Upload folder using huggingface_hub

.gitignore

@@ -0,0 +1,34 @@
+__pycache__
+*.ckpt
+*.safetensors
+*.pth
+/ESRGAN/*
+/SwinIR/*
+/repositories
+/venv
+/tmp
+/model.ckpt
+/GFPGANv1.3.pth
+/gfpgan/weights/*.pth
+/ui-config.json
+/outputs
+/config.json
+/log
+/start.settings.bat
+/embeddings
+/styles.csv
+/params.txt
+/styles.csv.bak
+/start-user.bat
+/start-user.sh
+/interrogate
+/user.css
+/.idea
+notification.mp3
+/SwinIR
+/textual_inversion
+.vscode
+/test/stdout.txt
+/test/stderr.txt
+/cache.json*
+/config_states/

.pylintrc

@@ -0,0 +1,3 @@
+# See https://pylint.pycqa.org/en/latest/user_guide/messages/message_control.html
+[MESSAGES CONTROL]
+disable=C,R,W,E,I

CHANGELOG.md

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+## 1.1.1
+
+### Bug Fixes:
+
+- fix an error that prevents running on torch<2.0 without --disable-safe-unpickle
+
+## 1.1.0
+
+### Features:
+
+- switch to torch 2.0.0 (except for AMD GPUs)
+- visual improvements to custom code scripts
+- add filename patterns: [clip_skip], [hasprompt<>], [batch_number], [generation_number]
+- add support for saving init images in img2img, and record their hashes in infotext for reproducability
+- automatically select current word when adjusting weight with ctrl+up/down
+- add dropdowns for X/Y/Z plot
+- setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs
+- support Gradio's theme API
+- use TCMalloc on Linux by default; possible fix for memory leaks
+- (optimization) option to remove negative conditioning at low sigma values #9177
+- embed model merge metadata in .safetensors file
+- extension settings backup/restore feature #9169
+- add "resize by" and "resize to" tabs to img2img
+- add option "keep original size" to textual inversion images preprocess
+- image viewer scrolling via analog stick
+- button to restore the progress from session lost / tab reload
+
+### Minor:
+
+- gradio bumped to 3.28.1
+- in extra tab, change extras "scale to" to sliders
+- add labels to tool buttons to make it possible to hide them
+- add tiled inference support for ScuNET
+- add branch support for extension installation
+- change linux installation script to insall into current directory rather than /home/username
+- sort textual inversion embeddings by name (case insensitive)
+- allow styles.csv to be symlinked or mounted in docker
+- remove the "do not add watermark to images" option
+- make selected tab configurable with UI config
+- extra networks UI in now fixed height and scrollable
+- add disable_tls_verify arg for use with self-signed certs
+
+### Extensions:
+
+- Add reload callback
+- add is_hr_pass field for processing
+
+### Bug Fixes:
+
+- fix broken batch image processing on 'Extras/Batch Process' tab
+- add "None" option to extra networks dropdowns
+- fix FileExistsError for CLIP Interrogator
+- fix /sdapi/v1/txt2img endpoint not working on Linux #9319
+- fix disappearing live previews and progressbar during slow tasks
+- fix fullscreen image view not working properly in some cases
+- prevent alwayson_scripts args param resizing script_arg list when they are inserted in it
+- fix prompt schedule for second order samplers
+- fix image mask/composite for weird resolutions #9628
+- use correct images for previews when using AND (see #9491)
+- one broken image in img2img batch won't stop all processing
+- fix image orientation bug in train/preprocess
+- fix Ngrok recreating tunnels every reload
+- fix --realesrgan-models-path and --ldsr-models-path not working
+- fix --skip-install not working
+- outpainting Mk2 & Poorman should use the SAMPLE file format to save images, not GRID file format
+- do not fail all Loras if some have failed to load when making a picture
+
+## 1.0.0
+
+- everything

CODEOWNERS

@@ -0,0 +1,17 @@
+-       @AUTOMATIC1111
+
+# if you were managing a localization and were removed from this file, this is because
+
+# the intended way to do localizations now is via extensions. See:
+
+# https://github.com/AUTOMATIC1111/stable-diffusion-/wiki/Developing-extensions
+
+# Make a repo with your localization and since you are still listed as a collaborator
+
+# you can add it to the wiki page yourself. This change is because some people complained
+
+# the git commit log is cluttered with things unrelated to almost everyone and
+
+# because I believe this is the best overall for the project to handle localizations almost
+
+# entirely without my oversight.

LICENSE.txt

@@ -0,0 +1,663 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
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+
+  A patent license is "discriminatory" if it does not include within
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+conditioned on the non-exercise of one or more of the rights that are
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+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
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+not convey it at all.  For example, if you agree to terms that obligate you
+to collect a royalty for further conveying from those to whom you convey
+the Program, the only way you could satisfy both those terms and this
+License would be to refrain entirely from conveying the Program.
+
+  13. Remote Network Interaction; Use with the GNU General Public License.
+
+  Notwithstanding any other provision of this License, if you modify the
+Program, your modified version must prominently offer all users
+interacting with it remotely through a computer network (if your version
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+License will continue to apply to the part which is the covered work,
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+3 of the GNU General Public License.
+
+  14. Revised Versions of this License.
+
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+the GNU Affero General Public License from time to time.  Such new versions
+will be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
+Program specifies that a certain numbered version of the GNU Affero General
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+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
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+
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+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
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+to attach them to the start of each source file to most effectively
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+
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+    Copyright (C) <year>  <name of author>
+
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+    it under the terms of the GNU Affero General Public License as published by
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+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

README.md

@@ -0,0 +1 @@
+# lite-kaggle

assets/blocks.py

@@ -0,0 +1,2025 @@
+from __future__ import annotations
+
+import copy
+import inspect
+import json
+import os
+import random
+import secrets
+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 gradio_client import serializing
+from gradio_client import utils as client_utils
+from gradio_client.documentation import document, set_documentation_group
+from typing_extensions import Literal
+
+from gradio import (
+    components,
+    external,
+    networking,
+    queueing,
+    routes,
+    strings,
+    themes,
+    utils,
+)
+from gradio.context import Context
+from gradio.deprecation import check_deprecated_parameters
+from gradio.exceptions import DuplicateBlockError, InvalidApiName
+from gradio.helpers import EventData, create_tracker, skip, special_args
+from gradio.themes import Default as DefaultTheme
+from gradio.themes import ThemeClass as Theme
+from gradio.tunneling import CURRENT_TUNNELS
+from gradio.utils import (
+    GRADIO_VERSION,
+    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).
+    from fastapi.applications import FastAPI
+
+    from gradio.components import Component
+
+BUILT_IN_THEMES: Dict[str, Theme] = {
+    t.name: t
+    for t in [
+        themes.Base(),
+        themes.Default(),
+        themes.Monochrome(),
+        themes.Soft(),
+        themes.Glass(),
+    ]
+}
+
+
+class Block:
+    def __init__(
+        self,
+        *,
+        render: bool = True,
+        elem_id: str | None = None,
+        elem_classes: List[str] | 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.elem_classes = (
+            [elem_classes] if isinstance(elem_classes, str) else elem_classes
+        )
+        self.root_url = root_url
+        self.share_token = secrets.token_urlsafe(32)
+        self._skip_init_processing = _skip_init_processing
+        self._style = {}
+        self.parent: BlockContext | None = None
+        self.root = ""
+
+        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.IOComponent):
+                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,
+        collects_event_data: bool | None = None,
+        trigger_after: int | None = None,
+        trigger_only_on_success: bool = False,
+    ) -> Tuple[Dict[str, Any], int]:
+        """
+        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: Experimental parameter (API may change): 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
+            queue: If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
+            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.
+            every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.
+            collects_event_data: whether to collect event data for this event
+            trigger_after: if set, this event will be triggered after 'trigger_after' function index
+            trigger_only_on_success: if True, this event will only be triggered if the previous event was successful (only applies if `trigger_after` is set)
+        Returns: dependency information, dependency index
+        """
+        # 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`.")
+
+        _, progress_index, event_data_index = (
+            special_args(fn) if fn else (None, None, None)
+        )
+        Context.root_block.fns.append(
+            BlockFunction(
+                fn,
+                inputs,
+                outputs,
+                preprocess,
+                postprocess,
+                inputs_as_dict,
+                progress_index is not None,
+            )
+        )
+        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(f"api_name {api_name} already exists, using {api_name_}")
+                api_name = api_name_
+
+        if collects_event_data is None:
+            collects_event_data = event_data_index is not None
+
+        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 [],
+            "types": {
+                "continuous": bool(every),
+                "generator": inspect.isgeneratorfunction(fn) or bool(every),
+            },
+            "collects_event_data": collects_event_data,
+            "trigger_after": trigger_after,
+            "trigger_only_on_success": trigger_only_on_success,
+        }
+        Context.root_block.dependencies.append(dependency)
+        return dependency, len(Context.root_block.dependencies) - 1
+
+    def get_config(self):
+        return {
+            "visible": self.visible,
+            "elem_id": self.elem_id,
+            "elem_classes": self.elem_classes,
+            "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:
+        generic_update = generic_update.copy()
+        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] = []
+        Block.__init__(self, 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,
+        tracks_progress: bool = False,
+    ):
+        self.fn = fn
+        self.inputs = inputs
+        self.outputs = outputs
+        self.preprocess = preprocess
+        self.postprocess = postprocess
+        self.tracks_progress = tracks_progress
+        self.total_runtime = 0
+        self.total_runs = 0
+        self.inputs_as_dict = inputs_as_dict
+        self.name = getattr(fn, "__name__", "fn") if fn is not None else None
+
+    def __str__(self):
+        return str(
+            {
+                "fn": self.name,
+                "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")
+    interactive = update_dict.pop("interactive", None)
+    if interactive is not None:
+        update_dict["mode"] = "dynamic" if interactive else "static"
+    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
+
+
+def get_api_info(config: Dict, serialize: bool = True):
+    """
+    Gets the information needed to generate the API docs from a Blocks config.
+    Parameters:
+        config: a Blocks config dictionary
+        serialize: If True, returns the serialized version of the typed information. If False, returns the raw version.
+    """
+    api_info = {"named_endpoints": {}, "unnamed_endpoints": {}}
+    mode = config.get("mode", None)
+
+    for d, dependency in enumerate(config["dependencies"]):
+        dependency_info = {"parameters": [], "returns": []}
+        skip_endpoint = False
+
+        inputs = dependency["inputs"]
+        for i in inputs:
+            for component in config["components"]:
+                if component["id"] == i:
+                    break
+            else:
+                skip_endpoint = True  # if component not found, skip this endpoint
+                break
+            type = component["type"]
+            if (
+                not component.get("serializer")
+                and type not in serializing.COMPONENT_MAPPING
+            ):
+                skip_endpoint = (
+                    True  # if component is not serializable, skip this endpoint
+                )
+                break
+            label = component["props"].get("label", f"parameter_{i}")
+            # The config has the most specific API info (taking into account the parameters
+            # of the component), so we use that if it exists. Otherwise, we fallback to the
+            # Serializer's API info.
+            if component.get("api_info"):
+                if serialize:
+                    info = component["api_info"]["serialized_input"]
+                    example = component["example_inputs"]["serialized"]
+                else:
+                    info = component["api_info"]["raw_input"]
+                    example = component["example_inputs"]["raw"]
+            else:
+                serializer = serializing.COMPONENT_MAPPING[type]()
+                assert isinstance(serializer, serializing.Serializable)
+                if serialize:
+                    info = serializer.api_info()["serialized_input"]
+                    example = serializer.example_inputs()["serialized"]
+                else:
+                    info = serializer.api_info()["raw_input"]
+                    example = serializer.example_inputs()["raw"]
+            dependency_info["parameters"].append(
+                {
+                    "label": label,
+                    "type_python": info[0],
+                    "type_description": info[1],
+                    "component": type.capitalize(),
+                    "example_input": example,
+                }
+            )
+
+        outputs = dependency["outputs"]
+        for o in outputs:
+            for component in config["components"]:
+                if component["id"] == o:
+                    break
+            else:
+                skip_endpoint = True  # if component not found, skip this endpoint
+                break
+            type = component["type"]
+            if (
+                not component.get("serializer")
+                and type not in serializing.COMPONENT_MAPPING
+            ):
+                skip_endpoint = (
+                    True  # if component is not serializable, skip this endpoint
+                )
+                break
+            label = component["props"].get("label", f"value_{o}")
+            serializer = serializing.COMPONENT_MAPPING[type]()
+            assert isinstance(serializer, serializing.Serializable)
+            if serialize:
+                info = serializer.api_info()["serialized_output"]
+            else:
+                info = serializer.api_info()["raw_output"]
+            dependency_info["returns"].append(
+                {
+                    "label": label,
+                    "type_python": info[0],
+                    "type_description": info[1],
+                    "component": type.capitalize(),
+                }
+            )
+
+        if not dependency["backend_fn"]:
+            skip_endpoint = True
+
+        if skip_endpoint:
+            continue
+        if dependency["api_name"]:
+            api_info["named_endpoints"][f"/{dependency['api_name']}"] = dependency_info
+        elif mode == "interface" or mode == "tabbed_interface":
+            pass  # Skip unnamed endpoints in interface mode
+        else:
+            api_info["unnamed_endpoints"][str(d)] = dependency_info
+
+    return api_info
+
+
+@document("launch", "queue", "integrate", "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: Theme | str | None = None,
+        analytics_enabled: bool | None = None,
+        mode: str = "blocks",
+        title: str = "Gradio",
+        css: str | None = None,
+        **kwargs,
+    ):
+        """
+        Parameters:
+            theme: a Theme object or a string representing a theme. If a string, will look for a built-in theme with that name (e.g. "soft" or "default"), or will attempt to load a theme from the HF Hub (e.g. "gradio/monochrome"). If None, will use the Default theme.
+            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
+        if theme is None:
+            theme = DefaultTheme()
+        elif isinstance(theme, str):
+            if theme.lower() in BUILT_IN_THEMES:
+                theme = BUILT_IN_THEMES[theme.lower()]
+            else:
+                try:
+                    theme = Theme.from_hub(theme)
+                except Exception as e:
+                    warnings.warn(f"Cannot load {theme}. Caught Exception: {str(e)}")
+                    theme = DefaultTheme()
+        if not isinstance(theme, Theme):
+            warnings.warn("Theme should be a class loaded from gradio.themes")
+            theme = DefaultTheme()
+        self.theme: Theme = theme
+        self.theme_css = theme._get_theme_css()
+        self.stylesheets = theme._stylesheets
+        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"
+        )
+        if not self.analytics_enabled:
+            os.environ["HF_HUB_DISABLE_TELEMETRY"] = "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.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
+        self.ssl_verify = True
+
+        self.file_directories = []
+
+        if self.analytics_enabled:
+            is_custom_theme = not any(
+                self.theme.to_dict() == built_in_theme.to_dict()
+                for built_in_theme in BUILT_IN_THEMES.values()
+            )
+            data = {
+                "mode": self.mode,
+                "custom_css": self.css is not None,
+                "theme": self.theme.name,
+                "is_custom_theme": is_custom_theme,
+                "version": GRADIO_VERSION,
+            }
+            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"]
+        theme = config.get("theme", "default")
+        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(f"Cannot find block with id {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"] = f"{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)
+
+        derived_fields = ["types"]
+
+        with Blocks(theme=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.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
+                for field in derived_fields:
+                    dependency.pop(field, None)
+                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
+                    )[0]
+                    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 += f"\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 += f"\n |-{block}"
+                repr += "\n outputs:"
+                for output_id in dependency["outputs"]:
+                    block = self.blocks[output_id]
+                    repr += f"\n |-{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(
+                            f"api_name {api_name} already exists, using {api_name_}"
+                        )
+                        dependency["api_name"] = api_name_
+                dependency["cancels"] = [
+                    c + dependency_offset for c in dependency["cancels"]
+                ]
+                if dependency.get("trigger_after") is not None:
+                    dependency["trigger_after"] += dependency_offset
+                # 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 = client_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,
+        event_data: EventData | 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
+            event_data: data associated with event trigger
+        """
+        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, event_data
+        )
+        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], root_url=block.root_url, hf_token=Context.hf_token
+            )
+            predictions.append(deserialized)
+
+        return predictions
+
+    def validate_inputs(self, fn_index: int, inputs: List[Any]):
+        block_fn = self.fns[fn_index]
+        dependency = self.dependencies[fn_index]
+
+        dep_inputs = dependency["inputs"]
+
+        # This handles incorrect inputs when args are changed by a JS function
+        # Only check not enough args case, ignore extra arguments (for now)
+        # TODO: make this stricter?
+        if len(inputs) < len(dep_inputs):
+            name = (
+                f" ({block_fn.name})"
+                if block_fn.name and block_fn.name != "<lambda>"
+                else ""
+            )
+
+            wanted_args = []
+            received_args = []
+            for input_id in dep_inputs:
+                block = self.blocks[input_id]
+                wanted_args.append(str(block))
+            for inp in inputs:
+                if isinstance(inp, str):
+                    v = f'"{inp}"'
+                else:
+                    v = str(inp)
+                received_args.append(v)
+
+            wanted = ", ".join(wanted_args)
+            received = ", ".join(received_args)
+
+            # JS func didn't pass enough arguments
+            raise ValueError(
+                f"""An event handler{name} didn't receive enough input values (needed: {len(dep_inputs)}, got: {len(inputs)}).
+Check if the event handler calls a Javascript function, and make sure its return value is correct.
+Wanted inputs:
+    [{wanted}]
+Received inputs:
+    [{received}]"""
+            )
+
+    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]
+
+        self.validate_inputs(fn_index, inputs)
+
+        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 validate_outputs(self, fn_index: int, predictions: Any | List[Any]):
+        block_fn = self.fns[fn_index]
+        dependency = self.dependencies[fn_index]
+
+        dep_outputs = dependency["outputs"]
+
+        if type(predictions) is not list and type(predictions) is not tuple:
+            predictions = [predictions]
+
+        if len(predictions) < len(dep_outputs):
+            name = (
+                f" ({block_fn.name})"
+                if block_fn.name and block_fn.name != "<lambda>"
+                else ""
+            )
+
+            wanted_args = []
+            received_args = []
+            for output_id in dep_outputs:
+                block = self.blocks[output_id]
+                wanted_args.append(str(block))
+            for pred in predictions:
+                if isinstance(pred, str):
+                    v = f'"{pred}"'
+                else:
+                    v = str(pred)
+                received_args.append(v)
+
+            wanted = ", ".join(wanted_args)
+            received = ", ".join(received_args)
+
+            raise ValueError(
+                f"""An event handler{name} didn't receive enough output values (needed: {len(dep_outputs)}, received: {len(predictions)}).
+Wanted outputs:
+    [{wanted}]
+Received outputs:
+    [{received}]"""
+            )
+
+    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,
+            ]
+
+        self.validate_outputs(fn_index, predictions)  # type: ignore
+
+        output = []
+        for i, output_id in enumerate(dependency["outputs"]):
+            try:
+                if predictions[i] is components._Keywords.FINISHED_ITERATING:
+                    output.append(None)
+                    continue
+            except (IndexError, KeyError):
+                raise ValueError(
+                    f"Number of output components does not match number of values returned from from function {block_fn.name}"
+                )
+            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,
+        event_data: EventData | 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
+            state: data stored from stateful components for session (key is input block id)
+            request: the gr.Request object containing information about the network request (e.g. IP address, headers, query parameters, username)
+            iterators: the in-progress iterators for each generator function (key is function index)
+            event_id: id of event that triggered this API call
+            event_data: data associated with the event trigger itself
+        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, event_id, event_data
+            )
+            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, event_data
+            )
+            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,
+            "analytics_enabled": self.analytics_enabled,
+            "components": [],
+            "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(),
+            "stylesheets": self.stylesheets,
+            "root": self.root,
+            "theme": self.theme.name,
+        }
+
+        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():
+            props = block.get_config() if hasattr(block, "get_config") else {}
+            block_config = {
+                "id": _id,
+                "type": block.get_block_name(),
+                "props": utils.delete_none(props),
+            }
+            serializer = utils.get_serializer_name(block)
+            if serializer:
+                assert isinstance(block, serializing.Serializable)
+                block_config["serializer"] = serializer
+                block_config["api_info"] = block.api_info()  # type: ignore
+                block_config["example_inputs"] = block.example_inputs()  # type: ignore
+            config["components"].append(block_config)
+        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
+        self.exited = False
+        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.tracks_progress for block_fn in self.fns)
+        self.exited = True
+
+    @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. Warning: this method will be deprecated. Use the equivalent `gradio.load()` instead.
+
+
+        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()
+        """
+        if isinstance(self_or_cls, type):
+            warnings.warn("gr.Blocks.load() will be deprecated. Use gr.load() instead.")
+            if name is None:
+                raise ValueError(
+                    "Blocks.load() requires passing parameters as keyword arguments"
+                )
+            return external.load(
+                name=name, src=src, hf_token=api_key, alias=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,
+            )[0]
+
+    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: (Blocks)
+            with gr.Blocks() as demo:
+                button = gr.Button(label="Generate Image")
+                button.click(fn=image_generator, inputs=gr.Textbox(), outputs=gr.Image())
+            demo.queue(concurrency_count=3)
+            demo.launch()
+        Example: (Interface)
+            demo = gr.Interface(image_generator, gr.Textbox(), gr.Image())
+            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()
+        self.app = routes.App.create_app(self)
+        return self
+
+    def validate_queue_settings(self):
+        if not self.enable_queue and self.progress_tracking:
+            raise ValueError("Progress tracking requires queuing to be enabled.")
+
+        for fn_index, dep in enumerate(self.dependencies):
+            if not self.enable_queue and self.queue_enabled_for_fn(fn_index):
+                raise ValueError(
+                    f"The queue is enabled for event {dep['api_name'] if dep['api_name'] else fn_index} "
+                    "but the queue has not been enabled for the app. Please call .queue() "
+                    "on your app. Consult https://gradio.app/docs/#blocks-queue for information on how "
+                    "to configure the queue."
+                )
+            for i in dep["cancels"]:
+                if not self.queue_enabled_for_fn(i):
+                    raise ValueError(
+                        "Queue needs to 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.")
+
+    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 | None = None,
+        favicon_path: str | None = None,
+        ssl_keyfile: str | None = None,
+        ssl_certfile: str | None = None,
+        ssl_keyfile_password: str | None = None,
+        ssl_verify: bool = True,
+        quiet: bool = False,
+        show_api: bool = True,
+        file_directories: List[str] | None = None,
+        _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: DEPRECATED. Has no effect.
+            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.
+            ssl_verify: If False, skips certificate validation which allows self-signed certificates to be used.
+            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.
+            file_directories: List of directories that gradio is allowed to serve files from (in addition to the directory containing the gradio python file). Must be absolute paths. Warning: any files in these directories or its children are potentially accessible to all users of your app.
+        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: (Blocks)
+            import gradio as gr
+            def reverse(text):
+                return text[::-1]
+            with gr.Blocks() as demo:
+                button = gr.Button(value="Reverse")
+                button.click(reverse, gr.Textbox(), gr.Textbox())
+            demo.launch(share=True, auth=("username", "password"))
+        Example:  (Interface)
+            import gradio as gr
+            def reverse(text):
+                return text[::-1]
+            demo = gr.Interface(reverse, "text", "text")
+            demo.launch(share=True, auth=("username", "password"))
+        """
+        if not self.exited:
+            self.__exit__()
+
+        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
+        self.ssl_verify = ssl_verify
+
+        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 encrypt is not None:
+            warnings.warn(
+                "The `encrypt` parameter has been deprecated and has no effect.",
+                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
+
+        self.file_directories = file_directories if file_directories is not None else []
+        if not isinstance(self.file_directories, list):
+            raise ValueError("file_directories must be a list of directories.")
+
+        self.validate_queue_settings()
+
+        self.config = self.get_config_file()
+        self.max_threads = max(
+            self._queue.max_thread_count if self.enable_queue else 0, max_threads
+        )
+
+        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.is_kaggle = utils.kaggle_check()
+            self.is_sagemaker = utils.sagemaker_check()
+
+            self.protocol = (
+                "https"
+            )
+
+            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", verify=ssl_verify)
+
+        utils.launch_counter()
+
+        if share is None:
+            if self.is_colab and self.enable_queue:
+                if not quiet:
+                    print(
+                        "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n"
+                    )
+                self.share = True
+            elif self.is_kaggle:
+                if not quiet:
+                    print(
+                        "Kaggle notebooks require sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n"
+                    )
+                self.share = True
+            elif self.is_sagemaker:
+                if not quiet:
+                    print(
+                        "Sagemaker notebooks may require sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n"
+                    )
+                self.share = True
+            else:
+                self.share = False
+        else:
+            self.share = share
+
+        # 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, self.share_token
+                    )
+                print(strings.en["SHARE_LINK_DISPLAY"].format(self.share_url))
+                if not (quiet):
+                    print('\u2714 Connected')
+            except (RuntimeError, requests.exceptions.ConnectionError):
+                if self.analytics_enabled:
+                    utils.error_analytics("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,
+                "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.launched_telemetry(self, 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=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(f"gradio: {self.share_url}")
+                comet_ml.end()
+            elif self.local_url:
+                comet_ml.log_text(f"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
+            # So that the startup events (starting the queue)
+            # happen the next time the app is launched
+            self.app.startup_events_triggered = False
+            if verbose:
+                print(f"Closing server running on port: {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
+                    dep = self.set_event_trigger(
+                        "load",
+                        load_fn,
+                        None,
+                        component,
+                        no_target=True,
+                        # If every is None, for sure skip the queue
+                        # else, let the enable_queue parameter take precedence
+                        # this will raise a nice error message is every is used
+                        # without queue
+                        queue=False if every is None else None,
+                        every=every,
+                    )[0]
+                    component.load_event = dep
+
+    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, self.ssl_verify
+            )
+            # So that processing can resume in case the queue was stopped
+            self._queue.stopped = False
+        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"]

