a crime

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+A1111_dep.tar.zst filter=lfs diff=lfs merge=lfs -text
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fm23da_files/CN_models.txt

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+https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny.pth
+https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth.pth
+https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart.pth
+https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_mlsd.pth
+https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_normalbae.pth
+https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose.pth
+https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_scribble.pth
+https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_seg.pth
+https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11e_sd15_ip2p.pth
+https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11e_sd15_shuffle.pth
+https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_inpaint.pth
+https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge.pth
+https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15s2_lineart_anime.pth
+https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11u_sd15_tile.pth
+https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_keypose-fp16.safetensors
+https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_seg-fp16.safetensors
+https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_sketch-fp16.safetensors
+https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_depth-fp16.safetensors
+https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_canny-fp16.safetensors
+https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_color-fp16.safetensors
+https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_style-fp16.safetensors
+https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/t2iadapter_openpose-fp16.safetensors

fm23da_files/CN_models_v2.txt

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+https://huggingface.co/thibaud/controlnet-sd21/resolve/main/control_v11p_sd21_depth.safetensors
+https://huggingface.co/thibaud/controlnet-sd21/resolve/main/control_v11p_sd21_hed.safetensors
+https://huggingface.co/thibaud/controlnet-sd21/resolve/main/control_v11p_sd21_openpose.safetensors
+https://huggingface.co/thibaud/controlnet-sd21/resolve/main/control_v11p_sd21_scribble.safetensors

fm23da_files/blocks.py

@@ -0,0 +1,1670 @@
+from __future__ import annotations
+
+import copy
+import getpass
+import inspect
+import json
+import os
+import pkgutil
+import random
+import sys
+import time
+import warnings
+import webbrowser
+from abc import abstractmethod
+from types import ModuleType
+from typing import TYPE_CHECKING, Any, Callable, Dict, Iterator, List, Set, Tuple, Type
+
+import anyio
+import requests
+from anyio import CapacityLimiter
+from typing_extensions import Literal
+
+from gradio import (
+    components,
+    encryptor,
+    external,
+    networking,
+    queueing,
+    routes,
+    strings,
+    utils,
+)
+from gradio.context import Context
+from gradio.deprecation import check_deprecated_parameters
+from gradio.documentation import document, set_documentation_group
+from gradio.exceptions import DuplicateBlockError, InvalidApiName
+from gradio.helpers import create_tracker, skip, special_args
+from gradio.tunneling import CURRENT_TUNNELS
+from gradio.utils import (
+    TupleNoPrint,
+    check_function_inputs_match,
+    component_or_layout_class,
+    delete_none,
+    get_cancel_function,
+    get_continuous_fn,
+)
+
+set_documentation_group("blocks")
+
+
+if TYPE_CHECKING:  # Only import for type checking (is False at runtime).
+    import comet_ml
+    from fastapi.applications import FastAPI
+
+    from gradio.components import Component
+
+
+class Block:
+    def __init__(
+        self,
+        *,
+        render: bool = True,
+        elem_id: str | None = None,
+        visible: bool = True,
+        root_url: str | None = None,  # URL that is prepended to all file paths
+        _skip_init_processing: bool = False,  # Used for loading from Spaces
+        **kwargs,
+    ):
+        self._id = Context.id
+        Context.id += 1
+        self.visible = visible
+        self.elem_id = elem_id
+        self.root_url = root_url
+        self._skip_init_processing = _skip_init_processing
+        self._style = {}
+        self.parent: BlockContext | None = None
+
+        if render:
+            self.render()
+        check_deprecated_parameters(self.__class__.__name__, **kwargs)
+
+    def render(self):
+        """
+        Adds self into appropriate BlockContext
+        """
+        if Context.root_block is not None and self._id in Context.root_block.blocks:
+            raise DuplicateBlockError(
+                f"A block with id: {self._id} has already been rendered in the current Blocks."
+            )
+        if Context.block is not None:
+            Context.block.add(self)
+        if Context.root_block is not None:
+            Context.root_block.blocks[self._id] = self
+            if isinstance(self, components.TempFileManager):
+                Context.root_block.temp_file_sets.append(self.temp_files)
+        return self
+
+    def unrender(self):
+        """
+        Removes self from BlockContext if it has been rendered (otherwise does nothing).
+        Removes self from the layout and collection of blocks, but does not delete any event triggers.
+        """
+        if Context.block is not None:
+            try:
+                Context.block.children.remove(self)
+            except ValueError:
+                pass
+        if Context.root_block is not None:
+            try:
+                del Context.root_block.blocks[self._id]
+            except KeyError:
+                pass
+        return self
+
+    def get_block_name(self) -> str:
+        """
+        Gets block's class name.
+
+        If it is template component it gets the parent's class name.
+
+        @return: class name
+        """
+        return (
+            self.__class__.__base__.__name__.lower()
+            if hasattr(self, "is_template")
+            else self.__class__.__name__.lower()
+        )
+
+    def get_expected_parent(self) -> Type[BlockContext] | None:
+        return None
+
+    def set_event_trigger(
+        self,
+        event_name: str,
+        fn: Callable | None,
+        inputs: Component | List[Component] | Set[Component] | None,
+        outputs: Component | List[Component] | None,
+        preprocess: bool = True,
+        postprocess: bool = True,
+        scroll_to_output: bool = False,
+        show_progress: bool = True,
+        api_name: str | None = None,
+        js: str | None = None,
+        no_target: bool = False,
+        queue: bool | None = None,
+        batch: bool = False,
+        max_batch_size: int = 4,
+        cancels: List[int] | None = None,
+        every: float | None = None,
+    ) -> Dict[str, Any]:
+        """
+        Adds an event to the component's dependencies.
+        Parameters:
+            event_name: event name
+            fn: Callable function
+            inputs: input list
+            outputs: output list
+            preprocess: whether to run the preprocess methods of components
+            postprocess: whether to run the postprocess methods of components
+            scroll_to_output: whether to scroll to output of dependency on trigger
+            show_progress: whether to show progress animation while running.
+            api_name: Defining this parameter exposes the endpoint in the api docs
+            js: Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components
+            no_target: if True, sets "targets" to [], used for Blocks "load" event
+            batch: whether this function takes in a batch of inputs
+            max_batch_size: the maximum batch size to send to the function
+            cancels: a list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method.
+        Returns: None
+        """
+        # Support for singular parameter
+        if isinstance(inputs, set):
+            inputs_as_dict = True
+            inputs = sorted(inputs, key=lambda x: x._id)
+        else:
+            inputs_as_dict = False
+            if inputs is None:
+                inputs = []
+            elif not isinstance(inputs, list):
+                inputs = [inputs]
+
+        if isinstance(outputs, set):
+            outputs = sorted(outputs, key=lambda x: x._id)
+        else:
+            if outputs is None:
+                outputs = []
+            elif not isinstance(outputs, list):
+                outputs = [outputs]
+
+        if fn is not None and not cancels:
+            check_function_inputs_match(fn, inputs, inputs_as_dict)
+
+        if Context.root_block is None:
+            raise AttributeError(
+                f"{event_name}() and other events can only be called within a Blocks context."
+            )
+        if every is not None and every <= 0:
+            raise ValueError("Parameter every must be positive or None")
+        if every and batch:
+            raise ValueError(
+                f"Cannot run {event_name} event in a batch and every {every} seconds. "
+                "Either batch is True or every is non-zero but not both."
+            )
+
+        if every and fn:
+            fn = get_continuous_fn(fn, every)
+        elif every:
+            raise ValueError("Cannot set a value for `every` without a `fn`.")
+
+        Context.root_block.fns.append(
+            BlockFunction(fn, inputs, outputs, preprocess, postprocess, inputs_as_dict)
+        )
+        if api_name is not None:
+            api_name_ = utils.append_unique_suffix(
+                api_name, [dep["api_name"] for dep in Context.root_block.dependencies]
+            )
+            if not (api_name == api_name_):
+                warnings.warn(
+                    "api_name {} already exists, using {}".format(api_name, api_name_)
+                )
+                api_name = api_name_
+
+        dependency = {
+            "targets": [self._id] if not no_target else [],
+            "trigger": event_name,
+            "inputs": [block._id for block in inputs],
+            "outputs": [block._id for block in outputs],
+            "backend_fn": fn is not None,
+            "js": js,
+            "queue": False if fn is None else queue,
+            "api_name": api_name,
+            "scroll_to_output": scroll_to_output,
+            "show_progress": show_progress,
+            "every": every,
+            "batch": batch,
+            "max_batch_size": max_batch_size,
+            "cancels": cancels or [],
+        }
+        Context.root_block.dependencies.append(dependency)
+        return dependency
+
+    def get_config(self):
+        return {
+            "visible": self.visible,
+            "elem_id": self.elem_id,
+            "style": self._style,
+            "root_url": self.root_url,
+        }
+
+    @staticmethod
+    @abstractmethod
+    def update(**kwargs) -> Dict:
+        return {}
+
+    @classmethod
+    def get_specific_update(cls, generic_update: Dict[str, Any]) -> Dict:
+        del generic_update["__type__"]
+        specific_update = cls.update(**generic_update)
+        return specific_update
+
+
+class BlockContext(Block):
+    def __init__(
+        self,
+        visible: bool = True,
+        render: bool = True,
+        **kwargs,
+    ):
+        """
+        Parameters:
+            visible: If False, this will be hidden but included in the Blocks config file (its visibility can later be updated).
