mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
NSFW checker and watermark nodes (#3923)
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X ] Yes
- [] No
## Description
This PR adds NSFW checker and invisible watermark fields. The NSFW
checker takes an image input and produces an image output. If NSFW
content is detected, the output image will be blurred and a "caution"
icon pasted into its upper left corner. A boolean `active` field
controls whether the checker is active. If turned off it simply returns
a copy of the image.
The invisible watermark node adds an invisible text to the image,
defaulting to "InvokeAI". To decode the watermark use the
`invisible-watermark` command, which is part of the
`invisible-watermark` library:
```
$ invisible-watermark -v -a decode -t bytes -m dwtDct -l 64 ./bluebird-watermark.png
decode time ms: 14.129877090454102
InvokeAI
```
Note that the `-l` (length) argument is mandatory. It is set to 64 here
because the watermark `InvokeAI` is 8 bytes/64 bits long. The length
must match in order for the watermark to be decoded correctly.
Both nodes are now incorporated into the linear Text2Image and
Image2Image UIs, including the canvas. They are not implemented for
inpaint currently.
The nodes can be disabled with configuration options:
```
invisible_watermark: false
nsfw_checker: false
```
or at launch time with `--no-invisible_watermark` and
`--no-nsfw_checker`.
This commit is contained in:
Lincoln Stein
GitHub
commit
f060e321eb
@ -136,19 +136,16 @@ command-line options by giving the `--help` argument:
|
||||
|
||||
```
|
||||
(.venv) > invokeai-web --help
|
||||
usage: InvokeAI [-h] [--host HOST] [--port PORT] [--allow_origins [ALLOW_ORIGINS ...]] [--allow_credentials | --no-allow_credentials]
|
||||
[--allow_methods [ALLOW_METHODS ...]] [--allow_headers [ALLOW_HEADERS ...]] [--esrgan | --no-esrgan]
|
||||
[--internet_available | --no-internet_available] [--log_tokenization | --no-log_tokenization]
|
||||
[--nsfw_checker | --no-nsfw_checker] [--patchmatch | --no-patchmatch] [--restore | --no-restore]
|
||||
[--always_use_cpu | --no-always_use_cpu] [--free_gpu_mem | --no-free_gpu_mem] [--max_cache_size MAX_CACHE_SIZE]
|
||||
[--max_vram_cache_size MAX_VRAM_CACHE_SIZE] [--precision {auto,float16,float32,autocast}]
|
||||
[--sequential_guidance | --no-sequential_guidance] [--xformers_enabled | --no-xformers_enabled]
|
||||
[--tiled_decode | --no-tiled_decode] [--root ROOT] [--autoimport_dir AUTOIMPORT_DIR] [--lora_dir LORA_DIR]
|
||||
[--embedding_dir EMBEDDING_DIR] [--controlnet_dir CONTROLNET_DIR] [--conf_path CONF_PATH] [--models_dir MODELS_DIR]
|
||||
[--legacy_conf_dir LEGACY_CONF_DIR] [--db_dir DB_DIR] [--outdir OUTDIR] [--from_file FROM_FILE]
|
||||
[--use_memory_db | --no-use_memory_db] [--model MODEL] [--log_handlers [LOG_HANDLERS ...]]
|
||||
[--log_format {plain,color,syslog,legacy}] [--log_level {debug,info,warning,error,critical}]
|
||||
...
|
||||
usage: InvokeAI [-h] [--host HOST] [--port PORT] [--allow_origins [ALLOW_ORIGINS ...]] [--allow_credentials | --no-allow_credentials] [--allow_methods [ALLOW_METHODS ...]]
|
||||
[--allow_headers [ALLOW_HEADERS ...]] [--esrgan | --no-esrgan] [--internet_available | --no-internet_available] [--log_tokenization | --no-log_tokenization]
|
||||
[--nsfw_checker | --no-nsfw_checker] [--invisible_watermark | --no-invisible_watermark] [--patchmatch | --no-patchmatch] [--restore | --no-restore]
|
||||
[--always_use_cpu | --no-always_use_cpu] [--free_gpu_mem | --no-free_gpu_mem] [--max_loaded_models MAX_LOADED_MODELS] [--max_cache_size MAX_CACHE_SIZE]
|
||||
[--max_vram_cache_size MAX_VRAM_CACHE_SIZE] [--gpu_mem_reserved GPU_MEM_RESERVED] [--precision {auto,float16,float32,autocast}]
|
||||
[--sequential_guidance | --no-sequential_guidance] [--xformers_enabled | --no-xformers_enabled] [--tiled_decode | --no-tiled_decode] [--root ROOT]
|
||||
[--autoimport_dir AUTOIMPORT_DIR] [--lora_dir LORA_DIR] [--embedding_dir EMBEDDING_DIR] [--controlnet_dir CONTROLNET_DIR] [--conf_path CONF_PATH]
|
||||
[--models_dir MODELS_DIR] [--legacy_conf_dir LEGACY_CONF_DIR] [--db_dir DB_DIR] [--outdir OUTDIR] [--from_file FROM_FILE]
|
||||
[--use_memory_db | --no-use_memory_db] [--model MODEL] [--log_handlers [LOG_HANDLERS ...]] [--log_format {plain,color,syslog,legacy}]
|
||||
[--log_level {debug,info,warning,error,critical}] [--version | --no-version]
|
||||
```
|
||||
|
||||
## The Configuration Settings
|
||||
@ -179,6 +176,7 @@ These configuration settings allow you to enable and disable various InvokeAI fe
|
||||
| `internet_available` | `true` | When a resource is not available locally, try to fetch it via the internet |
|
||||
| `log_tokenization` | `false` | Before each text2image generation, print a color-coded representation of the prompt to the console; this can help understand why a prompt is not working as expected |
|
||||
| `nsfw_checker` | `true` | Activate the NSFW checker to blur out risque images |
|
||||
| `invisible_watermark` | `true` | Write an invisible watermark 'InvokeAI' into generated images for use by AI image detectors |
|
||||
| `patchmatch` | `true` | Activate the "patchmatch" algorithm for improved inpainting |
|
||||
| `restore` | `true` | Activate the facial restoration features (DEPRECATED; restoration features will be removed in 3.0.0) |
|
||||
|
||||
|
||||
@ -61,11 +61,13 @@ A noise scheduler (eg. DPM++ 2M Karras) schedules the subtraction of noise from
|
||||
| ImageInverseLerp | Inverse linear interpolation of all pixels of an image |
|
||||
| ImageLerp | Linear interpolation of all pixels of an image |
|
||||
| ImageMultiply | Multiplies two images together using `PIL.ImageChops.Multiply()` |
|
||||
| ImageNSFWBlurInvocation | Detects and blurs images that may contain sexually explicit content |
|
||||
| ImagePaste | Pastes an image into another image |
|
||||
| ImageProcessor | Base class for invocations that reprocess images for ControlNet |
|
||||
| ImageResize | Resizes an image to specific dimensions |
|
||||
| ImageScale | Scales an image by a factor |
|
||||
| ImageToLatents | Scales latents by a given factor |
|
||||
| ImageWatermarkInvocation | Adds an invisible watermark to images |
|
||||
| InfillColor | Infills transparent areas of an image with a solid color |
|
||||
| InfillPatchMatch | Infills transparent areas of an image using the PatchMatch algorithm |
|
||||
| InfillTile | Infills transparent areas of an image with tiles of the image |
|
||||
|
||||
@ -16,21 +16,24 @@ Output Example:
|
||||
|
||||
---
|
||||
|
||||
## **Seamless Tiling**
|
||||
## **Invisible Watermark**
|
||||
|
||||
The seamless tiling mode causes generated images to seamlessly tile
|
||||
with itself creating repetitive wallpaper-like patterns. To use it,
|
||||
activate the Seamless Tiling option in the Web GUI and then select
|
||||
whether to tile on the X (horizontal) and/or Y (vertical) axes. Tiling
|
||||
will then be active for the next set of generations.
|
||||
In keeping with the principles for responsible AI generation, and to
|
||||
help AI researchers avoid synthetic images contaminating their
|
||||
training sets, InvokeAI adds an invisible watermark to each of the
|
||||
final images it generates. The watermark consists of the text
|
||||
"InvokeAI" and can be viewed using the
|
||||
[invisible-watermarks](https://github.com/ShieldMnt/invisible-watermark)
|
||||
tool.
|
||||
|
||||
A nice prompt to test seamless tiling with is:
|
||||
Watermarking is controlled using the `invisible-watermark` setting in
|
||||
`invokeai.yaml`. To turn it off, add the following line under the `Features`
|
||||
category.
|
||||
|
||||
```
|
||||
pond garden with lotus by claude monet"
|
||||
invisible_watermark: false
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## **Weighted Prompts**
|
||||
|
||||
@ -39,34 +42,10 @@ priority to them, by adding `:<percent>` to the end of the section you wish to u
|
||||
example consider this prompt:
|
||||
|
||||
```bash
|
||||
tabby cat:0.25 white duck:0.75 hybrid
|
||||
(tabby cat):0.25 (white duck):0.75 hybrid
|
||||
```
|
||||
|
||||
This will tell the sampler to invest 25% of its effort on the tabby cat aspect of the image and 75%
|
||||
on the white duck aspect (surprisingly, this example actually works). The prompt weights can use any
|
||||
combination of integers and floating point numbers, and they do not need to add up to 1.
|
||||
|
||||
## **Thresholding and Perlin Noise Initialization Options**
|
||||
|
||||
Under the Noise section of the Web UI, you will find two options named
|
||||
Perlin Noise and Noise Threshold. [Perlin
|
||||
noise](https://en.wikipedia.org/wiki/Perlin_noise) is a type of
|
||||
structured noise used to simulate terrain and other natural
|
||||
textures. The slider controls the percentage of perlin noise that will
|
||||
be mixed into the image at the beginning of generation. Adding a little
|
||||
perlin noise to a generation will alter the image substantially.
|
||||
|
||||
The noise threshold limits the range of the latent values during
|
||||
sampling and helps combat the oversharpening seem with higher CFG
|
||||
scale values.
|
||||
|
||||
For better intuition into what these options do in practice:
|
||||
|
||||
|
||||
|
||||
In generating this graphic, perlin noise at initialization was
|
||||
programmatically varied going across on the diagram by values 0.0,
|
||||
0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1.0; and the threshold was varied
|
||||
going down from 0, 1, 2, 3, 4, 5, 10, 20, 100. The other options are
|
||||
fixed using the prompt "a portrait of a beautiful young lady" a CFG of
|
||||
20, 100 steps, and a seed of 1950357039.
|
||||
|
||||
@ -1,9 +1,15 @@
|
||||
import typing
|
||||
from enum import Enum
|
||||
from fastapi import Body
|
||||
from fastapi.routing import APIRouter
|
||||
from pathlib import Path
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from invokeai.backend.image_util.patchmatch import PatchMatch
|
||||
from invokeai.backend.image_util.safety_checker import SafetyChecker
|
||||
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
|
||||
from invokeai.app.invocations.upscale import ESRGAN_MODELS
|
||||
|
||||
from invokeai.version import __version__
|
||||
|
||||
from ..dependencies import ApiDependencies
|
||||
@ -16,6 +22,10 @@ class LogLevel(int, Enum):
|
||||
Warning = logging.WARNING
|
||||
Error = logging.ERROR
|
||||
Critical = logging.CRITICAL
|
||||
|
||||
class Upscaler(BaseModel):
|
||||
upscaling_method: str = Field(description="Name of upscaling method")
|
||||
upscaling_models: list[str] = Field(description="List of upscaling models for this method")
|
||||
|
||||
app_router = APIRouter(prefix="/v1/app", tags=["app"])
|
||||
|
||||
@ -30,6 +40,9 @@ class AppConfig(BaseModel):
|
||||
"""App Config Response"""
|
||||
|
||||
infill_methods: list[str] = Field(description="List of available infill methods")
|
||||
upscaling_methods: list[Upscaler] = Field(description="List of upscaling methods")
|
||||
nsfw_methods: list[str] = Field(description="List of NSFW checking methods")
|
||||
watermarking_methods: list[str] = Field(description="List of invisible watermark methods")
|
||||
|
||||
|
||||
@app_router.get(
|
||||
@ -46,7 +59,30 @@ async def get_config() -> AppConfig:
|
||||
infill_methods = ['tile']
|
||||
if PatchMatch.patchmatch_available():
|
||||
infill_methods.append('patchmatch')
|
||||
return AppConfig(infill_methods=infill_methods)
|
||||
|
||||
|
||||
upscaling_models = []
|
||||
for model in typing.get_args(ESRGAN_MODELS):
|
||||
upscaling_models.append(str(Path(model).stem))
|
||||
upscaler = Upscaler(
|
||||
upscaling_method = 'esrgan',
|
||||
upscaling_models = upscaling_models
|
||||
)
|
||||
|
||||
nsfw_methods = []
|
||||
if SafetyChecker.safety_checker_available():
|
||||
nsfw_methods.append('nsfw_checker')
|
||||
|
||||
watermarking_methods = []
|
||||
if InvisibleWatermark.invisible_watermark_available():
|
||||
watermarking_methods.append('invisible_watermark')
|
||||
|
||||
return AppConfig(
|
||||
infill_methods=infill_methods,
|
||||
upscaling_methods=[upscaler],
|
||||
nsfw_methods=nsfw_methods,
|
||||
watermarking_methods=watermarking_methods,
|
||||
)
|
||||
|
||||
@app_router.get(
|
||||
"/logging",
|
||||
|
||||
|
Before Width: | Height: | Size: 33 KiB After Width: | Height: | Size: 33 KiB |
@ -20,7 +20,7 @@ from ...backend.model_management import BaseModelType, ModelType
|
||||
from ..models.image import ImageCategory, ImageField, ResourceOrigin
|
||||
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
|
||||
InvocationConfig, InvocationContext)
|
||||
from .image import ImageOutput, PILInvocationConfig
|
||||
from ..models.image import ImageOutput, PILInvocationConfig
|
||||
|
||||
CONTROLNET_DEFAULT_MODELS = [
|
||||
###########################################
|
||||
|
||||
@ -4,61 +4,21 @@ from typing import Literal, Optional
|
||||
|
||||
import numpy
|
||||
from PIL import Image, ImageFilter, ImageOps, ImageChops
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic import Field
|
||||
from pathlib import Path
|
||||
from typing import Union
|
||||
|
||||
from ..models.image import ImageCategory, ImageField, ResourceOrigin
|
||||
from invokeai.app.invocations.metadata import CoreMetadata
|
||||
from ..models.image import (
|
||||
ImageCategory, ImageField, ResourceOrigin,
|
||||
PILInvocationConfig, ImageOutput, MaskOutput,
|
||||
)
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
InvocationContext,
|
||||
InvocationConfig,
|
||||
)
|
||||
|
||||
|
||||
class PILInvocationConfig(BaseModel):
|
||||
"""Helper class to provide all PIL invocations with additional config"""
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"tags": ["PIL", "image"],
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
class ImageOutput(BaseInvocationOutput):
|
||||
"""Base class for invocations that output an image"""
|
||||
|
||||
# fmt: off
|
||||
type: Literal["image_output"] = "image_output"
|
||||
image: ImageField = Field(default=None, description="The output image")
|
||||
width: int = Field(description="The width of the image in pixels")
|
||||
height: int = Field(description="The height of the image in pixels")
|
||||
# fmt: on
|
||||
|
||||
class Config:
|
||||
schema_extra = {"required": ["type", "image", "width", "height"]}
|
||||
|
||||
|
||||
class MaskOutput(BaseInvocationOutput):
|
||||
"""Base class for invocations that output a mask"""
|
||||
|
||||
# fmt: off
|
||||
type: Literal["mask"] = "mask"
|
||||
mask: ImageField = Field(default=None, description="The output mask")
|
||||
width: int = Field(description="The width of the mask in pixels")
|
||||
height: int = Field(description="The height of the mask in pixels")
|
||||
# fmt: on
|
||||
|
||||
class Config:
|
||||
schema_extra = {
|
||||
"required": [
|
||||
"type",
|
||||
"mask",
|
||||
]
|
||||
}
|
||||
|
||||
from invokeai.backend.image_util.safety_checker import SafetyChecker
|
||||
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
|
||||
|
||||
class LoadImageInvocation(BaseInvocation):
|
||||
"""Load an image and provide it as output."""
|
||||
@ -397,7 +357,6 @@ class ImageConvertInvocation(BaseInvocation, PILInvocationConfig):
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
|
||||
class ImageBlurInvocation(BaseInvocation, PILInvocationConfig):
|
||||
"""Blurs an image"""
|
||||
|
||||
@ -602,7 +561,6 @@ class ImageLerpInvocation(BaseInvocation, PILInvocationConfig):
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
|
||||
class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
|
||||
"""Inverse linear interpolation of all pixels of an image"""
|
||||
|
||||
@ -650,3 +608,97 @@ class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
class ImageNSFWBlurInvocation(BaseInvocation, PILInvocationConfig):
|
||||
"""Add blur to NSFW-flagged images"""
|
||||
|
||||
# fmt: off
|
||||
type: Literal["img_nsfw"] = "img_nsfw"
|
||||
|
||||
# Inputs
|
||||
image: Optional[ImageField] = Field(default=None, description="The image to check")
|
||||
metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Blur NSFW Images",
|
||||
"tags": ["image", "nsfw", "checker"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
logger = context.services.logger
|
||||
logger.debug("Running NSFW checker")
|
||||
if SafetyChecker.has_nsfw_concept(image):
|
||||
logger.info("A potentially NSFW image has been detected. Image will be blurred.")
|
||||
blurry_image = image.filter(filter=ImageFilter.GaussianBlur(radius=32))
|
||||
caution = self._get_caution_img()
|
||||
blurry_image.paste(caution,(0,0),caution)
|
||||
image = blurry_image
|
||||
|
||||
image_dto = context.services.images.create(
|
||||
image=image,
|
||||
image_origin=ResourceOrigin.INTERNAL,
|
||||
image_category=ImageCategory.GENERAL,
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata.dict() if self.metadata else None,
|
||||
)
|
||||
|
||||
return ImageOutput(
|
||||
image=ImageField(image_name=image_dto.image_name),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
def _get_caution_img(self)->Image:
|
||||
import invokeai.app.assets.images as image_assets
|
||||
caution = Image.open(Path(image_assets.__path__[0]) / 'caution.png')
|
||||
return caution.resize((caution.width // 2, caution.height //2))
|
||||
|
||||
class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig):
|
||||
""" Add an invisible watermark to an image """
|
||||
|
||||
# fmt: off
|
||||
type: Literal["img_watermark"] = "img_watermark"
|
||||
|
||||
# Inputs
|
||||
image: Optional[ImageField] = Field(default=None, description="The image to check")
|
||||
text: str = Field(default='InvokeAI', description="Watermark text")
|
||||
metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Add Invisible Watermark",
|
||||
"tags": ["image", "watermark", "invisible"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
new_image = InvisibleWatermark.add_watermark(image, self.text)
|
||||
image_dto = context.services.images.create(
|
||||
image=new_image,
|
||||
image_origin=ResourceOrigin.INTERNAL,
|
||||
image_category=ImageCategory.GENERAL,
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata.dict() if self.metadata else None,
|
||||
)
|
||||
|
||||
return ImageOutput(
|
||||
image=ImageField(image_name=image_dto.image_name),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
|
||||
|
||||
|
||||
@ -501,7 +501,7 @@ class LatentsToImageInvocation(BaseInvocation):
|
||||
vae: VaeField = Field(default=None, description="Vae submodel")
|
||||
tiled: bool = Field(
|
||||
default=False,
|
||||
description="Decode latents by overlaping tiles(less memory consumption)")
|
||||
description="Decode latents by overlapping tiles(less memory consumption)")
|
||||
fp32: bool = Field(DEFAULT_PRECISION=='float32', description="Decode in full precision")
|
||||
metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")
|
||||
|
||||
|
||||
@ -11,7 +11,6 @@ from invokeai.app.invocations.baseinvocation import (
|
||||
from invokeai.app.invocations.controlnet_image_processors import ControlField
|
||||
from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
|
||||
|
||||
|
||||
class LoRAMetadataField(BaseModel):
|
||||
"""LoRA metadata for an image generated in InvokeAI."""
|
||||
|
||||
|
||||
@ -1,9 +1,80 @@
|
||||
from enum import Enum
|
||||
from typing import Optional, Tuple
|
||||
from typing import Optional, Tuple, Literal
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from invokeai.app.util.metaenum import MetaEnum
|
||||
from ..invocations.baseinvocation import (
|
||||
BaseInvocationOutput,
|
||||
InvocationConfig,
|
||||
)
|
||||
|
||||
class ImageField(BaseModel):
|
||||
"""An image field used for passing image objects between invocations"""
|
||||
|
||||
image_name: Optional[str] = Field(default=None, description="The name of the image")
|
||||
|
||||
class Config:
|
||||
schema_extra = {"required": ["image_name"]}
|
||||
|
||||
|
||||
class ColorField(BaseModel):
|
||||
r: int = Field(ge=0, le=255, description="The red component")
|
||||
g: int = Field(ge=0, le=255, description="The green component")
|
||||
b: int = Field(ge=0, le=255, description="The blue component")
|
||||
a: int = Field(ge=0, le=255, description="The alpha component")
|
||||
|
||||
def tuple(self) -> Tuple[int, int, int, int]:
|
||||
return (self.r, self.g, self.b, self.a)
|
||||
|
||||
|
||||
class ProgressImage(BaseModel):
|
||||
"""The progress image sent intermittently during processing"""
|
||||
|
||||
width: int = Field(description="The effective width of the image in pixels")
|
||||
height: int = Field(description="The effective height of the image in pixels")
|
||||
dataURL: str = Field(description="The image data as a b64 data URL")
|
||||
|
||||
class PILInvocationConfig(BaseModel):
|
||||
"""Helper class to provide all PIL invocations with additional config"""
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"tags": ["PIL", "image"],
|
||||
},
|
||||
}
|
||||
|
||||
class ImageOutput(BaseInvocationOutput):
|
||||
"""Base class for invocations that output an image"""
|
||||
|
||||
# fmt: off
|
||||
type: Literal["image_output"] = "image_output"
|
||||
image: ImageField = Field(default=None, description="The output image")
|
||||
width: int = Field(description="The width of the image in pixels")
|
||||
height: int = Field(description="The height of the image in pixels")
|
||||
# fmt: on
|
||||
|
||||
class Config:
|
||||
schema_extra = {"required": ["type", "image", "width", "height"]}
|
||||
|
||||
|
||||
class MaskOutput(BaseInvocationOutput):
|
||||
"""Base class for invocations that output a mask"""
|
||||
|
||||
# fmt: off
|
||||
type: Literal["mask"] = "mask"
|
||||
mask: ImageField = Field(default=None, description="The output mask")
|
||||
width: int = Field(description="The width of the mask in pixels")
|
||||
height: int = Field(description="The height of the mask in pixels")
|
||||
# fmt: on
|
||||
|
||||
class Config:
|
||||
schema_extra = {
|
||||
"required": [
|
||||
"type",
|
||||
"mask",
|
||||
]
|
||||
}
|
||||
|
||||
class ResourceOrigin(str, Enum, metaclass=MetaEnum):
|
||||
"""The origin of a resource (eg image).
|
||||
@ -63,28 +134,3 @@ class InvalidImageCategoryException(ValueError):
|
||||
super().__init__(message)
|
||||
|
||||
|
||||
class ImageField(BaseModel):
|
||||
"""An image field used for passing image objects between invocations"""
|
||||
|
||||
image_name: Optional[str] = Field(default=None, description="The name of the image")
|
||||
|
||||
class Config:
|
||||
schema_extra = {"required": ["image_name"]}
|
||||
|
||||
|
||||
class ColorField(BaseModel):
|
||||
r: int = Field(ge=0, le=255, description="The red component")
|
||||
g: int = Field(ge=0, le=255, description="The green component")
|
||||
b: int = Field(ge=0, le=255, description="The blue component")
|
||||
a: int = Field(ge=0, le=255, description="The alpha component")
|
||||
|
||||
def tuple(self) -> Tuple[int, int, int, int]:
|
||||
return (self.r, self.g, self.b, self.a)
|
||||
|
||||
|
||||
class ProgressImage(BaseModel):
|
||||
"""The progress image sent intermittently during processing"""
|
||||
|
||||
width: int = Field(description="The effective width of the image in pixels")
|
||||
height: int = Field(description="The effective height of the image in pixels")
|
||||
dataURL: str = Field(description="The image data as a b64 data URL")
|
||||
|
||||
@ -28,7 +28,6 @@ InvokeAI:
|
||||
always_use_cpu: false
|
||||
free_gpu_mem: false
|
||||
Features:
|
||||
nsfw_checker: true
|
||||
restore: true
|
||||
esrgan: true
|
||||
patchmatch: true
|
||||
@ -92,18 +91,18 @@ Typical usage at the top level file:
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
# get global configuration and print its nsfw_checker value
|
||||
# get global configuration and print its cache size
|
||||
conf = InvokeAIAppConfig.get_config()
|
||||
conf.parse_args()
|
||||
print(conf.nsfw_checker)
|
||||
print(conf.max_cache_size)
|
||||
|
||||
Typical usage in a backend module:
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
# get global configuration and print its nsfw_checker value
|
||||
# get global configuration and print its cache size value
|
||||
conf = InvokeAIAppConfig.get_config()
|
||||
print(conf.nsfw_checker)
|
||||
print(conf.max_cache_size)
|
||||
|
||||
|
||||
Computed properties:
|
||||
@ -277,7 +276,7 @@ class InvokeAISettings(BaseSettings):
|
||||
@classmethod
|
||||
def _excluded_from_yaml(self)->List[str]:
|
||||
# combination of deprecated parameters and internal ones that shouldn't be exposed as invokeai.yaml options
|
||||
return ['type','initconf', 'gpu_mem_reserved', 'max_loaded_models', 'version', 'from_file', 'model', 'restore', 'root']
|
||||
return ['type','initconf', 'gpu_mem_reserved', 'max_loaded_models', 'version', 'from_file', 'model', 'restore', 'root', 'nsfw_checker']
|
||||
|
||||
class Config:
|
||||
env_file_encoding = 'utf-8'
|
||||
@ -364,7 +363,6 @@ setting environment variables INVOKEAI_<setting>.
|
||||
esrgan : bool = Field(default=True, description="Enable/disable upscaling code", category='Features')
|
||||
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')
|
||||
nsfw_checker : bool = Field(default=True, description="Enable/disable the NSFW checker", 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')
|
||||
|
||||
@ -374,6 +372,7 @@ setting environment variables INVOKEAI_<setting>.
|
||||
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')
|
||||
@ -525,6 +524,16 @@ setting environment variables INVOKEAI_<setting>.
|
||||
"""Return true if patchmatch true"""
|
||||
return self.patchmatch
|
||||
|
||||
@property
|
||||
def nsfw_checker(self)->bool:
|
||||
""" NSFW node is always active and disabled from Web UIe"""
|
||||
return True
|
||||
|
||||
@property
|
||||
def invisible_watermark(self)->bool:
|
||||
""" invisible watermark node is always active and disabled from Web UIe"""
|
||||
return True
|
||||
|
||||
@staticmethod
|
||||
def find_root()->Path:
|
||||
'''
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
from ..invocations.latent import LatentsToImageInvocation, TextToLatentsInvocation
|
||||
from ..invocations.image import ImageNSFWBlurInvocation
|
||||
from ..invocations.noise import NoiseInvocation
|
||||
from ..invocations.compel import CompelInvocation
|
||||
from ..invocations.params import ParamIntInvocation
|
||||
@ -24,6 +25,7 @@ def create_text_to_image() -> LibraryGraph:
|
||||
'5': CompelInvocation(id='5'),
|
||||
'6': TextToLatentsInvocation(id='6'),
|
||||
'7': LatentsToImageInvocation(id='7'),
|
||||
'8': ImageNSFWBlurInvocation(id='8'),
|
||||
},
|
||||
edges=[
|
||||
Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='3', field='width')),
|
||||
@ -33,6 +35,7 @@ def create_text_to_image() -> LibraryGraph:
|
||||
Edge(source=EdgeConnection(node_id='6', field='latents'), destination=EdgeConnection(node_id='7', field='latents')),
|
||||
Edge(source=EdgeConnection(node_id='4', field='conditioning'), destination=EdgeConnection(node_id='6', field='positive_conditioning')),
|
||||
Edge(source=EdgeConnection(node_id='5', field='conditioning'), destination=EdgeConnection(node_id='6', field='negative_conditioning')),
|
||||
Edge(source=EdgeConnection(node_id='7', field='image'), destination=EdgeConnection(node_id='8', field='image')),
|
||||
]
|
||||
),
|
||||
exposed_inputs=[
|
||||
@ -43,7 +46,7 @@ def create_text_to_image() -> LibraryGraph:
|
||||
ExposedNodeInput(node_path='seed', field='a', alias='seed'),
|
||||
],
|
||||
exposed_outputs=[
|
||||
ExposedNodeOutput(node_path='7', field='image', alias='image')
|
||||
ExposedNodeOutput(node_path='8', field='image', alias='image')
|
||||
])
|
||||
|
||||
|
||||
|
||||
@ -216,16 +216,13 @@ class ImageService(ImageServiceABC):
|
||||
metadata=metadata,
|
||||
session_id=session_id,
|
||||
)
|
||||
|
||||
if board_id is not None:
|
||||
self._services.board_image_records.add_image_to_board(
|
||||
board_id=board_id, image_name=image_name
|
||||
)
|
||||
|
||||
self._services.image_files.save(
|
||||
image_name=image_name, image=image, metadata=metadata, graph=graph
|
||||
)
|
||||
|
||||
image_dto = self.get_dto(image_name)
|
||||
|
||||
return image_dto
|
||||
@ -236,7 +233,7 @@ class ImageService(ImageServiceABC):
|
||||
self._services.logger.error("Failed to save image file")
|
||||
raise
|
||||
except Exception as e:
|
||||
self._services.logger.error("Problem saving image record and file")
|
||||
self._services.logger.error(f"Problem saving image record and file: {str(e)}")
|
||||
raise e
|
||||
|
||||
def update(
|
||||
|
||||
@ -12,4 +12,4 @@ from .model_management import (
|
||||
ModelManager, ModelCache, BaseModelType,
|
||||
ModelType, SubModelType, ModelInfo
|
||||
)
|
||||
from .safety_checker import SafetyChecker
|
||||
from .model_management.models import SilenceWarnings
|
||||
|
||||
@ -28,7 +28,6 @@ from diffusers.schedulers import SchedulerMixin as Scheduler
|
||||
import invokeai.backend.util.logging as logger
|
||||
from ..image_util import configure_model_padding
|
||||
from ..util.util import rand_perlin_2d
|
||||
from ..safety_checker import SafetyChecker
|
||||
from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
|
||||
from ..stable_diffusion.schedulers import SCHEDULER_MAP
|
||||
|
||||
@ -52,7 +51,6 @@ class InvokeAIGeneratorBasicParams:
|
||||
v_symmetry_time_pct: Optional[float]=None
|
||||
variation_amount: float = 0.0
|
||||
with_variations: list=field(default_factory=list)
|
||||
safety_checker: Optional[SafetyChecker]=None
|
||||
|
||||
@dataclass
|
||||
class InvokeAIGeneratorOutput:
|
||||
@ -240,7 +238,6 @@ class Generator:
|
||||
self.seed = None
|
||||
self.latent_channels = model.unet.config.in_channels
|
||||
self.downsampling_factor = downsampling # BUG: should come from model or config
|
||||
self.safety_checker = None
|
||||
self.perlin = 0.0
|
||||
self.threshold = 0
|
||||
self.variation_amount = 0
|
||||
@ -277,12 +274,10 @@ class Generator:
|
||||
perlin=0.0,
|
||||
h_symmetry_time_pct=None,
|
||||
v_symmetry_time_pct=None,
|
||||
safety_checker: SafetyChecker=None,
|
||||
free_gpu_mem: bool = False,
|
||||
**kwargs,
|
||||
):
|
||||
scope = nullcontext
|
||||
self.safety_checker = safety_checker
|
||||
self.free_gpu_mem = free_gpu_mem
|
||||
attention_maps_images = []
|
||||
attention_maps_callback = lambda saver: attention_maps_images.append(
|
||||
@ -329,9 +324,6 @@ class Generator:
|
||||
# Pass on the seed in case a layer beneath us needs to generate noise on its own.
|
||||
image = make_image(x_T, seed)
|
||||
|
||||
if self.safety_checker is not None:
|
||||
image = self.safety_checker.check(image)
|
||||
|
||||
results.append([image, seed, attention_maps_images])
|
||||
|
||||
if image_callback is not None:
|
||||
|
||||
34
invokeai/backend/image_util/invisible_watermark.py
Normal file
34
invokeai/backend/image_util/invisible_watermark.py
Normal file
@ -0,0 +1,34 @@
|
||||
"""
|
||||
This module defines a singleton object, "invisible_watermark" that
|
||||
wraps the invisible watermark model. It respects the global "invisible_watermark"
|
||||
configuration variable, that allows the watermarking to be supressed.
|
||||
"""
|
||||
import numpy as np
|
||||
import cv2
|
||||
from PIL import Image
|
||||
from imwatermark import WatermarkEncoder
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
import invokeai.backend.util.logging as logger
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
class InvisibleWatermark:
|
||||
"""
|
||||
Wrapper around InvisibleWatermark module.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def invisible_watermark_available(self) -> bool:
|
||||
return config.invisible_watermark
|
||||
|
||||
@classmethod
|
||||
def add_watermark(self, image: Image, watermark_text:str) -> Image:
|
||||
if not self.invisible_watermark_available():
|
||||
return image
|
||||
logger.debug(f'Applying invisible watermark "{watermark_text}"')
|
||||
bgr = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
|
||||
encoder = WatermarkEncoder()
|
||||
encoder.set_watermark('bytes', watermark_text.encode('utf-8'))
|
||||
bgr_encoded = encoder.encode(bgr, 'dwtDct')
|
||||
return Image.fromarray(
|
||||
cv2.cvtColor(bgr_encoded, cv2.COLOR_BGR2RGB)
|
||||
).convert("RGBA")
|
||||
63
invokeai/backend/image_util/safety_checker.py
Normal file
63
invokeai/backend/image_util/safety_checker.py
Normal file
@ -0,0 +1,63 @@
|
||||
"""
|
||||
This module defines a singleton object, "safety_checker" that
|
||||
wraps the safety_checker model. It respects the global "nsfw_checker"
|
||||
configuration variable, that allows the checker to be supressed.
|
||||
"""
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from invokeai.backend import SilenceWarnings
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from invokeai.backend.util.devices import choose_torch_device
|
||||
import invokeai.backend.util.logging as logger
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
CHECKER_PATH = 'core/convert/stable-diffusion-safety-checker'
|
||||
|
||||
class SafetyChecker:
|
||||
"""
|
||||
Wrapper around SafetyChecker model.
|
||||
"""
|
||||
safety_checker = None
|
||||
feature_extractor = None
|
||||
tried_load: bool = False
|
||||
|
||||
@classmethod
|
||||
def _load_safety_checker(self):
|
||||
if self.tried_load:
|
||||
return
|
||||
|
||||
if config.nsfw_checker:
|
||||
try:
|
||||
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
|
||||
from transformers import AutoFeatureExtractor
|
||||
self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(
|
||||
config.models_path / CHECKER_PATH
|
||||
)
|
||||
self.feature_extractor = AutoFeatureExtractor.from_pretrained(
|
||||
config.models_path / CHECKER_PATH)
|
||||
logger.info('NSFW checker initialized')
|
||||
except Exception as e:
|
||||
logger.warning(f'Could not load NSFW checker: {str(e)}')
|
||||
else:
|
||||
logger.info('NSFW checker loading disabled')
|
||||
self.tried_load = True
|
||||
|
||||
@classmethod
|
||||
def safety_checker_available(self) -> bool:
|
||||
self._load_safety_checker()
|
||||
return self.safety_checker is not None
|
||||
|
||||
@classmethod
|
||||
def has_nsfw_concept(self, image: Image) -> bool:
|
||||
if not self.safety_checker_available():
|
||||
return False
|
||||
|
||||
device = choose_torch_device()
|
||||
features = self.feature_extractor([image], return_tensors="pt")
|
||||
features.to(device)
|
||||
self.safety_checker.to(device)
|
||||
x_image = np.array(image).astype(np.float32) / 255.0
|
||||
x_image = x_image[None].transpose(0, 3, 1, 2)
|
||||
with SilenceWarnings():
|
||||
checked_image, has_nsfw_concept = self.safety_checker(images=x_image, clip_input=features.pixel_values)
|
||||
return has_nsfw_concept[0]
|
||||
@ -298,47 +298,6 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
|
||||
color="CONTROL",
|
||||
)
|
||||
|
||||
self.nextrely += 1
|
||||
self.add_widget_intelligent(
|
||||
npyscreen.TitleFixedText,
|
||||
name="== BASIC OPTIONS ==",
|
||||
begin_entry_at=0,
|
||||
editable=False,
|
||||
color="CONTROL",
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely -= 1
|
||||
self.add_widget_intelligent(
|
||||
npyscreen.FixedText,
|
||||
value="Select an output directory for images:",
|
||||
editable=False,
|
||||
color="CONTROL",
|
||||
)
|
||||
self.outdir = self.add_widget_intelligent(
|
||||
npyscreen.TitleFilename,
|
||||
name="(<tab> autocompletes, ctrl-N advances):",
|
||||
value=str(default_output_dir()),
|
||||
select_dir=True,
|
||||
must_exist=False,
|
||||
use_two_lines=False,
|
||||
labelColor="GOOD",
|
||||
begin_entry_at=40,
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely += 1
|
||||
self.add_widget_intelligent(
|
||||
npyscreen.FixedText,
|
||||
value="Activate the NSFW checker to blur images showing potential sexual imagery:",
|
||||
editable=False,
|
||||
color="CONTROL",
|
||||
)
|
||||
self.nsfw_checker = self.add_widget_intelligent(
|
||||
npyscreen.Checkbox,
|
||||
name="NSFW checker",
|
||||
value=old_opts.nsfw_checker,
|
||||
relx=5,
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely += 1
|
||||
label = """HuggingFace access token (OPTIONAL) for automatic model downloads. See https://huggingface.co/settings/tokens."""
|
||||
for line in textwrap.wrap(label,width=window_width-6):
|
||||
@ -358,15 +317,6 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely += 1
|
||||
self.add_widget_intelligent(
|
||||
npyscreen.TitleFixedText,
|
||||
name="== ADVANCED OPTIONS ==",
|
||||
begin_entry_at=0,
|
||||
editable=False,
|
||||
color="CONTROL",
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely -= 1
|
||||
self.add_widget_intelligent(
|
||||
npyscreen.TitleFixedText,
|
||||
name="GPU Management",
|
||||
@ -426,6 +376,18 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
|
||||
editable=False,
|
||||
color="CONTROL",
|
||||
)
|
||||
self.outdir = self.add_widget_intelligent(
|
||||
FileBox,
|
||||
name="Output directory for images (<tab> autocompletes, ctrl-N advances):",
|
||||
value=str(default_output_dir()),
|
||||
select_dir=True,
|
||||
must_exist=False,
|
||||
use_two_lines=False,
|
||||
labelColor="GOOD",
|
||||
begin_entry_at=40,
|
||||
max_height=3,
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.autoimport_dirs = {}
|
||||
self.autoimport_dirs['autoimport_dir'] = self.add_widget_intelligent(
|
||||
FileBox,
|
||||
@ -517,7 +479,6 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
|
||||
|
||||
for attr in [
|
||||
"outdir",
|
||||
"nsfw_checker",
|
||||
"free_gpu_mem",
|
||||
"max_cache_size",
|
||||
"xformers_enabled",
|
||||
@ -553,7 +514,7 @@ class EditOptApplication(npyscreen.NPSAppManaged):
|
||||
"MAIN",
|
||||
editOptsForm,
|
||||
name="InvokeAI Startup Options",
|
||||
cycle_widgets=True,
|
||||
cycle_widgets=False,
|
||||
)
|
||||
if not (self.program_opts.skip_sd_weights or self.program_opts.default_only):
|
||||
self.model_select = self.addForm(
|
||||
@ -561,7 +522,7 @@ class EditOptApplication(npyscreen.NPSAppManaged):
|
||||
addModelsForm,
|
||||
name="Install Stable Diffusion Models",
|
||||
multipage=True,
|
||||
cycle_widgets=True,
|
||||
cycle_widgets=False,
|
||||
)
|
||||
|
||||
def new_opts(self):
|
||||
@ -575,8 +536,6 @@ def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Nam
|
||||
|
||||
def default_startup_options(init_file: Path) -> Namespace:
|
||||
opts = InvokeAIAppConfig.get_config()
|
||||
if not init_file.exists():
|
||||
opts.nsfw_checker = True
|
||||
return opts
|
||||
|
||||
def default_user_selections(program_opts: Namespace) -> InstallSelections:
|
||||
@ -700,7 +659,6 @@ def migrate_init_file(legacy_format:Path):
|
||||
|
||||
# a few places where the field names have changed and we have to
|
||||
# manually add in the new names/values
|
||||
new.nsfw_checker = old.safety_checker
|
||||
new.xformers_enabled = old.xformers
|
||||
new.conf_path = old.conf
|
||||
new.root = legacy_format.parent.resolve()
|
||||
|
||||
@ -1,77 +0,0 @@
|
||||
'''
|
||||
SafetyChecker class - checks images against the StabilityAI NSFW filter
|
||||
and blurs images that contain potential NSFW content.
|
||||
'''
|
||||
import diffusers
|
||||
import numpy as np
|
||||
import torch
|
||||
import traceback
|
||||
from diffusers.pipelines.stable_diffusion.safety_checker import (
|
||||
StableDiffusionSafetyChecker,
|
||||
)
|
||||
from pathlib import Path
|
||||
from PIL import Image, ImageFilter
|
||||
from transformers import AutoFeatureExtractor
|
||||
|
||||
import invokeai.assets.web as web_assets
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from .util import CPU_DEVICE
|
||||
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
class SafetyChecker(object):
|
||||
CAUTION_IMG = "caution.png"
|
||||
|
||||
def __init__(self, device: torch.device):
|
||||
path = Path(web_assets.__path__[0]) / self.CAUTION_IMG
|
||||
caution = Image.open(path)
|
||||
self.caution_img = caution.resize((caution.width // 2, caution.height // 2))
|
||||
self.device = device
|
||||
|
||||
try:
|
||||
safety_model_id = config.models_path / 'core/convert/stable-diffusion-safety-checker'
|
||||
feature_extractor_id = config.models_path / 'core/convert/stable-diffusion-safety-checker-extractor'
|
||||
self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
|
||||
self.safety_feature_extractor = AutoFeatureExtractor.from_pretrained(feature_extractor_id)
|
||||
except Exception:
|
||||
logger.error(
|
||||
"An error was encountered while installing the safety checker:"
|
||||
)
|
||||
print(traceback.format_exc())
|
||||
|
||||
def check(self, image: Image.Image):
|
||||
"""
|
||||
Check provided image against the StabilityAI safety checker and return
|
||||
|
||||
"""
|
||||
|
||||
self.safety_checker.to(self.device)
|
||||
features = self.safety_feature_extractor([image], return_tensors="pt")
|
||||
features.to(self.device)
|
||||
|
||||
# unfortunately checker requires the numpy version, so we have to convert back
|
||||
x_image = np.array(image).astype(np.float32) / 255.0
|
||||
x_image = x_image[None].transpose(0, 3, 1, 2)
|
||||
|
||||
diffusers.logging.set_verbosity_error()
|
||||
checked_image, has_nsfw_concept = self.safety_checker(
|
||||
images=x_image, clip_input=features.pixel_values
|
||||
)
|
||||
self.safety_checker.to(CPU_DEVICE) # offload
|
||||
if has_nsfw_concept[0]:
|
||||
logger.warning(
|
||||
"An image with potential non-safe content has been detected. A blurred image will be returned."
|
||||
)
|
||||
return self.blur(image)
|
||||
else:
|
||||
return image
|
||||
|
||||
def blur(self, input):
|
||||
blurry = input.filter(filter=ImageFilter.GaussianBlur(radius=32))
|
||||
try:
|
||||
if caution := self.caution_img:
|
||||
blurry.paste(caution, (0, 0), caution)
|
||||
except FileNotFoundError:
|
||||
pass
|
||||
return blurry
|
||||
@ -553,7 +553,7 @@ class AddModelApplication(npyscreen.NPSAppManaged):
|
||||
def onStart(self):
|
||||
npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
|
||||
self.main_form = self.addForm(
|
||||
"MAIN", addModelsForm, name="Install Stable Diffusion Models", cycle_widgets=True,
|
||||
"MAIN", addModelsForm, name="Install Stable Diffusion Models", cycle_widgets=False,
|
||||
)
|
||||
|
||||
class StderrToMessage():
|
||||
|
||||
169
invokeai/frontend/web/dist/assets/App-06ea4e5e.js
vendored
169
invokeai/frontend/web/dist/assets/App-06ea4e5e.js
vendored
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
125
invokeai/frontend/web/dist/assets/index-e2437518.js
vendored
125
invokeai/frontend/web/dist/assets/index-e2437518.js
vendored
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-e2437518.js"></script>
|
||||
<script type="module" crossorigin src="./assets/index-e45bf5a6.js"></script>
|
||||
</head>
|
||||
|
||||
<body dir="ltr">
|
||||
|
||||
15
invokeai/frontend/web/dist/locales/en.json
vendored
15
invokeai/frontend/web/dist/locales/en.json
vendored
@ -102,8 +102,7 @@
|
||||
"openInNewTab": "Open in New Tab",
|
||||
"dontAskMeAgain": "Don't ask me again",
|
||||
"areYouSure": "Are you sure?",
|
||||
"imagePrompt": "Image Prompt",
|
||||
"clearNodes": "Are you sure you want to clear all nodes?"
|
||||
"imagePrompt": "Image Prompt"
|
||||
},
|
||||
"gallery": {
|
||||
"generations": "Generations",
|
||||
@ -615,6 +614,11 @@
|
||||
"initialImageNotSetDesc": "Could not load initial image",
|
||||
"nodesSaved": "Nodes Saved",
|
||||
"nodesLoaded": "Nodes Loaded",
|
||||
"nodesNotValidGraph": "Not a valid InvokeAI Node Graph",
|
||||
"nodesNotValidJSON": "Not a valid JSON",
|
||||
"nodesCorruptedGraph": "Cannot load. Graph seems to be corrupted.",
|
||||
"nodesUnrecognizedTypes": "Cannot load. Graph has unrecognized types",
|
||||
"nodesBrokenConnections": "Cannot load. Some connections are broken.",
|
||||
"nodesLoadedFailed": "Failed To Load Nodes",
|
||||
"nodesCleared": "Nodes Cleared"
|
||||
},
|
||||
@ -700,9 +704,10 @@
|
||||
},
|
||||
"nodes": {
|
||||
"reloadSchema": "Reload Schema",
|
||||
"saveNodes": "Save Nodes",
|
||||
"loadNodes": "Load Nodes",
|
||||
"clearNodes": "Clear Nodes",
|
||||
"saveGraph": "Save Graph",
|
||||
"loadGraph": "Load Graph (saved from Node Editor) (Do not copy-paste metadata)",
|
||||
"clearGraph": "Clear Graph",
|
||||
"clearGraphDesc": "Are you sure you want to clear all nodes?",
|
||||
"zoomInNodes": "Zoom In",
|
||||
"zoomOutNodes": "Zoom Out",
|
||||
"fitViewportNodes": "Fit View",
|
||||
|
||||
@ -1,4 +1,8 @@
|
||||
import { setInfillMethod } from 'features/parameters/store/generationSlice';
|
||||
import {
|
||||
shouldUseNSFWCheckerChanged,
|
||||
shouldUseWatermarkerChanged,
|
||||
} from 'features/system/store/systemSlice';
|
||||
import { appInfoApi } from 'services/api/endpoints/appInfo';
|
||||
import { startAppListening } from '..';
|
||||
|
||||
@ -6,12 +10,21 @@ export const addAppConfigReceivedListener = () => {
|
||||
startAppListening({
|
||||
matcher: appInfoApi.endpoints.getAppConfig.matchFulfilled,
|
||||
effect: async (action, { getState, dispatch }) => {
|
||||
const { infill_methods } = action.payload;
|
||||
const { infill_methods, nsfw_methods, watermarking_methods } =
|
||||
action.payload;
|
||||
const infillMethod = getState().generation.infillMethod;
|
||||
|
||||
if (!infill_methods.includes(infillMethod)) {
|
||||
dispatch(setInfillMethod(infill_methods[0]));
|
||||
}
|
||||
|
||||
if (!nsfw_methods.includes('nsfw_checker')) {
|
||||
dispatch(shouldUseNSFWCheckerChanged(false));
|
||||
}
|
||||
|
||||
if (!watermarking_methods.includes('invisible_watermark')) {
|
||||
dispatch(shouldUseWatermarkerChanged(false));
|
||||
}
|
||||
},
|
||||
});
|
||||
};
|
||||
|
||||
@ -1,13 +1,10 @@
|
||||
import { logger } from 'app/logging/logger';
|
||||
import { LIST_TAG } from 'services/api';
|
||||
import { appInfoApi } from 'services/api/endpoints/appInfo';
|
||||
import { modelsApi } from 'services/api/endpoints/models';
|
||||
import { receivedOpenAPISchema } from 'services/api/thunks/schema';
|
||||
import { appSocketConnected, socketConnected } from 'services/events/actions';
|
||||
import { startAppListening } from '../..';
|
||||
import {
|
||||
ALL_BASE_MODELS,
|
||||
NON_REFINER_BASE_MODELS,
|
||||
REFINER_BASE_MODELS,
|
||||
} from 'services/api/constants';
|
||||
|
||||
export const addSocketConnectedEventListener = () => {
|
||||
startAppListening({
|
||||
@ -29,15 +26,18 @@ export const addSocketConnectedEventListener = () => {
|
||||
dispatch(appSocketConnected(action.payload));
|
||||
|
||||
// update all server state
|
||||
dispatch(modelsApi.endpoints.getMainModels.initiate(REFINER_BASE_MODELS));
|
||||
dispatch(
|
||||
modelsApi.endpoints.getMainModels.initiate(NON_REFINER_BASE_MODELS)
|
||||
modelsApi.util.invalidateTags([
|
||||
{ type: 'MainModel', id: LIST_TAG },
|
||||
{ type: 'SDXLRefinerModel', id: LIST_TAG },
|
||||
{ type: 'LoRAModel', id: LIST_TAG },
|
||||
{ type: 'ControlNetModel', id: LIST_TAG },
|
||||
{ type: 'VaeModel', id: LIST_TAG },
|
||||
{ type: 'TextualInversionModel', id: LIST_TAG },
|
||||
{ type: 'ScannedModels', id: LIST_TAG },
|
||||
])
|
||||
);
|
||||
dispatch(modelsApi.endpoints.getMainModels.initiate(ALL_BASE_MODELS));
|
||||
dispatch(modelsApi.endpoints.getControlNetModels.initiate());
|
||||
dispatch(modelsApi.endpoints.getLoRAModels.initiate());
|
||||
dispatch(modelsApi.endpoints.getTextualInversionModels.initiate());
|
||||
dispatch(modelsApi.endpoints.getVaeModels.initiate());
|
||||
dispatch(appInfoApi.util.invalidateTags(['AppConfig', 'AppVersion']));
|
||||
},
|
||||
});
|
||||
};
|
||||
|
||||
@ -5,8 +5,8 @@ import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
|
||||
// import { validateSeedWeights } from 'common/util/seedWeightPairs';
|
||||
import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
|
||||
import { forEach } from 'lodash-es';
|
||||
import { NON_REFINER_BASE_MODELS } from 'services/api/constants';
|
||||
import { modelsApi } from '../../services/api/endpoints/models';
|
||||
import { ALL_BASE_MODELS } from 'services/api/constants';
|
||||
|
||||
const readinessSelector = createSelector(
|
||||
[stateSelector, activeTabNameSelector],
|
||||
@ -25,7 +25,7 @@ const readinessSelector = createSelector(
|
||||
}
|
||||
|
||||
const { isSuccess: mainModelsSuccessfullyLoaded } =
|
||||
modelsApi.endpoints.getMainModels.select(ALL_BASE_MODELS)(state);
|
||||
modelsApi.endpoints.getMainModels.select(NON_REFINER_BASE_MODELS)(state);
|
||||
if (!mainModelsSuccessfullyLoaded) {
|
||||
isReady = false;
|
||||
reasonsWhyNotReady.push('Models are not loaded');
|
||||
|
||||
@ -0,0 +1,70 @@
|
||||
import { RootState } from 'app/store/store';
|
||||
import { NonNullableGraph } from 'features/nodes/types/types';
|
||||
import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
|
||||
import {
|
||||
ImageNSFWBlurInvocation,
|
||||
LatentsToImageInvocation,
|
||||
MetadataAccumulatorInvocation,
|
||||
} from 'services/api/types';
|
||||
import {
|
||||
LATENTS_TO_IMAGE,
|
||||
METADATA_ACCUMULATOR,
|
||||
NSFW_CHECKER,
|
||||
} from './constants';
|
||||
|
||||
export const addNSFWCheckerToGraph = (
|
||||
state: RootState,
|
||||
graph: NonNullableGraph,
|
||||
nodeIdToAddTo = LATENTS_TO_IMAGE
|
||||
): void => {
|
||||
const activeTabName = activeTabNameSelector(state);
|
||||
|
||||
const is_intermediate =
|
||||
activeTabName === 'unifiedCanvas' ? !state.canvas.shouldAutoSave : false;
|
||||
|
||||
const nodeToAddTo = graph.nodes[nodeIdToAddTo] as
|
||||
| LatentsToImageInvocation
|
||||
| undefined;
|
||||
|
||||
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
|
||||
| MetadataAccumulatorInvocation
|
||||
| undefined;
|
||||
|
||||
if (!nodeToAddTo) {
|
||||
// something has gone terribly awry
|
||||
return;
|
||||
}
|
||||
|
||||
nodeToAddTo.is_intermediate = true;
|
||||
|
||||
const nsfwCheckerNode: ImageNSFWBlurInvocation = {
|
||||
id: NSFW_CHECKER,
|
||||
type: 'img_nsfw',
|
||||
is_intermediate,
|
||||
};
|
||||
|
||||
graph.nodes[NSFW_CHECKER] = nsfwCheckerNode;
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: nodeIdToAddTo,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: NSFW_CHECKER,
|
||||
field: 'image',
|
||||
},
|
||||
});
|
||||
|
||||
if (metadataAccumulator) {
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: METADATA_ACCUMULATOR,
|
||||
field: 'metadata',
|
||||
},
|
||||
destination: {
|
||||
node_id: NSFW_CHECKER,
|
||||
field: 'metadata',
|
||||
},
|
||||
});
|
||||
}
|
||||
};
|
||||
@ -0,0 +1,95 @@
|
||||
import { RootState } from 'app/store/store';
|
||||
import { NonNullableGraph } from 'features/nodes/types/types';
|
||||
import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
|
||||
import {
|
||||
ImageNSFWBlurInvocation,
|
||||
ImageWatermarkInvocation,
|
||||
LatentsToImageInvocation,
|
||||
MetadataAccumulatorInvocation,
|
||||
} from 'services/api/types';
|
||||
import {
|
||||
LATENTS_TO_IMAGE,
|
||||
METADATA_ACCUMULATOR,
|
||||
NSFW_CHECKER,
|
||||
WATERMARKER,
|
||||
} from './constants';
|
||||
|
||||
export const addWatermarkerToGraph = (
|
||||
state: RootState,
|
||||
graph: NonNullableGraph,
|
||||
nodeIdToAddTo = LATENTS_TO_IMAGE
|
||||
): void => {
|
||||
const activeTabName = activeTabNameSelector(state);
|
||||
|
||||
const is_intermediate =
|
||||
activeTabName === 'unifiedCanvas' ? !state.canvas.shouldAutoSave : false;
|
||||
|
||||
const nodeToAddTo = graph.nodes[nodeIdToAddTo] as
|
||||
| LatentsToImageInvocation
|
||||
| undefined;
|
||||
|
||||
const nsfwCheckerNode = graph.nodes[NSFW_CHECKER] as
|
||||
| ImageNSFWBlurInvocation
|
||||
| undefined;
|
||||
|
||||
const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
|
||||
| MetadataAccumulatorInvocation
|
||||
| undefined;
|
||||
|
||||
if (!nodeToAddTo) {
|
||||
// something has gone terribly awry
|
||||
return;
|
||||
}
|
||||
|
||||
const watermarkerNode: ImageWatermarkInvocation = {
|
||||
id: WATERMARKER,
|
||||
type: 'img_watermark',
|
||||
is_intermediate,
|
||||
};
|
||||
|
||||
graph.nodes[WATERMARKER] = watermarkerNode;
|
||||
|
||||
// no matter the situation, we want the l2i node to be intermediate
|
||||
nodeToAddTo.is_intermediate = true;
|
||||
|
||||
if (nsfwCheckerNode) {
|
||||
// if we are using NSFW checker, we need to "disable" it output by marking it intermediate,
|
||||
// then connect it to the watermark node
|
||||
nsfwCheckerNode.is_intermediate = true;
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: NSFW_CHECKER,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: WATERMARKER,
|
||||
field: 'image',
|
||||
},
|
||||
});
|
||||
} else {
|
||||
// otherwise we just connect to the watermark node
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: nodeIdToAddTo,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: WATERMARKER,
|
||||
field: 'image',
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
if (metadataAccumulator) {
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: METADATA_ACCUMULATOR,
|
||||
field: 'metadata',
|
||||
},
|
||||
destination: {
|
||||
node_id: WATERMARKER,
|
||||
field: 'metadata',
|
||||
},
|
||||
});
|
||||
}
|
||||
};
|
||||
@ -10,7 +10,9 @@ import {
|
||||
import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
|
||||
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
|
||||
import { addLoRAsToGraph } from './addLoRAsToGraph';
|
||||
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
|
||||
import { addVAEToGraph } from './addVAEToGraph';
|
||||
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
|
||||
import {
|
||||
CLIP_SKIP,
|
||||
IMAGE_TO_IMAGE_GRAPH,
|
||||
@ -103,11 +105,6 @@ export const buildCanvasImageToImageGraph = (
|
||||
is_intermediate: true,
|
||||
skipped_layers: clipSkip,
|
||||
},
|
||||
[LATENTS_TO_IMAGE]: {
|
||||
is_intermediate: !shouldAutoSave,
|
||||
type: 'l2i',
|
||||
id: LATENTS_TO_IMAGE,
|
||||
},
|
||||
[LATENTS_TO_LATENTS]: {
|
||||
type: 'l2l',
|
||||
id: LATENTS_TO_LATENTS,
|
||||
@ -126,6 +123,11 @@ export const buildCanvasImageToImageGraph = (
|
||||
// image_name: initialImage.image_name,
|
||||
// },
|
||||
},
|
||||
[LATENTS_TO_IMAGE]: {
|
||||
type: 'l2i',
|
||||
id: LATENTS_TO_IMAGE,
|
||||
is_intermediate: !shouldAutoSave,
|
||||
},
|
||||
},
|
||||
edges: [
|
||||
{
|
||||
@ -310,17 +312,6 @@ export const buildCanvasImageToImageGraph = (
|
||||
init_image: initialImage.image_name,
|
||||
};
|
||||
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: METADATA_ACCUMULATOR,
|
||||
field: 'metadata',
|
||||
},
|
||||
destination: {
|
||||
node_id: LATENTS_TO_IMAGE,
|
||||
field: 'metadata',
|
||||
},
|
||||
});
|
||||
|
||||
// add LoRA support
|
||||
addLoRAsToGraph(state, graph, LATENTS_TO_LATENTS);
|
||||
|
||||
@ -333,5 +324,16 @@ export const buildCanvasImageToImageGraph = (
|
||||
// add controlnet, mutating `graph`
|
||||
addControlNetToLinearGraph(state, graph, LATENTS_TO_LATENTS);
|
||||
|
||||
// NSFW & watermark - must be last thing added to graph
|
||||
if (state.system.shouldUseNSFWChecker) {
|
||||
// must add before watermarker!
|
||||
addNSFWCheckerToGraph(state, graph);
|
||||
}
|
||||
|
||||
if (state.system.shouldUseWatermarker) {
|
||||
// must add after nsfw checker!
|
||||
addWatermarkerToGraph(state, graph);
|
||||
}
|
||||
|
||||
return graph;
|
||||
};
|
||||
|
||||
@ -20,6 +20,8 @@ import {
|
||||
RANDOM_INT,
|
||||
RANGE_OF_SIZE,
|
||||
} from './constants';
|
||||
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
|
||||
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
|
||||
|
||||
/**
|
||||
* Builds the Canvas tab's Inpaint graph.
|
||||
@ -249,5 +251,16 @@ export const buildCanvasInpaintGraph = (
|
||||
(graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
|
||||
}
|
||||
|
||||
// NSFW & watermark - must be last thing added to graph
|
||||
if (state.system.shouldUseNSFWChecker) {
|
||||
// must add before watermarker!
|
||||
addNSFWCheckerToGraph(state, graph, INPAINT);
|
||||
}
|
||||
|
||||
if (state.system.shouldUseWatermarker) {
|
||||
// must add after nsfw checker!
|
||||
addWatermarkerToGraph(state, graph, INPAINT);
|
||||
}
|
||||
|
||||
return graph;
|
||||
};
|
||||
|
||||
@ -5,7 +5,9 @@ import { initialGenerationState } from 'features/parameters/store/generationSlic
|
||||
import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
|
||||
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
|
||||
import { addLoRAsToGraph } from './addLoRAsToGraph';
|
||||
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
|
||||
import { addVAEToGraph } from './addVAEToGraph';
|
||||
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
|
||||
import {
|
||||
CLIP_SKIP,
|
||||
LATENTS_TO_IMAGE,
|
||||
@ -215,17 +217,6 @@ export const buildCanvasTextToImageGraph = (
|
||||
clip_skip: clipSkip,
|
||||
};
|
||||
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: METADATA_ACCUMULATOR,
|
||||
field: 'metadata',
|
||||
},
|
||||
destination: {
|
||||
node_id: LATENTS_TO_IMAGE,
|
||||
field: 'metadata',
|
||||
},
|
||||
});
|
||||
|
||||
// add LoRA support
|
||||
addLoRAsToGraph(state, graph, TEXT_TO_LATENTS);
|
||||
|
||||
@ -238,5 +229,16 @@ export const buildCanvasTextToImageGraph = (
|
||||
// add controlnet, mutating `graph`
|
||||
addControlNetToLinearGraph(state, graph, TEXT_TO_LATENTS);
|
||||
|
||||
// NSFW & watermark - must be last thing added to graph
|
||||
if (state.system.shouldUseNSFWChecker) {
|
||||
// must add before watermarker!
|
||||
addNSFWCheckerToGraph(state, graph);
|
||||
}
|
||||
|
||||
if (state.system.shouldUseWatermarker) {
|
||||
// must add after nsfw checker!
|
||||
addWatermarkerToGraph(state, graph);
|
||||
}
|
||||
|
||||
return graph;
|
||||
};
|
||||
|
||||
@ -9,7 +9,9 @@ import {
|
||||
import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
|
||||
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
|
||||
import { addLoRAsToGraph } from './addLoRAsToGraph';
|
||||
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
|
||||
import { addVAEToGraph } from './addVAEToGraph';
|
||||
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
|
||||
import {
|
||||
CLIP_SKIP,
|
||||
IMAGE_TO_IMAGE_GRAPH,
|
||||
@ -297,42 +299,6 @@ export const buildLinearImageToImageGraph = (
|
||||
});
|
||||
}
|
||||
|
||||
// TODO: add batch functionality
|
||||
// if (isBatchEnabled && asInitialImage && batchImageNames.length > 0) {
|
||||
// // we are going to connect an iterate up to the init image
|
||||
// delete (graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image;
|
||||
|
||||
// const imageCollection: ImageCollectionInvocation = {
|
||||
// id: IMAGE_COLLECTION,
|
||||
// type: 'image_collection',
|
||||
// images: batchImageNames.map((image_name) => ({ image_name })),
|
||||
// };
|
||||
|
||||
// const imageCollectionIterate: IterateInvocation = {
|
||||
// id: IMAGE_COLLECTION_ITERATE,
|
||||
// type: 'iterate',
|
||||
// };
|
||||
|
||||
// graph.nodes[IMAGE_COLLECTION] = imageCollection;
|
||||
// graph.nodes[IMAGE_COLLECTION_ITERATE] = imageCollectionIterate;
|
||||
|
||||
// graph.edges.push({
|
||||
// source: { node_id: IMAGE_COLLECTION, field: 'collection' },
|
||||
// destination: {
|
||||
// node_id: IMAGE_COLLECTION_ITERATE,
|
||||
// field: 'collection',
|
||||
// },
|
||||
// });
|
||||
|
||||
// graph.edges.push({
|
||||
// source: { node_id: IMAGE_COLLECTION_ITERATE, field: 'item' },
|
||||
// destination: {
|
||||
// node_id: IMAGE_TO_LATENTS,
|
||||
// field: 'image',
|
||||
// },
|
||||
// });
|
||||
// }
|
||||
|
||||
// add metadata accumulator, which is only mostly populated - some fields are added later
|
||||
graph.nodes[METADATA_ACCUMULATOR] = {
|
||||
id: METADATA_ACCUMULATOR,
|
||||
@ -356,17 +322,6 @@ export const buildLinearImageToImageGraph = (
|
||||
init_image: initialImage.imageName,
|
||||
};
|
||||
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: METADATA_ACCUMULATOR,
|
||||
field: 'metadata',
|
||||
},
|
||||
destination: {
|
||||
node_id: LATENTS_TO_IMAGE,
|
||||
field: 'metadata',
|
||||
},
|
||||
});
|
||||
|
||||
// add LoRA support
|
||||
addLoRAsToGraph(state, graph, LATENTS_TO_LATENTS);
|
||||
|
||||
@ -379,5 +334,16 @@ export const buildLinearImageToImageGraph = (
|
||||
// add controlnet, mutating `graph`
|
||||
addControlNetToLinearGraph(state, graph, LATENTS_TO_LATENTS);
|
||||
|
||||
// NSFW & watermark - must be last thing added to graph
|
||||
if (state.system.shouldUseNSFWChecker) {
|
||||
// must add before watermarker!
|
||||
addNSFWCheckerToGraph(state, graph);
|
||||
}
|
||||
|
||||
if (state.system.shouldUseWatermarker) {
|
||||
// must add after nsfw checker!
|
||||
addWatermarkerToGraph(state, graph);
|
||||
}
|
||||
|
||||
return graph;
|
||||
};
|
||||
|
||||
@ -20,6 +20,8 @@ import {
|
||||
SDXL_LATENTS_TO_LATENTS,
|
||||
SDXL_MODEL_LOADER,
|
||||
} from './constants';
|
||||
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
|
||||
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
|
||||
|
||||
/**
|
||||
* Builds the Image to Image tab graph.
|
||||
@ -365,5 +367,16 @@ export const buildLinearSDXLImageToImageGraph = (
|
||||
// add dynamic prompts - also sets up core iteration and seed
|
||||
addDynamicPromptsToGraph(state, graph);
|
||||
|
||||
// NSFW & watermark - must be last thing added to graph
|
||||
if (state.system.shouldUseNSFWChecker) {
|
||||
// must add before watermarker!
|
||||
addNSFWCheckerToGraph(state, graph);
|
||||
}
|
||||
|
||||
if (state.system.shouldUseWatermarker) {
|
||||
// must add after nsfw checker!
|
||||
addWatermarkerToGraph(state, graph);
|
||||
}
|
||||
|
||||
return graph;
|
||||
};
|
||||
|
||||
@ -14,6 +14,8 @@ import {
|
||||
SDXL_TEXT_TO_IMAGE_GRAPH,
|
||||
SDXL_TEXT_TO_LATENTS,
|
||||
} from './constants';
|
||||
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
|
||||
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
|
||||
|
||||
export const buildLinearSDXLTextToImageGraph = (
|
||||
state: RootState
|
||||
@ -247,5 +249,16 @@ export const buildLinearSDXLTextToImageGraph = (
|
||||
// add dynamic prompts - also sets up core iteration and seed
|
||||
addDynamicPromptsToGraph(state, graph);
|
||||
|
||||
// NSFW & watermark - must be last thing added to graph
|
||||
if (state.system.shouldUseNSFWChecker) {
|
||||
// must add before watermarker!
|
||||
addNSFWCheckerToGraph(state, graph);
|
||||
}
|
||||
|
||||
if (state.system.shouldUseWatermarker) {
|
||||
// must add after nsfw checker!
|
||||
addWatermarkerToGraph(state, graph);
|
||||
}
|
||||
|
||||
return graph;
|
||||
};
|
||||
|
||||
@ -5,7 +5,9 @@ import { initialGenerationState } from 'features/parameters/store/generationSlic
|
||||
import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
|
||||
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
|
||||
import { addLoRAsToGraph } from './addLoRAsToGraph';
|
||||
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
|
||||
import { addVAEToGraph } from './addVAEToGraph';
|
||||
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
|
||||
import {
|
||||
CLIP_SKIP,
|
||||
LATENTS_TO_IMAGE,
|
||||
@ -204,17 +206,6 @@ export const buildLinearTextToImageGraph = (
|
||||
clip_skip: clipSkip,
|
||||
};
|
||||
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: METADATA_ACCUMULATOR,
|
||||
field: 'metadata',
|
||||
},
|
||||
destination: {
|
||||
node_id: LATENTS_TO_IMAGE,
|
||||
field: 'metadata',
|
||||
},
|
||||
});
|
||||
|
||||
// add LoRA support
|
||||
addLoRAsToGraph(state, graph, TEXT_TO_LATENTS);
|
||||
|
||||
@ -227,5 +218,16 @@ export const buildLinearTextToImageGraph = (
|
||||
// add controlnet, mutating `graph`
|
||||
addControlNetToLinearGraph(state, graph, TEXT_TO_LATENTS);
|
||||
|
||||
// NSFW & watermark - must be last thing added to graph
|
||||
if (state.system.shouldUseNSFWChecker) {
|
||||
// must add before watermarker!
|
||||
addNSFWCheckerToGraph(state, graph);
|
||||
}
|
||||
|
||||
if (state.system.shouldUseWatermarker) {
|
||||
// must add after nsfw checker!
|
||||
addWatermarkerToGraph(state, graph);
|
||||
}
|
||||
|
||||
return graph;
|
||||
};
|
||||
|
||||
@ -3,6 +3,8 @@ export const POSITIVE_CONDITIONING = 'positive_conditioning';
|
||||
export const NEGATIVE_CONDITIONING = 'negative_conditioning';
|
||||
export const TEXT_TO_LATENTS = 'text_to_latents';
|
||||
export const LATENTS_TO_IMAGE = 'latents_to_image';
|
||||
export const NSFW_CHECKER = 'nsfw_checker';
|
||||
export const WATERMARKER = 'invisible_watermark';
|
||||
export const NOISE = 'noise';
|
||||
export const RANDOM_INT = 'rand_int';
|
||||
export const RANGE_OF_SIZE = 'range_of_size';
|
||||
|
||||
@ -27,6 +27,8 @@ import {
|
||||
setShouldConfirmOnDelete,
|
||||
shouldAntialiasProgressImageChanged,
|
||||
shouldLogToConsoleChanged,
|
||||
shouldUseNSFWCheckerChanged,
|
||||
shouldUseWatermarkerChanged,
|
||||
} from 'features/system/store/systemSlice';
|
||||
import {
|
||||
setShouldShowProgressInViewer,
|
||||
@ -43,6 +45,7 @@ import {
|
||||
} from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import { LogLevelName } from 'roarr';
|
||||
import { useGetAppConfigQuery } from 'services/api/endpoints/appInfo';
|
||||
import SettingSwitch from './SettingSwitch';
|
||||
import SettingsClearIntermediates from './SettingsClearIntermediates';
|
||||
import SettingsSchedulers from './SettingsSchedulers';
|
||||
@ -62,6 +65,8 @@ const selector = createSelector(
|
||||
shouldLogToConsole,
|
||||
shouldAntialiasProgressImage,
|
||||
isNodesEnabled,
|
||||
shouldUseNSFWChecker,
|
||||
shouldUseWatermarker,
|
||||
} = system;
|
||||
|
||||
const {
|
||||
@ -83,6 +88,8 @@ const selector = createSelector(
|
||||
shouldAntialiasProgressImage,
|
||||
shouldShowAdvancedOptions,
|
||||
isNodesEnabled,
|
||||
shouldUseNSFWChecker,
|
||||
shouldUseWatermarker,
|
||||
};
|
||||
},
|
||||
{
|
||||
@ -125,6 +132,16 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => {
|
||||
}
|
||||
}, [shouldShowDeveloperSettings, dispatch]);
|
||||
|
||||
const { isNSFWCheckerAvailable, isWatermarkerAvailable } =
|
||||
useGetAppConfigQuery(undefined, {
|
||||
selectFromResult: ({ data }) => ({
|
||||
isNSFWCheckerAvailable:
|
||||
data?.nsfw_methods.includes('nsfw_checker') ?? false,
|
||||
isWatermarkerAvailable:
|
||||
data?.watermarking_methods.includes('invisible_watermark') ?? false,
|
||||
}),
|
||||
});
|
||||
|
||||
const {
|
||||
isOpen: isSettingsModalOpen,
|
||||
onOpen: onSettingsModalOpen,
|
||||
@ -148,6 +165,8 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => {
|
||||
shouldAntialiasProgressImage,
|
||||
shouldShowAdvancedOptions,
|
||||
isNodesEnabled,
|
||||
shouldUseNSFWChecker,
|
||||
shouldUseWatermarker,
|
||||
} = useAppSelector(selector);
|
||||
|
||||
const handleClickResetWebUI = useCallback(() => {
|
||||
@ -239,6 +258,22 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => {
|
||||
<StyledFlex>
|
||||
<Heading size="sm">{t('settings.generation')}</Heading>
|
||||
<SettingsSchedulers />
|
||||
<SettingSwitch
|
||||
label="Enable NSFW Checker"
|
||||
isDisabled={!isNSFWCheckerAvailable}
|
||||
isChecked={shouldUseNSFWChecker}
|
||||
onChange={(e: ChangeEvent<HTMLInputElement>) =>
|
||||
dispatch(shouldUseNSFWCheckerChanged(e.target.checked))
|
||||
}
|
||||
/>
|
||||
<SettingSwitch
|
||||
label="Enable Invisible Watermark"
|
||||
isDisabled={!isWatermarkerAvailable}
|
||||
isChecked={shouldUseWatermarker}
|
||||
onChange={(e: ChangeEvent<HTMLInputElement>) =>
|
||||
dispatch(shouldUseWatermarkerChanged(e.target.checked))
|
||||
}
|
||||
/>
|
||||
</StyledFlex>
|
||||
|
||||
<StyledFlex>
|
||||
|
||||
@ -87,6 +87,8 @@ export interface SystemState {
|
||||
language: keyof typeof LANGUAGES;
|
||||
isUploading: boolean;
|
||||
isNodesEnabled: boolean;
|
||||
shouldUseNSFWChecker: boolean;
|
||||
shouldUseWatermarker: boolean;
|
||||
}
|
||||
|
||||
export const initialSystemState: SystemState = {
|
||||
@ -119,6 +121,8 @@ export const initialSystemState: SystemState = {
|
||||
language: 'en',
|
||||
isUploading: false,
|
||||
isNodesEnabled: false,
|
||||
shouldUseNSFWChecker: false,
|
||||
shouldUseWatermarker: false,
|
||||
};
|
||||
|
||||
export const systemSlice = createSlice({
|
||||
@ -194,6 +198,12 @@ export const systemSlice = createSlice({
|
||||
setIsNodesEnabled(state, action: PayloadAction<boolean>) {
|
||||
state.isNodesEnabled = action.payload;
|
||||
},
|
||||
shouldUseNSFWCheckerChanged(state, action: PayloadAction<boolean>) {
|
||||
state.shouldUseNSFWChecker = action.payload;
|
||||
},
|
||||
shouldUseWatermarkerChanged(state, action: PayloadAction<boolean>) {
|
||||
state.shouldUseWatermarker = action.payload;
|
||||
},
|
||||
},
|
||||
extraReducers(builder) {
|
||||
/**
|
||||
@ -409,6 +419,8 @@ export const {
|
||||
languageChanged,
|
||||
progressImageSet,
|
||||
setIsNodesEnabled,
|
||||
shouldUseNSFWCheckerChanged,
|
||||
shouldUseWatermarkerChanged,
|
||||
} = systemSlice.actions;
|
||||
|
||||
export default systemSlice.reducer;
|
||||
|
||||
@ -8,6 +8,7 @@ export const appInfoApi = api.injectEndpoints({
|
||||
url: `app/version`,
|
||||
method: 'GET',
|
||||
}),
|
||||
providesTags: ['AppVersion'],
|
||||
keepUnusedDataFor: 86400000, // 1 day
|
||||
}),
|
||||
getAppConfig: build.query<AppConfig, void>({
|
||||
@ -15,6 +16,7 @@ export const appInfoApi = api.injectEndpoints({
|
||||
url: `app/config`,
|
||||
method: 'GET',
|
||||
}),
|
||||
providesTags: ['AppConfig'],
|
||||
keepUnusedDataFor: 86400000, // 1 day
|
||||
}),
|
||||
}),
|
||||
|
||||
108
invokeai/frontend/web/src/services/api/schema.d.ts
vendored
108
invokeai/frontend/web/src/services/api/schema.d.ts
vendored
@ -318,6 +318,21 @@ export type components = {
|
||||
* @description List of available infill methods
|
||||
*/
|
||||
infill_methods: (string)[];
|
||||
/**
|
||||
* Upscaling Methods
|
||||
* @description List of upscaling methods
|
||||
*/
|
||||
upscaling_methods: (components["schemas"]["Upscaler"])[];
|
||||
/**
|
||||
* Nsfw Methods
|
||||
* @description List of NSFW checking methods
