Merge branch 'main' into install/refactor-configure-and-model-select

2024-08-30 20:32:17 +00:00 · 2023-02-16 03:24:02 -05:00 · 2023-02-16 03:24:02 -05:00 · 314ed7d8f6
commit 314ed7d8f6
parent b1341bc611 caf07479ec
42 changed files with 1263 additions and 773 deletions
--- a/README.md
+++ b/README.md
@ -33,7 +33,7 @@

 InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products.

-**Quick links**: [[How to Install](#installation)] [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</a>] [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<a href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]
+**Quick links**: [[How to Install](https://invoke-ai.github.io/InvokeAI/#installation)] [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</a>] [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<a href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]

 _Note: InvokeAI is rapidly evolving. Please use the
 [Issues](https://github.com/invoke-ai/InvokeAI/issues) tab to report bugs and make feature
--- a/binary_installer/install.bat.in
+++ b/binary_installer/install.bat.in
@ -147,7 +147,7 @@ echo ***** Installed invoke launcher script ******
 rd /s /q binary_installer installer_files

@rem preload the models
-call .venv\Scripts\python scripts\configure_invokeai.py
+call .venv\Scripts\python ldm\invoke\config\invokeai_configure.py
 set err_msg=----- model download clone failed -----
 if %errorlevel% neq 0 goto err_exit
 deactivate
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@ -12,7 +12,7 @@ LABEL org.opencontainers.image.authors="mauwii@outlook.de"
 RUN rm -f /etc/apt/apt.conf.d/docker-clean \
  && echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' >/etc/apt/apt.conf.d/keep-cache

-# Install necesarry packages
+# Install necessary packages
 RUN \
  --mount=type=cache,target=/var/cache/apt,sharing=locked \
  --mount=type=cache,target=/var/lib/apt,sharing=locked \
@ -78,7 +78,7 @@ RUN python3 -c "from patchmatch import patch_match"
 #####################
 FROM python-base AS runtime

-# Create a new User
+# Create a new user
 ARG UNAME=appuser
 RUN useradd \
  --no-log-init \
--- a/docker/build.sh
+++ b/docker/build.sh
@ -36,7 +36,7 @@ echo -e "Container Image:\t${CONTAINER_IMAGE}\n"
 if [[ -n "$(docker volume ls -f name="${VOLUMENAME}" -q)" ]]; then
    echo -e "Volume already exists\n"
 else
-    echo -n "createing docker volume "
+    echo -n "creating docker volume "
    docker volume create "${VOLUMENAME}"
 fi

--- a/invokeai/backend/invoke_ai_web_server.py
+++ b/invokeai/backend/invoke_ai_web_server.py
@ -28,7 +28,7 @@ from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
 from ldm.invoke.conditioning import get_tokens_for_prompt, get_prompt_structure
 from ldm.invoke.generator.diffusers_pipeline import PipelineIntermediateState
 from ldm.invoke.generator.inpaint import infill_methods
-from ldm.invoke.globals import Globals
+from ldm.invoke.globals import Globals, global_converted_ckpts_dir
 from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
 from ldm.invoke.prompt_parser import split_weighted_subprompts, Blend

@ -43,7 +43,8 @@ if not os.path.isabs(args.outdir):

 # normalize the config directory relative to root
 if not os.path.isabs(opt.conf):
-    opt.conf = os.path.normpath(os.path.join(Globals.root,opt.conf))
+    opt.conf = os.path.normpath(os.path.join(Globals.root, opt.conf))
+

 class InvokeAIWebServer:
    def __init__(self, generate: Generate, gfpgan, codeformer, esrgan) -> None:
@ -189,7 +190,8 @@ class InvokeAIWebServer:
                (width, height) = pil_image.size

                thumbnail_path = save_thumbnail(
-                    pil_image, os.path.basename(file_path), self.thumbnail_image_path
+                    pil_image, os.path.basename(
+                        file_path), self.thumbnail_image_path
                )

                response = {
@ -264,14 +266,16 @@ class InvokeAIWebServer:
        # location for "finished" images
        self.result_path = args.outdir
        # temporary path for intermediates
-        self.intermediate_path = os.path.join(self.result_path, "intermediates/")
+        self.intermediate_path = os.path.join(
+            self.result_path, "intermediates/")
        # path for user-uploaded init images and masks
        self.init_image_path = os.path.join(self.result_path, "init-images/")
        self.mask_image_path = os.path.join(self.result_path, "mask-images/")
        # path for temp images e.g. gallery generations which are not committed
        self.temp_image_path = os.path.join(self.result_path, "temp-images/")
        # path for thumbnail images
-        self.thumbnail_image_path = os.path.join(self.result_path, "thumbnails/")
+        self.thumbnail_image_path = os.path.join(
+            self.result_path, "thumbnails/")
        # txt log
        self.log_path = os.path.join(self.result_path, "invoke_log.txt")
        # make all output paths
@ -290,7 +294,7 @@ class InvokeAIWebServer:
    def load_socketio_listeners(self, socketio):
        @socketio.on("requestSystemConfig")
        def handle_request_capabilities():
-            print(f">> System config requested")
+            print(">> System config requested")
            config = self.get_system_config()
            config["model_list"] = self.generate.model_manager.list_models()
            config["infill_methods"] = infill_methods()
@ -301,14 +305,16 @@ class InvokeAIWebServer:
            try:
                if not search_folder:
                    socketio.emit(
-                    "foundModels",
-                    {'search_folder': None, 'found_models': None},
-                )
+                        "foundModels",
+                        {'search_folder': None, 'found_models': None},
+                    )
                else:
-                    search_folder, found_models = self.generate.model_manager.search_models(search_folder)
+                    search_folder, found_models = self.generate.model_manager.search_models(
+                        search_folder)
                    socketio.emit(
                        "foundModels",
-                        {'search_folder': search_folder, 'found_models': found_models},
+                        {'search_folder': search_folder,
+                            'found_models': found_models},
                    )
            except Exception as e:
                self.socketio.emit("error", {"message": (str(e))})
@ -393,6 +399,67 @@ class InvokeAIWebServer:
                traceback.print_exc()
                print("\n")

+        @socketio.on('convertToDiffusers')
+        def convert_to_diffusers(model_to_convert: dict):
+            try:
+                if (model_info := self.generate.model_manager.model_info(model_name=model_to_convert['model_name'])):
+                    if 'weights' in model_info:
+                        ckpt_path = Path(model_info['weights'])
+                        original_config_file = Path(model_info['config'])
+                        model_name = model_to_convert['model_name']
+                        model_description = model_info['description']
+                    else:
+                        self.socketio.emit(
+                            "error", {"message": "Model is not a valid checkpoint file"})
+                else:
+                    self.socketio.emit(
+                        "error", {"message": "Could not retrieve model info."})
+
+                if not ckpt_path.is_absolute():
+                    ckpt_path = Path(Globals.root, ckpt_path)
+
+                if original_config_file and not original_config_file.is_absolute():
+                    original_config_file = Path(
+                        Globals.root, original_config_file)
+
+                diffusers_path = Path(
+                    ckpt_path.parent.absolute(),
+                    f'{model_name}_diffusers'
+                )
+
+                if model_to_convert['save_location'] == 'root':
+                    diffusers_path = Path(global_converted_ckpts_dir(), f'{model_name}_diffusers')
+                
+                if model_to_convert['save_location'] == 'custom' and model_to_convert['custom_location'] is not None:
+                    diffusers_path = Path(model_to_convert['custom_location'], f'{model_name}_diffusers')
+
+                if diffusers_path.exists():
+                    shutil.rmtree(diffusers_path)
+
+                self.generate.model_manager.convert_and_import(
+                    ckpt_path,
+                    diffusers_path,
+                    model_name=model_name,
+                    model_description=model_description,
+                    vae=None,
+                    original_config_file=original_config_file,
+                    commit_to_conf=opt.conf,
+                )
+
+                new_model_list = self.generate.model_manager.list_models()
+                socketio.emit(
+                    "modelConverted",
+                    {"new_model_name": model_name,
+                     "model_list": new_model_list, 'update': True},
+                )
+                print(f">> Model Converted: {model_name}")
+            except Exception as e:
+                self.socketio.emit("error", {"message": (str(e))})
+                print("\n")
+
+                traceback.print_exc()
+                print("\n")
+
        @socketio.on("requestEmptyTempFolder")
        def empty_temp_folder():
            try:
@ -406,7 +473,8 @@ class InvokeAIWebServer:
                        )
                        os.remove(thumbnail_path)
                    except Exception as e:
-                        socketio.emit("error", {"message": f"Unable to delete {f}: {str(e)}"})
+                        socketio.emit(
+                            "error", {"message": f"Unable to delete {f}: {str(e)}"})
                        pass

