mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge remote-tracking branch 'origin/main' into refactor/model_manager_instantiate
# Conflicts: # invokeai/backend/model_management/model_manager.py
This commit is contained in:
Kevin Turner
commit
0e48c98330
@ -14,20 +14,25 @@ The nodes linked below have been developed and contributed by members of the Inv
|
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## List of Nodes
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### Face Mask
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### FaceTools
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**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.
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**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.
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**Node Link:** https://github.com/ymgenesis/InvokeAI/blob/facemaskmediapipe/invokeai/app/invocations/facemask.py
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**Node Link:** https://github.com/ymgenesis/FaceTools/
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**Example Node Graph:** https://www.mediafire.com/file/gohn5sb1bfp8use/21-July_2023-FaceMask.json/file
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**FaceMask Output Examples**
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**Output Examples**
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<hr>
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### Ideal Size
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**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.
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**Node Link:** https://github.com/JPPhoto/ideal-size-node
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--------------------------------
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### Super Cool Node Template
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@ -42,11 +47,5 @@ The nodes linked below have been developed and contributed by members of the Inv
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### Ideal Size
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**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.
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**Node Link:** https://github.com/JPPhoto/ideal-size-node
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## Help
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If you run into any issues with a node, please post in the [InvokeAI Discord](https://discord.gg/ZmtBAhwWhy).
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@ -13,7 +13,7 @@ from pathlib import Path
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from tempfile import TemporaryDirectory
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from typing import Union
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SUPPORTED_PYTHON = ">=3.9.0,<3.11"
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SUPPORTED_PYTHON = ">=3.9.0,<=3.11.100"
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INSTALLER_REQS = ["rich", "semver", "requests", "plumbum", "prompt-toolkit"]
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BOOTSTRAP_VENV_PREFIX = "invokeai-installer-tmp"
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@ -149,7 +149,7 @@ class Installer:
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return venv_dir
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def install(
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self, root: str = "~/invokeai-3", version: str = "latest", yes_to_all=False, find_links: Path = None
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self, root: str = "~/invokeai", version: str = "latest", yes_to_all=False, find_links: Path = None
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) -> None:
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"""
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Install the InvokeAI application into the given runtime path
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@ -168,7 +168,8 @@ class Installer:
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messages.welcome()
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self.dest = Path(root).expanduser().resolve() if yes_to_all else messages.dest_path(root)
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default_path = os.environ.get("INVOKEAI_ROOT") or Path(root).expanduser().resolve()
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self.dest = default_path if yes_to_all else messages.dest_path(root)
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# create the venv for the app
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self.venv = self.app_venv()
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@ -248,6 +249,9 @@ class InvokeAiInstance:
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pip[
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"install",
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"--require-virtualenv",
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"numpy~=1.24.0", # choose versions that won't be uninstalled during phase 2
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"urllib3~=1.26.0",
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"requests~=2.28.0",
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"torch~=2.0.0",
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"torchmetrics==0.11.4",
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"torchvision>=0.14.1",
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@ -3,6 +3,7 @@ InvokeAI Installer
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"""
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import argparse
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import os
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from pathlib import Path
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from installer import Installer
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@ -15,7 +16,7 @@ if __name__ == "__main__":
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dest="root",
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type=str,
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help="Destination path for installation",
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default="~/invokeai",
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default=os.environ.get("INVOKEAI_ROOT") or "~/invokeai",
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)
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parser.add_argument(
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"-y",
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@ -41,7 +41,7 @@ IF /I "%choice%" == "1" (
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python .venv\Scripts\invokeai-configure.exe --skip-sd-weight --skip-support-models
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) ELSE IF /I "%choice%" == "7" (
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echo Running invokeai-configure...
