Commit c96e4750 authored by AUTOMATIC1111's avatar AUTOMATIC1111
Browse files

SD VAE rework 2 

- the setting for preferring opts.sd_vae has been inverted and reworded
- resolve_vae function made easier to read and now returns an object rather than a tuple
- if the checkbox for overriding per-model preferences is checked, opts.sd_vae overrides checkpoint user metadata
- changing VAE in user metadata  for currently loaded model immediately applies the selection
parent 5a38a9c0

modules/sd_models.py

+1 −1
Original line number Diff line number Diff line
@@ -356,7 +356,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer 


    sd_vae.delete_base_vae()

    sd_vae.clear_loaded_vae()

    vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)

    vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename).tuple()

    sd_vae.load_vae(model, vae_file, vae_source)

    timer.record("load VAE")

modules/sd_vae.py

+55 −16
Original line number Diff line number Diff line 
import os

import collections

from dataclasses import dataclass



from modules import paths, shared, devices, script_callbacks, sd_models, extra_networks

import glob

from copy import deepcopy
@@ -97,37 +99,74 @@ def find_vae_near_checkpoint(checkpoint_file): 
    return None





def resolve_vae(checkpoint_file):

    if shared.cmd_opts.vae_path is not None:

        return shared.cmd_opts.vae_path, 'from commandline argument'

@dataclass

class VaeResolution:

    vae: str = None

    source: str = None

    resolved: bool = True



    def tuple(self):

        return self.vae, self.source





def is_automatic():

    return shared.opts.sd_vae in {"Automatic", "auto"}  # "auto" for people with old config





def resolve_vae_from_setting() -> VaeResolution:

    if shared.opts.sd_vae == "None":

        return VaeResolution()



    vae_from_options = vae_dict.get(shared.opts.sd_vae, None)

    if vae_from_options is not None:

        return VaeResolution(vae_from_options, 'specified in settings')



    if not is_automatic():

        print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead")



    return VaeResolution(resolved=False)





def resolve_vae_from_user_metadata(checkpoint_file) -> VaeResolution:

    metadata = extra_networks.get_user_metadata(checkpoint_file)

    vae_metadata = metadata.get("vae", None)

    if vae_metadata is not None and vae_metadata != "Automatic":

        if vae_metadata == "None":

            return None, None

            return VaeResolution()



        vae_from_metadata = vae_dict.get(vae_metadata, None)

        if vae_from_metadata is not None:

            return vae_from_metadata, "from user metadata"

            return VaeResolution(vae_from_metadata, "from user metadata")



    return VaeResolution(resolved=False)



    is_automatic = shared.opts.sd_vae in {"Automatic", "auto"}  # "auto" for people with old config



def resolve_vae_near_checkpoint(checkpoint_file) -> VaeResolution:

    vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)

    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'

        return VaeResolution(vae_near_checkpoint, 'found near the checkpoint')



    if shared.opts.sd_vae == "None":

        return None, None

    return VaeResolution(resolved=False)



    vae_from_options = vae_dict.get(shared.opts.sd_vae, None)

    if vae_from_options is not None:

        return vae_from_options, 'specified in settings'



    if not is_automatic:

        print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead")

def resolve_vae(checkpoint_file) -> VaeResolution:

    if shared.cmd_opts.vae_path is not None:

        return VaeResolution(shared.cmd_opts.vae_path, 'from commandline argument')



    if shared.opts.sd_vae_overrides_per_model_preferences and not is_automatic():

        return resolve_vae_from_setting()



    res = resolve_vae_from_user_metadata(checkpoint_file)

    if res.resolved:

        return res



    res = resolve_vae_near_checkpoint(checkpoint_file)

    if res.resolved:

        return res



    res = resolve_vae_from_setting()



    return None, None

    return res





def load_vae_dict(filename, map_location):
@@ -201,7 +240,7 @@ def reload_vae_weights(sd_model=None, vae_file=unspecified): 
    checkpoint_file = checkpoint_info.filename



    if vae_file == unspecified:

        vae_file, vae_source = resolve_vae(checkpoint_file)

        vae_file, vae_source = resolve_vae(checkpoint_file).tuple()

    else:

        vae_source = "from function argument"

modules/shared.py

+5 −1
Original line number Diff line number Diff line
@@ -479,7 +479,7 @@ For img2img, VAE is used to process user's input image before the sampling, and 
"""),

    "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": 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"),

    "sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),

    "sd_vae_overrides_per_model_preferences": OptionInfo(True, "Selected VAE overrides per-model preferences").info("you can set per-model VAE either by editing user metadata for checkpoints, or by making the VAE have same name as checkpoint"),

    "auto_vae_precision": OptionInfo(True, "Automaticlly revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),

    "sd_vae_encode_method": OptionInfo("Full", "VAE type for encode", gr.Radio, {"choices": ["Full", "TAESD"]}).info("method to encode image to latent (use in img2img, hires-fix or inpaint mask)"),

    "sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD"]}).info("method to decode latent to image"),
@@ -733,6 +733,10 @@ class Options: 
        with open(filename, "r", encoding="utf8") as file:

            self.data = json.load(file)



        # 1.6.0 VAE defaults

        if self.data.get('sd_vae_as_default') is not None and self.data.get('sd_vae_overrides_per_model_preferences') is None:

            self.data['sd_vae_overrides_per_model_preferences'] = not self.data.get('sd_vae_as_default')



        # 1.1.1 quicksettings list migration

        if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:

            self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]

modules/ui_extra_networks_checkpoints_user_metadata.py

+7 −1
Original line number Diff line number Diff line 
import gradio as gr



from modules import ui_extra_networks_user_metadata, sd_vae

from modules import ui_extra_networks_user_metadata, sd_vae, shared

from modules.ui_common import create_refresh_button
@@ -18,6 +18,10 @@ class CheckpointUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataE 


        self.write_user_metadata(name, user_metadata)



    def update_vae(self, name):

        if name == shared.sd_model.sd_checkpoint_info.name_for_extra:

            sd_vae.reload_vae_weights()



    def put_values_into_components(self, name):

        user_metadata = self.get_user_metadata(name)

        values = super().put_values_into_components(name)
@@ -58,3 +62,5 @@ class CheckpointUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataE 
        ]



        self.setup_save_handler(self.button_save, self.save_user_metadata, edited_components)

        self.button_save.click(fn=self.update_vae, inputs=[self.edit_name_input])

webui.py

+1 −1
Original line number Diff line number Diff line
@@ -211,7 +211,7 @@ def configure_sigint_handler(): 
def configure_opts_onchange():

    shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()), call=False)

    shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)

    shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)

    shared.opts.onchange("sd_vae_overrides_per_model_preferences", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)

    shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)

    shared.opts.onchange("gradio_theme", shared.reload_gradio_theme)

    shared.opts.onchange("cross_attention_optimization", wrap_queued_call(lambda: modules.sd_hijack.model_hijack.redo_hijack(shared.sd_model)), call=False)

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