                socketio.emit("tempFolderEmptied")
@ -421,7 +489,8 @@ class InvokeAIWebServer:
        def save_temp_image_to_gallery(url):
            try:
                image_path = self.get_image_path_from_url(url)
-                new_path = os.path.join(self.result_path, os.path.basename(image_path))
+                new_path = os.path.join(
+                    self.result_path, os.path.basename(image_path))
                shutil.copy2(image_path, new_path)

                if os.path.splitext(new_path)[1] == ".png":
@ -434,7 +503,8 @@ class InvokeAIWebServer:
                (width, height) = pil_image.size

                thumbnail_path = save_thumbnail(
-                    pil_image, os.path.basename(new_path), self.thumbnail_image_path
+                    pil_image, os.path.basename(
+                        new_path), self.thumbnail_image_path
                )

                image_array = [
@ -497,7 +567,8 @@ class InvokeAIWebServer:
                        (width, height) = pil_image.size

                        thumbnail_path = save_thumbnail(
-                            pil_image, os.path.basename(path), self.thumbnail_image_path
+                            pil_image, os.path.basename(
+                                path), self.thumbnail_image_path
                        )

                        image_array.append(
@ -515,7 +586,8 @@ class InvokeAIWebServer:
                            }
                        )
                    except Exception as e:
-                        socketio.emit("error", {"message": f"Unable to load {path}: {str(e)}"})
+                        socketio.emit(
+                            "error", {"message": f"Unable to load {path}: {str(e)}"})
                        pass

                socketio.emit(
@ -569,7 +641,8 @@ class InvokeAIWebServer:
                        (width, height) = pil_image.size

                        thumbnail_path = save_thumbnail(
-                            pil_image, os.path.basename(path), self.thumbnail_image_path
+                            pil_image, os.path.basename(
+                                path), self.thumbnail_image_path
                        )

                        image_array.append(
@ -588,7 +661,8 @@ class InvokeAIWebServer:
                        )
                    except Exception as e:
                        print(f">> Unable to load {path}")
-                        socketio.emit("error", {"message": f"Unable to load {path}: {str(e)}"})
+                        socketio.emit(
+                            "error", {"message": f"Unable to load {path}: {str(e)}"})
                        pass

                socketio.emit(
@ -626,7 +700,8 @@ class InvokeAIWebServer:
                        printable_parameters["init_mask"][:64] + "..."
                    )

-                print(f'\n>> Image Generation Parameters:\n\n{printable_parameters}\n')
+                print(
+                    f'\n>> Image Generation Parameters:\n\n{printable_parameters}\n')
                print(f'>> ESRGAN Parameters: {esrgan_parameters}')
                print(f'>> Facetool Parameters: {facetool_parameters}')

@ -662,16 +737,18 @@ class InvokeAIWebServer:

                try:
                    seed = original_image["metadata"]["image"]["seed"]
-                except (KeyError) as e:
+                except KeyError:
                    seed = "unknown_seed"
                    pass

                if postprocessing_parameters["type"] == "esrgan":
                    progress.set_current_status("common:statusUpscalingESRGAN")
                elif postprocessing_parameters["type"] == "gfpgan":
-                    progress.set_current_status("common:statusRestoringFacesGFPGAN")
+                    progress.set_current_status(
+                        "common:statusRestoringFacesGFPGAN")
                elif postprocessing_parameters["type"] == "codeformer":
-                    progress.set_current_status("common:statusRestoringFacesCodeFormer")
+                    progress.set_current_status(
+                        "common:statusRestoringFacesCodeFormer")

                socketio.emit("progressUpdate", progress.to_formatted_dict())
                eventlet.sleep(0)
@ -760,7 +837,7 @@ class InvokeAIWebServer:

        @socketio.on("cancel")
        def handle_cancel():
-            print(f">> Cancel processing requested")
+            print(">> Cancel processing requested")
            self.canceled.set()

        # TODO: I think this needs a safety mechanism.
@ -842,12 +919,10 @@ class InvokeAIWebServer:
                So we need to convert each into a PIL Image.
                """

-                truncated_outpaint_image_b64 = generation_parameters["init_img"][:64]
-                truncated_outpaint_mask_b64 = generation_parameters["init_mask"][:64]
-
                init_img_url = generation_parameters["init_img"]

-                original_bounding_box = generation_parameters["bounding_box"].copy()
+                original_bounding_box = generation_parameters["bounding_box"].copy(
+                )

                initial_image = dataURL_to_image(
                    generation_parameters["init_img"]
@ -924,7 +999,8 @@ class InvokeAIWebServer:
            elif generation_parameters["generation_mode"] == "img2img":
                init_img_url = generation_parameters["init_img"]
                init_img_path = self.get_image_path_from_url(init_img_url)
-                generation_parameters["init_img"] = Image.open(init_img_path).convert('RGB')
+                generation_parameters["init_img"] = Image.open(
+                    init_img_path).convert('RGB')

            def image_progress(sample, step):
                if self.canceled.is_set():
@ -983,9 +1059,9 @@ class InvokeAIWebServer:
                        },
                    )

-
                if generation_parameters["progress_latents"]:
-                    image = self.generate.sample_to_lowres_estimated_image(sample)
+                    image = self.generate.sample_to_lowres_estimated_image(
+                        sample)
                    (width, height) = image.size
                    width *= 8
                    height *= 8
@ -1004,7 +1080,8 @@ class InvokeAIWebServer:
                        },
                    )

-                self.socketio.emit("progressUpdate", progress.to_formatted_dict())
+                self.socketio.emit(
+                    "progressUpdate", progress.to_formatted_dict())
                eventlet.sleep(0)

            def image_done(image, seed, first_seed, attention_maps_image=None):
@ -1016,7 +1093,6 @@ class InvokeAIWebServer:
                nonlocal facetool_parameters
                nonlocal progress

-                step_index = 1
                nonlocal prior_variations

                """
@ -1032,7 +1108,8 @@ class InvokeAIWebServer:

                progress.set_current_status("common:statusGenerationComplete")

-                self.socketio.emit("progressUpdate", progress.to_formatted_dict())
+                self.socketio.emit(
+                    "progressUpdate", progress.to_formatted_dict())
                eventlet.sleep(0)

                all_parameters = generation_parameters
@ -1043,7 +1120,8 @@ class InvokeAIWebServer:
                    and all_parameters["variation_amount"] > 0
                ):
                    first_seed = first_seed or seed
-                    this_variation = [[seed, all_parameters["variation_amount"]]]
+                    this_variation = [
+                        [seed, all_parameters["variation_amount"]]]
                    all_parameters["with_variations"] = (
                        prior_variations + this_variation
                    )
@ -1059,7 +1137,8 @@ class InvokeAIWebServer:
                if esrgan_parameters:
                    progress.set_current_status("common:statusUpscaling")
                    progress.set_current_status_has_steps(False)
-                    self.socketio.emit("progressUpdate", progress.to_formatted_dict())
+                    self.socketio.emit(
+                        "progressUpdate", progress.to_formatted_dict())
                    eventlet.sleep(0)

                    image = self.esrgan.process(
@ -1082,12 +1161,15 @@ class InvokeAIWebServer:

                if facetool_parameters:
                    if facetool_parameters["type"] == "gfpgan":
-                        progress.set_current_status("common:statusRestoringFacesGFPGAN")
+                        progress.set_current_status(
+                            "common:statusRestoringFacesGFPGAN")
                    elif facetool_parameters["type"] == "codeformer":
-                        progress.set_current_status("common:statusRestoringFacesCodeFormer")
+                        progress.set_current_status(
+                            "common:statusRestoringFacesCodeFormer")

                    progress.set_current_status_has_steps(False)
-                    self.socketio.emit("progressUpdate", progress.to_formatted_dict())
+                    self.socketio.emit(
+                        "progressUpdate", progress.to_formatted_dict())
                    eventlet.sleep(0)

                    if facetool_parameters["type"] == "gfpgan":
@ -1117,7 +1199,8 @@ class InvokeAIWebServer:
                    all_parameters["facetool_type"] = facetool_parameters["type"]

                progress.set_current_status("common:statusSavingImage")
-                self.socketio.emit("progressUpdate", progress.to_formatted_dict())
+                self.socketio.emit(
+                    "progressUpdate", progress.to_formatted_dict())
                eventlet.sleep(0)

