First working version of dynamic backends, with Forge and ed_diffusers (v3) and ed_classic (v2). Does not auto-install Forge yet

ui/easydiffusion/app.py

@@ -11,7 +11,7 @@ from ruamel.yaml import YAML
 import urllib
 import warnings
 
-from easydiffusion import task_manager
+from easydiffusion import task_manager, backend_manager
 from easydiffusion.utils import log
 from rich.logging import RichHandler
 from rich.console import Console
@@ -60,7 +60,7 @@ APP_CONFIG_DEFAULTS = {
     "ui": {
         "open_browser_on_start": True,
     },
-    "use_v3_engine": True,
+    "backend": "ed_diffusers",
 }
 
 IMAGE_EXTENSIONS = [
@@ -108,6 +108,8 @@ def init():
     if config_models_dir is not None and config_models_dir != "":
         MODELS_DIR = config_models_dir
 
+    backend_manager.start_backend()
+
 
 def init_render_threads():
     load_server_plugins()
@@ -124,9 +126,9 @@ def getConfig(default_val=APP_CONFIG_DEFAULTS):
         shutil.move(config_legacy_yaml, config_yaml_path)
 
     def set_config_on_startup(config: dict):
-        if getConfig.__use_v3_engine_on_startup is None:
-            getConfig.__use_v3_engine_on_startup = config.get("use_v3_engine", True)
-        config["config_on_startup"] = {"use_v3_engine": getConfig.__use_v3_engine_on_startup}
+        if getConfig.__use_backend_on_startup is None:
+            getConfig.__use_backend_on_startup = config.get("backend", "ed_diffusers")
+        config["config_on_startup"] = {"backend": getConfig.__use_backend_on_startup}
 
     if os.path.isfile(config_yaml_path):
         try:
@@ -144,6 +146,15 @@ def getConfig(default_val=APP_CONFIG_DEFAULTS):
                 else:
                     config["net"]["listen_to_network"] = True
 
+            if "backend" not in config:
+                if "use_v3_engine" in config:
+                    config["backend"] = "ed_diffusers" if config["use_v3_engine"] else "ed_classic"
+                else:
+                    config["backend"] = "ed_diffusers"
+                    # this default will need to be smarter when WebUI becomes the main backend, but needs to maintain backwards
+                    # compatibility with existing ED 3.0 installations that haven't opted into the WebUI backend, and haven't
+                    # set a "use_v3_engine" flag in their config
+
             set_config_on_startup(config)
 
             return config
@@ -174,7 +185,7 @@ def getConfig(default_val=APP_CONFIG_DEFAULTS):
             return default_val
 
 
-getConfig.__use_v3_engine_on_startup = None
+getConfig.__use_backend_on_startup = None
 
 
 def setConfig(config):

ui/easydiffusion/backend_manager.py

@@ -0,0 +1,105 @@
+import os
+import ast
+import sys
+import importlib.util
+import traceback
+
+from easydiffusion.utils import log
+
+backend = None
+curr_backend_name = None
+
+
+def is_valid_backend(file_path):
+    with open(file_path, "r", encoding="utf-8") as file:
+        node = ast.parse(file.read())
+
+    # Check for presence of a dictionary named 'ed_info'
+    for item in node.body:
+        if isinstance(item, ast.Assign):
+            for target in item.targets:
+                if isinstance(target, ast.Name) and target.id == "ed_info":
+                    return True
+    return False
+
+
+def find_valid_backends(root_dir) -> dict:
+    backends_path = os.path.join(root_dir, "backends")
+    valid_backends = {}
+
+    if not os.path.exists(backends_path):
+        return valid_backends
+
+    for item in os.listdir(backends_path):
+        item_path = os.path.join(backends_path, item)
+
+        if os.path.isdir(item_path):
+            init_file = os.path.join(item_path, "__init__.py")
+            if os.path.exists(init_file) and is_valid_backend(init_file):
+                valid_backends[item] = item_path
+        elif item.endswith(".py"):
+            if is_valid_backend(item_path):
+                backend_name = os.path.splitext(item)[0]  # strip the .py extension
+                valid_backends[backend_name] = item_path
+
+    return valid_backends
+
+
+def load_backend_module(backend_name, backend_dict):
+    if backend_name not in backend_dict:
+        raise ValueError(f"Backend '{backend_name}' not found.")
+
+    module_path = backend_dict[backend_name]
+
+    mod_dir = os.path.dirname(module_path)
+
+    sys.path.insert(0, mod_dir)
+
+    # If it's a package (directory), add its parent directory to sys.path
+    if os.path.isdir(module_path):
+        module_path = os.path.join(module_path, "__init__.py")
+
+    spec = importlib.util.spec_from_file_location(backend_name, module_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+
+    if mod_dir in sys.path:
+        sys.path.remove(mod_dir)
+
+    log.info(f"Loaded backend: {module}")
+
+    return module
+
+
+def start_backend():
+    global backend, curr_backend_name
+
+    from easydiffusion.app import getConfig, ROOT_DIR
+
+    curr_dir = os.path.dirname(__file__)
+
+    backends = find_valid_backends(curr_dir)
+    plugin_backends = find_valid_backends(ROOT_DIR)
+    backends.update(plugin_backends)
+
+    config = getConfig()
+    backend_name = config["backend"]
+
+    if backend_name not in backends:
+        raise RuntimeError(
+            f"Couldn't find the backend configured in config.yaml: {backend_name}. Please check the name!"
+        )
+
+    if backend is not None and backend_name != curr_backend_name:
+        try:
+            backend.stop_backend()
+        except:
+            log.exception(traceback.format_exc())
+
+    log.info(f"Loading backend: {backend_name}")
+    backend = load_backend_module(backend_name, backends)
+
+    try:
+        backend.start_backend()
+    except:
+        log.exception(traceback.format_exc())

ui/easydiffusion/backends/ed_classic.py

@@ -0,0 +1,27 @@
+from sdkit_common import (
+    start_backend,
+    stop_backend,
+    install_backend,
+    uninstall_backend,
+    create_sdkit_context,
+    ping,
+    load_model,
+    unload_model,
+    set_options,
+    generate_images,
+    filter_images,
+    get_url,
+    stop_rendering,
+    refresh_models,
+    list_controlnet_filters,
+)
+
+ed_info = {
+    "name": "Classic backend for Easy Diffusion v2",
+    "version": (1, 0, 0),
+    "type": "backend",
+}
+
+
+def create_context():
+    return create_sdkit_context(use_diffusers=False)

ui/easydiffusion/backends/ed_diffusers.py

@@ -0,0 +1,27 @@
+from sdkit_common import (
+    start_backend,
+    stop_backend,
+    install_backend,
+    uninstall_backend,
+    create_sdkit_context,
+    ping,
+    load_model,
+    unload_model,
+    set_options,
+    generate_images,
+    filter_images,
+    get_url,
+    stop_rendering,
+    refresh_models,
+    list_controlnet_filters,
+)
+
+ed_info = {
+    "name": "Diffusers Backend for Easy Diffusion v3",
+    "version": (1, 0, 0),
+    "type": "backend",
+}
+
+
+def create_context():
+    return create_sdkit_context(use_diffusers=True)

ui/easydiffusion/backends/sdkit_common.py

@@ -0,0 +1,237 @@
+from sdkit import Context
+
+from easydiffusion.types import UserInitiatedStop
+
+from sdkit.utils import (
+    diffusers_latent_samples_to_images,
+    gc,
+    img_to_base64_str,
+    latent_samples_to_images,
+)
+
+opts = {}
+
+
+def install_backend():
+    pass
+
+
+def start_backend():
+    print("Started sdkit backend")
+
+
+def stop_backend():
+    pass
+
+
+def uninstall_backend():
+    pass
+
+
+def create_sdkit_context(use_diffusers):
+    c = Context()
+    c.test_diffusers = use_diffusers
+    return c
+
+
+def ping(timeout=1):
+    return True
+
+
+def load_model(context, model_type, **kwargs):
+    from sdkit.models import load_model
+
+    load_model(context, model_type, **kwargs)
+
+
+def unload_model(context, model_type, **kwargs):
+    from sdkit.models import unload_model
+
+    unload_model(context, model_type, **kwargs)
+
+
+def set_options(context, **kwargs):
+    if "vae_tiling" in kwargs and context.test_diffusers:
+        pipe = context.models["stable-diffusion"]["default"]
+        vae_tiling = kwargs["vae_tiling"]
+
+        if vae_tiling:
+            if hasattr(pipe, "enable_vae_tiling"):
+                pipe.enable_vae_tiling()
+        else:
+            if hasattr(pipe, "disable_vae_tiling"):
+                pipe.disable_vae_tiling()
+
+    for key in (
+        "output_format",
+        "output_quality",
+        "output_lossless",
+        "stream_image_progress",
+        "stream_image_progress_interval",
+    ):
+        if key in kwargs:
+            opts[key] = kwargs[key]
+
+
+def generate_images(
+    context: Context,
+    callback=None,
+    controlnet_filter=None,
+    output_type="pil",
+    **req,
+):
+    from sdkit.generate import generate_images
+
+    if req["init_image"] is not None and not context.test_diffusers:
+        req["sampler_name"] = "ddim"
+
+    gc(context)
+
+    context.stop_processing = False
+
+    if req["control_image"] and controlnet_filter:
+        controlnet_filter = convert_ED_controlnet_filter_name(controlnet_filter)
+        req["control_image"] = filter_images(context, req["control_image"], controlnet_filter)[0]
+
+    callback = make_step_callback(context, callback)
+
+    try:
+        images = generate_images(context, callback=callback, **req)
+    except UserInitiatedStop:
+        images = []
+        if context.partial_x_samples is not None:
+            if context.test_diffusers:
+                images = diffusers_latent_samples_to_images(context, context.partial_x_samples)
+            else:
+                images = latent_samples_to_images(context, context.partial_x_samples)
+    finally:
+        if hasattr(context, "partial_x_samples") and context.partial_x_samples is not None:
+            if not context.test_diffusers:
+                del context.partial_x_samples
+            context.partial_x_samples = None
+
+    gc(context)
+
+    if output_type == "base64":
+        output_format = opts.get("output_format", "jpeg")
+        output_quality = opts.get("output_quality", 75)
+        output_lossless = opts.get("output_lossless", False)
+        images = [img_to_base64_str(img, output_format, output_quality, output_lossless) for img in images]
+
+    return images
+
+
+def filter_images(context: Context, images, filters, filter_params={}, input_type="pil"):
+    gc(context)
+
+    if "nsfw_checker" in filters:
+        filters.remove("nsfw_checker")  # handled by ED directly
+
+    images = _filter_images(context, images, filters, filter_params)
+
+    if input_type == "base64":
+        output_format = opts.get("output_format", "jpg")
+        output_quality = opts.get("output_quality", 75)
+        output_lossless = opts.get("output_lossless", False)
+        images = [img_to_base64_str(img, output_format, output_quality, output_lossless) for img in images]
+
+    return images
+
+
+def _filter_images(context, images, filters, filter_params={}):
+    from sdkit.filter import apply_filters
+
+    filters = filters if isinstance(filters, list) else [filters]
+    filters = convert_ED_controlnet_filter_name(filters)
+
+    for filter_name in filters:
+        params = filter_params.get(filter_name, {})
+
+        previous_state = before_filter(context, filter_name, params)
+
+        try:
+            images = apply_filters(context, filter_name, images, **params)
+        finally:
+            after_filter(context, filter_name, params, previous_state)
+
+    return images
+
+
+def before_filter(context, filter_name, filter_params):
+    if filter_name == "codeformer":
+        from easydiffusion.model_manager import DEFAULT_MODELS, resolve_model_to_use
+
+        default_realesrgan = DEFAULT_MODELS["realesrgan"][0]["file_name"]
+        prev_realesrgan_path = None
+
+        upscale_faces = filter_params.get("upscale_faces", False)
+        if upscale_faces and default_realesrgan not in context.model_paths["realesrgan"]:
+            prev_realesrgan_path = context.model_paths.get("realesrgan")
+            context.model_paths["realesrgan"] = resolve_model_to_use(default_realesrgan, "realesrgan")
+            load_model(context, "realesrgan")
+
+        return prev_realesrgan_path
+
+
+def after_filter(context, filter_name, filter_params, previous_state):
+    if filter_name == "codeformer":
+        prev_realesrgan_path = previous_state
+        if prev_realesrgan_path:
+            context.model_paths["realesrgan"] = prev_realesrgan_path
+            load_model(context, "realesrgan")
+
+
+def get_url():
+    pass
+
+
+def stop_rendering(context):
+    context.stop_processing = True
+
+
+def refresh_models():
+    pass
+
+
+def list_controlnet_filters():
+    from sdkit.models.model_loader.controlnet_filters import filters as cn_filters
+
+    return cn_filters
+
+
+def make_step_callback(context, callback):
+    def on_step(x_samples, i, *args):
+        stream_image_progress = opts.get("stream_image_progress", False)
+        stream_image_progress_interval = opts.get("stream_image_progress_interval", 3)
+
+        if context.test_diffusers:
+            context.partial_x_samples = (x_samples, args[0])
+        else:
+            context.partial_x_samples = x_samples
+
+        if stream_image_progress and stream_image_progress_interval > 0 and i % stream_image_progress_interval == 0:
+            if context.test_diffusers:
+                images = diffusers_latent_samples_to_images(context, context.partial_x_samples)
+            else:
+                images = latent_samples_to_images(context, context.partial_x_samples)
+        else:
+            images = None
+
+        if callback:
+            callback(images, i, *args)
+
+        if context.stop_processing:
+            raise UserInitiatedStop("User requested that we stop processing")
+
+    return on_step
+
+
+def convert_ED_controlnet_filter_name(filter):
+    def cn(n):
+        if n.startswith("controlnet_"):
+            return n[len("controlnet_") :]
+        return n
+
+    if isinstance(filter, list):
+        return [cn(f) for f in filter]
+    return cn(filter)