configs/alt-diffusion-inference.yaml

@@ -0,0 +1,72 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: modules.xlmr.BertSeriesModelWithTransformation
+      params:
+        name: "XLMR-Large"

configs/instruct-pix2pix.yaml

@@ -0,0 +1,98 @@
+# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
+# See more details in LICENSE.
+
+model:
+  base_learning_rate: 1.0e-04
+  target: modules.models.diffusion.ddpm_edit.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: edited
+    cond_stage_key: edit
+    # image_size: 64
+    # image_size: 32
+    image_size: 16
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: hybrid
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: false
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 0 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 128
+    num_workers: 1
+    wrap: false
+    validation:
+      target: edit_dataset.EditDataset
+      params:
+        path: data/clip-filtered-dataset
+        cache_dir:  data/
+        cache_name: data_10k
+        split: val
+        min_text_sim: 0.2
+        min_image_sim: 0.75
+        min_direction_sim: 0.2
+        max_samples_per_prompt: 1
+        min_resize_res: 512
+        max_resize_res: 512
+        crop_res: 512
+        output_as_edit: False
+        real_input: True

configs/v1-inference.yaml

@@ -0,0 +1,70 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder

configs/v1-inpainting-inference.yaml

@@ -0,0 +1,70 @@
+model:
+  base_learning_rate: 7.5e-05
+  target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: hybrid   # important
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    finetune_keys: null
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 9  # 4 data + 4 downscaled image + 1 mask
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder

environment-wsl2.yaml

@@ -0,0 +1,11 @@
+name: automatic
+channels:
+  - pytorch
+  - defaults
+dependencies:
+  - python=3.10
+  - pip=23.0
+  - cudatoolkit=11.8
+  - pytorch=2.0
+  - torchvision=0.15
+  - numpy=1.23

extensions-builtin/LDSR/ldsr_model_arch.py

@@ -0,0 +1,253 @@
+import os
+import gc
+import time
+
+import numpy as np
+import torch
+import torchvision
+from PIL import Image
+from einops import rearrange, repeat
+from omegaconf import OmegaConf
+import safetensors.torch
+
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.util import instantiate_from_config, ismap
+from modules import shared, sd_hijack
+
+cached_ldsr_model: torch.nn.Module = None
+
+
+# Create LDSR Class
+class LDSR:
+    def load_model_from_config(self, half_attention):
+        global cached_ldsr_model
+
+        if shared.opts.ldsr_cached and cached_ldsr_model is not None:
+            print("Loading model from cache")
+            model: torch.nn.Module = cached_ldsr_model
+        else:
+            print(f"Loading model from {self.modelPath}")
+            _, extension = os.path.splitext(self.modelPath)
+            if extension.lower() == ".safetensors":
+                pl_sd = safetensors.torch.load_file(self.modelPath, device="cpu")
+            else:
+                pl_sd = torch.load(self.modelPath, map_location="cpu")
+            sd = pl_sd["state_dict"] if "state_dict" in pl_sd else pl_sd
+            config = OmegaConf.load(self.yamlPath)
+            config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1"
+            model: torch.nn.Module = instantiate_from_config(config.model)
+            model.load_state_dict(sd, strict=False)
+            model = model.to(shared.device)
+            if half_attention:
+                model = model.half()
+            if shared.cmd_opts.opt_channelslast:
+                model = model.to(memory_format=torch.channels_last)
+
+            sd_hijack.model_hijack.hijack(model) # apply optimization
+            model.eval()
+
+            if shared.opts.ldsr_cached:
+                cached_ldsr_model = model
+
+        return {"model": model}
+
+    def __init__(self, model_path, yaml_path):
+        self.modelPath = model_path
+        self.yamlPath = yaml_path
+
+    @staticmethod
+    def run(model, selected_path, custom_steps, eta):
+        example = get_cond(selected_path)
+
+        n_runs = 1
+        guider = None
+        ckwargs = None
+        ddim_use_x0_pred = False
+        temperature = 1.
+        eta = eta
+        custom_shape = None
+
+        height, width = example["image"].shape[1:3]
+        split_input = height >= 128 and width >= 128
+
+        if split_input:
+            ks = 128
+            stride = 64
+            vqf = 4  #
+            model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
+                                        "vqf": vqf,
+                                        "patch_distributed_vq": True,
+                                        "tie_braker": False,
+                                        "clip_max_weight": 0.5,
+                                        "clip_min_weight": 0.01,
+                                        "clip_max_tie_weight": 0.5,
+                                        "clip_min_tie_weight": 0.01}
+        else:
+            if hasattr(model, "split_input_params"):
+                delattr(model, "split_input_params")
+
+        x_t = None
+        logs = None
+        for n in range(n_runs):
+            if custom_shape is not None:
+                x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
+                x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
+
+            logs = make_convolutional_sample(example, model,
+                                             custom_steps=custom_steps,
+                                             eta=eta, quantize_x0=False,
+                                             custom_shape=custom_shape,
+                                             temperature=temperature, noise_dropout=0.,
+                                             corrector=guider, corrector_kwargs=ckwargs, x_T=x_t,
+                                             ddim_use_x0_pred=ddim_use_x0_pred
+                                             )
+        return logs
+
+    def super_resolution(self, image, steps=100, target_scale=2, half_attention=False):
+        model = self.load_model_from_config(half_attention)
+
+        # Run settings
+        diffusion_steps = int(steps)
+        eta = 1.0
+
+        down_sample_method = 'Lanczos'
+
+        gc.collect()
+        if torch.cuda.is_available:
+            torch.cuda.empty_cache()
+
+        im_og = image
+        width_og, height_og = im_og.size
+        # If we can adjust the max upscale size, then the 4 below should be our variable
+        down_sample_rate = target_scale / 4
+        wd = width_og * down_sample_rate
+        hd = height_og * down_sample_rate
+        width_downsampled_pre = int(np.ceil(wd))
+        height_downsampled_pre = int(np.ceil(hd))
+
+        if down_sample_rate != 1:
+            print(
+                f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
+            im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
+        else:
+            print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
+        
+        # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
+        pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
+        im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
+        
+        logs = self.run(model["model"], im_padded, diffusion_steps, eta)
+
+        sample = logs["sample"]
+        sample = sample.detach().cpu()
+        sample = torch.clamp(sample, -1., 1.)
+        sample = (sample + 1.) / 2. * 255
+        sample = sample.numpy().astype(np.uint8)
+        sample = np.transpose(sample, (0, 2, 3, 1))
+        a = Image.fromarray(sample[0])
+
+        # remove padding
+        a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
+
+        del model
+        gc.collect()
+        if torch.cuda.is_available:
+            torch.cuda.empty_cache()
+
+        return a
+
+
+def get_cond(selected_path):
+    example = dict()
+    up_f = 4
+    c = selected_path.convert('RGB')
+    c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
+    c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
+                                                    antialias=True)
+    c_up = rearrange(c_up, '1 c h w -> 1 h w c')
+    c = rearrange(c, '1 c h w -> 1 h w c')
+    c = 2. * c - 1.
+
+    c = c.to(shared.device)
+    example["LR_image"] = c
+    example["image"] = c_up
+
+    return example
+
+
+@torch.no_grad()
+def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
+                    mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None,
+                    corrector_kwargs=None, x_t=None
+                    ):
+    ddim = DDIMSampler(model)
+    bs = shape[0]
+    shape = shape[1:]
+    print(f"Sampling with eta = {eta}; steps: {steps}")
+    samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
+                                         normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
+                                         mask=mask, x0=x0, temperature=temperature, verbose=False,
+                                         score_corrector=score_corrector,
+                                         corrector_kwargs=corrector_kwargs, x_t=x_t)
+
+    return samples, intermediates
+
+
+@torch.no_grad()
+def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
+                              corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
+    log = dict()
+
+    z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
+                                        return_first_stage_outputs=True,
+                                        force_c_encode=not (hasattr(model, 'split_input_params')
+                                                            and model.cond_stage_key == 'coordinates_bbox'),
+                                        return_original_cond=True)
+
+    if custom_shape is not None:
+        z = torch.randn(custom_shape)
+        print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
+
+    z0 = None
+
+    log["input"] = x
+    log["reconstruction"] = xrec
+
+    if ismap(xc):
+        log["original_conditioning"] = model.to_rgb(xc)
+        if hasattr(model, 'cond_stage_key'):
+            log[model.cond_stage_key] = model.to_rgb(xc)
+
+    else:
+        log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
+        if model.cond_stage_model:
+            log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
+            if model.cond_stage_key == 'class_label':
+                log[model.cond_stage_key] = xc[model.cond_stage_key]
+
+    with model.ema_scope("Plotting"):
+        t0 = time.time()
+
+        sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
+                                                eta=eta,
+                                                quantize_x0=quantize_x0, mask=None, x0=z0,
+                                                temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs,
+                                                x_t=x_T)
+        t1 = time.time()
+
+        if ddim_use_x0_pred:
+            sample = intermediates['pred_x0'][-1]
+
+    x_sample = model.decode_first_stage(sample)
+
+    try:
+        x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
+        log["sample_noquant"] = x_sample_noquant
+        log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
+    except:
+        pass
+
+    log["sample"] = x_sample
+    log["time"] = t1 - t0
+
+    return log

extensions-builtin/LDSR/preload.py

@@ -0,0 +1,6 @@
+import os
+from modules import paths
+
+
+def preload(parser):
+    parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(paths.models_path, 'LDSR'))

extensions-builtin/LDSR/scripts/ldsr_model.py

@@ -0,0 +1,75 @@
+import os
+import sys
+import traceback
+
+from basicsr.utils.download_util import load_file_from_url
+
+from modules.upscaler import Upscaler, UpscalerData
+from ldsr_model_arch import LDSR
+from modules import shared, script_callbacks
+import sd_hijack_autoencoder, sd_hijack_ddpm_v1
+
+
+class UpscalerLDSR(Upscaler):
+    def __init__(self, user_path):
+        self.name = "LDSR"
+        self.user_path = user_path
+        self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
+        self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
+        super().__init__()
+        scaler_data = UpscalerData("LDSR", None, self)
+        self.scalers = [scaler_data]
+
+    def load_model(self, path: str):
+        # Remove incorrect project.yaml file if too big
+        yaml_path = os.path.join(self.model_path, "project.yaml")
+        old_model_path = os.path.join(self.model_path, "model.pth")
+        new_model_path = os.path.join(self.model_path, "model.ckpt")
+
+        local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"])
+        local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None)
+        local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None)
+        local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None)
+
+        if os.path.exists(yaml_path):
+            statinfo = os.stat(yaml_path)
+            if statinfo.st_size >= 10485760:
+                print("Removing invalid LDSR YAML file.")
+                os.remove(yaml_path)
+
+        if os.path.exists(old_model_path):
+            print("Renaming model from model.pth to model.ckpt")
+            os.rename(old_model_path, new_model_path)
+
+        if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
+            model = local_safetensors_path
+        else:
+            model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="model.ckpt", progress=True)
+
+        yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_path, file_name="project.yaml", progress=True)
+
+        try:
+            return LDSR(model, yaml)
+
+        except Exception:
+            print("Error importing LDSR:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+        return None
+
+    def do_upscale(self, img, path):
+        ldsr = self.load_model(path)
+        if ldsr is None:
+            print("NO LDSR!")
+            return img
+        ddim_steps = shared.opts.ldsr_steps
+        return ldsr.super_resolution(img, ddim_steps, self.scale)
+
+
+def on_ui_settings():
+    import gradio as gr
+
+    shared.opts.add_option("ldsr_steps", shared.OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}, section=('upscaling', "Upscaling")))
+    shared.opts.add_option("ldsr_cached", shared.OptionInfo(False, "Cache LDSR model in memory", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")))
+
+
+script_callbacks.on_ui_settings(on_ui_settings)

extensions-builtin/LDSR/sd_hijack_autoencoder.py

@@ -0,0 +1,286 @@
+# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
+# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
+# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
+
+import torch
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from contextlib import contextmanager
+from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
+from ldm.modules.diffusionmodules.model import Encoder, Decoder
+from ldm.util import instantiate_from_config
+
+import ldm.models.autoencoder
+
+class VQModel(pl.LightningModule):
+    def __init__(self,
+                 ddconfig,
+                 lossconfig,
+                 n_embed,
+                 embed_dim,
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 image_key="image",
+                 colorize_nlabels=None,
+                 monitor=None,
+                 batch_resize_range=None,
+                 scheduler_config=None,
+                 lr_g_factor=1.0,
+                 remap=None,
+                 sane_index_shape=False, # tell vector quantizer to return indices as bhw
+                 use_ema=False
+                 ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.n_embed = n_embed
+        self.image_key = image_key
+        self.encoder = Encoder(**ddconfig)
+        self.decoder = Decoder(**ddconfig)
+        self.loss = instantiate_from_config(lossconfig)
+        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
+                                        remap=remap,
+                                        sane_index_shape=sane_index_shape)
+        self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        if colorize_nlabels is not None:
+            assert type(colorize_nlabels)==int
+            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+        if monitor is not None:
+            self.monitor = monitor
+        self.batch_resize_range = batch_resize_range
+        if self.batch_resize_range is not None:
+            print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
+
+        self.use_ema = use_ema
+        if self.use_ema:
+            self.model_ema = LitEma(self)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+        self.scheduler_config = scheduler_config
+        self.lr_g_factor = lr_g_factor
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.parameters())
+            self.model_ema.copy_to(self)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    def init_from_ckpt(self, path, ignore_keys=list()):
+        sd = torch.load(path, map_location="cpu")["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        missing, unexpected = self.load_state_dict(sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+            print(f"Unexpected Keys: {unexpected}")
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self)
+
+    def encode(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        quant, emb_loss, info = self.quantize(h)
+        return quant, emb_loss, info
+
+    def encode_to_prequant(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        return h
+
+    def decode(self, quant):
+        quant = self.post_quant_conv(quant)
+        dec = self.decoder(quant)
+        return dec
+
+    def decode_code(self, code_b):
+        quant_b = self.quantize.embed_code(code_b)
+        dec = self.decode(quant_b)
+        return dec
+
+    def forward(self, input, return_pred_indices=False):
+        quant, diff, (_,_,ind) = self.encode(input)
+        dec = self.decode(quant)
+        if return_pred_indices:
+            return dec, diff, ind
+        return dec, diff
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+        if self.batch_resize_range is not None:
+            lower_size = self.batch_resize_range[0]
+            upper_size = self.batch_resize_range[1]
+            if self.global_step <= 4:
+                # do the first few batches with max size to avoid later oom
+                new_resize = upper_size
+            else:
+                new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
+            if new_resize != x.shape[2]:
+                x = F.interpolate(x, size=new_resize, mode="bicubic")
+            x = x.detach()
+        return x
+
+    def training_step(self, batch, batch_idx, optimizer_idx):
+        # https://github.com/pytorch/pytorch/issues/37142
+        # try not to fool the heuristics
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss, ind = self(x, return_pred_indices=True)
+
+        if optimizer_idx == 0:
+            # autoencode
+            aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train",
+                                            predicted_indices=ind)
+
+            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            return aeloss
+
+        if optimizer_idx == 1:
+            # discriminator
+            discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train")
+            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            return discloss
+
+    def validation_step(self, batch, batch_idx):
+        log_dict = self._validation_step(batch, batch_idx)
+        with self.ema_scope():
+            log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
+        return log_dict
+
+    def _validation_step(self, batch, batch_idx, suffix=""):
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss, ind = self(x, return_pred_indices=True)
+        aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
+                                        self.global_step,
+                                        last_layer=self.get_last_layer(),
+                                        split="val"+suffix,
+                                        predicted_indices=ind
+                                        )
+
+        discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
+                                            self.global_step,
+                                            last_layer=self.get_last_layer(),
+                                            split="val"+suffix,
+                                            predicted_indices=ind
+                                            )
+        rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
+        self.log(f"val{suffix}/rec_loss", rec_loss,
+                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+        self.log(f"val{suffix}/aeloss", aeloss,
+                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+        if version.parse(pl.__version__) >= version.parse('1.4.0'):
+            del log_dict_ae[f"val{suffix}/rec_loss"]
+        self.log_dict(log_dict_ae)
+        self.log_dict(log_dict_disc)
+        return self.log_dict
+
+    def configure_optimizers(self):
+        lr_d = self.learning_rate
+        lr_g = self.lr_g_factor*self.learning_rate
+        print("lr_d", lr_d)
+        print("lr_g", lr_g)
+        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
+                                  list(self.decoder.parameters())+
+                                  list(self.quantize.parameters())+
+                                  list(self.quant_conv.parameters())+
+                                  list(self.post_quant_conv.parameters()),
+                                  lr=lr_g, betas=(0.5, 0.9))
+        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+                                    lr=lr_d, betas=(0.5, 0.9))
+
+        if self.scheduler_config is not None:
+            scheduler = instantiate_from_config(self.scheduler_config)
+
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                },
+                {
+                    'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                },
+            ]
+            return [opt_ae, opt_disc], scheduler
+        return [opt_ae, opt_disc], []
+
+    def get_last_layer(self):
+        return self.decoder.conv_out.weight
+
+    def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.image_key)
+        x = x.to(self.device)
+        if only_inputs:
+            log["inputs"] = x
+            return log
+        xrec, _ = self(x)
+        if x.shape[1] > 3:
+            # colorize with random projection
+            assert xrec.shape[1] > 3
+            x = self.to_rgb(x)
+            xrec = self.to_rgb(xrec)
+        log["inputs"] = x
+        log["reconstructions"] = xrec
+        if plot_ema:
+            with self.ema_scope():
+                xrec_ema, _ = self(x)
+                if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
+                log["reconstructions_ema"] = xrec_ema
+        return log
+
+    def to_rgb(self, x):
+        assert self.image_key == "segmentation"
+        if not hasattr(self, "colorize"):
+            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+        x = F.conv2d(x, weight=self.colorize)
+        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+        return x
+
+
+class VQModelInterface(VQModel):
+    def __init__(self, embed_dim, *args, **kwargs):
+        super().__init__(embed_dim=embed_dim, *args, **kwargs)
+        self.embed_dim = embed_dim
+
+    def encode(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        return h
+
+    def decode(self, h, force_not_quantize=False):
+        # also go through quantization layer
+        if not force_not_quantize:
+            quant, emb_loss, info = self.quantize(h)
+        else:
+            quant = h
+        quant = self.post_quant_conv(quant)
+        dec = self.decoder(quant)
+        return dec
+
+setattr(ldm.models.autoencoder, "VQModel", VQModel)
+setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface)

