handle installer exceptions and update requirements

installer.py

@@ -7,7 +7,12 @@ import logging
 import platform
 import subprocess
 import cProfile
-import pkg_resources
+
+try:
+    import pkg_resources # python 3.12 no longer packages it built-in
+except ImportError:
+    stdout = subprocess.run(f'"{sys.executable}" -m pip install setuptools', shell=True, check=False, env=os.environ, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    import pkg_resources
 
 
 class Dot(dict): # dot notation access to dictionary attributes
@@ -163,8 +168,11 @@ def installed(package, friendly: str = None, reload = False, quiet = False):
     ok = True
     try:
         if reload:
-            import imp # pylint: disable=deprecated-module
-            imp.reload(pkg_resources)
+            try:
+                import imp # pylint: disable=deprecated-module
+                imp.reload(pkg_resources)
+            except Exception:
+                pass
         if friendly:
             pkgs = friendly.split()
         else:
@@ -201,10 +209,15 @@ def installed(package, friendly: str = None, reload = False, quiet = False):
         return False
 
 
-def uninstall(package):
-    if installed(package, package, quiet=True):
-        log.warning(f'Uninstalling: {package}')
-        pip(f"uninstall {package} --yes --quiet", ignore=True, quiet=True)
+def uninstall(package, quiet = False):
+    packages = package if isinstance(package, list) else [package]
+    res = ''
+    for p in packages:
+        if installed(p, p, quiet=True):
+            if not quiet:
+                log.warning(f'Uninstalling: {p}')
+            res += pip(f"uninstall {p} --yes --quiet", ignore=True, quiet=True)
+    return res
 
 
 def pip(arg: str, ignore: bool = False, quiet: bool = False):
@@ -229,11 +242,18 @@ def pip(arg: str, ignore: bool = False, quiet: bool = False):
 
 # install package using pip if not already installed
 def install(package, friendly: str = None, ignore: bool = False):
+    res = ''
     if args.reinstall or args.upgrade:
         global quick_allowed # pylint: disable=global-statement
         quick_allowed = False
     if args.reinstall or not installed(package, friendly):
-        pip(f"install --upgrade {package}", ignore=ignore)
+        res = pip(f"install --upgrade {package}", ignore=ignore)
+        try:
+            import imp # pylint: disable=deprecated-module
+            imp.reload(pkg_resources)
+        except Exception:
+            pass
+    return res
 
 
 # execute git command
@@ -362,6 +382,12 @@ def check_python():
         log.debug(f'Git {git_version.replace("git version", "").strip()}')
 
