mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'main' into feat/refactor_generation_backend
This commit is contained in:
Sergey Borisov
commit
ade78b9591
23
README.md
23
README.md
@ -306,13 +306,30 @@ InvokeAI. The second will prepare the 2.3 directory for use with 3.0.
|
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You may now launch the WebUI in the usual way, by selecting option [1]
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from the launcher script
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#### Migration Caveats
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#### Migrating Images
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|
||||
The migration script will migrate your invokeai settings and models,
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including textual inversion models, LoRAs and merges that you may have
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installed previously. However it does **not** migrate the generated
|
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images stored in your 2.3-format outputs directory. You will need to
|
||||
manually import selected images into the 3.0 gallery via drag-and-drop.
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images stored in your 2.3-format outputs directory. To do this, you
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||||
need to run an additional step:
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1. From a working InvokeAI 3.0 root directory, start the launcher and
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enter menu option [8] to open the "developer's console".
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2. At the developer's console command line, type the command:
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|
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```bash
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invokeai-import-images
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```
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3. This will lead you through the process of confirming the desired
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source and destination for the imported images. The images will
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appear in the gallery board of your choice, and contain the
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||||
original prompt, model name, and other parameters used to generate
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the image.
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(Many kudos to **techjedi** for contributing this script.)
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## Hardware Requirements
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@ -264,7 +264,7 @@ experimental versions later.
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you can create several levels of subfolders and drop your models into
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whichever ones you want.
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- ***Autoimport FolderLICENSE***
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- ***LICENSE***
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||||
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||||
At the bottom of the screen you will see a checkbox for accepting
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||||
the CreativeML Responsible AI Licenses. You need to accept the license
|
||||
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@ -55,7 +55,7 @@ logger = InvokeAILogger.getLogger()
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class ApiDependencies:
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"""Contains and initializes all dependencies for the API"""
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invoker: Optional[Invoker] = None
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invoker: Invoker
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@staticmethod
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def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger):
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@ -68,8 +68,9 @@ class ApiDependencies:
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output_folder = config.output_path
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# TODO: build a file/path manager?
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db_location = config.db_path
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db_location.parent.mkdir(parents=True, exist_ok=True)
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db_path = config.db_path
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db_path.parent.mkdir(parents=True, exist_ok=True)
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db_location = str(db_path)
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graph_execution_manager = SqliteItemStorage[GraphExecutionState](
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filename=db_location, table_name="graph_executions"
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@ -1,22 +1,20 @@
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import io
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from typing import Optional
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from PIL import Image
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from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
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from fastapi.responses import FileResponse
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from fastapi.routing import APIRouter
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from PIL import Image
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from pydantic import BaseModel, Field
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from pydantic import BaseModel
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from invokeai.app.invocations.metadata import ImageMetadata
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from invokeai.app.models.image import ImageCategory, ResourceOrigin
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from invokeai.app.services.image_record_storage import OffsetPaginatedResults
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from invokeai.app.services.item_storage import PaginatedResults
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from invokeai.app.services.models.image_record import (
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ImageDTO,
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ImageRecordChanges,
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ImageUrlsDTO,
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)
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from ..dependencies import ApiDependencies
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images_router = APIRouter(prefix="/v1/images", tags=["images"])
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@ -152,8 +150,9 @@ async def get_image_metadata(
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raise HTTPException(status_code=404)
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@images_router.get(
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@images_router.api_route(
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"/i/{image_name}/full",
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methods=["GET", "HEAD"],
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operation_id="get_image_full",
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response_class=Response,
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responses={
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@ -4,6 +4,7 @@ from typing import Literal, Optional
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import cv2
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import numpy
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import cv2
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from PIL import Image, ImageFilter, ImageOps, ImageChops
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from pydantic import Field
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from pathlib import Path
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@ -502,7 +503,7 @@ class ImageLerpInvocation(BaseInvocation, PILInvocationConfig):
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image = context.services.images.get_pil_image(self.image.image_name)
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image_arr = numpy.asarray(image, dtype=numpy.float32) / 255
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image_arr = image_arr * (self.max - self.min) + self.max
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image_arr = image_arr * (self.max - self.min) + self.min
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lerp_image = Image.fromarray(numpy.uint8(image_arr))
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@ -653,6 +654,7 @@ class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig):
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height=image_dto.height,
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)
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class MaskEdgeInvocation(BaseInvocation, PILInvocationConfig):
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"""Applies an edge mask to an image"""
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@ -702,6 +704,7 @@ class MaskEdgeInvocation(BaseInvocation, PILInvocationConfig):
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height=image_dto.height,
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)
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class ColorCorrectInvocation(BaseInvocation, PILInvocationConfig):
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type: Literal["color_correct"] = "color_correct"
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@ -817,3 +820,142 @@ class ColorCorrectInvocation(BaseInvocation, PILInvocationConfig):
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height=image_dto.height,
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)
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class ImageHueAdjustmentInvocation(BaseInvocation):
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"""Adjusts the Hue of an image."""
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# fmt: off
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type: Literal["img_hue_adjust"] = "img_hue_adjust"
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# Inputs
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image: ImageField = Field(default=None, description="The image to adjust")
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hue: int = Field(default=0, description="The degrees by which to rotate the hue, 0-360")
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# fmt: on
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def invoke(self, context: InvocationContext) -> ImageOutput:
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pil_image = context.services.images.get_pil_image(self.image.image_name)
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# Convert image to HSV color space
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hsv_image = numpy.array(pil_image.convert("HSV"))
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# Convert hue from 0..360 to 0..256
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hue = int(256 * ((self.hue % 360) / 360))
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# Increment each hue and wrap around at 255
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hsv_image[:, :, 0] = (hsv_image[:, :, 0] + hue) % 256
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# Convert back to PIL format and to original color mode
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pil_image = Image.fromarray(hsv_image, mode="HSV").convert("RGBA")
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image_dto = context.services.images.create(
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image=pil_image,
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image_origin=ResourceOrigin.INTERNAL,
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image_category=ImageCategory.GENERAL,
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node_id=self.id,
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is_intermediate=self.is_intermediate,
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session_id=context.graph_execution_state_id,
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)
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return ImageOutput(
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image=ImageField(
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image_name=image_dto.image_name,
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),
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width=image_dto.width,
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height=image_dto.height,
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)
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class ImageLuminosityAdjustmentInvocation(BaseInvocation):
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"""Adjusts the Luminosity (Value) of an image."""
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# fmt: off
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type: Literal["img_luminosity_adjust"] = "img_luminosity_adjust"
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# Inputs
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image: ImageField = Field(default=None, description="The image to adjust")
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luminosity: float = Field(default=1.0, ge=0, le=1, description="The factor by which to adjust the luminosity (value)")
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# fmt: on
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def invoke(self, context: InvocationContext) -> ImageOutput:
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pil_image = context.services.images.get_pil_image(self.image.image_name)
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# Convert PIL image to OpenCV format (numpy array), note color channel
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# ordering is changed from RGB to BGR
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image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1]
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# Convert image to HSV color space
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hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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# Adjust the luminosity (value)
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hsv_image[:, :, 2] = numpy.clip(hsv_image[:, :, 2] * self.luminosity, 0, 255)
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# Convert image back to BGR color space
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image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
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# Convert back to PIL format and to original color mode
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pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
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image_dto = context.services.images.create(
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image=pil_image,
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image_origin=ResourceOrigin.INTERNAL,
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image_category=ImageCategory.GENERAL,
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node_id=self.id,
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is_intermediate=self.is_intermediate,
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session_id=context.graph_execution_state_id,
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)
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return ImageOutput(
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image=ImageField(
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image_name=image_dto.image_name,
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),
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width=image_dto.width,
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height=image_dto.height,
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)
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class ImageSaturationAdjustmentInvocation(BaseInvocation):
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"""Adjusts the Saturation of an image."""
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# fmt: off
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type: Literal["img_saturation_adjust"] = "img_saturation_adjust"
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# Inputs
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image: ImageField = Field(default=None, description="The image to adjust")
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saturation: float = Field(default=1.0, ge=0, le=1, description="The factor by which to adjust the saturation")
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# fmt: on
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def invoke(self, context: InvocationContext) -> ImageOutput:
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pil_image = context.services.images.get_pil_image(self.image.image_name)
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# Convert PIL image to OpenCV format (numpy array), note color channel
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# ordering is changed from RGB to BGR
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image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1]
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# Convert image to HSV color space
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hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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# Adjust the saturation
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hsv_image[:, :, 1] = numpy.clip(hsv_image[:, :, 1] * self.saturation, 0, 255)
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# Convert image back to BGR color space
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image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
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# Convert back to PIL format and to original color mode
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pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
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image_dto = context.services.images.create(
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image=pil_image,
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image_origin=ResourceOrigin.INTERNAL,
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image_category=ImageCategory.GENERAL,
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node_id=self.id,
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is_intermediate=self.is_intermediate,
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session_id=context.graph_execution_state_id,
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)
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return ImageOutput(
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image=ImageField(
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image_name=image_dto.image_name,
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),
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width=image_dto.width,
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height=image_dto.height,
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||||
)
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@ -5,15 +5,26 @@ from typing import List, Literal, Optional, Union
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import einops
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import torch
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from diffusers import ControlNetModel
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from diffusers.image_processor import VaeImageProcessor
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from diffusers.models.attention_processor import (
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AttnProcessor2_0,
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LoRAAttnProcessor2_0,
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LoRAXFormersAttnProcessor,
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XFormersAttnProcessor,
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)
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from diffusers.schedulers import SchedulerMixin as Scheduler
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from pydantic import BaseModel, Field, validator
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from invokeai.app.invocations.metadata import CoreMetadata
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from invokeai.app.util.controlnet_utils import prepare_control_image
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from invokeai.app.util.step_callback import stable_diffusion_step_callback
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from invokeai.backend.model_management.models import ModelType, SilenceWarnings
|
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|
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from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
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from .compel import ConditioningField
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from .controlnet_image_processors import ControlField
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from .image import ImageOutput
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from .model import ModelInfo, UNetField, VaeField
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from ..models.image import ImageCategory, ImageField, ResourceOrigin
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from ...backend.model_management import ModelPatcher
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from ...backend.stable_diffusion import PipelineIntermediateState
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from ...backend.stable_diffusion.diffusers_pipeline import (
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@ -239,7 +250,6 @@ class TextToLatentsInvocation(BaseInvocation):
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safety_checker=None,
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feature_extractor=None,
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requires_safety_checker=False,
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precision="float16" if unet.dtype == torch.float16 else "float32",
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||||
)
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def prep_control_data(
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@ -24,11 +24,10 @@ InvokeAI:
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||||
sequential_guidance: false
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||||
precision: float16
|
||||
max_cache_size: 6
|
||||
max_vram_cache_size: 2.7
|
||||
max_vram_cache_size: 0.5
|
||||
always_use_cpu: false
|
||||
free_gpu_mem: false
|
||||
Features:
|
||||
restore: true
|
||||
esrgan: true
|
||||
patchmatch: true
|
||||
internet_available: true
|
||||
@ -165,7 +164,7 @@ import pydoc
|
||||
import os
|
||||
import sys
|
||||
from argparse import ArgumentParser
|
||||
from omegaconf import OmegaConf, DictConfig
|
||||
from omegaconf import OmegaConf, DictConfig, ListConfig
|
||||
from pathlib import Path
|
||||
from pydantic import BaseSettings, Field, parse_obj_as
|
||||
from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args
|
||||
@ -173,6 +172,7 @@ from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_ty
|
||||
INIT_FILE = Path("invokeai.yaml")
|
||||
DB_FILE = Path("invokeai.db")
|
||||
LEGACY_INIT_FILE = Path("invokeai.init")
|
||||
DEFAULT_MAX_VRAM = 0.5
|
||||
|
||||
|
||||
class InvokeAISettings(BaseSettings):
|
||||
@ -189,7 +189,12 @@ class InvokeAISettings(BaseSettings):
|
||||
opt = parser.parse_args(argv)
|
||||
for name in self.__fields__:
|
||||
if name not in self._excluded():
|
||||
setattr(self, name, getattr(opt, name))
|
||||
value = getattr(opt, name)
|
||||
if isinstance(value, ListConfig):
|
||||
value = list(value)
|
||||
elif isinstance(value, DictConfig):
|
||||
value = dict(value)
|
||||
setattr(self, name, value)
|
||||
|
||||
def to_yaml(self) -> str:
|
||||
"""
|
||||
@ -282,14 +287,10 @@ class InvokeAISettings(BaseSettings):
|
||||
return [
|
||||
"type",
|
||||
"initconf",
|
||||
"gpu_mem_reserved",
|
||||
"max_loaded_models",
|
||||
"version",
|
||||
"from_file",
|
||||
"model",
|
||||
"restore",
|
||||
"root",
|
||||
"nsfw_checker",
|
||||
]
|
||||
|
||||
class Config:
|
||||
@ -388,15 +389,11 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features')
|
||||
log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
|
||||
patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
|
||||
restore : bool = Field(default=True, description="Enable/disable face restoration code (DEPRECATED)", category='DEPRECATED')
|
||||
|
||||
always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
|
||||
free_gpu_mem : bool = Field(default=False, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
|
||||
max_loaded_models : int = Field(default=3, gt=0, description="(DEPRECATED: use max_cache_size) Maximum number of models to keep in memory for rapid switching", category='DEPRECATED')
|
||||
max_cache_size : float = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance')
|
||||
max_vram_cache_size : float = Field(default=2.75, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance')
|
||||
gpu_mem_reserved : float = Field(default=2.75, ge=0, description="DEPRECATED: use max_vram_cache_size. Amount of VRAM reserved for model storage", category='DEPRECATED')
|
||||
nsfw_checker : bool = Field(default=True, description="DEPRECATED: use Web settings to enable/disable", category='DEPRECATED')
|
||||
precision : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='auto',description='Floating point precision', category='Memory/Performance')
|
||||
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
|
||||
xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
|
||||
@ -414,9 +411,7 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
outdir : Path = Field(default='outputs', description='Default folder for output images', category='Paths')
|
||||
from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
|
||||
use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths')
|
||||
ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert')
|
||||
|
||||
model : str = Field(default='stable-diffusion-1.5', description='Initial model name', category='Models')
|
||||
ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features')
|
||||
|
||||
log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', category="Logging")
|
||||
# note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
|
||||
@ -426,6 +421,9 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
version : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
|
||||
# fmt: on
|
||||
|
||||
class Config:
|
||||
validate_assignment = True
|
||||
|
||||
def parse_args(self, argv: List[str] = None, conf: DictConfig = None, clobber=False):
|
||||
"""
|
||||
Update settings with contents of init file, environment, and
|
||||
|
||||
@ -3,9 +3,10 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from logging import Logger
|
||||
from pathlib import Path
|
||||
from pydantic import Field
|
||||
from typing import Optional, Union, Callable, List, Tuple, TYPE_CHECKING
|
||||
from typing import Literal, Optional, Union, Callable, List, Tuple, TYPE_CHECKING
|
||||
from types import ModuleType
|
||||
|
||||
from invokeai.backend.model_management import (
|
||||
@ -193,7 +194,7 @@ class ModelManagerServiceBase(ABC):
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: Union[ModelType.Main, ModelType.Vae],
|
||||
model_type: Literal[ModelType.Main, ModelType.Vae],
|
||||
) -> AddModelResult:
|
||||
"""
|
||||
Convert a checkpoint file into a diffusers folder, deleting the cached
|
||||
@ -292,7 +293,7 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
def __init__(
|
||||
self,
|
||||
config: InvokeAIAppConfig,
|
||||
logger: ModuleType,
|
||||
logger: Logger,
|
||||
):
|
||||
"""
|
||||
Initialize with the path to the models.yaml config file.