extensions-builtin/LDSR/sd_hijack_ddpm_v1.py

@@ -0,0 +1,1449 @@
+# This script is copied from the compvis/stable-diffusion repo (aka the SD V1 repo)
+# Original filename: ldm/models/diffusion/ddpm.py
+# The purpose to reinstate the old DDPM logic which works with VQ, whereas the V2 one doesn't
+# Some models such as LDSR require VQ to work correctly
+# The classes are suffixed with "V1" and added back to the "ldm.models.diffusion.ddpm" module
+
+import torch
+import torch.nn as nn
+import numpy as np
+import pytorch_lightning as pl
+from torch.optim.lr_scheduler import LambdaLR
+from einops import rearrange, repeat
+from contextlib import contextmanager
+from functools import partial
+from tqdm import tqdm
+from torchvision.utils import make_grid
+from pytorch_lightning.utilities.distributed import rank_zero_only
+
+from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from ldm.modules.ema import LitEma
+from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
+from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
+from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from ldm.models.diffusion.ddim import DDIMSampler
+
+import ldm.models.diffusion.ddpm
+
+__conditioning_keys__ = {'concat': 'c_concat',
+                         'crossattn': 'c_crossattn',
+                         'adm': 'y'}
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+def uniform_on_device(r1, r2, shape, device):
+    return (r1 - r2) * torch.rand(*shape, device=device) + r2
+
+
+class DDPMV1(pl.LightningModule):
+    # classic DDPM with Gaussian diffusion, in image space
+    def __init__(self,
+                 unet_config,
+                 timesteps=1000,
+                 beta_schedule="linear",
+                 loss_type="l2",
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 load_only_unet=False,
+                 monitor="val/loss",
+                 use_ema=True,
+                 first_stage_key="image",
+                 image_size=256,
+                 channels=3,
+                 log_every_t=100,
+                 clip_denoised=True,
+                 linear_start=1e-4,
+                 linear_end=2e-2,
+                 cosine_s=8e-3,
+                 given_betas=None,
+                 original_elbo_weight=0.,
+                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+                 l_simple_weight=1.,
+                 conditioning_key=None,
+                 parameterization="eps",  # all assuming fixed variance schedules
+                 scheduler_config=None,
+                 use_positional_encodings=False,
+                 learn_logvar=False,
+                 logvar_init=0.,
+                 ):
+        super().__init__()
+        assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
+        self.parameterization = parameterization
+        print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+        self.cond_stage_model = None
+        self.clip_denoised = clip_denoised
+        self.log_every_t = log_every_t
+        self.first_stage_key = first_stage_key
+        self.image_size = image_size  # try conv?
+        self.channels = channels
+        self.use_positional_encodings = use_positional_encodings
+        self.model = DiffusionWrapperV1(unet_config, conditioning_key)
+        count_params(self.model, verbose=True)
+        self.use_ema = use_ema
+        if self.use_ema:
+            self.model_ema = LitEma(self.model)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        self.use_scheduler = scheduler_config is not None
+        if self.use_scheduler:
+            self.scheduler_config = scheduler_config
+
+        self.v_posterior = v_posterior
+        self.original_elbo_weight = original_elbo_weight
+        self.l_simple_weight = l_simple_weight
+
+        if monitor is not None:
+            self.monitor = monitor
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+
+        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+
+        self.loss_type = loss_type
+
+        self.learn_logvar = learn_logvar
+        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+        if self.learn_logvar:
+            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+
+
+    def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        if exists(given_betas):
+            betas = given_betas
+        else:
+            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+                                       cosine_s=cosine_s)
+        alphas = 1. - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.linear_start = linear_start
+        self.linear_end = linear_end
+        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+        to_torch = partial(torch.tensor, dtype=torch.float32)
+
+        self.register_buffer('betas', to_torch(betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
+                    1. - alphas_cumprod) + self.v_posterior * betas
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.register_buffer('posterior_variance', to_torch(posterior_variance))
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+        self.register_buffer('posterior_mean_coef1', to_torch(
+            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+        self.register_buffer('posterior_mean_coef2', to_torch(
+            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+        if self.parameterization == "eps":
+            lvlb_weights = self.betas ** 2 / (
+                        2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
+        elif self.parameterization == "x0":
+            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+        else:
+            raise NotImplementedError("mu not supported")
+        # TODO how to choose this term
+        lvlb_weights[0] = lvlb_weights[1]
+        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
+        assert not torch.isnan(self.lvlb_weights).all()
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.model.parameters())
+            self.model_ema.copy_to(self.model)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.model.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+        sd = torch.load(path, map_location="cpu")
+        if "state_dict" in list(sd.keys()):
+            sd = sd["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+            sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+        if len(unexpected) > 0:
+            print(f"Unexpected Keys: {unexpected}")
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
+        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+        return mean, variance, log_variance
+
+    def predict_start_from_noise(self, x_t, t, noise):
+        return (
+                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+        )
+
+    def q_posterior(self, x_start, x_t, t):
+        posterior_mean = (
+                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self, x, t, clip_denoised: bool):
+        model_out = self.model(x, t)
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+        b, *_, device = *x.shape, x.device
+        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+        noise = noise_like(x.shape, device, repeat_noise)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def p_sample_loop(self, shape, return_intermediates=False):
+        device = self.betas.device
+        b = shape[0]
+        img = torch.randn(shape, device=device)
+        intermediates = [img]
+        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
+            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
+                                clip_denoised=self.clip_denoised)
+            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
+                intermediates.append(img)
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+    @torch.no_grad()
+    def sample(self, batch_size=16, return_intermediates=False):
+        image_size = self.image_size
+        channels = self.channels
+        return self.p_sample_loop((batch_size, channels, image_size, image_size),
+                                  return_intermediates=return_intermediates)
+
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+    def get_loss(self, pred, target, mean=True):
+        if self.loss_type == 'l1':
+            loss = (target - pred).abs()
+            if mean:
+                loss = loss.mean()
+        elif self.loss_type == 'l2':
+            if mean:
+                loss = torch.nn.functional.mse_loss(target, pred)
+            else:
+                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
+        else:
+            raise NotImplementedError("unknown loss type '{loss_type}'")
+
+        return loss
+
+    def p_losses(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        model_out = self.model(x_noisy, t)
+
+        loss_dict = {}
+        if self.parameterization == "eps":
+            target = noise
+        elif self.parameterization == "x0":
+            target = x_start
+        else:
+            raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
+
+        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
+
+        log_prefix = 'train' if self.training else 'val'
+
+        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
+        loss_simple = loss.mean() * self.l_simple_weight
+
+        loss_vlb = (self.lvlb_weights[t] * loss).mean()
+        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
+
+        loss = loss_simple + self.original_elbo_weight * loss_vlb
+
+        loss_dict.update({f'{log_prefix}/loss': loss})
+
+        return loss, loss_dict
+
+    def forward(self, x, *args, **kwargs):
+        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
+        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
+        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+        return self.p_losses(x, t, *args, **kwargs)
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = rearrange(x, 'b h w c -> b c h w')
+        x = x.to(memory_format=torch.contiguous_format).float()
+        return x
+
+    def shared_step(self, batch):
+        x = self.get_input(batch, self.first_stage_key)
+        loss, loss_dict = self(x)
+        return loss, loss_dict
+
+    def training_step(self, batch, batch_idx):
+        loss, loss_dict = self.shared_step(batch)
+
+        self.log_dict(loss_dict, prog_bar=True,
+                      logger=True, on_step=True, on_epoch=True)
+
+        self.log("global_step", self.global_step,
+                 prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+        if self.use_scheduler:
+            lr = self.optimizers().param_groups[0]['lr']
+            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+        return loss
+
+    @torch.no_grad()
+    def validation_step(self, batch, batch_idx):
+        _, loss_dict_no_ema = self.shared_step(batch)
+        with self.ema_scope():
+            _, loss_dict_ema = self.shared_step(batch)
+            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
+        self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+        self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self.model)
+
+    def _get_rows_from_list(self, samples):
+        n_imgs_per_row = len(samples)
+        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
+        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+        return denoise_grid
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.first_stage_key)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        x = x.to(self.device)[:N]
+        log["inputs"] = x
+
+        # get diffusion row
+        diffusion_row = list()
+        x_start = x[:n_row]
+
+        for t in range(self.num_timesteps):
+            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                t = t.to(self.device).long()
+                noise = torch.randn_like(x_start)
+                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+                diffusion_row.append(x_noisy)
+
+        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+
+        if sample:
+            # get denoise row
+            with self.ema_scope("Plotting"):
+                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+
+            log["samples"] = samples
+            log["denoise_row"] = self._get_rows_from_list(denoise_row)
+
+        if return_keys:
+            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+                return log
+            else:
+                return {key: log[key] for key in return_keys}
+        return log
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        if self.learn_logvar:
+            params = params + [self.logvar]
+        opt = torch.optim.AdamW(params, lr=lr)
+        return opt
+
+
+class LatentDiffusionV1(DDPMV1):
+    """main class"""
+    def __init__(self,
+                 first_stage_config,
+                 cond_stage_config,
+                 num_timesteps_cond=None,
+                 cond_stage_key="image",
+                 cond_stage_trainable=False,
+                 concat_mode=True,
+                 cond_stage_forward=None,
+                 conditioning_key=None,
+                 scale_factor=1.0,
+                 scale_by_std=False,
+                 *args, **kwargs):
+        self.num_timesteps_cond = default(num_timesteps_cond, 1)
+        self.scale_by_std = scale_by_std
+        assert self.num_timesteps_cond <= kwargs['timesteps']
+        # for backwards compatibility after implementation of DiffusionWrapper
+        if conditioning_key is None:
+            conditioning_key = 'concat' if concat_mode else 'crossattn'
+        if cond_stage_config == '__is_unconditional__':
+            conditioning_key = None
+        ckpt_path = kwargs.pop("ckpt_path", None)
+        ignore_keys = kwargs.pop("ignore_keys", [])
+        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
+        self.concat_mode = concat_mode
+        self.cond_stage_trainable = cond_stage_trainable
+        self.cond_stage_key = cond_stage_key
+        try:
+            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+        except:
+            self.num_downs = 0
+        if not scale_by_std:
+            self.scale_factor = scale_factor
+        else:
+            self.register_buffer('scale_factor', torch.tensor(scale_factor))
+        self.instantiate_first_stage(first_stage_config)
+        self.instantiate_cond_stage(cond_stage_config)
+        self.cond_stage_forward = cond_stage_forward
+        self.clip_denoised = False
+        self.bbox_tokenizer = None  
+
+        self.restarted_from_ckpt = False
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys)
+            self.restarted_from_ckpt = True
+
+    def make_cond_schedule(self, ):
+        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+        self.cond_ids[:self.num_timesteps_cond] = ids
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
+        # only for very first batch
+        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
+            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
+            # set rescale weight to 1./std of encodings
+            print("### USING STD-RESCALING ###")
+            x = super().get_input(batch, self.first_stage_key)
+            x = x.to(self.device)
+            encoder_posterior = self.encode_first_stage(x)
+            z = self.get_first_stage_encoding(encoder_posterior).detach()
+            del self.scale_factor
+            self.register_buffer('scale_factor', 1. / z.flatten().std())
+            print(f"setting self.scale_factor to {self.scale_factor}")
+            print("### USING STD-RESCALING ###")
+
+    def register_schedule(self,
+                          given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
+
+        self.shorten_cond_schedule = self.num_timesteps_cond > 1
+        if self.shorten_cond_schedule:
+            self.make_cond_schedule()
+
+    def instantiate_first_stage(self, config):
+        model = instantiate_from_config(config)
+        self.first_stage_model = model.eval()
+        self.first_stage_model.train = disabled_train
+        for param in self.first_stage_model.parameters():
+            param.requires_grad = False
+
+    def instantiate_cond_stage(self, config):
+        if not self.cond_stage_trainable:
+            if config == "__is_first_stage__":
+                print("Using first stage also as cond stage.")
+                self.cond_stage_model = self.first_stage_model
+            elif config == "__is_unconditional__":
+                print(f"Training {self.__class__.__name__} as an unconditional model.")
+                self.cond_stage_model = None
+                # self.be_unconditional = True
+            else:
+                model = instantiate_from_config(config)
+                self.cond_stage_model = model.eval()
+                self.cond_stage_model.train = disabled_train
+                for param in self.cond_stage_model.parameters():
+                    param.requires_grad = False
+        else:
+            assert config != '__is_first_stage__'
+            assert config != '__is_unconditional__'
+            model = instantiate_from_config(config)
+            self.cond_stage_model = model
+
+    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
+        denoise_row = []
+        for zd in tqdm(samples, desc=desc):
+            denoise_row.append(self.decode_first_stage(zd.to(self.device),
+                                                            force_not_quantize=force_no_decoder_quantization))
+        n_imgs_per_row = len(denoise_row)
+        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
+        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+        return denoise_grid
+
+    def get_first_stage_encoding(self, encoder_posterior):
+        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+            z = encoder_posterior.sample()
+        elif isinstance(encoder_posterior, torch.Tensor):
+            z = encoder_posterior
+        else:
+            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+        return self.scale_factor * z
+
+    def get_learned_conditioning(self, c):
+        if self.cond_stage_forward is None:
+            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+                c = self.cond_stage_model.encode(c)
+                if isinstance(c, DiagonalGaussianDistribution):
+                    c = c.mode()
+            else:
+                c = self.cond_stage_model(c)
+        else:
+            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+        return c
+
+    def meshgrid(self, h, w):
+        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
+        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
+
+        arr = torch.cat([y, x], dim=-1)
+        return arr
+
+    def delta_border(self, h, w):
+        """
+        :param h: height
+        :param w: width
+        :return: normalized distance to image border,
+         wtith min distance = 0 at border and max dist = 0.5 at image center
+        """
+        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
+        arr = self.meshgrid(h, w) / lower_right_corner
+        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
+        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
+        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
+        return edge_dist
+
+    def get_weighting(self, h, w, Ly, Lx, device):
+        weighting = self.delta_border(h, w)
+        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
+                               self.split_input_params["clip_max_weight"], )
+        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+
+        if self.split_input_params["tie_braker"]:
+            L_weighting = self.delta_border(Ly, Lx)
+            L_weighting = torch.clip(L_weighting,
+                                     self.split_input_params["clip_min_tie_weight"],
+                                     self.split_input_params["clip_max_tie_weight"])
+
+            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
+            weighting = weighting * L_weighting
+        return weighting
+
+    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
+        """
+        :param x: img of size (bs, c, h, w)
+        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
+        """
+        bs, nc, h, w = x.shape
+
+        # number of crops in image
+        Ly = (h - kernel_size[0]) // stride[0] + 1
+        Lx = (w - kernel_size[1]) // stride[1] + 1
+
+        if uf == 1 and df == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
+
+            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
+
+        elif uf > 1 and df == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
+                                dilation=1, padding=0,
+                                stride=(stride[0] * uf, stride[1] * uf))
+            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
+
+            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
+
+        elif df > 1 and uf == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
+                                dilation=1, padding=0,
+                                stride=(stride[0] // df, stride[1] // df))
+            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
+
+            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
+
+        else:
+            raise NotImplementedError
+
+        return fold, unfold, normalization, weighting
+
+    @torch.no_grad()
+    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
+                  cond_key=None, return_original_cond=False, bs=None):
+        x = super().get_input(batch, k)
+        if bs is not None:
+            x = x[:bs]
+        x = x.to(self.device)
+        encoder_posterior = self.encode_first_stage(x)
+        z = self.get_first_stage_encoding(encoder_posterior).detach()
+
+        if self.model.conditioning_key is not None:
+            if cond_key is None:
+                cond_key = self.cond_stage_key
+            if cond_key != self.first_stage_key:
+                if cond_key in ['caption', 'coordinates_bbox']:
+                    xc = batch[cond_key]
+                elif cond_key == 'class_label':
+                    xc = batch
+                else:
+                    xc = super().get_input(batch, cond_key).to(self.device)
+            else:
+                xc = x
+            if not self.cond_stage_trainable or force_c_encode:
+                if isinstance(xc, dict) or isinstance(xc, list):
+                    # import pudb; pudb.set_trace()
+                    c = self.get_learned_conditioning(xc)
+                else:
+                    c = self.get_learned_conditioning(xc.to(self.device))
+            else:
+                c = xc
+            if bs is not None:
+                c = c[:bs]
+
+            if self.use_positional_encodings:
+                pos_x, pos_y = self.compute_latent_shifts(batch)
+                ckey = __conditioning_keys__[self.model.conditioning_key]
+                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
+
+        else:
+            c = None
+            xc = None
+            if self.use_positional_encodings:
+                pos_x, pos_y = self.compute_latent_shifts(batch)
+                c = {'pos_x': pos_x, 'pos_y': pos_y}
+        out = [z, c]
+        if return_first_stage_outputs:
+            xrec = self.decode_first_stage(z)
+            out.extend([x, xrec])
+        if return_original_cond:
+            out.append(xc)
+        return out
+
+    @torch.no_grad()
+    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+        if predict_cids:
+            if z.dim() == 4:
+                z = torch.argmax(z.exp(), dim=1).long()
+            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+            z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+        z = 1. / self.scale_factor * z
+
+        if hasattr(self, "split_input_params"):
+            if self.split_input_params["patch_distributed_vq"]:
+                ks = self.split_input_params["ks"]  # eg. (128, 128)
+                stride = self.split_input_params["stride"]  # eg. (64, 64)
+                uf = self.split_input_params["vqf"]
+                bs, nc, h, w = z.shape
+                if ks[0] > h or ks[1] > w:
+                    ks = (min(ks[0], h), min(ks[1], w))
+                    print("reducing Kernel")
+
+                if stride[0] > h or stride[1] > w:
+                    stride = (min(stride[0], h), min(stride[1], w))
+                    print("reducing stride")
+
+                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+
+                z = unfold(z)  # (bn, nc * prod(**ks), L)
+                # 1. Reshape to img shape
+                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                # 2. apply model loop over last dim
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
+                                                                 force_not_quantize=predict_cids or force_not_quantize)
+                                   for i in range(z.shape[-1])]
+                else:
+
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
+                                   for i in range(z.shape[-1])]
+
+                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
+                o = o * weighting
+                # Reverse 1. reshape to img shape
+                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+                # stitch crops together
+                decoded = fold(o)
+                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
+                return decoded
+            else:
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+                else:
+                    return self.first_stage_model.decode(z)
+
+        else:
+            if isinstance(self.first_stage_model, VQModelInterface):
+                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+            else:
+                return self.first_stage_model.decode(z)
+
+    # same as above but without decorator
+    def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+        if predict_cids:
+            if z.dim() == 4:
+                z = torch.argmax(z.exp(), dim=1).long()
+            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+            z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+        z = 1. / self.scale_factor * z
+
+        if hasattr(self, "split_input_params"):
+            if self.split_input_params["patch_distributed_vq"]:
+                ks = self.split_input_params["ks"]  # eg. (128, 128)
+                stride = self.split_input_params["stride"]  # eg. (64, 64)
+                uf = self.split_input_params["vqf"]
+                bs, nc, h, w = z.shape
+                if ks[0] > h or ks[1] > w:
+                    ks = (min(ks[0], h), min(ks[1], w))
+                    print("reducing Kernel")
+
+                if stride[0] > h or stride[1] > w:
+                    stride = (min(stride[0], h), min(stride[1], w))
+                    print("reducing stride")
+