 
+# check onnx version
+def check_onnx():
+    if not installed('onnxruntime', quiet=True) and not installed('onnxruntime-gpu', quiet=True): # allow either
+        install('onnxruntime', 'onnxruntime', ignore=True)
+
+
 # check torch version
 def check_torch():
     if args.skip_torch:
@@ -379,8 +405,13 @@ def check_torch():
     log.debug(f'Torch allowed: cuda={allow_cuda} rocm={allow_rocm} ipex={allow_ipex} diml={allow_directml} openvino={allow_openvino}')
     torch_command = os.environ.get('TORCH_COMMAND', '')
     xformers_package = os.environ.get('XFORMERS_PACKAGE', 'none')
-    if not installed('onnxruntime', quiet=True) and not installed('onnxruntime-gpu', quiet=True): # allow either
-        install('onnxruntime', 'onnxruntime', ignore=True)
+    def is_rocm_available():
+        if not allow_rocm:
+            return False
+        if platform.system() == 'Windows':
+            hip_path = os.environ.get('HIP_PATH', None)
+            return hip_path is not None and os.path.exists(os.path.join(hip_path, 'bin'))
+        return shutil.which('rocminfo') is not None or os.path.exists('/opt/rocm/bin/rocminfo') or os.path.exists('/dev/kfd')
     if torch_command != '':
         pass
     elif allow_cuda and (shutil.which('nvidia-smi') is not None or args.use_xformers or os.path.exists(os.path.join(os.environ.get('SystemRoot') or r'C:\Windows', 'System32', 'nvidia-smi.exe'))):
@@ -391,14 +422,21 @@ def check_torch():
             torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision --index-url https://download.pytorch.org/whl/cu118')
         xformers_package = os.environ.get('XFORMERS_PACKAGE', '--pre xformers' if opts.get('cross_attention_optimization', '') == 'xFormers' else 'none')
         install('onnxruntime-gpu', 'onnxruntime-gpu', ignore=True)
-    elif allow_rocm and (shutil.which('rocminfo') is not None or os.path.exists('/opt/rocm/bin/rocminfo') or os.path.exists('/dev/kfd')):
+    elif is_rocm_available():
+        is_windows = platform.system() == 'Windows' # provides more better logs for ZLUDA users and ROCm for Windows users in future.
         log.info('AMD ROCm toolkit detected')
         os.environ.setdefault('PYTORCH_HIP_ALLOC_CONF', 'garbage_collection_threshold:0.8,max_split_size_mb:512')
-        os.environ.setdefault('TENSORFLOW_PACKAGE', 'tensorflow-rocm')
+        if not is_windows:
+            os.environ.setdefault('TENSORFLOW_PACKAGE', 'tensorflow-rocm')
         try:
-            command = subprocess.run('rocm_agent_enumerator', shell=True, check=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-            amd_gpus = command.stdout.decode(encoding="utf8", errors="ignore").split('\n')
-            amd_gpus = [x for x in amd_gpus if x and x != 'gfx000']
+            if is_windows:
+                command = subprocess.run('hipinfo', shell=True, check=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+                amd_gpus = command.stdout.decode(encoding="utf8", errors="ignore").split('\n')
+                amd_gpus = [x.split(' ')[-1].strip() for x in amd_gpus if x.startswith('gcnArchName:')]
+            else:
+                command = subprocess.run('rocm_agent_enumerator', shell=True, check=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+                amd_gpus = command.stdout.decode(encoding="utf8", errors="ignore").split('\n')
+                amd_gpus = [x for x in amd_gpus if x and x != 'gfx000']
             log.debug(f'ROCm agents detected: {amd_gpus}')
         except Exception as e:
             log.debug(f'Run rocm_agent_enumerator failed: {e}')
@@ -433,22 +471,17 @@ def check_torch():
         except Exception as e:
             log.debug(f'ROCm hipconfig failed: {e}')
             rocm_ver = None
-        if rocm_ver in {"5.7"}:
-            torch_command = os.environ.get('TORCH_COMMAND', f'torch torchvision --pre --index-url https://download.pytorch.org/whl/nightly/rocm{rocm_ver}')
-        elif rocm_ver in {"5.5", "5.6"}:
-            torch_command = os.environ.get('TORCH_COMMAND', f'torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm{rocm_ver}')
-        else:
-            # ROCm 5.5 is oldest for PyTorch 2.1
-            torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision --index-url https://download.pytorch.org/whl/rocm5.5')
+        if not is_windows: # remove after PyTorch built with ROCm for Windows is launched
+            if rocm_ver in {"5.7"}:
+                torch_command = os.environ.get('TORCH_COMMAND', f'torch torchvision --pre --index-url https://download.pytorch.org/whl/nightly/rocm{rocm_ver}')
+            elif rocm_ver in {"5.5", "5.6"}:
+                torch_command = os.environ.get('TORCH_COMMAND', f'torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm{rocm_ver}')
+            else:
+                # ROCm 5.5 is oldest for PyTorch 2.1
+                torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision --index-url https://download.pytorch.org/whl/rocm5.5')
+            if rocm_ver is not None:
+                install(os.environ.get('ONNXRUNTIME_PACKAGE', get_onnxruntime_source_for_rocm(arr)), "onnxruntime-training built with ROCm", ignore=True)
         xformers_package = os.environ.get('XFORMERS_PACKAGE', 'none')
-        if rocm_ver is not None:
-            install(os.environ.get('ONNXRUNTIME_PACKAGE', get_onnxruntime_source_for_rocm(arr)), "onnxruntime-training built with ROCm", ignore=True)
-            try:
-                import onnxruntime
-                if "ROCMExecutionProvider" not in onnxruntime.get_available_providers():
-                    log.warning('Failed to automatically install onxnruntime package for ROCm. Please manually install it if you need.')
-            except Exception:
-                pass
     elif allow_ipex and (args.use_ipex or shutil.which('sycl-ls') is not None or shutil.which('sycl-ls.exe') is not None or os.environ.get('ONEAPI_ROOT') is not None or os.path.exists('/opt/intel/oneapi') or os.path.exists("C:/Program Files (x86)/Intel/oneAPI") or os.path.exists("C:/oneAPI")):
         args.use_ipex = True # pylint: disable=attribute-defined-outside-init
         log.info('Intel OneAPI Toolkit detected')
@@ -482,10 +515,10 @@ def check_torch():
         install('onnxruntime-openvino', 'onnxruntime-openvino', ignore=True)
     elif allow_openvino and args.use_openvino:
         log.info('Using OpenVINO')
-        torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cpu')
+        torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.2.0 torchvision==0.17.0 --index-url https://download.pytorch.org/whl/cpu')
         install(os.environ.get('OPENVINO_PACKAGE', 'openvino==2023.3.0'), 'openvino')
         install('onnxruntime-openvino', 'onnxruntime-openvino', ignore=True) # TODO openvino: numpy version conflicts with tensorflow and doesn't support Python 3.11
-        install('nncf==2.8.0', 'nncf')
+        install('nncf==2.8.1', 'nncf')
         os.environ.setdefault('PYTORCH_TRACING_MODE', 'TORCHFX')
         os.environ.setdefault('NEOReadDebugKeys', '1')
         os.environ.setdefault('ClDeviceGlobalMemSizeAvailablePercent', '100')
@@ -555,6 +588,8 @@ def check_torch():
         log.debug(f'Cannot install xformers package: {e}')
     if opts.get('cuda_compile_backend', '') == 'hidet':
         install('hidet', 'hidet')
+    if opts.get('cuda_compile_backend', '') == 'deep-cache':
+        install('DeepCache')
     if opts.get('nncf_compress_weights', False) and not args.use_openvino:
         install('nncf==2.7.0', 'nncf')
     if args.profile:
@@ -613,10 +648,11 @@ def run_extension_installer(folder):
         env = os.environ.copy()
         env['PYTHONPATH'] = os.path.abspath(".")
         result = subprocess.run(f'"{sys.executable}" "{path_installer}"', shell=True, env=env, check=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=folder)
+        txt = result.stdout.decode(encoding="utf8", errors="ignore")
+        debug(f'Extension installer: file={path_installer} {txt}')
         if result.returncode != 0:
             global errors # pylint: disable=global-statement
             errors += 1
-            txt = result.stdout.decode(encoding="utf8", errors="ignore")
             if len(result.stderr) > 0:
                 txt = txt + '\n' + result.stderr.decode(encoding="utf8", errors="ignore")
             log.error(f'Error running extension installer: {path_installer}')
@@ -843,21 +879,15 @@ def get_version():
 