                # restore the stashed URLS and discard the paths, we are about to send the result to client
@ -1128,12 +1211,14 @@ class InvokeAIWebServer:
                )

                if "init_mask" in all_parameters:
-                    all_parameters["init_mask"] = ""  # TODO: store the mask in metadata
+                    # TODO: store the mask in metadata
+                    all_parameters["init_mask"] = ""

                if generation_parameters["generation_mode"] == "unifiedCanvas":
                    all_parameters["bounding_box"] = original_bounding_box

-                metadata = self.parameters_to_generated_image_metadata(all_parameters)
+                metadata = self.parameters_to_generated_image_metadata(
+                    all_parameters)

                command = parameters_to_command(all_parameters)

@ -1163,15 +1248,18 @@ class InvokeAIWebServer:

                if progress.total_iterations > progress.current_iteration:
                    progress.set_current_step(1)
-                    progress.set_current_status("common:statusIterationComplete")
+                    progress.set_current_status(
+                        "common:statusIterationComplete")
                    progress.set_current_status_has_steps(False)
                else:
                    progress.mark_complete()

-                self.socketio.emit("progressUpdate", progress.to_formatted_dict())
+                self.socketio.emit(
+                    "progressUpdate", progress.to_formatted_dict())
                eventlet.sleep(0)

-                parsed_prompt, _ = get_prompt_structure(generation_parameters["prompt"])
+                parsed_prompt, _ = get_prompt_structure(
+                    generation_parameters["prompt"])
                tokens = None if type(parsed_prompt) is Blend else \
                    get_tokens_for_prompt(self.generate.model, parsed_prompt)
                attention_maps_image_base64_url = None if attention_maps_image is None \
@ -1345,7 +1433,8 @@ class InvokeAIWebServer:
        self, parameters, original_image_path
    ):
        try:
-            current_metadata = retrieve_metadata(original_image_path)["sd-metadata"]
+            current_metadata = retrieve_metadata(
+                original_image_path)["sd-metadata"]
            postprocessing_metadata = {}

            """
@ -1385,7 +1474,8 @@ class InvokeAIWebServer:
                    postprocessing_metadata
                )
            else:
-                current_metadata["image"]["postprocessing"] = [postprocessing_metadata]
+                current_metadata["image"]["postprocessing"] = [
+                    postprocessing_metadata]

            return current_metadata

@ -1424,7 +1514,7 @@ class InvokeAIWebServer:
            if step_index:
                filename += f".{step_index}"
            if postprocessing:
-                filename += f".postprocessed"
+                filename += ".postprocessed"

            filename += ".png"

@ -1497,7 +1587,8 @@ class InvokeAIWebServer:
                )
            elif "thumbnails" in url:
                return os.path.abspath(
-                    os.path.join(self.thumbnail_image_path, os.path.basename(url))
+                    os.path.join(self.thumbnail_image_path,
+                                 os.path.basename(url))
                )
            else:
                return os.path.abspath(
@ -1666,10 +1757,12 @@ def dataURL_to_image(dataURL: str) -> ImageType:
    )
    return image

+
 """
 Converts an image into a base64 image dataURL.
 """

+
 def image_to_dataURL(image: ImageType) -> str:
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")
@ -1679,7 +1772,6 @@ def image_to_dataURL(image: ImageType) -> str:
    return image_base64


-
 """
 Converts a base64 image dataURL into bytes.
 The dataURL is split on the first commma.
--- a/invokeai/frontend/dist/assets/index-6b9f1e33.js
+++ b/invokeai/frontend/dist/assets/index-6b9f1e33.js
--- a/invokeai/frontend/dist/assets/index-a93d4500.js
+++ b/invokeai/frontend/dist/assets/index-a93d4500.js
--- a/invokeai/frontend/dist/index.html
+++ b/invokeai/frontend/dist/index.html
@ -5,7 +5,7 @@
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>InvokeAI - A Stable Diffusion Toolkit</title>
    <link rel="shortcut icon" type="icon" href="./assets/favicon-0d253ced.ico" />
-    <script type="module" crossorigin src="./assets/index-a93d4500.js"></script>
+    <script type="module" crossorigin src="./assets/index-6b9f1e33.js"></script>
    <link rel="stylesheet" href="./assets/index-fecb6dd4.css">
  </head>

--- a/invokeai/frontend/dist/locales/common/en.json
+++ b/invokeai/frontend/dist/locales/common/en.json
@ -58,5 +58,7 @@
  "statusUpscaling": "Upscaling",
  "statusUpscalingESRGAN": "Upscaling (ESRGAN)",
  "statusLoadingModel": "Loading Model",
-  "statusModelChanged": "Model Changed"
+  "statusModelChanged": "Model Changed",
+  "statusConvertingModel": "Converting Model",
+  "statusModelConverted": "Model Converted"
 }
--- a/invokeai/frontend/dist/locales/modelmanager/en-US.json
+++ b/invokeai/frontend/dist/locales/modelmanager/en-US.json
@ -63,5 +63,23 @@
  "formMessageDiffusersModelLocation": "Diffusers Model Location",
  "formMessageDiffusersModelLocationDesc": "Please enter at least one.",
  "formMessageDiffusersVAELocation": "VAE Location",
-  "formMessageDiffusersVAELocationDesc": "If not provided, InvokeAI will look for the VAE file inside the model location given above."
+  "formMessageDiffusersVAELocationDesc": "If not provided, InvokeAI will look for the VAE file inside the model location given above.",
+  "convert": "Convert",
+  "convertToDiffusers": "Convert To Diffusers",
+  "convertToDiffusersHelpText1": "This model will be converted to the 🧨 Diffusers format.",
+  "convertToDiffusersHelpText2": "This process will replace your Model Manager entry with the Diffusers version of the same model.",
+  "convertToDiffusersHelpText3": "Your checkpoint file on the disk will NOT be deleted or modified in anyway. You can add your checkpoint to the Model Manager again if you want to.",
+  "convertToDiffusersHelpText4": "This is a one time process only. It might take around 30s-60s depending on the specifications of your computer.",
+  "convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 4GB-7GB in size.",
+  "convertToDiffusersHelpText6": "Do you wish to convert this model?",
+  "v1": "v1",
+  "v2": "v2",
+  "inpainting": "v1 Inpainting",
+  "customConfig": "Custom Config",
+  "pathToCustomConfig": "Path To Custom Config",
+  "statusConverting": "Converting",
+  "sameFolder": "Same Folder",
+  "invokeRoot": "Invoke Models",
+  "custom": "Custom",
+  "customSaveLocation": "Custom Save Location"
 }
--- a/invokeai/frontend/dist/locales/modelmanager/en.json
+++ b/invokeai/frontend/dist/locales/modelmanager/en.json
@ -63,5 +63,25 @@
  "formMessageDiffusersModelLocation": "Diffusers Model Location",
  "formMessageDiffusersModelLocationDesc": "Please enter at least one.",
  "formMessageDiffusersVAELocation": "VAE Location",
-  "formMessageDiffusersVAELocationDesc": "If not provided, InvokeAI will look for the VAE file inside the model location given above."
+  "formMessageDiffusersVAELocationDesc": "If not provided, InvokeAI will look for the VAE file inside the model location given above.",
+  "convert": "Convert",
+  "convertToDiffusers": "Convert To Diffusers",
+  "convertToDiffusersHelpText1": "This model will be converted to the 🧨 Diffusers format.",
+  "convertToDiffusersHelpText2": "This process will replace your Model Manager entry with the Diffusers version of the same model.",
+  "convertToDiffusersHelpText3": "Your checkpoint file on the disk will NOT be deleted or modified in anyway. You can add your checkpoint to the Model Manager again if you want to.",
+  "convertToDiffusersHelpText4": "This is a one time process only. It might take around 30s-60s depending on the specifications of your computer.",
+  "convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 4GB-7GB in size.",
+  "convertToDiffusersHelpText6": "Do you wish to convert this model?",
+  "convertToDiffusersSaveLocation": "Save Location",
+  "v1": "v1",
+  "v2": "v2",
+  "inpainting": "v1 Inpainting",
+  "customConfig": "Custom Config",  
+  "pathToCustomConfig": "Path To Custom Config",
+  "statusConverting": "Converting",
+  "modelConverted": "Model Converted",
+  "sameFolder": "Same folder",
+  "invokeRoot": "InvokeAI folder",
+  "custom": "Custom",
+  "customSaveLocation": "Custom Save Location"
 }
--- a/invokeai/frontend/dist/locales/parameters/en-US.json
+++ b/invokeai/frontend/dist/locales/parameters/en-US.json
@ -57,6 +57,6 @@
  "useInitImg": "Use Initial Image",
  "info": "Info",
  "deleteImage": "Delete Image",
-  "initialImage": "Inital Image",
+  "initialImage": "Initial Image",
  "showOptionsPanel": "Show Options Panel"
 }
--- a/invokeai/frontend/dist/locales/parameters/en.json
+++ b/invokeai/frontend/dist/locales/parameters/en.json
@ -60,6 +60,6 @@
  "useInitImg": "Use Initial Image",
  "info": "Info",
  "deleteImage": "Delete Image",
-  "initialImage": "Inital Image",
+  "initialImage": "Initial Image",
  "showOptionsPanel": "Show Options Panel"
 }
--- a/invokeai/frontend/dist/locales/parameters/ja.json
+++ b/invokeai/frontend/dist/locales/parameters/ja.json
@ -56,7 +56,7 @@
    "useInitImg": "Use Initial Image",
    "info": "情報",
    "deleteImage": "画像を削除",
-    "initialImage": "Inital Image",
+    "initialImage": "Initial Image",
    "showOptionsPanel": "オプションパネルを表示"
  }
  
