fix bug with "Ignore selected VAE for..." option completely disabling VAE election

rework VAE resolving code to be more simple

modules/sd_models.py

@@ -224,7 +224,7 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
     return sd
 
 
-def load_model_weights(model, checkpoint_info: CheckpointInfo, vae_file="auto"):
+def load_model_weights(model, checkpoint_info: CheckpointInfo):
     sd_model_hash = checkpoint_info.calculate_shorthash()
 
     cache_enabled = shared.opts.sd_checkpoint_cache > 0
@@ -277,8 +277,8 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, vae_file="auto"):
 
     sd_vae.delete_base_vae()
     sd_vae.clear_loaded_vae()
-    vae_file = sd_vae.resolve_vae(checkpoint_info.filename, vae_file=vae_file)
+    vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
-    sd_vae.load_vae(model, vae_file)
+    sd_vae.load_vae(model, vae_file, vae_source)
 
 
 def enable_midas_autodownload():

modules/sd_vae.py

@@ -9,23 +9,9 @@ import glob
 from copy import deepcopy
 
 
-model_dir = "Stable-diffusion"
+vae_path = os.path.abspath(os.path.join(models_path, "VAE"))
-model_path = os.path.abspath(os.path.join(models_path, model_dir))
-vae_dir = "VAE"
-vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
-
-
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
-
+vae_dict = {}
-
-default_vae_dict = {"auto": "auto", "None": None, None: None}
-default_vae_list = ["auto", "None"]
-
-
-default_vae_values = [default_vae_dict[x] for x in default_vae_list]
-vae_dict = dict(default_vae_dict)
-vae_list = list(default_vae_list)
-first_load = True
 
 
 base_vae = None
@@ -64,100 +50,69 @@ def restore_base_vae(model):
 
 
 def get_filename(filepath):
-    return os.path.splitext(os.path.basename(filepath))[0]
+    return os.path.basename(filepath)
 
 
-def refresh_vae_list(vae_path=vae_path, model_path=model_path):
+def refresh_vae_list():
-    global vae_dict, vae_list
+    vae_dict.clear()
-    res = {}
+
-    candidates = [
+    paths = [
-        *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
+        os.path.join(sd_models.model_path, '**/*.vae.ckpt'),
-        *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
+        os.path.join(sd_models.model_path, '**/*.vae.pt'),
-        *glob.iglob(os.path.join(model_path, '**/*.vae.safetensors'), recursive=True),
+        os.path.join(sd_models.model_path, '**/*.vae.safetensors'),
-        *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True),
+        os.path.join(vae_path, '**/*.ckpt'),
-        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True),
+        os.path.join(vae_path, '**/*.pt'),
-        *glob.iglob(os.path.join(vae_path, '**/*.safetensors'), recursive=True),
+        os.path.join(vae_path, '**/*.safetensors'),
     ]
-    if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path):
+
-        candidates.append(shared.cmd_opts.vae_path)
+    if shared.cmd_opts.ckpt_dir is not None and os.path.isdir(shared.cmd_opts.ckpt_dir):
+        paths += [
+            os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.ckpt'),
+            os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.pt'),
+            os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.safetensors'),
+        ]
+
+    candidates = []
+    for path in paths:
+        candidates += glob.iglob(path, recursive=True)
+
     for filepath in candidates:
         name = get_filename(filepath)
-        res[name] = filepath
+        vae_dict[name] = filepath
-    vae_list.clear()
-    vae_list.extend(default_vae_list)
-    vae_list.extend(list(res.keys()))
-    vae_dict.clear()
-    vae_dict.update(res)
-    vae_dict.update(default_vae_dict)
-    return vae_list
 
 
-def get_vae_from_settings(vae_file="auto"):
+def find_vae_near_checkpoint(checkpoint_file):
-    # else, we load from settings, if not set to be default
+    checkpoint_path = os.path.splitext(checkpoint_file)[0]
-    if vae_file == "auto" and shared.opts.sd_vae is not None:
+    for vae_location in [checkpoint_path + ".vae.pt", checkpoint_path + ".vae.ckpt", checkpoint_path + ".vae.safetensors"]:
-        # if saved VAE settings isn't recognized, fallback to auto
+        if os.path.isfile(vae_location):
-        vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
+            return vae_location
-        # if VAE selected but not found, fallback to auto
+
-        if vae_file not in default_vae_values and not os.path.isfile(vae_file):
+    return None
-            vae_file = "auto"
-            print(f"Selected VAE doesn't exist: {vae_file}")
-    return vae_file
 