+            render: If False, this will not be included in the Blocks config file at all.
+        """
+        self.children: List[Block] = []
+        super().__init__(visible=visible, render=render, **kwargs)
+
+    def __enter__(self):
+        self.parent = Context.block
+        Context.block = self
+        return self
+
+    def add(self, child: Block):
+        child.parent = self
+        self.children.append(child)
+
+    def fill_expected_parents(self):
+        children = []
+        pseudo_parent = None
+        for child in self.children:
+            expected_parent = child.get_expected_parent()
+            if not expected_parent or isinstance(self, expected_parent):
+                pseudo_parent = None
+                children.append(child)
+            else:
+                if pseudo_parent is not None and isinstance(
+                    pseudo_parent, expected_parent
+                ):
+                    pseudo_parent.children.append(child)
+                else:
+                    pseudo_parent = expected_parent(render=False)
+                    children.append(pseudo_parent)
+                    pseudo_parent.children = [child]
+                    if Context.root_block:
+                        Context.root_block.blocks[pseudo_parent._id] = pseudo_parent
+                child.parent = pseudo_parent
+        self.children = children
+
+    def __exit__(self, *args):
+        if getattr(self, "allow_expected_parents", True):
+            self.fill_expected_parents()
+        Context.block = self.parent
+
+    def postprocess(self, y):
+        """
+        Any postprocessing needed to be performed on a block context.
+        """
+        return y
+
+
+class BlockFunction:
+    def __init__(
+        self,
+        fn: Callable | None,
+        inputs: List[Component],
+        outputs: List[Component],
+        preprocess: bool,
+        postprocess: bool,
+        inputs_as_dict: bool,
+    ):
+        self.fn = fn
+        self.inputs = inputs
+        self.outputs = outputs
+        self.preprocess = preprocess
+        self.postprocess = postprocess
+        self.total_runtime = 0
+        self.total_runs = 0
+        self.inputs_as_dict = inputs_as_dict
+
+    def __str__(self):
+        return str(
+            {
+                "fn": getattr(self.fn, "__name__", "fn")
+                if self.fn is not None
+                else None,
+                "preprocess": self.preprocess,
+                "postprocess": self.postprocess,
+            }
+        )
+
+    def __repr__(self):
+        return str(self)
+
+
+class class_or_instancemethod(classmethod):
+    def __get__(self, instance, type_):
+        descr_get = super().__get__ if instance is None else self.__func__.__get__
+        return descr_get(instance, type_)
+
+
+def postprocess_update_dict(block: Block, update_dict: Dict, postprocess: bool = True):
+    """
+    Converts a dictionary of updates into a format that can be sent to the frontend.
+    E.g. {"__type__": "generic_update", "value": "2", "interactive": False}
+    Into -> {"__type__": "update", "value": 2.0, "mode": "static"}
+
+    Parameters:
+        block: The Block that is being updated with this update dictionary.
+        update_dict: The original update dictionary
+        postprocess: Whether to postprocess the "value" key of the update dictionary.
+    """
+    if update_dict.get("__type__", "") == "generic_update":
+        update_dict = block.get_specific_update(update_dict)
+    if update_dict.get("value") is components._Keywords.NO_VALUE:
+        update_dict.pop("value")
+    prediction_value = delete_none(update_dict, skip_value=True)
+    if "value" in prediction_value and postprocess:
+        assert isinstance(
+            block, components.IOComponent
+        ), f"Component {block.__class__} does not support value"
+        prediction_value["value"] = block.postprocess(prediction_value["value"])
+    return prediction_value
+
+
+def convert_component_dict_to_list(
+    outputs_ids: List[int], predictions: Dict
+) -> List | Dict:
+    """
+    Converts a dictionary of component updates into a list of updates in the order of
+    the outputs_ids and including every output component. Leaves other types of dictionaries unchanged.
+    E.g. {"textbox": "hello", "number": {"__type__": "generic_update", "value": "2"}}
+    Into -> ["hello", {"__type__": "generic_update"}, {"__type__": "generic_update", "value": "2"}]
+    """
+    keys_are_blocks = [isinstance(key, Block) for key in predictions.keys()]
+    if all(keys_are_blocks):
+        reordered_predictions = [skip() for _ in outputs_ids]
+        for component, value in predictions.items():
+            if component._id not in outputs_ids:
+                raise ValueError(
+                    f"Returned component {component} not specified as output of function."
+                )
+            output_index = outputs_ids.index(component._id)
+            reordered_predictions[output_index] = value
+        predictions = utils.resolve_singleton(reordered_predictions)
+    elif any(keys_are_blocks):
+        raise ValueError(
+            "Returned dictionary included some keys as Components. Either all keys must be Components to assign Component values, or return a List of values to assign output values in order."
+        )
+    return predictions
+
+
+@document("load")
+class Blocks(BlockContext):
+    """
+    Blocks is Gradio's low-level API that allows you to create more custom web
+    applications and demos than Interfaces (yet still entirely in Python).
+
+
+    Compared to the Interface class, Blocks offers more flexibility and control over:
+    (1) the layout of components (2) the events that
+    trigger the execution of functions (3) data flows (e.g. inputs can trigger outputs,
+    which can trigger the next level of outputs). Blocks also offers ways to group
+    together related demos such as with tabs.
+
+
+    The basic usage of Blocks is as follows: create a Blocks object, then use it as a
+    context (with the "with" statement), and then define layouts, components, or events
+    within the Blocks context. Finally, call the launch() method to launch the demo.
+
+    Example:
+        import gradio as gr
+        def update(name):
+            return f"Welcome to Gradio, {name}!"
+
+        with gr.Blocks() as demo:
+            gr.Markdown("Start typing below and then click **Run** to see the output.")
+            with gr.Row():
+                inp = gr.Textbox(placeholder="What is your name?")
+                out = gr.Textbox()
+            btn = gr.Button("Run")
+            btn.click(fn=update, inputs=inp, outputs=out)
+
+        demo.launch()
+    Demos: blocks_hello, blocks_flipper, blocks_speech_text_sentiment, generate_english_german, sound_alert
+    Guides: blocks_and_event_listeners, controlling_layout, state_in_blocks, custom_CSS_and_JS, custom_interpretations_with_blocks, using_blocks_like_functions
+    """
+
+    def __init__(
+        self,
+        theme: str = "default",
+        analytics_enabled: bool | None = None,
+        mode: str = "blocks",
+        title: str = "Gradio",
+        css: str | None = None,
+        **kwargs,
+    ):
+        """
+        Parameters:
+            theme: which theme to use - right now, only "default" is supported.
+            analytics_enabled: whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable or default to True.
+            mode: a human-friendly name for the kind of Blocks or Interface being created.
+            title: The tab title to display when this is opened in a browser window.
+            css: custom css or path to custom css file to apply to entire Blocks
+        """
+        # Cleanup shared parameters with Interface #TODO: is this part still necessary after Interface with Blocks?
+        self.limiter = None
+        self.save_to = None
+        self.theme = theme
+        self.encrypt = False
+        self.share = False
+        self.enable_queue = None
+        self.max_threads = 40
+        self.show_error = True
+        if css is not None and os.path.exists(css):
+            with open(css) as css_file:
+                self.css = css_file.read()
+        else:
+            self.css = css
+
+        # For analytics_enabled and allow_flagging: (1) first check for
+        # parameter, (2) check for env variable, (3) default to True/"manual"
+        self.analytics_enabled = (
+            analytics_enabled
+            if analytics_enabled is not None
+            else os.getenv("GRADIO_ANALYTICS_ENABLED", "True") == "True"
+        )
+
+        super().__init__(render=False, **kwargs)
+        self.blocks: Dict[int, Block] = {}
+        self.fns: List[BlockFunction] = []
+        self.dependencies = []
+        self.mode = mode
+
+        self.is_running = False
+        self.local_url = None
+        self.share_url = None
+        self.width = None
+        self.height = None
+        self.api_open = True
+
+        self.ip_address = ""
+        self.is_space = True if os.getenv("SYSTEM") == "spaces" else False
+        self.favicon_path = None
+        self.auth = None
+        self.dev_mode = True
+        self.app_id = random.getrandbits(64)
+        self.temp_file_sets = []
+        self.title = title
+        self.show_api = True
+
+        # Only used when an Interface is loaded from a config
+        self.predict = None
+        self.input_components = None
+        self.output_components = None
+        self.__name__ = None
+        self.api_mode = None
+        self.progress_tracking = None
+
+        if self.analytics_enabled:
+            self.ip_address = utils.get_local_ip_address()
+            data = {
+                "mode": self.mode,
+                "ip_address": self.ip_address,
+                "custom_css": self.css is not None,
+                "theme": self.theme,
+                "version": (pkgutil.get_data(__name__, "version.txt") or b"")
+                .decode("ascii")
+                .strip(),
+            }
+            utils.initiated_analytics(data)
+
+    @classmethod
+    def from_config(
+        cls, config: dict, fns: List[Callable], root_url: str | None = None
+    ) -> Blocks:
+        """
+        Factory method that creates a Blocks from a config and list of functions.
