mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'main' into sdxl-convert-safetensors
This commit is contained in:
Brandon
GitHub
commit
72db2ee352
@ -169,7 +169,7 @@ the command `npm install -g pnpm` if needed)
|
||||
_For Linux with an AMD GPU:_
|
||||
|
||||
```sh
|
||||
pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
|
||||
pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.6
|
||||
```
|
||||
|
||||
_For non-GPU systems:_
|
||||
|
||||
@ -477,7 +477,7 @@ Then type the following commands:
|
||||
|
||||
=== "AMD System"
|
||||
```bash
|
||||
pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
|
||||
pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.6
|
||||
```
|
||||
|
||||
### Corrupted configuration file
|
||||
|
||||
@ -154,7 +154,7 @@ manager, please follow these steps:
|
||||
=== "ROCm (AMD)"
|
||||
|
||||
```bash
|
||||
pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
|
||||
pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.6
|
||||
```
|
||||
|
||||
=== "CPU (Intel Macs & non-GPU systems)"
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||||
@ -313,7 +313,7 @@ code for InvokeAI. For this to work, you will need to install the
|
||||
on your system, please see the [Git Installation
|
||||
Guide](https://github.com/git-guides/install-git)
|
||||
|
||||
You will also need to install the [frontend development toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/docs/contributing/contribution_guides/contributingToFrontend.md).
|
||||
You will also need to install the [frontend development toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/README.md).
|
||||
|
||||
If you have a "normal" installation, you should create a totally separate virtual environment for the git-based installation, else the two may interfere.
|
||||
|
||||
@ -345,7 +345,7 @@ installation protocol (important!)
|
||||
|
||||
=== "ROCm (AMD)"
|
||||
```bash
|
||||
pip install -e . --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
|
||||
pip install -e . --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.6
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||||
```
|
||||
|
||||
=== "CPU (Intel Macs & non-GPU systems)"
|
||||
@ -361,7 +361,7 @@ installation protocol (important!)
|
||||
Be sure to pass `-e` (for an editable install) and don't forget the
|
||||
dot ("."). It is part of the command.
|
||||
|
||||
5. Install the [frontend toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/docs/contributing/contribution_guides/contributingToFrontend.md) and do a production build of the UI as described.
|
||||
5. Install the [frontend toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/frontend/web/README.md) and do a production build of the UI as described.
|
||||
|
||||
6. You can now run `invokeai` and its related commands. The code will be
|
||||
read from the repository, so that you can edit the .py source files
|
||||
|
||||
@ -134,7 +134,7 @@ recipes are available
|
||||
|
||||
When installing torch and torchvision manually with `pip`, remember to provide
|
||||
the argument `--extra-index-url
|
||||
https://download.pytorch.org/whl/rocm5.4.2` as described in the [Manual
|
||||
https://download.pytorch.org/whl/rocm5.6` as described in the [Manual
|
||||
Installation Guide](020_INSTALL_MANUAL.md).
|
||||
|
||||
This will be done automatically for you if you use the installer
|
||||
|
||||
@ -25,6 +25,7 @@ To use a community workflow, download the the `.json` node graph file and load i
|
||||
+ [GPT2RandomPromptMaker](#gpt2randompromptmaker)
|
||||
+ [Grid to Gif](#grid-to-gif)
|
||||
+ [Halftone](#halftone)
|
||||
+ [Hand Refiner with MeshGraphormer](#hand-refiner-with-meshgraphormer)
|
||||
+ [Image and Mask Composition Pack](#image-and-mask-composition-pack)
|
||||
+ [Image Dominant Color](#image-dominant-color)
|
||||
+ [Image to Character Art Image Nodes](#image-to-character-art-image-nodes)
|
||||
@ -196,6 +197,18 @@ CMYK Halftone Output:
|
||||
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c59c578f-db8e-4d66-8c66-2851752d75ea" width="300" />
|
||||
|
||||
--------------------------------
|
||||
|
||||
### Hand Refiner with MeshGraphormer
|
||||
|
||||
**Description**: Hand Refiner takes in your image and automatically generates a fixed depth map for the hands along with a mask of the hands region that will conveniently allow you to use them along with ControlNet to fix the wonky hands generated by Stable Diffusion
|
||||
|
||||
**Node Link:** https://github.com/blessedcoolant/invoke_meshgraphormer
|
||||
|
||||
**View**
|
||||
<img src="https://raw.githubusercontent.com/blessedcoolant/invoke_meshgraphormer/main/assets/preview.jpg" />
|
||||
|
||||
--------------------------------
|
||||
|
||||
### Image and Mask Composition Pack
|
||||
|
||||
**Description:** This is a pack of nodes for composing masks and images, including a simple text mask creator and both image and latent offset nodes. The offsets wrap around, so these can be used in conjunction with the Seamless node to progressively generate centered on different parts of the seamless tiling.
|
||||
|
||||
@ -455,7 +455,7 @@ def get_torch_source() -> (Union[str, None], str):
|
||||
optional_modules = "[onnx]"
|
||||
if OS == "Linux":
|
||||
if device == "rocm":
|
||||
url = "https://download.pytorch.org/whl/rocm5.4.2"
|
||||
url = "https://download.pytorch.org/whl/rocm5.6"
|
||||
elif device == "cpu":
|
||||
url = "https://download.pytorch.org/whl/cpu"
|
||||
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
|
||||
from logging import Logger
|
||||
|
||||
from invokeai.app.services.item_storage.item_storage_memory import ItemStorageMemory
|
||||
from invokeai.app.services.shared.sqlite.sqlite_util import init_db
|
||||
from invokeai.backend.model_manager.metadata import ModelMetadataStore
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
@ -22,7 +23,6 @@ from ..services.invocation_queue.invocation_queue_memory import MemoryInvocation
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||||
from ..services.invocation_services import InvocationServices
|
||||
from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
|
||||
from ..services.invoker import Invoker
|
||||
from ..services.item_storage.item_storage_sqlite import SqliteItemStorage
|
||||
from ..services.latents_storage.latents_storage_disk import DiskLatentsStorage
|
||||
from ..services.latents_storage.latents_storage_forward_cache import ForwardCacheLatentsStorage
|
||||
from ..services.model_install import ModelInstallService
|
||||
@ -80,7 +80,7 @@ class ApiDependencies:
|
||||
board_records = SqliteBoardRecordStorage(db=db)
|
||||
boards = BoardService()
|
||||
events = FastAPIEventService(event_handler_id)
|
||||
graph_execution_manager = SqliteItemStorage[GraphExecutionState](db=db, table_name="graph_executions")
|
||||
graph_execution_manager = ItemStorageMemory[GraphExecutionState]()
|
||||
image_records = SqliteImageRecordStorage(db=db)
|
||||
images = ImageService()
|
||||
invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
|
||||
|
||||
@ -274,7 +274,7 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
attention_type : Literal["auto", "normal", "xformers", "sliced", "torch-sdp"] = Field(default="auto", description="Attention type", json_schema_extra=Categories.Generation)
|
||||
attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', json_schema_extra=Categories.Generation)
|
||||
force_tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.Generation)
|
||||
png_compress_level : int = Field(default=6, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize", json_schema_extra=Categories.Generation)
|
||||
png_compress_level : int = Field(default=1, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize", json_schema_extra=Categories.Generation)
|
||||
|
||||
# QUEUE
|
||||
max_queue_size : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", json_schema_extra=Categories.Queue)
|
||||
|
||||
@ -1,10 +1,8 @@
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Callable, Generic, Optional, TypeVar
|
||||
from typing import Callable, Generic, TypeVar
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from invokeai.app.services.shared.pagination import PaginatedResults
|
||||
|
||||
T = TypeVar("T", bound=BaseModel)
|
||||
|
||||
|
||||
@ -25,23 +23,14 @@ class ItemStorageABC(ABC, Generic[T]):
|
||||
"""Gets the item, parsing it into a Pydantic model"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_raw(self, item_id: str) -> Optional[str]:
|
||||
"""Gets the raw item as a string, skipping Pydantic parsing"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def set(self, item: T) -> None:
|
||||
"""Sets the item"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def list(self, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
|
||||
"""Gets a paginated list of items"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def search(self, query: str, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
|
||||
def delete(self, item_id: str) -> None:
|
||||
"""Deletes the item"""
|
||||
pass
|
||||
|
||||
def on_changed(self, on_changed: Callable[[T], None]) -> None:
|
||||
|
||||
50
invokeai/app/services/item_storage/item_storage_memory.py
Normal file
50
invokeai/app/services/item_storage/item_storage_memory.py
Normal file
@ -0,0 +1,50 @@
|
||||
from collections import OrderedDict
|
||||
from contextlib import suppress
|
||||
from typing import Generic, Optional, TypeVar
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from invokeai.app.services.item_storage.item_storage_base import ItemStorageABC
|
||||
|
||||
T = TypeVar("T", bound=BaseModel)
|
||||
|
||||
|
||||
class ItemStorageMemory(ItemStorageABC, Generic[T]):
|
||||
"""
|
||||
Provides a simple in-memory storage for items, with a maximum number of items to store.
|
||||
The storage uses the LRU strategy to evict items from storage when the max has been reached.
|
||||
"""
|
||||
|
||||
def __init__(self, id_field: str = "id", max_items: int = 10) -> None:
|
||||
super().__init__()
|
||||
if max_items < 1:
|
||||
raise ValueError("max_items must be at least 1")
|
||||
if not id_field:
|
||||
raise ValueError("id_field must not be empty")
|
||||
self._id_field = id_field
|
||||
self._items: OrderedDict[str, T] = OrderedDict()
|
||||
self._max_items = max_items
|
||||
|
||||
def get(self, item_id: str) -> Optional[T]:
|
||||
# If the item exists, move it to the end of the OrderedDict.
|
||||
item = self._items.pop(item_id, None)
|
||||
if item is not None:
|
||||
self._items[item_id] = item
|
||||
return self._items.get(item_id)
|
||||
|
||||
def set(self, item: T) -> None:
|
||||
item_id = getattr(item, self._id_field)
|
||||
if item_id in self._items:
|
||||
# If item already exists, remove it and add it to the end
|
||||
self._items.pop(item_id)
|
||||
elif len(self._items) >= self._max_items:
|
||||
# If cache is full, evict the least recently used item
|
||||
self._items.popitem(last=False)
|
||||
self._items[item_id] = item
|
||||
self._on_changed(item)
|
||||
|
||||
def delete(self, item_id: str) -> None:
|
||||
# This is a no-op if the item doesn't exist.
|
||||
with suppress(KeyError):
|
||||
del self._items[item_id]
|
||||
self._on_deleted(item_id)
|
||||
@ -1,147 +0,0 @@
|
||||
import sqlite3
|
||||
import threading
|
||||
from typing import Generic, Optional, TypeVar, get_args
|
||||
|
||||
from pydantic import BaseModel, TypeAdapter
|
||||
|
||||
from invokeai.app.services.shared.pagination import PaginatedResults
|
||||
from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
|
||||
|
||||
from .item_storage_base import ItemStorageABC
|
||||
|
||||
T = TypeVar("T", bound=BaseModel)
|
||||
|
||||
|
||||
class SqliteItemStorage(ItemStorageABC, Generic[T]):
|
||||
_table_name: str
|
||||
_conn: sqlite3.Connection
|
||||
_cursor: sqlite3.Cursor
|
||||
_id_field: str
|
||||
_lock: threading.RLock
|
||||
_validator: Optional[TypeAdapter[T]]
|
||||
|
||||
def __init__(self, db: SqliteDatabase, table_name: str, id_field: str = "id"):
|
||||
super().__init__()
|
||||
|
||||
self._lock = db.lock
|
||||
self._conn = db.conn
|
||||
self._table_name = table_name
|
||||
self._id_field = id_field # TODO: validate that T has this field
|
||||
self._cursor = self._conn.cursor()
|
||||
self._validator: Optional[TypeAdapter[T]] = None
|
||||
|
||||
self._create_table()
|
||||
|
||||
def _create_table(self):
|
||||
try:
|
||||
self._lock.acquire()
|
||||
self._cursor.execute(
|
||||
f"""CREATE TABLE IF NOT EXISTS {self._table_name} (
|
||||
item TEXT,
|
||||
id TEXT GENERATED ALWAYS AS (json_extract(item, '$.{self._id_field}')) VIRTUAL NOT NULL);"""
|
||||
)
|
||||
self._cursor.execute(
|
||||
f"""CREATE UNIQUE INDEX IF NOT EXISTS {self._table_name}_id ON {self._table_name}(id);"""
|
||||
)
|
||||
finally:
|
||||
self._lock.release()
|
||||
|
||||
def _parse_item(self, item: str) -> T:
|
||||
if self._validator is None:
|
||||
"""
|
||||
We don't get access to `__orig_class__` in `__init__()`, and we need this before start(), so
|
||||
we can create it when it is first needed instead.
|
||||
__orig_class__ is technically an implementation detail of the typing module, not a supported API
|
||||
"""
|
||||
self._validator = TypeAdapter(get_args(self.__orig_class__)[0]) # type: ignore [attr-defined]
|
||||
return self._validator.validate_json(item)
|
||||
|
||||
def set(self, item: T):
|
||||
try:
|
||||
self._lock.acquire()
|
||||
self._cursor.execute(
|
||||
f"""INSERT OR REPLACE INTO {self._table_name} (item) VALUES (?);""",
|
||||
(item.model_dump_json(warnings=False, exclude_none=True),),
|
||||
)
|
||||
self._conn.commit()
|
||||
finally:
|
||||
self._lock.release()
|
||||
self._on_changed(item)
|
||||
|
||||
def get(self, id: str) -> Optional[T]:
|
||||
try:
|
||||
self._lock.acquire()
|
||||
self._cursor.execute(f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),))
|
||||
result = self._cursor.fetchone()
|
||||
finally:
|
||||
self._lock.release()
|
||||
|
||||
if not result:
|
||||
return None
|
||||
|
||||
return self._parse_item(result[0])
|
||||
|
||||
def get_raw(self, id: str) -> Optional[str]:
|
||||
try:
|
||||
self._lock.acquire()
|
||||
self._cursor.execute(f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),))
|
||||
result = self._cursor.fetchone()
|
||||
finally:
|
||||
self._lock.release()
|
||||
|
||||
if not result:
|
||||
return None
|
||||
|
||||
return result[0]
|
||||
|
||||
def delete(self, id: str):
|
||||
try:
|
||||
self._lock.acquire()
|
||||
self._cursor.execute(f"""DELETE FROM {self._table_name} WHERE id = ?;""", (str(id),))
|
||||
self._conn.commit()
|
||||
finally:
|
||||
self._lock.release()
|
||||
self._on_deleted(id)
|
||||
|
||||
def list(self, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
|
||||
try:
|
||||
self._lock.acquire()
|
||||
self._cursor.execute(
|
||||
f"""SELECT item FROM {self._table_name} LIMIT ? OFFSET ?;""",
|
||||
(per_page, page * per_page),
|
||||
)
|
||||
result = self._cursor.fetchall()
|
||||
|
||||
items = [self._parse_item(r[0]) for r in result]
|
||||
|
||||
self._cursor.execute(f"""SELECT count(*) FROM {self._table_name};""")
|
||||
count = self._cursor.fetchone()[0]
|
||||
finally:
|
||||
self._lock.release()
|
||||
|
||||
pageCount = int(count / per_page) + 1
|
||||
|
||||
return PaginatedResults[T](items=items, page=page, pages=pageCount, per_page=per_page, total=count)
|
||||
|
||||
def search(self, query: str, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
|
||||
try:
|
||||
self._lock.acquire()
|
||||
self._cursor.execute(
|
||||
f"""SELECT item FROM {self._table_name} WHERE item LIKE ? LIMIT ? OFFSET ?;""",
|
||||
(f"%{query}%", per_page, page * per_page),
|
||||
)
|
||||
result = self._cursor.fetchall()
|
||||
|
||||
items = [self._parse_item(r[0]) for r in result]
|
||||
|
||||
self._cursor.execute(
|
||||
f"""SELECT count(*) FROM {self._table_name} WHERE item LIKE ?;""",
|
||||
(f"%{query}%",),
|
||||
)
|
||||
count = self._cursor.fetchone()[0]
|
||||
finally:
|
||||
self._lock.release()
|
||||
|
||||
pageCount = int(count / per_page) + 1
|
||||
|
||||
return PaginatedResults[T](items=items, page=page, pages=pageCount, per_page=per_page, total=count)
|
||||
@ -7,6 +7,7 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_1 import
|
||||
from invokeai.app.services.shared.sqlite_migrator.migrations.migration_2 import build_migration_2
|
||||
from invokeai.app.services.shared.sqlite_migrator.migrations.migration_3 import build_migration_3
|
||||
from invokeai.app.services.shared.sqlite_migrator.migrations.migration_4 import build_migration_4
|
||||
from invokeai.app.services.shared.sqlite_migrator.migrations.migration_5 import build_migration_5
|
||||
from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
|
||||
|
||||
|
||||
@ -31,6 +32,7 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
|
||||
migrator.register_migration(build_migration_2(image_files=image_files, logger=logger))
|
||||
migrator.register_migration(build_migration_3(app_config=config, logger=logger))
|
||||
migrator.register_migration(build_migration_4())
|
||||
migrator.register_migration(build_migration_5())
|
||||
migrator.run_migrations()
|
||||
|
||||
return db
|
||||
|
||||
@ -0,0 +1,34 @@
|
||||
import sqlite3
|
||||
|
||||
from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
|
||||
|
||||
|
||||
class Migration5Callback:
|
||||
def __call__(self, cursor: sqlite3.Cursor) -> None:
|
||||
self._drop_graph_executions(cursor)
|
||||
|
||||
def _drop_graph_executions(self, cursor: sqlite3.Cursor) -> None:
|
||||
"""Drops the `graph_executions` table."""
|
||||
|
||||
cursor.execute(
|
||||
"""--sql
|
||||
DROP TABLE IF EXISTS graph_executions;
|
||||
"""
|
||||
)
|
||||
|
||||
|
||||
def build_migration_5() -> Migration:
|
||||
"""
|
||||
Build the migration from database version 4 to 5.
|
||||
|
||||
Introduced in v3.6.3, this migration:
|
||||
- Drops the `graph_executions` table. We are able to do this because we are moving the graph storage
|
||||
to be purely in-memory.
|
||||
"""
|
||||
migration_5 = Migration(
|
||||
from_version=4,
|
||||
to_version=5,
|
||||
callback=Migration5Callback(),
|
||||
)
|
||||
|
||||
return migration_5
|
||||
@ -12,7 +12,7 @@ import psutil
|
||||
import torch
|
||||
from compel.cross_attention_control import Arguments
|
||||
from diffusers.models.attention_processor import Attention, AttentionProcessor, AttnProcessor, SlicedAttnProcessor
|
||||
from diffusers.models.unet_2d_condition import UNet2DConditionModel
|
||||
from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
|
||||
from torch import nn
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
|
||||
@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
import diffusers
|
||||
import torch
|
||||
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
||||
from diffusers.loaders import FromOriginalControlnetMixin
|
||||
from diffusers.loaders import FromOriginalControlNetMixin
|
||||
from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor
|
||||
from diffusers.models.controlnet import ControlNetConditioningEmbedding, ControlNetOutput, zero_module
|
||||
from diffusers.models.embeddings import (
|
||||
@ -14,8 +14,13 @@ from diffusers.models.embeddings import (
|
||||
Timesteps,
|
||||
)
|
||||
from diffusers.models.modeling_utils import ModelMixin
|
||||
from diffusers.models.unet_2d_blocks import CrossAttnDownBlock2D, DownBlock2D, UNetMidBlock2DCrossAttn, get_down_block
|
||||
from diffusers.models.unet_2d_condition import UNet2DConditionModel
|
||||
from diffusers.models.unets.unet_2d_blocks import (
|
||||
CrossAttnDownBlock2D,
|
||||
DownBlock2D,
|
||||
UNetMidBlock2DCrossAttn,
|
||||
get_down_block,
|
||||
)
|
||||
from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
|
||||
from torch import nn
|
||||
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
@ -27,7 +32,7 @@ from invokeai.backend.util.logging import InvokeAILogger
|
||||
logger = InvokeAILogger.get_logger(__name__)
|
||||
|
||||
|
||||
class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
|
||||
class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlNetMixin):
|
||||
"""
|
||||
A ControlNet model.
|
||||
|
||||
|
||||
@ -5,14 +5,14 @@ pip install <path_to_git_source>.
|
||||
import os
|
||||
import platform
|
||||
from distutils.version import LooseVersion
|
||||
from importlib.metadata import PackageNotFoundError, distribution, distributions
|
||||
|
||||
import pkg_resources
|
||||
import psutil
|
||||
import requests
|
||||
from rich import box, print
|
||||
from rich.console import Console, group
|
||||
from rich.panel import Panel
|
||||
from rich.prompt import Prompt
|
||||
from rich.prompt import Confirm, Prompt
|
||||
from rich.style import Style
|
||||
|
||||
from invokeai.version import __version__
|
||||
@ -61,6 +61,65 @@ def get_pypi_versions():
|
||||
return latest_version, latest_release_candidate, versions
|
||||
|
||||
|
||||
def get_torch_extra_index_url() -> str | None:
|
||||
"""
|
||||
Determine torch wheel source URL and optional modules based on the user's OS.
|
||||
"""
|
||||
|
||||
resolved_url = None
|
||||
|
||||
# In all other cases (like MacOS (MPS) or Linux+CUDA), there is no need to specify the extra index URL.
|
||||
torch_package_urls = {
|
||||
"windows_cuda": "https://download.pytorch.org/whl/cu121",
|
||||
"linux_rocm": "https://download.pytorch.org/whl/rocm5.6",
|
||||
"linux_cpu": "https://download.pytorch.org/whl/cpu",
|
||||
}
|
||||
|
||||
nvidia_packages_present = (
|
||||
len([d.metadata["Name"] for d in distributions() if d.metadata["Name"].startswith("nvidia")]) > 0
|
||||
)
|
||||
device = "cuda" if nvidia_packages_present else None
|
||||
manual_gpu_selection_prompt = (
|
||||
"[bold]We tried and failed to guess your GPU capabilities[/] :thinking_face:. Please select the GPU type:"
|
||||
)
|
||||
|
||||
if OS == "Linux":
|
||||
if not device:
|
||||
# do we even need to offer a CPU-only install option?
|
||||
print(manual_gpu_selection_prompt)
|
||||
print("1: NVIDIA (CUDA)")
|
||||
print("2: AMD (ROCm)")
|
||||
print("3: No GPU - CPU only")
|
||||
answer = Prompt.ask("Choice:", choices=["1", "2", "3"], default="1")
|
||||
match answer:
|
||||
case "1":
|
||||
device = "cuda"
|
||||
case "2":
|
||||
device = "rocm"
|
||||
case "3":
|
||||
device = "cpu"
|
||||
|
||||
if device != "cuda":
|
||||
resolved_url = torch_package_urls[f"linux_{device}"]
|
||||
|
||||
if OS == "Windows":
|
||||
if not device:
|
||||
print(manual_gpu_selection_prompt)
|
||||
print("1: NVIDIA (CUDA)")
|
||||
print("2: No GPU - CPU only")
|
||||
answer = Prompt.ask("Your choice:", choices=["1", "2"], default="1")
|
||||
match answer:
|
||||
case "1":
|
||||
device = "cuda"
|
||||
case "2":
|
||||
device = "cpu"
|
||||
|
||||
if device == "cuda":
|
||||
resolved_url = torch_package_urls[f"windows_{device}"]
|
||||
|
||||
return resolved_url
|
||||
|
||||
|
||||
def welcome(latest_release: str, latest_prerelease: str):
|
||||
@group()
|
||||
def text():
|
||||
@ -89,12 +148,11 @@ def welcome(latest_release: str, latest_prerelease: str):
|
||||
|
||||
|
||||
def get_extras():
|
||||
extras = ""
|
||||
try:
|
||||
_ = pkg_resources.get_distribution("xformers")
|
||||
distribution("xformers")
|
||||
extras = "[xformers]"
|
||||
except pkg_resources.DistributionNotFound:
|
||||
pass
|
||||
except PackageNotFoundError:
|
||||
extras = ""
|
||||
return extras
|
||||
|
||||
|
||||
@ -125,8 +183,22 @@ def main():
|
||||
|
||||
extras = get_extras()
|
||||
|
||||
console.line()
|
||||
force_reinstall = Confirm.ask(
|
||||
"[bold]Force reinstallation of all dependencies?[/] This [i]may[/] help fix a broken upgrade, but is usually not necessary.",
|
||||
default=False,
|
||||
)
|
||||
|
||||
console.line()
|
||||
flags = []
|
||||
if (index_url := get_torch_extra_index_url()) is not None:
|
||||
flags.append(f"--extra-index-url {index_url}")
|
||||
if force_reinstall:
|
||||
flags.append("--force-reinstall")
|
||||
flags = " ".join(flags)
|
||||
|
||||
print(f":crossed_fingers: Upgrading to [yellow]{release}[/yellow]")
|
||||
cmd = f'pip install "invokeai{extras}=={release}" --use-pep517 --upgrade'
|
||||
cmd = f'pip install "invokeai{extras}=={release}" --use-pep517 --upgrade {flags}'
|
||||
|
||||
print("")
|
||||
print("")
|
||||
|
||||
@ -1,9 +1,26 @@
|
||||
module.exports = {
|
||||
extends: ['@invoke-ai/eslint-config-react'],
|
||||
plugins: ['path', 'i18next'],
|
||||
rules: {
|
||||
// TODO(psyche): Enable this rule. Requires no default exports in components - many changes.
|
||||
'react-refresh/only-export-components': 'off',
|
||||
// TODO(psyche): Enable this rule. Requires a lot of eslint-disable-next-line comments.
|
||||
'@typescript-eslint/consistent-type-assertions': 'off',
|
||||
// https://github.com/qdanik/eslint-plugin-path
|
||||
'path/no-relative-imports': ['error', { maxDepth: 0 }],
|
||||
// https://github.com/edvardchen/eslint-plugin-i18next/blob/HEAD/docs/rules/no-literal-string.md
|
||||
'i18next/no-literal-string': 'error',
|
||||
},
|
||||
overrides: [
|
||||
/**
|
||||
* Overrides for stories
|
||||
*/
|
||||
{
|
||||
files: ['*.stories.tsx'],
|
||||
rules: {
|
||||
// We may not have i18n available in stories.
|
||||
'i18next/no-literal-string': 'off',
|
||||
},
|
||||
},
|
||||
],
|
||||
};
|
||||
|
||||
@ -111,7 +111,7 @@
|
||||
},
|
||||
"devDependencies": {
|
||||
"@arthurgeron/eslint-plugin-react-usememo": "^2.2.3",
|
||||
"@invoke-ai/eslint-config-react": "^0.0.12",
|
||||
"@invoke-ai/eslint-config-react": "^0.0.13",
|
||||
"@invoke-ai/prettier-config-react": "^0.0.6",
|
||||
"@storybook/addon-docs": "^7.6.10",
|
||||
"@storybook/addon-essentials": "^7.6.10",
|
||||
|
||||
@ -178,8 +178,8 @@ devDependencies:
|
||||
specifier: ^2.2.3
|
||||
version: 2.2.3
|
||||
'@invoke-ai/eslint-config-react':
|
||||
specifier: ^0.0.12
|
||||
version: 0.0.12(@typescript-eslint/eslint-plugin@6.19.0)(@typescript-eslint/parser@6.19.0)(eslint-config-prettier@9.1.0)(eslint-plugin-i18next@6.0.3)(eslint-plugin-import@2.29.1)(eslint-plugin-react-hooks@4.6.0)(eslint-plugin-react-refresh@0.4.5)(eslint-plugin-react@7.33.2)(eslint-plugin-simple-import-sort@10.0.0)(eslint-plugin-storybook@0.6.15)(eslint-plugin-unused-imports@3.0.0)(eslint@8.56.0)
|
||||
specifier: ^0.0.13
|
||||
version: 0.0.13(@typescript-eslint/eslint-plugin@6.19.0)(@typescript-eslint/parser@6.19.0)(eslint-config-prettier@9.1.0)(eslint-plugin-import@2.29.1)(eslint-plugin-react-hooks@4.6.0)(eslint-plugin-react-refresh@0.4.5)(eslint-plugin-react@7.33.2)(eslint-plugin-simple-import-sort@10.0.0)(eslint-plugin-storybook@0.6.15)(eslint-plugin-unused-imports@3.0.0)(eslint@8.56.0)
|
||||
'@invoke-ai/prettier-config-react':
|
||||
specifier: ^0.0.6
|
||||
version: 0.0.6(prettier@3.2.4)
|
||||
@ -3551,14 +3551,13 @@ packages:
|
||||
'@swc/helpers': 0.5.3
|
||||
dev: false
|
||||
|
||||
/@invoke-ai/eslint-config-react@0.0.12(@typescript-eslint/eslint-plugin@6.19.0)(@typescript-eslint/parser@6.19.0)(eslint-config-prettier@9.1.0)(eslint-plugin-i18next@6.0.3)(eslint-plugin-import@2.29.1)(eslint-plugin-react-hooks@4.6.0)(eslint-plugin-react-refresh@0.4.5)(eslint-plugin-react@7.33.2)(eslint-plugin-simple-import-sort@10.0.0)(eslint-plugin-storybook@0.6.15)(eslint-plugin-unused-imports@3.0.0)(eslint@8.56.0):
|
||||
resolution: {integrity: sha512-6IXENcSa7vv+YPO/TYmC8qXXJFQt3JqDY+Yc1AMf4/d3b3o+CA7/mqepXIhydG9Gqo5jTRknXdDmjSaLxgCJ/g==}
|
||||
/@invoke-ai/eslint-config-react@0.0.13(@typescript-eslint/eslint-plugin@6.19.0)(@typescript-eslint/parser@6.19.0)(eslint-config-prettier@9.1.0)(eslint-plugin-import@2.29.1)(eslint-plugin-react-hooks@4.6.0)(eslint-plugin-react-refresh@0.4.5)(eslint-plugin-react@7.33.2)(eslint-plugin-simple-import-sort@10.0.0)(eslint-plugin-storybook@0.6.15)(eslint-plugin-unused-imports@3.0.0)(eslint@8.56.0):
|
||||
resolution: {integrity: sha512-dfo9k+wPHdvpy1z6ABoYXR/Ttzs1FAnbC46ttIxVhZuqDq8K5cLWznivrOfl7f0hJb8Cb8HiuQb4pHDxhHBDqA==}
|
||||
peerDependencies:
|
||||
'@typescript-eslint/eslint-plugin': ^6.19.0
|
||||
'@typescript-eslint/parser': ^6.19.0
|
||||
eslint: ^8.56.0
|
||||
eslint-config-prettier: ^9.1.0
|
||||
eslint-plugin-i18next: ^6.0.3
|
||||
eslint-plugin-import: ^2.29.1
|
||||
eslint-plugin-react: ^7.33.2
|
||||
eslint-plugin-react-hooks: ^4.6.0
|
||||
@ -3571,7 +3570,6 @@ packages:
|
||||
'@typescript-eslint/parser': 6.19.0(eslint@8.56.0)(typescript@5.3.3)
|
||||
eslint: 8.56.0
|
||||
eslint-config-prettier: 9.1.0(eslint@8.56.0)
|
||||
eslint-plugin-i18next: 6.0.3
|
||||
eslint-plugin-import: 2.29.1(@typescript-eslint/parser@6.19.0)(eslint@8.56.0)
|
||||
eslint-plugin-react: 7.33.2(eslint@8.56.0)
|
||||
eslint-plugin-react-hooks: 4.6.0(eslint@8.56.0)
|
||||
|
||||
@ -98,7 +98,7 @@
|
||||
"outputs": "Ausgabe",
|
||||
"data": "Daten",
|
||||
"safetensors": "Safetensors",
|
||||
"outpaint": "outpaint",
|
||||
"outpaint": "Ausmalen",
|
||||
"details": "Details",
|
||||
"format": "Format",
|
||||
"unknown": "Unbekannt",
|
||||
@ -131,7 +131,8 @@
|
||||
"localSystem": "Lokales System",
|
||||
"orderBy": "Ordnen nach",
|
||||
"saveAs": "Speicher als",
|
||||
"updated": "Aktualisiert"
|
||||
"updated": "Aktualisiert",
|
||||
"copy": "Kopieren"
|
||||
},
|
||||
"gallery": {
|
||||
"generations": "Erzeugungen",
|
||||
@ -161,7 +162,13 @@
|
||||
"currentlyInUse": "Dieses Bild wird derzeit in den folgenden Funktionen verwendet:",
|
||||
"deleteImagePermanent": "Gelöschte Bilder können nicht wiederhergestellt werden.",
|
||||
"autoAssignBoardOnClick": "Board per Klick automatisch zuweisen",
|
||||
"noImageSelected": "Kein Bild ausgewählt"
|
||||
"noImageSelected": "Kein Bild ausgewählt",
|
||||
"problemDeletingImagesDesc": "Eins oder mehr Bilder könnten nicht gelöscht werden",
|
||||
"starImage": "Bild markieren",
|
||||
"assets": "Ressourcen",
|
||||
"unstarImage": "Markierung Entfernen",
|
||||
"image": "Bild",
|
||||
"deleteSelection": "Lösche markierte"
|
||||
},
|
||||
"hotkeys": {
|
||||
"keyboardShortcuts": "Tastenkürzel",
|
||||
@ -365,7 +372,13 @@
|
||||
"addNodes": {
|
||||
"title": "Knotenpunkt hinzufügen",
|
||||
"desc": "Öffnet das Menü zum Hinzufügen von Knoten"
|
||||
}
|
||||
},
|
||||
"cancelAndClear": {
|
||||
"title": "Abbruch und leeren"
|
||||
},
|
||||
"noHotkeysFound": "Kein Hotkey gefunden",
|
||||
"searchHotkeys": "Hotkeys durchsuchen",
|
||||
"clearSearch": "Suche leeren"
|
||||
},
|
||||
"modelManager": {
|
||||
"modelAdded": "Model hinzugefügt",
|
||||
@ -832,7 +845,13 @@
|
||||
"hedDescription": "Ganzheitlich verschachtelte Kantenerkennung",
|
||||
"scribble": "Scribble",
|
||||
"maxFaces": "Maximal Anzahl Gesichter",
|
||||
"resizeSimple": "Größe ändern (einfach)"
|
||||
"resizeSimple": "Größe ändern (einfach)",
|
||||
"large": "Groß",
|
||||
"modelSize": "Modell Größe",
|
||||
"small": "Klein",
|
||||
"base": "Basis",
|
||||
"depthAnything": "Depth Anything",
|
||||
"depthAnythingDescription": "Erstellung einer Tiefenkarte mit der Depth Anything-Technik"
|
||||
},
|
||||
"queue": {
|
||||
"status": "Status",
|
||||
@ -865,7 +884,7 @@
|
||||
"item": "Auftrag",
|
||||
"notReady": "Warteschlange noch nicht bereit",
|
||||
"batchValues": "Stapel Werte",
|
||||
"queueCountPrediction": "{{predicted}} zur Warteschlange hinzufügen",
|
||||
"queueCountPrediction": "{{promptsCount}} Prompts × {{iterations}} Iterationen -> {{count}} Generationen",
|
||||
"queuedCount": "{{pending}} wartenden Elemente",
|
||||
"clearQueueAlertDialog": "Die Warteschlange leeren, stoppt den aktuellen Prozess und leert die Warteschlange komplett.",
|
||||
"completedIn": "Fertig in",
|
||||
@ -887,7 +906,9 @@
|
||||
"back": "Hinten",
|
||||
"resumeSucceeded": "Prozessor wieder aufgenommen",
|
||||
"resumeTooltip": "Prozessor wieder aufnehmen",
|
||||
"time": "Zeit"
|
||||
"time": "Zeit",
|
||||
"batchQueuedDesc_one": "{{count}} Eintrage ans {{direction}} der Wartschlange hinzugefügt",
|
||||
"batchQueuedDesc_other": "{{count}} Einträge ans {{direction}} der Wartschlange hinzugefügt"
|
||||
},
|
||||
"metadata": {
|
||||
"negativePrompt": "Negativ Beschreibung",
|
||||
@ -956,7 +977,8 @@
|
||||
"enable": "Aktivieren",
|
||||
"clear": "Leeren",
|
||||
"maxCacheSize": "Maximale Cache Größe",
|
||||
"cacheSize": "Cache Größe"
|
||||
"cacheSize": "Cache Größe",
|
||||
"useCache": "Benutze Cache"
|
||||
},
|
||||
"embedding": {
|
||||
"noMatchingEmbedding": "Keine passenden Embeddings",
|
||||
@ -1042,7 +1064,8 @@
|
||||
},
|
||||
"compositing": {
|
||||
"coherenceTab": "Kohärenzpass",
|
||||
"infillTab": "Füllung"
|
||||
"infillTab": "Füllung",
|
||||
"title": "Compositing"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -1376,6 +1376,7 @@
|
||||
"problemCopyingCanvasDesc": "Unable to export base layer",
|
||||
"problemCopyingImage": "Unable to Copy Image",
|
||||
"problemCopyingImageLink": "Unable to Copy Image Link",
|
||||
"problemDownloadingImage": "Unable to Download Image",
|
||||
"problemDownloadingCanvas": "Problem Downloading Canvas",
|
||||
"problemDownloadingCanvasDesc": "Unable to export base layer",
|
||||
"problemImportingMask": "Problem Importing Mask",
|
||||
|
||||
43
invokeai/frontend/web/src/common/hooks/useDownloadImage.ts
Normal file
43
invokeai/frontend/web/src/common/hooks/useDownloadImage.ts
Normal file
@ -0,0 +1,43 @@
|
||||
import { useAppToaster } from 'app/components/Toaster';
|
||||
import { useCallback } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
|
||||
import { useImageUrlToBlob } from './useImageUrlToBlob';
|
||||
|
||||
export const useDownloadImage = () => {
|
||||
const toaster = useAppToaster();
|
||||
const { t } = useTranslation();
|
||||
const imageUrlToBlob = useImageUrlToBlob();
|
||||
|
||||
const downloadImage = useCallback(
|
||||
async (image_url: string, image_name: string) => {
|
||||
try {
|
||||
const blob = await imageUrlToBlob(image_url);
|
||||
|
||||
if (!blob) {
|
||||
throw new Error('Unable to create Blob');
|
||||
}
|
||||
|
||||
const url = window.URL.createObjectURL(blob);
|
||||
const a = document.createElement('a');
|
||||
a.style.display = 'none';
|
||||
a.href = url;
|
||||
a.download = image_name;
|
||||
document.body.appendChild(a);
|
||||
a.click();
|
||||
window.URL.revokeObjectURL(url);
|
||||
} catch (err) {
|
||||
toaster({
|
||||
title: t('toast.problemDownloadingImage'),
|
||||
description: String(err),
|
||||
status: 'error',
|
||||
duration: 2500,
|
||||
isClosable: true,
|
||||
});
|
||||
}
|
||||
},
|
||||
[t, toaster, imageUrlToBlob]
|
||||
);
|
||||
|
||||
return { downloadImage };
|
||||
};
|
||||
@ -4,6 +4,7 @@ import { useAppToaster } from 'app/components/Toaster';
|
||||
import { $customStarUI } from 'app/store/nanostores/customStarUI';
|
||||
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import { useCopyImageToClipboard } from 'common/hooks/useCopyImageToClipboard';
|
||||
import { useDownloadImage } from 'common/hooks/useDownloadImage';
|
||||
import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
|
||||
import { imagesToChangeSelected, isModalOpenChanged } from 'features/changeBoardModal/store/slice';
|
||||
import { imagesToDeleteSelected } from 'features/deleteImageModal/store/slice';
|
||||
@ -47,7 +48,7 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
|
||||
const toaster = useAppToaster();
|
||||
const isCanvasEnabled = useFeatureStatus('unifiedCanvas').isFeatureEnabled;
|
||||
const customStarUi = useStore($customStarUI);
|
||||
|
||||
const { downloadImage } = useDownloadImage();
|
||||
const { metadata, isLoading: isLoadingMetadata } = useDebouncedMetadata(imageDTO?.image_name);
|
||||
|
||||
const { getAndLoadEmbeddedWorkflow, getAndLoadEmbeddedWorkflowResult } = useGetAndLoadEmbeddedWorkflow({});
|
||||
@ -143,6 +144,10 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
|
||||
}
|
||||
}, [unstarImages, imageDTO]);
|
||||
|
||||
const handleDownloadImage = useCallback(() => {
|
||||
downloadImage(imageDTO.image_url, imageDTO.image_name);
|
||||
}, [downloadImage, imageDTO.image_name, imageDTO.image_url]);
|
||||
|
||||
return (
|
||||
<>
|
||||
<MenuItem as="a" href={imageDTO.image_url} target="_blank" icon={<PiShareFatBold />}>
|
||||
@ -153,14 +158,7 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
|
||||
{t('parameters.copyImage')}
|
||||
</MenuItem>
|
||||
)}
|
||||
<MenuItem
|
||||
as="a"
|
||||
download={true}
|
||||
href={imageDTO.image_url}
|
||||
target="_blank"
|
||||
icon={<PiDownloadSimpleBold />}
|
||||
w="100%"
|
||||
>
|
||||
<MenuItem icon={<PiDownloadSimpleBold />} onClickCapture={handleDownloadImage}>
|
||||
{t('parameters.downloadImage')}
|
||||
</MenuItem>
|
||||
<MenuDivider />
|
||||
|
||||
@ -1,13 +1,12 @@
|
||||
import { ConfirmationAlertDialog, Flex, IconButton, Text, useDisclosure } from '@invoke-ai/ui-library';
|
||||
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
|
||||
import { addToast } from 'features/system/store/systemSlice';
|
||||
import { makeToast } from 'features/system/util/makeToast';
|
||||
import { memo, useCallback } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import { PiTrashSimpleFill } from 'react-icons/pi';
|
||||
|
||||
import { addToast } from '../../../../../system/store/systemSlice';
|
||||
import { makeToast } from '../../../../../system/util/makeToast';
|
||||
import { nodeEditorReset } from '../../../../store/nodesSlice';
|
||||
|
||||
const ClearFlowButton = () => {
|
||||
const dispatch = useAppDispatch();
|
||||
const { t } = useTranslation();
|
||||
|
||||
@ -1,13 +1,12 @@
|
||||
import { IconButton } from '@invoke-ai/ui-library';
|
||||
import { useAppSelector } from 'app/store/storeHooks';
|
||||
import { $builtWorkflow } from 'features/nodes/hooks/useWorkflowWatcher';
|
||||
import { useSaveWorkflowAsDialog } from 'features/workflowLibrary/components/SaveWorkflowAsDialog/useSaveWorkflowAsDialog';
|
||||
import { isWorkflowWithID, useSaveLibraryWorkflow } from 'features/workflowLibrary/hooks/useSaveWorkflow';
|
||||
import { memo, useCallback } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import { PiFloppyDiskBold } from 'react-icons/pi';
|
||||
|
||||
import { isWorkflowWithID, useSaveLibraryWorkflow } from '../../../../../workflowLibrary/hooks/useSaveWorkflow';
|
||||
import { $builtWorkflow } from '../../../../hooks/useWorkflowWatcher';
|
||||
|
||||
const SaveWorkflowButton = () => {
|
||||
const { t } = useTranslation();
|
||||
const isTouched = useAppSelector((s) => s.workflow.isTouched);
|
||||
|
||||
@ -26,7 +26,7 @@ import { ImageSizeLinear } from './ImageSizeLinear';
|
||||
const selector = createMemoizedSelector(
|
||||
[selectGenerationSlice, selectCanvasSlice, selectHrfSlice, activeTabNameSelector],
|
||||
(generation, canvas, hrf, activeTabName) => {
|
||||
const { shouldRandomizeSeed } = generation;
|
||||
const { shouldRandomizeSeed, model } = generation;
|
||||
const { hrfEnabled } = hrf;
|
||||
const badges: string[] = [];
|
||||
|
||||
@ -56,7 +56,7 @@ const selector = createMemoizedSelector(
|
||||
if (hrfEnabled) {
|
||||
badges.push('HiRes Fix');
|
||||
}
|
||||
return { badges, activeTabName };
|
||||
return { badges, activeTabName, isSDXL: model?.base_model === 'sdxl' };
|
||||
}
|
||||
);
|
||||
|
||||
@ -66,7 +66,7 @@ const scalingLabelProps: FormLabelProps = {
|
||||
|
||||
export const ImageSettingsAccordion = memo(() => {
|
||||
const { t } = useTranslation();
|
||||
const { badges, activeTabName } = useAppSelector(selector);
|
||||
const { badges, activeTabName, isSDXL } = useAppSelector(selector);
|
||||
const { isOpen: isOpenAccordion, onToggle: onToggleAccordion } = useStandaloneAccordionToggle({
|
||||
id: 'image-settings',
|
||||
defaultIsOpen: true,
|
||||
@ -94,7 +94,7 @@ export const ImageSettingsAccordion = memo(() => {
|
||||
</Flex>
|
||||
{(activeTabName === 'img2img' || activeTabName === 'unifiedCanvas') && <ImageToImageStrength />}
|
||||
{activeTabName === 'img2img' && <ImageToImageFit />}
|
||||
{activeTabName === 'txt2img' && <HrfSettings />}
|
||||
{activeTabName === 'txt2img' && !isSDXL && <HrfSettings />}
|
||||
{activeTabName === 'unifiedCanvas' && (
|
||||
<>
|
||||
<ParamScaleBeforeProcessing />
|
||||
|
||||
@ -13,14 +13,13 @@ import {
|
||||
Input,
|
||||
} from '@invoke-ai/ui-library';
|
||||
import { useStore } from '@nanostores/react';
|
||||
import { $workflowCategories } from 'app/store/nanostores/workflowCategories';
|
||||
import { useSaveWorkflowAsDialog } from 'features/workflowLibrary/components/SaveWorkflowAsDialog/useSaveWorkflowAsDialog';
|
||||
import { useSaveWorkflowAs } from 'features/workflowLibrary/hooks/useSaveWorkflowAs';
|
||||
import { t } from 'i18next';
|
||||
import type { ChangeEvent } from 'react';
|
||||
import { useCallback, useRef } from 'react';
|
||||
|
||||
import { $workflowCategories } from '../../../../app/store/nanostores/workflowCategories';
|
||||
import { useSaveWorkflowAs } from '../../hooks/useSaveWorkflowAs';
|
||||
|
||||
export const SaveWorkflowAsDialog = () => {
|
||||
const { isOpen, onClose, workflowName, setWorkflowName, shouldSaveToProject, setShouldSaveToProject } =
|
||||
useSaveWorkflowAsDialog();
|
||||
|
||||
@ -1,13 +1,12 @@
|
||||
import { MenuItem } from '@invoke-ai/ui-library';
|
||||
import { useAppSelector } from 'app/store/storeHooks';
|
||||
import { $builtWorkflow } from 'features/nodes/hooks/useWorkflowWatcher';
|
||||
import { useSaveWorkflowAsDialog } from 'features/workflowLibrary/components/SaveWorkflowAsDialog/useSaveWorkflowAsDialog';
|
||||
import { isWorkflowWithID, useSaveLibraryWorkflow } from 'features/workflowLibrary/hooks/useSaveWorkflow';
|
||||
import { memo, useCallback } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import { PiFloppyDiskBold } from 'react-icons/pi';
|
||||
|
||||
import { useAppSelector } from '../../../../app/store/storeHooks';
|
||||
import { $builtWorkflow } from '../../../nodes/hooks/useWorkflowWatcher';
|
||||
|
||||
const SaveWorkflowMenuItem = () => {
|
||||
const { t } = useTranslation();
|
||||
const { saveWorkflow } = useSaveLibraryWorkflow();
|
||||
|
||||
@ -8,12 +8,11 @@ import {
|
||||
workflowNameChanged,
|
||||
workflowSaved,
|
||||
} from 'features/nodes/store/workflowSlice';
|
||||
import type { WorkflowCategory } from 'features/nodes/types/workflow';
|
||||
import { useCallback, useRef } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import { useCreateWorkflowMutation, workflowsApi } from 'services/api/endpoints/workflows';
|
||||
|
||||
import type { WorkflowCategory } from '../../nodes/types/workflow';
|
||||
|
||||
type SaveWorkflowAsArg = {
|
||||
name: string;
|
||||
category: WorkflowCategory;
|
||||
|
||||
@ -107,7 +107,6 @@ plugins:
|
||||
extra_javascript:
|
||||
- https://unpkg.com/tablesort@5.3.0/dist/tablesort.min.js
|
||||
- javascripts/tablesort.js
|
||||
- https://widget.kapa.ai/kapa-widget.bundle.js
|
||||
- javascript/init_kapa_widget.js
|
||||
|
||||
extra:
|
||||
|
||||
@ -38,7 +38,7 @@ dependencies = [
|
||||
"clip_anytorch==2.5.2", # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
|
||||
"compel==2.0.2",
|
||||
"controlnet-aux==0.0.7",
|
||||
"diffusers[torch]==0.25.1",
|
||||
"diffusers[torch]==0.26.0",
|
||||
"invisible-watermark==0.2.0", # needed to install SDXL base and refiner using their repo_ids
|
||||
"mediapipe==0.10.7", # needed for "mediapipeface" controlnet model
|
||||
"numpy==1.26.3", # >1.24.0 is needed to use the 'strict' argument to np.testing.assert_array_equal()
|
||||
|
||||
@ -2,6 +2,8 @@ import logging
|
||||
|
||||
import pytest
|
||||
|
||||
from invokeai.app.services.item_storage.item_storage_memory import ItemStorageMemory
|
||||
|
||||
# This import must happen before other invoke imports or test in other files(!!) break
|
||||
from .test_nodes import ( # isort: split
|
||||
PromptCollectionTestInvocation,
|
||||
@ -19,7 +21,6 @@ from invokeai.app.services.invocation_processor.invocation_processor_default imp
|
||||
from invokeai.app.services.invocation_queue.invocation_queue_memory import MemoryInvocationQueue
|
||||
from invokeai.app.services.invocation_services import InvocationServices
|
||||
from invokeai.app.services.invocation_stats.invocation_stats_default import InvocationStatsService
|
||||
from invokeai.app.services.item_storage.item_storage_sqlite import SqliteItemStorage
|
||||
from invokeai.app.services.session_queue.session_queue_common import DEFAULT_QUEUE_ID
|
||||
from invokeai.app.services.shared.graph import (
|
||||
CollectInvocation,
|
||||
@ -27,8 +28,6 @@ from invokeai.app.services.shared.graph import (
|
||||
GraphExecutionState,
|
||||
IterateInvocation,
|
||||
)
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
from tests.fixtures.sqlite_database import create_mock_sqlite_database
|
||||
|
||||
from .test_invoker import create_edge
|
||||
|
||||
@ -48,10 +47,8 @@ def simple_graph():
|
||||
@pytest.fixture
|
||||
def mock_services() -> InvocationServices:
|
||||
configuration = InvokeAIAppConfig(use_memory_db=True, node_cache_size=0)
|
||||
logger = InvokeAILogger.get_logger()
|
||||
db = create_mock_sqlite_database(configuration, logger)
|
||||
# NOTE: none of these are actually called by the test invocations
|
||||
graph_execution_manager = SqliteItemStorage[GraphExecutionState](db=db, table_name="graph_executions")
|
||||
graph_execution_manager = ItemStorageMemory[GraphExecutionState]()
|
||||
return InvocationServices(
|
||||
board_image_records=None, # type: ignore
|
||||
board_images=None, # type: ignore
|
||||
|
||||
@ -3,8 +3,7 @@ import logging
|
||||
import pytest
|
||||
|
||||
from invokeai.app.services.config.config_default import InvokeAIAppConfig
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
from tests.fixtures.sqlite_database import create_mock_sqlite_database
|
||||
from invokeai.app.services.item_storage.item_storage_memory import ItemStorageMemory
|
||||
|
||||
# This import must happen before other invoke imports or test in other files(!!) break
|
||||
from .test_nodes import ( # isort: split
|
||||
@ -22,7 +21,6 @@ from invokeai.app.services.invocation_queue.invocation_queue_memory import Memor
|
||||
from invokeai.app.services.invocation_services import InvocationServices
|
||||
from invokeai.app.services.invocation_stats.invocation_stats_default import InvocationStatsService
|
||||
from invokeai.app.services.invoker import Invoker
|
||||
from invokeai.app.services.item_storage.item_storage_sqlite import SqliteItemStorage
|
||||
from invokeai.app.services.session_queue.session_queue_common import DEFAULT_QUEUE_ID
|
||||
from invokeai.app.services.shared.graph import Graph, GraphExecutionState, GraphInvocation
|
||||
|
||||
@ -53,11 +51,6 @@ def graph_with_subgraph():
|
||||
@pytest.fixture
|
||||
def mock_services() -> InvocationServices:
|
||||
configuration = InvokeAIAppConfig(use_memory_db=True, node_cache_size=0)
|
||||
logger = InvokeAILogger.get_logger()
|
||||
db = create_mock_sqlite_database(configuration, logger)
|
||||
|
||||
# NOTE: none of these are actually called by the test invocations
|
||||
graph_execution_manager = SqliteItemStorage[GraphExecutionState](db=db, table_name="graph_executions")
|
||||
return InvocationServices(
|
||||
board_image_records=None, # type: ignore
|
||||
board_images=None, # type: ignore
|
||||
@ -65,7 +58,7 @@ def mock_services() -> InvocationServices:
|
||||
boards=None, # type: ignore
|
||||
configuration=configuration,
|
||||
events=TestEventService(),
|
||||
graph_execution_manager=graph_execution_manager,
|
||||
graph_execution_manager=ItemStorageMemory[GraphExecutionState](),
|
||||
image_files=None, # type: ignore
|
||||
image_records=None, # type: ignore
|
||||
images=None, # type: ignore
|
||||
|
||||
@ -1,139 +0,0 @@
|
||||
import pytest
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from invokeai.app.services.config.config_default import InvokeAIAppConfig
|
||||
from invokeai.app.services.item_storage.item_storage_sqlite import SqliteItemStorage
|
||||
from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
|
||||
|
||||
class TestModel(BaseModel):
|
||||
id: str = Field(description="ID")
|
||||
name: str = Field(description="Name")
|
||||
__test__ = False # not a pytest test case
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def db() -> SqliteItemStorage[TestModel]:
|
||||
config = InvokeAIAppConfig(use_memory_db=True)
|
||||
logger = InvokeAILogger.get_logger()
|
||||
db_path = None if config.use_memory_db else config.db_path
|
||||
db = SqliteDatabase(db_path=db_path, logger=logger, verbose=config.log_sql)
|
||||
sqlite_item_storage = SqliteItemStorage[TestModel](db=db, table_name="test", id_field="id")
|
||||
return sqlite_item_storage
|
||||
|
||||
|
||||
def test_sqlite_service_can_create_and_get(db: SqliteItemStorage[TestModel]):
|
||||
db.set(TestModel(id="1", name="Test"))
|
||||
assert db.get("1") == TestModel(id="1", name="Test")
|
||||
|
||||
|
||||
def test_sqlite_service_can_list(db: SqliteItemStorage[TestModel]):
|
||||
db.set(TestModel(id="1", name="Test"))
|
||||
db.set(TestModel(id="2", name="Test"))
|
||||
db.set(TestModel(id="3", name="Test"))
|
||||
results = db.list()
|
||||
assert results.page == 0
|
||||
assert results.pages == 1
|
||||
assert results.per_page == 10
|
||||
assert results.total == 3
|
||||
assert results.items == [
|
||||
TestModel(id="1", name="Test"),
|
||||
TestModel(id="2", name="Test"),
|
||||
TestModel(id="3", name="Test"),
|
||||
]
|
||||
|
||||
|
||||
def test_sqlite_service_can_delete(db: SqliteItemStorage[TestModel]):
|
||||
db.set(TestModel(id="1", name="Test"))
|
||||
db.delete("1")
|
||||
assert db.get("1") is None
|
||||
|
||||
|
||||
def test_sqlite_service_calls_set_callback(db: SqliteItemStorage[TestModel]):
|
||||
called = False
|
||||
|
||||
def on_changed(item: TestModel):
|
||||
nonlocal called
|
||||
called = True
|
||||
|
||||
db.on_changed(on_changed)
|
||||
db.set(TestModel(id="1", name="Test"))
|
||||
assert called
|
||||
|
||||
|
||||
def test_sqlite_service_calls_delete_callback(db: SqliteItemStorage[TestModel]):
|
||||
called = False
|
||||
|
||||
def on_deleted(item_id: str):
|
||||
nonlocal called
|
||||
called = True
|
||||
|
||||
db.on_deleted(on_deleted)
|
||||
db.set(TestModel(id="1", name="Test"))
|
||||
db.delete("1")
|
||||
assert called
|
||||
|
||||
|
||||
def test_sqlite_service_can_list_with_pagination(db: SqliteItemStorage[TestModel]):
|
||||
db.set(TestModel(id="1", name="Test"))
|
||||
db.set(TestModel(id="2", name="Test"))
|
||||
db.set(TestModel(id="3", name="Test"))
|
||||
results = db.list(page=0, per_page=2)
|
||||
assert results.page == 0
|
||||
assert results.pages == 2
|
||||
assert results.per_page == 2
|
||||
assert results.total == 3
|
||||
assert results.items == [TestModel(id="1", name="Test"), TestModel(id="2", name="Test")]
|
||||
|
||||
|
||||
def test_sqlite_service_can_list_with_pagination_and_offset(db: SqliteItemStorage[TestModel]):
|
||||
db.set(TestModel(id="1", name="Test"))
|
||||
db.set(TestModel(id="2", name="Test"))
|
||||
db.set(TestModel(id="3", name="Test"))
|
||||
results = db.list(page=1, per_page=2)
|
||||
assert results.page == 1
|
||||
assert results.pages == 2
|
||||
assert results.per_page == 2
|
||||
assert results.total == 3
|
||||
assert results.items == [TestModel(id="3", name="Test")]
|
||||
|
||||
|
||||
def test_sqlite_service_can_search(db: SqliteItemStorage[TestModel]):
|
||||
db.set(TestModel(id="1", name="Test"))
|
||||
db.set(TestModel(id="2", name="Test"))
|
||||
db.set(TestModel(id="3", name="Test"))
|
||||
results = db.search(query="Test")
|
||||
assert results.page == 0
|
||||
assert results.pages == 1
|
||||
assert results.per_page == 10
|
||||
assert results.total == 3
|
||||
assert results.items == [
|
||||
TestModel(id="1", name="Test"),
|
||||
TestModel(id="2", name="Test"),
|
||||
TestModel(id="3", name="Test"),
|
||||
]
|
||||
|
||||
|
||||
def test_sqlite_service_can_search_with_pagination(db: SqliteItemStorage[TestModel]):
|
||||
db.set(TestModel(id="1", name="Test"))
|
||||
db.set(TestModel(id="2", name="Test"))
|
||||
db.set(TestModel(id="3", name="Test"))
|
||||
results = db.search(query="Test", page=0, per_page=2)
|
||||
assert results.page == 0
|
||||
assert results.pages == 2
|
||||
assert results.per_page == 2
|
||||
assert results.total == 3
|
||||
assert results.items == [TestModel(id="1", name="Test"), TestModel(id="2", name="Test")]
|
||||
|
||||
|
||||
def test_sqlite_service_can_search_with_pagination_and_offset(db: SqliteItemStorage[TestModel]):
|
||||
db.set(TestModel(id="1", name="Test"))
|
||||
db.set(TestModel(id="2", name="Test"))
|
||||
db.set(TestModel(id="3", name="Test"))
|
||||
results = db.search(query="Test", page=1, per_page=2)
|
||||
assert results.page == 1
|
||||
assert results.pages == 2
|
||||
assert results.per_page == 2
|
||||
assert results.total == 3
|
||||
assert results.items == [TestModel(id="3", name="Test")]
|
||||
110
tests/test_item_storage_memory.py
Normal file
110
tests/test_item_storage_memory.py
Normal file
@ -0,0 +1,110 @@
|
||||
import re
|
||||
|
||||
import pytest
|
||||
from pydantic import BaseModel
|
||||
|
||||
from invokeai.app.services.item_storage.item_storage_memory import ItemStorageMemory
|
||||
|
||||
|
||||
class MockItemModel(BaseModel):
|
||||
id: str
|
||||
value: int
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def item_storage_memory():
|
||||
return ItemStorageMemory[MockItemModel]()
|
||||
|
||||
|
||||
def test_item_storage_memory_initializes():
|
||||
item_storage_memory = ItemStorageMemory()
|
||||
assert item_storage_memory._items == {}
|
||||
assert item_storage_memory._id_field == "id"
|
||||
assert item_storage_memory._max_items == 10
|
||||
|
||||
item_storage_memory = ItemStorageMemory(id_field="bananas", max_items=20)
|
||||
assert item_storage_memory._id_field == "bananas"
|
||||
assert item_storage_memory._max_items == 20
|
||||
|
||||
with pytest.raises(ValueError, match=re.escape("max_items must be at least 1")):
|
||||
item_storage_memory = ItemStorageMemory(max_items=0)
|
||||
with pytest.raises(ValueError, match=re.escape("id_field must not be empty")):
|
||||
item_storage_memory = ItemStorageMemory(id_field="")
|
||||
|
||||
|
||||
def test_item_storage_memory_sets(item_storage_memory: ItemStorageMemory[MockItemModel]):
|
||||
item_1 = MockItemModel(id="1", value=1)
|
||||
item_storage_memory.set(item_1)
|
||||
assert item_storage_memory._items == {"1": item_1}
|
||||
|
||||
item_2 = MockItemModel(id="2", value=2)
|
||||
item_storage_memory.set(item_2)
|
||||
assert item_storage_memory._items == {"1": item_1, "2": item_2}
|
||||
|
||||
# Updating value of existing item
|
||||
item_2_updated = MockItemModel(id="2", value=9001)
|
||||
item_storage_memory.set(item_2_updated)
|
||||
assert item_storage_memory._items == {"1": item_1, "2": item_2_updated}
|
||||
|
||||
|
||||
def test_item_storage_memory_gets(item_storage_memory: ItemStorageMemory[MockItemModel]):
|
||||
item_1 = MockItemModel(id="1", value=1)
|
||||
item_storage_memory.set(item_1)
|
||||
item = item_storage_memory.get("1")
|
||||
assert item == item_1
|
||||
|
||||
item_2 = MockItemModel(id="2", value=2)
|
||||
item_storage_memory.set(item_2)
|
||||
item = item_storage_memory.get("2")
|
||||
assert item == item_2
|
||||
|
||||
item = item_storage_memory.get("3")
|
||||
assert item is None
|
||||
|
||||
|
||||
def test_item_storage_memory_deletes(item_storage_memory: ItemStorageMemory[MockItemModel]):
|
||||
item_1 = MockItemModel(id="1", value=1)
|
||||
item_2 = MockItemModel(id="2", value=2)
|
||||
item_storage_memory.set(item_1)
|
||||
item_storage_memory.set(item_2)
|
||||
|
||||
item_storage_memory.delete("2")
|
||||
assert item_storage_memory._items == {"1": item_1}
|
||||
|
||||
|
||||
def test_item_storage_memory_respects_max():
|
||||
item_storage_memory = ItemStorageMemory(max_items=3)
|
||||
for i in range(10):
|
||||
item_storage_memory.set(MockItemModel(id=str(i), value=i))
|
||||
assert item_storage_memory._items == {
|
||||
"7": MockItemModel(id="7", value=7),
|
||||
"8": MockItemModel(id="8", value=8),
|
||||
"9": MockItemModel(id="9", value=9),
|
||||
}
|
||||
|
||||
|
||||
def test_item_storage_memory_calls_set_callback(item_storage_memory: ItemStorageMemory[MockItemModel]):
|
||||
called_item = None
|
||||
item = MockItemModel(id="1", value=1)
|
||||
|
||||
def on_changed(item: MockItemModel):
|
||||
nonlocal called_item
|
||||
called_item = item
|
||||
|
||||
item_storage_memory.on_changed(on_changed)
|
||||
item_storage_memory.set(item)
|
||||
assert called_item == item
|
||||
|
||||
|
||||
def test_item_storage_memory_calls_delete_callback(item_storage_memory: ItemStorageMemory[MockItemModel]):
|
||||
called_item_id = None
|
||||
item = MockItemModel(id="1", value=1)
|
||||
|
||||
def on_deleted(item_id: str):
|
||||
nonlocal called_item_id
|
||||
called_item_id = item_id
|
||||
|
||||
item_storage_memory.on_deleted(on_deleted)
|
||||
item_storage_memory.set(item)
|
||||
item_storage_memory.delete("1")
|
||||
assert called_item_id == "1"
|
||||
Loading…
Reference in New Issue
Block a user