|
||||
@ -396,7 +397,7 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
model_type,
|
||||
)
|
||||
|
||||
def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
|
||||
def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> Union[dict, None]:
|
||||
"""
|
||||
Given a model name returns a dict-like (OmegaConf) object describing it.
|
||||
"""
|
||||
@ -416,7 +417,7 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
"""
|
||||
return self.mgr.list_models(base_model, model_type)
|
||||
|
||||
def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
|
||||
def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> Union[dict, None]:
|
||||
"""
|
||||
Return information about the model using the same format as list_models()
|
||||
"""
|
||||
@ -429,7 +430,7 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
model_type: ModelType,
|
||||
model_attributes: dict,
|
||||
clobber: bool = False,
|
||||
) -> None:
|
||||
) -> AddModelResult:
|
||||
"""
|
||||
Update the named model with a dictionary of attributes. Will fail with an
|
||||
assertion error if the name already exists. Pass clobber=True to overwrite.
|
||||
@ -478,7 +479,7 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: Union[ModelType.Main, ModelType.Vae],
|
||||
model_type: Literal[ModelType.Main, ModelType.Vae],
|
||||
convert_dest_directory: Optional[Path] = Field(
|
||||
default=None, description="Optional directory location for merged model"
|
||||
),
|
||||
@ -573,9 +574,9 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
default=None, description="Base model shared by all models to be merged"
|
||||
),
|
||||
merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
|
||||
alpha: Optional[float] = 0.5,
|
||||
alpha: float = 0.5,
|
||||
interp: Optional[MergeInterpolationMethod] = None,
|
||||
force: Optional[bool] = False,
|
||||
force: bool = False,
|
||||
merge_dest_directory: Optional[Path] = Field(
|
||||
default=None, description="Optional directory location for merged model"
|
||||
),
|
||||
@ -633,8 +634,8 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
new_name: str = None,
|
||||
new_base: BaseModelType = None,
|
||||
new_name: Optional[str] = None,
|
||||
new_base: Optional[BaseModelType] = None,
|
||||
):
|
||||
"""
|
||||
Rename the indicated model. Can provide a new name and/or a new base.
|
||||
|
||||
@ -10,12 +10,15 @@ import sys
|
||||
import argparse
|
||||
import io
|
||||
import os
|
||||
import psutil
|
||||
import shutil
|
||||
import textwrap
|
||||
import torch
|
||||
import traceback
|
||||
import yaml
|
||||
import warnings
|
||||
from argparse import Namespace
|
||||
from enum import Enum
|
||||
from pathlib import Path
|
||||
from shutil import get_terminal_size
|
||||
from typing import get_type_hints
|
||||
@ -44,6 +47,8 @@ from invokeai.app.services.config import (
|
||||
)
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
|
||||
|
||||
# TO DO - Move all the frontend code into invokeai.frontend.install
|
||||
from invokeai.frontend.install.widgets import (
|
||||
SingleSelectColumns,
|
||||
CenteredButtonPress,
|
||||
@ -53,6 +58,7 @@ from invokeai.frontend.install.widgets import (
|
||||
CyclingForm,
|
||||
MIN_COLS,
|
||||
MIN_LINES,
|
||||
WindowTooSmallException,
|
||||
)
|
||||
from invokeai.backend.install.legacy_arg_parsing import legacy_parser
|
||||
from invokeai.backend.install.model_install_backend import (
|
||||
@ -61,6 +67,7 @@ from invokeai.backend.install.model_install_backend import (
|
||||
ModelInstall,
|
||||
)
|
||||
from invokeai.backend.model_management.model_probe import ModelType, BaseModelType
|
||||
from pydantic.error_wrappers import ValidationError
|
||||
|
||||
warnings.filterwarnings("ignore")
|
||||
transformers.logging.set_verbosity_error()
|
||||
@ -76,6 +83,13 @@ Default_config_file = config.model_conf_path
|
||||
SD_Configs = config.legacy_conf_path
|
||||
|
||||
PRECISION_CHOICES = ["auto", "float16", "float32"]
|
||||
GB = 1073741824 # GB in bytes
|
||||
HAS_CUDA = torch.cuda.is_available()
|
||||
_, MAX_VRAM = torch.cuda.mem_get_info() if HAS_CUDA else (0, 0)
|
||||
|
||||
|
||||
MAX_VRAM /= GB
|
||||
MAX_RAM = psutil.virtual_memory().total / GB
|
||||
|
||||
INIT_FILE_PREAMBLE = """# InvokeAI initialization file
|
||||
# This is the InvokeAI initialization file, which contains command-line default values.
|
||||
@ -86,6 +100,12 @@ INIT_FILE_PREAMBLE = """# InvokeAI initialization file
|
||||
logger = InvokeAILogger.getLogger()
|
||||
|
||||
|
||||
class DummyWidgetValue(Enum):
|
||||
zero = 0
|
||||
true = True
|
||||
false = False
|
||||
|
||||
|
||||
# --------------------------------------------
|
||||
def postscript(errors: None):
|
||||
if not any(errors):
|
||||
@ -378,13 +398,35 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
|
||||
)
|
||||
self.max_cache_size = self.add_widget_intelligent(
|
||||
IntTitleSlider,
|
||||
name="Size of the RAM cache used for fast model switching (GB)",
|
||||
name="RAM cache size (GB). Make this at least large enough to hold a single full model.",
|
||||
value=old_opts.max_cache_size,
|
||||
out_of=20,
|
||||
out_of=MAX_RAM,
|
||||
lowest=3,
|
||||
begin_entry_at=6,
|
||||
scroll_exit=True,
|
||||
)
|
||||
if HAS_CUDA:
|
||||
self.nextrely += 1
|
||||
self.add_widget_intelligent(
|
||||
npyscreen.TitleFixedText,
|
||||
name="VRAM cache size (GB). Reserving a small amount of VRAM will modestly speed up the start of image generation.",
|
||||
begin_entry_at=0,
|
||||
editable=False,
|
||||
color="CONTROL",
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely -= 1
|
||||
self.max_vram_cache_size = self.add_widget_intelligent(
|
||||
npyscreen.Slider,
|
||||
value=old_opts.max_vram_cache_size,
|
||||
out_of=round(MAX_VRAM * 2) / 2,
|
||||
lowest=0.0,
|
||||
relx=8,
|
||||
step=0.25,
|
||||
scroll_exit=True,
|
||||
)
|
||||
else:
|
||||
self.max_vram_cache_size = DummyWidgetValue.zero
|
||||
self.nextrely += 1
|
||||
self.outdir = self.add_widget_intelligent(
|
||||
FileBox,
|
||||
@ -401,7 +443,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
|
||||
self.autoimport_dirs = {}
|
||||
self.autoimport_dirs["autoimport_dir"] = self.add_widget_intelligent(
|
||||
FileBox,
|
||||
name=f"Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models",
|
||||
name="Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models",
|
||||
value=str(config.root_path / config.autoimport_dir),
|
||||
select_dir=True,
|
||||
must_exist=False,
|
||||
@ -476,6 +518,7 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
|
||||
"outdir",
|
||||
"free_gpu_mem",
|
||||
"max_cache_size",
|
||||
"max_vram_cache_size",
|
||||
"xformers_enabled",
|
||||
"always_use_cpu",
|
||||
]:
|
||||
@ -592,13 +635,13 @@ def maybe_create_models_yaml(root: Path):
|
||||
|
||||
# -------------------------------------
|
||||
def run_console_ui(program_opts: Namespace, initfile: Path = None) -> (Namespace, Namespace):
|
||||
# parse_args() will read from init file if present
|
||||
invokeai_opts = default_startup_options(initfile)
|
||||
invokeai_opts.root = program_opts.root
|
||||
|
||||
# The third argument is needed in the Windows 11 environment to
|
||||
# launch a console window running this program.
|
||||
set_min_terminal_size(MIN_COLS, MIN_LINES)
|
||||
if not set_min_terminal_size(MIN_COLS, MIN_LINES):
|
||||
raise WindowTooSmallException(
|
||||
"Could not increase terminal size. Try running again with a larger window or smaller font size."
|
||||
)
|
||||
|
||||
# the install-models application spawns a subprocess to install
|
||||
# models, and will crash unless this is set before running.
|
||||
@ -654,10 +697,13 @@ def migrate_init_file(legacy_format: Path):
|
||||
old = legacy_parser.parse_args([f"@{str(legacy_format)}"])
|
||||
new = InvokeAIAppConfig.get_config()
|
||||
|
||||
fields = list(get_type_hints(InvokeAIAppConfig).keys())
|
||||
fields = [x for x, y in InvokeAIAppConfig.__fields__.items() if y.field_info.extra.get("category") != "DEPRECATED"]
|
||||
for attr in fields:
|
||||
if hasattr(old, attr):
|
||||
setattr(new, attr, getattr(old, attr))
|
||||
try:
|
||||
setattr(new, attr, getattr(old, attr))
|
||||
except ValidationError as e:
|
||||
print(f"* Ignoring incompatible value for field {attr}:\n {str(e)}")
|
||||
|
||||
# a few places where the field names have changed and we have to
|
||||
# manually add in the new names/values
|
||||
@ -777,6 +823,7 @@ def main():
|
||||
|
||||
models_to_download = default_user_selections(opt)
|
||||
new_init_file = config.root_path / "invokeai.yaml"
|
||||
|
||||
if opt.yes_to_all:
|
||||
write_default_options(opt, new_init_file)
|
||||
init_options = Namespace(precision="float32" if opt.full_precision else "float16")
|
||||
@ -802,6 +849,8 @@ def main():
|
||||
postscript(errors=errors)
|
||||
if not opt.yes_to_all:
|
||||
input("Press any key to continue...")
|
||||
except WindowTooSmallException as e:
|
||||
logger.error(str(e))
|
||||
except KeyboardInterrupt:
|
||||
print("\nGoodbye! Come back soon.")
|
||||
|
||||
|
||||
@ -101,9 +101,9 @@ class ModelInstall(object):
|
||||
def __init__(
|
||||
self,
|
||||
config: InvokeAIAppConfig,
|
||||
prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None,
|
||||
model_manager: ModelManager = None,
|
||||
access_token: str = None,
|
||||
prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
|
||||
model_manager: Optional[ModelManager] = None,
|
||||
access_token: Optional[str] = None,
|
||||
):
|
||||
self.config = config
|
||||
self.mgr = model_manager or ModelManager(config.model_conf_path)
|
||||
|
||||
@ -228,19 +228,19 @@ the root is the InvokeAI ROOTDIR.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import hashlib
|
||||
import os
|
||||
import textwrap
|
||||
import yaml
|
||||
import types
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from typing import Optional, List, Tuple, Union, Dict, Set, Callable, types
|
||||
from shutil import rmtree, move
|
||||
from typing import Optional, List, Literal, Tuple, Union, Dict, Set, Callable
|
||||
|
||||
import torch
|
||||
import yaml
|
||||
from omegaconf import OmegaConf
|
||||
from omegaconf.dictconfig import DictConfig
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
@ -259,6 +259,7 @@ from .models import (
|
||||
ModelNotFoundException,
|
||||
InvalidModelException,
|
||||
DuplicateModelException,
|
||||
ModelBase,
|
||||
)
|
||||
|
||||
# We are only starting to number the config file with release 3.
|
||||
@ -361,7 +362,7 @@ class ModelManager(object):
|
||||
if model_key.startswith("_"):
|
||||
continue
|
||||
model_name, base_model, model_type = self.parse_key(model_key)
|
||||
model_class = MODEL_CLASSES[base_model][model_type]
|
||||
model_class = self._get_implementation(base_model, model_type)
|
||||
# alias for config file
|
||||
model_config["model_format"] = model_config.pop("format")
|
||||
self.models[model_key] = model_class.create_config(**model_config)
|
||||
@ -381,18 +382,24 @@ class ModelManager(object):
|
||||
# causing otherwise unreferenced models to be removed from memory
|
||||
self._read_models()
|
||||
|
||||
def model_exists(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
) -> bool:
|
||||
def model_exists(self, model_name: str, base_model: BaseModelType, model_type: ModelType, *, rescan=False) -> bool:
|
||||
"""
|
||||
Given a model name, returns True if it is a valid
|
||||
identifier.
|
||||
Given a model name, returns True if it is a valid identifier.
|
||||
|
||||
:param model_name: symbolic name of the model in models.yaml
|
||||
:param model_type: ModelType enum indicating the type of model to return
|
||||
:param base_model: BaseModelType enum indicating the base model used by this model
|
||||
:param rescan: if True, scan_models_directory
|
||||
"""
|
||||
model_key = self.create_key(model_name, base_model, model_type)
|
||||
return model_key in self.models
|
||||
exists = model_key in self.models
|
||||
|
||||
# if model not found try to find it (maybe file just pasted)
|
||||
if rescan and not exists:
|
||||
self.scan_models_directory(base_model=base_model, model_type=model_type)
|
||||
exists = self.model_exists(model_name, base_model, model_type, rescan=False)
|
||||
|
||||
return exists
|
||||
|
||||
@classmethod
|
||||
def create_key(
|
||||
@ -443,39 +450,32 @@ class ModelManager(object):
|
||||
:param model_name: symbolic name of the model in models.yaml
|
||||
:param model_type: ModelType enum indicating the type of model to return
|
||||
:param base_model: BaseModelType enum indicating the base model used by this model
|
||||
:param submode_typel: an ModelType enum indicating the portion of
|
||||
:param submodel_type: an ModelType enum indicating the portion of
|
||||
the model to retrieve (e.g. ModelType.Vae)
|
||||
"""
|
||||
model_class = MODEL_CLASSES[base_model][model_type]
|
||||
model_key = self.create_key(model_name, base_model, model_type)
|
||||
|
||||
# if model not found try to find it (maybe file just pasted)
|
||||
if model_key not in self.models:
|
||||
self.scan_models_directory(base_model=base_model, model_type=model_type)
|
||||
if model_key not in self.models:
|
||||
raise ModelNotFoundException(f"Model not found - {model_key}")
|
||||
if not self.model_exists(model_name, base_model, model_type, rescan=True):
|
||||
raise ModelNotFoundException(f"Model not found - {model_key}")
|
||||
|
||||
model_config = self.models[model_key]
|
||||
model_path = self.resolve_model_path(model_config.path)
|
||||
model_config = self._get_model_config(base_model, model_name, model_type)
|
||||
|
||||
model_path, is_submodel_override = self._get_model_path(model_config, submodel_type)
|
||||
|
||||
if is_submodel_override:
|
||||
model_type = submodel_type
|
||||
submodel_type = None
|
||||
|
||||
model_class = self._get_implementation(base_model, model_type)
|
||||
|
||||
if not model_path.exists():
|
||||
if model_class.save_to_config:
|
||||
self.models[model_key].error = ModelError.NotFound
|
||||
raise Exception(f'Files for model "{model_key}" not found')
|
||||
raise Exception(f'Files for model "{model_key}" not found at {model_path}')
|
||||
|
||||
else:
|
||||
self.models.pop(model_key, None)
|
||||
raise ModelNotFoundException(f"Model not found - {model_key}")
|
||||
|
||||
# vae/movq override
|
||||
# TODO:
|
||||
if submodel_type is not None and hasattr(model_config, submodel_type):
|
||||
override_path = getattr(model_config, submodel_type)
|
||||
if override_path:
|
||||
model_path = self.resolve_path(override_path)
|
||||
model_type = submodel_type
|
||||
submodel_type = None
|
||||
model_class = MODEL_CLASSES[base_model][model_type]
|
||||
raise ModelNotFoundException(f'Files for model "{model_key}" not found at {model_path}')
|
||||
|
||||
# TODO: path
|
||||
# TODO: is it accurate to use path as id
|
||||
@ -513,12 +513,61 @@ class ModelManager(object):
|
||||
_cache=self.cache,
|
||||
)
|
||||
|
||||
def _get_model_path(
|
||||
self, model_config: ModelConfigBase, submodel_type: Optional[SubModelType] = None
|
||||
) -> (Path, bool):
|
||||
"""Extract a model's filesystem path from its config.