+
+        Parameters:
+        config: a dictionary containing the configuration of the Blocks.
+        fns: a list of functions that are used in the Blocks. Must be in the same order as the dependencies in the config.
+        root_url: an optional root url to use for the components in the Blocks. Allows serving files from an external URL.
+        """
+        config = copy.deepcopy(config)
+        components_config = config["components"]
+        original_mapping: Dict[int, Block] = {}
+
+        def get_block_instance(id: int) -> Block:
+            for block_config in components_config:
+                if block_config["id"] == id:
+                    break
+            else:
+                raise ValueError("Cannot find block with id {}".format(id))
+            cls = component_or_layout_class(block_config["type"])
+            block_config["props"].pop("type", None)
+            block_config["props"].pop("name", None)
+            style = block_config["props"].pop("style", None)
+            if block_config["props"].get("root_url") is None and root_url:
+                block_config["props"]["root_url"] = root_url + "/"
+            # Any component has already processed its initial value, so we skip that step here
+            block = cls(**block_config["props"], _skip_init_processing=True)
+            if style and isinstance(block, components.IOComponent):
+                block.style(**style)
+            return block
+
+        def iterate_over_children(children_list):
+            for child_config in children_list:
+                id = child_config["id"]
+                block = get_block_instance(id)
+
+                original_mapping[id] = block
+
+                children = child_config.get("children")
+                if children is not None:
+                    assert isinstance(
+                        block, BlockContext
+                    ), f"Invalid config, Block with id {id} has children but is not a BlockContext."
+                    with block:
+                        iterate_over_children(children)
+
+        with Blocks(theme=config["theme"], css=config["theme"]) as blocks:
+            # ID 0 should be the root Blocks component
+            original_mapping[0] = Context.root_block or blocks
+
+            iterate_over_children(config["layout"]["children"])
+
+            first_dependency = None
+
+            # add the event triggers
+            for dependency, fn in zip(config["dependencies"], fns):
+                # We used to add a "fake_event" to the config to cache examples
+                # without removing it. This was causing bugs in calling gr.Interface.load
+                # We fixed the issue by removing "fake_event" from the config in examples.py
+                # but we still need to skip these events when loading the config to support
+                # older demos
+                if dependency["trigger"] == "fake_event":
+                    continue
+                targets = dependency.pop("targets")
+                trigger = dependency.pop("trigger")
+                dependency.pop("backend_fn")
+                dependency.pop("documentation", None)
+                dependency["inputs"] = [
+                    original_mapping[i] for i in dependency["inputs"]
+                ]
+                dependency["outputs"] = [
+                    original_mapping[o] for o in dependency["outputs"]
+                ]
+                dependency.pop("status_tracker", None)
+                dependency["preprocess"] = False
+                dependency["postprocess"] = False
+
+                for target in targets:
+                    dependency = original_mapping[target].set_event_trigger(
+                        event_name=trigger, fn=fn, **dependency
+                    )
+                    if first_dependency is None:
+                        first_dependency = dependency
+
+            # Allows some use of Interface-specific methods with loaded Spaces
+            if first_dependency and Context.root_block:
+                blocks.predict = [fns[0]]
+                blocks.input_components = [
+                    Context.root_block.blocks[i] for i in first_dependency["inputs"]
+                ]
+                blocks.output_components = [
+                    Context.root_block.blocks[o] for o in first_dependency["outputs"]
+                ]
+                blocks.__name__ = "Interface"
+                blocks.api_mode = True
+
+        return blocks
+
+    def __str__(self):
+        return self.__repr__()
+
+    def __repr__(self):
+        num_backend_fns = len([d for d in self.dependencies if d["backend_fn"]])
+        repr = f"Gradio Blocks instance: {num_backend_fns} backend functions"
+        repr += "\n" + "-" * len(repr)
+        for d, dependency in enumerate(self.dependencies):
+            if dependency["backend_fn"]:
+                repr += f"\nfn_index={d}"
+                repr += "\n inputs:"
+                for input_id in dependency["inputs"]:
+                    block = self.blocks[input_id]
+                    repr += "\n |-{}".format(str(block))
+                repr += "\n outputs:"
+                for output_id in dependency["outputs"]:
+                    block = self.blocks[output_id]
+                    repr += "\n |-{}".format(str(block))
+        return repr
+
+    def render(self):
+        if Context.root_block is not None:
+            if self._id in Context.root_block.blocks:
+                raise DuplicateBlockError(
+                    f"A block with id: {self._id} has already been rendered in the current Blocks."
+                )
+            if not set(Context.root_block.blocks).isdisjoint(self.blocks):
+                raise DuplicateBlockError(
+                    "At least one block in this Blocks has already been rendered."
+                )
+
+            Context.root_block.blocks.update(self.blocks)
+            Context.root_block.fns.extend(self.fns)
+            dependency_offset = len(Context.root_block.dependencies)
+            for i, dependency in enumerate(self.dependencies):
+                api_name = dependency["api_name"]
+                if api_name is not None:
+                    api_name_ = utils.append_unique_suffix(
+                        api_name,
+                        [dep["api_name"] for dep in Context.root_block.dependencies],
+                    )
+                    if not (api_name == api_name_):
+                        warnings.warn(
+                            "api_name {} already exists, using {}".format(
+                                api_name, api_name_
+                            )
+                        )
+                        dependency["api_name"] = api_name_
+                dependency["cancels"] = [
+                    c + dependency_offset for c in dependency["cancels"]
+                ]
+                # Recreate the cancel function so that it has the latest
+                # dependency fn indices. This is necessary to properly cancel
+                # events in the backend
+                if dependency["cancels"]:
+                    updated_cancels = [
+                        Context.root_block.dependencies[i]
+                        for i in dependency["cancels"]
+                    ]
+                    new_fn = BlockFunction(
+                        get_cancel_function(updated_cancels)[0],
+                        [],
+                        [],
+                        False,
+                        True,
+                        False,
+                    )
+                    Context.root_block.fns[dependency_offset + i] = new_fn
+                Context.root_block.dependencies.append(dependency)
+            Context.root_block.temp_file_sets.extend(self.temp_file_sets)
+
+        if Context.block is not None:
+            Context.block.children.extend(self.children)
+        return self
+
+    def is_callable(self, fn_index: int = 0) -> bool:
+        """Checks if a particular Blocks function is callable (i.e. not stateful or a generator)."""
+        block_fn = self.fns[fn_index]
+        dependency = self.dependencies[fn_index]
+
+        if inspect.isasyncgenfunction(block_fn.fn):
+            return False
+        if inspect.isgeneratorfunction(block_fn.fn):
+            return False
+        for input_id in dependency["inputs"]:
+            block = self.blocks[input_id]
+            if getattr(block, "stateful", False):
+                return False
+        for output_id in dependency["outputs"]:
+            block = self.blocks[output_id]
+            if getattr(block, "stateful", False):
+                return False
+
+        return True
+
+    def __call__(self, *inputs, fn_index: int = 0, api_name: str | None = None):
+        """
+        Allows Blocks objects to be called as functions. Supply the parameters to the
+        function as positional arguments. To choose which function to call, use the
+        fn_index parameter, which must be a keyword argument.
+
+        Parameters:
+        *inputs: the parameters to pass to the function
+        fn_index: the index of the function to call (defaults to 0, which for Interfaces, is the default prediction function)
+        api_name: The api_name of the dependency to call. Will take precedence over fn_index.
+        """
+        if api_name is not None:
+            inferred_fn_index = next(
+                (
+                    i
+                    for i, d in enumerate(self.dependencies)
+                    if d.get("api_name") == api_name
+                ),
+                None,
+            )
+            if inferred_fn_index is None:
+                raise InvalidApiName(f"Cannot find a function with api_name {api_name}")
+            fn_index = inferred_fn_index
+        if not (self.is_callable(fn_index)):
+            raise ValueError(
+                "This function is not callable because it is either stateful or is a generator. Please use the .launch() method instead to create an interactive user interface."
+            )
+
+        inputs = list(inputs)
+        processed_inputs = self.serialize_data(fn_index, inputs)
+        batch = self.dependencies[fn_index]["batch"]
+        if batch:
+            processed_inputs = [[inp] for inp in processed_inputs]
+
+        outputs = utils.synchronize_async(
+            self.process_api,
+            fn_index=fn_index,
+            inputs=processed_inputs,
+            request=None,
+            state={},
+        )
+        outputs = outputs["data"]
+
+        if batch:
+            outputs = [out[0] for out in outputs]
+
+        processed_outputs = self.deserialize_data(fn_index, outputs)
+        processed_outputs = utils.resolve_singleton(processed_outputs)
+
+        return processed_outputs
+
+    async def call_function(
+        self,
+        fn_index: int,
+        processed_input: List[Any],
+        iterator: Iterator[Any] | None = None,
+        requests: routes.Request | List[routes.Request] | None = None,
+        event_id: str | None = None,
+    ):
+        """
+        Calls function with given index and preprocessed input, and measures process time.
