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support old configs that say "auto" for ssd_vae 

change sd_vae_as_default to True by default as it's a more sensible setting
This commit is contained in:
AUTOMATIC 2023-01-16 00:44:46 +03:00
parent 300d4a80df
commit 3f887f7f61
2 changed files with 5 additions and 3 deletions
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6
modules/sd_vae.py
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@ -94,8 +94,10 @@ def resolve_vae(checkpoint_file):
if shared.cmd_opts.vae_path is not None:
return shared.cmd_opts.vae_path, 'from commandline argument'
is_automatic = shared.opts.sd_vae in {"Automatic", "auto"} # "auto" for people with old config
vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)
if vae_near_checkpoint is not None and (shared.opts.sd_vae_as_default or shared.opts.sd_vae == "Automatic"):
if vae_near_checkpoint is not None and (shared.opts.sd_vae_as_default or is_automatic):
return vae_near_checkpoint, 'found near the checkpoint'
if shared.opts.sd_vae == "None":
@ -105,7 +107,7 @@ def resolve_vae(checkpoint_file):
if vae_from_options is not None:
return vae_from_options, 'specified in settings'
if shared.opts.sd_vae != "Automatic":
if is_automatic:
print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead")
return None, None

2
modules/shared.py
View File

@ -394,7 +394,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": ["Automatic", "None"] + list(sd_vae.vae_dict)}, refresh=sd_vae.refresh_vae_list),
"sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
"sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
"sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
"sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),