Support python paths in legacy installations in the dev console

Tjcomserv patch 1 pydantic

CHANGES.md

@@ -1,21 +1,5 @@
 # What's new?
 
-## v3.5 (preview)
-### Major Changes
-- **Flux** - full support for the Flux model, including quantized bnb and nf4 models.
-- **LyCORIS** - including `LoCon`, `Hada`, `IA3` and `Lokr`.
-- **11 new samplers** - `DDIM CFG++`, `DPM Fast`, `DPM++ 2m SDE Heun`, `DPM++ 3M SDE`, `Restart`, `Heun PP2`, `IPNDM`, `IPNDM_V`, `LCM`, `[Forge] Flux Realistic`, `[Forge] Flux Realistic (Slow)`.
-- **15 new schedulers** - `Uniform`, `Karras`, `Exponential`, `Polyexponential`, `SGM Uniform`, `KL Optimal`, `Align Your Steps`, `Normal`, `DDIM`, `Beta`, `Turbo`, `Align Your Steps GITS`, `Align Your Steps 11`, `Align Your Steps 32`.
-- **42 new Controlnet filters, and support for lots of new ControlNet models** (including QR ControlNets).
-- **5 upscalers** - `SwinIR`, `ScuNET`, `Nearest`, `Lanczos`, `ESRGAN`.
-- **Faster than v3.0**
-- **Major rewrite of the code** - We've switched to `Forge WebUI` under the hood, which brings a lot of new features, faster image generation, and support for all the extensions in the Forge/Automatic1111 community. This allows Easy Diffusion to stay up-to-date with the latest features, and focus on making the UI and installation experience even easier.
-
-v3.5 is currently an optional upgrade, and you can switch between the v3.0 (diffusers) engine and the v3.5 (webui) engine using the `Settings` tab in the UI.
-
-### Detailed changelog
-* 3.5.0 - 11 Oct 2024 - **Preview release** of the new v3.5 engine, powered by Forge WebUI (a fork of Automatic1111). This enables Flux, SD3, LyCORIS and lots of new features, while using the same familiar Easy Diffusion interface.
-
 ## v3.0
 ### Major Changes
 - **ControlNet** - Full support for ControlNet, with native integration of the common ControlNet models. Just select a control image, then choose the ControlNet filter/model and run. No additional configuration or download necessary. Supports custom ControlNets as well.
@@ -33,7 +17,9 @@ v3.5 is currently an optional upgrade, and you can switch between the v3.0 (diff
 - **Major rewrite of the code** - We've switched to using diffusers under-the-hood, which allows us to release new features faster, and focus on making the UI and installer even easier to use.
 
 ### Detailed changelog
-* 3.0.10 - 11 Oct 2024 - **Major Update** - An option to upgrade to v3.5, which enables Flux, Stable Diffusion 3, LyCORIS models and lots more.
+* 3.0.9c - 6 Feb 2025 - (Internal code change) Remove hardcoded references to `torch.cuda`, and replace with torchruntime's device utilities.
+* 3.0.9b - 28 Jan 2025 - Fix a bug affecting older versions of Easy Diffusion, which tried to upgrade to an incompatible version of PyTorch.
+* 3.0.9b - 4 Jan 2025 - Replace the use of WMIC (deprecated) with a powershell call.
 * 3.0.9 - 28 May 2024 - Slider for controlling the strength of controlnets.
 * 3.0.8 - 27 May 2024 - SDXL ControlNets for Img2Img and Inpainting.
 * 3.0.7 - 11 Dec 2023 - Setting to enable/disable VAE tiling (in the Image Settings panel). Sometimes VAE tiling reduces the quality of the image, so this setting will help control that.

README.md

@@ -1,9 +1,9 @@
 # Easy Diffusion 3.0
-### The easiest way to install and use [Stable Diffusion](https://github.com/CompVis/stable-diffusion) on your computer.
+### An easy way to install and use [Stable Diffusion](https://github.com/CompVis/stable-diffusion) on your computer.
 
 Does not require technical knowledge, does not require pre-installed software. 1-click install, powerful features, friendly community.
 
-️‍🔥🎉 **New!** Support for SDXL, ControlNet, multiple LoRA files, embeddings (and a lot more) have been added!
+️‍🔥🎉 **New!** Support for Flux has been added in the beta branch (v3.5 engine)!
 
 [Installation guide](#installation) | [Troubleshooting guide](https://github.com/easydiffusion/easydiffusion/wiki/Troubleshooting) | [User guide](https://github.com/easydiffusion/easydiffusion/wiki) | <sub>[![Discord Server](https://img.shields.io/discord/1014774730907209781?label=Discord)](https://discord.com/invite/u9yhsFmEkB)</sub> <sup>(for support queries, and development discussions)</sup>
 
@@ -21,15 +21,15 @@ Click the download button for your operating system:
 </p>
 
 **Hardware requirements:**
-- **Windows:** NVIDIA graphics card¹ (minimum 2 GB RAM), or run on your CPU.
+- **Windows:** NVIDIA¹ or AMD graphics card (minimum 2 GB RAM), or run on your CPU.
 - **Linux:** NVIDIA¹ or AMD² graphics card (minimum 2 GB RAM), or run on your CPU.
-- **Mac:** M1 or M2, or run on your CPU.
+- **Mac:** M1/M2/M3/M4 or AMD graphics card (Intel Mac), or run on your CPU.
 - Minimum 8 GB of system RAM.
 - Atleast 25 GB of space on the hard disk.
 
 ¹) [CUDA Compute capability](https://en.wikipedia.org/wiki/CUDA#GPUs_supported) level of 3.7 or higher required.
 
-²) ROCm 5.2 support required.
+²) ROCm 5.2 (or newer) support required.
 
 The installer will take care of whatever is needed. If you face any problems, you can join the friendly [Discord community](https://discord.com/invite/u9yhsFmEkB) and ask for assistance.
 

scripts/Developer Console.cmd

@@ -4,7 +4,7 @@ echo "Opening Stable Diffusion UI - Developer Console.." & echo.
 
 cd /d %~dp0
 
-set PATH=C:\Windows\System32;%PATH%
+set PATH=C:\Windows\System32;C:\Windows\System32\WindowsPowerShell\v1.0;%PATH%
 
 @rem set legacy and new installer's PATH, if they exist
 if exist "installer" set PATH=%cd%\installer;%cd%\installer\Library\bin;%cd%\installer\Scripts;%cd%\installer\Library\usr\bin;%PATH%
@@ -26,18 +26,23 @@ call conda --version
 echo.
 echo COMSPEC=%COMSPEC%
 echo.
+powershell -Command "(Get-WmiObject Win32_VideoController | Select-Object Name, AdapterRAM, DriverDate, DriverVersion)"
 
 @rem activate the legacy environment (if present) and set PYTHONPATH
 if exist "installer_files\env" (
     set PYTHONPATH=%cd%\installer_files\env\lib\site-packages
+    set PYTHON=%cd%\installer_files\env\python.exe
+    echo PYTHON=%PYTHON%
 )
 if exist "stable-diffusion\env" (
     call conda activate .\stable-diffusion\env
     set PYTHONPATH=%cd%\stable-diffusion\env\lib\site-packages
+    set PYTHON=%cd%\stable-diffusion\env\python.exe
+    echo PYTHON=%PYTHON%
 )
 
-call where python
+@REM call where python
-call python --version
+call "%PYTHON%" --version
 
 echo PYTHONPATH=%PYTHONPATH%
 

scripts/Start Stable Diffusion UI.cmd

@@ -3,7 +3,7 @@
 cd /d %~dp0
 echo Install dir: %~dp0
 
-set PATH=C:\Windows\System32;C:\Windows\System32\wbem;%PATH%
+set PATH=C:\Windows\System32;C:\Windows\System32\WindowsPowerShell\v1.0;%PATH%
 set PYTHONHOME=
 
 if exist "on_sd_start.bat" (
@@ -39,7 +39,7 @@ call where conda
 call conda --version
 echo .
 echo COMSPEC=%COMSPEC%
-wmic path win32_VideoController get name,AdapterRAM,DriverDate,DriverVersion
+powershell -Command "(Get-WmiObject Win32_VideoController | Select-Object Name, AdapterRAM, DriverDate, DriverVersion)"
 
 @rem Download the rest of the installer and UI
 call scripts\on_env_start.bat

scripts/bootstrap.bat

@@ -24,7 +24,7 @@ set USERPROFILE=%cd%\profile
 @rem figure out whether git and conda needs to be installed
 if exist "%INSTALL_ENV_DIR%" set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Library\bin;%INSTALL_ENV_DIR%\Scripts;%INSTALL_ENV_DIR%\Library\usr\bin;%PATH%
 
-set PACKAGES_TO_INSTALL=git python=3.8.5
+set PACKAGES_TO_INSTALL=git python=3.9
 
 if not exist "%LEGACY_INSTALL_ENV_DIR%\etc\profile.d\conda.sh" (
     if not exist "%INSTALL_ENV_DIR%\etc\profile.d\conda.sh" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% conda

scripts/bootstrap.sh

@@ -46,7 +46,7 @@ if [ -e "$INSTALL_ENV_DIR" ]; then export PATH="$INSTALL_ENV_DIR/bin:$PATH"; fi
 
 PACKAGES_TO_INSTALL=""
 
-if [ ! -e "$LEGACY_INSTALL_ENV_DIR/etc/profile.d/conda.sh" ] && [ ! -e "$INSTALL_ENV_DIR/etc/profile.d/conda.sh" ]; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL conda python=3.8.5"; fi
+if [ ! -e "$LEGACY_INSTALL_ENV_DIR/etc/profile.d/conda.sh" ] && [ ! -e "$INSTALL_ENV_DIR/etc/profile.d/conda.sh" ]; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL conda python=3.9"; fi
 if ! hash "git" &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL git"; fi
 
 if "$MAMBA_ROOT_PREFIX/micromamba" --version &>/dev/null; then umamba_exists="T"; fi

scripts/get_config.py

@@ -6,7 +6,6 @@ import shutil
 # The config file is in the same directory as this script
 config_directory = os.path.dirname(__file__)
 config_yaml = os.path.join(config_directory, "..", "config.yaml")
-config_yaml = os.path.abspath(config_yaml)
 config_json = os.path.join(config_directory, "config.json")
 
 parser = argparse.ArgumentParser(description='Get values from config file')

scripts/on_env_start.bat

@@ -26,19 +26,19 @@ if "%update_branch%"=="" (
     set update_branch=main
 )
 
-@>nul findstr /m "conda_sd_ui_deps_installed" scripts\install_status.txt
+@REM @>nul findstr /m "sd_install_complete" scripts\install_status.txt
-@if "%ERRORLEVEL%" NEQ "0" (
+@REM @if "%ERRORLEVEL%" NEQ "0" (
-    for /f "tokens=*" %%a in ('python -c "import os; parts = os.getcwd().split(os.path.sep); print(len(parts))"') do if "%%a" NEQ "2" (
+@REM     for /f "tokens=*" %%a in ('python -c "import os; parts = os.getcwd().split(os.path.sep); print(len(parts))"') do if "%%a" NEQ "2" (
-        echo. & echo "!!!! WARNING !!!!" & echo.
+@REM         echo. & echo "!!!! WARNING !!!!" & echo.
-        echo "Your 'stable-diffusion-ui' folder is at %cd%" & echo.
+@REM         echo "Your 'stable-diffusion-ui' folder is at %cd%" & echo.
-        echo "The 'stable-diffusion-ui' folder needs to be at the top of your drive, for e.g. 'C:\stable-diffusion-ui' or 'D:\stable-diffusion-ui' etc."
+@REM         echo "The 'stable-diffusion-ui' folder needs to be at the top of your drive, for e.g. 'C:\stable-diffusion-ui' or 'D:\stable-diffusion-ui' etc."
-        echo "Not placing this folder at the top of a drive can cause errors on some computers."
+@REM         echo "Not placing this folder at the top of a drive can cause errors on some computers."
-        echo. & echo "Recommended: Please close this window and move the 'stable-diffusion-ui' folder to the top of a drive. For e.g. 'C:\stable-diffusion-ui'. Then run the installer again." & echo.
+@REM         echo. & echo "Recommended: Please close this window and move the 'stable-diffusion-ui' folder to the top of a drive. For e.g. 'C:\stable-diffusion-ui'. Then run the installer again." & echo.
-        echo "Not Recommended: If you're sure that you want to install at the current location, please press any key to continue." & echo.
+@REM         echo "Not Recommended: If you're sure that you want to install at the current location, please press any key to continue." & echo.
 