|
||||
|
||||
:return: The fully qualified Path of the module (or submodule).
|
||||
"""
|
||||
model_path = model_config.path
|
||||
is_submodel_override = False
|
||||
|
||||
# Does the config explicitly override the submodel?
|
||||
if submodel_type is not None and hasattr(model_config, submodel_type):
|
||||
submodel_path = getattr(model_config, submodel_type)
|
||||
if submodel_path is not None:
|
||||
model_path = getattr(model_config, submodel_type)
|
||||
is_submodel_override = True
|
||||
|
||||
model_path = self.resolve_model_path(model_path)
|
||||
return model_path, is_submodel_override
|
||||
|
||||
def _get_model_config(self, base_model: BaseModelType, model_name: str, model_type: ModelType) -> ModelConfigBase:
|
||||
"""Get a model's config object."""
|
||||
model_key = self.create_key(model_name, base_model, model_type)
|
||||
try:
|
||||
model_config = self.models[model_key]
|
||||
except KeyError:
|
||||
raise ModelNotFoundException(f"Model not found - {model_key}")
|
||||
return model_config
|
||||
|
||||
def _get_implementation(self, base_model: BaseModelType, model_type: ModelType) -> type[ModelBase]:
|
||||
"""Get the concrete implementation class for a specific model type."""
|
||||
model_class = MODEL_CLASSES[base_model][model_type]
|
||||
return model_class
|
||||
|
||||
def _instantiate(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
submodel_type: Optional[SubModelType] = None,
|
||||
) -> ModelBase:
|
||||
"""Make a new instance of this model, without loading it."""
|
||||
model_config = self._get_model_config(base_model, model_name, model_type)
|
||||
model_path, is_submodel_override = self._get_model_path(model_config, submodel_type)
|
||||
# FIXME: do non-overriden submodels get the right class?
|
||||
constructor = self._get_implementation(base_model, model_type)
|
||||
instance = constructor(model_path, base_model, model_type)
|
||||
return instance
|
||||
|
||||
def model_info(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
) -> dict:
|
||||
) -> Union[dict, None]:
|
||||
"""
|
||||
Given a model name returns the OmegaConf (dict-like) object describing it.
|
||||
"""
|
||||
@ -540,13 +589,16 @@ class ModelManager(object):
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
) -> dict:
|
||||
) -> Union[dict, None]:
|
||||
"""
|
||||
Returns a dict describing one installed model, using
|
||||
the combined format of the list_models() method.
|
||||
"""
|
||||
models = self.list_models(base_model, model_type, model_name)
|
||||
return models[0] if models else None
|
||||
if len(models) >= 1:
|
||||
return models[0]
|
||||
else:
|
||||
return None
|
||||
|
||||
def list_models(
|
||||
self,
|
||||
@ -560,7 +612,7 @@ class ModelManager(object):
|
||||
|
||||
model_keys = (
|
||||
[self.create_key(model_name, base_model, model_type)]
|
||||
if model_name
|
||||
if model_name and base_model and model_type
|
||||
else sorted(self.models, key=str.casefold)
|
||||
)
|
||||
models = []
|
||||
@ -596,7 +648,7 @@ class ModelManager(object):
|
||||
Print a table of models and their descriptions. This needs to be redone
|
||||
"""
|
||||
# TODO: redo
|
||||
for model_type, model_dict in self.list_models().items():
|
||||
for model_dict in self.list_models():
|
||||
for model_name, model_info in model_dict.items():
|
||||
line = f'{model_info["name"]:25s} {model_info["type"]:10s} {model_info["description"]}'
|
||||
print(line)
|
||||
@ -658,7 +710,7 @@ class ModelManager(object):
|
||||
if path := model_attributes.get("path"):
|
||||
model_attributes["path"] = str(self.relative_model_path(Path(path)))
|
||||
|
||||
model_class = MODEL_CLASSES[base_model][model_type]
|
||||
model_class = self._get_implementation(base_model, model_type)
|
||||
model_config = model_class.create_config(**model_attributes)
|
||||
model_key = self.create_key(model_name, base_model, model_type)
|
||||
|
||||
@ -699,8 +751,8 @@ class ModelManager(object):
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
new_name: str = None,
|
||||
new_base: BaseModelType = None,
|
||||
new_name: Optional[str] = None,
|
||||
new_base: Optional[BaseModelType] = None,
|
||||
):
|
||||
"""
|
||||
Rename or rebase a model.
|
||||
@ -753,7 +805,7 @@ class ModelManager(object):
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: Union[ModelType.Main, ModelType.Vae],
|
||||
model_type: Literal[ModelType.Main, ModelType.Vae],
|
||||
dest_directory: Optional[Path] = None,
|
||||
) -> AddModelResult:
|
||||
"""
|
||||
@ -767,6 +819,10 @@ class ModelManager(object):
|
||||
This will raise a ValueError unless the model is a checkpoint.
|
||||
"""
|
||||
info = self.model_info(model_name, base_model, model_type)
|
||||
|
||||
if info is None:
|
||||
raise FileNotFoundError(f"model not found: {model_name}")
|
||||
|
||||
if info["model_format"] != "checkpoint":
|
||||
raise ValueError(f"not a checkpoint format model: {model_name}")
|
||||
|
||||
@ -836,7 +892,7 @@ class ModelManager(object):
|
||||
|
||||
return search_folder, found_models
|
||||
|
||||
def commit(self, conf_file: Path = None) -> None:
|
||||
def commit(self, conf_file: Optional[Path] = None) -> None:
|
||||
"""
|
||||
Write current configuration out to the indicated file.
|
||||
"""
|
||||
@ -845,7 +901,7 @@ class ModelManager(object):
|
||||
|
||||
for model_key, model_config in self.models.items():
|
||||
model_name, base_model, model_type = self.parse_key(model_key)
|
||||
model_class = MODEL_CLASSES[base_model][model_type]
|
||||
model_class = self._get_implementation(base_model, model_type)
|
||||
if model_class.save_to_config:
|
||||
# TODO: or exclude_unset better fits here?
|
||||
data_to_save[model_key] = model_config.dict(exclude_defaults=True, exclude={"error"})
|
||||
@ -903,7 +959,7 @@ class ModelManager(object):
|
||||
|
||||
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]
|
||||
model_class = self._get_implementation(cur_base_model, cur_model_type)
|
||||
if model_class.save_to_config:
|
||||
model_config.error = ModelError.NotFound
|
||||
self.models.pop(model_key, None)
|
||||
@ -919,7 +975,7 @@ class ModelManager(object):
|
||||
for cur_model_type in ModelType:
|
||||
if model_type is not None and cur_model_type != model_type:
|
||||
continue
|
||||
model_class = MODEL_CLASSES[cur_base_model][cur_model_type]
|
||||
model_class = self._get_implementation(cur_base_model, cur_model_type)
|
||||
models_dir = self.resolve_model_path(Path(cur_base_model.value, cur_model_type.value))
|
||||
|
||||
if not models_dir.exists():
|
||||
@ -935,7 +991,9 @@ class ModelManager(object):
|
||||
raise DuplicateModelException(f"Model with key {model_key} added twice")
|
||||
|
||||
model_path = self.relative_model_path(model_path)
|
||||
model_config: ModelConfigBase = model_class.probe_config(str(model_path))
|
||||
model_config: ModelConfigBase = model_class.probe_config(
|
||||
str(model_path), model_base=cur_base_model
|
||||
)
|
||||
self.models[model_key] = model_config
|
||||
new_models_found = True
|
||||
except DuplicateModelException as e:
|
||||
@ -983,7 +1041,7 @@ 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({self.resolve_model_path("core/convert/sd-vae-ft-mse")})
|
||||
self.heuristic_import({str(self.resolve_model_path("core/convert/sd-vae-ft-mse"))})
|
||||
except:
|
||||
pass
|
||||
|
||||
@ -1011,7 +1069,7 @@ class ModelManager(object):
|
||||
def heuristic_import(
|
||||
self,
|
||||
items_to_import: Set[str],
|
||||
prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None,
|
||||
prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
|
||||
) -> Dict[str, AddModelResult]:
|
||||
"""Import a list of paths, repo_ids or URLs. Returns the set of
|
||||
successfully imported items.
|
||||
|
||||
@ -33,7 +33,7 @@ class ModelMerger(object):
|
||||
self,
|
||||
model_paths: List[Path],
|
||||
alpha: float = 0.5,
|
||||
interp: MergeInterpolationMethod = None,
|
||||
interp: Optional[MergeInterpolationMethod] = None,
|
||||
force: bool = False,
|
||||
**kwargs,
|
||||
) -> DiffusionPipeline:
|
||||
@ -73,7 +73,7 @@ class ModelMerger(object):
|
||||
base_model: Union[BaseModelType, str],
|
||||
merged_model_name: str,
|
||||
alpha: float = 0.5,
|
||||
interp: MergeInterpolationMethod = None,
|
||||
interp: Optional[MergeInterpolationMethod] = None,
|
||||
force: bool = False,
|
||||
merge_dest_directory: Optional[Path] = None,
|
||||
**kwargs,
|
||||
@ -122,7 +122,7 @@ class ModelMerger(object):
|
||||
dump_path.mkdir(parents=True, exist_ok=True)
|
||||
dump_path = dump_path / merged_model_name
|
||||
|
||||
merged_pipe.save_pretrained(dump_path, safe_serialization=1)
|
||||
merged_pipe.save_pretrained(dump_path, safe_serialization=True)
|
||||
attributes = dict(
|
||||
path=str(dump_path),
|
||||
description=f"Merge of models {', '.join(model_names)}",
|
||||
|
||||
@ -80,8 +80,10 @@ class StableDiffusionXLModel(DiffusersModel):
|
||||
raise Exception("Unkown stable diffusion 2.* model format")
|
||||
|
||||
if ckpt_config_path is None:
|
||||
# TO DO: implement picking
|
||||
pass
|
||||
# avoid circular import
|
||||
from .stable_diffusion import _select_ckpt_config
|
||||
|
||||
ckpt_config_path = _select_ckpt_config(kwargs.get("model_base", BaseModelType.StableDiffusionXL), variant)
|
||||
|
||||
return cls.create_config(
|
||||
path=path,
|
||||
|
||||
@ -1,9 +1,14 @@
|
||||
import os
|
||||
import torch
|
||||
import safetensors
|
||||
from enum import Enum
|
||||
from pathlib import Path
|
||||
from typing import Optional, Union, Literal
|
||||
from typing import Optional
|
||||
|
||||
import safetensors
|
||||
import torch
|
||||
from diffusers.utils import is_safetensors_available
|
||||
from omegaconf import OmegaConf
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from .base import (
|
||||
ModelBase,
|
||||
ModelConfigBase,
|
||||
@ -18,9 +23,6 @@ from .base import (
|
||||
InvalidModelException,
|
||||
ModelNotFoundException,
|
||||
)
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from diffusers.utils import is_safetensors_available
|
||||
from omegaconf import OmegaConf
|
||||
|
||||
|
||||
class VaeModelFormat(str, Enum):
|
||||
@ -80,7 +82,7 @@ class VaeModel(ModelBase):
|
||||
@classmethod
|
||||
def detect_format(cls, path: str):
|
||||
if not os.path.exists(path):
|
||||
raise ModelNotFoundException()
|
||||
raise ModelNotFoundException(f"Does not exist as local file: {path}")
|
||||
|
||||
if os.path.isdir(path):
|
||||
if os.path.exists(os.path.join(path, "config.json")):
|
||||
|
||||
@ -1,18 +1,19 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import dataclasses
|
||||
import inspect
|
||||
import math
|
||||
import secrets
|
||||
from dataclasses import dataclass, field
|
||||
import inspect
|
||||
from typing import Any, Callable, Generic, List, Optional, Type, TypeVar, Union
|
||||
from pydantic import Field
|
||||
|
||||
import math
|
||||
import einops
|
||||
import PIL.Image
|
||||
import numpy as np
|
||||
import einops
|
||||
import psutil
|
||||
import torch
|
||||
import torchvision.transforms as T
|
||||
from accelerate.utils import set_seed
|
||||
from diffusers.models import AutoencoderKL, UNet2DConditionModel
|
||||
from diffusers.models.controlnet import ControlNetModel
|
||||
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
|
||||
@ -27,17 +28,18 @@ from diffusers.schedulers import KarrasDiffusionSchedulers
|
||||
from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput
|
||||
from diffusers.utils.import_utils import is_xformers_available
|
||||
from diffusers.utils.outputs import BaseOutput
|
||||
from pydantic import Field
|
||||
from torchvision.transforms.functional import resize as tv_resize
|
||||
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
|
||||
from typing_extensions import ParamSpec
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from ..util import CPU_DEVICE, normalize_device
|
||||
from .diffusion import (
|
||||
AttentionMapSaver,
|
||||
InvokeAIDiffuserComponent,
|
||||
PostprocessingSettings,
|
||||
)
|
||||
from ..util import normalize_device
|
||||
|
||||
|
||||
@dataclass
|
||||
@ -292,9 +294,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
safety_checker: Optional[StableDiffusionSafetyChecker],
|
||||
feature_extractor: Optional[CLIPFeatureExtractor],
|
||||
requires_safety_checker: bool = False,
|
||||
precision: str = "float32",
|
||||
control_model: ControlNetModel = None,
|
||||
execution_device: Optional[torch.device] = None,
|
||||
):
|
||||
super().__init__(
|
||||
vae,
|
||||
@ -335,12 +335,12 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
return
|
||||
|
||||
|
||||
if self.device.type == "cpu" or self.device.type == "mps":
|
||||
if self.unet.device.type == "cpu" or self.unet.device.type == "mps":
|
||||
mem_free = psutil.virtual_memory().free
|
||||
elif self.device.type == "cuda":
|
||||
mem_free, _ = torch.cuda.mem_get_info(normalize_device(self.device))
|
||||
elif self.unet.device.type == "cuda":
|
||||
mem_free, _ = torch.cuda.mem_get_info(normalize_device(self.unet.device))
|
||||
else:
|
||||
raise ValueError(f"unrecognized device {self.device}")
|
||||
raise ValueError(f"unrecognized device {self.unet.device}")
|
||||
# input tensor of [1, 4, h/8, w/8]
|
||||
# output tensor of [16, (h/8 * w/8), (h/8 * w/8)]
|
||||
bytes_per_element_needed_for_baddbmm_duplication = latents.element_size() + 4
|
||||
@ -363,10 +363,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings=False):
|
||||
raise Exception("Should not be called")
|
||||
|
||||
@property
|
||||
def device(self) -> torch.device:
|
||||
return self.unet.device
|
||||
|
||||
def latents_from_embeddings(
|
||||
self,
|
||||
latents: torch.Tensor,
|
||||
|
||||
795
invokeai/frontend/install/import_images.py
Normal file
795
invokeai/frontend/install/import_images.py
Normal file
@ -0,0 +1,795 @@
|
||||
# Copyright (c) 2023 - The InvokeAI Team
|
||||
# Primary Author: David Lovell (github @f412design, discord @techjedi)
|
||||
# co-author, minor tweaks - Lincoln Stein
|
||||
|
||||
# pylint: disable=line-too-long
|
||||
# pylint: disable=broad-exception-caught
|
||||
"""Script to import images into the new database system for 3.0.0"""
|
||||
|
||||
import os
|
||||
import datetime
|
||||
import shutil
|
||||
import locale
|
||||
import sqlite3
|
||||
import json
|
||||
import glob
|
||||
import re
|
||||
import uuid
|
||||
import yaml
|
||||
import PIL
|
||||
import PIL.ImageOps
|
||||
import PIL.PngImagePlugin
|
||||
|
||||
from pathlib import Path
|
||||
from prompt_toolkit import prompt
|
||||
from prompt_toolkit.shortcuts import message_dialog
|
||||
from prompt_toolkit.completion import PathCompleter
|
||||
from prompt_toolkit.key_binding import KeyBindings
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
app_config = InvokeAIAppConfig.get_config()
|
||||
|
||||
bindings = KeyBindings()
|
||||
|
||||
|
||||
@bindings.add("c-c")
|
||||
def _(event):
|
||||
raise KeyboardInterrupt
|
||||
|
||||
|
||||
# release notes
|
||||
# "Use All" with size dimensions not selectable in the UI will not load dimensions
|
||||
|
||||
|
||||
class Config:
|
||||
"""Configuration loader."""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
TIMESTAMP_STRING = datetime.datetime.utcnow().strftime("%Y%m%dT%H%M%SZ")
|
||||
|
||||
INVOKE_DIRNAME = "invokeai"
|
||||
YAML_FILENAME = "invokeai.yaml"
|
||||
DATABASE_FILENAME = "invokeai.db"
|
||||
|
||||
database_path = None
|
||||
database_backup_dir = None
|
||||
outputs_path = None
|
||||
thumbnail_path = None
|
||||
|
||||
def find_and_load(self):
|
||||
"""find the yaml config file and load"""
|
||||
root = app_config.root_path
|
||||
if not self.confirm_and_load(os.path.abspath(root)):
|
||||
print("\r\nSpecify custom database and outputs paths:")
|
||||
self.confirm_and_load_from_user()
|
||||
|
||||
self.database_backup_dir = os.path.join(os.path.dirname(self.database_path), "backup")
|
||||
self.thumbnail_path = os.path.join(self.outputs_path, "thumbnails")
|
||||
|
||||
def confirm_and_load(self, invoke_root):
|
||||
"""Validates a yaml path exists, confirms the user wants to use it and loads config."""