+        Parameters:
+            fn_index: index of function to call
+            processed_input: preprocessed input to pass to function
+            iterator: iterator to use if function is a generator
+            requests: requests to pass to function
+            event_id: id of event in queue
+        """
+        block_fn = self.fns[fn_index]
+        assert block_fn.fn, f"function with index {fn_index} not defined."
+        is_generating = False
+
+        if block_fn.inputs_as_dict:
+            processed_input = [
+                {
+                    input_component: data
+                    for input_component, data in zip(block_fn.inputs, processed_input)
+                }
+            ]
+
+        if isinstance(requests, list):
+            request = requests[0]
+        else:
+            request = requests
+        processed_input, progress_index = special_args(
+            block_fn.fn,
+            processed_input,
+            request,
+        )
+        progress_tracker = (
+            processed_input[progress_index] if progress_index is not None else None
+        )
+
+        start = time.time()
+
+        if iterator is None:  # If not a generator function that has already run
+            if progress_tracker is not None and progress_index is not None:
+                progress_tracker, fn = create_tracker(
+                    self, event_id, block_fn.fn, progress_tracker.track_tqdm
+                )
+                processed_input[progress_index] = progress_tracker
+            else:
+                fn = block_fn.fn
+
+            if inspect.iscoroutinefunction(fn):
+                prediction = await fn(*processed_input)
+            else:
+                prediction = await anyio.to_thread.run_sync(
+                    fn, *processed_input, limiter=self.limiter
+                )
+        else:
+            prediction = None
+
+        if inspect.isasyncgenfunction(block_fn.fn):
+            raise ValueError("Gradio does not support async generators.")
+        if inspect.isgeneratorfunction(block_fn.fn):
+            if not self.enable_queue:
+                raise ValueError("Need to enable queue to use generators.")
+            try:
+                if iterator is None:
+                    iterator = prediction
+                prediction = await anyio.to_thread.run_sync(
+                    utils.async_iteration, iterator, limiter=self.limiter
+                )
+                is_generating = True
+            except StopAsyncIteration:
+                n_outputs = len(self.dependencies[fn_index].get("outputs"))
+                prediction = (
+                    components._Keywords.FINISHED_ITERATING
+                    if n_outputs == 1
+                    else (components._Keywords.FINISHED_ITERATING,) * n_outputs
+                )
+                iterator = None
+
+        duration = time.time() - start
+
+        return {
+            "prediction": prediction,
+            "duration": duration,
+            "is_generating": is_generating,
+            "iterator": iterator,
+        }
+
+    def serialize_data(self, fn_index: int, inputs: List[Any]) -> List[Any]:
+        dependency = self.dependencies[fn_index]
+        processed_input = []
+
+        for i, input_id in enumerate(dependency["inputs"]):
+            block = self.blocks[input_id]
+            assert isinstance(
+                block, components.IOComponent
+            ), f"{block.__class__} Component with id {input_id} not a valid input component."
+            serialized_input = block.serialize(inputs[i])
+            processed_input.append(serialized_input)
+
+        return processed_input
+
+    def deserialize_data(self, fn_index: int, outputs: List[Any]) -> List[Any]:
+        dependency = self.dependencies[fn_index]
+        predictions = []
+
+        for o, output_id in enumerate(dependency["outputs"]):
+            block = self.blocks[output_id]
+            assert isinstance(
+                block, components.IOComponent
+            ), f"{block.__class__} Component with id {output_id} not a valid output component."
+            deserialized = block.deserialize(outputs[o])
+            predictions.append(deserialized)
+
+        return predictions
+
+    def preprocess_data(self, fn_index: int, inputs: List[Any], state: Dict[int, Any]):
+        block_fn = self.fns[fn_index]
+        dependency = self.dependencies[fn_index]
+
+        if block_fn.preprocess:
+            processed_input = []
+            for i, input_id in enumerate(dependency["inputs"]):
+                block = self.blocks[input_id]
+                assert isinstance(
+                    block, components.Component
+                ), f"{block.__class__} Component with id {input_id} not a valid input component."
+                if getattr(block, "stateful", False):
+                    processed_input.append(state.get(input_id))
+                else:
+                    processed_input.append(block.preprocess(inputs[i]))
+        else:
+            processed_input = inputs
+        return processed_input
+
+    def postprocess_data(
+        self, fn_index: int, predictions: List | Dict, state: Dict[int, Any]
+    ):
+        block_fn = self.fns[fn_index]
+        dependency = self.dependencies[fn_index]
+        batch = dependency["batch"]
+
+        if type(predictions) is dict and len(predictions) > 0:
+            predictions = convert_component_dict_to_list(
+                dependency["outputs"], predictions
+            )
+
+        if len(dependency["outputs"]) == 1 and not (batch):
+            predictions = [
+                predictions,
+            ]
+
+        output = []
+        for i, output_id in enumerate(dependency["outputs"]):
+            if predictions[i] is components._Keywords.FINISHED_ITERATING:
+                output.append(None)
+                continue
+            block = self.blocks[output_id]
+            if getattr(block, "stateful", False):
+                if not utils.is_update(predictions[i]):
+                    state[output_id] = predictions[i]
+                output.append(None)
+            else:
+                prediction_value = predictions[i]
+                if utils.is_update(prediction_value):
+                    assert isinstance(prediction_value, dict)
+                    prediction_value = postprocess_update_dict(
+                        block=block,
+                        update_dict=prediction_value,
+                        postprocess=block_fn.postprocess,
+                    )
+                elif block_fn.postprocess:
+                    assert isinstance(
+                        block, components.Component
+                    ), f"{block.__class__} Component with id {output_id} not a valid output component."
+                    prediction_value = block.postprocess(prediction_value)
+                output.append(prediction_value)
+        return output
+
+    async def process_api(
+        self,
+        fn_index: int,
+        inputs: List[Any],
+        state: Dict[int, Any],
+        request: routes.Request | List[routes.Request] | None = None,
+        iterators: Dict[int, Any] | None = None,
+        event_id: str | None = None,
+    ) -> Dict[str, Any]:
+        """
+        Processes API calls from the frontend. First preprocesses the data,
+        then runs the relevant function, then postprocesses the output.
+        Parameters:
+            fn_index: Index of function to run.
+            inputs: input data received from the frontend
+            username: name of user if authentication is set up (not used)
+            state: data stored from stateful components for session (key is input block id)
+            iterators: the in-progress iterators for each generator function (key is function index)
+        Returns: None
+        """
+        block_fn = self.fns[fn_index]
+        batch = self.dependencies[fn_index]["batch"]
+
+        if batch:
+            max_batch_size = self.dependencies[fn_index]["max_batch_size"]
+            batch_sizes = [len(inp) for inp in inputs]
+            batch_size = batch_sizes[0]
+            if inspect.isasyncgenfunction(block_fn.fn) or inspect.isgeneratorfunction(
+                block_fn.fn
+            ):
+                raise ValueError("Gradio does not support generators in batch mode.")
+            if not all(x == batch_size for x in batch_sizes):
+                raise ValueError(
+                    f"All inputs to a batch function must have the same length but instead have sizes: {batch_sizes}."
