InvokeAI/invokeai/frontend/install/import_images.py

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# Copyright (c) 2023 - The InvokeAI Team
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# Primary Author: David Lovell (github @f412design, discord @techjedi)
# co-author, minor tweaks - Lincoln Stein
# pylint: disable=line-too-long
# pylint: disable=broad-exception-caught
"""Script to import images into the new database system for 3.0.0"""
import datetime
import glob
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import json
import locale
import os
import re
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import shutil
import sqlite3
from pathlib import Path
import PIL
import PIL.ImageOps
import PIL.PngImagePlugin
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import yaml
from prompt_toolkit import prompt
from prompt_toolkit.completion import PathCompleter
from prompt_toolkit.key_binding import KeyBindings
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from prompt_toolkit.shortcuts import message_dialog
from invokeai.app.services.config.config_default import get_config
feat: queued generation (#4502) * fix(config): fix typing issues in `config/` `config/invokeai_config.py`: - use `Optional` for things that are optional - fix typing of `ram_cache_size()` and `vram_cache_size()` - remove unused and incorrectly typed method `autoconvert_path` - fix types and logic for `parse_args()`, in which `InvokeAIAppConfig.initconf` *must* be a `DictConfig`, but function would allow it to be set as a `ListConfig`, which presumably would cause issues elsewhere `config/base.py`: - use `cls` for first arg of class methods - use `Optional` for things that are optional - fix minor type issue related to setting of `env_prefix` - remove unused `add_subparser()` method, which calls `add_parser()` on an `ArgumentParser` (method only available on the `_SubParsersAction` object, which is returned from ArgumentParser.add_subparsers()`) * feat: queued generation and batches Due to a very messy branch with broad addition of `isort` on `main` alongside it, some git surgery was needed to get an agreeable git history. This commit represents all of the work on queued generation. See PR for notes. * chore: flake8, isort, black * fix(nodes): fix incorrect service stop() method * fix(nodes): improve names of a few variables * fix(tests): fix up tests after changes to batches/queue * feat(tests): add unit tests for session queue helper functions * feat(ui): dynamic prompts is always enabled * feat(queue): add queue_status_changed event * feat(ui): wip queue graphs * feat(nodes): move cleanup til after invoker startup * feat(nodes): add cancel_by_batch_ids * feat(ui): wip batch graphs & UI * fix(nodes): remove `Batch.batch_id` from required * fix(ui): cleanup and use fixedCacheKey for all mutations * fix(ui): remove orphaned nodes from canvas graphs * fix(nodes): fix cancel_by_batch_ids result count * fix(ui): only show cancel batch tooltip when batches were canceled * chore: isort * fix(api): return `[""]` when dynamic prompts generates no prompts Just a simple fallback so we always have a prompt. * feat(ui): dynamicPrompts.combinatorial is always on There seems to be little purpose in using the combinatorial generation for dynamic prompts. I've disabled it by hiding it from the UI and defaulting combinatorial to true. If we want to enable it again in the future it's straightforward to do so. * feat: add queue_id & support logic * feat(ui): fix upscale button It prepends the upscale operation to queue * feat(nodes): return queue item when enqueuing a single graph This facilitates one-off graph async workflows in the client. * feat(ui): move controlnet autoprocess to queue * fix(ui): fix non-serializable DOMRect in redux state * feat(ui): QueueTable performance tweaks * feat(ui): update queue list Queue items expand to show the full queue item. Just as JSON for now. * wip threaded session_processor * feat(nodes,ui): fully migrate queue to session_processor * feat(nodes,ui): add processor events * feat(ui): ui tweaks * feat(nodes,ui): consolidate events, reduce network requests * feat(ui): cleanup & abstract queue hooks * feat(nodes): optimize batch permutation Use a generator to do only as much work as is needed. Previously, though we only ended up creating exactly as many queue items as was needed, there was still some intermediary work that calculated *all* permutations. When that number was very high, the system had a very hard time and used a lot of memory. The logic has been refactored to use a generator. Additionally, the batch validators are optimized to return early and use less memory. * feat(ui): add seed behaviour parameter This dynamic prompts parameter allows the seed to be randomized per prompt or per iteration: - Per