changelog

Support for CodeFormer face restoration

CHANGES.md

@@ -22,6 +22,10 @@
 Our focus continues to remain on an easy installation experience, and an easy user-interface. While still remaining pretty powerful, in terms of features and speed.
 
 ### Detailed changelog
+* 2.5.40 - 3 Jun 2023 - Added CodeFormer as another option for fixing faces and eyes. CodeFormer tends to perform better than GFPGAN for many images. Thanks @patriceac for the implementation, and for contacting the CodeFormer team (who were supportive of it being integrated into Easy Diffusion).
+* 2.5.39 - 25 May 2023 - (beta-only) Seamless Tiling - make seamlessly tiled images, e.g. rock and grass textures. Thanks @JeLuf.
+* 2.5.38 - 24 May 2023 - Better reporting of errors, and show an explanation if the user cannot disable the "Use CPU" setting.
+* 2.5.38 - 23 May 2023 - Add Latent Upscaler as another option for upscaling images. Thanks @JeLuf for the implementation of the Latent Upscaler model.
 * 2.5.37 - 19 May 2023 - (beta-only) Two more samplers: DDPM and DEIS. Also disables the samplers that aren't working yet in the Diffusers version. Thanks @ogmaresca.
 * 2.5.37 - 19 May 2023 - (beta-only) Support CLIP-Skip. You can set this option under the models dropdown. Thanks @JeLuf.
 * 2.5.37 - 19 May 2023 - (beta-only) More VRAM optimizations for all modes in diffusers. The VRAM usage for diffusers in "low" and "balanced" should now be equal or less than the non-diffusers version. Performs softmax in half precision, like sdkit does.

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": "1.0.97",
+    "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

@@ -10,6 +10,8 @@ import warnings
 from easydiffusion import task_manager
 from easydiffusion.utils import log
 from rich.logging import RichHandler
+from rich.console import Console
+from rich.panel import Panel
 from sdkit.utils import log as sdkit_log  # hack, so we can overwrite the log config
 
 # Remove all handlers associated with the root logger object.
@@ -88,8 +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()
@@ -213,11 +215,48 @@ def open_browser():
     ui = config.get("ui", {})
     net = config.get("net", {})
     port = net.get("listen_port", 9000)
+
     if ui.get("open_browser_on_start", True):
         import webbrowser
 
         webbrowser.open(f"http://localhost:{port}")
 
+    Console().print(
+        Panel(
+            "\n"
+            + "[white]Easy Diffusion is ready to serve requests.\n\n"
+            + "A new browser tab should have been opened by now.\n"
+            + 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():
     modifiers_json_path = os.path.join(SD_UI_DIR, "modifiers.json")

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
-        "custom-model",  # only one custom model file was supported initially, creatively named 'custom-model'
-        "sd-v1-4",  # Default fallback.
+    "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"},
     ],
-    "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
 
 
@@ -53,15 +68,21 @@ def load_default_models(context: Context):
                 scan_model=context.model_paths[model_type] != None
                 and not context.model_paths[model_type].endswith(".safetensors"),
             )
+            if model_type in context.model_load_errors:
+                del context.model_load_errors[model_type]
         except Exception as e:
             log.error(f"[red]Error while loading {model_type} model: {context.model_paths[model_type]}[/red]")
             log.exception(e)
             del context.model_paths[model_type]
 
+            context.model_load_errors[model_type] = str(e)  # storing the entire Exception can lead to memory leaks
+
 
 def unload_all(context: Context):
     for model_type in KNOWN_MODEL_TYPES:
         unload_model(context, model_type)
+        if model_type in context.model_load_errors:
+            del context.model_load_errors[model_type]
 
 
 def resolve_model_to_use(model_name: str = None, model_type: str = None):
@@ -69,7 +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"]:
@@ -77,42 +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):
-                return models_dir_path + model_extension
+            if os.path.exists(model_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:
-        for model_dir in model_dirs:
-            default_model_path = os.path.join(model_dir, default_model)
-            for model_extension in model_extensions:
-                if os.path.exists(default_model_path + model_extension):
-                    if model_name is not None:
-                        log.warn(
-                            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 + model_extension
+    if model_type == "stable-diffusion":
+        for default_model in default_models:
+            default_model_path = os.path.join(model_dir, default_model["file_name"])
+            if os.path.exists(default_model_path):
+                if model_name is not None:
+                    log.warn(
+                        f"Could not find the configured custom model {model_name}. Using the default one: {default_model_path}"
+                    )
+                return default_model_path
 
     return None
 
 
 def reload_models_if_necessary(context: Context, task_data: TaskData):
+    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,
-        "realesrgan": task_data.use_upscale,
+        "codeformer": task_data.use_face_correction if "codeformer" in face_fix_lower else None,
+        "gfpgan": task_data.use_face_correction if "gfpgan" in face_fix_lower 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,
     }
@@ -122,6 +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"]
 
