pass regression tests when translator package not installed

This is a relatively stable release that corrects the urgent windows
install and model manager problems in 3.0.1. It still has two known
bugs:

1. Many inpainting models are not loading correctly.
2. The merge script is failing to start.

docs/installation/010_INSTALL_AUTOMATED.md

@@ -394,7 +394,7 @@ rm .\.venv -r -force
 python -mvenv .venv
 .\.venv\Scripts\activate
 pip install invokeai
-invokeai-configure --root .
+invokeai-configure --yes --root .
 ```
 
 If you see anything marked as an error during this process please stop

docs/nodes/communityNodes.md

@@ -14,20 +14,25 @@ The nodes linked below have been developed and contributed by members of the Inv
 
 ## List of Nodes
 
-### Face Mask
+### FaceTools
 
-**Description:** This node autodetects a face in the image using MediaPipe and masks it by making it transparent. Via outpainting you can swap faces with other faces, or invert the mask and swap things around the face with other things. Additionally, you can supply X and Y offset values to scale/change the shape of the mask for finer control. The node also outputs an all-white mask in the same dimensions as the input image. This is needed by the inpaint node (and unified canvas) for outpainting.
+**Description:** FaceTools is a collection of nodes created to manipulate faces as you would in Unified Canvas. It includes FaceMask, FaceOff, and FacePlace. FaceMask autodetects a face in the image using MediaPipe and creates a mask from it. FaceOff similarly detects a face, then takes the face off of the image by adding a square bounding box around it and cropping/scaling it. FacePlace puts the bounded face image from FaceOff back onto the original image. Using these nodes with other inpainting node(s), you can put new faces on existing things, put new things around existing faces, and work closer with a face as a bounded image. Additionally, you can supply X and Y offset values to scale/change the shape of the mask for finer control on FaceMask and FaceOff. See GitHub repository below for usage examples.
 
-**Node Link:** https://github.com/ymgenesis/InvokeAI/blob/facemaskmediapipe/invokeai/app/invocations/facemask.py
+**Node Link:** https://github.com/ymgenesis/FaceTools/
 
-**Example Node Graph:** https://www.mediafire.com/file/gohn5sb1bfp8use/21-July_2023-FaceMask.json/file
+**FaceMask Output Examples** 
 
-**Output Examples** 
+![5cc8abce-53b0-487a-b891-3bf94dcc8960](https://github.com/invoke-ai/InvokeAI/assets/25252829/43f36d24-1429-4ab1-bd06-a4bedfe0955e)
+![b920b710-1882-49a0-8d02-82dff2cca907](https://github.com/invoke-ai/InvokeAI/assets/25252829/7660c1ed-bf7d-4d0a-947f-1fc1679557ba)
+![71a91805-fda5-481c-b380-264665703133](https://github.com/invoke-ai/InvokeAI/assets/25252829/f8f6a2ee-2b68-4482-87da-b90221d5c3e2)
 
-![2e3168cb-af6a-475d-bfac-c7b7fd67b4c2](https://github.com/ymgenesis/InvokeAI/assets/25252829/a5ad7d44-2ada-4b3c-a56e-a21f8244a1ac)
-![2_annotated](https://github.com/ymgenesis/InvokeAI/assets/25252829/53416c8a-a23b-4d76-bb6d-3cfd776e0096)
-![2fe2150c-fd08-4e26-8c36-f0610bf441bb](https://github.com/ymgenesis/InvokeAI/assets/25252829/b0f7ecfe-f093-4147-a904-b9f131b41dc9)
-![831b6b98-4f0f-4360-93c8-69a9c1338cbe](https://github.com/ymgenesis/InvokeAI/assets/25252829/fc7b0622-e361-4155-8a76-082894d084f0)
+<hr>
+
+### Ideal Size
+
+**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
+
+**Node Link:** https://github.com/JPPhoto/ideal-size-node
 
 --------------------------------
 ### Super Cool Node Template
@@ -42,11 +47,5 @@ The nodes linked below have been developed and contributed by members of the Inv
 
 ![Invoke AI](https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png)
 
-### Ideal Size
-
-**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
-
-**Node Link:** https://github.com/JPPhoto/ideal-size-node
-
 ## Help
 If you run into any issues with a node, please post in the [InvokeAI Discord](https://discord.gg/ZmtBAhwWhy). 

flake.lock

@@ -0,0 +1,25 @@
+{
+  "nodes": {
+    "nixpkgs": {
+      "locked": {
+        "lastModified": 1690630721,
+        "narHash": "sha256-Y04onHyBQT4Erfr2fc82dbJTfXGYrf4V0ysLUYnPOP8=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "d2b52322f35597c62abf56de91b0236746b2a03d",
+        "type": "github"
+      },
+      "original": {
+        "id": "nixpkgs",
+        "type": "indirect"
+      }
+    },
+    "root": {
+      "inputs": {
+        "nixpkgs": "nixpkgs"
+      }
+    }
+  },
+  "root": "root",
+  "version": 7
+}

flake.nix

@@ -0,0 +1,81 @@
+# Important note: this flake does not attempt to create a fully isolated, 'pure'
+# Python environment for InvokeAI. Instead, it depends on local invocations of
+# virtualenv/pip to install the required (binary) packages, most importantly the
+# prebuilt binary pytorch packages with CUDA support.
+# ML Python packages with CUDA support, like pytorch, are notoriously expensive
+# to compile so it's purposefuly not what this flake does.
+
+{
+  description = "An (impure) flake to develop on InvokeAI.";
+
+  outputs = { self, nixpkgs }:
+    let
+      system = "x86_64-linux";
+      pkgs = import nixpkgs {
+        inherit system;
+        config.allowUnfree = true;
+      };
+
+      python = pkgs.python310;
+
+      mkShell = { dir, install }:
+        let
+          setupScript = pkgs.writeScript "setup-invokai" ''
+            # This must be sourced using 'source', not executed.
+            ${python}/bin/python -m venv ${dir}
+            ${dir}/bin/python -m pip install ${install}
+            # ${dir}/bin/python -c 'import torch; assert(torch.cuda.is_available())'
+            source ${dir}/bin/activate
+          '';
+        in
+        pkgs.mkShell rec {
+          buildInputs = with pkgs; [
+            # Backend: graphics, CUDA.
+            cudaPackages.cudnn
+            cudaPackages.cuda_nvrtc
+            cudatoolkit
+            freeglut
+            glib
+            gperf
+            procps
+            libGL
+            libGLU
+            linuxPackages.nvidia_x11
+            python
+            stdenv.cc
+            stdenv.cc.cc.lib
+            xorg.libX11
+            xorg.libXext
+            xorg.libXi
+            xorg.libXmu
+            xorg.libXrandr
+            xorg.libXv
+            zlib
+
+            # Pre-commit hooks.
+            black
+
+            # Frontend.
+            yarn
+            nodejs
+          ];
+          LD_LIBRARY_PATH = pkgs.lib.makeLibraryPath buildInputs;
+          CUDA_PATH = pkgs.cudatoolkit;
+          EXTRA_LDFLAGS = "-L${pkgs.linuxPackages.nvidia_x11}/lib";
+          shellHook = ''
+            if [[ -f "${dir}/bin/activate" ]]; then
+              source "${dir}/bin/activate"
+              echo "Using Python: $(which python)"
+            else
+              echo "Use 'source ${setupScript}' to set up the environment."
+            fi
+          '';
+        };
+    in
+    {
+      devShells.${system} = rec {
+        develop = mkShell { dir = "venv"; install = "-e '.[xformers]' --extra-index-url https://download.pytorch.org/whl/cu118"; };
+        default = develop;
+      };
+    };
+}

installer/lib/installer.py

@@ -13,7 +13,7 @@ from pathlib import Path
 from tempfile import TemporaryDirectory
 from typing import Union
 
-SUPPORTED_PYTHON = ">=3.9.0,<3.11"
+SUPPORTED_PYTHON = ">=3.9.0,<=3.11.100"
 INSTALLER_REQS = ["rich", "semver", "requests", "plumbum", "prompt-toolkit"]
 BOOTSTRAP_VENV_PREFIX = "invokeai-installer-tmp"
 
@@ -149,7 +149,7 @@ class Installer:
         return venv_dir
 
     def install(
-        self, root: str = "~/invokeai-3", version: str = "latest", yes_to_all=False, find_links: Path = None
+        self, root: str = "~/invokeai", version: str = "latest", yes_to_all=False, find_links: Path = None
     ) -> None:
         """
         Install the InvokeAI application into the given runtime path
@@ -168,7 +168,8 @@ class Installer:
 
         messages.welcome()
 
-        self.dest = Path(root).expanduser().resolve() if yes_to_all else messages.dest_path(root)
+        default_path = os.environ.get("INVOKEAI_ROOT") or Path(root).expanduser().resolve()
+        self.dest = default_path if yes_to_all else messages.dest_path(root)
 
         # create the venv for the app
         self.venv = self.app_venv()
@@ -248,6 +249,9 @@ class InvokeAiInstance:
             pip[
                 "install",
                 "--require-virtualenv",
+                "numpy~=1.24.0",  # choose versions that won't be uninstalled during phase 2
+                "urllib3~=1.26.0",
+                "requests~=2.28.0",
                 "torch~=2.0.0",
                 "torchmetrics==0.11.4",
                 "torchvision>=0.14.1",

installer/lib/main.py

@@ -3,6 +3,7 @@ InvokeAI Installer
 """
 
 import argparse
+import os
 from pathlib import Path
 from installer import Installer
 
@@ -15,7 +16,7 @@ if __name__ == "__main__":
         dest="root",
         type=str,
         help="Destination path for installation",
-        default="~/invokeai",
+        default=os.environ.get("INVOKEAI_ROOT") or "~/invokeai",
     )
     parser.add_argument(
         "-y",

installer/templates/invoke.bat.in

@@ -41,7 +41,7 @@ IF /I "%choice%" == "1" (
     python .venv\Scripts\invokeai-configure.exe --skip-sd-weight --skip-support-models
 ) ELSE IF /I "%choice%" == "7" (
     echo Running invokeai-configure...