-        pause
+@REM         pause
-    )
+@REM     )
-)
+@REM )
 
 @>nul findstr /m "sd_ui_git_cloned" scripts\install_status.txt
 @if "%ERRORLEVEL%" EQU "0" (
@@ -71,7 +71,6 @@ if "%update_branch%"=="" (
 @copy sd-ui-files\scripts\check_modules.py scripts\ /Y
 @copy sd-ui-files\scripts\get_config.py scripts\ /Y
 @copy sd-ui-files\scripts\config.yaml.sample scripts\ /Y
-@copy sd-ui-files\scripts\webui_console.py scripts\ /Y
 @copy "sd-ui-files\scripts\Start Stable Diffusion UI.cmd" . /Y
 @copy "sd-ui-files\scripts\Developer Console.cmd" . /Y
 

scripts/on_env_start.sh

@@ -54,7 +54,6 @@ cp sd-ui-files/scripts/bootstrap.sh scripts/
 cp sd-ui-files/scripts/check_modules.py scripts/
 cp sd-ui-files/scripts/get_config.py scripts/
 cp sd-ui-files/scripts/config.yaml.sample scripts/
-cp sd-ui-files/scripts/webui_console.py scripts/
 cp sd-ui-files/scripts/start.sh .
 cp sd-ui-files/scripts/developer_console.sh .
 cp sd-ui-files/scripts/functions.sh scripts/

scripts/on_sd_start.bat

@@ -7,7 +7,6 @@
 @copy sd-ui-files\scripts\check_modules.py scripts\ /Y
 @copy sd-ui-files\scripts\get_config.py scripts\ /Y
 @copy sd-ui-files\scripts\config.yaml.sample scripts\ /Y
-@copy sd-ui-files\scripts\webui_console.py scripts\ /Y
 
 if exist "%cd%\profile" (
     set HF_HOME=%cd%\profile\.cache\huggingface
@@ -67,11 +66,17 @@ set PYTHONNOUSERSITE=1
 set PYTHONPATH=%INSTALL_ENV_DIR%\lib\site-packages
 echo PYTHONPATH=%PYTHONPATH%
 
-@rem Download the required packages
+set PYTHON=%INSTALL_ENV_DIR%\python.exe
-call where python
+echo PYTHON=%PYTHON%
-call python --version
 
-call python scripts\check_modules.py --launch-uvicorn
+@rem Download the required packages
+@REM call where python
+call "%PYTHON%" --version
+
+@rem this is outside check_modules.py to ensure that the required version of torchruntime is present
+call "%PYTHON%" -m pip install -q "torchruntime>=1.19.1"
+
+call "%PYTHON%" scripts\check_modules.py --launch-uvicorn
 pause
 exit /b
 

scripts/on_sd_start.sh

@@ -6,20 +6,16 @@ cp sd-ui-files/scripts/bootstrap.sh scripts/
 cp sd-ui-files/scripts/check_modules.py scripts/
 cp sd-ui-files/scripts/get_config.py scripts/
 cp sd-ui-files/scripts/config.yaml.sample scripts/
-cp sd-ui-files/scripts/webui_console.py scripts/
      
 
 source ./scripts/functions.sh
 
 # activate the installer env
-export CONDA_BASEPATH=$(conda info --base)
+CONDA_BASEPATH=$(conda info --base)
 source "$CONDA_BASEPATH/etc/profile.d/conda.sh" # avoids the 'shell not initialized' error
 
 conda activate || fail "Failed to activate conda"
 
-# hack to fix conda 4.14 on older installations
-cp $CONDA_BASEPATH/condabin/conda $CONDA_BASEPATH/bin/conda
-
 # remove the old version of the dev console script, if it's still present
 if [ -e "open_dev_console.sh" ]; then
     rm "open_dev_console.sh"
@@ -50,6 +46,9 @@ fi
 if [ -e "src" ]; then mv src src-old; fi
 if [ -e "ldm" ]; then mv ldm ldm-old; fi
 
+# this is outside check_modules.py to ensure that the required version of torchruntime is present
+python -m pip install -q "torchruntime>=1.19.1"
+
 cd ..
 # Download the required packages
 python scripts/check_modules.py --launch-uvicorn

scripts/webui_console.py

@@ -1,101 +0,0 @@
-import os
-import platform
-import subprocess
-
-
-def configure_env(dir):
-    env_entries = {
-        "PATH": [
-            f"{dir}",
-            f"{dir}/bin",
-            f"{dir}/Library/bin",
-            f"{dir}/Scripts",
-            f"{dir}/usr/bin",
-        ],
-        "PYTHONPATH": [
-            f"{dir}",
-            f"{dir}/lib/site-packages",
-            f"{dir}/lib/python3.10/site-packages",
-        ],
-        "PYTHONHOME": [],
-        "PY_LIBS": [
-            f"{dir}/Scripts/Lib",
-            f"{dir}/Scripts/Lib/site-packages",
-            f"{dir}/lib",
-            f"{dir}/lib/python3.10/site-packages",
-        ],
-        "PY_PIP": [f"{dir}/Scripts", f"{dir}/bin"],
-    }
-
-    if platform.system() == "Windows":
-        env_entries["PATH"].append("C:/Windows/System32")
-        env_entries["PATH"].append("C:/Windows/System32/wbem")
-        env_entries["PYTHONNOUSERSITE"] = ["1"]
-        env_entries["PYTHON"] = [f"{dir}/python"]
-        env_entries["GIT"] = [f"{dir}/Library/bin/git"]
-    else:
-        env_entries["PATH"].append("/bin")
-        env_entries["PATH"].append("/usr/bin")
-        env_entries["PATH"].append("/usr/sbin")
-        env_entries["PYTHONNOUSERSITE"] = ["y"]
-        env_entries["PYTHON"] = [f"{dir}/bin/python"]
-        env_entries["GIT"] = [f"{dir}/bin/git"]
-
-    env = {}
-    for key, paths in env_entries.items():
-        paths = [p.replace("/", os.path.sep) for p in paths]
-        paths = os.pathsep.join(paths)
-
-        os.environ[key] = paths
-
-    return env
-
-
-def print_env_info():
-    which_cmd = "where" if platform.system() == "Windows" else "which"
-
-    python = "python"
-
-    def locate_python():
-        nonlocal python
-
-        python = subprocess.getoutput(f"{which_cmd} python")
-        python = python.split("\n")
-        python = python[0].strip()
-        print("python: ", python)
-
-    locate_python()
-
-    def run(cmd):
-        with subprocess.Popen(cmd) as p:
-            p.wait()
-
-    run([which_cmd, "git"])
-    run(["git", "--version"])
-    run([which_cmd, "python"])
-    run([python, "--version"])
-
-    print(f"PATH={os.environ['PATH']}")
-
-    if platform.system() == "Windows":
-        print(f"COMSPEC={os.environ['COMSPEC']}")
-        print("")
-        run("wmic path win32_VideoController get name,AdapterRAM,DriverDate,DriverVersion".split(" "))
-
-    print(f"PYTHONPATH={os.environ['PYTHONPATH']}")
-    print("")
-
-
-def open_dev_shell():
-    if platform.system() == "Windows":
-        subprocess.Popen("cmd").communicate()
-    else:
-        subprocess.Popen("bash").communicate()
-
-
-if __name__ == "__main__":
-    env_dir = os.path.abspath(os.path.join("webui", "system"))
-
-    configure_env(env_dir)
-    print_env_info()
-    open_dev_shell()

ui/easydiffusion/app.py

@@ -11,7 +11,7 @@ from ruamel.yaml import YAML
 import urllib
 import warnings
 
-from easydiffusion import task_manager, backend_manager
+from easydiffusion import task_manager
 from easydiffusion.utils import log
 from rich.logging import RichHandler
 from rich.console import Console
@@ -36,10 +36,10 @@ ROOT_DIR = os.path.abspath(os.path.join(SD_DIR, ".."))
 
 SD_UI_DIR = os.getenv("SD_UI_PATH", None)
 
-CONFIG_DIR = os.path.abspath(os.path.join(ROOT_DIR, "scripts"))
+CONFIG_DIR = os.path.abspath(os.path.join(SD_UI_DIR, "..", "scripts"))
-BUCKET_DIR = os.path.abspath(os.path.join(ROOT_DIR, "bucket"))
+BUCKET_DIR = os.path.abspath(os.path.join(SD_DIR, "..", "bucket"))
 
-USER_PLUGINS_DIR = os.path.abspath(os.path.join(ROOT_DIR, "plugins"))
+USER_PLUGINS_DIR = os.path.abspath(os.path.join(SD_DIR, "..", "plugins"))
 CORE_PLUGINS_DIR = os.path.abspath(os.path.join(SD_UI_DIR, "plugins"))
 
 USER_UI_PLUGINS_DIR = os.path.join(USER_PLUGINS_DIR, "ui")
@@ -54,13 +54,12 @@ OUTPUT_DIRNAME = "Stable Diffusion UI"  # in the user's home folder
 PRESERVE_CONFIG_VARS = ["FORCE_FULL_PRECISION"]
 TASK_TTL = 15 * 60  # Discard last session's task timeout
 APP_CONFIG_DEFAULTS = {
-    # auto: selects the cuda device with the most free memory, cuda: use the currently active cuda device.
+    "render_devices": "auto",
-    "render_devices": "auto",  # valid entries: 'auto', 'cpu' or 'cuda:N' (where N is a GPU index)
     "update_branch": "main",
     "ui": {
         "open_browser_on_start": True,
     },
-    "backend": "ed_diffusers",
+    "use_v3_engine": True,
 }
 
 IMAGE_EXTENSIONS = [
@@ -77,7 +76,7 @@ IMAGE_EXTENSIONS = [
     ".avif",
     ".svg",
 ]
-CUSTOM_MODIFIERS_DIR = os.path.abspath(os.path.join(ROOT_DIR, "modifiers"))
+CUSTOM_MODIFIERS_DIR = os.path.abspath(os.path.join(SD_DIR, "..", "modifiers"))
 CUSTOM_MODIFIERS_PORTRAIT_EXTENSIONS = [
     ".portrait",
     "_portrait",
@@ -91,7 +90,7 @@ CUSTOM_MODIFIERS_LANDSCAPE_EXTENSIONS = [
     "-landscape",
 ]
 
-MODELS_DIR = os.path.abspath(os.path.join(ROOT_DIR, "models"))
+MODELS_DIR = os.path.abspath(os.path.join(SD_DIR, "..", "models"))
 
 
 def init():
@@ -105,11 +104,9 @@ def init():
 
     config = getConfig()
     config_models_dir = config.get("models_dir", None)
-    if config_models_dir is not None and config_models_dir != "":
+    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()
@@ -119,7 +116,6 @@ def init_render_threads():
 
 def getConfig(default_val=APP_CONFIG_DEFAULTS):
     config_yaml_path = os.path.join(CONFIG_DIR, "..", "config.yaml")
-    config_yaml_path = os.path.abspath(config_yaml_path)
 
     # migrate the old config yaml location
     config_legacy_yaml = os.path.join(CONFIG_DIR, "config.yaml")
@@ -127,9 +123,9 @@ 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_backend_on_startup is None:
+        if getConfig.__use_v3_engine_on_startup is None:
-            getConfig.__use_backend_on_startup = config.get("backend", "ed_diffusers")
+            getConfig.__use_v3_engine_on_startup = config.get("use_v3_engine", True)
-        config["config_on_startup"] = {"backend": getConfig.__use_backend_on_startup}
+        config["config_on_startup"] = {"use_v3_engine": getConfig.__use_v3_engine_on_startup}
 
     if os.path.isfile(config_yaml_path):
         try:
@@ -147,15 +143,6 @@ 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
@@ -186,7 +173,7 @@ def getConfig(default_val=APP_CONFIG_DEFAULTS):
             return default_val
 
 
-getConfig.__use_backend_on_startup = None
+getConfig.__use_v3_engine_on_startup = None
 
 
 def setConfig(config):
@@ -319,43 +306,28 @@ def getIPConfig():
 
 
 def open_browser():
-    from easydiffusion.backend_manager import backend
-
     config = getConfig()
     ui = config.get("ui", {})
     net = config.get("net", {})
     port = net.get("listen_port", 9000)
 
-    if backend.is_installed():
+    if ui.get("open_browser_on_start", True):
-        if ui.get("open_browser_on_start", True):
+        import webbrowser
-            import webbrowser
 
-            log.info("Opening browser..")
+        log.info("Opening browser..")
 
-            webbrowser.open(f"http://localhost:{port}")
+        webbrowser.open(f"http://localhost:{port}")
 
-        Console().print(
+    Console().print(
-            Panel(
+        Panel(
-                "\n"
+            "\n"
-                + "[white]Easy Diffusion is ready to serve requests.\n\n"
+            + "[white]Easy Diffusion is ready to serve requests.\n\n"
-                + "A new browser tab should have been opened by now.\n"
+            + "A new browser tab should have been opened by now.\n"
-                + f"If not, please open your web browser and navigate to [bold yellow underline]http://localhost:{port}/\n",
+            + f"If not, please open your web browser and navigate to [bold yellow underline]http://localhost:{port}/\n",
-                title="Easy Diffusion is ready",
+            title="Easy Diffusion is ready",
-                style="bold yellow on blue",
+            style="bold yellow on blue",
-            )
-        )
-    else:
-        backend_name = config["backend"]
-        Console().print(
-            Panel(
-                "\n"
-                + f"[white]Backend: {backend_name} is still installing..\n\n"
-                + "A new browser tab will open automatically after it finishes.\n"
-                + f"If it does not, please open your web browser and navigate to [bold yellow underline]http://localhost:{port}/\n",
-                title=f"Backend engine is installing",
-                style="bold yellow on blue",
-            )
         )
+    )
 
 
 def fail_and_die(fail_type: str, data: str):

ui/easydiffusion/backend_manager.py

@@ -1,105 +0,0 @@
-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

@@ -1,28 +0,0 @@
-from sdkit_common import (
-    start_backend,
-    stop_backend,
-    install_backend,
-    uninstall_backend,
-    is_installed,
-    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

@@ -1,28 +0,0 @@
-from sdkit_common import (
-    start_backend,
-    stop_backend,
-    install_backend,
-    uninstall_backend,
-    is_installed,
-    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

@@ -1,246 +0,0 @@
-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 is_installed():
-    return True
-
-
-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,
-    distilled_guidance_scale: float = 3.5,
-    scheduler_name: str = "simple",
-    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
-
-    if len(filters) == 0:
-        return images
-
-    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

