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99
docs/features/POSTPROCESS.md
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@ -4,35 +4,13 @@ title: Postprocessing
# :material-image-edit: Postprocessing
## Intro
This extension provides the ability to restore faces and upscale images.
This sections details the ability to improve faces and upscale images.
## Face Fixing
The default face restoration module is GFPGAN. The default upscale is
Real-ESRGAN. For an alternative face restoration module, see
[CodeFormer Support](#codeformer-support) below.
As of InvokeAI 3.0, the easiest way to improve faces created during image generation is through the Inpainting functionality of the Unified Canvas. Simply add the image containing the faces that you would like to improve to the canvas, mask the face to be improved and run the invocation. For best results, make sure to use an inpainting specific model; these are usually identified by the "-inpainting" term in the model name.
As of version 1.14, environment.yaml will install the Real-ESRGAN package into
the standard install location for python packages, and will put GFPGAN into a
subdirectory of "src" in the InvokeAI directory. Upscaling with Real-ESRGAN
should "just work" without further intervention. Simply indicate the desired scale on
the popup in the Web GUI.
**GFPGAN** requires a series of downloadable model files to work. These are
loaded when you run `invokeai-configure`. If GFPAN is failing with an
error, please run the following from the InvokeAI directory:
```bash
invokeai-configure
```
If you do not run this script in advance, the GFPGAN module will attempt to
download the models files the first time you try to perform facial
reconstruction.
### Upscaling
## Upscaling
Open the upscaling dialog by clicking on the "expand" icon located
above the image display area in the Web UI:
@ -41,82 +19,23 @@ above the image display area in the Web UI:
![upscale1](../assets/features/upscale-dialog.png)
</figure>
There are three different upscaling parameters that you can
adjust. The first is the scale itself, either 2x or 4x.
The default upscaling option is Real-ESRGAN x2 Plus, which will scale your image by a factor of two. This means upscaling a 512x512 image will result in a new 1024x1024 image.
The second is the "Denoising Strength." Higher values will smooth out
the image and remove digital chatter, but may lose fine detail at
higher values.
Other options are the x4 upscalers, which will scale your image by a factor of 4.
Third, "Upscale Strength" allows you to adjust how the You can set the
scaling stength between `0` and `1.0` to control the intensity of the
scaling. AI upscalers generally tend to smooth out texture details. If
you wish to retain some of those for natural looking results, we
recommend using values between `0.5 to 0.8`.
[This figure](../assets/features/upscaling-montage.png) illustrates
the effects of denoising and strength. The original image was 512x512,
4x scaled to 2048x2048. The "original" version on the upper left was
scaled using simple pixel averaging. The remainder use the ESRGAN
upscaling algorithm at different levels of denoising and strength.
<figure markdown>
![upscaling](../assets/features/upscaling-montage.png){ width=720 }
</figure>
Both denoising and strength default to 0.75.
### Face Restoration
InvokeAI offers alternative two face restoration algorithms,
[GFPGAN](https://github.com/TencentARC/GFPGAN) and
[CodeFormer](https://huggingface.co/spaces/sczhou/CodeFormer). These
algorithms improve the appearance of faces, particularly eyes and
mouths. Issues with faces are less common with the latest set of
Stable Diffusion models than with the original 1.4 release, but the
restoration algorithms can still make a noticeable improvement in
certain cases. You can also apply restoration to old photographs you
upload.
To access face restoration, click the "smiley face" icon in the
toolbar above the InvokeAI image panel. You will be presented with a
dialog that offers a choice between the two algorithm and sliders that
allow you to adjust their parameters. Alternatively, you may open the
left-hand accordion panel labeled "Face Restoration" and have the
restoration algorithm of your choice applied to generated images
automatically.
Like upscaling, there are a number of parameters that adjust the face
restoration output. GFPGAN has a single parameter, `strength`, which
controls how much the algorithm is allowed to adjust the
image. CodeFormer has two parameters, `strength`, and `fidelity`,
which together control the quality of the output image as described in
the [CodeFormer project
page](https://shangchenzhou.com/projects/CodeFormer/). Default values
are 0.75 for both parameters, which achieves a reasonable balance
between changing the image too much and not enough.
[This figure](../assets/features/restoration-montage.png) illustrates
the effects of adjusting GFPGAN and CodeFormer parameters.
<figure markdown>
![upscaling](../assets/features/restoration-montage.png){ width=720 }
</figure>
!!! note
GFPGAN and Real-ESRGAN are both memory intensive. In order to avoid crashes and memory overloads
Real-ESRGAN is memory intensive. In order to avoid crashes and memory overloads
during the Stable Diffusion process, these effects are applied after Stable Diffusion has completed
its work.
In single image generations, you will see the output right away but when you are using multiple
iterations, the images will first be generated and then upscaled and face restored after that
iterations, the images will first be generated and then upscaled after that
process is complete. While the image generation is taking place, you will still be able to preview
the base images.
## How to disable
If, for some reason, you do not wish to load the GFPGAN and/or ESRGAN libraries,
you can disable them on the invoke.py command line with the `--no_restore` and
`--no_esrgan` options, respectively.
If, for some reason, you do not wish to load the ESRGAN libraries,
you can disable them on the invoke.py command line with the `--no_esrgan` options.

241
invokeai/app/invocations/compel.py
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@ -16,7 +16,7 @@ from ...backend.util.devices import torch_dtype
from ...backend.model_management import ModelType
from ...backend.model_management.models import ModelNotFoundException
from ...backend.model_management.lora import ModelPatcher
from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
from ...backend.stable_diffusion import InvokeAIDiffuserComponent, BasicConditioningInfo, SDXLConditioningInfo
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
from .model import ClipField
from dataclasses import dataclass
@ -29,28 +29,9 @@ class ConditioningField(BaseModel):
schema_extra = {"required": ["conditioning_name"]}
@dataclass
class BasicConditioningInfo:
# type: Literal["basic_conditioning"] = "basic_conditioning"
embeds: torch.Tensor
extra_conditioning: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo]
# weight: float
# mode: ConditioningAlgo
@dataclass
class SDXLConditioningInfo(BasicConditioningInfo):
# type: Literal["sdxl_conditioning"] = "sdxl_conditioning"
pooled_embeds: torch.Tensor
add_time_ids: torch.Tensor
ConditioningInfoType = Annotated[Union[BasicConditioningInfo, SDXLConditioningInfo], Field(discriminator="type")]
@dataclass
class ConditioningFieldData:
conditionings: List[Union[BasicConditioningInfo, SDXLConditioningInfo]]
conditionings: List[BasicConditioningInfo]
# unconditioned: Optional[torch.Tensor]
@ -176,7 +157,15 @@ class CompelInvocation(BaseInvocation):
class SDXLPromptInvocationBase:
def run_clip_raw(self, context, clip_field, prompt, get_pooled, lora_prefix):
def run_clip_compel(
self,
context: InvocationContext,
clip_field: ClipField,
prompt: str,
get_pooled: bool,
lora_prefix: str,
zero_on_empty: bool,
):
tokenizer_info = context.services.model_manager.get_model(
**clip_field.tokenizer.dict(),
context=context,
@ -186,82 +175,21 @@ class SDXLPromptInvocationBase:
context=context,
)
def _lora_loader():
for lora in clip_field.loras:
lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
yield (lora_info.context.model, lora.weight)
del lora_info
return
# loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
ti_list = []
for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
name = trigger[1:-1]
try:
ti_list.append(
(
name,
context.services.model_manager.get_model(
model_name=name,
base_model=clip_field.text_encoder.base_model,
model_type=ModelType.TextualInversion,
context=context,
).context.model,
)
)
except ModelNotFoundException:
# print(e)
# import traceback
# print(traceback.format_exc())
print(f'Warn: trigger: "{trigger}" not found')
with ModelPatcher.apply_lora(
text_encoder_info.context.model, _lora_loader(), lora_prefix
), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
tokenizer,
ti_manager,
), ModelPatcher.apply_clip_skip(
text_encoder_info.context.model, clip_field.skipped_layers
), text_encoder_info as text_encoder:
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = text_encoder(
text_input_ids.to(text_encoder.device),
output_hidden_states=True,
# return zero on empty
if prompt == "" and zero_on_empty:
cpu_text_encoder = text_encoder_info.context.model
c = torch.zeros(
(1, cpu_text_encoder.config.max_position_embeddings, cpu_text_encoder.config.hidden_size),
dtype=text_encoder_info.context.cache.precision,
)
if get_pooled:
c_pooled = prompt_embeds[0]
c_pooled = torch.zeros(
(1, cpu_text_encoder.config.hidden_size),
dtype=c.dtype,
)
else:
c_pooled = None
c = prompt_embeds.hidden_states[-2]
del tokenizer
del text_encoder
del tokenizer_info
del text_encoder_info
c = c.detach().to("cpu")
if c_pooled is not None:
c_pooled = c_pooled.detach().to("cpu")
return c, c_pooled, None
def run_clip_compel(self, context, clip_field, prompt, get_pooled, lora_prefix):
tokenizer_info = context.services.model_manager.get_model(
**clip_field.tokenizer.dict(),
context=context,
)
text_encoder_info = context.services.model_manager.get_model(
**clip_field.text_encoder.dict(),
context=context,
)
return c, c_pooled, None
def _lora_loader():
for lora in clip_field.loras:
@ -366,11 +294,17 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
@torch.no_grad()
def invoke(self, context: InvocationContext) -> CompelOutput:
c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip, self.prompt, False, "lora_te1_")
c1, c1_pooled, ec1 = self.run_clip_compel(
context, self.clip, self.prompt, False, "lora_te1_", zero_on_empty=True
)
if self.style.strip() == "":
c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.prompt, True, "lora_te2_")
c2, c2_pooled, ec2 = self.run_clip_compel(
context, self.clip2, self.prompt, True, "lora_te2_", zero_on_empty=True
)
else:
c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "lora_te2_")
c2, c2_pooled, ec2 = self.run_clip_compel(
context, self.clip2, self.style, True, "lora_te2_", zero_on_empty=True
)
original_size = (self.original_height, self.original_width)
crop_coords = (self.crop_top, self.crop_left)
@ -425,118 +359,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
@torch.no_grad()
def invoke(self, context: InvocationContext) -> CompelOutput:
# TODO: if there will appear lora for refiner - write proper prefix
c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "<NONE>")
original_size = (self.original_height, self.original_width)
crop_coords = (self.crop_top, self.crop_left)
add_time_ids = torch.tensor([original_size + crop_coords + (self.aesthetic_score,)])
conditioning_data = ConditioningFieldData(
conditionings=[
SDXLConditioningInfo(
embeds=c2,
pooled_embeds=c2_pooled,
add_time_ids=add_time_ids,
extra_conditioning=ec2, # or None
)
]
)
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
context.services.latents.save(conditioning_name, conditioning_data)
return CompelOutput(
conditioning=ConditioningField(
conditioning_name=conditioning_name,
),
)
class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Pass unmodified prompt to conditioning without compel processing."""
type: Literal["sdxl_raw_prompt"] = "sdxl_raw_prompt"
prompt: str = Field(default="", description="Prompt")
style: str = Field(default="", description="Style prompt")
original_width: int = Field(1024, description="")
original_height: int = Field(1024, description="")
crop_top: int = Field(0, description="")
crop_left: int = Field(0, description="")
target_width: int = Field(1024, description="")
target_height: int = Field(1024, description="")
clip: ClipField = Field(None, description="Clip to use")
clip2: ClipField = Field(None, description="Clip2 to use")
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {"title": "SDXL Prompt (Raw)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
}
@torch.no_grad()
def invoke(self, context: InvocationContext) -> CompelOutput:
c1, c1_pooled, ec1 = self.run_clip_raw(context, self.clip, self.prompt, False, "lora_te1_")
if self.style.strip() == "":
c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.prompt, True, "lora_te2_")
else:
c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True, "lora_te2_")
original_size = (self.original_height, self.original_width)
crop_coords = (self.crop_top, self.crop_left)
target_size = (self.target_height, self.target_width)
add_time_ids = torch.tensor([original_size + crop_coords + target_size])
conditioning_data = ConditioningFieldData(
conditionings=[
SDXLConditioningInfo(
embeds=torch.cat([c1, c2], dim=-1),
pooled_embeds=c2_pooled,
add_time_ids=add_time_ids,
extra_conditioning=ec1,
)
]
)
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
context.services.latents.save(conditioning_name, conditioning_data)
return CompelOutput(
conditioning=ConditioningField(
conditioning_name=conditioning_name,
),
)
class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning."""
type: Literal["sdxl_refiner_raw_prompt"] = "sdxl_refiner_raw_prompt"
style: str = Field(default="", description="Style prompt") # TODO: ?
original_width: int = Field(1024, description="")
original_height: int = Field(1024, description="")
crop_top: int = Field(0, description="")
crop_left: int = Field(0, description="")
aesthetic_score: float = Field(6.0, description="")
clip2: ClipField = Field(None, description="Clip to use")
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "SDXL Refiner Prompt (Raw)",
"tags": ["prompt", "compel"],
"type_hints": {"model": "model"},
},
}
@torch.no_grad()
def invoke(self, context: InvocationContext) -> CompelOutput:
# TODO: if there will appear lora for refiner - write proper prefix
c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True, "<NONE>")
c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "<NONE>", zero_on_empty=False)
original_size = (self.original_height, self.original_width)
crop_coords = (self.crop_top, self.crop_left)

248
invokeai/app/invocations/generate.py
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@ -1,248 +0,0 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
from contextlib import contextmanager, ContextDecorator
from functools import partial
from typing import Literal, Optional, get_args
from pydantic import Field
from invokeai.app.models.image import ColorField, ImageCategory, ImageField, ResourceOrigin
from invokeai.app.util.misc import SEED_MAX, get_random_seed
from invokeai.backend.generator.inpaint import infill_methods
from .baseinvocation import BaseInvocation, InvocationConfig, InvocationContext
from .compel import ConditioningField
from .image import ImageOutput
from .model import UNetField, VaeField
from ..util.step_callback import stable_diffusion_step_callback
from ...backend.generator import Inpaint, InvokeAIGenerator
from ...backend.model_management.lora import ModelPatcher
from ...backend.stable_diffusion import PipelineIntermediateState
from ...backend.stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
INFILL_METHODS = Literal[tuple(infill_methods())]
DEFAULT_INFILL_METHOD = "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
from .latent import get_scheduler
class OldModelContext(ContextDecorator):
model: StableDiffusionGeneratorPipeline
def __init__(self, model):
self.model = model
def __enter__(self):
return self.model
def __exit__(self, *exc):
return False
class OldModelInfo:
name: str
hash: str
context: OldModelContext
def __init__(self, name: str, hash: str, model: StableDiffusionGeneratorPipeline):
self.name = name
self.hash = hash
self.context = OldModelContext(
model=model,
)
class InpaintInvocation(BaseInvocation):
"""Generates an image using inpaint."""
type: Literal["inpaint"] = "inpaint"
positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
seed: int = Field(
ge=0, le=SEED_MAX, description="The seed to use (omit for random)", default_factory=get_random_seed
)
steps: int = Field(default=30, gt=0, description="The number of steps to use to generate the image")
width: int = Field(
default=512,
multiple_of=8,
gt=0,
description="The width of the resulting image",
)
height: int = Field(
default=512,
multiple_of=8,
gt=0,
description="The height of the resulting image",
)
cfg_scale: float = Field(
default=7.5,
ge=1,
description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt",
)
scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use")
unet: UNetField = Field(default=None, description="UNet model")
vae: VaeField = Field(default=None, description="Vae model")
# Inputs
image: Optional[ImageField] = Field(description="The input image")
strength: float = Field(default=0.75, gt=0, le=1, description="The strength of the original image")
fit: bool = Field(
default=True,
description="Whether or not the result should be fit to the aspect ratio of the input image",
)
# Inputs
mask: Optional[ImageField] = Field(description="The mask")
seam_size: int = Field(default=96, ge=1, description="The seam inpaint size (px)")
seam_blur: int = Field(default=16, ge=0, description="The seam inpaint blur radius (px)")
seam_strength: float = Field(default=0.75, gt=0, le=1, description="The seam inpaint strength")
seam_steps: int = Field(default=30, ge=1, description="The number of steps to use for seam inpaint")
tile_size: int = Field(default=32, ge=1, description="The tile infill method size (px)")
infill_method: INFILL_METHODS = Field(
default=DEFAULT_INFILL_METHOD,
description="The method used to infill empty regions (px)",
)
inpaint_width: Optional[int] = Field(
default=None,
multiple_of=8,
gt=0,
description="The width of the inpaint region (px)",
)
inpaint_height: Optional[int] = Field(
default=None,
multiple_of=8,
gt=0,
description="The height of the inpaint region (px)",
)
inpaint_fill: Optional[ColorField] = Field(
default=ColorField(r=127, g=127, b=127, a=255),
description="The solid infill method color",
)
inpaint_replace: float = Field(
default=0.0,
ge=0.0,
le=1.0,
description="The amount by which to replace masked areas with latent noise",
)
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {"tags": ["stable-diffusion", "image"], "title": "Inpaint"},
}
def dispatch_progress(
self,
context: InvocationContext,
source_node_id: str,
intermediate_state: PipelineIntermediateState,
) -> None:
stable_diffusion_step_callback(
context=context,
intermediate_state=intermediate_state,
node=self.dict(),
source_node_id=source_node_id,
)
def get_conditioning(self, context, unet):
positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
c = positive_cond_data.conditionings[0].embeds.to(device=unet.device, dtype=unet.dtype)
extra_conditioning_info = positive_cond_data.conditionings[0].extra_conditioning
negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
uc = negative_cond_data.conditionings[0].embeds.to(device=unet.device, dtype=unet.dtype)
return (uc, c, extra_conditioning_info)
@contextmanager
def load_model_old_way(self, context, scheduler):
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}),
context=context,
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(),
context=context,
)
vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(),
context=context,
)
with vae_info as vae, ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()), unet_info as unet:
device = context.services.model_manager.mgr.cache.execution_device
dtype = context.services.model_manager.mgr.cache.precision
vae.to(dtype=unet.dtype)
pipeline = StableDiffusionGeneratorPipeline(
vae=vae,
text_encoder=None,
tokenizer=None,
unet=unet,
scheduler=scheduler,
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
)
yield OldModelInfo(
name=self.unet.unet.model_name,
hash="<NO-HASH>",
model=pipeline,
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = None if self.image is None else context.services.images.get_pil_image(self.image.image_name)
mask = None if self.mask is None else context.services.images.get_pil_image(self.mask.image_name)
# Get the source node id (we are invoking the prepared node)
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
scheduler = get_scheduler(
context=context,
scheduler_info=self.unet.scheduler,
scheduler_name=self.scheduler,
)
with self.load_model_old_way(context, scheduler) as model:
conditioning = self.get_conditioning(context, model.context.model.unet)
outputs = Inpaint(model).generate(
conditioning=conditioning,
scheduler=scheduler,
init_image=image,
mask_image=mask,
step_callback=partial(self.dispatch_progress, context, source_node_id),
**self.dict(
exclude={"positive_conditioning", "negative_conditioning", "scheduler", "image", "mask"}
), # Shorthand for passing all of the parameters above manually
)
# Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
# each time it is called. We only need the first one.
generator_output = next(outputs)
image_dto = context.services.images.create(
image=generator_output.image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
session_id=context.graph_execution_state_id,
node_id=self.id,
is_intermediate=self.is_intermediate,
)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)

228
invokeai/app/invocations/image.py
View File

@ -1,29 +1,19 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
from typing import Literal, Optional
import numpy
import cv2
from PIL import Image, ImageFilter, ImageOps, ImageChops
from pydantic import Field
from pathlib import Path
from typing import Union
from typing import Literal, Optional, Union
import cv2
import numpy
from PIL import Image, ImageChops, ImageFilter, ImageOps
from pydantic import Field
from invokeai.app.invocations.metadata import CoreMetadata
from ..models.image import (
ImageCategory,
ImageField,
ResourceOrigin,
PILInvocationConfig,
ImageOutput,
MaskOutput,
)
from .baseinvocation import (
BaseInvocation,
InvocationContext,
InvocationConfig,
)
from invokeai.backend.image_util.safety_checker import SafetyChecker
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.backend.image_util.safety_checker import SafetyChecker
from ..models.image import ImageCategory, ImageField, ImageOutput, MaskOutput, PILInvocationConfig, ResourceOrigin
from .baseinvocation import BaseInvocation, InvocationConfig, InvocationContext
class LoadImageInvocation(BaseInvocation):
@ -143,9 +133,10 @@ class ImagePasteInvocation(BaseInvocation, PILInvocationConfig):
def invoke(self, context: InvocationContext) -> ImageOutput:
base_image = context.services.images.get_pil_image(self.base_image.image_name)
image = context.services.images.get_pil_image(self.image.image_name)
mask = (
None if self.mask is None else ImageOps.invert(context.services.images.get_pil_image(self.mask.image_name))
)
mask = None
if self.mask is not None:
mask = context.services.images.get_pil_image(self.mask.image_name)
mask = ImageOps.invert(mask.convert("L"))
# TODO: probably shouldn't invert mask here... should user be required to do it?
min_x = min(0, self.x)
@ -653,6 +644,195 @@ class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig):
)
class MaskEdgeInvocation(BaseInvocation, PILInvocationConfig):
"""Applies an edge mask to an image"""
# fmt: off
type: Literal["mask_edge"] = "mask_edge"
# Inputs
image: Optional[ImageField] = Field(default=None, description="The image to apply the mask to")
edge_size: int = Field(description="The size of the edge")
edge_blur: int = Field(description="The amount of blur on the edge")
low_threshold: int = Field(description="First threshold for the hysteresis procedure in Canny edge detection")
high_threshold: int = Field(description="Second threshold for the hysteresis procedure in Canny edge detection")
# fmt: on
def invoke(self, context: InvocationContext) -> MaskOutput:
mask = context.services.images.get_pil_image(self.image.image_name)
npimg = numpy.asarray(mask, dtype=numpy.uint8)
npgradient = numpy.uint8(255 * (1.0 - numpy.floor(numpy.abs(0.5 - numpy.float32(npimg) / 255.0) * 2.0)))
npedge = cv2.Canny(npimg, threshold1=self.low_threshold, threshold2=self.high_threshold)
npmask = npgradient + npedge
npmask = cv2.dilate(npmask, numpy.ones((3, 3), numpy.uint8), iterations=int(self.edge_size / 2))
new_mask = Image.fromarray(npmask)
if self.edge_blur > 0:
new_mask = new_mask.filter(ImageFilter.BoxBlur(self.edge_blur))
new_mask = ImageOps.invert(new_mask)
image_dto = context.services.images.create(
image=new_mask,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.MASK,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
)
return MaskOutput(
mask=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
class MaskCombineInvocation(BaseInvocation, PILInvocationConfig):
"""Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
# fmt: off
type: Literal["mask_combine"] = "mask_combine"
# Inputs
mask1: ImageField = Field(default=None, description="The first mask to combine")
mask2: ImageField = Field(default=None, description="The second image to combine")
# fmt: on
class Config(InvocationConfig):
schema_extra = {
"ui": {"title": "Mask Combine", "tags": ["mask", "combine"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
mask1 = context.services.images.get_pil_image(self.mask1.image_name).convert("L")
mask2 = context.services.images.get_pil_image(self.mask2.image_name).convert("L")
combined_mask = ImageChops.multiply(mask1, mask2)
image_dto = context.services.images.create(
image=combined_mask,
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),
width=image_dto.width,
height=image_dto.height,
)
class ColorCorrectInvocation(BaseInvocation, PILInvocationConfig):
"""
Shifts the colors of a target image to match the reference image, optionally
using a mask to only color-correct certain regions of the target image.
"""
type: Literal["color_correct"] = "color_correct"
image: Optional[ImageField] = Field(default=None, description="The image to color-correct")
reference: Optional[ImageField] = Field(default=None, description="Reference image for color-correction")
mask: Optional[ImageField] = Field(default=None, description="Mask to use when applying color-correction")
mask_blur_radius: float = Field(default=8, description="Mask blur radius")
def invoke(self, context: InvocationContext) -> ImageOutput:
pil_init_mask = None
if self.mask is not None:
pil_init_mask = context.services.images.get_pil_image(self.mask.image_name).convert("L")
init_image = context.services.images.get_pil_image(self.reference.image_name)
result = context.services.images.get_pil_image(self.image.image_name).convert("RGBA")
# if init_image is None or init_mask is None:
# return result
# Get the original alpha channel of the mask if there is one.
# Otherwise it is some other black/white image format ('1', 'L' or 'RGB')
# pil_init_mask = (
# init_mask.getchannel("A")
# if init_mask.mode == "RGBA"
# else init_mask.convert("L")
# )
pil_init_image = init_image.convert("RGBA") # Add an alpha channel if one doesn't exist
# Build an image with only visible pixels from source to use as reference for color-matching.
init_rgb_pixels = numpy.asarray(init_image.convert("RGB"), dtype=numpy.uint8)
init_a_pixels = numpy.asarray(pil_init_image.getchannel("A"), dtype=numpy.uint8)
init_mask_pixels = numpy.asarray(pil_init_mask, dtype=numpy.uint8)
# Get numpy version of result
np_image = numpy.asarray(result.convert("RGB"), dtype=numpy.uint8)
# Mask and calculate mean and standard deviation
mask_pixels = init_a_pixels * init_mask_pixels > 0
np_init_rgb_pixels_masked = init_rgb_pixels[mask_pixels, :]
np_image_masked = np_image[mask_pixels, :]
if np_init_rgb_pixels_masked.size > 0:
init_means = np_init_rgb_pixels_masked.mean(axis=0)
init_std = np_init_rgb_pixels_masked.std(axis=0)
gen_means = np_image_masked.mean(axis=0)
gen_std = np_image_masked.std(axis=0)
# Color correct
np_matched_result = np_image.copy()
np_matched_result[:, :, :] = (
(
(
(np_matched_result[:, :, :].astype(numpy.float32) - gen_means[None, None, :])
/ gen_std[None, None, :]
)
* init_std[None, None, :]
+ init_means[None, None, :]
)
.clip(0, 255)
.astype(numpy.uint8)
)
matched_result = Image.fromarray(np_matched_result, mode="RGB")
else:
matched_result = Image.fromarray(np_image, mode="RGB")
# Blur the mask out (into init image) by specified amount
if self.mask_blur_radius > 0:
nm = numpy.asarray(pil_init_mask, dtype=numpy.uint8)
nmd = cv2.erode(
nm,
kernel=numpy.ones((3, 3), dtype=numpy.uint8),
iterations=int(self.mask_blur_radius / 2),
)
pmd = Image.fromarray(nmd, mode="L")
blurred_init_mask = pmd.filter(ImageFilter.BoxBlur(self.mask_blur_radius))
else:
blurred_init_mask = pil_init_mask
multiplied_blurred_init_mask = ImageChops.multiply(blurred_init_mask, result.split()[-1])
# Paste original on color-corrected generation (using blurred mask)
matched_result.paste(init_image, (0, 0), mask=multiplied_blurred_init_mask)
image_dto = context.services.images.create(
image=matched_result,
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),
width=image_dto.width,
height=image_dto.height,
)
class ImageHueAdjustmentInvocation(BaseInvocation):
"""Adjusts the Hue of an image."""

264
invokeai/app/invocations/latent.py
View File

@ -5,6 +5,7 @@ from typing import List, Literal, Optional, Union
import einops
import torch
import torchvision.transforms as T
from diffusers.image_processor import VaeImageProcessor
from diffusers.models.attention_processor import (
AttnProcessor2_0,
@ -12,20 +13,16 @@ from diffusers.models.attention_processor import (
LoRAXFormersAttnProcessor,
XFormersAttnProcessor,
)
from diffusers.schedulers import SchedulerMixin as Scheduler
from diffusers.schedulers import DPMSolverSDEScheduler, SchedulerMixin as Scheduler
from pydantic import BaseModel, Field, validator
from torchvision.transforms.functional import resize as tv_resize
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.util.controlnet_utils import prepare_control_image
from invokeai.app.util.step_callback import stable_diffusion_step_callback
from invokeai.backend.model_management.models import ModelType, SilenceWarnings
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
from .compel import ConditioningField
from .controlnet_image_processors import ControlField
from .image import ImageOutput
from .model import ModelInfo, UNetField, VaeField
from ..models.image import ImageCategory, ImageField, ResourceOrigin
from ...backend.model_management import ModelPatcher
from ...backend.model_management import BaseModelType, ModelPatcher
from ...backend.stable_diffusion import PipelineIntermediateState
from ...backend.stable_diffusion.diffusers_pipeline import (
ConditioningData,
@ -35,7 +32,13 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
)
from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
from ...backend.util.devices import choose_torch_device, torch_dtype, choose_precision
from ...backend.util.devices import choose_precision, choose_torch_device, torch_dtype
from ..models.image import ImageCategory, ImageField, ResourceOrigin
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
from .compel import ConditioningField
from .controlnet_image_processors import ControlField
from .image import ImageOutput
from .model import ModelInfo, UNetField, VaeField
DEFAULT_PRECISION = choose_precision(choose_torch_device())
@ -44,6 +47,7 @@ class LatentsField(BaseModel):
"""A latents field used for passing latents between invocations"""
latents_name: Optional[str] = Field(default=None, description="The name of the latents")
seed: Optional[int] = Field(description="Seed used to generate this latents")
class Config:
schema_extra = {"required": ["latents_name"]}
@ -62,9 +66,9 @@ class LatentsOutput(BaseInvocationOutput):
# fmt: on
def build_latents_output(latents_name: str, latents: torch.Tensor):
def build_latents_output(latents_name: str, latents: torch.Tensor, seed: Optional[int]):
return LatentsOutput(
latents=LatentsField(latents_name=latents_name),
latents=LatentsField(latents_name=latents_name, seed=seed),
width=latents.size()[3] * 8,
height=latents.size()[2] * 8,
)
@ -77,6 +81,7 @@ def get_scheduler(
context: InvocationContext,
scheduler_info: ModelInfo,
scheduler_name: str,
seed: int,
) -> Scheduler:
scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
orig_scheduler_info = context.services.model_manager.get_model(
@ -93,6 +98,11 @@ def get_scheduler(
**scheduler_extra_config,
"_backup": scheduler_config,
}
# make dpmpp_sde reproducable(seed can be passed only in initializer)
if scheduler_class is DPMSolverSDEScheduler:
scheduler_config["noise_sampler_seed"] = seed
scheduler = scheduler_class.from_config(scheduler_config)
# hack copied over from generate.py
@ -101,25 +111,31 @@ def get_scheduler(
return scheduler
# Text to image
class TextToLatentsInvocation(BaseInvocation):
"""Generates latents from conditionings."""
class DenoiseLatentsInvocation(BaseInvocation):
"""Denoises noisy latents to decodable images"""
type: Literal["t2l"] = "t2l"
type: Literal["denoise_latents"] = "denoise_latents"
# Inputs
# fmt: off
positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
noise: Optional[LatentsField] = Field(description="The noise to use")
steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
cfg_scale: Union[float, List[float]] = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
cfg_scale: Union[float, List[float]] = Field(
default=7.5,
ge=1,
description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt",
)
denoising_start: float = Field(default=0.0, ge=0, le=1, description="")
denoising_end: float = Field(default=1.0, ge=0, le=1, description="")
scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use")
unet: UNetField = Field(default=None, description="UNet submodel")
control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
# seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
# seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
# fmt: on
latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
mask: Optional[ImageField] = Field(
None,
description="Mask",
)
@validator("cfg_scale")
def ge_one(cls, v):
@ -137,12 +153,11 @@ class TextToLatentsInvocation(BaseInvocation):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Text To Latents",
"tags": ["latents"],
"title": "Denoise Latents",
"tags": ["denoise", "latents"],
"type_hints": {
"model": "model",
"control": "control",
# "cfg_scale": "float",
"cfg_scale": "number",
},
},
@ -154,12 +169,14 @@ class TextToLatentsInvocation(BaseInvocation):
context: InvocationContext,
source_node_id: str,
intermediate_state: PipelineIntermediateState,
base_model: BaseModelType,
) -> None:
stable_diffusion_step_callback(
context=context,
intermediate_state=intermediate_state,
node=self.dict(),
source_node_id=source_node_id,
base_model=base_model,
)
def get_conditioning_data(
@ -167,13 +184,14 @@ class TextToLatentsInvocation(BaseInvocation):
context: InvocationContext,
scheduler,
unet,
seed,
) -> ConditioningData:
positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
c = positive_cond_data.conditionings[0].embeds.to(device=unet.device, dtype=unet.dtype)
extra_conditioning_info = positive_cond_data.conditionings[0].extra_conditioning
c = positive_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
extra_conditioning_info = c.extra_conditioning
negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
uc = negative_cond_data.conditionings[0].embeds.to(device=unet.device, dtype=unet.dtype)
uc = negative_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
conditioning_data = ConditioningData(
unconditioned_embeddings=uc,
@ -193,7 +211,8 @@ class TextToLatentsInvocation(BaseInvocation):
# for ddim scheduler
eta=0.0, # ddim_eta
# for ancestral and sde schedulers
generator=torch.Generator(device=unet.device).manual_seed(0),
# flip all bits to have noise different from initial
generator=torch.Generator(device=unet.device).manual_seed(seed ^ 0xFFFFFFFF),
)
return conditioning_data
@ -304,110 +323,83 @@ class TextToLatentsInvocation(BaseInvocation):
# MultiControlNetModel has been refactored out, just need list[ControlNetData]
return control_data
@torch.no_grad()
def invoke(self, context: InvocationContext) -> LatentsOutput:
with SilenceWarnings():
noise = context.services.latents.get(self.noise.latents_name)
# original idea by https://github.com/AmericanPresidentJimmyCarter
# TODO: research more for second order schedulers timesteps
def init_scheduler(self, scheduler, device, steps, denoising_start, denoising_end):
num_inference_steps = steps
if scheduler.config.get("cpu_only", False):
scheduler.set_timesteps(num_inference_steps, device="cpu")
timesteps = scheduler.timesteps.to(device=device)
else:
scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = scheduler.timesteps
# Get the source node id (we are invoking the prepared node)
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
# apply denoising_start
t_start_val = int(round(scheduler.config.num_train_timesteps * (1 - denoising_start)))
t_start_idx = len(list(filter(lambda ts: ts >= t_start_val, timesteps)))
timesteps = timesteps[t_start_idx:]
if scheduler.order == 2 and t_start_idx > 0:
timesteps = timesteps[1:]
def step_callback(state: PipelineIntermediateState):
self.dispatch_progress(context, source_node_id, state)
# save start timestep to apply noise
init_timestep = timesteps[:1]
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}),
context=context,
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
# apply denoising_end
t_end_val = int(round(scheduler.config.num_train_timesteps * (1 - denoising_end)))
t_end_idx = len(list(filter(lambda ts: ts >= t_end_val, timesteps)))
if scheduler.order == 2 and t_end_idx > 0:
t_end_idx += 1
timesteps = timesteps[:t_end_idx]
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(),
context=context,
)
with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
unet_info.context.model, _lora_loader()
), unet_info as unet:
noise = noise.to(device=unet.device, dtype=unet.dtype)
# calculate step count based on scheduler order
num_inference_steps = len(timesteps)
if scheduler.order == 2:
num_inference_steps += num_inference_steps % 2
num_inference_steps = num_inference_steps // 2
scheduler = get_scheduler(
context=context,
scheduler_info=self.unet.scheduler,
scheduler_name=self.scheduler,
)
return num_inference_steps, timesteps, init_timestep
pipeline = self.create_pipeline(unet, scheduler)
conditioning_data = self.get_conditioning_data(context, scheduler, unet)
def prep_mask_tensor(self, mask, context, lantents):
if mask is None:
return None
control_data = self.prep_control_data(
model=pipeline,
context=context,
control_input=self.control,
latents_shape=noise.shape,
# do_classifier_free_guidance=(self.cfg_scale >= 1.0))
do_classifier_free_guidance=True,
exit_stack=exit_stack,
)
# TODO: Verify the noise is the right size
result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
latents=torch.zeros_like(noise, dtype=torch_dtype(unet.device)),
noise=noise,
num_inference_steps=self.steps,
conditioning_data=conditioning_data,
control_data=control_data, # list[ControlNetData]
callback=step_callback,
)
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
result_latents = result_latents.to("cpu")
torch.cuda.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, result_latents)
return build_latents_output(latents_name=name, latents=result_latents)
class LatentsToLatentsInvocation(TextToLatentsInvocation):
"""Generates latents using latents as base image."""
type: Literal["l2l"] = "l2l"
# Inputs
latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
strength: float = Field(default=0.7, ge=0, le=1, description="The strength of the latents to use")
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Latent To Latents",
"tags": ["latents"],
"type_hints": {
"model": "model",
"control": "control",
"cfg_scale": "number",
},
},
}
mask_image = context.services.images.get_pil_image(mask.image_name)
if mask_image.mode != "L":
# FIXME: why do we get passed an RGB image here? We can only use single-channel.
mask_image = mask_image.convert("L")
mask_tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
if mask_tensor.dim() == 3:
mask_tensor = mask_tensor.unsqueeze(0)
mask_tensor = tv_resize(mask_tensor, lantents.shape[-2:], T.InterpolationMode.BILINEAR)
return 1 - mask_tensor
@torch.no_grad()
def invoke(self, context: InvocationContext) -> LatentsOutput:
with SilenceWarnings(): # this quenches NSFW nag from diffusers
noise = context.services.latents.get(self.noise.latents_name)
latent = context.services.latents.get(self.latents.latents_name)
seed = None
noise = None
if self.noise is not None:
noise = context.services.latents.get(self.noise.latents_name)
seed = self.noise.seed
if self.latents is not None:
latents = context.services.latents.get(self.latents.latents_name)
if seed is None:
seed = self.latents.seed
else:
latents = torch.zeros_like(noise)
if seed is None:
seed = 0
mask = self.prep_mask_tensor(self.mask, context, latents)
# Get the source node id (we are invoking the prepared node)
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
def step_callback(state: PipelineIntermediateState):
self.dispatch_progress(context, source_node_id, state)
self.dispatch_progress(context, source_node_id, state, self.unet.unet.base_model)
def _lora_loader():
for lora in self.unet.loras:
@ -426,44 +418,48 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
unet_info.context.model, _lora_loader()
), unet_info as unet:
noise = noise.to(device=unet.device, dtype=unet.dtype)
latent = latent.to(device=unet.device, dtype=unet.dtype)
latents = latents.to(device=unet.device, dtype=unet.dtype)
if noise is not None:
noise = noise.to(device=unet.device, dtype=unet.dtype)
if mask is not None:
mask = mask.to(device=unet.device, dtype=unet.dtype)
scheduler = get_scheduler(
context=context,
scheduler_info=self.unet.scheduler,
scheduler_name=self.scheduler,
seed=seed,
)
pipeline = self.create_pipeline(unet, scheduler)
conditioning_data = self.get_conditioning_data(context, scheduler, unet)
conditioning_data = self.get_conditioning_data(context, scheduler, unet, seed)
control_data = self.prep_control_data(
model=pipeline,
context=context,
control_input=self.control,
latents_shape=noise.shape,
latents_shape=latents.shape,
# do_classifier_free_guidance=(self.cfg_scale >= 1.0))
do_classifier_free_guidance=True,
exit_stack=exit_stack,
)
# TODO: Verify the noise is the right size
initial_latents = (
latent if self.strength < 1.0 else torch.zeros_like(latent, device=unet.device, dtype=latent.dtype)
)
timesteps, _ = pipeline.get_img2img_timesteps(
self.steps,
self.strength,
num_inference_steps, timesteps, init_timestep = self.init_scheduler(
scheduler,
device=unet.device,
steps=self.steps,
denoising_start=self.denoising_start,
denoising_end=self.denoising_end,
)
result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
latents=initial_latents,
latents=latents,
timesteps=timesteps,
init_timestep=init_timestep,
noise=noise,
num_inference_steps=self.steps,
seed=seed,
mask=mask,
num_inference_steps=num_inference_steps,
conditioning_data=conditioning_data,
control_data=control_data, # list[ControlNetData]
callback=step_callback,
@ -475,7 +471,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, result_latents)
return build_latents_output(latents_name=name, latents=result_latents)
return build_latents_output(latents_name=name, latents=result_latents, seed=seed)
# Latent to image
@ -617,7 +613,7 @@ class ResizeLatentsInvocation(BaseInvocation):
name = f"{context.graph_execution_state_id}__{self.id}"
# context.services.latents.set(name, resized_latents)
context.services.latents.save(name, resized_latents)
return build_latents_output(latents_name=name, latents=resized_latents)
return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
class ScaleLatentsInvocation(BaseInvocation):
@ -659,7 +655,7 @@ class ScaleLatentsInvocation(BaseInvocation):
name = f"{context.graph_execution_state_id}__{self.id}"
# context.services.latents.set(name, resized_latents)
context.services.latents.save(name, resized_latents)
return build_latents_output(latents_name=name, latents=resized_latents)
return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
class ImageToLatentsInvocation(BaseInvocation):
@ -740,4 +736,4 @@ class ImageToLatentsInvocation(BaseInvocation):
name = f"{context.graph_execution_state_id}__{self.id}"
latents = latents.to("cpu")
context.services.latents.save(name, latents)
return build_latents_output(latents_name=name, latents=latents)
return build_latents_output(latents_name=name, latents=latents, seed=None)