-    python .venv\Scripts\invokeai-configure.exe --yes --default_only
+    python .venv\Scripts\invokeai-configure.exe --yes --skip-sd-weight
 ) ELSE IF /I "%choice%" == "8" (
     echo Developer Console
     echo Python command is:

installer/templates/invoke.sh.in

@@ -82,7 +82,7 @@ do_choice() {
     7)
         clear
         printf "Re-run the configure script to fix a broken install or to complete a major upgrade\n"
-        invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
+        invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only --skip-sd-weights
         ;;
     8)
         clear

invokeai/app/invocations/params.py

@@ -4,6 +4,8 @@ from typing import Literal
 
 from pydantic import Field
 
+from invokeai.app.invocations.prompt import PromptOutput
+
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
 from .math import FloatOutput, IntOutput
 
@@ -64,3 +66,18 @@ class ParamStringInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> StringOutput:
         return StringOutput(text=self.text)
+
+
+class ParamPromptInvocation(BaseInvocation):
+    """A prompt input parameter"""
+
+    type: Literal["param_prompt"] = "param_prompt"
+    prompt: str = Field(default="", description="The prompt value")
+
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {"tags": ["param", "prompt"], "title": "Prompt"},
+        }
+
+    def invoke(self, context: InvocationContext) -> PromptOutput:
+        return PromptOutput(prompt=self.prompt)

invokeai/app/invocations/translate.py

@@ -0,0 +1,52 @@
+# Copyright (c) 2023 Lincoln D. Stein
+
+from typing import Literal, Union, List
+from pydantic import Field
+from .baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    InvocationContext,
+    InvocationConfig,
+)
+
+# from .params import StringOutput
+
+translate_available = False
+try:
+    import translators as ts
+
+    translate_available = True
+    TRANSLATORS = tuple(ts.translators_pool)
+except:
+    TRANSLATORS = ("google", "bing")
+
+DEFAULT_PROMPT = "" if translate_available else "To use this node, please 'pip install --upgrade translators'"
+
+
+class TranslateOutput(BaseInvocationOutput):
+    """Translated string output"""
+
+    type: Literal["translated_string_output"] = "translated_string_output"
+    prompt: str = Field(default=None, description="The translated prompt string")
+
+
+class TranslateInvocation(BaseInvocation):
+    """Use the translators package to translate 330 languages into English prompts"""
+
+    # fmt: off
+    type: Literal["translate"] = "translate"
+
+    # Inputs
+    text: str = Field(default=DEFAULT_PROMPT, description="Prompt in any language")
+    translator: Literal[TRANSLATORS] = Field(default="google", description="The translator service to use")
+    # fmt: on
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {"title": "Translate", "tags": ["prompt", "translate", "translator"]},
+        }
+
+    def invoke(self, context: InvocationContext) -> TranslateOutput:
+        translation: str = ts.translate_text(self.text, translator=self.translator)
+        return TranslateOutput(prompt=translation)

invokeai/app/services/config.py

@@ -171,7 +171,6 @@ from pydantic import BaseSettings, Field, parse_obj_as
 from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args
 
 INIT_FILE = Path("invokeai.yaml")
-MODEL_CORE = Path("models/core")
 DB_FILE = Path("invokeai.db")
 LEGACY_INIT_FILE = Path("invokeai.init")
 
@@ -275,7 +274,7 @@ class InvokeAISettings(BaseSettings):
     @classmethod
     def _excluded(self) -> List[str]:
         # internal fields that shouldn't be exposed as command line options
-        return ["type", "initconf"]
+        return ["type", "initconf", "cached_root"]
 
     @classmethod
     def _excluded_from_yaml(self) -> List[str]:
@@ -291,6 +290,7 @@ class InvokeAISettings(BaseSettings):
             "restore",
             "root",
             "nsfw_checker",
+            "cached_root",
         ]
 
     class Config:
@@ -357,7 +357,7 @@ def _find_root() -> Path:
     venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
     if os.environ.get("INVOKEAI_ROOT"):
         root = Path(os.environ.get("INVOKEAI_ROOT")).resolve()
-    elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE, MODEL_CORE]]):
+    elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]]):
         root = (venv.parent).resolve()
     else:
         root = Path("~/invokeai").expanduser().resolve()
@@ -424,6 +424,7 @@ class InvokeAIAppConfig(InvokeAISettings):
     log_level           : Literal[tuple(["debug","info","warning","error","critical"])] = Field(default="info", description="Emit logging messages at this level or  higher", category="Logging")
 
     version             : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
+    cached_root         : Path = Field(default=None,  description="internal use only", category="DEPRECATED")
     # fmt: on
 
     def parse_args(self, argv: List[str] = None, conf: DictConfig = None, clobber=False):
@@ -471,10 +472,15 @@ class InvokeAIAppConfig(InvokeAISettings):
         """
         Path to the runtime root directory
         """
-        if self.root:
-            return Path(self.root).expanduser().absolute()
+        # we cache value of root to protect against it being '.' and the cwd changing
+        if self.cached_root:
+            root = self.cached_root
+        elif self.root:
+            root = Path(self.root).expanduser().absolute()
         else:
-            return self.find_root()
+            root = self.find_root()
+        self.cached_root = root
+        return self.cached_root
 
     @property
     def root_dir(self) -> Path:

invokeai/backend/install/invokeai_configure.py

@@ -181,7 +181,7 @@ def download_with_progress_bar(model_url: str, model_dest: str, label: str = "th
 
 
 def download_conversion_models():
-    target_dir = config.root_path / "models/core/convert"
+    target_dir = config.models_path / "core/convert"
     kwargs = dict()  # for future use
     try:
         logger.info("Downloading core tokenizers and text encoders")

invokeai/backend/install/model_install_backend.py

@@ -7,7 +7,7 @@ import warnings
 from dataclasses import dataclass, field
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import List, Dict, Callable, Union, Set
+from typing import List, Dict, Callable, Union, Set, Optional
 
 import requests
 from diffusers import DiffusionPipeline
@@ -128,7 +128,9 @@ class ModelInstall(object):
             model_dict[key] = ModelLoadInfo(**value)
 
         # supplement with entries in models.yaml
-        installed_models = self.mgr.list_models()
+        installed_models = [x for x in self.mgr.list_models()]
+        # suppresses autoloaded models
+        # installed_models = [x for x in self.mgr.list_models() if not self._is_autoloaded(x)]
 
         for md in installed_models:
             base = md["base_model"]
@@ -147,6 +149,17 @@ class ModelInstall(object):
                 )
         return {x: model_dict[x] for x in sorted(model_dict.keys(), key=lambda y: model_dict[y].name.lower())}
 
+    def _is_autoloaded(self, model_info: dict) -> bool:
+        path = model_info.get("path")
+        if not path:
+            return False
+        for autodir in ["autoimport_dir", "lora_dir", "embedding_dir", "controlnet_dir"]:
+            if autodir_path := getattr(self.config, autodir):
+                autodir_path = self.config.root_path / autodir_path
+                if Path(path).is_relative_to(autodir_path):
+                    return True
+        return False
+
     def list_models(self, model_type):
         installed = self.mgr.list_models(model_type=model_type)
         print(f"Installed models of type `{model_type}`:")
@@ -273,6 +286,7 @@ class ModelInstall(object):
                 logger.error(f"Unable to download {url}. Skipping.")
             info = ModelProbe().heuristic_probe(location)
             dest = self.config.models_path / info.base_type.value / info.model_type.value / location.name
+            dest.parent.mkdir(parents=True, exist_ok=True)
             models_path = shutil.move(location, dest)
 
         # staged version will be garbage-collected at this time
@@ -346,7 +360,7 @@ class ModelInstall(object):
             if key in self.datasets:
                 description = self.datasets[key].get("description") or description
 
-        rel_path = self.relative_to_root(path)
+        rel_path = self.relative_to_root(path, self.config.models_path)
 
         attributes = dict(
             path=str(rel_path),
@@ -386,8 +400,8 @@ class ModelInstall(object):
             attributes.update(dict(config=str(legacy_conf)))
         return attributes
 
-    def relative_to_root(self, path: Path) -> Path:
-        root = self.config.root_path
+    def relative_to_root(self, path: Path, root: Optional[Path] = None) -> Path:
+        root = root or self.config.root_path
         if path.is_relative_to(root):
             return path.relative_to(root)
         else:

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -63,7 +63,7 @@ from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionS
 from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
 
 from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.app.services.config import InvokeAIAppConfig, MODEL_CORE
+from invokeai.app.services.config import InvokeAIAppConfig
 
 from picklescan.scanner import scan_file_path
 from .models import BaseModelType, ModelVariantType
@@ -81,7 +81,7 @@ if is_accelerate_available():
     from accelerate.utils import set_module_tensor_to_device
 
 logger = InvokeAILogger.getLogger(__name__)
-CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().root_path / MODEL_CORE / "convert"
+CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().models_path / "core/convert"
 
 
 def shave_segments(path, n_shave_prefix_segments=1):
@@ -1070,7 +1070,7 @@ def convert_controlnet_checkpoint(
     extract_ema,
     use_linear_projection=None,
     cross_attention_dim=None,
-    precision: torch.dtype = torch.float32,
+    precision: Optional[torch.dtype] = None,
 ):
     ctrlnet_config = create_unet_diffusers_config(original_config, image_size=image_size, controlnet=True)
     ctrlnet_config["upcast_attention"] = upcast_attention
@@ -1111,7 +1111,6 @@ def convert_controlnet_checkpoint(
     return controlnet.to(precision)
 
 
-# TO DO - PASS PRECISION
 def download_from_original_stable_diffusion_ckpt(
     checkpoint_path: str,
     model_version: BaseModelType,
@@ -1121,7 +1120,7 @@ def download_from_original_stable_diffusion_ckpt(
     prediction_type: str = None,
     model_type: str = None,
     extract_ema: bool = False,
-    precision: torch.dtype = torch.float32,
+    precision: Optional[torch.dtype] = None,
     scheduler_type: str = "pndm",
     num_in_channels: Optional[int] = None,
     upcast_attention: Optional[bool] = None,
@@ -1194,6 +1193,8 @@ def download_from_original_stable_diffusion_ckpt(
             [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer)
             to use. If this parameter is `None`, the function will load a new instance of [CLIPTokenizer] by itself, if
             needed.