@@ -1,450 +0,0 @@
-import os
-import platform
-import subprocess
-import threading
-from threading import local
-import psutil
-import time
-import shutil
-
-from easydiffusion.app import ROOT_DIR, getConfig
-from easydiffusion.model_manager import get_model_dirs
-from easydiffusion.utils import log
-
-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",
-}
-
-WEBUI_REPO = "https://github.com/lllyasviel/stable-diffusion-webui-forge.git"
-WEBUI_COMMIT = "f4d5e8cac16a42fa939e78a0956b4c30e2b47bb5"
-
-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")
-
-OS_NAME = platform.system()
-
-MODELS_TO_OVERRIDE = {
-    "stable-diffusion": "--ckpt-dir",
-    "vae": "--vae-dir",
-    "hypernetwork": "--hypernetwork-dir",
-    "gfpgan": "--gfpgan-models-path",
-    "realesrgan": "--realesrgan-models-path",
-    "lora": "--lora-dir",
-    "codeformer": "--codeformer-models-path",
-    "embeddings": "--embeddings-dir",
-    "controlnet": "--controlnet-dir",
-}
-
-backend_process = None
-conda = "conda"
-
-
-def locate_conda():
-    global conda
-
-    which = "where" if OS_NAME == "Windows" else "which"
-    conda = subprocess.getoutput(f"{which} conda")
-    conda = conda.split("\n")
-    conda = conda[0].strip()
-    print("conda: ", conda)
-
-
-locate_conda()
-
-
-def install_backend():
-    print("Installing the WebUI backend..")
-
-    # create the conda env
-    run([conda, "create", "-y", "--prefix", SYSTEM_DIR], cwd=ROOT_DIR)
-
-    print("Installing packages..")
-
-    # install python 3.10 and git in the conda env
-    run([conda, "install", "-y", "--prefix", SYSTEM_DIR, "-c", "conda-forge", "python=3.10", "git"], cwd=ROOT_DIR)
-
-    # print info
-    run_in_conda(["git", "--version"], cwd=ROOT_DIR)
-    run_in_conda(["python", "--version"], cwd=ROOT_DIR)
-
-    # clone webui
-    run_in_conda(["git", "clone", WEBUI_REPO, WEBUI_DIR], cwd=ROOT_DIR)
-
-    # install cpu-only torch if the PC doesn't have a graphics card (for Windows and Linux).
-    # this avoids WebUI installing a CUDA version and trying to activate it
-    if OS_NAME in ("Windows", "Linux") and not has_discrete_graphics_card():
-        run_in_conda(["python", "-m", "pip", "install", "torch", "torchvision"], cwd=WEBUI_DIR)
-
-
-def start_backend():
-    config = getConfig()
-    backend_config = config.get("backend_config", {})
-
-    if not os.path.exists(BACKEND_DIR):
-        install_backend()
-
-    was_still_installing = not is_installed()
-
-    if backend_config.get("auto_update", True):
-        run_in_conda(["git", "add", "-A", "."], cwd=WEBUI_DIR)
-        run_in_conda(["git", "stash"], cwd=WEBUI_DIR)
-        run_in_conda(["git", "reset", "--hard"], cwd=WEBUI_DIR)
-        run_in_conda(["git", "fetch"], cwd=WEBUI_DIR)
-        run_in_conda(["git", "-c", "advice.detachedHead=false", "checkout", WEBUI_COMMIT], cwd=WEBUI_DIR)
-
-    # hack to prevent webui-macos-env.sh from overwriting the COMMANDLINE_ARGS env variable
-    mac_webui_file = os.path.join(WEBUI_DIR, "webui-macos-env.sh")
-    if os.path.exists(mac_webui_file):
-        os.remove(mac_webui_file)
-
-    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 restart_if_webui_dies_after_starting():
-        has_started = False
-
-        while True:
-            try:
-                impl.ping(timeout=1)
-
-                is_first_start = not has_started
-                has_started = True
-
-                if was_still_installing and is_first_start:
-                    ui = config.get("ui", {})
-                    net = config.get("net", {})
-                    port = net.get("listen_port", 9000)
-
-                    if ui.get("open_browser_on_start", True):
-                        import webbrowser
-
-                        log.info("Opening browser..")
-
-                        webbrowser.open(f"http://localhost:{port}")
-            except (TimeoutError, ConnectionError):
-                if has_started:  # process probably died
-                    print("######################## WebUI probably died. Restarting...")
-                    stop_backend()
-                    backend_thread = threading.Thread(target=target)
-                    backend_thread.start()
-                    break
-            except Exception:
-                import traceback
-
-                log.exception(traceback.format_exc())
-
-            time.sleep(1)
-
-    def target():
-        global backend_process
-
-        cmd = "webui.bat" if OS_NAME == "Windows" else "./webui.sh"
-
-        print("starting", cmd, WEBUI_DIR)
-        backend_process = run_in_conda([cmd], cwd=WEBUI_DIR, env=env, wait=False, output_prefix="[WebUI] ")
-
-        restart_if_dead_thread = threading.Thread(target=restart_if_webui_dies_after_starting)
-        restart_if_dead_thread.start()
-
-        backend_process.wait()
-
-    backend_thread = threading.Thread(target=target)
-    backend_thread.start()
-
-    start_proxy()
-
-
-def start_proxy():
-    # proxy
-    from easydiffusion.server import server_api
-    from fastapi import FastAPI, Request
-    from fastapi.responses import Response
-    import json
-
-    URI_PREFIX = "/webui"
-
-    webui_proxy = FastAPI(root_path=f"{URI_PREFIX}", docs_url="/swagger")
-
-    @webui_proxy.get("{uri:path}")
-    def proxy_get(uri: str, req: Request):
-        if uri == "/openapi-proxy.json":
-            uri = "/openapi.json"
-
-        res = impl.webui_get(uri, headers=req.headers)
-
-        content = res.content
-        headers = dict(res.headers)
-
-        if uri == "/docs":
-            content = res.text.replace("url: '/openapi.json'", f"url: '{URI_PREFIX}/openapi-proxy.json'")
-        elif uri == "/openapi.json":
-            content = res.json()
-            content["paths"] = {f"{URI_PREFIX}{k}": v for k, v in content["paths"].items()}
-            content = json.dumps(content)
-
-        if isinstance(content, str):
-            content = bytes(content, encoding="utf-8")
-            headers["content-length"] = str(len(content))
-
-        # Return the same response back to the client
-        return Response(content=content, status_code=res.status_code, headers=headers)
-
-    @webui_proxy.post("{uri:path}")
-    async def proxy_post(uri: str, req: Request):
-        body = await req.body()
-        res = impl.webui_post(uri, data=body, headers=req.headers)
-
-        # Return the same response back to the client
-        return Response(content=res.content, status_code=res.status_code, headers=dict(res.headers))
-
-    server_api.mount(f"{URI_PREFIX}", webui_proxy)
-
-
-def stop_backend():
-    global backend_process
-
-    if backend_process:
-        try:
-            kill(backend_process.pid)
-        except:
-            pass
-
-    backend_process = None
-
-
-def uninstall_backend():
-    shutil.rmtree(BACKEND_DIR)
-
-
-def is_installed():
-    if not os.path.exists(BACKEND_DIR) or not os.path.exists(SYSTEM_DIR) or not os.path.exists(WEBUI_DIR):
-        return True
-
-    env = dict(os.environ)
-    env.update(get_env())
-
-    try:
-        out = check_output_in_conda(["python", "-m", "pip", "show", "torch"], env=env)
-        return "Version" in out.decode()
-    except subprocess.CalledProcessError:
-        pass
-
-    return False
-
-
-def read_output(pipe, prefix=""):
-    while True:
-        output = pipe.readline()
-        if output:
-            print(f"{prefix}{output.decode('utf-8')}", end="")
-        else:
-            break  # Pipe is closed, subprocess has likely exited
-
-
-def run(cmds: list, cwd=None, env=None, stream_output=True, wait=True, output_prefix=""):
-    p = subprocess.Popen(cmds, cwd=cwd, env=env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
-    if stream_output:
-        output_thread = threading.Thread(target=read_output, args=(p.stdout, output_prefix))
-        output_thread.start()
-
-    if wait:
-        p.wait()
-
-    return p
-
-
-def run_in_conda(cmds: list, *args, **kwargs):
-    cmds = [conda, "run", "--no-capture-output", "--prefix", SYSTEM_DIR] + cmds
-    return run(cmds, *args, **kwargs)
-
-
-def check_output_in_conda(cmds: list, cwd=None, env=None):
-    cmds = [conda, "run", "--no-capture-output", "--prefix", SYSTEM_DIR] + cmds
-    return subprocess.check_output(cmds, cwd=cwd, env=env, stderr=subprocess.PIPE)
-
-
-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"))
-
-    model_path_args = get_model_path_args()
-
-    env_entries = {
-        "PATH": [
-            f"{dir}",
-            f"{dir}/bin",
-            f"{dir}/Library/bin",
-            f"{dir}/Scripts",
-            f"{dir}/usr/bin",
-        ],
-        "PYTHONPATH": [
-            f"{dir}",
-            f"{dir}/lib/site-packages",
-            f"{dir}/lib/python3.10/site-packages",
-        ],
-        "PYTHONHOME": [],
-        "PY_LIBS": [
-            f"{dir}/Scripts/Lib",
-            f"{dir}/Scripts/Lib/site-packages",
-            f"{dir}/lib",
-            f"{dir}/lib/python3.10/site-packages",
-        ],
-        "PY_PIP": [f"{dir}/Scripts", f"{dir}/bin"],
-        "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}" {model_path_args} --skip-torch-cuda-test'],
-        "SKIP_VENV": ["1"],
-        "SD_WEBUI_RESTARTING": ["1"],
-    }
-
-    if OS_NAME == "Windows":
-        env_entries["PATH"].append("C:/Windows/System32")
-        env_entries["PATH"].append("C:/Windows/System32/wbem")
-        env_entries["PYTHONNOUSERSITE"] = ["1"]
-        env_entries["PYTHON"] = [f"{dir}/python"]
-        env_entries["GIT"] = [f"{dir}/Library/bin/git"]
-    else:
-        env_entries["PATH"].append("/bin")
-        env_entries["PATH"].append("/usr/bin")
-        env_entries["PATH"].append("/usr/sbin")
-        env_entries["PYTHONNOUSERSITE"] = ["y"]
-        env_entries["PYTHON"] = [f"{dir}/bin/python"]
-        env_entries["GIT"] = [f"{dir}/bin/git"]
-        env_entries["venv_dir"] = ["-"]
-
-    if OS_NAME == "Darwin":
-        # based on https://github.com/lllyasviel/stable-diffusion-webui-forge/blob/e26abf87ecd1eefd9ab0a198eee56f9c643e4001/webui-macos-env.sh
-        # hack - have to define these here, otherwise webui-macos-env.sh will overwrite COMMANDLINE_ARGS
-        env_entries["COMMANDLINE_ARGS"][0] += " --upcast-sampling --no-half-vae --use-cpu interrogate"
-        env_entries["PYTORCH_ENABLE_MPS_FALLBACK"] = ["1"]
-
-        cpu_name = str(subprocess.check_output(["sysctl", "-n", "machdep.cpu.brand_string"]))
-        if "Intel" in cpu_name:
-            env_entries["TORCH_COMMAND"] = ["pip install torch==2.1.2 torchvision==0.16.2"]
-        else:
-            env_entries["TORCH_COMMAND"] = ["pip install torch==2.3.1 torchvision==0.18.1"]
-    else:
-        import torch
-        from easydiffusion.device_manager import needs_to_force_full_precision, is_cuda_available
-
-        vram_usage_level = config.get("vram_usage_level", "balanced")
-        if config.get("render_devices", "auto") == "cpu" or not has_discrete_graphics_card() or not is_cuda_available():
-            env_entries["COMMANDLINE_ARGS"][0] += " --always-cpu"
-        else:
-            c = local()
-            c.device_name = torch.cuda.get_device_name()
-
-            if needs_to_force_full_precision(c):
-                env_entries["COMMANDLINE_ARGS"][0] += " --no-half --precision full"
-
-            if vram_usage_level == "low":
-                env_entries["COMMANDLINE_ARGS"][0] += " --always-low-vram"
-            elif vram_usage_level == "high":
-                env_entries["COMMANDLINE_ARGS"][0] += " --always-high-vram"
-
-    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
-
-
-def has_discrete_graphics_card():
-    system = OS_NAME
-
-    if system == "Windows":
-        try:
-            output = subprocess.check_output(
-                ["wmic", "path", "win32_videocontroller", "get", "name"], stderr=subprocess.STDOUT
-            )
-            # Filter for discrete graphics cards (NVIDIA, AMD, etc.)
-            discrete_gpus = ["NVIDIA", "AMD", "ATI"]
-            return any(gpu in output.decode() for gpu in discrete_gpus)
-        except subprocess.CalledProcessError:
-            return False
-
-    elif system == "Linux":
-        try:
-            output = subprocess.check_output(["lspci"], stderr=subprocess.STDOUT)
-            # Check for discrete GPUs (NVIDIA, AMD)
-            discrete_gpus = ["NVIDIA", "AMD", "Advanced Micro Devices"]
-            return any(gpu in line for line in output.decode().splitlines() for gpu in discrete_gpus)
-        except subprocess.CalledProcessError:
-            return False
-
-    elif system == "Darwin":  # macOS
-        try:
-            output = subprocess.check_output(["system_profiler", "SPDisplaysDataType"], stderr=subprocess.STDOUT)
-            # Check for discrete GPU in the output
-            return "NVIDIA" in output.decode() or "AMD" in output.decode()
-        except subprocess.CalledProcessError:
-            return False
-
-    return False
-
-
-# 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()
-
-
-def get_model_path_args():
-    args = []
-    for model_type, flag in MODELS_TO_OVERRIDE.items():
-        model_dir = get_model_dirs(model_type)[0]
-        args.append(f'{flag} "{model_dir}"')
-
-    return " ".join(args)

ui/easydiffusion/backends/webui/impl.py

@@ -1,654 +0,0 @@
-import os
-import requests
-from requests.exceptions import ConnectTimeout, ConnectionError
-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,
-    "forge_additional_modules": [],
-}
-
-
-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:
-        res = webui_get("/internal/ping", timeout=timeout)
-
-        if res.status_code != 200:
-            raise ConnectTimeout(res.text)
-
-        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 getting options: {e}")
-
-        return True
-    except (ConnectTimeout, ConnectionError) 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):
-    if model_type == "vae":
-        context.model_paths[model_type] = None
-        load_model(context, model_type)
-
-
-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,
-    distilled_guidance_scale: float = 3.5,
-    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",
-    scheduler_name: str = "simple",
-    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": scheduler_name,
-        "steps": num_inference_steps,
-        "seed": seed,
-        "cfg_scale": guidance_scale,
-        "distilled_cfg_scale": distilled_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()
-        else:
-            raise RuntimeError(f"Unexpected progress response. Status code: {res.status_code}. Res: {res.text}")
-
-        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/device_manager.py

@@ -6,6 +6,15 @@ import traceback
 import torch
 from easydiffusion.utils import log
 
+from torchruntime.utils import (
+    get_installed_torch_platform,
+    get_device,
+    get_device_count,
+    get_device_name,
+    SUPPORTED_BACKENDS,
+)
+from sdkit.utils import mem_get_info, is_cpu_device, has_half_precision_bug
+
 """
 Set `FORCE_FULL_PRECISION` in the environment variables, or in `config.bat`/`config.sh` to set full precision (i.e. float32).
 Otherwise the models will load at half-precision (i.e. float16).
@@ -22,33 +31,15 @@ mem_free_threshold = 0
 
 def get_device_delta(render_devices, active_devices):
     """
-    render_devices: 'cpu', or 'auto', or 'mps' or ['cuda:N'...]
+    render_devices: 'auto' or backends listed in `torchruntime.utils.SUPPORTED_BACKENDS`
-    active_devices: ['cpu', 'mps', 'cuda:N'...]
+    active_devices: [backends listed in `torchruntime.utils.SUPPORTED_BACKENDS`]
     """
 