+                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+
+                z = unfold(z)  # (bn, nc * prod(**ks), L)
+                # 1. Reshape to img shape
+                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                # 2. apply model loop over last dim
+                if isinstance(self.first_stage_model, VQModelInterface):  
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
+                                                                 force_not_quantize=predict_cids or force_not_quantize)
+                                   for i in range(z.shape[-1])]
+                else:
+
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
+                                   for i in range(z.shape[-1])]
+
+                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
+                o = o * weighting
+                # Reverse 1. reshape to img shape
+                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+                # stitch crops together
+                decoded = fold(o)
+                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
+                return decoded
+            else:
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+                else:
+                    return self.first_stage_model.decode(z)
+
+        else:
+            if isinstance(self.first_stage_model, VQModelInterface):
+                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+            else:
+                return self.first_stage_model.decode(z)
+
+    @torch.no_grad()
+    def encode_first_stage(self, x):
+        if hasattr(self, "split_input_params"):
+            if self.split_input_params["patch_distributed_vq"]:
+                ks = self.split_input_params["ks"]  # eg. (128, 128)
+                stride = self.split_input_params["stride"]  # eg. (64, 64)
+                df = self.split_input_params["vqf"]
+                self.split_input_params['original_image_size'] = x.shape[-2:]
+                bs, nc, h, w = x.shape
+                if ks[0] > h or ks[1] > w:
+                    ks = (min(ks[0], h), min(ks[1], w))
+                    print("reducing Kernel")
+
+                if stride[0] > h or stride[1] > w:
+                    stride = (min(stride[0], h), min(stride[1], w))
+                    print("reducing stride")
+
+                fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
+                z = unfold(x)  # (bn, nc * prod(**ks), L)
+                # Reshape to img shape
+                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
+                               for i in range(z.shape[-1])]
+
+                o = torch.stack(output_list, axis=-1)
+                o = o * weighting
+
+                # Reverse reshape to img shape
+                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+                # stitch crops together
+                decoded = fold(o)
+                decoded = decoded / normalization
+                return decoded
+
+            else:
+                return self.first_stage_model.encode(x)
+        else:
+            return self.first_stage_model.encode(x)
+
+    def shared_step(self, batch, **kwargs):
+        x, c = self.get_input(batch, self.first_stage_key)
+        loss = self(x, c)
+        return loss
+
+    def forward(self, x, c, *args, **kwargs):
+        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+        if self.model.conditioning_key is not None:
+            assert c is not None
+            if self.cond_stage_trainable:
+                c = self.get_learned_conditioning(c)
+            if self.shorten_cond_schedule:  # TODO: drop this option
+                tc = self.cond_ids[t].to(self.device)
+                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
+        return self.p_losses(x, c, t, *args, **kwargs)
+
+    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
+        def rescale_bbox(bbox):
+            x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
+            y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
+            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
+            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
+            return x0, y0, w, h
+
+        return [rescale_bbox(b) for b in bboxes]
+
+    def apply_model(self, x_noisy, t, cond, return_ids=False):
+
+        if isinstance(cond, dict):
+            # hybrid case, cond is exptected to be a dict
+            pass
+        else:
+            if not isinstance(cond, list):
+                cond = [cond]
+            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+            cond = {key: cond}
+
+        if hasattr(self, "split_input_params"):
+            assert len(cond) == 1  # todo can only deal with one conditioning atm
+            assert not return_ids  
+            ks = self.split_input_params["ks"]  # eg. (128, 128)
+            stride = self.split_input_params["stride"]  # eg. (64, 64)
+
+            h, w = x_noisy.shape[-2:]
+
+            fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
+
+            z = unfold(x_noisy)  # (bn, nc * prod(**ks), L)
+            # Reshape to img shape
+            z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+            z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
+
+            if self.cond_stage_key in ["image", "LR_image", "segmentation",
+                                       'bbox_img'] and self.model.conditioning_key:  # todo check for completeness
+                c_key = next(iter(cond.keys()))  # get key
+                c = next(iter(cond.values()))  # get value
+                assert (len(c) == 1)  # todo extend to list with more than one elem
+                c = c[0]  # get element
+
+                c = unfold(c)
+                c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
+
+            elif self.cond_stage_key == 'coordinates_bbox':
+                assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
+
+                # assuming padding of unfold is always 0 and its dilation is always 1
+                n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
+                full_img_h, full_img_w = self.split_input_params['original_image_size']
+                # as we are operating on latents, we need the factor from the original image size to the
+                # spatial latent size to properly rescale the crops for regenerating the bbox annotations
+                num_downs = self.first_stage_model.encoder.num_resolutions - 1
+                rescale_latent = 2 ** (num_downs)
+
+                # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
+                # need to rescale the tl patch coordinates to be in between (0,1)
+                tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
+                                         rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
+                                        for patch_nr in range(z.shape[-1])]
+
+                # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
+                patch_limits = [(x_tl, y_tl,
+                                 rescale_latent * ks[0] / full_img_w,
+                                 rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
+                # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
+
+                # tokenize crop coordinates for the bounding boxes of the respective patches
+                patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
+                                      for bbox in patch_limits]  # list of length l with tensors of shape (1, 2)
+                print(patch_limits_tknzd[0].shape)
+                # cut tknzd crop position from conditioning
+                assert isinstance(cond, dict), 'cond must be dict to be fed into model'
+                cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
+                print(cut_cond.shape)
+
+                adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
+                adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
+                print(adapted_cond.shape)
+                adapted_cond = self.get_learned_conditioning(adapted_cond)
+                print(adapted_cond.shape)
+                adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
+                print(adapted_cond.shape)
+
+                cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
+
+            else:
+                cond_list = [cond for i in range(z.shape[-1])]  # Todo make this more efficient
+
+            # apply model by loop over crops
+            output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
+            assert not isinstance(output_list[0],
+                                  tuple)  # todo cant deal with multiple model outputs check this never happens
+
+            o = torch.stack(output_list, axis=-1)
+            o = o * weighting
+            # Reverse reshape to img shape
+            o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+            # stitch crops together
+            x_recon = fold(o) / normalization
+
+        else:
+            x_recon = self.model(x_noisy, t, **cond)
+
+        if isinstance(x_recon, tuple) and not return_ids:
+            return x_recon[0]
+        else:
+            return x_recon
+
+    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
+               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+    def _prior_bpd(self, x_start):
+        """
+        Get the prior KL term for the variational lower-bound, measured in
+        bits-per-dim.
+        This term can't be optimized, as it only depends on the encoder.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :return: a batch of [N] KL values (in bits), one per batch element.
+        """
+        batch_size = x_start.shape[0]
+        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+        return mean_flat(kl_prior) / np.log(2.0)
+
+    def p_losses(self, x_start, cond, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        model_output = self.apply_model(x_noisy, t, cond)
+
+        loss_dict = {}
+        prefix = 'train' if self.training else 'val'
+
+        if self.parameterization == "x0":
+            target = x_start
+        elif self.parameterization == "eps":
+            target = noise
+        else:
+            raise NotImplementedError()
+
+        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
+        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
+
+        logvar_t = self.logvar[t].to(self.device)
+        loss = loss_simple / torch.exp(logvar_t) + logvar_t
+        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
+        if self.learn_logvar:
+            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
+            loss_dict.update({'logvar': self.logvar.data.mean()})
+
+        loss = self.l_simple_weight * loss.mean()
+
+        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
+        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
+        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
+        loss += (self.original_elbo_weight * loss_vlb)
+        loss_dict.update({f'{prefix}/loss': loss})
+
+        return loss, loss_dict
+
+    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
+                        return_x0=False, score_corrector=None, corrector_kwargs=None):
+        t_in = t
+        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
+
+        if score_corrector is not None:
+            assert self.parameterization == "eps"
+            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+        if return_codebook_ids:
+            model_out, logits = model_out
+
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        else:
+            raise NotImplementedError()
+
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+        if quantize_denoised:
+            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        if return_codebook_ids:
+            return model_mean, posterior_variance, posterior_log_variance, logits
+        elif return_x0:
+            return model_mean, posterior_variance, posterior_log_variance, x_recon
+        else:
+            return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
+                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
+                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
+        b, *_, device = *x.shape, x.device
+        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
+                                       return_codebook_ids=return_codebook_ids,
+                                       quantize_denoised=quantize_denoised,
+                                       return_x0=return_x0,
+                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+        if return_codebook_ids:
+            raise DeprecationWarning("Support dropped.")
+            model_mean, _, model_log_variance, logits = outputs
+        elif return_x0:
+            model_mean, _, model_log_variance, x0 = outputs
+        else:
+            model_mean, _, model_log_variance = outputs
+
+        noise = noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+        if return_codebook_ids:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
+        if return_x0:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+        else:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
+                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
+                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
+                              log_every_t=None):
+        if not log_every_t:
+            log_every_t = self.log_every_t
+        timesteps = self.num_timesteps
+        if batch_size is not None:
+            b = batch_size if batch_size is not None else shape[0]
+            shape = [batch_size] + list(shape)
+        else:
+            b = batch_size = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=self.device)
+        else:
+            img = x_T
+        intermediates = []
+        if cond is not None:
+            if isinstance(cond, dict):
+                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+            else:
+                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+
+        if start_T is not None:
+            timesteps = min(timesteps, start_T)
+        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
+                        total=timesteps) if verbose else reversed(
+            range(0, timesteps))
+        if type(temperature) == float:
+            temperature = [temperature] * timesteps
+
+        for i in iterator:
+            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
+            if self.shorten_cond_schedule:
+                assert self.model.conditioning_key != 'hybrid'
+                tc = self.cond_ids[ts].to(cond.device)
+                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+            img, x0_partial = self.p_sample(img, cond, ts,
+                                            clip_denoised=self.clip_denoised,
+                                            quantize_denoised=quantize_denoised, return_x0=True,
+                                            temperature=temperature[i], noise_dropout=noise_dropout,
+                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+            if mask is not None:
+                assert x0 is not None
+                img_orig = self.q_sample(x0, ts)
+                img = img_orig * mask + (1. - mask) * img
+
+            if i % log_every_t == 0 or i == timesteps - 1:
+                intermediates.append(x0_partial)
+            if callback: callback(i)
+            if img_callback: img_callback(img, i)
+        return img, intermediates
+
+    @torch.no_grad()
+    def p_sample_loop(self, cond, shape, return_intermediates=False,
+                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, start_T=None,
+                      log_every_t=None):
+
+        if not log_every_t:
+            log_every_t = self.log_every_t
+        device = self.betas.device
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+
+        intermediates = [img]
+        if timesteps is None:
+            timesteps = self.num_timesteps
+
+        if start_T is not None:
+            timesteps = min(timesteps, start_T)
+        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
+            range(0, timesteps))
+
+        if mask is not None:
+            assert x0 is not None
+            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
+
+        for i in iterator:
+            ts = torch.full((b,), i, device=device, dtype=torch.long)
+            if self.shorten_cond_schedule:
+                assert self.model.conditioning_key != 'hybrid'
+                tc = self.cond_ids[ts].to(cond.device)
+                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+            img = self.p_sample(img, cond, ts,
+                                clip_denoised=self.clip_denoised,
+                                quantize_denoised=quantize_denoised)
+            if mask is not None:
+                img_orig = self.q_sample(x0, ts)
+                img = img_orig * mask + (1. - mask) * img
+
+            if i % log_every_t == 0 or i == timesteps - 1:
+                intermediates.append(img)
+            if callback: callback(i)
+            if img_callback: img_callback(img, i)
+
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+    @torch.no_grad()
+    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
+               verbose=True, timesteps=None, quantize_denoised=False,
+               mask=None, x0=None, shape=None,**kwargs):
+        if shape is None:
+            shape = (batch_size, self.channels, self.image_size, self.image_size)
+        if cond is not None:
+            if isinstance(cond, dict):
+                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+            else:
+                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+        return self.p_sample_loop(cond,
+                                  shape,
+                                  return_intermediates=return_intermediates, x_T=x_T,
+                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
+                                  mask=mask, x0=x0)
+
+    @torch.no_grad()
+    def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
+
+        if ddim:
+            ddim_sampler = DDIMSampler(self)
+            shape = (self.channels, self.image_size, self.image_size)
+            samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
+                                                        shape,cond,verbose=False,**kwargs)
+
+        else:
+            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
+                                                 return_intermediates=True,**kwargs)
+
+        return samples, intermediates
+
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+                   plot_diffusion_rows=True, **kwargs):
+
+        use_ddim = ddim_steps is not None
+
+        log = dict()
+        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
+                                           return_first_stage_outputs=True,
+                                           force_c_encode=True,
+                                           return_original_cond=True,
+                                           bs=N)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        log["inputs"] = x
+        log["reconstruction"] = xrec
+        if self.model.conditioning_key is not None:
+            if hasattr(self.cond_stage_model, "decode"):
+                xc = self.cond_stage_model.decode(c)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ["caption"]:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
+                log["conditioning"] = xc
+            elif self.cond_stage_key == 'class_label':
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
+                log['conditioning'] = xc
+            elif isimage(xc):
+                log["conditioning"] = xc
+            if ismap(xc):
+                log["original_conditioning"] = self.to_rgb(xc)
+
+        if plot_diffusion_rows:
+            # get diffusion row
+            diffusion_row = list()
+            z_start = z[:n_row]
+            for t in range(self.num_timesteps):
+                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                    t = t.to(self.device).long()
+                    noise = torch.randn_like(z_start)
+                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+                    diffusion_row.append(self.decode_first_stage(z_noisy))
+
+            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
+            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+            log["diffusion_row"] = diffusion_grid
+
+        if sample:
+            # get denoise row
+            with self.ema_scope("Plotting"):
+                samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
+                                                         ddim_steps=ddim_steps,eta=ddim_eta)
+                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+            x_samples = self.decode_first_stage(samples)
+            log["samples"] = x_samples
+            if plot_denoise_rows:
+                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+                log["denoise_row"] = denoise_grid
+
+            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
+                    self.first_stage_model, IdentityFirstStage):
+                # also display when quantizing x0 while sampling
+                with self.ema_scope("Plotting Quantized Denoised"):
+                    samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
+                                                             ddim_steps=ddim_steps,eta=ddim_eta,
+                                                             quantize_denoised=True)
+                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
+                    #                                      quantize_denoised=True)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_x0_quantized"] = x_samples
+
+            if inpaint:
+                # make a simple center square
+                b, h, w = z.shape[0], z.shape[2], z.shape[3]
+                mask = torch.ones(N, h, w).to(self.device)
+                # zeros will be filled in
+                mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
+                mask = mask[:, None, ...]
+                with self.ema_scope("Plotting Inpaint"):
+
+                    samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
+                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_inpainting"] = x_samples
+                log["mask"] = mask
+
+                # outpaint
+                with self.ema_scope("Plotting Outpaint"):
+                    samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
+                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_outpainting"] = x_samples
+
+        if plot_progressive_rows:
+            with self.ema_scope("Plotting Progressives"):
+                img, progressives = self.progressive_denoising(c,
+                                                               shape=(self.channels, self.image_size, self.image_size),
+                                                               batch_size=N)
+            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+            log["progressive_row"] = prog_row
+
+        if return_keys:
+            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+                return log
+            else:
+                return {key: log[key] for key in return_keys}
+        return log
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        if self.cond_stage_trainable:
+            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
+            params = params + list(self.cond_stage_model.parameters())
+        if self.learn_logvar:
+            print('Diffusion model optimizing logvar')
+            params.append(self.logvar)
+        opt = torch.optim.AdamW(params, lr=lr)
+        if self.use_scheduler:
+            assert 'target' in self.scheduler_config
+            scheduler = instantiate_from_config(self.scheduler_config)
+
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                }]
+            return [opt], scheduler
+        return opt
+
+    @torch.no_grad()
+    def to_rgb(self, x):
+        x = x.float()
+        if not hasattr(self, "colorize"):
+            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+        x = nn.functional.conv2d(x, weight=self.colorize)
+        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+        return x
+
+
+class DiffusionWrapperV1(pl.LightningModule):
+    def __init__(self, diff_model_config, conditioning_key):
+        super().__init__()
+        self.diffusion_model = instantiate_from_config(diff_model_config)
+        self.conditioning_key = conditioning_key
+        assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
+
+    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
+        if self.conditioning_key is None:
+            out = self.diffusion_model(x, t)
+        elif self.conditioning_key == 'concat':
+            xc = torch.cat([x] + c_concat, dim=1)
+            out = self.diffusion_model(xc, t)
+        elif self.conditioning_key == 'crossattn':
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(x, t, context=cc)
+        elif self.conditioning_key == 'hybrid':
+            xc = torch.cat([x] + c_concat, dim=1)
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(xc, t, context=cc)
+        elif self.conditioning_key == 'adm':
+            cc = c_crossattn[0]
+            out = self.diffusion_model(x, t, y=cc)
+        else:
+            raise NotImplementedError()
+
+        return out
+
+
+class Layout2ImgDiffusionV1(LatentDiffusionV1):
+    # TODO: move all layout-specific hacks to this class
+    def __init__(self, cond_stage_key, *args, **kwargs):
+        assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
+        super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
+
+    def log_images(self, batch, N=8, *args, **kwargs):
+        logs = super().log_images(batch=batch, N=N, *args, **kwargs)
+
+        key = 'train' if self.training else 'validation'
+        dset = self.trainer.datamodule.datasets[key]
+        mapper = dset.conditional_builders[self.cond_stage_key]
+
+        bbox_imgs = []
+        map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
+        for tknzd_bbox in batch[self.cond_stage_key][:N]:
+            bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
+            bbox_imgs.append(bboximg)
+
+        cond_img = torch.stack(bbox_imgs, dim=0)
+        logs['bbox_image'] = cond_img
+        return logs
+
+setattr(ldm.models.diffusion.ddpm, "DDPMV1", DDPMV1)
+setattr(ldm.models.diffusion.ddpm, "LatentDiffusionV1", LatentDiffusionV1)
+setattr(ldm.models.diffusion.ddpm, "DiffusionWrapperV1", DiffusionWrapperV1)
+setattr(ldm.models.diffusion.ddpm, "Layout2ImgDiffusionV1", Layout2ImgDiffusionV1)