+        precision (`torch.dtype`, *optional*, defauts to `None`):
+            If not provided the precision will be set to the precision of the original file.
         return: A StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file.
     """
 
@@ -1252,6 +1253,10 @@ def download_from_original_stable_diffusion_ckpt(
 
     logger.debug(f"model_type = {model_type}; original_config_file = {original_config_file}")
 
+    precision_probing_key = "model.diffusion_model.input_blocks.0.0.bias"
+    logger.debug(f"original checkpoint precision == {checkpoint[precision_probing_key].dtype}")
+    precision = precision or checkpoint[precision_probing_key].dtype
+
     if original_config_file is None:
         key_name_v2_1 = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
         key_name_sd_xl_base = "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias"
@@ -1279,9 +1284,12 @@ def download_from_original_stable_diffusion_ckpt(
         original_config_file = BytesIO(requests.get(config_url).content)
 
     original_config = OmegaConf.load(original_config_file)
+    if original_config["model"]["params"].get("use_ema") is not None:
+        extract_ema = original_config["model"]["params"]["use_ema"]
+
     if (
         model_version == BaseModelType.StableDiffusion2
-        and original_config["model"]["params"]["parameterization"] == "v"
+        and original_config["model"]["params"].get("parameterization") == "v"
     ):
         prediction_type = "v_prediction"
         upcast_attention = True
@@ -1447,7 +1455,7 @@ def download_from_original_stable_diffusion_ckpt(
             if controlnet:
                 pipe = pipeline_class(
                     vae=vae.to(precision),
-                    text_encoder=text_model,
+                    text_encoder=text_model.to(precision),
                     tokenizer=tokenizer,
                     unet=unet.to(precision),
                     scheduler=scheduler,
@@ -1459,7 +1467,7 @@ def download_from_original_stable_diffusion_ckpt(
             else:
                 pipe = pipeline_class(
                     vae=vae.to(precision),
-                    text_encoder=text_model,
+                    text_encoder=text_model.to(precision),
                     tokenizer=tokenizer,
                     unet=unet.to(precision),
                     scheduler=scheduler,
@@ -1484,8 +1492,8 @@ def download_from_original_stable_diffusion_ckpt(
                     image_noising_scheduler=image_noising_scheduler,
                     # regular denoising components
                     tokenizer=tokenizer,
-                    text_encoder=text_model,
-                    unet=unet,
+                    text_encoder=text_model.to(precision),
+                    unet=unet.to(precision),
                     scheduler=scheduler,
                     # vae
                     vae=vae,
@@ -1560,7 +1568,7 @@ def download_from_original_stable_diffusion_ckpt(
         if controlnet:
             pipe = pipeline_class(
                 vae=vae.to(precision),
-                text_encoder=text_model,
+                text_encoder=text_model.to(precision),
                 tokenizer=tokenizer,
                 unet=unet.to(precision),
                 controlnet=controlnet,
@@ -1571,7 +1579,7 @@ def download_from_original_stable_diffusion_ckpt(
         else:
             pipe = pipeline_class(
                 vae=vae.to(precision),
-                text_encoder=text_model,
+                text_encoder=text_model.to(precision),
                 tokenizer=tokenizer,
                 unet=unet.to(precision),
                 scheduler=scheduler,
@@ -1594,9 +1602,9 @@ def download_from_original_stable_diffusion_ckpt(
 
             pipe = StableDiffusionXLPipeline(
                 vae=vae.to(precision),
-                text_encoder=text_encoder,
+                text_encoder=text_encoder.to(precision),
                 tokenizer=tokenizer,
-                text_encoder_2=text_encoder_2,
+                text_encoder_2=text_encoder_2.to(precision),
                 tokenizer_2=tokenizer_2,
                 unet=unet.to(precision),
                 scheduler=scheduler,
@@ -1639,7 +1647,7 @@ def download_controlnet_from_original_ckpt(
     original_config_file: str,
     image_size: int = 512,
     extract_ema: bool = False,
-    precision: torch.dtype = torch.float32,
+    precision: Optional[torch.dtype] = None,
     num_in_channels: Optional[int] = None,
     upcast_attention: Optional[bool] = None,
     device: str = None,
@@ -1680,6 +1688,12 @@ def download_controlnet_from_original_ckpt(
     while "state_dict" in checkpoint:
         checkpoint = checkpoint["state_dict"]
 
+    # use original precision
+    precision_probing_key = "input_blocks.0.0.bias"
+    ckpt_precision = checkpoint[precision_probing_key].dtype
+    logger.debug(f"original controlnet precision = {ckpt_precision}")
+    precision = precision or ckpt_precision
+
     original_config = OmegaConf.load(original_config_file)
 
     if num_in_channels is not None:
@@ -1699,7 +1713,7 @@ def download_controlnet_from_original_ckpt(
         cross_attention_dim=cross_attention_dim,
     )
 
-    return controlnet
+    return controlnet.to(precision)
 
 
 def convert_ldm_vae_to_diffusers(checkpoint, vae_config: DictConfig, image_size: int) -> AutoencoderKL:

invokeai/backend/model_management/model_cache.py

@@ -187,7 +187,9 @@ class ModelCache(object):
         # TODO: lock for no copies on simultaneous calls?
         cache_entry = self._cached_models.get(key, None)
         if cache_entry is None:
-            self.logger.info(f"Loading model {model_path}, type {base_model}:{model_type}:{submodel}")
+            self.logger.info(
+                f"Loading model {model_path}, type {base_model.value}:{model_type.value}:{submodel.value if submodel else ''}"
+            )
 
             # this will remove older cached models until
             # there is sufficient room to load the requested model

invokeai/backend/model_management/model_manager.py

@@ -423,7 +423,7 @@ class ModelManager(object):
         return (model_name, base_model, model_type)
 
     def _get_model_cache_path(self, model_path):
-        return self.app_config.models_path / ".cache" / hashlib.md5(str(model_path).encode()).hexdigest()
+        return self.resolve_model_path(Path(".cache") / hashlib.md5(str(model_path).encode()).hexdigest())
 
     @classmethod
     def initialize_model_config(cls, config_path: Path):
@@ -456,7 +456,7 @@ class ModelManager(object):
                 raise ModelNotFoundException(f"Model not found - {model_key}")
 
         model_config = self.models[model_key]
-        model_path = self.app_config.root_path / model_config.path
+        model_path = self.resolve_model_path(model_config.path)
 
         if not model_path.exists():
             if model_class.save_to_config:
@@ -586,7 +586,7 @@ class ModelManager(object):
 
             # expose paths as absolute to help web UI
             if path := model_dict.get("path"):
-                model_dict["path"] = str(self.app_config.root_path / path)
+                model_dict["path"] = str(self.resolve_model_path(path))
             models.append(model_dict)
 
         return models
@@ -623,7 +623,7 @@ class ModelManager(object):
             self.cache.uncache_model(cache_id)
 
         # if model inside invoke models folder - delete files
-        model_path = self.app_config.root_path / model_cfg.path
+        model_path = self.resolve_model_path(model_cfg.path)
         cache_path = self._get_model_cache_path(model_path)
         if cache_path.exists():
             rmtree(str(cache_path))
@@ -654,10 +654,9 @@ class ModelManager(object):
         The returned dict has the same format as the dict returned by
         model_info().
         """
-        # relativize paths as they go in - this makes it easier to move the root directory around
+        # relativize paths as they go in - this makes it easier to move the models directory around
         if path := model_attributes.get("path"):
-            if Path(path).is_relative_to(self.app_config.root_path):
-                model_attributes["path"] = str(Path(path).relative_to(self.app_config.root_path))
+            model_attributes["path"] = str(self.relative_model_path(Path(path)))
 
         model_class = MODEL_CLASSES[base_model][model_type]
         model_config = model_class.create_config(**model_attributes)
@@ -715,7 +714,7 @@ class ModelManager(object):
         if not model_cfg:
             raise ModelNotFoundException(f"Unknown model: {model_key}")
 
-        old_path = self.app_config.root_path / model_cfg.path
+        old_path = self.resolve_model_path(model_cfg.path)
         new_name = new_name or model_name
         new_base = new_base or base_model
         new_key = self.create_key(new_name, new_base, model_type)
@@ -724,15 +723,15 @@ class ModelManager(object):
 
         # if this is a model file/directory that we manage ourselves, we need to move it
         if old_path.is_relative_to(self.app_config.models_path):
-            new_path = (
-                self.app_config.root_path
-                / "models"
-                / BaseModelType(new_base).value
-                / ModelType(model_type).value
-                / new_name
+            new_path = self.resolve_model_path(
+                Path(
+                    BaseModelType(new_base).value,
+                    ModelType(model_type).value,
+                    new_name,
+                )
             )
             move(old_path, new_path)
-            model_cfg.path = str(new_path.relative_to(self.app_config.root_path))
+            model_cfg.path = str(new_path.relative_to(self.app_config.models_path))
 
         # clean up caches
         old_model_cache = self._get_model_cache_path(old_path)
@@ -782,7 +781,7 @@ class ModelManager(object):
             **submodel,
         )
         checkpoint_path = self.app_config.root_path / info["path"]
-        old_diffusers_path = self.app_config.models_path / model.location
+        old_diffusers_path = self.resolve_model_path(model.location)
         new_diffusers_path = (
             dest_directory or self.app_config.models_path / base_model.value / model_type.value
         ) / model_name
@@ -795,7 +794,7 @@ class ModelManager(object):
             info["path"] = (
                 str(new_diffusers_path)
                 if dest_directory
-                else str(new_diffusers_path.relative_to(self.app_config.root_path))
+                else str(new_diffusers_path.relative_to(self.app_config.models_path))
             )
             info.pop("config")
 
@@ -810,6 +809,15 @@ class ModelManager(object):
 
         return result
 
+    def resolve_model_path(self, path: Union[Path, str]) -> Path:
+        """return relative paths based on configured models_path"""
+        return self.app_config.models_path / path
+
+    def relative_model_path(self, model_path: Path) -> Path:
+        if model_path.is_relative_to(self.app_config.models_path):
+            model_path = model_path.relative_to(self.app_config.models_path)
+        return model_path
+
     def search_models(self, search_folder):
         self.logger.info(f"Finding Models In: {search_folder}")
         models_folder_ckpt = Path(search_folder).glob("**/*.ckpt")
@@ -883,10 +891,17 @@ class ModelManager(object):
         new_models_found = False
 
         self.logger.info(f"Scanning {self.app_config.models_path} for new models")
-        with Chdir(self.app_config.root_path):
+        with Chdir(self.app_config.models_path):
             for model_key, model_config in list(self.models.items()):
                 model_name, cur_base_model, cur_model_type = self.parse_key(model_key)
-                model_path = self.app_config.root_path.absolute() / model_config.path
+
+                # Patch for relative path bug in older models.yaml - paths should not
+                # be starting with a hard-coded 'models'. This will also fix up
+                # models.yaml when committed.