-    if render_devices in ("cpu", "auto", "mps"):
+    render_devices = render_devices or "auto"
-        render_devices = [render_devices]
+    render_devices = [render_devices] if isinstance(render_devices, str) else render_devices
-    elif render_devices is not None:
-        if isinstance(render_devices, str):
-            render_devices = [render_devices]
-        if isinstance(render_devices, list) and len(render_devices) > 0:
-            render_devices = list(filter(lambda x: x.startswith("cuda:") or x == "mps", render_devices))
-            if len(render_devices) == 0:
-                raise Exception(
-                    'Invalid render_devices value in config.json. Valid: {"render_devices": ["cuda:0", "cuda:1"...]}, or {"render_devices": "cpu"} or {"render_devices": "mps"} or {"render_devices": "auto"}'
-                )
 
-            render_devices = list(filter(lambda x: is_device_compatible(x), render_devices))
+    # check for backend support
-            if len(render_devices) == 0:
+    validate_render_devices(render_devices)
-                raise Exception(
-                    "Sorry, none of the render_devices configured in config.json are compatible with Stable Diffusion"
-                )
-        else:
-            raise Exception(
-                'Invalid render_devices value in config.json. Valid: {"render_devices": ["cuda:0", "cuda:1"...]}, or {"render_devices": "cpu"} or {"render_devices": "auto"}'
-            )
-    else:
-        render_devices = ["auto"]
 
     if "auto" in render_devices:
         render_devices = auto_pick_devices(active_devices)
@@ -64,47 +55,39 @@ def get_device_delta(render_devices, active_devices):
     return devices_to_start, devices_to_stop
 
 
-def is_mps_available():
+def validate_render_devices(render_devices):
-    return (
+    supported_backends = ("auto",) + SUPPORTED_BACKENDS
-        platform.system() == "Darwin"
+    unsupported_render_devices = [d for d in render_devices if not d.lower().startswith(supported_backends)]
-        and hasattr(torch.backends, "mps")
-        and torch.backends.mps.is_available()
-        and torch.backends.mps.is_built()
-    )
 
-
+    if unsupported_render_devices:
-def is_cuda_available():
+        raise ValueError(
-    return torch.cuda.is_available()
+            f"Invalid render devices in config: {unsupported_render_devices}. Valid render devices: {supported_backends}"
+        )
 
 
 def auto_pick_devices(currently_active_devices):
     global mem_free_threshold
 
-    if is_mps_available():
+    torch_platform_name = get_installed_torch_platform()[0]
-        return ["mps"]
 
-    if not is_cuda_available():
+    if is_cpu_device(torch_platform_name):
-        return ["cpu"]
+        return [torch_platform_name]
-
-    device_count = torch.cuda.device_count()
-    if device_count == 1:
-        return ["cuda:0"] if is_device_compatible("cuda:0") else ["cpu"]
 
+    device_count = get_device_count()
     log.debug("Autoselecting GPU. Using most free memory.")
     devices = []
-    for device in range(device_count):
+    for device_id in range(device_count):
-        device = f"cuda:{device}"
+        device_id = f"{torch_platform_name}:{device_id}" if device_count > 1 else torch_platform_name
-        if not is_device_compatible(device):
+        device = get_device(device_id)
-            continue
 
-        mem_free, mem_total = torch.cuda.mem_get_info(device)
+        mem_free, mem_total = mem_get_info(device)
         mem_free /= float(10**9)
         mem_total /= float(10**9)
-        device_name = torch.cuda.get_device_name(device)
+        device_name = get_device_name(device)
         log.debug(
-            f"{device} detected: {device_name} - Memory (free/total): {round(mem_free, 2)}Gb / {round(mem_total, 2)}Gb"
+            f"{device_id} detected: {device_name} - Memory (free/total): {round(mem_free, 2)}Gb / {round(mem_total, 2)}Gb"
         )
-        devices.append({"device": device, "device_name": device_name, "mem_free": mem_free})
+        devices.append({"device": device_id, "device_name": device_name, "mem_free": mem_free})
 
     devices.sort(key=lambda x: x["mem_free"], reverse=True)
     max_mem_free = devices[0]["mem_free"]
@@ -117,69 +100,45 @@ def auto_pick_devices(currently_active_devices):
     #    always be very low (since their VRAM contains the model).
     #    These already-running devices probably aren't terrible, since they were picked in the past.
     #    Worst case, the user can restart the program and that'll get rid of them.
-    devices = list(
+    devices = [
-        filter(
+        x["device"] for x in devices if x["mem_free"] >= mem_free_threshold or x["device"] in currently_active_devices
-            (lambda x: x["mem_free"] > mem_free_threshold or x["device"] in currently_active_devices),
+    ]
-            devices,
-        )
-    )
-    devices = list(map(lambda x: x["device"], devices))
     return devices
 
 
-def device_init(context, device):
+def device_init(context, device_id):
-    """
+    context.device = device_id
-    This function assumes the 'device' has already been verified to be compatible.
-    `get_device_delta()` has already filtered out incompatible devices.
-    """
 
-    validate_device_id(device, log_prefix="device_init")
+    if is_cpu_device(context.torch_device):
-
-    if "cuda" not in device:
-        context.device = device
         context.device_name = get_processor_name()
         context.half_precision = False
-        log.debug(f"Render device available as {context.device_name}")
+    else:
-        return
+        context.device_name = get_device_name(context.torch_device)
 
-    context.device_name = torch.cuda.get_device_name(device)
+        # Some graphics cards have bugs in their firmware that prevent image generation at half precision
-    context.device = device
+        if needs_to_force_full_precision(context.device_name):
+            log.warn(f"forcing full precision on this GPU, to avoid corrupted images. GPU: {context.device_name}")
+            context.half_precision = False
 
-    # Force full precision on 1660 and 1650 NVIDIA cards to avoid creating green images
+    log.info(f'Setting {device_id} as active, with precision: {"half" if context.half_precision else "full"}')
-    if needs_to_force_full_precision(context):
-        log.warn(f"forcing full precision on this GPU, to avoid green images. GPU detected: {context.device_name}")
-        # Apply force_full_precision now before models are loaded.
-        context.half_precision = False
-
-    log.info(f'Setting {device} as active, with precision: {"half" if context.half_precision else "full"}')
-    torch.cuda.device(device)
 
 
-def needs_to_force_full_precision(context):
+def needs_to_force_full_precision(device_name):
     if "FORCE_FULL_PRECISION" in os.environ:
         return True
 
-    device_name = context.device_name.lower()
+    return has_half_precision_bug(device_name.lower())
-    return (
-        ("nvidia" in device_name or "geforce" in device_name or "quadro" in device_name)
-        and (
-            " 1660" in device_name
-            or " 1650" in device_name
-            or " 1630" in device_name
-            or " t400" in device_name
-            or " t550" in device_name
-            or " t600" in device_name
-            or " t1000" in device_name
-            or " t1200" in device_name
-            or " t2000" in device_name
-        )
-    ) or ("tesla k40m" in device_name)
 
 
 def get_max_vram_usage_level(device):
-    if "cuda" in device:
+    "Expects a torch.device as the argument"
-        _, mem_total = torch.cuda.mem_get_info(device)
+
-    else:
+    if is_cpu_device(device):
+        return "high"
+
+    _, mem_total = mem_get_info(device)
+
+    if mem_total < 0.001:  # probably a torch platform without a mem_get_info() implementation
         return "high"
 
     mem_total /= float(10**9)
@@ -191,51 +150,6 @@ def get_max_vram_usage_level(device):
     return "high"
 
 
-def validate_device_id(device, log_prefix=""):
-    def is_valid():
-        if not isinstance(device, str):
-            return False
-        if device == "cpu" or device == "mps":
-            return True
-        if not device.startswith("cuda:") or not device[5:].isnumeric():
-            return False
-        return True
-
-    if not is_valid():
-        raise EnvironmentError(
-            f"{log_prefix}: device id should be 'cpu', 'mps', or 'cuda:N' (where N is an integer index for the GPU). Got: {device}"
-        )
-
-
-def is_device_compatible(device):
-    """
-    Returns True/False, and prints any compatibility errors
-    """
-    # static variable "history".
-    is_device_compatible.history = getattr(is_device_compatible, "history", {})
-    try:
-        validate_device_id(device, log_prefix="is_device_compatible")
-    except:
-        log.error(str(e))
-        return False
-
-    if device in ("cpu", "mps"):
-        return True
-    # Memory check
-    try:
-        _, mem_total = torch.cuda.mem_get_info(device)
-        mem_total /= float(10**9)
-        if mem_total < 1.9:
-            if is_device_compatible.history.get(device) == None:
-                log.warn(f"GPU {device} with less than 2 GB of VRAM is not compatible with Stable Diffusion")
-                is_device_compatible.history[device] = 1
-            return False
-    except RuntimeError as e:
-        log.error(str(e))
-        return False
-    return True
-
-
 def get_processor_name():
     try:
         import subprocess

ui/easydiffusion/easydb/schemas.py

@@ -33,3 +33,4 @@ 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 scan_model, download_model, get_model_info_from_db
+from sdkit.models import load_model, scan_model, unload_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
@@ -25,19 +25,19 @@ KNOWN_MODEL_TYPES = [
     "controlnet",
 ]
 MODEL_EXTENSIONS = {
-    "stable-diffusion": [".ckpt", ".safetensors", ".sft", ".gguf"],
+    "stable-diffusion": [".ckpt", ".safetensors"],
-    "vae": [".vae.pt", ".ckpt", ".safetensors", ".sft"],
+    "vae": [".vae.pt", ".ckpt", ".safetensors"],
-    "hypernetwork": [".pt", ".safetensors", ".sft"],
+    "hypernetwork": [".pt", ".safetensors"],
     "gfpgan": [".pth"],
     "realesrgan": [".pth"],
-    "lora": [".ckpt", ".safetensors", ".sft", ".pt"],
+    "lora": [".ckpt", ".safetensors", ".pt"],
     "codeformer": [".pth"],
-    "embeddings": [".pt", ".bin", ".safetensors", ".sft"],
+    "embeddings": [".pt", ".bin", ".safetensors"],
-    "controlnet": [".pth", ".safetensors", ".sft"],
+    "controlnet": [".pth", ".safetensors"],
 }
 DEFAULT_MODELS = {
     "stable-diffusion": [
-        {"file_name": "sd-v1-4.ckpt", "model_id": "1.4"},
+        {"file_name": "sd-v1-5.safetensors", "model_id": "1.5-pruned-emaonly-fp16"},
     ],
     "gfpgan": [
         {"file_name": "GFPGANv1.4.pth", "model_id": "1.4"},
@@ -51,16 +51,6 @@ DEFAULT_MODELS = {
     ],
 }
 MODELS_TO_LOAD_ON_START = ["stable-diffusion", "vae", "hypernetwork", "lora"]
-ALTERNATE_FOLDER_NAMES = {  # for WebUI compatibility
-    "stable-diffusion": "Stable-diffusion",
-    "vae": "VAE",
-    "hypernetwork": "hypernetworks",
-    "codeformer": "Codeformer",
-    "gfpgan": "GFPGAN",
-    "realesrgan": "RealESRGAN",
-    "lora": "Lora",
-    "controlnet": "ControlNet",
-}
 
 known_models = {}
 
@@ -73,7 +63,6 @@ def init():
 
 def load_default_models(context: Context):
     from easydiffusion import runtime
-    from easydiffusion.backend_manager import backend
 
     runtime.set_vram_optimizations(context)
 
@@ -81,13 +70,13 @@ 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:
-            backend.load_model(
+            load_model(
                 context,
                 model_type,
                 scan_model=context.model_paths[model_type] != None
                 and not context.model_paths[model_type].endswith(".safetensors"),
             )
-            if model_type in context.model_load_errors:
+            if hasattr(context, "model_load_errors") and model_type in context.model_load_errors:
                 del context.model_load_errors[model_type]
         except Exception as e:
             log.error(f"[red]Error while loading {model_type} model: {context.model_paths[model_type]}[/red]")
@@ -99,15 +88,15 @@ def load_default_models(context: Context):
                 log.exception(e)
             del context.model_paths[model_type]
 
+            if not hasattr(context, "model_load_errors"):
+                context.model_load_errors = {}
             context.model_load_errors[model_type] = str(e)  # storing the entire Exception can lead to memory leaks
 
 
 def unload_all(context: Context):
-    from easydiffusion.backend_manager import backend
-
     for model_type in KNOWN_MODEL_TYPES:
-        backend.unload_model(context, model_type)
+        unload_model(context, model_type)
-        if hasattr(context, "model_load_errors") and model_type in context.model_load_errors:
+        if model_type in context.model_load_errors:
             del context.model_load_errors[model_type]
 
 
@@ -132,33 +121,33 @@ def resolve_model_to_use_single(model_name: str = None, model_type: str = None,
     default_models = DEFAULT_MODELS.get(model_type, [])
     config = app.getConfig()
 
+    model_dir = os.path.join(app.MODELS_DIR, model_type)
     if not model_name:  # When None try user configured model.
         # config = getConfig()
         if "model" in config and model_type in config["model"]:
             model_name = config["model"][model_type]
 
-    for model_dir in get_model_dirs(model_type):
+    if model_name:
-        if model_name:
+        # Check models directory
-            # Check models directory
+        model_path = os.path.join(model_dir, model_name)
-            model_path = os.path.join(model_dir, model_name)
+        if os.path.exists(model_path):
-            if os.path.exists(model_path):
+            return model_path
-                return model_path
+        for model_extension in model_extensions:
-            for model_extension in model_extensions:
+            if os.path.exists(model_path + model_extension):
-                if os.path.exists(model_path + model_extension):
+                return model_path + model_extension
-                    return model_path + model_extension
+            if os.path.exists(model_name + model_extension):
-                if os.path.exists(model_name + model_extension):
+                return os.path.abspath(model_name + model_extension)
-                    return os.path.abspath(model_name + model_extension)
 