+                )
+            if batch_size > max_batch_size:
+                raise ValueError(
+                    f"Batch size ({batch_size}) exceeds the max_batch_size for this function ({max_batch_size})"
+                )
+
+            inputs = [
+                self.preprocess_data(fn_index, list(i), state) for i in zip(*inputs)
+            ]
+            result = await self.call_function(
+                fn_index, list(zip(*inputs)), None, request
+            )
+            preds = result["prediction"]
+            data = [
+                self.postprocess_data(fn_index, list(o), state) for o in zip(*preds)
+            ]
+            data = list(zip(*data))
+            is_generating, iterator = None, None
+        else:
+            inputs = self.preprocess_data(fn_index, inputs, state)
+            iterator = iterators.get(fn_index, None) if iterators else None
+            result = await self.call_function(
+                fn_index, inputs, iterator, request, event_id
+            )
+            data = self.postprocess_data(fn_index, result["prediction"], state)
+            is_generating, iterator = result["is_generating"], result["iterator"]
+
+        block_fn.total_runtime += result["duration"]
+        block_fn.total_runs += 1
+
+        return {
+            "data": data,
+            "is_generating": is_generating,
+            "iterator": iterator,
+            "duration": result["duration"],
+            "average_duration": block_fn.total_runtime / block_fn.total_runs,
+        }
+
+    async def create_limiter(self):
+        self.limiter = (
+            None
+            if self.max_threads == 40
+            else CapacityLimiter(total_tokens=self.max_threads)
+        )
+
+    def get_config(self):
+        return {"type": "column"}
+
+    def get_config_file(self):
+        config = {
+            "version": routes.VERSION,
+            "mode": self.mode,
+            "dev_mode": self.dev_mode,
+            "components": [],
+            "theme": self.theme,
+            "css": self.css,
+            "title": self.title or "Gradio",
+            "is_space": self.is_space,
+            "enable_queue": getattr(self, "enable_queue", False),  # launch attributes
+            "show_error": getattr(self, "show_error", False),
+            "show_api": self.show_api,
+            "is_colab": utils.colab_check(),
+        }
+
+        def getLayout(block):
+            if not isinstance(block, BlockContext):
+                return {"id": block._id}
+            children_layout = []
+            for child in block.children:
+                children_layout.append(getLayout(child))
+            return {"id": block._id, "children": children_layout}
+
+        config["layout"] = getLayout(self)
+
+        for _id, block in self.blocks.items():
+            config["components"].append(
+                {
+                    "id": _id,
+                    "type": (block.get_block_name()),
+                    "props": utils.delete_none(block.get_config())
+                    if hasattr(block, "get_config")
+                    else {},
+                }
+            )
+        config["dependencies"] = self.dependencies
+        return config
+
+    def __enter__(self):
+        if Context.block is None:
+            Context.root_block = self
+        self.parent = Context.block
+        Context.block = self
+        return self
+
+    def __exit__(self, *args):
+        super().fill_expected_parents()
+        Context.block = self.parent
+        # Configure the load events before root_block is reset
+        self.attach_load_events()
+        if self.parent is None:
+            Context.root_block = None
+        else:
+            self.parent.children.extend(self.children)
+        self.config = self.get_config_file()
+        self.app = routes.App.create_app(self)
+        self.progress_tracking = any(
+            block_fn.fn is not None and special_args(block_fn.fn)[1] is not None
+            for block_fn in self.fns
+        )
+
+    @class_or_instancemethod
+    def load(
+        self_or_cls,
+        fn: Callable | None = None,
+        inputs: List[Component] | None = None,
+        outputs: List[Component] | None = None,
+        api_name: str | None = None,
+        scroll_to_output: bool = False,
+        show_progress: bool = True,
+        queue=None,
+        batch: bool = False,
+        max_batch_size: int = 4,
+        preprocess: bool = True,
+        postprocess: bool = True,
+        every: float | None = None,
+        _js: str | None = None,
+        *,
+        name: str | None = None,
+        src: str | None = None,
+        api_key: str | None = None,
+        alias: str | None = None,
+        **kwargs,
+    ) -> Blocks | Dict[str, Any] | None:
+        """
+        For reverse compatibility reasons, this is both a class method and an instance
+        method, the two of which, confusingly, do two completely different things.
+
+
+        Class method: loads a demo from a Hugging Face Spaces repo and creates it locally and returns a block instance. Equivalent to gradio.Interface.load()
+
+
+        Instance method: adds event that runs as soon as the demo loads in the browser. Example usage below.
+        Parameters:
+            name: Class Method - the name of the model (e.g. "gpt2" or "facebook/bart-base") or space (e.g. "flax-community/spanish-gpt2"), can include the `src` as prefix (e.g. "models/facebook/bart-base")
+            src: Class Method - the source of the model: `models` or `spaces` (or leave empty if source is provided as a prefix in `name`)
+            api_key: Class Method - optional access token for loading private Hugging Face Hub models or spaces. Find your token here: https://huggingface.co/settings/tokens
+            alias: Class Method - optional string used as the name of the loaded model instead of the default name (only applies if loading a Space running Gradio 2.x)
+            fn: Instance Method - the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
+            inputs: Instance Method - List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
+            outputs: Instance Method - List of gradio.components to use as inputs. If the function returns no outputs, this should be an empty list.
+            api_name: Instance Method - Defining this parameter exposes the endpoint in the api docs
+            scroll_to_output: Instance Method - If True, will scroll to output component on completion
+            show_progress: Instance Method - If True, will show progress animation while pending
+            queue: Instance Method - If True, will place the request on the queue, if the queue exists
+            batch: Instance Method - If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
+            max_batch_size: Instance Method - Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
+            preprocess: Instance Method - If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
+            postprocess: Instance Method - If False, will not run postprocessing of component data before returning 'fn' output to the browser.
+            every: Instance Method - Run this event 'every' number of seconds. Interpreted in seconds. Queue must be enabled.
+        Example:
+            import gradio as gr
+            import datetime
+            with gr.Blocks() as demo:
+                def get_time():
+                    return datetime.datetime.now().time()
+                dt = gr.Textbox(label="Current time")
+                demo.load(get_time, inputs=None, outputs=dt)
+            demo.launch()
+        """
+        # _js: Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
+        if isinstance(self_or_cls, type):
+            if name is None:
+                raise ValueError(
+                    "Blocks.load() requires passing parameters as keyword arguments"
+                )
+            return external.load_blocks_from_repo(name, src, api_key, alias, **kwargs)
+        else:
+            return self_or_cls.set_event_trigger(
+                event_name="load",
+                fn=fn,
+                inputs=inputs,
+                outputs=outputs,
+                api_name=api_name,
+                preprocess=preprocess,
+                postprocess=postprocess,
+                scroll_to_output=scroll_to_output,
+                show_progress=show_progress,
+                js=_js,
+                queue=queue,
+                batch=batch,
+                max_batch_size=max_batch_size,
+                every=every,
+                no_target=True,
+            )
+
+    def clear(self):
+        """Resets the layout of the Blocks object."""
+        self.blocks = {}
+        self.fns = []
+        self.dependencies = []
+        self.children = []
+        return self
+
+    @document()
+    def queue(
+        self,
+        concurrency_count: int = 1,
+        status_update_rate: float | Literal["auto"] = "auto",
+        client_position_to_load_data: int | None = None,
+        default_enabled: bool | None = None,
+        api_open: bool = True,
+        max_size: int | None = None,
+    ):
+        """
+        You can control the rate of processed requests by creating a queue. This will allow you to set the number of requests to be processed at one time, and will let users know their position in the queue.
+        Parameters:
+            concurrency_count: Number of worker threads that will be processing requests from the queue concurrently. Increasing this number will increase the rate at which requests are processed, but will also increase the memory usage of the queue.
+            status_update_rate: If "auto", Queue will send status estimations to all clients whenever a job is finished. Otherwise Queue will send status at regular intervals set by this parameter as the number of seconds.
+            client_position_to_load_data: DEPRECATED. This parameter is deprecated and has no effect.
+            default_enabled: Deprecated and has no effect.
+            api_open: If True, the REST routes of the backend will be open, allowing requests made directly to those endpoints to skip the queue.
+            max_size: The maximum number of events the queue will store at any given moment. If the queue is full, new events will not be added and a user will receive a message saying that the queue is full. If None, the queue size will be unlimited.
+        Example:
+            demo = gr.Interface(gr.Textbox(), gr.Image(), image_generator)
+            demo.queue(concurrency_count=3)
+            demo.launch()
+        """
+        if default_enabled is not None:
+            warnings.warn(
+                "The default_enabled parameter of queue has no effect and will be removed "
+                "in a future version of gradio."
+            )
+        self.enable_queue = True
+        self.api_open = api_open
+        if client_position_to_load_data is not None:
+            warnings.warn("The client_position_to_load_data parameter is deprecated.")
+        self._queue = queueing.Queue(
+            live_updates=status_update_rate == "auto",
+            concurrency_count=concurrency_count,
+            update_intervals=status_update_rate if status_update_rate != "auto" else 1,
+            max_size=max_size,
+            blocks_dependencies=self.dependencies,
+        )
+        self.config = self.get_config_file()
+        return self
+
+    def launch(
+        self,
+        inline: bool | None = None,
+        inbrowser: bool = False,
+        share: bool | None = None,
+        debug: bool = False,
+        enable_queue: bool | None = None,
+        max_threads: int = 40,
+        auth: Callable | Tuple[str, str] | List[Tuple[str, str]] | None = None,
+        auth_message: str | None = None,
+        prevent_thread_lock: bool = False,
+        show_error: bool = False,
+        server_name: str | None = None,
+        server_port: int | None = None,
+        show_tips: bool = False,
+        height: int = 500,
+        width: int | str = "100%",
+        encrypt: bool = False,
+        favicon_path: str | None = None,
+        ssl_keyfile: str | None = None,
+        ssl_certfile: str | None = None,
+        ssl_keyfile_password: str | None = None,
+        quiet: bool = False,
+        show_api: bool = True,
+        _frontend: bool = True,
+    ) -> Tuple[FastAPI, str, str]:
+        """
+        Launches a simple web server that serves the demo. Can also be used to create a
+        public link used by anyone to access the demo from their browser by setting share=True.
+
+        Parameters:
+            inline: whether to display in the interface inline in an iframe. Defaults to True in python notebooks; False otherwise.
+            inbrowser: whether to automatically launch the interface in a new tab on the default browser.
+            share: whether to create a publicly shareable link for the interface. Creates an SSH tunnel to make your UI accessible from anywhere. If not provided, it is set to False by default every time, except when running in Google Colab. When localhost is not accessible (e.g. Google Colab), setting share=False is not supported.
+            debug: if True, blocks the main thread from running. If running in Google Colab, this is needed to print the errors in the cell output.