+                if model_config.path.startswith("models"):
+                    model_config.path = str(Path(*Path(model_config.path).parts[1:]))
+
+                model_path = self.resolve_model_path(model_config.path).absolute()
                 if not model_path.exists():
                     model_class = MODEL_CLASSES[cur_base_model][cur_model_type]
                     if model_class.save_to_config:
@@ -905,7 +920,7 @@ class ModelManager(object):
                     if model_type is not None and cur_model_type != model_type:
                         continue
                     model_class = MODEL_CLASSES[cur_base_model][cur_model_type]
-                    models_dir = self.app_config.models_path / cur_base_model.value / cur_model_type.value
+                    models_dir = self.resolve_model_path(Path(cur_base_model.value, cur_model_type.value))
 
                     if not models_dir.exists():
                         continue  # TODO: or create all folders?
@@ -919,9 +934,7 @@ class ModelManager(object):
                                 if model_key in self.models:
                                     raise DuplicateModelException(f"Model with key {model_key} added twice")
 
-                                if model_path.is_relative_to(self.app_config.root_path):
-                                    model_path = model_path.relative_to(self.app_config.root_path)
-
+                                model_path = self.relative_model_path(model_path)
                                 model_config: ModelConfigBase = model_class.probe_config(str(model_path))
                                 self.models[model_key] = model_config
                                 new_models_found = True
@@ -932,12 +945,11 @@ class ModelManager(object):
                             except NotImplementedError as e:
                                 self.logger.warning(e)
 
-        imported_models = self.autoimport()
-
+        imported_models = self.scan_autoimport_directory()
         if (new_models_found or imported_models) and self.config_path:
             self.commit()
 
-    def autoimport(self) -> Dict[str, AddModelResult]:
+    def scan_autoimport_directory(self) -> Dict[str, AddModelResult]:
         """
         Scan the autoimport directory (if defined) and import new models, delete defunct models.
         """
@@ -971,7 +983,7 @@ class ModelManager(object):
         # LS: hacky
         # Patch in the SD VAE from core so that it is available for use by the UI
         try:
-            self.heuristic_import({config.root_path / "models/core/convert/sd-vae-ft-mse"})
+            self.heuristic_import({self.resolve_model_path("core/convert/sd-vae-ft-mse")})
         except:
             pass
 

invokeai/backend/model_management/models/controlnet.py

@@ -17,6 +17,7 @@ from .base import (
     ModelNotFoundException,
 )
 from invokeai.app.services.config import InvokeAIAppConfig
+import invokeai.backend.util.logging as logger
 
 
 class ControlNetModelFormat(str, Enum):
@@ -66,7 +67,7 @@ class ControlNetModel(ModelBase):
         child_type: Optional[SubModelType] = None,
     ):
         if child_type is not None:
-            raise Exception("There is no child models in controlnet model")
+            raise Exception("There are no child models in controlnet model")
 
         model = None
         for variant in ["fp16", None]:
@@ -124,9 +125,7 @@ class ControlNetModel(ModelBase):
             return model_path
 
 
-@classmethod
 def _convert_controlnet_ckpt_and_cache(
-    cls,
     model_path: str,
     output_path: str,
     base_model: BaseModelType,
@@ -141,6 +140,7 @@ def _convert_controlnet_ckpt_and_cache(
     weights = app_config.root_path / model_path
     output_path = Path(output_path)
 
+    logger.info(f"Converting {weights} to diffusers format")
     # return cached version if it exists
     if output_path.exists():
         return output_path

invokeai/backend/model_management/models/stable_diffusion.py

@@ -123,6 +123,7 @@ class StableDiffusion1Model(DiffusersModel):
             return _convert_ckpt_and_cache(
                 version=BaseModelType.StableDiffusion1,
                 model_config=config,
+                load_safety_checker=False,
                 output_path=output_path,
             )
         else:
@@ -259,7 +260,7 @@ def _convert_ckpt_and_cache(
     """
     app_config = InvokeAIAppConfig.get_config()
 
-    weights = app_config.root_path / model_config.path
+    weights = app_config.models_path / model_config.path
     config_file = app_config.root_path / model_config.config
     output_path = Path(output_path)
 

invokeai/frontend/install/invokeai_update.py

@@ -112,7 +112,7 @@ def main():
 
     extras = get_extras()
 
-    print(f":crossed_fingers: Upgrading to [yellow]{tag if tag else release}[/yellow]")
+    print(f":crossed_fingers: Upgrading to [yellow]{tag or release or branch}[/yellow]")
     if release:
         cmd = f'pip install "invokeai{extras} @ {INVOKE_AI_SRC}/{release}.zip" --use-pep517 --upgrade'
     elif tag:

invokeai/frontend/install/model_install.py

@@ -58,6 +58,9 @@ logger = InvokeAILogger.getLogger()
 # from https://stackoverflow.com/questions/92438/stripping-non-printable-characters-from-a-string-in-python
 NOPRINT_TRANS_TABLE = {i: None for i in range(0, sys.maxunicode + 1) if not chr(i).isprintable()}
 
+# maximum number of installed models we can display before overflowing vertically
+MAX_OTHER_MODELS = 72
+
 
 def make_printable(s: str) -> str:
     """Replace non-printable characters in a string"""
@@ -102,7 +105,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             SingleSelectColumns,
             values=[
                 "STARTER MODELS",
-                "MORE MODELS",
+                "MAIN MODELS",
                 "CONTROLNETS",
                 "LORA/LYCORIS",
                 "TEXTUAL INVERSION",
@@ -153,7 +156,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             BufferBox,
             name="Log Messages",
             editable=False,
-            max_height=8,
+            max_height=15,
         )
 
         self.nextrely += 1
@@ -253,6 +256,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         model_labels = [self.model_labels[x] for x in model_list]
 
         show_recommended = len(self.installed_models) == 0
+        truncated = False
         if len(model_list) > 0:
             max_width = max([len(x) for x in model_labels])
             columns = window_width // (max_width + 8)  # 8 characters for "[x] " and padding
@@ -271,6 +275,10 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
                 )
             )
 
+            if len(model_labels) > MAX_OTHER_MODELS:
+                model_labels = model_labels[0:MAX_OTHER_MODELS]
+                truncated = True
+
             widgets.update(
                 models_selected=self.add_widget_intelligent(
                     MultiSelectColumns,
@@ -289,6 +297,16 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
                 models=model_list,
             )
 
+        if truncated:
+            widgets.update(
+                warning_message=self.add_widget_intelligent(
+                    npyscreen.FixedText,
+                    value=f"Too many models to display (max={MAX_OTHER_MODELS}). Some are not displayed.",
+                    editable=False,
+                    color="CAUTION",
+                )
+            )
+
         self.nextrely += 1
         widgets.update(
             download_ids=self.add_widget_intelligent(
@@ -313,7 +331,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         widgets = self.add_model_widgets(
             model_type=model_type,
             window_width=window_width,
-            install_prompt=f"Additional {model_type.value.title()} models already installed.",
+            install_prompt=f"Installed {model_type.value.title()} models. Unchecked models in the InvokeAI root directory will be deleted. Enter URLs, paths or repo_ids to import.",
             **kwargs,
         )
 
@@ -399,7 +417,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         self.ok_button.hidden = True
         self.display()
 
-        # for communication with the subprocess
+        # TO DO: Spawn a worker thread, not a subprocess
         parent_conn, child_conn = Pipe()
         p = Process(
             target=process_and_execute,
@@ -414,7 +432,6 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
         self.subprocess_connection = parent_conn
         self.subprocess = p
         app.install_selections = InstallSelections()
-        # process_and_execute(app.opt, app.install_selections)
 
     def on_back(self):
         self.parentApp.switchFormPrevious()
@@ -489,8 +506,6 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
 
         # rebuild the form, saving and restoring some of the fields that need to be preserved.
         saved_messages = self.monitor.entry_widget.values
-        # autoload_dir = str(config.root_path / self.pipeline_models['autoload_directory'].value)
-        # autoscan = self.pipeline_models['autoscan_on_startup'].value
 
         app.main_form = app.addForm(
             "MAIN",
@@ -544,12 +559,6 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             if downloads := section.get("download_ids"):
                 selections.install_models.extend(downloads.value.split())
 
-        # load directory and whether to scan on startup
-        # if self.parentApp.autoload_pending:
-        #     selections.scan_directory = str(config.root_path / self.pipeline_models['autoload_directory'].value)
-        #     self.parentApp.autoload_pending = False
-        # selections.autoscan_on_startup = self.pipeline_models['autoscan_on_startup'].value
-
 
 class AddModelApplication(npyscreen.NPSAppManaged):
     def __init__(self, opt):
@@ -639,6 +648,11 @@ def process_and_execute(
     selections: InstallSelections,
     conn_out: Connection = None,
 ):
+    # need to reinitialize config in subprocess
+    config = InvokeAIAppConfig.get_config()
+    args = ["--root", opt.root] if opt.root else []
+    config.parse_args(args)
+
     # set up so that stderr is sent to conn_out
     if conn_out:
         translator = StderrToMessage(conn_out)
@@ -656,38 +670,11 @@ def process_and_execute(
         conn_out.close()
 
 
-def do_listings(opt) -> bool:
-    """List installed models of various sorts, and return
-    True if any were requested."""