@@ -129,7 +154,14 @@ def reload_models_if_necessary(context: Context, task_data: TaskData):
         context.model_paths[model_type] = model_path_in_req
 
         action_fn = unload_model if context.model_paths[model_type] is None else load_model
-        action_fn(context, model_type, scan_model=False)  # we've scanned them already
+        try:
+            action_fn(context, model_type, scan_model=False)  # we've scanned them already
+            if model_type in context.model_load_errors:
+                del context.model_load_errors[model_type]
+        except Exception as e:
+            log.exception(e)
+            if action_fn == load_model:
+                context.model_load_errors[model_type] = str(e)  # storing the entire Exception can lead to memory leaks
 
 
 def resolve_model_paths(task_data: TaskData):
@@ -141,11 +173,49 @@ 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 task_data.use_upscale:
+        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")
 
 
+def fail_if_models_did_not_load(context: Context):
+    for model_type in KNOWN_MODEL_TYPES:
+        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)
+
+
+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):
     config = app.getConfig()
     vram_usage_level = config.get("vram_usage_level", "balanced")
@@ -157,6 +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
 
@@ -214,17 +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"],
         },
     }
 
@@ -285,9 +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

@@ -33,6 +33,8 @@ def init(device):
     context.stop_processing = False
     context.temp_images = {}
     context.partial_x_samples = None
+    context.model_load_errors = {}
+    context.enable_codeformer = True
 
     from easydiffusion import app
 
@@ -95,7 +97,7 @@ def make_images_internal(
         task_data.stream_image_progress_interval,
     )
     gc(context)
-    filtered_images = filter_images(task_data, images, user_stopped)
+    filtered_images = filter_images(req, task_data, images, user_stopped)
 
     if task_data.save_to_disk_path is not None:
         save_images_to_disk(images, filtered_images, req, task_data)
@@ -151,22 +153,40 @@ def generate_images_internal(
     return images, user_stopped
 
 
-def filter_images(task_data: TaskData, images: list, user_stopped):
+def filter_images(req: GenerateImageRequest, task_data: TaskData, images: list, user_stopped):
     if user_stopped:
         return images
 
     filters_to_apply = []
+    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 and "realesrgan" in task_data.use_upscale.lower():
-        filters_to_apply.append("realesrgan")
+    if task_data.use_upscale:
+        if "realesrgan" in task_data.use_upscale.lower():
+            filters_to_apply.append("realesrgan")
+        elif task_data.use_upscale == "latent_upscaler":
+            filters_to_apply.append("latent_upscaler")
+
+            filter_params["latent_upscaler_options"] = {
+                "prompt": req.prompt,
+                "negative_prompt": req.negative_prompt,
+                "seed": req.seed,
+                "num_inference_steps": task_data.latent_upscaler_steps,
+                "guidance_scale": 0,
+            }
+
+        filter_params["scale"] = task_data.upscale_amount
 
     if len(filters_to_apply) == 0:
         return images
 
-    return apply_filters(context, filters_to_apply, images, scale=task_data.upscale_amount)
+    return apply_filters(context, filters_to_apply, images, **filter_params)
 
 
 def construct_response(images: list, seeds: list, task_data: TaskData, base_seed: int):

ui/easydiffusion/task_manager.py

@@ -336,6 +336,7 @@ def thread_render(device):
             current_state = ServerStates.LoadingModel
             model_manager.resolve_model_paths(task.task_data)
             model_manager.reload_models_if_necessary(renderer.context, task.task_data)
+            model_manager.fail_if_models_did_not_load(renderer.context)
 
             current_state = ServerStates.Rendering
             task.response = renderer.make_images(

ui/easydiffusion/types.py

@@ -23,6 +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"
 
 
 class TaskData(BaseModel):
@@ -32,8 +33,9 @@ class TaskData(BaseModel):
     vram_usage_level: str = "balanced"  # or "low" or "medium"
 
     use_face_correction: str = None  # or "GFPGANv1.3"
-    use_upscale: str = None  # or "RealESRGAN_x4plus" or "RealESRGAN_x4plus_anime_6B"
+    use_upscale: str = None  # or "RealESRGAN_x4plus" or "RealESRGAN_x4plus_anime_6B" or "latent_upscaler"
     upscale_amount: int = 4  # or 2
+    latent_upscaler_steps: int = 10
     use_stable_diffusion_model: str = "sd-v1-4"
     # use_stable_diffusion_config: str = "v1-inference"
     use_vae_model: str = None
@@ -49,6 +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/easydiffusion/utils/save_utils.py

