Merge pull request #1322 from cmdr2/cf

CodeFormer

scripts/check_models.py

@@ -1,101 +0,0 @@
-# this script runs inside the legacy "stable-diffusion" folder
-
-from sdkit.models import download_model, get_model_info_from_db
-from sdkit.utils import hash_file_quick
-
-import os
-import shutil
-from glob import glob
-import traceback
-
-models_base_dir = os.path.abspath(os.path.join("..", "models"))
-
-models_to_check = {
-    "stable-diffusion": [
-        {"file_name": "sd-v1-4.ckpt", "model_id": "1.4"},
-    ],
-    "gfpgan": [
-        {"file_name": "GFPGANv1.4.pth", "model_id": "1.4"},
-    ],
-    "realesrgan": [
-        {"file_name": "RealESRGAN_x4plus.pth", "model_id": "x4plus"},
-        {"file_name": "RealESRGAN_x4plus_anime_6B.pth", "model_id": "x4plus_anime_6"},
-    ],
-    "vae": [
-        {"file_name": "vae-ft-mse-840000-ema-pruned.ckpt", "model_id": "vae-ft-mse-840000-ema-pruned"},
-    ],
-}
-MODEL_EXTENSIONS = {  # copied from easydiffusion/model_manager.py
-    "stable-diffusion": [".ckpt", ".safetensors"],
-    "vae": [".vae.pt", ".ckpt", ".safetensors"],
-    "hypernetwork": [".pt", ".safetensors"],
-    "gfpgan": [".pth"],
-    "realesrgan": [".pth"],
-    "lora": [".ckpt", ".safetensors"],
-}
-
-
-def download_if_necessary(model_type: str, file_name: str, model_id: str):
-    model_path = os.path.join(models_base_dir, model_type, file_name)
-    expected_hash = get_model_info_from_db(model_type=model_type, model_id=model_id)["quick_hash"]
-
-    other_models_exist = any_model_exists(model_type)
-    known_model_exists = os.path.exists(model_path)
-    known_model_is_corrupt = known_model_exists and hash_file_quick(model_path) != expected_hash
-
-    if known_model_is_corrupt or (not other_models_exist and not known_model_exists):
-        print("> download", model_type, model_id)
-        download_model(model_type, model_id, download_base_dir=models_base_dir)
-
-
-def init():
-    migrate_legacy_model_location()
-
-    for model_type, models in models_to_check.items():
-        for model in models:
-            try:
-                download_if_necessary(model_type, model["file_name"], model["model_id"])
-            except:
-                traceback.print_exc()
-                fail(model_type)
-
-        print(model_type, "model(s) found.")
-
-
-### utilities
-def any_model_exists(model_type: str) -> bool:
-    extensions = MODEL_EXTENSIONS.get(model_type, [])
-    for ext in extensions:
-        if any(glob(f"{models_base_dir}/{model_type}/**/*{ext}", recursive=True)):
-            return True
-
-    return False
-
-
-def migrate_legacy_model_location():
-    'Move the models inside the legacy "stable-diffusion" folder, to their respective folders'
-
-    for model_type, models in models_to_check.items():
-        for model in models:
-            file_name = model["file_name"]
-            if os.path.exists(file_name):
-                dest_dir = os.path.join(models_base_dir, model_type)
-                os.makedirs(dest_dir, exist_ok=True)
-                shutil.move(file_name, os.path.join(dest_dir, file_name))
-
-
-def fail(model_name):
-    print(
-        f"""Error downloading the {model_name} model. Sorry about that, please try to:
-1. Run this installer again.
-2. If that doesn't fix it, please try to download the file manually. The address to download from, and the destination to save to are printed above this message.
-3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB
-4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues
-Thanks!"""
-    )
-    exit(1)
-
-
-### start
-
-init()

scripts/check_modules.py

@@ -18,7 +18,7 @@ os_name = platform.system()
 modules_to_check = {
     "torch": ("1.11.0", "1.13.1", "2.0.0"),
     "torchvision": ("0.12.0", "0.14.1", "0.15.1"),
-    "sdkit": "1.0.98",
+    "sdkit": "1.0.101",
     "stable-diffusion-sdkit": "2.1.4",
     "rich": "12.6.0",
     "uvicorn": "0.19.0",

scripts/on_sd_start.bat

@@ -79,13 +79,6 @@ call WHERE uvicorn > .tmp
     @echo conda_sd_ui_deps_installed >> ..\scripts\install_status.txt
 )
 