 
-def resolve_vae(checkpoint_file=None, vae_file="auto"):
+def resolve_vae(checkpoint_file):
-    global first_load, vae_dict, vae_list
+    if shared.cmd_opts.vae_path is not None:
+        return shared.cmd_opts.vae_path, 'from commandline argument'
 
-    # if vae_file argument is provided, it takes priority, but not saved
+    vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)
-    if vae_file and vae_file not in default_vae_list:
+    if vae_near_checkpoint is not None and (shared.opts.sd_vae_as_default or shared.opts.sd_vae == "auto"):
-        if not os.path.isfile(vae_file):
+        return vae_near_checkpoint, 'found near the checkpoint'
-            print(f"VAE provided as function argument doesn't exist: {vae_file}")
-            vae_file = "auto"
-    # for the first load, if vae-path is provided, it takes priority, saved, and failure is reported
-    if first_load and shared.cmd_opts.vae_path is not None:
-        if os.path.isfile(shared.cmd_opts.vae_path):
-            vae_file = shared.cmd_opts.vae_path
-            shared.opts.data['sd_vae'] = get_filename(vae_file)
-        else:
-            print(f"VAE provided as command line argument doesn't exist: {vae_file}")
-    # fallback to selector in settings, if vae selector not set to act as default fallback
-    if not shared.opts.sd_vae_as_default:
-        vae_file = get_vae_from_settings(vae_file)
-    # vae-path cmd arg takes priority for auto
-    if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
-        if os.path.isfile(shared.cmd_opts.vae_path):
-            vae_file = shared.cmd_opts.vae_path
-            print(f"Using VAE provided as command line argument: {vae_file}")
-    # if still not found, try look for ".vae.pt" beside model
-    model_path = os.path.splitext(checkpoint_file)[0]
-    if vae_file == "auto":
-        vae_file_try = model_path + ".vae.pt"
-        if os.path.isfile(vae_file_try):
-            vae_file = vae_file_try
-            print(f"Using VAE found similar to selected model: {vae_file}")
-    # if still not found, try look for ".vae.ckpt" beside model
-    if vae_file == "auto":
-        vae_file_try = model_path + ".vae.ckpt"
-        if os.path.isfile(vae_file_try):
-            vae_file = vae_file_try
-            print(f"Using VAE found similar to selected model: {vae_file}")
-    # if still not found, try look for ".vae.safetensors" beside model
-    if vae_file == "auto":
-        vae_file_try = model_path + ".vae.safetensors"
-        if os.path.isfile(vae_file_try):
-            vae_file = vae_file_try
-            print(f"Using VAE found similar to selected model: {vae_file}")
-    # No more fallbacks for auto
-    if vae_file == "auto":
-        vae_file = None
-    # Last check, just because
-    if vae_file and not os.path.exists(vae_file):
-        vae_file = None
 
-    return vae_file
+    if shared.opts.sd_vae == "None":
+        return None, None
+
+    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 shared.opts.sd_vae != "Automatic":
+        print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead")
+
+    return None, None
 
 
-def load_vae(model, vae_file=None):
+def load_vae(model, vae_file=None, vae_source="from unknown source"):
-    global first_load, vae_dict, vae_list, loaded_vae_file
+    global vae_dict, loaded_vae_file
     # save_settings = False
 
     cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0
@@ -165,12 +120,12 @@ def load_vae(model, vae_file=None):
     if vae_file:
         if cache_enabled and vae_file in checkpoints_loaded:
             # use vae checkpoint cache
-            print(f"Loading VAE weights [{get_filename(vae_file)}] from cache")
+            print(f"Loading VAE weights {vae_source}: cached {get_filename(vae_file)}")
             store_base_vae(model)
             _load_vae_dict(model, checkpoints_loaded[vae_file])
         else:
-            assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
+            assert os.path.isfile(vae_file), f"VAE {vae_source} doesn't exist: {vae_file}"
-            print(f"Loading VAE weights from: {vae_file}")
+            print(f"Loading VAE weights {vae_source}: {vae_file}")
             store_base_vae(model)
 