10
invokeai/app/invocations/metadata.py
View File

@ -67,7 +67,10 @@ class CoreMetadata(BaseModelExcludeNull):
)
refiner_steps: Union[int, None] = Field(default=None, description="The number of steps used for the refiner")
refiner_scheduler: Union[str, None] = Field(default=None, description="The scheduler used for the refiner")
refiner_aesthetic_store: Union[float, None] = Field(
refiner_positive_aesthetic_store: Union[float, None] = Field(
default=None, description="The aesthetic score used for the refiner"
)
refiner_negative_aesthetic_store: Union[float, None] = Field(
default=None, description="The aesthetic score used for the refiner"
)
refiner_start: Union[float, None] = Field(default=None, description="The start value used for refiner denoising")
@ -136,7 +139,10 @@ class MetadataAccumulatorInvocation(BaseInvocation):
)
refiner_steps: Union[int, None] = Field(default=None, description="The number of steps used for the refiner")
refiner_scheduler: Union[str, None] = Field(default=None, description="The scheduler used for the refiner")
refiner_aesthetic_store: Union[float, None] = Field(
refiner_positive_aesthetic_score: Union[float, None] = Field(
default=None, description="The aesthetic score used for the refiner"
)
refiner_negative_aesthetic_score: Union[float, None] = Field(
default=None, description="The aesthetic score used for the refiner"
)
refiner_start: Union[float, None] = Field(default=None, description="The start value used for refiner denoising")

6
invokeai/app/invocations/noise.py
View File

@ -71,9 +71,9 @@ class NoiseOutput(BaseInvocationOutput):
# fmt: on
def build_noise_output(latents_name: str, latents: torch.Tensor):
def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
return NoiseOutput(
noise=LatentsField(latents_name=latents_name),
noise=LatentsField(latents_name=latents_name, seed=seed),
width=latents.size()[3] * 8,
height=latents.size()[2] * 8,
)
@ -132,4 +132,4 @@ class NoiseInvocation(BaseInvocation):
)
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, noise)
return build_noise_output(latents_name=name, latents=noise)
return build_noise_output(latents_name=name, latents=noise, seed=self.seed)

1
invokeai/app/invocations/onnx.py
View File

@ -212,6 +212,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
context=context,
scheduler_info=self.unet.scheduler,
scheduler_name=self.scheduler,
seed=0, # TODO: refactor this node
)
def torch2numpy(latent: torch.Tensor):

536
invokeai/app/invocations/sdxl.py
View File

@ -1,17 +1,10 @@
import torch
import inspect
from tqdm import tqdm
from typing import List, Literal, Optional, Union
from typing import Literal
from pydantic import Field
from pydantic import Field, validator
from ...backend.model_management import ModelType, SubModelType, ModelPatcher
from invokeai.app.util.step_callback import stable_diffusion_xl_step_callback
from ...backend.model_management import ModelType, SubModelType
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
from .model import UNetField, ClipField, VaeField, MainModelField, ModelInfo
from .compel import ConditioningField
from .latent import LatentsField, SAMPLER_NAME_VALUES, LatentsOutput, get_scheduler, build_latents_output
class SDXLModelLoaderOutput(BaseInvocationOutput):
@ -201,526 +194,3 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
),
),
)
# Text to image
class SDXLTextToLatentsInvocation(BaseInvocation):
"""Generates latents from conditionings."""
type: Literal["t2l_sdxl"] = "t2l_sdxl"
# Inputs
# fmt: off
positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
noise: Optional[LatentsField] = Field(description="The noise to use")
steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
cfg_scale: Union[float, List[float]] = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
unet: UNetField = Field(default=None, description="UNet submodel")
denoising_end: float = Field(default=1.0, gt=0, le=1, description="")
# control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
# seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
# seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
# fmt: on
@validator("cfg_scale")
def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1"""
if isinstance(v, list):
for i in v:
if i < 1:
raise ValueError("cfg_scale must be greater than 1")
else:
if v < 1:
raise ValueError("cfg_scale must be greater than 1")
return v
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "SDXL Text To Latents",
"tags": ["latents"],
"type_hints": {
"model": "model",
# "cfg_scale": "float",
"cfg_scale": "number",
},
},
}
def dispatch_progress(
self,
context: InvocationContext,
source_node_id: str,
sample,
step,
total_steps,
) -> None:
stable_diffusion_xl_step_callback(
context=context,
node=self.dict(),
source_node_id=source_node_id,
sample=sample,
step=step,
total_steps=total_steps,
)
# based on
# https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
@torch.no_grad()
def invoke(self, context: InvocationContext) -> LatentsOutput:
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
latents = context.services.latents.get(self.noise.latents_name)
positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
prompt_embeds = positive_cond_data.conditionings[0].embeds
pooled_prompt_embeds = positive_cond_data.conditionings[0].pooled_embeds
add_time_ids = positive_cond_data.conditionings[0].add_time_ids
negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
negative_prompt_embeds = negative_cond_data.conditionings[0].embeds
negative_pooled_prompt_embeds = negative_cond_data.conditionings[0].pooled_embeds
add_neg_time_ids = negative_cond_data.conditionings[0].add_time_ids
scheduler = get_scheduler(
context=context,
scheduler_info=self.unet.scheduler,
scheduler_name=self.scheduler,
)
num_inference_steps = self.steps
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}),
context=context,
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model(**self.unet.unet.dict(), context=context)
do_classifier_free_guidance = True
cross_attention_kwargs = None
with ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()), unet_info as unet:
scheduler.set_timesteps(num_inference_steps, device=unet.device)
timesteps = scheduler.timesteps
latents = latents.to(device=unet.device, dtype=unet.dtype) * scheduler.init_noise_sigma
extra_step_kwargs = dict()
if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
extra_step_kwargs.update(
eta=0.0,
)
if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
extra_step_kwargs.update(
generator=torch.Generator(device=unet.device).manual_seed(0),
)
num_warmup_steps = len(timesteps) - self.steps * scheduler.order
# apply denoising_end
skipped_final_steps = int(round((1 - self.denoising_end) * self.steps))
num_inference_steps = num_inference_steps - skipped_final_steps
timesteps = timesteps[: num_warmup_steps + scheduler.order * num_inference_steps]
if not context.services.configuration.sequential_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)
prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype)
add_text_embeds = add_text_embeds.to(device=unet.device, dtype=unet.dtype)
add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype)
latents = latents.to(device=unet.device, dtype=unet.dtype)
with tqdm(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
noise_pred = unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
# del noise_pred_uncond
# del noise_pred_text
# if do_classifier_free_guidance and guidance_rescale > 0.0:
# # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
# noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
# compute the previous noisy sample x_t -> x_t-1
latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
progress_bar.update()
self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
# if callback is not None and i % callback_steps == 0:
# callback(i, t, latents)
else:
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
negative_prompt_embeds = negative_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
add_neg_time_ids = add_neg_time_ids.to(device=unet.device, dtype=unet.dtype)
pooled_prompt_embeds = pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype)
add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype)
latents = latents.to(device=unet.device, dtype=unet.dtype)
with tqdm(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
# latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = scheduler.scale_model_input(latents, t)
# import gc
# gc.collect()
# torch.cuda.empty_cache()
# predict the noise residual
added_cond_kwargs = {"text_embeds": negative_pooled_prompt_embeds, "time_ids": add_neg_time_ids}
noise_pred_uncond = unet(
latent_model_input,
t,
encoder_hidden_states=negative_prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
added_cond_kwargs = {"text_embeds": pooled_prompt_embeds, "time_ids": add_time_ids}
noise_pred_text = unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
# perform guidance
noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
# del noise_pred_text
# del noise_pred_uncond
# import gc
# gc.collect()
# torch.cuda.empty_cache()
# if do_classifier_free_guidance and guidance_rescale > 0.0:
# # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
# noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
# compute the previous noisy sample x_t -> x_t-1
latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# del noise_pred
# import gc
# gc.collect()
# torch.cuda.empty_cache()
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
progress_bar.update()
self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
# if callback is not None and i % callback_steps == 0:
# callback(i, t, latents)
#################
latents = latents.to("cpu")
torch.cuda.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, latents)
return build_latents_output(latents_name=name, latents=latents)
class SDXLLatentsToLatentsInvocation(BaseInvocation):
"""Generates latents from conditionings."""
type: Literal["l2l_sdxl"] = "l2l_sdxl"
# Inputs
# fmt: off
positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
noise: Optional[LatentsField] = Field(description="The noise to use")
steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
cfg_scale: Union[float, List[float]] = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
unet: UNetField = Field(default=None, description="UNet submodel")
latents: Optional[LatentsField] = Field(description="Initial latents")
denoising_start: float = Field(default=0.0, ge=0, le=1, description="")
denoising_end: float = Field(default=1.0, ge=0, le=1, description="")
# control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
# seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
# seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
# fmt: on
@validator("cfg_scale")
def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1"""
if isinstance(v, list):
for i in v:
if i < 1:
raise ValueError("cfg_scale must be greater than 1")
else:
if v < 1:
raise ValueError("cfg_scale must be greater than 1")
return v
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "SDXL Latents to Latents",
"tags": ["latents"],
"type_hints": {
"model": "model",
# "cfg_scale": "float",
"cfg_scale": "number",
},
},
}
def dispatch_progress(
self,
context: InvocationContext,
source_node_id: str,
sample,
step,
total_steps,
) -> None:
stable_diffusion_xl_step_callback(
context=context,
node=self.dict(),
source_node_id=source_node_id,
sample=sample,
step=step,
total_steps=total_steps,
)
# based on
# https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
@torch.no_grad()
def invoke(self, context: InvocationContext) -> LatentsOutput:
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
latents = context.services.latents.get(self.latents.latents_name)
positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
prompt_embeds = positive_cond_data.conditionings[0].embeds
pooled_prompt_embeds = positive_cond_data.conditionings[0].pooled_embeds
add_time_ids = positive_cond_data.conditionings[0].add_time_ids
negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
negative_prompt_embeds = negative_cond_data.conditionings[0].embeds
negative_pooled_prompt_embeds = negative_cond_data.conditionings[0].pooled_embeds
add_neg_time_ids = negative_cond_data.conditionings[0].add_time_ids
scheduler = get_scheduler(
context=context,
scheduler_info=self.unet.scheduler,
scheduler_name=self.scheduler,
)
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(),
context=context,
)
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}),
context=context,
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
do_classifier_free_guidance = True
cross_attention_kwargs = None
with ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()), unet_info as unet:
# apply denoising_start
num_inference_steps = self.steps
scheduler.set_timesteps(num_inference_steps, device=unet.device)
t_start = int(round(self.denoising_start * num_inference_steps))
timesteps = scheduler.timesteps[t_start * scheduler.order :]
num_inference_steps = num_inference_steps - t_start
# apply noise(if provided)
if self.noise is not None and timesteps.shape[0] > 0:
noise = context.services.latents.get(self.noise.latents_name)
latents = scheduler.add_noise(latents, noise, timesteps[:1])
del noise
# apply scheduler extra args
extra_step_kwargs = dict()
if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
extra_step_kwargs.update(
eta=0.0,
)
if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
extra_step_kwargs.update(
generator=torch.Generator(device=unet.device).manual_seed(0),
)
num_warmup_steps = max(len(timesteps) - num_inference_steps * scheduler.order, 0)
# apply denoising_end
skipped_final_steps = int(round((1 - self.denoising_end) * self.steps))
num_inference_steps = num_inference_steps - skipped_final_steps
timesteps = timesteps[: num_warmup_steps + scheduler.order * num_inference_steps]
if not context.services.configuration.sequential_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)
prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype)
add_text_embeds = add_text_embeds.to(device=unet.device, dtype=unet.dtype)
add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype)
latents = latents.to(device=unet.device, dtype=unet.dtype)
with tqdm(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
noise_pred = unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
# del noise_pred_uncond
# del noise_pred_text
# if do_classifier_free_guidance and guidance_rescale > 0.0:
# # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
# noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
# compute the previous noisy sample x_t -> x_t-1
latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
progress_bar.update()
self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
# if callback is not None and i % callback_steps == 0:
# callback(i, t, latents)
else:
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
negative_prompt_embeds = negative_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
add_neg_time_ids = add_neg_time_ids.to(device=unet.device, dtype=unet.dtype)
pooled_prompt_embeds = pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype)
add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype)
latents = latents.to(device=unet.device, dtype=unet.dtype)
with tqdm(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
# latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = scheduler.scale_model_input(latents, t)
# import gc
# gc.collect()
# torch.cuda.empty_cache()
# predict the noise residual
added_cond_kwargs = {"text_embeds": negative_pooled_prompt_embeds, "time_ids": add_time_ids}
noise_pred_uncond = unet(
latent_model_input,
t,
encoder_hidden_states=negative_prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
added_cond_kwargs = {"text_embeds": pooled_prompt_embeds, "time_ids": add_time_ids}
noise_pred_text = unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
# perform guidance
noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
# del noise_pred_text
# del noise_pred_uncond
# import gc
# gc.collect()
# torch.cuda.empty_cache()
# if do_classifier_free_guidance and guidance_rescale > 0.0:
# # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
# noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
# compute the previous noisy sample x_t -> x_t-1
latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# del noise_pred
# import gc
# gc.collect()
# torch.cuda.empty_cache()
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
progress_bar.update()
self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
# if callback is not None and i % callback_steps == 0:
# callback(i, t, latents)
#################
latents = latents.to("cpu")
torch.cuda.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, latents)
return build_latents_output(latents_name=name, latents=latents)

4
invokeai/app/services/default_graphs.py
View File

@ -1,4 +1,4 @@
from ..invocations.latent import LatentsToImageInvocation, TextToLatentsInvocation
from ..invocations.latent import LatentsToImageInvocation, DenoiseLatentsInvocation
from ..invocations.image import ImageNSFWBlurInvocation
from ..invocations.noise import NoiseInvocation
from ..invocations.compel import CompelInvocation
@ -23,7 +23,7 @@ def create_text_to_image() -> LibraryGraph:
"3": NoiseInvocation(id="3"),
"4": CompelInvocation(id="4"),
"5": CompelInvocation(id="5"),
"6": TextToLatentsInvocation(id="6"),
"6": DenoiseLatentsInvocation(id="6"),
"7": LatentsToImageInvocation(id="7"),
"8": ImageNSFWBlurInvocation(id="8"),
},

2
invokeai/app/services/events.py
View File

@ -35,6 +35,7 @@ class EventServiceBase:
source_node_id: str,
progress_image: Optional[ProgressImage],
step: int,
order: int,
total_steps: int,
) -> None:
"""Emitted when there is generation progress"""
@ -46,6 +47,7 @@ class EventServiceBase:
source_node_id=source_node_id,
progress_image=progress_image.dict() if progress_image is not None else None,
step=step,
order=order,
total_steps=total_steps,
),
)

117
invokeai/app/util/step_callback.py
View File

@ -4,9 +4,9 @@ from invokeai.app.models.exceptions import CanceledException
from invokeai.app.models.image import ProgressImage
from ..invocations.baseinvocation import InvocationContext
from ...backend.util.util import image_to_dataURL
from ...backend.generator.base import Generator
from ...backend.stable_diffusion import PipelineIntermediateState
from invokeai.app.services.config import InvokeAIAppConfig
from ...backend.model_management.models import BaseModelType
def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix=None):
@ -29,6 +29,7 @@ def stable_diffusion_step_callback(
intermediate_state: PipelineIntermediateState,
node: dict,
source_node_id: str,
base_model: BaseModelType,
):
if context.services.queue.is_canceled(context.graph_execution_state_id):
raise CanceledException
@ -56,23 +57,50 @@ def stable_diffusion_step_callback(
# TODO: only output a preview image when requested
# origingally adapted from code by @erucipe and @keturn here:
# https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
if base_model in [BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner]:
# fast latents preview matrix for sdxl
# generated by @StAlKeR7779
sdxl_latent_rgb_factors = torch.tensor(
[
# R G B
[0.3816, 0.4930, 0.5320],
[-0.3753, 0.1631, 0.1739],
[0.1770, 0.3588, -0.2048],
[-0.4350, -0.2644, -0.4289],
],
dtype=sample.dtype,
device=sample.device,
)
# these updated numbers for v1.5 are from @torridgristle
v1_5_latent_rgb_factors = torch.tensor(
[
# R G B
[0.3444, 0.1385, 0.0670], # L1
[0.1247, 0.4027, 0.1494], # L2
[-0.3192, 0.2513, 0.2103], # L3
[-0.1307, -0.1874, -0.7445], # L4
],
dtype=sample.dtype,
device=sample.device,
)
sdxl_smooth_matrix = torch.tensor(
[
[0.0358, 0.0964, 0.0358],
[0.0964, 0.4711, 0.0964],
[0.0358, 0.0964, 0.0358],
],
dtype=sample.dtype,
device=sample.device,
)
image = sample_to_lowres_estimated_image(sample, v1_5_latent_rgb_factors)
image = sample_to_lowres_estimated_image(sample, sdxl_latent_rgb_factors, sdxl_smooth_matrix)
else:
# origingally adapted from code by @erucipe and @keturn here:
# https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
# these updated numbers for v1.5 are from @torridgristle
v1_5_latent_rgb_factors = torch.tensor(
[
# R G B
[0.3444, 0.1385, 0.0670], # L1
[0.1247, 0.4027, 0.1494], # L2
[-0.3192, 0.2513, 0.2103], # L3
[-0.1307, -0.1874, -0.7445], # L4
],
dtype=sample.dtype,
device=sample.device,
)
image = sample_to_lowres_estimated_image(sample, v1_5_latent_rgb_factors)
(width, height) = image.size
width *= 8
@ -86,59 +114,6 @@ def stable_diffusion_step_callback(
source_node_id=source_node_id,
progress_image=ProgressImage(width=width, height=height, dataURL=dataURL),
step=intermediate_state.step,
total_steps=node["steps"],
)
def stable_diffusion_xl_step_callback(
context: InvocationContext,
node: dict,
source_node_id: str,
sample,
step,
total_steps,
):
if context.services.queue.is_canceled(context.graph_execution_state_id):
raise CanceledException
sdxl_latent_rgb_factors = torch.tensor(
[
# R G B
[0.3816, 0.4930, 0.5320],
[-0.3753, 0.1631, 0.1739],
[0.1770, 0.3588, -0.2048],
[-0.4350, -0.2644, -0.4289],
],
dtype=sample.dtype,
device=sample.device,
)
sdxl_smooth_matrix = torch.tensor(
[
# [ 0.0478, 0.1285, 0.0478],
# [ 0.1285, 0.2948, 0.1285],
# [ 0.0478, 0.1285, 0.0478],
[0.0358, 0.0964, 0.0358],
[0.0964, 0.4711, 0.0964],
[0.0358, 0.0964, 0.0358],
],
dtype=sample.dtype,
device=sample.device,
)
image = sample_to_lowres_estimated_image(sample, sdxl_latent_rgb_factors, sdxl_smooth_matrix)
(width, height) = image.size
width *= 8
height *= 8
dataURL = image_to_dataURL(image, image_format="JPEG")
context.services.events.emit_generator_progress(
graph_execution_state_id=context.graph_execution_state_id,
node=node,
source_node_id=source_node_id,
progress_image=ProgressImage(width=width, height=height, dataURL=dataURL),
step=step,
total_steps=total_steps,
order=intermediate_state.order,
total_steps=intermediate_state.total_steps,
)

1
invokeai/backend/__init__.py
View File

@ -1,6 +1,5 @@
"""
Initialization file for invokeai.backend
"""
from .generator import InvokeAIGeneratorBasicParams, InvokeAIGenerator, InvokeAIGeneratorOutput, Img2Img, Inpaint
from .model_management import ModelManager, ModelCache, BaseModelType, ModelType, SubModelType, ModelInfo
from .model_management.models import SilenceWarnings

12
invokeai/backend/generator/__init__.py
View File

@ -1,12 +0,0 @@
"""
Initialization file for the invokeai.generator package
"""
from .base import (
InvokeAIGenerator,
InvokeAIGeneratorBasicParams,
InvokeAIGeneratorOutput,
Img2Img,
Inpaint,
Generator,
)
from .inpaint import infill_methods

559
invokeai/backend/generator/base.py
View File

@ -1,559 +0,0 @@
"""
Base class for invokeai.backend.generator.*
including img2img, txt2img, and inpaint
"""
from __future__ import annotations
import itertools
import dataclasses
import diffusers
import os
import random
import traceback
from abc import ABCMeta
from argparse import Namespace
from contextlib import nullcontext
import cv2
import numpy as np
import torch
from PIL import Image, ImageChops, ImageFilter
from accelerate.utils import set_seed
from diffusers import DiffusionPipeline
from tqdm import trange
from typing import Callable, List, Iterator, Optional, Type, Union
from dataclasses import dataclass, field
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 ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
from ..stable_diffusion.schedulers import SCHEDULER_MAP
downsampling = 8
@dataclass
class InvokeAIGeneratorBasicParams:
seed: Optional[int] = None
width: int = 512
height: int = 512
cfg_scale: float = 7.5
steps: int = 20
ddim_eta: float = 0.0
scheduler: str = "ddim"
precision: str = "float16"
perlin: float = 0.0
threshold: float = 0.0
seamless: bool = False
seamless_axes: List[str] = field(default_factory=lambda: ["x", "y"])
h_symmetry_time_pct: Optional[float] = None
v_symmetry_time_pct: Optional[float] = None
variation_amount: float = 0.0
with_variations: list = field(default_factory=list)
@dataclass
class InvokeAIGeneratorOutput:
"""
InvokeAIGeneratorOutput is a dataclass that contains the outputs of a generation
operation, including the image, its seed, the model name used to generate the image
and the model hash, as well as all the generate() parameters that went into
generating the image (in .params, also available as attributes)
"""
image: Image.Image
seed: int
model_hash: str
attention_maps_images: List[Image.Image]
params: Namespace
# we are interposing a wrapper around the original Generator classes so that
# old code that calls Generate will continue to work.
class InvokeAIGenerator(metaclass=ABCMeta):
def __init__(
self,
model_info: dict,
params: InvokeAIGeneratorBasicParams = InvokeAIGeneratorBasicParams(),
**kwargs,
):
self.model_info = model_info
self.params = params
self.kwargs = kwargs
def generate(
self,
conditioning: tuple,
scheduler,
callback: Optional[Callable] = None,
step_callback: Optional[Callable] = None,
iterations: int = 1,
**keyword_args,
) -> Iterator[InvokeAIGeneratorOutput]:
"""
Return an iterator across the indicated number of generations.
Each time the iterator is called it will return an InvokeAIGeneratorOutput
object. Use like this:
outputs = txt2img.generate(prompt='banana sushi', iterations=5)
for result in outputs:
print(result.image, result.seed)
In the typical case of wanting to get just a single image, iterations
defaults to 1 and do:
output = next(txt2img.generate(prompt='banana sushi')
Pass None to get an infinite iterator.
outputs = txt2img.generate(prompt='banana sushi', iterations=None)
for o in outputs:
print(o.image, o.seed)
"""
generator_args = dataclasses.asdict(self.params)
generator_args.update(keyword_args)
model_info = self.model_info
model_name = model_info.name
model_hash = model_info.hash
with model_info.context as model:
gen_class = self._generator_class()
generator = gen_class(model, self.params.precision, **self.kwargs)
if self.params.variation_amount > 0:
generator.set_variation(
generator_args.get("seed"),
generator_args.get("variation_amount"),
generator_args.get("with_variations"),
)
if isinstance(model, DiffusionPipeline):
for component in [model.unet, model.vae]:
configure_model_padding(
component, generator_args.get("seamless", False), generator_args.get("seamless_axes")
)
else:
configure_model_padding(
model, generator_args.get("seamless", False), generator_args.get("seamless_axes")
)
iteration_count = range(iterations) if iterations else itertools.count(start=0, step=1)
for i in iteration_count:
results = generator.generate(
conditioning=conditioning,
step_callback=step_callback,
sampler=scheduler,
**generator_args,
)
output = InvokeAIGeneratorOutput(
image=results[0][0],
seed=results[0][1],
attention_maps_images=results[0][2],
model_hash=model_hash,
params=Namespace(model_name=model_name, **generator_args),
)
if callback:
callback(output)
yield output
@classmethod
def schedulers(self) -> List[str]:
"""
Return list of all the schedulers that we currently handle.
"""
return list(SCHEDULER_MAP.keys())
def load_generator(self, model: StableDiffusionGeneratorPipeline, generator_class: Type[Generator]):
return generator_class(model, self.params.precision)
@classmethod
def _generator_class(cls) -> Type[Generator]:
"""
In derived classes return the name of the generator to apply.
If you don't override will return the name of the derived
class, which nicely parallels the generator class names.
"""
return Generator
# ------------------------------------
class Img2Img(InvokeAIGenerator):
def generate(
self, init_image: Union[Image.Image, torch.FloatTensor], strength: float = 0.75, **keyword_args
) -> Iterator[InvokeAIGeneratorOutput]:
return super().generate(init_image=init_image, strength=strength, **keyword_args)
@classmethod
def _generator_class(cls):
from .img2img import Img2Img
return Img2Img
# ------------------------------------
# Takes all the arguments of Img2Img and adds the mask image and the seam/infill stuff
class Inpaint(Img2Img):
def generate(
self,
mask_image: Union[Image.Image, torch.FloatTensor],
# Seam settings - when 0, doesn't fill seam
seam_size: int = 96,
seam_blur: int = 16,
seam_strength: float = 0.7,
seam_steps: int = 30,
tile_size: int = 32,
inpaint_replace=False,
infill_method=None,
inpaint_width=None,
inpaint_height=None,
inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF),
**keyword_args,
) -> Iterator[InvokeAIGeneratorOutput]:
return super().generate(
mask_image=mask_image,
seam_size=seam_size,
seam_blur=seam_blur,
seam_strength=seam_strength,
seam_steps=seam_steps,
tile_size=tile_size,
inpaint_replace=inpaint_replace,
infill_method=infill_method,
inpaint_width=inpaint_width,
inpaint_height=inpaint_height,
inpaint_fill=inpaint_fill,
**keyword_args,
)
@classmethod
def _generator_class(cls):
from .inpaint import Inpaint
return Inpaint
class Generator:
downsampling_factor: int
latent_channels: int
precision: str
model: DiffusionPipeline
def __init__(self, model: DiffusionPipeline, precision: str, **kwargs):
self.model = model
self.precision = precision
self.seed = None
self.latent_channels = model.unet.config.in_channels
self.downsampling_factor = downsampling # BUG: should come from model or config
self.perlin = 0.0
self.threshold = 0
self.variation_amount = 0
self.with_variations = []
self.use_mps_noise = False
self.free_gpu_mem = None
# this is going to be overridden in img2img.py, txt2img.py and inpaint.py
def get_make_image(self, **kwargs):
"""
Returns a function returning an image derived from the prompt and the initial image
Return value depends on the seed at the time you call it
"""
raise NotImplementedError("image_iterator() must be implemented in a descendent class")
def set_variation(self, seed, variation_amount, with_variations):
self.seed = seed
self.variation_amount = variation_amount
self.with_variations = with_variations
def generate(
self,
width,
height,
sampler,
init_image=None,
iterations=1,
seed=None,
image_callback=None,
step_callback=None,
threshold=0.0,
perlin=0.0,
h_symmetry_time_pct=None,
v_symmetry_time_pct=None,
free_gpu_mem: bool = False,
**kwargs,
):
scope = nullcontext
self.free_gpu_mem = free_gpu_mem
attention_maps_images = []
attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image())
make_image = self.get_make_image(
sampler=sampler,
init_image=init_image,
width=width,
height=height,
step_callback=step_callback,
threshold=threshold,
perlin=perlin,
h_symmetry_time_pct=h_symmetry_time_pct,
v_symmetry_time_pct=v_symmetry_time_pct,
attention_maps_callback=attention_maps_callback,
**kwargs,
)
results = []
seed = seed if seed is not None and seed >= 0 else self.new_seed()
first_seed = seed
seed, initial_noise = self.generate_initial_noise(seed, width, height)
# There used to be an additional self.model.ema_scope() here, but it breaks
# the inpaint-1.5 model. Not sure what it did.... ?
with scope(self.model.device.type):
for n in trange(iterations, desc="Generating"):
x_T = None
if self.variation_amount > 0:
set_seed(seed)
target_noise = self.get_noise(width, height)
x_T = self.slerp(self.variation_amount, initial_noise, target_noise)
elif initial_noise is not None:
# i.e. we specified particular variations
x_T = initial_noise
else:
set_seed(seed)
try:
x_T = self.get_noise(width, height)
except:
logger.error("An error occurred while getting initial noise")
print(traceback.format_exc())
# Pass on the seed in case a layer beneath us needs to generate noise on its own.
image = make_image(x_T, seed)
results.append([image, seed, attention_maps_images])
if image_callback is not None:
attention_maps_image = None if len(attention_maps_images) == 0 else attention_maps_images[-1]
image_callback(
image,
seed,
first_seed=first_seed,
attention_maps_image=attention_maps_image,
)
seed = self.new_seed()
# Free up memory from the last generation.
clear_cuda_cache = kwargs["clear_cuda_cache"] if "clear_cuda_cache" in kwargs else None
if clear_cuda_cache is not None:
clear_cuda_cache()
return results
def sample_to_image(self, samples) -> Image.Image:
"""
Given samples returned from a sampler, converts
it into a PIL Image
"""
with torch.inference_mode():
image = self.model.decode_latents(samples)
return self.model.numpy_to_pil(image)[0]
def repaste_and_color_correct(
self,
result: Image.Image,
init_image: Image.Image,
init_mask: Image.Image,
mask_blur_radius: int = 8,
) -> Image.Image:
if init_image is None or init_mask is None:
return result
# Get the original alpha channel of the mask if there is one.
# Otherwise it is some other black/white image format ('1', 'L' or 'RGB')
pil_init_mask = init_mask.getchannel("A") if init_mask.mode == "RGBA" else init_mask.convert("L")
pil_init_image = init_image.convert("RGBA") # Add an alpha channel if one doesn't exist
# Build an image with only visible pixels from source to use as reference for color-matching.
init_rgb_pixels = np.asarray(init_image.convert("RGB"), dtype=np.uint8)
init_a_pixels = np.asarray(pil_init_image.getchannel("A"), dtype=np.uint8)
init_mask_pixels = np.asarray(pil_init_mask, dtype=np.uint8)
# Get numpy version of result
np_image = np.asarray(result, dtype=np.uint8)
# Mask and calculate mean and standard deviation
mask_pixels = init_a_pixels * init_mask_pixels > 0
np_init_rgb_pixels_masked = init_rgb_pixels[mask_pixels, :]
np_image_masked = np_image[mask_pixels, :]
if np_init_rgb_pixels_masked.size > 0:
init_means = np_init_rgb_pixels_masked.mean(axis=0)
init_std = np_init_rgb_pixels_masked.std(axis=0)
gen_means = np_image_masked.mean(axis=0)
gen_std = np_image_masked.std(axis=0)
# Color correct
np_matched_result = np_image.copy()
np_matched_result[:, :, :] = (
(
(
(np_matched_result[:, :, :].astype(np.float32) - gen_means[None, None, :])
/ gen_std[None, None, :]
)
* init_std[None, None, :]
+ init_means[None, None, :]
)
.clip(0, 255)
.astype(np.uint8)
)
matched_result = Image.fromarray(np_matched_result, mode="RGB")
else:
matched_result = Image.fromarray(np_image, mode="RGB")
# Blur the mask out (into init image) by specified amount
if mask_blur_radius > 0:
nm = np.asarray(pil_init_mask, dtype=np.uint8)
nmd = cv2.erode(
nm,
kernel=np.ones((3, 3), dtype=np.uint8),
iterations=int(mask_blur_radius / 2),
)
pmd = Image.fromarray(nmd, mode="L")
blurred_init_mask = pmd.filter(ImageFilter.BoxBlur(mask_blur_radius))
else:
blurred_init_mask = pil_init_mask
multiplied_blurred_init_mask = ImageChops.multiply(blurred_init_mask, self.pil_image.split()[-1])
# Paste original on color-corrected generation (using blurred mask)
matched_result.paste(init_image, (0, 0), mask=multiplied_blurred_init_mask)
return matched_result
@staticmethod
def sample_to_lowres_estimated_image(samples):
# origingally adapted from code by @erucipe and @keturn here:
# https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
# these updated numbers for v1.5 are from @torridgristle
v1_5_latent_rgb_factors = torch.tensor(
[
# R G B
[0.3444, 0.1385, 0.0670], # L1
[0.1247, 0.4027, 0.1494], # L2
[-0.3192, 0.2513, 0.2103], # L3
[-0.1307, -0.1874, -0.7445], # L4
],
dtype=samples.dtype,
device=samples.device,
)
latent_image = samples[0].permute(1, 2, 0) @ v1_5_latent_rgb_factors
latents_ubyte = (
((latent_image + 1) / 2).clamp(0, 1).mul(0xFF).byte() # change scale from -1..1 to 0..1 # to 0..255
).cpu()
return Image.fromarray(latents_ubyte.numpy())
def generate_initial_noise(self, seed, width, height):
initial_noise = None
if self.variation_amount > 0 or len(self.with_variations) > 0:
# use fixed initial noise plus random noise per iteration
set_seed(seed)
initial_noise = self.get_noise(width, height)
for v_seed, v_weight in self.with_variations:
seed = v_seed
set_seed(seed)
next_noise = self.get_noise(width, height)
initial_noise = self.slerp(v_weight, initial_noise, next_noise)
if self.variation_amount > 0:
random.seed() # reset RNG to an actually random state, so we can get a random seed for variations
seed = random.randrange(0, np.iinfo(np.uint32).max)
return (seed, initial_noise)
def get_perlin_noise(self, width, height):
fixdevice = "cpu" if (self.model.device.type == "mps") else self.model.device
# limit noise to only the diffusion image channels, not the mask channels
input_channels = min(self.latent_channels, 4)
# round up to the nearest block of 8
temp_width = int((width + 7) / 8) * 8
temp_height = int((height + 7) / 8) * 8
noise = torch.stack(
[
rand_perlin_2d((temp_height, temp_width), (8, 8), device=self.model.device).to(fixdevice)
for _ in range(input_channels)
],
dim=0,
).to(self.model.device)
return noise[0:4, 0:height, 0:width]
def new_seed(self):
self.seed = random.randrange(0, np.iinfo(np.uint32).max)
return self.seed
def slerp(self, t, v0, v1, DOT_THRESHOLD=0.9995):
"""
Spherical linear interpolation
Args:
t (float/np.ndarray): Float value between 0.0 and 1.0
v0 (np.ndarray): Starting vector
v1 (np.ndarray): Final vector
DOT_THRESHOLD (float): Threshold for considering the two vectors as
colineal. Not recommended to alter this.
Returns:
v2 (np.ndarray): Interpolation vector between v0 and v1
"""
inputs_are_torch = False
if not isinstance(v0, np.ndarray):
inputs_are_torch = True
v0 = v0.detach().cpu().numpy()
if not isinstance(v1, np.ndarray):
inputs_are_torch = True
v1 = v1.detach().cpu().numpy()
dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
if np.abs(dot) > DOT_THRESHOLD:
v2 = (1 - t) * v0 + t * v1
else:
theta_0 = np.arccos(dot)
sin_theta_0 = np.sin(theta_0)
theta_t = theta_0 * t
sin_theta_t = np.sin(theta_t)
s0 = np.sin(theta_0 - theta_t) / sin_theta_0
s1 = sin_theta_t / sin_theta_0
v2 = s0 * v0 + s1 * v1
if inputs_are_torch:
v2 = torch.from_numpy(v2).to(self.model.device)
return v2
# this is a handy routine for debugging use. Given a generated sample,
# convert it into a PNG image and store it at the indicated path
def save_sample(self, sample, filepath):
image = self.sample_to_image(sample)
dirname = os.path.dirname(filepath) or "."
if not os.path.exists(dirname):
logger.info(f"creating directory {dirname}")
os.makedirs(dirname, exist_ok=True)
image.save(filepath, "PNG")
def torch_dtype(self) -> torch.dtype:
return torch.float16 if self.precision == "float16" else torch.float32
# returns a tensor filled with random numbers from a normal distribution
def get_noise(self, width, height):
device = self.model.device
# limit noise to only the diffusion image channels, not the mask channels
input_channels = min(self.latent_channels, 4)
x = torch.randn(
[
1,
input_channels,
height // self.downsampling_factor,
width // self.downsampling_factor,
],
dtype=self.torch_dtype(),
device=device,
)
if self.perlin > 0.0:
perlin_noise = self.get_perlin_noise(width // self.downsampling_factor, height // self.downsampling_factor)
x = (1 - self.perlin) * x + self.perlin * perlin_noise
return x