-    model_manager = ModelManager(config.model_conf_path)
-    if opt.list_models == "diffusers":
-        print("Diffuser models:")
-        model_manager.print_models()
-    elif opt.list_models == "controlnets":
-        print("Installed Controlnet Models:")
-        cnm = model_manager.list_controlnet_models()
-        print(textwrap.indent("\n".join([x for x in cnm if cnm[x]]), prefix="   "))
-    elif opt.list_models == "loras":
-        print("Installed LoRA/LyCORIS Models:")
-        cnm = model_manager.list_lora_models()
-        print(textwrap.indent("\n".join([x for x in cnm if cnm[x]]), prefix="   "))
-    elif opt.list_models == "tis":
-        print("Installed Textual Inversion Embeddings:")
-        cnm = model_manager.list_ti_models()
-        print(textwrap.indent("\n".join([x for x in cnm if cnm[x]]), prefix="   "))
-    else:
-        return False
-    return True
-
-
 # --------------------------------------------------------
 def select_and_download_models(opt: Namespace):
     precision = "float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
     config.precision = precision
     helper = lambda x: ask_user_for_prediction_type(x)
-    # if do_listings(opt):
-    # pass
-
     installer = ModelInstall(config, prediction_type_helper=helper)
     if opt.list_models:
         installer.list_models(opt.list_models)
@@ -706,8 +693,6 @@ def select_and_download_models(opt: Namespace):
         # needed to support the probe() method running under a subprocess
         torch.multiprocessing.set_start_method("spawn")
 
-        # the third argument is needed in the Windows 11 environment in
-        # order to launch and resize a console window running this program
         set_min_terminal_size(MIN_COLS, MIN_LINES)
         installApp = AddModelApplication(opt)
         try:

invokeai/frontend/merge/merge_diffusers.py

@@ -320,7 +320,7 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
 
     def get_model_names(self, base_model: BaseModelType = None) -> List[str]:
         model_names = [
-            info["name"]
+            info["model_name"]
             for info in self.model_manager.list_models(model_type=ModelType.Main, base_model=base_model)
             if info["model_format"] == "diffusers"
         ]

invokeai/frontend/web/dist/index.html

@@ -12,7 +12,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-bad7ff83.js"></script>
+    <script type="module" crossorigin src="./assets/index-9bb68e3a.js"></script>
   </head>
 
   <body dir="ltr">

invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx

@@ -139,8 +139,19 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
   useHotkeys('s', handleUseSeed, [imageDTO]);
 
   const handleUsePrompt = useCallback(() => {
-    recallBothPrompts(metadata?.positive_prompt, metadata?.negative_prompt);
-  }, [metadata?.negative_prompt, metadata?.positive_prompt, recallBothPrompts]);
+    recallBothPrompts(
+      metadata?.positive_prompt,
+      metadata?.negative_prompt,
+      metadata?.positive_style_prompt,
+      metadata?.negative_style_prompt
+    );
+  }, [
+    metadata?.negative_prompt,
+    metadata?.positive_prompt,
+    metadata?.positive_style_prompt,
+    metadata?.negative_style_prompt,
+    recallBothPrompts,
+  ]);
 
   useHotkeys('p', handleUsePrompt, [imageDTO]);
 

invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx

@@ -102,8 +102,19 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
 
   // Recall parameters handlers
   const handleRecallPrompt = useCallback(() => {
-    recallBothPrompts(metadata?.positive_prompt, metadata?.negative_prompt);
-  }, [metadata?.negative_prompt, metadata?.positive_prompt, recallBothPrompts]);
+    recallBothPrompts(
+      metadata?.positive_prompt,
+      metadata?.negative_prompt,
+      metadata?.positive_style_prompt,
+      metadata?.negative_style_prompt
+    );
+  }, [
+    metadata?.negative_prompt,
+    metadata?.positive_prompt,
+    metadata?.positive_style_prompt,
+    metadata?.negative_style_prompt,
+    recallBothPrompts,
+  ]);
 
   const handleRecallSeed = useCallback(() => {
     recallSeed(metadata?.seed);

invokeai/frontend/web/src/features/nodes/components/fields/StringInputFieldComponent.tsx

@@ -1,4 +1,4 @@
-import { Input } from '@chakra-ui/react';
+import { Input, Textarea } from '@chakra-ui/react';
 import { useAppDispatch } from 'app/store/storeHooks';
 import { fieldValueChanged } from 'features/nodes/store/nodesSlice';
 import {
@@ -12,10 +12,11 @@ const StringInputFieldComponent = (
   props: FieldComponentProps<StringInputFieldValue, StringInputFieldTemplate>
 ) => {
   const { nodeId, field } = props;
-
   const dispatch = useAppDispatch();
 
-  const handleValueChanged = (e: ChangeEvent<HTMLInputElement>) => {
+  const handleValueChanged = (
+    e: ChangeEvent<HTMLInputElement | HTMLTextAreaElement>
+  ) => {
     dispatch(
       fieldValueChanged({
         nodeId,
@@ -25,7 +26,11 @@ const StringInputFieldComponent = (
     );
   };
 
-  return <Input onChange={handleValueChanged} value={field.value}></Input>;
+  return ['prompt', 'style'].includes(field.name.toLowerCase()) ? (
+    <Textarea onChange={handleValueChanged} value={field.value} rows={2} />
+  ) : (
+    <Input onChange={handleValueChanged} value={field.value} />
+  );
 };
 
 export default memo(StringInputFieldComponent);

invokeai/frontend/web/src/features/parameters/hooks/useRecallParameters.ts

@@ -1,5 +1,15 @@
 import { useAppToaster } from 'app/components/Toaster';
 import { useAppDispatch } from 'app/store/storeHooks';
+import {
+  refinerModelChanged,
+  setNegativeStylePromptSDXL,
+  setPositiveStylePromptSDXL,
+  setRefinerAestheticScore,
+  setRefinerCFGScale,
+  setRefinerScheduler,
+  setRefinerStart,
+  setRefinerSteps,
+} from 'features/sdxl/store/sdxlSlice';
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { UnsafeImageMetadata } from 'services/api/endpoints/images';
@@ -22,6 +32,10 @@ import {
   isValidMainModel,
   isValidNegativePrompt,
   isValidPositivePrompt,
+  isValidSDXLNegativeStylePrompt,
+  isValidSDXLPositiveStylePrompt,
+  isValidSDXLRefinerAestheticScore,
+  isValidSDXLRefinerStart,
   isValidScheduler,
   isValidSeed,
   isValidSteps,
@@ -74,17 +88,34 @@ export const useRecallParameters = () => {
    * Recall both prompts with toast
    */
   const recallBothPrompts = useCallback(
-    (positivePrompt: unknown, negativePrompt: unknown) => {
+    (
+      positivePrompt: unknown,
+      negativePrompt: unknown,
+      positiveStylePrompt: unknown,
+      negativeStylePrompt: unknown
+    ) => {
       if (
         isValidPositivePrompt(positivePrompt) ||
-        isValidNegativePrompt(negativePrompt)
+        isValidNegativePrompt(negativePrompt) ||
+        isValidSDXLPositiveStylePrompt(positiveStylePrompt) ||
+        isValidSDXLNegativeStylePrompt(negativeStylePrompt)
       ) {
         if (isValidPositivePrompt(positivePrompt)) {
           dispatch(setPositivePrompt(positivePrompt));
         }
+
         if (isValidNegativePrompt(negativePrompt)) {
           dispatch(setNegativePrompt(negativePrompt));
         }
+
+        if (isValidSDXLPositiveStylePrompt(positiveStylePrompt)) {
+          dispatch(setPositiveStylePromptSDXL(positiveStylePrompt));
+        }
+
+        if (isValidSDXLPositiveStylePrompt(negativeStylePrompt)) {
+          dispatch(setNegativeStylePromptSDXL(negativeStylePrompt));
+        }
+
         parameterSetToast();
         return;
       }
@@ -123,6 +154,36 @@ export const useRecallParameters = () => {
     [dispatch, parameterSetToast, parameterNotSetToast]
   );
 
+  /**
+   * Recall SDXL Positive Style Prompt with toast
+   */
+  const recallSDXLPositiveStylePrompt = useCallback(
+    (positiveStylePrompt: unknown) => {
+      if (!isValidSDXLPositiveStylePrompt(positiveStylePrompt)) {
+        parameterNotSetToast();
+        return;
+      }
+      dispatch(setPositiveStylePromptSDXL(positiveStylePrompt));
+      parameterSetToast();
+    },
+    [dispatch, parameterSetToast, parameterNotSetToast]
+  );
+
+  /**
+   * Recall SDXL Negative Style Prompt with toast
+   */
+  const recallSDXLNegativeStylePrompt = useCallback(
+    (negativeStylePrompt: unknown) => {
+      if (!isValidSDXLNegativeStylePrompt(negativeStylePrompt)) {
+        parameterNotSetToast();
+        return;
+      }
+      dispatch(setNegativeStylePromptSDXL(negativeStylePrompt));
+      parameterSetToast();
+    },
+    [dispatch, parameterSetToast, parameterNotSetToast]
+  );
+
   /**
    * Recall seed with toast
    */
@@ -271,6 +332,14 @@ export const useRecallParameters = () => {
         steps,
         width,
         strength,
+        positive_style_prompt,
+        negative_style_prompt,
+        refiner_model,
+        refiner_cfg_scale,
+        refiner_steps,
+        refiner_scheduler,
+        refiner_aesthetic_store,
+        refiner_start,
       } = metadata;
 
       if (isValidCfgScale(cfg_scale)) {
@@ -304,6 +373,38 @@ export const useRecallParameters = () => {
         dispatch(setImg2imgStrength(strength));
       }
 
+      if (isValidSDXLPositiveStylePrompt(positive_style_prompt)) {
+        dispatch(setPositiveStylePromptSDXL(positive_style_prompt));
+      }
+
+      if (isValidSDXLNegativeStylePrompt(negative_style_prompt)) {
+        dispatch(setNegativeStylePromptSDXL(negative_style_prompt));
+      }
+
+      if (isValidMainModel(refiner_model)) {
+        dispatch(refinerModelChanged(refiner_model));
+      }
+
+      if (isValidSteps(refiner_steps)) {
+        dispatch(setRefinerSteps(refiner_steps));
+      }
+