iteration: Use the same seed for all prompts in a single dynamic prompt expansion - Per prompt: Use a different seed for every single prompt "Per iteration" is appropriate for exploring a the latents space with a stable starting noise, while "Per prompt" provides more variation. * fix(ui): remove extraneous random seed nodes from linear graphs * fix(ui): fix controlnet autoprocess not working when queue is running * feat(queue): add timestamps to queue status updates Also show execution time in queue list * feat(queue): change all execution-related events to use the `queue_id` as the room, also include `queue_item_id` in InvocationQueueItem This allows for much simpler handling of queue items. * feat(api): deprecate sessions router * chore(backend): tidy logging in `dependencies.py` * fix(backend): respect `use_memory_db` * feat(backend): add `config.log_sql` (enables sql trace logging) * feat: add invocation cache Supersedes #4574 The invocation cache provides simple node memoization functionality. Nodes that use the cache are memoized and not re-executed if their inputs haven't changed. Instead, the stored output is returned. ## Results This feature provides anywhere some significant to massive performance improvement. The improvement is most marked on large batches of generations where you only change a couple things (e.g. different seed or prompt for each iteration) and low-VRAM systems, where skipping an extraneous model load is a big deal. ## Overview A new `invocation_cache` service is added to handle the caching. There's not much to it. All nodes now inherit a boolean `use_cache` field from `BaseInvocation`. This is a node field and not a class attribute, because specific instances of nodes may want to opt in or out of caching. The recently-added `invoke_internal()` method on `BaseInvocation` is used as an entrypoint for the cache logic. To create a cache key, the invocation is first serialized using pydantic's provided `json()` method, skipping the unique `id` field. Then python's very fast builtin `hash()` is used to create an integer key. All implementations of `InvocationCacheBase` must provide a class method `create_key()` which accepts an invocation and outputs a string or integer key. ## In-Memory Implementation An in-memory implementation is provided. In this implementation, the node outputs are stored in memory as python classes. The in-memory cache does not persist application restarts. Max node cache size is added as `node_cache_size` under the `Generation` config category. It defaults to 512 - this number is up for discussion, but given that these are relatively lightweight pydantic models, I think it's safe to up this even higher. Note that the cache isn't storing the big stuff - tensors and images are store on disk, and outputs include only references to them. ## Node Definition The default for all nodes is to use the cache. The `@invocation` decorator now accepts an optional `use_cache: bool` argument to override the default of `True`. Non-deterministic nodes, however, should set this to `False`. Currently, all random-stuff nodes, including `dynamic_prompt`, are set to `False`. The field name `use_cache` is now effectively a reserved field name and possibly a breaking change if any community nodes use this as a field name. In hindsight, all our reserved field names should have been prefixed with underscores or something. ## One Gotcha Leaf nodes probably want to opt out of the cache, because if they are not cached, their outputs are not saved again. If you run the same graph multiple times, you only end up with a single image output, because the image storage side-effects are in the `invoke()` method, which is bypassed if we have a cache hit. ## Linear UI The linear graphs _almost_ just work, but due to the gotcha, we need to be careful about the final image-outputting node. To resolve this, a `SaveImageInvocation` node is added and used in the linear graphs. This node is similar to `ImagePrimitive`, except it saves a copy of its input image, and has `use_cache` set to `False` by default. This is now the leaf node in all linear graphs, and is the only node in those graphs with `use_cache == False` _and_ the only node with `is_intermedate == False`. ## Workflow Editor All nodes now have a footer with a new `Use Cache [ ]` checkbox. It defaults to the value set by the invocation in its python definition, but can be changed by the user. The workflow/node validation logic has been updated to migrate old workflows to use the new default values for `use_cache`. Users may still want to review the settings that have been chosen. In the event of catastrophic failure when running this migration, the default value of `True` is applied, as this is correct for most nodes. Users should consider saving their workflows after