|
||||
yaml_path = os.path.join(invoke_root, self.YAML_FILENAME)
|
||||
if os.path.exists(yaml_path):
|
||||
db_dir, outdir = self.load_paths_from_yaml(yaml_path)
|
||||
if os.path.isabs(db_dir):
|
||||
database_path = os.path.join(db_dir, self.DATABASE_FILENAME)
|
||||
else:
|
||||
database_path = os.path.join(invoke_root, db_dir, self.DATABASE_FILENAME)
|
||||
|
||||
if os.path.isabs(outdir):
|
||||
outputs_path = os.path.join(outdir, "images")
|
||||
else:
|
||||
outputs_path = os.path.join(invoke_root, outdir, "images")
|
||||
|
||||
db_exists = os.path.exists(database_path)
|
||||
outdir_exists = os.path.exists(outputs_path)
|
||||
|
||||
text = f"Found {self.YAML_FILENAME} file at {yaml_path}:"
|
||||
text += f"\n Database : {database_path}"
|
||||
text += f"\n Outputs : {outputs_path}"
|
||||
text += "\n\nUse these paths for import (yes) or choose different ones (no) [Yn]: "
|
||||
|
||||
if db_exists and outdir_exists:
|
||||
if (prompt(text).strip() or "Y").upper().startswith("Y"):
|
||||
self.database_path = database_path
|
||||
self.outputs_path = outputs_path
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
else:
|
||||
print(" Invalid: One or more paths in this config did not exist and cannot be used.")
|
||||
|
||||
else:
|
||||
message_dialog(
|
||||
title="Path not found",
|
||||
text=f"Auto-discovery of configuration failed! Could not find ({yaml_path}), Custom paths can be specified.",
|
||||
).run()
|
||||
return False
|
||||
|
||||
def confirm_and_load_from_user(self):
|
||||
default = ""
|
||||
while True:
|
||||
database_path = os.path.expanduser(
|
||||
prompt(
|
||||
"Database: Specify absolute path to the database to import into: ",
|
||||
completer=PathCompleter(
|
||||
expanduser=True, file_filter=lambda x: Path(x).is_dir() or x.endswith((".db"))
|
||||
),
|
||||
default=default,
|
||||
)
|
||||
)
|
||||
if database_path.endswith(".db") and os.path.isabs(database_path) and os.path.exists(database_path):
|
||||
break
|
||||
default = database_path + "/" if Path(database_path).is_dir() else database_path
|
||||
|
||||
default = ""
|
||||
while True:
|
||||
outputs_path = os.path.expanduser(
|
||||
prompt(
|
||||
"Outputs: Specify absolute path to outputs/images directory to import into: ",
|
||||
completer=PathCompleter(expanduser=True, only_directories=True),
|
||||
default=default,
|
||||
)
|
||||
)
|
||||
|
||||
if outputs_path.endswith("images") and os.path.isabs(outputs_path) and os.path.exists(outputs_path):
|
||||
break
|
||||
default = outputs_path + "/" if Path(outputs_path).is_dir() else outputs_path
|
||||
|
||||
self.database_path = database_path
|
||||
self.outputs_path = outputs_path
|
||||
|
||||
return
|
||||
|
||||
def load_paths_from_yaml(self, yaml_path):
|
||||
"""Load an Invoke AI yaml file and get the database and outputs paths."""
|
||||
try:
|
||||
with open(yaml_path, "rt", encoding=locale.getpreferredencoding()) as file:
|
||||
yamlinfo = yaml.safe_load(file)
|
||||
db_dir = yamlinfo.get("InvokeAI", {}).get("Paths", {}).get("db_dir", None)
|
||||
outdir = yamlinfo.get("InvokeAI", {}).get("Paths", {}).get("outdir", None)
|
||||
return db_dir, outdir
|
||||
except Exception:
|
||||
print(f"Failed to load paths from yaml file! {yaml_path}!")
|
||||
return None, None
|
||||
|
||||
|
||||
class ImportStats:
|
||||
"""DTO for tracking work progress."""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
time_start = datetime.datetime.utcnow()
|
||||
count_source_files = 0
|
||||
count_skipped_file_exists = 0
|
||||
count_skipped_db_exists = 0
|
||||
count_imported = 0
|
||||
count_imported_by_version = {}
|
||||
count_file_errors = 0
|
||||
|
||||
@staticmethod
|
||||
def get_elapsed_time_string():
|
||||
"""Get a friendly time string for the time elapsed since processing start."""
|
||||
time_now = datetime.datetime.utcnow()
|
||||
total_seconds = (time_now - ImportStats.time_start).total_seconds()
|
||||
hours = int((total_seconds) / 3600)
|
||||
minutes = int(((total_seconds) % 3600) / 60)
|
||||
seconds = total_seconds % 60
|
||||
out_str = f"{hours} hour(s) -" if hours > 0 else ""
|
||||
out_str += f"{minutes} minute(s) -" if minutes > 0 else ""
|
||||
out_str += f"{seconds:.2f} second(s)"
|
||||
return out_str
|
||||
|
||||
|
||||
class InvokeAIMetadata:
|
||||
"""DTO for core Invoke AI generation properties parsed from metadata."""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def __str__(self):
|
||||
formatted_str = f"{self.generation_mode}~{self.steps}~{self.cfg_scale}~{self.model_name}~{self.scheduler}~{self.seed}~{self.width}~{self.height}~{self.rand_device}~{self.strength}~{self.init_image}"
|
||||
formatted_str += f"\r\npositive_prompt: {self.positive_prompt}"
|
||||
formatted_str += f"\r\nnegative_prompt: {self.negative_prompt}"
|
||||
return formatted_str
|
||||
|
||||
generation_mode = None
|
||||
steps = None
|
||||
cfg_scale = None
|
||||
model_name = None
|
||||
scheduler = None
|
||||
seed = None
|
||||
width = None
|
||||
height = None
|
||||
rand_device = None
|
||||
strength = None
|
||||
init_image = None
|
||||
positive_prompt = None
|
||||
negative_prompt = None
|
||||
imported_app_version = None
|
||||
|
||||
def to_json(self):
|
||||
"""Convert the active instance to json format."""
|
||||
prop_dict = {}
|
||||
prop_dict["generation_mode"] = self.generation_mode
|
||||
# dont render prompt nodes if neither are set to avoid the ui thinking it can set them
|
||||
# if at least one exists, render them both, but use empty string instead of None if one of them is empty
|
||||
# this allows the field that is empty to actually be cleared byt he UI instead of leaving the previous value
|
||||
if self.positive_prompt or self.negative_prompt:
|
||||
prop_dict["positive_prompt"] = "" if self.positive_prompt is None else self.positive_prompt
|
||||
prop_dict["negative_prompt"] = "" if self.negative_prompt is None else self.negative_prompt
|
||||
prop_dict["width"] = self.width
|
||||
prop_dict["height"] = self.height
|
||||
# only render seed if it has a value to avoid ui thinking it can set this and then error
|
||||
if self.seed:
|
||||
prop_dict["seed"] = self.seed
|
||||
prop_dict["rand_device"] = self.rand_device
|
||||
prop_dict["cfg_scale"] = self.cfg_scale
|
||||
prop_dict["steps"] = self.steps
|
||||
prop_dict["scheduler"] = self.scheduler
|
||||
prop_dict["clip_skip"] = 0
|
||||
prop_dict["model"] = {}
|
||||
prop_dict["model"]["model_name"] = self.model_name
|
||||
prop_dict["model"]["base_model"] = None
|
||||
prop_dict["controlnets"] = []
|
||||
prop_dict["loras"] = []
|
||||
prop_dict["vae"] = None
|
||||
prop_dict["strength"] = self.strength
|
||||
prop_dict["init_image"] = self.init_image
|
||||
prop_dict["positive_style_prompt"] = None
|
||||
prop_dict["negative_style_prompt"] = None
|
||||
prop_dict["refiner_model"] = None
|
||||
prop_dict["refiner_cfg_scale"] = None
|
||||
prop_dict["refiner_steps"] = None
|
||||
prop_dict["refiner_scheduler"] = None
|
||||
prop_dict["refiner_aesthetic_store"] = None
|
||||
prop_dict["refiner_start"] = None
|
||||
prop_dict["imported_app_version"] = self.imported_app_version
|
||||
|
||||
return json.dumps(prop_dict)
|
||||
|
||||
|
||||
class InvokeAIMetadataParser:
|
||||
"""Parses strings with json data to find Invoke AI core metadata properties."""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def parse_meta_tag_dream(self, dream_string):
|
||||
"""Take as input an png metadata json node for the 'dream' field variant from prior to 1.15"""
|
||||
props = InvokeAIMetadata()
|
||||
|
||||
props.imported_app_version = "pre1.15"
|
||||
seed_match = re.search("-S\\s*(\\d+)", dream_string)
|
||||
if seed_match is not None:
|
||||
try:
|
||||
props.seed = int(seed_match[1])
|
||||
except ValueError:
|
||||
props.seed = None
|
||||
raw_prompt = re.sub("(-S\\s*\\d+)", "", dream_string)
|
||||
else:
|
||||
raw_prompt = dream_string
|
||||
|
||||
pos_prompt, neg_prompt = self.split_prompt(raw_prompt)
|
||||
|
||||
props.positive_prompt = pos_prompt
|
||||
props.negative_prompt = neg_prompt
|
||||
|
||||
return props
|
||||
|
||||
def parse_meta_tag_sd_metadata(self, tag_value):
|
||||
"""Take as input an png metadata json node for the 'sd-metadata' field variant from 1.15 through 2.3.5 post 2"""
|
||||
props = InvokeAIMetadata()
|
||||
|
||||
props.imported_app_version = tag_value.get("app_version")
|
||||
props.model_name = tag_value.get("model_weights")
|
||||
img_node = tag_value.get("image")
|
||||
if img_node is not None:
|
||||
props.generation_mode = img_node.get("type")
|
||||
props.width = img_node.get("width")
|
||||
props.height = img_node.get("height")
|
||||
props.seed = img_node.get("seed")
|
||||
props.rand_device = "cuda" # hardcoded since all generations pre 3.0 used cuda random noise instead of cpu
|
||||
props.cfg_scale = img_node.get("cfg_scale")
|
||||
props.steps = img_node.get("steps")
|
||||
props.scheduler = self.map_scheduler(img_node.get("sampler"))
|
||||
props.strength = img_node.get("strength")
|
||||
if props.strength is None:
|
||||
props.strength = img_node.get("strength_steps") # try second name for this property
|
||||
props.init_image = img_node.get("init_image_path")
|
||||
if props.init_image is None: # try second name for this property
|
||||
props.init_image = img_node.get("init_img")
|
||||
# remove the path info from init_image so if we move the init image, it will be correctly relative in the new location
|
||||
if props.init_image is not None:
|
||||
props.init_image = os.path.basename(props.init_image)
|
||||
raw_prompt = img_node.get("prompt")
|
||||
if isinstance(raw_prompt, list):
|
||||
raw_prompt = raw_prompt[0].get("prompt")
|
||||
|
||||
props.positive_prompt, props.negative_prompt = self.split_prompt(raw_prompt)
|
||||
|
||||
return props
|
||||
|
||||
def parse_meta_tag_invokeai(self, tag_value):
|
||||
"""Take as input an png metadata json node for the 'invokeai' field variant from 3.0.0 beta 1 through 5"""
|
||||
props = InvokeAIMetadata()
|
||||
|
||||
props.imported_app_version = "3.0.0 or later"
|
||||
props.generation_mode = tag_value.get("type")
|
||||
if props.generation_mode is not None:
|
||||
props.generation_mode = props.generation_mode.replace("t2l", "txt2img").replace("l2l", "img2img")
|
||||
|
||||
props.width = tag_value.get("width")
|
||||
props.height = tag_value.get("height")
|
||||
props.seed = tag_value.get("seed")
|
||||
props.cfg_scale = tag_value.get("cfg_scale")
|
||||
props.steps = tag_value.get("steps")
|
||||
props.scheduler = tag_value.get("scheduler")
|
||||
props.strength = tag_value.get("strength")
|
||||
props.positive_prompt = tag_value.get("positive_conditioning")
|
||||
props.negative_prompt = tag_value.get("negative_conditioning")
|
||||
|
||||
return props
|
||||
|
||||
def map_scheduler(self, old_scheduler):
|
||||
"""Convert the legacy sampler names to matching 3.0 schedulers"""
|
||||
if old_scheduler is None:
|
||||
return None
|
||||
|
||||
match (old_scheduler):
|
||||
case "ddim":
|
||||
return "ddim"
|
||||
case "plms":
|
||||
return "pnmd"
|
||||
case "k_lms":
|
||||
return "lms"
|
||||
case "k_dpm_2":
|
||||
return "kdpm_2"
|
||||
case "k_dpm_2_a":
|
||||
return "kdpm_2_a"
|
||||
case "dpmpp_2":
|
||||
return "dpmpp_2s"
|
||||
case "k_dpmpp_2":
|
||||
return "dpmpp_2m"
|
||||
case "k_dpmpp_2_a":
|
||||
return None # invalid, in 2.3.x, selecting this sample would just fallback to last run or plms if new session
|
||||
case "k_euler":
|
||||
return "euler"
|
||||
case "k_euler_a":
|
||||
return "euler_a"
|
||||
case "k_heun":
|
||||
return "heun"
|
||||
return None
|
||||
|
||||
def split_prompt(self, raw_prompt: str):
|
||||
"""Split the unified prompt strings by extracting all negative prompt blocks out into the negative prompt."""
