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<abbr title='Variational autoencoder'>VAE</abbr> is a neural network that transforms a standard <abbr title='red/green/blue'>RGB</abbr>
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image into latent space representation and back. Latent space representation is what stable diffusion is working on during sampling
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(i.e. when the progress bar is between empty and full). For txt2img, VAE is used to create a resulting image after the sampling is finished.
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For img2img, VAE is used to process user's input image before the sampling, and to create an image after sampling.
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"""),
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"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
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"sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
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@ -619,6 +633,9 @@ class Options:
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assert not cmd_opts.freeze_settings, "changing settings is disabled"
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info = opts.data_labels.get(key, None)
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if info.do_not_save:
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return
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comp_args = info.component_args if info else None
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if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
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raise RuntimeError(f"not possible to set {key} because it is restricted")
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@ -648,6 +665,9 @@ class Options:
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if oldval == value:
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return False
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if self.data_labels[key].do_not_save:
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return False
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try:
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setattr(self, key, value)
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except RuntimeError:
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