-@rem Download the required models
-call python ..\scripts\check_models.py
-if "%ERRORLEVEL%" NEQ "0" (
-    pause
-    exit /b
-)
-
 @>nul findstr /m "sd_install_complete" ..\scripts\install_status.txt
 @if "%ERRORLEVEL%" NEQ "0" (
     @echo sd_weights_downloaded >> ..\scripts\install_status.txt

scripts/on_sd_start.sh

@@ -51,12 +51,6 @@ if ! command -v uvicorn &> /dev/null; then
     fail "UI packages not found!"
 fi
 
-# Download the required models
-if ! python ../scripts/check_models.py; then
-    read -p "Press any key to continue"
-    exit 1
-fi
-
 if [ `grep -c sd_install_complete ../scripts/install_status.txt` -gt "0" ]; then
     echo sd_weights_downloaded >> ../scripts/install_status.txt
     echo sd_install_complete >> ../scripts/install_status.txt

ui/easydiffusion/app.py

@@ -90,8 +90,8 @@ def init():
     os.makedirs(USER_SERVER_PLUGINS_DIR, exist_ok=True)
 
     # https://pytorch.org/docs/stable/storage.html
-    warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
+    warnings.filterwarnings("ignore", category=UserWarning, message="TypedStorage is deprecated")
-    
+
     load_server_plugins()
 
     update_render_threads()
@@ -221,12 +221,41 @@ def open_browser():
 
         webbrowser.open(f"http://localhost:{port}")
 
-    Console().print(Panel(
+    Console().print(
-        "\n" +
+        Panel(
-        "[white]Easy Diffusion is ready to serve requests.\n\n" +
+            "\n"
-        "A new browser tab should have been opened by now.\n" +
+            + "[white]Easy Diffusion is ready to serve requests.\n\n"
-        f"If not, please open your web browser and navigate to [bold yellow underline]http://localhost:{port}/\n",
+            + "A new browser tab should have been opened by now.\n"
-        title="Easy Diffusion is ready", style="bold yellow on blue"))
+            + f"If not, please open your web browser and navigate to [bold yellow underline]http://localhost:{port}/\n",
+            title="Easy Diffusion is ready",
+            style="bold yellow on blue",
+        )
+    )
+
+
+def fail_and_die(fail_type: str, data: str):
+    suggestions = [
+        "Run this installer again.",
+        "If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB",
+        "If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues",
+    ]
+
+    if fail_type == "model_download":
+        fail_label = f"Error downloading the {data} model"
+        suggestions.insert(
+            1,
+            "If that doesn't fix it, please try to download the file manually. The address to download from, and the destination to save to are printed above this message.",
+        )
+    else:
+        fail_label = "Error while installing Easy Diffusion"
+
+    msg = [f"{fail_label}. Sorry about that, please try to:"]
+    for i, suggestion in enumerate(suggestions):
+        msg.append(f"{i+1}. {suggestion}")
+    msg.append("Thanks!")
+
+    print("\n".join(msg))
+    exit(1)
 
 
 def get_image_modifiers():

ui/easydiffusion/model_manager.py

@@ -1,10 +1,14 @@
 import os
+import shutil
+from glob import glob
+import traceback
 
 from easydiffusion import app
 from easydiffusion.types import TaskData
 from easydiffusion.utils import log
 from sdkit import Context
-from sdkit.models import load_model, scan_model, unload_model
+from sdkit.models import load_model, scan_model, unload_model, download_model, get_model_info_from_db
+from sdkit.utils import hash_file_quick
 