extensions-builtin/Lora/extra_networks_lora.py

@@ -0,0 +1,26 @@
+from modules import extra_networks, shared
+import lora
+
+class ExtraNetworkLora(extra_networks.ExtraNetwork):
+    def __init__(self):
+        super().__init__('lora')
+
+    def activate(self, p, params_list):
+        additional = shared.opts.sd_lora
+
+        if additional != "None" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
+            p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
+            params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
+
+        names = []
+        multipliers = []
+        for params in params_list:
+            assert len(params.items) > 0
+
+            names.append(params.items[0])
+            multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)
+
+        lora.load_loras(names, multipliers)
+
+    def deactivate(self, p):
+        pass

extensions-builtin/Lora/lora.py

@@ -0,0 +1,366 @@
+import glob
+import os
+import re
+import torch
+from typing import Union
+
+from modules import shared, devices, sd_models, errors
+
+metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
+
+re_digits = re.compile(r"\d+")
+re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
+re_compiled = {}
+
+suffix_conversion = {
+    "attentions": {},
+    "resnets": {
+        "conv1": "in_layers_2",
+        "conv2": "out_layers_3",
+        "time_emb_proj": "emb_layers_1",
+        "conv_shortcut": "skip_connection",
+    }
+}
+
+
+def convert_diffusers_name_to_compvis(key, is_sd2):
+    def match(match_list, regex_text):
+        regex = re_compiled.get(regex_text)
+        if regex is None:
+            regex = re.compile(regex_text)
+            re_compiled[regex_text] = regex
+
+        r = re.match(regex, key)
+        if not r:
+            return False
+
+        match_list.clear()
+        match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
+        return True
+
+    m = []
+
+    if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+        return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
+
+    if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
+        suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
+        return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
+
+    if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+        return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
+
+    if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
+        return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
+
+    if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
+        return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
+
+    if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
+        if is_sd2:
+            if 'mlp_fc1' in m[1]:
+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
+            elif 'mlp_fc2' in m[1]:
+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
+            else:
+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
+
+        return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
+
+    return key
+
+
+class LoraOnDisk:
+    def __init__(self, name, filename):
+        self.name = name
+        self.filename = filename
+        self.metadata = {}
+
+        _, ext = os.path.splitext(filename)
+        if ext.lower() == ".safetensors":
+            try:
+                self.metadata = sd_models.read_metadata_from_safetensors(filename)
+            except Exception as e:
+                errors.display(e, f"reading lora {filename}")
+
+        if self.metadata:
+            m = {}
+            for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
+                m[k] = v
+
+            self.metadata = m
+
+        self.ssmd_cover_images = self.metadata.pop('ssmd_cover_images', None)  # those are cover images and they are too big to display in UI as text
+
+
+class LoraModule:
+    def __init__(self, name):
+        self.name = name
+        self.multiplier = 1.0
+        self.modules = {}
+        self.mtime = None
+
+
+class LoraUpDownModule:
+    def __init__(self):
+        self.up = None
+        self.down = None
+        self.alpha = None
+
+
+def assign_lora_names_to_compvis_modules(sd_model):
+    lora_layer_mapping = {}
+
+    for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
+        lora_name = name.replace(".", "_")
+        lora_layer_mapping[lora_name] = module
+        module.lora_layer_name = lora_name
+
+    for name, module in shared.sd_model.model.named_modules():
+        lora_name = name.replace(".", "_")
+        lora_layer_mapping[lora_name] = module
+        module.lora_layer_name = lora_name
+
+    sd_model.lora_layer_mapping = lora_layer_mapping
+
+
+def load_lora(name, filename):
+    lora = LoraModule(name)
+    lora.mtime = os.path.getmtime(filename)
+
+    sd = sd_models.read_state_dict(filename)
+
+    keys_failed_to_match = {}
+    is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping
+
+    for key_diffusers, weight in sd.items():
+        key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1)
+        key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2)
+
+        sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
+
+        if sd_module is None:
+            m = re_x_proj.match(key)
+            if m:
+                sd_module = shared.sd_model.lora_layer_mapping.get(m.group(1), None)
+
+        if sd_module is None:
+            keys_failed_to_match[key_diffusers] = key
+            continue
+
+        lora_module = lora.modules.get(key, None)
+        if lora_module is None:
+            lora_module = LoraUpDownModule()
+            lora.modules[key] = lora_module
+
+        if lora_key == "alpha":
+            lora_module.alpha = weight.item()
+            continue
+
+        if type(sd_module) == torch.nn.Linear:
+            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+        elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear:
+            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+        elif type(sd_module) == torch.nn.MultiheadAttention:
+            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+        elif type(sd_module) == torch.nn.Conv2d:
+            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
+        else:
+            print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
+            continue
+            assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
+
+        with torch.no_grad():
+            module.weight.copy_(weight)
+
+        module.to(device=devices.cpu, dtype=devices.dtype)
+
+        if lora_key == "lora_up.weight":
+            lora_module.up = module
+        elif lora_key == "lora_down.weight":
+            lora_module.down = module
+        else:
+            assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha'
+
+    if len(keys_failed_to_match) > 0:
+        print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}")
+
+    return lora
+
+
+def load_loras(names, multipliers=None):
+    already_loaded = {}
+
+    for lora in loaded_loras:
+        if lora.name in names:
+            already_loaded[lora.name] = lora
+
+    loaded_loras.clear()
+
+    loras_on_disk = [available_loras.get(name, None) for name in names]
+    if any([x is None for x in loras_on_disk]):
+        list_available_loras()
+
+        loras_on_disk = [available_loras.get(name, None) for name in names]
+
+    for i, name in enumerate(names):
+        lora = already_loaded.get(name, None)
+
+        lora_on_disk = loras_on_disk[i]
+        if lora_on_disk is not None:
+            if lora is None or os.path.getmtime(lora_on_disk.filename) > lora.mtime:
+                try:
+                    lora = load_lora(name, lora_on_disk.filename)
+                except Exception as e:
+                    errors.display(e, f"loading Lora {lora_on_disk.filename}")
+                    continue
+
+        if lora is None:
+            print(f"Couldn't find Lora with name {name}")
+            continue
+
+        lora.multiplier = multipliers[i] if multipliers else 1.0
+        loaded_loras.append(lora)
+
+
+def lora_calc_updown(lora, module, target):
+    with torch.no_grad():
+        up = module.up.weight.to(target.device, dtype=target.dtype)
+        down = module.down.weight.to(target.device, dtype=target.dtype)
+
+        if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
+            updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
+        else:
+            updown = up @ down
+
+        updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+
+        return updown
+
+
+def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+    """
+    Applies the currently selected set of Loras to the weights of torch layer self.
+    If weights already have this particular set of loras applied, does nothing.
+    If not, restores orginal weights from backup and alters weights according to loras.
+    """
+
+    lora_layer_name = getattr(self, 'lora_layer_name', None)
+    if lora_layer_name is None:
+        return
+
+    current_names = getattr(self, "lora_current_names", ())
+    wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras)
+
+    weights_backup = getattr(self, "lora_weights_backup", None)
+    if weights_backup is None:
+        if isinstance(self, torch.nn.MultiheadAttention):
+            weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
+        else:
+            weights_backup = self.weight.to(devices.cpu, copy=True)
+
+        self.lora_weights_backup = weights_backup
+
+    if current_names != wanted_names:
+        if weights_backup is not None:
+            if isinstance(self, torch.nn.MultiheadAttention):
+                self.in_proj_weight.copy_(weights_backup[0])
+                self.out_proj.weight.copy_(weights_backup[1])
+            else:
+                self.weight.copy_(weights_backup)
+
+        for lora in loaded_loras:
+            module = lora.modules.get(lora_layer_name, None)
+            if module is not None and hasattr(self, 'weight'):
+                self.weight += lora_calc_updown(lora, module, self.weight)
+                continue
+
+            module_q = lora.modules.get(lora_layer_name + "_q_proj", None)
+            module_k = lora.modules.get(lora_layer_name + "_k_proj", None)
+            module_v = lora.modules.get(lora_layer_name + "_v_proj", None)
+            module_out = lora.modules.get(lora_layer_name + "_out_proj", None)
+
+            if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
+                updown_q = lora_calc_updown(lora, module_q, self.in_proj_weight)
+                updown_k = lora_calc_updown(lora, module_k, self.in_proj_weight)
+                updown_v = lora_calc_updown(lora, module_v, self.in_proj_weight)
+                updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
+
+                self.in_proj_weight += updown_qkv
+                self.out_proj.weight += lora_calc_updown(lora, module_out, self.out_proj.weight)
+                continue
+
+            if module is None:
+                continue
+
+            print(f'failed to calculate lora weights for layer {lora_layer_name}')
+
+        setattr(self, "lora_current_names", wanted_names)
+
+
+def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
+    setattr(self, "lora_current_names", ())
+    setattr(self, "lora_weights_backup", None)
+
+
+def lora_Linear_forward(self, input):
+    lora_apply_weights(self)
+
+    return torch.nn.Linear_forward_before_lora(self, input)
+
+
+def lora_Linear_load_state_dict(self, *args, **kwargs):
+    lora_reset_cached_weight(self)
+
+    return torch.nn.Linear_load_state_dict_before_lora(self, *args, **kwargs)
+
+
+def lora_Conv2d_forward(self, input):
+    lora_apply_weights(self)
+
+    return torch.nn.Conv2d_forward_before_lora(self, input)
+
+
+def lora_Conv2d_load_state_dict(self, *args, **kwargs):
+    lora_reset_cached_weight(self)
+
+    return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs)
+
+
+def lora_MultiheadAttention_forward(self, *args, **kwargs):
+    lora_apply_weights(self)
+
+    return torch.nn.MultiheadAttention_forward_before_lora(self, *args, **kwargs)
+
+
+def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs):
+    lora_reset_cached_weight(self)
+
+    return torch.nn.MultiheadAttention_load_state_dict_before_lora(self, *args, **kwargs)
+
+
+def list_available_loras():
+    available_loras.clear()
+
+    os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
+
+    candidates = \
+        glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.pt'), recursive=True) + \
+        glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.safetensors'), recursive=True) + \
+        glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.ckpt'), recursive=True)
+
+    for filename in sorted(candidates, key=str.lower):
+        if os.path.isdir(filename):
+            continue
+
+        name = os.path.splitext(os.path.basename(filename))[0]
+
+        available_loras[name] = LoraOnDisk(name, filename)
+
+
+available_loras = {}
+loaded_loras = []
+
+list_available_loras()

extensions-builtin/Lora/preload.py

@@ -0,0 +1,6 @@
+import os
+from modules import paths
+
+
+def preload(parser):
+    parser.add_argument("--lora-dir", type=str, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))

extensions-builtin/Lora/scripts/lora_script.py

@@ -0,0 +1,56 @@
+import torch
+import gradio as gr
+
+import lora
+import extra_networks_lora
+import ui_extra_networks_lora
+from modules import script_callbacks, ui_extra_networks, extra_networks, shared
+
+
+def unload():
+    torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
+    torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
+    torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
+    torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora
+    torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora
+    torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora
+
+
+def before_ui():
+    ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
+    extra_networks.register_extra_network(extra_networks_lora.ExtraNetworkLora())
+
+
+if not hasattr(torch.nn, 'Linear_forward_before_lora'):
+    torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward
+
+if not hasattr(torch.nn, 'Linear_load_state_dict_before_lora'):
+    torch.nn.Linear_load_state_dict_before_lora = torch.nn.Linear._load_from_state_dict
+
+if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
+    torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward
+
+if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'):
+    torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict
+
+if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lora'):
+    torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward
+
+if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'):
+    torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict
+
+torch.nn.Linear.forward = lora.lora_Linear_forward
+torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict
+torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
+torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict
+torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward
+torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict
+
+script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
+script_callbacks.on_script_unloaded(unload)
+script_callbacks.on_before_ui(before_ui)
+
+
+shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
+    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
+}))

extensions-builtin/Lora/ui_extra_networks_lora.py

@@ -0,0 +1,31 @@
+import json
+import os
+import lora
+
+from modules import shared, ui_extra_networks
+
+
+class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
+    def __init__(self):
+        super().__init__('Lora')
+
+    def refresh(self):
+        lora.list_available_loras()
+
+    def list_items(self):
+        for name, lora_on_disk in lora.available_loras.items():
+            path, ext = os.path.splitext(lora_on_disk.filename)
+            yield {
+                "name": name,
+                "filename": path,
+                "preview": self.find_preview(path),
+                "description": self.find_description(path),
+                "search_term": self.search_terms_from_path(lora_on_disk.filename),
+                "prompt": json.dumps(f"<lora:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
+                "local_preview": f"{path}.{shared.opts.samples_format}",
+                "metadata": json.dumps(lora_on_disk.metadata, indent=4) if lora_on_disk.metadata else None,
+            }
+
+    def allowed_directories_for_previews(self):
+        return [shared.cmd_opts.lora_dir]
+

extensions-builtin/ScuNET/preload.py

@@ -0,0 +1,6 @@
+import os
+from modules import paths
+
+
+def preload(parser):
+    parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(paths.models_path, 'ScuNET'))

extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -0,0 +1,140 @@
+import os.path
+import sys
+import traceback
+
+import PIL.Image
+import numpy as np
+import torch
+from tqdm import tqdm
+
+from basicsr.utils.download_util import load_file_from_url
+
+import modules.upscaler
+from modules import devices, modelloader
+from scunet_model_arch import SCUNet as net
+from modules.shared import opts
+from modules import images
+
+
+class UpscalerScuNET(modules.upscaler.Upscaler):
+    def __init__(self, dirname):
+        self.name = "ScuNET"
+        self.model_name = "ScuNET GAN"
+        self.model_name2 = "ScuNET PSNR"
+        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
+        self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
+        self.user_path = dirname
+        super().__init__()
+        model_paths = self.find_models(ext_filter=[".pth"])
+        scalers = []
+        add_model2 = True
+        for file in model_paths:
+            if "http" in file:
+                name = self.model_name
+            else:
+                name = modelloader.friendly_name(file)
+            if name == self.model_name2 or file == self.model_url2:
+                add_model2 = False
+            try:
+                scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
+                scalers.append(scaler_data)
+            except Exception:
+                print(f"Error loading ScuNET model: {file}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+        if add_model2:
+            scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
+            scalers.append(scaler_data2)
+        self.scalers = scalers
+
+    @staticmethod
+    @torch.no_grad()
+    def tiled_inference(img, model):
+        # test the image tile by tile
+        h, w = img.shape[2:]
+        tile = opts.SCUNET_tile
+        tile_overlap = opts.SCUNET_tile_overlap
+        if tile == 0:
+            return model(img)
+
+        device = devices.get_device_for('scunet')
+        assert tile % 8 == 0, "tile size should be a multiple of window_size"
+        sf = 1
+
+        stride = tile - tile_overlap
+        h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
+        w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
+        E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
+        W = torch.zeros_like(E, dtype=devices.dtype, device=device)
+
+        with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
+            for h_idx in h_idx_list:
+
+                for w_idx in w_idx_list:
+
+                    in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+
+                    out_patch = model(in_patch)
+                    out_patch_mask = torch.ones_like(out_patch)
+
+                    E[
+                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                    ].add_(out_patch)
+                    W[
+                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                    ].add_(out_patch_mask)
+                    pbar.update(1)
+        output = E.div_(W)
+
+        return output
+
+    def do_upscale(self, img: PIL.Image.Image, selected_file):
+
+        torch.cuda.empty_cache()
+
+        model = self.load_model(selected_file)
+        if model is None:
+            print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
+            return img
+
+        device = devices.get_device_for('scunet')
+        tile = opts.SCUNET_tile
+        h, w = img.height, img.width
+        np_img = np.array(img)
+        np_img = np_img[:, :, ::-1]  # RGB to BGR
+        np_img = np_img.transpose((2, 0, 1)) / 255  # HWC to CHW
+        torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device)  # type: ignore
+
+        if tile > h or tile > w:
+            _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
+            _img[:, :, :h, :w] = torch_img # pad image
+            torch_img = _img
+
+        torch_output = self.tiled_inference(torch_img, model).squeeze(0)
+        torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
+        np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
+        del torch_img, torch_output
+        torch.cuda.empty_cache()
+
+        output = np_output.transpose((1, 2, 0))  # CHW to HWC
+        output = output[:, :, ::-1]  # BGR to RGB
+        return PIL.Image.fromarray((output * 255).astype(np.uint8))
+
+    def load_model(self, path: str):
+        device = devices.get_device_for('scunet')
+        if "http" in path:
+            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
+                                          progress=True)
+        else:
+            filename = path
+        if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
+            print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
+            return None
+
+        model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
+        model.load_state_dict(torch.load(filename), strict=True)
+        model.eval()
+        for k, v in model.named_parameters():
+            v.requires_grad = False
+        model = model.to(device)
+
+        return model

extensions-builtin/ScuNET/scunet_model_arch.py

@@ -0,0 +1,265 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from einops.layers.torch import Rearrange
+from timm.models.layers import trunc_normal_, DropPath
+
+
+class WMSA(nn.Module):
+    """ Self-attention module in Swin Transformer
+    """
+
+    def __init__(self, input_dim, output_dim, head_dim, window_size, type):
+        super(WMSA, self).__init__()
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.head_dim = head_dim
+        self.scale = self.head_dim ** -0.5
+        self.n_heads = input_dim // head_dim
+        self.window_size = window_size
+        self.type = type
+        self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
+
+        self.relative_position_params = nn.Parameter(
+            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
+
+        self.linear = nn.Linear(self.input_dim, self.output_dim)
+
+        trunc_normal_(self.relative_position_params, std=.02)
+        self.relative_position_params = torch.nn.Parameter(
+            self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
+                                                                                                                 2).transpose(
+                0, 1))
+
+    def generate_mask(self, h, w, p, shift):
+        """ generating the mask of SW-MSA
+        Args:
+            shift: shift parameters in CyclicShift.
+        Returns:
+            attn_mask: should be (1 1 w p p),
+        """
+        # supporting square.
+        attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
+        if self.type == 'W':
+            return attn_mask
+
+        s = p - shift
+        attn_mask[-1, :, :s, :, s:, :] = True
+        attn_mask[-1, :, s:, :, :s, :] = True
+        attn_mask[:, -1, :, :s, :, s:] = True
+        attn_mask[:, -1, :, s:, :, :s] = True
+        attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
+        return attn_mask
+
+    def forward(self, x):
+        """ Forward pass of Window Multi-head Self-attention module.
+        Args:
+            x: input tensor with shape of [b h w c];
+            attn_mask: attention mask, fill -inf where the value is True;
+        Returns:
+            output: tensor shape [b h w c]
+        """
+        if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
+        x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
+        h_windows = x.size(1)
+        w_windows = x.size(2)
+        # square validation
+        # assert h_windows == w_windows
+
+        x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
+        qkv = self.embedding_layer(x)
+        q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
+        sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
+        # Adding learnable relative embedding
+        sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
+        # Using Attn Mask to distinguish different subwindows.
+        if self.type != 'W':
+            attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
+            sim = sim.masked_fill_(attn_mask, float("-inf"))
+
+        probs = nn.functional.softmax(sim, dim=-1)
+        output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
+        output = rearrange(output, 'h b w p c -> b w p (h c)')
+        output = self.linear(output)
+        output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
+
+        if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
+                                                 dims=(1, 2))
+        return output
+
+    def relative_embedding(self):
+        cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
+        relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
+        # negative is allowed
+        return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
+
+
+class Block(nn.Module):
+    def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
+        """ SwinTransformer Block
+        """
+        super(Block, self).__init__()
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        assert type in ['W', 'SW']
+        self.type = type
+        if input_resolution <= window_size:
+            self.type = 'W'
+
+        self.ln1 = nn.LayerNorm(input_dim)
+        self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.ln2 = nn.LayerNorm(input_dim)
+        self.mlp = nn.Sequential(
+            nn.Linear(input_dim, 4 * input_dim),
+            nn.GELU(),
+            nn.Linear(4 * input_dim, output_dim),
+        )
+
+    def forward(self, x):
+        x = x + self.drop_path(self.msa(self.ln1(x)))
+        x = x + self.drop_path(self.mlp(self.ln2(x)))
+        return x
+
+
+class ConvTransBlock(nn.Module):
+    def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
+        """ SwinTransformer and Conv Block
+        """
+        super(ConvTransBlock, self).__init__()
+        self.conv_dim = conv_dim
+        self.trans_dim = trans_dim
+        self.head_dim = head_dim
+        self.window_size = window_size
+        self.drop_path = drop_path
+        self.type = type
+        self.input_resolution = input_resolution
+
+        assert self.type in ['W', 'SW']
+        if self.input_resolution <= self.window_size:
+            self.type = 'W'
+
+        self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
+                                 self.type, self.input_resolution)
+        self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
+        self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
+
+        self.conv_block = nn.Sequential(
+            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
+            nn.ReLU(True),
+            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
+        )
+
+    def forward(self, x):
+        conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
+        conv_x = self.conv_block(conv_x) + conv_x
+        trans_x = Rearrange('b c h w -> b h w c')(trans_x)
+        trans_x = self.trans_block(trans_x)
+        trans_x = Rearrange('b h w c -> b c h w')(trans_x)
+        res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
+        x = x + res
+
+        return x
+
+
+class SCUNet(nn.Module):
+    # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
+    def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
+        super(SCUNet, self).__init__()
+        if config is None:
+            config = [2, 2, 2, 2, 2, 2, 2]
+        self.config = config
+        self.dim = dim
+        self.head_dim = 32
+        self.window_size = 8
+
+        # drop path rate for each layer
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
+
+        self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
+
+        begin = 0
+        self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
+                                       'W' if not i % 2 else 'SW', input_resolution)
+                        for i in range(config[0])] + \
+                       [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
+
+        begin += config[0]
+        self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
+                                       'W' if not i % 2 else 'SW', input_resolution // 2)
+                        for i in range(config[1])] + \
+                       [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
+
+        begin += config[1]
+        self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
+                                       'W' if not i % 2 else 'SW', input_resolution // 4)
+                        for i in range(config[2])] + \
+                       [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
+
+        begin += config[2]
+        self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
+                                      'W' if not i % 2 else 'SW', input_resolution // 8)
+                       for i in range(config[3])]
+
+        begin += config[3]
+        self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
+                     [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
+                                     'W' if not i % 2 else 'SW', input_resolution // 4)
+                      for i in range(config[4])]
+
+        begin += config[4]
+        self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
+                     [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
+                                     'W' if not i % 2 else 'SW', input_resolution // 2)
+                      for i in range(config[5])]
+
+        begin += config[5]
+        self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
+                     [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
+                                     'W' if not i % 2 else 'SW', input_resolution)
+                      for i in range(config[6])]
+
+        self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
+
+        self.m_head = nn.Sequential(*self.m_head)
+        self.m_down1 = nn.Sequential(*self.m_down1)
+        self.m_down2 = nn.Sequential(*self.m_down2)
+        self.m_down3 = nn.Sequential(*self.m_down3)
+        self.m_body = nn.Sequential(*self.m_body)
+        self.m_up3 = nn.Sequential(*self.m_up3)
+        self.m_up2 = nn.Sequential(*self.m_up2)
+        self.m_up1 = nn.Sequential(*self.m_up1)
+        self.m_tail = nn.Sequential(*self.m_tail)
+        # self.apply(self._init_weights)
+
+    def forward(self, x0):
+
+        h, w = x0.size()[-2:]
+        paddingBottom = int(np.ceil(h / 64) * 64 - h)
+        paddingRight = int(np.ceil(w / 64) * 64 - w)
+        x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
+
+        x1 = self.m_head(x0)
+        x2 = self.m_down1(x1)
+        x3 = self.m_down2(x2)
+        x4 = self.m_down3(x3)
+        x = self.m_body(x4)
+        x = self.m_up3(x + x4)
+        x = self.m_up2(x + x3)
+        x = self.m_up1(x + x2)
+        x = self.m_tail(x + x1)
+
+        x = x[..., :h, :w]
+
+        return x
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)