|
||||
if raw_prompt is None:
|
||||
return "", ""
|
||||
raw_prompt_search = raw_prompt.replace("\r", "").replace("\n", "")
|
||||
matches = re.findall(r"\[(.+?)\]", raw_prompt_search)
|
||||
if len(matches) > 0:
|
||||
negative_prompt = ""
|
||||
if len(matches) == 1:
|
||||
negative_prompt = matches[0].strip().strip(",")
|
||||
else:
|
||||
for match in matches:
|
||||
negative_prompt += f"({match.strip().strip(',')})"
|
||||
positive_prompt = re.sub(r"(\[.+?\])", "", raw_prompt_search).strip()
|
||||
else:
|
||||
positive_prompt = raw_prompt_search.strip()
|
||||
negative_prompt = ""
|
||||
|
||||
return positive_prompt, negative_prompt
|
||||
|
||||
|
||||
class DatabaseMapper:
|
||||
"""Class to abstract database functionality."""
|
||||
|
||||
def __init__(self, database_path, database_backup_dir):
|
||||
self.database_path = database_path
|
||||
self.database_backup_dir = database_backup_dir
|
||||
self.connection = None
|
||||
self.cursor = None
|
||||
|
||||
def connect(self):
|
||||
"""Open connection to the database."""
|
||||
self.connection = sqlite3.connect(self.database_path)
|
||||
self.cursor = self.connection.cursor()
|
||||
|
||||
def get_board_names(self):
|
||||
"""Get a list of the current board names from the database."""
|
||||
sql_get_board_name = "SELECT board_name FROM boards"
|
||||
self.cursor.execute(sql_get_board_name)
|
||||
rows = self.cursor.fetchall()
|
||||
return [row[0] for row in rows]
|
||||
|
||||
def does_image_exist(self, image_name):
|
||||
"""Check database if a image name already exists and return a boolean."""
|
||||
sql_get_image_by_name = f"SELECT image_name FROM images WHERE image_name='{image_name}'"
|
||||
self.cursor.execute(sql_get_image_by_name)
|
||||
rows = self.cursor.fetchall()
|
||||
return True if len(rows) > 0 else False
|
||||
|
||||
def add_new_image_to_database(self, filename, width, height, metadata, modified_date_string):
|
||||
"""Add an image to the database."""
|
||||
sql_add_image = f"""INSERT INTO images (image_name, image_origin, image_category, width, height, session_id, node_id, metadata, is_intermediate, created_at, updated_at)
|
||||
VALUES ('{filename}', 'internal', 'general', {width}, {height}, null, null, '{metadata}', 0, '{modified_date_string}', '{modified_date_string}')"""
|
||||
self.cursor.execute(sql_add_image)
|
||||
self.connection.commit()
|
||||
|
||||
def get_board_id_with_create(self, board_name):
|
||||
"""Get the board id for supplied name, and create the board if one does not exist."""
|
||||
sql_find_board = f"SELECT board_id FROM boards WHERE board_name='{board_name}' COLLATE NOCASE"
|
||||
self.cursor.execute(sql_find_board)
|
||||
rows = self.cursor.fetchall()
|
||||
if len(rows) > 0:
|
||||
return rows[0][0]
|
||||
else:
|
||||
board_date_string = datetime.datetime.utcnow().date().isoformat()
|
||||
new_board_id = str(uuid.uuid4())
|
||||
sql_insert_board = f"INSERT INTO boards (board_id, board_name, created_at, updated_at) VALUES ('{new_board_id}', '{board_name}', '{board_date_string}', '{board_date_string}')"
|
||||
self.cursor.execute(sql_insert_board)
|
||||
self.connection.commit()
|
||||
return new_board_id
|
||||
|
||||
def add_image_to_board(self, filename, board_id):
|
||||
"""Add an image mapping to a board."""
|
||||
add_datetime_str = datetime.datetime.utcnow().isoformat()
|
||||
sql_add_image_to_board = f"""INSERT INTO board_images (board_id, image_name, created_at, updated_at)
|
||||
VALUES ('{board_id}', '{filename}', '{add_datetime_str}', '{add_datetime_str}')"""
|
||||
self.cursor.execute(sql_add_image_to_board)
|
||||
self.connection.commit()
|
||||
|
||||
def disconnect(self):
|
||||
"""Disconnect from the db, cleaning up connections and cursors."""
|
||||
if self.cursor is not None:
|
||||
self.cursor.close()
|
||||
if self.connection is not None:
|
||||
self.connection.close()
|
||||
|
||||
def backup(self, timestamp_string):
|
||||
"""Take a backup of the database."""
|
||||
if not os.path.exists(self.database_backup_dir):
|
||||
print(f"Database backup directory {self.database_backup_dir} does not exist -> creating...", end="")
|
||||
os.makedirs(self.database_backup_dir)
|
||||
print("Done!")
|
||||
database_backup_path = os.path.join(self.database_backup_dir, f"backup-{timestamp_string}-invokeai.db")
|
||||
print(f"Making DB Backup at {database_backup_path}...", end="")
|
||||
shutil.copy2(self.database_path, database_backup_path)
|
||||
print("Done!")
|
||||
|
||||
|
||||
class MediaImportProcessor:
|
||||
"""Containing class for script functionality."""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
board_name_id_map = {}
|
||||
|
||||
def get_import_file_list(self):
|
||||
"""Ask the user for the import folder and scan for the list of files to return."""
|
||||
while True:
|
||||
default = ""
|
||||
while True:
|
||||
import_dir = os.path.expanduser(
|
||||
prompt(
|
||||
"Inputs: Specify absolute path containing InvokeAI .png images to import: ",
|
||||
completer=PathCompleter(expanduser=True, only_directories=True),
|
||||
default=default,
|
||||
)
|
||||
)
|
||||
if len(import_dir) > 0 and Path(import_dir).is_dir():
|
||||
break
|
||||
default = import_dir
|
||||
|
||||
recurse_directories = (
|
||||
(prompt("Include files from subfolders recursively [yN]? ").strip() or "N").upper().startswith("N")
|
||||
)
|
||||
if recurse_directories:
|
||||
is_recurse = False
|
||||
matching_file_list = glob.glob(import_dir + "/*.png", recursive=False)
|
||||
else:
|
||||
is_recurse = True
|
||||
matching_file_list = glob.glob(import_dir + "/**/*.png", recursive=True)
|
||||
|
||||
if len(matching_file_list) > 0:
|
||||
return import_dir, is_recurse, matching_file_list
|
||||
else:
|
||||
print(f"The specific path {import_dir} exists, but does not contain .png files!")
|
||||
|
||||
def get_file_details(self, filepath):
|
||||
"""Retrieve the embedded metedata fields and dimensions from an image file."""
|
||||
with PIL.Image.open(filepath) as img:
|
||||
img.load()
|
||||
png_width, png_height = img.size
|
||||
img_info = img.info
|
||||
return img_info, png_width, png_height
|
||||
|
||||
def select_board_option(self, board_names, timestamp_string):
|
||||
"""Allow the user to choose how a board is selected for imported files."""
|
||||
while True:
|
||||
print("\r\nOptions for board selection for imported images:")
|
||||
print(f"1) Select an existing board name. (found {len(board_names)})")
|
||||
print("2) Specify a board name to create/add to.")
|
||||
print("3) Create/add to board named 'IMPORT'.")
|
||||
print(
|
||||
f"4) Create/add to board named 'IMPORT' with the current datetime string appended (.e.g IMPORT_{timestamp_string})."
|
||||
)
|
||||
print(
|
||||
"5) Create/add to board named 'IMPORT' with a the original file app_version appended (.e.g IMPORT_2.2.5)."
|
||||
)
|
||||
input_option = input("Specify desired board option: ")
|
||||
match (input_option):
|
||||
case "1":
|
||||
if len(board_names) < 1:
|
||||
print("\r\nThere are no existing board names to choose from. Select another option!")
|
||||
continue
|
||||
board_name = self.select_item_from_list(
|
||||
board_names, "board name", True, "Cancel, go back and choose a different board option."
|
||||
)
|
||||
if board_name is not None:
|
||||
return board_name
|
||||
case "2":
|
||||
while True:
|
||||
board_name = input("Specify new/existing board name: ")
|
||||
if board_name:
|
||||
return board_name
|
||||
case "3":
|
||||
return "IMPORT"
|
||||
case "4":
|
||||
return f"IMPORT_{timestamp_string}"
|
||||
case "5":
|
||||
return "IMPORT_APPVERSION"
|
||||
|
||||
def select_item_from_list(self, items, entity_name, allow_cancel, cancel_string):
|
||||
"""A general function to render a list of items to select in the console, prompt the user for a selection and ensure a valid entry is selected."""
|
||||
print(f"Select a {entity_name.lower()} from the following list:")
|
||||
index = 1
|
||||
for item in items:
|
||||
print(f"{index}) {item}")
|
||||
index += 1
|
||||
if allow_cancel:
|
||||
print(f"{index}) {cancel_string}")
|
||||
while True:
|
||||
try:
|
||||
option_number = int(input("Specify number of selection: "))
|
||||
except ValueError:
|
||||
continue
|
||||
if allow_cancel and option_number == index:
|
||||
return None
|
||||
if option_number >= 1 and option_number <= len(items):
|
||||
return items[option_number - 1]
|
||||
|
||||
def import_image(self, filepath: str, board_name_option: str, db_mapper: DatabaseMapper, config: Config):
|
||||
"""Import a single file by its path"""
|
||||
parser = InvokeAIMetadataParser()
|
||||
file_name = os.path.basename(filepath)
|
||||
file_destination_path = os.path.join(config.outputs_path, file_name)
|
||||
|
||||
print("===============================================================================")
|
||||
print(f"Importing {filepath}")
|
||||
|
||||
# check destination to see if the file was previously imported
|
||||
if os.path.exists(file_destination_path):
|
||||
print("File already exists in the destination, skipping!")
|
||||
ImportStats.count_skipped_file_exists += 1
|
||||
return
|
||||
|
||||
# check if file name is already referenced in the database
|
||||
if db_mapper.does_image_exist(file_name):
|
||||
print("A reference to a file with this name already exists in the database, skipping!")
|
||||
ImportStats.count_skipped_db_exists += 1
|
||||
return
|
||||
|
||||
# load image info and dimensions
|
||||
img_info, png_width, png_height = self.get_file_details(filepath)
|
||||
|
||||
# parse metadata
|
||||
destination_needs_meta_update = True
|
||||
log_version_note = "(Unknown)"
|
||||
if "invokeai_metadata" in img_info:
|
||||
# for the latest, we will just re-emit the same json, no need to parse/modify
|
||||
converted_field = None
|
||||
latest_json_string = img_info.get("invokeai_metadata")
|
||||
log_version_note = "3.0.0+"
|
||||
destination_needs_meta_update = False
|
||||
else:
|
||||
if "sd-metadata" in img_info:
|
||||
converted_field = parser.parse_meta_tag_sd_metadata(json.loads(img_info.get("sd-metadata")))
|
||||
elif "invokeai" in img_info:
|
||||
converted_field = parser.parse_meta_tag_invokeai(json.loads(img_info.get("invokeai")))
|
||||
elif "dream" in img_info:
|
||||
converted_field = parser.parse_meta_tag_dream(img_info.get("dream"))
|
||||
elif "Dream" in img_info:
|
||||
converted_field = parser.parse_meta_tag_dream(img_info.get("Dream"))
|
||||
else:
|
||||
converted_field = InvokeAIMetadata()
|
||||
destination_needs_meta_update = False
|
||||
print("File does not have metadata from known Invoke AI versions, add only, no update!")
|
||||
|
||||
# use the loaded img dimensions if the metadata didnt have them
|
||||
if converted_field.width is None:
|
||||
converted_field.width = png_width
|
||||
if converted_field.height is None:
|
||||
converted_field.height = png_height
|
||||
|
||||
log_version_note = converted_field.imported_app_version if converted_field else "NoVersion"
|
||||
log_version_note = log_version_note or "NoVersion"
|
||||
|
||||
latest_json_string = converted_field.to_json()
|
||||
|
||||
print(f"From Invoke AI Version {log_version_note} with dimensions {png_width} x {png_height}.")
|
||||
|
||||
# if metadata needs update, then update metdata and copy in one shot
|
||||
if destination_needs_meta_update:
|
||||
print("Updating metadata while copying...", end="")
|
||||
self.update_file_metadata_while_copying(
|
||||
filepath, file_destination_path, "invokeai_metadata", latest_json_string
|
||||
)
|
||||
print("Done!")
|
||||
else:
|
||||
print("No metadata update necessary, copying only...", end="")
|
||||
shutil.copy2(filepath, file_destination_path)
|
||||
print("Done!")
|
||||
|
||||
# create thumbnail
|
||||
print("Creating thumbnail...", end="")
|
||||
thumbnail_path = os.path.join(config.thumbnail_path, os.path.splitext(file_name)[0]) + ".webp"
|
||||
thumbnail_size = 256, 256
|
||||
with PIL.Image.open(filepath) as source_image:
|
||||
source_image.thumbnail(thumbnail_size)
|
||||
source_image.save(thumbnail_path, "webp")
|
||||
print("Done!")
|
||||
|
||||
# finalize the dynamic board name if there is an APPVERSION token in it.
|
||||
if converted_field is not None:
|
||||
board_name = board_name_option.replace("APPVERSION", converted_field.imported_app_version or "NoVersion")
|
||||
else:
|
||||
board_name = board_name_option.replace("APPVERSION", "Latest")
|
||||
|
||||
# maintain a map of alrady created/looked up ids to avoid DB queries
|
||||
print("Finding/Creating board...", end="")
|
||||
if board_name in self.board_name_id_map:
|
||||
board_id = self.board_name_id_map[board_name]
|
||||
else:
|
||||
board_id = db_mapper.get_board_id_with_create(board_name)
|
||||
self.board_name_id_map[board_name] = board_id
|
||||
print("Done!")
|
||||
|
||||
# add image to db
|
||||
print("Adding image to database......", end="")
|
||||
modified_time = datetime.datetime.utcfromtimestamp(os.path.getmtime(filepath))
|
||||
db_mapper.add_new_image_to_database(file_name, png_width, png_height, latest_json_string, modified_time)
|
||||
print("Done!")
|
||||
|
||||
# add image to board
|
||||
print("Adding image to board......", end="")
|
||||
db_mapper.add_image_to_board(file_name, board_id)
|
||||
print("Done!")
|
||||
|
||||
ImportStats.count_imported += 1
|
||||
if log_version_note in ImportStats.count_imported_by_version:
|
||||
ImportStats.count_imported_by_version[log_version_note] += 1
|
||||
else:
|
||||
ImportStats.count_imported_by_version[log_version_note] = 1
|
||||
|
||||
def update_file_metadata_while_copying(self, filepath, file_destination_path, tag_name, tag_value):
|
||||
"""Perform a metadata update with save to a new destination which accomplishes a copy while updating metadata."""
|
||||
with PIL.Image.open(filepath) as target_image:
|
||||
existing_img_info = target_image.info
|
||||
metadata = PIL.PngImagePlugin.PngInfo()
|
||||
# re-add any existing invoke ai tags unless they are the one we are trying to add
|
||||
for key in existing_img_info:
|
||||
if key != tag_name and key in ("dream", "Dream", "sd-metadata", "invokeai", "invokeai_metadata"):
|
||||
metadata.add_text(key, existing_img_info[key])
|
||||
metadata.add_text(tag_name, tag_value)
|
||||
target_image.save(file_destination_path, pnginfo=metadata)
|
||||
|
||||
def process(self):
|
||||
"""Begin main processing."""
|
||||
|
||||
print("===============================================================================")
|
||||
print("This script will import images generated by earlier versions of")
|
||||
print("InvokeAI into the currently installed root directory:")
|
||||
print(f" {app_config.root_path}")
|
||||
print("If this is not what you want to do, type ctrl-C now to cancel.")