 KNOWN_MODEL_TYPES = [
     "stable-diffusion",
@@ -13,6 +17,7 @@ KNOWN_MODEL_TYPES = [
     "gfpgan",
     "realesrgan",
     "lora",
+    "codeformer",
 ]
 MODEL_EXTENSIONS = {
     "stable-diffusion": [".ckpt", ".safetensors"],
@@ -21,14 +26,22 @@ MODEL_EXTENSIONS = {
     "gfpgan": [".pth"],
     "realesrgan": [".pth"],
     "lora": [".ckpt", ".safetensors"],
+    "codeformer": [".pth"],
 }
 DEFAULT_MODELS = {
-    "stable-diffusion": [  # needed to support the legacy installations
+    "stable-diffusion": [
-        "custom-model",  # only one custom model file was supported initially, creatively named 'custom-model'
+        {"file_name": "sd-v1-4.ckpt", "model_id": "1.4"},
-        "sd-v1-4",  # Default fallback.
+    ],
+    "gfpgan": [
+        {"file_name": "GFPGANv1.4.pth", "model_id": "1.4"},
+    ],
+    "realesrgan": [
+        {"file_name": "RealESRGAN_x4plus.pth", "model_id": "x4plus"},
+        {"file_name": "RealESRGAN_x4plus_anime_6B.pth", "model_id": "x4plus_anime_6"},
+    ],
+    "vae": [
+        {"file_name": "vae-ft-mse-840000-ema-pruned.ckpt", "model_id": "vae-ft-mse-840000-ema-pruned"},
     ],
-    "gfpgan": ["GFPGANv1.3"],
-    "realesrgan": ["RealESRGAN_x4plus"],
 }
 MODELS_TO_LOAD_ON_START = ["stable-diffusion", "vae", "hypernetwork", "lora"]
 
@@ -37,6 +50,8 @@ known_models = {}
 
 def init():
     make_model_folders()
+    migrate_legacy_model_location()  # if necessary
+    download_default_models_if_necessary()
     getModels()  # run this once, to cache the picklescan results
 
 
@@ -75,7 +90,7 @@ def resolve_model_to_use(model_name: str = None, model_type: str = None):
     default_models = DEFAULT_MODELS.get(model_type, [])
     config = app.getConfig()
 
-    model_dirs = [os.path.join(app.MODELS_DIR, model_type), app.SD_DIR]
+    model_dir = os.path.join(app.MODELS_DIR, model_type)
     if not model_name:  # When None try user configured model.
         # config = getConfig()
         if "model" in config and model_type in config["model"]:
@@ -83,45 +98,41 @@ def resolve_model_to_use(model_name: str = None, model_type: str = None):
 
     if model_name:
         # Check models directory
-        models_dir_path = os.path.join(app.MODELS_DIR, model_type, model_name)
+        model_path = os.path.join(model_dir, model_name)
+        if os.path.exists(model_path):
+            return model_path
         for model_extension in model_extensions:
-            if os.path.exists(models_dir_path + model_extension):
+            if os.path.exists(model_path + model_extension):
-                return models_dir_path + model_extension
+                return model_path + model_extension
             if os.path.exists(model_name + model_extension):
                 return os.path.abspath(model_name + model_extension)
 