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python .venv\Scripts\invokeai-configure.exe --yes --default_only
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python .venv\Scripts\invokeai-configure.exe --yes --skip-sd-weight
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) ELSE IF /I "%choice%" == "8" (
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echo Developer Console
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echo Python command is:
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@ -82,7 +82,7 @@ do_choice() {
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7)
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clear
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printf "Re-run the configure script to fix a broken install or to complete a major upgrade\n"
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invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
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invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only --skip-sd-weights
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;;
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8)
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clear
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@ -171,7 +171,6 @@ from pydantic import BaseSettings, Field, parse_obj_as
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from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args
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INIT_FILE = Path("invokeai.yaml")
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MODEL_CORE = Path("models/core")
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DB_FILE = Path("invokeai.db")
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LEGACY_INIT_FILE = Path("invokeai.init")
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@ -357,7 +356,7 @@ def _find_root() -> Path:
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venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
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if os.environ.get("INVOKEAI_ROOT"):
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root = Path(os.environ.get("INVOKEAI_ROOT")).resolve()
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elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE, MODEL_CORE]]):
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elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]]):
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root = (venv.parent).resolve()
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else:
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root = Path("~/invokeai").expanduser().resolve()
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@ -181,7 +181,7 @@ def download_with_progress_bar(model_url: str, model_dest: str, label: str = "th
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def download_conversion_models():
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target_dir = config.root_path / "models/core/convert"
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target_dir = config.models_path / "core/convert"
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kwargs = dict() # for future use
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try:
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logger.info("Downloading core tokenizers and text encoders")
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@ -7,7 +7,7 @@ import warnings
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from dataclasses import dataclass, field
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from pathlib import Path
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from tempfile import TemporaryDirectory
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from typing import List, Dict, Callable, Union, Set
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from typing import List, Dict, Callable, Union, Set, Optional
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import requests
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from diffusers import DiffusionPipeline
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@ -128,7 +128,9 @@ class ModelInstall(object):
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model_dict[key] = ModelLoadInfo(**value)
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# supplement with entries in models.yaml
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installed_models = self.mgr.list_models()
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installed_models = [x for x in self.mgr.list_models()]
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# suppresses autoloaded models
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# installed_models = [x for x in self.mgr.list_models() if not self._is_autoloaded(x)]
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for md in installed_models:
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base = md["base_model"]
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@ -147,6 +149,17 @@ class ModelInstall(object):
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)
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return {x: model_dict[x] for x in sorted(model_dict.keys(), key=lambda y: model_dict[y].name.lower())}
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def _is_autoloaded(self, model_info: dict) -> bool:
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path = model_info.get("path")
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if not path:
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return False
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for autodir in ["autoimport_dir", "lora_dir", "embedding_dir", "controlnet_dir"]:
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if autodir_path := getattr(self.config, autodir):
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autodir_path = self.config.root_path / autodir_path
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if Path(path).is_relative_to(autodir_path):
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return True
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return False
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def list_models(self, model_type):
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installed = self.mgr.list_models(model_type=model_type)
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print(f"Installed models of type `{model_type}`:")
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@ -273,6 +286,7 @@ class ModelInstall(object):
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logger.error(f"Unable to download {url}. Skipping.")
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info = ModelProbe().heuristic_probe(location)
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dest = self.config.models_path / info.base_type.value / info.model_type.value / location.name
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dest.parent.mkdir(parents=True, exist_ok=True)
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models_path = shutil.move(location, dest)
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# staged version will be garbage-collected at this time
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@ -346,7 +360,7 @@ class ModelInstall(object):
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if key in self.datasets:
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description = self.datasets[key].get("description") or description
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rel_path = self.relative_to_root(path)
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rel_path = self.relative_to_root(path, self.config.models_path)
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attributes = dict(
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path=str(rel_path),
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@ -386,8 +400,8 @@ class ModelInstall(object):
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attributes.update(dict(config=str(legacy_conf)))
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return attributes
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def relative_to_root(self, path: Path) -> Path:
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root = self.config.root_path
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def relative_to_root(self, path: Path, root: Optional[Path] = None) -> Path:
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root = root or self.config.root_path
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if path.is_relative_to(root):
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return path.relative_to(root)
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else:
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@ -63,7 +63,7 @@ from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionS
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from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
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from invokeai.backend.util.logging import InvokeAILogger
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from invokeai.app.services.config import InvokeAIAppConfig, MODEL_CORE
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from invokeai.app.services.config import InvokeAIAppConfig
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from picklescan.scanner import scan_file_path
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from .models import BaseModelType, ModelVariantType
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@ -81,7 +81,7 @@ if is_accelerate_available():
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from accelerate.utils import set_module_tensor_to_device
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logger = InvokeAILogger.getLogger(__name__)
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CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().root_path / MODEL_CORE / "convert"
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CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().models_path / "core/convert"
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def shave_segments(path, n_shave_prefix_segments=1):
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@ -1070,7 +1070,7 @@ def convert_controlnet_checkpoint(
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extract_ema,
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use_linear_projection=None,
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cross_attention_dim=None,
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precision: torch.dtype = torch.float32,
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precision: Optional[torch.dtype] = None,
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):
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ctrlnet_config = create_unet_diffusers_config(original_config, image_size=image_size, controlnet=True)
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ctrlnet_config["upcast_attention"] = upcast_attention
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@ -1111,7 +1111,6 @@ def convert_controlnet_checkpoint(
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return controlnet.to(precision)
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# TO DO - PASS PRECISION
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def download_from_original_stable_diffusion_ckpt(
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checkpoint_path: str,
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model_version: BaseModelType,
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@ -1121,7 +1120,7 @@ def download_from_original_stable_diffusion_ckpt(
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prediction_type: str = None,
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model_type: str = None,
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extract_ema: bool = False,
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precision: torch.dtype = torch.float32,
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precision: Optional[torch.dtype] = None,
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scheduler_type: str = "pndm",
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num_in_channels: Optional[int] = None,
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upcast_attention: Optional[bool] = None,
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@ -1194,6 +1193,8 @@ def download_from_original_stable_diffusion_ckpt(
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[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer)
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to use. If this parameter is `None`, the function will load a new instance of [CLIPTokenizer] by itself, if
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needed.