loading them in and having them updated. ## Future Enhancements - Callback A future enhancement would be to provide a callback to the `use_cache` flag that would be run as the node is executed to determine, based on its own internal state, if the cache should be used or not. This would be useful for `DynamicPromptInvocation`, where the deterministic behaviour is determined by the `combinatorial: bool` field. ## Future Enhancements - Persisted Cache Similar to how the latents storage is backed by disk, the invocation cache could be persisted to the database or disk. We'd need to be very careful about deserializing outputs, but it's perhaps worth exploring in the future. * fix(ui): fix queue list item width * feat(nodes): do not send the whole node on every generator progress * feat(ui): strip out old logic related to sessions Things like `isProcessing` are no longer relevant with queue. Removed them all & updated everything be appropriate for queue. May be a few little quirks I've missed... * feat(ui): fix up param collapse labels * feat(ui): click queue count to go to queue tab * tidy(queue): update comment, query format * feat(ui): fix progress bar when canceling * fix(ui): fix circular dependency * feat(nodes): bail on node caching logic if `node_cache_size == 0` * feat(nodes): handle KeyError on node cache pop * feat(nodes): bypass cache codepath if caches is disabled more better no do thing * fix(ui): reset api cache on connect/disconnect * feat(ui): prevent enqueue when no prompts generated * feat(ui): add queue controls to workflow editor * feat(ui): update floating buttons & other incidental UI tweaks * fix(ui): fix missing/incorrect translation keys * fix(tests): add config service to mock invocation services invoking needs access to `node_cache_size` to occur * optionally remove pause/resume buttons from queue UI * option to disable prepending * chore(ui): remove unused file * feat(queue): remove `order_id` entirely, `item_id` is now an autoinc pk --------- Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
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from invokeai.app.util.misc import uuid_string
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app_config = get_config()
bindings = KeyBindings()
@bindings.add("c-c")
def _(event):
raise KeyboardInterrupt
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# release notes
# "Use All" with size dimensions not selectable in the UI will not load dimensions
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class Config:
"""Configuration loader."""
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def __init__(self):
pass
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TIMESTAMP_STRING = datetime.datetime.utcnow().strftime("%Y%m%dT%H%M%SZ")
INVOKE_DIRNAME = "invokeai"
YAML_FILENAME = "invokeai.yaml"
DATABASE_FILENAME = "invokeai.db"
database_path = None
database_backup_dir = None
outputs_path = None
thumbnail_path = None
def find_and_load(self):
"""Find the yaml config file and load"""
root = app_config.root_path
if not self.confirm_and_load(os.path.abspath(root)):
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print("\r\nSpecify custom database and outputs paths:")
self.confirm_and_load_from_user()
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self.database_backup_dir = os.path.join(os.path.dirname(self.database_path), "backup")
self.thumbnail_path = os.path.join(self.outputs_path, "thumbnails")
def confirm_and_load(self, invoke_root):
"""Validate a yaml path exists, confirms the user wants to use it and loads config."""
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yaml_path = os.path.join(invoke_root, self.YAML_FILENAME)
if os.path.exists(yaml_path):
db_dir, outdir = self.load_paths_from_yaml(yaml_path)
if os.path.isabs(db_dir):
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database_path = os.path.join(db_dir, self.DATABASE_FILENAME)
else:
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database_path = os.path.join(invoke_root, db_dir, self.DATABASE_FILENAME)
if os.path.isabs(outdir):
outputs_path = os.path.join(outdir, "images")
else:
outputs_path = os.path.join(invoke_root, outdir, "images")
db_exists = os.path.exists(database_path)
outdir_exists = os.path.exists(outputs_path)
text = f"Found {self.YAML_FILENAME} file at {yaml_path}:"
text += f"\n Database : {database_path}"
text += f"\n Outputs : {outputs_path}"
text += "\n\nUse these paths for import (yes) or choose different ones (no) [Yn]: "
if db_exists and outdir_exists:
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if (prompt(text).strip() or "Y").upper().startswith("Y"):
self.database_path = database_path
self.outputs_path = outputs_path
return True
else:
return False
else:
print(" Invalid: One or more paths in this config did not exist and cannot be used.")