@@ -30,9 +30,11 @@ TASK_TEXT_MAPPING = {
     "lora_alpha": "LoRA Strength",
     "use_hypernetwork_model": "Hypernetwork model",
     "hypernetwork_strength": "Hypernetwork Strength",
+    "tiling": "Seamless Tiling",
     "use_face_correction": "Use Face Correction",
     "use_upscale": "Use Upscaling",
     "upscale_amount": "Upscale By",
+    "latent_upscaler_steps": "Latent Upscaler Steps"
 }
 
 time_placeholders = {
@@ -169,21 +171,23 @@ def save_images_to_disk(images: list, filtered_images: list, req: GenerateImageR
             output_quality=task_data.output_quality,
             output_lossless=task_data.output_lossless,
         )
-        if task_data.metadata_output_format.lower() in ["json", "txt", "embed"]:
-            save_dicts(
-                metadata_entries,
-                save_dir_path,
-                file_name=make_filter_filename,
-                output_format=task_data.metadata_output_format,
-                file_format=task_data.output_format,
-            )
+        if task_data.metadata_output_format:
+            for metadata_output_format in task_data.metadata_output_format.split(","):
+                if metadata_output_format.lower() in ["json", "txt", "embed"]:
+                    save_dicts(
+                        metadata_entries,
+                        save_dir_path,
+                        file_name=make_filter_filename,
+                        output_format=task_data.metadata_output_format,
+                        file_format=task_data.output_format,
+                    )
 
 
 def get_metadata_entries_for_request(req: GenerateImageRequest, task_data: TaskData):
     metadata = get_printable_request(req, task_data)
 
     # if text, format it in the text format expected by the UI
-    is_txt_format = task_data.metadata_output_format.lower() == "txt"
+    is_txt_format = task_data.metadata_output_format and "txt" in task_data.metadata_output_format.lower().split(",")
     if is_txt_format:
         metadata = {TASK_TEXT_MAPPING[key]: val for key, val in metadata.items() if key in TASK_TEXT_MAPPING}
 
@@ -215,10 +219,12 @@ def get_printable_request(req: GenerateImageRequest, task_data: TaskData):
         del metadata["hypernetwork_strength"]
     if task_data.use_lora_model is None and "lora_alpha" in metadata:
         del metadata["lora_alpha"]
+    if task_data.use_upscale != "latent_upscaler" and "latent_upscaler_steps" in metadata:
+        del metadata["latent_upscaler_steps"]
 
     app_config = app.getConfig()
     if not app_config.get("test_diffusers", False):
-        for key in (x for x in ["use_lora_model", "lora_alpha", "clip_skip"] if x in metadata):
+        for key in (x for x in ["use_lora_model", "lora_alpha", "clip_skip", "tiling", "latent_upscaler_steps"] if x in metadata):
             del metadata[key]
 
     return metadata

ui/index.html

@@ -30,7 +30,7 @@
             <h1>
                 <img id="logo_img" src="/media/images/icon-512x512.png" >
                 Easy Diffusion
-                <small>v2.5.37 <span id="updateBranchLabel"></span></small>
+                <small>v2.5.40 <span id="updateBranchLabel"></span></small>
             </h1>
         </div>
         <div id="server-status">
@@ -167,7 +167,7 @@
                             <option value="unipc_snr" class="k_diffusion-only">UniPC SNR</option>
                             <option value="unipc_tu">UniPC TU</option>
                             <option value="unipc_snr_2" class="k_diffusion-only">UniPC SNR 2</option>
-                            <option value="unipc_tu_2">UniPC TU 2</option>
+                            <option value="unipc_tu_2" class="k_diffusion-only">UniPC TU 2</option>
                             <option value="unipc_tq" class="k_diffusion-only">UniPC TQ</option>
                         </select>
                         <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/How-to-Use#samplers" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about samplers</span></i></a>
@@ -236,6 +236,15 @@
                         <td><label for="hypernetwork_strength_slider">Hypernetwork Strength:</label></td>
                         <td> <input id="hypernetwork_strength_slider" name="hypernetwork_strength_slider" class="editor-slider" value="100" type="range" min="0" max="100"> <input id="hypernetwork_strength" name="hypernetwork_strength" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)"><br/></td>
                     </tr>
+                    <tr id="tiling_container" class="pl-5"><td><label for="tiling">Seamless Tiling:</label></td><td>
+                        <select id="tiling" name="tiling">
+                            <option value="none" selected>None</option>
+                            <option value="x">Horizontal</option>
+                            <option value="y">Vertical</option>
+                            <option value="xy">Both</option>
+                        </select>
+                        <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Seamless-Tiling" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about Seamless Tiling</span></i></a>
+                    </td></tr>
                     <tr class="pl-5"><td><label for="output_format">Output Format:</label></td><td>
                         <select id="output_format" name="output_format">
                             <option value="jpeg" selected>jpeg</option>
@@ -254,18 +263,27 @@
                     <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">
-                            <option value="2">2x</option>
-                            <option value="4" selected>4x</option>
+                            <option id="upscale_amount_2x" value="2">2x</option>
+                            <option id="upscale_amount_4x" value="4" selected>4x</option>
                         </select>
                         with 
                         <select id="upscale_model" name="upscale_model">
                             <option value="RealESRGAN_x4plus" selected>RealESRGAN_x4plus</option>
                             <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 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>
                     <li class="pl-5"><input id="show_only_filtered_image" name="show_only_filtered_image" type="checkbox" checked> <label for="show_only_filtered_image">Show only the corrected/upscaled image</label></li>
                     </ul></div>