31
invokeai/backend/generator/img2img.py
View File

@ -1,31 +0,0 @@
"""
invokeai.backend.generator.img2img descends from .generator
"""
from .base import Generator
class Img2Img(Generator):
def get_make_image(
self,
sampler,
steps,
cfg_scale,
ddim_eta,
conditioning,
init_image,
strength,
step_callback=None,
threshold=0.0,
warmup=0.2,
perlin=0.0,
h_symmetry_time_pct=None,
v_symmetry_time_pct=None,
attention_maps_callback=None,
**kwargs,
):
"""
Returns a function returning an image derived from the prompt and the initial image
Return value depends on the seed at the time you call it.
"""
raise NotImplementedError("replaced by invokeai.app.invocations.latent.LatentsToLatentsInvocation")

387
invokeai/backend/generator/inpaint.py
View File

@ -1,387 +0,0 @@
"""
invokeai.backend.generator.inpaint descends from .generator
"""
from __future__ import annotations
import math
from typing import Tuple, Union, Optional
import cv2
import numpy as np
import torch
from PIL import Image, ImageChops, ImageFilter, ImageOps
from ..image_util import PatchMatch, debug_image
from ..stable_diffusion.diffusers_pipeline import (
ConditioningData,
StableDiffusionGeneratorPipeline,
image_resized_to_grid_as_tensor,
)
from .img2img import Img2Img
def infill_methods() -> list[str]:
methods = [
"tile",
"solid",
]
if PatchMatch.patchmatch_available():
methods.insert(0, "patchmatch")
return methods
class Inpaint(Img2Img):
def __init__(self, model, precision):
self.inpaint_height = 0
self.inpaint_width = 0
self.enable_image_debugging = False
self.init_latent = None
self.pil_image = None
self.pil_mask = None
self.mask_blur_radius = 0
self.infill_method = None
super().__init__(model, precision)
# Outpaint support code
def get_tile_images(self, image: np.ndarray, width=8, height=8):
_nrows, _ncols, depth = image.shape
_strides = image.strides
nrows, _m = divmod(_nrows, height)
ncols, _n = divmod(_ncols, width)
if _m != 0 or _n != 0:
return None
return np.lib.stride_tricks.as_strided(
np.ravel(image),
shape=(nrows, ncols, height, width, depth),
strides=(height * _strides[0], width * _strides[1], *_strides),
writeable=False,
)
def infill_patchmatch(self, im: Image.Image) -> Image.Image:
if im.mode != "RGBA":
return im
# Skip patchmatch if patchmatch isn't available
if not PatchMatch.patchmatch_available():
return im
# Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though)
im_patched_np = PatchMatch.inpaint(im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3)
im_patched = Image.fromarray(im_patched_np, mode="RGB")
return im_patched
def tile_fill_missing(self, im: Image.Image, tile_size: int = 16, seed: Optional[int] = None) -> Image.Image:
# Only fill if there's an alpha layer
if im.mode != "RGBA":
return im
a = np.asarray(im, dtype=np.uint8)
tile_size_tuple = (tile_size, tile_size)
# Get the image as tiles of a specified size
tiles = self.get_tile_images(a, *tile_size_tuple).copy()
# Get the mask as tiles
tiles_mask = tiles[:, :, :, :, 3]
# Find any mask tiles with any fully transparent pixels (we will be replacing these later)
tmask_shape = tiles_mask.shape
tiles_mask = tiles_mask.reshape(math.prod(tiles_mask.shape))
n, ny = (math.prod(tmask_shape[0:2])), math.prod(tmask_shape[2:])
tiles_mask = tiles_mask > 0
tiles_mask = tiles_mask.reshape((n, ny)).all(axis=1)
# Get RGB tiles in single array and filter by the mask
tshape = tiles.shape
tiles_all = tiles.reshape((math.prod(tiles.shape[0:2]), *tiles.shape[2:]))
filtered_tiles = tiles_all[tiles_mask]
if len(filtered_tiles) == 0:
return im
# Find all invalid tiles and replace with a random valid tile
replace_count = (tiles_mask == False).sum()
rng = np.random.default_rng(seed=seed)
tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[
rng.choice(filtered_tiles.shape[0], replace_count), :, :, :
]
# Convert back to an image
tiles_all = tiles_all.reshape(tshape)
tiles_all = tiles_all.swapaxes(1, 2)
st = tiles_all.reshape(
(
math.prod(tiles_all.shape[0:2]),
math.prod(tiles_all.shape[2:4]),
tiles_all.shape[4],
)
)
si = Image.fromarray(st, mode="RGBA")
return si
def mask_edge(self, mask: Image.Image, edge_size: int, edge_blur: int) -> Image.Image:
npimg = np.asarray(mask, dtype=np.uint8)
# Detect any partially transparent regions
npgradient = np.uint8(255 * (1.0 - np.floor(np.abs(0.5 - np.float32(npimg) / 255.0) * 2.0)))
# Detect hard edges
npedge = cv2.Canny(npimg, threshold1=100, threshold2=200)
# Combine
npmask = npgradient + npedge
# Expand
npmask = cv2.dilate(npmask, np.ones((3, 3), np.uint8), iterations=int(edge_size / 2))
new_mask = Image.fromarray(npmask)
if edge_blur > 0:
new_mask = new_mask.filter(ImageFilter.BoxBlur(edge_blur))
return ImageOps.invert(new_mask)
def seam_paint(
self,
im: Image.Image,
seam_size: int,
seam_blur: int,
seed,
steps,
cfg_scale,
ddim_eta,
conditioning,
strength,
noise,
infill_method,
step_callback,
) -> Image.Image:
hard_mask = self.pil_image.split()[-1].copy()
mask = self.mask_edge(hard_mask, seam_size, seam_blur)
make_image = self.get_make_image(
steps,
cfg_scale,
ddim_eta,
conditioning,
init_image=im.copy().convert("RGBA"),
mask_image=mask,
strength=strength,
mask_blur_radius=0,
seam_size=0,
step_callback=step_callback,
inpaint_width=im.width,
inpaint_height=im.height,
infill_method=infill_method,
)
seam_noise = self.get_noise(im.width, im.height)
result = make_image(seam_noise, seed=None)
return result
@torch.no_grad()
def get_make_image(
self,
steps,
cfg_scale,
ddim_eta,
conditioning,
init_image: Union[Image.Image, torch.FloatTensor],
mask_image: Union[Image.Image, torch.FloatTensor],
strength: float,
mask_blur_radius: int = 8,
# Seam settings - when 0, doesn't fill seam
seam_size: int = 96,
seam_blur: int = 16,
seam_strength: float = 0.7,
seam_steps: int = 30,
tile_size: int = 32,
step_callback=None,
inpaint_replace=False,
enable_image_debugging=False,
infill_method=None,
inpaint_width=None,
inpaint_height=None,
inpaint_fill: Tuple[int, int, int, int] = (0x7F, 0x7F, 0x7F, 0xFF),
attention_maps_callback=None,
**kwargs,
):
"""
Returns a function returning an image derived from the prompt and
the initial image + mask. Return value depends on the seed at
the time you call it. kwargs are 'init_latent' and 'strength'
"""
self.enable_image_debugging = enable_image_debugging
infill_method = infill_method or infill_methods()[0]
self.infill_method = infill_method
self.inpaint_width = inpaint_width
self.inpaint_height = inpaint_height
if isinstance(init_image, Image.Image):
self.pil_image = init_image.copy()
# Do infill
if infill_method == "patchmatch" and PatchMatch.patchmatch_available():
init_filled = self.infill_patchmatch(self.pil_image.copy())
elif infill_method == "tile":
init_filled = self.tile_fill_missing(self.pil_image.copy(), seed=self.seed, tile_size=tile_size)
elif infill_method == "solid":
solid_bg = Image.new("RGBA", init_image.size, inpaint_fill)
init_filled = Image.alpha_composite(solid_bg, init_image)
else:
raise ValueError(f"Non-supported infill type {infill_method}", infill_method)
init_filled.paste(init_image, (0, 0), init_image.split()[-1])
# Resize if requested for inpainting
if inpaint_width and inpaint_height:
init_filled = init_filled.resize((inpaint_width, inpaint_height))
debug_image(init_filled, "init_filled", debug_status=self.enable_image_debugging)
# Create init tensor
init_image = image_resized_to_grid_as_tensor(init_filled.convert("RGB"))
if isinstance(mask_image, Image.Image):
self.pil_mask = mask_image.copy()
debug_image(
mask_image,
"mask_image BEFORE multiply with pil_image",
debug_status=self.enable_image_debugging,
)
init_alpha = self.pil_image.getchannel("A")
if mask_image.mode != "L":
# FIXME: why do we get passed an RGB image here? We can only use single-channel.
mask_image = mask_image.convert("L")
mask_image = ImageChops.multiply(mask_image, init_alpha)
self.pil_mask = mask_image
# Resize if requested for inpainting
if inpaint_width and inpaint_height:
mask_image = mask_image.resize((inpaint_width, inpaint_height))
debug_image(
mask_image,
"mask_image AFTER multiply with pil_image",
debug_status=self.enable_image_debugging,
)
mask: torch.FloatTensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
else:
mask: torch.FloatTensor = mask_image
self.mask_blur_radius = mask_blur_radius
# noinspection PyTypeChecker
pipeline: StableDiffusionGeneratorPipeline = self.model
# todo: support cross-attention control
uc, c, _ = conditioning
conditioning_data = ConditioningData(uc, c, cfg_scale).add_scheduler_args_if_applicable(
pipeline.scheduler, eta=ddim_eta
)
def make_image(x_T: torch.Tensor, seed: int):
pipeline_output = pipeline.inpaint_from_embeddings(
init_image=init_image,
mask=1 - mask, # expects white means "paint here."
strength=strength,
num_inference_steps=steps,
conditioning_data=conditioning_data,
noise_func=self.get_noise_like,
callback=step_callback,
seed=seed,
)
if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
attention_maps_callback(pipeline_output.attention_map_saver)
result = self.postprocess_size_and_mask(pipeline.numpy_to_pil(pipeline_output.images)[0])
# Seam paint if this is our first pass (seam_size set to 0 during seam painting)
if seam_size > 0:
old_image = self.pil_image or init_image
old_mask = self.pil_mask or mask_image
result = self.seam_paint(
result,
seam_size,
seam_blur,
seed,
seam_steps,
cfg_scale,
ddim_eta,
conditioning,
seam_strength,
x_T,
infill_method,
step_callback,
)
# Restore original settings
self.get_make_image(
steps,
cfg_scale,
ddim_eta,
conditioning,
old_image,
old_mask,
strength,
mask_blur_radius,
seam_size,
seam_blur,
seam_strength,
seam_steps,
tile_size,
step_callback,
inpaint_replace,
enable_image_debugging,
inpaint_width=inpaint_width,
inpaint_height=inpaint_height,
infill_method=infill_method,
**kwargs,
)
return result
return make_image
def sample_to_image(self, samples) -> Image.Image:
gen_result = super().sample_to_image(samples).convert("RGB")
return self.postprocess_size_and_mask(gen_result)
def postprocess_size_and_mask(self, gen_result: Image.Image) -> Image.Image:
debug_image(gen_result, "gen_result", debug_status=self.enable_image_debugging)
# Resize if necessary
if self.inpaint_width and self.inpaint_height:
gen_result = gen_result.resize(self.pil_image.size)
if self.pil_image is None or self.pil_mask is None:
return gen_result
corrected_result = self.repaste_and_color_correct(
gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius
)
debug_image(
corrected_result,
"corrected_result",
debug_status=self.enable_image_debugging,
)
return corrected_result
def get_noise_like(self, like: torch.Tensor):
device = like.device
x = torch.randn_like(like, device=device)
if self.perlin > 0.0:
shape = like.shape
x = (1 - self.perlin) * x + self.perlin * self.get_perlin_noise(shape[3], shape[2])
return x

2
invokeai/backend/stable_diffusion/__init__.py
View File

@ -8,4 +8,4 @@ from .diffusers_pipeline import (
)
from .diffusion import InvokeAIDiffuserComponent
from .diffusion.cross_attention_map_saving import AttentionMapSaver
from .diffusion.shared_invokeai_diffusion import PostprocessingSettings
from .diffusion.shared_invokeai_diffusion import PostprocessingSettings, BasicConditioningInfo, SDXLConditioningInfo

485
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@ -5,23 +5,19 @@ import inspect
import math
import secrets
from dataclasses import dataclass, field
from typing import Any, Callable, Generic, List, Optional, Type, TypeVar, Union
from typing import Any, Callable, Generic, List, Optional, Type, Union
import PIL.Image
import einops
import psutil
import torch
import torchvision.transforms as T
from accelerate.utils import set_seed
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.models.controlnet import ControlNetModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import (
StableDiffusionPipeline,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import (
StableDiffusionImg2ImgPipeline,
)
from diffusers.pipelines.stable_diffusion.safety_checker import (
StableDiffusionSafetyChecker,
)
@ -30,23 +26,23 @@ from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutpu
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.outputs import BaseOutput
from pydantic import Field
from torchvision.transforms.functional import resize as tv_resize
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from typing_extensions import ParamSpec
from invokeai.app.services.config import InvokeAIAppConfig
from .diffusion import (
AttentionMapSaver,
InvokeAIDiffuserComponent,
PostprocessingSettings,
BasicConditioningInfo,
)
from ..util import normalize_device
@dataclass
class PipelineIntermediateState:
run_id: str
step: int
order: int
total_steps: int
timestep: int
latents: torch.Tensor
predicted_original: Optional[torch.Tensor] = None
@ -97,7 +93,6 @@ class AddsMaskGuidance:
mask_latents: torch.FloatTensor
scheduler: SchedulerMixin
noise: torch.Tensor
_debug: Optional[Callable] = None
def __call__(self, step_output: Union[BaseOutput, SchedulerOutput], t: torch.Tensor, conditioning) -> BaseOutput:
output_class = step_output.__class__ # We'll create a new one with masked data.
@ -134,8 +129,6 @@ class AddsMaskGuidance:
# mask_latents = self.scheduler.scale_model_input(mask_latents, t)
mask_latents = einops.repeat(mask_latents, "b c h w -> (repeat b) c h w", repeat=batch_size)
masked_input = torch.lerp(mask_latents.to(dtype=latents.dtype), latents, mask.to(dtype=latents.dtype))
if self._debug:
self._debug(masked_input, f"t={t} lerped")
return masked_input
@ -167,33 +160,6 @@ def is_inpainting_model(unet: UNet2DConditionModel):
return unet.conv_in.in_channels == 9
CallbackType = TypeVar("CallbackType")
ReturnType = TypeVar("ReturnType")
ParamType = ParamSpec("ParamType")
@dataclass(frozen=True)
class GeneratorToCallbackinator(Generic[ParamType, ReturnType, CallbackType]):
"""Convert a generator to a function with a callback and a return value."""
generator_method: Callable[ParamType, ReturnType]
callback_arg_type: Type[CallbackType]
def __call__(
self,
*args: ParamType.args,
callback: Callable[[CallbackType], Any] = None,
**kwargs: ParamType.kwargs,
) -> ReturnType:
result = None
for result in self.generator_method(*args, **kwargs):
if callback is not None and isinstance(result, self.callback_arg_type):
callback(result)
if result is None:
raise AssertionError("why was that an empty generator?")
return result
@dataclass
class ControlNetData:
model: ControlNetModel = Field(default=None)
@ -207,8 +173,8 @@ class ControlNetData:
@dataclass
class ConditioningData:
unconditioned_embeddings: torch.Tensor
text_embeddings: torch.Tensor
unconditioned_embeddings: BasicConditioningInfo
text_embeddings: BasicConditioningInfo
guidance_scale: Union[float, List[float]]
"""
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
@ -284,7 +250,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
ID_LENGTH = 8
def __init__(
self,
@ -328,33 +293,41 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
if xformers is available, use it, otherwise use sliced attention.
"""
config = InvokeAIAppConfig.get_config()
if torch.cuda.is_available() and is_xformers_available() and not config.disable_xformers:
self.enable_xformers_memory_efficient_attention()
if self.unet.device.type == "cuda":
if is_xformers_available() and not config.disable_xformers:
self.enable_xformers_memory_efficient_attention()
return
elif hasattr(torch.nn.functional, "scaled_dot_product_attention"):
# diffusers enable sdp automatically
return
if self.unet.device.type == "cpu" or self.unet.device.type == "mps":
mem_free = psutil.virtual_memory().free
elif self.unet.device.type == "cuda":
mem_free, _ = torch.cuda.mem_get_info(normalize_device(self.unet.device))
else:
if self.device.type == "cpu" or self.device.type == "mps":
mem_free = psutil.virtual_memory().free
elif self.device.type == "cuda":
mem_free, _ = torch.cuda.mem_get_info(normalize_device(self.device))
else:
raise ValueError(f"unrecognized device {self.device}")
# input tensor of [1, 4, h/8, w/8]
# output tensor of [16, (h/8 * w/8), (h/8 * w/8)]
bytes_per_element_needed_for_baddbmm_duplication = latents.element_size() + 4
max_size_required_for_baddbmm = (
16
* latents.size(dim=2)
* latents.size(dim=3)
* latents.size(dim=2)
* latents.size(dim=3)
* bytes_per_element_needed_for_baddbmm_duplication
)
if max_size_required_for_baddbmm > (mem_free * 3.0 / 4.0): # 3.3 / 4.0 is from old Invoke code
self.enable_attention_slicing(slice_size="max")
elif torch.backends.mps.is_available():
# diffusers recommends always enabling for mps
self.enable_attention_slicing(slice_size="max")
else:
self.disable_attention_slicing()
raise ValueError(f"unrecognized device {self.unet.device}")
# input tensor of [1, 4, h/8, w/8]
# output tensor of [16, (h/8 * w/8), (h/8 * w/8)]
bytes_per_element_needed_for_baddbmm_duplication = latents.element_size() + 4
max_size_required_for_baddbmm = (
16
* latents.size(dim=2)
* latents.size(dim=3)
* latents.size(dim=2)
* latents.size(dim=3)
* bytes_per_element_needed_for_baddbmm_duplication
)
if max_size_required_for_baddbmm > (mem_free * 3.0 / 4.0): # 3.3 / 4.0 is from old Invoke code
self.enable_attention_slicing(slice_size="max")
elif torch.backends.mps.is_available():
# diffusers recommends always enabling for mps
self.enable_attention_slicing(slice_size="max")
else:
self.disable_attention_slicing()
def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings=False):
raise Exception("Should not be called")
def latents_from_embeddings(
self,
@ -362,35 +335,72 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
num_inference_steps: int,
conditioning_data: ConditioningData,
*,
noise: torch.Tensor,
timesteps=None,
noise: Optional[torch.Tensor],
timesteps: torch.Tensor,
init_timestep: torch.Tensor,
additional_guidance: List[Callable] = None,
run_id=None,
callback: Callable[[PipelineIntermediateState], None] = None,
control_data: List[ControlNetData] = None,
mask: Optional[torch.Tensor] = None,
seed: Optional[int] = None,
) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
if self.scheduler.config.get("cpu_only", False):
scheduler_device = torch.device("cpu")
else:
scheduler_device = self.unet.device
if init_timestep.shape[0] == 0:
return latents, None
if timesteps is None:
self.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
timesteps = self.scheduler.timesteps
infer_latents_from_embeddings = GeneratorToCallbackinator(
self.generate_latents_from_embeddings, PipelineIntermediateState
)
result: PipelineIntermediateState = infer_latents_from_embeddings(
latents,
timesteps,
conditioning_data,
noise=noise,
run_id=run_id,
additional_guidance=additional_guidance,
control_data=control_data,
callback=callback,
)
return result.latents, result.attention_map_saver
if additional_guidance is None:
additional_guidance = []
orig_latents = latents.clone()
batch_size = latents.shape[0]
batched_t = init_timestep.expand(batch_size)
if noise is not None:
# latents = noise * self.scheduler.init_noise_sigma # it's like in t2l according to diffusers
latents = self.scheduler.add_noise(latents, noise, batched_t)
if mask is not None:
if is_inpainting_model(self.unet):
# You'd think the inpainting model wouldn't be paying attention to the area it is going to repaint
# (that's why there's a mask!) but it seems to really want that blanked out.
# masked_latents = latents * torch.where(mask < 0.5, 1, 0) TODO: inpaint/outpaint/infill
# TODO: we should probably pass this in so we don't have to try/finally around setting it.
self.invokeai_diffuser.model_forward_callback = AddsMaskLatents(self._unet_forward, mask, orig_latents)
else:
# if no noise provided, noisify unmasked area based on seed(or 0 as fallback)
if noise is None:
noise = torch.randn(
orig_latents.shape,
dtype=torch.float32,
device="cpu",
generator=torch.Generator(device="cpu").manual_seed(seed or 0),
).to(device=orig_latents.device, dtype=orig_latents.dtype)
latents = self.scheduler.add_noise(latents, noise, batched_t)
latents = torch.lerp(
orig_latents, latents.to(dtype=orig_latents.dtype), mask.to(dtype=orig_latents.dtype)
)
additional_guidance.append(AddsMaskGuidance(mask, orig_latents, self.scheduler, noise))
try:
latents, attention_map_saver = self.generate_latents_from_embeddings(
latents,
timesteps,
conditioning_data,
additional_guidance=additional_guidance,
control_data=control_data,
callback=callback,
)
finally:
self.invokeai_diffuser.model_forward_callback = self._unet_forward
# restore unmasked part
if mask is not None:
latents = torch.lerp(orig_latents, latents.to(dtype=orig_latents.dtype), mask.to(dtype=orig_latents.dtype))
return latents, attention_map_saver
def generate_latents_from_embeddings(
self,
@ -398,42 +408,40 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
timesteps,
conditioning_data: ConditioningData,
*,
noise: torch.Tensor,
run_id: str = None,
additional_guidance: List[Callable] = None,
control_data: List[ControlNetData] = None,
callback: Callable[[PipelineIntermediateState], None] = None,
):
self._adjust_memory_efficient_attention(latents)
if run_id is None:
run_id = secrets.token_urlsafe(self.ID_LENGTH)
if additional_guidance is None:
additional_guidance = []
batch_size = latents.shape[0]
attention_map_saver: Optional[AttentionMapSaver] = None
if timesteps.shape[0] == 0:
return latents, attention_map_saver
extra_conditioning_info = conditioning_data.extra
with self.invokeai_diffuser.custom_attention_context(
self.invokeai_diffuser.model,
extra_conditioning_info=extra_conditioning_info,
step_count=len(self.scheduler.timesteps),
):
yield PipelineIntermediateState(
run_id=run_id,
step=-1,
timestep=self.scheduler.config.num_train_timesteps,
latents=latents,
)
if callback is not None:
callback(
PipelineIntermediateState(
step=-1,
order=self.scheduler.order,
total_steps=len(timesteps),
timestep=self.scheduler.config.num_train_timesteps,
latents=latents,
)
)
batch_size = latents.shape[0]
batched_t = torch.full(
(batch_size,),
timesteps[0],
dtype=timesteps.dtype,
device=self.unet.device,
)
latents = self.scheduler.add_noise(latents, noise, batched_t)
attention_map_saver: Optional[AttentionMapSaver] = None
# print("timesteps:", timesteps)
for i, t in enumerate(self.progress_bar(timesteps)):
batched_t.fill_(t)
batched_t = t.expand(batch_size)
step_output = self.step(
batched_t,
latents,
@ -462,14 +470,18 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
# attention_map_saver = AttentionMapSaver(token_ids=attention_map_token_ids, latents_shape=latents.shape[-2:])
# self.invokeai_diffuser.setup_attention_map_saving(attention_map_saver)
yield PipelineIntermediateState(
run_id=run_id,
step=i,
timestep=int(t),
latents=latents,
predicted_original=predicted_original,
attention_map_saver=attention_map_saver,
)
if callback is not None:
callback(
PipelineIntermediateState(
step=i,
order=self.scheduler.order,
total_steps=len(timesteps),
timestep=int(t),
latents=latents,
predicted_original=predicted_original,
attention_map_saver=attention_map_saver,
)
)
return latents, attention_map_saver
@ -491,95 +503,39 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
# TODO: should this scaling happen here or inside self._unet_forward?
# i.e. before or after passing it to InvokeAIDiffuserComponent
unet_latent_input = self.scheduler.scale_model_input(latents, timestep)
latent_model_input = self.scheduler.scale_model_input(latents, timestep)
# default is no controlnet, so set controlnet processing output to None
down_block_res_samples, mid_block_res_sample = None, None
controlnet_down_block_samples, controlnet_mid_block_sample = None, None
if control_data is not None:
# control_data should be type List[ControlNetData]
# this loop covers both ControlNet (one ControlNetData in list)
# and MultiControlNet (multiple ControlNetData in list)
for i, control_datum in enumerate(control_data):
control_mode = control_datum.control_mode
# soft_injection and cfg_injection are the two ControlNet control_mode booleans
# that are combined at higher level to make control_mode enum
# soft_injection determines whether to do per-layer re-weighting adjustment (if True)
# or default weighting (if False)
soft_injection = control_mode == "more_prompt" or control_mode == "more_control"
# cfg_injection = determines whether to apply ControlNet to only the conditional (if True)
# or the default both conditional and unconditional (if False)
cfg_injection = control_mode == "more_control" or control_mode == "unbalanced"
controlnet_down_block_samples, controlnet_mid_block_sample = self.invokeai_diffuser.do_controlnet_step(
control_data=control_data,
sample=latent_model_input,
timestep=timestep,
step_index=step_index,
total_step_count=total_step_count,
conditioning_data=conditioning_data,
)
first_control_step = math.floor(control_datum.begin_step_percent * total_step_count)
last_control_step = math.ceil(control_datum.end_step_percent * total_step_count)
# only apply controlnet if current step is within the controlnet's begin/end step range
if step_index >= first_control_step and step_index <= last_control_step:
if cfg_injection:
control_latent_input = unet_latent_input
else:
# expand the latents input to control model if doing classifier free guidance
# (which I think for now is always true, there is conditional elsewhere that stops execution if
# classifier_free_guidance is <= 1.0 ?)
control_latent_input = torch.cat([unet_latent_input] * 2)
if cfg_injection: # only applying ControlNet to conditional instead of in unconditioned
encoder_hidden_states = conditioning_data.text_embeddings
encoder_attention_mask = None
else:
(
encoder_hidden_states,
encoder_attention_mask,
) = self.invokeai_diffuser._concat_conditionings_for_batch(
conditioning_data.unconditioned_embeddings,
conditioning_data.text_embeddings,
)
if isinstance(control_datum.weight, list):
# if controlnet has multiple weights, use the weight for the current step
controlnet_weight = control_datum.weight[step_index]
else:
# if controlnet has a single weight, use it for all steps
controlnet_weight = control_datum.weight
# controlnet(s) inference
down_samples, mid_sample = control_datum.model(
sample=control_latent_input,
timestep=timestep,
encoder_hidden_states=encoder_hidden_states,
controlnet_cond=control_datum.image_tensor,
conditioning_scale=controlnet_weight, # controlnet specific, NOT the guidance scale
encoder_attention_mask=encoder_attention_mask,
guess_mode=soft_injection, # this is still called guess_mode in diffusers ControlNetModel
return_dict=False,
)
if cfg_injection:
# Inferred ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# prepend zeros for unconditional batch
down_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_samples]
mid_sample = torch.cat([torch.zeros_like(mid_sample), mid_sample])
if down_block_res_samples is None and mid_block_res_sample is None:
down_block_res_samples, mid_block_res_sample = down_samples, mid_sample
else:
# add controlnet outputs together if have multiple controlnets
down_block_res_samples = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)
]
mid_block_res_sample += mid_sample
# predict the noise residual
noise_pred = self.invokeai_diffuser.do_diffusion_step(
x=unet_latent_input,
sigma=t,
unconditioning=conditioning_data.unconditioned_embeddings,
conditioning=conditioning_data.text_embeddings,
unconditional_guidance_scale=conditioning_data.guidance_scale,
uc_noise_pred, c_noise_pred = self.invokeai_diffuser.do_unet_step(
sample=latent_model_input,
timestep=t, # TODO: debug how handled batched and non batched timesteps
step_index=step_index,
total_step_count=total_step_count,
down_block_additional_residuals=down_block_res_samples, # from controlnet(s)
mid_block_additional_residual=mid_block_res_sample, # from controlnet(s)
conditioning_data=conditioning_data,
# extra:
down_block_additional_residuals=controlnet_down_block_samples, # from controlnet(s)
mid_block_additional_residual=controlnet_mid_block_sample, # from controlnet(s)
)
guidance_scale = conditioning_data.guidance_scale
if isinstance(guidance_scale, list):
guidance_scale = guidance_scale[step_index]
noise_pred = self.invokeai_diffuser._combine(
uc_noise_pred,
c_noise_pred,
guidance_scale,
)
# compute the previous noisy sample x_t -> x_t-1
@ -621,126 +577,3 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
cross_attention_kwargs=cross_attention_kwargs,
**kwargs,
).sample
def get_img2img_timesteps(self, num_inference_steps: int, strength: float, device=None) -> (torch.Tensor, int):
img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
assert img2img_pipeline.scheduler is self.scheduler
if self.scheduler.config.get("cpu_only", False):
scheduler_device = torch.device("cpu")
else:
scheduler_device = self.unet.device
img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
timesteps, adjusted_steps = img2img_pipeline.get_timesteps(
num_inference_steps, strength, device=scheduler_device
)
# Workaround for low strength resulting in zero timesteps.
# TODO: submit upstream fix for zero-step img2img
if timesteps.numel() == 0:
timesteps = self.scheduler.timesteps[-1:]
adjusted_steps = timesteps.numel()
return timesteps, adjusted_steps
def inpaint_from_embeddings(
self,
init_image: torch.FloatTensor,
mask: torch.FloatTensor,
strength: float,
num_inference_steps: int,
conditioning_data: ConditioningData,
*,
callback: Callable[[PipelineIntermediateState], None] = None,
run_id=None,
noise_func=None,
seed=None,
) -> InvokeAIStableDiffusionPipelineOutput:
device = self.unet.device
latents_dtype = self.unet.dtype
if isinstance(init_image, PIL.Image.Image):
init_image = image_resized_to_grid_as_tensor(init_image.convert("RGB"))
init_image = init_image.to(device=device, dtype=latents_dtype)
mask = mask.to(device=device, dtype=latents_dtype)
if init_image.dim() == 3:
init_image = init_image.unsqueeze(0)
timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength)
# 6. Prepare latent variables
# can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents
# because we have our own noise function
init_image_latents = self.non_noised_latents_from_image(init_image, device=device, dtype=latents_dtype)
if seed is not None:
set_seed(seed)
noise = noise_func(init_image_latents)
if mask.dim() == 3:
mask = mask.unsqueeze(0)
latent_mask = tv_resize(mask, init_image_latents.shape[-2:], T.InterpolationMode.BILINEAR).to(
device=device, dtype=latents_dtype
)
guidance: List[Callable] = []
if is_inpainting_model(self.unet):
# You'd think the inpainting model wouldn't be paying attention to the area it is going to repaint
# (that's why there's a mask!) but it seems to really want that blanked out.
masked_init_image = init_image * torch.where(mask < 0.5, 1, 0)
masked_latents = self.non_noised_latents_from_image(masked_init_image, device=device, dtype=latents_dtype)
# TODO: we should probably pass this in so we don't have to try/finally around setting it.
self.invokeai_diffuser.model_forward_callback = AddsMaskLatents(
self._unet_forward, latent_mask, masked_latents
)
else:
guidance.append(AddsMaskGuidance(latent_mask, init_image_latents, self.scheduler, noise))
try:
result_latents, result_attention_maps = self.latents_from_embeddings(
latents=init_image_latents
if strength < 1.0
else torch.zeros_like(
init_image_latents, device=init_image_latents.device, dtype=init_image_latents.dtype
),
num_inference_steps=num_inference_steps,
conditioning_data=conditioning_data,
noise=noise,
timesteps=timesteps,
additional_guidance=guidance,
run_id=run_id,
callback=callback,
)
finally:
self.invokeai_diffuser.model_forward_callback = self._unet_forward
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
torch.cuda.empty_cache()
with torch.inference_mode():
image = self.decode_latents(result_latents)
output = InvokeAIStableDiffusionPipelineOutput(
images=image,
nsfw_content_detected=[],
attention_map_saver=result_attention_maps,
)
return output
def non_noised_latents_from_image(self, init_image, *, device: torch.device, dtype):
init_image = init_image.to(device=device, dtype=dtype)
with torch.inference_mode():
init_latent_dist = self.vae.encode(init_image).latent_dist
init_latents = init_latent_dist.sample().to(dtype=dtype) # FIXME: uses torch.randn. make reproducible!
init_latents = 0.18215 * init_latents
return init_latents
def debug_latents(self, latents, msg):
from invokeai.backend.image_util import debug_image
with torch.inference_mode():
decoded = self.numpy_to_pil(self.decode_latents(latents))
for i, img in enumerate(decoded):
debug_image(img, f"latents {msg} {i+1}/{len(decoded)}", debug_status=True)

7
invokeai/backend/stable_diffusion/diffusion/__init__.py
View File

@ -3,4 +3,9 @@ Initialization file for invokeai.models.diffusion
"""
from .cross_attention_control import InvokeAICrossAttentionMixin
from .cross_attention_map_saving import AttentionMapSaver
from .shared_invokeai_diffusion import InvokeAIDiffuserComponent, PostprocessingSettings
from .shared_invokeai_diffusion import (
InvokeAIDiffuserComponent,
PostprocessingSettings,
BasicConditioningInfo,
SDXLConditioningInfo,
)