|
||||
|
||||
# load config
|
||||
print("===============================================================================")
|
||||
print("= Configuration & Settings")
|
||||
|
||||
config = Config()
|
||||
config.find_and_load()
|
||||
db_mapper = DatabaseMapper(config.database_path, config.database_backup_dir)
|
||||
db_mapper.connect()
|
||||
|
||||
import_dir, is_recurse, import_file_list = self.get_import_file_list()
|
||||
ImportStats.count_source_files = len(import_file_list)
|
||||
|
||||
board_names = db_mapper.get_board_names()
|
||||
board_name_option = self.select_board_option(board_names, config.TIMESTAMP_STRING)
|
||||
|
||||
print("\r\n===============================================================================")
|
||||
print("= Import Settings Confirmation")
|
||||
|
||||
print()
|
||||
print(f"Database File Path : {config.database_path}")
|
||||
print(f"Outputs/Images Directory : {config.outputs_path}")
|
||||
print(f"Import Image Source Directory : {import_dir}")
|
||||
print(f" Recurse Source SubDirectories : {'Yes' if is_recurse else 'No'}")
|
||||
print(f"Count of .png file(s) found : {len(import_file_list)}")
|
||||
print(f"Board name option specified : {board_name_option}")
|
||||
print(f"Database backup will be taken at : {config.database_backup_dir}")
|
||||
|
||||
print("\r\nNotes about the import process:")
|
||||
print("- Source image files will not be modified, only copied to the outputs directory.")
|
||||
print("- If the same file name already exists in the destination, the file will be skipped.")
|
||||
print("- If the same file name already has a record in the database, the file will be skipped.")
|
||||
print("- Invoke AI metadata tags will be updated/written into the imported copy only.")
|
||||
print(
|
||||
"- On the imported copy, only Invoke AI known tags (latest and legacy) will be retained (dream, sd-metadata, invokeai, invokeai_metadata)"
|
||||
)
|
||||
print(
|
||||
"- A property 'imported_app_version' will be added to metadata that can be viewed in the UI's metadata viewer."
|
||||
)
|
||||
print(
|
||||
"- The new 3.x InvokeAI outputs folder structure is flat so recursively found source imges will all be placed into the single outputs/images folder."
|
||||
)
|
||||
|
||||
while True:
|
||||
should_continue = prompt("\nDo you wish to continue with the import [Yn] ? ").lower() or "y"
|
||||
if should_continue == "n":
|
||||
print("\r\nCancelling Import")
|
||||
return
|
||||
elif should_continue == "y":
|
||||
print()
|
||||
break
|
||||
|
||||
db_mapper.backup(config.TIMESTAMP_STRING)
|
||||
|
||||
print()
|
||||
ImportStats.time_start = datetime.datetime.utcnow()
|
||||
|
||||
for filepath in import_file_list:
|
||||
try:
|
||||
self.import_image(filepath, board_name_option, db_mapper, config)
|
||||
except sqlite3.Error as sql_ex:
|
||||
print(f"A database related exception was found processing {filepath}, will continue to next file. ")
|
||||
print("Exception detail:")
|
||||
print(sql_ex)
|
||||
ImportStats.count_file_errors += 1
|
||||
except Exception as ex:
|
||||
print(f"Exception processing {filepath}, will continue to next file. ")
|
||||
print("Exception detail:")
|
||||
print(ex)
|
||||
ImportStats.count_file_errors += 1
|
||||
|
||||
print("\r\n===============================================================================")
|
||||
print(f"= Import Complete - Elpased Time: {ImportStats.get_elapsed_time_string()}")
|
||||
print()
|
||||
print(f"Source File(s) : {ImportStats.count_source_files}")
|
||||
print(f"Total Imported : {ImportStats.count_imported}")
|
||||
print(f"Skipped b/c file already exists on disk : {ImportStats.count_skipped_file_exists}")
|
||||
print(f"Skipped b/c file already exists in db : {ImportStats.count_skipped_db_exists}")
|
||||
print(f"Errors during import : {ImportStats.count_file_errors}")
|
||||
if ImportStats.count_imported > 0:
|
||||
print("\r\nBreakdown of imported files by version:")
|
||||
for key, version in ImportStats.count_imported_by_version.items():
|
||||
print(f" {key:20} : {version}")
|
||||
|
||||
|
||||
def main():
|
||||
try:
|
||||
processor = MediaImportProcessor()
|
||||
processor.process()
|
||||
except KeyboardInterrupt:
|
||||
print("\r\n\r\nUser cancelled execution.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -28,7 +28,6 @@ from npyscreen import widget
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
|
||||
from invokeai.backend.install.model_install_backend import (
|
||||
ModelInstallList,
|
||||
InstallSelections,
|
||||
ModelInstall,
|
||||
SchedulerPredictionType,
|
||||
@ -41,12 +40,12 @@ from invokeai.frontend.install.widgets import (
|
||||
SingleSelectColumns,
|
||||
TextBox,
|
||||
BufferBox,
|
||||
FileBox,
|
||||
set_min_terminal_size,
|
||||
select_stable_diffusion_config_file,
|
||||
CyclingForm,
|
||||
MIN_COLS,
|
||||
MIN_LINES,
|
||||
WindowTooSmallException,
|
||||
)
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
@ -156,7 +155,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
BufferBox,
|
||||
name="Log Messages",
|
||||
editable=False,
|
||||
max_height=15,
|
||||
max_height=6,
|
||||
)
|
||||
|
||||
self.nextrely += 1
|
||||
@ -693,7 +692,11 @@ def select_and_download_models(opt: Namespace):
|
||||
# needed to support the probe() method running under a subprocess
|
||||
torch.multiprocessing.set_start_method("spawn")
|
||||
|
||||
set_min_terminal_size(MIN_COLS, MIN_LINES)
|
||||
if not set_min_terminal_size(MIN_COLS, MIN_LINES):
|
||||
raise WindowTooSmallException(
|
||||
"Could not increase terminal size. Try running again with a larger window or smaller font size."
|
||||
)
|
||||
|
||||
installApp = AddModelApplication(opt)
|
||||
try:
|
||||
installApp.run()
|
||||
@ -787,6 +790,8 @@ def main():
|
||||
curses.echo()
|
||||
curses.endwin()
|
||||
logger.info("Goodbye! Come back soon.")
|
||||
except WindowTooSmallException as e:
|
||||
logger.error(str(e))
|
||||
except widget.NotEnoughSpaceForWidget as e:
|
||||
if str(e).startswith("Height of 1 allocated"):
|
||||
logger.error("Insufficient vertical space for the interface. Please make your window taller and try again")
|
||||
|
||||
@ -21,31 +21,40 @@ MIN_COLS = 130
|
||||
MIN_LINES = 38
|
||||
|
||||
|
||||
class WindowTooSmallException(Exception):
|
||||
pass
|
||||
|
||||
|
||||
# -------------------------------------
|
||||
def set_terminal_size(columns: int, lines: int):
|
||||
ts = get_terminal_size()
|
||||
width = max(columns, ts.columns)
|
||||
height = max(lines, ts.lines)
|
||||
|
||||
def set_terminal_size(columns: int, lines: int) -> bool:
|
||||
OS = platform.uname().system
|
||||
if OS == "Windows":
|
||||
pass
|
||||
# not working reliably - ask user to adjust the window
|
||||
# _set_terminal_size_powershell(width,height)
|
||||
elif OS in ["Darwin", "Linux"]:
|
||||
_set_terminal_size_unix(width, height)
|
||||
screen_ok = False
|
||||
while not screen_ok:
|
||||
ts = get_terminal_size()
|
||||
width = max(columns, ts.columns)
|
||||
height = max(lines, ts.lines)
|
||||
|
||||
# check whether it worked....
|
||||
ts = get_terminal_size()
|
||||
pause = False
|
||||
if ts.columns < columns:
|
||||
print("\033[1mThis window is too narrow for the user interface.\033[0m")
|
||||
pause = True
|
||||
if ts.lines < lines:
|
||||
print("\033[1mThis window is too short for the user interface.\033[0m")
|
||||
pause = True
|
||||
if pause:
|
||||
input("Maximize the window then press any key to continue..")
|
||||
if OS == "Windows":
|
||||
pass
|
||||
# not working reliably - ask user to adjust the window
|
||||
# _set_terminal_size_powershell(width,height)
|
||||
elif OS in ["Darwin", "Linux"]:
|
||||
_set_terminal_size_unix(width, height)
|
||||
|
||||
# check whether it worked....
|
||||
ts = get_terminal_size()
|
||||
if ts.columns < columns or ts.lines < lines:
|
||||
print(
|
||||
f"\033[1mThis window is too small for the interface. InvokeAI requires {columns}x{lines} (w x h) characters, but window is {ts.columns}x{ts.lines}\033[0m"
|
||||
)
|
||||
resp = input(
|
||||
"Maximize the window and/or decrease the font size then press any key to continue. Type [Q] to give up.."
|
||||
)
|
||||
if resp.upper().startswith("Q"):
|
||||
break
|
||||
else:
|
||||
screen_ok = True
|
||||
return screen_ok
|
||||
|
||||
|
||||
def _set_terminal_size_powershell(width: int, height: int):
|
||||
@ -80,14 +89,14 @@ def _set_terminal_size_unix(width: int, height: int):
|
||||
sys.stdout.flush()
|
||||
|
||||
|
||||
def set_min_terminal_size(min_cols: int, min_lines: int):
|
||||
def set_min_terminal_size(min_cols: int, min_lines: int) -> bool:
|
||||
# make sure there's enough room for the ui
|
||||
term_cols, term_lines = get_terminal_size()
|
||||
if term_cols >= min_cols and term_lines >= min_lines:
|
||||
return
|
||||
return True
|
||||
cols = max(term_cols, min_cols)
|
||||
lines = max(term_lines, min_lines)
|
||||
set_terminal_size(cols, lines)
|
||||
return set_terminal_size(cols, lines)
|
||||
|
||||
|
||||
class IntSlider(npyscreen.Slider):
|
||||
@ -164,7 +173,7 @@ class FloatSlider(npyscreen.Slider):
|
||||
|
||||
|
||||
class FloatTitleSlider(npyscreen.TitleText):
|
||||
_entry_type = FloatSlider
|
||||
_entry_type = npyscreen.Slider
|
||||
|
||||
|
||||
class SelectColumnBase:
|
||||
|
||||
File diff suppressed because one or more lines are too long
@ -1,4 +1,4 @@
|
||||
import{B as m,g7 as Je,A as y,a5 as Ka,g8 as Xa,af as va,aj as d,g9 as b,ga as t,gb as Ya,gc as h,gd as ua,ge as Ja,gf as Qa,aL as Za,gg as et,ad as rt,gh as at}from"./index-de589048.js";import{s as fa,n as o,t as tt,o as ha,p as ot,q as ma,v as ga,w as ya,x as it,y as Sa,z as pa,A as xr,B as nt,D as lt,E as st,F as xa,G as $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./menu-11348abc.js";var za=String.raw,Ca=za`
|
||||
import{B as m,g7 as Je,A as y,a5 as Ka,g8 as Xa,af as va,aj as d,g9 as b,ga as t,gb as Ya,gc as h,gd as ua,ge as Ja,gf as Qa,aL as Za,gg as et,ad as rt,gh as at}from"./index-dd054634.js";import{s as fa,n as o,t as tt,o as ha,p as ot,q as ma,v as ga,w as ya,x as it,y as Sa,z as pa,A as xr,B as nt,D as lt,E as st,F as xa,G as $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./menu-b42141e3.js";var za=String.raw,Ca=za`
|
||||
:root,
|
||||
:host {
|
||||
--chakra-vh: 100vh;
|
||||
151
invokeai/frontend/web/dist/assets/index-dd054634.js
vendored
Normal file
151
invokeai/frontend/web/dist/assets/index-dd054634.js
vendored
Normal file
File diff suppressed because one or more lines are too long
151
invokeai/frontend/web/dist/assets/index-de589048.js
vendored
151
invokeai/frontend/web/dist/assets/index-de589048.js
vendored
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
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-de589048.js"></script>
|
||||
<script type="module" crossorigin src="./assets/index-dd054634.js"></script>
|
||||
</head>
|
||||
|
||||
<body dir="ltr">
|
||||
|
||||
@ -96,7 +96,8 @@ export type AppFeature =
|
||||
| 'consoleLogging'
|
||||
| 'dynamicPrompting'
|
||||
| 'batches'
|
||||
| 'syncModels';
|
||||
| 'syncModels'
|
||||
| 'multiselect';
|
||||
|
||||
/**
|
||||
* A disable-able Stable Diffusion feature
|
||||
|
||||
@ -9,6 +9,7 @@ import { useListImagesQuery } from 'services/api/endpoints/images';
|
||||
import { ImageDTO } from 'services/api/types';
|
||||
import { selectionChanged } from '../store/gallerySlice';
|
||||
import { imagesSelectors } from 'services/api/util';
|
||||
import { useFeatureStatus } from '../../system/hooks/useFeatureStatus';
|
||||
|
||||
const selector = createSelector(
|
||||
[stateSelector, selectListImagesBaseQueryArgs],
|
||||
@ -33,11 +34,18 @@ export const useMultiselect = (imageDTO?: ImageDTO) => {
|
||||
}),
|
||||
});
|
||||
|
||||
const isMultiSelectEnabled = useFeatureStatus('multiselect').isFeatureEnabled;
|
||||
|
||||
const handleClick = useCallback(
|
||||
(e: MouseEvent<HTMLDivElement>) => {
|
||||
if (!imageDTO) {
|
||||
return;
|
||||
}
|
||||
if (!isMultiSelectEnabled) {
|
||||
dispatch(selectionChanged([imageDTO]));
|
||||
return;
|
||||
}
|
||||
|
||||
if (e.shiftKey) {
|
||||
const rangeEndImageName = imageDTO.image_name;
|
||||
const lastSelectedImage = selection[selection.length - 1]?.image_name;
|
||||
@ -71,7 +79,7 @@ export const useMultiselect = (imageDTO?: ImageDTO) => {
|
||||
dispatch(selectionChanged([imageDTO]));
|
||||
}
|
||||
},
|
||||
[dispatch, imageDTO, imageDTOs, selection]
|
||||
[dispatch, imageDTO, imageDTOs, selection, isMultiSelectEnabled]
|
||||
);
|
||||
|
||||
const isSelected = useMemo(
|
||||
|
||||
@ -31,7 +31,7 @@ const ParamLoraCollapse = () => {
|
||||
}
|
||||
|
||||
return (
|
||||
<IAICollapse label={'LoRA'} activeLabel={activeLabel}>
|
||||
<IAICollapse label="LoRA" activeLabel={activeLabel}>
|
||||
<Flex sx={{ flexDir: 'column', gap: 2 }}>
|
||||
<ParamLoRASelect />
|
||||
<ParamLoraList />
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
import { Divider } from '@chakra-ui/react';
|
||||
import { createSelector } from '@reduxjs/toolkit';
|
||||
import { stateSelector } from 'app/store/store';
|
||||
import { useAppSelector } from 'app/store/storeHooks';
|
||||
@ -8,20 +9,21 @@ import ParamLora from './ParamLora';
|
||||
const selector = createSelector(
|
||||
stateSelector,
|
||||
({ lora }) => {
|
||||
const { loras } = lora;
|
||||
|
||||
return { loras };
|
||||
return { lorasArray: map(lora.loras) };
|
||||
},
|
||||
defaultSelectorOptions
|
||||
);
|
||||
|
||||
const ParamLoraList = () => {
|
||||
const { loras } = useAppSelector(selector);
|
||||
const { lorasArray } = useAppSelector(selector);
|
||||
|
||||
return (
|
||||
<>
|
||||
{map(loras, (lora) => (
|
||||
<ParamLora key={lora.model_name} lora={lora} />
|
||||
{lorasArray.map((lora, i) => (
|
||||
<>
|
||||
{i > 0 && <Divider key={`${lora.model_name}-divider`} pt={1} />}
|
||||
<ParamLora key={lora.model_name} lora={lora} />
|
||||
</>
|
||||
))}
|
||||
</>
|
||||
);
|
||||
|
||||
@ -9,7 +9,6 @@ import {
|
||||
CLIP_SKIP,
|
||||
LORA_LOADER,
|
||||
MAIN_MODEL_LOADER,
|
||||
ONNX_MODEL_LOADER,
|
||||
METADATA_ACCUMULATOR,
|
||||
NEGATIVE_CONDITIONING,
|
||||
POSITIVE_CONDITIONING,
|
||||
@ -36,15 +35,11 @@ export const addLoRAsToGraph = (
|
||||
| undefined;
|
||||
|
||||
if (loraCount > 0) {
|
||||
// Remove MAIN_MODEL_LOADER unet connection to feed it to LoRAs
|
||||
// Remove modelLoaderNodeId unet connection to feed it to LoRAs
|
||||
graph.edges = graph.edges.filter(
|
||||
(e) =>
|
||||
!(
|
||||
e.source.node_id === MAIN_MODEL_LOADER &&
|
||||
['unet'].includes(e.source.field)
|
||||
) &&
|
||||
!(
|
||||
e.source.node_id === ONNX_MODEL_LOADER &&
|
||||
e.source.node_id === modelLoaderNodeId &&
|
||||
['unet'].includes(e.source.field)
|
||||
)
|
||||
);
|
||||
|
||||
@ -0,0 +1,212 @@
|
||||
import { RootState } from 'app/store/store';
|
||||
import { NonNullableGraph } from 'features/nodes/types/types';
|
||||
import { forEach, size } from 'lodash-es';
|
||||
import {
|
||||
MetadataAccumulatorInvocation,
|
||||
SDXLLoraLoaderInvocation,
|
||||
} from 'services/api/types';
|
||||
import {
|
||||
LORA_LOADER,
|
||||
METADATA_ACCUMULATOR,
|
||||
NEGATIVE_CONDITIONING,
|
||||
POSITIVE_CONDITIONING,
|
||||
SDXL_MODEL_LOADER,
|
||||
} from './constants';
|
||||
|
||||
export const addSDXLLoRAsToGraph = (
|
||||
state: RootState,
|
||||
graph: NonNullableGraph,
|
||||
baseNodeId: string,
|
||||
modelLoaderNodeId: string = SDXL_MODEL_LOADER
|
||||
): void => {
|
||||
/**
|
||||
* LoRA nodes get the UNet and CLIP models from the main model loader and apply the LoRA to them.