--- a/invokeai/frontend/public/locales/common/en.json
+++ b/invokeai/frontend/public/locales/common/en.json
@ -58,5 +58,7 @@
  "statusUpscaling": "Upscaling",
  "statusUpscalingESRGAN": "Upscaling (ESRGAN)",
  "statusLoadingModel": "Loading Model",
-  "statusModelChanged": "Model Changed"
+  "statusModelChanged": "Model Changed",
+  "statusConvertingModel": "Converting Model",
+  "statusModelConverted": "Model Converted"
 }
--- a/invokeai/frontend/public/locales/modelmanager/en-US.json
+++ b/invokeai/frontend/public/locales/modelmanager/en-US.json
@ -63,5 +63,23 @@
  "formMessageDiffusersModelLocation": "Diffusers Model Location",
  "formMessageDiffusersModelLocationDesc": "Please enter at least one.",
  "formMessageDiffusersVAELocation": "VAE Location",
-  "formMessageDiffusersVAELocationDesc": "If not provided, InvokeAI will look for the VAE file inside the model location given above."
+  "formMessageDiffusersVAELocationDesc": "If not provided, InvokeAI will look for the VAE file inside the model location given above.",
+  "convert": "Convert",
+  "convertToDiffusers": "Convert To Diffusers",
+  "convertToDiffusersHelpText1": "This model will be converted to the 🧨 Diffusers format.",
+  "convertToDiffusersHelpText2": "This process will replace your Model Manager entry with the Diffusers version of the same model.",
+  "convertToDiffusersHelpText3": "Your checkpoint file on the disk will NOT be deleted or modified in anyway. You can add your checkpoint to the Model Manager again if you want to.",
+  "convertToDiffusersHelpText4": "This is a one time process only. It might take around 30s-60s depending on the specifications of your computer.",
+  "convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 4GB-7GB in size.",
+  "convertToDiffusersHelpText6": "Do you wish to convert this model?",
+  "v1": "v1",
+  "v2": "v2",
+  "inpainting": "v1 Inpainting",
+  "customConfig": "Custom Config",
+  "pathToCustomConfig": "Path To Custom Config",
+  "statusConverting": "Converting",
+  "sameFolder": "Same Folder",
+  "invokeRoot": "Invoke Models",
+  "custom": "Custom",
+  "customSaveLocation": "Custom Save Location"
 }
--- a/invokeai/frontend/public/locales/modelmanager/en.json
+++ b/invokeai/frontend/public/locales/modelmanager/en.json
@ -63,5 +63,25 @@
  "formMessageDiffusersModelLocation": "Diffusers Model Location",
  "formMessageDiffusersModelLocationDesc": "Please enter at least one.",
  "formMessageDiffusersVAELocation": "VAE Location",
-  "formMessageDiffusersVAELocationDesc": "If not provided, InvokeAI will look for the VAE file inside the model location given above."
+  "formMessageDiffusersVAELocationDesc": "If not provided, InvokeAI will look for the VAE file inside the model location given above.",
+  "convert": "Convert",
+  "convertToDiffusers": "Convert To Diffusers",
+  "convertToDiffusersHelpText1": "This model will be converted to the 🧨 Diffusers format.",
+  "convertToDiffusersHelpText2": "This process will replace your Model Manager entry with the Diffusers version of the same model.",
+  "convertToDiffusersHelpText3": "Your checkpoint file on the disk will NOT be deleted or modified in anyway. You can add your checkpoint to the Model Manager again if you want to.",
+  "convertToDiffusersHelpText4": "This is a one time process only. It might take around 30s-60s depending on the specifications of your computer.",
+  "convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 4GB-7GB in size.",
+  "convertToDiffusersHelpText6": "Do you wish to convert this model?",
+  "convertToDiffusersSaveLocation": "Save Location",
+  "v1": "v1",
+  "v2": "v2",
+  "inpainting": "v1 Inpainting",
+  "customConfig": "Custom Config",  
+  "pathToCustomConfig": "Path To Custom Config",
+  "statusConverting": "Converting",
+  "modelConverted": "Model Converted",
+  "sameFolder": "Same folder",
+  "invokeRoot": "InvokeAI folder",
+  "custom": "Custom",
+  "customSaveLocation": "Custom Save Location"
 }
--- a/invokeai/frontend/public/locales/parameters/en-US.json
+++ b/invokeai/frontend/public/locales/parameters/en-US.json
@ -57,6 +57,6 @@
  "useInitImg": "Use Initial Image",
  "info": "Info",
  "deleteImage": "Delete Image",
-  "initialImage": "Inital Image",
+  "initialImage": "Initial Image",
  "showOptionsPanel": "Show Options Panel"
 }
--- a/invokeai/frontend/public/locales/parameters/en.json
+++ b/invokeai/frontend/public/locales/parameters/en.json
@ -60,6 +60,6 @@
  "useInitImg": "Use Initial Image",
  "info": "Info",
  "deleteImage": "Delete Image",
-  "initialImage": "Inital Image",
+  "initialImage": "Initial Image",
  "showOptionsPanel": "Show Options Panel"
 }
--- a/invokeai/frontend/public/locales/parameters/ja.json
+++ b/invokeai/frontend/public/locales/parameters/ja.json
@ -56,7 +56,7 @@
    "useInitImg": "Use Initial Image",
    "info": "情報",
    "deleteImage": "画像を削除",
-    "initialImage": "Inital Image",
+    "initialImage": "Initial Image",
    "showOptionsPanel": "オプションパネルを表示"
  }
  
--- a/invokeai/frontend/src/app/invokeai.d.ts
+++ b/invokeai/frontend/src/app/invokeai.d.ts
@ -219,6 +219,12 @@ export declare type InvokeDiffusersModelConfigProps = {
  };
 };

+export declare type InvokeModelConversionProps = {
+  model_name: string;
+  save_location: string;
+  custom_location: string | null;
+};
+
 /**
 * These types type data received from the server via socketio.
 */
@ -228,6 +234,11 @@ export declare type ModelChangeResponse = {
  model_list: ModelList;
 };