ui/easydiffusion/backends/webui/__init__.py

@@ -0,0 +1,155 @@
+import os
+import platform
+import subprocess
+import threading
+from threading import local
+import psutil
+
+from easydiffusion.app import ROOT_DIR, getConfig
+
+from . import impl
+from .impl import (
+    ping,
+    load_model,
+    unload_model,
+    set_options,
+    generate_images,
+    filter_images,
+    get_url,
+    stop_rendering,
+    refresh_models,
+    list_controlnet_filters,
+)
+
+
+ed_info = {
+    "name": "WebUI backend for Easy Diffusion",
+    "version": (1, 0, 0),
+    "type": "backend",
+}
+
+BACKEND_DIR = os.path.abspath(os.path.join(ROOT_DIR, "webui"))
+SYSTEM_DIR = os.path.join(BACKEND_DIR, "system")
+WEBUI_DIR = os.path.join(BACKEND_DIR, "webui")
+
+backend_process = None
+
+
+def install_backend():
+    pass
+
+
+def start_backend():
+    config = getConfig()
+    backend_config = config.get("backend_config", {})
+
+    if not os.path.exists(BACKEND_DIR):
+        install_backend()
+
+    impl.WEBUI_HOST = backend_config.get("host", "localhost")
+    impl.WEBUI_PORT = backend_config.get("port", "7860")
+
+    env = dict(os.environ)
+    env.update(get_env())
+
+    def target():
+        global backend_process
+
+        cmd = "webui.bat" if platform.system() == "Windows" else "webui.sh"
+        print("starting", cmd, WEBUI_DIR)
+        backend_process = subprocess.Popen([cmd], shell=True, cwd=WEBUI_DIR, env=env)
+
+    backend_thread = threading.Thread(target=target)
+    backend_thread.start()
+
+
+def stop_backend():
+    global backend_process
+
+    if backend_process:
+        kill(backend_process.pid)
+
+    backend_process = None
+
+
+def uninstall_backend():
+    pass
+
+
+def create_context():
+    context = local()
+
+    # temp hack, throws an attribute not found error otherwise
+    context.device = "cuda:0"
+    context.half_precision = True
+    context.vram_usage_level = None
+
+    context.models = {}
+    context.model_paths = {}
+    context.model_configs = {}
+    context.device_name = None
+    context.vram_optimizations = set()
+    context.vram_usage_level = "balanced"
+    context.test_diffusers = False
+    context.enable_codeformer = False
+
+    return context
+
+
+def get_env():
+    dir = os.path.abspath(SYSTEM_DIR)
+
+    if not os.path.exists(dir):
+        raise RuntimeError("The system folder is missing!")
+
+    config = getConfig()
+    models_dir = config.get("models_dir", os.path.join(ROOT_DIR, "models"))
+    embeddings_dir = os.path.join(models_dir, "embeddings")
+
+    env_entries = {
+        "PATH": [
+            f"{dir}/git/bin",
+            f"{dir}/python",
+            f"{dir}/python/Library/bin",
+            f"{dir}/python/Scripts",
+            f"{dir}/python/Library/usr/bin",
+        ],
+        "PYTHONPATH": [
+            f"{dir}/python",
+            f"{dir}/python/lib/site-packages",
+            f"{dir}/python/lib/python3.10/site-packages",
+        ],
+        "PYTHONHOME": [],
+        "PY_LIBS": [f"{dir}/python/Scripts/Lib", f"{dir}/python/Scripts/Lib/site-packages"],
+        "PY_PIP": [f"{dir}/python/Scripts"],
+        "PIP_INSTALLER_LOCATION": [f"{dir}/python/get-pip.py"],
+        "TRANSFORMERS_CACHE": [f"{dir}/transformers-cache"],
+        "HF_HUB_DISABLE_SYMLINKS_WARNING": ["true"],
+        "COMMANDLINE_ARGS": [f'--api --models-dir "{models_dir}" --embeddings-dir "{embeddings_dir}"'],
+        "SKIP_VENV": ["1"],
+        "SD_WEBUI_RESTARTING": ["1"],
+        "PYTHON": [f"{dir}/python/python"],
+        "GIT": [f"{dir}/git/bin/git"],
+    }
+
+    if platform.system() == "Windows":
+        env_entries["PYTHONNOUSERSITE"] = ["1"]
+    else:
+        env_entries["PYTHONNOUSERSITE"] = ["y"]
+
+    env = {}
+    for key, paths in env_entries.items():
+        paths = [p.replace("/", os.path.sep) for p in paths]
+        paths = os.pathsep.join(paths)
+
+        env[key] = paths
+
+    return env
+
+
+# https://stackoverflow.com/a/25134985
+def kill(proc_pid):
+    process = psutil.Process(proc_pid)
+    for proc in process.children(recursive=True):
+        proc.kill()
+    process.kill()