|
||||
* They then output the UNet and CLIP models references on to either the next LoRA in the chain,
|
||||
* or to the inference/conditioning nodes.
|
||||
*
|
||||
* So we need to inject a LoRA chain into the graph.
|
||||
*/
|
||||
|
||||
const { loras } = state.lora;
|
||||
const loraCount = size(loras);
|
||||
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
|
||||
| MetadataAccumulatorInvocation
|
||||
| undefined;
|
||||
|
||||
if (loraCount > 0) {
|
||||
// Remove modelLoaderNodeId unet/clip/clip2 connections to feed it to LoRAs
|
||||
graph.edges = graph.edges.filter(
|
||||
(e) =>
|
||||
!(
|
||||
e.source.node_id === modelLoaderNodeId &&
|
||||
['unet'].includes(e.source.field)
|
||||
) &&
|
||||
!(
|
||||
e.source.node_id === modelLoaderNodeId &&
|
||||
['clip'].includes(e.source.field)
|
||||
) &&
|
||||
!(
|
||||
e.source.node_id === modelLoaderNodeId &&
|
||||
['clip2'].includes(e.source.field)
|
||||
)
|
||||
);
|
||||
}
|
||||
|
||||
// we need to remember the last lora so we can chain from it
|
||||
let lastLoraNodeId = '';
|
||||
let currentLoraIndex = 0;
|
||||
|
||||
forEach(loras, (lora) => {
|
||||
const { model_name, base_model, weight } = lora;
|
||||
const currentLoraNodeId = `${LORA_LOADER}_${model_name.replace('.', '_')}`;
|
||||
|
||||
const loraLoaderNode: SDXLLoraLoaderInvocation = {
|
||||
type: 'sdxl_lora_loader',
|
||||
id: currentLoraNodeId,
|
||||
is_intermediate: true,
|
||||
lora: { model_name, base_model },
|
||||
weight,
|
||||
};
|
||||
|
||||
// add the lora to the metadata accumulator
|
||||
if (metadataAccumulator) {
|
||||
metadataAccumulator.loras.push({
|
||||
lora: { model_name, base_model },
|
||||
weight,
|
||||
});
|
||||
}
|
||||
|
||||
// add to graph
|
||||
graph.nodes[currentLoraNodeId] = loraLoaderNode;
|
||||
if (currentLoraIndex === 0) {
|
||||
// first lora = start the lora chain, attach directly to model loader
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: modelLoaderNodeId,
|
||||
field: 'unet',
|
||||
},
|
||||
destination: {
|
||||
node_id: currentLoraNodeId,
|
||||
field: 'unet',
|
||||
},
|
||||
});
|
||||
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: modelLoaderNodeId,
|
||||
field: 'clip',
|
||||
},
|
||||
destination: {
|
||||
node_id: currentLoraNodeId,
|
||||
field: 'clip',
|
||||
},
|
||||
});
|
||||
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: modelLoaderNodeId,
|
||||
field: 'clip2',
|
||||
},
|
||||
destination: {
|
||||
node_id: currentLoraNodeId,
|
||||
field: 'clip2',
|
||||
},
|
||||
});
|
||||
} else {
|
||||
// we are in the middle of the lora chain, instead connect to the previous lora
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: lastLoraNodeId,
|
||||
field: 'unet',
|
||||
},
|
||||
destination: {
|
||||
node_id: currentLoraNodeId,
|
||||
field: 'unet',
|
||||
},
|
||||
});
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: lastLoraNodeId,
|
||||
field: 'clip',
|
||||
},
|
||||
destination: {
|
||||
node_id: currentLoraNodeId,
|
||||
field: 'clip',
|
||||
},
|
||||
});
|
||||
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: lastLoraNodeId,
|
||||
field: 'clip2',
|
||||
},
|
||||
destination: {
|
||||
node_id: currentLoraNodeId,
|
||||
field: 'clip2',
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
if (currentLoraIndex === loraCount - 1) {
|
||||
// final lora, end the lora chain - we need to connect up to inference and conditioning nodes
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: currentLoraNodeId,
|
||||
field: 'unet',
|
||||
},
|
||||
destination: {
|
||||
node_id: baseNodeId,
|
||||
field: 'unet',
|
||||
},
|
||||
});
|
||||
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: currentLoraNodeId,
|
||||
field: 'clip',
|
||||
},
|
||||
destination: {
|
||||
node_id: POSITIVE_CONDITIONING,
|
||||
field: 'clip',
|
||||
},
|
||||
});
|
||||
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: currentLoraNodeId,
|
||||
field: 'clip',
|
||||
},
|
||||
destination: {
|
||||
node_id: NEGATIVE_CONDITIONING,
|
||||
field: 'clip',
|
||||
},
|
||||
});
|
||||
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: currentLoraNodeId,
|
||||
field: 'clip2',
|
||||
},
|
||||
destination: {
|
||||
node_id: POSITIVE_CONDITIONING,
|
||||
field: 'clip2',
|
||||
},
|
||||
});
|
||||
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: currentLoraNodeId,
|
||||
field: 'clip2',
|
||||
},
|
||||
destination: {
|
||||
node_id: NEGATIVE_CONDITIONING,
|
||||
field: 'clip2',
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
// increment the lora for the next one in the chain
|
||||
lastLoraNodeId = currentLoraNodeId;
|
||||
currentLoraIndex += 1;
|
||||
});
|
||||
};
|
||||
@ -22,6 +22,7 @@ import {
|
||||
SDXL_LATENTS_TO_LATENTS,
|
||||
SDXL_MODEL_LOADER,
|
||||
} from './constants';
|
||||
import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
|
||||
|
||||
/**
|
||||
* Builds the Image to Image tab graph.
|
||||
@ -364,6 +365,8 @@ export const buildLinearSDXLImageToImageGraph = (
|
||||
},
|
||||
});
|
||||
|
||||
addSDXLLoRAsToGraph(state, graph, SDXL_LATENTS_TO_LATENTS, SDXL_MODEL_LOADER);
|
||||
|
||||
// Add Refiner if enabled
|
||||
if (shouldUseSDXLRefiner) {
|
||||
addSDXLRefinerToGraph(state, graph, SDXL_LATENTS_TO_LATENTS);
|
||||
|
||||
@ -4,6 +4,7 @@ import { NonNullableGraph } from 'features/nodes/types/types';
|
||||
import { initialGenerationState } from 'features/parameters/store/generationSlice';
|
||||
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
|
||||
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
|
||||
import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
|
||||
import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
|
||||
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
|
||||
import {
|
||||
@ -246,6 +247,8 @@ export const buildLinearSDXLTextToImageGraph = (
|
||||
},
|
||||
});
|
||||
|
||||
addSDXLLoRAsToGraph(state, graph, SDXL_TEXT_TO_LATENTS, SDXL_MODEL_LOADER);
|
||||
|
||||
// Add Refiner if enabled
|
||||
if (shouldUseSDXLRefiner) {
|
||||
addSDXLRefinerToGraph(state, graph, SDXL_TEXT_TO_LATENTS);
|
||||
|
||||
@ -4,6 +4,7 @@ import ProcessButtons from 'features/parameters/components/ProcessButtons/Proces
|
||||
import ParamSDXLPromptArea from './ParamSDXLPromptArea';
|
||||
import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
|
||||
import SDXLImageToImageTabCoreParameters from './SDXLImageToImageTabCoreParameters';
|
||||
import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
|
||||
|
||||
const SDXLImageToImageTabParameters = () => {
|
||||
return (
|
||||
@ -12,6 +13,7 @@ const SDXLImageToImageTabParameters = () => {
|
||||
<ProcessButtons />
|
||||
<SDXLImageToImageTabCoreParameters />
|
||||
<ParamSDXLRefinerCollapse />
|
||||
<ParamLoraCollapse />
|
||||
<ParamDynamicPromptsCollapse />
|
||||
<ParamNoiseCollapse />
|
||||
</>
|
||||
|
||||
@ -4,6 +4,7 @@ import ProcessButtons from 'features/parameters/components/ProcessButtons/Proces
|
||||
import TextToImageTabCoreParameters from 'features/ui/components/tabs/TextToImage/TextToImageTabCoreParameters';
|
||||
import ParamSDXLPromptArea from './ParamSDXLPromptArea';
|
||||
import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
|
||||
import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
|
||||
|
||||
const SDXLTextToImageTabParameters = () => {
|
||||
return (
|
||||
@ -12,6 +13,7 @@ const SDXLTextToImageTabParameters = () => {
|
||||
<ProcessButtons />
|
||||
<TextToImageTabCoreParameters />
|
||||
<ParamSDXLRefinerCollapse />
|
||||
<ParamLoraCollapse />
|
||||
<ParamDynamicPromptsCollapse />
|
||||
<ParamNoiseCollapse />
|
||||
</>
|
||||
|
||||
@ -4,6 +4,7 @@ import {
|
||||
ASSETS_CATEGORIES,
|
||||
BoardId,
|
||||
IMAGE_CATEGORIES,
|
||||
IMAGE_LIMIT,
|
||||
} from 'features/gallery/store/types';
|
||||
import { keyBy } from 'lodash';
|
||||
import { ApiFullTagDescription, LIST_TAG, api } from '..';
|
||||
@ -167,7 +168,14 @@ export const imagesApi = api.injectEndpoints({
|
||||
},
|
||||
};
|
||||
},
|
||||
invalidatesTags: (result, error, imageDTOs) => [],
|
||||
invalidatesTags: (result, error, { imageDTOs }) => {
|
||||
// for now, assume bulk delete is all on one board
|
||||
const boardId = imageDTOs[0]?.board_id;
|
||||
return [
|
||||
{ type: 'BoardImagesTotal', id: boardId ?? 'none' },
|
||||
{ type: 'BoardAssetsTotal', id: boardId ?? 'none' },
|
||||
];
|
||||
},
|
||||
async onQueryStarted({ imageDTOs }, { dispatch, queryFulfilled }) {
|
||||
/**
|
||||
* Cache changes for `deleteImages`:
|
||||
@ -889,18 +897,25 @@ export const imagesApi = api.injectEndpoints({
|
||||
board_id,
|
||||
},
|
||||
}),
|
||||
invalidatesTags: (result, error, { board_id }) => [
|
||||
// update the destination board
|
||||
{ type: 'Board', id: board_id ?? 'none' },
|
||||
// update old board totals
|
||||
{ type: 'BoardImagesTotal', id: board_id ?? 'none' },
|
||||
{ type: 'BoardAssetsTotal', id: board_id ?? 'none' },
|
||||
// update the no_board totals
|
||||
{ type: 'BoardImagesTotal', id: 'none' },
|
||||
{ type: 'BoardAssetsTotal', id: 'none' },
|
||||
],
|
||||
invalidatesTags: (result, error, { imageDTOs, board_id }) => {
|
||||
//assume all images are being moved from one board for now
|
||||
const oldBoardId = imageDTOs[0]?.board_id;
|
||||
return [
|
||||
// update the destination board
|
||||
{ type: 'Board', id: board_id ?? 'none' },
|
||||
// update new board totals
|
||||
{ type: 'BoardImagesTotal', id: board_id ?? 'none' },
|
||||
{ type: 'BoardAssetsTotal', id: board_id ?? 'none' },
|
||||
// update old board totals
|
||||
{ type: 'BoardImagesTotal', id: oldBoardId ?? 'none' },
|
||||
{ type: 'BoardAssetsTotal', id: oldBoardId ?? 'none' },
|
||||
// update the no_board totals
|
||||
{ type: 'BoardImagesTotal', id: 'none' },
|
||||
{ type: 'BoardAssetsTotal', id: 'none' },
|
||||
];
|
||||
},
|
||||
async onQueryStarted(
|
||||
{ board_id, imageDTOs },
|
||||
{ board_id: new_board_id, imageDTOs },
|
||||
{ dispatch, queryFulfilled, getState }
|
||||
) {
|
||||
try {
|
||||
@ -920,7 +935,7 @@ export const imagesApi = api.injectEndpoints({
|
||||
'getImageDTO',
|
||||
image_name,
|
||||
(draft) => {
|
||||
draft.board_id = board_id;
|
||||
draft.board_id = new_board_id;
|
||||
}
|
||||
)
|
||||
);
|
||||
@ -946,7 +961,7 @@ export const imagesApi = api.injectEndpoints({
|
||||
);
|
||||
|
||||
const queryArgs = {
|
||||
board_id,
|
||||
board_id: new_board_id,
|
||||
categories,
|
||||
};
|
||||
|
||||
@ -954,23 +969,24 @@ export const imagesApi = api.injectEndpoints({
|
||||
queryArgs
|
||||
)(getState());
|
||||
|
||||
const { data: total } = IMAGE_CATEGORIES.includes(
|
||||
const { data: previousTotal } = IMAGE_CATEGORIES.includes(
|
||||
imageDTO.image_category
|
||||
)
|
||||
? boardsApi.endpoints.getBoardImagesTotal.select(
|
||||
imageDTO.board_id ?? 'none'
|
||||
new_board_id ?? 'none'
|
||||
)(getState())
|
||||
: boardsApi.endpoints.getBoardAssetsTotal.select(
|
||||
imageDTO.board_id ?? 'none'
|
||||
new_board_id ?? 'none'
|
||||
)(getState());
|
||||
|
||||
const isCacheFullyPopulated =
|
||||
currentCache.data && currentCache.data.ids.length >= (total ?? 0);
|
||||
currentCache.data &&
|
||||
currentCache.data.ids.length >= (previousTotal ?? 0);
|
||||
|
||||
const isInDateRange = getIsImageInDateRange(
|
||||
currentCache.data,
|
||||
imageDTO
|
||||
);
|
||||
const isInDateRange =
|
||||
(previousTotal || 0) >= IMAGE_LIMIT
|
||||
? getIsImageInDateRange(currentCache.data, imageDTO)
|
||||
: true;
|
||||
|
||||
if (isCacheFullyPopulated || isInDateRange) {
|
||||
// *upsert* to $cache
|
||||
@ -981,7 +997,7 @@ export const imagesApi = api.injectEndpoints({
|
||||
(draft) => {
|
||||
imagesAdapter.upsertOne(draft, {
|
||||
...imageDTO,
|
||||
board_id,
|
||||
board_id: new_board_id,
|
||||
});
|
||||
}
|
||||
)
|
||||
@ -1097,10 +1113,10 @@ export const imagesApi = api.injectEndpoints({
|
||||
const isCacheFullyPopulated =
|
||||
currentCache.data && currentCache.data.ids.length >= (total ?? 0);
|
||||
|
||||
const isInDateRange = getIsImageInDateRange(
|
||||
currentCache.data,
|
||||
imageDTO
|
||||
);
|
||||
const isInDateRange =
|
||||
(total || 0) >= IMAGE_LIMIT
|
||||
? getIsImageInDateRange(currentCache.data, imageDTO)
|
||||
: true;
|
||||
|
||||
if (isCacheFullyPopulated || isInDateRange) {
|
||||
// *upsert* to $cache
|
||||
@ -1111,7 +1127,7 @@ export const imagesApi = api.injectEndpoints({
|
||||
(draft) => {
|
||||
imagesAdapter.upsertOne(draft, {
|
||||
...imageDTO,
|
||||
board_id: undefined,
|
||||
board_id: 'none',
|
||||
});
|
||||
}
|
||||
)
|
||||
|
||||
222
invokeai/frontend/web/src/services/api/schema.d.ts
vendored
222
invokeai/frontend/web/src/services/api/schema.d.ts
vendored
@ -1443,7 +1443,7 @@ 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"]["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"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | 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"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | 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"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | 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"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | 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"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | 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
|
||||
@ -1486,7 +1486,7 @@ 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"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["NoiseOutput"] | 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"]["SDXLLoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
|
||||
};
|
||||
/**
|
||||
* Errors
|
||||
@ -1904,6 +1904,40 @@ export type components = {
|
||||
*/
|
||||
image_name: string;
|
||||
};
|
||||
/**
|
||||
* ImageHueAdjustmentInvocation
|
||||
* @description Adjusts the Hue of an image.