+export declare type ModelConvertedResponse = {
+  converted_model_name: string;
+  model_list: ModelList;
+};
+
 export declare type ModelAddedResponse = {
  new_model_name: string;
  model_list: ModelList;
--- a/invokeai/frontend/src/app/socketio/actions.ts
+++ b/invokeai/frontend/src/app/socketio/actions.ts
@ -38,6 +38,11 @@ export const addNewModel = createAction<

 export const deleteModel = createAction<string>('socketio/deleteModel');

+export const convertToDiffusers =
+  createAction<InvokeAI.InvokeModelConversionProps>(
+    'socketio/convertToDiffusers'
+  );
+
 export const requestModelChange = createAction<string>(
  'socketio/requestModelChange'
 );
--- a/invokeai/frontend/src/app/socketio/emitters.ts
+++ b/invokeai/frontend/src/app/socketio/emitters.ts
@ -15,6 +15,7 @@ import {
  addLogEntry,
  generationRequested,
  modelChangeRequested,
+  modelConvertRequested,
  setIsProcessing,
 } from 'features/system/store/systemSlice';
 import { InvokeTabName } from 'features/ui/store/tabMap';
@ -178,6 +179,12 @@ const makeSocketIOEmitters = (
    emitDeleteModel: (modelName: string) => {
      socketio.emit('deleteModel', modelName);
    },
+    emitConvertToDiffusers: (
+      modelToConvert: InvokeAI.InvokeModelConversionProps
+    ) => {
+      dispatch(modelConvertRequested());
+      socketio.emit('convertToDiffusers', modelToConvert);
+    },
    emitRequestModelChange: (modelName: string) => {
      dispatch(modelChangeRequested());
      socketio.emit('requestModelChange', modelName);
--- a/invokeai/frontend/src/app/socketio/listeners.ts
+++ b/invokeai/frontend/src/app/socketio/listeners.ts
@ -365,6 +365,7 @@ const makeSocketIOListeners = (
      const { new_model_name, model_list, update } = data;
      dispatch(setModelList(model_list));
      dispatch(setIsProcessing(false));
+      dispatch(setCurrentStatus(i18n.t('modelmanager:modelAdded')));
      dispatch(
        addLogEntry({
          timestamp: dateFormat(new Date(), 'isoDateTime'),
@ -407,6 +408,30 @@ const makeSocketIOListeners = (
        })
      );
    },
+    onModelConverted: (data: InvokeAI.ModelConvertedResponse) => {
+      const { converted_model_name, model_list } = data;
+      dispatch(setModelList(model_list));
+      dispatch(setCurrentStatus(i18n.t('common:statusModelConverted')));
+      dispatch(setIsProcessing(false));
+      dispatch(setIsCancelable(true));
+      dispatch(
+        addLogEntry({
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
+          message: `Model converted: ${converted_model_name}`,
+          level: 'info',
+        })
+      );
+      dispatch(
+        addToast({
+          title: `${i18n.t(
+            'modelmanager:modelConverted'
+          )}: ${converted_model_name}`,
+          status: 'success',
+          duration: 2500,
+          isClosable: true,
+        })
+      );
+    },
    onModelChanged: (data: InvokeAI.ModelChangeResponse) => {
      const { model_name, model_list } = data;
      dispatch(setModelList(model_list));
--- a/invokeai/frontend/src/app/socketio/middleware.ts
+++ b/invokeai/frontend/src/app/socketio/middleware.ts
@ -48,6 +48,7 @@ export const socketioMiddleware = () => {
      onFoundModels,
      onNewModelAdded,
      onModelDeleted,
+      onModelConverted,
      onModelChangeFailed,
      onTempFolderEmptied,
    } = makeSocketIOListeners(store);
@ -64,6 +65,7 @@ export const socketioMiddleware = () => {
      emitSearchForModels,
      emitAddNewModel,
      emitDeleteModel,
+      emitConvertToDiffusers,
      emitRequestModelChange,
      emitSaveStagingAreaImageToGallery,
      emitRequestEmptyTempFolder,
@ -125,6 +127,10 @@ export const socketioMiddleware = () => {
        onModelDeleted(data);
      });

+      socketio.on('modelConverted', (data: InvokeAI.ModelConvertedResponse) => {
+        onModelConverted(data);
+      });
+
      socketio.on('modelChanged', (data: InvokeAI.ModelChangeResponse) => {
        onModelChanged(data);
      });
@ -199,6 +205,11 @@ export const socketioMiddleware = () => {
        break;
      }

+      case 'socketio/convertToDiffusers': {
+        emitConvertToDiffusers(action.payload);
+        break;
+      }
+
      case 'socketio/requestModelChange': {
        emitRequestModelChange(action.payload);
        break;
--- a/invokeai/frontend/src/common/util/parameterTranslation.ts
+++ b/invokeai/frontend/src/common/util/parameterTranslation.ts
@ -178,12 +178,16 @@ export const frontendToBackendParameters = (
    ? randomInt(NUMPY_RAND_MIN, NUMPY_RAND_MAX)
    : seed;

-  // parameters common to txt2img and img2img
-  if (['txt2img', 'img2img'].includes(generationMode)) {
-    generationParameters.seamless = seamless;
+  // txt2img exclusive parameters
+  if (generationMode === 'txt2img') {
    generationParameters.hires_fix = hiresFix;

    if (hiresFix) generationParameters.strength = hiresStrength;
+  }
+
+  // parameters common to txt2img and img2img
+  if (['txt2img', 'img2img'].includes(generationMode)) {
+    generationParameters.seamless = seamless;

    if (shouldRunESRGAN) {
      esrganParameters = {
--- a/invokeai/frontend/src/features/system/components/ModelManager/CheckpointModelEdit.tsx
+++ b/invokeai/frontend/src/features/system/components/ModelManager/CheckpointModelEdit.tsx
@ -27,6 +27,7 @@ import type { InvokeModelConfigProps } from 'app/invokeai';
 import type { RootState } from 'app/store';
 import type { FieldInputProps, FormikProps } from 'formik';
 import { isEqual, pickBy } from 'lodash';
+import ModelConvert from './ModelConvert';

 const selector = createSelector(
  [systemSelector],
@ -101,10 +102,11 @@ export default function CheckpointModelEdit() {