 
 def get_onnxruntime_source_for_rocm(rocm_ver):
-    ort_version = "1.16.3"
-
-    try:
-        import onnxruntime
-        ort_version = onnxruntime.__version__
-    except ImportError:
-        pass
-
+    ort_version = "1.16.3" # hardcoded
     cp_str = f"{sys.version_info.major}{sys.version_info.minor}"
-
     if rocm_ver is None:
         command = subprocess.run('hipconfig --version', shell=True, check=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
         rocm_ver = command.stdout.decode(encoding="utf8", errors="ignore").split('.')
-
-    return f"https://download.onnxruntime.ai/onnxruntime_training-{ort_version}%2Brocm{rocm_ver[0]}{rocm_ver[1]}-cp{cp_str}-cp{cp_str}-manylinux_2_17_x86_64.manylinux2014_x86_64.whl"
+    if "linux" in sys.platform:
+        return f"https://download.onnxruntime.ai/onnxruntime_training-{ort_version}%2Brocm{rocm_ver[0]}{rocm_ver[1]}-cp{cp_str}-cp{cp_str}-manylinux_2_17_x86_64.manylinux2014_x86_64.whl"
+    else:
+        return 'onnxruntime-gpu'
 
 
 # check version of the main repo and optionally upgrade it
@@ -971,6 +1001,7 @@ def add_args(parser):
     group.add_argument('--skip-git', default = os.environ.get("SD_SKIPGIT",False), action='store_true', help = "Skips running all GIT operations, default: %(default)s")
     group.add_argument('--skip-torch', default = os.environ.get("SD_SKIPTORCH",False), action='store_true', help = "Skips running Torch checks, default: %(default)s")
     group.add_argument('--skip-all', default = os.environ.get("SD_SKIPALL",False), action='store_true', help = "Skips running all checks, default: %(default)s")
+    group.add_argument('--skip-env', default = os.environ.get("SD_SKIPENV",False), action='store_true', help = "Skips setting of env variables during startup, default: %(default)s")
     group.add_argument('--experimental', default = os.environ.get("SD_EXPERIMENTAL",False), action='store_true', help = "Allow unsupported versions of libraries, default: %(default)s")
     group.add_argument('--reinstall', default = os.environ.get("SD_REINSTALL",False), action='store_true', help = "Force reinstallation of all requirements, default: %(default)s")
     group.add_argument('--test', default = os.environ.get("SD_TEST",False), action='store_true', help = "Run test only and exit")