ui/media/css/main.css

@@ -1303,6 +1303,12 @@ body.wait-pause {
     display:none !important;
 }
 
+.sub-settings {
+    padding-top: 3pt;
+    padding-bottom: 3pt;
+    padding-left: 5pt;
+}
+
 /* TOAST NOTIFICATIONS */
 .toast-notification {
     position: fixed;
@@ -1316,7 +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/auto-save.js

@@ -25,6 +25,7 @@ const SETTINGS_IDS_LIST = [
     "prompt_strength",
     "hypernetwork_strength",
     "lora_alpha",
+    "tiling",
     "output_format",
     "output_quality",
     "output_lossless",
@@ -34,6 +35,7 @@ const SETTINGS_IDS_LIST = [
     "gfpgan_model",
     "use_upscale",
     "upscale_amount",
+    "latent_upscaler_steps",
     "block_nsfw",
     "show_only_filtered_image",
     "upscale_model",

ui/media/js/dnd.js

@@ -79,6 +79,7 @@ const TASK_MAPPING = {
             if (!widthField.value) {
                 widthField.value = oldVal
             }
+            widthField.dispatchEvent(new Event("change"))
         },
         readUI: () => parseInt(widthField.value),
         parse: (val) => parseInt(val),
@@ -91,6 +92,7 @@ const TASK_MAPPING = {
             if (!heightField.value) {
                 heightField.value = oldVal
             }
+            heightField.dispatchEvent(new Event("change"))
         },
         readUI: () => parseInt(heightField.value),
         parse: (val) => parseInt(val),
@@ -172,16 +174,22 @@ const TASK_MAPPING = {
         name: "Use Face Correction",
         setUI: (use_face_correction) => {
             const oldVal = gfpganModelField.value
-            gfpganModelField.value = getModelPath(use_face_correction, [".pth"])
-            if (gfpganModelField.value) {
-                // Is a valid value for the field.
-                useFaceCorrectionField.checked = true
-                gfpganModelField.disabled = false
-            } else {
-                // Not a valid value, restore the old value and disable the filter.
+            console.log("use face correction", use_face_correction)
+            if (use_face_correction == null || use_face_correction == "None") {
                 gfpganModelField.disabled = true
-                gfpganModelField.value = oldVal
                 useFaceCorrectionField.checked = false
+            } else {
+                gfpganModelField.value = getModelPath(use_face_correction, [".pth"])
+                if (gfpganModelField.value) {
+                    // Is a valid value for the field.
+                    useFaceCorrectionField.checked = true
+                    gfpganModelField.disabled = false
+                } else {
+                    // Not a valid value, restore the old value and disable the filter.
+                    gfpganModelField.disabled = true
+                    gfpganModelField.value = oldVal
+                    useFaceCorrectionField.checked = false
+                }
             }
 