-        # Can't find requested model, check the default paths.
+    # Can't find requested model, check the default paths.
-        if model_type == "stable-diffusion" and not fail_if_not_found:
+    if model_type == "stable-diffusion" and not fail_if_not_found:
-            for default_model in default_models:
+        for default_model in default_models:
-                default_model_path = os.path.join(model_dir, default_model["file_name"])
+            default_model_path = os.path.join(model_dir, default_model["file_name"])
-                if os.path.exists(default_model_path):
+            if os.path.exists(default_model_path):
-                    if model_name is not None:
+                if model_name is not None:
-                        log.warn(
+                    log.warn(
-                            f"Could not find the configured custom model {model_name}. Using the default one: {default_model_path}"
+                        f"Could not find the configured custom model {model_name}. Using the default one: {default_model_path}"
-                        )
+                    )
-                    return default_model_path
+                return default_model_path
 
     if model_name and fail_if_not_found:
         raise FileNotFoundError(
@@ -167,8 +156,6 @@ 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()
@@ -190,31 +177,24 @@ 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 = backend.unload_model if context.model_paths[model_type] is None else backend.load_model
+        action_fn = unload_model if context.model_paths[model_type] is None else 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
-            if model_type in context.model_load_errors:
+            if hasattr(context, "model_load_errors") and model_type in context.model_load_errors:
                 del context.model_load_errors[model_type]
         except Exception as e:
             log.exception(e)
-            if action_fn == backend.load_model:
+            if action_fn == load_model:
+                if not hasattr(context, "model_load_errors"):
+                    context.model_load_errors = {}
                 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]:
@@ -231,7 +211,7 @@ def resolve_model_paths(models_data: ModelsData):
 
 def fail_if_models_did_not_load(context: Context):
     for model_type in KNOWN_MODEL_TYPES:
-        if model_type in context.model_load_errors:
+        if hasattr(context, "model_load_errors") and model_type in context.model_load_errors:
             e = context.model_load_errors[model_type]
             raise Exception(f"Could not load the {model_type} model! Reason: " + e)
 
@@ -249,8 +229,7 @@ def download_default_models_if_necessary():
 
 
 def download_if_necessary(model_type: str, file_name: str, model_id: str, skip_if_others_exist=True):
-    model_dir = get_model_dirs(model_type)[0]
+    model_path = os.path.join(app.MODELS_DIR, model_type, file_name)
-    model_path = os.path.join(model_dir, file_name)
     expected_hash = get_model_info_from_db(model_type=model_type, model_id=model_id)["quick_hash"]
 
     other_models_exist = any_model_exists(model_type) and skip_if_others_exist
@@ -270,23 +249,21 @@ def migrate_legacy_model_location():
             file_name = model["file_name"]
             legacy_path = os.path.join(app.SD_DIR, file_name)
             if os.path.exists(legacy_path):
-                model_dir = get_model_dirs(model_type)[0]
+                shutil.move(legacy_path, os.path.join(app.MODELS_DIR, model_type, file_name))
-                shutil.move(legacy_path, os.path.join(model_dir, file_name))
 
 
 def any_model_exists(model_type: str) -> bool:
     extensions = MODEL_EXTENSIONS.get(model_type, [])
-    for model_dir in get_model_dirs(model_type):
+    for ext in extensions:
-        for ext in extensions:
+        if any(glob(f"{app.MODELS_DIR}/{model_type}/**/*{ext}", recursive=True)):
-            if any(glob(f"{model_dir}/**/*{ext}", recursive=True)):
+            return True
-                return True
 
     return False
 
 
 def make_model_folders():
     for model_type in KNOWN_MODEL_TYPES:
-        model_dir_path = get_model_dirs(model_type)[0]
+        model_dir_path = os.path.join(app.MODELS_DIR, model_type)
 
         try:
             os.makedirs(model_dir_path, exist_ok=True)
@@ -316,7 +293,7 @@ def make_model_folders():
 
 def is_malicious_model(file_path):
     try:
-        if file_path.endswith(".safetensors"):
+        if file_path.endswith((".safetensors", ".sft", ".gguf")):
             return False
         scan_result = scan_model(file_path)
         if scan_result.issues_count > 0 or scan_result.infected_files > 0:
@@ -347,10 +324,6 @@ 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": [],
@@ -360,19 +333,19 @@ def getModels(scan_for_malicious: bool = True):
             "codeformer": [{"codeformer": "CodeFormer"}],
             "embeddings": [],
             "controlnet": [
-                # {"control_v11p_sd15_canny": "Canny (*)"},
+                {"control_v11p_sd15_canny": "Canny (*)"},
-                # {"control_v11p_sd15_openpose": "OpenPose (*)"},
+                {"control_v11p_sd15_openpose": "OpenPose (*)"},
-                # {"control_v11p_sd15_normalbae": "Normal BAE (*)"},
+                {"control_v11p_sd15_normalbae": "Normal BAE (*)"},
-                # {"control_v11f1p_sd15_depth": "Depth (*)"},
+                {"control_v11f1p_sd15_depth": "Depth (*)"},
-                # {"control_v11p_sd15_scribble": "Scribble"},
+                {"control_v11p_sd15_scribble": "Scribble"},
-                # {"control_v11p_sd15_softedge": "Soft Edge"},
+                {"control_v11p_sd15_softedge": "Soft Edge"},
-                # {"control_v11p_sd15_inpaint": "Inpaint"},
+                {"control_v11p_sd15_inpaint": "Inpaint"},
-                # {"control_v11p_sd15_lineart": "Line Art"},
+                {"control_v11p_sd15_lineart": "Line Art"},
-                # {"control_v11p_sd15s2_lineart_anime": "Line Art Anime"},
+                {"control_v11p_sd15s2_lineart_anime": "Line Art Anime"},
-                # {"control_v11p_sd15_mlsd": "Straight Lines"},
+                {"control_v11p_sd15_mlsd": "Straight Lines"},
-                # {"control_v11p_sd15_seg": "Segment"},
+                {"control_v11p_sd15_seg": "Segment"},
-                # {"control_v11e_sd15_shuffle": "Shuffle"},
+                {"control_v11e_sd15_shuffle": "Shuffle"},
-                # {"control_v11f1e_sd15_tile": "Tile"},
+                {"control_v11f1e_sd15_tile": "Tile"},
             ],
         },
     }
@@ -387,9 +360,6 @@ def getModels(scan_for_malicious: bool = True):
 
         tree = list(default_entries)
 
-        if not os.path.exists(directory):
-            return tree
-
         for entry in sorted(
             os.scandir(directory),
             key=lambda entry: (entry.is_file() == directoriesFirst, entry.name.lower()),
@@ -412,8 +382,6 @@ 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
@@ -434,18 +402,17 @@ def getModels(scan_for_malicious: bool = True):
         nonlocal models_scanned
 
         model_extensions = MODEL_EXTENSIONS.get(model_type, [])
-        models_dirs = get_model_dirs(model_type)
+        models_dir = os.path.join(app.MODELS_DIR, model_type)
-        if not os.path.exists(models_dirs[0]):
+        if not os.path.exists(models_dir):
-            os.makedirs(models_dirs[0])
+            os.makedirs(models_dir)
 
-        for model_dir in models_dirs:
+        try:
-            try:
+            default_tree = models["options"].get(model_type, [])
-                default_tree = models["options"].get(model_type, [])
+            models["options"][model_type] = scan_directory(
-                models["options"][model_type] = scan_directory(
+                models_dir, model_extensions, default_entries=default_tree, nameFilter=nameFilter
-                    model_dir, model_extensions, default_entries=default_tree, nameFilter=nameFilter
+            )
-                )
+        except MaliciousModelException as e:
-            except MaliciousModelException as e:
+            models["scan-error"] = str(e)
-                models["scan-error"] = str(e)
 
     if scan_for_malicious:
         log.info(f"[green]Scanning all model folders for models...[/]")
@@ -453,7 +420,7 @@ def getModels(scan_for_malicious: bool = True):
     listModels(model_type="stable-diffusion")
     listModels(model_type="vae")
     listModels(model_type="hypernetwork")
-    listModels(model_type="gfpgan", nameFilter=lambda x: (x if "gfpgan" in x.lower() else None))
+    listModels(model_type="gfpgan")
     listModels(model_type="lora")
     listModels(model_type="embeddings", nameFilter=get_embedding_token)
     listModels(model_type="controlnet")
@@ -462,20 +429,3 @@ def getModels(scan_for_malicious: bool = True):
         log.info(f"[green]Scanned {models_scanned} models. Nothing infected[/]")
 
     return models
-
-
-def get_model_dirs(model_type: str, base_dir=None):
-    "Returns the possible model directory paths for the given model type. Mainly used for WebUI compatibility"
-
-    if base_dir is None:
-        base_dir = app.MODELS_DIR
-
-    dirs = [os.path.join(base_dir, model_type)]
-
-    if model_type in ALTERNATE_FOLDER_NAMES:
-        alt_dir = ALTERNATE_FOLDER_NAMES[model_type]
-        alt_dir = os.path.join(base_dir, alt_dir)
-        if os.path.exists(alt_dir) and os.path.isdir(alt_dir):
-            dirs.append(alt_dir)
-
-    return dirs

ui/easydiffusion/package_manager.py

@@ -3,9 +3,12 @@ import os
 import platform
 from importlib.metadata import version as pkg_version
 
+from sdkit.utils import log
+
 from easydiffusion import app
 
-# future home of scripts/check_modules.py
+# was meant to be a rewrite of scripts/check_modules.py
+# but probably dead for now
 
 manifest = {
     "tensorrt": {
@@ -48,8 +51,6 @@ def is_installed(module_name) -> bool:
 
 
 def install(module_name):
-    from easydiffusion.utils import log
-
     if is_installed(module_name):
         log.info(f"{module_name} has already been installed!")
         return
@@ -79,8 +80,6 @@ def install(module_name):
 
 
 def uninstall(module_name):
-    from easydiffusion.utils import log
-
     if not is_installed(module_name):
         log.info(f"{module_name} hasn't been installed!")
         return

ui/easydiffusion/runtime.py

@@ -1,5 +1,4 @@
 """
-(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.
@@ -7,35 +6,42 @@ 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.
 """
 
-context = None
+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
+"""
 
 
 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):
+def set_vram_optimizations(context: Context):
-    from easydiffusion.app import getConfig
+    from easydiffusion import app
 
-    config = getConfig()
+    config = app.getConfig()
     vram_usage_level = config.get("vram_usage_level", "balanced")
 
     if vram_usage_level != context.vram_usage_level:

ui/easydiffusion/server.py

@@ -2,7 +2,6 @@
 Notes:
     async endpoints always run on the main thread. Without they run on the thread pool.
 """
-
 import datetime
 import mimetypes
 import os
@@ -21,7 +20,6 @@ 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
@@ -69,9 +67,7 @@ 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
-    vram_usage_level: str = "balanced"
 
 
 def init():
@@ -159,12 +155,6 @@ 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):
@@ -186,10 +176,8 @@ def set_app_config_internal(req: SetAppConfigRequest):
             config["net"] = {}
         config["net"]["listen_port"] = int(req.listen_port)
 
-    config["use_v3_engine"] = req.backend == "ed_diffusers"
+    config["use_v3_engine"] = req.use_v3_engine
-    config["backend"] = req.backend
     config["models_dir"] = req.models_dir
-    config["vram_usage_level"] = req.vram_usage_level
 
     for property, property_value in req.dict().items():
         if property_value is not None and property not in req.__fields__ and property not in PROTECTED_CONFIG_KEYS:
@@ -208,11 +196,13 @@ def set_app_config_internal(req: SetAppConfigRequest):
 
 
 def update_render_devices_in_config(config, render_devices):
-    if render_devices not in ("cpu", "auto") and not render_devices.startswith("cuda:"):
+    from easydiffusion.device_manager import validate_render_devices
-        raise HTTPException(status_code=400, detail=f"Invalid render device requested: {render_devices}")
 
-    if render_devices.startswith("cuda:"):
+    try:
         render_devices = render_devices.split(",")
+        validate_render_devices(render_devices)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
 
     config["render_devices"] = render_devices
 
@@ -228,8 +218,6 @@ 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))
@@ -240,7 +228,6 @@ 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)
@@ -324,15 +311,6 @@ 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)
@@ -366,13 +344,15 @@ def model_merge_internal(req: dict):
 
         mergeReq: MergeRequest = MergeRequest.parse_obj(req)
 
-        sd_model_dir = model_manager.get_model_dir("stable-diffusion")[0]
-
         merge_models(
             model_manager.resolve_model_to_use(mergeReq.model0, "stable-diffusion"),
             model_manager.resolve_model_to_use(mergeReq.model1, "stable-diffusion"),
             mergeReq.ratio,
-            os.path.join(sd_model_dir, filename_regex.sub("_", mergeReq.out_path)),
+            os.path.join(
+                app.MODELS_DIR,
+                "stable-diffusion",
+                filename_regex.sub("_", mergeReq.out_path),
+            ),
             mergeReq.use_fp16,
         )
         return JSONResponse({"status": "OK"}, headers=NOCACHE_HEADERS)

ui/easydiffusion/task_manager.py

@@ -4,7 +4,6 @@ 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
 
@@ -20,6 +19,10 @@ import torch
 from easydiffusion import device_manager
 from easydiffusion.tasks import Task
 from easydiffusion.utils import log
+from sdkit.utils import gc
+
+from torchruntime.utils import get_device_count, get_device, get_device_name, get_installed_torch_platform
+from sdkit.utils import is_cpu_device, mem_get_info
 
 THREAD_NAME_PREFIX = ""
 ERR_LOCK_FAILED = " failed to acquire lock within timeout."
@@ -233,8 +236,6 @@ def thread_render(device):
     global current_state, current_state_error
 
     from easydiffusion import model_manager, runtime
-    from easydiffusion.backend_manager import backend
-    from requests import ConnectionError
 
     try:
         runtime.init(device)
@@ -246,17 +247,8 @@ def thread_render(device):
         }
 
         current_state = ServerStates.LoadingModel
-
-        while True:
-            try:
-                if backend.ping(timeout=1):
-                    break
-
-                time.sleep(1)
-            except (TimeoutError, ConnectionError):
-                time.sleep(1)
-
         model_manager.load_default_models(runtime.context)
+
         current_state = ServerStates.Online
     except Exception as e:
         log.error(traceback.format_exc())
@@ -302,6 +294,7 @@ 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)
@@ -339,34 +332,33 @@ def get_devices():
         "active": {},
     }
 