modules/generation_parameters_copypaste.py

@@ -6,13 +6,12 @@ import json
 from PIL import Image
 import gradio as gr
 from modules.paths import data_path
-from modules import shared, ui_tempdir, script_callbacks, images
+from modules import shared, gr_tempdir, script_callbacks, images
 
 
 re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
 re_imagesize = re.compile(r"^(\d+)x(\d+)$")
-re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$") # pylint: disable=anomalous-backslash-in-string
 type_of_gr_update = type(gr.update())
 paste_fields = {}
 registered_param_bindings = []
@@ -58,7 +57,7 @@ def image_from_url_text(filedata):
         filedata = filedata[0]
     if type(filedata) == dict and filedata.get("is_file", False):
         filename = filedata["name"]
-        is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
+        is_in_right_dir = gr_tempdir.check_tmp_file(shared.demo, filename)
         if is_in_right_dir:
             filename = filename.rsplit('?', 1)[0]
             if not os.path.exists(filename):
@@ -188,28 +187,6 @@ def send_image_and_dimensions(x):
     return img, w, h
 
 
-def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
-    """Determines the config parameter name to use for the hypernet based on the parameters in the infotext.
-    Example: an infotext provides "Hypernet: ke-ta" and "Hypernet hash: 1234abcd". For the "Hypernet" config
-    parameter this means there should be an entry that looks like "ke-ta-10000(1234abcd)" to set it to.
-    If the infotext has no hash, then a hypernet with the same name will be selected instead.
-    """
-    hypernet_name = hypernet_name.lower()
-    if hypernet_hash is not None:
-        # Try to match the hash in the name
-        for hypernet_key in shared.hypernetworks.keys():
-            result = re_hypernet_hash.search(hypernet_key)
-            if result is not None and result[1] == hypernet_hash:
-                return hypernet_key
-    else:
-        # Fall back to a hypernet with the same name
-        for hypernet_key in shared.hypernetworks.keys():
-            if hypernet_key.lower().startswith(hypernet_name):
-                return hypernet_key
-
-    return None
-
-
 def parse_generation_parameters(x: str):
     res = {}
     if x is None:

modules/gr_tempdir.py

@@ -0,0 +1,99 @@
+import os
+import tempfile
+from collections import namedtuple
+from pathlib import Path
+from PIL import Image, PngImagePlugin
+from modules import shared, errors, paths
+
+
+Savedfile = namedtuple("Savedfile", ["name"])
+debug = errors.log.trace if os.environ.get('SD_PATH_DEBUG', None) is not None else lambda *args, **kwargs: None
+
+
+def register_tmp_file(gradio, filename):
+    if hasattr(gradio, 'temp_file_sets'):
+        gradio.temp_file_sets[0] = gradio.temp_file_sets[0] | {os.path.abspath(filename)}
+
+
+def check_tmp_file(gradio, filename):
+    ok = False
+    if hasattr(gradio, 'temp_file_sets'):
+        ok = ok or any(filename in fileset for fileset in gradio.temp_file_sets)
+    if shared.opts.outdir_samples != '':
+        ok = ok or Path(shared.opts.outdir_samples).resolve() in Path(filename).resolve().parents
+    else:
+        ok = ok or Path(shared.opts.outdir_txt2img_samples).resolve() in Path(filename).resolve().parents
+        ok = ok or Path(shared.opts.outdir_img2img_samples).resolve() in Path(filename).resolve().parents
+        ok = ok or Path(shared.opts.outdir_extras_samples).resolve() in Path(filename).resolve().parents
+    if shared.opts.outdir_grids != '':
+        ok = ok or Path(shared.opts.outdir_grids).resolve() in Path(filename).resolve().parents
+    else:
+        ok = ok or Path(shared.opts.outdir_txt2img_grids).resolve() in Path(filename).resolve().parents
+        ok = ok or Path(shared.opts.outdir_img2img_grids).resolve() in Path(filename).resolve().parents
+    ok = ok or Path(shared.opts.outdir_save).resolve() in Path(filename).resolve().parents
+    ok = ok or Path(shared.opts.outdir_init_images).resolve() in Path(filename).resolve().parents
+    return ok
+
+
+def pil_to_temp_file(self, img: Image, dir: str, format="png") -> str: # pylint: disable=redefined-builtin,unused-argument
+    """
+    # original gradio implementation
+    bytes_data = gr.processing_utils.encode_pil_to_bytes(img, format)
+    temp_dir = Path(dir) / self.hash_bytes(bytes_data)
+    temp_dir.mkdir(exist_ok=True, parents=True)
+    filename = str(temp_dir / f"image.{format}")
+    img.save(filename, pnginfo=gr.processing_utils.get_pil_metadata(img))
+    """
+    folder = dir
+    already_saved_as = getattr(img, 'already_saved_as', None)
+    exists = os.path.isfile(already_saved_as) if already_saved_as is not None else False
+    debug(f'Image lookup: {already_saved_as} exists={exists}')
+    if already_saved_as and exists:
+        register_tmp_file(shared.demo, already_saved_as)
+        file_obj = Savedfile(already_saved_as)
+        name = file_obj.name
+        debug(f'Image registered: {name}')
+        return name
+    if shared.opts.temp_dir != "":
+        folder = shared.opts.temp_dir
+    use_metadata = False
+    metadata = PngImagePlugin.PngInfo()
+    for key, value in img.info.items():
+        if isinstance(key, str) and isinstance(value, str):
+            metadata.add_text(key, value)
+            use_metadata = True
+    if not os.path.exists(folder):
+        os.makedirs(folder, exist_ok=True)
+        shared.log.debug(f'Created temp folder: path="{folder}"')
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=folder) as tmp:
+        name = tmp.name
+        img.save(name, pnginfo=(metadata if use_metadata else None))
+        img.already_saved_as = name
+        size = os.path.getsize(name)
+        shared.log.debug(f'Saving temp: image="{name}" resolution={img.width}x{img.height} size={size}')
+    params = ', '.join([f'{k}: {v}' for k, v in img.info.items()])
+    params = params[12:] if params.startswith('parameters: ') else params
+    with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
+        file.write(params)
+    return name
+
+
+# override save to file function so that it also writes PNG info
+
+def on_tmpdir_changed():
+    if shared.opts.temp_dir == "":
+        return
+    register_tmp_file(shared.demo, os.path.join(shared.opts.temp_dir, "x"))
+
+
+def cleanup_tmpdr():
+    temp_dir = shared.opts.temp_dir
+    if temp_dir == "" or not os.path.isdir(temp_dir):
+        return
+    for root, _dirs, files in os.walk(temp_dir, topdown=False):
+        for name in files:
+            _, extension = os.path.splitext(name)
+            if extension != ".png" and extension != ".jpg" and extension != ".webp":
+                continue
+            filename = os.path.join(root, name)
+            os.remove(filename)

modules/onnx_impl/__init__.py

@@ -95,7 +95,7 @@ class OnnxRuntimeModel(TorchCompatibleModule, diffusers.OnnxRuntimeModel):
         device = extract_device(args, kwargs)
         if device is not None:
             self.device = device
-            self.model = move_inference_session(self.model, device)
+            self.model = move_inference_session(self.model, device) # pylint: disable=attribute-defined-outside-init
         return self
 
 
@@ -203,7 +203,6 @@ def ORTDiffusionModelPart_to(self, *args, **kwargs):
 
 def initialize():
     global initialized # pylint: disable=global-statement
-
     if initialized:
         return
 
@@ -211,52 +210,53 @@ def initialize():
     from modules import devices
     from modules.paths import models_path
     from modules.shared import opts
-    from .execution_providers import ExecutionProvider, TORCH_DEVICE_TO_EP, available_execution_providers
 
-    onnx_dir = os.path.join(models_path, "ONNX")
-    if not os.path.isdir(onnx_dir):
-        os.mkdir(onnx_dir)
+    try: # may fail on onnx import
+        import onnx # pylint: disable=unused-import
+        from .execution_providers import ExecutionProvider, TORCH_DEVICE_TO_EP, available_execution_providers
 