else:
message_dialog(
title="Path not found",
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text=f"Auto-discovery of configuration failed! Could not find ({yaml_path}), Custom paths can be specified.",
).run()
return False
def confirm_and_load_from_user(self):
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default = ""
while True:
database_path = os.path.expanduser(
prompt(
"Database: Specify absolute path to the database to import into: ",
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completer=PathCompleter(
expanduser=True, file_filter=lambda x: Path(x).is_dir() or x.endswith((".db"))
),
default=default,
)
)
if database_path.endswith(".db") and os.path.isabs(database_path) and os.path.exists(database_path):
break
default = database_path + "/" if Path(database_path).is_dir() else database_path
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default = ""
while True:
outputs_path = os.path.expanduser(
prompt(
"Outputs: Specify absolute path to outputs/images directory to import into: ",
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completer=PathCompleter(expanduser=True, only_directories=True),
default=default,
)
)
if outputs_path.endswith("images") and os.path.isabs(outputs_path) and os.path.exists(outputs_path):
break
default = outputs_path + "/" if Path(outputs_path).is_dir() else outputs_path
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self.database_path = database_path
self.outputs_path = outputs_path
return
def load_paths_from_yaml(self, yaml_path):
"""Load an Invoke AI yaml file and get the database and outputs paths."""
try:
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with open(yaml_path, "rt", encoding=locale.getpreferredencoding()) as file:
yamlinfo = yaml.safe_load(file)
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db_dir = yamlinfo.get("InvokeAI", {}).get("Paths", {}).get("db_dir", None)
outdir = yamlinfo.get("InvokeAI", {}).get("Paths", {}).get("outdir", None)
return db_dir, outdir
except Exception:
print(f"Failed to load paths from yaml file! {yaml_path}!")
return None, None
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class ImportStats:
"""DTO for tracking work progress."""
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def __init__(self):
pass
time_start = datetime.datetime.utcnow()
count_source_files = 0
count_skipped_file_exists = 0
count_skipped_db_exists = 0
count_imported = 0
count_imported_by_version = {}
count_file_errors = 0
@staticmethod
def get_elapsed_time_string():
"""Get a friendly time string for the time elapsed since processing start."""
time_now = datetime.datetime.utcnow()
total_seconds = (time_now - ImportStats.time_start).total_seconds()
hours = int((total_seconds) / 3600)
minutes = int(((total_seconds) % 3600) / 60)
seconds = total_seconds % 60
out_str = f"{hours} hour(s) -" if hours > 0 else ""
out_str += f"{minutes} minute(s) -" if minutes > 0 else ""
out_str += f"{seconds:.2f} second(s)"
return out_str
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class InvokeAIMetadata:
"""DTO for core Invoke AI generation properties parsed from metadata."""
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def __init__(self):
pass
def __str__(self):
formatted_str = f"{self.generation_mode}~{self.steps}~{self.cfg_scale}~{self.model_name}~{self.scheduler}~{self.seed}~{self.width}~{self.height}~{self.rand_device}~{self.strength}~{self.init_image}"
formatted_str += f"\r\npositive_prompt: {self.positive_prompt}"
formatted_str += f"\r\nnegative_prompt: {self.negative_prompt}"
return formatted_str
generation_mode = None
steps = None
cfg_scale = None
model_name = None
scheduler = None
seed = None
width = None
height = None
rand_device = None
strength = None
init_image = None
positive_prompt = None
negative_prompt = None
imported_app_version = None
def to_json(self):
"""Convert the active instance to json format."""
prop_dict = {}
prop_dict["generation_mode"] = self.generation_mode
# dont render prompt nodes if neither are set to avoid the ui thinking it can set them
# if at least one exists, render them both, but use empty string instead of None if one of them is empty
# this allows the field that is empty to actually be cleared byt he UI instead of leaving the previous value
if self.positive_prompt or self.negative_prompt:
prop_dict["positive_prompt"] = "" if self.positive_prompt is None else self.positive_prompt
prop_dict["negative_prompt"] = "" if self.negative_prompt is None else self.negative_prompt
prop_dict["width"] = self.width
prop_dict["height"] = self.height
# only render seed if it has a value to avoid ui thinking it can set this and then error
if self.seed:
prop_dict["seed"] = self.seed
prop_dict["rand_device"] = self.rand_device
prop_dict["cfg_scale"] = self.cfg_scale
prop_dict["steps"] = self.steps
prop_dict["scheduler"] = self.scheduler
prop_dict["clip_skip"] = 0
prop_dict["model"] = {}
prop_dict["model"]["model_name"] = self.model_name
prop_dict["model"]["base_model"] = None
prop_dict["controlnets"] = []
prop_dict["loras"] = []
prop_dict["vae"] = None
prop_dict["strength"] = self.strength
prop_dict["init_image"] = self.init_image
prop_dict["positive_style_prompt"] = None
prop_dict["negative_style_prompt"] = None
prop_dict["refiner_model"] = None
prop_dict["refiner_cfg_scale"] = None
prop_dict["refiner_steps"] = None
prop_dict["refiner_scheduler"] = None
prop_dict["refiner_aesthetic_store"] = None
prop_dict["refiner_start"] = None
prop_dict["imported_app_version"] = self.imported_app_version
return json.dumps(prop_dict)
class InvokeAIMetadataParser:
"""Parses strings with json data to find Invoke AI core metadata properties."""
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def __init__(self):
pass
def parse_meta_tag_dream(self, dream_string):
"""Take as input an png metadata json node for the 'dream' field variant from prior to 1.15"""
props = InvokeAIMetadata()
props.imported_app_version = "pre1.15"
seed_match = re.search("-S\\s*(\\d+)", dream_string)
if seed_match is not None:
try:
props.seed = int(seed_match[1])
except ValueError:
props.seed = None
raw_prompt = re.sub("(-S\\s*\\d+)", "", dream_string)
else:
raw_prompt = dream_string
pos_prompt, neg_prompt = self.split_prompt(raw_prompt)
props.positive_prompt = pos_prompt
props.negative_prompt = neg_prompt
return props
def parse_meta_tag_sd_metadata(self, tag_value):
"""Take as input an png metadata json node for the 'sd-metadata' field variant from 1.15 through 2.3.5 post 2"""
props = InvokeAIMetadata()
props.imported_app_version = tag_value.get("app_version")
props.model_name = tag_value.get("model_weights")
img_node = tag_value.get("image")
if img_node is not None:
props.generation_mode = img_node.get("type")
props.width = img_node.get("width")
props.height = img_node.get("height")
props.seed = img_node.get("seed")
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props.rand_device = "cuda" # hardcoded since all generations pre 3.0 used cuda random noise instead of cpu
props.cfg_scale = img_node.get("cfg_scale")
props.steps = img_node.get("steps")
props.scheduler = self.map_scheduler(img_node.get("sampler"))
props.strength = img_node.get("strength")
if props.strength is None:
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props.strength = img_node.get("strength_steps") # try second name for this property
props.init_image = img_node.get("init_image_path")
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if props.init_image is None: # try second name for this property
props.init_image = img_node.get("init_img")
# remove the path info from init_image so if we move the init image, it will be correctly relative in the new location
if props.init_image is not None:
props.init_image = os.path.basename(props.init_image)
raw_prompt = img_node.get("prompt")
if isinstance(raw_prompt, list):
raw_prompt = raw_prompt[0].get("prompt")
props.positive_prompt, props.negative_prompt = self.split_prompt(raw_prompt)
return props
def parse_meta_tag_invokeai(self, tag_value):
"""Take as input an png metadata json node for the 'invokeai' field variant from 3.0.0 beta 1 through 5"""
props = InvokeAIMetadata()
props.imported_app_version = "3.0.0 or later"
props.generation_mode = tag_value.get("type")
if props.generation_mode is not None:
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props.generation_mode = props.generation_mode.replace("t2l", "txt2img").replace("l2l", "img2img")
props.width = tag_value.get("width")
props.height = tag_value.get("height")
props.seed = tag_value.get("seed")
props.cfg_scale = tag_value.get("cfg_scale")
props.steps = tag_value.get("steps")
props.scheduler = tag_value.get("scheduler")
props.strength = tag_value.get("strength")
props.positive_prompt = tag_value.get("positive_conditioning")
props.negative_prompt = tag_value.get("negative_conditioning")
return props
def map_scheduler(self, old_scheduler):
"""Convert the legacy sampler names to matching 3.0 schedulers"""
# this was more elegant as a case statement, but that's not available in python 3.9
if old_scheduler is None:
return None
scheduler_map = {
"ddim": "ddim",
"plms": "pnmd",
"k_lms": "lms",
"k_dpm_2": "kdpm_2",
"k_dpm_2_a": "kdpm_2_a",
"dpmpp_2": "dpmpp_2s",
"k_dpmpp_2": "dpmpp_2m",
"k_dpmpp_2_a": None, # invalid, in 2.3.x, selecting this sample would just fallback to last run or plms if new session
"k_euler": "euler",
"k_euler_a": "euler_a",
"k_heun": "heun",
}
return scheduler_map.get(old_scheduler)
def split_prompt(self, raw_prompt: str):
"""Split the unified prompt strings by extracting all negative prompt blocks out into the negative prompt."""
if raw_prompt is None:
return "", ""
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raw_prompt_search = raw_prompt.replace("\r", "").replace("\n", "")
matches = re.findall(r"\[(.+?)\]", raw_prompt_search)
if len(matches) > 0:
negative_prompt = ""
if len(matches) == 1:
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negative_prompt = matches[0].strip().strip(",")
else:
for match in matches:
negative_prompt += f"({match.strip().strip(',')})"
positive_prompt = re.sub(r"(\[.+?\])", "", raw_prompt_search).strip()
else:
positive_prompt = raw_prompt_search.strip()
negative_prompt = ""
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return positive_prompt, negative_prompt
class DatabaseMapper:
"""Class to abstract database functionality."""
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def __init__(self, database_path, database_backup_dir):
self.database_path = database_path
self.database_backup_dir = database_backup_dir
self.connection = None
self.cursor = None
def connect(self):
"""Open connection to the database."""
self.connection = sqlite3.connect(self.database_path)
self.cursor = self.connection.cursor()
def get_board_names(self):
"""Get a list of the current board names from the database."""
sql_get_board_name = "SELECT board_name FROM boards"
self.cursor.execute(sql_get_board_name)
rows = self.cursor.fetchall()
return [row[0] for row in rows]
def does_image_exist(self, image_name):
"""Check database if a image name already exists and return a boolean."""
sql_get_image_by_name = f"SELECT image_name FROM images WHERE image_name='{image_name}'"
self.cursor.execute(sql_get_image_by_name)
rows = self.cursor.fetchall()
return True if len(rows) > 0 else False
def add_new_image_to_database(self, filename, width, height, metadata, modified_date_string):
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"""Add an image to the database."""
sql_add_image = f"""INSERT INTO images (image_name, image_origin, image_category, width, height, session_id, node_id, metadata, is_intermediate, created_at, updated_at)
VALUES ('{filename}', 'internal', 'general', {width}, {height}, null, null, '{metadata}', 0, '{modified_date_string}', '{modified_date_string}')"""
self.cursor.execute(sql_add_image)
self.connection.commit()
def get_board_id_with_create(self, board_name):
"""Get the board id for supplied name, and create the board if one does not exist."""