ui/easydiffusion/backends/webui/impl.py

@@ -0,0 +1,639 @@
+import os
+import requests
+from requests.exceptions import ConnectTimeout
+from typing import Union, List
+from threading import local as Context
+from threading import Thread
+import uuid
+import time
+from copy import deepcopy
+
+from sdkit.utils import base64_str_to_img, img_to_base64_str
+
+WEBUI_HOST = "localhost"
+WEBUI_PORT = "7860"
+
+DEFAULT_WEBUI_OPTIONS = {"show_progress_every_n_steps": 3, "show_progress_grid": True, "live_previews_enable": False}
+
+
+webui_opts: dict = None
+
+
+curr_models = {
+    "stable-diffusion": None,
+    "vae": None,
+}
+
+
+def set_options(context, **kwargs):
+    changed_opts = {}
+
+    opts_mapping = {
+        "stream_image_progress": ("live_previews_enable", bool),
+        "stream_image_progress_interval": ("show_progress_every_n_steps", int),
+        "clip_skip": ("CLIP_stop_at_last_layers", int),
+        "clip_skip_sdxl": ("sdxl_clip_l_skip", bool),
+        "output_format": ("samples_format", str),
+    }
+
+    for ed_key, webui_key in opts_mapping.items():
+        webui_key, webui_type = webui_key
+
+        if ed_key in kwargs and (webui_opts is None or webui_opts.get(webui_key, False) != webui_type(kwargs[ed_key])):
+            changed_opts[webui_key] = webui_type(kwargs[ed_key])
+
+    if changed_opts:
+        changed_opts["sd_model_checkpoint"] = curr_models["stable-diffusion"]
+
+        print(f"Got options: {kwargs}. Sending options: {changed_opts}")
+
+        try:
+            res = webui_post("/sdapi/v1/options", json=changed_opts)
+            if res.status_code != 200:
+                raise Exception(res.text)
+
+            webui_opts.update(changed_opts)
+        except Exception as e:
+            print(f"Error setting options: {e}")
+
+
+def ping(timeout=1):
+    "timeout (in seconds)"
+
+    global webui_opts
+
+    try:
+        webui_get("/internal/ping", timeout=timeout)
+
+        if webui_opts is None:
+            try:
+                res = webui_post("/sdapi/v1/options", json=DEFAULT_WEBUI_OPTIONS)
+                if res.status_code != 200:
+                    raise Exception(res.text)
+            except Exception as e:
+                print(f"Error setting options: {e}")
+
+            try:
+                res = webui_get("/sdapi/v1/options")
+                if res.status_code != 200:
+                    raise Exception(res.text)
+
+                webui_opts = res.json()
+            except Exception as e:
+                print(f"Error setting options: {e}")
+
+        return True
+    except ConnectTimeout as e:
+        raise TimeoutError(e)
+
+
+def load_model(context, model_type, **kwargs):
+    model_path = context.model_paths[model_type]
+
+    if webui_opts is None:
+        print("Server not ready, can't set the model")
+        return
+
+    if model_type == "stable-diffusion":
+        model_name = os.path.basename(model_path)
+        model_name = os.path.splitext(model_name)[0]
+        print(f"setting sd model: {model_name}")
+        if curr_models[model_type] != model_name:
+            try:
+                res = webui_post("/sdapi/v1/options", json={"sd_model_checkpoint": model_name})
+                if res.status_code != 200:
+                    raise Exception(res.text)
+            except Exception as e:
+                raise RuntimeError(
+                    f"The engine failed to set the required options. Please check the logs in the command line window for more details."
+                )
+
+            curr_models[model_type] = model_name
+    elif model_type == "vae":
+        if curr_models[model_type] != model_path:
+            vae_model = [model_path] if model_path else []
+
+            opts = {"sd_model_checkpoint": curr_models["stable-diffusion"], "forge_additional_modules": vae_model}
+            print("setting opts 2", opts)
+
+            try:
+                res = webui_post("/sdapi/v1/options", json=opts)
+                if res.status_code != 200:
+                    raise Exception(res.text)
+            except Exception as e:
+                raise RuntimeError(
+                    f"The engine failed to set the required options. Please check the logs in the command line window for more details."
+                )
+
+            curr_models[model_type] = model_path
+
+
+def unload_model(context, model_type, **kwargs):
+    pass
+
+
+def generate_images(
+    context: Context,
+    prompt: str = "",
+    negative_prompt: str = "",
+    seed: int = 42,
+    width: int = 512,
+    height: int = 512,
+    num_outputs: int = 1,
+    num_inference_steps: int = 25,
+    guidance_scale: float = 7.5,
+    init_image=None,
+    init_image_mask=None,
+    control_image=None,
+    control_alpha=1.0,
+    controlnet_filter=None,
+    prompt_strength: float = 0.8,
+    preserve_init_image_color_profile=False,
+    strict_mask_border=False,
+    sampler_name: str = "euler_a",
+    hypernetwork_strength: float = 0,
+    tiling=None,
+    lora_alpha: Union[float, List[float]] = 0,
+    sampler_params={},
+    callback=None,
+    output_type="pil",
+):
+
+    task_id = str(uuid.uuid4())
+
+    sampler_name = convert_ED_sampler_names(sampler_name)
+    controlnet_filter = convert_ED_controlnet_filter_name(controlnet_filter)
+
+    cmd = {
+        "force_task_id": task_id,
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "sampler_name": sampler_name,
+        "scheduler": "simple",
+        "steps": num_inference_steps,
+        "seed": seed,
+        "cfg_scale": guidance_scale,
+        "batch_size": num_outputs,
+        "width": width,
+        "height": height,
+    }
+
+    if init_image:
+        cmd["init_images"] = [init_image]
+        cmd["denoising_strength"] = prompt_strength
+    if init_image_mask:
+        cmd["mask"] = init_image_mask
+        cmd["include_init_images"] = True
+        cmd["inpainting_fill"] = 1
+        cmd["initial_noise_multiplier"] = 1
+        cmd["inpaint_full_res"] = 1
+
+    if context.model_paths.get("lora"):
+        lora_model = context.model_paths["lora"]
+        lora_model = lora_model if isinstance(lora_model, list) else [lora_model]
+        lora_alpha = lora_alpha if isinstance(lora_alpha, list) else [lora_alpha]
+
+        for lora, alpha in zip(lora_model, lora_alpha):
+            lora = os.path.basename(lora)
+            lora = os.path.splitext(lora)[0]
+            cmd["prompt"] += f" <lora:{lora}:{alpha}>"
+
+    if controlnet_filter and control_image and context.model_paths.get("controlnet"):
+        controlnet_model = context.model_paths["controlnet"]
+
+        model_hash = auto1111_hash(controlnet_model)
+        controlnet_model = os.path.basename(controlnet_model)
+        controlnet_model = os.path.splitext(controlnet_model)[0]
+        print(f"setting controlnet model: {controlnet_model}")
+        controlnet_model = f"{controlnet_model} [{model_hash}]"
+
+        cmd["alwayson_scripts"] = {
+            "controlnet": {
+                "args": [
+                    {
+                        "image": control_image,
+                        "weight": control_alpha,
+                        "module": controlnet_filter,
+                        "model": controlnet_model,
+                        "resize_mode": "Crop and Resize",
+                        "threshold_a": 50,
+                        "threshold_b": 130,
+                    }
+                ]
+            }
+        }
+
+    operation_to_apply = "img2img" if init_image else "txt2img"
+
+    stream_image_progress = webui_opts.get("live_previews_enable", False)
+
+    progress_thread = Thread(
+        target=image_progress_thread, args=(task_id, callback, stream_image_progress, num_outputs, num_inference_steps)
+    )
+    progress_thread.start()
+
+    print(f"task id: {task_id}")
+    print_request(operation_to_apply, cmd)
+
+    res = webui_post(f"/sdapi/v1/{operation_to_apply}", json=cmd)
+    if res.status_code == 200:
+        res = res.json()
+    else:
+        raise Exception(
+            "The engine failed while generating this image. Please check the logs in the command-line window for more details."
+        )
+
+    import json
+
+    print(json.loads(res["info"])["infotexts"])
+
+    images = res["images"]
+    if output_type == "pil":
+        images = [base64_str_to_img(img) for img in images]
+    elif output_type == "base64":
+        images = [base64_buffer_to_base64_img(img) for img in images]
+
+    return images
+
+
+def filter_images(context: Context, images, filters, filter_params={}, input_type="pil"):
+    """
+    * context: Context
+    * images: str or PIL.Image or list of str/PIL.Image - image to filter. if a string is passed, it needs to be a base64-encoded image
+    * filters: filter_type (string) or list of strings
+    * filter_params: dict
+
+    returns: [PIL.Image] - list of filtered images
+    """
+    images = images if isinstance(images, list) else [images]
+    filters = filters if isinstance(filters, list) else [filters]
+
+    if "nsfw_checker" in filters:
+        filters.remove("nsfw_checker")  # handled by ED directly
+
+    args = {}
+    controlnet_filters = []
+
+    print(filter_params)
+
+    for filter_name in filters:
+        params = filter_params.get(filter_name, {})
+
+        if filter_name == "gfpgan":
+            args["gfpgan_visibility"] = 1
+
+        if filter_name in ("realesrgan", "esrgan_4x", "lanczos", "nearest", "scunet", "swinir"):
+            args["upscaler_1"] = params.get("upscaler", "RealESRGAN_x4plus")
+            args["upscaling_resize"] = params.get("scale", 4)
+
+            if args["upscaler_1"] == "RealESRGAN_x4plus":
+                args["upscaler_1"] = "R-ESRGAN 4x+"
+            elif args["upscaler_1"] == "RealESRGAN_x4plus_anime_6B":
+                args["upscaler_1"] = "R-ESRGAN 4x+ Anime6B"
+
+        if filter_name == "codeformer":
+            args["codeformer_visibility"] = 1
+            args["codeformer_weight"] = params.get("codeformer_fidelity", 0.5)
+
+        if filter_name.startswith("controlnet_"):
+            filter_name = convert_ED_controlnet_filter_name(filter_name)
+            controlnet_filters.append(filter_name)
+
+    print(f"filtering {len(images)} images with {args}. {controlnet_filters=}")
+
+    if len(filters) > len(controlnet_filters):
+        filtered_images = extra_batch_images(images, input_type=input_type, **args)
+    else:
+        filtered_images = images
+
+    for filter_name in controlnet_filters:
+        filtered_images = controlnet_filter(filtered_images, module=filter_name, input_type=input_type)
+
+    return filtered_images
+
+
+def get_url():
+    return f"//{WEBUI_HOST}:{WEBUI_PORT}/?__theme=dark"
+
+
+def stop_rendering(context):
+    try:
+        res = webui_post("/sdapi/v1/interrupt")
+        if res.status_code != 200:
+            raise Exception(res.text)
+    except Exception as e:
+        print(f"Error interrupting webui: {e}")
+
+
+def refresh_models():
+    def make_refresh_call(type):
+        try:
+            webui_post(f"/sdapi/v1/refresh-{type}")
+        except:
+            pass
+
+    try:
+        for type in ("checkpoints", "vae"):
+            t = Thread(target=make_refresh_call, args=(type,))
+            t.start()
+    except Exception as e:
+        print(f"Error refreshing models: {e}")
+
+
+def list_controlnet_filters():
+    return [
+        "openpose",
+        "openpose_face",
+        "openpose_faceonly",
+        "openpose_hand",
+        "openpose_full",
+        "animal_openpose",
+        "densepose_parula (black bg & blue torso)",
+        "densepose (pruple bg & purple torso)",
+        "dw_openpose_full",
+        "mediapipe_face",
+        "instant_id_face_keypoints",
+        "InsightFace+CLIP-H (IPAdapter)",
+        "InsightFace (InstantID)",
+        "canny",
+        "mlsd",
+        "scribble_hed",
+        "scribble_hedsafe",
+        "scribble_pidinet",
+        "scribble_pidsafe",
+        "scribble_xdog",
+        "softedge_hed",
+        "softedge_hedsafe",
+        "softedge_pidinet",
+        "softedge_pidsafe",
+        "softedge_teed",
+        "normal_bae",
+        "depth_midas",
+        "normal_midas",
+        "depth_zoe",
+        "depth_leres",
+        "depth_leres++",
+        "depth_anything_v2",
+        "depth_anything",
+        "depth_hand_refiner",
+        "depth_marigold",
+        "lineart_coarse",
+        "lineart_realistic",
+        "lineart_anime",
+        "lineart_standard (from white bg & black line)",
+        "lineart_anime_denoise",
+        "reference_adain",
+        "reference_only",
+        "reference_adain+attn",
+        "tile_colorfix",
+        "tile_resample",
+        "tile_colorfix+sharp",
+        "CLIP-ViT-H (IPAdapter)",
+        "CLIP-G (Revision)",
+        "CLIP-G (Revision ignore prompt)",
+        "CLIP-ViT-bigG (IPAdapter)",
+        "InsightFace+CLIP-H (IPAdapter)",
+        "inpaint_only",
+        "inpaint_only+lama",
+        "inpaint_global_harmonious",
+        "seg_ufade20k",
+        "seg_ofade20k",
+        "seg_anime_face",
+        "seg_ofcoco",
+        "shuffle",
+        "segment",
+        "invert (from white bg & black line)",
+        "threshold",
+        "t2ia_sketch_pidi",
+        "t2ia_color_grid",
+        "recolor_intensity",
+        "recolor_luminance",
+        "blur_gaussian",
+    ]
+
+
+def controlnet_filter(images, module="none", processor_res=512, threshold_a=64, threshold_b=64, input_type="pil"):
+    if input_type == "pil":
+        images = [img_to_base64_str(x) for x in images]
+
+    payload = {
+        "controlnet_module": module,
+        "controlnet_input_images": images,
+        "controlnet_processor_res": processor_res,
+        "controlnet_threshold_a": threshold_a,
+        "controlnet_threshold_b": threshold_b,
+    }
+    res = webui_post("/controlnet/detect", json=payload)
+    res = res.json()
+    filtered_images = res["images"]
+
+    if input_type == "pil":
+        filtered_images = [base64_str_to_img(img) for img in filtered_images]
+    elif input_type == "base64":
+        filtered_images = [base64_buffer_to_base64_img(img) for img in filtered_images]
+
+    return filtered_images
+
+
+def image_progress_thread(task_id, callback, stream_image_progress, total_images, total_steps):
+    from PIL import Image
+
+    last_preview_id = -1
+
+    EMPTY_IMAGE = Image.new("RGB", (1, 1))
+
+    while True:
+        res = webui_post(
+            f"/internal/progress",
+            json={"id_task": task_id, "live_preview": stream_image_progress, "id_live_preview": last_preview_id},
+        )
+        if res.status_code == 200:
+            res = res.json()
+
+        last_preview_id = res["id_live_preview"]
+
+        if res["progress"] is not None:
+            step_num = int(res["progress"] * total_steps)
+
+            if res["live_preview"] is not None:
+                img = res["live_preview"]
+                img = base64_str_to_img(img)
+                images = [EMPTY_IMAGE] * total_images
+                images[0] = img
+            else:
+                images = None
+
+            callback(images, step_num)
+
+        if res["completed"] == True:
+            print("Complete!")
+            break
+
+        time.sleep(0.5)
+
+
+def webui_get(uri, *args, **kwargs):
+    url = f"http://{WEBUI_HOST}:{WEBUI_PORT}{uri}"
+    return requests.get(url, *args, **kwargs)
+
+
+def webui_post(uri, *args, **kwargs):
+    url = f"http://{WEBUI_HOST}:{WEBUI_PORT}{uri}"
+    return requests.post(url, *args, **kwargs)
+
+
+def print_request(operation_to_apply, args):
+    args = deepcopy(args)
+    if "init_images" in args:
+        args["init_images"] = ["img" for _ in args["init_images"]]
+    if "mask" in args:
+        args["mask"] = "mask_img"
+
+    controlnet_args = args.get("alwayson_scripts", {}).get("controlnet", {}).get("args", [])
+    if controlnet_args:
+        controlnet_args[0]["image"] = "control_image"
+
+    print(f"operation: {operation_to_apply}, args: {args}")
+
+
+def auto1111_hash(file_path):
+    import hashlib
+
+    with open(file_path, "rb") as f:
+        f.seek(0x100000)
+        b = f.read(0x10000)
+        return hashlib.sha256(b).hexdigest()[:8]
+
+
+def extra_batch_images(
+    images,  # list of PIL images
+    name_list=None,  # list of image names
+    resize_mode=0,
+    show_extras_results=True,
+    gfpgan_visibility=0,
+    codeformer_visibility=0,
+    codeformer_weight=0,
+    upscaling_resize=2,
+    upscaling_resize_w=512,
+    upscaling_resize_h=512,
+    upscaling_crop=True,
+    upscaler_1="None",
+    upscaler_2="None",
+    extras_upscaler_2_visibility=0,
+    upscale_first=False,
+    use_async=False,
+    input_type="pil",
+):
+    if name_list is not None:
+        if len(name_list) != len(images):
+            raise RuntimeError("len(images) != len(name_list)")
+    else:
+        name_list = [f"image{i + 1:05}" for i in range(len(images))]
+
+    if input_type == "pil":
+        images = [img_to_base64_str(x) for x in images]
+
+    image_list = []
+    for name, image in zip(name_list, images):
+        image_list.append({"data": image, "name": name})
+
+    payload = {
+        "resize_mode": resize_mode,
+        "show_extras_results": show_extras_results,
+        "gfpgan_visibility": gfpgan_visibility,
+        "codeformer_visibility": codeformer_visibility,
+        "codeformer_weight": codeformer_weight,
+        "upscaling_resize": upscaling_resize,
+        "upscaling_resize_w": upscaling_resize_w,
+        "upscaling_resize_h": upscaling_resize_h,
+        "upscaling_crop": upscaling_crop,
+        "upscaler_1": upscaler_1,
+        "upscaler_2": upscaler_2,
+        "extras_upscaler_2_visibility": extras_upscaler_2_visibility,
+        "upscale_first": upscale_first,
+        "imageList": image_list,
+    }
+
+    res = webui_post("/sdapi/v1/extra-batch-images", json=payload)
+    if res.status_code == 200:
+        res = res.json()
+    else:
+        raise Exception(
+            "The engine failed while filtering this image. Please check the logs in the command-line window for more details."
+        )
+
+    images = res["images"]
+
+    if input_type == "pil":
+        images = [base64_str_to_img(img) for img in images]
+    elif input_type == "base64":
+        images = [base64_buffer_to_base64_img(img) for img in images]
+
+    return images
+
+
+def base64_buffer_to_base64_img(img):
+    output_format = webui_opts.get("samples_format", "jpeg")
+    mime_type = f"image/{output_format.lower()}"
+    return f"data:{mime_type};base64," + img
+
+
+def convert_ED_sampler_names(sampler_name):
+    name_mapping = {
+        "dpmpp_2m": "DPM++ 2M",
+        "dpmpp_sde": "DPM++ SDE",
+        "dpmpp_2m_sde": "DPM++ 2M SDE",
+        "dpmpp_2m_sde_heun": "DPM++ 2M SDE Heun",
+        "dpmpp_2s_a": "DPM++ 2S a",
+        "dpmpp_3m_sde": "DPM++ 3M SDE",
+        "euler_a": "Euler a",
+        "euler": "Euler",
+        "lms": "LMS",
+        "heun": "Heun",
+        "dpm2": "DPM2",
+        "dpm2_a": "DPM2 a",
+        "dpm_fast": "DPM fast",
+        "dpm_adaptive": "DPM adaptive",
+        "restart": "Restart",
+        "heun_pp2": "HeunPP2",
+        "ipndm": "IPNDM",
+        "ipndm_v": "IPNDM_V",
+        "deis": "DEIS",
+        "ddim": "DDIM",
+        "ddim_cfgpp": "DDIM CFG++",
+        "plms": "PLMS",
+        "unipc": "UniPC",
+        "lcm": "LCM",
+        "ddpm": "DDPM",
+        "forge_flux_realistic": "[Forge] Flux Realistic",
+        "forge_flux_realistic_slow": "[Forge] Flux Realistic (Slow)",
+        # deprecated samplers in 3.5
+        "dpm_solver_stability": None,
+        "unipc_snr": None,
+        "unipc_tu": None,
+        "unipc_snr_2": None,
+        "unipc_tu_2": None,
+        "unipc_tq": None,
+    }
+    return name_mapping.get(sampler_name)
+
+
+def convert_ED_controlnet_filter_name(filter):
+    if filter is None:
+        return None
+
+    def cn(n):
+        if n.startswith("controlnet_"):
+            return n[len("controlnet_") :]
+        return n
+
+    mapping = {
+        "controlnet_scribble_hedsafe": None,
+        "controlnet_scribble_pidsafe": None,
+        "controlnet_softedge_pidsafe": "controlnet_softedge_pidisafe",
+        "controlnet_normal_bae": "controlnet_normalbae",
+        "controlnet_segment": None,
+    }
+    if isinstance(filter, list):
+        return [cn(mapping.get(f, f)) for f in filter]
+    return cn(mapping.get(filter, filter))

ui/easydiffusion/easydb/schemas.py

@@ -33,4 +33,3 @@ class Bucket(BucketBase):
 
     class Config:
         orm_mode = True
-

ui/easydiffusion/model_manager.py

@@ -8,7 +8,7 @@ from easydiffusion import app
 from easydiffusion.types import ModelsData
 from easydiffusion.utils import log
 from sdkit import Context
-from sdkit.models import load_model, scan_model, unload_model, download_model, get_model_info_from_db
+from sdkit.models import scan_model, download_model, get_model_info_from_db
 from sdkit.models.model_loader.controlnet_filters import filters as cn_filters
 from sdkit.utils import hash_file_quick
 from sdkit.models.model_loader.embeddings import get_embedding_token
@@ -25,15 +25,15 @@ KNOWN_MODEL_TYPES = [
     "controlnet",
 ]
 MODEL_EXTENSIONS = {
-    "stable-diffusion": [".ckpt", ".safetensors"],
-    "vae": [".vae.pt", ".ckpt", ".safetensors"],
-    "hypernetwork": [".pt", ".safetensors"],
+    "stable-diffusion": [".ckpt", ".safetensors", ".sft", ".gguf"],
+    "vae": [".vae.pt", ".ckpt", ".safetensors", ".sft"],
+    "hypernetwork": [".pt", ".safetensors", ".sft"],
     "gfpgan": [".pth"],
     "realesrgan": [".pth"],
-    "lora": [".ckpt", ".safetensors", ".pt"],
+    "lora": [".ckpt", ".safetensors", ".sft", ".pt"],
     "codeformer": [".pth"],
-    "embeddings": [".pt", ".bin", ".safetensors"],
-    "controlnet": [".pth", ".safetensors"],
+    "embeddings": [".pt", ".bin", ".safetensors", ".sft"],
+    "controlnet": [".pth", ".safetensors", ".sft"],
 }
 DEFAULT_MODELS = {
     "stable-diffusion": [
@@ -63,6 +63,7 @@ def init():
 
 def load_default_models(context: Context):
     from easydiffusion import runtime
+    from easydiffusion.backend_manager import backend
 
     runtime.set_vram_optimizations(context)
 