-    if devices.backend == "rocm":
-        TORCH_DEVICE_TO_EP["cuda"] = ExecutionProvider.ROCm
+        onnx_dir = os.path.join(models_path, "ONNX")
+        if not os.path.isdir(onnx_dir):
+            os.mkdir(onnx_dir)
+        if devices.backend == "rocm":
+            TORCH_DEVICE_TO_EP["cuda"] = ExecutionProvider.ROCm
 
-    from .pipelines.onnx_stable_diffusion_pipeline import OnnxStableDiffusionPipeline
-    from .pipelines.onnx_stable_diffusion_img2img_pipeline import OnnxStableDiffusionImg2ImgPipeline
-    from .pipelines.onnx_stable_diffusion_inpaint_pipeline import OnnxStableDiffusionInpaintPipeline
-    from .pipelines.onnx_stable_diffusion_upscale_pipeline import OnnxStableDiffusionUpscalePipeline
-    from .pipelines.onnx_stable_diffusion_xl_pipeline import OnnxStableDiffusionXLPipeline
-    from .pipelines.onnx_stable_diffusion_xl_img2img_pipeline import OnnxStableDiffusionXLImg2ImgPipeline
+        from .pipelines.onnx_stable_diffusion_pipeline import OnnxStableDiffusionPipeline
+        from .pipelines.onnx_stable_diffusion_img2img_pipeline import OnnxStableDiffusionImg2ImgPipeline
+        from .pipelines.onnx_stable_diffusion_inpaint_pipeline import OnnxStableDiffusionInpaintPipeline
+        from .pipelines.onnx_stable_diffusion_upscale_pipeline import OnnxStableDiffusionUpscalePipeline
+        from .pipelines.onnx_stable_diffusion_xl_pipeline import OnnxStableDiffusionXLPipeline
+        from .pipelines.onnx_stable_diffusion_xl_img2img_pipeline import OnnxStableDiffusionXLImg2ImgPipeline
 
-    # OnnxRuntimeModel Hijack.
-    OnnxRuntimeModel.__module__ = 'diffusers'
-    diffusers.OnnxRuntimeModel = OnnxRuntimeModel
+        OnnxRuntimeModel.__module__ = 'diffusers' # OnnxRuntimeModel Hijack.
+        diffusers.OnnxRuntimeModel = OnnxRuntimeModel
 
-    diffusers.OnnxStableDiffusionPipeline = OnnxStableDiffusionPipeline
-    diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion"] = diffusers.OnnxStableDiffusionPipeline
+        diffusers.OnnxStableDiffusionPipeline = OnnxStableDiffusionPipeline
+        diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion"] = diffusers.OnnxStableDiffusionPipeline
 
-    diffusers.OnnxStableDiffusionImg2ImgPipeline = OnnxStableDiffusionImg2ImgPipeline
-    diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion"] = diffusers.OnnxStableDiffusionImg2ImgPipeline
+        diffusers.OnnxStableDiffusionImg2ImgPipeline = OnnxStableDiffusionImg2ImgPipeline
+        diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion"] = diffusers.OnnxStableDiffusionImg2ImgPipeline
 
-    diffusers.OnnxStableDiffusionInpaintPipeline = OnnxStableDiffusionInpaintPipeline
-    diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["onnx-stable-diffusion"] = diffusers.OnnxStableDiffusionInpaintPipeline
+        diffusers.OnnxStableDiffusionInpaintPipeline = OnnxStableDiffusionInpaintPipeline
+        diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["onnx-stable-diffusion"] = diffusers.OnnxStableDiffusionInpaintPipeline
 
-    diffusers.OnnxStableDiffusionUpscalePipeline = OnnxStableDiffusionUpscalePipeline
+        diffusers.OnnxStableDiffusionUpscalePipeline = OnnxStableDiffusionUpscalePipeline
 
-    diffusers.OnnxStableDiffusionXLPipeline = OnnxStableDiffusionXLPipeline
-    diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion-xl"] = diffusers.OnnxStableDiffusionXLPipeline
+        diffusers.OnnxStableDiffusionXLPipeline = OnnxStableDiffusionXLPipeline
+        diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion-xl"] = diffusers.OnnxStableDiffusionXLPipeline
 
-    diffusers.OnnxStableDiffusionXLImg2ImgPipeline = OnnxStableDiffusionXLImg2ImgPipeline
-    diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion-xl"] = diffusers.OnnxStableDiffusionXLImg2ImgPipeline
+        diffusers.OnnxStableDiffusionXLImg2ImgPipeline = OnnxStableDiffusionXLImg2ImgPipeline
+        diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion-xl"] = diffusers.OnnxStableDiffusionXLImg2ImgPipeline
 