+            auth: If provided, username and password (or list of username-password tuples) required to access interface. Can also provide function that takes username and password and returns True if valid login.
+            auth_message: If provided, HTML message provided on login page.
+            prevent_thread_lock: If True, the interface will block the main thread while the server is running.
+            show_error: If True, any errors in the interface will be displayed in an alert modal and printed in the browser console log
+            server_port: will start gradio app on this port (if available). Can be set by environment variable GRADIO_SERVER_PORT. If None, will search for an available port starting at 7860.
+            server_name: to make app accessible on local network, set this to "0.0.0.0". Can be set by environment variable GRADIO_SERVER_NAME. If None, will use "127.0.0.1".
+            show_tips: if True, will occasionally show tips about new Gradio features
+            enable_queue: DEPRECATED (use .queue() method instead.) if True, inference requests will be served through a queue instead of with parallel threads. Required for longer inference times (> 1min) to prevent timeout. The default option in HuggingFace Spaces is True. The default option elsewhere is False.
+            max_threads: the maximum number of total threads that the Gradio app can generate in parallel. The default is inherited from the starlette library (currently 40). Applies whether the queue is enabled or not. But if queuing is enabled, this parameter is increaseed to be at least the concurrency_count of the queue.
+            width: The width in pixels of the iframe element containing the interface (used if inline=True)
+            height: The height in pixels of the iframe element containing the interface (used if inline=True)
+            encrypt: If True, flagged data will be encrypted by key provided by creator at launch
+            favicon_path: If a path to a file (.png, .gif, or .ico) is provided, it will be used as the favicon for the web page.
+            ssl_keyfile: If a path to a file is provided, will use this as the private key file to create a local server running on https.
+            ssl_certfile: If a path to a file is provided, will use this as the signed certificate for https. Needs to be provided if ssl_keyfile is provided.
+            ssl_keyfile_password: If a password is provided, will use this with the ssl certificate for https.
+            quiet: If True, suppresses most print statements.
+            show_api: If True, shows the api docs in the footer of the app. Default True. If the queue is enabled, then api_open parameter of .queue() will determine if the api docs are shown, independent of the value of show_api.
+        Returns:
+            app: FastAPI app object that is running the demo
+            local_url: Locally accessible link to the demo
+            share_url: Publicly accessible link to the demo (if share=True, otherwise None)
+        Example:
+            import gradio as gr
+            def reverse(text):
+                return text[::-1]
+            demo = gr.Interface(reverse, "text", "text")
+            demo.launch(share=True, auth=("username", "password"))
+        """
+        self.dev_mode = False
+        if (
+            auth
+            and not callable(auth)
+            and not isinstance(auth[0], tuple)
+            and not isinstance(auth[0], list)
+        ):
+            self.auth = [auth]
+        else:
+            self.auth = auth
+        self.auth_message = auth_message
+        self.show_tips = show_tips
+        self.show_error = show_error
+        self.height = height
+        self.width = width
+        self.favicon_path = favicon_path
+
+        if enable_queue is not None:
+            self.enable_queue = enable_queue
+            warnings.warn(
+                "The `enable_queue` parameter has been deprecated. Please use the `.queue()` method instead.",
+                DeprecationWarning,
+            )
+
+        if self.is_space:
+            self.enable_queue = self.enable_queue is not False
+        else:
+            self.enable_queue = self.enable_queue is True
+        if self.enable_queue and not hasattr(self, "_queue"):
+            self.queue()
+        self.show_api = self.api_open if self.enable_queue else show_api
+
+        if not self.enable_queue and self.progress_tracking:
+            raise ValueError("Progress tracking requires queuing to be enabled.")
+
+        for dep in self.dependencies:
+            for i in dep["cancels"]:
+                if not self.queue_enabled_for_fn(i):
+                    raise ValueError(
+                        "In order to cancel an event, the queue for that event must be enabled! "
+                        "You may get this error by either 1) passing a function that uses the yield keyword "
+                        "into an interface without enabling the queue or 2) defining an event that cancels "
+                        "another event without enabling the queue. Both can be solved by calling .queue() "
+                        "before .launch()"
+                    )
+            if dep["batch"] and (
+                dep["queue"] is False
+                or (dep["queue"] is None and not self.enable_queue)
+            ):
+                raise ValueError("In order to use batching, the queue must be enabled.")
+
+        self.config = self.get_config_file()
+        self.encrypt = encrypt
+        self.max_threads = max(
+            self._queue.max_thread_count if self.enable_queue else 0, max_threads
+        )
+        if self.encrypt:
+            self.encryption_key = encryptor.get_key(
+                getpass.getpass("Enter key for encryption: ")
+            )
+
+        if self.is_running:
+            assert isinstance(
+                self.local_url, str
+            ), f"Invalid local_url: {self.local_url}"
+            if not (quiet):
+                print(
+                    "Rerunning server... use `close()` to stop if you need to change `launch()` parameters.\n----"
+                )
+        else:
+            server_name, server_port, local_url, app, server = networking.start_server(
+                self,
+                server_name,
+                server_port,
+                ssl_keyfile,
+                ssl_certfile,
+                ssl_keyfile_password,
+            )
+            self.server_name = server_name
+            self.local_url = local_url
+            self.server_port = server_port
+            self.server_app = app
+            self.server = server
+            self.is_running = True
+            self.is_colab = utils.colab_check()
+            self.protocol = (
+                "https"
+                if self.local_url.startswith("https") or self.is_colab
+                else "http"
+            )
+
+            if self.enable_queue:
+                self._queue.set_url(self.local_url)
+
+            # Cannot run async functions in background other than app's scope.
+            # Workaround by triggering the app endpoint
+            requests.get(f"{self.local_url}startup-events")
+
+            if self.enable_queue:
+                if self.encrypt:
+                    raise ValueError("Cannot queue with encryption enabled.")
+        utils.launch_counter()
+
+        self.share = (
+            share
+            if share is not None
+            else True
+            if self.is_colab and self.enable_queue
+            else False
+        )
+
+        # If running in a colab or not able to access localhost,
+        # a shareable link must be created.
+        if _frontend and (not networking.url_ok(self.local_url)) and (not self.share):
+            raise ValueError(
+                "When localhost is not accessible, a shareable link must be created. Please set share=True."
+            )
+
+        if self.is_colab:
+            if not quiet:
+                if debug:
+                    print(strings.en["COLAB_DEBUG_TRUE"])
+                else:
+                    print(strings.en["COLAB_DEBUG_FALSE"])
+                if not self.share:
+                    print(strings.en["COLAB_WARNING"].format(self.server_port))
+            if self.enable_queue and not self.share:
+                raise ValueError(
+                    "When using queueing in Colab, a shareable link must be created. Please set share=True."
+                )
+        else:
+            if not self.share:
+              print(f'Running on local URL: https://{self.server_name}') 
+
+        if self.share:
+            if self.is_space:
+                raise RuntimeError("Share is not supported when you are in Spaces")
+            try:
+                if self.share_url is None:
+                    self.share_url = networking.setup_tunnel(
+                        self.server_name, self.server_port
+                    )
+                print(strings.en["SHARE_LINK_DISPLAY"].format(self.share_url))
+                if not (quiet):
+                    print('\u2714 Connected')
+            except RuntimeError:
+                if self.analytics_enabled:
+                    utils.error_analytics(self.ip_address, "Not able to set up tunnel")
+                self.share_url = None
+                self.share = False
+                print(strings.en["COULD_NOT_GET_SHARE_LINK"])
+        else:
+            if not (quiet):
+                print('\u2714 Connected')
+            self.share_url = None
+
+        if inbrowser:
+            link = self.share_url if self.share and self.share_url else self.local_url
+            webbrowser.open(link)
+
+        # Check if running in a Python notebook in which case, display inline
+        if inline is None:
+            inline = utils.ipython_check() and (self.auth is None)
+        if inline:
+            if self.auth is not None:
+                print(
+                    "Warning: authentication is not supported inline. Please"
+                    "click the link to access the interface in a new tab."