@@ -70,7 +71,7 @@ def load_default_models(context: Context):
     for model_type in MODELS_TO_LOAD_ON_START:
         context.model_paths[model_type] = resolve_model_to_use(model_type=model_type, fail_if_not_found=False)
         try:
-            load_model(
+            backend.load_model(
                 context,
                 model_type,
                 scan_model=context.model_paths[model_type] != None
@@ -92,8 +93,10 @@ def load_default_models(context: Context):
 
 
 def unload_all(context: Context):
+    from easydiffusion.backend_manager import backend
+
     for model_type in KNOWN_MODEL_TYPES:
-        unload_model(context, model_type)
+        backend.unload_model(context, model_type)
         if model_type in context.model_load_errors:
             del context.model_load_errors[model_type]
 
@@ -154,6 +157,8 @@ def resolve_model_to_use_single(model_name: str = None, model_type: str = None,
 
 
 def reload_models_if_necessary(context: Context, models_data: ModelsData, models_to_force_reload: list = []):
+    from easydiffusion.backend_manager import backend
+
     models_to_reload = {
         model_type: path
         for model_type, path in models_data.model_paths.items()
@@ -175,7 +180,7 @@ def reload_models_if_necessary(context: Context, models_data: ModelsData, models
     for model_type, model_path_in_req in models_to_reload.items():
         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 = backend.unload_model if context.model_paths[model_type] is None else backend.load_model
         extra_params = models_data.model_params.get(model_type, {})
         try:
             action_fn(context, model_type, scan_model=False, **extra_params)  # we've scanned them already
@@ -183,14 +188,23 @@ def reload_models_if_necessary(context: Context, models_data: ModelsData, models
                 del context.model_load_errors[model_type]
         except Exception as e:
             log.exception(e)
-            if action_fn == load_model:
+            if action_fn == backend.load_model:
                 context.model_load_errors[model_type] = str(e)  # storing the entire Exception can lead to memory leaks
 
 
 def resolve_model_paths(models_data: ModelsData):
     model_paths = models_data.model_paths
+    skip_models = cn_filters + [
+        "latent_upscaler",
+        "nsfw_checker",
+        "esrgan_4x",
+        "lanczos",
+        "nearest",
+        "scunet",
+        "swinir",
+    ]
+
     for model_type in model_paths:
-        skip_models = cn_filters + ["latent_upscaler", "nsfw_checker"]
         if model_type in skip_models:  # doesn't use model paths
             continue
         if model_type == "codeformer" and model_paths[model_type]:
@@ -320,6 +334,10 @@ def is_malicious_model(file_path):
 
 
 def getModels(scan_for_malicious: bool = True):
+    from easydiffusion.backend_manager import backend
+
+    backend.refresh_models()
+
     models = {
         "options": {
             "stable-diffusion": [],
@@ -329,19 +347,19 @@ def getModels(scan_for_malicious: bool = True):
             "codeformer": [{"codeformer": "CodeFormer"}],
             "embeddings": [],
             "controlnet": [
-                {"control_v11p_sd15_canny": "Canny (*)"},
-                {"control_v11p_sd15_openpose": "OpenPose (*)"},
-                {"control_v11p_sd15_normalbae": "Normal BAE (*)"},
-                {"control_v11f1p_sd15_depth": "Depth (*)"},
-                {"control_v11p_sd15_scribble": "Scribble"},
-                {"control_v11p_sd15_softedge": "Soft Edge"},
-                {"control_v11p_sd15_inpaint": "Inpaint"},
-                {"control_v11p_sd15_lineart": "Line Art"},
-                {"control_v11p_sd15s2_lineart_anime": "Line Art Anime"},
-                {"control_v11p_sd15_mlsd": "Straight Lines"},
-                {"control_v11p_sd15_seg": "Segment"},
-                {"control_v11e_sd15_shuffle": "Shuffle"},
-                {"control_v11f1e_sd15_tile": "Tile"},
+                # {"control_v11p_sd15_canny": "Canny (*)"},
+                # {"control_v11p_sd15_openpose": "OpenPose (*)"},
+                # {"control_v11p_sd15_normalbae": "Normal BAE (*)"},
+                # {"control_v11f1p_sd15_depth": "Depth (*)"},
+                # {"control_v11p_sd15_scribble": "Scribble"},
+                # {"control_v11p_sd15_softedge": "Soft Edge"},
+                # {"control_v11p_sd15_inpaint": "Inpaint"},
+                # {"control_v11p_sd15_lineart": "Line Art"},
+                # {"control_v11p_sd15s2_lineart_anime": "Line Art Anime"},
+                # {"control_v11p_sd15_mlsd": "Straight Lines"},
+                # {"control_v11p_sd15_seg": "Segment"},
+                # {"control_v11e_sd15_shuffle": "Shuffle"},
+                # {"control_v11f1e_sd15_tile": "Tile"},
             ],
         },
     }
@@ -378,6 +396,8 @@ def getModels(scan_for_malicious: bool = True):
                 model_id = entry.name[: -len(matching_suffix)]
                 if callable(nameFilter):
                     model_id = nameFilter(model_id)
+                    if model_id is None:
+                        continue
 
                 model_exists = False
                 for m in tree:  # allows default "named" models, like CodeFormer and known ControlNet models
@@ -416,7 +436,7 @@ def getModels(scan_for_malicious: bool = True):
     listModels(model_type="stable-diffusion")
     listModels(model_type="vae")
     listModels(model_type="hypernetwork")
-    listModels(model_type="gfpgan")
+    listModels(model_type="gfpgan", nameFilter=lambda x: (x if "gfpgan" in x.lower() else None))
     listModels(model_type="lora")
     listModels(model_type="embeddings", nameFilter=get_embedding_token)
     listModels(model_type="controlnet")

ui/easydiffusion/runtime.py

@@ -1,4 +1,5 @@
 """
+(OUTDATED DOC)
 A runtime that runs on a specific device (in a thread).
 
 It can run various tasks like image generation, image filtering, model merge etc by using that thread-local context.
@@ -6,42 +7,35 @@ It can run various tasks like image generation, image filtering, model merge etc
 This creates an `sdkit.Context` that's bound to the device specified while calling the `init()` function.
 """
 
-from easydiffusion import device_manager
-from easydiffusion.utils import log
-from sdkit import Context
-from sdkit.utils import get_device_usage
-
-context = Context()  # thread-local
-"""
-runtime data (bound locally to this thread), for e.g. device, references to loaded models, optimization flags etc
-"""
+context = None
 
 
 def init(device):
     """
     Initializes the fields that will be bound to this runtime's context, and sets the current torch device
     """
+
+    global context
+
+    from easydiffusion import device_manager
+    from easydiffusion.backend_manager import backend
+    from easydiffusion.app import getConfig
+
+    context = backend.create_context()
+
     context.stop_processing = False
     context.temp_images = {}
     context.partial_x_samples = None
     context.model_load_errors = {}
     context.enable_codeformer = True
 
-    from easydiffusion import app
-
-    app_config = app.getConfig()
-    context.test_diffusers = app_config.get("use_v3_engine", True)
-
-    log.info("Device usage during initialization:")
-    get_device_usage(device, log_info=True, process_usage_only=False)
-
     device_manager.device_init(context, device)
 
 
-def set_vram_optimizations(context: Context):
-    from easydiffusion import app
+def set_vram_optimizations(context):
+    from easydiffusion.app import getConfig
 
-    config = app.getConfig()
+    config = getConfig()
     vram_usage_level = config.get("vram_usage_level", "balanced")
 
     if vram_usage_level != context.vram_usage_level:

ui/easydiffusion/server.py

@@ -2,6 +2,7 @@
 Notes:
     async endpoints always run on the main thread. Without they run on the thread pool.
 """
+
 import datetime
 import mimetypes
 import os
@@ -20,6 +21,7 @@ from easydiffusion.types import (
     OutputFormatData,
     SaveToDiskData,
     convert_legacy_render_req_to_new,
+    convert_legacy_controlnet_filter_name,
 )
 from easydiffusion.utils import log
 from fastapi import FastAPI, HTTPException
@@ -67,6 +69,7 @@ class SetAppConfigRequest(BaseModel, extra=Extra.allow):
     listen_to_network: bool = None
     listen_port: int = None
     use_v3_engine: bool = True
+    backend: str = "ed_diffusers"
     models_dir: str = None
 
 
@@ -155,6 +158,12 @@ def init():
     def shutdown_event():  # Signal render thread to close on shutdown
         task_manager.current_state_error = SystemExit("Application shutting down.")
 
+    @server_api.on_event("startup")
+    def start_event():
+        from easydiffusion.app import open_browser
+
+        open_browser()
+
 
 # API implementations
 def set_app_config_internal(req: SetAppConfigRequest):
@@ -176,7 +185,8 @@ def set_app_config_internal(req: SetAppConfigRequest):
             config["net"] = {}
         config["net"]["listen_port"] = int(req.listen_port)
 
-    config["use_v3_engine"] = req.use_v3_engine
+    config["use_v3_engine"] = req.backend == "ed_diffusers"
+    config["backend"] = req.backend
     config["models_dir"] = req.models_dir
 
     for property, property_value in req.dict().items():
@@ -216,6 +226,8 @@ def read_web_data_internal(key: str = None, **kwargs):
 
         return JSONResponse(config, headers=NOCACHE_HEADERS)
     elif key == "system_info":
+        from easydiffusion.backend_manager import backend
+
         config = app.getConfig()
 
         output_dir = config.get("force_save_path", os.path.join(os.path.expanduser("~"), app.OUTPUT_DIRNAME))
@@ -226,6 +238,7 @@ def read_web_data_internal(key: str = None, **kwargs):
             "default_output_dir": output_dir,
             "enforce_output_dir": ("force_save_path" in config),
             "enforce_output_metadata": ("force_save_metadata" in config),
+            "backend_url": backend.get_url(),
         }
         system_info["devices"]["config"] = config.get("render_devices", "auto")
         return JSONResponse(system_info, headers=NOCACHE_HEADERS)
@@ -309,6 +322,15 @@ def filter_internal(req: dict):
         output_format: OutputFormatData = OutputFormatData.parse_obj(req)
         save_data: SaveToDiskData = SaveToDiskData.parse_obj(req)
 
+        filter_req.filter = convert_legacy_controlnet_filter_name(filter_req.filter)
+
+        for model_name in ("realesrgan", "esrgan_4x", "lanczos", "nearest", "scunet", "swinir"):
+            if models_data.model_paths.get(model_name):
+                if model_name not in filter_req.filter_params:
+                    filter_req.filter_params[model_name] = {}
+
+                filter_req.filter_params[model_name]["upscaler"] = models_data.model_paths[model_name]
+
         # enqueue the task
         task = FilterTask(filter_req, task_data, models_data, output_format, save_data)
         return enqueue_task(task)

ui/easydiffusion/task_manager.py

@@ -4,6 +4,7 @@ Notes:
     Use weak_thread_data to store all other data using weak keys.
     This will allow for garbage collection after the thread dies.
 """
+
 import json
 import traceback
 
@@ -19,7 +20,6 @@ import torch
 from easydiffusion import device_manager
 from easydiffusion.tasks import Task
 from easydiffusion.utils import log
-from sdkit.utils import gc
 
 THREAD_NAME_PREFIX = ""
 ERR_LOCK_FAILED = " failed to acquire lock within timeout."
@@ -233,6 +233,7 @@ def thread_render(device):
     global current_state, current_state_error
 
     from easydiffusion import model_manager, runtime
+    from easydiffusion.backend_manager import backend
 
     try:
         runtime.init(device)
@@ -244,8 +245,17 @@ def thread_render(device):
         }
 
         current_state = ServerStates.LoadingModel
-        model_manager.load_default_models(runtime.context)
 
+        while True:
+            try:
+                if backend.ping(timeout=1):
+                    break
+
+                time.sleep(1)
+            except TimeoutError:
+                time.sleep(1)
+
+        model_manager.load_default_models(runtime.context)
         current_state = ServerStates.Online
     except Exception as e:
         log.error(traceback.format_exc())
@@ -291,7 +301,6 @@ def thread_render(device):
             task.buffer_queue.put(json.dumps(task.response))
             log.error(traceback.format_exc())
         finally:
-            gc(runtime.context)
             task.lock.release()
 
         keep_task_alive(task)

ui/easydiffusion/tasks/filter_images.py

@@ -5,9 +5,7 @@ import time
 
 from numpy import base_repr
 
-from sdkit.filter import apply_filters
-from sdkit.models import load_model
-from sdkit.utils import img_to_base64_str, get_image, log, save_images
+from sdkit.utils import img_to_base64_str, log, save_images, base64_str_to_img
 
 from easydiffusion import model_manager, runtime
 from easydiffusion.types import (
@@ -19,6 +17,7 @@ from easydiffusion.types import (
     TaskData,
     GenerateImageRequest,
 )
+from easydiffusion.utils import filter_nsfw
 from easydiffusion.utils.save_utils import format_folder_name
 
 from .task import Task
@@ -47,7 +46,9 @@ class FilterTask(Task):
 
         # convert to multi-filter format, if necessary
         if isinstance(req.filter, str):
-            req.filter_params = {req.filter: req.filter_params}
+            if req.filter not in req.filter_params:
+                req.filter_params = {req.filter: req.filter_params}
+
             req.filter = [req.filter]
 
         if not isinstance(req.image, list):
@@ -57,6 +58,7 @@ class FilterTask(Task):
         "Runs the image filtering task on the assigned thread"
 
         from easydiffusion import app
+        from easydiffusion.backend_manager import backend
 
         context = runtime.context
 
@@ -66,15 +68,24 @@ class FilterTask(Task):
 
         print_task_info(self.request, self.models_data, self.output_format, self.save_data)
 