-    # Huggingface model compatibility
-    diffusers.ORTStableDiffusionXLPipeline = diffusers.OnnxStableDiffusionXLPipeline
-    diffusers.ORTStableDiffusionXLImg2ImgPipeline = diffusers.OnnxStableDiffusionXLImg2ImgPipeline
+        diffusers.ORTStableDiffusionXLPipeline = diffusers.OnnxStableDiffusionXLPipeline # Huggingface model compatibility
+        diffusers.ORTStableDiffusionXLImg2ImgPipeline = diffusers.OnnxStableDiffusionXLImg2ImgPipeline
 
-    optimum.onnxruntime.modeling_diffusion._ORTDiffusionModelPart.to = ORTDiffusionModelPart_to # pylint: disable=protected-access
+        optimum.onnxruntime.modeling_diffusion._ORTDiffusionModelPart.to = ORTDiffusionModelPart_to # pylint: disable=protected-access
 
-    log.info(f'ONNX: selected={opts.onnx_execution_provider}, available={available_execution_providers}')
-
-    initialized = True
+        log.info(f'ONNX: selected={opts.onnx_execution_provider}, available={available_execution_providers}')
+        initialized = True
+    except Exception as e:
+        log.error(f'ONNX: failed to initialize: {e}')
 
 
 def initialize_olive():

modules/onnx_impl/pipelines/__init__.py

@@ -5,12 +5,11 @@ import tempfile
 from abc import ABCMeta
 from typing import Type, Tuple, List, Any, Dict
 from packaging import version
-import onnx
 import torch
 import diffusers
 import onnxruntime as ort
 import optimum.onnxruntime
-from installer import log
+from installer import log, install
 from modules import shared
 from modules.paths import sd_configs_path, models_path
 from modules.sd_models import CheckpointInfo
@@ -148,6 +147,8 @@ class OnnxRawPipeline(PipelineBase):
         return pipeline
 
     def convert(self, submodels: List[str], in_dir: os.PathLike, out_dir: os.PathLike):
+        install('onnx') # may not be installed yet, this performs check and installs as needed
+        import onnx
         shutil.rmtree("cache", ignore_errors=True)
         shutil.rmtree("footprints", ignore_errors=True)
 
@@ -328,7 +329,7 @@ class OnnxRawPipeline(PipelineBase):
         config.vae = os.path.join(models_path, "VAE", shared.opts.sd_vae)
         if not os.path.isfile(config.vae):
             del config.vae
-        config.vae_sdxl_fp16_fix = self._is_sdxl and not shared.opts.diffusers_vae_upcast
+        config.vae_sdxl_fp16_fix = self._is_sdxl and shared.opts.diffusers_vae_upcast == "false"
 
         config.width = p.width
         config.height = p.height

modules/postprocess/gfpgan_model.py

@@ -104,4 +104,4 @@ def setup_model(dirname):
 
         shared.face_restorers.append(FaceRestorerGFPGAN())
     except Exception as e:
-        errors.display(e, 'gfpgan')
+        errors.log.error(f'GFPGan failed to initialize: {e}')

modules/shared.py

@@ -562,7 +562,7 @@ options_templates.update(options_section(('ui', "User Interface Options"), {
     "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
     "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001, "visible": False}),
     "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001, "visible": False}),
-    "keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters", gr.Textbox, { "visible": False }), # pylint: disable=anomalous-backslash-in-string
+    "keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters", gr.Textbox, { "visible": False }),
     "quicksettings_list": OptionInfo(["sd_model_checkpoint"] if backend == Backend.ORIGINAL else ["sd_model_checkpoint", "sd_model_refiner"], "Quicksettings list", gr.Dropdown, lambda: {"multiselect":True, "choices": list(opts.data_labels.keys())}),
     "ui_scripts_reorder": OptionInfo("", "UI scripts order", gr.Textbox, { "visible": False }),
 }))