  return openModel ? (
    <Flex flexDirection="column" rowGap="1rem" width="100%">
-      <Flex alignItems="center">
+      <Flex alignItems="center" gap={4} justifyContent="space-between">
        <Text fontSize="lg" fontWeight="bold">
          {openModel}
        </Text>
+        <ModelConvert model={openModel} />
      </Flex>
      <Flex
        flexDirection="column"
--- a/invokeai/frontend/src/features/system/components/ModelManager/ModelConvert.tsx
+++ b/invokeai/frontend/src/features/system/components/ModelManager/ModelConvert.tsx
@ -0,0 +1,148 @@
+import {
+  Flex,
+  ListItem,
+  Radio,
+  RadioGroup,
+  Text,
+  UnorderedList,
+  Tooltip,
+} from '@chakra-ui/react';
+import { convertToDiffusers } from 'app/socketio/actions';
+import { RootState } from 'app/store';
+import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+import IAIAlertDialog from 'common/components/IAIAlertDialog';
+import IAIButton from 'common/components/IAIButton';
+import IAIInput from 'common/components/IAIInput';
+import { useState, useEffect } from 'react';
+import { useTranslation } from 'react-i18next';
+
+interface ModelConvertProps {
+  model: string;
+}
+
+export default function ModelConvert(props: ModelConvertProps) {
+  const { model } = props;
+
+  const model_list = useAppSelector(
+    (state: RootState) => state.system.model_list
+  );
+
+  const retrievedModel = model_list[model];
+
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const isProcessing = useAppSelector(
+    (state: RootState) => state.system.isProcessing
+  );
+
+  const isConnected = useAppSelector(
+    (state: RootState) => state.system.isConnected
+  );
+
+  const [saveLocation, setSaveLocation] = useState<string>('same');
+  const [customSaveLocation, setCustomSaveLocation] = useState<string>('');
+
+  useEffect(() => {
+    setSaveLocation('same');
+  }, [model]);
+
+  const modelConvertCancelHandler = () => {
+    setSaveLocation('same');
+  };
+
+  const modelConvertHandler = () => {
+    const modelToConvert = {
+      model_name: model,
+      save_location: saveLocation,
+      custom_location:
+        saveLocation === 'custom' && customSaveLocation !== ''
+          ? customSaveLocation
+          : null,
+    };
+    dispatch(convertToDiffusers(modelToConvert));
+  };
+
+  return (
+    <IAIAlertDialog
+      title={`${t('modelmanager:convert')} ${model}`}
+      acceptCallback={modelConvertHandler}
+      cancelCallback={modelConvertCancelHandler}
+      acceptButtonText={`${t('modelmanager:convert')}`}
+      triggerComponent={
+        <IAIButton
+          size={'sm'}
+          aria-label={t('modelmanager:convertToDiffusers')}
+          isDisabled={
+            retrievedModel.status === 'active' || isProcessing || !isConnected
+          }
+          className=" modal-close-btn"
+          marginRight="2rem"
+        >
+          🧨 {t('modelmanager:convertToDiffusers')}
+        </IAIButton>
+      }
+      motionPreset="slideInBottom"
+    >
+      <Flex flexDirection="column" rowGap={4}>
+        <Text>{t('modelmanager:convertToDiffusersHelpText1')}</Text>
+        <UnorderedList>
+          <ListItem>{t('modelmanager:convertToDiffusersHelpText2')}</ListItem>
+          <ListItem>{t('modelmanager:convertToDiffusersHelpText3')}</ListItem>
+          <ListItem>{t('modelmanager:convertToDiffusersHelpText4')}</ListItem>
+          <ListItem>{t('modelmanager:convertToDiffusersHelpText5')}</ListItem>
+        </UnorderedList>
+        <Text>{t('modelmanager:convertToDiffusersHelpText6')}</Text>
+      </Flex>
+
+      <Flex flexDir="column" gap={4}>
+        <Flex marginTop="1rem" flexDir="column" gap={2}>
+          <Text fontWeight="bold">
+            {t('modelmanager:convertToDiffusersSaveLocation')}
+          </Text>
+          <RadioGroup value={saveLocation} onChange={(v) => setSaveLocation(v)}>
+            <Flex gap={4}>
+              <Radio value="same">
+                <Tooltip label="Save converted model in the same folder">
+                  {t('modelmanager:sameFolder')}
+                </Tooltip>
+              </Radio>
+
+              <Radio value="root">
+                <Tooltip label="Save converted model in the InvokeAI root folder">
+                  {t('modelmanager:invokeRoot')}
+                </Tooltip>
+              </Radio>
+
+              <Radio value="custom">
+                <Tooltip label="Save converted model in a custom folder">
+                  {t('modelmanager:custom')}
+                </Tooltip>
+              </Radio>
+            </Flex>
+          </RadioGroup>
+        </Flex>
+
+        {saveLocation === 'custom' && (
+          <Flex flexDirection="column" rowGap={2}>
+            <Text
+              fontWeight="bold"
+              fontSize="sm"
+              color="var(--text-color-secondary)"
+            >
+              {t('modelmanager:customSaveLocation')}
+            </Text>
+            <IAIInput
+              value={customSaveLocation}
+              onChange={(e) => {
+                if (e.target.value !== '')
+                  setCustomSaveLocation(e.target.value);
+              }}
+              width="25rem"
+            />
+          </Flex>
+        )}
+      </Flex>
+    </IAIAlertDialog>
+  );
+}
--- a/invokeai/frontend/src/features/system/components/ModelManager/ModelListItem.tsx
+++ b/invokeai/frontend/src/features/system/components/ModelManager/ModelListItem.tsx
@ -83,6 +83,7 @@ export default function ModelListItem(props: ModelListItemProps) {
        >
          {t('modelmanager:load')}
        </Button>
+
        <IAIIconButton
          icon={<EditIcon />}
          size={'sm'}
--- a/invokeai/frontend/src/features/system/components/ModelManager/SearchModels.tsx
+++ b/invokeai/frontend/src/features/system/components/ModelManager/SearchModels.tsx
@ -3,7 +3,16 @@ import IAICheckbox from 'common/components/IAICheckbox';
 import IAIIconButton from 'common/components/IAIIconButton';
 import React from 'react';

-import { Box, Flex, FormControl, HStack, Text, VStack } from '@chakra-ui/react';
+import {
+  Box,
+  Flex,
+  FormControl,
+  HStack,
+  Radio,
+  RadioGroup,
+  Text,
+  VStack,
+} from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/storeHooks';
 import { systemSelector } from 'features/system/store/systemSelectors';
@ -135,6 +144,8 @@ export default function SearchModels() {
  );

  const [modelsToAdd, setModelsToAdd] = React.useState<string[]>([]);
+  const [modelType, setModelType] = React.useState<string>('v1');
+  const [pathToConfig, setPathToConfig] = React.useState<string>('');

  const resetSearchModelHandler = () => {
    dispatch(setSearchFolder(null));
@ -167,11 +178,19 @@ export default function SearchModels() {
    const modelsToBeAdded = foundModels?.filter((foundModel) =>
      modelsToAdd.includes(foundModel.name)
    );
+
+    const configFiles = {
+      v1: 'configs/stable-diffusion/v1-inference.yaml',
+      v2: 'configs/stable-diffusion/v2-inference-v.yaml',
+      inpainting: 'configs/stable-diffusion/v1-inpainting-inference.yaml',
+      custom: pathToConfig,
+    };
+
    modelsToBeAdded?.forEach((model) => {
      const modelFormat = {
        name: model.name,
        description: '',
-        config: 'configs/stable-diffusion/v1-inference.yaml',
+        config: configFiles[modelType as keyof typeof configFiles],
        weights: model.location,
        vae: '',
        width: 512,
@ -346,6 +365,55 @@ export default function SearchModels() {
              {t('modelmanager:addSelected')}
            </IAIButton>
          </Flex>
+
+          <Flex
+            gap={4}
+            backgroundColor="var(--background-color)"
+            padding="1rem 1rem"
+            borderRadius="0.2rem"
+            flexDirection="column"
+          >
+            <Flex gap={4}>
+              <Text fontWeight="bold" color="var(--text-color-secondary)">
+                Pick Model Type:
+              </Text>
+              <RadioGroup
+                value={modelType}
+                onChange={(v) => setModelType(v)}
+                defaultValue="v1"
+                name="model_type"
+              >
+                <Flex gap={4}>
+                  <Radio value="v1">{t('modelmanager:v1')}</Radio>
+                  <Radio value="v2">{t('modelmanager:v2')}</Radio>
+                  <Radio value="inpainting">
+                    {t('modelmanager:inpainting')}
+                  </Radio>
+                  <Radio value="custom">{t('modelmanager:customConfig')}</Radio>
+                </Flex>
+              </RadioGroup>
+            </Flex>
+
+            {modelType === 'custom' && (
+              <Flex flexDirection="column" rowGap={2}>
+                <Text
+                  fontWeight="bold"
+                  fontSize="sm"
+                  color="var(--text-color-secondary)"
+                >
+                  {t('modelmanager:pathToCustomConfig')}
+                </Text>
+                <IAIInput
+                  value={pathToConfig}
+                  onChange={(e) => {
+                    if (e.target.value !== '') setPathToConfig(e.target.value);
+                  }}
+                  width="42.5rem"
+                />
+              </Flex>
+            )}
+          </Flex>
+
          <Flex
            rowGap="1rem"
            flexDirection="column"
--- a/invokeai/frontend/src/features/system/store/systemSlice.ts
+++ b/invokeai/frontend/src/features/system/store/systemSlice.ts
@ -214,6 +214,12 @@ export const systemSlice = createSlice({
      state.isProcessing = true;
      state.currentStatusHasSteps = false;
    },
+    modelConvertRequested: (state) => {
+      state.currentStatus = i18n.t('common:statusConvertingModel');
+      state.isCancelable = false;
+      state.isProcessing = true;
+      state.currentStatusHasSteps = false;
+    },
    setSaveIntermediatesInterval: (state, action: PayloadAction<number>) => {
      state.saveIntermediatesInterval = action.payload;
    },
@ -265,6 +271,7 @@ export const {
  setModelList,
  setIsCancelable,
  modelChangeRequested,
+  modelConvertRequested,
  setSaveIntermediatesInterval,
  setEnableImageDebugging,
  generationRequested,
--- a/invokeai/frontend/src/features/ui/components/ImageToImage/ImageToImagePanel.tsx
+++ b/invokeai/frontend/src/features/ui/components/ImageToImage/ImageToImagePanel.tsx
@ -1,6 +1,5 @@
 import { Flex } from '@chakra-ui/react';
 import { Feature } from 'app/features';
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
 import FaceRestoreSettings from 'features/parameters/components/AdvancedParameters/FaceRestore/FaceRestoreSettings';
 import FaceRestoreToggle from 'features/parameters/components/AdvancedParameters/FaceRestore/FaceRestoreToggle';
 import ImageFit from 'features/parameters/components/AdvancedParameters/ImageToImage/ImageFit';
@ -16,10 +15,7 @@ import ParametersAccordion from 'features/parameters/components/ParametersAccord
 import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
 import NegativePromptInput from 'features/parameters/components/PromptInput/NegativePromptInput';
 import PromptInput from 'features/parameters/components/PromptInput/PromptInput';
-import { setHiresFix } from 'features/parameters/store/postprocessingSlice';
 import InvokeOptionsPanel from 'features/ui/components/InvokeParametersPanel';
-import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
-import { useEffect } from 'react';
 import { useTranslation } from 'react-i18next';