-        if isinstance(self.request.image, list):
-            images = [get_image(img) for img in self.request.image]
-        else:
-            images = get_image(self.request.image)
-
-        images = filter_images(context, images, self.request.filter, self.request.filter_params)
+        has_nsfw_filter = "nsfw_filter" in self.request.filter
 
         output_format = self.output_format
 
+        backend.set_options(
+            context,
+            output_format=output_format.output_format,
+            output_quality=output_format.output_quality,
+            output_lossless=output_format.output_lossless,
+        )
+
+        images = backend.filter_images(
+            context, self.request.image, self.request.filter, self.request.filter_params, input_type="base64"
+        )
+
+        if has_nsfw_filter:
+            images = filter_nsfw(images)
+
         if self.save_data.save_to_disk_path is not None:
             app_config = app.getConfig()
             folder_format = app_config.get("folder_format", "$id")
@@ -85,8 +96,9 @@ class FilterTask(Task):
             save_dir_path = os.path.join(
                 self.save_data.save_to_disk_path, format_folder_name(folder_format, dummy_req, self.task_data)
             )
+            images_pil = [base64_str_to_img(img) for img in images]
             save_images(
-                images,
+                images_pil,
                 save_dir_path,
                 file_name=img_id,
                 output_format=output_format.output_format,
@@ -94,13 +106,6 @@ class FilterTask(Task):
                 output_lossless=output_format.output_lossless,
             )
 
-        images = [
-            img_to_base64_str(
-                img, output_format.output_format, output_format.output_quality, output_format.output_lossless
-            )
-            for img in images
-        ]
-
         res = FilterImageResponse(self.request, self.models_data, images=images)
         res = res.json()
         self.buffer_queue.put(json.dumps(res))
@@ -110,46 +115,6 @@ class FilterTask(Task):
         self.response = res
 
 
-def filter_images(context, images, filters, filter_params={}):
-    filters = filters if isinstance(filters, list) else [filters]
-
-    for filter_name in filters:
-        params = filter_params.get(filter_name, {})
-
-        previous_state = before_filter(context, filter_name, params)
-
-        try:
-            images = apply_filters(context, filter_name, images, **params)
-        finally:
-            after_filter(context, filter_name, params, previous_state)
-
-    return images
-
-
-def before_filter(context, filter_name, filter_params):
-    if filter_name == "codeformer":
-        from easydiffusion.model_manager import DEFAULT_MODELS, resolve_model_to_use
-
-        default_realesrgan = DEFAULT_MODELS["realesrgan"][0]["file_name"]
-        prev_realesrgan_path = None
-
-        upscale_faces = filter_params.get("upscale_faces", False)
-        if upscale_faces and default_realesrgan not in context.model_paths["realesrgan"]:
-            prev_realesrgan_path = context.model_paths.get("realesrgan")
-            context.model_paths["realesrgan"] = resolve_model_to_use(default_realesrgan, "realesrgan")
-            load_model(context, "realesrgan")
-
-        return prev_realesrgan_path
-
-
-def after_filter(context, filter_name, filter_params, previous_state):
-    if filter_name == "codeformer":
-        prev_realesrgan_path = previous_state
-        if prev_realesrgan_path:
-            context.model_paths["realesrgan"] = prev_realesrgan_path
-            load_model(context, "realesrgan")
-
-
 def print_task_info(
     req: FilterImageRequest, models_data: ModelsData, output_format: OutputFormatData, save_data: SaveToDiskData
 ):

ui/easydiffusion/tasks/render_images.py

@@ -2,26 +2,23 @@ import json
 import pprint
 import queue
 import time
+from PIL import Image
 
 from easydiffusion import model_manager, runtime
 from easydiffusion.types import GenerateImageRequest, ModelsData, OutputFormatData, SaveToDiskData
 from easydiffusion.types import Image as ResponseImage
-from easydiffusion.types import GenerateImageResponse, RenderTaskData, UserInitiatedStop
-from easydiffusion.utils import get_printable_request, log, save_images_to_disk
-from sdkit.generate import generate_images
+from easydiffusion.types import GenerateImageResponse, RenderTaskData
+from easydiffusion.utils import get_printable_request, log, save_images_to_disk, filter_nsfw
 from sdkit.utils import (
-    diffusers_latent_samples_to_images,
-    gc,
     img_to_base64_str,
+    base64_str_to_img,
     img_to_buffer,
-    latent_samples_to_images,
     resize_img,
     get_image,
     log,
 )
 
 from .task import Task
-from .filter_images import filter_images
 
 
 class RenderTask(Task):
@@ -51,15 +48,13 @@ class RenderTask(Task):
         "Runs the image generation task on the assigned thread"
 
         from easydiffusion import task_manager, app
+        from easydiffusion.backend_manager import backend
 
         context = runtime.context
         config = app.getConfig()
 
         if config.get("block_nsfw", False):  # override if set on the server
             self.task_data.block_nsfw = True
-            if "nsfw_checker" not in self.task_data.filters:
-                self.task_data.filters.append("nsfw_checker")
-                self.models_data.model_paths["nsfw_checker"] = "nsfw_checker"
 
         def step_callback():
             task_manager.keep_task_alive(self)
@@ -68,7 +63,7 @@ class RenderTask(Task):
             if isinstance(task_manager.current_state_error, (SystemExit, StopAsyncIteration)) or isinstance(
                 self.error, StopAsyncIteration
             ):
-                context.stop_processing = True
+                backend.stop_rendering(context)
                 if isinstance(task_manager.current_state_error, StopAsyncIteration):
                     self.error = task_manager.current_state_error
                     task_manager.current_state_error = None
@@ -78,11 +73,7 @@ class RenderTask(Task):
         model_manager.resolve_model_paths(self.models_data)
 
         models_to_force_reload = []
-        if (
-            runtime.set_vram_optimizations(context)
-            or self.has_param_changed(context, "clip_skip")
-            or self.trt_needs_reload(context)
-        ):
+        if runtime.set_vram_optimizations(context) or self.has_param_changed(context, "clip_skip"):
             models_to_force_reload.append("stable-diffusion")
 
         model_manager.reload_models_if_necessary(context, self.models_data, models_to_force_reload)
@@ -99,10 +90,11 @@ class RenderTask(Task):
             self.buffer_queue,
             self.temp_images,
             step_callback,
+            self,
         )
 
     def has_param_changed(self, context, param_name):
-        if not context.test_diffusers:
+        if not getattr(context, "test_diffusers", False):
             return False
         if "stable-diffusion" not in context.models or "params" not in context.models["stable-diffusion"]:
             return True
@@ -111,29 +103,6 @@ class RenderTask(Task):
         new_val = self.models_data.model_params.get("stable-diffusion", {}).get(param_name, False)
         return model["params"].get(param_name) != new_val
 
-    def trt_needs_reload(self, context):
-        if not context.test_diffusers:
-            return False
-        if "stable-diffusion" not in context.models or "params" not in context.models["stable-diffusion"]:
-            return True
-
-        model = context.models["stable-diffusion"]
-
-        # curr_convert_to_trt = model["params"].get("convert_to_tensorrt")
-        new_convert_to_trt = self.models_data.model_params.get("stable-diffusion", {}).get("convert_to_tensorrt", False)
-
-        pipe = model["default"]
-        is_trt_loaded = hasattr(pipe.unet, "_allocate_trt_buffers") or hasattr(
-            pipe.unet, "_allocate_trt_buffers_backup"
-        )
-        if new_convert_to_trt and not is_trt_loaded:
-            return True
-
-        curr_build_config = model["params"].get("trt_build_config")
-        new_build_config = self.models_data.model_params.get("stable-diffusion", {}).get("trt_build_config", {})
-
-        return new_convert_to_trt and curr_build_config != new_build_config
-
 
 def make_images(
     context,
@@ -145,12 +114,21 @@ def make_images(
     data_queue: queue.Queue,
     task_temp_images: list,
     step_callback,
+    task,
 ):
-    context.stop_processing = False
     print_task_info(req, task_data, models_data, output_format, save_data)
 
     images, seeds = make_images_internal(
-        context, req, task_data, models_data, output_format, save_data, data_queue, task_temp_images, step_callback
+        context,
+        req,
+        task_data,
+        models_data,
+        output_format,
+        save_data,
+        data_queue,
+        task_temp_images,
+        step_callback,
+        task,
     )
 
     res = GenerateImageResponse(
@@ -170,7 +148,9 @@ def print_task_info(
     output_format: OutputFormatData,
     save_data: SaveToDiskData,
 ):
-    req_str = pprint.pformat(get_printable_request(req, task_data, models_data, output_format, save_data)).replace("[", "\[")
+    req_str = pprint.pformat(get_printable_request(req, task_data, models_data, output_format, save_data)).replace(
+        "[", "\["
+    )
     task_str = pprint.pformat(task_data.dict()).replace("[", "\[")
     models_data = pprint.pformat(models_data.dict()).replace("[", "\[")
     output_format = pprint.pformat(output_format.dict()).replace("[", "\[")
@@ -178,7 +158,7 @@ def print_task_info(
 
     log.info(f"request: {req_str}")
     log.info(f"task data: {task_str}")
-    # log.info(f"models data: {models_data}")
+    log.info(f"models data: {models_data}")
     log.info(f"output format: {output_format}")
     log.info(f"save data: {save_data}")
 
@@ -193,26 +173,41 @@ def make_images_internal(
     data_queue: queue.Queue,
     task_temp_images: list,
     step_callback,
+    task,
 ):
-    images, user_stopped = generate_images_internal(
+    from easydiffusion.backend_manager import backend
+
+    # prep the nsfw_filter
+    if task_data.block_nsfw:
+        filter_nsfw([Image.new("RGB", (1, 1))])  # hack - ensures that the model is available
+
+    images = generate_images_internal(
         context,
         req,
         task_data,
         models_data,
+        output_format,
         data_queue,
         task_temp_images,
         step_callback,
         task_data.stream_image_progress,
         task_data.stream_image_progress_interval,
     )
-
-    gc(context)
+    user_stopped = isinstance(task.error, StopAsyncIteration)
 
     filters, filter_params = task_data.filters, task_data.filter_params
-    filtered_images = filter_images(context, images, filters, filter_params) if not user_stopped else images
+    if len(filters) > 0 and not user_stopped:
+        filtered_images = backend.filter_images(context, images, filters, filter_params, input_type="base64")
+    else:
+        filtered_images = images
+
+    if task_data.block_nsfw:
+        filtered_images = filter_nsfw(filtered_images)
 
     if save_data.save_to_disk_path is not None:
-        save_images_to_disk(images, filtered_images, req, task_data, models_data, output_format, save_data)
+        images_pil = [base64_str_to_img(img) for img in images]
+        filtered_images_pil = [base64_str_to_img(img) for img in filtered_images]
+        save_images_to_disk(images_pil, filtered_images_pil, req, task_data, models_data, output_format, save_data)
 
     seeds = [*range(req.seed, req.seed + len(images))]
     if task_data.show_only_filtered_image or filtered_images is images:
@@ -226,97 +221,43 @@ def generate_images_internal(
     req: GenerateImageRequest,
     task_data: RenderTaskData,
     models_data: ModelsData,
+    output_format: OutputFormatData,
     data_queue: queue.Queue,
     task_temp_images: list,
     step_callback,
     stream_image_progress: bool,
     stream_image_progress_interval: int,
 ):
-    context.temp_images.clear()
+    from easydiffusion.backend_manager import backend
 
-    callback = make_step_callback(
+    callback = make_step_callback(context, req, task_data, data_queue, task_temp_images, step_callback)
+
+    req.width, req.height = map(lambda x: x - x % 8, (req.width, req.height))  # clamp to 8
+
+    if req.control_image and task_data.control_filter_to_apply:
+        req.controlnet_filter = task_data.control_filter_to_apply
+
+    if req.init_image is not None and int(req.num_inference_steps * req.prompt_strength) == 0:
+        req.prompt_strength = 1 / req.num_inference_steps if req.num_inference_steps > 0 else 1
+
+    backend.set_options(
         context,
-        req,
-        task_data,
-        data_queue,
-        task_temp_images,
-        step_callback,
-        stream_image_progress,
-        stream_image_progress_interval,
+        output_format=output_format.output_format,
+        output_quality=output_format.output_quality,
+        output_lossless=output_format.output_lossless,
+        vae_tiling=task_data.enable_vae_tiling,
+        stream_image_progress=stream_image_progress,
+        stream_image_progress_interval=stream_image_progress_interval,
+        clip_skip=2 if task_data.clip_skip else 1,
     )
 
-    try:
-        if req.init_image is not None and not context.test_diffusers:
-            req.sampler_name = "ddim"
+    images = backend.generate_images(context, callback=callback, output_type="base64", **req.dict())
 
-        req.width, req.height = map(lambda x: x - x % 8, (req.width, req.height))  # clamp to 8
-
-        if req.control_image and task_data.control_filter_to_apply:
-            req.control_image = get_image(req.control_image)
-            req.control_image = resize_img(req.control_image.convert("RGB"), req.width, req.height, clamp_to_8=True)
-            req.control_image = filter_images(context, req.control_image, task_data.control_filter_to_apply)[0]
-
-        if req.init_image is not None and int(req.num_inference_steps * req.prompt_strength) == 0:
-            req.prompt_strength = 1 / req.num_inference_steps if req.num_inference_steps > 0 else 1
-
-        if context.test_diffusers:
-            pipe = context.models["stable-diffusion"]["default"]
-            if hasattr(pipe.unet, "_allocate_trt_buffers_backup"):
-                setattr(pipe.unet, "_allocate_trt_buffers", pipe.unet._allocate_trt_buffers_backup)
-                delattr(pipe.unet, "_allocate_trt_buffers_backup")
-
-            if hasattr(pipe.unet, "_allocate_trt_buffers"):
-                convert_to_trt = models_data.model_params["stable-diffusion"].get("convert_to_tensorrt", False)
-                if convert_to_trt:
-                    pipe.unet.forward = pipe.unet._trt_forward
-                    # pipe.vae.decoder.forward = pipe.vae.decoder._trt_forward
-                    log.info(f"Setting unet.forward to TensorRT")
-                else:
-                    log.info(f"Not using TensorRT for unet.forward")
-                    pipe.unet.forward = pipe.unet._non_trt_forward
-                    # pipe.vae.decoder.forward = pipe.vae.decoder._non_trt_forward
-                    setattr(pipe.unet, "_allocate_trt_buffers_backup", pipe.unet._allocate_trt_buffers)
-                    delattr(pipe.unet, "_allocate_trt_buffers")
-
-            if task_data.enable_vae_tiling:
-                if hasattr(pipe, "enable_vae_tiling"):
-                    pipe.enable_vae_tiling()
-            else:
-                if hasattr(pipe, "disable_vae_tiling"):
-                    pipe.disable_vae_tiling()
-
-        images = generate_images(context, callback=callback, **req.dict())
-        user_stopped = False
-    except UserInitiatedStop:
-        images = []
-        user_stopped = True
-        if context.partial_x_samples is not None:
-            if context.test_diffusers:
-                images = diffusers_latent_samples_to_images(context, context.partial_x_samples)
-            else:
-                images = latent_samples_to_images(context, context.partial_x_samples)
-    finally:
-        if hasattr(context, "partial_x_samples") and context.partial_x_samples is not None:
-            if not context.test_diffusers:
-                del context.partial_x_samples
-            context.partial_x_samples = None
-
-    return images, user_stopped
+    return images
 
 
 def construct_response(images: list, seeds: list, output_format: OutputFormatData):
-    return [
-        ResponseImage(
-            data=img_to_base64_str(
-                img,
-                output_format.output_format,
-                output_format.output_quality,
-                output_format.output_lossless,
-            ),
-            seed=seed,
-        )
-        for img, seed in zip(images, seeds)
-    ]
+    return [ResponseImage(data=img, seed=seed) for img, seed in zip(images, seeds)]
 
 
 def make_step_callback(
@@ -326,53 +267,44 @@ def make_step_callback(
     data_queue: queue.Queue,
     task_temp_images: list,
     step_callback,
-    stream_image_progress: bool,
-    stream_image_progress_interval: int,
 ):
+    from easydiffusion.backend_manager import backend
+
     n_steps = req.num_inference_steps if req.init_image is None else int(req.num_inference_steps * req.prompt_strength)
     last_callback_time = -1
 