             //useFaceCorrectionField.checked = parseBoolean(use_face_correction)
@@ -218,6 +226,14 @@ const TASK_MAPPING = {
         readUI: () => upscaleAmountField.value,
         parse: (val) => val,
     },
+    latent_upscaler_steps: {
+        name: "Latent Upscaler Steps",
+        setUI: (latent_upscaler_steps) => {
+            latentUpscalerStepsField.value = latent_upscaler_steps
+        },
+        readUI: () => latentUpscalerStepsField.value,
+        parse: (val) => val,
+    },
     sampler_name: {
         name: "Sampler",
         setUI: (sampler_name) => {
@@ -249,6 +265,14 @@ const TASK_MAPPING = {
         readUI: () => clip_skip.checked,
         parse: (val) => Boolean(val),
     },
+    tiling: {
+        name: "Tiling",
+        setUI: (val) => {
+            tilingField.value = val
+        },
+        readUI: () => tilingField.value,
+        parse: (val) => val,
+    },
     use_vae_model: {
         name: "VAE model",
         setUI: (use_vae_model) => {
@@ -411,6 +435,7 @@ function restoreTaskToUI(task, fieldsToSkip) {
     if (!("original_prompt" in task.reqBody)) {
         promptField.value = task.reqBody.prompt
     }
+    promptField.dispatchEvent(new Event("input"))
 
     // properly reset checkboxes
     if (!("use_face_correction" in task.reqBody)) {

ui/media/js/engine.js

@@ -789,9 +789,10 @@
         use_hypernetwork_model: "string",
         hypernetwork_strength: "number",
         output_lossless: "boolean",
+        tiling: "string",
     }
 
-    // Higer values will result in...
+    // Higher values will result in...
     // pytorch_lightning/utilities/seed.py:60: UserWarning: X is not in bounds, numpy accepts from 0 to 4294967295
     const MAX_SEED_VALUE = 4294967295
 

ui/media/js/image-editor.js

@@ -834,6 +834,7 @@ function pixelCompare(int1, int2) {
 }
 
 // adapted from https://ben.akrin.com/canvas_fill/fill_04.html
+// May 2023 - look at using a library instead of custom code: https://github.com/shaneosullivan/example-canvas-fill
 function flood_fill(editor, the_canvas_context, x, y, color) {
     pixel_stack = [{ x: x, y: y }]
     pixels = the_canvas_context.getImageData(0, 0, editor.width, editor.height)

ui/media/js/main.js

@@ -18,6 +18,11 @@ const taskConfigSetup = {
             visible: ({ reqBody }) => reqBody?.clip_skip,
             value: ({ reqBody }) => "yes",
         },
+        tiling: {
+            label: "Tiling",
+            visible: ({ reqBody }) => reqBody?.tiling != "none",
+            value: ({ reqBody }) => reqBody?.tiling,
+        },
         use_vae_model: {
             label: "VAE",
             visible: ({ reqBody }) => reqBody?.use_vae_model !== undefined && reqBody?.use_vae_model.trim() !== "",
@@ -82,12 +87,16 @@ 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")
+let latentUpscalerSettings = document.querySelector("#latent_upscaler_settings")
+let latentUpscalerStepsSlider = document.querySelector("#latent_upscaler_steps_slider")
+let latentUpscalerStepsField = document.querySelector("#latent_upscaler_steps")
 let stableDiffusionModelField = new ModelDropdown(document.querySelector("#stable_diffusion_model"), "stable-diffusion")
 let clipSkipField = document.querySelector("#clip_skip")
+let tilingField = document.querySelector("#tiling")
 let vaeModelField = new ModelDropdown(document.querySelector("#vae_model"), "vae", "None")
 let hypernetworkModelField = new ModelDropdown(document.querySelector("#hypernetwork_model"), "hypernetwork", "None")
 let hypernetworkStrengthSlider = document.querySelector("#hypernetwork_strength_slider")
@@ -239,7 +248,7 @@ function setServerStatus(event) {
             break
     }
     if (SD.serverState.devices) {
-        document.dispatchEvent(new CustomEvent("system_info_update", { detail: SD.serverState.devices}))
+        document.dispatchEvent(new CustomEvent("system_info_update", { detail: SD.serverState.devices }))
     }
 }
 
@@ -258,20 +267,13 @@ function shiftOrConfirm(e, prompt, fn) {
     if (e.shiftKey || !confirmDangerousActionsField.checked) {
         fn(e)
     } else {
-        $.confirm({
-            theme: "modern",
-            title: prompt,
-            useBootstrap: false,
-            animateFromElement: false,
-            content:
-                '<small>Tip: To skip this dialog, use shift-click or disable the "Confirm dangerous actions" setting in the Settings tab.</small>',
-            buttons: {
-                yes: () => {
-                    fn(e)
-                },
-                cancel: () => {},
-            },
-        })
+        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)
+            }
+        )
     }
 }
 
@@ -293,6 +295,7 @@ function logError(msg, res, outputMsg) {
     logMsg(msg, "error", outputMsg)
 
     console.log("request error", res)
+    console.trace()
     setStatus("request", "error", "error")
 }
 