extensions-builtin/SwinIR/preload.py

@@ -0,0 +1,6 @@
+import os
+from modules import paths
+
+
+def preload(parser):
+    parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(paths.models_path, 'SwinIR'))

extensions-builtin/SwinIR/scripts/swinir_model.py

@@ -0,0 +1,178 @@
+import contextlib
+import os
+
+import numpy as np
+import torch
+from PIL import Image
+from basicsr.utils.download_util import load_file_from_url
+from tqdm import tqdm
+
+from modules import modelloader, devices, script_callbacks, shared
+from modules.shared import cmd_opts, opts, state
+from swinir_model_arch import SwinIR as net
+from swinir_model_arch_v2 import Swin2SR as net2
+from modules.upscaler import Upscaler, UpscalerData
+
+
+device_swinir = devices.get_device_for('swinir')
+
+
+class UpscalerSwinIR(Upscaler):
+    def __init__(self, dirname):
+        self.name = "SwinIR"
+        self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \
+                         "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
+                         "-L_x4_GAN.pth "
+        self.model_name = "SwinIR 4x"
+        self.user_path = dirname
+        super().__init__()
+        scalers = []
+        model_files = self.find_models(ext_filter=[".pt", ".pth"])
+        for model in model_files:
+            if "http" in model:
+                name = self.model_name
+            else:
+                name = modelloader.friendly_name(model)
+            model_data = UpscalerData(name, model, self)
+            scalers.append(model_data)
+        self.scalers = scalers
+
+    def do_upscale(self, img, model_file):
+        model = self.load_model(model_file)
+        if model is None:
+            return img
+        model = model.to(device_swinir, dtype=devices.dtype)
+        img = upscale(img, model)
+        try:
+            torch.cuda.empty_cache()
+        except:
+            pass
+        return img
+
+    def load_model(self, path, scale=4):
+        if "http" in path:
+            dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth")
+            filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True)
+        else:
+            filename = path
+        if filename is None or not os.path.exists(filename):
+            return None
+        if filename.endswith(".v2.pth"):
+            model = net2(
+            upscale=scale,
+            in_chans=3,
+            img_size=64,
+            window_size=8,
+            img_range=1.0,
+            depths=[6, 6, 6, 6, 6, 6],
+            embed_dim=180,
+            num_heads=[6, 6, 6, 6, 6, 6],
+            mlp_ratio=2,
+            upsampler="nearest+conv",
+            resi_connection="1conv",
+            )
+            params = None
+        else:
+            model = net(
+                upscale=scale,
+                in_chans=3,
+                img_size=64,
+                window_size=8,
+                img_range=1.0,
+                depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
+                embed_dim=240,
+                num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
+                mlp_ratio=2,
+                upsampler="nearest+conv",
+                resi_connection="3conv",
+            )
+            params = "params_ema"
+
+        pretrained_model = torch.load(filename)
+        if params is not None:
+            model.load_state_dict(pretrained_model[params], strict=True)
+        else:
+            model.load_state_dict(pretrained_model, strict=True)
+        return model
+
+
+def upscale(
+        img,
+        model,
+        tile=None,
+        tile_overlap=None,
+        window_size=8,
+        scale=4,
+):
+    tile = tile or opts.SWIN_tile
+    tile_overlap = tile_overlap or opts.SWIN_tile_overlap
+
+
+    img = np.array(img)
+    img = img[:, :, ::-1]
+    img = np.moveaxis(img, 2, 0) / 255
+    img = torch.from_numpy(img).float()
+    img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype)
+    with torch.no_grad(), devices.autocast():
+        _, _, h_old, w_old = img.size()
+        h_pad = (h_old // window_size + 1) * window_size - h_old
+        w_pad = (w_old // window_size + 1) * window_size - w_old
+        img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
+        img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
+        output = inference(img, model, tile, tile_overlap, window_size, scale)
+        output = output[..., : h_old * scale, : w_old * scale]
+        output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+        if output.ndim == 3:
+            output = np.transpose(
+                output[[2, 1, 0], :, :], (1, 2, 0)
+            )  # CHW-RGB to HCW-BGR
+        output = (output * 255.0).round().astype(np.uint8)  # float32 to uint8
+        return Image.fromarray(output, "RGB")
+
+
+def inference(img, model, tile, tile_overlap, window_size, scale):
+    # test the image tile by tile
+    b, c, h, w = img.size()
+    tile = min(tile, h, w)
+    assert tile % window_size == 0, "tile size should be a multiple of window_size"
+    sf = scale
+
+    stride = tile - tile_overlap
+    h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
+    w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
+    E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img)
+    W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir)
+
+    with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
+        for h_idx in h_idx_list:
+            if state.interrupted or state.skipped:
+                break
+
+            for w_idx in w_idx_list:
+                if state.interrupted or state.skipped:
+                    break
+                
+                in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+                out_patch = model(in_patch)
+                out_patch_mask = torch.ones_like(out_patch)
+
+                E[
+                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                ].add_(out_patch)
+                W[
+                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                ].add_(out_patch_mask)
+                pbar.update(1)
+    output = E.div_(W)
+
+    return output
+
+
+def on_ui_settings():
+    import gradio as gr
+
+    shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
+    shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
+
+
+script_callbacks.on_ui_settings(on_ui_settings)

extensions-builtin/SwinIR/swinir_model_arch.py

@@ -0,0 +1,867 @@
+# -----------------------------------------------------------------------------------
+# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
+# Originally Written by Ze Liu, Modified by Jingyun Liang.
+# -----------------------------------------------------------------------------------
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
+
+    def flops(self, N):
+        # calculate flops for 1 window with token length of N
+        flops = 0
+        # qkv = self.qkv(x)
+        flops += N * self.dim * 3 * self.dim
+        # attn = (q @ k.transpose(-2, -1))
+        flops += self.num_heads * N * (self.dim // self.num_heads) * N
+        #  x = (attn @ v)
+        flops += self.num_heads * N * N * (self.dim // self.num_heads)
+        # x = self.proj(x)
+        flops += N * self.dim * self.dim
+        return flops
+
+
+class SwinTransformerBlock(nn.Module):
+    r""" Swin Transformer Block.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if self.shift_size > 0:
+            attn_mask = self.calculate_mask(self.input_resolution)
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+
+    def calculate_mask(self, x_size):
+        # calculate attention mask for SW-MSA
+        H, W = x_size
+        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+        h_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        w_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+        return attn_mask
+
+    def forward(self, x, x_size):
+        H, W = x_size
+        B, L, C = x.shape
+        # assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+        else:
+            shifted_x = x
+
+        # partition windows
+        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
+        if self.input_resolution == x_size:
+            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
+        else:
+            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        nW = H * W / self.window_size / self.window_size
+        flops += nW * self.attn.flops(self.window_size * self.window_size)
+        # mlp
+        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+        # norm2
+        flops += self.dim * H * W
+        return flops
+
+
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x):
+        """
+        x: B, H*W, C
+        """
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, C)
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.dim
+        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+        return flops
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, window_size=window_size,
+                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                 drop=drop, attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                 norm_layer=norm_layer)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, x_size):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, x_size)
+            else:
+                x = blk(x, x_size)
+        if self.downsample is not None:
+            x = self.downsample(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        if self.downsample is not None:
+            flops += self.downsample.flops()
+        return flops
+
+
+class RSTB(nn.Module):
+    """Residual Swin Transformer Block (RSTB).
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        img_size: Input image size.
+        patch_size: Patch size.
+        resi_connection: The convolutional block before residual connection.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+                 img_size=224, patch_size=4, resi_connection='1conv'):
+        super(RSTB, self).__init__()
+
+        self.dim = dim
+        self.input_resolution = input_resolution
+
+        self.residual_group = BasicLayer(dim=dim,
+                                         input_resolution=input_resolution,
+                                         depth=depth,
+                                         num_heads=num_heads,
+                                         window_size=window_size,
+                                         mlp_ratio=mlp_ratio,
+                                         qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                         drop=drop, attn_drop=attn_drop,
+                                         drop_path=drop_path,
+                                         norm_layer=norm_layer,
+                                         downsample=downsample,
+                                         use_checkpoint=use_checkpoint)
+
+        if resi_connection == '1conv':
+            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
+                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
+            norm_layer=None)
+
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
+            norm_layer=None)
+
+    def forward(self, x, x_size):
+        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
+
+    def flops(self):
+        flops = 0
+        flops += self.residual_group.flops()
+        H, W = self.input_resolution
+        flops += H * W * self.dim * self.dim * 9
+        flops += self.patch_embed.flops()
+        flops += self.patch_unembed.flops()
+
+        return flops
+
+
+class PatchEmbed(nn.Module):
+    r""" Image to Patch Embedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+    def flops(self):
+        flops = 0
+        H, W = self.img_size
+        if self.norm is not None:
+            flops += H * W * self.embed_dim
+        return flops
+
+
+class PatchUnEmbed(nn.Module):
+    r""" Image to Patch Unembedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+    def forward(self, x, x_size):
+        B, HW, C = x.shape
+        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
+        return x
+
+    def flops(self):
+        flops = 0
+        return flops
+
+
+class Upsample(nn.Sequential):
+    """Upsample module.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+
+    def __init__(self, scale, num_feat):
+        m = []
+        if (scale & (scale - 1)) == 0:  # scale = 2^n
+            for _ in range(int(math.log(scale, 2))):
+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+                m.append(nn.PixelShuffle(2))
+        elif scale == 3:
+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+            m.append(nn.PixelShuffle(3))
+        else:
+            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+        super(Upsample, self).__init__(*m)
+
+
+class UpsampleOneStep(nn.Sequential):
+    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
+       Used in lightweight SR to save parameters.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+
+    """
+
+    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
+        self.num_feat = num_feat
+        self.input_resolution = input_resolution
+        m = []
+        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
+        m.append(nn.PixelShuffle(scale))
+        super(UpsampleOneStep, self).__init__(*m)
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.num_feat * 3 * 9
+        return flops
+
+
+class SwinIR(nn.Module):
+    r""" SwinIR
+        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
+
+    Args:
+        img_size (int | tuple(int)): Input image size. Default 64
+        patch_size (int | tuple(int)): Patch size. Default: 1
+        in_chans (int): Number of input image channels. Default: 3
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each Swin Transformer layer.
+        num_heads (tuple(int)): Number of attention heads in different layers.
+        window_size (int): Window size. Default: 7
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
+        drop_rate (float): Dropout rate. Default: 0
+        attn_drop_rate (float): Attention dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
+        img_range: Image range. 1. or 255.
+        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
+        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
+    """
+
+    def __init__(self, img_size=64, patch_size=1, in_chans=3,
+                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
+                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
+                 **kwargs):
+        super(SwinIR, self).__init__()
+        num_in_ch = in_chans
+        num_out_ch = in_chans
+        num_feat = 64
+        self.img_range = img_range
+        if in_chans == 3:
+            rgb_mean = (0.4488, 0.4371, 0.4040)
+            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
+        else:
+            self.mean = torch.zeros(1, 1, 1, 1)
+        self.upscale = upscale
+        self.upsampler = upsampler
+        self.window_size = window_size
+
+        #####################################################################################################
+        ################################### 1, shallow feature extraction ###################################
+        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
+
+        #####################################################################################################
+        ################################### 2, deep feature extraction ######################################
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.num_features = embed_dim
+        self.mlp_ratio = mlp_ratio
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+        num_patches = self.patch_embed.num_patches
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # merge non-overlapping patches into image
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        # absolute position embedding
+        if self.ape:
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+        # build Residual Swin Transformer blocks (RSTB)
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = RSTB(dim=embed_dim,
+                         input_resolution=(patches_resolution[0],
+                                           patches_resolution[1]),
+                         depth=depths[i_layer],
+                         num_heads=num_heads[i_layer],
+                         window_size=window_size,
+                         mlp_ratio=self.mlp_ratio,
+                         qkv_bias=qkv_bias, qk_scale=qk_scale,
+                         drop=drop_rate, attn_drop=attn_drop_rate,
+                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
+                         norm_layer=norm_layer,
+                         downsample=None,
+                         use_checkpoint=use_checkpoint,
+                         img_size=img_size,
+                         patch_size=patch_size,
+                         resi_connection=resi_connection
+
+                         )
+            self.layers.append(layer)
+        self.norm = norm_layer(self.num_features)
+
+        # build the last conv layer in deep feature extraction
+        if resi_connection == '1conv':
+            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
+
+        #####################################################################################################
+        ################################ 3, high quality image reconstruction ################################
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.upsample = Upsample(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR (to save parameters)
+            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
+                                            (patches_resolution[0], patches_resolution[1]))
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR (less artifacts)
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            if self.upscale == 4:
+                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'absolute_pos_embed'}
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'relative_position_bias_table'}
+
+    def check_image_size(self, x):
+        _, _, h, w = x.size()
+        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
+        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
+        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
+        return x
+
+    def forward_features(self, x):
+        x_size = (x.shape[2], x.shape[3])
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers:
+            x = layer(x, x_size)
+
+        x = self.norm(x)  # B L C
+        x = self.patch_unembed(x, x_size)
+
+        return x
+
+    def forward(self, x):
+        H, W = x.shape[2:]
+        x = self.check_image_size(x)
+        
+        self.mean = self.mean.type_as(x)
+        x = (x - self.mean) * self.img_range
+
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.conv_last(self.upsample(x))
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.upsample(x)
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            if self.upscale == 4:
+                x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            x = self.conv_last(self.lrelu(self.conv_hr(x)))
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            x_first = self.conv_first(x)
+            res = self.conv_after_body(self.forward_features(x_first)) + x_first
+            x = x + self.conv_last(res)
+
+        x = x / self.img_range + self.mean
+
+        return x[:, :, :H*self.upscale, :W*self.upscale]
+
+    def flops(self):
+        flops = 0
+        H, W = self.patches_resolution
+        flops += H * W * 3 * self.embed_dim * 9
+        flops += self.patch_embed.flops()
+        for i, layer in enumerate(self.layers):
+            flops += layer.flops()
+        flops += H * W * 3 * self.embed_dim * self.embed_dim
+        flops += self.upsample.flops()
+        return flops
+
+
+if __name__ == '__main__':
+    upscale = 4
+    window_size = 8
+    height = (1024 // upscale // window_size + 1) * window_size
+    width = (720 // upscale // window_size + 1) * window_size
+    model = SwinIR(upscale=2, img_size=(height, width),
+                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
+                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
+    print(model)
+    print(height, width, model.flops() / 1e9)
+
+    x = torch.randn((1, 3, height, width))
+    x = model(x)
+    print(x.shape)