377
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
View File

@ -1,6 +1,8 @@
from __future__ import annotations
from contextlib import contextmanager
from dataclasses import dataclass
from math import ceil
import math
from typing import Any, Callable, Dict, Optional, Union, List
import numpy as np
@ -32,6 +34,29 @@ ModelForwardCallback: TypeAlias = Union[
]
@dataclass
class BasicConditioningInfo:
embeds: torch.Tensor
extra_conditioning: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo]
# weight: float
# mode: ConditioningAlgo
def to(self, device, dtype=None):
self.embeds = self.embeds.to(device=device, dtype=dtype)
return self
@dataclass
class SDXLConditioningInfo(BasicConditioningInfo):
pooled_embeds: torch.Tensor
add_time_ids: torch.Tensor
def to(self, device, dtype=None):
self.pooled_embeds = self.pooled_embeds.to(device=device, dtype=dtype)
self.add_time_ids = self.add_time_ids.to(device=device, dtype=dtype)
return super().to(device=device, dtype=dtype)
@dataclass(frozen=True)
class PostprocessingSettings:
threshold: float
@ -127,33 +152,125 @@ class InvokeAIDiffuserComponent:
for _, module in tokens_cross_attention_modules:
module.set_attention_slice_calculated_callback(None)
def do_diffusion_step(
def do_controlnet_step(
self,
x: torch.Tensor,
sigma: torch.Tensor,
unconditioning: Union[torch.Tensor, dict],
conditioning: Union[torch.Tensor, dict],
# unconditional_guidance_scale: float,
unconditional_guidance_scale: Union[float, List[float]],
step_index: Optional[int] = None,
total_step_count: Optional[int] = None,
control_data,
sample: torch.Tensor,
timestep: torch.Tensor,
step_index: int,
total_step_count: int,
conditioning_data,
):
down_block_res_samples, mid_block_res_sample = None, None
# control_data should be type List[ControlNetData]
# this loop covers both ControlNet (one ControlNetData in list)
# and MultiControlNet (multiple ControlNetData in list)
for i, control_datum in enumerate(control_data):
control_mode = control_datum.control_mode
# soft_injection and cfg_injection are the two ControlNet control_mode booleans
# that are combined at higher level to make control_mode enum
# soft_injection determines whether to do per-layer re-weighting adjustment (if True)
# or default weighting (if False)
soft_injection = control_mode == "more_prompt" or control_mode == "more_control"
# cfg_injection = determines whether to apply ControlNet to only the conditional (if True)
# or the default both conditional and unconditional (if False)
cfg_injection = control_mode == "more_control" or control_mode == "unbalanced"
first_control_step = math.floor(control_datum.begin_step_percent * total_step_count)
last_control_step = math.ceil(control_datum.end_step_percent * total_step_count)
# only apply controlnet if current step is within the controlnet's begin/end step range
if step_index >= first_control_step and step_index <= last_control_step:
if cfg_injection:
sample_model_input = sample
else:
# expand the latents input to control model if doing classifier free guidance
# (which I think for now is always true, there is conditional elsewhere that stops execution if
# classifier_free_guidance is <= 1.0 ?)
sample_model_input = torch.cat([sample] * 2)
added_cond_kwargs = None
if cfg_injection: # only applying ControlNet to conditional instead of in unconditioned
if type(conditioning_data.text_embeddings) is SDXLConditioningInfo:
added_cond_kwargs = {
"text_embeds": conditioning_data.text_embeddings.pooled_embeds,
"time_ids": conditioning_data.text_embeddings.add_time_ids,
}
encoder_hidden_states = conditioning_data.text_embeddings.embeds
encoder_attention_mask = None
else:
if type(conditioning_data.text_embeddings) is SDXLConditioningInfo:
added_cond_kwargs = {
"text_embeds": torch.cat(
[
# TODO: how to pad? just by zeros? or even truncate?
conditioning_data.unconditioned_embeddings.pooled_embeds,
conditioning_data.text_embeddings.pooled_embeds,
],
dim=0,
),
"time_ids": torch.cat(
[
conditioning_data.unconditioned_embeddings.add_time_ids,
conditioning_data.text_embeddings.add_time_ids,
],
dim=0,
),
}
(
encoder_hidden_states,
encoder_attention_mask,
) = self._concat_conditionings_for_batch(
conditioning_data.unconditioned_embeddings.embeds,
conditioning_data.text_embeddings.embeds,
)
if isinstance(control_datum.weight, list):
# if controlnet has multiple weights, use the weight for the current step
controlnet_weight = control_datum.weight[step_index]
else:
# if controlnet has a single weight, use it for all steps
controlnet_weight = control_datum.weight
# controlnet(s) inference
down_samples, mid_sample = control_datum.model(
sample=sample_model_input,
timestep=timestep,
encoder_hidden_states=encoder_hidden_states,
controlnet_cond=control_datum.image_tensor,
conditioning_scale=controlnet_weight, # controlnet specific, NOT the guidance scale
encoder_attention_mask=encoder_attention_mask,
guess_mode=soft_injection, # this is still called guess_mode in diffusers ControlNetModel
return_dict=False,
)
if cfg_injection:
# Inferred ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# prepend zeros for unconditional batch
down_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_samples]
mid_sample = torch.cat([torch.zeros_like(mid_sample), mid_sample])
if down_block_res_samples is None and mid_block_res_sample is None:
down_block_res_samples, mid_block_res_sample = down_samples, mid_sample
else:
# add controlnet outputs together if have multiple controlnets
down_block_res_samples = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def do_unet_step(
self,
sample: torch.Tensor,
timestep: torch.Tensor,
conditioning_data, # TODO: type
step_index: int,
total_step_count: int,
**kwargs,
):
"""
:param x: current latents
:param sigma: aka t, passed to the internal model to control how much denoising will occur
:param unconditioning: embeddings for unconditioned output. for hybrid conditioning this is a dict of tensors [B x 77 x 768], otherwise a single tensor [B x 77 x 768]
:param conditioning: embeddings for conditioned output. for hybrid conditioning this is a dict of tensors [B x 77 x 768], otherwise a single tensor [B x 77 x 768]
:param unconditional_guidance_scale: aka CFG scale, controls how much effect the conditioning tensor has
:param step_index: counts upwards from 0 to (step_count-1) (as passed to setup_cross_attention_control, if using). May be called multiple times for a single step, therefore do not assume that its value will monotically increase. If None, will be estimated by comparing sigma against self.model.sigmas .
:return: the new latents after applying the model to x using unscaled unconditioning and CFG-scaled conditioning.
"""
if isinstance(unconditional_guidance_scale, list):
guidance_scale = unconditional_guidance_scale[step_index]
else:
guidance_scale = unconditional_guidance_scale
cross_attention_control_types_to_do = []
context: Context = self.cross_attention_control_context
if self.cross_attention_control_context is not None:
@ -163,25 +280,15 @@ class InvokeAIDiffuserComponent:
)
wants_cross_attention_control = len(cross_attention_control_types_to_do) > 0
wants_hybrid_conditioning = isinstance(conditioning, dict)
if wants_hybrid_conditioning:
unconditioned_next_x, conditioned_next_x = self._apply_hybrid_conditioning(
x,
sigma,
unconditioning,
conditioning,
**kwargs,
)
elif wants_cross_attention_control:
if wants_cross_attention_control:
(
unconditioned_next_x,
conditioned_next_x,
) = self._apply_cross_attention_controlled_conditioning(
x,
sigma,
unconditioning,
conditioning,
sample,
timestep,
conditioning_data,
cross_attention_control_types_to_do,
**kwargs,
)
@ -190,10 +297,9 @@ class InvokeAIDiffuserComponent:
unconditioned_next_x,
conditioned_next_x,
) = self._apply_standard_conditioning_sequentially(
x,
sigma,
unconditioning,
conditioning,
sample,
timestep,
conditioning_data,
**kwargs,
)
@ -202,21 +308,13 @@ class InvokeAIDiffuserComponent:
unconditioned_next_x,
conditioned_next_x,
) = self._apply_standard_conditioning(
x,
sigma,
unconditioning,
conditioning,
sample,
timestep,
conditioning_data,
**kwargs,
)
combined_next_x = self._combine(
# unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale
unconditioned_next_x,
conditioned_next_x,
guidance_scale,
)
return combined_next_x
return unconditioned_next_x, conditioned_next_x
def do_latent_postprocessing(
self,
@ -228,7 +326,6 @@ class InvokeAIDiffuserComponent:
) -> torch.Tensor:
if postprocessing_settings is not None:
percent_through = step_index / total_step_count
latents = self.apply_threshold(postprocessing_settings, latents, percent_through)
latents = self.apply_symmetry(postprocessing_settings, latents, percent_through)
return latents
@ -281,17 +378,40 @@ class InvokeAIDiffuserComponent:
# methods below are called from do_diffusion_step and should be considered private to this class.
def _apply_standard_conditioning(self, x, sigma, unconditioning, conditioning, **kwargs):
def _apply_standard_conditioning(self, x, sigma, conditioning_data, **kwargs):
# fast batched path
x_twice = torch.cat([x] * 2)
sigma_twice = torch.cat([sigma] * 2)
both_conditionings, encoder_attention_mask = self._concat_conditionings_for_batch(unconditioning, conditioning)
added_cond_kwargs = None
if type(conditioning_data.text_embeddings) is SDXLConditioningInfo:
added_cond_kwargs = {
"text_embeds": torch.cat(
[
# TODO: how to pad? just by zeros? or even truncate?
conditioning_data.unconditioned_embeddings.pooled_embeds,
conditioning_data.text_embeddings.pooled_embeds,
],
dim=0,
),
"time_ids": torch.cat(
[
conditioning_data.unconditioned_embeddings.add_time_ids,
conditioning_data.text_embeddings.add_time_ids,
],
dim=0,
),
}
both_conditionings, encoder_attention_mask = self._concat_conditionings_for_batch(
conditioning_data.unconditioned_embeddings.embeds, conditioning_data.text_embeddings.embeds
)
both_results = self.model_forward_callback(
x_twice,
sigma_twice,
both_conditionings,
encoder_attention_mask=encoder_attention_mask,
added_cond_kwargs=added_cond_kwargs,
**kwargs,
)
unconditioned_next_x, conditioned_next_x = both_results.chunk(2)
@ -301,8 +421,7 @@ class InvokeAIDiffuserComponent:
self,
x: torch.Tensor,
sigma,
unconditioning: torch.Tensor,
conditioning: torch.Tensor,
conditioning_data,
**kwargs,
):
# low-memory sequential path
@ -320,52 +439,46 @@ class InvokeAIDiffuserComponent:
if mid_block_additional_residual is not None:
uncond_mid_block, cond_mid_block = mid_block_additional_residual.chunk(2)
added_cond_kwargs = None
is_sdxl = type(conditioning_data.text_embeddings) is SDXLConditioningInfo
if is_sdxl:
added_cond_kwargs = {
"text_embeds": conditioning_data.unconditioned_embeddings.pooled_embeds,
"time_ids": conditioning_data.unconditioned_embeddings.add_time_ids,
}
unconditioned_next_x = self.model_forward_callback(
x,
sigma,
unconditioning,
conditioning_data.unconditioned_embeddings.embeds,
down_block_additional_residuals=uncond_down_block,
mid_block_additional_residual=uncond_mid_block,
added_cond_kwargs=added_cond_kwargs,
**kwargs,
)
if is_sdxl:
added_cond_kwargs = {
"text_embeds": conditioning_data.text_embeddings.pooled_embeds,
"time_ids": conditioning_data.text_embeddings.add_time_ids,
}
conditioned_next_x = self.model_forward_callback(
x,
sigma,
conditioning,
conditioning_data.text_embeddings.embeds,
down_block_additional_residuals=cond_down_block,
mid_block_additional_residual=cond_mid_block,
added_cond_kwargs=added_cond_kwargs,
**kwargs,
)
return unconditioned_next_x, conditioned_next_x
# TODO: looks unused
def _apply_hybrid_conditioning(self, x, sigma, unconditioning, conditioning, **kwargs):
assert isinstance(conditioning, dict)
assert isinstance(unconditioning, dict)
x_twice = torch.cat([x] * 2)
sigma_twice = torch.cat([sigma] * 2)
both_conditionings = dict()
for k in conditioning:
if isinstance(conditioning[k], list):
both_conditionings[k] = [
torch.cat([unconditioning[k][i], conditioning[k][i]]) for i in range(len(conditioning[k]))
]
else:
both_conditionings[k] = torch.cat([unconditioning[k], conditioning[k]])
unconditioned_next_x, conditioned_next_x = self.model_forward_callback(
x_twice,
sigma_twice,
both_conditionings,
**kwargs,
).chunk(2)
return unconditioned_next_x, conditioned_next_x
def _apply_cross_attention_controlled_conditioning(
self,
x: torch.Tensor,
sigma,
unconditioning,
conditioning,
conditioning_data,
cross_attention_control_types_to_do,
**kwargs,
):
@ -391,26 +504,43 @@ class InvokeAIDiffuserComponent:
mask=context.cross_attention_mask,
cross_attention_types_to_do=[],
)
added_cond_kwargs = None
is_sdxl = type(conditioning_data.text_embeddings) is SDXLConditioningInfo
if is_sdxl:
added_cond_kwargs = {
"text_embeds": conditioning_data.unconditioned_embeddings.pooled_embeds,
"time_ids": conditioning_data.unconditioned_embeddings.add_time_ids,
}
# no cross attention for unconditioning (negative prompt)
unconditioned_next_x = self.model_forward_callback(
x,
sigma,
unconditioning,
conditioning_data.unconditioned_embeddings.embeds,
{"swap_cross_attn_context": cross_attn_processor_context},
down_block_additional_residuals=uncond_down_block,
mid_block_additional_residual=uncond_mid_block,
added_cond_kwargs=added_cond_kwargs,
**kwargs,
)
if is_sdxl:
added_cond_kwargs = {
"text_embeds": conditioning_data.text_embeddings.pooled_embeds,
"time_ids": conditioning_data.text_embeddings.add_time_ids,
}
# do requested cross attention types for conditioning (positive prompt)
cross_attn_processor_context.cross_attention_types_to_do = cross_attention_control_types_to_do
conditioned_next_x = self.model_forward_callback(
x,
sigma,
conditioning,
conditioning_data.text_embeddings.embeds,
{"swap_cross_attn_context": cross_attn_processor_context},
down_block_additional_residuals=cond_down_block,
mid_block_additional_residual=cond_mid_block,
added_cond_kwargs=added_cond_kwargs,
**kwargs,
)
return unconditioned_next_x, conditioned_next_x
@ -421,63 +551,6 @@ class InvokeAIDiffuserComponent:
combined_next_x = unconditioned_next_x + scaled_delta
return combined_next_x
def apply_threshold(
self,
postprocessing_settings: PostprocessingSettings,
latents: torch.Tensor,
percent_through: float,
) -> torch.Tensor:
if postprocessing_settings.threshold is None or postprocessing_settings.threshold == 0.0:
return latents
threshold = postprocessing_settings.threshold
warmup = postprocessing_settings.warmup
if percent_through < warmup:
current_threshold = threshold + threshold * 5 * (1 - (percent_through / warmup))
else:
current_threshold = threshold
if current_threshold <= 0:
return latents
maxval = latents.max().item()
minval = latents.min().item()
scale = 0.7 # default value from #395
if self.debug_thresholding:
std, mean = [i.item() for i in torch.std_mean(latents)]
outside = torch.count_nonzero((latents < -current_threshold) | (latents > current_threshold))
logger.info(f"Threshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})")
logger.debug(f"min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}")
logger.debug(f"{outside / latents.numel() * 100:.2f}% values outside threshold")
if maxval < current_threshold and minval > -current_threshold:
return latents
num_altered = 0
# MPS torch.rand_like is fine because torch.rand_like is wrapped in generate.py!
if maxval > current_threshold:
latents = torch.clone(latents)
maxval = np.clip(maxval * scale, 1, current_threshold)
num_altered += torch.count_nonzero(latents > maxval)
latents[latents > maxval] = torch.rand_like(latents[latents > maxval]) * maxval
if minval < -current_threshold:
latents = torch.clone(latents)
minval = np.clip(minval * scale, -current_threshold, -1)
num_altered += torch.count_nonzero(latents < minval)
latents[latents < minval] = torch.rand_like(latents[latents < minval]) * minval
if self.debug_thresholding:
logger.debug(f"min, , max = {minval:.3f}, , {maxval:.3f}\t(scaled by {scale})")
logger.debug(f"{num_altered / latents.numel() * 100:.2f}% values altered")
return latents
def apply_symmetry(
self,
postprocessing_settings: PostprocessingSettings,
@ -539,18 +612,6 @@ class InvokeAIDiffuserComponent:
self.last_percent_through = percent_through
return latents.to(device=dev)
def estimate_percent_through(self, step_index, sigma):
if step_index is not None and self.cross_attention_control_context is not None:
# percent_through will never reach 1.0 (but this is intended)
return float(step_index) / float(self.cross_attention_control_context.step_count)
# find the best possible index of the current sigma in the sigma sequence
smaller_sigmas = torch.nonzero(self.model.sigmas <= sigma)
sigma_index = smaller_sigmas[-1].item() if smaller_sigmas.shape[0] > 0 else 0
# flip because sigmas[0] is for the fully denoised image
# percent_through must be <1
return 1.0 - float(sigma_index + 1) / float(self.model.sigmas.shape[0])
# print('estimated percent_through', percent_through, 'from sigma', sigma.item())
# todo: make this work
@classmethod
def apply_conjunction(cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale):
@ -564,7 +625,7 @@ class InvokeAIDiffuserComponent:
# below is fugly omg
conditionings = [uc] + [c for c, weight in weighted_cond_list]
weights = [1] + [weight for c, weight in weighted_cond_list]
chunk_count = ceil(len(conditionings) / 2)
chunk_count = math.ceil(len(conditionings) / 2)
deltas = None
for chunk_index in range(chunk_count):
offset = chunk_index * 2

3
invokeai/frontend/web/public/locales/en.json
View File

@ -503,6 +503,9 @@
"hiresStrength": "High Res Strength",
"imageFit": "Fit Initial Image To Output Size",
"codeformerFidelity": "Fidelity",
"maskAdjustmentsHeader": "Mask Adjustments",
"maskBlur": "Mask Blur",
"maskBlurMethod": "Mask Blur Method",
"seamSize": "Seam Size",
"seamBlur": "Seam Blur",
"seamStrength": "Seam Strength",

2
invokeai/frontend/web/scripts/typegen.js
View File

@ -1,7 +1,7 @@
import fs from 'node:fs';
import openapiTS from 'openapi-typescript';
const OPENAPI_URL = 'http://localhost:9090/openapi.json';
const OPENAPI_URL = 'http://127.0.0.1:9090/openapi.json';
const OUTPUT_FILE = 'src/services/api/schema.d.ts';
async function main() {

5
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationComplete.ts
View File

@ -7,6 +7,7 @@ import {
imageSelected,
} from 'features/gallery/store/gallerySlice';
import { IMAGE_CATEGORIES } from 'features/gallery/store/types';
import { CANVAS_OUTPUT } from 'features/nodes/util/graphBuilders/constants';
import { progressImageSet } from 'features/system/store/systemSlice';
import { imagesApi } from 'services/api/endpoints/images';
import { isImageOutput } from 'services/api/guards';
@ -52,7 +53,9 @@ export const addInvocationCompleteEventListener = () => {
// Add canvas images to the staging area
if (
graph_execution_state_id === canvas.layerState.stagingArea.sessionId
graph_execution_state_id ===
canvas.layerState.stagingArea.sessionId &&
[CANVAS_OUTPUT].includes(data.source_node_id)
) {
dispatch(addImageToStagingArea(imageDTO));
}

9
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/tabChanged.ts
View File

@ -12,7 +12,10 @@ export const addTabChangedListener = () => {
if (activeTabName === 'unifiedCanvas') {
const currentBaseModel = getState().generation.model?.base_model;
if (currentBaseModel && ['sd-1', 'sd-2'].includes(currentBaseModel)) {
if (
currentBaseModel &&
['sd-1', 'sd-2', 'sdxl'].includes(currentBaseModel)
) {
// if we're already on a valid model, no change needed
return;
}
@ -36,7 +39,9 @@ export const addTabChangedListener = () => {
const validCanvasModels = mainModelsAdapter
.getSelectors()
.selectAll(models)
.filter((model) => ['sd-1', 'sd-2'].includes(model.base_model));
.filter((model) =>
['sd-1', 'sd-2', 'sxdl'].includes(model.base_model)
);
const firstValidCanvasModel = validCanvasModels[0];

2
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedCanvas.ts
View File

@ -1,6 +1,7 @@
import { logger } from 'app/logging/logger';
import { userInvoked } from 'app/store/actions';
import openBase64ImageInTab from 'common/util/openBase64ImageInTab';
import { parseify } from 'common/util/serialize';
import {
canvasSessionIdChanged,
stagingAreaInitialized,
@ -15,7 +16,6 @@ import { imagesApi } from 'services/api/endpoints/images';
import { sessionCreated } from 'services/api/thunks/session';
import { ImageDTO } from 'services/api/types';
import { startAppListening } from '..';
import { parseify } from 'common/util/serialize';
/**
* This listener is responsible invoking the canvas. This involves a number of steps:

2
invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts
View File

@ -47,7 +47,7 @@ export const initialCanvasState: CanvasState = {
boundingBoxCoordinates: { x: 0, y: 0 },
boundingBoxDimensions: { width: 512, height: 512 },
boundingBoxPreviewFill: { r: 0, g: 0, b: 0, a: 0.5 },
boundingBoxScaleMethod: 'auto',
boundingBoxScaleMethod: 'none',
brushColor: { r: 90, g: 90, b: 255, a: 1 },
brushSize: 50,
canvasContainerDimensions: { width: 0, height: 0 },

2
invokeai/frontend/web/src/features/canvas/store/canvasTypes.ts
View File

@ -11,9 +11,9 @@ export const LAYER_NAMES = ['base', 'mask'] as const;
export type CanvasLayer = (typeof LAYER_NAMES)[number];
export const BOUNDING_BOX_SCALES_DICT = [
{ label: 'None', value: 'none' },
{ label: 'Auto', value: 'auto' },
{ label: 'Manual', value: 'manual' },
{ label: 'None', value: 'none' },
];
export const BOUNDING_BOX_SCALES = ['none', 'auto', 'manual'] as const;

10
invokeai/frontend/web/src/features/lora/components/ParamLoraList.tsx
View File

@ -1,4 +1,4 @@
import { Divider } from '@chakra-ui/react';
import { Divider, Flex } from '@chakra-ui/react';
import { createSelector } from '@reduxjs/toolkit';
import { stateSelector } from 'app/store/store';
import { useAppSelector } from 'app/store/storeHooks';
@ -20,10 +20,10 @@ const ParamLoraList = () => {
return (
<>
{lorasArray.map((lora, i) => (
<>
{i > 0 && <Divider key={`${lora.model_name}-divider`} pt={1} />}
<ParamLora key={lora.model_name} lora={lora} />
</>
<Flex key={lora.model_name} sx={{ flexDirection: 'column', gap: 2 }}>
{i > 0 && <Divider pt={1} />}
<ParamLora lora={lora} />
</Flex>
))}
</>
);

2
invokeai/frontend/web/src/features/lora/components/ParamLoraSelect.tsx
View File

@ -54,6 +54,8 @@ const ParamLoRASelect = () => {
});
});
data.sort((a, b) => (a.label && !b.label ? 1 : -1));
return data.sort((a, b) => (a.disabled && !b.disabled ? 1 : -1));
}, [loras, loraModels, currentMainModel?.base_model]);

118
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLRefinerToGraph.ts
View File

@ -2,28 +2,31 @@ import { RootState } from 'app/store/store';
import { MetadataAccumulatorInvocation } from 'services/api/types';
import { NonNullableGraph } from '../../types/types';
import {
IMAGE_TO_LATENTS,
CANVAS_OUTPUT,
LATENTS_TO_IMAGE,
MASK_BLUR,
METADATA_ACCUMULATOR,
SDXL_LATENTS_TO_LATENTS,
SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH,
SDXL_CANVAS_INPAINT_GRAPH,
SDXL_CANVAS_OUTPAINT_GRAPH,
SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH,
SDXL_MODEL_LOADER,
SDXL_REFINER_LATENTS_TO_LATENTS,
SDXL_REFINER_DENOISE_LATENTS,
SDXL_REFINER_MODEL_LOADER,
SDXL_REFINER_NEGATIVE_CONDITIONING,
SDXL_REFINER_POSITIVE_CONDITIONING,
} from './constants';
import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
export const addSDXLRefinerToGraph = (
state: RootState,
graph: NonNullableGraph,
baseNodeId: string
): void => {
const { positivePrompt, negativePrompt } = state.generation;
const {
refinerModel,
refinerAestheticScore,
positiveStylePrompt,
negativeStylePrompt,
refinerPositiveAestheticScore,
refinerNegativeAestheticScore,
refinerSteps,
refinerScheduler,
refinerCFGScale,
@ -38,13 +41,20 @@ export const addSDXLRefinerToGraph = (
if (metadataAccumulator) {
metadataAccumulator.refiner_model = refinerModel;
metadataAccumulator.refiner_aesthetic_store = refinerAestheticScore;
metadataAccumulator.refiner_positive_aesthetic_score =
refinerPositiveAestheticScore;
metadataAccumulator.refiner_negative_aesthetic_score =
refinerNegativeAestheticScore;
metadataAccumulator.refiner_cfg_scale = refinerCFGScale;
metadataAccumulator.refiner_scheduler = refinerScheduler;
metadataAccumulator.refiner_start = refinerStart;
metadataAccumulator.refiner_steps = refinerSteps;
}
// Construct Style Prompt
const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } =
craftSDXLStylePrompt(state, true);
// Unplug SDXL Latents Generation To Latents To Image
graph.edges = graph.edges.filter(
(e) =>
@ -59,21 +69,6 @@ export const addSDXLRefinerToGraph = (
)
);
// connect the VAE back to the i2l, which we just removed in the filter
// but only if we are doing l2l
if (baseNodeId === SDXL_LATENTS_TO_LATENTS) {
graph.edges.push({
source: {
node_id: SDXL_MODEL_LOADER,
field: 'vae',
},
destination: {
node_id: IMAGE_TO_LATENTS,
field: 'vae',
},
});
}
graph.nodes[SDXL_REFINER_MODEL_LOADER] = {
type: 'sdxl_refiner_model_loader',
id: SDXL_REFINER_MODEL_LOADER,
@ -82,20 +77,20 @@ export const addSDXLRefinerToGraph = (
graph.nodes[SDXL_REFINER_POSITIVE_CONDITIONING] = {
type: 'sdxl_refiner_compel_prompt',
id: SDXL_REFINER_POSITIVE_CONDITIONING,
style: `${positivePrompt} ${positiveStylePrompt}`,
aesthetic_score: refinerAestheticScore,
style: craftedPositiveStylePrompt,
aesthetic_score: refinerPositiveAestheticScore,
};
graph.nodes[SDXL_REFINER_NEGATIVE_CONDITIONING] = {
type: 'sdxl_refiner_compel_prompt',
id: SDXL_REFINER_NEGATIVE_CONDITIONING,
style: `${negativePrompt} ${negativeStylePrompt}`,
aesthetic_score: refinerAestheticScore,
style: craftedNegativeStylePrompt,
aesthetic_score: refinerNegativeAestheticScore,
};
graph.nodes[SDXL_REFINER_LATENTS_TO_LATENTS] = {
type: 'l2l_sdxl',
id: SDXL_REFINER_LATENTS_TO_LATENTS,
graph.nodes[SDXL_REFINER_DENOISE_LATENTS] = {
type: 'denoise_latents',
id: SDXL_REFINER_DENOISE_LATENTS,
cfg_scale: refinerCFGScale,
steps: refinerSteps / (1 - Math.min(refinerStart, 0.99)),
steps: refinerSteps,
scheduler: refinerScheduler,
denoising_start: refinerStart,
denoising_end: 1,
@ -108,20 +103,10 @@ export const addSDXLRefinerToGraph = (
field: 'unet',
},
destination: {
node_id: SDXL_REFINER_LATENTS_TO_LATENTS,
node_id: SDXL_REFINER_DENOISE_LATENTS,
field: 'unet',
},
},
{
source: {
node_id: SDXL_REFINER_MODEL_LOADER,
field: 'vae',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'vae',
},
},
{
source: {
node_id: SDXL_REFINER_MODEL_LOADER,
@ -148,7 +133,7 @@ export const addSDXLRefinerToGraph = (
field: 'conditioning',
},
destination: {
node_id: SDXL_REFINER_LATENTS_TO_LATENTS,
node_id: SDXL_REFINER_DENOISE_LATENTS,
field: 'positive_conditioning',
},
},
@ -158,7 +143,7 @@ export const addSDXLRefinerToGraph = (
field: 'conditioning',
},
destination: {
node_id: SDXL_REFINER_LATENTS_TO_LATENTS,
node_id: SDXL_REFINER_DENOISE_LATENTS,
field: 'negative_conditioning',
},
},
@ -168,19 +153,52 @@ export const addSDXLRefinerToGraph = (
field: 'latents',
},
destination: {
node_id: SDXL_REFINER_LATENTS_TO_LATENTS,
node_id: SDXL_REFINER_DENOISE_LATENTS,
field: 'latents',
},
},
{
}
);
if (
graph.id === SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH ||
graph.id === SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH
) {
graph.edges.push({
source: {
node_id: SDXL_REFINER_LATENTS_TO_LATENTS,
node_id: SDXL_REFINER_DENOISE_LATENTS,
field: 'latents',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'latents',
},
});
} else {
graph.edges.push({
source: {
node_id: SDXL_REFINER_DENOISE_LATENTS,
field: 'latents',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'latents',
},
}
);
});
}
if (
graph.id === SDXL_CANVAS_INPAINT_GRAPH ||
graph.id === SDXL_CANVAS_OUTPAINT_GRAPH
) {
graph.edges.push({
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: SDXL_REFINER_DENOISE_LATENTS,
field: 'mask',
},
});
}
};

84
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts
View File

@ -2,14 +2,24 @@ import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { MetadataAccumulatorInvocation } from 'services/api/types';
import {
CANVAS_IMAGE_TO_IMAGE_GRAPH,
CANVAS_INPAINT_GRAPH,
CANVAS_OUTPAINT_GRAPH,
CANVAS_OUTPUT,
CANVAS_TEXT_TO_IMAGE_GRAPH,
IMAGE_TO_IMAGE_GRAPH,
IMAGE_TO_LATENTS,
INPAINT,
INPAINT_GRAPH,
INPAINT_IMAGE,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
ONNX_MODEL_LOADER,
SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH,
SDXL_CANVAS_INPAINT_GRAPH,
SDXL_CANVAS_OUTPAINT_GRAPH,
SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH,
SDXL_IMAGE_TO_IMAGE_GRAPH,
SDXL_TEXT_TO_IMAGE_GRAPH,
TEXT_TO_IMAGE_GRAPH,
VAE_LOADER,
} from './constants';
@ -35,7 +45,13 @@ export const addVAEToGraph = (
};
}
const isOnnxModel = modelLoaderNodeId == ONNX_MODEL_LOADER;
if (graph.id === TEXT_TO_IMAGE_GRAPH || graph.id === IMAGE_TO_IMAGE_GRAPH) {
if (
graph.id === TEXT_TO_IMAGE_GRAPH ||
graph.id === IMAGE_TO_IMAGE_GRAPH ||
graph.id === SDXL_TEXT_TO_IMAGE_GRAPH ||
graph.id === SDXL_IMAGE_TO_IMAGE_GRAPH
) {
graph.edges.push({
source: {
node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
@ -48,7 +64,30 @@ export const addVAEToGraph = (
});
}
if (graph.id === IMAGE_TO_IMAGE_GRAPH) {
if (
graph.id === CANVAS_TEXT_TO_IMAGE_GRAPH ||
graph.id === CANVAS_IMAGE_TO_IMAGE_GRAPH ||
graph.id === SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH ||
graph.id == SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH
) {
graph.edges.push({
source: {
node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'vae',
},
});
}
if (
graph.id === IMAGE_TO_IMAGE_GRAPH ||
graph.id === SDXL_IMAGE_TO_IMAGE_GRAPH ||
graph.id === CANVAS_IMAGE_TO_IMAGE_GRAPH ||
graph.id === SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH
) {
graph.edges.push({
source: {
node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
@ -61,17 +100,34 @@ export const addVAEToGraph = (
});
}
if (graph.id === INPAINT_GRAPH) {
graph.edges.push({
source: {
node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae',
if (
graph.id === CANVAS_INPAINT_GRAPH ||
graph.id === CANVAS_OUTPAINT_GRAPH ||
graph.id === SDXL_CANVAS_INPAINT_GRAPH ||
graph.id === SDXL_CANVAS_OUTPAINT_GRAPH
) {
graph.edges.push(
{
source: {
node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae',
},
destination: {
node_id: INPAINT_IMAGE,
field: 'vae',
},
},
destination: {
node_id: INPAINT,
field: 'vae',
},
});
{
source: {
node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'vae',
},
}
);
}
if (vae && metadataAccumulator) {

54
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasGraph.ts
View File

@ -3,6 +3,11 @@ import { NonNullableGraph } from 'features/nodes/types/types';
import { ImageDTO } from 'services/api/types';
import { buildCanvasImageToImageGraph } from './buildCanvasImageToImageGraph';
import { buildCanvasInpaintGraph } from './buildCanvasInpaintGraph';
import { buildCanvasOutpaintGraph } from './buildCanvasOutpaintGraph';
import { buildCanvasSDXLImageToImageGraph } from './buildCanvasSDXLImageToImageGraph';
import { buildCanvasSDXLInpaintGraph } from './buildCanvasSDXLInpaintGraph';
import { buildCanvasSDXLOutpaintGraph } from './buildCanvasSDXLOutpaintGraph';
import { buildCanvasSDXLTextToImageGraph } from './buildCanvasSDXLTextToImageGraph';
import { buildCanvasTextToImageGraph } from './buildCanvasTextToImageGraph';
export const buildCanvasGraph = (
@ -14,17 +19,58 @@ export const buildCanvasGraph = (
let graph: NonNullableGraph;
if (generationMode === 'txt2img') {
graph = buildCanvasTextToImageGraph(state);
if (
state.generation.model &&
state.generation.model.base_model === 'sdxl'
) {
graph = buildCanvasSDXLTextToImageGraph(state);
} else {
graph = buildCanvasTextToImageGraph(state);
}
} else if (generationMode === 'img2img') {
if (!canvasInitImage) {
throw new Error('Missing canvas init image');
}
graph = buildCanvasImageToImageGraph(state, canvasInitImage);
} else {
if (
state.generation.model &&
state.generation.model.base_model === 'sdxl'
) {
graph = buildCanvasSDXLImageToImageGraph(state, canvasInitImage);
} else {
graph = buildCanvasImageToImageGraph(state, canvasInitImage);
}
} else if (generationMode === 'inpaint') {
if (!canvasInitImage || !canvasMaskImage) {
throw new Error('Missing canvas init and mask images');
}
graph = buildCanvasInpaintGraph(state, canvasInitImage, canvasMaskImage);
if (
state.generation.model &&
state.generation.model.base_model === 'sdxl'
) {
graph = buildCanvasSDXLInpaintGraph(
state,
canvasInitImage,
canvasMaskImage
);
} else {
graph = buildCanvasInpaintGraph(state, canvasInitImage, canvasMaskImage);
}
} else {
if (!canvasInitImage) {
throw new Error('Missing canvas init image');
}
if (
state.generation.model &&
state.generation.model.base_model === 'sdxl'
) {
graph = buildCanvasSDXLOutpaintGraph(
state,
canvasInitImage,
canvasMaskImage
);
} else {
graph = buildCanvasOutpaintGraph(state, canvasInitImage, canvasMaskImage);
}
}
return graph;