@@ -784,11 +787,6 @@ function getTaskUpdater(task, reqBody, outputContainer) {
                         }
                         msg += "</pre>"
                         logError(msg, event, outputMsg)
-                    } else {
-                        let msg = `Unexpected Read Error:<br/><pre>Error:${
-                            this.exception
-                        }<br/>EventInfo: ${JSON.stringify(event, undefined, 4)}</pre>`
-                        logError(msg, event, outputMsg)
                     }
                     break
             }
@@ -885,15 +883,15 @@ function onTaskCompleted(task, reqBody, instance, outputContainer, stepUpdate) {
                             1. If you have set an initial image, please try reducing its dimension to ${MAX_INIT_IMAGE_DIMENSION}x${MAX_INIT_IMAGE_DIMENSION} or smaller.<br/>
                             2. Try picking a lower level in the '<em>GPU Memory Usage</em>' setting (in the '<em>Settings</em>' tab).<br/>
                             3. Try generating a smaller image.<br/>`
-                } else if (msg.toLowerCase().includes("DefaultCPUAllocator: not enough memory")) {
+                } else if (msg.includes("DefaultCPUAllocator: not enough memory")) {
                     msg += `<br/><br/>
                             Reason: Your computer is running out of system RAM!
-                            <br/>
+                            <br/><br/>
                             <b>Suggestions</b>:
                             <br/>
                             1. Try closing unnecessary programs and browser tabs.<br/>
                             2. If that doesn't help, please increase your computer's virtual memory by following these steps for
-                             <a href="https://www.ibm.com/docs/en/opw/8.2.0?topic=tuning-optional-increasing-paging-file-size-windows-computers" target="_blank">Windows</a>, or
+                             <a href="https://www.ibm.com/docs/en/opw/8.2.0?topic=tuning-optional-increasing-paging-file-size-windows-computers" target="_blank">Windows</a> or
                              <a href="https://linuxhint.com/increase-swap-space-linux/" target="_blank">Linux</a>.<br/>
                             3. Try restarting your computer.<br/>`
                 }
@@ -1231,6 +1229,7 @@ function getCurrentUserRequest() {
             //render_device: undefined, // Set device affinity. Prefer this device, but wont activate.
             use_stable_diffusion_model: stableDiffusionModelField.value,
             clip_skip: clipSkipField.checked,
+            tiling: tilingField.value,
             use_vae_model: vaeModelField.value,
             stream_progress_updates: true,
             stream_image_progress: numOutputsTotal > 50 ? false : streamImageProgressField.checked,
@@ -1264,10 +1263,18 @@ 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
         newTask.reqBody.upscale_amount = upscaleAmountField.value
+        if (upscaleModelField.value === "latent_upscaler") {
+            newTask.reqBody.upscale_amount = "2"
+            newTask.reqBody.latent_upscaler_steps = latentUpscalerStepsField.value
+        }
     }
     if (hypernetworkModelField.value) {
         newTask.reqBody.use_hypernetwork_model = hypernetworkModelField.value
@@ -1573,15 +1580,44 @@ 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" && !upscaleModelField.disabled) {
+        upscale4x.disabled = true
+        upscaleAmountField.value = "2"
+        latentUpscalerSettings.classList.remove("displayNone")
+    } else {
+        upscale4x.disabled = false
+        latentUpscalerSettings.classList.add("displayNone")
+    }
+}
+upscaleModelField.addEventListener("change", onUpscaleModelChange)
+onUpscaleModelChange()
+
 makeImageBtn.addEventListener("click", makeImage)
 
 document.onkeydown = function(e) {
@@ -1591,6 +1627,27 @@ document.onkeydown = function(e) {
     }
 }
 
+/********************* Latent Upscaler Steps **************************/
+function updateLatentUpscalerSteps() {
+    latentUpscalerStepsField.value = latentUpscalerStepsSlider.value
+    latentUpscalerStepsField.dispatchEvent(new Event("change"))
+}
+
+function updateLatentUpscalerStepsSlider() {
+    if (latentUpscalerStepsField.value < 1) {
+        latentUpscalerStepsField.value = 1
+    } else if (latentUpscalerStepsField.value > 50) {
+        latentUpscalerStepsField.value = 50
+    }
+
+    latentUpscalerStepsSlider.value = latentUpscalerStepsField.value
+    latentUpscalerStepsSlider.dispatchEvent(new Event("change"))
+}
+
+latentUpscalerStepsSlider.addEventListener("input", updateLatentUpscalerSteps)
+latentUpscalerStepsField.addEventListener("input", updateLatentUpscalerStepsSlider)
+updateLatentUpscalerSteps()
+
 /********************* Guidance **************************/
 function updateGuidanceScale() {
     guidanceScaleField.value = guidanceScaleSlider.value / 10