 export default function ImageToImagePanel() {
@ -56,17 +52,6 @@ export default function ImageToImagePanel() {
    },
  };

-  const dispatch = useAppDispatch();
-
-  const activeTabName = useAppSelector(activeTabNameSelector);
-
-  useEffect(() => {
-    if (activeTabName === 'img2img') {
-      const handleChangeHiresFix = () => dispatch(setHiresFix(false));
-      handleChangeHiresFix();
-    }
-  }, [activeTabName, dispatch]);
-
  return (
    <InvokeOptionsPanel>
      <Flex flexDir="column" rowGap="0.5rem">
--- a/invokeai/frontend/stats.html
+++ b/invokeai/frontend/stats.html
--- a/ldm/invoke/generator/base.py
+++ b/ldm/invoke/generator/base.py
@ -247,11 +247,14 @@ class Generator:
        fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device
        # limit noise to only the diffusion image channels, not the mask channels
        input_channels = min(self.latent_channels, 4)
+        # round up to the nearest block of 8
+        temp_width = int((width + 7) / 8) * 8
+        temp_height = int((height + 7) / 8) * 8
        noise = torch.stack([
-            rand_perlin_2d((height, width),
+            rand_perlin_2d((temp_height, temp_width),
                           (8, 8),
                           device = self.model.device).to(fixdevice) for _ in range(input_channels)], dim=0).to(self.model.device)
-        return noise
+        return noise[0:4, 0:height, 0:width]

    def new_seed(self):
        self.seed = random.randrange(0, np.iinfo(np.uint32).max)
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@ -35,7 +35,7 @@ from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer

 from ldm.invoke.globals import Globals
-from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent, ThresholdSettings
+from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent, PostprocessingSettings
 from ldm.modules.textual_inversion_manager import TextualInversionManager


@ -200,8 +200,10 @@ class ConditioningData:
    """
    extra: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo] = None
    scheduler_args: dict[str, Any] = field(default_factory=dict)
-    """Additional arguments to pass to scheduler.step."""
-    threshold: Optional[ThresholdSettings] = None
+    """
+    Additional arguments to pass to invokeai_diffuser.do_latent_postprocessing().
+    """
+    postprocessing_settings: Optional[PostprocessingSettings] = None

    @property
    def dtype(self):
@ -420,6 +422,15 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                                        total_step_count=len(timesteps),
                                        additional_guidance=additional_guidance)
                latents = step_output.prev_sample
+
+                latents = self.invokeai_diffuser.do_latent_postprocessing(
+                    postprocessing_settings=conditioning_data.postprocessing_settings,
+                    latents=latents,
+                    sigma=batched_t,
+                    step_index=i,
+                    total_step_count=len(timesteps)
+                )
+
                predicted_original = getattr(step_output, 'pred_original_sample', None)

                # TODO resuscitate attention map saving
@ -456,7 +467,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
            conditioning_data.guidance_scale,
            step_index=step_index,
            total_step_count=total_step_count,
-            threshold=conditioning_data.threshold
        )

        # compute the previous noisy sample x_t -> x_t-1
--- a/ldm/invoke/generator/img2img.py
+++ b/ldm/invoke/generator/img2img.py
@ -7,7 +7,7 @@ from diffusers import logging

 from ldm.invoke.generator.base import Generator
 from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline, ConditioningData
-from ldm.models.diffusion.shared_invokeai_diffusion import ThresholdSettings
+from ldm.models.diffusion.shared_invokeai_diffusion import PostprocessingSettings


 class Img2Img(Generator):
@ -33,7 +33,7 @@ class Img2Img(Generator):
        conditioning_data = (
            ConditioningData(
                uc, c, cfg_scale, extra_conditioning_info,
-                threshold = ThresholdSettings(threshold, warmup=0.2) if threshold else None)
+                postprocessing_settings = PostprocessingSettings(threshold, warmup=0.2) if threshold else None)
            .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))


--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@ -6,7 +6,7 @@ import torch

 from .base import Generator
 from .diffusers_pipeline import StableDiffusionGeneratorPipeline, ConditioningData
-from ...models.diffusion.shared_invokeai_diffusion import ThresholdSettings
+from ...models.diffusion.shared_invokeai_diffusion import PostprocessingSettings


 class Txt2Img(Generator):
@ -33,7 +33,7 @@ class Txt2Img(Generator):
        conditioning_data = (
            ConditioningData(
                uc, c, cfg_scale, extra_conditioning_info,
-                threshold = ThresholdSettings(threshold, warmup=0.2) if threshold else None)
+                postprocessing_settings = PostprocessingSettings(threshold, warmup=0.2) if threshold else None)
            .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))

        def make_image(x_T) -> PIL.Image.Image:
--- a/ldm/invoke/generator/txt2img2img.py
+++ b/ldm/invoke/generator/txt2img2img.py
@ -11,7 +11,7 @@ from diffusers.utils.logging import get_verbosity, set_verbosity, set_verbosity_
 from ldm.invoke.generator.base import Generator
 from ldm.invoke.generator.diffusers_pipeline import trim_to_multiple_of, StableDiffusionGeneratorPipeline, \
    ConditioningData
-from ldm.models.diffusion.shared_invokeai_diffusion import ThresholdSettings
+from ldm.models.diffusion.shared_invokeai_diffusion import PostprocessingSettings


 class Txt2Img2Img(Generator):
@ -36,7 +36,7 @@ class Txt2Img2Img(Generator):
        conditioning_data = (
            ConditioningData(
                uc, c, cfg_scale, extra_conditioning_info,
-                threshold = ThresholdSettings(threshold, warmup=0.2) if threshold else None)
+                postprocessing_settings = PostprocessingSettings(threshold=threshold, warmup=0.2) if threshold else None)
            .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))

        def make_image(x_T):
@ -47,7 +47,6 @@ class Txt2Img2Img(Generator):
                conditioning_data=conditioning_data,
                noise=x_T,
                callback=step_callback,
-                # TODO: threshold = threshold,
            )

            # Get our initial generation width and height directly from the latent output so
--- a/ldm/invoke/globals.py
+++ b/ldm/invoke/globals.py
@ -33,7 +33,7 @@ Globals.models_file = 'models.yaml'
 Globals.models_dir = 'models'
 Globals.config_dir = 'configs'
 Globals.autoscan_dir = 'weights'
-Globals.converted_ckpts_dir = 'converted-ckpts'
+Globals.converted_ckpts_dir = 'converted_ckpts'

 # Try loading patchmatch
 Globals.try_patchmatch = True
@ -66,6 +66,9 @@ def global_models_dir()->Path:
 def global_autoscan_dir()->Path:
    return Path(Globals.root, Globals.autoscan_dir)

+def global_converted_ckpts_dir()->Path:
+    return Path(global_models_dir(), Globals.converted_ckpts_dir)
+
 def global_set_root(root_dir:Union[str,Path]):
    Globals.root = root_dir