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precision (`torch.dtype`, *optional*, defauts to `None`):
|
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If not provided the precision will be set to the precision of the original file.
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return: A StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file.
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"""
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@ -1252,6 +1253,10 @@ def download_from_original_stable_diffusion_ckpt(
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|
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logger.debug(f"model_type = {model_type}; original_config_file = {original_config_file}")
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|
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precision_probing_key = "model.diffusion_model.input_blocks.0.0.bias"
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logger.debug(f"original checkpoint precision == {checkpoint[precision_probing_key].dtype}")
|
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precision = precision or checkpoint[precision_probing_key].dtype
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|
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if original_config_file is None:
|
||||
key_name_v2_1 = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
|
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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)
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||||
|
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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
|
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@ -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:
|
||||
|
||||
@ -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
|
||||
|
||||
@ -426,7 +426,7 @@ 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):
|
||||
@ -520,7 +520,7 @@ class ModelManager(object):
|
||||
model_path = getattr(model_config, submodel_type)
|
||||
is_submodel_override = True
|
||||
|
||||
model_path = self.app_config.root_path / model_path
|
||||
model_path = self.resolve_model_path(model_path)
|
||||
return model_path, is_submodel_override
|
||||
|
||||
def _get_model_config(self, base_model, model_name, model_type) -> ModelConfigBase:
|
||||
@ -618,7 +618,7 @@ 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
|
||||
@ -655,7 +655,7 @@ 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))
|
||||
@ -686,10 +686,9 @@ 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 = self._get_implementation(base_model, model_type)
|
||||
model_config = model_class.create_config(**model_attributes)
|
||||
@ -747,7 +746,7 @@ 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)
|
||||
@ -756,15 +755,15 @@ 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)
|
||||
@ -814,7 +813,7 @@ 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
|
||||
@ -827,7 +826,7 @@ 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")
|
||||
|
||||
@ -842,6 +841,15 @@ 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")
|
||||
@ -915,10 +923,17 @@ 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 = self._get_implementation(cur_base_model, cur_model_type)
|
||||
if model_class.save_to_config:
|
||||
@ -937,7 +952,7 @@ class ModelManager(object):
|
||||
if model_type is not None and cur_model_type != model_type:
|
||||
continue
|
||||
model_class = self._get_implementation(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?
|
||||
@ -951,9 +966,7 @@ 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
|
||||
@ -964,12 +977,11 @@ 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.
|
||||
"""
|
||||
@ -1003,7 +1015,7 @@ 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
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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)
|
||||
|
||||
|
||||
@ -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:
|
||||
|
||||
@ -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:
|
||||
|
||||
169
invokeai/frontend/web/dist/assets/App-d6f88f50.js
vendored
169
invokeai/frontend/web/dist/assets/App-d6f88f50.js
vendored
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169
invokeai/frontend/web/dist/assets/App-ea7b7298.js
vendored
Normal file
169
invokeai/frontend/web/dist/assets/App-ea7b7298.js
vendored
Normal file
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2
invokeai/frontend/web/dist/index.html
vendored
2
invokeai/frontend/web/dist/index.html
vendored
@ -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">
|
||||
|
||||
@ -1 +1 @@
|
||||
__version__ = "3.0.1"
|
||||
__version__ = "3.0.1post3"
|
||||
|
||||
Loading…
Reference in New Issue
Block a user