resolve conflicts with main

This commit is contained in:
Lincoln Stein
2023-07-01 14:42:48 -04:00
626 changed files with 32220 additions and 28402 deletions

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@ -19,31 +19,56 @@ An invocation looks like this:
```py
class UpscaleInvocation(BaseInvocation):
"""Upscales an image."""
type: Literal['upscale'] = 'upscale'
# fmt: off
type: Literal["upscale"] = "upscale"
# Inputs
image: Union[ImageField,None] = Field(description="The input image")
strength: float = Field(default=0.75, gt=0, le=1, description="The strength")
level: Literal[2,4] = Field(default=2, description = "The upscale level")
image: Union[ImageField, None] = Field(description="The input image", default=None)
strength: float = Field(default=0.75, gt=0, le=1, description="The strength")
level: Literal[2, 4] = Field(default=2, description="The upscale level")
# fmt: on
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"tags": ["upscaling", "image"],
},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get(self.image.image_type, self.image.image_name)
results = context.services.generate.upscale_and_reconstruct(
image_list = [[image, 0]],
upscale = (self.level, self.strength),
strength = 0.0, # GFPGAN strength
save_original = False,
image_callback = None,
image = context.services.images.get_pil_image(
self.image.image_origin, self.image.image_name
)
results = context.services.restoration.upscale_and_reconstruct(
image_list=[[image, 0]],
upscale=(self.level, self.strength),
strength=0.0, # GFPGAN strength
save_original=False,
image_callback=None,
)
# Results are image and seed, unwrap for now
# TODO: can this return multiple results?
image_type = ImageType.RESULT
image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
context.services.images.save(image_type, image_name, results[0][0])
return ImageOutput(
image = ImageField(image_type = image_type, image_name = image_name)
image_dto = context.services.images.create(
image=results[0][0],
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
)
return ImageOutput(
image=ImageField(
image_name=image_dto.image_name,
image_origin=image_dto.image_origin,
),
width=image_dto.width,
height=image_dto.height,
)
```
Each portion is important to implement correctly.
@ -95,25 +120,67 @@ Finally, note that for all linking, the `type` of the linked fields must match.
If the `name` also matches, then the field can be **automatically linked** to a
previous invocation by name and matching.
### Config
```py
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"tags": ["upscaling", "image"],
},
}
```
This is an optional configuration for the invocation. It inherits from
pydantic's model `Config` class, and it used primarily to customize the
autogenerated OpenAPI schema.
The UI relies on the OpenAPI schema in two ways:
- An API client & Typescript types are generated from it. This happens at build
time.
- The node editor parses the schema into a template used by the UI to create the
node editor UI. This parsing happens at runtime.
In this example, a `ui` key has been added to the `schema_extra` dict to provide
some tags for the UI, to facilitate filtering nodes.
See the Schema Generation section below for more information.
### Invoke Function
```py
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get(self.image.image_type, self.image.image_name)
results = context.services.generate.upscale_and_reconstruct(
image_list = [[image, 0]],
upscale = (self.level, self.strength),
strength = 0.0, # GFPGAN strength
save_original = False,
image_callback = None,
image = context.services.images.get_pil_image(
self.image.image_origin, self.image.image_name
)
results = context.services.restoration.upscale_and_reconstruct(
image_list=[[image, 0]],
upscale=(self.level, self.strength),
strength=0.0, # GFPGAN strength
save_original=False,
image_callback=None,
)
# Results are image and seed, unwrap for now
image_type = ImageType.RESULT
image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
context.services.images.save(image_type, image_name, results[0][0])
# TODO: can this return multiple results?
image_dto = context.services.images.create(
image=results[0][0],
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
)
return ImageOutput(
image = ImageField(image_type = image_type, image_name = image_name)
image=ImageField(
image_name=image_dto.image_name,
image_origin=image_dto.image_origin,
),
width=image_dto.width,
height=image_dto.height,
)
```
@ -135,9 +202,16 @@ scenarios. If you need functionality, please provide it as a service in the
```py
class ImageOutput(BaseInvocationOutput):
"""Base class for invocations that output an image"""
type: Literal['image'] = 'image'
image: ImageField = Field(default=None, description="The output 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"]}
```
Output classes look like an invocation class without the invoke method. Prefer
@ -168,35 +242,36 @@ Here's that `ImageOutput` class, without the needed schema customisation:
class ImageOutput(BaseInvocationOutput):
"""Base class for invocations that output an image"""
type: Literal["image"] = "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
```
The generated OpenAPI schema, and all clients/types generated from it, will have
the `type` and `image` properties marked as optional, even though we know they
will always have a value by the time we can interact with them via the API.
Here's the same class, but with the schema customisation added:
The OpenAPI schema that results from this `ImageOutput` will have the `type`,
`image`, `width` and `height` properties marked as optional, even though we know
they will always have a value.
```python
class ImageOutput(BaseInvocationOutput):
"""Base class for invocations that output an image"""
type: Literal["image"] = "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
# Add schema customization
class Config:
schema_extra = {
'required': [
'type',
'image',
]
}
schema_extra = {"required": ["type", "image", "width", "height"]}
```
The resultant schema (and any API client or types generated from it) will now
have see `type` as string literal `"image"` and `image` as an `ImageField`
object.
With the customization in place, the schema will now show these properties as
required, obviating the need for extensive null checks in client code.
See this `pydantic` issue for discussion on this solution:
<https://github.com/pydantic/pydantic/discussions/4577>

171
docs/features/LOGGING.md Normal file
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@ -0,0 +1,171 @@
---
title: Controlling Logging
---
# :material-image-off: Controlling Logging
## Controlling How InvokeAI Logs Status Messages
InvokeAI logs status messages using a configurable logging system. You
can log to the terminal window, to a designated file on the local
machine, to the syslog facility on a Linux or Mac, or to a properly
configured web server. You can configure several logs at the same
time, and control the level of message logged and the logging format
(to a limited extent).
Three command-line options control logging:
### `--log_handlers <handler1> <handler2> ...`
This option activates one or more log handlers. Options are "console",
"file", "syslog" and "http". To specify more than one, separate them
by spaces:
```bash
invokeai-web --log_handlers console syslog=/dev/log file=C:\Users\fred\invokeai.log
```
The format of these options is described below.
### `--log_format {plain|color|legacy|syslog}`
This controls the format of log messages written to the console. Only
the "console" log handler is currently affected by this setting.
* "plain" provides formatted messages like this:
```bash
[2023-05-24 23:18:2[2023-05-24 23:18:50,352]::[InvokeAI]::DEBUG --> this is a debug message
[2023-05-24 23:18:50,352]::[InvokeAI]::INFO --> this is an informational messages
[2023-05-24 23:18:50,352]::[InvokeAI]::WARNING --> this is a warning
[2023-05-24 23:18:50,352]::[InvokeAI]::ERROR --> this is an error
[2023-05-24 23:18:50,352]::[InvokeAI]::CRITICAL --> this is a critical error
```
* "color" produces similar output, but the text will be color coded to
indicate the severity of the message.
* "legacy" produces output similar to InvokeAI versions 2.3 and earlier:
```bash
### this is a critical error
*** this is an error
** this is a warning
>> this is an informational messages
| this is a debug message
```
* "syslog" produces messages suitable for syslog entries:
```bash
InvokeAI [2691178] <CRITICAL> this is a critical error
InvokeAI [2691178] <ERROR> this is an error
InvokeAI [2691178] <WARNING> this is a warning
InvokeAI [2691178] <INFO> this is an informational messages
InvokeAI [2691178] <DEBUG> this is a debug message
```
(note that the date, time and hostname will be added by the syslog
system)
### `--log_level {debug|info|warning|error|critical}`
Providing this command-line option will cause only messages at the
specified level or above to be emitted.
## Console logging
When "console" is provided to `--log_handlers`, messages will be
written to the command line window in which InvokeAI was launched. By
default, the color formatter will be used unless overridden by
`--log_format`.
## File logging
When "file" is provided to `--log_handlers`, entries will be written
to the file indicated in the path argument. By default, the "plain"
format will be used:
```bash
invokeai-web --log_handlers file=/var/log/invokeai.log
```
## Syslog logging
When "syslog" is requested, entries will be sent to the syslog
system. There are a variety of ways to control where the log message
is sent:
* Send to the local machine using the `/dev/log` socket:
```
invokeai-web --log_handlers syslog=/dev/log
```
* Send to the local machine using a UDP message:
```
invokeai-web --log_handlers syslog=localhost
```
* Send to the local machine using a UDP message on a nonstandard
port:
```
invokeai-web --log_handlers syslog=localhost:512
```
* Send to a remote machine named "loghost" on the local LAN using
facility LOG_USER and UDP packets:
```
invokeai-web --log_handlers syslog=loghost,facility=LOG_USER,socktype=SOCK_DGRAM
```
This can be abbreviated `syslog=loghost`, as LOG_USER and SOCK_DGRAM
are defaults.
* Send to a remote machine named "loghost" using the facility LOCAL0
and using a TCP socket:
```
invokeai-web --log_handlers syslog=loghost,facility=LOG_LOCAL0,socktype=SOCK_STREAM
```
If no arguments are specified (just a bare "syslog"), then the logging
system will look for a UNIX socket named `/dev/log`, and if not found
try to send a UDP message to `localhost`. The Macintosh OS used to
support logging to a socket named `/var/run/syslog`, but this feature
has since been disabled.
## Web logging
If you have access to a web server that is configured to log messages
when a particular URL is requested, you can log using the "http"
method:
```
invokeai-web --log_handlers http=http://my.server/path/to/logger,method=POST
```
The optional [,method=] part can be used to specify whether the URL
accepts GET (default) or POST messages.
Currently password authentication and SSL are not supported.
## Using the configuration file
You can set and forget logging options by adding a "Logging" section
to `invokeai.yaml`:
```
InvokeAI:
[... other settings...]
Logging:
log_handlers:
- console
- syslog=/dev/log
log_level: info
log_format: color
```

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@ -45,6 +45,9 @@ Personalize models by adding your own style or subjects.
## * [The NSFW Checker](NSFW.md)
Prevent InvokeAI from displaying unwanted racy images.
## * [Controlling Logging](LOGGING.md)
Control how InvokeAI logs status messages.
## * [Miscellaneous](OTHER.md)
Run InvokeAI on Google Colab, generate images with repeating patterns,
batch process a file of prompts, increase the "creativity" of image

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@ -68,7 +68,7 @@ title: Home
implementation of Stable Diffusion, the open source text-to-image and
image-to-image generator. It provides a streamlined process with various new
features and options to aid the image generation process. It runs on Windows,
Mac and Linux machines, and runs on GPU cards with as little as 4 GB or RAM.
Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
**Quick links**: [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>]
[<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a

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@ -87,18 +87,18 @@ Prior to installing PyPatchMatch, you need to take the following steps:
sudo pacman -S --needed base-devel
```
2. Install `opencv`:
2. Install `opencv` and `blas`:
```sh
sudo pacman -S opencv
sudo pacman -S opencv blas
```
or for CUDA support
```sh
sudo pacman -S opencv-cuda
sudo pacman -S opencv-cuda blas
```
3. Fix the naming of the `opencv` package configuration file:
```sh