-    # Default locations
-    if model_name in default_models:
-        default_model_path = os.path.join(app.SD_DIR, model_name)
-        for model_extension in model_extensions:
-            if os.path.exists(default_model_path + model_extension):
-                return default_model_path + model_extension
-
     # Can't find requested model, check the default paths.
-    for default_model in default_models:
+    if model_type == "stable-diffusion":
-        for model_dir in model_dirs:
+        for default_model in default_models:
-            default_model_path = os.path.join(model_dir, default_model)
+            default_model_path = os.path.join(model_dir, default_model["file_name"])
-            for model_extension in model_extensions:
+            if os.path.exists(default_model_path):
-                if os.path.exists(default_model_path + model_extension):
+                if model_name is not None:
-                    if model_name is not None:
+                    log.warn(
-                        log.warn(
+                        f"Could not find the configured custom model {model_name}. Using the default one: {default_model_path}"
-                            f"Could not find the configured custom model {model_name}{model_extension}. Using the default one: {default_model_path}{model_extension}"
+                    )
-                        )
+                return default_model_path
-                    return default_model_path + model_extension
 
     return None
 
 
 def reload_models_if_necessary(context: Context, task_data: TaskData):
-    use_upscale_lower = task_data.use_upscale.lower() if task_data.use_upscale else ""
+    face_fix_lower = task_data.use_face_correction.lower() if task_data.use_face_correction else ""
+    upscale_lower = task_data.use_upscale.lower() if task_data.use_upscale else ""
 
     model_paths_in_req = {
         "stable-diffusion": task_data.use_stable_diffusion_model,
         "vae": task_data.use_vae_model,
         "hypernetwork": task_data.use_hypernetwork_model,
-        "gfpgan": task_data.use_face_correction,
+        "codeformer": task_data.use_face_correction if "codeformer" in face_fix_lower else None,
-        "realesrgan": task_data.use_upscale if "realesrgan" in use_upscale_lower else None,
+        "gfpgan": task_data.use_face_correction if "gfpgan" in face_fix_lower else None,
-        "latent_upscaler": True if task_data.use_upscale == "latent_upscaler" else None,
+        "realesrgan": task_data.use_upscale if "realesrgan" in upscale_lower else None,
+        "latent_upscaler": True if "latent_upscaler" in upscale_lower else None,
         "nsfw_checker": True if task_data.block_nsfw else None,
         "lora": task_data.use_lora_model,
     }
@@ -131,6 +142,11 @@ def reload_models_if_necessary(context: Context, task_data: TaskData):
         if context.model_paths.get(model_type) != path
     }
 
+    if task_data.codeformer_upscale_faces and "realesrgan" not in models_to_reload.keys():
+        models_to_reload["realesrgan"] = resolve_model_to_use(
+            DEFAULT_MODELS["realesrgan"][0]["file_name"], "realesrgan"
+        )
+
     if set_vram_optimizations(context) or set_clip_skip(context, task_data):  # reload SD
         models_to_reload["stable-diffusion"] = model_paths_in_req["stable-diffusion"]
 
@@ -157,7 +173,13 @@ def resolve_model_paths(task_data: TaskData):
     task_data.use_lora_model = resolve_model_to_use(task_data.use_lora_model, model_type="lora")
 
     if task_data.use_face_correction:
-        task_data.use_face_correction = resolve_model_to_use(task_data.use_face_correction, "gfpgan")
+        if "gfpgan" in task_data.use_face_correction.lower():
+            model_type = "gfpgan"
+        elif "codeformer" in task_data.use_face_correction.lower():
+            model_type = "codeformer"
+            download_if_necessary("codeformer", "codeformer.pth", "codeformer-0.1.0")
+
+        task_data.use_face_correction = resolve_model_to_use(task_data.use_face_correction, model_type)
     if task_data.use_upscale and "realesrgan" in task_data.use_upscale.lower():
         task_data.use_upscale = resolve_model_to_use(task_data.use_upscale, "realesrgan")
 