ui/media/js/parameters.js

@@ -191,7 +191,8 @@ var PARAMETERS = [
         id: "listen_port",
         type: ParameterType.custom,
         label: "Network port",
-        note: "Port that this server listens to. The '9000' part in 'http://localhost:9000'. Please restart the program after changing this.",
+        note:
+            "Port that this server listens to. The '9000' part in 'http://localhost:9000'. Please restart the program after changing this.",
         icon: "fa-anchor",
         render: (parameter) => {
             return `<input id="${parameter.id}" name="${parameter.id}" size="6" value="9000" onkeypress="preventNonNumericalInput(event)">`
@@ -395,15 +396,17 @@ async function getAppConfig() {
         if (!testDiffusersEnabled) {
             document.querySelector("#lora_model_container").style.display = "none"
             document.querySelector("#lora_alpha_container").style.display = "none"
+            document.querySelector("#tiling_container").style.display = "none"
 
-            document.querySelectorAll("#sampler_name option.diffusers-only").forEach(option => {
+            document.querySelectorAll("#sampler_name option.diffusers-only").forEach((option) => {
                 option.style.display = "none"
             })
         } else {
             document.querySelector("#lora_model_container").style.display = ""
             document.querySelector("#lora_alpha_container").style.display = loraModelField.value ? "" : "none"
+            document.querySelector("#tiling_container").style.display = ""
 
-            document.querySelectorAll("#sampler_name option.k_diffusion-only").forEach(option => {
+            document.querySelectorAll("#sampler_name option.k_diffusion-only").forEach((option) => {
                 option.disabled = true
             })
             document.querySelector("#clip_skip_config").classList.remove("displayNone")
@@ -568,6 +571,16 @@ async function getSystemInfo() {
         if (allDeviceIds.length === 0) {
             useCPUField.checked = true
             useCPUField.disabled = true // no compatible GPUs, so make the CPU mandatory
+
+            getParameterSettingsEntry("use_cpu").addEventListener("click", function() {
+                alert(
+                    "Sorry, we could not find a compatible graphics card! Easy Diffusion supports graphics cards with minimum 2 GB of RAM. " +
+                        "Only NVIDIA cards are supported on Windows. NVIDIA and AMD cards are supported on Linux.<br/><br/>" +
+                        "If you have a compatible graphics card, please try updating to the latest drivers.<br/><br/>" +
+                        "Only the CPU can be used for generating images, without a compatible graphics card.",
+                    "No compatible graphics card found!"
+                )
+            })
         }
 
         autoPickGPUsField.checked = devices["config"] === "auto"
@@ -586,7 +599,7 @@ async function getSystemInfo() {
             $("#use_gpus").val(activeDeviceIds)
         }
 
-        document.dispatchEvent(new CustomEvent("system_info_update", { detail: devices}))
+        document.dispatchEvent(new CustomEvent("system_info_update", { detail: devices }))
         setHostInfo(res["hosts"])
         let force = false
         if (res["enforce_output_dir"] !== undefined) {

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)
         )

ui/media/js/utils.js

@@ -843,57 +843,83 @@ function createTab(request) {
 
 /* TOAST NOTIFICATIONS */
 function showToast(message, duration = 5000, error = false) {
-    const toast = document.createElement("div");
-    toast.classList.add("toast-notification");
+    const toast = document.createElement("div")
+    toast.classList.add("toast-notification")
     if (error === true) {
-        toast.classList.add("toast-notification-error");
+        toast.classList.add("toast-notification-error")
     }
-    toast.innerHTML = message;
-    document.body.appendChild(toast);
+    toast.innerHTML = message
+    document.body.appendChild(toast)
 
     // Set the position of the toast on the screen
-    const toastCount = document.querySelectorAll(".toast-notification").length;
-    const toastHeight = toast.offsetHeight;
+    const toastCount = document.querySelectorAll(".toast-notification").length
+    const toastHeight = toast.offsetHeight
     const previousToastsHeight = Array.from(document.querySelectorAll(".toast-notification"))
         .slice(0, -1) // exclude current toast
-        .reduce((totalHeight, toast) => totalHeight + toast.offsetHeight + 10, 0); // add 10 pixels for spacing
-    toast.style.bottom = `${10 + previousToastsHeight}px`;
-    toast.style.right = "10px";
+        .reduce((totalHeight, toast) => totalHeight + toast.offsetHeight + 10, 0) // add 10 pixels for spacing
+    toast.style.bottom = `${10 + previousToastsHeight}px`
+    toast.style.right = "10px"
 