modules/shared_items.py

@@ -47,17 +47,20 @@ def get_pipelines():
         'Kandinsky 2.2': getattr(diffusers, 'KandinskyV22Pipeline', None),
         'Kandinsky 3': getattr(diffusers, 'Kandinsky3Pipeline', None),
         'DeepFloyd IF': getattr(diffusers, 'IFPipeline', None),
-        'ONNX Stable Diffusion': getattr(diffusers, 'OnnxStableDiffusionPipeline', None),
-        'ONNX Stable Diffusion Img2Img': getattr(diffusers, 'OnnxStableDiffusionImg2ImgPipeline', None),
-        'ONNX Stable Diffusion Inpaint': getattr(diffusers, 'OnnxStableDiffusionInpaintPipeline', None),
-        'ONNX Stable Diffusion Upscale': getattr(diffusers, 'OnnxStableDiffusionUpscalePipeline', None),
-        'ONNX Stable Diffusion XL': getattr(diffusers, 'OnnxStableDiffusionXLPipeline', None),
-        'ONNX Stable Diffusion XL Img2Img': getattr(diffusers, 'OnnxStableDiffusionXLImg2ImgPipeline', None),
         'Custom Diffusers Pipeline': getattr(diffusers, 'DiffusionPipeline', None),
         'InstaFlow': getattr(diffusers, 'StableDiffusionPipeline', None), # dynamically redefined and loaded in sd_models.load_diffuser
         'SegMoE': getattr(diffusers, 'StableDiffusionPipeline', None), # dynamically redefined and loaded in sd_models.load_diffuser
-        # Segmind SSD-1B, Segmind Tiny
     }
+    if hasattr(diffusers, 'OnnxStableDiffusionXLPipeline'):
+        onnx_pipelines = {
+            'ONNX Stable Diffusion': getattr(diffusers, 'OnnxStableDiffusionPipeline', None),
+            'ONNX Stable Diffusion Img2Img': getattr(diffusers, 'OnnxStableDiffusionImg2ImgPipeline', None),
+            'ONNX Stable Diffusion Inpaint': getattr(diffusers, 'OnnxStableDiffusionInpaintPipeline', None),
+            'ONNX Stable Diffusion Upscale': getattr(diffusers, 'OnnxStableDiffusionUpscalePipeline', None),
+            'ONNX Stable Diffusion XL': getattr(diffusers, 'OnnxStableDiffusionXLPipeline', None),
+            'ONNX Stable Diffusion XL Img2Img': getattr(diffusers, 'OnnxStableDiffusionXLImg2ImgPipeline', None),
+        }
+        pipelines.update(onnx_pipelines)
 
     for k, v in pipelines.items():
         if k != 'Autodetect' and v is None:

requirements.txt

@@ -1,4 +1,5 @@
 setuptools
+patch-ng
 addict
 aenum
 aiohttp
@@ -9,22 +10,18 @@ blendmodes
 clean-fid
 easydev
 extcolors
-facexlib
 filetype
 future
 gdown
-gfpgan
 GitPython
 httpcore
 inflection
 jsonmerge
 kornia
 lark
-lmdb
 lpips
 omegaconf
 open-clip-torch
-onnx
 optimum
 piexif
 psutil
@@ -40,7 +37,6 @@ torchdiffeq
 voluptuous
 yapf
 scikit-image
-basicsr
 fasteners
 dctorch
 pymatting
@@ -60,10 +56,10 @@ diffusers==0.26.2
 einops==0.4.1
 gradio==3.43.2
 huggingface_hub==0.20.3
-numexpr==2.8.4
-numpy==1.26.2
-numba==0.58.1
-pandas==1.5.3
+numexpr==2.8.8
+numpy==1.26.4
+numba==0.59.0
+pandas
 protobuf==3.20.3
 pytorch_lightning==1.9.4
 tokenizers==0.15.1

scripts/postprocessing_gfpgan.py

@@ -2,7 +2,6 @@ from PIL import Image
 import numpy as np
 import gradio as gr
 from modules import scripts_postprocessing
-from modules.postprocess import gfpgan_model
 
 
 class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):
@@ -15,8 +14,12 @@ class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):
         return { "gfpgan_visibility": gfpgan_visibility }
 
     def process(self, pp: scripts_postprocessing.PostprocessedImage, gfpgan_visibility): # pylint: disable=arguments-differ
+        from installer import install
+        install("facexlib")
+        install("gfpgan")
         if gfpgan_visibility == 0:
             return
+        from modules.postprocess import gfpgan_model
         restored_img = gfpgan_model.gfpgan_fix_faces(np.array(pp.image, dtype=np.uint8))
         res = Image.fromarray(restored_img)
         if gfpgan_visibility < 1.0: