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fix merge conflicts

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Lincoln Stein 2024-04-12 00:58:11 -04:00
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34
docs/features/PROMPTS.md
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@ -108,40 +108,6 @@ Can be used with .and():
Each will give you different results - try them out and see what you prefer!
### Cross-Attention Control ('prompt2prompt')
Sometimes an image you generate is almost right, and you just want to change one
detail without affecting the rest. You could use a photo editor and inpainting
to overpaint the area, but that's a pain. Here's where `prompt2prompt` comes in
handy.
Generate an image with a given prompt, record the seed of the image, and then
use the `prompt2prompt` syntax to substitute words in the original prompt for
words in a new prompt. This works for `img2img` as well.
For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because the words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
- `a cat playing with a ball in the forest`
- `a dog playing with a ball in the forest`
| `a cat playing with a ball in the forest` | `a dog playing with a ball in the forest` |
| --- | --- |
| img | img |
- For multiple word swaps, use parentheses: `a (fluffy cat).swap(barking dog) playing with a ball in the forest`.
- To swap a comma, use quotes: `a ("fluffy, grey cat").swap("big, barking dog") playing with a ball in the forest`.
- Supports options `t_start` and `t_end` (each 0-1) loosely corresponding to (bloc97's)[(https://github.com/bloc97/CrossAttentionControl)] `prompt_edit_tokens_start/_end` but with the math swapped to make it easier to
intuitively understand. `t_start` and `t_end` are used to control on which steps cross-attention control should run. With the default values `t_start=0` and `t_end=1`, cross-attention control is active on every step of image generation. Other values can be used to turn cross-attention control off for part of the image generation process.
- For example, if doing a diffusion with 10 steps for the prompt is `a cat.swap(dog, t_start=0.3, t_end=1.0) playing with a ball in the forest`, the first 3 steps will be run as `a cat playing with a ball in the forest`, while the last 7 steps will run as `a dog playing with a ball in the forest`, but the pixels that represent `dog` will be locked to the pixels that would have represented `cat` if the `cat` prompt had been used instead.
- Conversely, for `a cat.swap(dog, t_start=0, t_end=0.7) playing with a ball in the forest`, the first 7 steps will run as `a dog playing with a ball in the forest` with the pixels that represent `dog` locked to the same pixels that would have represented `cat` if the `cat` prompt was being used instead. The final 3 steps will just run `a cat playing with a ball in the forest`.
> For img2img, the step sequence does not start at 0 but instead at `(1.0-strength)` - so if the img2img `strength` is `0.7`, `t_start` and `t_end` must both be greater than `0.3` (`1.0-0.7`) to have any effect.
Prompt2prompt `.swap()` is not compatible with xformers, which will be temporarily disabled when doing a `.swap()` - so you should expect to use more VRAM and run slower that with xformers enabled.
The `prompt2prompt` code is based off
[bloc97's colab](https://github.com/bloc97/CrossAttentionControl).
### Escaping parentheses and speech marks
If the model you are using has parentheses () or speech marks "" as part of its

19
docs/help/FAQ.md
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@ -40,6 +40,25 @@ Follow the same steps to scan and import the missing models.
- Check the `ram` setting in `invokeai.yaml`. This setting tells Invoke how much of your system RAM can be used to cache models. Having this too high or too low can slow things down. That said, it's generally safest to not set this at all and instead let Invoke manage it.
- Check the `vram` setting in `invokeai.yaml`. This setting tells Invoke how much of your GPU VRAM can be used to cache models. Counter-intuitively, if this setting is too high, Invoke will need to do a lot of shuffling of models as it juggles the VRAM cache and the currently-loaded model. The default value of 0.25 is generally works well for GPUs without 16GB or more VRAM. Even on a 24GB card, the default works well.
- Check that your generations are happening on your GPU (if you have one). InvokeAI will log what is being used for generation upon startup. If your GPU isn't used, re-install to ensure the correct versions of torch get installed.
- If you are on Windows, you may have exceeded your GPU's VRAM capacity and are using slower [shared GPU memory](#shared-gpu-memory-windows). There's a guide to opt out of this behaviour in the linked FAQ entry.
## Shared GPU Memory (Windows)
!!! tip "Nvidia GPUs with driver 536.40"
This only applies to current Nvidia cards with driver 536.40 or later, released in June 2023.
When the GPU doesn't have enough VRAM for a task, Windows is able to allocate some of its CPU RAM to the GPU. This is much slower than VRAM, but it does allow the system to generate when it otherwise might no have enough VRAM.
When shared GPU memory is used, generation slows down dramatically - but at least it doesn't crash.
If you'd like to opt out of this behavior and instead get an error when you exceed your GPU's VRAM, follow [this guide from Nvidia](https://nvidia.custhelp.com/app/answers/detail/a_id/5490).
Here's how to get the python path required in the linked guide:
- Run `invoke.bat`.
- Select option 2 for developer console.
- At least one python path will be printed. Copy the path that includes your invoke installation directory (typically the first).
## Installer cannot find python (Windows)

5
installer/lib/installer.py
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@ -3,6 +3,7 @@
InvokeAI installer script
"""
import locale
import os
import platform
import re
@ -316,7 +317,9 @@ def upgrade_pip(venv_path: Path) -> str | None:
python = str(venv_path.expanduser().resolve() / python)
try:
result = subprocess.check_output([python, "-m", "pip", "install", "--upgrade", "pip"]).decode()
result = subprocess.check_output([python, "-m", "pip", "install", "--upgrade", "pip"]).decode(
encoding=locale.getpreferredencoding()
)
except subprocess.CalledProcessError as e:
print(e)
result = None

4
invokeai/app/api/routers/app_info.py
View File

@ -12,7 +12,7 @@ from pydantic import BaseModel, Field
from invokeai.app.invocations.upscale import ESRGAN_MODELS
from invokeai.app.services.invocation_cache.invocation_cache_common import InvocationCacheStatus
from invokeai.backend.image_util.patchmatch import PatchMatch
from invokeai.backend.image_util.infill_methods.patchmatch import PatchMatch
from invokeai.backend.image_util.safety_checker import SafetyChecker
from invokeai.backend.util.logging import logging
from invokeai.version import __version__
@ -100,7 +100,7 @@ async def get_app_deps() -> AppDependencyVersions:
@app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
async def get_config() -> AppConfig:
infill_methods = ["tile", "lama", "cv2"]
infill_methods = ["tile", "lama", "cv2", "color"] # TODO: add mosaic back
if PatchMatch.patchmatch_available():
infill_methods.append("patchmatch")

91
invokeai/app/invocations/compel.py
View File

@ -5,7 +5,15 @@ from compel import Compel, ReturnedEmbeddingsType
from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIComponent
from invokeai.app.invocations.fields import (
ConditioningField,
FieldDescriptions,
Input,
InputField,
OutputField,
TensorField,
UIComponent,
)
from invokeai.app.invocations.primitives import ConditioningOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.ti_utils import generate_ti_list
@ -14,7 +22,6 @@ from invokeai.backend.model_patcher import ModelPatcher
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
BasicConditioningInfo,
ConditioningFieldData,
ExtraConditioningInfo,
SDXLConditioningInfo,
)
from invokeai.backend.util.devices import torch_dtype
@ -36,7 +43,7 @@ from .model import CLIPField
title="Prompt",
tags=["prompt", "compel"],
category="conditioning",
version="1.1.1",
version="1.2.0",
)
class CompelInvocation(BaseInvocation):
"""Parse prompt using compel package to conditioning."""
@ -51,6 +58,9 @@ class CompelInvocation(BaseInvocation):
description=FieldDescriptions.clip,
input=Input.Connection,
)
mask: Optional[TensorField] = InputField(
default=None, description="A mask defining the region that this conditioning prompt applies to."
)
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ConditioningOutput:
@ -98,27 +108,19 @@ class CompelInvocation(BaseInvocation):
if context.config.get().log_tokenization:
log_tokenization_for_conjunction(conjunction, tokenizer)
c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
ec = ExtraConditioningInfo(
tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
cross_attention_control_args=options.get("cross_attention_control", None),
)
c, _options = compel.build_conditioning_tensor_for_conjunction(conjunction)
c = c.detach().to("cpu")
conditioning_data = ConditioningFieldData(
conditionings=[
BasicConditioningInfo(
embeds=c,
extra_conditioning=ec,
)
]
)
conditioning_data = ConditioningFieldData(conditionings=[BasicConditioningInfo(embeds=c)])
conditioning_name = context.conditioning.save(conditioning_data)
return ConditioningOutput.build(conditioning_name)
return ConditioningOutput(
conditioning=ConditioningField(
conditioning_name=conditioning_name,
mask=self.mask,
)
)
class SDXLPromptInvocationBase:
@ -132,7 +134,7 @@ class SDXLPromptInvocationBase:
get_pooled: bool,
lora_prefix: str,
zero_on_empty: bool,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[ExtraConditioningInfo]]:
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
tokenizer_info = context.models.load(clip_field.tokenizer)
tokenizer_model = tokenizer_info.model
assert isinstance(tokenizer_model, CLIPTokenizer)
@ -159,7 +161,7 @@ class SDXLPromptInvocationBase:
)
else:
c_pooled = None
return c, c_pooled, None
return c, c_pooled
def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
for lora in clip_field.loras:
@ -204,17 +206,12 @@ class SDXLPromptInvocationBase:
log_tokenization_for_conjunction(conjunction, tokenizer)
# TODO: ask for optimizations? to not run text_encoder twice
c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
c, _options = compel.build_conditioning_tensor_for_conjunction(conjunction)
if get_pooled:
c_pooled = compel.conditioning_provider.get_pooled_embeddings([prompt])
else:
c_pooled = None
ec = ExtraConditioningInfo(
tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
cross_attention_control_args=options.get("cross_attention_control", None),
)
del tokenizer
del text_encoder
del tokenizer_info
@ -224,7 +221,7 @@ class SDXLPromptInvocationBase:
if c_pooled is not None:
c_pooled = c_pooled.detach().to("cpu")
return c, c_pooled, ec
return c, c_pooled
@invocation(
@ -232,7 +229,7 @@ class SDXLPromptInvocationBase:
title="SDXL Prompt",
tags=["sdxl", "compel", "prompt"],
category="conditioning",
version="1.1.1",
version="1.2.0",
)
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning."""
@ -255,20 +252,19 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
target_height: int = InputField(default=1024, description="")
clip: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1")
clip2: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2")
mask: Optional[TensorField] = InputField(
default=None, description="A mask defining the region that this conditioning prompt applies to."
)
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ConditioningOutput:
c1, c1_pooled, ec1 = self.run_clip_compel(
context, self.clip, self.prompt, False, "lora_te1_", zero_on_empty=True
)
c1, c1_pooled = 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(
c2, c2_pooled = 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_", zero_on_empty=True
)
c2, c2_pooled = 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)
@ -307,17 +303,19 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
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,
embeds=torch.cat([c1, c2], dim=-1), pooled_embeds=c2_pooled, add_time_ids=add_time_ids
)
]
)
conditioning_name = context.conditioning.save(conditioning_data)
return ConditioningOutput.build(conditioning_name)
return ConditioningOutput(
conditioning=ConditioningField(
conditioning_name=conditioning_name,
mask=self.mask,
)
)
@invocation(
@ -345,7 +343,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ConditioningOutput:
# 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>", zero_on_empty=False)
c2, c2_pooled = 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)
@ -354,14 +352,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
assert c2_pooled is not None
conditioning_data = ConditioningFieldData(
conditionings=[
SDXLConditioningInfo(
embeds=c2,
pooled_embeds=c2_pooled,
add_time_ids=add_time_ids,
extra_conditioning=ec2, # or None
)
]
conditionings=[SDXLConditioningInfo(embeds=c2, pooled_embeds=c2_pooled, add_time_ids=add_time_ids)]
)
conditioning_name = context.conditioning.save(conditioning_data)

12
invokeai/app/invocations/fields.py
View File

@ -203,6 +203,12 @@ class DenoiseMaskField(BaseModel):
gradient: bool = Field(default=False, description="Used for gradient inpainting")
class TensorField(BaseModel):
"""A tensor primitive field."""
tensor_name: str = Field(description="The name of a tensor.")
class LatentsField(BaseModel):
"""A latents tensor primitive field"""
@ -226,7 +232,11 @@ class ConditioningField(BaseModel):
"""A conditioning tensor primitive value"""
conditioning_name: str = Field(description="The name of conditioning tensor")
# endregion
mask: Optional[TensorField] = Field(
default=None,
description="The mask associated with this conditioning tensor. Excluded regions should be set to False, "
"included regions should be set to True.",
)
class MetadataField(RootModel[dict[str, Any]]):

239
invokeai/app/invocations/infill.py
View File

@ -1,154 +1,91 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
from abc import abstractmethod
from typing import Literal, get_args
import math
from typing import Literal, Optional, get_args
import numpy as np
from PIL import Image, ImageOps
from PIL import Image
from invokeai.app.invocations.fields import ColorField, ImageField
from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.download_with_progress import download_with_progress_bar
from invokeai.app.util.misc import SEED_MAX
from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
from invokeai.backend.image_util.lama import LaMA
from invokeai.backend.image_util.patchmatch import PatchMatch
from invokeai.backend.image_util.infill_methods.cv2_inpaint import cv2_inpaint
from invokeai.backend.image_util.infill_methods.lama import LaMA
from invokeai.backend.image_util.infill_methods.mosaic import infill_mosaic
from invokeai.backend.image_util.infill_methods.patchmatch import PatchMatch, infill_patchmatch
from invokeai.backend.image_util.infill_methods.tile import infill_tile
from invokeai.backend.util.logging import InvokeAILogger
from .baseinvocation import BaseInvocation, invocation
from .fields import InputField, WithBoard, WithMetadata
from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
logger = InvokeAILogger.get_logger()
def infill_methods() -> list[str]:
methods = ["tile", "solid", "lama", "cv2"]
def get_infill_methods():
methods = Literal["tile", "color", "lama", "cv2"] # TODO: add mosaic back
if PatchMatch.patchmatch_available():
methods.insert(0, "patchmatch")
methods = Literal["patchmatch", "tile", "color", "lama", "cv2"] # TODO: add mosaic back
return methods
INFILL_METHODS = Literal[tuple(infill_methods())]
INFILL_METHODS = get_infill_methods()
DEFAULT_INFILL_METHOD = "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
def infill_lama(im: Image.Image) -> Image.Image:
lama = LaMA()
return lama(im)
class InfillImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Base class for invocations that preprocess images for Infilling"""
image: ImageField = InputField(description="The image to process")
def infill_patchmatch(im: Image.Image) -> Image.Image:
if im.mode != "RGBA":
return im
@abstractmethod
def infill(self, image: Image.Image) -> Image.Image:
"""Infill the image with the specified method"""
pass
# Skip patchmatch if patchmatch isn't available
if not PatchMatch.patchmatch_available():
return im
def load_image(self, context: InvocationContext) -> tuple[Image.Image, bool]:
"""Process the image to have an alpha channel before being infilled"""
image = context.images.get_pil(self.image.image_name)
has_alpha = True if image.mode == "RGBA" else False
return image, has_alpha
# 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 invoke(self, context: InvocationContext) -> ImageOutput:
# Retrieve and process image to be infilled
input_image, has_alpha = self.load_image(context)
# If the input image has no alpha channel, return it
if has_alpha is False:
return ImageOutput.build(context.images.get_dto(self.image.image_name))
def infill_cv2(im: Image.Image) -> Image.Image:
return cv2_inpaint(im)
# Perform Infill action
infilled_image = self.infill(input_image)
# Create ImageDTO for Infilled Image
infilled_image_dto = context.images.save(image=infilled_image)
def get_tile_images(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 tile_fill_missing(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 = 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() # noqa: E712
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
# Return Infilled Image
return ImageOutput.build(infilled_image_dto)
@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.2")
class InfillColorInvocation(BaseInvocation, WithMetadata, WithBoard):
class InfillColorInvocation(InfillImageProcessorInvocation):
"""Infills transparent areas of an image with a solid color"""
image: ImageField = InputField(description="The image to infill")
color: ColorField = InputField(
default=ColorField(r=127, g=127, b=127, a=255),
description="The color to use to infill",
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name)
def infill(self, image: Image.Image):
solid_bg = Image.new("RGBA", image.size, self.color.tuple())
infilled = Image.alpha_composite(solid_bg, image.convert("RGBA"))
infilled.paste(image, (0, 0), image.split()[-1])
image_dto = context.images.save(image=infilled)
return ImageOutput.build(image_dto)
return infilled
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.3")
class InfillTileInvocation(BaseInvocation, WithMetadata, WithBoard):
class InfillTileInvocation(InfillImageProcessorInvocation):
"""Infills transparent areas of an image with tiles of the image"""
image: ImageField = InputField(description="The image to infill")
tile_size: int = InputField(default=32, ge=1, description="The tile size (px)")
seed: int = InputField(
default=0,
@ -157,92 +94,74 @@ class InfillTileInvocation(BaseInvocation, WithMetadata, WithBoard):
description="The seed to use for tile generation (omit for random)",
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name)
infilled = tile_fill_missing(image.copy(), seed=self.seed, tile_size=self.tile_size)
infilled.paste(image, (0, 0), image.split()[-1])
image_dto = context.images.save(image=infilled)
return ImageOutput.build(image_dto)
def infill(self, image: Image.Image):
output = infill_tile(image, seed=self.seed, tile_size=self.tile_size)
return output.infilled
@invocation(
"infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.2"
)
class InfillPatchMatchInvocation(BaseInvocation, WithMetadata, WithBoard):
class InfillPatchMatchInvocation(InfillImageProcessorInvocation):
"""Infills transparent areas of an image using the PatchMatch algorithm"""
image: ImageField = InputField(description="The image to infill")
downscale: float = InputField(default=2.0, gt=0, description="Run patchmatch on downscaled image to speedup infill")
resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name).convert("RGBA")
def infill(self, image: Image.Image):
resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
infill_image = image.copy()
width = int(image.width / self.downscale)
height = int(image.height / self.downscale)
infill_image = infill_image.resize(
infilled = image.resize(
(width, height),
resample=resample_mode,
)
if PatchMatch.patchmatch_available():
infilled = infill_patchmatch(infill_image)
else:
raise ValueError("PatchMatch is not available on this system")
infilled = infill_patchmatch(image)
infilled = infilled.resize(
(image.width, image.height),
resample=resample_mode,
)
infilled.paste(image, (0, 0), mask=image.split()[-1])
# image.paste(infilled, (0, 0), mask=image.split()[-1])
image_dto = context.images.save(image=infilled)
return ImageOutput.build(image_dto)
return infilled
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.2")
class LaMaInfillInvocation(BaseInvocation, WithMetadata, WithBoard):
class LaMaInfillInvocation(InfillImageProcessorInvocation):
"""Infills transparent areas of an image using the LaMa model"""
image: ImageField = InputField(description="The image to infill")
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name)
# Downloads the LaMa model if it doesn't already exist
download_with_progress_bar(
name="LaMa Inpainting Model",
url="https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
dest_path=context.config.get().models_path / "core/misc/lama/lama.pt",
)
infilled = infill_lama(image.copy())
image_dto = context.images.save(image=infilled)
return ImageOutput.build(image_dto)
def infill(self, image: Image.Image):
lama = LaMA()
return lama(image)
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.2")
class CV2InfillInvocation(BaseInvocation, WithMetadata, WithBoard):
class CV2InfillInvocation(InfillImageProcessorInvocation):
"""Infills transparent areas of an image using OpenCV Inpainting"""
def infill(self, image: Image.Image):
return cv2_inpaint(image)
# @invocation(
# "infill_mosaic", title="Mosaic Infill", tags=["image", "inpaint", "outpaint"], category="inpaint", version="1.0.0"
# )
class MosaicInfillInvocation(InfillImageProcessorInvocation):
"""Infills transparent areas of an image with a mosaic pattern drawing colors from the rest of the image"""
image: ImageField = InputField(description="The image to infill")
tile_width: int = InputField(default=64, description="Width of the tile")
tile_height: int = InputField(default=64, description="Height of the tile")
min_color: ColorField = InputField(
default=ColorField(r=0, g=0, b=0, a=255),
description="The min threshold for color",
)
max_color: ColorField = InputField(
default=ColorField(r=255, g=255, b=255, a=255),
description="The max threshold for color",
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name)
infilled = infill_cv2(image.copy())
image_dto = context.images.save(image=infilled)
return ImageOutput.build(image_dto)
def infill(self, image: Image.Image):
return infill_mosaic(image, (self.tile_width, self.tile_height), self.min_color.tuple(), self.max_color.tuple())

40
invokeai/app/invocations/ip_adapter.py
View File

@ -1,11 +1,23 @@
from builtins import float
from typing import List, Literal, Union
from typing import List, Literal, Optional, Union
from pydantic import BaseModel, Field, field_validator, model_validator
from typing_extensions import Self
from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
invocation,
invocation_output,
)
from invokeai.app.invocations.fields import (
FieldDescriptions,
Input,
InputField,
OutputField,
TensorField,
UIType,
)
from invokeai.app.invocations.model import ModelIdentifierField
from invokeai.app.invocations.primitives import ImageField
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
@ -23,13 +35,18 @@ class IPAdapterField(BaseModel):
image: Union[ImageField, List[ImageField]] = Field(description="The IP-Adapter image prompt(s).")
ip_adapter_model: ModelIdentifierField = Field(description="The IP-Adapter model to use.")
image_encoder_model: ModelIdentifierField = Field(description="The name of the CLIP image encoder model.")
weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
weight: Union[float, List[float]] = Field(default=1, description="The weight given to the IP-Adapter.")
begin_step_percent: float = Field(
default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
)
end_step_percent: float = Field(
default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
)
mask: Optional[TensorField] = Field(
default=None,
description="The bool mask associated with this IP-Adapter. Excluded regions should be set to False, included "
"regions should be set to True.",
)
@field_validator("weight")
@classmethod
@ -52,7 +69,7 @@ class IPAdapterOutput(BaseInvocationOutput):
CLIP_VISION_MODEL_MAP = {"ViT-H": "ip_adapter_sd_image_encoder", "ViT-G": "ip_adapter_sdxl_image_encoder"}
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.2.2")
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.3.0")
class IPAdapterInvocation(BaseInvocation):
"""Collects IP-Adapter info to pass to other nodes."""
@ -65,9 +82,9 @@ class IPAdapterInvocation(BaseInvocation):
ui_order=-1,
ui_type=UIType.IPAdapterModel,
)
clip_vision_model: Literal["auto", "ViT-H", "ViT-G"] = InputField(
clip_vision_model: Literal["ViT-H", "ViT-G"] = InputField(
description="CLIP Vision model to use. Overrides model settings. Mandatory for checkpoint models.",
default="auto",
default="ViT-H",
ui_order=2,
)
weight: Union[float, List[float]] = InputField(
@ -79,6 +96,9 @@ class IPAdapterInvocation(BaseInvocation):
end_step_percent: float = InputField(
default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
)
mask: Optional[TensorField] = InputField(
default=None, description="A mask defining the region that this IP-Adapter applies to."
)
@field_validator("weight")
@classmethod
@ -96,14 +116,9 @@ class IPAdapterInvocation(BaseInvocation):
ip_adapter_info = context.models.get_config(self.ip_adapter_model.key)
assert isinstance(ip_adapter_info, (IPAdapterInvokeAIConfig, IPAdapterCheckpointConfig))
if self.clip_vision_model == "auto":
if isinstance(ip_adapter_info, IPAdapterInvokeAIConfig):
image_encoder_model_id = ip_adapter_info.image_encoder_model_id
image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
else:
raise RuntimeError(
"You need to set the appropriate CLIP Vision model for checkpoint IP Adapter models."
)
else:
image_encoder_model_name = CLIP_VISION_MODEL_MAP[self.clip_vision_model]
@ -117,6 +132,7 @@ class IPAdapterInvocation(BaseInvocation):
weight=self.weight,
begin_step_percent=self.begin_step_percent,
end_step_percent=self.end_step_percent,
mask=self.mask,
),
)

237
invokeai/app/invocations/latent.py
View File

@ -1,5 +1,5 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
import inspect
import math
from contextlib import ExitStack
from functools import singledispatchmethod
@ -9,6 +9,7 @@ import einops
import numpy as np
import numpy.typing as npt
import torch
import torchvision
import torchvision.transforms as T
from diffusers import AutoencoderKL, AutoencoderTiny
from diffusers.configuration_utils import ConfigMixin
@ -52,12 +53,20 @@ from invokeai.backend.lora import LoRAModelRaw
from invokeai.backend.model_manager import BaseModelType, LoadedModel
from invokeai.backend.model_patcher import ModelPatcher
from invokeai.backend.stable_diffusion import PipelineIntermediateState, set_seamless
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData, IPAdapterConditioningInfo
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
BasicConditioningInfo,
IPAdapterConditioningInfo,
IPAdapterData,
Range,
SDXLConditioningInfo,
TextConditioningData,
TextConditioningRegions,
)
from invokeai.backend.util.mask import to_standard_float_mask
from invokeai.backend.util.silence_warnings import SilenceWarnings
from ...backend.stable_diffusion.diffusers_pipeline import (
ControlNetData,
IPAdapterData,
StableDiffusionGeneratorPipeline,
T2IAdapterData,
image_resized_to_grid_as_tensor,
@ -275,10 +284,10 @@ def get_scheduler(
class DenoiseLatentsInvocation(BaseInvocation):
"""Denoises noisy latents to decodable images"""
positive_conditioning: ConditioningField = InputField(
positive_conditioning: Union[ConditioningField, list[ConditioningField]] = InputField(
description=FieldDescriptions.positive_cond, input=Input.Connection, ui_order=0
)
negative_conditioning: ConditioningField = InputField(
negative_conditioning: Union[ConditioningField, list[ConditioningField]] = InputField(
description=FieldDescriptions.negative_cond, input=Input.Connection, ui_order=1
)
noise: Optional[LatentsField] = InputField(
@ -356,33 +365,168 @@ class DenoiseLatentsInvocation(BaseInvocation):
raise ValueError("cfg_scale must be greater than 1")
return v
def _get_text_embeddings_and_masks(
self,
cond_list: list[ConditioningField],
context: InvocationContext,
device: torch.device,
dtype: torch.dtype,
) -> tuple[Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]], list[Optional[torch.Tensor]]]:
"""Get the text embeddings and masks from the input conditioning fields."""
text_embeddings: Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]] = []
text_embeddings_masks: list[Optional[torch.Tensor]] = []
for cond in cond_list:
cond_data = context.conditioning.load(cond.conditioning_name)
text_embeddings.append(cond_data.conditionings[0].to(device=device, dtype=dtype))
mask = cond.mask
if mask is not None:
mask = context.tensors.load(mask.tensor_name)
text_embeddings_masks.append(mask)
return text_embeddings, text_embeddings_masks
def _preprocess_regional_prompt_mask(
self, mask: Optional[torch.Tensor], target_height: int, target_width: int, dtype: torch.dtype
) -> torch.Tensor:
"""Preprocess a regional prompt mask to match the target height and width.
If mask is None, returns a mask of all ones with the target height and width.
If mask is not None, resizes the mask to the target height and width using 'nearest' interpolation.
Returns:
torch.Tensor: The processed mask. shape: (1, 1, target_height, target_width).
"""
if mask is None:
return torch.ones((1, 1, target_height, target_width), dtype=dtype)
mask = to_standard_float_mask(mask, out_dtype=dtype)
tf = torchvision.transforms.Resize(
(target_height, target_width), interpolation=torchvision.transforms.InterpolationMode.NEAREST
)
# Add a batch dimension to the mask, because torchvision expects shape (batch, channels, h, w).
mask = mask.unsqueeze(0) # Shape: (1, h, w) -> (1, 1, h, w)
resized_mask = tf(mask)
return resized_mask
def _concat_regional_text_embeddings(
self,
text_conditionings: Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]],
masks: Optional[list[Optional[torch.Tensor]]],
latent_height: int,
latent_width: int,
dtype: torch.dtype,
) -> tuple[Union[BasicConditioningInfo, SDXLConditioningInfo], Optional[TextConditioningRegions]]:
"""Concatenate regional text embeddings into a single embedding and track the region masks accordingly."""
if masks is None:
masks = [None] * len(text_conditionings)
assert len(text_conditionings) == len(masks)
is_sdxl = type(text_conditionings[0]) is SDXLConditioningInfo
all_masks_are_none = all(mask is None for mask in masks)
text_embedding = []
pooled_embedding = None
add_time_ids = None
cur_text_embedding_len = 0
processed_masks = []
embedding_ranges = []
for prompt_idx, text_embedding_info in enumerate(text_conditionings):
mask = masks[prompt_idx]
if is_sdxl:
# We choose a random SDXLConditioningInfo's pooled_embeds and add_time_ids here, with a preference for
# prompts without a mask. We prefer prompts without a mask, because they are more likely to contain
# global prompt information. In an ideal case, there should be exactly one global prompt without a
# mask, but we don't enforce this.
# HACK(ryand): The fact that we have to choose a single pooled_embedding and add_time_ids here is a
# fundamental interface issue. The SDXL Compel nodes are not designed to be used in the way that we use
# them for regional prompting. Ideally, the DenoiseLatents invocation should accept a single
# pooled_embeds tensor and a list of standard text embeds with region masks. This change would be a
# pretty major breaking change to a popular node, so for now we use this hack.
if pooled_embedding is None or mask is None:
pooled_embedding = text_embedding_info.pooled_embeds
if add_time_ids is None or mask is None:
add_time_ids = text_embedding_info.add_time_ids
text_embedding.append(text_embedding_info.embeds)
if not all_masks_are_none:
embedding_ranges.append(
Range(
start=cur_text_embedding_len, end=cur_text_embedding_len + text_embedding_info.embeds.shape[1]
)
)
processed_masks.append(
self._preprocess_regional_prompt_mask(mask, latent_height, latent_width, dtype=dtype)
)
cur_text_embedding_len += text_embedding_info.embeds.shape[1]
text_embedding = torch.cat(text_embedding, dim=1)
assert len(text_embedding.shape) == 3 # batch_size, seq_len, token_len
regions = None
if not all_masks_are_none:
regions = TextConditioningRegions(
masks=torch.cat(processed_masks, dim=1),
ranges=embedding_ranges,
)
if is_sdxl:
return SDXLConditioningInfo(
embeds=text_embedding, pooled_embeds=pooled_embedding, add_time_ids=add_time_ids
), regions
return BasicConditioningInfo(embeds=text_embedding), regions
def get_conditioning_data(
self,
context: InvocationContext,
scheduler: Scheduler,
unet: UNet2DConditionModel,
seed: int,
) -> ConditioningData:
positive_cond_data = context.conditioning.load(self.positive_conditioning.conditioning_name)
c = positive_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
latent_height: int,
latent_width: int,
) -> TextConditioningData:
# Normalize self.positive_conditioning and self.negative_conditioning to lists.
cond_list = self.positive_conditioning
if not isinstance(cond_list, list):
cond_list = [cond_list]
uncond_list = self.negative_conditioning
if not isinstance(uncond_list, list):
uncond_list = [uncond_list]
negative_cond_data = context.conditioning.load(self.negative_conditioning.conditioning_name)
uc = negative_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
conditioning_data = ConditioningData(
unconditioned_embeddings=uc,
text_embeddings=c,
guidance_scale=self.cfg_scale,
guidance_rescale_multiplier=self.cfg_rescale_multiplier,
cond_text_embeddings, cond_text_embedding_masks = self._get_text_embeddings_and_masks(
cond_list, context, unet.device, unet.dtype
)
uncond_text_embeddings, uncond_text_embedding_masks = self._get_text_embeddings_and_masks(
uncond_list, context, unet.device, unet.dtype
)
conditioning_data = conditioning_data.add_scheduler_args_if_applicable( # FIXME
scheduler,
# for ddim scheduler
eta=0.0, # ddim_eta
# for ancestral and sde schedulers
# flip all bits to have noise different from initial
generator=torch.Generator(device=unet.device).manual_seed(seed ^ 0xFFFFFFFF),
cond_text_embedding, cond_regions = self._concat_regional_text_embeddings(
text_conditionings=cond_text_embeddings,
masks=cond_text_embedding_masks,
latent_height=latent_height,
latent_width=latent_width,
dtype=unet.dtype,
)
uncond_text_embedding, uncond_regions = self._concat_regional_text_embeddings(
text_conditionings=uncond_text_embeddings,
masks=uncond_text_embedding_masks,
latent_height=latent_height,
latent_width=latent_width,
dtype=unet.dtype,
)
conditioning_data = TextConditioningData(
uncond_text=uncond_text_embedding,
cond_text=cond_text_embedding,
uncond_regions=uncond_regions,
cond_regions=cond_regions,
guidance_scale=self.cfg_scale,
guidance_rescale_multiplier=self.cfg_rescale_multiplier,
)
return conditioning_data
@ -488,8 +632,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
self,
context: InvocationContext,
ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]],
conditioning_data: ConditioningData,
exit_stack: ExitStack,
latent_height: int,
latent_width: int,
dtype: torch.dtype,
) -> Optional[list[IPAdapterData]]:
"""If IP-Adapter is enabled, then this function loads the requisite models, and adds the image prompt embeddings
to the `conditioning_data` (in-place).
@ -505,7 +651,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
return None
ip_adapter_data_list = []
conditioning_data.ip_adapter_conditioning = []
for single_ip_adapter in ip_adapter:
ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
context.models.load(single_ip_adapter.ip_adapter_model)
@ -528,9 +673,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
single_ipa_images, image_encoder_model
)
conditioning_data.ip_adapter_conditioning.append(
IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds)
)
mask = single_ip_adapter.mask
if mask is not None:
mask = context.tensors.load(mask.tensor_name)
mask = self._preprocess_regional_prompt_mask(mask, latent_height, latent_width, dtype=dtype)
ip_adapter_data_list.append(
IPAdapterData(
@ -538,6 +684,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
weight=single_ip_adapter.weight,
begin_step_percent=single_ip_adapter.begin_step_percent,
end_step_percent=single_ip_adapter.end_step_percent,
ip_adapter_conditioning=IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds),
mask=mask,
)
)
@ -627,6 +775,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
steps: int,
denoising_start: float,
denoising_end: float,
seed: int,
) -> Tuple[int, List[int], int]:
assert isinstance(scheduler, ConfigMixin)
if scheduler.config.get("cpu_only", False):
@ -655,7 +804,15 @@ class DenoiseLatentsInvocation(BaseInvocation):
timesteps = timesteps[t_start_idx : t_start_idx + t_end_idx]
num_inference_steps = len(timesteps) // scheduler.order
return num_inference_steps, timesteps, init_timestep
scheduler_step_kwargs = {}
scheduler_step_signature = inspect.signature(scheduler.step)
if "generator" in scheduler_step_signature.parameters:
# At some point, someone decided that schedulers that accept a generator should use the original seed with
# all bits flipped. I don't know the original rationale for this, but now we must keep it like this for
# reproducibility.
scheduler_step_kwargs = {"generator": torch.Generator(device=device).manual_seed(seed ^ 0xFFFFFFFF)}
return num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs
def prep_inpaint_mask(
self, context: InvocationContext, latents: torch.Tensor
@ -749,7 +906,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
)
pipeline = self.create_pipeline(unet, scheduler)
conditioning_data = self.get_conditioning_data(context, scheduler, unet, seed)
_, _, latent_height, latent_width = latents.shape
conditioning_data = self.get_conditioning_data(
context=context, unet=unet, latent_height=latent_height, latent_width=latent_width
)
controlnet_data = self.prep_control_data(
context=context,
@ -763,16 +924,19 @@ class DenoiseLatentsInvocation(BaseInvocation):
ip_adapter_data = self.prep_ip_adapter_data(
context=context,
ip_adapter=self.ip_adapter,
conditioning_data=conditioning_data,
exit_stack=exit_stack,
latent_height=latent_height,
latent_width=latent_width,
dtype=unet.dtype,
)
num_inference_steps, timesteps, init_timestep = self.init_scheduler(
num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs = self.init_scheduler(
scheduler,
device=unet.device,
steps=self.steps,
denoising_start=self.denoising_start,
denoising_end=self.denoising_end,
seed=seed,
)
result_latents = pipeline.latents_from_embeddings(
@ -785,6 +949,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
masked_latents=masked_latents,
gradient_mask=gradient_mask,
num_inference_steps=num_inference_steps,
scheduler_step_kwargs=scheduler_step_kwargs,
conditioning_data=conditioning_data,
control_data=controlnet_data,
ip_adapter_data=ip_adapter_data,
@ -799,7 +964,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
mps.empty_cache()
name = context.tensors.save(tensor=result_latents)
return LatentsOutput.build(latents_name=name, latents=result_latents, seed=seed)
return LatentsOutput.build(latents_name=name, latents=result_latents, seed=None)
@invocation(
@ -1254,7 +1419,7 @@ class IdealSizeInvocation(BaseInvocation):
return tuple((x - x % multiple_of) for x in args)
def invoke(self, context: InvocationContext) -> IdealSizeOutput:
unet_config = context.models.get_config(**self.unet.unet.model_dump())
unet_config = context.models.get_config(self.unet.unet.key)
aspect = self.width / self.height
dimension: float = 512
if unet_config.base == BaseModelType.StableDiffusion2:

36
invokeai/app/invocations/mask.py Normal file
View File

@ -0,0 +1,36 @@
import torch
from invokeai.app.invocations.baseinvocation import BaseInvocation, InvocationContext, invocation
from invokeai.app.invocations.fields import InputField, TensorField, WithMetadata
from invokeai.app.invocations.primitives import MaskOutput
@invocation(
"rectangle_mask",
title="Create Rectangle Mask",
tags=["conditioning"],
category="conditioning",
version="1.0.1",
)
class RectangleMaskInvocation(BaseInvocation, WithMetadata):
"""Create a rectangular mask."""
width: int = InputField(description="The width of the entire mask.")
height: int = InputField(description="The height of the entire mask.")
x_left: int = InputField(description="The left x-coordinate of the rectangular masked region (inclusive).")
y_top: int = InputField(description="The top y-coordinate of the rectangular masked region (inclusive).")
rectangle_width: int = InputField(description="The width of the rectangular masked region.")
rectangle_height: int = InputField(description="The height of the rectangular masked region.")
def invoke(self, context: InvocationContext) -> MaskOutput:
mask = torch.zeros((1, self.height, self.width), dtype=torch.bool)
mask[:, self.y_top : self.y_top + self.rectangle_height, self.x_left : self.x_left + self.rectangle_width] = (
True
)
mask_tensor_name = context.tensors.save(mask)
return MaskOutput(
mask=TensorField(tensor_name=mask_tensor_name),
width=self.width,
height=self.height,
)

11
invokeai/app/invocations/primitives.py
View File

@ -15,6 +15,7 @@ from invokeai.app.invocations.fields import (
InputField,
LatentsField,
OutputField,
TensorField,
UIComponent,
)
from invokeai.app.services.images.images_common import ImageDTO
@ -405,9 +406,19 @@ class ColorInvocation(BaseInvocation):
# endregion
# region Conditioning
@invocation_output("mask_output")
class MaskOutput(BaseInvocationOutput):
"""A torch mask tensor."""
mask: TensorField = OutputField(description="The mask.")
width: int = OutputField(description="The width of the mask in pixels.")
height: int = OutputField(description="The height of the mask in pixels.")
@invocation_output("conditioning_output")
class ConditioningOutput(BaseInvocationOutput):
"""Base class for nodes that output a single conditioning tensor"""

3
invokeai/app/services/config/config_default.py
View File

@ -3,6 +3,7 @@
from __future__ import annotations
import locale
import os
import re
import shutil
@ -401,7 +402,7 @@ def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
An instance of `InvokeAIAppConfig` with the loaded and migrated settings.
"""
assert config_path.suffix == ".yaml"
with open(config_path) as file:
with open(config_path, "rt", encoding=locale.getpreferredencoding()) as file:
loaded_config_dict = yaml.safe_load(file)
assert isinstance(loaded_config_dict, dict)

4
invokeai/app/services/model_install/model_install_default.py
View File

@ -1,5 +1,6 @@
"""Model installation class."""
import locale
import os
import re
import signal
@ -323,7 +324,8 @@ class ModelInstallService(ModelInstallServiceBase):
legacy_models_yaml_path = Path(self._app_config.root_path, legacy_models_yaml_path)
if legacy_models_yaml_path.exists():
legacy_models_yaml = yaml.safe_load(legacy_models_yaml_path.read_text())
with open(legacy_models_yaml_path, "rt", encoding=locale.getpreferredencoding()) as file:
legacy_models_yaml = yaml.safe_load(file)
yaml_metadata = legacy_models_yaml.pop("__metadata__")
yaml_version = yaml_metadata.get("version")

1
invokeai/app/services/model_manager/model_manager_default.py
View File

@ -80,6 +80,7 @@ class ModelManagerService(ModelManagerServiceBase):
ram_cache = ModelCache(
max_cache_size=app_config.ram,
max_vram_cache_size=app_config.vram,
lazy_offloading=app_config.lazy_offload,
logger=logger,
execution_device=execution_device,
)

6
invokeai/app/services/session_processor/session_processor_default.py
View File

@ -86,6 +86,12 @@ class DefaultSessionProcessor(SessionProcessorBase):
self._poll_now()
elif event_name == "batch_enqueued":
self._poll_now()
elif event_name == "queue_item_status_changed" and event[1]["data"]["queue_item"]["status"] in [
"completed",
"failed",
"canceled",
]:
self._poll_now()
def resume(self) -> SessionProcessorStatus:
if not self._resume_event.is_set():

12
invokeai/app/services/shared/invocation_context.py
View File

@ -249,6 +249,18 @@ class ImagesInterface(InvocationContextInterface):
"""
return self._services.images.get_dto(image_name)
def get_path(self, image_name: str, thumbnail: bool = False) -> Path:
"""Gets the internal path to an image or thumbnail.
Args:
image_name: The name of the image to get the path of.
thumbnail: Get the path of the thumbnail instead of the full image
Returns:
The local path of the image or thumbnail.
"""
return self._services.images.get_path(image_name, thumbnail)
class TensorsInterface(InvocationContextInterface):
def save(self, tensor: Tensor) -> str:

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

@ -2,7 +2,7 @@
Initialization file for invokeai.backend.image_util methods.
"""
from .patchmatch import PatchMatch # noqa: F401
from .infill_methods.patchmatch import PatchMatch # noqa: F401
from .pngwriter import PngWriter, PromptFormatter, retrieve_metadata, write_metadata # noqa: F401
from .seamless import configure_model_padding # noqa: F401
from .util import InitImageResizer, make_grid # noqa: F401

0
invokeai/backend/image_util/cv2_inpaint.py → invokeai/backend/image_util/infill_methods/cv2_inpaint.py
View File

9
invokeai/backend/image_util/lama.py → invokeai/backend/image_util/infill_methods/lama.py
View File

@ -7,6 +7,7 @@ from PIL import Image
import invokeai.backend.util.logging as logger
from invokeai.app.services.config.config_default import get_config
from invokeai.app.util.download_with_progress import download_with_progress_bar
from invokeai.backend.util.devices import choose_torch_device
@ -30,6 +31,14 @@ class LaMA:
def __call__(self, input_image: Image.Image, *args: Any, **kwds: Any) -> Any:
device = choose_torch_device()
model_location = get_config().models_path / "core/misc/lama/lama.pt"
if not model_location.exists():
download_with_progress_bar(
name="LaMa Inpainting Model",
url="https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
dest_path=model_location,
)
model = load_jit_model(model_location, device)
image = np.asarray(input_image.convert("RGB"))

60
invokeai/backend/image_util/infill_methods/mosaic.py Normal file
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@ -0,0 +1,60 @@
from typing import Tuple
import numpy as np
from PIL import Image
def infill_mosaic(
image: Image.Image,
tile_shape: Tuple[int, int] = (64, 64),
min_color: Tuple[int, int, int, int] = (0, 0, 0, 0),
max_color: Tuple[int, int, int, int] = (255, 255, 255, 0),
) -> Image.Image:
"""
image:PIL - A PIL Image
tile_shape: Tuple[int,int] - Tile width & Tile Height
min_color: Tuple[int,int,int] - RGB values for the lowest color to clip to (0-255)
max_color: Tuple[int,int,int] - RGB values for the highest color to clip to (0-255)
"""
np_image = np.array(image) # Convert image to np array
alpha = np_image[:, :, 3] # Get the mask from the alpha channel of the image
non_transparent_pixels = np_image[alpha != 0, :3] # List of non-transparent pixels
# Create color tiles to paste in the empty areas of the image
tile_width, tile_height = tile_shape
# Clip the range of colors in the image to a particular spectrum only
r_min, g_min, b_min, _ = min_color
r_max, g_max, b_max, _ = max_color
non_transparent_pixels[:, 0] = np.clip(non_transparent_pixels[:, 0], r_min, r_max)
non_transparent_pixels[:, 1] = np.clip(non_transparent_pixels[:, 1], g_min, g_max)
non_transparent_pixels[:, 2] = np.clip(non_transparent_pixels[:, 2], b_min, b_max)
tiles = []
for _ in range(256):
color = non_transparent_pixels[np.random.randint(len(non_transparent_pixels))]
tile = np.zeros((tile_height, tile_width, 3), dtype=np.uint8)
tile[:, :] = color
tiles.append(tile)
# Fill the transparent area with tiles
filled_image = np.zeros((image.height, image.width, 3), dtype=np.uint8)
for x in range(image.width):
for y in range(image.height):
tile = tiles[np.random.randint(len(tiles))]
try:
filled_image[
y - (y % tile_height) : y - (y % tile_height) + tile_height,
x - (x % tile_width) : x - (x % tile_width) + tile_width,
] = tile
except ValueError:
# Need to handle edge cases - literally
pass
filled_image = Image.fromarray(filled_image) # Convert the filled tiles image to PIL
image = Image.composite(
image, filled_image, image.split()[-1]
) # Composite the original image on top of the filled tiles
return image

67
invokeai/backend/image_util/infill_methods/patchmatch.py Normal file
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@ -0,0 +1,67 @@
"""
This module defines a singleton object, "patchmatch" that
wraps the actual patchmatch object. It respects the global
"try_patchmatch" attribute, so that patchmatch loading can
be suppressed or deferred
"""
import numpy as np
from PIL import Image
import invokeai.backend.util.logging as logger
from invokeai.app.services.config.config_default import get_config
class PatchMatch:
"""
Thin class wrapper around the patchmatch function.
"""
patch_match = None
tried_load: bool = False
def __init__(self):
super().__init__()
@classmethod
def _load_patch_match(cls):
if cls.tried_load:
return
if get_config().patchmatch:
from patchmatch import patch_match as pm
if pm.patchmatch_available:
logger.info("Patchmatch initialized")
cls.patch_match = pm
else:
logger.info("Patchmatch not loaded (nonfatal)")
else:
logger.info("Patchmatch loading disabled")
cls.tried_load = True
@classmethod
def patchmatch_available(cls) -> bool:
cls._load_patch_match()
if not cls.patch_match:
return False
return cls.patch_match.patchmatch_available
@classmethod
def inpaint(cls, image: Image.Image) -> Image.Image:
if cls.patch_match is None or not cls.patchmatch_available():
return image
np_image = np.array(image)
mask = 255 - np_image[:, :, 3]
infilled = cls.patch_match.inpaint(np_image[:, :, :3], mask, patch_size=3)
return Image.fromarray(infilled, mode="RGB")
def infill_patchmatch(image: Image.Image) -> Image.Image:
IS_PATCHMATCH_AVAILABLE = PatchMatch.patchmatch_available()
if not IS_PATCHMATCH_AVAILABLE:
logger.warning("PatchMatch is not available on this system")
return image
return PatchMatch.inpaint(image)

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95
invokeai/backend/image_util/infill_methods/tile.ipynb Normal file
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@ -0,0 +1,95 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"Smoke test for the tile infill\"\"\"\n",
"\n",
"from pathlib import Path\n",
"from typing import Optional\n",
"from PIL import Image\n",
"from invokeai.backend.image_util.infill_methods.tile import infill_tile\n",
"\n",
"images: list[tuple[str, Image.Image]] = []\n",
"\n",
"for i in sorted(Path(\"./test_images/\").glob(\"*.webp\")):\n",
" images.append((i.name, Image.open(i)))\n",
" images.append((i.name, Image.open(i).transpose(Image.FLIP_LEFT_RIGHT)))\n",
" images.append((i.name, Image.open(i).transpose(Image.FLIP_TOP_BOTTOM)))\n",
" images.append((i.name, Image.open(i).resize((512, 512))))\n",
" images.append((i.name, Image.open(i).resize((1234, 461))))\n",
"\n",
"outputs: list[tuple[str, Image.Image, Image.Image, Optional[Image.Image]]] = []\n",
"\n",
"for name, image in images:\n",
" try:\n",
" output = infill_tile(image, seed=0, tile_size=32)\n",
" outputs.append((name, image, output.infilled, output.tile_image))\n",
" except ValueError as e:\n",
" print(f\"Skipping image {name}: {e}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Display the images in jupyter notebook\n",
"import matplotlib.pyplot as plt\n",
"from PIL import ImageOps\n",
"\n",
"fig, axes = plt.subplots(len(outputs), 3, figsize=(10, 3 * len(outputs)))\n",
"plt.subplots_adjust(hspace=0)\n",
"\n",
"for i, (name, original, infilled, tile_image) in enumerate(outputs):\n",
" # Add a border to each image, helps to see the edges\n",
" size = original.size\n",
" original = ImageOps.expand(original, border=5, fill=\"red\")\n",
" filled = ImageOps.expand(infilled, border=5, fill=\"red\")\n",
" if tile_image:\n",
" tile_image = ImageOps.expand(tile_image, border=5, fill=\"red\")\n",
"\n",
" axes[i, 0].imshow(original)\n",
" axes[i, 0].axis(\"off\")\n",
" axes[i, 0].set_title(f\"Original ({name} - {size})\")\n",
"\n",
" if tile_image:\n",
" axes[i, 1].imshow(tile_image)\n",
" axes[i, 1].axis(\"off\")\n",
" axes[i, 1].set_title(\"Tile Image\")\n",
" else:\n",
" axes[i, 1].axis(\"off\")\n",
" axes[i, 1].set_title(\"NO TILES GENERATED (NO TRANSPARENCY)\")\n",
"\n",
" axes[i, 2].imshow(filled)\n",
" axes[i, 2].axis(\"off\")\n",
" axes[i, 2].set_title(\"Filled\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".invokeai",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

122
invokeai/backend/image_util/infill_methods/tile.py Normal file
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@ -0,0 +1,122 @@
from dataclasses import dataclass
from typing import Optional
import numpy as np
from PIL import Image
def create_tile_pool(img_array: np.ndarray, tile_size: tuple[int, int]) -> list[np.ndarray]:
"""
Create a pool of tiles from non-transparent areas of the image by systematically walking through the image.
Args:
img_array: numpy array of the image.
tile_size: tuple (tile_width, tile_height) specifying the size of each tile.
Returns:
A list of numpy arrays, each representing a tile.
"""
tiles: list[np.ndarray] = []
rows, cols = img_array.shape[:2]
tile_width, tile_height = tile_size
for y in range(0, rows - tile_height + 1, tile_height):
for x in range(0, cols - tile_width + 1, tile_width):
tile = img_array[y : y + tile_height, x : x + tile_width]
# Check if the image has an alpha channel and the tile is completely opaque
if img_array.shape[2] == 4 and np.all(tile[:, :, 3] == 255):
tiles.append(tile)
elif img_array.shape[2] == 3: # If no alpha channel, append the tile
tiles.append(tile)
if not tiles:
raise ValueError(
"Not enough opaque pixels to generate any tiles. Use a smaller tile size or a different image."
)
return tiles
def create_filled_image(
img_array: np.ndarray, tile_pool: list[np.ndarray], tile_size: tuple[int, int], seed: int
) -> np.ndarray:
"""
Create an image of the same dimensions as the original, filled entirely with tiles from the pool.
Args:
img_array: numpy array of the original image.
tile_pool: A list of numpy arrays, each representing a tile.
tile_size: tuple (tile_width, tile_height) specifying the size of each tile.
Returns:
A numpy array representing the filled image.
"""
rows, cols, _ = img_array.shape
tile_width, tile_height = tile_size
# Prep an empty RGB image
filled_img_array = np.zeros((rows, cols, 3), dtype=img_array.dtype)
# Make the random tile selection reproducible
rng = np.random.default_rng(seed)
for y in range(0, rows, tile_height):
for x in range(0, cols, tile_width):
# Pick a random tile from the pool
tile = tile_pool[rng.integers(len(tile_pool))]
# Calculate the space available (may be less than tile size near the edges)
space_y = min(tile_height, rows - y)
space_x = min(tile_width, cols - x)
# Crop the tile if necessary to fit into the available space
cropped_tile = tile[:space_y, :space_x, :3]
# Fill the available space with the (possibly cropped) tile
filled_img_array[y : y + space_y, x : x + space_x, :3] = cropped_tile
return filled_img_array
@dataclass
class InfillTileOutput:
infilled: Image.Image
tile_image: Optional[Image.Image] = None
def infill_tile(image_to_infill: Image.Image, seed: int, tile_size: int) -> InfillTileOutput:
"""Infills an image with random tiles from the image itself.
If the image is not an RGBA image, it is returned untouched.
Args:
image: The image to infill.
tile_size: The size of the tiles to use for infilling.
Raises:
ValueError: If there are not enough opaque pixels to generate any tiles.
"""
if image_to_infill.mode != "RGBA":
return InfillTileOutput(infilled=image_to_infill)
# Internally, we want a tuple of (tile_width, tile_height). In the future, the tile size can be any rectangle.
_tile_size = (tile_size, tile_size)
np_image = np.array(image_to_infill, dtype=np.uint8)
# Create the pool of tiles that we will use to infill
tile_pool = create_tile_pool(np_image, _tile_size)
# Create an image from the tiles, same size as the original
tile_np_image = create_filled_image(np_image, tile_pool, _tile_size, seed)
# Paste the OG image over the tile image, effectively infilling the area
tile_image = Image.fromarray(tile_np_image, "RGB")
infilled = tile_image.copy()
infilled.paste(image_to_infill, (0, 0), image_to_infill.split()[-1])
# I think we want this to be "RGBA"?
infilled.convert("RGBA")
return InfillTileOutput(infilled=infilled, tile_image=tile_image)

49
invokeai/backend/image_util/patchmatch.py
View File

@ -1,49 +0,0 @@
"""
This module defines a singleton object, "patchmatch" that
wraps the actual patchmatch object. It respects the global
"try_patchmatch" attribute, so that patchmatch loading can
be suppressed or deferred
"""
import numpy as np
import invokeai.backend.util.logging as logger
from invokeai.app.services.config.config_default import get_config
class PatchMatch:
"""
Thin class wrapper around the patchmatch function.
"""
patch_match = None
tried_load: bool = False
def __init__(self):
super().__init__()
@classmethod
def _load_patch_match(self):
if self.tried_load:
return
if get_config().patchmatch:
from patchmatch import patch_match as pm
if pm.patchmatch_available:
logger.info("Patchmatch initialized")
else:
logger.info("Patchmatch not loaded (nonfatal)")
self.patch_match = pm
else:
logger.info("Patchmatch loading disabled")
self.tried_load = True
@classmethod
def patchmatch_available(self) -> bool:
self._load_patch_match()
return self.patch_match and self.patch_match.patchmatch_available
@classmethod
def inpaint(self, *args, **kwargs) -> np.ndarray:
if self.patchmatch_available():
return self.patch_match.inpaint(*args, **kwargs)

182
invokeai/backend/ip_adapter/attention_processor.py
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@ -1,182 +0,0 @@
# copied from https://github.com/tencent-ailab/IP-Adapter (Apache License 2.0)
# and modified as needed
# tencent-ailab comment:
# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers.models.attention_processor import AttnProcessor2_0 as DiffusersAttnProcessor2_0
from invokeai.backend.ip_adapter.ip_attention_weights import IPAttentionProcessorWeights
# Create a version of AttnProcessor2_0 that is a sub-class of nn.Module. This is required for IP-Adapter state_dict
# loading.
class AttnProcessor2_0(DiffusersAttnProcessor2_0, nn.Module):
def __init__(self):
DiffusersAttnProcessor2_0.__init__(self)
nn.Module.__init__(self)
def __call__(
self,
attn,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
ip_adapter_image_prompt_embeds=None,
):
"""Re-definition of DiffusersAttnProcessor2_0.__call__(...) that accepts and ignores the
ip_adapter_image_prompt_embeds parameter.
"""
return DiffusersAttnProcessor2_0.__call__(
self, attn, hidden_states, encoder_hidden_states, attention_mask, temb
)
class IPAttnProcessor2_0(torch.nn.Module):
r"""
Attention processor for IP-Adapater for PyTorch 2.0.
Args:
hidden_size (`int`):
The hidden size of the attention layer.
cross_attention_dim (`int`):
The number of channels in the `encoder_hidden_states`.
scale (`float`, defaults to 1.0):
the weight scale of image prompt.
"""
def __init__(self, weights: list[IPAttentionProcessorWeights], scales: list[float]):
super().__init__()
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
assert len(weights) == len(scales)
self._weights = weights
self._scales = scales
def __call__(
self,
attn,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
ip_adapter_image_prompt_embeds=None,
):
"""Apply IP-Adapter attention.
Args:
ip_adapter_image_prompt_embeds (torch.Tensor): The image prompt embeddings.
Shape: (batch_size, num_ip_images, seq_len, ip_embedding_len).
"""
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
if encoder_hidden_states is not None:
# If encoder_hidden_states is not None, then we are doing cross-attention, not self-attention. In this case,
# we will apply IP-Adapter conditioning. We validate the inputs for IP-Adapter conditioning here.
assert ip_adapter_image_prompt_embeds is not None
assert len(ip_adapter_image_prompt_embeds) == len(self._weights)
for ipa_embed, ipa_weights, scale in zip(
ip_adapter_image_prompt_embeds, self._weights, self._scales, strict=True
):
# The batch dimensions should match.
assert ipa_embed.shape[0] == encoder_hidden_states.shape[0]
# The token_len dimensions should match.
assert ipa_embed.shape[-1] == encoder_hidden_states.shape[-1]
ip_hidden_states = ipa_embed
# Expected ip_hidden_state shape: (batch_size, num_ip_images, ip_seq_len, ip_image_embedding)
ip_key = ipa_weights.to_k_ip(ip_hidden_states)
ip_value = ipa_weights.to_v_ip(ip_hidden_states)
# Expected ip_key and ip_value shape: (batch_size, num_ip_images, ip_seq_len, head_dim * num_heads)
ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# Expected ip_key and ip_value shape: (batch_size, num_heads, num_ip_images * ip_seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
ip_hidden_states = F.scaled_dot_product_attention(
query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
)
# Expected ip_hidden_states shape: (batch_size, num_heads, query_seq_len, head_dim)
ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
ip_hidden_states = ip_hidden_states.to(query.dtype)
# Expected ip_hidden_states shape: (batch_size, query_seq_len, num_heads * head_dim)
hidden_states = hidden_states + scale * ip_hidden_states
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states

2
invokeai/backend/model_manager/load/load_default.py
View File

@ -37,7 +37,7 @@ class ModelLoader(ModelLoaderBase):
self._logger = logger
self._ram_cache = ram_cache
self._convert_cache = convert_cache
self._torch_dtype = torch_dtype(choose_torch_device(), app_config)
self._torch_dtype = torch_dtype(choose_torch_device())
def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
"""

2
invokeai/backend/model_manager/load/model_cache/model_cache_base.py
View File

@ -117,7 +117,7 @@ class ModelCacheBase(ABC, Generic[T]):
@property
@abstractmethod
def stats(self) -> CacheStats:
def stats(self) -> Optional[CacheStats]:
"""Return collected CacheStats object."""
pass

33
invokeai/backend/model_manager/load/model_cache/model_cache_default.py
View File

@ -270,12 +270,14 @@ class ModelCache(ModelCacheBase[AnyModel]):
if torch.device(source_device).type == torch.device(target_device).type:
return
# may raise an exception here if insufficient GPU VRAM
self._check_free_vram(target_device, cache_entry.size)
start_model_to_time = time.time()
snapshot_before = self._capture_memory_snapshot()
try:
cache_entry.model.to(target_device)
except Exception as e: # blow away cache entry
self._delete_cache_entry(cache_entry)
raise e
snapshot_after = self._capture_memory_snapshot()
end_model_to_time = time.time()
self.logger.debug(
@ -330,11 +332,11 @@ class ModelCache(ModelCacheBase[AnyModel]):
f" {in_ram_models}/{in_vram_models}({locked_in_vram_models})"
)
def make_room(self, model_size: int) -> None:
def make_room(self, size: int) -> None:
"""Make enough room in the cache to accommodate a new model of indicated size."""
# calculate how much memory this model will require
# multiplier = 2 if self.precision==torch.float32 else 1
bytes_needed = model_size
bytes_needed = size
maximum_size = self.max_cache_size * GIG # stored in GB, convert to bytes
current_size = self.cache_size()
@ -389,12 +391,11 @@ class ModelCache(ModelCacheBase[AnyModel]):
# 1 from onnx runtime object
if not cache_entry.locked and refs <= (3 if "onnx" in model_key else 2):
self.logger.debug(
f"Removing {model_key} from RAM cache to free at least {(model_size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
f"Removing {model_key} from RAM cache to free at least {(size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
)
current_size -= cache_entry.size
models_cleared += 1
del self._cache_stack[pos]
del self._cached_models[model_key]
self._delete_cache_entry(cache_entry)
del cache_entry
else:
@ -422,16 +423,6 @@ class ModelCache(ModelCacheBase[AnyModel]):
self.logger.debug(f"After making room: cached_models={len(self._cached_models)}")
def _check_free_vram(self, target_device: torch.device, needed_size: int) -> None:
if target_device.type != "cuda":
return
vram_device = ( # mem_get_info() needs an indexed device
target_device if target_device.index is not None else torch.device(str(target_device), index=0)
)
free_mem, _ = torch.cuda.mem_get_info(torch.device(vram_device))
if needed_size > free_mem:
needed_gb = round(needed_size / GIG, 2)
free_gb = round(free_mem / GIG, 2)
raise torch.cuda.OutOfMemoryError(
f"Insufficient VRAM to load model, requested {needed_gb}GB but only had {free_gb}GB free"
)
def _delete_cache_entry(self, cache_entry: CacheRecord[AnyModel]) -> None:
self._cache_stack.remove(cache_entry.key)
del self._cached_models[cache_entry.key]

2
invokeai/backend/model_manager/load/model_cache/model_locker.py
View File

@ -34,7 +34,6 @@ class ModelLocker(ModelLockerBase):
# NOTE that the model has to have the to() method in order for this code to move it into GPU!
self._cache_entry.lock()
try:
if self._cache.lazy_offloading:
self._cache.offload_unlocked_models(self._cache_entry.size)
@ -51,6 +50,7 @@ class ModelLocker(ModelLockerBase):
except Exception:
self._cache_entry.unlock()
raise
return self.model
def unlock(self) -> None:

99
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@ -21,10 +21,12 @@ from pydantic import Field
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from invokeai.app.services.config.config_default import get_config
from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
from invokeai.backend.ip_adapter.unet_patcher import UNetPatcher
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
IPAdapterData,
TextConditioningData,
)
from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher
from invokeai.backend.util.attention import auto_detect_slice_size
from invokeai.backend.util.devices import normalize_device
@ -149,16 +151,6 @@ class ControlNetData:
resize_mode: str = Field(default="just_resize")
@dataclass
class IPAdapterData:
ip_adapter_model: IPAdapter = Field(default=None)
# TODO: change to polymorphic so can do different weights per step (once implemented...)
weight: Union[float, List[float]] = Field(default=1.0)
# weight: float = Field(default=1.0)
begin_step_percent: float = Field(default=0.0)
end_step_percent: float = Field(default=1.0)
@dataclass
class T2IAdapterData:
"""A structure containing the information required to apply conditioning from a single T2I-Adapter model."""
@ -295,7 +287,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
self,
latents: torch.Tensor,
num_inference_steps: int,
conditioning_data: ConditioningData,
scheduler_step_kwargs: dict[str, Any],
conditioning_data: TextConditioningData,
*,
noise: Optional[torch.Tensor],
timesteps: torch.Tensor,
@ -308,7 +301,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
mask: Optional[torch.Tensor] = None,
masked_latents: Optional[torch.Tensor] = None,
gradient_mask: Optional[bool] = False,
seed: Optional[int] = None,
seed: int,
) -> torch.Tensor:
if init_timestep.shape[0] == 0:
return latents
@ -326,20 +319,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
latents = self.scheduler.add_noise(latents, noise, batched_t)
if mask is not None:
# 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)
)
if is_inpainting_model(self.unet):
if masked_latents is None:
raise Exception("Source image required for inpaint mask when inpaint model used!")
@ -348,6 +327,15 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
self._unet_forward, mask, masked_latents
)
else:
# if no noise provided, noisify unmasked area based on seed
if noise is None:
noise = torch.randn(
orig_latents.shape,
dtype=torch.float32,
device="cpu",
generator=torch.Generator(device="cpu").manual_seed(seed),
).to(device=orig_latents.device, dtype=orig_latents.dtype)
additional_guidance.append(AddsMaskGuidance(mask, orig_latents, self.scheduler, noise, gradient_mask))
try:
@ -355,6 +343,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
latents,
timesteps,
conditioning_data,
scheduler_step_kwargs=scheduler_step_kwargs,
additional_guidance=additional_guidance,
control_data=control_data,
ip_adapter_data=ip_adapter_data,
@ -380,7 +369,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
self,
latents: torch.Tensor,
timesteps,
conditioning_data: ConditioningData,
conditioning_data: TextConditioningData,
scheduler_step_kwargs: dict[str, Any],
*,
additional_guidance: List[Callable] = None,
control_data: List[ControlNetData] = None,
@ -397,22 +387,17 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
if timesteps.shape[0] == 0:
return latents
ip_adapter_unet_patcher = None
extra_conditioning_info = conditioning_data.text_embeddings.extra_conditioning
if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
attn_ctx = self.invokeai_diffuser.custom_attention_context(
self.invokeai_diffuser.model,
extra_conditioning_info=extra_conditioning_info,
use_ip_adapter = ip_adapter_data is not None
use_regional_prompting = (
conditioning_data.cond_regions is not None or conditioning_data.uncond_regions is not None
)
self.use_ip_adapter = False
elif ip_adapter_data is not None:
# TODO(ryand): Should we raise an exception if both custom attention and IP-Adapter attention are active?
# As it is now, the IP-Adapter will silently be skipped.
ip_adapter_unet_patcher = UNetPatcher([ipa.ip_adapter_model for ipa in ip_adapter_data])
attn_ctx = ip_adapter_unet_patcher.apply_ip_adapter_attention(self.invokeai_diffuser.model)
self.use_ip_adapter = True
else:
unet_attention_patcher = None
self.use_ip_adapter = use_ip_adapter
attn_ctx = nullcontext()
if use_ip_adapter or use_regional_prompting:
ip_adapters = [ipa.ip_adapter_model for ipa in ip_adapter_data] if use_ip_adapter else None
unet_attention_patcher = UNetAttentionPatcher(ip_adapters)
attn_ctx = unet_attention_patcher.apply_ip_adapter_attention(self.invokeai_diffuser.model)
with attn_ctx:
if callback is not None:
@ -435,11 +420,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
conditioning_data,
step_index=i,
total_step_count=len(timesteps),
scheduler_step_kwargs=scheduler_step_kwargs,
additional_guidance=additional_guidance,
control_data=control_data,
ip_adapter_data=ip_adapter_data,
t2i_adapter_data=t2i_adapter_data,
ip_adapter_unet_patcher=ip_adapter_unet_patcher,
)
latents = step_output.prev_sample
predicted_original = getattr(step_output, "pred_original_sample", None)
@ -463,14 +448,14 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
self,
t: torch.Tensor,
latents: torch.Tensor,
conditioning_data: ConditioningData,
conditioning_data: TextConditioningData,
step_index: int,
total_step_count: int,
scheduler_step_kwargs: dict[str, Any],
additional_guidance: List[Callable] = None,
control_data: List[ControlNetData] = None,
ip_adapter_data: Optional[list[IPAdapterData]] = None,
t2i_adapter_data: Optional[list[T2IAdapterData]] = None,
ip_adapter_unet_patcher: Optional[UNetPatcher] = None,
):
# invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
timestep = t[0]
@ -485,23 +470,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
# i.e. before or after passing it to InvokeAIDiffuserComponent
latent_model_input = self.scheduler.scale_model_input(latents, timestep)
# handle IP-Adapter
if self.use_ip_adapter and ip_adapter_data is not None: # somewhat redundant but logic is clearer
for i, single_ip_adapter_data in enumerate(ip_adapter_data):
first_adapter_step = math.floor(single_ip_adapter_data.begin_step_percent * total_step_count)
last_adapter_step = math.ceil(single_ip_adapter_data.end_step_percent * total_step_count)
weight = (
single_ip_adapter_data.weight[step_index]
if isinstance(single_ip_adapter_data.weight, List)
else single_ip_adapter_data.weight
)
if step_index >= first_adapter_step and step_index <= last_adapter_step:
# Only apply this IP-Adapter if the current step is within the IP-Adapter's begin/end step range.
ip_adapter_unet_patcher.set_scale(i, weight)
else:
# Otherwise, set the IP-Adapter's scale to 0, so it has no effect.
ip_adapter_unet_patcher.set_scale(i, 0.0)
# Handle ControlNet(s)
down_block_additional_residuals = None
mid_block_additional_residual = None
@ -550,6 +518,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
step_index=step_index,
total_step_count=total_step_count,
conditioning_data=conditioning_data,
ip_adapter_data=ip_adapter_data,
down_block_additional_residuals=down_block_additional_residuals, # for ControlNet
mid_block_additional_residual=mid_block_additional_residual, # for ControlNet
down_intrablock_additional_residuals=down_intrablock_additional_residuals, # for T2I-Adapter
@ -569,7 +538,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
)
# compute the previous noisy sample x_t -> x_t-1
step_output = self.scheduler.step(noise_pred, timestep, latents, **conditioning_data.scheduler_args)
step_output = self.scheduler.step(noise_pred, timestep, latents, **scheduler_step_kwargs)
# TODO: discuss injection point options. For now this is a patch to get progress images working with inpainting again.
for guidance in additional_guidance:

123
invokeai/backend/stable_diffusion/diffusion/conditioning_data.py
View File

@ -1,27 +1,17 @@
import dataclasses
import inspect
from dataclasses import dataclass, field
from typing import Any, List, Optional, Union
import math
from dataclasses import dataclass
from typing import List, Optional, Union
import torch
from .cross_attention_control import Arguments
@dataclass
class ExtraConditioningInfo:
tokens_count_including_eos_bos: int
cross_attention_control_args: Optional[Arguments] = None
@property
def wants_cross_attention_control(self):
return self.cross_attention_control_args is not None
from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
@dataclass
class BasicConditioningInfo:
"""SD 1/2 text conditioning information produced by Compel."""
embeds: torch.Tensor
extra_conditioning: Optional[ExtraConditioningInfo]
def to(self, device, dtype=None):
self.embeds = self.embeds.to(device=device, dtype=dtype)
@ -35,6 +25,8 @@ class ConditioningFieldData:
@dataclass
class SDXLConditioningInfo(BasicConditioningInfo):
"""SDXL text conditioning information produced by Compel."""
pooled_embeds: torch.Tensor
add_time_ids: torch.Tensor
@ -57,37 +49,74 @@ class IPAdapterConditioningInfo:
@dataclass
class ConditioningData:
unconditioned_embeddings: BasicConditioningInfo
text_embeddings: BasicConditioningInfo
"""
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
"""
guidance_scale: Union[float, List[float]]
""" for models trained using zero-terminal SNR ("ztsnr"), it's suggested to use guidance_rescale_multiplier of 0.7 .
ref [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf)
"""
guidance_rescale_multiplier: float = 0
scheduler_args: dict[str, Any] = field(default_factory=dict)
class IPAdapterData:
ip_adapter_model: IPAdapter
ip_adapter_conditioning: IPAdapterConditioningInfo
mask: torch.Tensor
ip_adapter_conditioning: Optional[list[IPAdapterConditioningInfo]] = None
# Either a single weight applied to all steps, or a list of weights for each step.
weight: Union[float, List[float]] = 1.0
begin_step_percent: float = 0.0
end_step_percent: float = 1.0
@property
def dtype(self):
return self.text_embeddings.dtype
def scale_for_step(self, step_index: int, total_steps: int) -> float:
first_adapter_step = math.floor(self.begin_step_percent * total_steps)
last_adapter_step = math.ceil(self.end_step_percent * total_steps)
weight = self.weight[step_index] if isinstance(self.weight, List) else self.weight
if step_index >= first_adapter_step and step_index <= last_adapter_step:
# Only apply this IP-Adapter if the current step is within the IP-Adapter's begin/end step range.
return weight
# Otherwise, set the IP-Adapter's scale to 0, so it has no effect.
return 0.0
def add_scheduler_args_if_applicable(self, scheduler, **kwargs):
scheduler_args = dict(self.scheduler_args)
step_method = inspect.signature(scheduler.step)
for name, value in kwargs.items():
try:
step_method.bind_partial(**{name: value})
except TypeError:
# FIXME: don't silently discard arguments
pass # debug("%s does not accept argument named %r", scheduler, name)
else:
scheduler_args[name] = value
return dataclasses.replace(self, scheduler_args=scheduler_args)
@dataclass
class Range:
start: int
end: int
class TextConditioningRegions:
def __init__(
self,
masks: torch.Tensor,
ranges: list[Range],
):
# A binary mask indicating the regions of the image that the prompt should be applied to.
# Shape: (1, num_prompts, height, width)
# Dtype: torch.bool
self.masks = masks
# A list of ranges indicating the start and end indices of the embeddings that corresponding mask applies to.
# ranges[i] contains the embedding range for the i'th prompt / mask.
self.ranges = ranges
assert self.masks.shape[1] == len(self.ranges)
class TextConditioningData:
def __init__(
self,
uncond_text: Union[BasicConditioningInfo, SDXLConditioningInfo],
cond_text: Union[BasicConditioningInfo, SDXLConditioningInfo],
uncond_regions: Optional[TextConditioningRegions],
cond_regions: Optional[TextConditioningRegions],
guidance_scale: Union[float, List[float]],
guidance_rescale_multiplier: float = 0,
):
self.uncond_text = uncond_text
self.cond_text = cond_text
self.uncond_regions = uncond_regions
self.cond_regions = cond_regions
# Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
# `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
# Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
# images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
self.guidance_scale = guidance_scale
# For models trained using zero-terminal SNR ("ztsnr"), it's suggested to use guidance_rescale_multiplier of 0.7.
# See [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
self.guidance_rescale_multiplier = guidance_rescale_multiplier
def is_sdxl(self):
assert isinstance(self.uncond_text, SDXLConditioningInfo) == isinstance(self.cond_text, SDXLConditioningInfo)
return isinstance(self.cond_text, SDXLConditioningInfo)

218
invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py
View File

@ -1,218 +0,0 @@
# adapted from bloc97's CrossAttentionControl colab
# https://github.com/bloc97/CrossAttentionControl
import enum
from dataclasses import dataclass, field
from typing import Optional
import torch
from compel.cross_attention_control import Arguments
from diffusers.models.attention_processor import Attention, SlicedAttnProcessor
from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
from invokeai.backend.util.devices import torch_dtype
class CrossAttentionType(enum.Enum):
SELF = 1
TOKENS = 2
class CrossAttnControlContext:
def __init__(self, arguments: Arguments):
"""
:param arguments: Arguments for the cross-attention control process
"""
self.cross_attention_mask: Optional[torch.Tensor] = None
self.cross_attention_index_map: Optional[torch.Tensor] = None
self.arguments = arguments
def get_active_cross_attention_control_types_for_step(
self, percent_through: float = None
) -> list[CrossAttentionType]:
"""
Should cross-attention control be applied on the given step?
:param percent_through: How far through the step sequence are we (0.0=pure noise, 1.0=completely denoised image). Expected range 0.0..<1.0.
:return: A list of attention types that cross-attention control should be performed for on the given step. May be [].
"""
if percent_through is None:
return [CrossAttentionType.SELF, CrossAttentionType.TOKENS]
opts = self.arguments.edit_options
to_control = []
if opts["s_start"] <= percent_through < opts["s_end"]:
to_control.append(CrossAttentionType.SELF)
if opts["t_start"] <= percent_through < opts["t_end"]:
to_control.append(CrossAttentionType.TOKENS)
return to_control
def setup_cross_attention_control_attention_processors(unet: UNet2DConditionModel, context: CrossAttnControlContext):
"""
Inject attention parameters and functions into the passed in model to enable cross attention editing.
:param model: The unet model to inject into.
:return: None
"""
# adapted from init_attention_edit
device = context.arguments.edited_conditioning.device
# urgh. should this be hardcoded?
max_length = 77
# mask=1 means use base prompt attention, mask=0 means use edited prompt attention
mask = torch.zeros(max_length, dtype=torch_dtype(device))
indices_target = torch.arange(max_length, dtype=torch.long)
indices = torch.arange(max_length, dtype=torch.long)
for name, a0, a1, b0, b1 in context.arguments.edit_opcodes:
if b0 < max_length:
if name == "equal": # or (name == "replace" and a1 - a0 == b1 - b0):
# these tokens have not been edited
indices[b0:b1] = indices_target[a0:a1]
mask[b0:b1] = 1
context.cross_attention_mask = mask.to(device)
context.cross_attention_index_map = indices.to(device)
old_attn_processors = unet.attn_processors
if torch.backends.mps.is_available():
# see note in StableDiffusionGeneratorPipeline.__init__ about borked slicing on MPS
unet.set_attn_processor(SwapCrossAttnProcessor())
else:
# try to re-use an existing slice size
default_slice_size = 4
slice_size = next(
(p.slice_size for p in old_attn_processors.values() if type(p) is SlicedAttnProcessor), default_slice_size
)
unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
@dataclass
class SwapCrossAttnContext:
modified_text_embeddings: torch.Tensor
index_map: torch.Tensor # maps from original prompt token indices to the equivalent tokens in the modified prompt
mask: torch.Tensor # in the target space of the index_map
cross_attention_types_to_do: list[CrossAttentionType] = field(default_factory=list)
def wants_cross_attention_control(self, attn_type: CrossAttentionType) -> bool:
return attn_type in self.cross_attention_types_to_do
@classmethod
def make_mask_and_index_map(
cls, edit_opcodes: list[tuple[str, int, int, int, int]], max_length: int
) -> tuple[torch.Tensor, torch.Tensor]:
# mask=1 means use original prompt attention, mask=0 means use modified prompt attention
mask = torch.zeros(max_length)
indices_target = torch.arange(max_length, dtype=torch.long)
indices = torch.arange(max_length, dtype=torch.long)
for name, a0, a1, b0, b1 in edit_opcodes:
if b0 < max_length:
if name == "equal":
# these tokens remain the same as in the original prompt
indices[b0:b1] = indices_target[a0:a1]
mask[b0:b1] = 1
return mask, indices
class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
# TODO: dynamically pick slice size based on memory conditions
def __call__(
self,
attn: Attention,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
# kwargs
swap_cross_attn_context: SwapCrossAttnContext = None,
**kwargs,
):
attention_type = CrossAttentionType.SELF if encoder_hidden_states is None else CrossAttentionType.TOKENS
# if cross-attention control is not in play, just call through to the base implementation.
if (
attention_type is CrossAttentionType.SELF
or swap_cross_attn_context is None
or not swap_cross_attn_context.wants_cross_attention_control(attention_type)
):
# print(f"SwapCrossAttnContext for {attention_type} not active - passing request to superclass")
return super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask)
# else:
# print(f"SwapCrossAttnContext for {attention_type} active")
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn.prepare_attention_mask(
attention_mask=attention_mask,
target_length=sequence_length,
batch_size=batch_size,
)
query = attn.to_q(hidden_states)
dim = query.shape[-1]
query = attn.head_to_batch_dim(query)
original_text_embeddings = encoder_hidden_states
modified_text_embeddings = swap_cross_attn_context.modified_text_embeddings
original_text_key = attn.to_k(original_text_embeddings)
modified_text_key = attn.to_k(modified_text_embeddings)
original_value = attn.to_v(original_text_embeddings)
modified_value = attn.to_v(modified_text_embeddings)
original_text_key = attn.head_to_batch_dim(original_text_key)
modified_text_key = attn.head_to_batch_dim(modified_text_key)
original_value = attn.head_to_batch_dim(original_value)
modified_value = attn.head_to_batch_dim(modified_value)
# compute slices and prepare output tensor
batch_size_attention = query.shape[0]
hidden_states = torch.zeros(
(batch_size_attention, sequence_length, dim // attn.heads),
device=query.device,
dtype=query.dtype,
)
# do slices
for i in range(max(1, hidden_states.shape[0] // self.slice_size)):
start_idx = i * self.slice_size
end_idx = (i + 1) * self.slice_size
query_slice = query[start_idx:end_idx]
original_key_slice = original_text_key[start_idx:end_idx]
modified_key_slice = modified_text_key[start_idx:end_idx]
attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None
original_attn_slice = attn.get_attention_scores(query_slice, original_key_slice, attn_mask_slice)
modified_attn_slice = attn.get_attention_scores(query_slice, modified_key_slice, attn_mask_slice)
# because the prompt modifications may result in token sequences shifted forwards or backwards,
# the original attention probabilities must be remapped to account for token index changes in the
# modified prompt
remapped_original_attn_slice = torch.index_select(
original_attn_slice, -1, swap_cross_attn_context.index_map
)
# only some tokens taken from the original attention probabilities. this is controlled by the mask.
mask = swap_cross_attn_context.mask
inverse_mask = 1 - mask
attn_slice = remapped_original_attn_slice * mask + modified_attn_slice * inverse_mask
del remapped_original_attn_slice, modified_attn_slice
attn_slice = torch.bmm(attn_slice, modified_value[start_idx:end_idx])
hidden_states[start_idx:end_idx] = attn_slice
# done
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
return hidden_states
class SwapCrossAttnProcessor(SlicedSwapCrossAttnProcesser):
def __init__(self):
super(SwapCrossAttnProcessor, self).__init__(slice_size=int(1e9)) # massive slice size = don't slice

198
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@ -0,0 +1,198 @@
from typing import Optional
import torch
import torch.nn.functional as F
from diffusers.models.attention_processor import Attention, AttnProcessor2_0
from invokeai.backend.ip_adapter.ip_attention_weights import IPAttentionProcessorWeights
from invokeai.backend.stable_diffusion.diffusion.regional_ip_data import RegionalIPData
from invokeai.backend.stable_diffusion.diffusion.regional_prompt_data import RegionalPromptData
class CustomAttnProcessor2_0(AttnProcessor2_0):
"""A custom implementation of AttnProcessor2_0 that supports additional Invoke features.
This implementation is based on
https://github.com/huggingface/diffusers/blame/fcfa270fbd1dc294e2f3a505bae6bcb791d721c3/src/diffusers/models/attention_processor.py#L1204
Supported custom features:
- IP-Adapter
- Regional prompt attention
"""
def __init__(
self,
ip_adapter_weights: Optional[list[IPAttentionProcessorWeights]] = None,
):
"""Initialize a CustomAttnProcessor2_0.
Note: Arguments that are the same for all attention layers are passed to __call__(). Arguments that are
layer-specific are passed to __init__().
Args:
ip_adapter_weights: The IP-Adapter attention weights. ip_adapter_weights[i] contains the attention weights
for the i'th IP-Adapter.
"""
super().__init__()
self._ip_adapter_weights = ip_adapter_weights
def _is_ip_adapter_enabled(self) -> bool:
return self._ip_adapter_weights is not None
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
temb: Optional[torch.FloatTensor] = None,
# For regional prompting:
regional_prompt_data: Optional[RegionalPromptData] = None,
percent_through: Optional[torch.FloatTensor] = None,
# For IP-Adapter:
regional_ip_data: Optional[RegionalIPData] = None,
) -> torch.FloatTensor:
"""Apply attention.
Args:
regional_prompt_data: The regional prompt data for the current batch. If not None, this will be used to
apply regional prompt masking.
regional_ip_data: The IP-Adapter data for the current batch.
"""
# If true, we are doing cross-attention, if false we are doing self-attention.
is_cross_attention = encoder_hidden_states is not None
# Start unmodified block from AttnProcessor2_0.
# vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# End unmodified block from AttnProcessor2_0.
_, query_seq_len, _ = hidden_states.shape
# Handle regional prompt attention masks.
if regional_prompt_data is not None and is_cross_attention:
assert percent_through is not None
prompt_region_attention_mask = regional_prompt_data.get_cross_attn_mask(
query_seq_len=query_seq_len, key_seq_len=sequence_length
)
if attention_mask is None:
attention_mask = prompt_region_attention_mask
else:
attention_mask = prompt_region_attention_mask + attention_mask
# Start unmodified block from AttnProcessor2_0.
# vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# End unmodified block from AttnProcessor2_0.
# Apply IP-Adapter conditioning.
if is_cross_attention:
if self._is_ip_adapter_enabled():
assert regional_ip_data is not None
ip_masks = regional_ip_data.get_masks(query_seq_len=query_seq_len)
assert (
len(regional_ip_data.image_prompt_embeds)
== len(self._ip_adapter_weights)
== len(regional_ip_data.scales)
== ip_masks.shape[1]
)
for ipa_index, ipa_embed in enumerate(regional_ip_data.image_prompt_embeds):
ipa_weights = self._ip_adapter_weights[ipa_index]
ipa_scale = regional_ip_data.scales[ipa_index]
ip_mask = ip_masks[0, ipa_index, ...]
# The batch dimensions should match.
assert ipa_embed.shape[0] == encoder_hidden_states.shape[0]
# The token_len dimensions should match.
assert ipa_embed.shape[-1] == encoder_hidden_states.shape[-1]
ip_hidden_states = ipa_embed
# Expected ip_hidden_state shape: (batch_size, num_ip_images, ip_seq_len, ip_image_embedding)
ip_key = ipa_weights.to_k_ip(ip_hidden_states)
ip_value = ipa_weights.to_v_ip(ip_hidden_states)
# Expected ip_key and ip_value shape: (batch_size, num_ip_images, ip_seq_len, head_dim * num_heads)
ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# Expected ip_key and ip_value shape: (batch_size, num_heads, num_ip_images * ip_seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
ip_hidden_states = F.scaled_dot_product_attention(
query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
)
# Expected ip_hidden_states shape: (batch_size, num_heads, query_seq_len, head_dim)
ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
ip_hidden_states = ip_hidden_states.to(query.dtype)
# Expected ip_hidden_states shape: (batch_size, query_seq_len, num_heads * head_dim)
hidden_states = hidden_states + ipa_scale * ip_hidden_states * ip_mask
else:
# If IP-Adapter is not enabled, then regional_ip_data should not be passed in.
assert regional_ip_data is None
# Start unmodified block from AttnProcessor2_0.
# vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states

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import torch
class RegionalIPData:
"""A class to manage the data for regional IP-Adapter conditioning."""
def __init__(
self,
image_prompt_embeds: list[torch.Tensor],
scales: list[float],
masks: list[torch.Tensor],
dtype: torch.dtype,
device: torch.device,
max_downscale_factor: int = 8,
):
"""Initialize a `IPAdapterConditioningData` object."""
assert len(image_prompt_embeds) == len(scales) == len(masks)
# The image prompt embeddings.
# regional_ip_data[i] contains the image prompt embeddings for the i'th IP-Adapter. Each tensor
# has shape (batch_size, num_ip_images, seq_len, ip_embedding_len).
self.image_prompt_embeds = image_prompt_embeds
# The scales for the IP-Adapter attention.
# scales[i] contains the attention scale for the i'th IP-Adapter.
self.scales = scales
# The IP-Adapter masks.
# self._masks_by_seq_len[s] contains the spatial masks for the downsampling level with query sequence length of
# s. It has shape (batch_size, num_ip_images, query_seq_len, 1). The masks have values of 1.0 for included
# regions and 0.0 for excluded regions.
self._masks_by_seq_len = self._prepare_masks(masks, max_downscale_factor, device, dtype)
def _prepare_masks(
self, masks: list[torch.Tensor], max_downscale_factor: int, device: torch.device, dtype: torch.dtype
) -> dict[int, torch.Tensor]:
"""Prepare the masks for the IP-Adapter attention."""
# Concatenate the masks so that they can be processed more efficiently.
mask_tensor = torch.cat(masks, dim=1)
mask_tensor = mask_tensor.to(device=device, dtype=dtype)
masks_by_seq_len: dict[int, torch.Tensor] = {}
# Downsample the spatial dimensions by factors of 2 until max_downscale_factor is reached.
downscale_factor = 1
while downscale_factor <= max_downscale_factor:
b, num_ip_adapters, h, w = mask_tensor.shape
# Assert that the batch size is 1, because I haven't thought through batch handling for this feature yet.
assert b == 1
# The IP-Adapters are applied in the cross-attention layers, where the query sequence length is the h * w of
# the spatial features.
query_seq_len = h * w
masks_by_seq_len[query_seq_len] = mask_tensor.view((b, num_ip_adapters, -1, 1))
downscale_factor *= 2
if downscale_factor <= max_downscale_factor:
# We use max pooling because we downscale to a pretty low resolution, so we don't want small mask
# regions to be lost entirely.
#
# ceil_mode=True is set to mirror the downsampling behavior of SD and SDXL.
#
# TODO(ryand): In the future, we may want to experiment with other downsampling methods.
mask_tensor = torch.nn.functional.max_pool2d(mask_tensor, kernel_size=2, stride=2, ceil_mode=True)
return masks_by_seq_len
def get_masks(self, query_seq_len: int) -> torch.Tensor:
"""Get the mask for the given query sequence length."""
return self._masks_by_seq_len[query_seq_len]

105
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@ -0,0 +1,105 @@
import torch
import torch.nn.functional as F
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
TextConditioningRegions,
)
class RegionalPromptData:
"""A class to manage the prompt data for regional conditioning."""
def __init__(
self,
regions: list[TextConditioningRegions],
device: torch.device,
dtype: torch.dtype,
max_downscale_factor: int = 8,
):
"""Initialize a `RegionalPromptData` object.
Args:
regions (list[TextConditioningRegions]): regions[i] contains the prompt regions for the i'th sample in the
batch.
device (torch.device): The device to use for the attention masks.
dtype (torch.dtype): The data type to use for the attention masks.
max_downscale_factor: Spatial masks will be prepared for downscale factors from 1 to max_downscale_factor
in steps of 2x.
"""
self._regions = regions
self._device = device
self._dtype = dtype
# self._spatial_masks_by_seq_len[b][s] contains the spatial masks for the b'th batch sample with a query
# sequence length of s.
self._spatial_masks_by_seq_len: list[dict[int, torch.Tensor]] = self._prepare_spatial_masks(
regions, max_downscale_factor
)
self._negative_cross_attn_mask_score = -10000.0
def _prepare_spatial_masks(
self, regions: list[TextConditioningRegions], max_downscale_factor: int = 8
) -> list[dict[int, torch.Tensor]]:
"""Prepare the spatial masks for all downscaling factors."""
# batch_masks_by_seq_len[b][s] contains the spatial masks for the b'th batch sample with a query sequence length
# of s.
batch_sample_masks_by_seq_len: list[dict[int, torch.Tensor]] = []
for batch_sample_regions in regions:
batch_sample_masks_by_seq_len.append({})
batch_sample_masks = batch_sample_regions.masks.to(device=self._device, dtype=self._dtype)
# Downsample the spatial dimensions by factors of 2 until max_downscale_factor is reached.
downscale_factor = 1
while downscale_factor <= max_downscale_factor:
b, _num_prompts, h, w = batch_sample_masks.shape
assert b == 1
query_seq_len = h * w
batch_sample_masks_by_seq_len[-1][query_seq_len] = batch_sample_masks
downscale_factor *= 2
if downscale_factor <= max_downscale_factor:
# We use max pooling because we downscale to a pretty low resolution, so we don't want small prompt
# regions to be lost entirely.
#
# ceil_mode=True is set to mirror the downsampling behavior of SD and SDXL.
#
# TODO(ryand): In the future, we may want to experiment with other downsampling methods (e.g.
# nearest interpolation), and could potentially use a weighted mask rather than a binary mask.
batch_sample_masks = F.max_pool2d(batch_sample_masks, kernel_size=2, stride=2, ceil_mode=True)
return batch_sample_masks_by_seq_len
def get_cross_attn_mask(self, query_seq_len: int, key_seq_len: int) -> torch.Tensor:
"""Get the cross-attention mask for the given query sequence length.
Args:
query_seq_len: The length of the flattened spatial features at the current downscaling level.
key_seq_len (int): The sequence length of the prompt embeddings (which act as the key in the cross-attention
layers). This is most likely equal to the max embedding range end, but we pass it explicitly to be sure.
Returns:
torch.Tensor: The cross-attention score mask.
shape: (batch_size, query_seq_len, key_seq_len).
dtype: float
"""
batch_size = len(self._spatial_masks_by_seq_len)
batch_spatial_masks = [self._spatial_masks_by_seq_len[b][query_seq_len] for b in range(batch_size)]
# Create an empty attention mask with the correct shape.
attn_mask = torch.zeros((batch_size, query_seq_len, key_seq_len), dtype=self._dtype, device=self._device)
for batch_idx in range(batch_size):
batch_sample_spatial_masks = batch_spatial_masks[batch_idx]
batch_sample_regions = self._regions[batch_idx]
# Flatten the spatial dimensions of the mask by reshaping to (1, num_prompts, query_seq_len, 1).
_, num_prompts, _, _ = batch_sample_spatial_masks.shape
batch_sample_query_masks = batch_sample_spatial_masks.view((1, num_prompts, query_seq_len, 1))
for prompt_idx, embedding_range in enumerate(batch_sample_regions.ranges):
batch_sample_query_scores = batch_sample_query_masks[0, prompt_idx, :, :].clone()
batch_sample_query_mask = batch_sample_query_scores > 0.5
batch_sample_query_scores[batch_sample_query_mask] = 0.0
batch_sample_query_scores[~batch_sample_query_mask] = self._negative_cross_attn_mask_score
attn_mask[batch_idx, :, embedding_range.start : embedding_range.end] = batch_sample_query_scores
return attn_mask

248
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
View File

@ -1,26 +1,20 @@
from __future__ import annotations
import math
from contextlib import contextmanager
from typing import Any, Callable, Optional, Union
import torch
from diffusers import UNet2DConditionModel
from typing_extensions import TypeAlias
from invokeai.app.services.config.config_default import get_config
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
ConditioningData,
ExtraConditioningInfo,
SDXLConditioningInfo,
)
from .cross_attention_control import (
CrossAttentionType,
CrossAttnControlContext,
SwapCrossAttnContext,
setup_cross_attention_control_attention_processors,
IPAdapterData,
Range,
TextConditioningData,
TextConditioningRegions,
)
from invokeai.backend.stable_diffusion.diffusion.regional_ip_data import RegionalIPData
from invokeai.backend.stable_diffusion.diffusion.regional_prompt_data import RegionalPromptData
ModelForwardCallback: TypeAlias = Union[
# x, t, conditioning, Optional[cross-attention kwargs]
@ -58,31 +52,8 @@ class InvokeAIDiffuserComponent:
self.conditioning = None
self.model = model
self.model_forward_callback = model_forward_callback
self.cross_attention_control_context = None
self.sequential_guidance = config.sequential_guidance
@contextmanager
def custom_attention_context(
self,
unet: UNet2DConditionModel,
extra_conditioning_info: Optional[ExtraConditioningInfo],
):
old_attn_processors = unet.attn_processors
try:
self.cross_attention_control_context = CrossAttnControlContext(
arguments=extra_conditioning_info.cross_attention_control_args,
)
setup_cross_attention_control_attention_processors(
unet,
self.cross_attention_control_context,
)
yield None
finally:
self.cross_attention_control_context = None
unet.set_attn_processor(old_attn_processors)
def do_controlnet_step(
self,
control_data,
@ -90,7 +61,7 @@ class InvokeAIDiffuserComponent:
timestep: torch.Tensor,
step_index: int,
total_step_count: int,
conditioning_data,
conditioning_data: TextConditioningData,
):
down_block_res_samples, mid_block_res_sample = None, None
@ -123,28 +94,28 @@ class InvokeAIDiffuserComponent:
added_cond_kwargs = None
if cfg_injection: # only applying ControlNet to conditional instead of in unconditioned
if type(conditioning_data.text_embeddings) is SDXLConditioningInfo:
if conditioning_data.is_sdxl():
added_cond_kwargs = {
"text_embeds": conditioning_data.text_embeddings.pooled_embeds,
"time_ids": conditioning_data.text_embeddings.add_time_ids,
"text_embeds": conditioning_data.cond_text.pooled_embeds,
"time_ids": conditioning_data.cond_text.add_time_ids,
}
encoder_hidden_states = conditioning_data.text_embeddings.embeds
encoder_hidden_states = conditioning_data.cond_text.embeds
encoder_attention_mask = None
else:
if type(conditioning_data.text_embeddings) is SDXLConditioningInfo:
if conditioning_data.is_sdxl():
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,
conditioning_data.uncond_text.pooled_embeds,
conditioning_data.cond_text.pooled_embeds,
],
dim=0,
),
"time_ids": torch.cat(
[
conditioning_data.unconditioned_embeddings.add_time_ids,
conditioning_data.text_embeddings.add_time_ids,
conditioning_data.uncond_text.add_time_ids,
conditioning_data.cond_text.add_time_ids,
],
dim=0,
),
@ -153,8 +124,8 @@ class InvokeAIDiffuserComponent:
encoder_hidden_states,
encoder_attention_mask,
) = self._concat_conditionings_for_batch(
conditioning_data.unconditioned_embeddings.embeds,
conditioning_data.text_embeddings.embeds,
conditioning_data.uncond_text.embeds,
conditioning_data.cond_text.embeds,
)
if isinstance(control_datum.weight, list):
# if controlnet has multiple weights, use the weight for the current step
@ -198,24 +169,15 @@ class InvokeAIDiffuserComponent:
self,
sample: torch.Tensor,
timestep: torch.Tensor,
conditioning_data: ConditioningData,
conditioning_data: TextConditioningData,
ip_adapter_data: Optional[list[IPAdapterData]],
step_index: int,
total_step_count: int,
down_block_additional_residuals: Optional[torch.Tensor] = None, # for ControlNet
mid_block_additional_residual: Optional[torch.Tensor] = None, # for ControlNet
down_intrablock_additional_residuals: Optional[torch.Tensor] = None, # for T2I-Adapter
):
cross_attention_control_types_to_do = []
if self.cross_attention_control_context is not None:
percent_through = step_index / total_step_count
cross_attention_control_types_to_do = (
self.cross_attention_control_context.get_active_cross_attention_control_types_for_step(percent_through)
)
wants_cross_attention_control = len(cross_attention_control_types_to_do) > 0
if wants_cross_attention_control or self.sequential_guidance:
# If wants_cross_attention_control is True, we force the sequential mode to be used, because cross-attention
# control is currently only supported in sequential mode.
if self.sequential_guidance:
(
unconditioned_next_x,
conditioned_next_x,
@ -223,7 +185,9 @@ class InvokeAIDiffuserComponent:
x=sample,
sigma=timestep,
conditioning_data=conditioning_data,
cross_attention_control_types_to_do=cross_attention_control_types_to_do,
ip_adapter_data=ip_adapter_data,
step_index=step_index,
total_step_count=total_step_count,
down_block_additional_residuals=down_block_additional_residuals,
mid_block_additional_residual=mid_block_additional_residual,
down_intrablock_additional_residuals=down_intrablock_additional_residuals,
@ -236,6 +200,9 @@ class InvokeAIDiffuserComponent:
x=sample,
sigma=timestep,
conditioning_data=conditioning_data,
ip_adapter_data=ip_adapter_data,
step_index=step_index,
total_step_count=total_step_count,
down_block_additional_residuals=down_block_additional_residuals,
mid_block_additional_residual=mid_block_additional_residual,
down_intrablock_additional_residuals=down_intrablock_additional_residuals,
@ -294,53 +261,84 @@ class InvokeAIDiffuserComponent:
def _apply_standard_conditioning(
self,
x,
sigma,
conditioning_data: ConditioningData,
x: torch.Tensor,
sigma: torch.Tensor,
conditioning_data: TextConditioningData,
ip_adapter_data: Optional[list[IPAdapterData]],
step_index: int,
total_step_count: int,
down_block_additional_residuals: Optional[torch.Tensor] = None, # for ControlNet
mid_block_additional_residual: Optional[torch.Tensor] = None, # for ControlNet
down_intrablock_additional_residuals: Optional[torch.Tensor] = None, # for T2I-Adapter
):
) -> tuple[torch.Tensor, torch.Tensor]:
"""Runs the conditioned and unconditioned UNet forward passes in a single batch for faster inference speed at
the cost of higher memory usage.
"""
x_twice = torch.cat([x] * 2)
sigma_twice = torch.cat([sigma] * 2)
cross_attention_kwargs = None
if conditioning_data.ip_adapter_conditioning is not None:
cross_attention_kwargs = {}
if ip_adapter_data is not None:
ip_adapter_conditioning = [ipa.ip_adapter_conditioning for ipa in ip_adapter_data]
# Note that we 'stack' to produce tensors of shape (batch_size, num_ip_images, seq_len, token_len).
cross_attention_kwargs = {
"ip_adapter_image_prompt_embeds": [
torch.stack(
[ipa_conditioning.uncond_image_prompt_embeds, ipa_conditioning.cond_image_prompt_embeds]
)
for ipa_conditioning in conditioning_data.ip_adapter_conditioning
image_prompt_embeds = [
torch.stack([ipa_conditioning.uncond_image_prompt_embeds, ipa_conditioning.cond_image_prompt_embeds])
for ipa_conditioning in ip_adapter_conditioning
]
}
scales = [ipa.scale_for_step(step_index, total_step_count) for ipa in ip_adapter_data]
ip_masks = [ipa.mask for ipa in ip_adapter_data]
regional_ip_data = RegionalIPData(
image_prompt_embeds=image_prompt_embeds, scales=scales, masks=ip_masks, dtype=x.dtype, device=x.device
)
cross_attention_kwargs["regional_ip_data"] = regional_ip_data
added_cond_kwargs = None
if type(conditioning_data.text_embeddings) is SDXLConditioningInfo:
if conditioning_data.is_sdxl():
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,
conditioning_data.uncond_text.pooled_embeds,
conditioning_data.cond_text.pooled_embeds,
],
dim=0,
),
"time_ids": torch.cat(
[
conditioning_data.unconditioned_embeddings.add_time_ids,
conditioning_data.text_embeddings.add_time_ids,
conditioning_data.uncond_text.add_time_ids,
conditioning_data.cond_text.add_time_ids,
],
dim=0,
),
}
if conditioning_data.cond_regions is not None or conditioning_data.uncond_regions is not None:
# TODO(ryand): We currently initialize RegionalPromptData for every denoising step. The text conditionings
# and masks are not changing from step-to-step, so this really only needs to be done once. While this seems
# painfully inefficient, the time spent is typically negligible compared to the forward inference pass of
# the UNet. The main reason that this hasn't been moved up to eliminate redundancy is that it is slightly
# awkward to handle both standard conditioning and sequential conditioning further up the stack.
regions = []
for c, r in [
(conditioning_data.uncond_text, conditioning_data.uncond_regions),
(conditioning_data.cond_text, conditioning_data.cond_regions),
]:
if r is None:
# Create a dummy mask and range for text conditioning that doesn't have region masks.
_, _, h, w = x.shape
r = TextConditioningRegions(
masks=torch.ones((1, 1, h, w), dtype=x.dtype),
ranges=[Range(start=0, end=c.embeds.shape[1])],
)
regions.append(r)
cross_attention_kwargs["regional_prompt_data"] = RegionalPromptData(
regions=regions, device=x.device, dtype=x.dtype
)
cross_attention_kwargs["percent_through"] = step_index / total_step_count
both_conditionings, encoder_attention_mask = self._concat_conditionings_for_batch(
conditioning_data.unconditioned_embeddings.embeds, conditioning_data.text_embeddings.embeds
conditioning_data.uncond_text.embeds, conditioning_data.cond_text.embeds
)
both_results = self.model_forward_callback(
x_twice,
@ -360,8 +358,10 @@ class InvokeAIDiffuserComponent:
self,
x: torch.Tensor,
sigma,
conditioning_data: ConditioningData,
cross_attention_control_types_to_do: list[CrossAttentionType],
conditioning_data: TextConditioningData,
ip_adapter_data: Optional[list[IPAdapterData]],
step_index: int,
total_step_count: int,
down_block_additional_residuals: Optional[torch.Tensor] = None, # for ControlNet
mid_block_additional_residual: Optional[torch.Tensor] = None, # for ControlNet
down_intrablock_additional_residuals: Optional[torch.Tensor] = None, # for T2I-Adapter
@ -391,53 +391,48 @@ class InvokeAIDiffuserComponent:
if mid_block_additional_residual is not None:
uncond_mid_block, cond_mid_block = mid_block_additional_residual.chunk(2)
# If cross-attention control is enabled, prepare the SwapCrossAttnContext.
cross_attn_processor_context = None
if self.cross_attention_control_context is not None:
# Note that the SwapCrossAttnContext is initialized with an empty list of cross_attention_types_to_do.
# This list is empty because cross-attention control is not applied in the unconditioned pass. This field
# will be populated before the conditioned pass.
cross_attn_processor_context = SwapCrossAttnContext(
modified_text_embeddings=self.cross_attention_control_context.arguments.edited_conditioning,
index_map=self.cross_attention_control_context.cross_attention_index_map,
mask=self.cross_attention_control_context.cross_attention_mask,
cross_attention_types_to_do=[],
)
#####################
# Unconditioned pass
#####################
cross_attention_kwargs = None
cross_attention_kwargs = {}
# Prepare IP-Adapter cross-attention kwargs for the unconditioned pass.
if conditioning_data.ip_adapter_conditioning is not None:
if ip_adapter_data is not None:
ip_adapter_conditioning = [ipa.ip_adapter_conditioning for ipa in ip_adapter_data]
# Note that we 'unsqueeze' to produce tensors of shape (batch_size=1, num_ip_images, seq_len, token_len).
cross_attention_kwargs = {
"ip_adapter_image_prompt_embeds": [
image_prompt_embeds = [
torch.unsqueeze(ipa_conditioning.uncond_image_prompt_embeds, dim=0)
for ipa_conditioning in conditioning_data.ip_adapter_conditioning
for ipa_conditioning in ip_adapter_conditioning
]
}
# Prepare cross-attention control kwargs for the unconditioned pass.
if cross_attn_processor_context is not None:
cross_attention_kwargs = {"swap_cross_attn_context": cross_attn_processor_context}
scales = [ipa.scale_for_step(step_index, total_step_count) for ipa in ip_adapter_data]
ip_masks = [ipa.mask for ipa in ip_adapter_data]
regional_ip_data = RegionalIPData(
image_prompt_embeds=image_prompt_embeds, scales=scales, masks=ip_masks, dtype=x.dtype, device=x.device
)
cross_attention_kwargs["regional_ip_data"] = regional_ip_data
# Prepare SDXL conditioning kwargs for the unconditioned pass.
added_cond_kwargs = None
is_sdxl = type(conditioning_data.text_embeddings) is SDXLConditioningInfo
if is_sdxl:
if conditioning_data.is_sdxl():
added_cond_kwargs = {
"text_embeds": conditioning_data.unconditioned_embeddings.pooled_embeds,
"time_ids": conditioning_data.unconditioned_embeddings.add_time_ids,
"text_embeds": conditioning_data.uncond_text.pooled_embeds,
"time_ids": conditioning_data.uncond_text.add_time_ids,
}
# Prepare prompt regions for the unconditioned pass.
if conditioning_data.uncond_regions is not None:
cross_attention_kwargs["regional_prompt_data"] = RegionalPromptData(
regions=[conditioning_data.uncond_regions], device=x.device, dtype=x.dtype
)
cross_attention_kwargs["percent_through"] = step_index / total_step_count
# Run unconditioned UNet denoising (i.e. negative prompt).
unconditioned_next_x = self.model_forward_callback(
x,
sigma,
conditioning_data.unconditioned_embeddings.embeds,
conditioning_data.uncond_text.embeds,
cross_attention_kwargs=cross_attention_kwargs,
down_block_additional_residuals=uncond_down_block,
mid_block_additional_residual=uncond_mid_block,
@ -449,36 +444,43 @@ class InvokeAIDiffuserComponent:
# Conditioned pass
###################
cross_attention_kwargs = None
cross_attention_kwargs = {}
# Prepare IP-Adapter cross-attention kwargs for the conditioned pass.
if conditioning_data.ip_adapter_conditioning is not None:
if ip_adapter_data is not None:
ip_adapter_conditioning = [ipa.ip_adapter_conditioning for ipa in ip_adapter_data]
# Note that we 'unsqueeze' to produce tensors of shape (batch_size=1, num_ip_images, seq_len, token_len).
cross_attention_kwargs = {
"ip_adapter_image_prompt_embeds": [
image_prompt_embeds = [
torch.unsqueeze(ipa_conditioning.cond_image_prompt_embeds, dim=0)
for ipa_conditioning in conditioning_data.ip_adapter_conditioning
for ipa_conditioning in ip_adapter_conditioning
]
}
# Prepare cross-attention control kwargs for the conditioned pass.
if cross_attn_processor_context is not None:
cross_attn_processor_context.cross_attention_types_to_do = cross_attention_control_types_to_do
cross_attention_kwargs = {"swap_cross_attn_context": cross_attn_processor_context}
scales = [ipa.scale_for_step(step_index, total_step_count) for ipa in ip_adapter_data]
ip_masks = [ipa.mask for ipa in ip_adapter_data]
regional_ip_data = RegionalIPData(
image_prompt_embeds=image_prompt_embeds, scales=scales, masks=ip_masks, dtype=x.dtype, device=x.device
)
cross_attention_kwargs["regional_ip_data"] = regional_ip_data
# Prepare SDXL conditioning kwargs for the conditioned pass.
added_cond_kwargs = None
if is_sdxl:
if conditioning_data.is_sdxl():
added_cond_kwargs = {
"text_embeds": conditioning_data.text_embeddings.pooled_embeds,
"time_ids": conditioning_data.text_embeddings.add_time_ids,
"text_embeds": conditioning_data.cond_text.pooled_embeds,
"time_ids": conditioning_data.cond_text.add_time_ids,
}
# Prepare prompt regions for the conditioned pass.
if conditioning_data.cond_regions is not None:
cross_attention_kwargs["regional_prompt_data"] = RegionalPromptData(
regions=[conditioning_data.cond_regions], device=x.device, dtype=x.dtype
)
cross_attention_kwargs["percent_through"] = step_index / total_step_count
# Run conditioned UNet denoising (i.e. positive prompt).
conditioned_next_x = self.model_forward_callback(
x,
sigma,
conditioning_data.text_embeddings.embeds,
conditioning_data.cond_text.embeds,
cross_attention_kwargs=cross_attention_kwargs,
down_block_additional_residuals=cond_down_block,
mid_block_additional_residual=cond_mid_block,

34
invokeai/backend/ip_adapter/unet_patcher.py → invokeai/backend/stable_diffusion/diffusion/unet_attention_patcher.py
View File

@ -1,52 +1,46 @@
from contextlib import contextmanager
from typing import Optional
from diffusers.models import UNet2DConditionModel
from invokeai.backend.ip_adapter.attention_processor import AttnProcessor2_0, IPAttnProcessor2_0
from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
from invokeai.backend.stable_diffusion.diffusion.custom_atttention import CustomAttnProcessor2_0
class UNetPatcher:
"""A class that contains multiple IP-Adapters and can apply them to a UNet."""
class UNetAttentionPatcher:
"""A class for patching a UNet with CustomAttnProcessor2_0 attention layers."""
def __init__(self, ip_adapters: list[IPAdapter]):
def __init__(self, ip_adapters: Optional[list[IPAdapter]]):
self._ip_adapters = ip_adapters
self._scales = [1.0] * len(self._ip_adapters)
def set_scale(self, idx: int, value: float):
self._scales[idx] = value
def _prepare_attention_processors(self, unet: UNet2DConditionModel):
"""Prepare a dict of attention processors that can be injected into a unet, and load the IP-Adapter attention
weights into them.
weights into them (if IP-Adapters are being applied).
Note that the `unet` param is only used to determine attention block dimensions and naming.
"""
# Construct a dict of attention processors based on the UNet's architecture.
attn_procs = {}
for idx, name in enumerate(unet.attn_processors.keys()):
if name.endswith("attn1.processor"):
attn_procs[name] = AttnProcessor2_0()
if name.endswith("attn1.processor") or self._ip_adapters is None:
# "attn1" processors do not use IP-Adapters.
attn_procs[name] = CustomAttnProcessor2_0()
else:
# Collect the weights from each IP Adapter for the idx'th attention processor.
attn_procs[name] = IPAttnProcessor2_0(
attn_procs[name] = CustomAttnProcessor2_0(
[ip_adapter.attn_weights.get_attention_processor_weights(idx) for ip_adapter in self._ip_adapters],
self._scales,
)
return attn_procs
@contextmanager
def apply_ip_adapter_attention(self, unet: UNet2DConditionModel):
"""A context manager that patches `unet` with IP-Adapter attention processors."""
"""A context manager that patches `unet` with CustomAttnProcessor2_0 attention layers."""
attn_procs = self._prepare_attention_processors(unet)
orig_attn_processors = unet.attn_processors
try:
# Note to future devs: set_attn_processor(...) does something slightly unexpected - it pops elements from the
# passed dict. So, if you wanted to keep the dict for future use, you'd have to make a moderately-shallow copy
# of it. E.g. `attn_procs_copy = {k: v for k, v in attn_procs.items()}`.
# Note to future devs: set_attn_processor(...) does something slightly unexpected - it pops elements from
# the passed dict. So, if you wanted to keep the dict for future use, you'd have to make a
# moderately-shallow copy of it. E.g. `attn_procs_copy = {k: v for k, v in attn_procs.items()}`.
unet.set_attn_processor(attn_procs)
yield None
finally:

40
invokeai/backend/util/devices.py
View File

@ -6,8 +6,7 @@ from typing import Literal, Optional, Union
import torch
from torch import autocast
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.config.config_default import get_config
from invokeai.app.services.config.config_default import PRECISION, get_config
CPU_DEVICE = torch.device("cpu")
CUDA_DEVICE = torch.device("cuda")
@ -33,35 +32,34 @@ def get_torch_device_name() -> str:
return torch.cuda.get_device_name(device) if device.type == "cuda" else device.type.upper()
# We are in transition here from using a single global AppConfig to allowing multiple
# configurations. It is strongly recommended to pass the app_config to this function.
def choose_precision(
device: torch.device, app_config: Optional[InvokeAIAppConfig] = None
) -> Literal["float32", "float16", "bfloat16"]:
def choose_precision(device: torch.device) -> Literal["float32", "float16", "bfloat16"]:
"""Return an appropriate precision for the given torch device."""
app_config = app_config or get_config()
app_config = get_config()
if device.type == "cuda":
device_name = torch.cuda.get_device_name(device)
if not ("GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name):
if app_config.precision == "float32":
if "GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name:
# These GPUs have limited support for float16
return "float32"
elif app_config.precision == "bfloat16":
return "bfloat16"
elif app_config.precision == "auto" or app_config.precision == "autocast":
# Default to float16 for CUDA devices
return "float16"
else:
return "float16"
# Use the user-defined precision
return app_config.precision
elif device.type == "mps":
if app_config.precision == "auto" or app_config.precision == "autocast":
# Default to float16 for MPS devices
return "float16"
else:
# Use the user-defined precision
return app_config.precision
# CPU / safe fallback
return "float32"
# We are in transition here from using a single global AppConfig to allowing multiple
# configurations. It is strongly recommended to pass the app_config to this function.
def torch_dtype(
device: Optional[torch.device] = None,
app_config: Optional[InvokeAIAppConfig] = None,
) -> torch.dtype:
def torch_dtype(device: Optional[torch.device] = None) -> torch.dtype:
device = device or choose_torch_device()
precision = choose_precision(device, app_config)
precision = choose_precision(device)
if precision == "float16":
return torch.float16
if precision == "bfloat16":
@ -71,7 +69,7 @@ def torch_dtype(
return torch.float32
def choose_autocast(precision):
def choose_autocast(precision: PRECISION):
"""Returns an autocast context or nullcontext for the given precision string"""
# float16 currently requires autocast to avoid errors like:
# 'expected scalar type Half but found Float'

53
invokeai/backend/util/mask.py Normal file
View File

@ -0,0 +1,53 @@
import torch
def to_standard_mask_dim(mask: torch.Tensor) -> torch.Tensor:
"""Standardize the dimensions of a mask tensor.
Args:
mask (torch.Tensor): A mask tensor. The shape can be (1, h, w) or (h, w).
Returns:
torch.Tensor: The output mask tensor. The shape is (1, h, w).
"""
# Get the mask height and width.
if mask.ndim == 2:
mask = mask.unsqueeze(0)
elif mask.ndim == 3 and mask.shape[0] == 1:
pass
else:
raise ValueError(f"Unsupported mask shape: {mask.shape}. Expected (1, h, w) or (h, w).")
return mask
def to_standard_float_mask(mask: torch.Tensor, out_dtype: torch.dtype) -> torch.Tensor:
"""Standardize the format of a mask tensor.
Args:
mask (torch.Tensor): A mask tensor. The dtype can be any bool, float, or int type. The shape must be (1, h, w)
or (h, w).
out_dtype (torch.dtype): The dtype of the output mask tensor. Must be a float type.
Returns:
torch.Tensor: The output mask tensor. The dtype is out_dtype. The shape is (1, h, w). All values are either 0.0
or 1.0.
"""
if not out_dtype.is_floating_point:
raise ValueError(f"out_dtype must be a float type, but got {out_dtype}")
mask = to_standard_mask_dim(mask)
mask = mask.to(out_dtype)
# Set masked regions to 1.0.
if mask.dtype == torch.bool:
mask = mask.to(out_dtype)
else:
mask = mask.to(out_dtype)
mask_region = mask > 0.5
mask[mask_region] = 1.0
mask[~mask_region] = 0.0
return mask

1
invokeai/frontend/web/package.json
View File

@ -52,6 +52,7 @@
},
"dependencies": {
"@chakra-ui/react-use-size": "^2.1.0",
"@dagrejs/dagre": "^1.1.1",
"@dagrejs/graphlib": "^2.2.1",
"@dnd-kit/core": "^6.1.0",
"@dnd-kit/sortable": "^8.0.0",

9
invokeai/frontend/web/pnpm-lock.yaml
View File

@ -11,6 +11,9 @@ dependencies:
'@chakra-ui/react-use-size':
specifier: ^2.1.0
version: 2.1.0(react@18.2.0)
'@dagrejs/dagre':
specifier: ^1.1.1
version: 1.1.1
'@dagrejs/graphlib':
specifier: ^2.2.1
version: 2.2.1
@ -3092,6 +3095,12 @@ packages:
dev: true
optional: true
/@dagrejs/dagre@1.1.1:
resolution: {integrity: sha512-AQfT6pffEuPE32weFzhS/u3UpX+bRXUARIXL7UqLaxz497cN8pjuBlX6axO4IIECE2gBV8eLFQkGCtKX5sDaUA==}
dependencies:
'@dagrejs/graphlib': 2.2.1
dev: false
/@dagrejs/graphlib@2.2.1:
resolution: {integrity: sha512-xJsN1v6OAxXk6jmNdM+OS/bBE8nDCwM0yDNprXR18ZNatL6to9ggod9+l2XtiLhXfLm0NkE7+Er/cpdlM+SkUA==}
engines: {node: '>17.0.0'}

1
invokeai/frontend/web/public/locales/ar.json
View File

@ -291,7 +291,6 @@
"canvasMerged": "تم دمج الخط",
"sentToImageToImage": "تم إرسال إلى صورة إلى صورة",
"sentToUnifiedCanvas": "تم إرسال إلى لوحة موحدة",
"parametersSet": "تم تعيين المعلمات",
"parametersNotSet": "لم يتم تعيين المعلمات",
"metadataLoadFailed": "فشل تحميل البيانات الوصفية"
},

24
invokeai/frontend/web/public/locales/de.json
View File

@ -75,7 +75,8 @@
"copy": "Kopieren",
"aboutHeading": "Nutzen Sie Ihre kreative Energie",
"toResolve": "Lösen",
"add": "Hinzufügen"
"add": "Hinzufügen",
"loglevel": "Protokoll Stufe"
},
"gallery": {
"galleryImageSize": "Bildgröße",
@ -388,7 +389,14 @@
"vaePrecision": "VAE-Präzision",
"variant": "Variante",
"modelDeleteFailed": "Modell konnte nicht gelöscht werden",
"noModelSelected": "Kein Modell ausgewählt"
"noModelSelected": "Kein Modell ausgewählt",
"huggingFace": "HuggingFace",
"defaultSettings": "Standardeinstellungen",
"edit": "Bearbeiten",
"cancel": "Stornieren",
"defaultSettingsSaved": "Standardeinstellungen gespeichert",
"addModels": "Model hinzufügen",
"deleteModelImage": "Lösche Model Bild"
},
"parameters": {
"images": "Bilder",
@ -472,7 +480,6 @@
"canvasMerged": "Leinwand zusammengeführt",
"sentToImageToImage": "Gesendet an Bild zu Bild",
"sentToUnifiedCanvas": "Gesendet an Leinwand",
"parametersSet": "Parameter festlegen",
"parametersNotSet": "Parameter nicht festgelegt",
"metadataLoadFailed": "Metadaten konnten nicht geladen werden",
"setCanvasInitialImage": "Ausgangsbild setzen",
@ -677,7 +684,8 @@
"body": "Körper",
"hands": "Hände",
"dwOpenpose": "DW Openpose",
"dwOpenposeDescription": "Posenschätzung mit DW Openpose"
"dwOpenposeDescription": "Posenschätzung mit DW Openpose",
"selectCLIPVisionModel": "Wähle ein CLIP Vision Model aus"
},
"queue": {
"status": "Status",
@ -765,7 +773,10 @@
"recallParameters": "Parameter wiederherstellen",
"cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
"allPrompts": "Alle Prompts",
"imageDimensions": "Bilder Auslösungen"
"imageDimensions": "Bilder Auslösungen",
"parameterSet": "Parameter {{parameter}} setzen",
"recallParameter": "{{label}} Abrufen",
"parsingFailed": "Parsing Fehlgeschlagen"
},
"popovers": {
"noiseUseCPU": {
@ -1030,7 +1041,8 @@
"title": "Bild"
},
"advanced": {
"title": "Erweitert"
"title": "Erweitert",
"options": "$t(accordions.advanced.title) Optionen"
},
"control": {
"title": "Kontrolle"

16
invokeai/frontend/web/public/locales/en.json
View File

@ -684,6 +684,7 @@
"noModelsInstalled": "No Models Installed",
"noModelsInstalledDesc1": "Install models with the",
"noModelSelected": "No Model Selected",
"noMatchingModels": "No matching Models",
"none": "none",
"path": "Path",
"pathToConfig": "Path To Config",
@ -848,6 +849,7 @@
"version": "Version",
"versionUnknown": " Version Unknown",
"workflow": "Workflow",
"graph": "Graph",
"workflowAuthor": "Author",
"workflowContact": "Contact",
"workflowDescription": "Short Description",
@ -887,6 +889,11 @@
"imageFit": "Fit Initial Image To Output Size",
"images": "Images",
"infillMethod": "Infill Method",
"infillMosaicTileWidth": "Tile Width",
"infillMosaicTileHeight": "Tile Height",
"infillMosaicMinColor": "Min Color",
"infillMosaicMaxColor": "Max Color",
"infillColorValue": "Fill Color",
"info": "Info",
"invoke": {
"addingImagesTo": "Adding images to",
@ -1035,10 +1042,10 @@
"metadataLoadFailed": "Failed to load metadata",
"modelAddedSimple": "Model Added to Queue",
"modelImportCanceled": "Model Import Canceled",
"parameters": "Parameters",
"parameterNotSet": "{{parameter}} not set",
"parameterSet": "{{parameter}} set",
"parametersNotSet": "Parameters Not Set",
"parametersSet": "Parameters Set",
"problemCopyingCanvas": "Problem Copying Canvas",
"problemCopyingCanvasDesc": "Unable to export base layer",
"problemCopyingImage": "Unable to Copy Image",
@ -1417,6 +1424,7 @@
"eraseBoundingBox": "Erase Bounding Box",
"eraser": "Eraser",
"fillBoundingBox": "Fill Bounding Box",
"initialFitImageSize": "Fit Image Size on Drop",
"invertBrushSizeScrollDirection": "Invert Scroll for Brush Size",
"layer": "Layer",
"limitStrokesToBox": "Limit Strokes to Box",
@ -1475,7 +1483,11 @@
"workflowName": "Workflow Name",
"newWorkflowCreated": "New Workflow Created",
"workflowCleared": "Workflow Cleared",
"workflowEditorMenu": "Workflow Editor Menu"
"workflowEditorMenu": "Workflow Editor Menu",
"loadFromGraph": "Load Workflow from Graph",
"convertGraph": "Convert Graph",
"loadWorkflow": "$t(common.load) Workflow",
"autoLayout": "Auto Layout"
},
"app": {
"storeNotInitialized": "Store is not initialized"

1
invokeai/frontend/web/public/locales/es.json
View File

@ -363,7 +363,6 @@
"canvasMerged": "Lienzo consolidado",
"sentToImageToImage": "Enviar hacia Imagen a Imagen",
"sentToUnifiedCanvas": "Enviar hacia Lienzo Consolidado",
"parametersSet": "Parámetros establecidos",
"parametersNotSet": "Parámetros no establecidos",
"metadataLoadFailed": "Error al cargar metadatos",
"serverError": "Error en el servidor",

1
invokeai/frontend/web/public/locales/fr.json
View File

@ -298,7 +298,6 @@
"canvasMerged": "Canvas fusionné",
"sentToImageToImage": "Envoyé à Image à Image",
"sentToUnifiedCanvas": "Envoyé à Canvas unifié",
"parametersSet": "Paramètres définis",
"parametersNotSet": "Paramètres non définis",
"metadataLoadFailed": "Échec du chargement des métadonnées"
},

1
invokeai/frontend/web/public/locales/he.json
View File

@ -306,7 +306,6 @@
"canvasMerged": "קנבס מוזג",
"sentToImageToImage": "נשלח לתמונה לתמונה",
"sentToUnifiedCanvas": "נשלח אל קנבס מאוחד",
"parametersSet": "הגדרת פרמטרים",
"parametersNotSet": "פרמטרים לא הוגדרו",
"metadataLoadFailed": "טעינת מטא-נתונים נכשלה"
},

9
invokeai/frontend/web/public/locales/it.json
View File

@ -366,7 +366,7 @@
"modelConverted": "Modello convertito",
"alpha": "Alpha",
"convertToDiffusersHelpText1": "Questo modello verrà convertito nel formato 🧨 Diffusori.",
"convertToDiffusersHelpText3": "Il file Checkpoint su disco verrà eliminato se si trova nella cartella principale di InvokeAI. Se si trova invece in una posizione personalizzata, NON verrà eliminato.",
"convertToDiffusersHelpText3": "Il file del modello su disco verrà eliminato se si trova nella cartella principale di InvokeAI. Se si trova invece in una posizione personalizzata, NON verrà eliminato.",
"v2_base": "v2 (512px)",
"v2_768": "v2 (768px)",
"none": "nessuno",
@ -443,7 +443,8 @@
"noModelsInstalled": "Nessun modello installato",
"hfTokenInvalidErrorMessage2": "Aggiornalo in ",
"main": "Principali",
"noModelsInstalledDesc1": "Installa i modelli con"
"noModelsInstalledDesc1": "Installa i modelli con",
"ipAdapters": "Adattatori IP"
},
"parameters": {
"images": "Immagini",
@ -568,7 +569,6 @@
"canvasMerged": "Tela unita",
"sentToImageToImage": "Inviato a Immagine a Immagine",
"sentToUnifiedCanvas": "Inviato a Tela Unificata",
"parametersSet": "Parametri impostati",
"parametersNotSet": "Parametri non impostati",
"metadataLoadFailed": "Impossibile caricare i metadati",
"serverError": "Errore del Server",
@ -937,7 +937,8 @@
"controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
"mediapipeFace": "Mediapipe Volto",
"ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
"t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))"
"t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))",
"selectCLIPVisionModel": "Seleziona un modello CLIP Vision"
},
"queue": {
"queueFront": "Aggiungi all'inizio della coda",

1
invokeai/frontend/web/public/locales/nl.json
View File

@ -420,7 +420,6 @@
"canvasMerged": "Canvas samengevoegd",
"sentToImageToImage": "Gestuurd naar Afbeelding naar afbeelding",
"sentToUnifiedCanvas": "Gestuurd naar Centraal canvas",
"parametersSet": "Parameters ingesteld",
"parametersNotSet": "Parameters niet ingesteld",
"metadataLoadFailed": "Fout bij laden metagegevens",
"serverError": "Serverfout",

1
invokeai/frontend/web/public/locales/pl.json
View File

@ -267,7 +267,6 @@
"canvasMerged": "Scalono widoczne warstwy",
"sentToImageToImage": "Wysłano do Obraz na obraz",
"sentToUnifiedCanvas": "Wysłano do trybu uniwersalnego",
"parametersSet": "Ustawiono parametry",
"parametersNotSet": "Nie ustawiono parametrów",
"metadataLoadFailed": "Błąd wczytywania metadanych"
},

1
invokeai/frontend/web/public/locales/pt.json
View File

@ -310,7 +310,6 @@
"canvasMerged": "Tela Fundida",
"sentToImageToImage": "Mandar Para Imagem Para Imagem",
"sentToUnifiedCanvas": "Enviada para a Tela Unificada",
"parametersSet": "Parâmetros Definidos",
"parametersNotSet": "Parâmetros Não Definidos",
"metadataLoadFailed": "Falha ao tentar carregar metadados"
},

1
invokeai/frontend/web/public/locales/pt_BR.json
View File

@ -307,7 +307,6 @@
"canvasMerged": "Tela Fundida",
"sentToImageToImage": "Mandar Para Imagem Para Imagem",
"sentToUnifiedCanvas": "Enviada para a Tela Unificada",
"parametersSet": "Parâmetros Definidos",
"parametersNotSet": "Parâmetros Não Definidos",
"metadataLoadFailed": "Falha ao tentar carregar metadados"
},

1
invokeai/frontend/web/public/locales/ru.json
View File

@ -575,7 +575,6 @@
"canvasMerged": "Холст объединен",
"sentToImageToImage": "Отправить в img2img",
"sentToUnifiedCanvas": "Отправлено на Единый холст",
"parametersSet": "Параметры заданы",
"parametersNotSet": "Параметры не заданы",
"metadataLoadFailed": "Не удалось загрузить метаданные",
"serverError": "Ошибка сервера",

1
invokeai/frontend/web/public/locales/uk.json
View File

@ -315,7 +315,6 @@
"canvasMerged": "Полотно об'єднане",
"sentToImageToImage": "Надіслати до img2img",
"sentToUnifiedCanvas": "Надіслати на полотно",
"parametersSet": "Параметри задані",
"parametersNotSet": "Параметри не задані",
"metadataLoadFailed": "Не вдалося завантажити метадані",
"serverError": "Помилка сервера",

1
invokeai/frontend/web/public/locales/zh_CN.json
View File

@ -487,7 +487,6 @@
"canvasMerged": "画布已合并",
"sentToImageToImage": "已发送到图生图",
"sentToUnifiedCanvas": "已发送到统一画布",
"parametersSet": "参数已设定",
"parametersNotSet": "参数未设定",
"metadataLoadFailed": "加载元数据失败",
"uploadFailedInvalidUploadDesc": "必须是单张的 PNG 或 JPEG 图片",

10
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/addCommitStagingAreaImageListener.ts
View File

@ -1,12 +1,18 @@
import { isAnyOf } from '@reduxjs/toolkit';
import { logger } from 'app/logging/logger';
import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
import { canvasBatchIdsReset, commitStagingAreaImage, discardStagedImages } from 'features/canvas/store/canvasSlice';
import {
canvasBatchIdsReset,
commitStagingAreaImage,
discardStagedImages,
resetCanvas,
setInitialCanvasImage,
} from 'features/canvas/store/canvasSlice';
import { addToast } from 'features/system/store/systemSlice';
import { t } from 'i18next';
import { queueApi } from 'services/api/endpoints/queue';
const matcher = isAnyOf(commitStagingAreaImage, discardStagedImages);
const matcher = isAnyOf(commitStagingAreaImage, discardStagedImages, resetCanvas, setInitialCanvasImage);
export const addCommitStagingAreaImageListener = (startAppListening: AppStartListening) => {
startAppListening({

9
invokeai/frontend/web/src/features/canvas/components/IAICanvasStagingAreaToolbar.tsx
View File

@ -49,14 +49,20 @@ const selector = createMemoizedSelector(selectCanvasSlice, (canvas) => {
const ClearStagingIntermediatesIconButton = () => {
const dispatch = useAppDispatch();
const { t } = useTranslation();
const totalStagedImages = useAppSelector((s) => s.canvas.layerState.stagingArea.images.length);
const handleDiscardStagingArea = useCallback(() => {
dispatch(discardStagedImages());
}, [dispatch]);
const handleDiscardStagingImage = useCallback(() => {
// Discarding all staged images triggers cancelation of all canvas batches. It's too easy to accidentally
// click the discard button, so to prevent accidental cancelation of all batches, we only discard the current
// image if there are more than one staged images.
if (totalStagedImages > 1) {
dispatch(discardStagedImage());
}, [dispatch]);
}
}, [dispatch, totalStagedImages]);
return (
<>
@ -67,6 +73,7 @@ const ClearStagingIntermediatesIconButton = () => {
onClick={handleDiscardStagingImage}
colorScheme="invokeBlue"
fontSize={16}
isDisabled={totalStagedImages <= 1}
/>
<IconButton
tooltip={`${t('unifiedCanvas.discardAll')} (Esc)`}

10
invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasSettingsButtonPopover.tsx
View File

@ -18,6 +18,7 @@ import {
setShouldAutoSave,
setShouldCropToBoundingBoxOnSave,
setShouldDarkenOutsideBoundingBox,
setShouldFitImageSize,
setShouldInvertBrushSizeScrollDirection,
setShouldRestrictStrokesToBox,
setShouldShowCanvasDebugInfo,
@ -48,6 +49,7 @@ const IAICanvasSettingsButtonPopover = () => {
const shouldSnapToGrid = useAppSelector((s) => s.canvas.shouldSnapToGrid);
const shouldRestrictStrokesToBox = useAppSelector((s) => s.canvas.shouldRestrictStrokesToBox);
const shouldAntialias = useAppSelector((s) => s.canvas.shouldAntialias);
const shouldFitImageSize = useAppSelector((s) => s.canvas.shouldFitImageSize);
useHotkeys(
['n'],
@ -102,6 +104,10 @@ const IAICanvasSettingsButtonPopover = () => {
(e: ChangeEvent<HTMLInputElement>) => dispatch(setShouldAntialias(e.target.checked)),
[dispatch]
);
const handleChangeShouldFitImageSize = useCallback(
(e: ChangeEvent<HTMLInputElement>) => dispatch(setShouldFitImageSize(e.target.checked)),
[dispatch]
);
return (
<Popover>
@ -165,6 +171,10 @@ const IAICanvasSettingsButtonPopover = () => {
<FormLabel>{t('unifiedCanvas.antialiasing')}</FormLabel>
<Checkbox isChecked={shouldAntialias} onChange={handleChangeShouldAntialias} />
</FormControl>
<FormControl>
<FormLabel>{t('unifiedCanvas.initialFitImageSize')}</FormLabel>
<Checkbox isChecked={shouldFitImageSize} onChange={handleChangeShouldFitImageSize} />
</FormControl>
</FormControlGroup>
<ClearCanvasHistoryButtonModal />
</Flex>

70
invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts
View File

@ -66,6 +66,7 @@ const initialCanvasState: CanvasState = {
shouldAutoSave: false,
shouldCropToBoundingBoxOnSave: false,
shouldDarkenOutsideBoundingBox: false,
shouldFitImageSize: true,
shouldInvertBrushSizeScrollDirection: false,
shouldLockBoundingBox: false,
shouldPreserveMaskedArea: false,
@ -144,11 +145,19 @@ export const canvasSlice = createSlice({
reducer: (state, action: PayloadActionWithOptimalDimension<ImageDTO>) => {
const { width, height, image_name } = action.payload;
const { optimalDimension } = action.meta;
const { stageDimensions } = state;
const { stageDimensions, shouldFitImageSize } = state;
const newBoundingBoxDimensions = {
const newBoundingBoxDimensions = shouldFitImageSize
? {
width: roundDownToMultiple(width, CANVAS_GRID_SIZE_FINE),
height: roundDownToMultiple(height, CANVAS_GRID_SIZE_FINE),
}
: {
width: roundDownToMultiple(clamp(width, CANVAS_GRID_SIZE_FINE, optimalDimension), CANVAS_GRID_SIZE_FINE),
height: roundDownToMultiple(clamp(height, CANVAS_GRID_SIZE_FINE, optimalDimension), CANVAS_GRID_SIZE_FINE),
height: roundDownToMultiple(
clamp(height, CANVAS_GRID_SIZE_FINE, optimalDimension),
CANVAS_GRID_SIZE_FINE
),
};
const newBoundingBoxCoordinates = {
@ -181,7 +190,6 @@ export const canvasSlice = createSlice({
],
};
state.futureLayerStates = [];
state.batchIds = [];
const newScale = calculateScale(
stageDimensions.width,
@ -277,33 +285,14 @@ export const canvasSlice = createSlice({
},
discardStagedImages: (state) => {
pushToPrevLayerStates(state);
state.layerState.stagingArea = deepClone(initialLayerState.stagingArea);
resetStagingArea(state);
state.futureLayerStates = [];
state.shouldShowStagingOutline = true;
state.shouldShowStagingImage = true;
state.batchIds = [];
},
discardStagedImage: (state) => {
const { images, selectedImageIndex } = state.layerState.stagingArea;
pushToPrevLayerStates(state);
if (!images.length) {
return;
}
images.splice(selectedImageIndex, 1);
if (selectedImageIndex >= images.length) {
state.layerState.stagingArea.selectedImageIndex = images.length - 1;
}
if (!images.length) {
state.shouldShowStagingImage = false;
state.shouldShowStagingOutline = false;
}
state.layerState.stagingArea.selectedImageIndex = Math.max(0, images.length - 1);
state.futureLayerStates = [];
},
addFillRect: (state) => {
@ -417,7 +406,6 @@ export const canvasSlice = createSlice({
pushToPrevLayerStates(state);
state.layerState = deepClone(initialLayerState);
state.futureLayerStates = [];
state.batchIds = [];
state.boundingBoxCoordinates = {
...initialCanvasState.boundingBoxCoordinates,
};
@ -518,12 +506,9 @@ export const canvasSlice = createSlice({
...imageToCommit,
});
}
state.layerState.stagingArea = deepClone(initialLayerState.stagingArea);
resetStagingArea(state);
state.futureLayerStates = [];
state.shouldShowStagingOutline = true;
state.shouldShowStagingImage = true;
state.batchIds = [];
},
setBoundingBoxScaleMethod: {
reducer: (state, action: PayloadActionWithOptimalDimension<BoundingBoxScaleMethod>) => {
@ -575,6 +560,9 @@ export const canvasSlice = createSlice({
setShouldAntialias: (state, action: PayloadAction<boolean>) => {
state.shouldAntialias = action.payload;
},
setShouldFitImageSize: (state, action: PayloadAction<boolean>) => {
state.shouldFitImageSize = action.payload;
},
setShouldCropToBoundingBoxOnSave: (state, action: PayloadAction<boolean>) => {
state.shouldCropToBoundingBoxOnSave = action.payload;
},
@ -628,12 +616,19 @@ export const canvasSlice = createSlice({
if (batch_status.in_progress === 0 && batch_status.pending === 0) {
state.batchIds = state.batchIds.filter((id) => id !== batch_status.batch_id);
}
const queueItemStatus = action.payload.data.queue_item.status;
if (queueItemStatus === 'canceled' || queueItemStatus === 'failed') {
resetStagingAreaIfEmpty(state);
}
});
builder.addMatcher(queueApi.endpoints.clearQueue.matchFulfilled, (state) => {
state.batchIds = [];
resetStagingAreaIfEmpty(state);
});
builder.addMatcher(queueApi.endpoints.cancelByBatchIds.matchFulfilled, (state, action) => {
state.batchIds = state.batchIds.filter((id) => !action.meta.arg.originalArgs.batch_ids.includes(id));
resetStagingAreaIfEmpty(state);
});
},
});
@ -685,6 +680,7 @@ export const {
setShouldRestrictStrokesToBox,
stagingAreaInitialized,
setShouldAntialias,
setShouldFitImageSize,
canvasResized,
canvasBatchIdAdded,
canvasBatchIdsReset,
@ -706,7 +702,7 @@ export const canvasPersistConfig: PersistConfig<CanvasState> = {
name: canvasSlice.name,
initialState: initialCanvasState,
migrate: migrateCanvasState,
persistDenylist: [],
persistDenylist: ['shouldShowStagingImage', 'shouldShowStagingOutline'],
};
const pushToPrevLayerStates = (state: CanvasState) => {
@ -722,3 +718,15 @@ const pushToFutureLayerStates = (state: CanvasState) => {
state.futureLayerStates = state.futureLayerStates.slice(0, MAX_HISTORY);
}
};
const resetStagingAreaIfEmpty = (state: CanvasState) => {
if (state.batchIds.length === 0 && state.layerState.stagingArea.images.length === 0) {
resetStagingArea(state);
}
};
const resetStagingArea = (state: CanvasState) => {
state.layerState.stagingArea = { ...initialCanvasState.layerState.stagingArea };
state.shouldShowStagingImage = initialCanvasState.shouldShowStagingImage;
state.shouldShowStagingOutline = initialCanvasState.shouldShowStagingOutline;
};

1
invokeai/frontend/web/src/features/canvas/store/canvasTypes.ts
View File

@ -120,6 +120,7 @@ export interface CanvasState {
shouldAutoSave: boolean;
shouldCropToBoundingBoxOnSave: boolean;
shouldDarkenOutsideBoundingBox: boolean;
shouldFitImageSize: boolean;
shouldInvertBrushSizeScrollDirection: boolean;
shouldLockBoundingBox: boolean;
shouldPreserveMaskedArea: boolean;

1
invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataActions.tsx
View File

@ -33,6 +33,7 @@ const ImageMetadataActions = (props: Props) => {
<MetadataItem metadata={metadata} handlers={handlers.scheduler} />
<MetadataItem metadata={metadata} handlers={handlers.cfgScale} />
<MetadataItem metadata={metadata} handlers={handlers.cfgRescaleMultiplier} />
<MetadataItem metadata={metadata} handlers={handlers.initialImage} />
<MetadataItem metadata={metadata} handlers={handlers.strength} />
<MetadataItem metadata={metadata} handlers={handlers.hrfEnabled} />
<MetadataItem metadata={metadata} handlers={handlers.hrfMethod} />

8
invokeai/frontend/web/src/features/metadata/util/handlers.ts
View File

@ -189,6 +189,12 @@ export const handlers = {
recaller: recallers.cfgScale,
}),
height: buildHandlers({ getLabel: () => t('metadata.height'), parser: parsers.height, recaller: recallers.height }),
initialImage: buildHandlers({
getLabel: () => t('metadata.initImage'),
parser: parsers.initialImage,
recaller: recallers.initialImage,
renderValue: async (imageDTO) => imageDTO.image_name,
}),
negativePrompt: buildHandlers({
getLabel: () => t('metadata.negativePrompt'),
parser: parsers.negativePrompt,
@ -405,6 +411,6 @@ export const parseAndRecallAllMetadata = async (metadata: unknown, skip: (keyof
})
);
if (results.some((result) => result.status === 'fulfilled')) {
parameterSetToast(t('toast.parametersSet'));
parameterSetToast(t('toast.parameters'));
}
};

12
invokeai/frontend/web/src/features/metadata/util/parsers.ts
View File

@ -1,3 +1,4 @@
import { getStore } from 'app/store/nanostores/store';
import {
initialControlNet,
initialIPAdapter,
@ -57,6 +58,8 @@ import {
isParameterWidth,
} from 'features/parameters/types/parameterSchemas';
import { get, isArray, isString } from 'lodash-es';
import { imagesApi } from 'services/api/endpoints/images';
import type { ImageDTO } from 'services/api/types';
import {
isControlNetModelConfig,
isIPAdapterModelConfig,
@ -135,6 +138,14 @@ const parseCFGRescaleMultiplier: MetadataParseFunc<ParameterCFGRescaleMultiplier
const parseScheduler: MetadataParseFunc<ParameterScheduler> = (metadata) =>
getProperty(metadata, 'scheduler', isParameterScheduler);
const parseInitialImage: MetadataParseFunc<ImageDTO> = async (metadata) => {
const imageName = await getProperty(metadata, 'init_image', isString);
const imageDTORequest = getStore().dispatch(imagesApi.endpoints.getImageDTO.initiate(imageName));
const imageDTO = await imageDTORequest.unwrap();
imageDTORequest.unsubscribe();
return imageDTO;
};
const parseWidth: MetadataParseFunc<ParameterWidth> = (metadata) => getProperty(metadata, 'width', isParameterWidth);
const parseHeight: MetadataParseFunc<ParameterHeight> = (metadata) =>
@ -402,6 +413,7 @@ export const parsers = {
cfgScale: parseCFGScale,
cfgRescaleMultiplier: parseCFGRescaleMultiplier,
scheduler: parseScheduler,
initialImage: parseInitialImage,
width: parseWidth,
height: parseHeight,
steps: parseSteps,

7
invokeai/frontend/web/src/features/metadata/util/recallers.ts
View File

@ -17,6 +17,7 @@ import type {
import { modelSelected } from 'features/parameters/store/actions';
import {
heightRecalled,
initialImageChanged,
setCfgRescaleMultiplier,
setCfgScale,
setImg2imgStrength,
@ -61,6 +62,7 @@ import {
setRefinerStart,
setRefinerSteps,
} from 'features/sdxl/store/sdxlSlice';
import type { ImageDTO } from 'services/api/types';
const recallPositivePrompt: MetadataRecallFunc<ParameterPositivePrompt> = (positivePrompt) => {
getStore().dispatch(setPositivePrompt(positivePrompt));
@ -94,6 +96,10 @@ const recallScheduler: MetadataRecallFunc<ParameterScheduler> = (scheduler) => {
getStore().dispatch(setScheduler(scheduler));
};
const recallInitialImage: MetadataRecallFunc<ImageDTO> = async (imageDTO) => {
getStore().dispatch(initialImageChanged(imageDTO));
};
const recallWidth: MetadataRecallFunc<ParameterWidth> = (width) => {
getStore().dispatch(widthRecalled(width));
};
@ -235,6 +241,7 @@ export const recallers = {
cfgScale: recallCFGScale,
cfgRescaleMultiplier: recallCFGRescaleMultiplier,
scheduler: recallScheduler,
initialImage: recallInitialImage,
width: recallWidth,
height: recallHeight,
steps: recallSteps,

2
invokeai/frontend/web/src/features/modelManagerV2/store/modelManagerV2Slice.ts
View File

@ -3,7 +3,7 @@ import { createSlice } from '@reduxjs/toolkit';
import type { PersistConfig } from 'app/store/store';
import type { ModelType } from 'services/api/types';
export type FilterableModelType = Exclude<ModelType, 'onnx' | 'clip_vision'>;
export type FilterableModelType = Exclude<ModelType, 'onnx' | 'clip_vision'> | 'refiner';
type ModelManagerState = {
_version: 1;

1
invokeai/frontend/web/src/features/modelManagerV2/subpanels/AddModelPanel/InstallModelForm.tsx
View File

@ -74,7 +74,6 @@ export const InstallModelForm = () => {
onClick={handleSubmit(onSubmit)}
isDisabled={!formState.dirtyFields.location}
isLoading={isLoading}
type="submit"
size="sm"
>
{t('modelManager.install')}

60
invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelManagerPanel/ModelList.tsx
View File

@ -1,6 +1,7 @@
import { Flex } from '@invoke-ai/ui-library';
import { Flex, Text } from '@invoke-ai/ui-library';
import { useAppSelector } from 'app/store/storeHooks';
import ScrollableContent from 'common/components/OverlayScrollbars/ScrollableContent';
import type { FilterableModelType } from 'features/modelManagerV2/store/modelManagerV2Slice';
import { memo, useMemo } from 'react';
import { useTranslation } from 'react-i18next';
import {
@ -9,10 +10,11 @@ import {
useIPAdapterModels,
useLoRAModels,
useMainModels,
useRefinerModels,
useT2IAdapterModels,
useVAEModels,
} from 'services/api/hooks/modelsByType';
import type { AnyModelConfig, ModelType } from 'services/api/types';
import type { AnyModelConfig } from 'services/api/types';
import { FetchingModelsLoader } from './FetchingModelsLoader';
import { ModelListWrapper } from './ModelListWrapper';
@ -27,6 +29,12 @@ const ModelList = () => {
[mainModels, searchTerm, filteredModelType]
);
const [refinerModels, { isLoading: isLoadingRefinerModels }] = useRefinerModels();
const filteredRefinerModels = useMemo(
() => modelsFilter(refinerModels, searchTerm, filteredModelType),
[refinerModels, searchTerm, filteredModelType]
);
const [loraModels, { isLoading: isLoadingLoRAModels }] = useLoRAModels();
const filteredLoRAModels = useMemo(
() => modelsFilter(loraModels, searchTerm, filteredModelType),
@ -63,6 +71,28 @@ const ModelList = () => {
[vaeModels, searchTerm, filteredModelType]
);
const totalFilteredModels = useMemo(() => {
return (
filteredMainModels.length +
filteredRefinerModels.length +
filteredLoRAModels.length +
filteredEmbeddingModels.length +
filteredControlNetModels.length +
filteredT2IAdapterModels.length +
filteredIPAdapterModels.length +
filteredVAEModels.length
);
}, [
filteredControlNetModels.length,
filteredEmbeddingModels.length,
filteredIPAdapterModels.length,
filteredLoRAModels.length,
filteredMainModels.length,
filteredRefinerModels.length,
filteredT2IAdapterModels.length,
filteredVAEModels.length,
]);
return (
<ScrollableContent>
<Flex flexDirection="column" w="full" h="full" gap={4}>
@ -71,6 +101,11 @@ const ModelList = () => {
{!isLoadingMainModels && filteredMainModels.length > 0 && (
<ModelListWrapper title={t('modelManager.main')} modelList={filteredMainModels} key="main" />
)}
{/* Refiner Model List */}
{isLoadingRefinerModels && <FetchingModelsLoader loadingMessage="Loading Refiner Models..." />}
{!isLoadingRefinerModels && filteredRefinerModels.length > 0 && (
<ModelListWrapper title={t('sdxl.refiner')} modelList={filteredRefinerModels} key="refiner" />
)}
{/* LoRAs List */}
{isLoadingLoRAModels && <FetchingModelsLoader loadingMessage="Loading LoRAs..." />}
{!isLoadingLoRAModels && filteredLoRAModels.length > 0 && (
@ -108,6 +143,11 @@ const ModelList = () => {
{!isLoadingT2IAdapterModels && filteredT2IAdapterModels.length > 0 && (
<ModelListWrapper title={t('common.t2iAdapter')} modelList={filteredT2IAdapterModels} key="t2i-adapters" />
)}
{totalFilteredModels === 0 && (
<Flex w="full" h="full" alignItems="center" justifyContent="center">
<Text>{t('modelManager.noMatchingModels')}</Text>
</Flex>
)}
</Flex>
</ScrollableContent>
);
@ -118,12 +158,24 @@ export default memo(ModelList);
const modelsFilter = <T extends AnyModelConfig>(
data: T[],
nameFilter: string,
filteredModelType: ModelType | null
filteredModelType: FilterableModelType | null
): T[] => {
return data.filter((model) => {
const matchesFilter = model.name.toLowerCase().includes(nameFilter.toLowerCase());
const matchesType = filteredModelType ? model.type === filteredModelType : true;
const matchesType = getMatchesType(model, filteredModelType);
return matchesFilter && matchesType;
});
};
const getMatchesType = (modelConfig: AnyModelConfig, filteredModelType: FilterableModelType | null): boolean => {
if (filteredModelType === 'refiner') {
return modelConfig.base === 'sdxl-refiner';
}
if (filteredModelType === 'main' && modelConfig.base === 'sdxl-refiner') {
return false;
}
return filteredModelType ? modelConfig.type === filteredModelType : true;
};

1
invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelManagerPanel/ModelTypeFilter.tsx
View File

@ -13,6 +13,7 @@ export const ModelTypeFilter = () => {
const MODEL_TYPE_LABELS: Record<FilterableModelType, string> = useMemo(
() => ({
main: t('modelManager.main'),
refiner: t('sdxl.refiner'),
lora: 'LoRA',
embedding: t('modelManager.textualInversions'),
controlnet: 'ControlNet',

1
invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/ControlNetOrT2IAdapterDefaultSettings/ControlNetOrT2IAdapterDefaultSettings.tsx
View File

@ -86,7 +86,6 @@ export const ControlNetOrT2IAdapterDefaultSettings = () => {
colorScheme="invokeYellow"
isDisabled={!formState.isDirty}
onClick={handleSubmit(onSubmit)}
type="submit"
isLoading={isLoadingUpdateModel}
>
{t('common.save')}

1
invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/MainModelDefaultSettings/MainModelDefaultSettings.tsx
View File

@ -116,7 +116,6 @@ export const MainModelDefaultSettings = () => {
colorScheme="invokeYellow"
isDisabled={!formState.isDirty}
onClick={handleSubmit(onSubmit)}
type="submit"
isLoading={isLoadingUpdateModel}
>
{t('common.save')}

1
invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/TriggerPhrases.tsx
View File

@ -88,7 +88,6 @@ export const TriggerPhrases = () => {
<Button
leftIcon={<PiPlusBold />}
size="sm"
type="submit"
onClick={addTriggerPhrase}
isDisabled={!phrase || Boolean(errors.length)}
isLoading={isLoading}

2
invokeai/frontend/web/src/features/nodes/components/NodeEditor.tsx
View File

@ -3,6 +3,7 @@ import 'reactflow/dist/style.css';
import { Flex } from '@invoke-ai/ui-library';
import { IAINoContentFallback } from 'common/components/IAIImageFallback';
import TopPanel from 'features/nodes/components/flow/panels/TopPanel/TopPanel';
import { LoadWorkflowFromGraphModal } from 'features/workflowLibrary/components/LoadWorkflowFromGraphModal/LoadWorkflowFromGraphModal';
import { SaveWorkflowAsDialog } from 'features/workflowLibrary/components/SaveWorkflowAsDialog/SaveWorkflowAsDialog';
import type { AnimationProps } from 'framer-motion';
import { AnimatePresence, motion } from 'framer-motion';
@ -61,6 +62,7 @@ const NodeEditor = () => {
<BottomLeftPanel />
<MinimapPanel />
<SaveWorkflowAsDialog />
<LoadWorkflowFromGraphModal />
</motion.div>
)}
</AnimatePresence>

36
invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/NumberFieldInputComponent.tsx
View File

@ -37,34 +37,50 @@ const NumberFieldInputComponent = (
);
const min = useMemo(() => {
let min = -NUMPY_RAND_MAX;
if (!isNil(fieldTemplate.minimum)) {
return fieldTemplate.minimum;
min = fieldTemplate.minimum;
}
if (!isNil(fieldTemplate.exclusiveMinimum)) {
return fieldTemplate.exclusiveMinimum + 0.01;
min = fieldTemplate.exclusiveMinimum + 0.01;
}
return;
return min;
}, [fieldTemplate.exclusiveMinimum, fieldTemplate.minimum]);
const max = useMemo(() => {
let max = NUMPY_RAND_MAX;
if (!isNil(fieldTemplate.maximum)) {
return fieldTemplate.maximum;
max = fieldTemplate.maximum;
}
if (!isNil(fieldTemplate.exclusiveMaximum)) {
return fieldTemplate.exclusiveMaximum - 0.01;
max = fieldTemplate.exclusiveMaximum - 0.01;
}
return;
return max;
}, [fieldTemplate.exclusiveMaximum, fieldTemplate.maximum]);
const step = useMemo(() => {
if (isNil(fieldTemplate.multipleOf)) {
return isIntegerField ? 1 : 0.1;
}
return fieldTemplate.multipleOf;
}, [fieldTemplate.multipleOf, isIntegerField]);
const fineStep = useMemo(() => {
if (isNil(fieldTemplate.multipleOf)) {
return isIntegerField ? 1 : 0.01;
}
return fieldTemplate.multipleOf;
}, [fieldTemplate.multipleOf, isIntegerField]);
return (
<CompositeNumberInput
defaultValue={fieldTemplate.default}
onChange={handleValueChanged}
value={field.value}
min={min ?? -NUMPY_RAND_MAX}
max={max ?? NUMPY_RAND_MAX}
step={isIntegerField ? 1 : 0.1}
fineStep={isIntegerField ? 1 : 0.01}
min={min}
max={max}
step={step}
fineStep={fineStep}
className="nodrag"
/>
);

82
invokeai/frontend/web/src/features/nodes/util/graph/addSDXLRefinerToGraph.ts
View File

@ -1,26 +1,18 @@
import type { RootState } from 'app/store/store';
import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
import {
type CreateDenoiseMaskInvocation,
type ImageDTO,
isRefinerMainModelModelConfig,
type NonNullableGraph,
type SeamlessModeInvocation,
} from 'services/api/types';
import type { NonNullableGraph, SeamlessModeInvocation } from 'services/api/types';
import { isRefinerMainModelModelConfig } from 'services/api/types';
import {
CANVAS_OUTPUT,
INPAINT_IMAGE_RESIZE_UP,
INPAINT_CREATE_MASK,
LATENTS_TO_IMAGE,
MASK_COMBINE,
MASK_RESIZE_UP,
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_DENOISE_LATENTS,
SDXL_REFINER_INPAINT_CREATE_MASK,
SDXL_REFINER_MODEL_LOADER,
SDXL_REFINER_NEGATIVE_CONDITIONING,
SDXL_REFINER_POSITIVE_CONDITIONING,
@ -33,9 +25,7 @@ export const addSDXLRefinerToGraph = async (
state: RootState,
graph: NonNullableGraph,
baseNodeId: string,
modelLoaderNodeId?: string,
canvasInitImage?: ImageDTO,
canvasMaskImage?: ImageDTO
modelLoaderNodeId?: string
): Promise<void> => {
const {
refinerModel,
@ -51,11 +41,9 @@ export const addSDXLRefinerToGraph = async (
return;
}
const { seamlessXAxis, seamlessYAxis, vaePrecision } = state.generation;
const { seamlessXAxis, seamlessYAxis } = state.generation;
const { boundingBoxScaleMethod } = state.canvas;
const fp32 = vaePrecision === 'fp32';
const isUsingScaledDimensions = ['auto', 'manual'].includes(boundingBoxScaleMethod);
const modelConfig = await fetchModelConfigWithTypeGuard(refinerModel.key, isRefinerMainModelModelConfig);
@ -214,67 +202,9 @@ export const addSDXLRefinerToGraph = async (
);
if (graph.id === SDXL_CANVAS_INPAINT_GRAPH || graph.id === SDXL_CANVAS_OUTPAINT_GRAPH) {
graph.nodes[SDXL_REFINER_INPAINT_CREATE_MASK] = {
type: 'create_denoise_mask',
id: SDXL_REFINER_INPAINT_CREATE_MASK,
is_intermediate: true,
fp32,
};
if (isUsingScaledDimensions) {
graph.edges.push({
source: {
node_id: INPAINT_IMAGE_RESIZE_UP,
field: 'image',
},
destination: {
node_id: SDXL_REFINER_INPAINT_CREATE_MASK,
field: 'image',
},
});
} else {
graph.nodes[SDXL_REFINER_INPAINT_CREATE_MASK] = {
...(graph.nodes[SDXL_REFINER_INPAINT_CREATE_MASK] as CreateDenoiseMaskInvocation),
image: canvasInitImage,
};
}
if (graph.id === SDXL_CANVAS_INPAINT_GRAPH) {
if (isUsingScaledDimensions) {
graph.edges.push({
source: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
destination: {
node_id: SDXL_REFINER_INPAINT_CREATE_MASK,
field: 'mask',
},
});
} else {
graph.nodes[SDXL_REFINER_INPAINT_CREATE_MASK] = {
...(graph.nodes[SDXL_REFINER_INPAINT_CREATE_MASK] as CreateDenoiseMaskInvocation),
mask: canvasMaskImage,
};
}
}
if (graph.id === SDXL_CANVAS_OUTPAINT_GRAPH) {
graph.edges.push({
source: {
node_id: isUsingScaledDimensions ? MASK_RESIZE_UP : MASK_COMBINE,
field: 'image',
},
destination: {
node_id: SDXL_REFINER_INPAINT_CREATE_MASK,
field: 'mask',
},
});
}
graph.edges.push({
source: {
node_id: SDXL_REFINER_INPAINT_CREATE_MASK,
node_id: INPAINT_CREATE_MASK,
field: 'denoise_mask',
},
destination: {

22
invokeai/frontend/web/src/features/nodes/util/graph/addVAEToGraph.ts
View File

@ -17,7 +17,6 @@ import {
SDXL_CANVAS_OUTPAINT_GRAPH,
SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH,
SDXL_IMAGE_TO_IMAGE_GRAPH,
SDXL_REFINER_INPAINT_CREATE_MASK,
SDXL_REFINER_SEAMLESS,
SDXL_TEXT_TO_IMAGE_GRAPH,
SEAMLESS,
@ -166,27 +165,6 @@ export const addVAEToGraph = async (
);
}
if (refinerModel) {
if (graph.id === SDXL_CANVAS_INPAINT_GRAPH || graph.id === SDXL_CANVAS_OUTPAINT_GRAPH) {
graph.edges.push({
source: {
node_id: isSeamlessEnabled
? isUsingRefiner
? SDXL_REFINER_SEAMLESS
: SEAMLESS
: isAutoVae
? modelLoaderNodeId
: VAE_LOADER,
field: 'vae',
},
destination: {
node_id: SDXL_REFINER_INPAINT_CREATE_MASK,
field: 'vae',
},
});
}
}
if (vae) {
upsertMetadata(graph, { vae });
}

27
invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasOutpaintGraph.ts
View File

@ -65,6 +65,11 @@ export const buildCanvasOutpaintGraph = async (
infillTileSize,
infillPatchmatchDownscaleSize,
infillMethod,
// infillMosaicTileWidth,
// infillMosaicTileHeight,
// infillMosaicMinColor,
// infillMosaicMaxColor,
infillColorValue,
clipSkip,
seamlessXAxis,
seamlessYAxis,
@ -356,6 +361,28 @@ export const buildCanvasOutpaintGraph = async (
};
}
// TODO: add mosaic back
// if (infillMethod === 'mosaic') {
// graph.nodes[INPAINT_INFILL] = {
// type: 'infill_mosaic',
// id: INPAINT_INFILL,
// is_intermediate,
// tile_width: infillMosaicTileWidth,
// tile_height: infillMosaicTileHeight,
// min_color: infillMosaicMinColor,
// max_color: infillMosaicMaxColor,
// };
// }
if (infillMethod === 'color') {
graph.nodes[INPAINT_INFILL] = {
type: 'infill_rgba',
id: INPAINT_INFILL,
color: infillColorValue,
is_intermediate,
};
}
// Handle Scale Before Processing
if (isUsingScaledDimensions) {
const scaledWidth: number = scaledBoundingBoxDimensions.width;

11
invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasSDXLInpaintGraph.ts
View File

@ -133,7 +133,7 @@ export const buildCanvasSDXLInpaintGraph = async (
id: INPAINT_CREATE_MASK,
is_intermediate,
coherence_mode: canvasCoherenceMode,
minimum_denoise: canvasCoherenceMinDenoise,
minimum_denoise: refinerModel ? Math.max(0.2, canvasCoherenceMinDenoise) : canvasCoherenceMinDenoise,
edge_radius: canvasCoherenceEdgeSize,
},
[SDXL_DENOISE_LATENTS]: {
@ -426,14 +426,7 @@ export const buildCanvasSDXLInpaintGraph = async (
// Add Refiner if enabled
if (refinerModel) {
await addSDXLRefinerToGraph(
state,
graph,
SDXL_DENOISE_LATENTS,
modelLoaderNodeId,
canvasInitImage,
canvasMaskImage
);
await addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
if (seamlessXAxis || seamlessYAxis) {
modelLoaderNodeId = SDXL_REFINER_SEAMLESS;
}

31
invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasSDXLOutpaintGraph.ts
View File

@ -66,6 +66,11 @@ export const buildCanvasSDXLOutpaintGraph = async (
infillTileSize,
infillPatchmatchDownscaleSize,
infillMethod,
// infillMosaicTileWidth,
// infillMosaicTileHeight,
// infillMosaicMinColor,
// infillMosaicMaxColor,
infillColorValue,
seamlessXAxis,
seamlessYAxis,
canvasCoherenceMode,
@ -151,7 +156,7 @@ export const buildCanvasSDXLOutpaintGraph = async (
is_intermediate,
coherence_mode: canvasCoherenceMode,
edge_radius: canvasCoherenceEdgeSize,
minimum_denoise: canvasCoherenceMinDenoise,
minimum_denoise: refinerModel ? Math.max(0.2, canvasCoherenceMinDenoise) : canvasCoherenceMinDenoise,
},
[SDXL_DENOISE_LATENTS]: {
type: 'denoise_latents',
@ -365,6 +370,28 @@ export const buildCanvasSDXLOutpaintGraph = async (
};
}
// TODO: add mosaic back
// if (infillMethod === 'mosaic') {
// graph.nodes[INPAINT_INFILL] = {
// type: 'infill_mosaic',
// id: INPAINT_INFILL,
// is_intermediate,
// tile_width: infillMosaicTileWidth,
// tile_height: infillMosaicTileHeight,
// min_color: infillMosaicMinColor,
// max_color: infillMosaicMaxColor,
// };
// }
if (infillMethod === 'color') {
graph.nodes[INPAINT_INFILL] = {
type: 'infill_rgba',
id: INPAINT_INFILL,
is_intermediate,
color: infillColorValue,
};
}
// Handle Scale Before Processing
if (isUsingScaledDimensions) {
const scaledWidth: number = scaledBoundingBoxDimensions.width;
@ -555,7 +582,7 @@ export const buildCanvasSDXLOutpaintGraph = async (
// Add Refiner if enabled
if (refinerModel) {
await addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId, canvasInitImage);
await addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
if (seamlessXAxis || seamlessYAxis) {
modelLoaderNodeId = SDXL_REFINER_SEAMLESS;
}

148
invokeai/frontend/web/src/features/nodes/util/workflow/graphToWorkflow.ts Normal file
View File