-    def update_temp_img(x_samples, task_temp_images: list):
+    def update_temp_img(images, task_temp_images: list):
         partial_images = []
 
-        if context.test_diffusers:
-            images = diffusers_latent_samples_to_images(context, x_samples)
-        else:
-            images = latent_samples_to_images(context, x_samples)
+        if images is None:
+            return []
 
         if task_data.block_nsfw:
-            images = filter_images(context, images, "nsfw_checker")
+            images = filter_nsfw(images, print_log=False)
 
         for i, img in enumerate(images):
+            img = img.convert("RGB")
+            img = resize_img(img, req.width, req.height)
             buf = img_to_buffer(img, output_format="JPEG")
 
-            context.temp_images[f"{task_data.request_id}/{i}"] = buf
             task_temp_images[i] = buf
             partial_images.append({"path": f"/image/tmp/{task_data.request_id}/{i}"})
         del images
         return partial_images
 
-    def on_image_step(x_samples, i, *args):
+    def on_image_step(images, i, *args):
         nonlocal last_callback_time
 
-        if context.test_diffusers:
-            context.partial_x_samples = (x_samples, args[0])
-        else:
-            context.partial_x_samples = x_samples
-
         step_time = time.time() - last_callback_time if last_callback_time != -1 else -1
         last_callback_time = time.time()
 
         progress = {"step": i, "step_time": step_time, "total_steps": n_steps}
 
-        if stream_image_progress and stream_image_progress_interval > 0 and i % stream_image_progress_interval == 0:
-            progress["output"] = update_temp_img(context.partial_x_samples, task_temp_images)
+        if images is not None:
+            progress["output"] = update_temp_img(images, task_temp_images)
 
         data_queue.put(json.dumps(progress))
 
         step_callback()
 
-        if context.stop_processing:
-            raise UserInitiatedStop("User requested that we stop processing")
-
     return on_image_step

ui/easydiffusion/types.py

@@ -19,9 +19,10 @@ class GenerateImageRequest(BaseModel):
     init_image_mask: Any = None
     control_image: Any = None
     control_alpha: Union[float, List[float]] = None
+    controlnet_filter: str = None
     prompt_strength: float = 0.8
-    preserve_init_image_color_profile = False
-    strict_mask_border = False
+    preserve_init_image_color_profile: bool = False
+    strict_mask_border: bool = False
 
     sampler_name: str = None  # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"
     hypernetwork_strength: float = 0
@@ -100,7 +101,7 @@ class MergeRequest(BaseModel):
     model1: str = None
     ratio: float = None
     out_path: str = "mix"
-    use_fp16 = True
+    use_fp16: bool = True
 
 
 class Image:
@@ -213,22 +214,19 @@ def convert_legacy_render_req_to_new(old_req: dict):
     model_paths["controlnet"] = old_req.get("use_controlnet_model")
     model_paths["embeddings"] = old_req.get("use_embeddings_model")
 
-    model_paths["gfpgan"] = old_req.get("use_face_correction", "")
-    model_paths["gfpgan"] = model_paths["gfpgan"] if "gfpgan" in model_paths["gfpgan"].lower() else None
+    ## ensure that the model name is in the model path
+    for model_name in ("gfpgan", "codeformer"):
+        model_paths[model_name] = old_req.get("use_face_correction", "")
+        model_paths[model_name] = model_paths[model_name] if model_name in model_paths[model_name].lower() else None
 
-    model_paths["codeformer"] = old_req.get("use_face_correction", "")
-    model_paths["codeformer"] = model_paths["codeformer"] if "codeformer" in model_paths["codeformer"].lower() else None
+    for model_name in ("realesrgan", "latent_upscaler", "esrgan_4x", "lanczos", "nearest", "scunet", "swinir"):
+        model_paths[model_name] = old_req.get("use_upscale", "")
+        model_paths[model_name] = model_paths[model_name] if model_name in model_paths[model_name].lower() else None
 
-    model_paths["realesrgan"] = old_req.get("use_upscale", "")
-    model_paths["realesrgan"] = model_paths["realesrgan"] if "realesrgan" in model_paths["realesrgan"].lower() else None
-
-    model_paths["latent_upscaler"] = old_req.get("use_upscale", "")
-    model_paths["latent_upscaler"] = (
-        model_paths["latent_upscaler"] if "latent_upscaler" in model_paths["latent_upscaler"].lower() else None
-    )
     if "control_filter_to_apply" in old_req:
         filter_model = old_req["control_filter_to_apply"]
         model_paths[filter_model] = filter_model
+        old_req["control_filter_to_apply"] = convert_legacy_controlnet_filter_name(old_req["control_filter_to_apply"])
 
     if old_req.get("block_nsfw"):
         model_paths["nsfw_checker"] = "nsfw_checker"
@@ -244,8 +242,12 @@ def convert_legacy_render_req_to_new(old_req: dict):
         }
 
     # move the filter params
-    if model_paths["realesrgan"]:
-        filter_params["realesrgan"] = {"scale": int(old_req.get("upscale_amount", 4))}
+    for model_name in ("realesrgan", "esrgan_4x", "lanczos", "nearest", "scunet", "swinir"):
+        if model_paths[model_name]:
+            filter_params[model_name] = {
+                "upscaler": model_paths[model_name],
+                "scale": int(old_req.get("upscale_amount", 4)),
+            }
     if model_paths["latent_upscaler"]:
         filter_params["latent_upscaler"] = {
             "prompt": old_req["prompt"],
@@ -264,14 +266,31 @@ def convert_legacy_render_req_to_new(old_req: dict):
     if old_req.get("block_nsfw"):
         filters.append("nsfw_checker")
 
-    if model_paths["codeformer"]:
-        filters.append("codeformer")
-    elif model_paths["gfpgan"]:
-        filters.append("gfpgan")
+    for model_name in ("gfpgan", "codeformer"):
+        if model_paths[model_name]:
+            filters.append(model_name)
+            break
 
-    if model_paths["realesrgan"]:
-        filters.append("realesrgan")
-    elif model_paths["latent_upscaler"]:
-        filters.append("latent_upscaler")
+    for model_name in ("realesrgan", "latent_upscaler", "esrgan_4x", "lanczos", "nearest", "scunet", "swinir"):
+        if model_paths[model_name]:
+            filters.append(model_name)
+            break
 
     return new_req
+
+
+def convert_legacy_controlnet_filter_name(filter):
+    from easydiffusion.backend_manager import backend
+
+    if filter is None:
+        return None
+
+    controlnet_filter_names = backend.list_controlnet_filters()
+
+    def apply(f):
+        return f"controlnet_{f}" if f in controlnet_filter_names else f
+
+    if isinstance(filter, list):
+        return [apply(f) for f in filter]
+
+    return apply(filter)

ui/easydiffusion/utils/__init__.py

@@ -7,6 +7,8 @@ from .save_utils import (
     save_images_to_disk,
     get_printable_request,
 )
+from .nsfw_checker import filter_nsfw
+
 
 def sha256sum(filename):
     sha256 = hashlib.sha256()
@@ -18,4 +20,3 @@ def sha256sum(filename):
             sha256.update(data)
 
     return sha256.hexdigest()
-

ui/easydiffusion/utils/nsfw_checker.py

@@ -0,0 +1,80 @@
+# possibly move this to sdkit in the future
+import os
+
+# mirror of https://huggingface.co/AdamCodd/vit-base-nsfw-detector/blob/main/onnx/model_quantized.onnx
+NSFW_MODEL_URL = (
+    "https://github.com/easydiffusion/sdkit-test-data/releases/download/assets/vit-base-nsfw-detector-quantized.onnx"
+)
+MODEL_HASH_QUICK = "220123559305b1b07b7a0894c3471e34dccd090d71cdf337dd8012f9e40d6c28"
+
+nsfw_check_model = None
+
+
+def filter_nsfw(images, blur_radius: float = 75, print_log=True):
+    global nsfw_check_model
+
+    from easydiffusion.app import MODELS_DIR
+    from sdkit.utils import base64_str_to_img, img_to_base64_str, download_file, log, hash_file_quick
+
+    import onnxruntime as ort
+    from PIL import ImageFilter
+    import numpy as np
+
+    if nsfw_check_model is None:
+        model_dir = os.path.join(MODELS_DIR, "nsfw-checker")
+        model_path = os.path.join(model_dir, "vit-base-nsfw-detector-quantized.onnx")
+
+        os.makedirs(model_dir, exist_ok=True)
+
+        if not os.path.exists(model_path) or hash_file_quick(model_path) != MODEL_HASH_QUICK:
+            download_file(NSFW_MODEL_URL, model_path)
+
+        nsfw_check_model = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
+
+    # Preprocess the input image
+    def preprocess_image(img):
+        img = img.convert("RGB")
+
+        # config based on based on https://huggingface.co/AdamCodd/vit-base-nsfw-detector/blob/main/onnx/preprocessor_config.json
+        # Resize the image
+        img = img.resize((384, 384))
+
+        # Normalize the image
+        img = np.array(img) / 255.0  # Scale pixel values to [0, 1]
+        mean = np.array([0.5, 0.5, 0.5])
+        std = np.array([0.5, 0.5, 0.5])
+        img = (img - mean) / std
+
+        # Transpose to match input shape (batch_size, channels, height, width)
+        img = np.transpose(img, (2, 0, 1)).astype(np.float32)
+
+        # Add batch dimension
+        img = np.expand_dims(img, axis=0)
+
+        return img
+
+    # Run inference
+    input_name = nsfw_check_model.get_inputs()[0].name
+    output_name = nsfw_check_model.get_outputs()[0].name
+
+    if print_log:
+        log.info("Running NSFW checker (onnx)")
+
+    results = []
+    for img in images:
+        is_base64 = isinstance(img, str)
+
+        input_img = base64_str_to_img(img) if is_base64 else img
+
+        result = nsfw_check_model.run([output_name], {input_name: preprocess_image(input_img)})
+        is_nsfw = [np.argmax(arr) == 1 for arr in result][0]
+
+        if is_nsfw:
+            output_img = input_img.filter(ImageFilter.GaussianBlur(blur_radius))
+            output_img = img_to_base64_str(output_img) if is_base64 else output_img
+        else:
+            output_img = img
+
+        results.append(output_img)
+
+    return results

ui/easydiffusion/utils/save_utils.py

@@ -247,7 +247,7 @@ def get_printable_request(
     task_data_metadata.update(save_data.dict())
 
     app_config = app.getConfig()
-    using_diffusers = app_config.get("use_v3_engine", True)
+    using_diffusers = app_config.get("backend", "ed_diffusers") in ("ed_diffusers", "webui")
 