@@ -167,7 +189,31 @@ def fail_if_models_did_not_load(context: Context):
         if model_type in context.model_load_errors:
             e = context.model_load_errors[model_type]
             raise Exception(f"Could not load the {model_type} model! Reason: " + e)
-            # concat 'e', don't use in format string (injection attack)
+
+
+def download_default_models_if_necessary():
+    for model_type, models in DEFAULT_MODELS.items():
+        for model in models:
+            try:
+                download_if_necessary(model_type, model["file_name"], model["model_id"])
+            except:
+                traceback.print_exc()
+                app.fail_and_die(fail_type="model_download", data=model_type)
+
+        print(model_type, "model(s) found.")
+
+
+def download_if_necessary(model_type: str, file_name: str, model_id: str):
+    model_path = os.path.join(app.MODELS_DIR, model_type, file_name)
+    expected_hash = get_model_info_from_db(model_type=model_type, model_id=model_id)["quick_hash"]
+
+    other_models_exist = any_model_exists(model_type)
+    known_model_exists = os.path.exists(model_path)
+    known_model_is_corrupt = known_model_exists and hash_file_quick(model_path) != expected_hash
+
+    if known_model_is_corrupt or (not other_models_exist and not known_model_exists):
+        print("> download", model_type, model_id)
+        download_model(model_type, model_id, download_base_dir=app.MODELS_DIR)
 
 
 def set_vram_optimizations(context: Context):
@@ -181,6 +227,26 @@ def set_vram_optimizations(context: Context):
     return False
 
 
+def migrate_legacy_model_location():
+    'Move the models inside the legacy "stable-diffusion" folder, to their respective folders'
+
+    for model_type, models in DEFAULT_MODELS.items():
+        for model in models:
+            file_name = model["file_name"]
+            legacy_path = os.path.join(app.SD_DIR, file_name)
+            if os.path.exists(legacy_path):
+                shutil.move(legacy_path, os.path.join(app.MODELS_DIR, model_type, file_name))
+
+
+def any_model_exists(model_type: str) -> bool:
+    extensions = MODEL_EXTENSIONS.get(model_type, [])
+    for ext in extensions:
+        if any(glob(f"{app.MODELS_DIR}/{model_type}/**/*{ext}", recursive=True)):
+            return True
+
+    return False
+
+
 def set_clip_skip(context: Context, task_data: TaskData):
     clip_skip = task_data.clip_skip
 
@@ -238,17 +304,12 @@ def is_malicious_model(file_path):
 
 def getModels():
     models = {
-        "active": {
-            "stable-diffusion": "sd-v1-4",
-            "vae": "",
-            "hypernetwork": "",
-            "lora": "",
-        },
         "options": {
             "stable-diffusion": ["sd-v1-4"],
             "vae": [],
             "hypernetwork": [],
             "lora": [],
+            "codeformer": ["codeformer"],
         },
     }
 
@@ -309,9 +370,4 @@ def getModels():
     if models_scanned > 0:
         log.info(f"[green]Scanned {models_scanned} models. Nothing infected[/]")
 
-    # legacy
-    custom_weight_path = os.path.join(app.SD_DIR, "custom-model.ckpt")
-    if os.path.exists(custom_weight_path):
-        models["options"]["stable-diffusion"].append("custom-model")
-
     return models

ui/easydiffusion/renderer.py

@@ -34,6 +34,7 @@ def init(device):
     context.temp_images = {}
     context.partial_x_samples = None
     context.model_load_errors = {}
+    context.enable_codeformer = True
 
     from easydiffusion import app
 
@@ -160,7 +161,11 @@ def filter_images(req: GenerateImageRequest, task_data: TaskData, images: list,
     filter_params = {}
     if task_data.block_nsfw:
         filters_to_apply.append("nsfw_checker")
-    if task_data.use_face_correction and "gfpgan" in task_data.use_face_correction.lower():
+    if task_data.use_face_correction and "codeformer" in task_data.use_face_correction.lower():
+        filters_to_apply.append("codeformer")
+
+        filter_params["upscale_faces"] = task_data.codeformer_upscale_faces
+    elif task_data.use_face_correction and "gfpgan" in task_data.use_face_correction.lower():
         filters_to_apply.append("gfpgan")
     if task_data.use_upscale:
         if "realesrgan" in task_data.use_upscale.lower():