+                )
+            try:
+                from IPython.display import HTML, Javascript, display  # type: ignore
+
+                if self.share and self.share_url:
+                    while not networking.url_ok(self.share_url):
+                        time.sleep(0.25)
+                    display(
+                        HTML(
+                            f'<div><iframe src="{self.share_url}" width="{self.width}" height="{self.height}" allow="autoplay; camera; microphone; clipboard-read; clipboard-write;" frameborder="0" allowfullscreen></iframe></div>'
+                        )
+                    )
+                elif self.is_colab:
+                    # modified from /usr/local/lib/python3.7/dist-packages/google/colab/output/_util.py within Colab environment
+                    code = """(async (port, path, width, height, cache, element) => {
+                        if (!google.colab.kernel.accessAllowed && !cache) {
+                            return;
+                        }
+                        element.appendChild(document.createTextNode(''));
+                        const url = await google.colab.kernel.proxyPort(port, {cache});
+
+                        const external_link = document.createElement('div');
+                        external_link.innerHTML = `
+                            <div style="font-family: monospace; margin-bottom: 0.5rem">
+                                Running on <a href=${new URL(path, url).toString()} target="_blank">
+                                    https://localhost:${port}${path}
+                                </a>
+                            </div>
+                        `;
+                        element.appendChild(external_link);
+
+                        const iframe = document.createElement('iframe');
+                        iframe.src = new URL(path, url).toString();
+                        iframe.height = height;
+                        iframe.allow = "autoplay; camera; microphone; clipboard-read; clipboard-write;"
+                        iframe.width = width;
+                        iframe.style.border = 0;
+                        element.appendChild(iframe);
+                    })""" + "({port}, {path}, {width}, {height}, {cache}, window.element)".format(
+                        port=json.dumps(self.server_port),
+                        path=json.dumps("/"),
+                        width=json.dumps(self.width),
+                        height=json.dumps(self.height),
+                        cache=json.dumps(False),
+                    )
+
+                    display(Javascript(code))
+                else:
+                    display(
+                        HTML(
+                            f'<div><iframe src="{self.local_url}" width="{self.width}" height="{self.height}" allow="autoplay; camera; microphone; clipboard-read; clipboard-write;" frameborder="0" allowfullscreen></iframe></div>'
+                        )
+                    )
+            except ImportError:
+                pass
+
+        if getattr(self, "analytics_enabled", False):
+            data = {
+                "launch_method": "browser" if inbrowser else "inline",
+                "is_google_colab": self.is_colab,
+                "is_sharing_on": self.share,
+                "share_url": self.share_url,
+                "ip_address": self.ip_address,
+                "enable_queue": self.enable_queue,
+                "show_tips": self.show_tips,
+                "server_name": server_name,
+                "server_port": server_port,
+                "is_spaces": self.is_space,
+                "mode": self.mode,
+            }
+            utils.launch_analytics(data)
+
+        utils.show_tip(self)
+
+        # Block main thread if debug==True
+        if debug or int(os.getenv("GRADIO_DEBUG", 0)) == 1:
+            self.block_thread()
+        # Block main thread if running in a script to stop script from exiting
+        is_in_interactive_mode = bool(getattr(sys, "ps1", sys.flags.interactive))
+
+        if not prevent_thread_lock and not is_in_interactive_mode:
+            self.block_thread()
+
+        return TupleNoPrint((self.server_app, self.local_url, self.share_url))
+
+    def integrate(
+        self,
+        comet_ml: comet_ml.Experiment | None = None,
+        wandb: ModuleType | None = None,
+        mlflow: ModuleType | None = None,
+    ) -> None:
+        """
+        A catch-all method for integrating with other libraries. This method should be run after launch()
+        Parameters:
+            comet_ml: If a comet_ml Experiment object is provided, will integrate with the experiment and appear on Comet dashboard
+            wandb: If the wandb module is provided, will integrate with it and appear on WandB dashboard
+            mlflow: If the mlflow module  is provided, will integrate with the experiment and appear on ML Flow dashboard
+        """
+        analytics_integration = ""
+        if comet_ml is not None:
+            analytics_integration = "CometML"
+            comet_ml.log_other("Created from", "Gradio")
+            if self.share_url is not None:
+                comet_ml.log_text("gradio: " + self.share_url)
+                comet_ml.end()
+            elif self.local_url:
+                comet_ml.log_text("gradio: " + self.local_url)
+                comet_ml.end()
+            else:
+                raise ValueError("Please run `launch()` first.")
+        if wandb is not None:
+            analytics_integration = "WandB"
+            if self.share_url is not None:
+                wandb.log(
+                    {
+                        "Gradio panel": wandb.Html(
+                            '<iframe src="'
+                            + self.share_url
+                            + '" width="'
+                            + str(self.width)
+                            + '" height="'
+                            + str(self.height)
+                            + '" frameBorder="0"></iframe>'
+                        )
+                    }
+                )
+            else:
+                print(
+                    "The WandB integration requires you to "
+                    "`launch(share=True)` first."
+                )
+        if mlflow is not None:
+            analytics_integration = "MLFlow"
+            if self.share_url is not None:
+                mlflow.log_param("Gradio Interface Share Link", self.share_url)
+            else:
+                mlflow.log_param("Gradio Interface Local Link", self.local_url)
+        if self.analytics_enabled and analytics_integration:
+            data = {"integration": analytics_integration}
+            utils.integration_analytics(data)
+
+    def close(self, verbose: bool = True) -> None:
+        """
+        Closes the Interface that was launched and frees the port.
+        """
+        try:
+            if self.enable_queue:
+                self._queue.close()
+            self.server.close()
+            self.is_running = False
+            if verbose:
+                print("Closing server running on port: {}".format(self.server_port))
+        except (AttributeError, OSError):  # can't close if not running
+            pass
+
+    def block_thread(
+        self,
+    ) -> None:
+        """Block main thread until interrupted by user."""
+        try:
+            while True:
+                time.sleep(0.1)
+        except (KeyboardInterrupt, OSError):
+            print("Keyboard interruption in main thread... closing server.")
+            self.server.close()
+            for tunnel in CURRENT_TUNNELS:
+                tunnel.kill()
+
+    def attach_load_events(self):
+        """Add a load event for every component whose initial value should be randomized."""
+        if Context.root_block:
+            for component in Context.root_block.blocks.values():
+                if (
+                    isinstance(component, components.IOComponent)
+                    and component.load_event_to_attach
+                ):
+                    load_fn, every = component.load_event_to_attach
+                    # Use set_event_trigger to avoid ambiguity between load class/instance method
+                    self.set_event_trigger(
+                        "load",
+                        load_fn,
+                        None,
+                        component,
+                        no_target=True,
+                        queue=False,
+                        every=every,
+                    )
+
+    def startup_events(self):
+        """Events that should be run when the app containing this block starts up."""
+
+        if self.enable_queue:
+            utils.run_coro_in_background(self._queue.start, (self.progress_tracking,))
+        utils.run_coro_in_background(self.create_limiter)
+
+    def queue_enabled_for_fn(self, fn_index: int):
+        if self.dependencies[fn_index]["queue"] is None:
+            return self.enable_queue
+        return self.dependencies[fn_index]["queue"]

fm23da_files/frozen_dict.py

@@ -0,0 +1,294 @@
+# Copyright 2022 The Flax Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Frozen Dictionary."""
+
+import collections
+from typing import Any, TypeVar, Mapping, Dict, Tuple, Union, Hashable
+
+from flax import serialization
+import jax
+
+
+class FrozenKeysView(collections.abc.KeysView):
+  """A wrapper for a more useful repr of the keys in a frozen dict."""
+
+  def __repr__(self):
+    return f'frozen_dict_keys({list(self)})'
+
+
+class FrozenValuesView(collections.abc.ValuesView):
+  """A wrapper for a more useful repr of the values in a frozen dict."""
+
+  def __repr__(self):
+    return f'frozen_dict_values({list(self)})'
+
+
+K = TypeVar('K')
+V = TypeVar('V')
+
+
+def _indent(x, num_spaces):
+  indent_str = ' ' * num_spaces
+  lines = x.split('\n')
+  assert not lines[-1]
+  # skip the final line because it's empty and should not be indented.
+  return '\n'.join(indent_str + line for line in lines[:-1]) + '\n'
+
+
+# TODO(ivyzheng): change to register_pytree_with_keys_class after JAX release.
+@jax.tree_util.register_pytree_node_class
+class FrozenDict(Mapping[K, V]):
+  """An immutable variant of the Python dict."""
+  __slots__ = ('_dict', '_hash')
+
+  def __init__(self, *args, __unsafe_skip_copy__=False, **kwargs):  # pylint: disable=invalid-name
+    # make sure the dict is as
+    xs = dict(*args, **kwargs)
+    if __unsafe_skip_copy__:
+      self._dict = xs
+    else:
+      self._dict = _prepare_freeze(xs)
+
+    self._hash = None
+
+  def __getitem__(self, key):
+    v = self._dict[key]
+    if isinstance(v, dict):
+      return FrozenDict(v)
+    return v
+
+  def __setitem__(self, key, value):
+    raise ValueError('FrozenDict is immutable.')
+
+  def __contains__(self, key):
+    return key in self._dict
+
+  def __iter__(self):
+    return iter(self._dict)
+
+  def __len__(self):
+    return len(self._dict)
+
+  def __repr__(self):
+    return self.pretty_repr()
+
+  def __reduce__(self):
+    return FrozenDict, (self.unfreeze(),)
+
+  def pretty_repr(self, num_spaces=4):
+    """Returns an indented representation of the nested dictionary."""
+    def pretty_dict(x):
+      if not isinstance(x, dict):
+        return repr(x)
+      rep = ''
+      for key, val in x.items():
+        rep += f'{key}: {pretty_dict(val)},\n'
+      if rep:
+        return '{\n' + _indent(rep, num_spaces) + '}'
+      else:
+        return '{}'
+    return f'FrozenDict({pretty_dict(self._dict)})'
+
+  def __hash__(self):
+    if self._hash is None:
+      h = 0
+      for key, value in self.items():
+        h ^= hash((key, value))
+      self._hash = h
+    return self._hash
+
+  def copy(self, add_or_replace: Mapping[K, V]) -> 'FrozenDict[K, V]':
+    """Create a new FrozenDict with additional or replaced entries."""