             vae_ckpt = sd_models.read_state_dict(vae_file, map_location=shared.weight_load_location)
@@ -191,14 +146,12 @@ def load_vae(model, vae_file=None):
         vae_opt = get_filename(vae_file)
         if vae_opt not in vae_dict:
             vae_dict[vae_opt] = vae_file
-            vae_list.append(vae_opt)
+
     elif loaded_vae_file:
         restore_base_vae(model)
 
     loaded_vae_file = vae_file
 
-    first_load = False
-
 
 # don't call this from outside
 def _load_vae_dict(model, vae_dict_1):
@@ -211,7 +164,10 @@ def clear_loaded_vae():
     loaded_vae_file = None
 
 
-def reload_vae_weights(sd_model=None, vae_file="auto"):
+unspecified = object()
+
+
+def reload_vae_weights(sd_model=None, vae_file=unspecified):
     from modules import lowvram, devices, sd_hijack
 
     if not sd_model:
@@ -219,7 +175,11 @@ def reload_vae_weights(sd_model=None, vae_file="auto"):
 
     checkpoint_info = sd_model.sd_checkpoint_info
     checkpoint_file = checkpoint_info.filename
-    vae_file = resolve_vae(checkpoint_file, vae_file=vae_file)
+
+    if vae_file == unspecified:
+        vae_file, vae_source = resolve_vae(checkpoint_file)
+    else:
+        vae_source = "from function argument"
 
     if loaded_vae_file == vae_file:
         return
@@ -231,7 +191,7 @@ def reload_vae_weights(sd_model=None, vae_file="auto"):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
-    load_vae(sd_model, vae_file)
+    load_vae(sd_model, vae_file, vae_source)
 
     sd_hijack.model_hijack.hijack(sd_model)
     script_callbacks.model_loaded_callback(sd_model)
@@ -239,5 +199,5 @@ def reload_vae_weights(sd_model=None, vae_file="auto"):
     if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
         sd_model.to(devices.device)
 
-    print("VAE Weights loaded.")
+    print("VAE weights loaded.")
     return sd_model

modules/shared.py

@@ -83,7 +83,7 @@ parser.add_argument("--theme", type=str, help="launches the UI with light or dar
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
-parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
+parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
 parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
@@ -383,7 +383,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
     "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("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
+    "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_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}),

scripts/xy_grid.py

@@ -125,24 +125,21 @@ def apply_upscale_latent_space(p, x, xs):
 
 
 def find_vae(name: str):
-    if name.lower() in ['auto', 'none']:
+    if name.lower() in ['auto', 'automatic']:
-        return name
+        return modules.sd_vae.unspecified
+    if name.lower() == 'none':
+        return None
     else:
-        vae_path = os.path.abspath(os.path.join(paths.models_path, 'VAE'))
+        choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]
-        found = glob.glob(os.path.join(vae_path, f'**/{name}.*pt'), recursive=True)
+        if len(choices) == 0:
-        if found:
+            print(f"No VAE found for {name}; using automatic")
-            return found[0]
+            return modules.sd_vae.unspecified
         else:
-            return 'auto'
+            return modules.sd_vae.vae_dict[choices[0]]
 
 
 def apply_vae(p, x, xs):
-    if x.lower().strip() == 'none':
+    modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))
-        modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file='None')
-    else:
-        found = find_vae(x)
-        if found:
-            v = modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=found)
 
 
 def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
@@ -271,7 +268,9 @@ class SharedSettingsStackHelper(object):
   
     def __exit__(self, exc_type, exc_value, tb):
         modules.sd_models.reload_model_weights(self.model)
-        modules.sd_vae.reload_vae_weights(self.model, vae_file=find_vae(self.vae))
+
+        opts.data["sd_vae"] = self.vae
+        modules.sd_vae.reload_vae_weights(self.model)
 
         hypernetwork.load_hypernetwork(self.hypernetwork)
         hypernetwork.apply_strength()