     # Save the metadata in the order defined in TASK_TEXT_MAPPING
     metadata = {}

ui/index.html

@@ -35,7 +35,10 @@
             <h1>
                 <img id="logo_img" src="/media/images/icon-512x512.png" >
                 Easy Diffusion
-                <small><span id="version">v3.0.9</span> <span id="updateBranchLabel"></span></small>
+                <small>
+                    <span id="version">v3.5.0</span> <span id="updateBranchLabel"></span>
+                    <div id="engine-logo" class="gated-feature" data-feature-keys="backend_webui">(Powered by <a id="backend-url" href="https://github.com/lllyasviel/stable-diffusion-webui-forge" target="_blank">Stable Diffusion WebUI Forge</a>)</div>
+                </small>
             </h1>
         </div>
         <div id="server-status">
@@ -73,7 +76,7 @@
                         </div>
                         <div id="prompt-toolbar-right" class="toolbar-right">
                             <button id="image-modifier-dropdown" class="tertiaryButton smallButton">+ Image Modifiers</button>
-                            <button id="embeddings-button" class="tertiaryButton smallButton displayNone">+ Embedding</button>
+                            <button id="embeddings-button" class="tertiaryButton smallButton gated-feature" data-feature-keys="backend_ed_diffusers backend_webui">+ Embedding</button>
                         </div>
                     </div>
                     <textarea id="prompt" class="col-free">a photograph of an astronaut riding a horse</textarea>
@@ -83,7 +86,7 @@
                         <a href="https://github.com/easydiffusion/easydiffusion/wiki/Writing-prompts#negative-prompts" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top">Click to learn more about Negative Prompts</span></i></a>
                         <small>(optional)</small>
                     </label>
-                    <button id="negative-embeddings-button" class="tertiaryButton smallButton displayNone">+ Negative Embedding</button>
+                    <button id="negative-embeddings-button" class="tertiaryButton smallButton gated-feature" data-feature-keys="backend_ed_diffusers backend_webui">+ Negative Embedding</button>
                     <div class="collapsible-content">
                         <textarea id="negative_prompt" name="negative_prompt" placeholder="list the things to remove from the image (e.g. fog, green)"></textarea>
                     </div>
@@ -174,14 +177,14 @@
                             <!-- <label><small>Takes upto 20 mins the first time</small></label> -->
                         </td>
                     </tr>
-                    <tr class="pl-5 displayNone" id="clip_skip_config">
+                    <tr class="pl-5 gated-feature" id="clip_skip_config" data-feature-keys="backend_ed_diffusers backend_webui">
                         <td><label for="clip_skip">Clip Skip:</label></td>
                         <td class="diffusers-restart-needed">
                             <input id="clip_skip" name="clip_skip" type="checkbox">
                             <a href="https://github.com/easydiffusion/easydiffusion/wiki/Clip-Skip" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about Clip Skip</span></i></a>
                         </td>
                     </tr>
-                    <tr id="controlnet_model_container" class="pl-5">
+                    <tr id="controlnet_model_container" class="pl-5 gated-feature" data-feature-keys="backend_ed_diffusers backend_webui">
                         <td><label for="controlnet_model">ControlNet Image:</label></td>
                         <td class="diffusers-restart-needed">
                             <div id="control_image_wrapper" class="preview_image_wrapper">
@@ -201,40 +204,92 @@
                                         <option value="openpose_faceonly">OpenPose face-only</option>
                                         <option value="openpose_hand">OpenPose hand</option>
                                         <option value="openpose_full">OpenPose full</option>
+                                        <option value="animal_openpose" class="gated-feature" data-feature-keys="backend_webui">animal_openpose</option>
+                                        <option value="densepose_parula (black bg & blue torso)" class="gated-feature" data-feature-keys="backend_webui">densepose_parula (black bg & blue torso)</option>
+                                        <option value="densepose (pruple bg & purple torso)" class="gated-feature" data-feature-keys="backend_webui">densepose (pruple bg & purple torso)</option>
+                                        <option value="dw_openpose_full" class="gated-feature" data-feature-keys="backend_webui">dw_openpose_full</option>
+                                        <option value="mediapipe_face" class="gated-feature" data-feature-keys="backend_webui">mediapipe_face</option>
+                                        <option value="instant_id_face_keypoints" class="gated-feature" data-feature-keys="backend_webui">instant_id_face_keypoints</option>
+                                        <option value="InsightFace+CLIP-H (IPAdapter)" class="gated-feature" data-feature-keys="backend_webui">InsightFace+CLIP-H (IPAdapter)</option>
+                                        <option value="InsightFace (InstantID)" class="gated-feature" data-feature-keys="backend_webui">InsightFace (InstantID)</option>
                                     </optgroup>
                                     <optgroup label="Outline">
                                         <option value="canny">Canny (*)</option>
                                         <option value="mlsd">Straight lines</option>
                                         <option value="scribble_hed">Scribble hed (*)</option>
-                                        <option value="scribble_hedsafe">Scribble hedsafe</option>
+                                        <option value="scribble_hedsafe" class="gated-feature" data-feature-keys="backend_diffusers">Scribble hedsafe</option>
                                         <option value="scribble_pidinet">Scribble pidinet</option>
-                                        <option value="scribble_pidsafe">Scribble pidsafe</option>
+                                        <option value="scribble_pidsafe" class="gated-feature" data-feature-keys="backend_diffusers">Scribble pidsafe</option>
+                                        <option value="scribble_xdog" class="gated-feature" data-feature-keys="backend_webui">scribble_xdog</option>
                                         <option value="softedge_hed">Softedge hed</option>
                                         <option value="softedge_hedsafe">Softedge hedsafe</option>
                                         <option value="softedge_pidinet">Softedge pidinet</option>
                                         <option value="softedge_pidsafe">Softedge pidsafe</option>
+                                        <option value="softedge_teed" class="gated-feature" data-feature-keys="backend_webui">softedge_teed</option>
                                     </optgroup>
                                     <optgroup label="Depth">
                                         <option value="normal_bae">Normal bae (*)</option>
                                         <option value="depth_midas">Depth midas</option>
+                                        <option value="normal_midas" class="gated-feature" data-feature-keys="backend_webui">normal_midas</option>
                                         <option value="depth_zoe">Depth zoe</option>
                                         <option value="depth_leres">Depth leres</option>
                                         <option value="depth_leres++">Depth leres++</option>
+                                        <option value="depth_anything_v2" class="gated-feature" data-feature-keys="backend_webui">depth_anything_v2</option>
+                                        <option value="depth_anything" class="gated-feature" data-feature-keys="backend_webui">depth_anything</option>
+                                        <option value="depth_hand_refiner" class="gated-feature" data-feature-keys="backend_webui">depth_hand_refiner</option>
+                                        <option value="depth_marigold" class="gated-feature" data-feature-keys="backend_webui">depth_marigold</option>
                                     </optgroup>
                                     <optgroup label="Line art">
                                         <option value="lineart_coarse">Lineart coarse</option>
                                         <option value="lineart_realistic">Lineart realistic</option>
                                         <option value="lineart_anime">Lineart anime</option>
+                                        <option value="lineart_standard (from white bg & black line)" class="gated-feature" data-feature-keys="backend_webui">lineart_standard (from white bg & black line)</option>
+                                        <option value="lineart_anime_denoise" class="gated-feature" data-feature-keys="backend_webui">lineart_anime_denoise</option>
+                                    </optgroup>
+                                    <optgroup label="Reference" class="gated-feature" data-feature-keys="backend_webui">
+                                        <option value="reference_adain">reference_adain</option>
+                                        <option value="reference_only">reference_only</option>
+                                        <option value="reference_adain+attn">reference_adain+attn</option>
+                                    </optgroup>
+                                    <optgroup label="Tile" class="gated-feature" data-feature-keys="backend_webui">
+                                        <option value="tile_colorfix">tile_colorfix</option>
+                                        <option value="tile_resample">tile_resample</option>
+                                        <option value="tile_colorfix+sharp">tile_colorfix+sharp</option>
+                                    </optgroup>
+                                    <optgroup label="CLIP (IPAdapter)" class="gated-feature" data-feature-keys="backend_webui">
+                                        <option value="CLIP-ViT-H (IPAdapter)">CLIP-ViT-H (IPAdapter)</option>
+                                        <option value="CLIP-G (Revision)">CLIP-G (Revision)</option>
+                                        <option value="CLIP-G (Revision ignore prompt)">CLIP-G (Revision ignore prompt)</option>
+                                        <option value="CLIP-ViT-bigG (IPAdapter)">CLIP-ViT-bigG (IPAdapter)</option>
+                                        <option value="InsightFace+CLIP-H (IPAdapter)">InsightFace+CLIP-H (IPAdapter)</option>
+                                    </optgroup>
+                                    <optgroup label="Inpaint" class="gated-feature" data-feature-keys="backend_webui">
+                                        <option value="inpaint_only">inpaint_only</option>
+                                        <option value="inpaint_only+lama">inpaint_only+lama</option>
+                                        <option value="inpaint_global_harmonious">inpaint_global_harmonious</option>
+                                    </optgroup>
+                                    <optgroup label="Segment" class="gated-feature" data-feature-keys="backend_webui">
+                                        <option value="seg_ufade20k">seg_ufade20k</option>
+                                        <option value="seg_ofade20k">seg_ofade20k</option>
+                                        <option value="seg_anime_face">seg_anime_face</option>
+                                        <option value="seg_ofcoco">seg_ofcoco</option>
                                     </optgroup>
                                     <optgroup label="Misc">
                                         <option value="shuffle">Shuffle</option>
-                                        <option value="segment">Segment</option>
+                                        <option value="segment" class="gated-feature" data-feature-keys="backend_diffusers">Segment</option>
+                                        <option value="invert (from white bg & black line)" class="gated-feature" data-feature-keys="backend_webui">invert (from white bg & black line)</option>
+                                        <option value="threshold" class="gated-feature" data-feature-keys="backend_webui">threshold</option>
+                                        <option value="t2ia_sketch_pidi" class="gated-feature" data-feature-keys="backend_webui">t2ia_sketch_pidi</option>
+                                        <option value="t2ia_color_grid" class="gated-feature" data-feature-keys="backend_webui">t2ia_color_grid</option>
+                                        <option value="recolor_intensity" class="gated-feature" data-feature-keys="backend_webui">recolor_intensity</option>
+                                        <option value="recolor_luminance" class="gated-feature" data-feature-keys="backend_webui">recolor_luminance</option>
+                                        <option value="blur_gaussian" class="gated-feature" data-feature-keys="backend_webui">blur_gaussian</option>
                                     </optgroup>
                                 </select>
                                 <br/>
                                 <label for="controlnet_model"><small>Model:</small></label> <input id="controlnet_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />
-                                <br/>
-                                <label><small>Will download the necessary models, the first time.</small></label>
+                                <!-- <br/>
+                                <label><small>Will download the necessary models, the first time.</small></label> -->
                                 <br/>
                                 <label for="controlnet_alpha_slider"><small>Strength:</small></label> <input id="controlnet_alpha_slider" name="controlnet_alpha_slider" class="editor-slider" value="10" type="range" min="0" max="10"> <input id="controlnet_alpha" name="controlnet_alpha" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="decimal">
                             </div>
@@ -248,27 +303,38 @@
                         <select id="sampler_name" name="sampler_name">
                             <option value="plms">PLMS</option>
                             <option value="ddim">DDIM</option>
+                            <option value="ddim_cfgpp" class="gated-feature" data-feature-keys="backend_webui">DDIM CFG++</option>
                             <option value="heun">Heun</option>
                             <option value="euler">Euler</option>
                             <option value="euler_a" selected>Euler Ancestral</option>
                             <option value="dpm2">DPM2</option>
                             <option value="dpm2_a">DPM2 Ancestral</option>
+                            <option value="dpm_fast" class="gated-feature" data-feature-keys="backend_webui">DPM Fast</option>
+                            <option value="dpm_adaptive" class="gated-feature" data-feature-keys="backend_ed_classic backend_webui">DPM Adaptive</option>
                             <option value="lms">LMS</option>
-                            <option value="dpm_solver_stability">DPM Solver (Stability AI)</option>
+                            <option value="dpm_solver_stability" class="gated-feature" data-feature-keys="backend_ed_classic backend_ed_diffusers">DPM Solver (Stability AI)</option>
                             <option value="dpmpp_2s_a">DPM++ 2s Ancestral (Karras)</option>
                             <option value="dpmpp_2m">DPM++ 2m (Karras)</option>
-                            <option value="dpmpp_2m_sde" class="diffusers-only">DPM++ 2m SDE (Karras)</option>
+                            <option value="dpmpp_2m_sde" class="gated-feature" data-feature-keys="backend_ed_diffusers backend_webui">DPM++ 2m SDE</option>
+                            <option value="dpmpp_2m_sde_heun" class="gated-feature" data-feature-keys="backend_webui">DPM++ 2m SDE Heun</option>
+                            <option value="dpmpp_3m_sde" class="gated-feature" data-feature-keys="backend_webui">DPM++ 3M SDE</option>
                             <option value="dpmpp_sde">DPM++ SDE (Karras)</option>
-                            <option value="dpm_adaptive" class="k_diffusion-only">DPM Adaptive (Karras)</option>
-                            <option value="ddpm" class="diffusers-only">DDPM</option>
-                            <option value="deis" class="diffusers-only">DEIS</option>
-                            <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" class="k_diffusion-only">UniPC TU 2</option>
-                            <option value="unipc_tq" class="k_diffusion-only">UniPC TQ</option>
+                            <option value="restart" class="gated-feature" data-feature-keys="backend_webui">Restart</option>
+                            <option value="heun_pp2" class="gated-feature" data-feature-keys="backend_webui">Heun PP2</option>
+                            <option value="ipndm" class="gated-feature" data-feature-keys="backend_webui">IPNDM</option>
+                            <option value="ipndm_v" class="gated-feature" data-feature-keys="backend_webui">IPNDM_V</option>
+                            <option value="ddpm" class="gated-feature" data-feature-keys="backend_ed_diffusers backend_webui">DDPM</option>
+                            <option value="deis" class="gated-feature" data-feature-keys="backend_ed_diffusers backend_webui">DEIS</option>
+                            <option value="lcm" class="gated-feature" data-feature-keys="backend_webui">LCM</option>
+                            <option value="forge_flux_realistic" class="gated-feature" data-feature-keys="backend_webui">[Forge] Flux Realistic</option>
+                            <option value="forge_flux_realistic_slow" class="gated-feature" data-feature-keys="backend_webui">[Forge] Flux Realistic (Slow)</option>
+                            <option value="unipc_snr" class="gated-feature" data-feature-keys="backend_ed_classic">UniPC SNR</option>
+                            <option value="unipc_tu" class="gated-feature" data-feature-keys="backend_ed_classic backend_ed_diffusers">UniPC TU</option>
+                            <option value="unipc_snr_2" class="gated-feature" data-feature-keys="backend_ed_classic">UniPC SNR 2</option>
+                            <option value="unipc_tu_2" class="gated-feature" data-feature-keys="backend_ed_classic">UniPC TU 2</option>
+                            <option value="unipc_tq" class="gated-feature" data-feature-keys="backend_ed_classic">UniPC TQ</option>
                         </select>
-                        <a href="https://github.com/easydiffusion/easydiffusion/wiki/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>
+                        <a href="https://github.com/easydiffusion/easydiffusion/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>
                     </td></tr>
                     <tr class="pl-5"><td><label>Image Size: </label></td><td id="image-size-options">
                         <select id="width" name="width" value="512">
@@ -349,7 +415,7 @@
                     <tr class="pl-5"><td><label for="num_inference_steps">Inference Steps:</label></td><td> <input id="num_inference_steps" name="num_inference_steps" type="number" min="1" step="1" style="width: 42pt" value="25" onkeypress="preventNonNumericalInput(event)" inputmode="numeric"></td></tr>
                     <tr class="pl-5"><td><label for="guidance_scale_slider">Guidance Scale:</label></td><td> <input id="guidance_scale_slider" name="guidance_scale_slider" class="editor-slider" value="75" type="range" min="11" max="500"> <input id="guidance_scale" name="guidance_scale" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="decimal"></td></tr>
                     <tr id="prompt_strength_container" class="pl-5"><td><label for="prompt_strength_slider">Prompt Strength:</label></td><td> <input id="prompt_strength_slider" name="prompt_strength_slider" class="editor-slider" value="80" type="range" min="0" max="99"> <input id="prompt_strength" name="prompt_strength" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="decimal"><br/></td></tr>
-                    <tr id="lora_model_container" class="pl-5">
+                    <tr id="lora_model_container" class="pl-5 gated-feature" data-feature-keys="backend_ed_diffusers backend_webui">
                         <td>
                             <label for="lora_model">LoRA:</label>
                         </td>
@@ -357,14 +423,14 @@
                             <div id="lora_model" data-path=""></div>
                         </td>
                     </tr>
-                    <tr id="hypernetwork_model_container" class="pl-5"><td><label for="hypernetwork_model">Hypernetwork:</label></td><td>
+                    <tr id="hypernetwork_model_container" class="pl-5 gated-feature" data-feature-keys="backend_ed_classic"><td><label for="hypernetwork_model">Hypernetwork:</label></td><td>
                         <input id="hypernetwork_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />
                     </td></tr>
-                    <tr id="hypernetwork_strength_container" class="pl-5">
+                    <tr id="hypernetwork_strength_container" class="pl-5 gated-feature" data-feature-keys="backend_ed_classic">
                         <td><label for="hypernetwork_strength_slider">Hypernetwork Strength:</label></td>
                         <td> <input id="hypernetwork_strength_slider" name="hypernetwork_strength_slider" class="editor-slider" value="100" type="range" min="0" max="100"> <input id="hypernetwork_strength" name="hypernetwork_strength" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="decimal"><br/></td>
                     </tr>
-                    <tr id="tiling_container" class="pl-5">
+                    <tr id="tiling_container" class="pl-5 gated-feature" data-feature-keys="backend_ed_diffusers">
                         <td><label for="tiling">Seamless Tiling:</label></td>
                         <td class="diffusers-restart-needed">
                             <select id="tiling" name="tiling">
@@ -389,7 +455,7 @@
                     <tr class="pl-5" id="output_quality_row"><td><label for="output_quality">Image Quality:</label></td><td>
                     <input id="output_quality_slider" name="output_quality" class="editor-slider" value="75" type="range" min="10" max="95"> <input id="output_quality" name="output_quality" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="numeric">
                     </td></tr>
-                    <tr class="pl-5">
+                    <tr class="pl-5 gated-feature" data-feature-keys="backend_ed_diffusers">
                         <td><label for="tiling">Enable VAE Tiling:</label></td>
                         <td class="diffusers-restart-needed">
                             <input id="enable_vae_tiling" name="enable_vae_tiling" type="checkbox" checked>
@@ -405,7 +471,7 @@
                         <input id="use_face_correction" name="use_face_correction" type="checkbox"> <label for="use_face_correction">Fix incorrect faces and eyes</label> <div style="display:inline-block;"><input id="gfpgan_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" /></div>
                         <table id="codeformer_settings" class="displayNone sub-settings">
                             <tr class="pl-5"><td><label for="codeformer_fidelity_slider">Strength:</label></td><td><input id="codeformer_fidelity_slider" name="codeformer_fidelity_slider" class="editor-slider" value="5" type="range" min="0" max="10"> <input id="codeformer_fidelity" name="codeformer_fidelity" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="decimal"></td></tr>
-                            <tr class="pl-5"><td><label for="codeformer_upscale_faces">Upscale Faces:</label></td><td><input id="codeformer_upscale_faces" name="codeformer_upscale_faces" type="checkbox" checked> <label><small>(improves the resolution of faces)</small></label></td></tr>
+                            <tr class="pl-5 gated-feature" data-feature-keys="backend_ed_diffusers"><td><label for="codeformer_upscale_faces">Upscale Faces:</label></td><td><input id="codeformer_upscale_faces" name="codeformer_upscale_faces" type="checkbox" checked> <label><small>(improves the resolution of faces)</small></label></td></tr>
                         </table>
                     </li>
                     <li class="pl-5">
@@ -418,7 +484,13 @@
                         <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>
+                            <option value="ESRGAN_4x" class="pl-5 gated-feature" data-feature-keys="backend_webui">ESRGAN_4x</option>
+                            <option value="Lanczos" class="pl-5 gated-feature" data-feature-keys="backend_webui">Lanczos</option>
+                            <option value="Nearest" class="pl-5 gated-feature" data-feature-keys="backend_webui">Nearest</option>
+                            <option value="ScuNET" class="pl-5 gated-feature" data-feature-keys="backend_webui">ScuNET GAN</option>
+                            <option value="ScuNET PSNR" class="pl-5 gated-feature" data-feature-keys="backend_webui">ScuNET PSNR</option>
+                            <option value="SwinIR_4x" class="pl-5 gated-feature" data-feature-keys="backend_webui">SwinIR 4x</option>
+                            <option value="latent_upscaler" class="pl-5 gated-feature" data-feature-keys="backend_ed_classic backend_ed_diffusers">Latent Upscaler 2x</option>
                         </select>
                         <table id="latent_upscaler_settings" class="displayNone sub-settings">
                             <tr class="pl-5"><td><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)" inputmode="numeric"></td></tr>
@@ -825,7 +897,8 @@
             <p>This license of this software forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, <br/>spread misinformation and target vulnerable groups. For the full list of restrictions please read <a href="https://github.com/easydiffusion/easydiffusion/blob/main/LICENSE" target="_blank">the license</a>.</p>
             <p>By using this software, you consent to the terms and conditions of the license.</p>
         </div>
-        <input id="test_diffusers" type="checkbox" style="display: none" checked />
+        <input id="test_diffusers" type="checkbox" style="display: none" checked /> <!-- for backwards compatibility -->
+        <input id="use_v3_engine" type="checkbox" style="display: none" checked /> <!-- for backwards compatibility -->
     </div>
 </div>
 </body>