ui/easydiffusion/types.py

@@ -23,7 +23,7 @@ class GenerateImageRequest(BaseModel):
     sampler_name: str = None  # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"
     hypernetwork_strength: float = 0
     lora_alpha: float = 0
-    tiling: str = "none" # "none", "x", "y", "xy"
+    tiling: str = "none"  # "none", "x", "y", "xy"
 
 
 class TaskData(BaseModel):
@@ -51,6 +51,7 @@ class TaskData(BaseModel):
     stream_image_progress: bool = False
     stream_image_progress_interval: int = 5
     clip_skip: bool = False
+    codeformer_upscale_faces: bool = False
 
 
 class MergeRequest(BaseModel):

ui/index.html

@@ -263,7 +263,12 @@
                     <div><ul>
                     <li><b class="settings-subheader">Render Settings</b></li>
                     <li class="pl-5"><input id="stream_image_progress" name="stream_image_progress" type="checkbox"> <label for="stream_image_progress">Show a live preview <small>(uses more VRAM, slower images)</small></label></li>
-                    <li class="pl-5"><input id="use_face_correction" name="use_face_correction" type="checkbox"> <label for="use_face_correction">Fix incorrect faces and eyes</label> <div style="display:inline-block;"><input id="gfpgan_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" /></div></li>
+                    <li class="pl-5">
+                        <input id="use_face_correction" name="use_face_correction" type="checkbox"> <label for="use_face_correction">Fix incorrect faces and eyes</label> <div style="display:inline-block;"><input id="gfpgan_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" /></div>
+                        <div id="codeformer_settings" class="displayNone sub-settings">
+                            <input id="codeformer_upscale_faces" name="codeformer_upscale_faces" type="checkbox"><label for="codeformer_upscale_faces">Upscale Faces <small>(improves the resolution of faces)</small></label>
+                        </div>
+                    </li>
                     <li class="pl-5">
                         <input id="use_upscale" name="use_upscale" type="checkbox"> <label for="use_upscale">Scale up by</label> 
                         <select id="upscale_amount" name="upscale_amount">
@@ -276,7 +281,7 @@
                             <option value="RealESRGAN_x4plus_anime_6B">RealESRGAN_x4plus_anime_6B</option>
                             <option value="latent_upscaler">Latent Upscaler 2x</option>
                         </select>
-                        <div id="latent_upscaler_settings" class="displayNone">
+                        <div id="latent_upscaler_settings" class="displayNone sub-settings">
                             <label for="latent_upscaler_steps_slider">Upscaling Steps:</label></td><td> <input id="latent_upscaler_steps_slider" name="latent_upscaler_steps_slider" class="editor-slider" value="10" type="range" min="1" max="50"> <input id="latent_upscaler_steps" name="latent_upscaler_steps" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)">
                         </div>
                     </li>

ui/media/css/main.css

@@ -1303,7 +1303,7 @@ body.wait-pause {
     display:none !important;
 }
 
-#latent_upscaler_settings {
+.sub-settings {
     padding-top: 3pt;
     padding-bottom: 3pt;
     padding-left: 5pt;
@@ -1322,7 +1322,7 @@ body.wait-pause {
     box-shadow: 0 0 10px rgba(0, 0, 0, 0.5);
     z-index: 9999;
     animation: slideInRight 0.5s ease forwards;
-    transition: bottom 0.5s ease; // Add a transition to smoothly reposition the toasts
+    transition: bottom 0.5s ease; /* Add a transition to smoothly reposition the toasts */
 }
 