+      if (isValidCfgScale(refiner_cfg_scale)) {
+        dispatch(setRefinerCFGScale(refiner_cfg_scale));
+      }
+
+      if (isValidScheduler(refiner_scheduler)) {
+        dispatch(setRefinerScheduler(refiner_scheduler));
+      }
+
+      if (isValidSDXLRefinerAestheticScore(refiner_aesthetic_store)) {
+        dispatch(setRefinerAestheticScore(refiner_aesthetic_store));
+      }
+
+      if (isValidSDXLRefinerStart(refiner_start)) {
+        dispatch(setRefinerStart(refiner_start));
+      }
+
       allParameterSetToast();
     },
     [allParameterNotSetToast, allParameterSetToast, dispatch]
@@ -313,6 +414,8 @@ export const useRecallParameters = () => {
     recallBothPrompts,
     recallPositivePrompt,
     recallNegativePrompt,
+    recallSDXLPositiveStylePrompt,
+    recallSDXLNegativeStylePrompt,
     recallSeed,
     recallCfgScale,
     recallModel,

invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts

@@ -310,6 +310,39 @@ export type PrecisionParam = z.infer<typeof zPrecision>;
 export const isValidPrecision = (val: unknown): val is PrecisionParam =>
   zPrecision.safeParse(val).success;
 
+/**
+ * Zod schema for SDXL refiner aesthetic score parameter
+ */
+export const zSDXLRefinerAestheticScore = z.number().min(1).max(10);
+/**
+ * Type alias for SDXL refiner aesthetic score parameter, inferred from its zod schema
+ */
+export type SDXLRefinerAestheticScoreParam = z.infer<
+  typeof zSDXLRefinerAestheticScore
+>;
+/**
+ * Validates/type-guards a value as a SDXL refiner aesthetic score parameter
+ */
+export const isValidSDXLRefinerAestheticScore = (
+  val: unknown
+): val is SDXLRefinerAestheticScoreParam =>
+  zSDXLRefinerAestheticScore.safeParse(val).success;
+
+/**
+ * Zod schema for SDXL start parameter
+ */
+export const zSDXLRefinerstart = z.number().min(0).max(1);
+/**
+ * Type alias for SDXL start, inferred from its zod schema
+ */
+export type SDXLRefinerStartParam = z.infer<typeof zSDXLRefinerstart>;
+/**
+ * Validates/type-guards a value as a SDXL refiner aesthetic score parameter
+ */
+export const isValidSDXLRefinerStart = (
+  val: unknown
+): val is SDXLRefinerStartParam => zSDXLRefinerstart.safeParse(val).success;
+
 // /**
 //  * Zod schema for BaseModelType
 //  */

invokeai/frontend/web/src/features/sdxl/components/ParamSDXLConcatButton.tsx

@@ -21,8 +21,8 @@ export default function ParamSDXLConcatButton() {
 
   return (
     <IAIIconButton
-      aria-label="Concat"
-      tooltip="Concatenates Basic Prompt with Style (Recommended)"
+      aria-label="Concatenate Prompt & Style"
+      tooltip="Concatenate Prompt & Style"
       variant="outline"
       isChecked={shouldConcatSDXLStylePrompt}
       onClick={handleShouldConcatPromptChange}

invokeai/frontend/web/src/services/api/schema.d.ts

@@ -1381,7 +1381,7 @@ export type components = {
        * @description The nodes in this graph
        */
       nodes?: {
-        [key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
+        [key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
       };
       /**
        * Edges 
@@ -1424,7 +1424,7 @@ export type components = {
        * @description The results of node executions
        */
       results: {
-        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
+        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
       };
       /**
        * Errors 
@@ -2752,7 +2752,7 @@ export type components = {
       vae?: components["schemas"]["VaeField"];
       /**
        * Tiled 
-       * @description Decode latents by overlapping tiles(less memory consumption) 
+       * @description Decode latents by overlaping tiles (less memory consumption) 
        * @default false
        */
       tiled?: boolean;
@@ -3965,6 +3965,35 @@ export type components = {
        */
       a?: number;
     };
+    /**
+     * ParamPromptInvocation 
+     * @description A prompt input parameter
+     */
+    ParamPromptInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default param_prompt 
+       * @enum {string}
+       */
+      type?: "param_prompt";
+      /**
+       * Prompt 
+       * @description The prompt value 
+       * @default
+       */
+      prompt?: string;
+    };
     /**
      * ParamStringInvocation 
      * @description A string parameter
@@ -5568,18 +5597,18 @@ export type components = {
      * @enum {string}
      */
     StableDiffusionXLModelFormat: "checkpoint" | "diffusers";
-    /**
-     * ControlNetModelFormat 
-     * @description An enumeration. 
-     * @enum {string}
-     */
-    ControlNetModelFormat: "checkpoint" | "diffusers";
     /**
      * StableDiffusion1ModelFormat 
      * @description An enumeration. 
      * @enum {string}
      */
     StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
+    /**
+     * ControlNetModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    ControlNetModelFormat: "checkpoint" | "diffusers";
   };
   responses: never;
   parameters: never;
@@ -5690,7 +5719,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {
@@ -5727,7 +5756,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "3.0.1"
+__version__ = "3.0.1post3"

notebooks/Stable_Diffusion_AI_Notebook.ipynb

@@ -1,281 +1,283 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ycYWcsEKc6w7"
-      },
-      "source": [
-        "# Stable Diffusion AI Notebook (Release 2.0.0)\n",
-        "\n",
-        "<img src=\"https://user-images.githubusercontent.com/60411196/186547976-d9de378a-9de8-4201-9c25-c057a9c59bad.jpeg\" alt=\"stable-diffusion-ai\" width=\"170px\"/> <br>\n",
-        "#### Instructions:\n",
-        "1. Execute each cell in order to mount a Dream bot and create images from text. <br>\n",
-        "2. Once cells 1-8 were run correctly you'll be executing a terminal in cell #9, you'll need to enter `python scripts/dream.py` command to run Dream bot.<br> \n",
-        "3. After launching dream bot, you'll see: <br> `Dream > ` in terminal. <br> Insert a command, eg. `Dream > Astronaut floating in a distant galaxy`, or type `-h` for help.\n",
-        "3. After completion you'll see your generated images in path `stable-diffusion/outputs/img-samples/`, you can also show last generated images in cell #10.\n",
-        "4. To quit Dream bot use `q` command. <br> \n",
-        "---\n",
-        "<font color=\"red\">Note:</font> It takes some time to load, but after installing all dependencies you can use the bot all time you want while colab instance is up. <br>\n",
-        "<font color=\"red\">Requirements:</font> For this notebook to work you need to have [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) stored in your Google Drive, it will be needed in cell #7\n",
-        "##### For more details visit Github repository: [invoke-ai/InvokeAI](https://github.com/invoke-ai/InvokeAI)\n",
-        "---\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dr32VLxlnouf"
-      },
-      "source": [
-        "## ◢ Installation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "a2Z5Qu_o8VtQ"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 1. Check current GPU assigned\n",
-        "!nvidia-smi -L\n",
-        "!nvidia-smi"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "vbI9ZsQHzjqF"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 2. Download stable-diffusion Repository\n",
-        "from os.path import exists\n",
-        "\n",
-        "!git clone --quiet https://github.com/invoke-ai/InvokeAI.git # Original repo\n",
-        "%cd /content/InvokeAI/\n",
-        "!git checkout --quiet tags/v2.0.0"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "QbXcGXYEFSNB"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 3. Install dependencies\n",
-        "import gc\n",
-        "\n",
-        "!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-base.txt\n",
-        "!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-win-colab-cuda.txt\n",
-        "!pip install colab-xterm\n",
-        "!pip install -r requirements-lin-win-colab-CUDA.txt\n",
-        "!pip install clean-fid torchtext\n",
-        "!pip install transformers\n",
-        "gc.collect()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "8rSMhgnAttQa"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 4. Restart Runtime\n",
-        "exit()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "ChIDWxLVHGGJ"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 5. Load small ML models required\n",
-        "import gc\n",
-        "%cd /content/InvokeAI/\n",
-        "!python scripts/preload_models.py\n",
-        "gc.collect()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "795x1tMoo8b1"
-      },
-      "source": [
-        "## ◢ Configuration"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "YEWPV-sF1RDM"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 6. Mount google Drive\n",
-        "from google.colab import drive\n",
-        "drive.mount('/content/drive')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "zRTJeZ461WGu"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 7. Drive Path to model\n",
-        "#@markdown Path should start with /content/drive/path-to-your-file <br>\n",
-        "#@markdown <font color=\"red\">Note:</font> Model should be downloaded from https://huggingface.co <br>\n",
-        "#@markdown Lastest release: [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)\n",
-        "from os.path import exists\n",
-        "\n",
-        "model_path = \"\" #@param {type:\"string\"}\n",
-        "if exists(model_path):\n",
-        "    print(\"✅ Valid directory\")\n",
-        "else: \n",
