Misc
Misc
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@ -220,6 +220,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
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model.sd_model_checkpoint = checkpoint_file
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model.sd_checkpoint_info = checkpoint_info
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sd_vae.delete_base_vae()
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sd_vae.clear_loaded_vae()
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sd_vae.load_vae(model, vae_file)
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@ -154,8 +154,7 @@ def load_vae(model, vae_file=None):
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if vae_opt not in vae_dict:
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vae_dict[vae_opt] = vae_file
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vae_list.append(vae_opt)
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# shared.opts.data['sd_vae'] = vae_opt
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else:
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elif loaded_vae_file:
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restore_base_vae(model)
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loaded_vae_file = vae_file
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@ -335,7 +335,7 @@ options_templates.update(options_section(('training', "Training"), {
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options_templates.update(options_section(('sd', "Stable Diffusion"), {
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"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
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"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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"sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
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"sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
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"sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
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"sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
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"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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