 .toast-notification-error {

ui/media/js/main.js

@@ -87,7 +87,7 @@ let promptStrengthField = document.querySelector("#prompt_strength")
 let samplerField = document.querySelector("#sampler_name")
 let samplerSelectionContainer = document.querySelector("#samplerSelection")
 let useFaceCorrectionField = document.querySelector("#use_face_correction")
-let gfpganModelField = new ModelDropdown(document.querySelector("#gfpgan_model"), "gfpgan")
+let gfpganModelField = new ModelDropdown(document.querySelector("#gfpgan_model"), ["codeformer", "gfpgan"])
 let useUpscalingField = document.querySelector("#use_upscale")
 let upscaleModelField = document.querySelector("#upscale_model")
 let upscaleAmountField = document.querySelector("#upscale_amount")
@@ -270,7 +270,9 @@ function shiftOrConfirm(e, prompt, fn) {
         confirm(
             '<small>Tip: To skip this dialog, use shift-click or disable the "Confirm dangerous actions" setting in the Settings tab.</small>',
             prompt,
-            () => { fn(e) }
+            () => {
+                fn(e)
+            }
         )
     }
 }
@@ -1261,6 +1263,10 @@ function getCurrentUserRequest() {
     }
     if (useFaceCorrectionField.checked) {
         newTask.reqBody.use_face_correction = gfpganModelField.value
+
+        if (gfpganModelField.value.includes("codeformer")) {
+            newTask.reqBody.codeformer_upscale_faces = document.querySelector("#codeformer_upscale_faces").checked
+        }
     }
     if (useUpscalingField.checked) {
         newTask.reqBody.use_upscale = upscaleModelField.value
@@ -1574,18 +1580,33 @@ metadataOutputFormatField.disabled = !saveToDiskField.checked
 gfpganModelField.disabled = !useFaceCorrectionField.checked
 useFaceCorrectionField.addEventListener("change", function(e) {
     gfpganModelField.disabled = !this.checked
+
+    onFixFaceModelChange()
 })
 
+function onFixFaceModelChange() {
+    let codeformerSettings = document.querySelector("#codeformer_settings")
+    if (gfpganModelField.value === "codeformer" && !gfpganModelField.disabled) {
+        codeformerSettings.classList.remove("displayNone")
+    } else {
+        codeformerSettings.classList.add("displayNone")
+    }
+}
+gfpganModelField.addEventListener("change", onFixFaceModelChange)
+onFixFaceModelChange()
+
 upscaleModelField.disabled = !useUpscalingField.checked
 upscaleAmountField.disabled = !useUpscalingField.checked
 useUpscalingField.addEventListener("change", function(e) {
     upscaleModelField.disabled = !this.checked
     upscaleAmountField.disabled = !this.checked
+
+    onUpscaleModelChange()
 })
 
 function onUpscaleModelChange() {
     let upscale4x = document.querySelector("#upscale_amount_4x")
-    if (upscaleModelField.value === "latent_upscaler") {
+    if (upscaleModelField.value === "latent_upscaler" && !upscaleModelField.disabled) {
         upscale4x.disabled = true
         upscaleAmountField.value = "2"
         latentUpscalerSettings.classList.remove("displayNone")

ui/media/js/searchable-models.js

@@ -90,7 +90,12 @@ class ModelDropdown {
 
         if (modelsOptions !== undefined) {
             // reuse models from cache (only useful for plugins, which are loaded after models)
-            this.inputModels = modelsOptions[this.modelKey]
+            this.inputModels = []
+            let modelKeys = Array.isArray(this.modelKey) ? this.modelKey : [this.modelKey]
+            for (let i = 0; i < modelKeys.length; i++) {
+                let key = modelKeys[i]
+                this.inputModels.push(...modelsOptions[key])
+            }
             this.populateModels()
         }
         document.addEventListener(
@@ -98,6 +103,12 @@ class ModelDropdown {
             this.bind(function(e) {
                 // reload the models
                 this.inputModels = modelsOptions[this.modelKey]
+                this.inputModels = []
+                let modelKeys = Array.isArray(this.modelKey) ? this.modelKey : [this.modelKey]
+                for (let i = 0; i < modelKeys.length; i++) {
+                    let key = modelKeys[i]
+                    this.inputModels.push(...modelsOptions[key])
+                }
                 this.populateModels()
             }, this)
         )