-        "    print(\"❌ File doesn't exist\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "UY-NNz4I8_aG"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 8. Symlink to model\n",
-        "\n",
-        "from os.path import exists\n",
-        "import os \n",
-        "\n",
-        "# Folder creation if it doesn't exist\n",
-        "if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1\"):\n",
-        "     print(\"❗ Dir stable-diffusion-v1 already exists\")\n",
-        "else:\n",
-        "    %mkdir /content/InvokeAI/models/ldm/stable-diffusion-v1\n",
-        "    print(\"✅ Dir stable-diffusion-v1 created\")\n",
-        "\n",
-        "# Symbolic link if it doesn't exist\n",
-        "if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt\"):\n",
-        "    print(\"❗ Symlink already created\")\n",
-        "else: \n",
-        "    src = model_path\n",
-        "    dst = '/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt'\n",
-        "    os.symlink(src, dst) \n",
-        "    print(\"✅ Symbolic link created successfully\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Mc28N0_NrCQH"
-      },
-      "source": [
-        "## ◢ Execution"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "ir4hCrMIuUpl"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 9. Run Terminal and Execute Dream bot\n",
-        "#@markdown <font color=\"blue\">Steps:</font> <br>\n",
-        "#@markdown 1. Execute command `python scripts/invoke.py` to run InvokeAI.<br>\n",
-        "#@markdown 2. After initialized you'll see `Dream>` line.<br>\n",
-        "#@markdown 3. Example text: `Astronaut floating in a distant galaxy` <br>\n",
-        "#@markdown 4. To quit Dream bot use: `q` command.<br>\n",
-        "\n",
-        "%load_ext colabxterm\n",
-        "%xterm\n",
-        "gc.collect()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "qnLohSHmKoGk"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 10. Show the last 15 generated images\n",
-        "import glob\n",
-        "import matplotlib.pyplot as plt\n",
-        "import matplotlib.image as mpimg\n",
-        "%matplotlib inline\n",
-        "\n",
-        "images = []\n",
-        "for img_path in sorted(glob.glob('/content/InvokeAI/outputs/img-samples/*.png'), reverse=True):\n",
-        "    images.append(mpimg.imread(img_path))\n",
-        "\n",
-        "images = images[:15] \n",
-        "\n",
-        "plt.figure(figsize=(20,10))\n",
-        "\n",
-        "columns = 5\n",
-        "for i, image in enumerate(images):\n",
-        "    ax = plt.subplot(len(images) / columns + 1, columns, i + 1)\n",
-        "    ax.axes.xaxis.set_visible(False)\n",
-        "    ax.axes.yaxis.set_visible(False)\n",
-        "    ax.axis('off')\n",
-        "    plt.imshow(image)\n",
-        "    gc.collect()\n",
-        "\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "collapsed_sections": [],
-      "private_outputs": true,
-      "provenance": []
-    },
-    "gpuClass": "standard",
-    "kernelspec": {
-      "display_name": "Python 3.9.12 64-bit",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python",
-      "version": "3.9.12"
-    },
-    "vscode": {
-      "interpreter": {
-        "hash": "4e870c5c5fe42db7e2c5647ae5af656ff3391bf8c2b729cbf7fa0e16ca8cb5af"
-      }
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ycYWcsEKc6w7"
+   },
+   "source": [
+    "# Stable Diffusion AI Notebook (Release 2.0.0)\n",
+    "\n",
+    "<img src=\"https://user-images.githubusercontent.com/60411196/186547976-d9de378a-9de8-4201-9c25-c057a9c59bad.jpeg\" alt=\"stable-diffusion-ai\" width=\"170px\"/> <br>\n",
+    "#### Instructions:\n",
+    "1. Execute each cell in order to mount a Dream bot and create images from text. <br>\n",
+    "2. Once cells 1-8 were run correctly you'll be executing a terminal in cell #9, you'll need to enter `python scripts/dream.py` command to run Dream bot.<br> \n",
+    "3. After launching dream bot, you'll see: <br> `Dream > ` in terminal. <br> Insert a command, eg. `Dream > Astronaut floating in a distant galaxy`, or type `-h` for help.\n",
+    "3. After completion you'll see your generated images in path `stable-diffusion/outputs/img-samples/`, you can also show last generated images in cell #10.\n",
+    "4. To quit Dream bot use `q` command. <br> \n",
+    "---\n",
+    "<font color=\"red\">Note:</font> It takes some time to load, but after installing all dependencies you can use the bot all time you want while colab instance is up. <br>\n",
+    "<font color=\"red\">Requirements:</font> For this notebook to work you need to have [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) stored in your Google Drive, it will be needed in cell #7\n",
+    "##### For more details visit Github repository: [invoke-ai/InvokeAI](https://github.com/invoke-ai/InvokeAI)\n",
+    "---\n"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dr32VLxlnouf"
+   },
+   "source": [
+    "## ◢ Installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "a2Z5Qu_o8VtQ"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 1. Check current GPU assigned\n",
+    "!nvidia-smi -L\n",
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "vbI9ZsQHzjqF"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 2. Download stable-diffusion Repository\n",
+    "from os.path import exists\n",
+    "\n",
+    "!git clone --quiet https://github.com/invoke-ai/InvokeAI.git # Original repo\n",
+    "%cd /content/InvokeAI/\n",
+    "!git checkout --quiet tags/v2.0.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "QbXcGXYEFSNB"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 3. Install dependencies\n",
+    "import gc\n",
+    "\n",
+    "!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-base.txt\n",
+    "!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-win-colab-cuda.txt\n",
+    "!pip install colab-xterm\n",
+    "!pip install -r requirements-lin-win-colab-CUDA.txt\n",
+    "!pip install clean-fid torchtext\n",
+    "!pip install transformers\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "8rSMhgnAttQa"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 4. Restart Runtime\n",
+    "exit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "ChIDWxLVHGGJ"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 5. Load small ML models required\n",
+    "import gc\n",
+    "\n",
+    "%cd /content/InvokeAI/\n",
+    "!python scripts/preload_models.py\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "795x1tMoo8b1"
+   },
+   "source": [
+    "## ◢ Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "YEWPV-sF1RDM"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 6. Mount google Drive\n",
+    "from google.colab import drive\n",
+    "\n",
+    "drive.mount(\"/content/drive\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "zRTJeZ461WGu"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 7. Drive Path to model\n",
+    "# @markdown Path should start with /content/drive/path-to-your-file <br>\n",
+    "# @markdown <font color=\"red\">Note:</font> Model should be downloaded from https://huggingface.co <br>\n",
+    "# @markdown Lastest release: [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)\n",
+    "from os.path import exists\n",
+    "\n",
+    "model_path = \"\"  # @param {type:\"string\"}\n",
+    "if exists(model_path):\n",
+    "    print(\"✅ Valid directory\")\n",
+    "else:\n",
+    "    print(\"❌ File doesn't exist\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "UY-NNz4I8_aG"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 8. Symlink to model\n",
+    "\n",
+    "from os.path import exists\n",
+    "import os\n",
+    "\n",
+    "# Folder creation if it doesn't exist\n",
+    "if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1\"):\n",
+    "    print(\"❗ Dir stable-diffusion-v1 already exists\")\n",
+    "else:\n",
+    "    %mkdir /content/InvokeAI/models/ldm/stable-diffusion-v1\n",
+    "    print(\"✅ Dir stable-diffusion-v1 created\")\n",
+    "\n",
+    "# Symbolic link if it doesn't exist\n",
+    "if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt\"):\n",
+    "    print(\"❗ Symlink already created\")\n",
+    "else:\n",
+    "    src = model_path\n",
+    "    dst = \"/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt\"\n",
+    "    os.symlink(src, dst)\n",
+    "    print(\"✅ Symbolic link created successfully\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Mc28N0_NrCQH"
+   },
+   "source": [
+    "## ◢ Execution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "ir4hCrMIuUpl"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 9. Run Terminal and Execute Dream bot\n",
+    "# @markdown <font color=\"blue\">Steps:</font> <br>\n",
+    "# @markdown 1. Execute command `python scripts/invoke.py` to run InvokeAI.<br>\n",
+    "# @markdown 2. After initialized you'll see `Dream>` line.<br>\n",
+    "# @markdown 3. Example text: `Astronaut floating in a distant galaxy` <br>\n",
+    "# @markdown 4. To quit Dream bot use: `q` command.<br>\n",
+    "\n",
+    "%load_ext colabxterm\n",
+    "%xterm\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "qnLohSHmKoGk"
+   },
+   "outputs": [],
+   "source": [
+    "#@title 10. Show the last 15 generated images\n",
+    "import glob\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.image as mpimg\n",
+    "%matplotlib inline\n",
+    "\n",
+    "images = []\n",
+    "for img_path in sorted(glob.glob('/content/InvokeAI/outputs/img-samples/*.png'), reverse=True):\n",
+    "    images.append(mpimg.imread(img_path))\n",
+    "\n",
+    "images = images[:15] \n",
+    "\n",