extensions-builtin/SwinIR/swinir_model_arch_v2.py

@@ -0,0 +1,1017 @@
+# -----------------------------------------------------------------------------------
+# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/
+# Written by Conde and Choi et al.
+# -----------------------------------------------------------------------------------
+
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+        pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
+                 pretrained_window_size=[0, 0]):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.pretrained_window_size = pretrained_window_size
+        self.num_heads = num_heads
+
+        self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
+
+        # mlp to generate continuous relative position bias
+        self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
+                                     nn.ReLU(inplace=True),
+                                     nn.Linear(512, num_heads, bias=False))
+
+        # get relative_coords_table
+        relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
+        relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
+        relative_coords_table = torch.stack(
+            torch.meshgrid([relative_coords_h,
+                            relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0)  # 1, 2*Wh-1, 2*Ww-1, 2
+        if pretrained_window_size[0] > 0:
+            relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
+            relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
+        else:
+            relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
+            relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
+        relative_coords_table *= 8  # normalize to -8, 8
+        relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
+            torch.abs(relative_coords_table) + 1.0) / np.log2(8)
+
+        self.register_buffer("relative_coords_table", relative_coords_table)
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=False)
+        if qkv_bias:
+            self.q_bias = nn.Parameter(torch.zeros(dim))
+            self.v_bias = nn.Parameter(torch.zeros(dim))
+        else:
+            self.q_bias = None
+            self.v_bias = None
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv_bias = None
+        if self.q_bias is not None:
+            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        # cosine attention
+        attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
+        logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp()
+        attn = attn * logit_scale
+
+        relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
+        relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f'dim={self.dim}, window_size={self.window_size}, ' \
+               f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
+
+    def flops(self, N):
+        # calculate flops for 1 window with token length of N
+        flops = 0
+        # qkv = self.qkv(x)
+        flops += N * self.dim * 3 * self.dim
+        # attn = (q @ k.transpose(-2, -1))
+        flops += self.num_heads * N * (self.dim // self.num_heads) * N
+        #  x = (attn @ v)
+        flops += self.num_heads * N * N * (self.dim // self.num_heads)
+        # x = self.proj(x)
+        flops += N * self.dim * self.dim
+        return flops
+
+class SwinTransformerBlock(nn.Module):
+    r""" Swin Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resulotion.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+        pretrained_window_size (int): Window size in pre-training.
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
+            pretrained_window_size=to_2tuple(pretrained_window_size))
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if self.shift_size > 0:
+            attn_mask = self.calculate_mask(self.input_resolution)
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+        
+    def calculate_mask(self, x_size):
+        # calculate attention mask for SW-MSA
+        H, W = x_size
+        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+        h_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        w_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+        return attn_mask        
+
+    def forward(self, x, x_size):
+        H, W = x_size
+        B, L, C = x.shape
+        #assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = x.view(B, H, W, C)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+        else:
+            shifted_x = x
+
+        # partition windows
+        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
+        if self.input_resolution == x_size:
+            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
+        else:
+            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
+            
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+        x = x.view(B, H * W, C)
+        x = shortcut + self.drop_path(self.norm1(x))
+
+        # FFN
+        x = x + self.drop_path(self.norm2(self.mlp(x)))
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        nW = H * W / self.window_size / self.window_size
+        flops += nW * self.attn.flops(self.window_size * self.window_size)
+        # mlp
+        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+        # norm2
+        flops += self.dim * H * W
+        return flops
+
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(2 * dim)
+
+    def forward(self, x):
+        """
+        x: B, H*W, C
+        """
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, C)
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.reduction(x)
+        x = self.norm(x)
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+        flops += H * W * self.dim // 2
+        return flops    
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        pretrained_window_size (int): Local window size in pre-training.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+                 pretrained_window_size=0):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, window_size=window_size,
+                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias,
+                                 drop=drop, attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                 norm_layer=norm_layer,
+                                 pretrained_window_size=pretrained_window_size)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, x_size):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, x_size)
+            else:
+                x = blk(x, x_size)
+        if self.downsample is not None:
+            x = self.downsample(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        if self.downsample is not None:
+            flops += self.downsample.flops()
+        return flops
+
+    def _init_respostnorm(self):
+        for blk in self.blocks:
+            nn.init.constant_(blk.norm1.bias, 0)
+            nn.init.constant_(blk.norm1.weight, 0)
+            nn.init.constant_(blk.norm2.bias, 0)
+            nn.init.constant_(blk.norm2.weight, 0)
+            
+class PatchEmbed(nn.Module):
+    r""" Image to Patch Embedding
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        # FIXME look at relaxing size constraints
+        # assert H == self.img_size[0] and W == self.img_size[1],
+        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+    def flops(self):
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops           
+
+class RSTB(nn.Module):
+    """Residual Swin Transformer Block (RSTB).
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        img_size: Input image size.
+        patch_size: Patch size.
+        resi_connection: The convolutional block before residual connection.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+                 img_size=224, patch_size=4, resi_connection='1conv'):
+        super(RSTB, self).__init__()
+
+        self.dim = dim
+        self.input_resolution = input_resolution
+
+        self.residual_group = BasicLayer(dim=dim,
+                                         input_resolution=input_resolution,
+                                         depth=depth,
+                                         num_heads=num_heads,
+                                         window_size=window_size,
+                                         mlp_ratio=mlp_ratio,
+                                         qkv_bias=qkv_bias, 
+                                         drop=drop, attn_drop=attn_drop,
+                                         drop_path=drop_path,
+                                         norm_layer=norm_layer,
+                                         downsample=downsample,
+                                         use_checkpoint=use_checkpoint)
+
+        if resi_connection == '1conv':
+            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
+                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
+            norm_layer=None)
+
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
+            norm_layer=None)
+
+    def forward(self, x, x_size):
+        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
+
+    def flops(self):
+        flops = 0
+        flops += self.residual_group.flops()
+        H, W = self.input_resolution
+        flops += H * W * self.dim * self.dim * 9
+        flops += self.patch_embed.flops()
+        flops += self.patch_unembed.flops()
+
+        return flops
+
+class PatchUnEmbed(nn.Module):
+    r""" Image to Patch Unembedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+    def forward(self, x, x_size):
+        B, HW, C = x.shape
+        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
+        return x
+
+    def flops(self):
+        flops = 0
+        return flops
+
+
+class Upsample(nn.Sequential):
+    """Upsample module.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+
+    def __init__(self, scale, num_feat):
+        m = []
+        if (scale & (scale - 1)) == 0:  # scale = 2^n
+            for _ in range(int(math.log(scale, 2))):
+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+                m.append(nn.PixelShuffle(2))
+        elif scale == 3:
+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+            m.append(nn.PixelShuffle(3))
+        else:
+            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+        super(Upsample, self).__init__(*m)
+        
+class Upsample_hf(nn.Sequential):
+    """Upsample module.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+
+    def __init__(self, scale, num_feat):
+        m = []
+        if (scale & (scale - 1)) == 0:  # scale = 2^n
+            for _ in range(int(math.log(scale, 2))):
+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+                m.append(nn.PixelShuffle(2))
+        elif scale == 3:
+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+            m.append(nn.PixelShuffle(3))
+        else:
+            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+        super(Upsample_hf, self).__init__(*m)        
+
+
+class UpsampleOneStep(nn.Sequential):
+    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
+       Used in lightweight SR to save parameters.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+
+    """
+
+    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
+        self.num_feat = num_feat
+        self.input_resolution = input_resolution
+        m = []
+        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
+        m.append(nn.PixelShuffle(scale))
+        super(UpsampleOneStep, self).__init__(*m)
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.num_feat * 3 * 9
+        return flops
+    
+    
+
+class Swin2SR(nn.Module):
+    r""" Swin2SR
+        A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.
+
+    Args:
+        img_size (int | tuple(int)): Input image size. Default 64
+        patch_size (int | tuple(int)): Patch size. Default: 1
+        in_chans (int): Number of input image channels. Default: 3
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each Swin Transformer layer.
+        num_heads (tuple(int)): Number of attention heads in different layers.
+        window_size (int): Window size. Default: 7
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        drop_rate (float): Dropout rate. Default: 0
+        attn_drop_rate (float): Attention dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
+        img_range: Image range. 1. or 255.
+        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
+        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
+    """
+
+    def __init__(self, img_size=64, patch_size=1, in_chans=3,
+                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
+                 window_size=7, mlp_ratio=4., qkv_bias=True, 
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
+                 **kwargs):
+        super(Swin2SR, self).__init__()
+        num_in_ch = in_chans
+        num_out_ch = in_chans
+        num_feat = 64
+        self.img_range = img_range
+        if in_chans == 3:
+            rgb_mean = (0.4488, 0.4371, 0.4040)
+            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
+        else:
+            self.mean = torch.zeros(1, 1, 1, 1)
+        self.upscale = upscale
+        self.upsampler = upsampler
+        self.window_size = window_size
+
+        #####################################################################################################
+        ################################### 1, shallow feature extraction ###################################
+        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
+
+        #####################################################################################################
+        ################################### 2, deep feature extraction ######################################
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.num_features = embed_dim
+        self.mlp_ratio = mlp_ratio
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+        num_patches = self.patch_embed.num_patches
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # merge non-overlapping patches into image
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        # absolute position embedding
+        if self.ape:
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+        # build Residual Swin Transformer blocks (RSTB)
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = RSTB(dim=embed_dim,
+                         input_resolution=(patches_resolution[0],
+                                           patches_resolution[1]),
+                         depth=depths[i_layer],
+                         num_heads=num_heads[i_layer],
+                         window_size=window_size,
+                         mlp_ratio=self.mlp_ratio,
+                         qkv_bias=qkv_bias, 
+                         drop=drop_rate, attn_drop=attn_drop_rate,
+                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
+                         norm_layer=norm_layer,
+                         downsample=None,
+                         use_checkpoint=use_checkpoint,
+                         img_size=img_size,
+                         patch_size=patch_size,
+                         resi_connection=resi_connection
+
+                         )
+            self.layers.append(layer)
+            
+        if self.upsampler == 'pixelshuffle_hf':
+            self.layers_hf = nn.ModuleList()
+            for i_layer in range(self.num_layers):
+                layer = RSTB(dim=embed_dim,
+                             input_resolution=(patches_resolution[0],
+                                               patches_resolution[1]),
+                             depth=depths[i_layer],
+                             num_heads=num_heads[i_layer],
+                             window_size=window_size,
+                             mlp_ratio=self.mlp_ratio,
+                             qkv_bias=qkv_bias, 
+                             drop=drop_rate, attn_drop=attn_drop_rate,
+                             drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
+                             norm_layer=norm_layer,
+                             downsample=None,
+                             use_checkpoint=use_checkpoint,
+                             img_size=img_size,
+                             patch_size=patch_size,
+                             resi_connection=resi_connection
+
+                             )
+                self.layers_hf.append(layer)
+                        
+        self.norm = norm_layer(self.num_features)
+
+        # build the last conv layer in deep feature extraction
+        if resi_connection == '1conv':
+            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
+
+        #####################################################################################################
+        ################################ 3, high quality image reconstruction ################################
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.upsample = Upsample(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+        elif self.upsampler == 'pixelshuffle_aux':
+            self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+            self.conv_before_upsample = nn.Sequential(
+                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                nn.LeakyReLU(inplace=True))
+            self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.conv_after_aux = nn.Sequential(
+                nn.Conv2d(3, num_feat, 3, 1, 1),
+                nn.LeakyReLU(inplace=True))            
+            self.upsample = Upsample(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            
+        elif self.upsampler == 'pixelshuffle_hf':
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.upsample = Upsample(upscale, num_feat)
+            self.upsample_hf = Upsample_hf(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+            self.conv_before_upsample_hf = nn.Sequential(
+                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                nn.LeakyReLU(inplace=True))
+            self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR (to save parameters)
+            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
+                                            (patches_resolution[0], patches_resolution[1]))
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR (less artifacts)
+            assert self.upscale == 4, 'only support x4 now.'
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'absolute_pos_embed'}
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'relative_position_bias_table'}
+
+    def check_image_size(self, x):
+        _, _, h, w = x.size()
+        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
+        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
+        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
+        return x
+
+    def forward_features(self, x):
+        x_size = (x.shape[2], x.shape[3])
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers:
+            x = layer(x, x_size)
+
+        x = self.norm(x)  # B L C
+        x = self.patch_unembed(x, x_size)
+
+        return x
+    
+    def forward_features_hf(self, x):
+        x_size = (x.shape[2], x.shape[3])
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers_hf:
+            x = layer(x, x_size)
+
+        x = self.norm(x)  # B L C
+        x = self.patch_unembed(x, x_size)
+
+        return x    
+
+    def forward(self, x):
+        H, W = x.shape[2:]
+        x = self.check_image_size(x)
+
+        self.mean = self.mean.type_as(x)
+        x = (x - self.mean) * self.img_range
+
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.conv_last(self.upsample(x))
+        elif self.upsampler == 'pixelshuffle_aux':
+            bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False)
+            bicubic = self.conv_bicubic(bicubic)
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            aux = self.conv_aux(x) # b, 3, LR_H, LR_W
+            x = self.conv_after_aux(aux)
+            x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale]
+            x = self.conv_last(x)
+            aux = aux / self.img_range + self.mean
+        elif self.upsampler == 'pixelshuffle_hf':
+            # for classical SR with HF
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x_before = self.conv_before_upsample(x)
+            x_out = self.conv_last(self.upsample(x_before))
+            
+            x_hf = self.conv_first_hf(x_before)
+            x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
+            x_hf = self.conv_before_upsample_hf(x_hf)
+            x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
+            x = x_out + x_hf
+            x_hf = x_hf / self.img_range + self.mean
+
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.upsample(x)
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            x = self.conv_last(self.lrelu(self.conv_hr(x)))
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            x_first = self.conv_first(x)
+            res = self.conv_after_body(self.forward_features(x_first)) + x_first
+            x = x + self.conv_last(res)
+        
+        x = x / self.img_range + self.mean
+        if self.upsampler == "pixelshuffle_aux":
+            return x[:, :, :H*self.upscale, :W*self.upscale], aux
+        
+        elif self.upsampler == "pixelshuffle_hf":
+            x_out = x_out / self.img_range + self.mean
+            return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
+        
+        else:
+            return x[:, :, :H*self.upscale, :W*self.upscale]
+
+    def flops(self):
+        flops = 0
+        H, W = self.patches_resolution
+        flops += H * W * 3 * self.embed_dim * 9
+        flops += self.patch_embed.flops()
+        for i, layer in enumerate(self.layers):
+            flops += layer.flops()
+        flops += H * W * 3 * self.embed_dim * self.embed_dim
+        flops += self.upsample.flops()
+        return flops
+
+
+if __name__ == '__main__':
+    upscale = 4
+    window_size = 8
+    height = (1024 // upscale // window_size + 1) * window_size
+    width = (720 // upscale // window_size + 1) * window_size
+    model = Swin2SR(upscale=2, img_size=(height, width),
+                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
+                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
+    print(model)
+    print(height, width, model.flops() / 1e9)
+
+    x = torch.randn((1, 3, height, width))
+    x = model(x)
+    print(x.shape)

extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js

@@ -0,0 +1,46 @@
+// Stable Diffusion  - Bracket checker
+// By Hingashi no Florin/Bwin4L & @akx
+// Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
+// If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
+
+function checkBrackets(textArea, counterElt) {
+  var counts = {};
+  (textArea.value.match(/[(){}\[\]]/g) || []).forEach((bracket) => {
+    counts[bracket] = (counts[bracket] || 0) + 1;
+  });
+  var errors = [];
+
+  function checkPair(open, close, kind) {
+    if (counts[open] !== counts[close]) {
+      errors.push(
+        `${open}...${close} - Detected ${counts[open] || 0} opening and ${
+          counts[close] || 0
+        } closing ${kind}.`
+      );
+    }
+  }
+
+  checkPair("(", ")", "round brackets");
+  checkPair("[", "]", "square brackets");
+  checkPair("{", "}", "curly brackets");
+  counterElt.title = errors.join("\n");
+  counterElt.classList.toggle("error", errors.length !== 0);
+}
+
+function setupBracketChecking(id_prompt, id_counter) {
+  var textarea = gradioApp().querySelector(
+    "#" + id_prompt + " > label > textarea"
+  );
+  var counter = gradioApp().getElementById(id_counter);
+
+  if (textarea && counter) {
+    textarea.addEventListener("input", () => checkBrackets(textarea, counter));
+  }
+}
+
+onUiLoaded(function () {
+  setupBracketChecking("txt2img_prompt", "txt2img_token_counter");
+  setupBracketChecking("txt2img_neg_prompt", "txt2img_negative_token_counter");
+  setupBracketChecking("img2img_prompt", "img2img_token_counter");
+  setupBracketChecking("img2img_neg_prompt", "img2img_negative_token_counter");
+});

extensions/lite-kaggle-controlnet/.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,84 @@
+name: Bug Report
+description: Create a report
+title: "[Bug]: "
+labels: ["bug-report"]
+
+body:
+  - type: checkboxes
+    attributes:
+      label: Is there an existing issue for this?
+      description: Please search to see if an issue already exists for the bug you encountered, and that it hasn't been fixed in a recent build/commit.
+      options:
+        - label: I have searched the existing issues and checked the recent builds/commits of both this extension and the startfk
+          required: true
+  - type: markdown
+    attributes:
+      value: |
+        *Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible**
+  - type: textarea
+    id: what-did
+    attributes:
+      label: What happened?
+      description: Tell us what happened in a very clear and simple way
+    validations:
+      required: true
+  - type: textarea
+    id: steps
+    attributes:
+      label: Steps to reproduce the problem
+      description: Please provide us with precise step by step information on how to reproduce the bug
+      value: |
+        1. Go to .... 
+        2. Press ....
+        3. ...
+    validations:
+      required: true
+  - type: textarea
+    id: what-should
+    attributes:
+      label: What should have happened?
+      description: Tell what you think the normal behavior should be
+    validations:
+      required: true
+  - type: textarea
+    id: commits
+    attributes:
+      label: Commit where the problem happens
+      description: Which commit of the extension are you running on? Please include the commit of both the extension and the startfk (Do not write *Latest version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Commit** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)
+      value: |
+        startfk: 
+        controlnet:
+    validations:
+      required: true
+  - type: dropdown
+    id: browsers
+    attributes:
+      label: What browsers do you use to access the UI ?
+      multiple: true
+      options:
+        - Mozilla Firefox
+        - Google Chrome
+        - Brave
+        - Apple Safari
+        - Microsoft Edge
+  - type: textarea
+    id: cmdargs
+    attributes:
+      label: Command Line Arguments
+      description: Are you using any launching parameters/command line arguments (modified startfk-user .bat/.sh) ? If yes, please write them below. Write "No" otherwise.
+      render: Shell
+    validations:
+      required: true
+  - type: textarea
+    id: logs
+    attributes:
+      label: Console logs
+      description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after your bug happened. If it's very long, provide a link to pastebin or similar service.
+      render: Shell
+    validations:
+      required: true
+  - type: textarea
+    id: misc
+    attributes:
+      label: Additional information
+      description: Please provide us with any relevant additional info or context.

extensions/lite-kaggle-controlnet/.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1 @@
+blank_issues_enabled: true

extensions/lite-kaggle-controlnet/.gitignore

@@ -0,0 +1,171 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea
+*.pt
+*.pth
+*.ckpt
+*.bin
+*.safetensors
+
+# Editor setting metadata
+.idea/
+.vscode/
+detected_maps/
+annotator/downloads/

extensions/lite-kaggle-controlnet/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Kakigōri Maker
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

extensions/lite-kaggle-controlnet/annotator/annotator_path.py

@@ -0,0 +1,22 @@
+import os
+from modules import shared
+
+models_path = shared.opts.data.get('control_net_modules_path', None)
+if not models_path:
+    models_path = getattr(shared.cmd_opts, 'controlnet_annotator_models_path', None)
+if not models_path:
+    models_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'downloads')
+
+if not os.path.isabs(models_path):
+    models_path = os.path.join(shared.data_path, models_path)
+
+clip_vision_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'clip_vision')
+# clip vision is always inside controlnet "extensions\controlnet"
+# and any problem can be solved by removing controlnet and reinstall
+
+models_path = os.path.realpath(models_path)
+os.makedirs(models_path, exist_ok=True)
+print(f'ControlNet preprocessor location: {models_path}')
+# Make sure that the default location is inside controlnet "extensions\controlnet"
+# so that any problem can be solved by removing controlnet and reinstall
+# if users do not change configs on their own (otherwise users will know what is wrong)

extensions/lite-kaggle-controlnet/annotator/binary/__init__.py

@@ -0,0 +1,14 @@
+import cv2
+
+
+def apply_binary(img, bin_threshold):
+    img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+
+    if bin_threshold == 0 or bin_threshold == 255:
+        # Otsu's threshold
+        otsu_threshold, img_bin = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+        print("Otsu threshold:", otsu_threshold)
+    else:
+        _, img_bin = cv2.threshold(img_gray, bin_threshold, 255, cv2.THRESH_BINARY_INV)
+
+    return cv2.cvtColor(img_bin, cv2.COLOR_GRAY2RGB)

extensions/lite-kaggle-controlnet/annotator/canny/__init__.py

@@ -0,0 +1,5 @@
+import cv2
+
+
+def apply_canny(img, low_threshold, high_threshold):
+    return cv2.Canny(img, low_threshold, high_threshold)

extensions/lite-kaggle-controlnet/annotator/clip/__init__.py

@@ -0,0 +1,39 @@
+import torch
+from transformers import CLIPProcessor, CLIPVisionModel
+from modules import devices
+import os
+from annotator.annotator_path import clip_vision_path
+
+
+remote_model_path = "https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin"
+clip_path = clip_vision_path
+print(f'ControlNet ClipVision location: {clip_path}')
+
+clip_proc = None
+clip_vision_model = None
+
+
+def apply_clip(img):
+    global clip_proc, clip_vision_model
+    
+    if clip_vision_model is None:
+        modelpath = os.path.join(clip_path, 'pytorch_model.bin')
+        if not os.path.exists(modelpath):
+            from basicsr.utils.download_util import load_file_from_url
+            load_file_from_url(remote_model_path, model_dir=clip_path)
+
+        clip_proc = CLIPProcessor.from_pretrained(clip_path)
+        clip_vision_model = CLIPVisionModel.from_pretrained(clip_path)
+
+    with torch.no_grad():
+        clip_vision_model = clip_vision_model.to(devices.get_device_for("controlnet"))
+        style_for_clip = clip_proc(images=img, return_tensors="pt")['pixel_values']
+        style_feat = clip_vision_model(style_for_clip.to(devices.get_device_for("controlnet")))['last_hidden_state']
+
+    return style_feat
+
+
+def unload_clip_model():
+    global clip_proc, clip_vision_model
+    if clip_vision_model is not None:
+        clip_vision_model.cpu()

extensions/lite-kaggle-controlnet/annotator/clip_vision/config.json

@@ -0,0 +1,171 @@
+{
+  "_name_or_path": "clip-vit-large-patch14/",
+  "architectures": [
+    "CLIPModel"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
+  "projection_dim": 768,
+  "text_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "bos_token_id": 0,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 768,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 77,
+    "min_length": 0,
+    "model_type": "clip_text_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 12,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 1,
+    "prefix": null,
+    "problem_type": null,
+    "projection_dim" : 768,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.16.0.dev0",
+    "use_bfloat16": false,
+    "vocab_size": 49408
+  },
+  "text_config_dict": {
+    "hidden_size": 768,
+    "intermediate_size": 3072,
+    "num_attention_heads": 12,
+    "num_hidden_layers": 12,
+    "projection_dim": 768
+  },
+  "torch_dtype": "float32",
+  "transformers_version": null,
+  "vision_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "image_size": 224,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "clip_vision_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 24,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "patch_size": 14,
+    "prefix": null,
+    "problem_type": null,
+    "projection_dim" : 768,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.16.0.dev0",
+    "use_bfloat16": false
+  },
+  "vision_config_dict": {
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "patch_size": 14,
+    "projection_dim": 768
+  }
+}

extensions/lite-kaggle-controlnet/annotator/clip_vision/preprocessor_config.json

@@ -0,0 +1,19 @@
+{
+  "crop_size": 224,
+  "do_center_crop": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "size": 224
+}

extensions/lite-kaggle-controlnet/annotator/clip_vision/tokenizer_config.json

@@ -0,0 +1,34 @@
+{
+    "unk_token": {
+        "content": "<|endoftext|>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "bos_token": {
+        "content": "<|startoftext|>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "eos_token": {
+        "content": "<|endoftext|>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "pad_token": "<|endoftext|>",
+    "add_prefix_space": false,
+    "errors": "replace",
+    "do_lower_case": true,
+    "name_or_path": "openai/clip-vit-base-patch32",
+    "model_max_length": 77,
+    "special_tokens_map_file": "./special_tokens_map.json",
+    "tokenizer_class": "CLIPTokenizer"
+}

extensions/lite-kaggle-controlnet/annotator/color/__init__.py

@@ -0,0 +1,20 @@
+import cv2
+
+def cv2_resize_shortest_edge(image, size):
+    h, w = image.shape[:2]
+    if h < w:
+        new_h = size
+        new_w = int(round(w / h * size))
+    else:
+        new_w = size
+        new_h = int(round(h / w * size))
+    resized_image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_AREA)
+    return resized_image
+
+def apply_color(img, res=512):
+    img = cv2_resize_shortest_edge(img, res)
+    h, w = img.shape[:2]
+
+    input_img_color = cv2.resize(img, (w//64, h//64), interpolation=cv2.INTER_CUBIC)  
+    input_img_color = cv2.resize(input_img_color, (w, h), interpolation=cv2.INTER_NEAREST)
+    return input_img_color