ui/main.py

@@ -9,6 +9,3 @@ server.init()
 model_manager.init()
 app.init_render_threads()
 bucket_manager.init()
-
-# start the browser ui
-app.open_browser()

ui/media/css/auto-save.css

@@ -79,6 +79,7 @@
 }
 
 .parameters-table .fa-fire,
-.parameters-table .fa-bolt {
+.parameters-table .fa-bolt,
+.parameters-table .fa-robot {
     color: #F7630C;
 }

ui/media/css/main.css

@@ -36,6 +36,15 @@ code {
     transform: translateY(4px);
     cursor: pointer;
 }
+#engine-logo {
+    font-size: 8pt;
+    padding-left: 10pt;
+    color: var(--small-label-color);
+}
+#engine-logo a {
+    text-decoration: none;
+    /* color: var(--small-label-color); */
+}
 #prompt {
     width: 100%;
     height: 65pt;
@@ -541,7 +550,7 @@ div.img-preview img {
     position: relative;
     background: var(--background-color4);
     display: flex;
-    padding: 12px 0 0;
+    padding: 6px 0 0;
 }
 .tab .icon {
     padding-right: 4pt;
@@ -657,6 +666,10 @@ div.img-preview img {
     display: block;
 }
 
+.gated-feature {
+    display: none;
+}
+
 .display-settings {
     float: right;
     position: relative;

ui/media/js/main.js

@@ -981,7 +981,20 @@ function onRedoFilter(req, img, e, tools) {
 function onUpscaleClick(req, img, e, tools) {
     let path = upscaleModelField.value
     let scale = parseInt(upscaleAmountField.value)
-    let filterName = path.toLowerCase().includes("realesrgan") ? "realesrgan" : "latent_upscaler"
+
+    let filterName = null
+    const FILTERS = ["realesrgan", "latent_upscaler", "esrgan_4x", "lanczos", "nearest", "scunet", "swinir"]
+    for (let idx in FILTERS) {
+        let f = FILTERS[idx]
+        if (path.toLowerCase().includes(f)) {
+            filterName = f
+            break
+        }
+    }
+
+    if (!filterName) {
+        return
+    }
     let statusText = "Upscaling by " + scale + "x using " + filterName
     applyInlineFilter(filterName, path, { scale: scale }, img, statusText, tools)
 }

ui/media/js/parameters.js

@@ -249,14 +249,19 @@ var PARAMETERS = [
         default: false,
     },
     {
-        id: "use_v3_engine",
-        type: ParameterType.checkbox,
-        label: "Use the new v3 engine (diffusers)",
+        id: "backend",
+        type: ParameterType.select,
+        label: "Engine to use",
         note:
-            "Use our new v3 engine, with additional features like LoRA, ControlNet, SDXL, Embeddings, Tiling and lots more! Please press Save, then restart the program after changing this.",
-        icon: "fa-bolt",
-        default: true,
+            "Use our new v3.5 engine (Forge), with additional features like Flux, SD3, Lycoris and lots more! Please press Save, then restart the program after changing this.",
+        icon: "fa-robot",
         saveInAppConfig: true,
+        default: "ed_diffusers",
+        options: [
+            { value: "webui", label: "v3.5 (latest)" },
+            { value: "ed_diffusers", label: "v3.0" },
+            { value: "ed_classic", label: "v2.0" },
+        ],
     },
     {
         id: "cloudflare",
@@ -432,6 +437,7 @@ let useBetaChannelField = document.querySelector("#use_beta_channel")
 let uiOpenBrowserOnStartField = document.querySelector("#ui_open_browser_on_start")
 let confirmDangerousActionsField = document.querySelector("#confirm_dangerous_actions")
 let testDiffusers = document.querySelector("#use_v3_engine")
+let backendEngine = document.querySelector("#backend")
 let profileNameField = document.querySelector("#profileName")
 let modelsDirField = document.querySelector("#models_dir")
 
@@ -454,6 +460,23 @@ async function changeAppConfig(configDelta) {
     }
 }
 
+function getDefaultDisplay(element) {
+    const tag = element.tagName.toLowerCase();
+    const defaultDisplays = {
+        div: 'block',
+        span: 'inline',
+        p: 'block',
+        tr: 'table-row',
+        table: 'table',
+        li: 'list-item',
+        ul: 'block',
+        ol: 'block',
+        button: 'inline',
+        // Add more if needed
+    };
+    return defaultDisplays[tag] || 'block'; // Default to 'block' if not listed
+}
+
 async function getAppConfig() {
     try {
         let res = await fetch("/get/app_config")
@@ -478,14 +501,16 @@ async function getAppConfig() {
         modelsDirField.value = config.models_dir
 
         let testDiffusersEnabled = true
-        if (config.use_v3_engine === false) {
+        if (config.backend === "ed_classic") {
             testDiffusersEnabled = false
         }
         testDiffusers.checked = testDiffusersEnabled
+        backendEngine.value = config.backend
         document.querySelector("#test_diffusers").checked = testDiffusers.checked // don't break plugins
+        document.querySelector("#use_v3_engine").checked = testDiffusers.checked // don't break plugins
 
         if (config.config_on_startup) {
-            if (config.config_on_startup?.use_v3_engine) {
+            if (config.config_on_startup?.backend !== "ed_classic") {
                 document.body.classList.add("diffusers-enabled-on-startup")
                 document.body.classList.remove("diffusers-disabled-on-startup")
             } else {
@@ -494,37 +519,27 @@ async function getAppConfig() {
             }
         }
 
-        if (!testDiffusersEnabled) {
-            document.querySelector("#lora_model_container").style.display = "none"
-            document.querySelector("#tiling_container").style.display = "none"
-            document.querySelector("#controlnet_model_container").style.display = "none"
-            document.querySelector("#hypernetwork_model_container").style.display = ""
-            document.querySelector("#hypernetwork_strength_container").style.display = ""
-            document.querySelector("#negative-embeddings-button").style.display = "none"
-
-            document.querySelectorAll("#sampler_name option.diffusers-only").forEach((option) => {
-                option.style.display = "none"
-            })
+        if (config.backend === "ed_classic") {
             IMAGE_STEP_SIZE = 64
-            customWidthField.step = IMAGE_STEP_SIZE
-            customHeightField.step = IMAGE_STEP_SIZE
         } else {
-            document.querySelector("#lora_model_container").style.display = ""
-            document.querySelector("#tiling_container").style.display = ""
-            document.querySelector("#controlnet_model_container").style.display = ""
-            document.querySelector("#hypernetwork_model_container").style.display = "none"
-            document.querySelector("#hypernetwork_strength_container").style.display = "none"
-
-            document.querySelectorAll("#sampler_name option.k_diffusion-only").forEach((option) => {
-                option.style.display = "none"
-            })
-            document.querySelector("#clip_skip_config").classList.remove("displayNone")
-            document.querySelector("#embeddings-button").classList.remove("displayNone")
             IMAGE_STEP_SIZE = 8
-            customWidthField.step = IMAGE_STEP_SIZE
-            customHeightField.step = IMAGE_STEP_SIZE
         }
 
+        customWidthField.step = IMAGE_STEP_SIZE
+        customHeightField.step = IMAGE_STEP_SIZE
+
+        const currentBackendKey = "backend_" + config.backend
+
+        document.querySelectorAll('.gated-feature').forEach((element) => {
+            const featureKeys = element.getAttribute('data-feature-keys').split(' ')
+
+            if (featureKeys.includes(currentBackendKey)) {
+                element.style.display = getDefaultDisplay(element)
+            } else {
+                element.style.display = 'none'
+            }
+        });
+
         if (config.force_save_metadata) {
             metadataOutputFormatField.value = config.force_save_metadata
         }
@@ -749,6 +764,11 @@ async function getSystemInfo() {
             metadataOutputFormatField.disabled = !saveToDiskField.checked
         }
         setDiskPath(res["default_output_dir"], force)
+
+        // backend info
+        if (res["backend_url"]) {
+            document.querySelector("#backend-url").setAttribute("href", res["backend_url"])
+        }
     } catch (e) {
         console.log("error fetching devices", e)
     }