     // Delay the removal of the toast until animation has completed
     const removeToast = () => {
-        toast.classList.add("hide");
+        toast.classList.add("hide")
         const removeTimeoutId = setTimeout(() => {
-            toast.remove();
+            toast.remove()
             // Adjust the position of remaining toasts
-            const remainingToasts = document.querySelectorAll(".toast-notification");
-            const removedToastBottom = toast.getBoundingClientRect().bottom;
-        
+            const remainingToasts = document.querySelectorAll(".toast-notification")
+            const removedToastBottom = toast.getBoundingClientRect().bottom
+
             remainingToasts.forEach((toast) => {
                 if (toast.getBoundingClientRect().bottom < removedToastBottom) {
-                    toast.classList.add("slide-down");
+                    toast.classList.add("slide-down")
                 }
-            });
-        
+            })
+
             // Wait for the slide-down animation to complete
             setTimeout(() => {
                 // Remove the slide-down class after the animation has completed
-                const slidingToasts = document.querySelectorAll(".slide-down");
+                const slidingToasts = document.querySelectorAll(".slide-down")
                 slidingToasts.forEach((toast) => {
-                    toast.classList.remove("slide-down");
-                });
-        
+                    toast.classList.remove("slide-down")
+                })
+
                 // Adjust the position of remaining toasts again, in case there are multiple toasts being removed at once
-                const remainingToastsDown = document.querySelectorAll(".toast-notification");
-                let heightSoFar = 0;
+                const remainingToastsDown = document.querySelectorAll(".toast-notification")
+                let heightSoFar = 0
                 remainingToastsDown.forEach((toast) => {
-                    toast.style.bottom = `${10 + heightSoFar}px`;
-                    heightSoFar += toast.offsetHeight + 10; // add 10 pixels for spacing
-                });
-            }, 0); // The duration of the slide-down animation (in milliseconds)
-        }, 500);
-    };
+                    toast.style.bottom = `${10 + heightSoFar}px`
+                    heightSoFar += toast.offsetHeight + 10 // add 10 pixels for spacing
+                })
+            }, 0) // The duration of the slide-down animation (in milliseconds)
+        }, 500)
+    }
 
     // Remove the toast after specified duration
-    setTimeout(removeToast, duration);
+    setTimeout(removeToast, duration)
+}
+
+function alert(msg, title) {
+    title = title || ""
+    $.alert({
+        theme: "modern",
+        title: title,
+        useBootstrap: false,
+        animateFromElement: false,
+        content: msg,
+    })
+}
+
+function confirm(msg, title, fn) {
+    title = title || ""
+    $.confirm({
+        theme: "modern",
+        title: title,
+        useBootstrap: false,
+        animateFromElement: false,
+        content: msg,
+        buttons: {
+            yes: fn,
+            cancel: () => {},
+        },
+    })
 }

ui/plugins/ui/merge.plugin.js

@@ -403,16 +403,19 @@
                 // Batch main loop
                 for (let i = 0; i < iterations; i++) {
                     let alpha = (start + i * step) / 100
-                    switch (document.querySelector("#merge-interpolation").value) {
-                        case "SmoothStep":
-                            alpha = smoothstep(alpha)
-                            break
-                        case "SmootherStep":
-                            alpha = smootherstep(alpha)
-                            break
-                        case "SmoothestStep":
-                            alpha = smootheststep(alpha)
-                            break
+
+                    if (isTabActive(tabSettingsBatch)) {
+                        switch (document.querySelector("#merge-interpolation").value) {
+                            case "SmoothStep":
+                                alpha = smoothstep(alpha)
+                                break
+                            case "SmootherStep":
+                                alpha = smootherstep(alpha)
+                                break
+                            case "SmoothestStep":
+                                alpha = smootheststep(alpha)
+                                break
+                        }
                     }
                     addLogMessage(`merging batch job ${i + 1}/${iterations}, alpha = ${alpha.toFixed(5)}...`)
 
@@ -420,7 +423,8 @@
                     request["out_path"] += "-" + alpha.toFixed(5) + "." + document.querySelector("#merge-format").value
                     addLogMessage(`&nbsp;&nbsp;filename: ${request["out_path"]}`)
 
-                    request["ratio"] = alpha
+                    // sdkit documentation: "ratio - the ratio of the second model. 1 means only the second model will be used."
+                    request["ratio"] = 1-alpha 
                     let res = await fetch("/model/merge", {
                         method: "POST",
                         headers: { "Content-Type": "application/json" },