-    def get_device_info(device):
+    def get_device_info(device_id):
-        if device in ("cpu", "mps"):
+        if is_cpu_device(device_id):
             return {"name": device_manager.get_processor_name()}
 
-        mem_free, mem_total = torch.cuda.mem_get_info(device)
+        device = get_device(device_id)
+
+        mem_free, mem_total = mem_get_info(device)
         mem_free /= float(10**9)
         mem_total /= float(10**9)
 
         return {
-            "name": torch.cuda.get_device_name(device),
+            "name": get_device_name(device),
             "mem_free": mem_free,
             "mem_total": mem_total,
             "max_vram_usage_level": device_manager.get_max_vram_usage_level(device),
         }
 
     # list the compatible devices
-    cuda_count = torch.cuda.device_count()
+    torch_platform_name = get_installed_torch_platform()[0]
-    for device in range(cuda_count):
+    device_count = get_device_count()
-        device = f"cuda:{device}"
+    for device_id in range(device_count):
-        if not device_manager.is_device_compatible(device):
+        device_id = f"{torch_platform_name}:{device_id}" if device_count > 1 else torch_platform_name
-            continue
 
-        devices["all"].update({device: get_device_info(device)})
+        devices["all"].update({device_id: get_device_info(device_id)})
 
-    if device_manager.is_mps_available():
+    if torch_platform_name != "cpu":
-        devices["all"].update({"mps": get_device_info("mps")})
+        devices["all"].update({"cpu": get_device_info("cpu")})
-
-    devices["all"].update({"cpu": get_device_info("cpu")})
 
     # list the activated devices
     if not manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT):
@@ -378,8 +370,8 @@ def get_devices():
             weak_data = weak_thread_data.get(rthread)
             if not weak_data or not "device" in weak_data or not "device_name" in weak_data:
                 continue
-            device = weak_data["device"]
+            device_id = weak_data["device"]
-            devices["active"].update({device: get_device_info(device)})
+            devices["active"].update({device_id: get_device_info(device_id)})
     finally:
         manager_lock.release()
 
@@ -437,12 +429,6 @@ def start_render_thread(device):
 
 
 def stop_render_thread(device):
-    try:
-        device_manager.validate_device_id(device, log_prefix="stop_render_thread")
-    except:
-        log.error(traceback.format_exc())
-        return False
-
     if not manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT):
         raise Exception("stop_render_thread" + ERR_LOCK_FAILED)
     log.info(f"Stopping Rendering Thread on device: {device}")

ui/easydiffusion/tasks/filter_images.py

@@ -5,7 +5,9 @@ import time
 
 from numpy import base_repr
 
-from sdkit.utils import img_to_base64_str, log, save_images, base64_str_to_img
+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 easydiffusion import model_manager, runtime
 from easydiffusion.types import (
@@ -17,7 +19,6 @@ 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
@@ -46,9 +47,7 @@ class FilterTask(Task):
 
         # convert to multi-filter format, if necessary
         if isinstance(req.filter, str):
-            if req.filter not in req.filter_params:
+            req.filter_params = {req.filter: req.filter_params}
-                req.filter_params = {req.filter: req.filter_params}
-
             req.filter = [req.filter]
 
         if not isinstance(req.image, list):
@@ -58,7 +57,6 @@ 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
 
@@ -68,24 +66,15 @@ class FilterTask(Task):
 
         print_task_info(self.request, self.models_data, self.output_format, self.save_data)
 
-        has_nsfw_filter = "nsfw_filter" in self.request.filter
+        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)
 
         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")
@@ -96,9 +85,8 @@ 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_pil,
+                images,
                 save_dir_path,
                 file_name=img_id,
                 output_format=output_format.output_format,
@@ -106,6 +94,13 @@ 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))
@@ -115,6 +110,46 @@ 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

@@ -2,23 +2,26 @@ 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
+from easydiffusion.types import GenerateImageResponse, RenderTaskData, UserInitiatedStop
-from easydiffusion.utils import get_printable_request, log, save_images_to_disk, filter_nsfw
+from easydiffusion.utils import get_printable_request, log, save_images_to_disk
+from sdkit.generate import generate_images
 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):
@@ -48,13 +51,15 @@ 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)
@@ -63,7 +68,7 @@ class RenderTask(Task):
             if isinstance(task_manager.current_state_error, (SystemExit, StopAsyncIteration)) or isinstance(
                 self.error, StopAsyncIteration
             ):
-                backend.stop_rendering(context)
+                context.stop_processing = True
                 if isinstance(task_manager.current_state_error, StopAsyncIteration):
                     self.error = task_manager.current_state_error
                     task_manager.current_state_error = None
@@ -73,7 +78,11 @@ 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"):
+        if (
+            runtime.set_vram_optimizations(context)
+            or self.has_param_changed(context, "clip_skip")
+            or self.trt_needs_reload(context)
+        ):
             models_to_force_reload.append("stable-diffusion")
 
         model_manager.reload_models_if_necessary(context, self.models_data, models_to_force_reload)
@@ -90,11 +99,10 @@ class RenderTask(Task):
             self.buffer_queue,
             self.temp_images,
             step_callback,
-            self,
         )
 
     def has_param_changed(self, context, param_name):
-        if not getattr(context, "test_diffusers", False):
+        if not context.test_diffusers:
             return False
         if "stable-diffusion" not in context.models or "params" not in context.models["stable-diffusion"]:
             return True
@@ -103,6 +111,29 @@ 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,
@@ -114,21 +145,12 @@ 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,
+        context, req, task_data, models_data, output_format, save_data, data_queue, task_temp_images, step_callback
-        req,
-        task_data,
-        models_data,
-        output_format,
-        save_data,
-        data_queue,
-        task_temp_images,
-        step_callback,
-        task,
     )
 
     res = GenerateImageResponse(
@@ -148,9 +170,7 @@ 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("[", "\[")
@@ -158,7 +178,7 @@ 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}")
 
@@ -173,41 +193,26 @@ def make_images_internal(
     data_queue: queue.Queue,
     task_temp_images: list,
     step_callback,
-    task,
 ):
-    from easydiffusion.backend_manager import backend
+    images, user_stopped = generate_images_internal(
-
-    # 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,
     )
-    user_stopped = isinstance(task.error, StopAsyncIteration)
+
+    gc(context)
 
     filters, filter_params = task_data.filters, task_data.filter_params
-    if len(filters) > 0 and not user_stopped:
+    filtered_images = filter_images(context, images, filters, filter_params) if not user_stopped else images
-        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:
-        images_pil = [base64_str_to_img(img) for img in images]
+        save_images_to_disk(images, filtered_images, req, task_data, models_data, output_format, save_data)
-        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:
@@ -221,43 +226,97 @@ 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,
 ):
-    from easydiffusion.backend_manager import backend
+    context.temp_images.clear()
 
-    callback = make_step_callback(context, req, task_data, data_queue, task_temp_images, step_callback)
+    callback = make_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,
-        output_format=output_format.output_format,
+        req,
-        output_quality=output_format.output_quality,
+        task_data,
-        output_lossless=output_format.output_lossless,
+        data_queue,
-        vae_tiling=task_data.enable_vae_tiling,
+        task_temp_images,
-        stream_image_progress=stream_image_progress,
+        step_callback,
-        stream_image_progress_interval=stream_image_progress_interval,
+        stream_image_progress,
-        clip_skip=2 if task_data.clip_skip else 1,
+        stream_image_progress_interval,
     )
 
-    images = backend.generate_images(context, callback=callback, output_type="base64", **req.dict())
+    try:
+        if req.init_image is not None and not context.test_diffusers:
+            req.sampler_name = "ddim"
 
-    return images
+        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
 
 
 def construct_response(images: list, seeds: list, output_format: OutputFormatData):
-    return [ResponseImage(data=img, seed=seed) for img, seed in zip(images, seeds)]
+    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)
+    ]
 
 
 def make_step_callback(
@@ -267,44 +326,53 @@ 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(images, task_temp_images: list):
+    def update_temp_img(x_samples, task_temp_images: list):
         partial_images = []
 
-        if images is None:
+        if context.test_diffusers:
-            return []
+            images = diffusers_latent_samples_to_images(context, x_samples)
+        else:
+            images = latent_samples_to_images(context, x_samples)
 
         if task_data.block_nsfw:
-            images = filter_nsfw(images, print_log=False)
+            images = filter_images(context, images, "nsfw_checker")
 
         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(images, i, *args):
+    def on_image_step(x_samples, 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 images is not None:
+        if stream_image_progress and stream_image_progress_interval > 0 and i % stream_image_progress_interval == 0:
-            progress["output"] = update_temp_img(images, task_temp_images)
+            progress["output"] = update_temp_img(context.partial_x_samples, 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

@@ -14,19 +14,16 @@ class GenerateImageRequest(BaseModel):
     num_outputs: int = 1
     num_inference_steps: int = 50
     guidance_scale: float = 7.5
-    distilled_guidance_scale: float = 3.5
 
     init_image: Any = None
     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: bool = False
     strict_mask_border: bool = False
 
     sampler_name: str = None  # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"
-    scheduler_name: str = None
     hypernetwork_strength: float = 0
     lora_alpha: Union[float, List[float]] = 0
     tiling: str = None  # None, "x", "y", "xy"
@@ -216,19 +213,22 @@ 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")
 
-    ## ensure that the model name is in the model path
+    model_paths["gfpgan"] = old_req.get("use_face_correction", "")
-    for model_name in ("gfpgan", "codeformer"):
+    model_paths["gfpgan"] = model_paths["gfpgan"] if "gfpgan" in model_paths["gfpgan"].lower() else None
-        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
 
-    for model_name in ("realesrgan", "latent_upscaler", "esrgan_4x", "lanczos", "nearest", "scunet", "swinir"):
+    model_paths["codeformer"] = old_req.get("use_face_correction", "")
-        model_paths[model_name] = old_req.get("use_upscale", "")
+    model_paths["codeformer"] = model_paths["codeformer"] if "codeformer" in model_paths["codeformer"].lower() else None
-        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"
@@ -244,12 +244,8 @@ def convert_legacy_render_req_to_new(old_req: dict):
         }
 
     # move the filter params
-    for model_name in ("realesrgan", "esrgan_4x", "lanczos", "nearest", "scunet", "swinir"):
+    if model_paths["realesrgan"]:
-        if model_paths[model_name]:
+        filter_params["realesrgan"] = {"scale": int(old_req.get("upscale_amount", 4))}
-            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"],
@@ -268,31 +264,14 @@ def convert_legacy_render_req_to_new(old_req: dict):
     if old_req.get("block_nsfw"):
         filters.append("nsfw_checker")
 
-    for model_name in ("gfpgan", "codeformer"):
+    if model_paths["codeformer"]:
-        if model_paths[model_name]:
+        filters.append("codeformer")
-            filters.append(model_name)
+    elif model_paths["gfpgan"]:
-            break
+        filters.append("gfpgan")
 
-    for model_name in ("realesrgan", "latent_upscaler", "esrgan_4x", "lanczos", "nearest", "scunet", "swinir"):
+    if model_paths["realesrgan"]:
-        if model_paths[model_name]:
+        filters.append("realesrgan")
-            filters.append(model_name)
+    elif model_paths["latent_upscaler"]:
-            break
+        filters.append("latent_upscaler")
 
     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

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

ui/easydiffusion/utils/nsfw_checker.py

@@ -1,80 +0,0 @@
-# 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.model_manager import get_model_dirs
-    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 = get_model_dirs("nsfw-checker")[0]
-        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

@@ -34,12 +34,10 @@ TASK_TEXT_MAPPING = {
     "control_alpha": "ControlNet Strength",
     "use_vae_model": "VAE model",
     "sampler_name": "Sampler",
-    "scheduler_name": "Scheduler",
     "width": "Width",
     "height": "Height",
     "num_inference_steps": "Steps",
     "guidance_scale": "Guidance Scale",
-    "distilled_guidance_scale": "Distilled Guidance",
     "prompt_strength": "Prompt Strength",
     "use_lora_model": "LoRA model",
     "lora_alpha": "LoRA Strength",
@@ -249,7 +247,7 @@ def get_printable_request(
     task_data_metadata.update(save_data.dict())
 
     app_config = app.getConfig()
-    using_diffusers = app_config.get("backend", "ed_diffusers") in ("ed_diffusers", "webui")
+    using_diffusers = app_config.get("use_v3_engine", True)
 