|
||||
*/
|
||||
ImageHueAdjustmentInvocation: {
|
||||
/**
|
||||
* 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 img_hue_adjust
|
||||
* @enum {string}
|
||||
*/
|
||||
type?: "img_hue_adjust";
|
||||
/**
|
||||
* Image
|
||||
* @description The image to adjust
|
||||
*/
|
||||
image?: components["schemas"]["ImageField"];
|
||||
/**
|
||||
* Hue
|
||||
* @description The degrees by which to rotate the hue, 0-360
|
||||
* @default 0
|
||||
*/
|
||||
hue?: number;
|
||||
};
|
||||
/**
|
||||
* ImageInverseLerpInvocation
|
||||
* @description Inverse linear interpolation of all pixels of an image
|
||||
@ -1984,6 +2018,40 @@ export type components = {
|
||||
*/
|
||||
max?: number;
|
||||
};
|
||||
/**
|
||||
* ImageLuminosityAdjustmentInvocation
|
||||
* @description Adjusts the Luminosity (Value) of an image.
|
||||
*/
|
||||
ImageLuminosityAdjustmentInvocation: {
|
||||
/**
|
||||
* 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 img_luminosity_adjust
|
||||
* @enum {string}
|
||||
*/
|
||||
type?: "img_luminosity_adjust";
|
||||
/**
|
||||
* Image
|
||||
* @description The image to adjust
|
||||
*/
|
||||
image?: components["schemas"]["ImageField"];
|
||||
/**
|
||||
* Luminosity
|
||||
* @description The factor by which to adjust the luminosity (value)
|
||||
* @default 1
|
||||
*/
|
||||
luminosity?: number;
|
||||
};
|
||||
/**
|
||||
* ImageMetadata
|
||||
* @description An image's generation metadata
|
||||
@ -2239,6 +2307,40 @@ export type components = {
|
||||
*/
|
||||
resample_mode?: "nearest" | "box" | "bilinear" | "hamming" | "bicubic" | "lanczos";
|
||||
};
|
||||
/**
|
||||
* ImageSaturationAdjustmentInvocation
|
||||
* @description Adjusts the Saturation of an image.
|
||||
*/
|
||||
ImageSaturationAdjustmentInvocation: {
|
||||
/**
|
||||
* 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 img_saturation_adjust
|
||||
* @enum {string}
|
||||
*/
|
||||
type?: "img_saturation_adjust";
|
||||
/**
|
||||
* Image
|
||||
* @description The image to adjust
|
||||
*/
|
||||
image?: components["schemas"]["ImageField"];
|
||||
/**
|
||||
* Saturation
|
||||
* @description The factor by which to adjust the saturation
|
||||
* @default 1
|
||||
*/
|
||||
saturation?: number;
|
||||
};
|
||||
/**
|
||||
* ImageScaleInvocation
|
||||
* @description Scales an image by a factor
|
||||
@ -4912,6 +5014,82 @@ export type components = {
|
||||
*/
|
||||
denoising_end?: number;
|
||||
};
|
||||
/**
|
||||
* SDXLLoraLoaderInvocation
|
||||
* @description Apply selected lora to unet and text_encoder.
|
||||
*/
|
||||
SDXLLoraLoaderInvocation: {
|
||||
/**
|
||||
* 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 sdxl_lora_loader
|
||||
* @enum {string}
|
||||
*/
|
||||
type?: "sdxl_lora_loader";
|
||||
/**
|
||||
* Lora
|
||||
* @description Lora model name
|
||||
*/
|
||||
lora?: components["schemas"]["LoRAModelField"];
|
||||
/**
|
||||
* Weight
|
||||
* @description With what weight to apply lora
|
||||
* @default 0.75
|
||||
*/
|
||||
weight?: number;
|
||||
/**
|
||||
* Unet
|
||||
* @description UNet model for applying lora
|
||||
*/
|
||||
unet?: components["schemas"]["UNetField"];
|
||||
/**
|
||||
* Clip
|
||||
* @description Clip model for applying lora
|
||||
*/
|
||||
clip?: components["schemas"]["ClipField"];
|
||||
/**
|
||||
* Clip2
|
||||
* @description Clip2 model for applying lora
|
||||
*/
|
||||
clip2?: components["schemas"]["ClipField"];
|
||||
};
|
||||
/**
|
||||
* SDXLLoraLoaderOutput
|
||||
* @description Model loader output
|
||||
*/
|
||||
SDXLLoraLoaderOutput: {
|
||||
/**
|
||||
* Type
|
||||
* @default sdxl_lora_loader_output
|
||||
* @enum {string}
|
||||
*/
|
||||
type?: "sdxl_lora_loader_output";
|
||||
/**
|
||||
* Unet
|
||||
* @description UNet submodel
|
||||
*/
|
||||
unet?: components["schemas"]["UNetField"];
|
||||
/**
|
||||
* Clip
|
||||
* @description Tokenizer and text_encoder submodels
|
||||
*/
|
||||
clip?: components["schemas"]["ClipField"];
|
||||
/**
|
||||
* Clip2
|
||||
* @description Tokenizer2 and text_encoder2 submodels
|
||||
*/
|
||||
clip2?: components["schemas"]["ClipField"];
|
||||
};
|
||||
/**
|
||||
* SDXLModelLoaderInvocation
|
||||
* @description Loads an sdxl base model, outputting its submodels.
|
||||
@ -5961,6 +6139,24 @@ export type components = {
|
||||
*/
|
||||
image?: components["schemas"]["ImageField"];
|
||||
};
|
||||
/**
|
||||
* ControlNetModelFormat
|
||||
* @description An enumeration.
|
||||
* @enum {string}
|
||||
*/
|
||||
ControlNetModelFormat: "checkpoint" | "diffusers";
|
||||
/**
|
||||
* StableDiffusionXLModelFormat
|
||||
* @description An enumeration.
|
||||
* @enum {string}
|
||||
*/
|
||||
StableDiffusionXLModelFormat: "checkpoint" | "diffusers";
|
||||
/**
|
||||
* StableDiffusion1ModelFormat
|
||||
* @description An enumeration.
|
||||
* @enum {string}
|
||||
*/
|
||||
StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
|
||||
/**
|
||||
* StableDiffusionOnnxModelFormat
|
||||
* @description An enumeration.
|
||||
@ -5973,24 +6169,6 @@ export type components = {
|
||||
* @enum {string}
|
||||
*/
|
||||
StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
|
||||
/**
|
||||
* StableDiffusion1ModelFormat
|
||||
* @description An enumeration.
|
||||
* @enum {string}
|
||||
*/
|
||||
StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
|
||||
/**
|
||||
* StableDiffusionXLModelFormat
|
||||
* @description An enumeration.
|
||||
* @enum {string}
|
||||
*/
|
||||
StableDiffusionXLModelFormat: "checkpoint" | "diffusers";
|
||||
/**
|
||||
* ControlNetModelFormat
|
||||
* @description An enumeration.
|
||||
* @enum {string}
|
||||
*/
|
||||
ControlNetModelFormat: "checkpoint" | "diffusers";
|
||||
};
|
||||
responses: never;
|
||||
parameters: never;
|
||||
@ -6101,7 +6279,7 @@ 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"]["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"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | 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"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | 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"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | 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"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | 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"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | 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: {
|
||||
@ -6138,7 +6316,7 @@ 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"]["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"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | 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"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | 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"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | 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"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | 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"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | 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: {
|
||||
|
||||
@ -166,6 +166,9 @@ export type OnnxModelLoaderInvocation = TypeReq<
|
||||
export type LoraLoaderInvocation = TypeReq<
|
||||
components['schemas']['LoraLoaderInvocation']
|
||||
>;
|
||||
export type SDXLLoraLoaderInvocation = TypeReq<
|
||||
components['schemas']['SDXLLoraLoaderInvocation']
|
||||
>;
|
||||
export type MetadataAccumulatorInvocation = TypeReq<
|
||||
components['schemas']['MetadataAccumulatorInvocation']
|
||||
>;
|
||||
|
||||
@ -1 +1 @@
|
||||
__version__ = "3.0.2a1"
|
||||
__version__ = "3.0.2rc1"
|
||||
|
||||
@ -77,7 +77,7 @@ dependencies = [
|
||||
"realesrgan",
|
||||
"requests~=2.28.2",
|
||||
"rich~=13.3",
|
||||
"safetensors~=0.3.0",
|
||||
"safetensors==0.3.1",
|
||||
"scikit-image~=0.21.0",
|
||||
"send2trash",
|
||||
"test-tube~=0.7.5",
|
||||
@ -100,7 +100,7 @@ dependencies = [
|
||||
"dev" = [
|
||||
"pudb",
|
||||
]
|
||||
"test" = ["pytest>6.0.0", "pytest-cov", "black"]
|
||||
"test" = ["pytest>6.0.0", "pytest-cov", "pytest-datadir", "black"]
|
||||
"xformers" = [
|
||||
"xformers~=0.0.19; sys_platform!='darwin'",
|
||||
"triton; sys_platform=='linux'",
|
||||
@ -139,6 +139,7 @@ dependencies = [
|
||||
"invokeai-metadata" = "invokeai.frontend.CLI.sd_metadata:print_metadata"
|
||||
"invokeai-node-cli" = "invokeai.app.cli_app:invoke_cli"
|
||||
"invokeai-node-web" = "invokeai.app.api_app:invoke_api"
|
||||
"invokeai-import-images" = "invokeai.frontend.install.import_images:main"
|
||||
|
||||
[project.urls]
|
||||
"Homepage" = "https://invoke-ai.github.io/InvokeAI/"
|
||||
|
||||
38
tests/test_model_manager.py
Normal file
38
tests/test_model_manager.py
Normal file
@ -0,0 +1,38 @@
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from invokeai.backend import ModelManager, BaseModelType, ModelType, SubModelType
|
||||
|
||||
BASIC_MODEL_NAME = ("SDXL base", BaseModelType.StableDiffusionXL, ModelType.Main)
|
||||
VAE_OVERRIDE_MODEL_NAME = ("SDXL with VAE", BaseModelType.StableDiffusionXL, ModelType.Main)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def model_manager(datadir) -> ModelManager:
|
||||
InvokeAIAppConfig.get_config(root=datadir)
|
||||
return ModelManager(datadir / "configs" / "relative_sub.models.yaml")
|
||||
|
||||
|
||||
def test_get_model_names(model_manager: ModelManager):
|
||||
names = model_manager.model_names()
|
||||
assert names[:2] == [BASIC_MODEL_NAME, VAE_OVERRIDE_MODEL_NAME]
|
||||
|
||||
|
||||
def test_get_model_path_for_diffusers(model_manager: ModelManager, datadir: Path):
|
||||
model_config = model_manager._get_model_config(BASIC_MODEL_NAME[1], BASIC_MODEL_NAME[0], BASIC_MODEL_NAME[2])
|
||||
top_model_path, is_override = model_manager._get_model_path(model_config)
|
||||
expected_model_path = datadir / "models" / "sdxl" / "main" / "SDXL base 1_0"
|
||||
assert top_model_path == expected_model_path
|
||||
assert not is_override
|
||||
|
||||
|
||||
def test_get_model_path_for_overridden_vae(model_manager: ModelManager, datadir: Path):
|
||||
model_config = model_manager._get_model_config(
|
||||
VAE_OVERRIDE_MODEL_NAME[1], VAE_OVERRIDE_MODEL_NAME[0], VAE_OVERRIDE_MODEL_NAME[2]
|
||||
)
|
||||
vae_model_path, is_override = model_manager._get_model_path(model_config, SubModelType.Vae)
|
||||
expected_vae_path = datadir / "models" / "sdxl" / "vae" / "sdxl-vae-fp16-fix"
|
||||
assert vae_model_path == expected_vae_path
|
||||
assert is_override
|
||||
15
tests/test_model_manager/configs/relative_sub.models.yaml
Normal file
15
tests/test_model_manager/configs/relative_sub.models.yaml
Normal file
@ -0,0 +1,15 @@
|
||||
__metadata__:
|
||||
version: 3.0.0
|
||||
|
||||
sdxl/main/SDXL base:
|
||||
path: sdxl/main/SDXL base 1_0
|
||||
description: SDXL base v1.0
|
||||
variant: normal
|
||||
format: diffusers
|
||||
|
||||
sdxl/main/SDXL with VAE:
|
||||
path: sdxl/main/SDXL base 1_0
|
||||
description: SDXL with customized VAE
|
||||
vae: sdxl/vae/sdxl-vae-fp16-fix/
|
||||
variant: normal
|
||||
format: diffusers
|
||||
Loading…
Reference in New Issue
Block a user