|
||||
*/
|
||||
nsfw_methods: (string)[];
|
||||
/**
|
||||
* Watermarking Methods
|
||||
* @description List of invisible watermark methods
|
||||
*/
|
||||
watermarking_methods: (string)[];
|
||||
};
|
||||
/**
|
||||
* AppVersion
|
||||
@ -1345,7 +1360,7 @@ export type components = {
|
||||
* @description The nodes in this graph
|
||||
*/
|
||||
nodes?: {
|
||||
[key: string]: (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"]["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"]["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"]["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"]["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"]["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"]["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"]["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
|
||||
@ -1935,6 +1950,39 @@ export type components = {
|
||||
*/
|
||||
image2?: components["schemas"]["ImageField"];
|
||||
};
|
||||
/**
|
||||
* ImageNSFWBlurInvocation
|
||||
* @description Add blur to NSFW-flagged images
|
||||
*/
|
||||
ImageNSFWBlurInvocation: {
|
||||
/**
|
||||
* 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_nsfw
|
||||
* @enum {string}
|
||||
*/
|
||||
type?: "img_nsfw";
|
||||
/**
|
||||
* Image
|
||||
* @description The image to check
|
||||
*/
|
||||
image?: components["schemas"]["ImageField"];
|
||||
/**
|
||||
* Metadata
|
||||
* @description Optional core metadata to be written to the image
|
||||
*/
|
||||
metadata?: components["schemas"]["CoreMetadata"];
|
||||
};
|
||||
/**
|
||||
* ImageOutput
|
||||
* @description Base class for invocations that output an image
|
||||
@ -2215,6 +2263,45 @@ export type components = {
|
||||
*/
|
||||
thumbnail_url: string;
|
||||
};
|
||||
/**
|
||||
* ImageWatermarkInvocation
|
||||
* @description Add an invisible watermark to an image
|
||||
*/
|
||||
ImageWatermarkInvocation: {
|
||||
/**
|
||||
* 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_watermark
|
||||
* @enum {string}
|
||||
*/
|
||||
type?: "img_watermark";
|
||||
/**
|
||||
* Image
|
||||
* @description The image to check
|
||||
*/
|
||||
image?: components["schemas"]["ImageField"];
|
||||
/**
|
||||
* Text
|
||||
* @description Watermark text
|
||||
* @default InvokeAI
|
||||
*/
|
||||
text?: string;
|
||||
/**
|
||||
* Metadata
|
||||
* @description Optional core metadata to be written to the image
|
||||
*/
|
||||
metadata?: components["schemas"]["CoreMetadata"];
|
||||
};
|
||||
/**
|
||||
* InfillColorInvocation
|
||||
* @description Infills transparent areas of an image with a solid color
|
||||
@ -2644,7 +2731,7 @@ export type components = {
|
||||
vae?: components["schemas"]["VaeField"];
|
||||
/**
|
||||
* Tiled
|
||||
* @description Decode latents by overlaping tiles(less memory consumption)
|
||||
* @description Decode latents by overlapping tiles(less memory consumption)
|
||||
* @default false
|
||||
*/
|
||||
tiled?: boolean;
|
||||
@ -5309,6 +5396,19 @@ export type components = {
|
||||
*/
|
||||
loras: (components["schemas"]["LoraInfo"])[];
|
||||
};
|
||||
/** Upscaler */
|
||||
Upscaler: {
|
||||
/**
|
||||
* Upscaling Method
|
||||
* @description Name of upscaling method
|
||||
*/
|
||||
upscaling_method: string;
|
||||
/**
|
||||
* Upscaling Models
|
||||
* @description List of upscaling models for this method
|
||||
*/
|
||||
upscaling_models: (string)[];
|
||||
};
|
||||
/**
|
||||
* VAEModelField
|
||||
* @description Vae model field
|
||||
@ -5563,7 +5663,7 @@ export type operations = {
|
||||
};
|
||||
requestBody: {
|
||||
content: {
|
||||
"application/json": 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"]["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"]["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"]["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"]["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"]["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"]["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"]["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: {
|
||||
@ -5600,7 +5700,7 @@ export type operations = {
|
||||
};
|
||||
requestBody: {
|
||||
content: {
|
||||
"application/json": 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"]["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"]["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"]["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"]["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"]["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"]["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"]["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: {
|
||||
|
||||
@ -134,6 +134,12 @@ export type ESRGANInvocation = TypeReq<
|
||||
export type DivideInvocation = TypeReq<
|
||||
components['schemas']['DivideInvocation']
|
||||
>;
|
||||
export type ImageNSFWBlurInvocation = TypeReq<
|
||||
components['schemas']['ImageNSFWBlurInvocation']
|
||||
>;
|
||||
export type ImageWatermarkInvocation = TypeReq<
|
||||
components['schemas']['ImageWatermarkInvocation']
|
||||
>;
|
||||
|
||||
// ControlNet Nodes
|
||||
export type ControlNetInvocation = TypeReq<
|
||||
|
||||
@ -462,16 +462,16 @@ def test_graph_subgraph_t2i():
|
||||
|
||||
n4 = ShowImageInvocation(id = "4")
|
||||
g.add_node(n4)
|
||||
g.add_edge(create_edge("1.7","image","4","image"))
|
||||
g.add_edge(create_edge("1.8","image","4","image"))
|
||||
|
||||
# Validate
|
||||
dg = g.nx_graph_flat()
|
||||
assert set(dg.nodes) == set(['1.width', '1.height', '1.seed', '1.3', '1.4', '1.5', '1.6', '1.7', '2', '3', '4'])
|
||||
assert set(dg.nodes) == set(['1.width', '1.height', '1.seed', '1.3', '1.4', '1.5', '1.6', '1.7', '1.8', '2', '3', '4'])
|
||||
expected_edges = [(f'1.{e.source.node_id}',f'1.{e.destination.node_id}') for e in lg.graph.edges]
|
||||
expected_edges.extend([
|
||||
('2','1.width'),
|
||||
('3','1.height'),
|
||||
('1.7','4')
|
||||
('1.8','4')
|
||||
])
|
||||
print(expected_edges)
|
||||
print(list(dg.edges))
|
||||
|
||||
@ -13,9 +13,10 @@ init1 = OmegaConf.create(
|
||||
'''
|
||||
InvokeAI:
|
||||
Features:
|
||||
nsfw_checker: False
|
||||
always_use_cpu: false
|
||||
Memory/Performance:
|
||||
max_cache_size: 5
|
||||
tiled_decode: false
|
||||
'''
|
||||
)
|
||||
|
||||
@ -23,9 +24,10 @@ init2 = OmegaConf.create(
|
||||
'''
|
||||
InvokeAI:
|
||||
Features:
|
||||
nsfw_checker: true
|
||||
always_use_cpu: true
|
||||
Memory/Performance:
|
||||
max_cache_size: 2
|
||||
tiled_decode: true
|
||||
'''
|
||||
)
|
||||
|
||||
@ -36,20 +38,21 @@ def test_use_init():
|
||||
conf1 = InvokeAIAppConfig.get_config()
|
||||
assert conf1
|
||||
conf1.parse_args(conf=init1,argv=[])
|
||||
assert not conf1.tiled_decode
|
||||
assert conf1.max_cache_size==5
|
||||
assert not conf1.nsfw_checker
|
||||
assert not conf1.always_use_cpu
|
||||
|
||||
conf2 = InvokeAIAppConfig.get_config()
|
||||
assert conf2
|
||||
conf2.parse_args(conf=init2,argv=[])
|
||||
assert conf2.nsfw_checker
|
||||
assert conf2.tiled_decode
|
||||
assert conf2.max_cache_size==2
|
||||
assert not hasattr(conf2,'invalid_attribute')
|
||||
|
||||
def test_argv_override():
|
||||
conf = InvokeAIAppConfig.get_config()
|
||||
conf.parse_args(conf=init1,argv=['--nsfw_checker','--max_cache=10'])
|
||||
assert conf.nsfw_checker
|
||||
conf.parse_args(conf=init1,argv=['--always_use_cpu','--max_cache=10'])
|
||||
assert conf.always_use_cpu
|
||||
assert conf.max_cache_size==10
|
||||
assert conf.outdir==Path('outputs') # this is the default
|
||||
|
||||
@ -57,10 +60,10 @@ def test_env_override():
|
||||
# argv overrides
|
||||
conf = InvokeAIAppConfig()
|
||||
conf.parse_args(conf=init1,argv=['--max_cache=10'])
|
||||
assert conf.nsfw_checker==False
|
||||
os.environ['INVOKEAI_nsfw_checker'] = 'True'
|
||||
assert conf.always_use_cpu==False
|
||||
os.environ['INVOKEAI_always_use_cpu'] = 'True'
|
||||
conf.parse_args(conf=init1,argv=['--max_cache=10'])
|
||||
assert conf.nsfw_checker==True
|
||||
assert conf.always_use_cpu==True
|
||||
|
||||
# environment variables should be case insensitive
|
||||
os.environ['InvokeAI_Max_Cache_Size'] = '15'
|
||||
@ -69,8 +72,8 @@ def test_env_override():
|
||||
assert conf.max_cache_size == 15
|
||||
|
||||
conf = InvokeAIAppConfig()
|
||||
conf.parse_args(conf=init1,argv=['--no-nsfw_checker','--max_cache=10'])
|
||||
assert conf.nsfw_checker==False
|
||||
conf.parse_args(conf=init1,argv=['--no-always_use_cpu','--max_cache=10'])
|
||||
assert conf.always_use_cpu==False
|
||||
assert conf.max_cache_size==10
|
||||
|
||||
conf = InvokeAIAppConfig.get_config(max_cache_size=20)
|
||||
|
||||
@ -9,7 +9,7 @@ from PIL import Image
|
||||
|
||||
import invokeai.frontend.web.dist as frontend
|
||||
import invokeai.configs as configs
|
||||
import invokeai.assets.web as assets_web
|
||||
import invokeai.app.assets.images as image_assets
|
||||
|
||||
class ConfigsTestCase(unittest.TestCase):
|
||||
"""Test the configuration related imports and objects"""
|
||||
@ -35,7 +35,7 @@ class ConfigsTestCase(unittest.TestCase):
|
||||
|
||||
def test_caution_img(self):
|
||||
"""Verify the caution image"""
|
||||
caution_img = Image.open(osp.join(assets_web.__path__[0], "caution.png"))
|
||||
caution_img = Image.open(osp.join(image_assets.__path__[0], "caution.png"))
|
||||
assert caution_img.width == int(500)
|
||||
assert caution_img.height == int(441)
|
||||
assert caution_img.format == str("PNG")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user