     # Save the metadata in the order defined in TASK_TEXT_MAPPING
     metadata = {}

ui/index.html

@@ -35,13 +35,7 @@
             <h1>
                 <img id="logo_img" src="/media/images/icon-512x512.png" >
                 Easy Diffusion
-                <small>
+                <small><span id="version">v3.0.9c</span> <span id="updateBranchLabel"></span></small>
-                    <span id="version">
-                        <span class="gated-feature" data-feature-keys="backend_ed_classic backend_ed_diffusers">v3.0.10</span>
-                        <span class="gated-feature" data-feature-keys="backend_webui">v3.5.0</span>
-                    </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">
@@ -79,7 +73,7 @@
                         </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 gated-feature" data-feature-keys="backend_ed_diffusers backend_webui">+ Embedding</button>
+                            <button id="embeddings-button" class="tertiaryButton smallButton displayNone">+ Embedding</button>
                         </div>
                     </div>
                     <textarea id="prompt" class="col-free">a photograph of an astronaut riding a horse</textarea>
@@ -89,7 +83,7 @@
                         <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 gated-feature" data-feature-keys="backend_ed_diffusers backend_webui">+ Negative Embedding</button>
+                    <button id="negative-embeddings-button" class="tertiaryButton smallButton displayNone">+ 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>
@@ -180,14 +174,14 @@
                             <!-- <label><small>Takes upto 20 mins the first time</small></label> -->
                         </td>
                     </tr>
-                    <tr class="pl-5 gated-feature" id="clip_skip_config" data-feature-keys="backend_ed_diffusers backend_webui">
+                    <tr class="pl-5 displayNone" id="clip_skip_config">
                         <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 gated-feature" data-feature-keys="backend_ed_diffusers backend_webui">
+                    <tr id="controlnet_model_container" class="pl-5">
                         <td><label for="controlnet_model">ControlNet Image:</label></td>
                         <td class="diffusers-restart-needed">
                             <div id="control_image_wrapper" class="preview_image_wrapper">
@@ -207,92 +201,40 @@
                                         <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" class="gated-feature" data-feature-keys="backend_diffusers">Scribble hedsafe</option>
+                                        <option value="scribble_hedsafe">Scribble hedsafe</option>
                                         <option value="scribble_pidinet">Scribble pidinet</option>
-                                        <option value="scribble_pidsafe" class="gated-feature" data-feature-keys="backend_diffusers">Scribble pidsafe</option>
+                                        <option value="scribble_pidsafe">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" class="gated-feature" data-feature-keys="backend_diffusers">Segment</option>
+                                        <option value="segment">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/>
+                                <br/>
-                                <label><small>Will download the necessary models, the first time.</small></label> -->
+                                <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>
@@ -306,59 +248,27 @@
                         <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" class="gated-feature" data-feature-keys="backend_ed_classic backend_ed_diffusers">DPM Solver (Stability AI)</option>
+                            <option value="dpm_solver_stability">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="gated-feature" data-feature-keys="backend_ed_diffusers backend_webui">DPM++ 2m SDE</option>
+                            <option value="dpmpp_2m_sde" class="diffusers-only">DPM++ 2m SDE (Karras)</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="restart" class="gated-feature" data-feature-keys="backend_webui">Restart</option>
+                            <option value="dpm_adaptive" class="k_diffusion-only">DPM Adaptive (Karras)</option>
-                            <option value="heun_pp2" class="gated-feature" data-feature-keys="backend_webui">Heun PP2</option>
+                            <option value="ddpm" class="diffusers-only">DDPM</option>
-                            <option value="ipndm" class="gated-feature" data-feature-keys="backend_webui">IPNDM</option>
+                            <option value="deis" class="diffusers-only">DEIS</option>
-                            <option value="ipndm_v" class="gated-feature" data-feature-keys="backend_webui">IPNDM_V</option>
+                            <option value="unipc_snr" class="k_diffusion-only">UniPC SNR</option>
-                            <option value="ddpm" class="gated-feature" data-feature-keys="backend_ed_diffusers backend_webui">DDPM</option>
+                            <option value="unipc_tu">UniPC TU</option>
-                            <option value="deis" class="gated-feature" data-feature-keys="backend_ed_diffusers backend_webui">DEIS</option>
+                            <option value="unipc_snr_2" class="k_diffusion-only">UniPC SNR 2</option>
-                            <option value="lcm" class="gated-feature" data-feature-keys="backend_webui">LCM</option>
+                            <option value="unipc_tu_2" class="k_diffusion-only">UniPC TU 2</option>
-                            <option value="forge_flux_realistic" class="gated-feature" data-feature-keys="backend_webui">[Forge] Flux Realistic</option>
+                            <option value="unipc_tq" class="k_diffusion-only">UniPC TQ</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/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 warning-label displayNone" id="fluxSamplerWarning"><td></td><td>Please avoid 'Euler Ancestral' with Flux!</td></tr>
-                    <tr id="schedulerSelection" class="pl-5 gated-feature" data-feature-keys="backend_webui"><td><label for="scheduler_name">Scheduler:</label></td><td>
-                        <select id="scheduler_name" name="scheduler_name">
-                            <option value="automatic">Automatic</option>
-                            <option value="uniform">Uniform</option>
-                            <option value="karras">Karras</option>
-                            <option value="exponential">Exponential</option>
-                            <option value="polyexponential">Polyexponential</option>
-                            <option value="sgm_uniform">SGM Uniform</option>
-                            <option value="kl_optimal">KL Optimal</option>
-                            <option value="align_your_steps">Align Your Steps</option>
-                            <option value="simple" selected>Simple</option>
-                            <option value="normal">Normal</option>
-                            <option value="ddim">DDIM</option>
-                            <option value="beta">Beta</option>
-                            <option value="turbo">Turbo</option>
-                            <option value="align_your_steps_GITS">Align Your Steps GITS</option>
-                            <option value="align_your_steps_11">Align Your Steps 11</option>
-                            <option value="align_your_steps_32">Align Your Steps 32</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>
                     </td></tr>
                     <tr class="pl-5"><td><label>Image Size: </label></td><td id="image-size-options">
                         <select id="width" name="width" value="512">
@@ -434,14 +344,12 @@
                                 </div>
                             </div>
                         </div>
-                        <div id="small_image_warning" class="displayNone warning-label">Small image sizes can cause bad image quality</div>
+                        <div id="small_image_warning" class="displayNone">Small image sizes can cause bad image quality</div>
                     </td></tr>
                     <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 class="pl-5 displayNone warning-label" id="guidanceWarning"><td></td><td id="guidanceWarningText"></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\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="decimal"><br/></td></tr>
-                    <tr id="distilled_guidance_scale_container" class="pl-5 displayNone"><td><label for="distilled_guidance_scale_slider">Distilled Guidance:</label></td><td> <input id="distilled_guidance_scale_slider" name="distilled_guidance_scale_slider" class="editor-slider" value="35" type="range" min="11" max="500"> <input id="distilled_guidance_scale" name="distilled_guidance_scale" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="decimal"></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>
@@ -449,14 +357,14 @@
                             <div id="lora_model" data-path=""></div>
                         </td>
                     </tr>
-                    <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>
+                    <tr id="hypernetwork_model_container" class="pl-5"><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 gated-feature" data-feature-keys="backend_ed_classic">
+                    <tr id="hypernetwork_strength_container" class="pl-5">
                         <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\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="decimal"><br/></td>
                     </tr>
-                    <tr id="tiling_container" class="pl-5 gated-feature" data-feature-keys="backend_ed_diffusers">
+                    <tr id="tiling_container" class="pl-5">
                         <td><label for="tiling">Seamless Tiling:</label></td>
                         <td class="diffusers-restart-needed">
                             <select id="tiling" name="tiling">
@@ -481,7 +389,7 @@
                     <tr class="pl-5" id="output_quality_row"><td><label for="output_quality">Image Quality:</label></td><td>
                     <input id="output_quality_slider" name="output_quality" class="editor-slider" value="75" type="range" min="10" max="95"> <input id="output_quality" name="output_quality" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="numeric">
                     </td></tr>
-                    <tr class="pl-5 gated-feature" data-feature-keys="backend_ed_diffusers">
+                    <tr class="pl-5">
                         <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>
@@ -497,7 +405,7 @@
                         <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 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>
+                            <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>
                         </table>
                     </li>
                     <li class="pl-5">
@@ -510,13 +418,7 @@
                         <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="ESRGAN_4x" class="pl-5 gated-feature" data-feature-keys="backend_webui">ESRGAN_4x</option>
+                            <option value="latent_upscaler">Latent Upscaler 2x</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>
@@ -923,8 +825,7 @@
             <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 /> <!-- for backwards compatibility -->
+        <input id="test_diffusers" type="checkbox" style="display: none" checked />
-        <input id="use_v3_engine" type="checkbox" style="display: none" checked /> <!-- for backwards compatibility -->
     </div>
 </div>
 </body>

ui/main.py

@@ -9,3 +9,6 @@ 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

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

ui/media/css/main.css

@@ -36,15 +36,6 @@ 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;
@@ -550,7 +541,7 @@ div.img-preview img {
     position: relative;
     background: var(--background-color4);
     display: flex;
-    padding: 6px 0 0;
+    padding: 12px 0 0;
 }
 .tab .icon {
     padding-right: 4pt;
@@ -666,15 +657,6 @@ div.img-preview img {
     display: block;
 }
 
-.gated-feature {
-    display: none;
-}
-
-.warning-label {
-    font-size: smaller;
-    color: var(--status-orange);
-}
-
 .display-settings {
     float: right;
     position: relative;
@@ -1477,6 +1459,11 @@ div.top-right {
     margin-top: 6px;
 }
 
+#small_image_warning {
+    font-size: smaller;
+    color: var(--status-orange);
+}
+
 button#save-system-settings-btn {
     padding: 4pt 8pt;
 }

ui/media/js/auto-save.js

@@ -16,12 +16,10 @@ const SETTINGS_IDS_LIST = [
     "clip_skip",
     "vae_model",
     "sampler_name",
-    "scheduler_name",
     "width",
     "height",
     "num_inference_steps",
     "guidance_scale",
-    "distilled_guidance_scale",
     "prompt_strength",
     "tiling",
     "output_format",
@@ -180,23 +178,6 @@ function loadSettings() {
             }
         })
         CURRENTLY_LOADING_SETTINGS = false
-    } else if (localStorage.length < 2) {
-        // localStorage is too short for OldSettings
-        // So this is likely the first time Easy Diffusion is running.
-        // Initialize vram_usage_level based on the available VRAM
-        function initGPUProfile(event) {
-            if (
-                "detail" in event &&
-                "active" in event.detail &&
-                "cuda:0" in event.detail.active &&
-                event.detail.active["cuda:0"].mem_total < 4.5
-            ) {
-                vramUsageLevelField.value = "low"
-                vramUsageLevelField.dispatchEvent(new Event("change"))
-            }
-            document.removeEventListener("system_info_update", initGPUProfile)
-        }
-        document.addEventListener("system_info_update", initGPUProfile)
     } else {
         CURRENTLY_LOADING_SETTINGS = true
         tryLoadOldSettings()

ui/media/js/dnd.js

@@ -131,15 +131,6 @@ const TASK_MAPPING = {
         readUI: () => parseFloat(guidanceScaleField.value),
         parse: (val) => parseFloat(val),
     },
-    distilled_guidance_scale: {
-        name: "Distilled Guidance",
-        setUI: (distilled_guidance_scale) => {
-            distilledGuidanceScaleField.value = distilled_guidance_scale
-            updateDistilledGuidanceScaleSlider()
-        },
-        readUI: () => parseFloat(distilledGuidanceScaleField.value),
-        parse: (val) => parseFloat(val),
-    },
     prompt_strength: {
         name: "Prompt Strength",
         setUI: (prompt_strength) => {
@@ -251,14 +242,6 @@ const TASK_MAPPING = {
         readUI: () => samplerField.value,
         parse: (val) => val,
     },
-    scheduler_name: {
-        name: "Scheduler",
-        setUI: (scheduler_name) => {
-            schedulerField.value = scheduler_name
-        },
-        readUI: () => schedulerField.value,
-        parse: (val) => val,
-    },
     use_stable_diffusion_model: {
         name: "Stable Diffusion model",
         setUI: (use_stable_diffusion_model) => {
@@ -607,13 +590,11 @@ const TASK_TEXT_MAPPING = {
     seed: "Seed",
     num_inference_steps: "Steps",
     guidance_scale: "Guidance Scale",
-    distilled_guidance_scale: "Distilled Guidance",
     prompt_strength: "Prompt Strength",
     use_face_correction: "Use Face Correction",
     use_upscale: "Use Upscaling",
     upscale_amount: "Upscale By",
     sampler_name: "Sampler",
-    scheduler_name: "Scheduler",
     negative_prompt: "Negative Prompt",
     use_stable_diffusion_model: "Stable Diffusion model",
     use_hypernetwork_model: "Hypernetwork model",

ui/media/js/main.js

@@ -12,16 +12,8 @@ const taskConfigSetup = {
         seed: { value: ({ seed }) => seed, label: "Seed" },
         dimensions: { value: ({ reqBody }) => `${reqBody?.width}x${reqBody?.height}`, label: "Dimensions" },
         sampler_name: "Sampler",
-        scheduler_name: {
-            label: "Scheduler",
-            visible: ({ reqBody }) => reqBody?.scheduler_name,
-        },
         num_inference_steps: "Inference Steps",
         guidance_scale: "Guidance Scale",
-        distilled_guidance_scale: {
-            label: "Distilled Guidance Scale",
-            visible: ({ reqBody }) => reqBody?.distilled_guidance_scale,
-        },
         use_stable_diffusion_model: "Model",
         clip_skip: {
             label: "Clip Skip",
@@ -84,8 +76,6 @@ let numOutputsParallelField = document.querySelector("#num_outputs_parallel")
 let numInferenceStepsField = document.querySelector("#num_inference_steps")
 let guidanceScaleSlider = document.querySelector("#guidance_scale_slider")
 let guidanceScaleField = document.querySelector("#guidance_scale")
-let distilledGuidanceScaleSlider = document.querySelector("#distilled_guidance_scale_slider")
-let distilledGuidanceScaleField = document.querySelector("#distilled_guidance_scale")
 let outputQualitySlider = document.querySelector("#output_quality_slider")
 let outputQualityField = document.querySelector("#output_quality")
 let outputQualityRow = document.querySelector("#output_quality_row")
@@ -123,8 +113,6 @@ let promptStrengthSlider = document.querySelector("#prompt_strength_slider")
 let promptStrengthField = document.querySelector("#prompt_strength")
 let samplerField = document.querySelector("#sampler_name")
 let samplerSelectionContainer = document.querySelector("#samplerSelection")
-let schedulerField = document.querySelector("#scheduler_name")
-let schedulerSelectionContainer = document.querySelector("#schedulerSelection")
 let useFaceCorrectionField = document.querySelector("#use_face_correction")
 let gfpganModelField = new ModelDropdown(document.querySelector("#gfpgan_model"), ["gfpgan", "codeformer"], "", false)
 let useUpscalingField = document.querySelector("#use_upscale")
@@ -623,6 +611,13 @@ function onUseAsInputClick(req, img) {
     initImagePreview.src = imgData
 
     maskSetting.checked = false
+
+    //Force the image settings size to match the input, as inpaint currently only works correctly
+    //if input image and generate sizes match.
+    addImageSizeOption(img.naturalWidth);
+    addImageSizeOption(img.naturalHeight);
+    widthField.value = img.naturalWidth;
+    heightField.value = img.naturalHeight;
 }
 
 function onUseForControlnetClick(req, img) {
@@ -993,20 +988,7 @@ 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)
 }
@@ -1063,9 +1045,6 @@ function makeImage() {
     if (guidanceScaleField.value == "") {
         guidanceScaleField.value = guidanceScaleSlider.value / 10
     }
-    if (distilledGuidanceScaleField.value == "") {
-        distilledGuidanceScaleField.value = distilledGuidanceScaleSlider.value / 10
-    }
     if (hypernetworkStrengthField.value == "") {
         hypernetworkStrengthField.value = hypernetworkStrengthSlider.value / 100
     }
@@ -1434,12 +1413,6 @@ function getCurrentUserRequest() {
             newTask.reqBody.control_filter_to_apply = controlImageFilterField.value
         }
     }
-    if (stableDiffusionModelField.value.toLowerCase().includes("flux")) {
-        newTask.reqBody.distilled_guidance_scale = parseFloat(distilledGuidanceScaleField.value)
-    }
-    if (schedulerSelectionContainer.style.display !== "none") {
-        newTask.reqBody.scheduler_name = schedulerField.value
-    }
 
     return newTask
 }
@@ -1879,93 +1852,36 @@ controlImagePreview.addEventListener("load", onControlnetModelChange)
 controlImagePreview.addEventListener("unload", onControlnetModelChange)
 onControlnetModelChange()
 