extensions/lite-kaggle-controlnet/annotator/hed/__init__.py

@@ -0,0 +1,98 @@
+# This is an improved version and model of HED edge detection with Apache License, Version 2.0.
+# Please use this implementation in your products
+# This implementation may produce slightly different results from Saining Xie's official implementations,
+# but it generates smoother edges and is more suitable for ControlNet as well as other image-to-image translations.
+# Different from official models and other implementations, this is an RGB-input model (rather than BGR)
+# and in this way it works better for gradio's RGB protocol
+
+import os
+import cv2
+import torch
+import numpy as np
+
+from einops import rearrange
+import os
+from modules import devices
+from annotator.annotator_path import models_path
+from annotator.util import safe_step, nms
+
+
+class DoubleConvBlock(torch.nn.Module):
+    def __init__(self, input_channel, output_channel, layer_number):
+        super().__init__()
+        self.convs = torch.nn.Sequential()
+        self.convs.append(torch.nn.Conv2d(in_channels=input_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1))
+        for i in range(1, layer_number):
+            self.convs.append(torch.nn.Conv2d(in_channels=output_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1))
+        self.projection = torch.nn.Conv2d(in_channels=output_channel, out_channels=1, kernel_size=(1, 1), stride=(1, 1), padding=0)
+
+    def __call__(self, x, down_sampling=False):
+        h = x
+        if down_sampling:
+            h = torch.nn.functional.max_pool2d(h, kernel_size=(2, 2), stride=(2, 2))
+        for conv in self.convs:
+            h = conv(h)
+            h = torch.nn.functional.relu(h)
+        return h, self.projection(h)
+
+
+class ControlNetHED_Apache2(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.norm = torch.nn.Parameter(torch.zeros(size=(1, 3, 1, 1)))
+        self.block1 = DoubleConvBlock(input_channel=3, output_channel=64, layer_number=2)
+        self.block2 = DoubleConvBlock(input_channel=64, output_channel=128, layer_number=2)
+        self.block3 = DoubleConvBlock(input_channel=128, output_channel=256, layer_number=3)
+        self.block4 = DoubleConvBlock(input_channel=256, output_channel=512, layer_number=3)
+        self.block5 = DoubleConvBlock(input_channel=512, output_channel=512, layer_number=3)
+
+    def __call__(self, x):
+        h = x - self.norm
+        h, projection1 = self.block1(h)
+        h, projection2 = self.block2(h, down_sampling=True)
+        h, projection3 = self.block3(h, down_sampling=True)
+        h, projection4 = self.block4(h, down_sampling=True)
+        h, projection5 = self.block5(h, down_sampling=True)
+        return projection1, projection2, projection3, projection4, projection5
+
+
+netNetwork = None
+remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth"
+modeldir = os.path.join(models_path, "hed")
+old_modeldir = os.path.dirname(os.path.realpath(__file__))
+
+
+def apply_hed(input_image, is_safe=False):
+    global netNetwork
+    if netNetwork is None:
+        modelpath = os.path.join(modeldir, "ControlNetHED.pth")
+        old_modelpath = os.path.join(old_modeldir, "ControlNetHED.pth")
+        if os.path.exists(old_modelpath):
+            modelpath = old_modelpath
+        elif not os.path.exists(modelpath):
+            from basicsr.utils.download_util import load_file_from_url
+            load_file_from_url(remote_model_path, model_dir=modeldir)
+        netNetwork = ControlNetHED_Apache2().to(devices.get_device_for("controlnet"))
+        netNetwork.load_state_dict(torch.load(modelpath, map_location='cpu'))
+    netNetwork.to(devices.get_device_for("controlnet")).float().eval()
+
+    assert input_image.ndim == 3
+    H, W, C = input_image.shape
+    with torch.no_grad():
+        image_hed = torch.from_numpy(input_image.copy()).float().to(devices.get_device_for("controlnet"))
+        image_hed = rearrange(image_hed, 'h w c -> 1 c h w')
+        edges = netNetwork(image_hed)
+        edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges]
+        edges = [cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR) for e in edges]
+        edges = np.stack(edges, axis=2)
+        edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64)))
+        if is_safe:
+            edge = safe_step(edge)
+        edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
+        return edge
+
+    
+def unload_hed_model():
+    global netNetwork
+    if netNetwork is not None:
+        netNetwork.cpu()

extensions/lite-kaggle-controlnet/annotator/keypose/__init__.py

@@ -0,0 +1,212 @@
+import numpy as np
+import cv2
+import torch
+
+import os
+from modules import devices
+from annotator.annotator_path import models_path
+
+import mmcv
+from mmdet.apis import inference_detector, init_detector
+from mmpose.apis import inference_top_down_pose_model
+from mmpose.apis import init_pose_model, process_mmdet_results, vis_pose_result
+
+
+def preprocessing(image, device):
+    # Resize
+    scale = 640 / max(image.shape[:2])
+    image = cv2.resize(image, dsize=None, fx=scale, fy=scale)
+    raw_image = image.astype(np.uint8)
+
+    # Subtract mean values
+    image = image.astype(np.float32)
+    image -= np.array(
+        [
+            float(104.008),
+            float(116.669),
+            float(122.675),
+        ]
+    )
+
+    # Convert to torch.Tensor and add "batch" axis
+    image = torch.from_numpy(image.transpose(2, 0, 1)).float().unsqueeze(0)
+    image = image.to(device)
+
+    return image, raw_image
+
+
+def imshow_keypoints(img,
+                     pose_result,
+                     skeleton=None,
+                     kpt_score_thr=0.1,
+                     pose_kpt_color=None,
+                     pose_link_color=None,
+                     radius=4,
+                     thickness=1):
+    """Draw keypoints and links on an image.
+    Args:
+            img (ndarry): The image to draw poses on.
+            pose_result (list[kpts]): The poses to draw. Each element kpts is
+                a set of K keypoints as an Kx3 numpy.ndarray, where each
+                keypoint is represented as x, y, score.
+            kpt_score_thr (float, optional): Minimum score of keypoints
+                to be shown. Default: 0.3.
+            pose_kpt_color (np.array[Nx3]`): Color of N keypoints. If None,
+                the keypoint will not be drawn.
+            pose_link_color (np.array[Mx3]): Color of M links. If None, the
+                links will not be drawn.
+            thickness (int): Thickness of lines.
+    """
+
+    img_h, img_w, _ = img.shape
+    img = np.zeros(img.shape)
+
+    for idx, kpts in enumerate(pose_result):
+        if idx > 1:
+            continue
+        kpts = kpts['keypoints']
+        # print(kpts)
+        kpts = np.array(kpts, copy=False)
+
+        # draw each point on image
+        if pose_kpt_color is not None:
+            assert len(pose_kpt_color) == len(kpts)
+
+            for kid, kpt in enumerate(kpts):
+                x_coord, y_coord, kpt_score = int(kpt[0]), int(kpt[1]), kpt[2]
+
+                if kpt_score < kpt_score_thr or pose_kpt_color[kid] is None:
+                    # skip the point that should not be drawn
+                    continue
+
+                color = tuple(int(c) for c in pose_kpt_color[kid])
+                cv2.circle(img, (int(x_coord), int(y_coord)),
+                           radius, color, -1)
+
+        # draw links
+        if skeleton is not None and pose_link_color is not None:
+            assert len(pose_link_color) == len(skeleton)
+
+            for sk_id, sk in enumerate(skeleton):
+                pos1 = (int(kpts[sk[0], 0]), int(kpts[sk[0], 1]))
+                pos2 = (int(kpts[sk[1], 0]), int(kpts[sk[1], 1]))
+
+                if (pos1[0] <= 0 or pos1[0] >= img_w or pos1[1] <= 0 or pos1[1] >= img_h or pos2[0] <= 0
+                        or pos2[0] >= img_w or pos2[1] <= 0 or pos2[1] >= img_h or kpts[sk[0], 2] < kpt_score_thr
+                        or kpts[sk[1], 2] < kpt_score_thr or pose_link_color[sk_id] is None):
+                    # skip the link that should not be drawn
+                    continue
+                color = tuple(int(c) for c in pose_link_color[sk_id])
+                cv2.line(img, pos1, pos2, color, thickness=thickness)
+
+    return img
+
+
+human_det, pose_model = None, None
+det_model_path = "https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth"
+pose_model_path = "https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth"
+
+modeldir = os.path.join(models_path, "keypose")
+old_modeldir = os.path.dirname(os.path.realpath(__file__))
+
+det_config = 'faster_rcnn_r50_fpn_coco.py'
+pose_config = 'hrnet_w48_coco_256x192.py'
+
+det_checkpoint = 'faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
+pose_checkpoint = 'hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth'
+det_cat_id = 1
+bbox_thr = 0.2
+
+skeleton = [
+    [15, 13], [13, 11], [16, 14], [14, 12], [11, 12], [5, 11], [6, 12], [5, 6], [5, 7], [6, 8],
+    [7, 9], [8, 10],
+    [1, 2], [0, 1], [0, 2], [1, 3], [2, 4], [3, 5], [4, 6]
+]
+
+pose_kpt_color = [
+    [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255],
+    [0, 255, 0],
+    [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0],
+    [255, 128, 0],
+    [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0]
+]
+
+pose_link_color = [
+    [0, 255, 0], [0, 255, 0], [255, 128, 0], [255, 128, 0],
+    [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [0, 255, 0],
+    [255, 128, 0],
+    [0, 255, 0], [255, 128, 0], [51, 153, 255], [51, 153, 255], [51, 153, 255],
+    [51, 153, 255],
+    [51, 153, 255], [51, 153, 255], [51, 153, 255]
+]
+
+def find_download_model(checkpoint, remote_path):
+    modelpath = os.path.join(modeldir, checkpoint)
+    old_modelpath = os.path.join(old_modeldir, checkpoint)
+        
+    if os.path.exists(old_modelpath):
+        modelpath = old_modelpath
+    elif not os.path.exists(modelpath):
+        from basicsr.utils.download_util import load_file_from_url
+        load_file_from_url(remote_path, model_dir=modeldir)
+        
+    return modelpath
+
+def apply_keypose(input_image):
+    global human_det, pose_model
+    if netNetwork is None:
+        det_model_local = find_download_model(det_checkpoint, det_model_path)
+        hrnet_model_local = find_download_model(pose_checkpoint, pose_model_path)
+        det_config_mmcv = mmcv.Config.fromfile(det_config)
+        pose_config_mmcv = mmcv.Config.fromfile(pose_config)
+        human_det = init_detector(det_config_mmcv, det_model_local, device=devices.get_device_for("controlnet"))
+        pose_model = init_pose_model(pose_config_mmcv, hrnet_model_local, device=devices.get_device_for("controlnet"))
+
+    assert input_image.ndim == 3
+    input_image = input_image.copy()
+    with torch.no_grad():
+        image = torch.from_numpy(input_image).float().to(devices.get_device_for("controlnet"))
+        image = image / 255.0
+        mmdet_results = inference_detector(human_det, image)
+        
+        # keep the person class bounding boxes.
+        person_results = process_mmdet_results(mmdet_results, det_cat_id)
+        
+        return_heatmap = False
+        dataset = pose_model.cfg.data['test']['type']
+        
+        # e.g. use ('backbone', ) to return backbone feature
+        output_layer_names = None
+        pose_results, _ = inference_top_down_pose_model(
+            pose_model,
+            image,
+            person_results,
+            bbox_thr=bbox_thr,
+            format='xyxy',
+            dataset=dataset,
+            dataset_info=None,
+            return_heatmap=return_heatmap,
+            outputs=output_layer_names
+        )
+        
+        im_keypose_out = imshow_keypoints(
+            image,
+            pose_results,
+            skeleton=skeleton,
+            pose_kpt_color=pose_kpt_color,
+            pose_link_color=pose_link_color,
+            radius=2,
+            thickness=2
+        )
+        im_keypose_out = im_keypose_out.astype(np.uint8)
+
+        # image_hed = rearrange(image_hed, 'h w c -> 1 c h w')
+        # edge = netNetwork(image_hed)[0]
+        # edge = (edge.cpu().numpy() * 255.0).clip(0, 255).astype(np.uint8)
+        return im_keypose_out
+
+
+def unload_hed_model():
+    global netNetwork
+    if netNetwork is not None:
+        netNetwork.cpu()

extensions/lite-kaggle-controlnet/annotator/keypose/faster_rcnn_r50_fpn_coco.py

@@ -0,0 +1,182 @@
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook')
+    ])
+# yapf:enable
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[8, 11])
+total_epochs = 12
+
+model = dict(
+    type='FasterRCNN',
+    pretrained='torchvision://resnet50',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch'),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    roi_head=dict(
+        type='StandardRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='Shared2FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=False,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100)
+        # soft-nms is also supported for rcnn testing
+        # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
+    ))
+
+dataset_type = 'CocoDataset'
+data_root = 'data/coco'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=f'{data_root}/annotations/instances_train2017.json',
+        img_prefix=f'{data_root}/train2017/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=f'{data_root}/annotations/instances_val2017.json',
+        img_prefix=f'{data_root}/val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=f'{data_root}/annotations/instances_val2017.json',
+        img_prefix=f'{data_root}/val2017/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='bbox')

extensions/lite-kaggle-controlnet/annotator/keypose/hrnet_w48_coco_256x192.py

@@ -0,0 +1,169 @@
+# _base_ = [
+#     '../../../../_base_/default_runtime.py',
+#     '../../../../_base_/datasets/coco.py'
+# ]
+evaluation = dict(interval=10, metric='mAP', save_best='AP')
+
+optimizer = dict(
+    type='Adam',
+    lr=5e-4,
+)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[170, 200])
+total_epochs = 210
+channel_cfg = dict(
+    num_output_channels=17,
+    dataset_joints=17,
+    dataset_channel=[
+        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
+    ],
+    inference_channel=[
+        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
+    ])
+
+# model settings
+model = dict(
+    type='TopDown',
+    pretrained='https://download.openmmlab.com/mmpose/'
+    'pretrain_models/hrnet_w48-8ef0771d.pth',
+    backbone=dict(
+        type='HRNet',
+        in_channels=3,
+        extra=dict(
+            stage1=dict(
+                num_modules=1,
+                num_branches=1,
+                block='BOTTLENECK',
+                num_blocks=(4, ),
+                num_channels=(64, )),
+            stage2=dict(
+                num_modules=1,
+                num_branches=2,
+                block='BASIC',
+                num_blocks=(4, 4),
+                num_channels=(48, 96)),
+            stage3=dict(
+                num_modules=4,
+                num_branches=3,
+                block='BASIC',
+                num_blocks=(4, 4, 4),
+                num_channels=(48, 96, 192)),
+            stage4=dict(
+                num_modules=3,
+                num_branches=4,
+                block='BASIC',
+                num_blocks=(4, 4, 4, 4),
+                num_channels=(48, 96, 192, 384))),
+    ),
+    keypoint_head=dict(
+        type='TopdownHeatmapSimpleHead',
+        in_channels=48,
+        out_channels=channel_cfg['num_output_channels'],
+        num_deconv_layers=0,
+        extra=dict(final_conv_kernel=1, ),
+        loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
+    train_cfg=dict(),
+    test_cfg=dict(
+        flip_test=True,
+        post_process='default',
+        shift_heatmap=True,
+        modulate_kernel=11))
+
+data_cfg = dict(
+    image_size=[192, 256],
+    heatmap_size=[48, 64],
+    num_output_channels=channel_cfg['num_output_channels'],
+    num_joints=channel_cfg['dataset_joints'],
+    dataset_channel=channel_cfg['dataset_channel'],
+    inference_channel=channel_cfg['inference_channel'],
+    soft_nms=False,
+    nms_thr=1.0,
+    oks_thr=0.9,
+    vis_thr=0.2,
+    use_gt_bbox=False,
+    det_bbox_thr=0.0,
+    bbox_file='data/coco/person_detection_results/'
+    'COCO_val2017_detections_AP_H_56_person.json',
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='TopDownGetBboxCenterScale', padding=1.25),
+    dict(type='TopDownRandomShiftBboxCenter', shift_factor=0.16, prob=0.3),
+    dict(type='TopDownRandomFlip', flip_prob=0.5),
+    dict(
+        type='TopDownHalfBodyTransform',
+        num_joints_half_body=8,
+        prob_half_body=0.3),
+    dict(
+        type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
+    dict(type='TopDownAffine'),
+    dict(type='ToTensor'),
+    dict(
+        type='NormalizeTensor',
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]),
+    dict(type='TopDownGenerateTarget', sigma=2),
+    dict(
+        type='Collect',
+        keys=['img', 'target', 'target_weight'],
+        meta_keys=[
+            'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
+            'rotation', 'bbox_score', 'flip_pairs'
+        ]),
+]
+
+val_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='TopDownGetBboxCenterScale', padding=1.25),
+    dict(type='TopDownAffine'),
+    dict(type='ToTensor'),
+    dict(
+        type='NormalizeTensor',
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]),
+    dict(
+        type='Collect',
+        keys=['img'],
+        meta_keys=[
+            'image_file', 'center', 'scale', 'rotation', 'bbox_score',
+            'flip_pairs'
+        ]),
+]
+
+test_pipeline = val_pipeline
+
+data_root = 'data/coco'
+data = dict(
+    samples_per_gpu=32,
+    workers_per_gpu=2,
+    val_dataloader=dict(samples_per_gpu=32),
+    test_dataloader=dict(samples_per_gpu=32),
+    train=dict(
+        type='TopDownCocoDataset',
+        ann_file=f'{data_root}/annotations/person_keypoints_train2017.json',
+        img_prefix=f'{data_root}/train2017/',
+        data_cfg=data_cfg,
+        pipeline=train_pipeline,
+        dataset_info={{_base_.dataset_info}}),
+    val=dict(
+        type='TopDownCocoDataset',
+        ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
+        img_prefix=f'{data_root}/val2017/',
+        data_cfg=data_cfg,
+        pipeline=val_pipeline,
+        dataset_info={{_base_.dataset_info}}),
+    test=dict(
+        type='TopDownCocoDataset',
+        ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
+        img_prefix=f'{data_root}/val2017/',
+        data_cfg=data_cfg,
+        pipeline=test_pipeline,
+        dataset_info={{_base_.dataset_info}}),
+)

extensions/lite-kaggle-controlnet/annotator/leres/__init__.py

@@ -0,0 +1,113 @@
+import cv2
+import numpy as np
+import torch
+import os
+from modules import devices, shared
+from annotator.annotator_path import models_path
+from torchvision.transforms import transforms
+
+# AdelaiDepth/LeReS imports
+from .leres.depthmap import estimateleres, estimateboost
+from .leres.multi_depth_model_woauxi import RelDepthModel
+from .leres.net_tools import strip_prefix_if_present
+
+# pix2pix/merge net imports
+from .pix2pix.options.test_options import TestOptions
+from .pix2pix.models.pix2pix4depth_model import Pix2Pix4DepthModel
+
+base_model_path = os.path.join(models_path, "leres")
+old_modeldir = os.path.dirname(os.path.realpath(__file__))
+
+remote_model_path_leres = "https://huggingface.co/lllyasviel/Annotators/resolve/main/res101.pth"
+remote_model_path_pix2pix = "https://huggingface.co/lllyasviel/Annotators/resolve/main/latest_net_G.pth"
+
+model = None
+pix2pixmodel = None
+
+def unload_leres_model():
+    global model, pix2pixmodel
+    if model is not None:
+        model = model.cpu()
+    if pix2pixmodel is not None:
+        pix2pixmodel = pix2pixmodel.unload_network('G')
+
+
+def apply_leres(input_image, thr_a, thr_b, boost=False):
+    global model, pix2pixmodel
+    if model is None:
+        model_path = os.path.join(base_model_path, "res101.pth")
+        old_model_path = os.path.join(old_modeldir, "res101.pth")
+        
+        if os.path.exists(old_model_path):
+            model_path = old_model_path
+        elif not os.path.exists(model_path):
+            from basicsr.utils.download_util import load_file_from_url
+            load_file_from_url(remote_model_path_leres, model_dir=base_model_path)
+
+        if torch.cuda.is_available():
+            checkpoint = torch.load(model_path)
+        else:
+            checkpoint = torch.load(model_path, map_location=torch.device('cpu'))
+
+        model = RelDepthModel(backbone='resnext101')
+        model.load_state_dict(strip_prefix_if_present(checkpoint['depth_model'], "module."), strict=True)
+        del checkpoint
+
+    if boost and pix2pixmodel is None:
+        pix2pixmodel_path = os.path.join(base_model_path, "latest_net_G.pth")
+        if not os.path.exists(pix2pixmodel_path):
+            from basicsr.utils.download_util import load_file_from_url
+            load_file_from_url(remote_model_path_pix2pix, model_dir=base_model_path)
+
+        opt = TestOptions().parse()
+        if not torch.cuda.is_available():
+            opt.gpu_ids = []  # cpu mode
+        pix2pixmodel = Pix2Pix4DepthModel(opt)
+        pix2pixmodel.save_dir = base_model_path
+        pix2pixmodel.load_networks('latest')
+        pix2pixmodel.eval()
+    
+    if devices.get_device_for("controlnet").type != 'mps':
+        model = model.to(devices.get_device_for("controlnet"))
+
+    assert input_image.ndim == 3
+    height, width, dim = input_image.shape
+
+    with torch.no_grad():
+
+        if boost:
+            depth = estimateboost(input_image, model, 0, pix2pixmodel, max(width, height))
+        else:
+            depth = estimateleres(input_image, model, width, height)
+
+        numbytes=2
+        depth_min = depth.min()
+        depth_max = depth.max()
+        max_val = (2**(8*numbytes))-1
+
+        # check output before normalizing and mapping to 16 bit
+        if depth_max - depth_min > np.finfo("float").eps:
+            out = max_val * (depth - depth_min) / (depth_max - depth_min)
+        else:
+            out = np.zeros(depth.shape)
+        
+        # single channel, 16 bit image
+        depth_image = out.astype("uint16")
+
+        # convert to uint8
+        depth_image = cv2.convertScaleAbs(depth_image, alpha=(255.0/65535.0))
+
+        # remove near
+        if thr_a != 0:
+            thr_a = ((thr_a/100)*255) 
+            depth_image = cv2.threshold(depth_image, thr_a, 255, cv2.THRESH_TOZERO)[1]
+
+        # invert image
+        depth_image = cv2.bitwise_not(depth_image)
+
+        # remove bg
+        if thr_b != 0:
+            thr_b = ((thr_b/100)*255)
+            depth_image = cv2.threshold(depth_image, thr_b, 255, cv2.THRESH_TOZERO)[1]
+
+        return depth_image