--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@ -888,15 +888,17 @@ class ModelManager(object):
        models_folder_safetensors = Path(search_folder).glob("**/*.safetensors")

        ckpt_files = [x for x in models_folder_ckpt if x.is_file()]
-        safetensor_files = [x for x in models_folder_safetensors if x.is_file]
+        safetensor_files = [x for x in models_folder_safetensors if x.is_file()]

        files = ckpt_files + safetensor_files

        found_models = []
        for file in files:
-            found_models.append(
-                {"name": file.stem, "location": str(file.resolve()).replace("\\", "/")}
-            )
+            location = str(file.resolve()).replace("\\", "/")
+            if 'model.safetensors' not in location and 'diffusion_pytorch_model.safetensors' not in location:
+                found_models.append(
+                    {"name": file.stem, "location": location}
+                )

        return search_folder, found_models

--- a/ldm/models/diffusion/shared_invokeai_diffusion.py
+++ b/ldm/models/diffusion/shared_invokeai_diffusion.py
@ -15,7 +15,7 @@ from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver


@dataclass(frozen=True)
-class ThresholdSettings:
+class PostprocessingSettings:
    threshold: float
    warmup: float

@ -121,7 +121,6 @@ class InvokeAIDiffuserComponent:
                                unconditional_guidance_scale: float,
                                step_index: Optional[int]=None,
                                total_step_count: Optional[int]=None,
-                                threshold: Optional[ThresholdSettings]=None,
                          ):
        """
        :param x: current latents
@ -130,7 +129,6 @@ class InvokeAIDiffuserComponent:
        :param conditioning: embeddings for conditioned output. for hybrid conditioning this is a dict of tensors [B x 77 x 768], otherwise a single tensor [B x 77 x 768]
        :param unconditional_guidance_scale: aka CFG scale, controls how much effect the conditioning tensor has
        :param step_index: counts upwards from 0 to (step_count-1) (as passed to setup_cross_attention_control, if using). May be called multiple times for a single step, therefore do not assume that its value will monotically increase. If None, will be estimated by comparing sigma against self.model.sigmas .
-        :param threshold: threshold to apply after each step
        :return: the new latents after applying the model to x using unscaled unconditioning and CFG-scaled conditioning.
        """

@ -138,15 +136,7 @@ class InvokeAIDiffuserComponent:
        cross_attention_control_types_to_do = []
        context: Context = self.cross_attention_control_context
        if self.cross_attention_control_context is not None:
-            if step_index is not None and total_step_count is not None:
-                # 🧨diffusers codepath
-                percent_through = step_index / total_step_count  # will never reach 1.0 - this is deliberate
-            else:
-                # legacy compvis codepath
-                # TODO remove when compvis codepath support is dropped
-                if step_index is None and sigma is None:
-                    raise ValueError(f"Either step_index or sigma is required when doing cross attention control, but both are None.")
-                percent_through = self.estimate_percent_through(step_index, sigma)
+            percent_through = self.calculate_percent_through(sigma, step_index, total_step_count)
            cross_attention_control_types_to_do = context.get_active_cross_attention_control_types_for_step(percent_through)

        wants_cross_attention_control = (len(cross_attention_control_types_to_do) > 0)
@ -161,11 +151,34 @@ class InvokeAIDiffuserComponent:

        combined_next_x = self._combine(unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale)

-        if threshold:
-            combined_next_x = self._threshold(threshold.threshold, threshold.warmup, combined_next_x, sigma)
-
        return combined_next_x

+    def do_latent_postprocessing(
+        self,
+        postprocessing_settings: PostprocessingSettings,
+        latents: torch.Tensor,
+        sigma,
+        step_index,
+        total_step_count
+    ) -> torch.Tensor:
+        if postprocessing_settings is not None:
+            percent_through = self.calculate_percent_through(sigma, step_index, total_step_count)
+            latents = self.apply_threshold(postprocessing_settings, latents, percent_through)
+        return latents
+
+    def calculate_percent_through(self, sigma, step_index, total_step_count):
+        if step_index is not None and total_step_count is not None:
+            # 🧨diffusers codepath
+            percent_through = step_index / total_step_count  # will never reach 1.0 - this is deliberate
+        else:
+            # legacy compvis codepath
+            # TODO remove when compvis codepath support is dropped
+            if step_index is None and sigma is None:
+                raise ValueError(
+                    f"Either step_index or sigma is required when doing cross attention control, but both are None.")
+            percent_through = self.estimate_percent_through(step_index, sigma)
+        return percent_through
+
    # methods below are called from do_diffusion_step and should be considered private to this class.

    def apply_standard_conditioning(self, x, sigma, unconditioning, conditioning):
@ -275,17 +288,23 @@ class InvokeAIDiffuserComponent:
        combined_next_x = unconditioned_next_x + scaled_delta
        return combined_next_x

-    def _threshold(self, threshold, warmup, latents: torch.Tensor, sigma) -> torch.Tensor:
-        warmup_scale = (1 - sigma.item() / 1000) / warmup if warmup else math.inf
-        if warmup_scale < 1:
-            # This arithmetic based on https://github.com/invoke-ai/InvokeAI/pull/395
-            warming_threshold = 1 + (threshold - 1) * warmup_scale
-            current_threshold = np.clip(warming_threshold, 1, threshold)
+    def apply_threshold(
+        self,
+        postprocessing_settings: PostprocessingSettings,
+        latents: torch.Tensor,
+        percent_through
+    ) -> torch.Tensor:
+        threshold = postprocessing_settings.threshold
+        warmup = postprocessing_settings.warmup
+
+        if percent_through < warmup:
+            current_threshold = threshold + threshold * 5 * (1 - (percent_through / warmup))
        else:
            current_threshold = threshold

        if current_threshold <= 0:
            return latents
+
        maxval = latents.max().item()
        minval = latents.min().item()

@ -294,25 +313,34 @@ class InvokeAIDiffuserComponent:
        if self.debug_thresholding:
            std, mean = [i.item() for i in torch.std_mean(latents)]
            outside = torch.count_nonzero((latents < -current_threshold) | (latents > current_threshold))
-            print(f"\nThreshold: 𝜎={sigma.item()} threshold={current_threshold:.3f} (of {threshold:.3f})\n"
+            print(f"\nThreshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})\n"
                  f"  | min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}\n"
                  f"  | {outside / latents.numel() * 100:.2f}% values outside threshold")

        if maxval < current_threshold and minval > -current_threshold:
            return latents

+        num_altered = 0
+
+        # MPS torch.rand_like is fine because torch.rand_like is wrapped in generate.py!
+
        if maxval > current_threshold:
+            latents = torch.clone(latents)
            maxval = np.clip(maxval * scale, 1, current_threshold)
+            num_altered += torch.count_nonzero(latents > maxval)
+            latents[latents > maxval] = torch.rand_like(latents[latents > maxval]) * maxval

        if minval < -current_threshold:
+            latents = torch.clone(latents)
            minval = np.clip(minval * scale, -current_threshold, -1)
+            num_altered += torch.count_nonzero(latents < minval)
+            latents[latents < minval] = torch.rand_like(latents[latents < minval]) * minval

        if self.debug_thresholding:
-            outside = torch.count_nonzero((latents < minval) | (latents > maxval))
            print(f"  | min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})\n"
-                  f"  | {outside / latents.numel() * 100:.2f}% values will be clamped")
+                  f"  | {num_altered / latents.numel() * 100:.2f}% values altered")

-        return latents.clamp(minval, maxval)
+        return latents

    def estimate_percent_through(self, step_index, sigma):
        if step_index is not None and self.cross_attention_control_context is not None:
@ -376,4 +404,3 @@ class InvokeAIDiffuserComponent:
        # assert(0 == len(torch.nonzero(old_return_value - (uncond_latents + deltas_merged * cond_scale))))

        return uncond_latents + deltas_merged * global_guidance_scale
-
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@ -5,5 +5,5 @@ import warnings
 from ldm.invoke.config import invokeai_configure

 if __name__ == '__main__':
-    warnings.warn("configure_invokeai.py is deprecated, please run 'invokai-configure'", DeprecationWarning)
-    configure_invokeai.main()
+    warnings.warn("configure_invokeai.py is deprecated, running 'invokeai-configure'...", DeprecationWarning)
+    invokeai_configure.main()