125
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts
View File

@ -14,11 +14,11 @@ import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CANVAS_IMAGE_TO_IMAGE_GRAPH,
CANVAS_OUTPUT,
CLIP_SKIP,
IMAGE_TO_IMAGE_GRAPH,
DENOISE_LATENTS,
IMAGE_TO_LATENTS,
LATENTS_TO_IMAGE,
LATENTS_TO_LATENTS,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
@ -73,8 +73,20 @@ export const buildCanvasImageToImageGraph = (
// copy-pasted graph from node editor, filled in with state values & friendly node ids
const graph: NonNullableGraph = {
id: IMAGE_TO_IMAGE_GRAPH,
id: CANVAS_IMAGE_TO_IMAGE_GRAPH,
nodes: {
[MAIN_MODEL_LOADER]: {
type: 'main_model_loader',
id: MAIN_MODEL_LOADER,
is_intermediate: true,
model,
},
[CLIP_SKIP]: {
type: 'clip_skip',
id: CLIP_SKIP,
is_intermediate: true,
skipped_layers: clipSkip,
},
[POSITIVE_CONDITIONING]: {
type: 'compel',
id: POSITIVE_CONDITIONING,
@ -93,27 +105,6 @@ export const buildCanvasImageToImageGraph = (
is_intermediate: true,
use_cpu,
},
[MAIN_MODEL_LOADER]: {
type: 'main_model_loader',
id: MAIN_MODEL_LOADER,
is_intermediate: true,
model,
},
[CLIP_SKIP]: {
type: 'clip_skip',
id: CLIP_SKIP,
is_intermediate: true,
skipped_layers: clipSkip,
},
[LATENTS_TO_LATENTS]: {
type: 'l2l',
id: LATENTS_TO_LATENTS,
is_intermediate: true,
cfg_scale,
scheduler,
steps,
strength,
},
[IMAGE_TO_LATENTS]: {
type: 'i2l',
id: IMAGE_TO_LATENTS,
@ -123,13 +114,34 @@ export const buildCanvasImageToImageGraph = (
// image_name: initialImage.image_name,
// },
},
[LATENTS_TO_IMAGE]: {
[DENOISE_LATENTS]: {
type: 'denoise_latents',
id: DENOISE_LATENTS,
is_intermediate: true,
cfg_scale,
scheduler,
steps,
denoising_start: 1 - strength,
denoising_end: 1,
},
[CANVAS_OUTPUT]: {
type: 'l2i',
id: LATENTS_TO_IMAGE,
id: CANVAS_OUTPUT,
is_intermediate: !shouldAutoSave,
},
},
edges: [
// Connect Model Loader to CLIP Skip and UNet
{
source: {
node_id: MAIN_MODEL_LOADER,
field: 'unet',
},
destination: {
node_id: DENOISE_LATENTS,
field: 'unet',
},
},
{
source: {
node_id: MAIN_MODEL_LOADER,
@ -140,6 +152,7 @@ export const buildCanvasImageToImageGraph = (
field: 'clip',
},
},
// Connect CLIP Skip To Conditioning
{
source: {
node_id: CLIP_SKIP,
@ -160,24 +173,25 @@ export const buildCanvasImageToImageGraph = (
field: 'clip',
},
},
// Connect Everything To Denoise Latents
{
source: {
node_id: LATENTS_TO_LATENTS,
field: 'latents',
node_id: POSITIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'latents',
node_id: DENOISE_LATENTS,
field: 'positive_conditioning',
},
},
{
source: {
node_id: IMAGE_TO_LATENTS,
field: 'latents',
node_id: NEGATIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: LATENTS_TO_LATENTS,
field: 'latents',
node_id: DENOISE_LATENTS,
field: 'negative_conditioning',
},
},
{
@ -186,38 +200,29 @@ export const buildCanvasImageToImageGraph = (
field: 'noise',
},
destination: {
node_id: LATENTS_TO_LATENTS,
node_id: DENOISE_LATENTS,
field: 'noise',
},
},
{
source: {
node_id: MAIN_MODEL_LOADER,
field: 'unet',
node_id: IMAGE_TO_LATENTS,
field: 'latents',
},
destination: {
node_id: LATENTS_TO_LATENTS,
field: 'unet',
node_id: DENOISE_LATENTS,
field: 'latents',
},
},
// Decode the denoised latents to an image
{
source: {
node_id: NEGATIVE_CONDITIONING,
field: 'conditioning',
node_id: DENOISE_LATENTS,
field: 'latents',
},
destination: {
node_id: LATENTS_TO_LATENTS,
field: 'negative_conditioning',
},
},
{
source: {
node_id: POSITIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: LATENTS_TO_LATENTS,
field: 'positive_conditioning',
node_id: CANVAS_OUTPUT,
field: 'latents',
},
},
],
@ -318,32 +323,32 @@ export const buildCanvasImageToImageGraph = (
field: 'metadata',
},
destination: {
node_id: LATENTS_TO_IMAGE,
node_id: CANVAS_OUTPUT,
field: 'metadata',
},
});
// add LoRA support
addLoRAsToGraph(state, graph, LATENTS_TO_LATENTS);
addLoRAsToGraph(state, graph, DENOISE_LATENTS);
// optionally add custom VAE
addVAEToGraph(state, graph);
addVAEToGraph(state, graph, MAIN_MODEL_LOADER);
// add dynamic prompts - also sets up core iteration and seed
addDynamicPromptsToGraph(state, graph);
// add controlnet, mutating `graph`
addControlNetToLinearGraph(state, graph, LATENTS_TO_LATENTS);
addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
// NSFW & watermark - must be last thing added to graph
if (state.system.shouldUseNSFWChecker) {
// must add before watermarker!
addNSFWCheckerToGraph(state, graph);
addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT);
}
if (state.system.shouldUseWatermarker) {
// must add after nsfw checker!
addWatermarkerToGraph(state, graph);
addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
}
return graph;

396
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts
View File

@ -2,22 +2,35 @@ import { logger } from 'app/logging/logger';
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import {
ImageBlurInvocation,
ImageDTO,
InpaintInvocation,
ImageToLatentsInvocation,
NoiseInvocation,
RandomIntInvocation,
RangeOfSizeInvocation,
} from 'services/api/types';
import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
import { addLoRAsToGraph } from './addLoRAsToGraph';
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CANVAS_INPAINT_GRAPH,
CANVAS_OUTPUT,
CLIP_SKIP,
INPAINT,
INPAINT_GRAPH,
COLOR_CORRECT,
DENOISE_LATENTS,
INPAINT_IMAGE,
INPAINT_IMAGE_RESIZE_DOWN,
INPAINT_IMAGE_RESIZE_UP,
ITERATE,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
MASK_BLUR,
MASK_RESIZE_DOWN,
MASK_RESIZE_UP,
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
RANDOM_INT,
RANGE_OF_SIZE,
@ -40,16 +53,14 @@ export const buildCanvasInpaintGraph = (
scheduler,
steps,
img2imgStrength: strength,
shouldFitToWidthHeight,
iterations,
seed,
shouldRandomizeSeed,
seamSize,
seamBlur,
seamSteps,
seamStrength,
tileSize,
infillMethod,
vaePrecision,
shouldUseNoiseSettings,
shouldUseCpuNoise,
maskBlur,
maskBlurMethod,
clipSkip,
} = state.generation;
@ -68,40 +79,24 @@ export const buildCanvasInpaintGraph = (
shouldAutoSave,
} = state.canvas;
const use_cpu = shouldUseNoiseSettings
? shouldUseCpuNoise
: shouldUseCpuNoise;
const graph: NonNullableGraph = {
id: INPAINT_GRAPH,
id: CANVAS_INPAINT_GRAPH,
nodes: {
[INPAINT]: {
is_intermediate: !shouldAutoSave,
type: 'inpaint',
id: INPAINT,
steps,
width,
height,
cfg_scale,
scheduler,
image: {
image_name: canvasInitImage.image_name,
},
strength,
fit: shouldFitToWidthHeight,
mask: {
image_name: canvasMaskImage.image_name,
},
seam_size: seamSize,
seam_blur: seamBlur,
seam_strength: seamStrength,
seam_steps: seamSteps,
tile_size: infillMethod === 'tile' ? tileSize : undefined,
infill_method: infillMethod as InpaintInvocation['infill_method'],
inpaint_width:
boundingBoxScaleMethod !== 'none'
? scaledBoundingBoxDimensions.width
: undefined,
inpaint_height:
boundingBoxScaleMethod !== 'none'
? scaledBoundingBoxDimensions.height
: undefined,
[MAIN_MODEL_LOADER]: {
type: 'main_model_loader',
id: MAIN_MODEL_LOADER,
is_intermediate: true,
model,
},
[CLIP_SKIP]: {
type: 'clip_skip',
id: CLIP_SKIP,
is_intermediate: true,
skipped_layers: clipSkip,
},
[POSITIVE_CONDITIONING]: {
type: 'compel',
@ -115,17 +110,52 @@ export const buildCanvasInpaintGraph = (
is_intermediate: true,
prompt: negativePrompt,
},
[MAIN_MODEL_LOADER]: {
type: 'main_model_loader',
id: MAIN_MODEL_LOADER,
[MASK_BLUR]: {
type: 'img_blur',
id: MASK_BLUR,
is_intermediate: true,
model,
radius: maskBlur,
blur_type: maskBlurMethod,
},
[CLIP_SKIP]: {
type: 'clip_skip',
id: CLIP_SKIP,
[INPAINT_IMAGE]: {
type: 'i2l',
id: INPAINT_IMAGE,
is_intermediate: true,
skipped_layers: clipSkip,
fp32: vaePrecision === 'fp32' ? true : false,
},
[NOISE]: {
type: 'noise',
id: NOISE,
use_cpu,
is_intermediate: true,
},
[DENOISE_LATENTS]: {
type: 'denoise_latents',
id: DENOISE_LATENTS,
is_intermediate: true,
steps: steps,
cfg_scale: cfg_scale,
scheduler: scheduler,
denoising_start: 1 - strength,
denoising_end: 1,
},
[LATENTS_TO_IMAGE]: {
type: 'l2i',
id: LATENTS_TO_IMAGE,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
},
[COLOR_CORRECT]: {
type: 'color_correct',
id: COLOR_CORRECT,
is_intermediate: true,
reference: canvasInitImage,
},
[CANVAS_OUTPUT]: {
type: 'img_paste',
id: CANVAS_OUTPUT,
is_intermediate: !shouldAutoSave,
base_image: canvasInitImage,
},
[RANGE_OF_SIZE]: {
type: 'range_of_size',
@ -143,13 +173,14 @@ export const buildCanvasInpaintGraph = (
},
},
edges: [
// Connect Model Loader to CLIP Skip and UNet
{
source: {
node_id: MAIN_MODEL_LOADER,
field: 'unet',
},
destination: {
node_id: INPAINT,
node_id: DENOISE_LATENTS,
field: 'unet',
},
},
@ -163,6 +194,7 @@ export const buildCanvasInpaintGraph = (
field: 'clip',
},
},
// Connect CLIP Skip to Conditioning
{
source: {
node_id: CLIP_SKIP,
@ -183,26 +215,58 @@ export const buildCanvasInpaintGraph = (
field: 'clip',
},
},
// Connect Everything To Inpaint Node
{
source: {
node_id: POSITIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: DENOISE_LATENTS,
field: 'positive_conditioning',
},
},
{
source: {
node_id: NEGATIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: INPAINT,
node_id: DENOISE_LATENTS,
field: 'negative_conditioning',
},
},
{
source: {
node_id: POSITIVE_CONDITIONING,
field: 'conditioning',
node_id: NOISE,
field: 'noise',
},
destination: {
node_id: INPAINT,
field: 'positive_conditioning',
node_id: DENOISE_LATENTS,
field: 'noise',
},
},
{
source: {
node_id: INPAINT_IMAGE,
field: 'latents',
},
destination: {
node_id: DENOISE_LATENTS,
field: 'latents',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: DENOISE_LATENTS,
field: 'mask',
},
},
// Iterate
{
source: {
node_id: RANGE_OF_SIZE,
@ -219,19 +283,216 @@ export const buildCanvasInpaintGraph = (
field: 'item',
},
destination: {
node_id: INPAINT,
node_id: NOISE,
field: 'seed',
},
},
// Decode Inpainted Latents To Image
{
source: {
node_id: DENOISE_LATENTS,
field: 'latents',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'latents',
},
},
],
};
addLoRAsToGraph(state, graph, INPAINT);
// Handle Scale Before Processing
if (['auto', 'manual'].includes(boundingBoxScaleMethod)) {
const scaledWidth: number = scaledBoundingBoxDimensions.width;
const scaledHeight: number = scaledBoundingBoxDimensions.height;
// Add VAE
addVAEToGraph(state, graph);
// Add Scaling Nodes
graph.nodes[INPAINT_IMAGE_RESIZE_UP] = {
type: 'img_resize',
id: INPAINT_IMAGE_RESIZE_UP,
is_intermediate: true,
width: scaledWidth,
height: scaledHeight,
image: canvasInitImage,
};
graph.nodes[MASK_RESIZE_UP] = {
type: 'img_resize',
id: MASK_RESIZE_UP,
is_intermediate: true,
width: scaledWidth,
height: scaledHeight,
image: canvasMaskImage,
};
graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = {
type: 'img_resize',
id: INPAINT_IMAGE_RESIZE_DOWN,
is_intermediate: true,
width: width,
height: height,
};
graph.nodes[MASK_RESIZE_DOWN] = {
type: 'img_resize',
id: MASK_RESIZE_DOWN,
is_intermediate: true,
width: width,
height: height,
};
// handle seed
graph.nodes[NOISE] = {
...(graph.nodes[NOISE] as NoiseInvocation),
width: scaledWidth,
height: scaledHeight,
};
// Connect Nodes
graph.edges.push(
// Scale Inpaint Image and Mask
{
source: {
node_id: INPAINT_IMAGE_RESIZE_UP,
field: 'image',
},
destination: {
node_id: INPAINT_IMAGE,
field: 'image',
},
},
{
source: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
destination: {
node_id: MASK_BLUR,
field: 'image',
},
},
// Color Correct The Inpainted Result
{
source: {
node_id: LATENTS_TO_IMAGE,
field: 'image',
},
destination: {
node_id: INPAINT_IMAGE_RESIZE_DOWN,
field: 'image',
},
},
{
source: {
node_id: INPAINT_IMAGE_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'image',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: MASK_RESIZE_DOWN,
field: 'image',
},
},
{
source: {
node_id: MASK_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'mask',
},
},
// Paste Back Onto Original Image
{
source: {
node_id: COLOR_CORRECT,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'image',
},
},
{
source: {
node_id: MASK_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'mask',
},
}
);
} else {
// Add Images To Nodes
graph.nodes[NOISE] = {
...(graph.nodes[NOISE] as NoiseInvocation),
width: width,
height: height,
};
graph.nodes[INPAINT_IMAGE] = {
...(graph.nodes[INPAINT_IMAGE] as ImageToLatentsInvocation),
image: canvasInitImage,
};
graph.nodes[MASK_BLUR] = {
...(graph.nodes[MASK_BLUR] as ImageBlurInvocation),
image: canvasMaskImage,
};
graph.edges.push(
// Color Correct The Inpainted Result
{
source: {
node_id: LATENTS_TO_IMAGE,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'image',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'mask',
},
},
// Paste Back Onto Original Image
{
source: {
node_id: COLOR_CORRECT,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'image',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'mask',
},
}
);
}
// Handle Seed
if (shouldRandomizeSeed) {
// Random int node to generate the starting seed
const randomIntNode: RandomIntInvocation = {
@ -251,15 +512,24 @@ export const buildCanvasInpaintGraph = (
(graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
}
// Add VAE
addVAEToGraph(state, graph, MAIN_MODEL_LOADER);
// add LoRA support
addLoRAsToGraph(state, graph, DENOISE_LATENTS, MAIN_MODEL_LOADER);
// add controlnet, mutating `graph`
addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
// NSFW & watermark - must be last thing added to graph
if (state.system.shouldUseNSFWChecker) {
// must add before watermarker!
addNSFWCheckerToGraph(state, graph, INPAINT);
addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT);
}
if (state.system.shouldUseWatermarker) {
// must add after nsfw checker!
addWatermarkerToGraph(state, graph, INPAINT);
addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
}
return graph;

677
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasOutpaintGraph.ts Normal file
View File

@ -0,0 +1,677 @@
import { logger } from 'app/logging/logger';
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import {
ImageBlurInvocation,
ImageDTO,
ImageToLatentsInvocation,
InfillPatchmatchInvocation,
InfillTileInvocation,
NoiseInvocation,
RandomIntInvocation,
RangeOfSizeInvocation,
} from 'services/api/types';
import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
import { addLoRAsToGraph } from './addLoRAsToGraph';
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CANVAS_OUTPAINT_GRAPH,
CANVAS_OUTPUT,
CLIP_SKIP,
COLOR_CORRECT,
DENOISE_LATENTS,
INPAINT_IMAGE,
INPAINT_IMAGE_RESIZE_DOWN,
INPAINT_IMAGE_RESIZE_UP,
INPAINT_INFILL,
INPAINT_INFILL_RESIZE_DOWN,
ITERATE,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
MASK_BLUR,
MASK_COMBINE,
MASK_FROM_ALPHA,
MASK_RESIZE_DOWN,
MASK_RESIZE_UP,
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
RANDOM_INT,
RANGE_OF_SIZE,
} from './constants';
/**
* Builds the Canvas tab's Outpaint graph.
*/
export const buildCanvasOutpaintGraph = (
state: RootState,
canvasInitImage: ImageDTO,
canvasMaskImage?: ImageDTO
): NonNullableGraph => {
const log = logger('nodes');
const {
positivePrompt,
negativePrompt,
model,
cfgScale: cfg_scale,
scheduler,
steps,
img2imgStrength: strength,
iterations,
seed,
shouldRandomizeSeed,
vaePrecision,
shouldUseNoiseSettings,
shouldUseCpuNoise,
maskBlur,
maskBlurMethod,
tileSize,
infillMethod,
clipSkip,
} = state.generation;
if (!model) {
log.error('No model found in state');
throw new Error('No model found in state');
}
// The bounding box determines width and height, not the width and height params
const { width, height } = state.canvas.boundingBoxDimensions;
// We may need to set the inpaint width and height to scale the image
const {
scaledBoundingBoxDimensions,
boundingBoxScaleMethod,
shouldAutoSave,
} = state.canvas;
const use_cpu = shouldUseNoiseSettings
? shouldUseCpuNoise
: shouldUseCpuNoise;
const graph: NonNullableGraph = {
id: CANVAS_OUTPAINT_GRAPH,
nodes: {
[MAIN_MODEL_LOADER]: {
type: 'main_model_loader',
id: MAIN_MODEL_LOADER,
is_intermediate: true,
model,
},
[CLIP_SKIP]: {
type: 'clip_skip',
id: CLIP_SKIP,
is_intermediate: true,
skipped_layers: clipSkip,
},
[POSITIVE_CONDITIONING]: {
type: 'compel',
id: POSITIVE_CONDITIONING,
is_intermediate: true,
prompt: positivePrompt,
},
[NEGATIVE_CONDITIONING]: {
type: 'compel',
id: NEGATIVE_CONDITIONING,
is_intermediate: true,
prompt: negativePrompt,
},
[MASK_FROM_ALPHA]: {
type: 'tomask',
id: MASK_FROM_ALPHA,
is_intermediate: true,
image: canvasInitImage,
},
[MASK_COMBINE]: {
type: 'mask_combine',
id: MASK_COMBINE,
is_intermediate: true,
mask2: canvasMaskImage,
},
[MASK_BLUR]: {
type: 'img_blur',
id: MASK_BLUR,
is_intermediate: true,
radius: maskBlur,
blur_type: maskBlurMethod,
},
[INPAINT_INFILL]: {
type: 'infill_tile',
id: INPAINT_INFILL,
is_intermediate: true,
tile_size: tileSize,
},
[INPAINT_IMAGE]: {
type: 'i2l',
id: INPAINT_IMAGE,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
},
[NOISE]: {
type: 'noise',
id: NOISE,
use_cpu,
is_intermediate: true,
},
[DENOISE_LATENTS]: {
type: 'denoise_latents',
id: DENOISE_LATENTS,
is_intermediate: true,
steps: steps,
cfg_scale: cfg_scale,
scheduler: scheduler,
denoising_start: 1 - strength,
denoising_end: 1,
},
[LATENTS_TO_IMAGE]: {
type: 'l2i',
id: LATENTS_TO_IMAGE,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
},
[COLOR_CORRECT]: {
type: 'color_correct',
id: COLOR_CORRECT,
is_intermediate: true,
},
[CANVAS_OUTPUT]: {
type: 'img_paste',
id: CANVAS_OUTPUT,
is_intermediate: !shouldAutoSave,
},
[RANGE_OF_SIZE]: {
type: 'range_of_size',
id: RANGE_OF_SIZE,
is_intermediate: true,
// seed - must be connected manually
// start: 0,
size: iterations,
step: 1,
},
[ITERATE]: {
type: 'iterate',
id: ITERATE,
is_intermediate: true,
},
},
edges: [
// Connect Model Loader To UNet & Clip Skip
{
source: {
node_id: MAIN_MODEL_LOADER,
field: 'unet',
},
destination: {
node_id: DENOISE_LATENTS,
field: 'unet',
},
},
{
source: {
node_id: MAIN_MODEL_LOADER,
field: 'clip',
},
destination: {
node_id: CLIP_SKIP,
field: 'clip',
},
},
// Connect CLIP Skip to Conditioning
{
source: {
node_id: CLIP_SKIP,
field: 'clip',
},
destination: {
node_id: POSITIVE_CONDITIONING,
field: 'clip',
},
},
{
source: {
node_id: CLIP_SKIP,
field: 'clip',
},
destination: {
node_id: NEGATIVE_CONDITIONING,
field: 'clip',
},
},
// Connect Infill Result To Inpaint Image
{
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: INPAINT_IMAGE,
field: 'image',
},
},
// Combine Mask from Init Image with User Painted Mask
{
source: {
node_id: MASK_FROM_ALPHA,
field: 'mask',
},
destination: {
node_id: MASK_COMBINE,
field: 'mask1',
},
},
// Plug Everything Into Inpaint Node
{
source: {
node_id: POSITIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: DENOISE_LATENTS,
field: 'positive_conditioning',
},
},
{
source: {
node_id: NEGATIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: DENOISE_LATENTS,
field: 'negative_conditioning',
},
},
{
source: {
node_id: NOISE,
field: 'noise',
},
destination: {
node_id: DENOISE_LATENTS,
field: 'noise',
},
},
{
source: {
node_id: INPAINT_IMAGE,
field: 'latents',
},
destination: {
node_id: DENOISE_LATENTS,
field: 'latents',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: DENOISE_LATENTS,
field: 'mask',
},
},
// Iterate
{
source: {
node_id: RANGE_OF_SIZE,
field: 'collection',
},
destination: {
node_id: ITERATE,
field: 'collection',
},
},
{
source: {
node_id: ITERATE,
field: 'item',
},
destination: {
node_id: NOISE,
field: 'seed',
},
},
// Decode the result from Inpaint
{
source: {
node_id: DENOISE_LATENTS,
field: 'latents',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'latents',
},
},
],
};
// Add Infill Nodes
if (infillMethod === 'patchmatch') {
graph.nodes[INPAINT_INFILL] = {
type: 'infill_patchmatch',
id: INPAINT_INFILL,
is_intermediate: true,
};
}
// Handle Scale Before Processing
if (['auto', 'manual'].includes(boundingBoxScaleMethod)) {
const scaledWidth: number = scaledBoundingBoxDimensions.width;
const scaledHeight: number = scaledBoundingBoxDimensions.height;
// Add Scaling Nodes
graph.nodes[INPAINT_IMAGE_RESIZE_UP] = {
type: 'img_resize',
id: INPAINT_IMAGE_RESIZE_UP,
is_intermediate: true,
width: scaledWidth,
height: scaledHeight,
image: canvasInitImage,
};
graph.nodes[MASK_RESIZE_UP] = {
type: 'img_resize',
id: MASK_RESIZE_UP,
is_intermediate: true,
width: scaledWidth,
height: scaledHeight,
};
graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = {
type: 'img_resize',
id: INPAINT_IMAGE_RESIZE_DOWN,
is_intermediate: true,
width: width,
height: height,
};
graph.nodes[INPAINT_INFILL_RESIZE_DOWN] = {
type: 'img_resize',
id: INPAINT_INFILL_RESIZE_DOWN,
is_intermediate: true,
width: width,
height: height,
};
graph.nodes[MASK_RESIZE_DOWN] = {
type: 'img_resize',
id: MASK_RESIZE_DOWN,
is_intermediate: true,
width: width,
height: height,
};
graph.nodes[NOISE] = {
...(graph.nodes[NOISE] as NoiseInvocation),
width: scaledWidth,
height: scaledHeight,
};
// Connect Nodes
graph.edges.push(
// Scale Inpaint Image
{
source: {
node_id: INPAINT_IMAGE_RESIZE_UP,
field: 'image',
},
destination: {
node_id: INPAINT_INFILL,
field: 'image',
},
},
// Take combined mask and resize and then blur
{
source: {
node_id: MASK_COMBINE,
field: 'image',
},
destination: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
},
{
source: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
destination: {
node_id: MASK_BLUR,
field: 'image',
},
},
// Resize Results Down
{
source: {
node_id: LATENTS_TO_IMAGE,
field: 'image',
},
destination: {
node_id: INPAINT_IMAGE_RESIZE_DOWN,
field: 'image',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: MASK_RESIZE_DOWN,
field: 'image',
},
},
{
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: INPAINT_INFILL_RESIZE_DOWN,
field: 'image',
},
},
// Color Correct The Inpainted Result
{
source: {
node_id: INPAINT_INFILL_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'reference',
},
},
{
source: {
node_id: INPAINT_IMAGE_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'image',
},
},
{
source: {
node_id: MASK_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'mask',
},
},
// Paste Everything Back
{
source: {
node_id: INPAINT_INFILL_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'base_image',
},
},
{
source: {
node_id: COLOR_CORRECT,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'image',
},
},
{
source: {
node_id: MASK_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'mask',
},
}
);
} else {
// Add Images To Nodes
graph.nodes[INPAINT_INFILL] = {
...(graph.nodes[INPAINT_INFILL] as
| InfillTileInvocation
| InfillPatchmatchInvocation),
image: canvasInitImage,
};
graph.nodes[NOISE] = {
...(graph.nodes[NOISE] as NoiseInvocation),
width: width,
height: height,
};
graph.nodes[INPAINT_IMAGE] = {
...(graph.nodes[INPAINT_IMAGE] as ImageToLatentsInvocation),
image: canvasInitImage,
};
graph.nodes[MASK_BLUR] = {
...(graph.nodes[MASK_BLUR] as ImageBlurInvocation),
image: canvasMaskImage,
};
graph.edges.push(
// Take combined mask and plug it to blur
{
source: {
node_id: MASK_COMBINE,
field: 'image',
},
destination: {
node_id: MASK_BLUR,
field: 'image',
},
},
// Color Correct The Inpainted Result
{
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'reference',
},
},
{
source: {
node_id: LATENTS_TO_IMAGE,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'image',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'mask',
},
},
// Paste Everything Back
{
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'base_image',
},
},
{
source: {
node_id: COLOR_CORRECT,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'image',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'mask',
},
}
);
}
// Handle Seed
if (shouldRandomizeSeed) {
// Random int node to generate the starting seed
const randomIntNode: RandomIntInvocation = {
id: RANDOM_INT,
type: 'rand_int',
};
graph.nodes[RANDOM_INT] = randomIntNode;
// Connect random int to the start of the range of size so the range starts on the random first seed
graph.edges.push({
source: { node_id: RANDOM_INT, field: 'a' },
destination: { node_id: RANGE_OF_SIZE, field: 'start' },
});
} else {
// User specified seed, so set the start of the range of size to the seed
(graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
}
// Add VAE
addVAEToGraph(state, graph, MAIN_MODEL_LOADER);
// add LoRA support
addLoRAsToGraph(state, graph, DENOISE_LATENTS, MAIN_MODEL_LOADER);
// add controlnet, mutating `graph`
addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
// NSFW & watermark - must be last thing added to graph
if (state.system.shouldUseNSFWChecker) {
// must add before watermarker!
addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT);
}
if (state.system.shouldUseWatermarker) {
// must add after nsfw checker!
addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
}
return graph;
};

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import { logger } from 'app/logging/logger';
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { initialGenerationState } from 'features/parameters/store/generationSlice';
import {
ImageDTO,
ImageResizeInvocation,
ImageToLatentsInvocation,
} from 'services/api/types';
import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CANVAS_OUTPUT,
IMAGE_TO_LATENTS,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
RESIZE,
SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH,
SDXL_DENOISE_LATENTS,
SDXL_MODEL_LOADER,
} from './constants';
import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
/**
* Builds the Canvas tab's Image to Image graph.
*/
export const buildCanvasSDXLImageToImageGraph = (
state: RootState,
initialImage: ImageDTO
): NonNullableGraph => {
const log = logger('nodes');
const {
positivePrompt,
negativePrompt,
model,
cfgScale: cfg_scale,
scheduler,
steps,
vaePrecision,
clipSkip,
shouldUseCpuNoise,
shouldUseNoiseSettings,
} = state.generation;
const {
shouldUseSDXLRefiner,
refinerStart,
sdxlImg2ImgDenoisingStrength: strength,
shouldConcatSDXLStylePrompt,
} = state.sdxl;
// The bounding box determines width and height, not the width and height params
const { width, height } = state.canvas.boundingBoxDimensions;
const { shouldAutoSave } = state.canvas;
if (!model) {
log.error('No model found in state');
throw new Error('No model found in state');
}
const use_cpu = shouldUseNoiseSettings
? shouldUseCpuNoise
: initialGenerationState.shouldUseCpuNoise;
// Construct Style Prompt
const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } =
craftSDXLStylePrompt(state, shouldConcatSDXLStylePrompt);
/**
* The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
* full graph here as a template. Then use the parameters from app state and set friendlier node
* ids.
*
* The only thing we need extra logic for is handling randomized seed, control net, and for img2img,
* the `fit` param. These are added to the graph at the end.
*/
// copy-pasted graph from node editor, filled in with state values & friendly node ids
const graph: NonNullableGraph = {
id: SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH,
nodes: {
[SDXL_MODEL_LOADER]: {
type: 'sdxl_model_loader',
id: SDXL_MODEL_LOADER,
model,
},
[POSITIVE_CONDITIONING]: {
type: 'sdxl_compel_prompt',
id: POSITIVE_CONDITIONING,
prompt: positivePrompt,
style: craftedPositiveStylePrompt,
},
[NEGATIVE_CONDITIONING]: {
type: 'sdxl_compel_prompt',
id: NEGATIVE_CONDITIONING,
prompt: negativePrompt,
style: craftedNegativeStylePrompt,
},
[NOISE]: {
type: 'noise',
id: NOISE,
is_intermediate: true,
use_cpu,
},
[IMAGE_TO_LATENTS]: {
type: 'i2l',
id: IMAGE_TO_LATENTS,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
// must be set manually later, bc `fit` parameter may require a resize node inserted
// image: {
// image_name: initialImage.image_name,
// },
},
[SDXL_DENOISE_LATENTS]: {
type: 'denoise_latents',
id: SDXL_DENOISE_LATENTS,
is_intermediate: true,
cfg_scale,
scheduler,
steps,
denoising_start: shouldUseSDXLRefiner
? Math.min(refinerStart, 1 - strength)
: 1 - strength,
denoising_end: shouldUseSDXLRefiner ? refinerStart : 1,
},
[CANVAS_OUTPUT]: {
type: 'l2i',
id: CANVAS_OUTPUT,
is_intermediate: !shouldAutoSave,
fp32: vaePrecision === 'fp32' ? true : false,
},
},
edges: [
// Connect Model Loader To UNet & CLIP
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'unet',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'unet',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'clip',
},
destination: {
node_id: POSITIVE_CONDITIONING,
field: 'clip',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'clip2',
},
destination: {
node_id: POSITIVE_CONDITIONING,
field: 'clip2',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'clip',
},
destination: {
node_id: NEGATIVE_CONDITIONING,
field: 'clip',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'clip2',
},
destination: {
node_id: NEGATIVE_CONDITIONING,
field: 'clip2',
},
},
// Connect Everything to Denoise Latents
{
source: {
node_id: POSITIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'positive_conditioning',
},
},
{
source: {
node_id: NEGATIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'negative_conditioning',
},
},
{
source: {
node_id: NOISE,
field: 'noise',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'noise',
},
},
{
source: {
node_id: IMAGE_TO_LATENTS,
field: 'latents',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'latents',
},
},
// Decode denoised latents to an image
{
source: {
node_id: SDXL_DENOISE_LATENTS,
field: 'latents',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'latents',
},
},
],
};
// handle `fit`
if (initialImage.width !== width || initialImage.height !== height) {
// The init image needs to be resized to the specified width and height before being passed to `IMAGE_TO_LATENTS`
// Create a resize node, explicitly setting its image
const resizeNode: ImageResizeInvocation = {
id: RESIZE,
type: 'img_resize',
image: {
image_name: initialImage.image_name,
},
is_intermediate: true,
width,
height,
};
graph.nodes[RESIZE] = resizeNode;
// The `RESIZE` node then passes its image to `IMAGE_TO_LATENTS`
graph.edges.push({
source: { node_id: RESIZE, field: 'image' },
destination: {
node_id: IMAGE_TO_LATENTS,
field: 'image',
},
});
// The `RESIZE` node also passes its width and height to `NOISE`
graph.edges.push({
source: { node_id: RESIZE, field: 'width' },
destination: {
node_id: NOISE,
field: 'width',
},
});
graph.edges.push({
source: { node_id: RESIZE, field: 'height' },
destination: {
node_id: NOISE,
field: 'height',
},
});
} else {
// We are not resizing, so we need to set the image on the `IMAGE_TO_LATENTS` node explicitly
(graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image = {
image_name: initialImage.image_name,
};
// Pass the image's dimensions to the `NOISE` node
graph.edges.push({
source: { node_id: IMAGE_TO_LATENTS, field: 'width' },
destination: {
node_id: NOISE,
field: 'width',
},
});
graph.edges.push({
source: { node_id: IMAGE_TO_LATENTS, field: 'height' },
destination: {
node_id: NOISE,
field: 'height',
},
});
}
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
generation_mode: 'img2img',
cfg_scale,
height,
width,
positive_prompt: '', // set in addDynamicPromptsToGraph
negative_prompt: negativePrompt,
model,
seed: 0, // set in addDynamicPromptsToGraph
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined, // option; set in addVAEToGraph
controlnets: [], // populated in addControlNetToLinearGraph
loras: [], // populated in addLoRAsToGraph
clip_skip: clipSkip,
strength,
init_image: initialImage.image_name,
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'metadata',
},
});
// add LoRA support
addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER);
// Add Refiner if enabled
if (shouldUseSDXLRefiner) {
addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS);
}
// optionally add custom VAE
addVAEToGraph(state, graph, SDXL_MODEL_LOADER);
// add dynamic prompts - also sets up core iteration and seed
addDynamicPromptsToGraph(state, graph);
// add controlnet, mutating `graph`
addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
// NSFW & watermark - must be last thing added to graph
if (state.system.shouldUseNSFWChecker) {
// must add before watermarker!
addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT);
}
if (state.system.shouldUseWatermarker) {
// must add after nsfw checker!
addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
}
return graph;
};

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import { logger } from 'app/logging/logger';
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import {
ImageBlurInvocation,
ImageDTO,
ImageToLatentsInvocation,
NoiseInvocation,
RandomIntInvocation,
RangeOfSizeInvocation,
} from 'services/api/types';
import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CANVAS_OUTPUT,
COLOR_CORRECT,
INPAINT_IMAGE,
INPAINT_IMAGE_RESIZE_DOWN,
INPAINT_IMAGE_RESIZE_UP,
ITERATE,
LATENTS_TO_IMAGE,
MASK_BLUR,
MASK_RESIZE_DOWN,
MASK_RESIZE_UP,
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
RANDOM_INT,
RANGE_OF_SIZE,
SDXL_CANVAS_INPAINT_GRAPH,
SDXL_DENOISE_LATENTS,
SDXL_MODEL_LOADER,
} from './constants';
import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
/**
* Builds the Canvas tab's Inpaint graph.
*/
export const buildCanvasSDXLInpaintGraph = (
state: RootState,
canvasInitImage: ImageDTO,
canvasMaskImage: ImageDTO
): NonNullableGraph => {
const log = logger('nodes');
const {
positivePrompt,
negativePrompt,
model,
cfgScale: cfg_scale,
scheduler,
steps,
iterations,
seed,
shouldRandomizeSeed,
vaePrecision,
shouldUseNoiseSettings,
shouldUseCpuNoise,
maskBlur,
maskBlurMethod,
} = state.generation;
const {
sdxlImg2ImgDenoisingStrength: strength,
shouldUseSDXLRefiner,
refinerStart,
shouldConcatSDXLStylePrompt,
} = state.sdxl;
if (!model) {
log.error('No model found in state');
throw new Error('No model found in state');
}
// The bounding box determines width and height, not the width and height params
const { width, height } = state.canvas.boundingBoxDimensions;
// We may need to set the inpaint width and height to scale the image
const {
scaledBoundingBoxDimensions,
boundingBoxScaleMethod,
shouldAutoSave,
} = state.canvas;
const use_cpu = shouldUseNoiseSettings
? shouldUseCpuNoise
: shouldUseCpuNoise;
// Construct Style Prompt
const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } =
craftSDXLStylePrompt(state, shouldConcatSDXLStylePrompt);
const graph: NonNullableGraph = {
id: SDXL_CANVAS_INPAINT_GRAPH,
nodes: {
[SDXL_MODEL_LOADER]: {
type: 'sdxl_model_loader',
id: SDXL_MODEL_LOADER,
model,
},
[POSITIVE_CONDITIONING]: {
type: 'sdxl_compel_prompt',
id: POSITIVE_CONDITIONING,
prompt: positivePrompt,
style: craftedPositiveStylePrompt,
},
[NEGATIVE_CONDITIONING]: {
type: 'sdxl_compel_prompt',
id: NEGATIVE_CONDITIONING,
prompt: negativePrompt,
style: craftedNegativeStylePrompt,
},
[MASK_BLUR]: {
type: 'img_blur',
id: MASK_BLUR,
is_intermediate: true,
radius: maskBlur,
blur_type: maskBlurMethod,
},
[INPAINT_IMAGE]: {
type: 'i2l',
id: INPAINT_IMAGE,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
},
[NOISE]: {
type: 'noise',
id: NOISE,
use_cpu,
is_intermediate: true,
},
[SDXL_DENOISE_LATENTS]: {
type: 'denoise_latents',
id: SDXL_DENOISE_LATENTS,
is_intermediate: true,
steps: steps,
cfg_scale: cfg_scale,
scheduler: scheduler,
denoising_start: shouldUseSDXLRefiner
? Math.min(refinerStart, 1 - strength)
: 1 - strength,
denoising_end: shouldUseSDXLRefiner ? refinerStart : 1,
},
[LATENTS_TO_IMAGE]: {
type: 'l2i',
id: LATENTS_TO_IMAGE,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
},
[COLOR_CORRECT]: {
type: 'color_correct',
id: COLOR_CORRECT,
is_intermediate: true,
reference: canvasInitImage,
},
[CANVAS_OUTPUT]: {
type: 'img_paste',
id: CANVAS_OUTPUT,
is_intermediate: !shouldAutoSave,
base_image: canvasInitImage,
},
[RANGE_OF_SIZE]: {
type: 'range_of_size',
id: RANGE_OF_SIZE,
is_intermediate: true,
// seed - must be connected manually
// start: 0,
size: iterations,
step: 1,
},
[ITERATE]: {
type: 'iterate',
id: ITERATE,
is_intermediate: true,
},
},
edges: [
// Connect Model Loader to UNet and CLIP
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'unet',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'unet',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'clip',
},
destination: {
node_id: POSITIVE_CONDITIONING,
field: 'clip',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'clip2',
},
destination: {
node_id: POSITIVE_CONDITIONING,
field: 'clip2',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'clip',
},
destination: {
node_id: NEGATIVE_CONDITIONING,
field: 'clip',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'clip2',
},
destination: {
node_id: NEGATIVE_CONDITIONING,
field: 'clip2',
},
},
// Connect everything to Inpaint
{
source: {
node_id: POSITIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'positive_conditioning',
},
},
{
source: {
node_id: NEGATIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'negative_conditioning',
},
},
{
source: {
node_id: NOISE,
field: 'noise',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'noise',
},
},
{
source: {
node_id: INPAINT_IMAGE,
field: 'latents',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'latents',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'mask',
},
},
// Iterate
{
source: {
node_id: RANGE_OF_SIZE,
field: 'collection',
},
destination: {
node_id: ITERATE,
field: 'collection',
},
},
{
source: {
node_id: ITERATE,
field: 'item',
},
destination: {
node_id: NOISE,
field: 'seed',
},
},
// Decode inpainted latents to image
{
source: {
node_id: SDXL_DENOISE_LATENTS,
field: 'latents',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'latents',
},
},
],
};
// Handle Scale Before Processing
if (['auto', 'manual'].includes(boundingBoxScaleMethod)) {
const scaledWidth: number = scaledBoundingBoxDimensions.width;
const scaledHeight: number = scaledBoundingBoxDimensions.height;
// Add Scaling Nodes
graph.nodes[INPAINT_IMAGE_RESIZE_UP] = {
type: 'img_resize',
id: INPAINT_IMAGE_RESIZE_UP,
is_intermediate: true,
width: scaledWidth,
height: scaledHeight,
image: canvasInitImage,
};
graph.nodes[MASK_RESIZE_UP] = {
type: 'img_resize',
id: MASK_RESIZE_UP,
is_intermediate: true,
width: scaledWidth,
height: scaledHeight,
image: canvasMaskImage,
};
graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = {
type: 'img_resize',
id: INPAINT_IMAGE_RESIZE_DOWN,
is_intermediate: true,
width: width,
height: height,
};
graph.nodes[MASK_RESIZE_DOWN] = {
type: 'img_resize',
id: MASK_RESIZE_DOWN,
is_intermediate: true,
width: width,
height: height,
};
graph.nodes[NOISE] = {
...(graph.nodes[NOISE] as NoiseInvocation),
width: scaledWidth,
height: scaledHeight,
};
// Connect Nodes
graph.edges.push(
// Scale Inpaint Image and Mask
{
source: {
node_id: INPAINT_IMAGE_RESIZE_UP,
field: 'image',
},
destination: {
node_id: INPAINT_IMAGE,
field: 'image',
},
},
{
source: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
destination: {
node_id: MASK_BLUR,
field: 'image',
},
},
// Color Correct The Inpainted Result
{
source: {
node_id: LATENTS_TO_IMAGE,
field: 'image',
},
destination: {
node_id: INPAINT_IMAGE_RESIZE_DOWN,
field: 'image',
},
},
{
source: {
node_id: INPAINT_IMAGE_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'image',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: MASK_RESIZE_DOWN,
field: 'image',
},
},
{
source: {
node_id: MASK_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'mask',
},
},
// Paste Back Onto Original Image
{
source: {
node_id: COLOR_CORRECT,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'image',
},
},
{
source: {
node_id: MASK_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'mask',
},
}
);
} else {
// Add Images To Nodes
graph.nodes[NOISE] = {
...(graph.nodes[NOISE] as NoiseInvocation),
width: width,
height: height,
};
graph.nodes[INPAINT_IMAGE] = {
...(graph.nodes[INPAINT_IMAGE] as ImageToLatentsInvocation),
image: canvasInitImage,
};
graph.nodes[MASK_BLUR] = {
...(graph.nodes[MASK_BLUR] as ImageBlurInvocation),
image: canvasMaskImage,
};
graph.edges.push(
// Color Correct The Inpainted Result
{
source: {
node_id: LATENTS_TO_IMAGE,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'image',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'mask',
},
},
// Paste Back Onto Original Image
{
source: {
node_id: COLOR_CORRECT,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'image',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'mask',
},
}
);
}
// Handle Seed
if (shouldRandomizeSeed) {
// Random int node to generate the starting seed
const randomIntNode: RandomIntInvocation = {
id: RANDOM_INT,
type: 'rand_int',
};
graph.nodes[RANDOM_INT] = randomIntNode;
// Connect random int to the start of the range of size so the range starts on the random first seed
graph.edges.push({
source: { node_id: RANDOM_INT, field: 'a' },
destination: { node_id: RANGE_OF_SIZE, field: 'start' },
});
} else {
// User specified seed, so set the start of the range of size to the seed
(graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
}
// Add Refiner if enabled
if (shouldUseSDXLRefiner) {
addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS);
}
// optionally add custom VAE
addVAEToGraph(state, graph, SDXL_MODEL_LOADER);
// add LoRA support
addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER);
// add controlnet, mutating `graph`
addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
// NSFW & watermark - must be last thing added to graph
if (state.system.shouldUseNSFWChecker) {
// must add before watermarker!
addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT);
}
if (state.system.shouldUseWatermarker) {
// must add after nsfw checker!
addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
}
return graph;
};

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import { logger } from 'app/logging/logger';
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import {
ImageBlurInvocation,
ImageDTO,
ImageToLatentsInvocation,
InfillPatchmatchInvocation,
InfillTileInvocation,
NoiseInvocation,
RandomIntInvocation,
RangeOfSizeInvocation,
} from 'services/api/types';
import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CANVAS_OUTPUT,
COLOR_CORRECT,
INPAINT_IMAGE,
INPAINT_IMAGE_RESIZE_DOWN,
INPAINT_IMAGE_RESIZE_UP,
INPAINT_INFILL,
INPAINT_INFILL_RESIZE_DOWN,
ITERATE,
LATENTS_TO_IMAGE,
MASK_BLUR,
MASK_COMBINE,
MASK_FROM_ALPHA,
MASK_RESIZE_DOWN,
MASK_RESIZE_UP,
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
RANDOM_INT,
RANGE_OF_SIZE,
SDXL_CANVAS_OUTPAINT_GRAPH,
SDXL_DENOISE_LATENTS,
SDXL_MODEL_LOADER,
} from './constants';
import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
/**
* Builds the Canvas tab's Outpaint graph.
*/
export const buildCanvasSDXLOutpaintGraph = (
state: RootState,
canvasInitImage: ImageDTO,
canvasMaskImage?: ImageDTO
): NonNullableGraph => {
const log = logger('nodes');
const {
positivePrompt,
negativePrompt,
model,
cfgScale: cfg_scale,
scheduler,
steps,
iterations,
seed,
shouldRandomizeSeed,
vaePrecision,
shouldUseNoiseSettings,
shouldUseCpuNoise,
maskBlur,
maskBlurMethod,
tileSize,
infillMethod,
} = state.generation;
const {
sdxlImg2ImgDenoisingStrength: strength,
shouldUseSDXLRefiner,
refinerStart,
shouldConcatSDXLStylePrompt,
} = state.sdxl;
if (!model) {
log.error('No model found in state');
throw new Error('No model found in state');
}
// The bounding box determines width and height, not the width and height params
const { width, height } = state.canvas.boundingBoxDimensions;
// We may need to set the inpaint width and height to scale the image
const {
scaledBoundingBoxDimensions,
boundingBoxScaleMethod,
shouldAutoSave,
} = state.canvas;
const use_cpu = shouldUseNoiseSettings
? shouldUseCpuNoise
: shouldUseCpuNoise;
// Construct Style Prompt
const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } =
craftSDXLStylePrompt(state, shouldConcatSDXLStylePrompt);
const graph: NonNullableGraph = {
id: SDXL_CANVAS_OUTPAINT_GRAPH,
nodes: {
[SDXL_MODEL_LOADER]: {
type: 'sdxl_model_loader',
id: SDXL_MODEL_LOADER,
model,
},
[POSITIVE_CONDITIONING]: {
type: 'sdxl_compel_prompt',
id: POSITIVE_CONDITIONING,
prompt: positivePrompt,
style: craftedPositiveStylePrompt,
},
[NEGATIVE_CONDITIONING]: {
type: 'sdxl_compel_prompt',
id: NEGATIVE_CONDITIONING,
prompt: negativePrompt,
style: craftedNegativeStylePrompt,
},
[MASK_FROM_ALPHA]: {
type: 'tomask',
id: MASK_FROM_ALPHA,
is_intermediate: true,
image: canvasInitImage,
},
[MASK_COMBINE]: {
type: 'mask_combine',
id: MASK_COMBINE,
is_intermediate: true,
mask2: canvasMaskImage,
},
[MASK_BLUR]: {
type: 'img_blur',
id: MASK_BLUR,
is_intermediate: true,
radius: maskBlur,
blur_type: maskBlurMethod,
},
[INPAINT_INFILL]: {
type: 'infill_tile',
id: INPAINT_INFILL,
is_intermediate: true,
tile_size: tileSize,
},
[INPAINT_IMAGE]: {
type: 'i2l',
id: INPAINT_IMAGE,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
},
[NOISE]: {
type: 'noise',
id: NOISE,
use_cpu,
is_intermediate: true,
},
[SDXL_DENOISE_LATENTS]: {
type: 'denoise_latents',
id: SDXL_DENOISE_LATENTS,
is_intermediate: true,
steps: steps,
cfg_scale: cfg_scale,
scheduler: scheduler,
denoising_start: shouldUseSDXLRefiner
? Math.min(refinerStart, 1 - strength)
: 1 - strength,
denoising_end: shouldUseSDXLRefiner ? refinerStart : 1,
},
[LATENTS_TO_IMAGE]: {
type: 'l2i',
id: LATENTS_TO_IMAGE,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
},
[COLOR_CORRECT]: {
type: 'color_correct',
id: COLOR_CORRECT,
is_intermediate: true,
},
[CANVAS_OUTPUT]: {
type: 'img_paste',
id: CANVAS_OUTPUT,
is_intermediate: !shouldAutoSave,
},
[RANGE_OF_SIZE]: {
type: 'range_of_size',
id: RANGE_OF_SIZE,
is_intermediate: true,
// seed - must be connected manually
// start: 0,
size: iterations,
step: 1,
},
[ITERATE]: {
type: 'iterate',
id: ITERATE,
is_intermediate: true,
},
},
edges: [
// Connect Model Loader To UNet and CLIP
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'unet',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'unet',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'clip',
},
destination: {
node_id: POSITIVE_CONDITIONING,
field: 'clip',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'clip2',
},
destination: {
node_id: POSITIVE_CONDITIONING,
field: 'clip2',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'clip',
},
destination: {
node_id: NEGATIVE_CONDITIONING,
field: 'clip',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'clip2',
},
destination: {
node_id: NEGATIVE_CONDITIONING,
field: 'clip2',
},
},
// Connect Infill Result To Inpaint Image
{
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: INPAINT_IMAGE,
field: 'image',
},
},
// Combine Mask from Init Image with User Painted Mask
{
source: {
node_id: MASK_FROM_ALPHA,
field: 'mask',
},
destination: {
node_id: MASK_COMBINE,
field: 'mask1',
},
},
// Connect Everything To Inpaint
{
source: {
node_id: POSITIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'positive_conditioning',
},
},
{
source: {
node_id: NEGATIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'negative_conditioning',
},
},
{
source: {
node_id: NOISE,
field: 'noise',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'noise',
},
},
{
source: {
node_id: INPAINT_IMAGE,
field: 'latents',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'latents',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'mask',
},
},
// Iterate
{
source: {
node_id: RANGE_OF_SIZE,
field: 'collection',
},
destination: {
node_id: ITERATE,
field: 'collection',
},
},
{
source: {
node_id: ITERATE,
field: 'item',
},
destination: {
node_id: NOISE,
field: 'seed',
},
},
// Decode inpainted latents to image
{
source: {
node_id: SDXL_DENOISE_LATENTS,
field: 'latents',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'latents',
},
},
],
};
// Add Infill Nodes
if (infillMethod === 'patchmatch') {
graph.nodes[INPAINT_INFILL] = {
type: 'infill_patchmatch',
id: INPAINT_INFILL,
is_intermediate: true,
};
}
// Handle Scale Before Processing
if (['auto', 'manual'].includes(boundingBoxScaleMethod)) {
const scaledWidth: number = scaledBoundingBoxDimensions.width;
const scaledHeight: number = scaledBoundingBoxDimensions.height;
// Add Scaling Nodes
graph.nodes[INPAINT_IMAGE_RESIZE_UP] = {
type: 'img_resize',
id: INPAINT_IMAGE_RESIZE_UP,
is_intermediate: true,
width: scaledWidth,
height: scaledHeight,
image: canvasInitImage,
};
graph.nodes[MASK_RESIZE_UP] = {
type: 'img_resize',
id: MASK_RESIZE_UP,
is_intermediate: true,
width: scaledWidth,
height: scaledHeight,
};
graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = {
type: 'img_resize',
id: INPAINT_IMAGE_RESIZE_DOWN,
is_intermediate: true,
width: width,
height: height,
};
graph.nodes[INPAINT_INFILL_RESIZE_DOWN] = {
type: 'img_resize',
id: INPAINT_INFILL_RESIZE_DOWN,
is_intermediate: true,
width: width,
height: height,
};
graph.nodes[MASK_RESIZE_DOWN] = {
type: 'img_resize',
id: MASK_RESIZE_DOWN,
is_intermediate: true,
width: width,
height: height,
};
graph.nodes[NOISE] = {
...(graph.nodes[NOISE] as NoiseInvocation),
width: scaledWidth,
height: scaledHeight,
};
// Connect Nodes
graph.edges.push(
// Scale Inpaint Image
{
source: {
node_id: INPAINT_IMAGE_RESIZE_UP,
field: 'image',
},
destination: {
node_id: INPAINT_INFILL,
field: 'image',
},
},
// Take combined mask and resize and then blur
{
source: {
node_id: MASK_COMBINE,
field: 'image',
},
destination: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
},
{
source: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
destination: {
node_id: MASK_BLUR,
field: 'image',
},
},
// Resize Results Down
{
source: {
node_id: LATENTS_TO_IMAGE,
field: 'image',
},
destination: {
node_id: INPAINT_IMAGE_RESIZE_DOWN,
field: 'image',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: MASK_RESIZE_DOWN,
field: 'image',
},
},
{
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: INPAINT_INFILL_RESIZE_DOWN,
field: 'image',
},
},
// Color Correct The Inpainted Result
{
source: {
node_id: INPAINT_INFILL_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'reference',
},
},
{
source: {
node_id: INPAINT_IMAGE_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'image',
},
},
{
source: {
node_id: MASK_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'mask',
},
},
// Paste Everything Back
{
source: {
node_id: INPAINT_INFILL_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'base_image',
},
},
{
source: {
node_id: COLOR_CORRECT,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'image',
},
},
{
source: {
node_id: MASK_RESIZE_DOWN,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'mask',
},
}
);
} else {
// Add Images To Nodes
graph.nodes[INPAINT_INFILL] = {
...(graph.nodes[INPAINT_INFILL] as
| InfillTileInvocation
| InfillPatchmatchInvocation),
image: canvasInitImage,
};
graph.nodes[NOISE] = {
...(graph.nodes[NOISE] as NoiseInvocation),
width: width,
height: height,
};
graph.nodes[INPAINT_IMAGE] = {
...(graph.nodes[INPAINT_IMAGE] as ImageToLatentsInvocation),
image: canvasInitImage,
};
graph.nodes[MASK_BLUR] = {
...(graph.nodes[MASK_BLUR] as ImageBlurInvocation),
image: canvasMaskImage,
};
graph.edges.push(
// Take combined mask and plug it to blur
{
source: {
node_id: MASK_COMBINE,
field: 'image',
},
destination: {
node_id: MASK_BLUR,
field: 'image',
},
},
// Color Correct The Inpainted Result
{
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'reference',
},
},
{
source: {
node_id: LATENTS_TO_IMAGE,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'image',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: COLOR_CORRECT,
field: 'mask',
},
},
// Paste Everything Back
{
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'base_image',
},
},
{
source: {
node_id: COLOR_CORRECT,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'image',
},
},
{
source: {
node_id: MASK_BLUR,
field: 'image',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'mask',
},
}
);
}
// Handle seed
if (shouldRandomizeSeed) {
// Random int node to generate the starting seed
const randomIntNode: RandomIntInvocation = {
id: RANDOM_INT,
type: 'rand_int',
};
graph.nodes[RANDOM_INT] = randomIntNode;
// Connect random int to the start of the range of size so the range starts on the random first seed
graph.edges.push({
source: { node_id: RANDOM_INT, field: 'a' },
destination: { node_id: RANGE_OF_SIZE, field: 'start' },
});
} else {
// User specified seed, so set the start of the range of size to the seed
(graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
}
// Add Refiner if enabled
if (shouldUseSDXLRefiner) {
addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS);
}
// optionally add custom VAE
addVAEToGraph(state, graph, SDXL_MODEL_LOADER);
// add LoRA support
addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER);
// add controlnet, mutating `graph`
addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
// NSFW & watermark - must be last thing added to graph
if (state.system.shouldUseNSFWChecker) {
// must add before watermarker!
addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT);
}
if (state.system.shouldUseWatermarker) {
// must add after nsfw checker!
addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
}
return graph;
};

309
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLTextToImageGraph.ts Normal file
View File

@ -0,0 +1,309 @@
import { logger } from 'app/logging/logger';
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { initialGenerationState } from 'features/parameters/store/generationSlice';
import {
DenoiseLatentsInvocation,
ONNXTextToLatentsInvocation,
} from 'services/api/types';
import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CANVAS_OUTPUT,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
ONNX_MODEL_LOADER,
POSITIVE_CONDITIONING,
SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH,
SDXL_DENOISE_LATENTS,
SDXL_MODEL_LOADER,
} from './constants';
import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
/**
* Builds the Canvas tab's Text to Image graph.
*/
export const buildCanvasSDXLTextToImageGraph = (
state: RootState
): NonNullableGraph => {
const log = logger('nodes');
const {
positivePrompt,
negativePrompt,
model,
cfgScale: cfg_scale,
scheduler,
steps,
vaePrecision,
clipSkip,
shouldUseCpuNoise,
shouldUseNoiseSettings,
} = state.generation;
// The bounding box determines width and height, not the width and height params
const { width, height } = state.canvas.boundingBoxDimensions;
const { shouldAutoSave } = state.canvas;
const { shouldUseSDXLRefiner, refinerStart, shouldConcatSDXLStylePrompt } =
state.sdxl;
if (!model) {
log.error('No model found in state');
throw new Error('No model found in state');
}
const use_cpu = shouldUseNoiseSettings
? shouldUseCpuNoise
: initialGenerationState.shouldUseCpuNoise;
const isUsingOnnxModel = model.model_type === 'onnx';
const modelLoaderNodeId = isUsingOnnxModel
? ONNX_MODEL_LOADER
: SDXL_MODEL_LOADER;
const modelLoaderNodeType = isUsingOnnxModel
? 'onnx_model_loader'
: 'sdxl_model_loader';
const t2lNode: DenoiseLatentsInvocation | ONNXTextToLatentsInvocation =
isUsingOnnxModel
? {
type: 't2l_onnx',
id: SDXL_DENOISE_LATENTS,
is_intermediate: true,
cfg_scale,
scheduler,
steps,
}
: {
type: 'denoise_latents',
id: SDXL_DENOISE_LATENTS,
is_intermediate: true,
cfg_scale,
scheduler,
steps,
denoising_start: 0,
denoising_end: shouldUseSDXLRefiner ? refinerStart : 1,
};
// Construct Style Prompt
const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } =
craftSDXLStylePrompt(state, shouldConcatSDXLStylePrompt);
/**
* The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
* full graph here as a template. Then use the parameters from app state and set friendlier node
* ids.
*
* The only thing we need extra logic for is handling randomized seed, control net, and for img2img,
* the `fit` param. These are added to the graph at the end.
*/
// copy-pasted graph from node editor, filled in with state values & friendly node ids
// TODO: Actually create the graph correctly for ONNX
const graph: NonNullableGraph = {
id: SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH,
nodes: {
[modelLoaderNodeId]: {
type: modelLoaderNodeType,
id: modelLoaderNodeId,
is_intermediate: true,
model,
},
[POSITIVE_CONDITIONING]: {
type: isUsingOnnxModel ? 'prompt_onnx' : 'sdxl_compel_prompt',
id: POSITIVE_CONDITIONING,
is_intermediate: true,
prompt: positivePrompt,
style: craftedPositiveStylePrompt,
},
[NEGATIVE_CONDITIONING]: {
type: isUsingOnnxModel ? 'prompt_onnx' : 'sdxl_compel_prompt',
id: NEGATIVE_CONDITIONING,
is_intermediate: true,
prompt: negativePrompt,
style: craftedNegativeStylePrompt,
},
[NOISE]: {
type: 'noise',
id: NOISE,
is_intermediate: true,
width,
height,
use_cpu,
},
[t2lNode.id]: t2lNode,
[CANVAS_OUTPUT]: {
type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
id: CANVAS_OUTPUT,
is_intermediate: !shouldAutoSave,
fp32: vaePrecision === 'fp32' ? true : false,
},
},
edges: [
// Connect Model Loader to UNet and CLIP
{
source: {
node_id: modelLoaderNodeId,
field: 'unet',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'unet',
},
},
{
source: {
node_id: modelLoaderNodeId,
field: 'clip',
},
destination: {
node_id: POSITIVE_CONDITIONING,
field: 'clip',
},
},
{
source: {
node_id: modelLoaderNodeId,
field: 'clip2',
},
destination: {
node_id: POSITIVE_CONDITIONING,
field: 'clip2',
},
},
{
source: {
node_id: modelLoaderNodeId,
field: 'clip',
},
destination: {
node_id: NEGATIVE_CONDITIONING,
field: 'clip',
},
},
{
source: {
node_id: modelLoaderNodeId,
field: 'clip2',
},
destination: {
node_id: NEGATIVE_CONDITIONING,
field: 'clip2',
},
},
// Connect everything to Denoise Latents
{
source: {
node_id: POSITIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'positive_conditioning',
},
},
{
source: {
node_id: NEGATIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'negative_conditioning',
},
},
{
source: {
node_id: NOISE,
field: 'noise',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'noise',
},
},
// Decode Denoised Latents To Image
{
source: {
node_id: SDXL_DENOISE_LATENTS,
field: 'latents',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'latents',
},
},
],
};
// add metadata accumulator, which is only mostly populated - some fields are added later
graph.nodes[METADATA_ACCUMULATOR] = {
id: METADATA_ACCUMULATOR,
type: 'metadata_accumulator',
generation_mode: 'txt2img',
cfg_scale,
height,
width,
positive_prompt: '', // set in addDynamicPromptsToGraph
negative_prompt: negativePrompt,
model,
seed: 0, // set in addDynamicPromptsToGraph
steps,
rand_device: use_cpu ? 'cpu' : 'cuda',
scheduler,
vae: undefined, // option; set in addVAEToGraph
controlnets: [], // populated in addControlNetToLinearGraph
loras: [], // populated in addLoRAsToGraph
clip_skip: clipSkip,
};
graph.edges.push({
source: {
node_id: METADATA_ACCUMULATOR,
field: 'metadata',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'metadata',
},
});
// Add Refiner if enabled
if (shouldUseSDXLRefiner) {
addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS);
}
// add LoRA support
addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
// optionally add custom VAE
addVAEToGraph(state, graph, modelLoaderNodeId);
// add dynamic prompts - also sets up core iteration and seed
addDynamicPromptsToGraph(state, graph);
// add controlnet, mutating `graph`
addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
// NSFW & watermark - must be last thing added to graph
if (state.system.shouldUseNSFWChecker) {
// must add before watermarker!
addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT);
}
if (state.system.shouldUseWatermarker) {
// must add after nsfw checker!
addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
}
return graph;
};

177
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts
View File

@ -2,6 +2,10 @@ import { logger } from 'app/logging/logger';
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { initialGenerationState } from 'features/parameters/store/generationSlice';
import {
DenoiseLatentsInvocation,
ONNXTextToLatentsInvocation,
} from 'services/api/types';
import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
import { addLoRAsToGraph } from './addLoRAsToGraph';
@ -9,21 +13,17 @@ import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CANVAS_OUTPUT,
CANVAS_TEXT_TO_IMAGE_GRAPH,
CLIP_SKIP,
LATENTS_TO_IMAGE,
DENOISE_LATENTS,
MAIN_MODEL_LOADER,
ONNX_MODEL_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
ONNX_MODEL_LOADER,
POSITIVE_CONDITIONING,
TEXT_TO_IMAGE_GRAPH,
TEXT_TO_LATENTS,
} from './constants';
import {
ONNXTextToLatentsInvocation,
TextToLatentsInvocation,
} from 'services/api/types';
/**
* Builds the Canvas tab's Text to Image graph.
@ -57,31 +57,38 @@ export const buildCanvasTextToImageGraph = (
const use_cpu = shouldUseNoiseSettings
? shouldUseCpuNoise
: initialGenerationState.shouldUseCpuNoise;
const isUsingOnnxModel = model.model_type === 'onnx';
const modelLoaderNodeId = isUsingOnnxModel
? ONNX_MODEL_LOADER
: MAIN_MODEL_LOADER;
const modelLoaderNodeType = isUsingOnnxModel
? 'onnx_model_loader'
: 'main_model_loader';
const t2lNode: TextToLatentsInvocation | ONNXTextToLatentsInvocation =
const t2lNode: DenoiseLatentsInvocation | ONNXTextToLatentsInvocation =
isUsingOnnxModel
? {
type: 't2l_onnx',
id: TEXT_TO_LATENTS,
id: DENOISE_LATENTS,
is_intermediate: true,
cfg_scale,
scheduler,
steps,
}
: {
type: 't2l',
id: TEXT_TO_LATENTS,
type: 'denoise_latents',
id: DENOISE_LATENTS,
is_intermediate: true,
cfg_scale,
scheduler,
steps,
denoising_start: 0,
denoising_end: 1,
};
/**
* The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
* full graph here as a template. Then use the parameters from app state and set friendlier node
@ -94,8 +101,20 @@ export const buildCanvasTextToImageGraph = (
// copy-pasted graph from node editor, filled in with state values & friendly node ids
// TODO: Actually create the graph correctly for ONNX
const graph: NonNullableGraph = {
id: TEXT_TO_IMAGE_GRAPH,
id: CANVAS_TEXT_TO_IMAGE_GRAPH,
nodes: {
[modelLoaderNodeId]: {
type: modelLoaderNodeType,
id: modelLoaderNodeId,
is_intermediate: true,
model,
},
[CLIP_SKIP]: {
type: 'clip_skip',
id: CLIP_SKIP,
is_intermediate: true,
skipped_layers: clipSkip,
},
[POSITIVE_CONDITIONING]: {
type: isUsingOnnxModel ? 'prompt_onnx' : 'compel',
id: POSITIVE_CONDITIONING,
@ -117,93 +136,74 @@ export const buildCanvasTextToImageGraph = (
use_cpu,
},
[t2lNode.id]: t2lNode,
[modelLoaderNodeId]: {
type: modelLoaderNodeType,
id: modelLoaderNodeId,
is_intermediate: true,
model,
},
[CLIP_SKIP]: {
type: 'clip_skip',
id: CLIP_SKIP,
is_intermediate: true,
skipped_layers: clipSkip,
},
[LATENTS_TO_IMAGE]: {
[CANVAS_OUTPUT]: {
type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
id: LATENTS_TO_IMAGE,
id: CANVAS_OUTPUT,
is_intermediate: !shouldAutoSave,
},
},
edges: [
// Connect Model Loader to UNet & CLIP Skip
{
source: {
node_id: NEGATIVE_CONDITIONING,
field: 'conditioning',
node_id: modelLoaderNodeId,
field: 'unet',
},
destination: {
node_id: TEXT_TO_LATENTS,
field: 'negative_conditioning',
node_id: DENOISE_LATENTS,
field: 'unet',
},
},
{
source: {
node_id: modelLoaderNodeId,
field: 'clip',
},
destination: {
node_id: CLIP_SKIP,
field: 'clip',
},
},
// Connect CLIP Skip to Conditioning
{
source: {
node_id: CLIP_SKIP,
field: 'clip',
},
destination: {
node_id: POSITIVE_CONDITIONING,
field: 'clip',
},
},
{
source: {
node_id: CLIP_SKIP,
field: 'clip',
},
destination: {
node_id: NEGATIVE_CONDITIONING,
field: 'clip',
},
},
// Connect everything to Denoise Latents
{
source: {
node_id: POSITIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: TEXT_TO_LATENTS,
node_id: DENOISE_LATENTS,
field: 'positive_conditioning',
},
},
{
source: {
node_id: modelLoaderNodeId,
field: 'clip',
},
destination: {
node_id: CLIP_SKIP,
field: 'clip',
},
},
{
source: {
node_id: CLIP_SKIP,
field: 'clip',
},
destination: {
node_id: POSITIVE_CONDITIONING,
field: 'clip',
},
},
{
source: {
node_id: CLIP_SKIP,
field: 'clip',
},
destination: {
node_id: NEGATIVE_CONDITIONING,
field: 'clip',
},
},
{
source: {
node_id: modelLoaderNodeId,
field: 'unet',
field: 'conditioning',
},
destination: {
node_id: TEXT_TO_LATENTS,
field: 'unet',
},
},
{
source: {
node_id: TEXT_TO_LATENTS,
field: 'latents',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'latents',
node_id: DENOISE_LATENTS,
field: 'negative_conditioning',
},
},
{
@ -212,10 +212,21 @@ export const buildCanvasTextToImageGraph = (
field: 'noise',
},
destination: {
node_id: TEXT_TO_LATENTS,
node_id: DENOISE_LATENTS,
field: 'noise',
},
},
// Decode denoised latents to image
{
source: {
node_id: DENOISE_LATENTS,
field: 'latents',
},
destination: {
node_id: CANVAS_OUTPUT,
field: 'latents',
},
},
],
};
@ -246,32 +257,32 @@ export const buildCanvasTextToImageGraph = (
field: 'metadata',
},
destination: {
node_id: LATENTS_TO_IMAGE,
node_id: CANVAS_OUTPUT,
field: 'metadata',
},
});
// add LoRA support
addLoRAsToGraph(state, graph, TEXT_TO_LATENTS, modelLoaderNodeId);
// optionally add custom VAE
addVAEToGraph(state, graph, modelLoaderNodeId);
// add LoRA support
addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
// add dynamic prompts - also sets up core iteration and seed
addDynamicPromptsToGraph(state, graph);
// add controlnet, mutating `graph`
addControlNetToLinearGraph(state, graph, TEXT_TO_LATENTS);
addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
// NSFW & watermark - must be last thing added to graph
if (state.system.shouldUseNSFWChecker) {
// must add before watermarker!
addNSFWCheckerToGraph(state, graph);
addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT);
}
if (state.system.shouldUseWatermarker) {
// must add after nsfw checker!
addWatermarkerToGraph(state, graph);
addWatermarkerToGraph(state, graph, CANVAS_OUTPUT);
}
return graph;

61
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts
View File

@ -14,10 +14,10 @@ import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CLIP_SKIP,
DENOISE_LATENTS,
IMAGE_TO_IMAGE_GRAPH,
IMAGE_TO_LATENTS,
LATENTS_TO_IMAGE,
LATENTS_TO_LATENTS,
MAIN_MODEL_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
@ -118,13 +118,14 @@ export const buildLinearImageToImageGraph = (
id: LATENTS_TO_IMAGE,
fp32: vaePrecision === 'fp32' ? true : false,
},
[LATENTS_TO_LATENTS]: {
type: 'l2l',
id: LATENTS_TO_LATENTS,
[DENOISE_LATENTS]: {
type: 'denoise_latents',
id: DENOISE_LATENTS,
cfg_scale,
scheduler,
steps,
strength,
denoising_start: 1 - strength,
denoising_end: 1,
},
[IMAGE_TO_LATENTS]: {
type: 'i2l',
@ -137,13 +138,14 @@ export const buildLinearImageToImageGraph = (
},
},
edges: [
// Connect Model Loader to UNet and CLIP Skip
{
source: {
node_id: MAIN_MODEL_LOADER,
field: 'unet',
},
destination: {
node_id: LATENTS_TO_LATENTS,
node_id: DENOISE_LATENTS,
field: 'unet',
},
},
@ -157,6 +159,7 @@ export const buildLinearImageToImageGraph = (
field: 'clip',
},
},
// Connect CLIP Skip to Conditioning
{
source: {
node_id: CLIP_SKIP,
@ -177,24 +180,25 @@ export const buildLinearImageToImageGraph = (
field: 'clip',
},
},
// Connect everything to Denoise Latents
{
source: {
node_id: LATENTS_TO_LATENTS,
field: 'latents',
node_id: POSITIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'latents',
node_id: DENOISE_LATENTS,
field: 'positive_conditioning',
},
},
{
source: {
node_id: IMAGE_TO_LATENTS,
field: 'latents',
node_id: NEGATIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: LATENTS_TO_LATENTS,
field: 'latents',
node_id: DENOISE_LATENTS,
field: 'negative_conditioning',
},
},
{
@ -203,28 +207,29 @@ export const buildLinearImageToImageGraph = (
field: 'noise',
},
destination: {
node_id: LATENTS_TO_LATENTS,
node_id: DENOISE_LATENTS,
field: 'noise',
},
},
{
source: {
node_id: NEGATIVE_CONDITIONING,
field: 'conditioning',
node_id: IMAGE_TO_LATENTS,
field: 'latents',
},
destination: {
node_id: LATENTS_TO_LATENTS,
field: 'negative_conditioning',
node_id: DENOISE_LATENTS,
field: 'latents',
},
},
// Decode denoised latents to image
{
source: {
node_id: POSITIVE_CONDITIONING,
field: 'conditioning',
node_id: DENOISE_LATENTS,
field: 'latents',
},
destination: {
node_id: LATENTS_TO_LATENTS,
field: 'positive_conditioning',
node_id: LATENTS_TO_IMAGE,
field: 'latents',
},
},
],
@ -333,17 +338,17 @@ export const buildLinearImageToImageGraph = (
},
});
// add LoRA support
addLoRAsToGraph(state, graph, LATENTS_TO_LATENTS);
// optionally add custom VAE
addVAEToGraph(state, graph);
addVAEToGraph(state, graph, MAIN_MODEL_LOADER);
// add LoRA support
addLoRAsToGraph(state, graph, DENOISE_LATENTS);
// add dynamic prompts - also sets up core iteration and seed
addDynamicPromptsToGraph(state, graph);
// add controlnet, mutating `graph`
addControlNetToLinearGraph(state, graph, LATENTS_TO_LATENTS);
addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
// NSFW & watermark - must be last thing added to graph
if (state.system.shouldUseNSFWChecker) {

124
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts
View File

@ -6,9 +6,12 @@ import {
ImageResizeInvocation,
ImageToLatentsInvocation,
} from 'services/api/types';
import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
IMAGE_TO_LATENTS,
@ -18,11 +21,11 @@ import {
NOISE,
POSITIVE_CONDITIONING,
RESIZE,
SDXL_DENOISE_LATENTS,
SDXL_IMAGE_TO_IMAGE_GRAPH,
SDXL_LATENTS_TO_LATENTS,
SDXL_MODEL_LOADER,
} from './constants';
import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
/**
* Builds the Image to Image tab graph.
@ -80,6 +83,10 @@ export const buildLinearSDXLImageToImageGraph = (
? shouldUseCpuNoise
: initialGenerationState.shouldUseCpuNoise;
// Construct Style Prompt
const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } =
craftSDXLStylePrompt(state, shouldConcatSDXLStylePrompt);
// copy-pasted graph from node editor, filled in with state values & friendly node ids
const graph: NonNullableGraph = {
id: SDXL_IMAGE_TO_IMAGE_GRAPH,
@ -93,17 +100,13 @@ export const buildLinearSDXLImageToImageGraph = (
type: 'sdxl_compel_prompt',
id: POSITIVE_CONDITIONING,
prompt: positivePrompt,
style: shouldConcatSDXLStylePrompt
? `${positivePrompt} ${positiveStylePrompt}`
: positiveStylePrompt,
style: craftedPositiveStylePrompt,
},
[NEGATIVE_CONDITIONING]: {
type: 'sdxl_compel_prompt',
id: NEGATIVE_CONDITIONING,
prompt: negativePrompt,
style: shouldConcatSDXLStylePrompt
? `${negativePrompt} ${negativeStylePrompt}`
: negativeStylePrompt,
style: craftedNegativeStylePrompt,
},
[NOISE]: {
type: 'noise',
@ -115,9 +118,9 @@ export const buildLinearSDXLImageToImageGraph = (
id: LATENTS_TO_IMAGE,
fp32: vaePrecision === 'fp32' ? true : false,
},
[SDXL_LATENTS_TO_LATENTS]: {
type: 'l2l_sdxl',
id: SDXL_LATENTS_TO_LATENTS,
[SDXL_DENOISE_LATENTS]: {
type: 'denoise_latents',
id: SDXL_DENOISE_LATENTS,
cfg_scale,
scheduler,
steps,
@ -137,36 +140,17 @@ export const buildLinearSDXLImageToImageGraph = (
},
},
edges: [
// Connect Model Loader to UNet, CLIP & VAE
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'unet',
},
destination: {
node_id: SDXL_LATENTS_TO_LATENTS,
node_id: SDXL_DENOISE_LATENTS,
field: 'unet',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'vae',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'vae',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'vae',
},
destination: {
node_id: IMAGE_TO_LATENTS,
field: 'vae',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
@ -207,43 +191,14 @@ export const buildLinearSDXLImageToImageGraph = (
field: 'clip2',
},
},
{
source: {
node_id: SDXL_LATENTS_TO_LATENTS,
field: 'latents',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'latents',
},
},
{
source: {
node_id: IMAGE_TO_LATENTS,
field: 'latents',
},
destination: {
node_id: SDXL_LATENTS_TO_LATENTS,
field: 'latents',
},
},
{
source: {
node_id: NOISE,
field: 'noise',
},
destination: {
node_id: SDXL_LATENTS_TO_LATENTS,
field: 'noise',
},
},
// Connect everything to Denoise Latents
{
source: {
node_id: POSITIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: SDXL_LATENTS_TO_LATENTS,
node_id: SDXL_DENOISE_LATENTS,
field: 'positive_conditioning',
},
},
@ -253,10 +208,41 @@ export const buildLinearSDXLImageToImageGraph = (
field: 'conditioning',
},
destination: {
node_id: SDXL_LATENTS_TO_LATENTS,
node_id: SDXL_DENOISE_LATENTS,
field: 'negative_conditioning',
},
},
{
source: {
node_id: NOISE,
field: 'noise',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'noise',
},
},
{
source: {
node_id: IMAGE_TO_LATENTS,
field: 'latents',
},
destination: {
node_id: SDXL_DENOISE_LATENTS,
field: 'latents',
},
},
// Decode Denoised Latents To Image
{
source: {
node_id: SDXL_DENOISE_LATENTS,
field: 'latents',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'latents',
},
},
],
};
@ -365,13 +351,19 @@ export const buildLinearSDXLImageToImageGraph = (
},
});
addSDXLLoRAsToGraph(state, graph, SDXL_LATENTS_TO_LATENTS, SDXL_MODEL_LOADER);
addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER);
// Add Refiner if enabled
if (shouldUseSDXLRefiner) {
addSDXLRefinerToGraph(state, graph, SDXL_LATENTS_TO_LATENTS);
addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS);
}
// optionally add custom VAE
addVAEToGraph(state, graph, SDXL_MODEL_LOADER);
// add controlnet, mutating `graph`
addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
// add dynamic prompts - also sets up core iteration and seed
addDynamicPromptsToGraph(state, graph);

62
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts
View File

@ -2,10 +2,12 @@ import { logger } from 'app/logging/logger';
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { initialGenerationState } from 'features/parameters/store/generationSlice';
import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
LATENTS_TO_IMAGE,
@ -13,10 +15,11 @@ import {
NEGATIVE_CONDITIONING,
NOISE,
POSITIVE_CONDITIONING,
SDXL_DENOISE_LATENTS,
SDXL_MODEL_LOADER,
SDXL_TEXT_TO_IMAGE_GRAPH,
SDXL_TEXT_TO_LATENTS,
} from './constants';
import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
export const buildLinearSDXLTextToImageGraph = (
state: RootState
@ -40,8 +43,8 @@ export const buildLinearSDXLTextToImageGraph = (
const {
positiveStylePrompt,
negativeStylePrompt,
shouldConcatSDXLStylePrompt,
shouldUseSDXLRefiner,
shouldConcatSDXLStylePrompt,
refinerStart,
} = state.sdxl;
@ -54,6 +57,10 @@ export const buildLinearSDXLTextToImageGraph = (
throw new Error('No model found in state');
}
// Construct Style Prompt
const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } =
craftSDXLStylePrompt(state, shouldConcatSDXLStylePrompt);
/**
* The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
* full graph here as a template. Then use the parameters from app state and set friendlier node
@ -76,17 +83,13 @@ export const buildLinearSDXLTextToImageGraph = (
type: 'sdxl_compel_prompt',
id: POSITIVE_CONDITIONING,
prompt: positivePrompt,
style: shouldConcatSDXLStylePrompt
? `${positivePrompt} ${positiveStylePrompt}`
: positiveStylePrompt,
style: craftedPositiveStylePrompt,
},
[NEGATIVE_CONDITIONING]: {
type: 'sdxl_compel_prompt',
id: NEGATIVE_CONDITIONING,
prompt: negativePrompt,
style: shouldConcatSDXLStylePrompt
? `${negativePrompt} ${negativeStylePrompt}`
: negativeStylePrompt,
style: craftedNegativeStylePrompt,
},
[NOISE]: {
type: 'noise',
@ -95,12 +98,13 @@ export const buildLinearSDXLTextToImageGraph = (
height,
use_cpu,
},
[SDXL_TEXT_TO_LATENTS]: {
type: 't2l_sdxl',
id: SDXL_TEXT_TO_LATENTS,
[SDXL_DENOISE_LATENTS]: {
type: 'denoise_latents',
id: SDXL_DENOISE_LATENTS,
cfg_scale,
scheduler,
steps,
denoising_start: 0,
denoising_end: shouldUseSDXLRefiner ? refinerStart : 1,
},
[LATENTS_TO_IMAGE]: {
@ -110,26 +114,17 @@ export const buildLinearSDXLTextToImageGraph = (
},
},
edges: [
// Connect Model Loader to UNet, VAE & CLIP
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'unet',
},
destination: {
node_id: SDXL_TEXT_TO_LATENTS,
node_id: SDXL_DENOISE_LATENTS,
field: 'unet',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
field: 'vae',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'vae',
},
},
{
source: {
node_id: SDXL_MODEL_LOADER,
@ -170,13 +165,14 @@ export const buildLinearSDXLTextToImageGraph = (
field: 'clip2',
},
},
// Connect everything to Denoise Latents
{
source: {
node_id: POSITIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: SDXL_TEXT_TO_LATENTS,
node_id: SDXL_DENOISE_LATENTS,
field: 'positive_conditioning',
},
},
@ -186,7 +182,7 @@ export const buildLinearSDXLTextToImageGraph = (
field: 'conditioning',
},
destination: {
node_id: SDXL_TEXT_TO_LATENTS,
node_id: SDXL_DENOISE_LATENTS,
field: 'negative_conditioning',
},
},
@ -196,13 +192,14 @@ export const buildLinearSDXLTextToImageGraph = (
field: 'noise',
},
destination: {
node_id: SDXL_TEXT_TO_LATENTS,
node_id: SDXL_DENOISE_LATENTS,
field: 'noise',
},
},
// Decode Denoised Latents To Image
{
source: {
node_id: SDXL_TEXT_TO_LATENTS,
node_id: SDXL_DENOISE_LATENTS,
field: 'latents',
},
destination: {
@ -247,13 +244,20 @@ export const buildLinearSDXLTextToImageGraph = (
},
});
addSDXLLoRAsToGraph(state, graph, SDXL_TEXT_TO_LATENTS, SDXL_MODEL_LOADER);
// Add Refiner if enabled
if (shouldUseSDXLRefiner) {
addSDXLRefinerToGraph(state, graph, SDXL_TEXT_TO_LATENTS);
addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS);
}
// optionally add custom VAE
addVAEToGraph(state, graph, SDXL_MODEL_LOADER);
// add LoRA support
addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER);
// add controlnet, mutating `graph`
addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
// add dynamic prompts - also sets up core iteration and seed
addDynamicPromptsToGraph(state, graph);

108
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts
View File

@ -2,6 +2,10 @@ import { logger } from 'app/logging/logger';
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { initialGenerationState } from 'features/parameters/store/generationSlice';
import {
DenoiseLatentsInvocation,
ONNXTextToLatentsInvocation,
} from 'services/api/types';
import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
import { addLoRAsToGraph } from './addLoRAsToGraph';
@ -10,20 +14,16 @@ import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CLIP_SKIP,
DENOISE_LATENTS,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
ONNX_MODEL_LOADER,
METADATA_ACCUMULATOR,
NEGATIVE_CONDITIONING,
NOISE,
ONNX_MODEL_LOADER,
POSITIVE_CONDITIONING,
TEXT_TO_IMAGE_GRAPH,
TEXT_TO_LATENTS,
} from './constants';
import {
ONNXTextToLatentsInvocation,
TextToLatentsInvocation,
} from 'services/api/types';
export const buildLinearTextToImageGraph = (
state: RootState
@ -54,30 +54,36 @@ export const buildLinearTextToImageGraph = (
}
const isUsingOnnxModel = model.model_type === 'onnx';
const modelLoaderNodeId = isUsingOnnxModel
? ONNX_MODEL_LOADER
: MAIN_MODEL_LOADER;
const modelLoaderNodeType = isUsingOnnxModel
? 'onnx_model_loader'
: 'main_model_loader';
const t2lNode: TextToLatentsInvocation | ONNXTextToLatentsInvocation =
const t2lNode: DenoiseLatentsInvocation | ONNXTextToLatentsInvocation =
isUsingOnnxModel
? {
type: 't2l_onnx',
id: TEXT_TO_LATENTS,
id: DENOISE_LATENTS,
is_intermediate: true,
cfg_scale,
scheduler,
steps,
}
: {
type: 't2l',
id: TEXT_TO_LATENTS,
type: 'denoise_latents',
id: DENOISE_LATENTS,
is_intermediate: true,
cfg_scale,
scheduler,
steps,
denoising_start: 0,
denoising_end: 1,
};
/**
* The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
* full graph here as a template. Then use the parameters from app state and set friendlier node
@ -93,6 +99,18 @@ export const buildLinearTextToImageGraph = (
const graph: NonNullableGraph = {
id: TEXT_TO_IMAGE_GRAPH,
nodes: {
[modelLoaderNodeId]: {
type: modelLoaderNodeType,
id: modelLoaderNodeId,
is_intermediate: true,
model,
},
[CLIP_SKIP]: {
type: 'clip_skip',
id: CLIP_SKIP,
skipped_layers: clipSkip,
is_intermediate: true,
},
[POSITIVE_CONDITIONING]: {
type: isUsingOnnxModel ? 'prompt_onnx' : 'compel',
id: POSITIVE_CONDITIONING,
@ -114,18 +132,6 @@ export const buildLinearTextToImageGraph = (
is_intermediate: true,
},
[t2lNode.id]: t2lNode,
[modelLoaderNodeId]: {
type: modelLoaderNodeType,
id: modelLoaderNodeId,
is_intermediate: true,
model,
},
[CLIP_SKIP]: {
type: 'clip_skip',
id: CLIP_SKIP,
skipped_layers: clipSkip,
is_intermediate: true,
},
[LATENTS_TO_IMAGE]: {
type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
id: LATENTS_TO_IMAGE,
@ -133,6 +139,17 @@ export const buildLinearTextToImageGraph = (
},
},
edges: [
// Connect Model Loader to UNet and CLIP Skip
{
source: {
node_id: modelLoaderNodeId,
field: 'unet',
},
destination: {
node_id: DENOISE_LATENTS,
field: 'unet',
},
},
{
source: {
node_id: modelLoaderNodeId,
@ -143,16 +160,7 @@ export const buildLinearTextToImageGraph = (
field: 'clip',
},
},
{
source: {
node_id: modelLoaderNodeId,
field: 'unet',
},
destination: {
node_id: TEXT_TO_LATENTS,
field: 'unet',
},
},
// Connect CLIP Skip to Conditioning
{
source: {
node_id: CLIP_SKIP,
@ -173,13 +181,14 @@ export const buildLinearTextToImageGraph = (
field: 'clip',
},
},
// Connect everything to Denoise Latents
{
source: {
node_id: POSITIVE_CONDITIONING,
field: 'conditioning',
},
destination: {
node_id: TEXT_TO_LATENTS,
node_id: DENOISE_LATENTS,
field: 'positive_conditioning',
},
},
@ -189,30 +198,31 @@ export const buildLinearTextToImageGraph = (
field: 'conditioning',
},
destination: {
node_id: TEXT_TO_LATENTS,
node_id: DENOISE_LATENTS,
field: 'negative_conditioning',
},
},
{
source: {
node_id: TEXT_TO_LATENTS,
field: 'latents',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'latents',
},
},
{
source: {
node_id: NOISE,
field: 'noise',
},
destination: {
node_id: TEXT_TO_LATENTS,
node_id: DENOISE_LATENTS,
field: 'noise',
},
},
// Decode Denoised Latents To Image
{
source: {
node_id: DENOISE_LATENTS,
field: 'latents',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'latents',
},
},
],
};
@ -248,17 +258,17 @@ export const buildLinearTextToImageGraph = (
},
});
// add LoRA support
addLoRAsToGraph(state, graph, TEXT_TO_LATENTS, modelLoaderNodeId);
// optionally add custom VAE
addVAEToGraph(state, graph, modelLoaderNodeId);
// add LoRA support
addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
// add dynamic prompts - also sets up core iteration and seed
addDynamicPromptsToGraph(state, graph);
// add controlnet, mutating `graph`
addControlNetToLinearGraph(state, graph, TEXT_TO_LATENTS);
addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
// NSFW & watermark - must be last thing added to graph
if (state.system.shouldUseNSFWChecker) {

37
invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts
View File

@ -1,7 +1,7 @@
// friendly node ids
export const POSITIVE_CONDITIONING = 'positive_conditioning';
export const NEGATIVE_CONDITIONING = 'negative_conditioning';
export const TEXT_TO_LATENTS = 'text_to_latents';
export const DENOISE_LATENTS = 'denoise_latents';
export const LATENTS_TO_IMAGE = 'latents_to_image';
export const NSFW_CHECKER = 'nsfw_checker';
export const WATERMARKER = 'invisible_watermark';
@ -17,7 +17,24 @@ export const CLIP_SKIP = 'clip_skip';
export const IMAGE_TO_LATENTS = 'image_to_latents';
export const LATENTS_TO_LATENTS = 'latents_to_latents';
export const RESIZE = 'resize_image';
export const CANVAS_OUTPUT = 'canvas_output';
export const INPAINT = 'inpaint';
export const INPAINT_SEAM_FIX = 'inpaint_seam_fix';
export const INPAINT_IMAGE = 'inpaint_image';
export const SCALED_INPAINT_IMAGE = 'scaled_inpaint_image';
export const INPAINT_IMAGE_RESIZE_UP = 'inpaint_image_resize_up';
export const INPAINT_IMAGE_RESIZE_DOWN = 'inpaint_image_resize_down';
export const INPAINT_INFILL = 'inpaint_infill';
export const INPAINT_INFILL_RESIZE_DOWN = 'inpaint_infill_resize_down';
export const INPAINT_FINAL_IMAGE = 'inpaint_final_image';
export const MASK_FROM_ALPHA = 'tomask';
export const MASK_EDGE = 'mask_edge';
export const MASK_BLUR = 'mask_blur';
export const MASK_COMBINE = 'mask_combine';
export const MASK_RESIZE_UP = 'mask_resize_up';
export const MASK_RESIZE_DOWN = 'mask_resize_down';
export const COLOR_CORRECT = 'color_correct';
export const PASTE_IMAGE = 'img_paste';
export const CONTROL_NET_COLLECT = 'control_net_collect';
export const DYNAMIC_PROMPT = 'dynamic_prompt';
export const IMAGE_COLLECTION = 'image_collection';
@ -27,18 +44,26 @@ export const REALESRGAN = 'esrgan';
export const DIVIDE = 'divide';
export const SCALE = 'scale_image';
export const SDXL_MODEL_LOADER = 'sdxl_model_loader';
export const SDXL_TEXT_TO_LATENTS = 't2l_sdxl';
export const SDXL_LATENTS_TO_LATENTS = 'l2l_sdxl';
export const SDXL_DENOISE_LATENTS = 'sdxl_denoise_latents';
export const SDXL_REFINER_MODEL_LOADER = 'sdxl_refiner_model_loader';
export const SDXL_REFINER_POSITIVE_CONDITIONING =
'sdxl_refiner_positive_conditioning';
export const SDXL_REFINER_NEGATIVE_CONDITIONING =
'sdxl_refiner_negative_conditioning';
export const SDXL_REFINER_LATENTS_TO_LATENTS = 'l2l_sdxl_refiner';
export const SDXL_REFINER_DENOISE_LATENTS = 'sdxl_refiner_denoise_latents';
// friendly graph ids
export const TEXT_TO_IMAGE_GRAPH = 'text_to_image_graph';
export const IMAGE_TO_IMAGE_GRAPH = 'image_to_image_graph';
export const CANVAS_TEXT_TO_IMAGE_GRAPH = 'canvas_text_to_image_graph';
export const CANVAS_IMAGE_TO_IMAGE_GRAPH = 'canvas_image_to_image_graph';
export const CANVAS_INPAINT_GRAPH = 'canvas_inpaint_graph';
export const CANVAS_OUTPAINT_GRAPH = 'canvas_outpaint_graph';
export const SDXL_TEXT_TO_IMAGE_GRAPH = 'sdxl_text_to_image_graph';
export const SDXL_IMAGE_TO_IMAGE_GRAPH = 'sxdl_image_to_image_graph';
export const IMAGE_TO_IMAGE_GRAPH = 'image_to_image_graph';
export const INPAINT_GRAPH = 'inpaint_graph';
export const SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH =
'sdxl_canvas_text_to_image_graph';
export const SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH =
'sdxl_canvas_image_to_image_graph';
export const SDXL_CANVAS_INPAINT_GRAPH = 'sdxl_canvas_inpaint_graph';
export const SDXL_CANVAS_OUTPAINT_GRAPH = 'sdxl_canvas_outpaint_graph';

28
invokeai/frontend/web/src/features/nodes/util/graphBuilders/helpers/craftSDXLStylePrompt.ts Normal file
View File

@ -0,0 +1,28 @@
import { RootState } from 'app/store/store';
export const craftSDXLStylePrompt = (
state: RootState,
shouldConcatSDXLStylePrompt: boolean
) => {
const { positivePrompt, negativePrompt } = state.generation;
const { positiveStylePrompt, negativeStylePrompt } = state.sdxl;
let craftedPositiveStylePrompt = positiveStylePrompt;
let craftedNegativeStylePrompt = negativeStylePrompt;
if (shouldConcatSDXLStylePrompt) {
if (positiveStylePrompt.length > 0) {
craftedPositiveStylePrompt = `${positivePrompt} ${positiveStylePrompt}`;
} else {
craftedPositiveStylePrompt = positivePrompt;
}
if (negativeStylePrompt.length > 0) {
craftedNegativeStylePrompt = `${negativePrompt} ${negativeStylePrompt}`;
} else {
craftedNegativeStylePrompt = negativePrompt;
}
}
return { craftedPositiveStylePrompt, craftedNegativeStylePrompt };
};

21
invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskAdjustmentCollapse.tsx Normal file
View File

@ -0,0 +1,21 @@
import { Flex } from '@chakra-ui/react';
import IAICollapse from 'common/components/IAICollapse';
import { memo } from 'react';
import { useTranslation } from 'react-i18next';
import ParamMaskBlur from './ParamMaskBlur';
import ParamMaskBlurMethod from './ParamMaskBlurMethod';
const ParamMaskAdjustmentCollapse = () => {
const { t } = useTranslation();
return (
<IAICollapse label={t('parameters.maskAdjustmentsHeader')}>
<Flex sx={{ flexDirection: 'column', gap: 2 }}>
<ParamMaskBlur />
<ParamMaskBlurMethod />
</Flex>
</IAICollapse>
);
};
export default memo(ParamMaskAdjustmentCollapse);

16
invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamBlur.tsx → invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskBlur.tsx
View File

@ -1,31 +1,31 @@
import type { RootState } from 'app/store/store';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import IAISlider from 'common/components/IAISlider';
import { setSeamBlur } from 'features/parameters/store/generationSlice';
import { setMaskBlur } from 'features/parameters/store/generationSlice';
import { useTranslation } from 'react-i18next';
export default function ParamSeamBlur() {
export default function ParamMaskBlur() {
const dispatch = useAppDispatch();
const seamBlur = useAppSelector(
(state: RootState) => state.generation.seamBlur
const maskBlur = useAppSelector(
(state: RootState) => state.generation.maskBlur
);
const { t } = useTranslation();
return (
<IAISlider
label={t('parameters.seamBlur')}
label={t('parameters.maskBlur')}
min={0}
max={64}
sliderNumberInputProps={{ max: 512 }}
value={seamBlur}
value={maskBlur}
onChange={(v) => {
dispatch(setSeamBlur(v));
dispatch(setMaskBlur(v));
}}
withInput
withSliderMarks
withReset
handleReset={() => {
dispatch(setSeamBlur(16));
dispatch(setMaskBlur(16));
}}
/>
);

36
invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskBlurMethod.tsx Normal file
View File

@ -0,0 +1,36 @@
import { SelectItem } from '@mantine/core';
import { RootState } from 'app/store/store';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import IAIMantineSelect from 'common/components/IAIMantineSelect';
import { setMaskBlurMethod } from 'features/parameters/store/generationSlice';
import { useTranslation } from 'react-i18next';
type MaskBlurMethods = 'box' | 'gaussian';
const maskBlurMethods: SelectItem[] = [
{ label: 'Box Blur', value: 'box' },
{ label: 'Gaussian Blur', value: 'gaussian' },
];
export default function ParamMaskBlurMethod() {
const maskBlurMethod = useAppSelector(
(state: RootState) => state.generation.maskBlurMethod
);
const dispatch = useAppDispatch();
const { t } = useTranslation();
const handleMaskBlurMethodChange = (v: string | null) => {
if (!v) return;
dispatch(setMaskBlurMethod(v as MaskBlurMethods));
};
return (
<IAIMantineSelect
value={maskBlurMethod}
onChange={handleMaskBlurMethodChange}
label={t('parameters.maskBlurMethod')}
data={maskBlurMethods}
/>
);
}

22
invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamCorrectionCollapse.tsx
View File

@ -1,22 +0,0 @@
import IAICollapse from 'common/components/IAICollapse';
import { memo } from 'react';
import { useTranslation } from 'react-i18next';
import ParamSeamBlur from './ParamSeamBlur';
import ParamSeamSize from './ParamSeamSize';
import ParamSeamSteps from './ParamSeamSteps';
import ParamSeamStrength from './ParamSeamStrength';
const ParamSeamCorrectionCollapse = () => {
const { t } = useTranslation();
return (
<IAICollapse label={t('parameters.seamCorrectionHeader')}>
<ParamSeamSize />
<ParamSeamBlur />
<ParamSeamStrength />
<ParamSeamSteps />
</IAICollapse>
);
};
export default memo(ParamSeamCorrectionCollapse);

31
invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamSize.tsx
View File

@ -1,31 +0,0 @@
import type { RootState } from 'app/store/store';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import IAISlider from 'common/components/IAISlider';
import { setSeamSize } from 'features/parameters/store/generationSlice';
import { useTranslation } from 'react-i18next';
export default function ParamSeamSize() {
const dispatch = useAppDispatch();
const { t } = useTranslation();
const seamSize = useAppSelector(
(state: RootState) => state.generation.seamSize
);
return (
<IAISlider
label={t('parameters.seamSize')}
min={1}
max={256}
sliderNumberInputProps={{ max: 512 }}
value={seamSize}
onChange={(v) => {
dispatch(setSeamSize(v));
}}
withInput
withSliderMarks
withReset
handleReset={() => dispatch(setSeamSize(96))}
/>
);
}

32
invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamSteps.tsx
View File

@ -1,32 +0,0 @@
import type { RootState } from 'app/store/store';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import IAISlider from 'common/components/IAISlider';
import { setSeamSteps } from 'features/parameters/store/generationSlice';
import { useTranslation } from 'react-i18next';
export default function ParamSeamSteps() {
const { t } = useTranslation();
const seamSteps = useAppSelector(
(state: RootState) => state.generation.seamSteps
);
const dispatch = useAppDispatch();
return (
<IAISlider
label={t('parameters.seamSteps')}
min={1}
max={100}
sliderNumberInputProps={{ max: 999 }}
value={seamSteps}
onChange={(v) => {
dispatch(setSeamSteps(v));
}}
withInput
withSliderMarks
withReset
handleReset={() => {
dispatch(setSeamSteps(30));
}}
/>
);
}

32
invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamStrength.tsx
View File

@ -1,32 +0,0 @@
import { RootState } from 'app/store/store';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import IAISlider from 'common/components/IAISlider';
import { setSeamStrength } from 'features/parameters/store/generationSlice';
import { useTranslation } from 'react-i18next';
export default function ParamSeamStrength() {
const dispatch = useAppDispatch();
const { t } = useTranslation();
const seamStrength = useAppSelector(
(state: RootState) => state.generation.seamStrength
);
return (
<IAISlider
label={t('parameters.seamStrength')}
min={0.01}
max={0.99}
step={0.01}
value={seamStrength}
onChange={(v) => {
dispatch(setSeamStrength(v));
}}
withInput
withSliderMarks
withReset
handleReset={() => {
dispatch(setSeamStrength(0.7));
}}
/>
);
}

7
invokeai/frontend/web/src/features/parameters/components/Parameters/MainModel/ParamMainModelSelect.tsx
View File

@ -15,11 +15,11 @@ import { modelIdToMainModelParam } from 'features/parameters/util/modelIdToMainM
import SyncModelsButton from 'features/ui/components/tabs/ModelManager/subpanels/ModelManagerSettingsPanel/SyncModelsButton';
import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
import { forEach } from 'lodash-es';
import { NON_REFINER_BASE_MODELS } from 'services/api/constants';
import {
useGetMainModelsQuery,
useGetOnnxModelsQuery,
} from 'services/api/endpoints/models';
import { NON_REFINER_BASE_MODELS } from 'services/api/constants';
import { useFeatureStatus } from '../../../../system/hooks/useFeatureStatus';
const selector = createSelector(
@ -52,10 +52,7 @@ const ParamMainModelSelect = () => {
const data: SelectItem[] = [];
forEach(mainModels.entities, (model, id) => {
if (
!model ||
(activeTabName === 'unifiedCanvas' && model.base_model === 'sdxl')
) {
if (!model) {
return;
}

32
invokeai/frontend/web/src/features/parameters/hooks/useRecallParameters.ts
View File

@ -4,16 +4,16 @@ import {
refinerModelChanged,
setNegativeStylePromptSDXL,
setPositiveStylePromptSDXL,
setRefinerAestheticScore,
setRefinerCFGScale,
setRefinerNegativeAestheticScore,
setRefinerPositiveAestheticScore,
setRefinerScheduler,
setRefinerStart,
setRefinerSteps,
} from 'features/sdxl/store/sdxlSlice';
import { useCallback } from 'react';
import { useTranslation } from 'react-i18next';
import { UnsafeImageMetadata } from 'services/api/types';
import { ImageDTO } from 'services/api/types';
import { ImageDTO, UnsafeImageMetadata } from 'services/api/types';
import { initialImageSelected, modelSelected } from '../store/actions';
import {
setCfgScale,
@ -34,8 +34,9 @@ import {
isValidPositivePrompt,
isValidSDXLNegativeStylePrompt,
isValidSDXLPositiveStylePrompt,
isValidSDXLRefinerAestheticScore,
isValidSDXLRefinerModel,
isValidSDXLRefinerNegativeAestheticScore,
isValidSDXLRefinerPositiveAestheticScore,
isValidSDXLRefinerStart,
isValidScheduler,
isValidSeed,
@ -339,7 +340,8 @@ export const useRecallParameters = () => {
refiner_cfg_scale,
refiner_steps,
refiner_scheduler,
refiner_aesthetic_store,
refiner_positive_aesthetic_store,
refiner_negative_aesthetic_store,
refiner_start,
} = metadata;
@ -398,8 +400,24 @@ export const useRecallParameters = () => {
dispatch(setRefinerScheduler(refiner_scheduler));
}
if (isValidSDXLRefinerAestheticScore(refiner_aesthetic_store)) {
dispatch(setRefinerAestheticScore(refiner_aesthetic_store));
if (
isValidSDXLRefinerPositiveAestheticScore(
refiner_positive_aesthetic_store
)
) {
dispatch(
setRefinerPositiveAestheticScore(refiner_positive_aesthetic_store)
);
}
if (
isValidSDXLRefinerNegativeAestheticScore(
refiner_negative_aesthetic_store
)
) {
dispatch(
setRefinerNegativeAestheticScore(refiner_negative_aesthetic_store)
);
}
if (isValidSDXLRefinerStart(refiner_start)) {

34
invokeai/frontend/web/src/features/parameters/store/generationSlice.ts
View File

@ -4,11 +4,13 @@ import { roundToMultiple } from 'common/util/roundDownToMultiple';
import { configChanged } from 'features/system/store/configSlice';
import { clamp } from 'lodash-es';
import { ImageDTO } from 'services/api/types';
import { clipSkipMap } from '../types/constants';
import {
CfgScaleParam,
HeightParam,
MainModelParam,
MaskBlurMethodParam,
NegativePromptParam,
OnnxModelParam,
PositivePromptParam,
@ -33,10 +35,8 @@ export interface GenerationState {
positivePrompt: PositivePromptParam;
negativePrompt: NegativePromptParam;
scheduler: SchedulerParam;
seamBlur: number;
seamSize: number;
seamSteps: number;
seamStrength: number;
maskBlur: number;
maskBlurMethod: MaskBlurMethodParam;
seed: SeedParam;
seedWeights: string;
shouldFitToWidthHeight: boolean;
@ -72,10 +72,8 @@ export const initialGenerationState: GenerationState = {
positivePrompt: '',
negativePrompt: '',
scheduler: 'euler',
seamBlur: 16,
seamSize: 96,
seamSteps: 30,
seamStrength: 0.7,
maskBlur: 16,
maskBlurMethod: 'box',
seed: 0,
seedWeights: '',
shouldFitToWidthHeight: true,
@ -196,17 +194,11 @@ export const generationSlice = createSlice({
clearInitialImage: (state) => {
state.initialImage = undefined;
},
setSeamSize: (state, action: PayloadAction<number>) => {
state.seamSize = action.payload;
setMaskBlur: (state, action: PayloadAction<number>) => {
state.maskBlur = action.payload;
},
setSeamBlur: (state, action: PayloadAction<number>) => {
state.seamBlur = action.payload;
},
setSeamStrength: (state, action: PayloadAction<number>) => {
state.seamStrength = action.payload;
},
setSeamSteps: (state, action: PayloadAction<number>) => {
state.seamSteps = action.payload;
setMaskBlurMethod: (state, action: PayloadAction<MaskBlurMethodParam>) => {
state.maskBlurMethod = action.payload;
},
setTileSize: (state, action: PayloadAction<number>) => {
state.tileSize = action.payload;
@ -312,10 +304,8 @@ export const {
setPositivePrompt,
setNegativePrompt,
setScheduler,
setSeamBlur,
setSeamSize,
setSeamSteps,
setSeamStrength,
setMaskBlur,
setMaskBlurMethod,
setSeed,
setSeedWeights,
setShouldFitToWidthHeight,

51
invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts
View File

@ -353,22 +353,40 @@ export const isValidPrecision = (val: unknown): val is PrecisionParam =>
zPrecision.safeParse(val).success;
/**
* Zod schema for SDXL refiner aesthetic score parameter
* Zod schema for SDXL refiner positive aesthetic score parameter
*/
export const zSDXLRefinerAestheticScore = z.number().min(1).max(10);
export const zSDXLRefinerPositiveAestheticScore = z.number().min(1).max(10);
/**
* Type alias for SDXL refiner aesthetic score parameter, inferred from its zod schema
* Type alias for SDXL refiner aesthetic positive score parameter, inferred from its zod schema
*/
export type SDXLRefinerAestheticScoreParam = z.infer<
typeof zSDXLRefinerAestheticScore
export type SDXLRefinerPositiveAestheticScoreParam = z.infer<
typeof zSDXLRefinerPositiveAestheticScore
>;
/**
* Validates/type-guards a value as a SDXL refiner aesthetic score parameter
* Validates/type-guards a value as a SDXL refiner positive aesthetic score parameter
*/
export const isValidSDXLRefinerAestheticScore = (
export const isValidSDXLRefinerPositiveAestheticScore = (
val: unknown
): val is SDXLRefinerAestheticScoreParam =>
zSDXLRefinerAestheticScore.safeParse(val).success;
): val is SDXLRefinerPositiveAestheticScoreParam =>
zSDXLRefinerPositiveAestheticScore.safeParse(val).success;
/**
* Zod schema for SDXL refiner negative aesthetic score parameter
*/
export const zSDXLRefinerNegativeAestheticScore = z.number().min(1).max(10);
/**
* Type alias for SDXL refiner aesthetic negative score parameter, inferred from its zod schema
*/
export type SDXLRefinerNegativeAestheticScoreParam = z.infer<
typeof zSDXLRefinerNegativeAestheticScore
>;
/**
* Validates/type-guards a value as a SDXL refiner negative aesthetic score parameter
*/
export const isValidSDXLRefinerNegativeAestheticScore = (
val: unknown
): val is SDXLRefinerNegativeAestheticScoreParam =>
zSDXLRefinerNegativeAestheticScore.safeParse(val).success;
/**
* Zod schema for SDXL start parameter
@ -385,6 +403,21 @@ export const isValidSDXLRefinerStart = (
val: unknown
): val is SDXLRefinerStartParam => zSDXLRefinerstart.safeParse(val).success;
/**
* Zod schema for a mask blur method parameter
*/
export const zMaskBlurMethod = z.enum(['box', 'gaussian']);
/**
* Type alias for mask blur method parameter, inferred from its zod schema
*/
export type MaskBlurMethodParam = z.infer<typeof zMaskBlurMethod>;
/**
* Validates/type-guards a value as a mask blur method parameter
*/
export const isValidMaskBlurMethod = (
val: unknown
): val is MaskBlurMethodParam => zMaskBlurMethod.safeParse(val).success;
// /**
// * Zod schema for BaseModelType
// */

6
invokeai/frontend/web/src/features/sdxl/components/ParamSDXLRefinerCollapse.tsx
View File

@ -4,9 +4,10 @@ import { stateSelector } from 'app/store/store';
import { useAppSelector } from 'app/store/storeHooks';
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
import IAICollapse from 'common/components/IAICollapse';
import ParamSDXLRefinerAestheticScore from './SDXLRefiner/ParamSDXLRefinerAestheticScore';
import ParamSDXLRefinerCFGScale from './SDXLRefiner/ParamSDXLRefinerCFGScale';
import ParamSDXLRefinerModelSelect from './SDXLRefiner/ParamSDXLRefinerModelSelect';
import ParamSDXLRefinerNegativeAestheticScore from './SDXLRefiner/ParamSDXLRefinerNegativeAestheticScore';
import ParamSDXLRefinerPositiveAestheticScore from './SDXLRefiner/ParamSDXLRefinerPositiveAestheticScore';
import ParamSDXLRefinerScheduler from './SDXLRefiner/ParamSDXLRefinerScheduler';
import ParamSDXLRefinerStart from './SDXLRefiner/ParamSDXLRefinerStart';
import ParamSDXLRefinerSteps from './SDXLRefiner/ParamSDXLRefinerSteps';
@ -38,7 +39,8 @@ const ParamSDXLRefinerCollapse = () => {
<ParamSDXLRefinerCFGScale />
</Flex>
<ParamSDXLRefinerScheduler />
<ParamSDXLRefinerAestheticScore />
<ParamSDXLRefinerPositiveAestheticScore />
<ParamSDXLRefinerNegativeAestheticScore />
<ParamSDXLRefinerStart />
</Flex>
</IAICollapse>

4
invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabParameters.tsx
View File

@ -1,10 +1,11 @@
import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
import ParamSDXLPromptArea from './ParamSDXLPromptArea';
import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
import SDXLImageToImageTabCoreParameters from './SDXLImageToImageTabCoreParameters';
import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
const SDXLImageToImageTabParameters = () => {
return (
@ -13,6 +14,7 @@ const SDXLImageToImageTabParameters = () => {
<ProcessButtons />
<SDXLImageToImageTabCoreParameters />
<ParamSDXLRefinerCollapse />
<ParamControlNetCollapse />
<ParamLoraCollapse />
<ParamDynamicPromptsCollapse />
<ParamNoiseCollapse />

60
invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerNegativeAestheticScore.tsx Normal file
View File

@ -0,0 +1,60 @@
import { createSelector } from '@reduxjs/toolkit';
import { stateSelector } from 'app/store/store';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
import IAISlider from 'common/components/IAISlider';
import { setRefinerNegativeAestheticScore } from 'features/sdxl/store/sdxlSlice';
import { memo, useCallback } from 'react';
import { useIsRefinerAvailable } from 'services/api/hooks/useIsRefinerAvailable';
const selector = createSelector(
[stateSelector],
({ sdxl, hotkeys }) => {
const { refinerNegativeAestheticScore } = sdxl;
const { shift } = hotkeys;
return {
refinerNegativeAestheticScore,
shift,
};
},
defaultSelectorOptions
);
const ParamSDXLRefinerNegativeAestheticScore = () => {
const { refinerNegativeAestheticScore, shift } = useAppSelector(selector);
const isRefinerAvailable = useIsRefinerAvailable();
const dispatch = useAppDispatch();
const handleChange = useCallback(
(v: number) => dispatch(setRefinerNegativeAestheticScore(v)),
[dispatch]
);
const handleReset = useCallback(
() => dispatch(setRefinerNegativeAestheticScore(2.5)),
[dispatch]
);
return (
<IAISlider
label="Negative Aesthetic Score"
step={shift ? 0.1 : 0.5}
min={1}
max={10}
onChange={handleChange}
handleReset={handleReset}
value={refinerNegativeAestheticScore}
sliderNumberInputProps={{ max: 10 }}
withInput
withReset
withSliderMarks
isInteger={false}
isDisabled={!isRefinerAvailable}
/>
);
};
export default memo(ParamSDXLRefinerNegativeAestheticScore);

20
invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerAestheticScore.tsx → invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerPositiveAestheticScore.tsx
View File

@ -3,50 +3,50 @@ import { stateSelector } from 'app/store/store';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
import IAISlider from 'common/components/IAISlider';
import { setRefinerAestheticScore } from 'features/sdxl/store/sdxlSlice';
import { setRefinerPositiveAestheticScore } from 'features/sdxl/store/sdxlSlice';
import { memo, useCallback } from 'react';
import { useIsRefinerAvailable } from 'services/api/hooks/useIsRefinerAvailable';
const selector = createSelector(
[stateSelector],
({ sdxl, hotkeys }) => {
const { refinerAestheticScore } = sdxl;
const { refinerPositiveAestheticScore } = sdxl;
const { shift } = hotkeys;
return {
refinerAestheticScore,
refinerPositiveAestheticScore,
shift,
};
},
defaultSelectorOptions
);
const ParamSDXLRefinerAestheticScore = () => {
const { refinerAestheticScore, shift } = useAppSelector(selector);
const ParamSDXLRefinerPositiveAestheticScore = () => {
const { refinerPositiveAestheticScore, shift } = useAppSelector(selector);
const isRefinerAvailable = useIsRefinerAvailable();
const dispatch = useAppDispatch();
const handleChange = useCallback(
(v: number) => dispatch(setRefinerAestheticScore(v)),
(v: number) => dispatch(setRefinerPositiveAestheticScore(v)),
[dispatch]
);
const handleReset = useCallback(
() => dispatch(setRefinerAestheticScore(6)),
() => dispatch(setRefinerPositiveAestheticScore(6)),
[dispatch]
);
return (
<IAISlider
label="Aesthetic Score"
label="Positive Aesthetic Score"
step={shift ? 0.1 : 0.5}
min={1}
max={10}
onChange={handleChange}
handleReset={handleReset}
value={refinerAestheticScore}
value={refinerPositiveAestheticScore}
sliderNumberInputProps={{ max: 10 }}
withInput
withReset
@ -57,4 +57,4 @@ const ParamSDXLRefinerAestheticScore = () => {
);
};
export default memo(ParamSDXLRefinerAestheticScore);
export default memo(ParamSDXLRefinerPositiveAestheticScore);

4
invokeai/frontend/web/src/features/sdxl/components/SDXLTextToImageTabParameters.tsx
View File

@ -1,10 +1,11 @@
import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
import TextToImageTabCoreParameters from 'features/ui/components/tabs/TextToImage/TextToImageTabCoreParameters';
import ParamSDXLPromptArea from './ParamSDXLPromptArea';
import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
const SDXLTextToImageTabParameters = () => {
return (
@ -13,6 +14,7 @@ const SDXLTextToImageTabParameters = () => {
<ProcessButtons />
<TextToImageTabCoreParameters />
<ParamSDXLRefinerCollapse />
<ParamControlNetCollapse />
<ParamLoraCollapse />
<ParamDynamicPromptsCollapse />
<ParamNoiseCollapse />

75
invokeai/frontend/web/src/features/sdxl/components/SDXLUnifiedCanvasTabCoreParameters.tsx Normal file
View File

@ -0,0 +1,75 @@
import { Box, Flex } from '@chakra-ui/react';
import { createSelector } from '@reduxjs/toolkit';
import { stateSelector } from 'app/store/store';
import { useAppSelector } from 'app/store/storeHooks';
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
import IAICollapse from 'common/components/IAICollapse';
import ParamBoundingBoxSize from 'features/parameters/components/Parameters/Canvas/BoundingBox/ParamBoundingBoxSize';
import ParamCFGScale from 'features/parameters/components/Parameters/Core/ParamCFGScale';
import ParamIterations from 'features/parameters/components/Parameters/Core/ParamIterations';
import ParamModelandVAEandScheduler from 'features/parameters/components/Parameters/Core/ParamModelandVAEandScheduler';
import ParamSteps from 'features/parameters/components/Parameters/Core/ParamSteps';
import ParamSeedFull from 'features/parameters/components/Parameters/Seed/ParamSeedFull';
import { memo } from 'react';
import ParamSDXLImg2ImgDenoisingStrength from './ParamSDXLImg2ImgDenoisingStrength';
const selector = createSelector(
stateSelector,
({ ui, generation }) => {
const { shouldUseSliders } = ui;
const { shouldRandomizeSeed } = generation;
const activeLabel = !shouldRandomizeSeed ? 'Manual Seed' : undefined;
return { shouldUseSliders, activeLabel };
},
defaultSelectorOptions
);
const SDXLUnifiedCanvasTabCoreParameters = () => {
const { shouldUseSliders, activeLabel } = useAppSelector(selector);
return (
<IAICollapse
label={'General'}
activeLabel={activeLabel}
defaultIsOpen={true}
>
<Flex
sx={{
flexDirection: 'column',
gap: 3,
}}
>
{shouldUseSliders ? (
<>
<ParamIterations />
<ParamSteps />
<ParamCFGScale />
<ParamModelandVAEandScheduler />
<Box pt={2}>
<ParamSeedFull />
</Box>
<ParamBoundingBoxSize />
</>
) : (
<>
<Flex gap={3}>
<ParamIterations />
<ParamSteps />
<ParamCFGScale />
</Flex>
<ParamModelandVAEandScheduler />
<Box pt={2}>
<ParamSeedFull />
</Box>
<ParamBoundingBoxSize />
</>
)}
<ParamSDXLImg2ImgDenoisingStrength />
</Flex>
</IAICollapse>
);
};
export default memo(SDXLUnifiedCanvasTabCoreParameters);

27
invokeai/frontend/web/src/features/sdxl/components/SDXLUnifiedCanvasTabParameters.tsx Normal file
View File

@ -0,0 +1,27 @@
import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
import ParamInfillAndScalingCollapse from 'features/parameters/components/Parameters/Canvas/InfillAndScaling/ParamInfillAndScalingCollapse';
import ParamMaskAdjustmentCollapse from 'features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskAdjustmentCollapse';
import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
import ParamSDXLPromptArea from './ParamSDXLPromptArea';
import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
import SDXLUnifiedCanvasTabCoreParameters from './SDXLUnifiedCanvasTabCoreParameters';
export default function SDXLUnifiedCanvasTabParameters() {
return (
<>
<ParamSDXLPromptArea />
<ProcessButtons />
<SDXLUnifiedCanvasTabCoreParameters />
<ParamSDXLRefinerCollapse />
<ParamControlNetCollapse />
<ParamLoraCollapse />
<ParamDynamicPromptsCollapse />
<ParamNoiseCollapse />
<ParamMaskAdjustmentCollapse />
<ParamInfillAndScalingCollapse />
</>
);
}

22
invokeai/frontend/web/src/features/sdxl/store/sdxlSlice.ts
View File

@ -16,7 +16,8 @@ type SDXLInitialState = {
refinerSteps: number;
refinerCFGScale: number;
refinerScheduler: SchedulerParam;
refinerAestheticScore: number;
refinerPositiveAestheticScore: number;
refinerNegativeAestheticScore: number;
refinerStart: number;
};
@ -30,7 +31,8 @@ const sdxlInitialState: SDXLInitialState = {
refinerSteps: 20,
refinerCFGScale: 7.5,
refinerScheduler: 'euler',
refinerAestheticScore: 6,
refinerPositiveAestheticScore: 6,
refinerNegativeAestheticScore: 2.5,
refinerStart: 0.7,
};
@ -68,8 +70,17 @@ const sdxlSlice = createSlice({
setRefinerScheduler: (state, action: PayloadAction<SchedulerParam>) => {
state.refinerScheduler = action.payload;
},
setRefinerAestheticScore: (state, action: PayloadAction<number>) => {
state.refinerAestheticScore = action.payload;
setRefinerPositiveAestheticScore: (
state,
action: PayloadAction<number>
) => {
state.refinerPositiveAestheticScore = action.payload;
},
setRefinerNegativeAestheticScore: (
state,
action: PayloadAction<number>
) => {
state.refinerNegativeAestheticScore = action.payload;
},
setRefinerStart: (state, action: PayloadAction<number>) => {
state.refinerStart = action.payload;
@ -87,7 +98,8 @@ export const {
setRefinerSteps,
setRefinerCFGScale,
setRefinerScheduler,
setRefinerAestheticScore,
setRefinerPositiveAestheticScore,
setRefinerNegativeAestheticScore,
setRefinerStart,
} = sdxlSlice.actions;

4
invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx
View File

@ -2,10 +2,10 @@ import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/Para
import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
import ParamAdvancedCollapse from 'features/parameters/components/Parameters/Advanced/ParamAdvancedCollapse';
import ParamInfillAndScalingCollapse from 'features/parameters/components/Parameters/Canvas/InfillAndScaling/ParamInfillAndScalingCollapse';
import ParamSeamCorrectionCollapse from 'features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamCorrectionCollapse';
import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
// import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse';
import ParamMaskAdjustmentCollapse from 'features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskAdjustmentCollapse';
import ParamPromptArea from 'features/parameters/components/Parameters/Prompt/ParamPromptArea';
import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
import UnifiedCanvasCoreParameters from './UnifiedCanvasCoreParameters';
@ -21,7 +21,7 @@ const UnifiedCanvasParameters = () => {
<ParamDynamicPromptsCollapse />
{/* <ParamVariationCollapse /> */}
<ParamSymmetryCollapse />
<ParamSeamCorrectionCollapse />
<ParamMaskAdjustmentCollapse />
<ParamInfillAndScalingCollapse />
<ParamAdvancedCollapse />
</>

10
invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasTab.tsx
View File

@ -1,14 +1,22 @@
import { Flex } from '@chakra-ui/react';
import { RootState } from 'app/store/store';
import { useAppSelector } from 'app/store/storeHooks';
import SDXLUnifiedCanvasTabParameters from 'features/sdxl/components/SDXLUnifiedCanvasTabParameters';
import { memo } from 'react';
import ParametersPinnedWrapper from '../../ParametersPinnedWrapper';
import UnifiedCanvasContent from './UnifiedCanvasContent';
import UnifiedCanvasParameters from './UnifiedCanvasParameters';
const UnifiedCanvasTab = () => {
const model = useAppSelector((state: RootState) => state.generation.model);
return (
<Flex sx={{ gap: 4, w: 'full', h: 'full' }}>
<ParametersPinnedWrapper>
<UnifiedCanvasParameters />
{model && model.base_model === 'sdxl' ? (
<SDXLUnifiedCanvasTabParameters />
) : (
<UnifiedCanvasParameters />
)}
</ParametersPinnedWrapper>
<UnifiedCanvasContent />
</Flex>

859
invokeai/frontend/web/src/services/api/schema.d.ts vendored
View File

@ -179,6 +179,11 @@ export type paths = {
* @description Gets a full-resolution image file
*/
get: operations["get_image_full"];
/**
* Get Image Full
* @description Gets a full-resolution image file
*/
head: operations["get_image_full"];
};
"/api/v1/images/i/{image_name}/thumbnail": {
/**
@ -707,6 +712,51 @@ export type components = {
*/
collection: (unknown)[];
};
/**
* ColorCorrectInvocation
* @description Shifts the colors of a target image to match the reference image, optionally
* using a mask to only color-correct certain regions of the target image.
*/
ColorCorrectInvocation: {
/**
* 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 color_correct
* @enum {string}
*/
type?: "color_correct";
/**
* Image
* @description The image to color-correct
*/
image?: components["schemas"]["ImageField"];
/**
* Reference
* @description Reference image for color-correction
*/
reference?: components["schemas"]["ImageField"];
/**
* Mask
* @description Mask to use when applying color-correction
*/
mask?: components["schemas"]["ImageField"];
/**
* Mask Blur Radius
* @description Mask blur radius
* @default 8
*/
mask_blur_radius?: number;
};
/** ColorField */
ColorField: {
/**
@ -1037,6 +1087,12 @@ export type components = {
* @description Core generation metadata for an image generated in InvokeAI.
*/
CoreMetadata: {
/**
* App Version
* @description The version of InvokeAI used to generate this image
* @default 3.0.2
*/
app_version?: string;
/**
* Generation Mode
* @description The generation mode that output this image
@ -1153,10 +1209,15 @@ export type components = {
*/
refiner_scheduler?: string;
/**
* Refiner Aesthetic Store
* Refiner Positive Aesthetic Store
* @description The aesthetic score used for the refiner
*/
refiner_aesthetic_store?: number;
refiner_positive_aesthetic_store?: number;
/**
* Refiner Negative Aesthetic Store
* @description The aesthetic score used for the refiner
*/
refiner_negative_aesthetic_store?: number;
/**
* Refiner Start
* @description The start value used for refiner denoising
@ -1219,6 +1280,93 @@ export type components = {
/** Deleted Images */
deleted_images: (string)[];
};
/**
* DenoiseLatentsInvocation
* @description Denoises noisy latents to decodable images
*/
DenoiseLatentsInvocation: {
/**
* 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 denoise_latents
* @enum {string}
*/
type?: "denoise_latents";
/**
* Positive Conditioning
* @description Positive conditioning for generation
*/
positive_conditioning?: components["schemas"]["ConditioningField"];
/**
* Negative Conditioning
* @description Negative conditioning for generation
*/
negative_conditioning?: components["schemas"]["ConditioningField"];
/**
* Noise
* @description The noise to use
*/
noise?: components["schemas"]["LatentsField"];
/**
* Steps
* @description The number of steps to use to generate the image
* @default 10
*/
steps?: number;
/**
* Cfg Scale
* @description The Classifier-Free Guidance, higher values may result in a result closer to the prompt
* @default 7.5
*/
cfg_scale?: number | (number)[];
/**
* Denoising Start
* @default 0
*/
denoising_start?: number;
/**
* Denoising End
* @default 1
*/
denoising_end?: number;
/**
* Scheduler
* @description The scheduler to use
* @default euler
* @enum {string}
*/
scheduler?: "ddim" | "ddpm" | "deis" | "lms" | "lms_k" | "pndm" | "heun" | "heun_k" | "euler" | "euler_k" | "euler_a" | "kdpm_2" | "kdpm_2_a" | "dpmpp_2s" | "dpmpp_2s_k" | "dpmpp_2m" | "dpmpp_2m_k" | "dpmpp_2m_sde" | "dpmpp_2m_sde_k" | "dpmpp_sde" | "dpmpp_sde_k" | "unipc";
/**
* Unet
* @description UNet submodel
*/
unet?: components["schemas"]["UNetField"];
/**
* Control
* @description The control to use
*/
control?: components["schemas"]["ControlField"] | (components["schemas"]["ControlField"])[];
/**
* Latents
* @description The latents to use as a base image
*/
latents?: components["schemas"]["LatentsField"];
/**
* Mask
* @description Mask
*/
mask?: components["schemas"]["ImageField"];
};
/**
* DivideInvocation
* @description Divides two numbers
@ -1443,7 +1591,7 @@ export type components = {
* @description The nodes in this graph
*/
nodes?: {
[key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
[key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | 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"]["MaskEdgeInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"]) | undefined;
};
/**
* Edges
@ -1486,7 +1634,7 @@ export type components = {
* @description The results of node executions
*/
results: {
[key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["SDXLLoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
[key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["SDXLLoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
};
/**
* Errors
@ -2593,171 +2741,6 @@ export type components = {
*/
seed?: number;
};
/**
* InpaintInvocation
* @description Generates an image using inpaint.
*/
InpaintInvocation: {
/**
* 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 inpaint
* @enum {string}
*/
type?: "inpaint";
/**
* Positive Conditioning
* @description Positive conditioning for generation
*/
positive_conditioning?: components["schemas"]["ConditioningField"];
/**
* Negative Conditioning
* @description Negative conditioning for generation
*/
negative_conditioning?: components["schemas"]["ConditioningField"];
/**
* Seed
* @description The seed to use (omit for random)
*/
seed?: number;
/**
* Steps
* @description The number of steps to use to generate the image
* @default 30
*/
steps?: number;
/**
* Width
* @description The width of the resulting image
* @default 512
*/
width?: number;
/**
* Height
* @description The height of the resulting image
* @default 512
*/
height?: number;
/**
* Cfg Scale
* @description The Classifier-Free Guidance, higher values may result in a result closer to the prompt
* @default 7.5
*/
cfg_scale?: number;
/**
* Scheduler
* @description The scheduler to use
* @default euler
* @enum {string}
*/
scheduler?: "ddim" | "ddpm" | "deis" | "lms" | "lms_k" | "pndm" | "heun" | "heun_k" | "euler" | "euler_k" | "euler_a" | "kdpm_2" | "kdpm_2_a" | "dpmpp_2s" | "dpmpp_2s_k" | "dpmpp_2m" | "dpmpp_2m_k" | "dpmpp_2m_sde" | "dpmpp_2m_sde_k" | "dpmpp_sde" | "dpmpp_sde_k" | "unipc";
/**
* Unet
* @description UNet model
*/
unet?: components["schemas"]["UNetField"];
/**
* Vae
* @description Vae model
*/
vae?: components["schemas"]["VaeField"];
/**
* Image
* @description The input image
*/
image?: components["schemas"]["ImageField"];
/**
* Strength
* @description The strength of the original image
* @default 0.75
*/
strength?: number;
/**
* Fit
* @description Whether or not the result should be fit to the aspect ratio of the input image
* @default true
*/
fit?: boolean;
/**
* Mask
* @description The mask
*/
mask?: components["schemas"]["ImageField"];
/**
* Seam Size
* @description The seam inpaint size (px)
* @default 96
*/
seam_size?: number;
/**
* Seam Blur
* @description The seam inpaint blur radius (px)
* @default 16
*/
seam_blur?: number;
/**
* Seam Strength
* @description The seam inpaint strength
* @default 0.75
*/
seam_strength?: number;
/**
* Seam Steps
* @description The number of steps to use for seam inpaint
* @default 30
*/
seam_steps?: number;
/**
* Tile Size
* @description The tile infill method size (px)
* @default 32
*/
tile_size?: number;
/**
* Infill Method
* @description The method used to infill empty regions (px)
* @default patchmatch
* @enum {string}
*/
infill_method?: "patchmatch" | "tile" | "solid";
/**
* Inpaint Width
* @description The width of the inpaint region (px)
*/
inpaint_width?: number;
/**
* Inpaint Height
* @description The height of the inpaint region (px)
*/
inpaint_height?: number;
/**
* Inpaint Fill
* @description The solid infill method color
* @default {
* "r": 127,
* "g": 127,
* "b": 127,
* "a": 255
* }
*/
inpaint_fill?: components["schemas"]["ColorField"];
/**
* Inpaint Replace
* @description The amount by which to replace masked areas with latent noise
* @default 0
*/
inpaint_replace?: number;
};
/**
* IntCollectionOutput
* @description A collection of integers
@ -2854,6 +2837,11 @@ export type components = {
* @description The name of the latents
*/
latents_name: string;
/**
* Seed
* @description Seed used to generate this latents
*/
seed?: number;
};
/**
* LatentsOutput
@ -2932,84 +2920,6 @@ export type components = {
*/
metadata?: components["schemas"]["CoreMetadata"];
};
/**
* LatentsToLatentsInvocation
* @description Generates latents using latents as base image.
*/
LatentsToLatentsInvocation: {
/**
* 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 l2l
* @enum {string}
*/
type?: "l2l";
/**
* Positive Conditioning
* @description Positive conditioning for generation
*/
positive_conditioning?: components["schemas"]["ConditioningField"];
/**
* Negative Conditioning
* @description Negative conditioning for generation
*/
negative_conditioning?: components["schemas"]["ConditioningField"];
/**
* Noise
* @description The noise to use
*/
noise?: components["schemas"]["LatentsField"];
/**
* Steps
* @description The number of steps to use to generate the image
* @default 10
*/
steps?: number;
/**
* Cfg Scale
* @description The Classifier-Free Guidance, higher values may result in a result closer to the prompt
* @default 7.5
*/
cfg_scale?: number | (number)[];
/**
* Scheduler
* @description The scheduler to use
* @default euler
* @enum {string}
*/
scheduler?: "ddim" | "ddpm" | "deis" | "lms" | "lms_k" | "pndm" | "heun" | "heun_k" | "euler" | "euler_k" | "euler_a" | "kdpm_2" | "kdpm_2_a" | "dpmpp_2s" | "dpmpp_2s_k" | "dpmpp_2m" | "dpmpp_2m_k" | "dpmpp_2m_sde" | "dpmpp_2m_sde_k" | "dpmpp_sde" | "dpmpp_sde_k" | "unipc";
/**
* Unet
* @description UNet submodel
*/
unet?: components["schemas"]["UNetField"];
/**
* Control
* @description The control to use
*/
control?: components["schemas"]["ControlField"] | (components["schemas"]["ControlField"])[];
/**
* Latents
* @description The latents to use as a base image
*/
latents?: components["schemas"]["LatentsField"];
/**
* Strength
* @description The strength of the latents to use
* @default 0.7
*/
strength?: number;
};
/**
* LeresImageProcessorInvocation
* @description Applies leres processing to image
@ -3368,6 +3278,87 @@ export type components = {
*/
model: components["schemas"]["MainModelField"];
};
/**
* MaskCombineInvocation
* @description Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`.
*/
MaskCombineInvocation: {
/**
* 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 mask_combine
* @enum {string}
*/
type?: "mask_combine";
/**
* Mask1
* @description The first mask to combine
*/
mask1?: components["schemas"]["ImageField"];
/**
* Mask2
* @description The second image to combine
*/
mask2?: components["schemas"]["ImageField"];
};
/**
* MaskEdgeInvocation
* @description Applies an edge mask to an image
*/
MaskEdgeInvocation: {
/**
* 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 mask_edge
* @enum {string}
*/
type?: "mask_edge";
/**
* Image
* @description The image to apply the mask to
*/
image?: components["schemas"]["ImageField"];
/**
* Edge Size
* @description The size of the edge
*/
edge_size: number;
/**
* Edge Blur
* @description The amount of blur on the edge
*/
edge_blur: number;
/**
* Low Threshold
* @description First threshold for the hysteresis procedure in Canny edge detection
*/
low_threshold: number;
/**
* High Threshold
* @description Second threshold for the hysteresis procedure in Canny edge detection
*/
high_threshold: number;
};
/**
* MaskFromAlphaInvocation
* @description Extracts the alpha channel of an image as a mask.
@ -3613,10 +3604,15 @@ export type components = {
*/
refiner_scheduler?: string;
/**
* Refiner Aesthetic Store
* Refiner Positive Aesthetic Score
* @description The aesthetic score used for the refiner
*/
refiner_aesthetic_store?: number;
refiner_positive_aesthetic_score?: number;
/**
* Refiner Negative Aesthetic Score
* @description The aesthetic score used for the refiner
*/
refiner_negative_aesthetic_score?: number;
/**
* Refiner Start
* @description The start value used for refiner denoising
@ -4937,83 +4933,6 @@ export type components = {
*/
clip2?: components["schemas"]["ClipField"];
};
/**
* SDXLLatentsToLatentsInvocation
* @description Generates latents from conditionings.
*/
SDXLLatentsToLatentsInvocation: {
/**
* 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 l2l_sdxl
* @enum {string}
*/
type?: "l2l_sdxl";
/**
* Positive Conditioning
* @description Positive conditioning for generation
*/
positive_conditioning?: components["schemas"]["ConditioningField"];
/**
* Negative Conditioning
* @description Negative conditioning for generation
*/
negative_conditioning?: components["schemas"]["ConditioningField"];
/**
* Noise
* @description The noise to use
*/
noise?: components["schemas"]["LatentsField"];
/**
* Steps
* @description The number of steps to use to generate the image
* @default 10
*/
steps?: number;
/**
* Cfg Scale
* @description The Classifier-Free Guidance, higher values may result in a result closer to the prompt
* @default 7.5
*/
cfg_scale?: number | (number)[];
/**
* Scheduler
* @description The scheduler to use
* @default euler
* @enum {string}
*/
scheduler?: "ddim" | "ddpm" | "deis" | "lms" | "lms_k" | "pndm" | "heun" | "heun_k" | "euler" | "euler_k" | "euler_a" | "kdpm_2" | "kdpm_2_a" | "dpmpp_2s" | "dpmpp_2s_k" | "dpmpp_2m" | "dpmpp_2m_k" | "dpmpp_2m_sde" | "dpmpp_2m_sde_k" | "dpmpp_sde" | "dpmpp_sde_k" | "unipc";
/**
* Unet
* @description UNet submodel
*/
unet?: components["schemas"]["UNetField"];
/**
* Latents
* @description Initial latents
*/
latents?: components["schemas"]["LatentsField"];
/**
* Denoising Start
* @default 0
*/
denoising_start?: number;
/**
* Denoising End
* @default 1
*/
denoising_end?: number;
};
/**
* SDXLLoraLoaderInvocation
* @description Apply selected lora to unet and text_encoder.
@ -5150,81 +5069,6 @@ export type components = {
*/
vae?: components["schemas"]["VaeField"];
};
/**
* SDXLRawPromptInvocation
* @description Pass unmodified prompt to conditioning without compel processing.
*/
SDXLRawPromptInvocation: {
/**
* Id
* @description The id of this node. Must be unique among all nodes.
*/
id: string;
/**
* Is Intermediate
* @description Whether or not this node is an intermediate node.
* @default false
*/
is_intermediate?: boolean;
/**
* Type
* @default sdxl_raw_prompt
* @enum {string}
*/
type?: "sdxl_raw_prompt";
/**
* Prompt
* @description Prompt
* @default
*/
prompt?: string;
/**
* Style
* @description Style prompt
* @default
*/
style?: string;
/**
* Original Width
* @default 1024
*/
original_width?: number;
/**
* Original Height
* @default 1024
*/
original_height?: number;
/**
* Crop Top
* @default 0
*/
crop_top?: number;
/**
* Crop Left
* @default 0
*/
crop_left?: number;
/**
* Target Width
* @default 1024
*/
target_width?: number;
/**
* Target Height
* @default 1024
*/
target_height?: number;
/**
* Clip
* @description Clip to use
*/
clip?: components["schemas"]["ClipField"];
/**
* Clip2
* @description Clip2 to use
*/
clip2?: components["schemas"]["ClipField"];
};
/**
* SDXLRefinerCompelPromptInvocation
* @description Parse prompt using compel package to conditioning.
@ -5339,132 +5183,6 @@ export type components = {
*/
vae?: components["schemas"]["VaeField"];
};
/**
* SDXLRefinerRawPromptInvocation
* @description Parse prompt using compel package to conditioning.
*/
SDXLRefinerRawPromptInvocation: {
/**
* Id
* @description The id of this node. Must be unique among all nodes.
*/
id: string;
/**
* Is Intermediate
* @description Whether or not this node is an intermediate node.
* @default false
*/
is_intermediate?: boolean;
/**
* Type
* @default sdxl_refiner_raw_prompt
* @enum {string}
*/
type?: "sdxl_refiner_raw_prompt";
/**
* Style
* @description Style prompt
* @default
*/
style?: string;
/**
* Original Width
* @default 1024
*/
original_width?: number;
/**
* Original Height
* @default 1024
*/
original_height?: number;
/**
* Crop Top
* @default 0
*/
crop_top?: number;
/**
* Crop Left
* @default 0
*/
crop_left?: number;
/**
* Aesthetic Score
* @default 6
*/
aesthetic_score?: number;
/**
* Clip2
* @description Clip to use
*/
clip2?: components["schemas"]["ClipField"];
};
/**
* SDXLTextToLatentsInvocation
* @description Generates latents from conditionings.
*/
SDXLTextToLatentsInvocation: {
/**
* 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 t2l_sdxl
* @enum {string}
*/
type?: "t2l_sdxl";
/**
* Positive Conditioning
* @description Positive conditioning for generation
*/
positive_conditioning?: components["schemas"]["ConditioningField"];
/**
* Negative Conditioning
* @description Negative conditioning for generation
*/
negative_conditioning?: components["schemas"]["ConditioningField"];
/**
* Noise
* @description The noise to use
*/
noise?: components["schemas"]["LatentsField"];
/**
* Steps
* @description The number of steps to use to generate the image
* @default 10
*/
steps?: number;
/**
* Cfg Scale
* @description The Classifier-Free Guidance, higher values may result in a result closer to the prompt
* @default 7.5
*/
cfg_scale?: number | (number)[];
/**
* Scheduler
* @description The scheduler to use
* @default euler
* @enum {string}
*/
scheduler?: "ddim" | "ddpm" | "deis" | "lms" | "lms_k" | "pndm" | "heun" | "heun_k" | "euler" | "euler_k" | "euler_a" | "kdpm_2" | "kdpm_2_a" | "dpmpp_2s" | "dpmpp_2s_k" | "dpmpp_2m" | "dpmpp_2m_k" | "dpmpp_2m_sde" | "dpmpp_2m_sde_k" | "dpmpp_sde" | "dpmpp_sde_k" | "unipc";
/**
* Unet
* @description UNet submodel
*/
unet?: components["schemas"]["UNetField"];
/**
* Denoising End
* @default 1
*/
denoising_end?: number;
};
/**
* ScaleLatentsInvocation
* @description Scales latents by a given factor.
@ -5863,73 +5581,6 @@ export type components = {
*/
b?: number;
};
/**
* TextToLatentsInvocation
* @description Generates latents from conditionings.
*/
TextToLatentsInvocation: {
/**
* 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 t2l
* @enum {string}
*/
type?: "t2l";
/**
* Positive Conditioning
* @description Positive conditioning for generation
*/
positive_conditioning?: components["schemas"]["ConditioningField"];
/**
* Negative Conditioning
* @description Negative conditioning for generation
*/
negative_conditioning?: components["schemas"]["ConditioningField"];
/**
* Noise
* @description The noise to use
*/
noise?: components["schemas"]["LatentsField"];
/**
* Steps
* @description The number of steps to use to generate the image
* @default 10
*/
steps?: number;
/**
* Cfg Scale
* @description The Classifier-Free Guidance, higher values may result in a result closer to the prompt
* @default 7.5
*/
cfg_scale?: number | (number)[];
/**
* Scheduler
* @description The scheduler to use
* @default euler
* @enum {string}
*/
scheduler?: "ddim" | "ddpm" | "deis" | "lms" | "lms_k" | "pndm" | "heun" | "heun_k" | "euler" | "euler_k" | "euler_a" | "kdpm_2" | "kdpm_2_a" | "dpmpp_2s" | "dpmpp_2s_k" | "dpmpp_2m" | "dpmpp_2m_k" | "dpmpp_2m_sde" | "dpmpp_2m_sde_k" | "dpmpp_sde" | "dpmpp_sde_k" | "unipc";
/**
* Unet
* @description UNet submodel
*/
unet?: components["schemas"]["UNetField"];
/**
* Control
* @description The control to use
*/
control?: components["schemas"]["ControlField"] | (components["schemas"]["ControlField"])[];
};
/** TextualInversionModelConfig */
TextualInversionModelConfig: {
/** Model Name */
@ -6145,18 +5796,18 @@ export type components = {
* @enum {string}
*/
ControlNetModelFormat: "checkpoint" | "diffusers";
/**
* StableDiffusion2ModelFormat
* @description An enumeration.
* @enum {string}
*/
StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
/**
* StableDiffusionXLModelFormat
* @description An enumeration.
* @enum {string}
*/
StableDiffusionXLModelFormat: "checkpoint" | "diffusers";
/**
* StableDiffusion1ModelFormat
* @description An enumeration.
* @enum {string}
*/
StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
/**
* StableDiffusionOnnxModelFormat
* @description An enumeration.
@ -6164,11 +5815,11 @@ export type components = {
*/
StableDiffusionOnnxModelFormat: "olive" | "onnx";
/**
* StableDiffusion2ModelFormat
* StableDiffusion1ModelFormat
* @description An enumeration.
* @enum {string}
*/
StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
};
responses: never;
parameters: never;
@ -6279,7 +5930,7 @@ export type operations = {
};
requestBody: {
content: {
"application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
"application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | 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"]["MaskEdgeInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"];
};
};
responses: {
@ -6316,7 +5967,7 @@ export type operations = {
};
requestBody: {
content: {
"application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
"application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | 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"]["MaskEdgeInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"];
};
};
responses: {

25
invokeai/frontend/web/src/services/api/types.ts
View File

@ -120,9 +120,6 @@ export type RandomRangeInvocation = TypeReq<
export type RangeOfSizeInvocation = TypeReq<
components['schemas']['RangeOfSizeInvocation']
>;
export type InpaintInvocation = TypeReq<
components['schemas']['InpaintInvocation']
>;
export type ImageResizeInvocation = TypeReq<
components['schemas']['ImageResizeInvocation']
>;
@ -139,14 +136,11 @@ export type DynamicPromptInvocation = TypeReq<
components['schemas']['DynamicPromptInvocation']
>;
export type NoiseInvocation = TypeReq<components['schemas']['NoiseInvocation']>;
export type TextToLatentsInvocation = TypeReq<
components['schemas']['TextToLatentsInvocation']
>;
export type ONNXTextToLatentsInvocation = TypeReq<
components['schemas']['ONNXTextToLatentsInvocation']
>;
export type LatentsToLatentsInvocation = TypeReq<
components['schemas']['LatentsToLatentsInvocation']
export type DenoiseLatentsInvocation = TypeReq<
components['schemas']['DenoiseLatentsInvocation']
>;
export type ImageToLatentsInvocation = TypeReq<
components['schemas']['ImageToLatentsInvocation']
@ -178,12 +172,27 @@ export type ESRGANInvocation = TypeReq<
export type DivideInvocation = TypeReq<
components['schemas']['DivideInvocation']
>;
export type InfillTileInvocation = TypeReq<
components['schemas']['InfillTileInvocation']
>;
export type InfillPatchmatchInvocation = TypeReq<
components['schemas']['InfillPatchMatchInvocation']
>;
export type ImageNSFWBlurInvocation = TypeReq<
components['schemas']['ImageNSFWBlurInvocation']
>;
export type ImageWatermarkInvocation = TypeReq<
components['schemas']['ImageWatermarkInvocation']
>;
export type ImageBlurInvocation = TypeReq<
components['schemas']['ImageBlurInvocation']
>;
export type ColorCorrectInvocation = TypeReq<
components['schemas']['ColorCorrectInvocation']
>;
export type ImagePasteInvocation = TypeReq<
components['schemas']['ImagePasteInvocation']
>;
// ControlNet Nodes
export type ControlNetInvocation = TypeReq<

2
pyproject.toml
View File

@ -40,7 +40,7 @@ dependencies = [
"controlnet-aux>=0.0.6",
"timm==0.6.13", # needed to override timm latest in controlnet_aux, see https://github.com/isl-org/ZoeDepth/issues/26
"datasets",
"diffusers[torch]~=0.19.0",
"diffusers[torch]~=0.19.3",
"dnspython~=2.4.0",
"dynamicprompts",
"easing-functions",