+    return type(self)({**self, **unfreeze(add_or_replace)}) # type: ignore[arg-type]
+
+  def keys(self):
+    return FrozenKeysView(self)
+
+  def values(self):
+    return FrozenValuesView(self)
+
+  def items(self):
+    for key in self._dict:
+      yield (key, self[key])
+
+  def pop(self, key: K) -> Tuple['FrozenDict[K, V]', V]:
+    """Create a new FrozenDict where one entry is removed.
+
+    Example::
+
+      state, params = variables.pop('params')
+
+    Args:
+      key: the key to remove from the dict
+    Returns:
+      A pair with the new FrozenDict and the removed value.
+    """
+    value = self[key]
+    new_dict = dict(self._dict)
+    new_dict.pop(key)
+    new_self = type(self)(new_dict)
+    return new_self, value
+
+  def unfreeze(self) -> Dict[K, V]:
+    """Unfreeze this FrozenDict.
+
+    Returns:
+      An unfrozen version of this FrozenDict instance.
+    """
+    return unfreeze(self)
+
+  # TODO(ivyzheng): remove this after JAX 0.4.6 release.
+  def tree_flatten(self) -> Tuple[Tuple[Any, ...], Hashable]:
+    """Flattens this FrozenDict.
+
+    Returns:
+      A flattened version of this FrozenDict instance.
+    """
+    sorted_keys = sorted(self._dict)
+    return tuple([self._dict[k] for k in sorted_keys]), tuple(sorted_keys)
+
+  @classmethod
+  def tree_unflatten(cls, keys, values):
+    # data is already deep copied due to tree map mechanism
+    # we can skip the deep copy in the constructor
+    return cls({k: v for k, v in zip(keys, values)}, __unsafe_skip_copy__=True)
+
+
+#jax.tree_util.register_keypaths(
+#    FrozenDict, lambda fd: tuple(jax.tree_util.DictKey(k) for k in sorted(fd))
+#)
+
+
+def _prepare_freeze(xs: Any) -> Any:
+  """Deep copy unfrozen dicts to make the dictionary FrozenDict safe."""
+  if isinstance(xs, FrozenDict):
+    # we can safely ref share the internal state of a FrozenDict
+    # because it is immutable.
+    return xs._dict  # pylint: disable=protected-access
+  if not isinstance(xs, dict):
+    # return a leaf as is.
+    return xs
+  # recursively copy dictionary to avoid ref sharing
+  return {key: _prepare_freeze(val) for key, val in xs.items()}
+
+
+def freeze(xs: Mapping[Any, Any]) -> FrozenDict[Any, Any]:
+  """Freeze a nested dict.
+
+  Makes a nested `dict` immutable by transforming it into `FrozenDict`.
+
+  Args:
+    xs: Dictionary to freeze (a regualr Python dict).
+  Returns:
+    The frozen dictionary.
+  """
+  return FrozenDict(xs)
+
+
+def unfreeze(x: Union[FrozenDict, Dict[str, Any]]) -> Dict[Any, Any]:
+  """Unfreeze a FrozenDict.
+
+  Makes a mutable copy of a `FrozenDict` mutable by transforming
+  it into (nested) dict.
+
+  Args:
+    x: Frozen dictionary to unfreeze.
+  Returns:
+    The unfrozen dictionary (a regular Python dict).
+  """
+  if isinstance(x, FrozenDict):
+    # deep copy internal state of a FrozenDict
+    # the dict branch would also work here but
+    # it is much less performant because jax.tree_util.tree_map
+    # uses an optimized C implementation.
+    return jax.tree_util.tree_map(lambda y: y, x._dict)  # type: ignore
+  elif isinstance(x, dict):
+    ys = {}
+    for key, value in x.items():
+      ys[key] = unfreeze(value)
+    return ys
+  else:
+    return x
+
+
+def copy(x: Union[FrozenDict, Dict[str, Any]], add_or_replace: Union[FrozenDict, Dict[str, Any]]) -> Union[FrozenDict, Dict[str, Any]]:
+  """Create a new dict with additional and/or replaced entries. This is a utility
+  function that can act on either a FrozenDict or regular dict and mimics the
+  behavior of `FrozenDict.copy`.
+
+  Example::
+
+  new_variables = copy(variables, {'additional_entries': 1})
+
+  Args:
+    x: the dictionary to be copied and updated
+    add_or_replace: dictionary of key-value pairs to add or replace in the dict x
+  Returns:
+    A new dict with the additional and/or replaced entries.
+  """
+
+  if isinstance(x, FrozenDict):
+    return x.copy(add_or_replace)
+  elif isinstance(x, dict):
+    new_dict = jax.tree_map(lambda x: x, x) # make a deep copy of dict x
+    new_dict.update(add_or_replace)
+    return new_dict
+  raise TypeError(f'Expected FrozenDict or dict, got {type(x)}')
+
+
+def pop(x: Union[FrozenDict, Dict[str, Any]], key: str) -> Tuple[Union[FrozenDict, Dict[str, Any]], Any]:
+  """Create a new dict where one entry is removed. This is a utility
+  function for regular dicts that mimics the behavior of `FrozenDict.pop`.
+
+  Example::
+
+    state, params = pop(variables, 'params')
+
+  Args:
+    x: the dictionary to remove the entry from
+    key: the key to remove from the dict
+  Returns:
+    A pair with the new dict and the removed value.
+  """
+
+  if isinstance(x, FrozenDict):
+    return x.pop(key)
+  elif isinstance(x, dict):
+    new_dict = jax.tree_map(lambda x: x, x) # make a deep copy of dict x
+    value = new_dict.pop(key)
+    return new_dict, value
+  raise TypeError(f'Expected FrozenDict or dict, got {type(x)}')
+
+
+def _frozen_dict_state_dict(xs):
+  return {key: serialization.to_state_dict(value) for key, value in xs.items()}
+
+
+def _restore_frozen_dict(xs, states):
+  diff = set(map(str, xs.keys())).difference(states.keys())
+  if diff:
+    raise ValueError('The target dict keys and state dict keys do not match,'
+                   f' target dict contains keys {diff} which are not present in state dict '
+                   f'at path {serialization.current_path()}')
+
+  return FrozenDict(
+      {key: serialization.from_state_dict(value, states[key], name=key)
+       for key, value in xs.items()})
+
+
+serialization.register_serialization_state(
+    FrozenDict,
+    _frozen_dict_state_dict,
+    _restore_frozen_dict)

fm23da_files/paths.py

@@ -0,0 +1,61 @@
+import argparse
+import os
+import sys
+import modules.safe
+
+script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+
+# Parse the --data-dir flag first so we can use it as a base for our other argument default values
+parser = argparse.ArgumentParser(add_help=False)
+parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
+cmd_opts_pre = parser.parse_known_args()[0]
+data_path = cmd_opts_pre.data_dir
+models_path = os.path.join(data_path, "models")
+
+# data_path = cmd_opts_pre.data
+sys.path.insert(0, script_path)
+
+# search for directory of stable diffusion in following places
+sd_path = None
+possible_sd_paths = [os.path.join(script_path, '/content/gdrive/MyDrive/sd/stablediffusion'), '.', os.path.dirname(script_path)]
+for possible_sd_path in possible_sd_paths:
+    if os.path.exists(os.path.join(possible_sd_path, 'ldm/models/diffusion/ddpm.py')):
+        sd_path = os.path.abspath(possible_sd_path)
+        break
+
+assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + str(possible_sd_paths)
+
+path_dirs = [
+    (sd_path, 'ldm', 'Stable Diffusion', []),
+    (os.path.join(sd_path, 'src/taming-transformers'), 'taming', 'Taming Transformers', []),
+    (os.path.join(sd_path, 'src/codeformer'), 'inference_codeformer.py', 'CodeFormer', []),
+    (os.path.join(sd_path, 'src/blip'), 'models/blip.py', 'BLIP', []),
+    (os.path.join(sd_path, 'src/k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
+]
+
+paths = {}
+
+for d, must_exist, what, options in path_dirs:
+    must_exist_path = os.path.abspath(os.path.join(script_path, d, must_exist))
+    if not os.path.exists(must_exist_path):
+        print(f"Warning: {what} not found at path {must_exist_path}", file=sys.stderr)
+    else:
+        d = os.path.abspath(d)
+        if "atstart" in options:
+            sys.path.insert(0, d)
+        else:
+            sys.path.append(d)
+        paths[what] = d
+        
+class Prioritize:
+    def __init__(self, name):
+        self.name = name
+        self.path = None
+
+    def __enter__(self):
+        self.path = sys.path.copy()
+        sys.path = [paths[self.name]] + sys.path
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        sys.path = self.path
+        self.path = None        

precompiled/README.md

@@ -0,0 +1,2 @@
+# precompiled xformers files
+T4 + P100 + V100 + A100

precompiled/T4/T4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7704cb0c745caa4612fe7664fb5d1f77e2ecd71bd056f48f968ad2c7c065762
+size 32252700