-// tip for Flux
+function onControlImageFilterChange() {
-let sdModelField = document.querySelector("#stable_diffusion_model")
+    let filterId = controlImageFilterField.value
-function checkGuidanceValue() {
+    if (filterId.includes("openpose")) {
-    let guidance = parseFloat(guidanceScaleField.value)
+        controlnetModelField.value = "control_v11p_sd15_openpose"
-    let guidanceWarning = document.querySelector("#guidanceWarning")
+    } else if (filterId === "canny") {
-    let guidanceWarningText = document.querySelector("#guidanceWarningText")
+        controlnetModelField.value = "control_v11p_sd15_canny"
-    if (sdModelField.value.toLowerCase().includes("flux")) {
+    } else if (filterId === "mlsd") {
-        if (guidance > 1.5) {
+        controlnetModelField.value = "control_v11p_sd15_mlsd"
-            guidanceWarningText.innerText = "Flux recommends a 'Guidance Scale' of 1"
+    } else if (filterId === "mlsd") {
-            guidanceWarning.classList.remove("displayNone")
+        controlnetModelField.value = "control_v11p_sd15_mlsd"
-        } else {
+    } else if (filterId.includes("scribble")) {
-            guidanceWarning.classList.add("displayNone")
+        controlnetModelField.value = "control_v11p_sd15_scribble"
-        }
+    } else if (filterId.includes("softedge")) {
-    } else {
+        controlnetModelField.value = "control_v11p_sd15_softedge"
-        if (guidance < 2) {
+    } else if (filterId === "normal_bae") {
-            guidanceWarningText.innerText = "A higher 'Guidance Scale' is recommended!"
+        controlnetModelField.value = "control_v11p_sd15_normalbae"
-            guidanceWarning.classList.remove("displayNone")
+    } else if (filterId.includes("depth")) {
-        } else {
+        controlnetModelField.value = "control_v11f1p_sd15_depth"
-            guidanceWarning.classList.add("displayNone")
+    } else if (filterId === "lineart_anime") {
-        }
+        controlnetModelField.value = "control_v11p_sd15s2_lineart_anime"
+    } else if (filterId.includes("lineart")) {
+        controlnetModelField.value = "control_v11p_sd15_lineart"
+    } else if (filterId === "shuffle") {
+        controlnetModelField.value = "control_v11e_sd15_shuffle"
+    } else if (filterId === "segment") {
+        controlnetModelField.value = "control_v11p_sd15_seg"
     }
 }
-sdModelField.addEventListener("change", checkGuidanceValue)
+controlImageFilterField.addEventListener("change", onControlImageFilterChange)
-guidanceScaleField.addEventListener("change", checkGuidanceValue)
+onControlImageFilterChange()
-guidanceScaleSlider.addEventListener("change", checkGuidanceValue)
-
-function checkGuidanceScaleVisibility() {
-    let guidanceScaleContainer = document.querySelector("#distilled_guidance_scale_container")
-    if (sdModelField.value.toLowerCase().includes("flux")) {
-        guidanceScaleContainer.classList.remove("displayNone")
-    } else {
-        guidanceScaleContainer.classList.add("displayNone")
-    }
-}
-sdModelField.addEventListener("change", checkGuidanceScaleVisibility)
-
-function checkFluxSampler() {
-    let samplerWarning = document.querySelector("#fluxSamplerWarning")
-    if (sdModelField.value.toLowerCase().includes("flux")) {
-        if (samplerField.value == "euler_a") {
-            samplerWarning.classList.remove("displayNone")
-        } else {
-            samplerWarning.classList.add("displayNone")
-        }
-    } else {
-        samplerWarning.classList.add("displayNone")
-    }
-}
-sdModelField.addEventListener("change", checkFluxSampler)
-samplerField.addEventListener("change", checkFluxSampler)
-
-document.addEventListener("refreshModels", function() {
-    checkGuidanceValue()
-    checkGuidanceScaleVisibility()
-    checkFluxSampler()
-})
-
-// function onControlImageFilterChange() {
-//     let filterId = controlImageFilterField.value
-//     if (filterId.includes("openpose")) {
-//         controlnetModelField.value = "control_v11p_sd15_openpose"
-//     } else if (filterId === "canny") {
-//         controlnetModelField.value = "control_v11p_sd15_canny"
-//     } else if (filterId === "mlsd") {
-//         controlnetModelField.value = "control_v11p_sd15_mlsd"
-//     } else if (filterId === "mlsd") {
-//         controlnetModelField.value = "control_v11p_sd15_mlsd"
-//     } else if (filterId.includes("scribble")) {
-//         controlnetModelField.value = "control_v11p_sd15_scribble"
-//     } else if (filterId.includes("softedge")) {
-//         controlnetModelField.value = "control_v11p_sd15_softedge"
-//     } else if (filterId === "normal_bae") {
-//         controlnetModelField.value = "control_v11p_sd15_normalbae"
-//     } else if (filterId.includes("depth")) {
-//         controlnetModelField.value = "control_v11f1p_sd15_depth"
-//     } else if (filterId === "lineart_anime") {
-//         controlnetModelField.value = "control_v11p_sd15s2_lineart_anime"
-//     } else if (filterId.includes("lineart")) {
-//         controlnetModelField.value = "control_v11p_sd15_lineart"
-//     } else if (filterId === "shuffle") {
-//         controlnetModelField.value = "control_v11e_sd15_shuffle"
-//     } else if (filterId === "segment") {
-//         controlnetModelField.value = "control_v11p_sd15_seg"
-//     }
-// }
-// controlImageFilterField.addEventListener("change", onControlImageFilterChange)
-// onControlImageFilterChange()
 
 upscaleModelField.disabled = !useUpscalingField.checked
 upscaleAmountField.disabled = !useUpscalingField.checked
@@ -2064,27 +1980,6 @@ guidanceScaleSlider.addEventListener("input", updateGuidanceScale)
 guidanceScaleField.addEventListener("input", updateGuidanceScaleSlider)
 updateGuidanceScale()
 
-/********************* Distilled Guidance **************************/
-function updateDistilledGuidanceScale() {
-    distilledGuidanceScaleField.value = distilledGuidanceScaleSlider.value / 10
-    distilledGuidanceScaleField.dispatchEvent(new Event("change"))
-}
-
-function updateDistilledGuidanceScaleSlider() {
-    if (distilledGuidanceScaleField.value < 0) {
-        distilledGuidanceScaleField.value = 0
-    } else if (distilledGuidanceScaleField.value > 50) {
-        distilledGuidanceScaleField.value = 50
-    }
-
-    distilledGuidanceScaleSlider.value = distilledGuidanceScaleField.value * 10
-    distilledGuidanceScaleSlider.dispatchEvent(new Event("change"))
-}
-
-distilledGuidanceScaleSlider.addEventListener("input", updateDistilledGuidanceScale)
-distilledGuidanceScaleField.addEventListener("input", updateDistilledGuidanceScaleSlider)
-updateDistilledGuidanceScale()
-
 /********************* Prompt Strength *******************/
 function updatePromptStrength() {
     promptStrengthField.value = promptStrengthSlider.value / 100

ui/media/js/parameters.js

@@ -102,7 +102,7 @@ var PARAMETERS = [
         type: ParameterType.custom,
         icon: "fa-folder-tree",
         label: "Models Folder",
-        note: "Path to the 'models' folder. Please save and restart Easy Diffusion after changing this.",
+        note: "Path to the 'models' folder. Please save and refresh the page after changing this.",
         saveInAppConfig: true,
         render: (parameter) => {
             return `<input id="${parameter.id}" name="${parameter.id}" size="30">`
@@ -161,7 +161,6 @@ var PARAMETERS = [
             "<b>Low:</b> slowest, recommended for GPUs with 3 to 4 GB memory",
         icon: "fa-forward",
         default: "balanced",
-        saveInAppConfig: true,
         options: [
             { value: "balanced", label: "Balanced" },
             { value: "high", label: "High" },
@@ -250,19 +249,14 @@ var PARAMETERS = [
         default: false,
     },
     {
-        id: "backend",
+        id: "use_v3_engine",
-        type: ParameterType.select,
+        type: ParameterType.checkbox,
-        label: "Engine to use",
+        label: "Use the new v3 engine (diffusers)",
         note:
-            "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.",
+            "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-robot",
+        icon: "fa-bolt",
+        default: true,
         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",
@@ -438,7 +432,6 @@ 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")
 
@@ -461,23 +454,6 @@ 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")
@@ -502,16 +478,14 @@ async function getAppConfig() {
         modelsDirField.value = config.models_dir
 
         let testDiffusersEnabled = true
-        if (config.backend === "ed_classic") {
+        if (config.use_v3_engine === false) {
             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?.backend !== "ed_classic") {
+            if (config.config_on_startup?.use_v3_engine) {
                 document.body.classList.add("diffusers-enabled-on-startup")
                 document.body.classList.remove("diffusers-disabled-on-startup")
             } else {
@@ -520,27 +494,37 @@ async function getAppConfig() {
             }
         }
 
-        if (config.backend === "ed_classic") {
+        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"
+            })
             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
         }
@@ -658,7 +642,7 @@ function setDeviceInfo(devices) {
 
     function ID_TO_TEXT(d) {
         let info = devices.all[d]
-        if ("mem_free" in info && "mem_total" in info) {
+        if ("mem_free" in info && "mem_total" in info && info["mem_total"] > 0) {
             return `${info.name} <small>(${d}) (${info.mem_free.toFixed(1)}Gb free / ${info.mem_total.toFixed(
                 1
             )} Gb total)</small>`
@@ -765,11 +749,6 @@ 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)
     }

ui/plugins/ui/snow.plugin.js

@@ -0,0 +1,80 @@
+// christmas hack, courtesy: https://pajasevi.github.io/CSSnowflakes/
+
+;(function(){
+    "use strict";
+
+    function makeItSnow() {
+        const styleSheet = document.createElement("style")
+        styleSheet.textContent = `
+        /* customizable snowflake styling */
+        .snowflake {
+            color: #fff;
+            font-size: 1em;
+            font-family: Arial, sans-serif;
+            text-shadow: 0 0 5px #000;
+        }
+
+        .snowflake,.snowflake .inner{animation-iteration-count:infinite;animation-play-state:running}@keyframes snowflakes-fall{0%{transform:translateY(0)}100%{transform:translateY(110vh)}}@keyframes snowflakes-shake{0%,100%{transform:translateX(0)}50%{transform:translateX(80px)}}.snowflake{position:fixed;top:-10%;z-index:9999;-webkit-user-select:none;user-select:none;cursor:default;animation-name:snowflakes-shake;animation-duration:3s;animation-timing-function:ease-in-out}.snowflake .inner{animation-duration:10s;animation-name:snowflakes-fall;animation-timing-function:linear}.snowflake:nth-of-type(0){left:1%;animation-delay:0s}.snowflake:nth-of-type(0) .inner{animation-delay:0s}.snowflake:first-of-type{left:10%;animation-delay:1s}.snowflake:first-of-type .inner,.snowflake:nth-of-type(8) .inner{animation-delay:1s}.snowflake:nth-of-type(2){left:20%;animation-delay:.5s}.snowflake:nth-of-type(2) .inner,.snowflake:nth-of-type(6) .inner{animation-delay:6s}.snowflake:nth-of-type(3){left:30%;animation-delay:2s}.snowflake:nth-of-type(11) .inner,.snowflake:nth-of-type(3) .inner{animation-delay:4s}.snowflake:nth-of-type(4){left:40%;animation-delay:2s}.snowflake:nth-of-type(10) .inner,.snowflake:nth-of-type(4) .inner{animation-delay:2s}.snowflake:nth-of-type(5){left:50%;animation-delay:3s}.snowflake:nth-of-type(5) .inner{animation-delay:8s}.snowflake:nth-of-type(6){left:60%;animation-delay:2s}.snowflake:nth-of-type(7){left:70%;animation-delay:1s}.snowflake:nth-of-type(7) .inner{animation-delay:2.5s}.snowflake:nth-of-type(8){left:80%;animation-delay:0s}.snowflake:nth-of-type(9){left:90%;animation-delay:1.5s}.snowflake:nth-of-type(9) .inner{animation-delay:3s}.snowflake:nth-of-type(10){left:25%;animation-delay:0s}.snowflake:nth-of-type(11){left:65%;animation-delay:2.5s}
+        `
+        document.head.appendChild(styleSheet)
+
+        const snowflakes = document.createElement("div")
+        snowflakes.id = "snowflakes-container"
+        snowflakes.innerHTML = `
+            <div class="snowflakes" aria-hidden="true">
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+            </div>`
+
+        document.body.appendChild(snowflakes)
+
+        const script = document.createElement("script")
+        script.innerHTML = `
+            $(document).ready(function() {
+                setTimeout(function() {
+                    $("#snowflakes-container").fadeOut("slow", function() {$(this).remove()})
+                }, 10 * 1000)
+            })
+        `
+        document.body.appendChild(script)
+    }
+
+    let date = new Date()
+    if (date.getMonth() === 11 && date.getDate() >= 12) {
+        makeItSnow()
+    }
+})()