+    "plt.figure(figsize=(20,10))\n",
+    "\n",
+    "columns = 5\n",
+    "for i, image in enumerate(images):\n",
+    "    ax = plt.subplot(len(images) / columns + 1, columns, i + 1)\n",
+    "    ax.axes.xaxis.set_visible(False)\n",
+    "    ax.axes.yaxis.set_visible(False)\n",
+    "    ax.axis('off')\n",
+    "    plt.imshow(image)\n",
+    "    gc.collect()\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "private_outputs": true,
+   "provenance": []
+  },
+  "gpuClass": "standard",
+  "kernelspec": {
+   "display_name": "Python 3.9.12 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.12"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4e870c5c5fe42db7e2c5647ae5af656ff3391bf8c2b729cbf7fa0e16ca8cb5af"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }

pyproject.toml

@@ -58,14 +58,14 @@ dependencies = [
   "invisible-watermark~=0.2.0", # needed to install SDXL base and refiner using their repo_ids
   "matplotlib",                 # needed for plotting of Penner easing functions
   "mediapipe",                  # needed for "mediapipeface" controlnet model
+  "numpy",
   "npyscreen",
-  "numpy==1.24.4",
   "omegaconf",
   "opencv-python",
+  "pydantic==1.*",
   "picklescan",
   "pillow",
   "prompt-toolkit",
-  "pydantic==1.10.10",
   "pympler~=1.0.1",
   "pypatchmatch",
   'pyperclip',
@@ -81,7 +81,7 @@ dependencies = [
   "test-tube~=0.7.5",
   "torch~=2.0.1",
   "torchvision~=0.15.2",
-  "torchmetrics~=1.0.1",
+  "torchmetrics~=0.11.0",
   "torchsde~=0.2.5",
   "transformers~=4.31.0",
   "uvicorn[standard]~=0.21.1",

scripts/orig_scripts/latent_imagenet_diffusion.ipynb

@@ -52,17 +52,17 @@
      "name": "stdout",
      "text": [
       "Cloning into 'latent-diffusion'...\n",
-      "remote: Enumerating objects: 992, done.\u001B[K\n",
-      "remote: Counting objects: 100% (695/695), done.\u001B[K\n",
-      "remote: Compressing objects: 100% (397/397), done.\u001B[K\n",
-      "remote: Total 992 (delta 375), reused 564 (delta 253), pack-reused 297\u001B[K\n",
+      "remote: Enumerating objects: 992, done.\u001b[K\n",
+      "remote: Counting objects: 100% (695/695), done.\u001b[K\n",
+      "remote: Compressing objects: 100% (397/397), done.\u001b[K\n",
+      "remote: Total 992 (delta 375), reused 564 (delta 253), pack-reused 297\u001b[K\n",
       "Receiving objects: 100% (992/992), 30.78 MiB | 29.43 MiB/s, done.\n",
       "Resolving deltas: 100% (510/510), done.\n",
       "Cloning into 'taming-transformers'...\n",
-      "remote: Enumerating objects: 1335, done.\u001B[K\n",
-      "remote: Counting objects: 100% (525/525), done.\u001B[K\n",
-      "remote: Compressing objects: 100% (493/493), done.\u001B[K\n",
-      "remote: Total 1335 (delta 58), reused 481 (delta 30), pack-reused 810\u001B[K\n",
+      "remote: Enumerating objects: 1335, done.\u001b[K\n",
+      "remote: Counting objects: 100% (525/525), done.\u001b[K\n",
+      "remote: Compressing objects: 100% (493/493), done.\u001b[K\n",
+      "remote: Total 1335 (delta 58), reused 481 (delta 30), pack-reused 810\u001b[K\n",
       "Receiving objects: 100% (1335/1335), 412.35 MiB | 30.53 MiB/s, done.\n",
       "Resolving deltas: 100% (267/267), done.\n",
       "Obtaining file:///content/taming-transformers\n",
@@ -73,23 +73,24 @@
       "Installing collected packages: taming-transformers\n",
       "  Running setup.py develop for taming-transformers\n",
       "Successfully installed taming-transformers-0.0.1\n",
-      "\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
       "tensorflow 2.8.0 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\n",
-      "arviz 0.11.4 requires typing-extensions<4,>=3.7.4.3, but you have typing-extensions 4.1.1 which is incompatible.\u001B[0m\n"
+      "arviz 0.11.4 requires typing-extensions<4,>=3.7.4.3, but you have typing-extensions 4.1.1 which is incompatible.\u001b[0m\n"
      ]
     }
    ],
    "source": [
-    "#@title Installation\n",
+    "# @title Installation\n",
     "!git clone https://github.com/CompVis/latent-diffusion.git\n",
     "!git clone https://github.com/CompVis/taming-transformers\n",
     "!pip install -e ./taming-transformers\n",
     "!pip install omegaconf>=2.0.0 pytorch-lightning>=1.0.8 torch-fidelity einops\n",
     "\n",
     "import sys\n",
+    "\n",
     "sys.path.append(\".\")\n",
-    "sys.path.append('./taming-transformers')\n",
-    "from taming.models import vqgan "
+    "sys.path.append(\"./taming-transformers\")\n",
+    "from taming.models import vqgan"
    ]
   },
   {
@@ -104,11 +105,11 @@
   {
    "cell_type": "code",
    "source": [
-    "#@title Download\n",
-    "%cd latent-diffusion/ \n",
+    "# @title Download\n",
+    "%cd latent-diffusion/\n",
     "\n",
     "!mkdir -p models/ldm/cin256-v2/\n",
-    "!wget -O models/ldm/cin256-v2/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/cin/model.ckpt "
+    "!wget -O models/ldm/cin256-v2/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/cin/model.ckpt"
    ],
    "metadata": {
     "colab": {
@@ -203,7 +204,7 @@
   {
    "cell_type": "code",
    "source": [
-    "#@title loading utils\n",
+    "# @title loading utils\n",
     "import torch\n",
     "from omegaconf import OmegaConf\n",
     "\n",
@@ -212,7 +213,7 @@
     "\n",
     "def load_model_from_config(config, ckpt):\n",
     "    print(f\"Loading model from {ckpt}\")\n",
-    "    pl_sd = torch.load(ckpt)#, map_location=\"cpu\")\n",
+    "    pl_sd = torch.load(ckpt)  # , map_location=\"cpu\")\n",
     "    sd = pl_sd[\"state_dict\"]\n",
     "    model = instantiate_from_config(config.model)\n",
     "    m, u = model.load_state_dict(sd, strict=False)\n",
@@ -222,7 +223,7 @@
     "\n",
     "\n",
     "def get_model():\n",
-    "    config = OmegaConf.load(\"configs/latent-diffusion/cin256-v2.yaml\")  \n",
+    "    config = OmegaConf.load(\"configs/latent-diffusion/cin256-v2.yaml\")\n",
     "    model = load_model_from_config(config, \"models/ldm/cin256-v2/model.ckpt\")\n",
     "    return model"
    ],
@@ -276,18 +277,18 @@
   {
    "cell_type": "code",
    "source": [
-    "import numpy as np \n",
+    "import numpy as np\n",
     "from PIL import Image\n",
     "from einops import rearrange\n",
     "from torchvision.utils import make_grid\n",
     "\n",
     "\n",
-    "classes = [25, 187, 448, 992]   # define classes to be sampled here\n",
+    "classes = [25, 187, 448, 992]  # define classes to be sampled here\n",
     "n_samples_per_class = 6\n",
     "\n",
     "ddim_steps = 20\n",
     "ddim_eta = 0.0\n",
-    "scale = 3.0   # for unconditional guidance\n",
+    "scale = 3.0  # for unconditional guidance\n",
     "\n",
     "\n",
     "all_samples = list()\n",
@@ -295,36 +296,39 @@
     "with torch.no_grad():\n",
     "    with model.ema_scope():\n",
     "        uc = model.get_learned_conditioning(\n",
-    "            {model.cond_stage_key: torch.tensor(n_samples_per_class*[1000]).to(model.device)}\n",
-    "            )\n",
-    "        \n",
+    "            {model.cond_stage_key: torch.tensor(n_samples_per_class * [1000]).to(model.device)}\n",
+    "        )\n",
+    "\n",
     "        for class_label in classes:\n",
-    "            print(f\"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.\")\n",
-    "            xc = torch.tensor(n_samples_per_class*[class_label])\n",
+    "            print(\n",
+    "                f\"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.\"\n",
+    "            )\n",
+    "            xc = torch.tensor(n_samples_per_class * [class_label])\n",
     "            c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)})\n",
-    "            \n",
-    "            samples_ddim, _ = sampler.sample(S=ddim_steps,\n",
-    "                                             conditioning=c,\n",
-    "                                             batch_size=n_samples_per_class,\n",
-    "                                             shape=[3, 64, 64],\n",
-    "                                             verbose=False,\n",
-    "                                             unconditional_guidance_scale=scale,\n",
-    "                                             unconditional_conditioning=uc, \n",
-    "                                             eta=ddim_eta)\n",
+    "\n",
+    "            samples_ddim, _ = sampler.sample(\n",
+    "                S=ddim_steps,\n",
+    "                conditioning=c,\n",
+    "                batch_size=n_samples_per_class,\n",
+    "                shape=[3, 64, 64],\n",
+    "                verbose=False,\n",
+    "                unconditional_guidance_scale=scale,\n",
+    "                unconditional_conditioning=uc,\n",
+    "                eta=ddim_eta,\n",
+    "            )\n",
     "\n",
     "            x_samples_ddim = model.decode_first_stage(samples_ddim)\n",
-    "            x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, \n",
-    "                                         min=0.0, max=1.0)\n",
+    "            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)\n",
     "            all_samples.append(x_samples_ddim)\n",
     "\n",
     "\n",
     "# display as grid\n",
     "grid = torch.stack(all_samples, 0)\n",
-    "grid = rearrange(grid, 'n b c h w -> (n b) c h w')\n",
+    "grid = rearrange(grid, \"n b c h w -> (n b) c h w\")\n",
     "grid = make_grid(grid, nrow=n_samples_per_class)\n",
     "\n",
     "# to image\n",
-    "grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()\n",
+    "grid = 255.0 * rearrange(grid, \"c h w -> h w c\").cpu().numpy()\n",
     "Image.fromarray(grid.astype(np.uint8))"
    ],
    "metadata": {