@ -0,0 +1,148 @@
import * as dagre from '@dagrejs/dagre';
import { logger } from 'app/logging/logger';
import { getStore } from 'app/store/nanostores/store';
import { NODE_WIDTH } from 'features/nodes/types/constants';
import type { FieldInputInstance } from 'features/nodes/types/field';
import type { WorkflowV3 } from 'features/nodes/types/workflow';
import { buildFieldInputInstance } from 'features/nodes/util/schema/buildFieldInputInstance';
import { t } from 'i18next';
import { forEach } from 'lodash-es';
import type { NonNullableGraph } from 'services/api/types';
import { v4 as uuidv4 } from 'uuid';
/**
* Converts a graph to a workflow. This is a best-effort conversion and may not be perfect.
* For example, if a graph references an unknown node type, that node will be skipped.
* @param graph The graph to convert to a workflow
* @param autoLayout Whether to auto-layout the nodes using `dagre`. If false, nodes will be simply stacked on top of one another with an offset.
* @returns The workflow.
*/
export const graphToWorkflow = (graph: NonNullableGraph, autoLayout = true): WorkflowV3 => {
const invocationTemplates = getStore().getState().nodes.templates;
if (!invocationTemplates) {
throw new Error(t('app.storeNotInitialized'));
}
// Initialize the workflow
const workflow: WorkflowV3 = {
name: '',
author: '',
contact: '',
description: '',
meta: {
category: 'user',
version: '3.0.0',
},
notes: '',
tags: '',
version: '',
exposedFields: [],
edges: [],
nodes: [],
};
// Convert nodes
forEach(graph.nodes, (node) => {
const template = invocationTemplates[node.type];
// Skip missing node templates - this is a best-effort
if (!template) {
logger('nodes').warn(`Node type ${node.type} not found in invocationTemplates`);
return;
}
// Build field input instances for each attr
const inputs: Record<string, FieldInputInstance> = {};
forEach(node, (value, key) => {
// Ignore the non-input keys - I think this is all of them?
if (key === 'id' || key === 'type' || key === 'is_intermediate' || key === 'use_cache') {
return;
}
const inputTemplate = template.inputs[key];
// Skip missing input templates
if (!inputTemplate) {
logger('nodes').warn(`Input ${key} not found in template for node type ${node.type}`);
return;
}
// This _should_ be all we need to do!
const inputInstance = buildFieldInputInstance(node.id, inputTemplate);
inputInstance.value = value;
inputs[key] = inputInstance;
});
workflow.nodes.push({
id: node.id,
type: 'invocation',
position: { x: 0, y: 0 }, // we'll do layout later, just need something here
data: {
id: node.id,
type: node.type,
version: template.version,
label: '',
notes: '',
isOpen: true,
isIntermediate: node.is_intermediate ?? false,
useCache: node.use_cache ?? true,
inputs,
},
});
});
forEach(graph.edges, (edge) => {
workflow.edges.push({
id: uuidv4(), // we don't have edge IDs in the graph
type: 'default',
source: edge.source.node_id,
sourceHandle: edge.source.field,
target: edge.destination.node_id,
targetHandle: edge.destination.field,
});
});
if (autoLayout) {
// Best-effort auto layout via dagre - not perfect but better than nothing
const dagreGraph = new dagre.graphlib.Graph();
// `rankdir` and `align` could be tweaked, but it's gonna be janky no matter what we choose
dagreGraph.setGraph({ rankdir: 'TB', align: 'UL' });
dagreGraph.setDefaultEdgeLabel(() => ({}));
// We don't know the dimensions of the nodes until we load the graph into `reactflow` - use a reasonable value
forEach(graph.nodes, (node) => {
const width = NODE_WIDTH;
const height = NODE_WIDTH * 1.5;
dagreGraph.setNode(node.id, { width, height });
});
graph.edges.forEach((edge) => {
dagreGraph.setEdge(edge.source.node_id, edge.destination.node_id);
});
// This does the magic
dagre.layout(dagreGraph);
// Update the workflow now that we've got the positions
workflow.nodes.forEach((node) => {
const nodeWithPosition = dagreGraph.node(node.id);
node.position = {
x: nodeWithPosition.x - nodeWithPosition.width / 2,
y: nodeWithPosition.y - nodeWithPosition.height / 2,
};
});
} else {
// Stack nodes with a 50px,50px offset from the previous ndoe
let x = 0;
let y = 0;
workflow.nodes.forEach((node) => {
node.position = { x, y };
x = x + 50;
y = y + 50;
});
}
return workflow;
};

46
invokeai/frontend/web/src/features/parameters/components/Canvas/InfillAndScaling/ParamInfillColorOptions.tsx Normal file
View File

@ -0,0 +1,46 @@
import { Box, Flex, FormControl, FormLabel } from '@invoke-ai/ui-library';
import { createSelector } from '@reduxjs/toolkit';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import IAIColorPicker from 'common/components/IAIColorPicker';
import { selectGenerationSlice, setInfillColorValue } from 'features/parameters/store/generationSlice';
import { memo, useCallback, useMemo } from 'react';
import type { RgbaColor } from 'react-colorful';
import { useTranslation } from 'react-i18next';
const ParamInfillColorOptions = () => {
const dispatch = useAppDispatch();
const selector = useMemo(
() =>
createSelector(selectGenerationSlice, (generation) => ({
infillColor: generation.infillColorValue,
})),
[]
);
const { infillColor } = useAppSelector(selector);
const infillMethod = useAppSelector((s) => s.generation.infillMethod);
const { t } = useTranslation();
const handleInfillColor = useCallback(
(v: RgbaColor) => {
dispatch(setInfillColorValue(v));
},
[dispatch]
);
return (
<Flex flexDir="column" gap={4}>
<FormControl isDisabled={infillMethod !== 'color'}>
<FormLabel>{t('parameters.infillColorValue')}</FormLabel>
<Box w="full" pt={2} pb={2}>
<IAIColorPicker color={infillColor} onChange={handleInfillColor} />
</Box>
</FormControl>
</Flex>
);
};
export default memo(ParamInfillColorOptions);

127
invokeai/frontend/web/src/features/parameters/components/Canvas/InfillAndScaling/ParamInfillMosaicOptions.tsx Normal file
View File

@ -0,0 +1,127 @@
import { Box, CompositeNumberInput, CompositeSlider, Flex, FormControl, FormLabel } from '@invoke-ai/ui-library';
import { createSelector } from '@reduxjs/toolkit';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import IAIColorPicker from 'common/components/IAIColorPicker';
import {
selectGenerationSlice,
setInfillMosaicMaxColor,
setInfillMosaicMinColor,
setInfillMosaicTileHeight,
setInfillMosaicTileWidth,
} from 'features/parameters/store/generationSlice';
import { memo, useCallback, useMemo } from 'react';
import type { RgbaColor } from 'react-colorful';
import { useTranslation } from 'react-i18next';
const ParamInfillMosaicTileSize = () => {
const dispatch = useAppDispatch();
const selector = useMemo(
() =>
createSelector(selectGenerationSlice, (generation) => ({
infillMosaicTileWidth: generation.infillMosaicTileWidth,
infillMosaicTileHeight: generation.infillMosaicTileHeight,
infillMosaicMinColor: generation.infillMosaicMinColor,
infillMosaicMaxColor: generation.infillMosaicMaxColor,
})),
[]
);
const { infillMosaicTileWidth, infillMosaicTileHeight, infillMosaicMinColor, infillMosaicMaxColor } =
useAppSelector(selector);
const infillMethod = useAppSelector((s) => s.generation.infillMethod);
const { t } = useTranslation();
const handleInfillMosaicTileWidthChange = useCallback(
(v: number) => {
dispatch(setInfillMosaicTileWidth(v));
},
[dispatch]
);
const handleInfillMosaicTileHeightChange = useCallback(
(v: number) => {
dispatch(setInfillMosaicTileHeight(v));
},
[dispatch]
);
const handleInfillMosaicMinColor = useCallback(
(v: RgbaColor) => {
dispatch(setInfillMosaicMinColor(v));
},
[dispatch]
);
const handleInfillMosaicMaxColor = useCallback(
(v: RgbaColor) => {
dispatch(setInfillMosaicMaxColor(v));
},
[dispatch]
);
return (
<Flex flexDir="column" gap={4}>
<FormControl isDisabled={infillMethod !== 'mosaic'}>
<FormLabel>{t('parameters.infillMosaicTileWidth')}</FormLabel>
<CompositeSlider
min={8}
max={256}
value={infillMosaicTileWidth}
defaultValue={64}
onChange={handleInfillMosaicTileWidthChange}
step={8}
fineStep={8}
marks
/>
<CompositeNumberInput
min={8}
max={1024}
value={infillMosaicTileWidth}
defaultValue={64}
onChange={handleInfillMosaicTileWidthChange}
step={8}
fineStep={8}
/>
</FormControl>
<FormControl isDisabled={infillMethod !== 'mosaic'}>
<FormLabel>{t('parameters.infillMosaicTileHeight')}</FormLabel>
<CompositeSlider
min={8}
max={256}
value={infillMosaicTileHeight}
defaultValue={64}
onChange={handleInfillMosaicTileHeightChange}
step={8}
fineStep={8}
marks
/>
<CompositeNumberInput
min={8}
max={1024}
value={infillMosaicTileHeight}
defaultValue={64}
onChange={handleInfillMosaicTileHeightChange}
step={8}
fineStep={8}
/>
</FormControl>
<FormControl isDisabled={infillMethod !== 'mosaic'}>
<FormLabel>{t('parameters.infillMosaicMinColor')}</FormLabel>
<Box w="full" pt={2} pb={2}>
<IAIColorPicker color={infillMosaicMinColor} onChange={handleInfillMosaicMinColor} />
</Box>
</FormControl>
<FormControl isDisabled={infillMethod !== 'mosaic'}>
<FormLabel>{t('parameters.infillMosaicMaxColor')}</FormLabel>
<Box w="full" pt={2} pb={2}>
<IAIColorPicker color={infillMosaicMaxColor} onChange={handleInfillMosaicMaxColor} />
</Box>
</FormControl>
</Flex>
);
};
export default memo(ParamInfillMosaicTileSize);

10
invokeai/frontend/web/src/features/parameters/components/Canvas/InfillAndScaling/ParamInfillOptions.tsx
View File

@ -1,6 +1,8 @@
import { useAppSelector } from 'app/store/storeHooks';
import { memo } from 'react';
import ParamInfillColorOptions from './ParamInfillColorOptions';
import ParamInfillMosaicOptions from './ParamInfillMosaicOptions';
import ParamInfillPatchmatchDownscaleSize from './ParamInfillPatchmatchDownscaleSize';
import ParamInfillTilesize from './ParamInfillTilesize';
@ -14,6 +16,14 @@ const ParamInfillOptions = () => {
return <ParamInfillPatchmatchDownscaleSize />;
}
if (infillMethod === 'mosaic') {
return <ParamInfillMosaicOptions />;
}
if (infillMethod === 'color') {
return <ParamInfillColorOptions />;
}
return null;
};

52
invokeai/frontend/web/src/features/parameters/store/generationSlice.ts
View File

@ -19,6 +19,7 @@ import type {
import { getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/optimalDimension';
import { configChanged } from 'features/system/store/configSlice';
import { clamp } from 'lodash-es';
import type { RgbaColor } from 'react-colorful';
import type { ImageDTO } from 'services/api/types';
import type { GenerationState } from './types';
@ -43,8 +44,6 @@ const initialGenerationState: GenerationState = {
shouldFitToWidthHeight: true,
shouldRandomizeSeed: true,
steps: 50,
infillTileSize: 32,
infillPatchmatchDownscaleSize: 1,
width: 512,
model: null,
vae: null,
@ -55,6 +54,13 @@ const initialGenerationState: GenerationState = {
shouldUseCpuNoise: true,
shouldShowAdvancedOptions: false,
aspectRatio: { ...initialAspectRatioState },
infillTileSize: 32,
infillPatchmatchDownscaleSize: 1,
infillMosaicTileWidth: 64,
infillMosaicTileHeight: 64,
infillMosaicMinColor: { r: 0, g: 0, b: 0, a: 1 },
infillMosaicMaxColor: { r: 255, g: 255, b: 255, a: 1 },
infillColorValue: { r: 0, g: 0, b: 0, a: 1 },
};
export const generationSlice = createSlice({
@ -116,15 +122,6 @@ export const generationSlice = createSlice({
setCanvasCoherenceMinDenoise: (state, action: PayloadAction<number>) => {
state.canvasCoherenceMinDenoise = action.payload;
},
setInfillMethod: (state, action: PayloadAction<string>) => {
state.infillMethod = action.payload;
},
setInfillTileSize: (state, action: PayloadAction<number>) => {
state.infillTileSize = action.payload;
},
setInfillPatchmatchDownscaleSize: (state, action: PayloadAction<number>) => {
state.infillPatchmatchDownscaleSize = action.payload;
},
initialImageChanged: (state, action: PayloadAction<ImageDTO>) => {
const { image_name, width, height } = action.payload;
state.initialImage = { imageName: image_name, width, height };
@ -206,6 +203,30 @@ export const generationSlice = createSlice({
aspectRatioChanged: (state, action: PayloadAction<AspectRatioState>) => {
state.aspectRatio = action.payload;
},
setInfillMethod: (state, action: PayloadAction<string>) => {
state.infillMethod = action.payload;
},
setInfillTileSize: (state, action: PayloadAction<number>) => {
state.infillTileSize = action.payload;
},
setInfillPatchmatchDownscaleSize: (state, action: PayloadAction<number>) => {
state.infillPatchmatchDownscaleSize = action.payload;
},
setInfillMosaicTileWidth: (state, action: PayloadAction<number>) => {
state.infillMosaicTileWidth = action.payload;
},
setInfillMosaicTileHeight: (state, action: PayloadAction<number>) => {
state.infillMosaicTileHeight = action.payload;
},
setInfillMosaicMinColor: (state, action: PayloadAction<RgbaColor>) => {
state.infillMosaicMinColor = action.payload;
},
setInfillMosaicMaxColor: (state, action: PayloadAction<RgbaColor>) => {
state.infillMosaicMaxColor = action.payload;
},
setInfillColorValue: (state, action: PayloadAction<RgbaColor>) => {
state.infillColorValue = action.payload;
},
},
extraReducers: (builder) => {
builder.addCase(configChanged, (state, action) => {
@ -249,8 +270,6 @@ export const {
setShouldFitToWidthHeight,
setShouldRandomizeSeed,
setSteps,
setInfillTileSize,
setInfillPatchmatchDownscaleSize,
initialImageChanged,
modelChanged,
vaeSelected,
@ -264,6 +283,13 @@ export const {
heightChanged,
widthRecalled,
heightRecalled,
setInfillTileSize,
setInfillPatchmatchDownscaleSize,
setInfillMosaicTileWidth,
setInfillMosaicTileHeight,
setInfillMosaicMinColor,
setInfillMosaicMaxColor,
setInfillColorValue,
} = generationSlice.actions;
export const { selectOptimalDimension } = generationSlice.selectors;

10
invokeai/frontend/web/src/features/parameters/store/types.ts
View File

@ -17,6 +17,7 @@ import type {
ParameterVAEModel,
ParameterWidth,
} from 'features/parameters/types/parameterSchemas';
import type { RgbaColor } from 'react-colorful';
export interface GenerationState {
_version: 2;
@ -39,8 +40,6 @@ export interface GenerationState {
shouldFitToWidthHeight: boolean;
shouldRandomizeSeed: boolean;
steps: ParameterSteps;
infillTileSize: number;
infillPatchmatchDownscaleSize: number;
width: ParameterWidth;
model: ParameterModel | null;
vae: ParameterVAEModel | null;
@ -51,6 +50,13 @@ export interface GenerationState {
shouldUseCpuNoise: boolean;
shouldShowAdvancedOptions: boolean;
aspectRatio: AspectRatioState;
infillTileSize: number;
infillPatchmatchDownscaleSize: number;
infillMosaicTileWidth: number;
infillMosaicTileHeight: number;
infillMosaicMinColor: RgbaColor;
infillMosaicMaxColor: RgbaColor;
infillColorValue: RgbaColor;
}
export type PayloadActionWithOptimalDimension<T = void> = PayloadAction<T, string, { optimalDimension: number }>;

4
invokeai/frontend/web/src/features/ui/store/uiSlice.ts
View File

@ -1,6 +1,7 @@
import type { PayloadAction } from '@reduxjs/toolkit';
import { createSlice } from '@reduxjs/toolkit';
import type { PersistConfig, RootState } from 'app/store/store';
import { workflowLoadRequested } from 'features/nodes/store/actions';
import { initialImageChanged } from 'features/parameters/store/generationSlice';
import type { InvokeTabName } from './tabMap';
@ -45,6 +46,9 @@ export const uiSlice = createSlice({
builder.addCase(initialImageChanged, (state) => {
state.activeTab = 'img2img';
});
builder.addCase(workflowLoadRequested, (state) => {
state.activeTab = 'nodes';
});
},
});

111
invokeai/frontend/web/src/features/workflowLibrary/components/LoadWorkflowFromGraphModal/LoadWorkflowFromGraphModal.tsx Normal file
View File

@ -0,0 +1,111 @@
import {
Button,
Checkbox,
Flex,
FormControl,
FormLabel,
Modal,
ModalBody,
ModalCloseButton,
ModalContent,
ModalHeader,
ModalOverlay,
Spacer,
Textarea,
} from '@invoke-ai/ui-library';
import { useStore } from '@nanostores/react';
import { useAppDispatch } from 'app/store/storeHooks';
import { workflowLoadRequested } from 'features/nodes/store/actions';
import { graphToWorkflow } from 'features/nodes/util/workflow/graphToWorkflow';
import { atom } from 'nanostores';
import type { ChangeEvent } from 'react';
import { useCallback, useState } from 'react';
import { useTranslation } from 'react-i18next';
const $isOpen = atom<boolean>(false);
export const useLoadWorkflowFromGraphModal = () => {
const isOpen = useStore($isOpen);
const onOpen = useCallback(() => {
$isOpen.set(true);
}, []);
const onClose = useCallback(() => {
$isOpen.set(false);
}, []);
return { isOpen, onOpen, onClose };
};
export const LoadWorkflowFromGraphModal = () => {
const { t } = useTranslation();
const dispatch = useAppDispatch();
const { isOpen, onClose } = useLoadWorkflowFromGraphModal();
const [graphRaw, setGraphRaw] = useState<string>('');
const [workflowRaw, setWorkflowRaw] = useState<string>('');
const [shouldAutoLayout, setShouldAutoLayout] = useState(true);
const onChangeGraphRaw = useCallback((e: ChangeEvent<HTMLTextAreaElement>) => {
setGraphRaw(e.target.value);
}, []);
const onChangeWorkflowRaw = useCallback((e: ChangeEvent<HTMLTextAreaElement>) => {
setWorkflowRaw(e.target.value);
}, []);
const onChangeShouldAutoLayout = useCallback((e: ChangeEvent<HTMLInputElement>) => {
setShouldAutoLayout(e.target.checked);
}, []);
const parse = useCallback(() => {
const graph = JSON.parse(graphRaw);
const workflow = graphToWorkflow(graph, shouldAutoLayout);
setWorkflowRaw(JSON.stringify(workflow, null, 2));
}, [graphRaw, shouldAutoLayout]);
const loadWorkflow = useCallback(() => {
const workflow = JSON.parse(workflowRaw);
dispatch(workflowLoadRequested({ workflow, asCopy: true }));
onClose();
}, [dispatch, onClose, workflowRaw]);
return (
<Modal isOpen={isOpen} onClose={onClose} isCentered>
<ModalOverlay />
<ModalContent w="80vw" h="80vh" maxW="unset" maxH="unset">
<ModalHeader>{t('workflows.loadFromGraph')}</ModalHeader>
<ModalCloseButton />
<ModalBody as={Flex} flexDir="column" gap={4} w="full" h="full" pb={4}>
<Flex gap={4}>
<Button onClick={parse} size="sm" flexShrink={0}>
{t('workflows.convertGraph')}
</Button>
<FormControl>
<FormLabel>{t('workflows.autoLayout')}</FormLabel>
<Checkbox isChecked={shouldAutoLayout} onChange={onChangeShouldAutoLayout} />
</FormControl>
<Spacer />
<Button onClick={loadWorkflow} size="sm" flexShrink={0}>
{t('workflows.loadWorkflow')}
</Button>
</Flex>
<FormControl orientation="vertical" h="50%">
<FormLabel>{t('nodes.graph')}</FormLabel>
<Textarea
h="full"
value={graphRaw}
fontFamily="monospace"
whiteSpace="pre-wrap"
overflowWrap="normal"
onChange={onChangeGraphRaw}
/>
</FormControl>
<FormControl orientation="vertical" h="50%">
<FormLabel>{t('nodes.workflow')}</FormLabel>
<Textarea
h="full"
value={workflowRaw}
fontFamily="monospace"
whiteSpace="pre-wrap"
overflowWrap="normal"
onChange={onChangeWorkflowRaw}
/>
</FormControl>
</ModalBody>
</ModalContent>
</Modal>
);
};

18
invokeai/frontend/web/src/features/workflowLibrary/components/WorkflowLibraryMenu/LoadWorkflowFromGraphMenuItem.tsx Normal file
View File

@ -0,0 +1,18 @@
import { MenuItem } from '@invoke-ai/ui-library';
import { useLoadWorkflowFromGraphModal } from 'features/workflowLibrary/components/LoadWorkflowFromGraphModal/LoadWorkflowFromGraphModal';
import { memo } from 'react';
import { useTranslation } from 'react-i18next';
import { PiFlaskBold } from 'react-icons/pi';
const LoadWorkflowFromGraphMenuItem = () => {
const { t } = useTranslation();
const { onOpen } = useLoadWorkflowFromGraphModal();
return (
<MenuItem as="button" icon={<PiFlaskBold />} onClick={onOpen}>
{t('workflows.loadFromGraph')}
</MenuItem>
);
};
export default memo(LoadWorkflowFromGraphMenuItem);

5
invokeai/frontend/web/src/features/workflowLibrary/components/WorkflowLibraryMenu/WorkflowLibraryMenu.tsx
View File

@ -6,8 +6,10 @@ import {
MenuList,
useDisclosure,
useGlobalMenuClose,
useShiftModifier,
} from '@invoke-ai/ui-library';
import DownloadWorkflowMenuItem from 'features/workflowLibrary/components/WorkflowLibraryMenu/DownloadWorkflowMenuItem';
import LoadWorkflowFromGraphMenuItem from 'features/workflowLibrary/components/WorkflowLibraryMenu/LoadWorkflowFromGraphMenuItem';
import { NewWorkflowMenuItem } from 'features/workflowLibrary/components/WorkflowLibraryMenu/NewWorkflowMenuItem';
import SaveWorkflowAsMenuItem from 'features/workflowLibrary/components/WorkflowLibraryMenu/SaveWorkflowAsMenuItem';
import SaveWorkflowMenuItem from 'features/workflowLibrary/components/WorkflowLibraryMenu/SaveWorkflowMenuItem';
@ -20,6 +22,7 @@ import { PiDotsThreeOutlineFill } from 'react-icons/pi';
const WorkflowLibraryMenu = () => {
const { t } = useTranslation();
const { isOpen, onOpen, onClose } = useDisclosure();
const shift = useShiftModifier();
useGlobalMenuClose(onClose);
return (
<Menu isOpen={isOpen} onOpen={onOpen} onClose={onClose}>
@ -38,6 +41,8 @@ const WorkflowLibraryMenu = () => {
<DownloadWorkflowMenuItem />
<MenuDivider />
<SettingsMenuItem />
{shift && <MenuDivider />}
{shift && <LoadWorkflowFromGraphMenuItem />}
</MenuList>
</Menu>
);

18
invokeai/frontend/web/src/services/api/schema.ts
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File diff suppressed because one or more lines are too long

1
invokeai/frontend/web/src/services/api/types.ts
View File

@ -134,7 +134,6 @@ export type CollectInvocation = S['CollectInvocation'];
export type ImageResizeInvocation = S['ImageResizeInvocation'];
export type InfillPatchMatchInvocation = S['InfillPatchMatchInvocation'];
export type InfillTileInvocation = S['InfillTileInvocation'];
export type CreateDenoiseMaskInvocation = S['CreateDenoiseMaskInvocation'];
export type CreateGradientMaskInvocation = S['CreateGradientMaskInvocation'];
export type CanvasPasteBackInvocation = S['CanvasPasteBackInvocation'];
export type NoiseInvocation = S['NoiseInvocation'];

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