sql_find_board = f"SELECT board_id FROM boards WHERE board_name='{board_name}' COLLATE NOCASE"
self.cursor.execute(sql_find_board)
rows = self.cursor.fetchall()
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if len(rows) > 0:
return rows[0][0]
else:
board_date_string = datetime.datetime.utcnow().date().isoformat()
feat: queued generation (#4502) * fix(config): fix typing issues in `config/` `config/invokeai_config.py`: - use `Optional` for things that are optional - fix typing of `ram_cache_size()` and `vram_cache_size()` - remove unused and incorrectly typed method `autoconvert_path` - fix types and logic for `parse_args()`, in which `InvokeAIAppConfig.initconf` *must* be a `DictConfig`, but function would allow it to be set as a `ListConfig`, which presumably would cause issues elsewhere `config/base.py`: - use `cls` for first arg of class methods - use `Optional` for things that are optional - fix minor type issue related to setting of `env_prefix` - remove unused `add_subparser()` method, which calls `add_parser()` on an `ArgumentParser` (method only available on the `_SubParsersAction` object, which is returned from ArgumentParser.add_subparsers()`) * feat: queued generation and batches Due to a very messy branch with broad addition of `isort` on `main` alongside it, some git surgery was needed to get an agreeable git history. This commit represents all of the work on queued generation. See PR for notes. * chore: flake8, isort, black * fix(nodes): fix incorrect service stop() method * fix(nodes): improve names of a few variables * fix(tests): fix up tests after changes to batches/queue * feat(tests): add unit tests for session queue helper functions * feat(ui): dynamic prompts is always enabled * feat(queue): add queue_status_changed event * feat(ui): wip queue graphs * feat(nodes): move cleanup til after invoker startup * feat(nodes): add cancel_by_batch_ids * feat(ui): wip batch graphs & UI * fix(nodes): remove `Batch.batch_id` from required * fix(ui): cleanup and use fixedCacheKey for all mutations * fix(ui): remove orphaned nodes from canvas graphs * fix(nodes): fix cancel_by_batch_ids result count * fix(ui): only show cancel batch tooltip when batches were canceled * chore: isort * fix(api): return `[""]` when dynamic prompts generates no prompts Just a simple fallback so we always have a prompt. * feat(ui): dynamicPrompts.combinatorial is always on There seems to be little purpose in using the combinatorial generation for dynamic prompts. I've disabled it by hiding it from the UI and defaulting combinatorial to true. If we want to enable it again in the future it's straightforward to do so. * feat: add queue_id & support logic * feat(ui): fix upscale button It prepends the upscale operation to queue * feat(nodes): return queue item when enqueuing a single graph This facilitates one-off graph async workflows in the client. * feat(ui): move controlnet autoprocess to queue * fix(ui): fix non-serializable DOMRect in redux state * feat(ui): QueueTable performance tweaks * feat(ui): update queue list Queue items expand to show the full queue item. Just as JSON for now. * wip threaded session_processor * feat(nodes,ui): fully migrate queue to session_processor * feat(nodes,ui): add processor events * feat(ui): ui tweaks * feat(nodes,ui): consolidate events, reduce network requests * feat(ui): cleanup & abstract queue hooks * feat(nodes): optimize batch permutation Use a generator to do only as much work as is needed. Previously, though we only ended up creating exactly as many queue items as was needed, there was still some intermediary work that calculated *all* permutations. When that number was very high, the system had a very hard time and used a lot of memory. The logic has been refactored to use a generator. Additionally, the batch validators are optimized to return early and use less memory. * feat(ui): add seed behaviour parameter This dynamic prompts parameter allows the seed to be randomized per prompt or per iteration: - Per iteration: Use the same seed for all prompts in a single dynamic prompt expansion - Per prompt: Use a different seed for every single prompt "Per iteration" is appropriate for exploring a the latents space with a stable starting noise, while "Per prompt" provides more variation. * fix(ui): remove extraneous random seed nodes from linear graphs * fix(ui): fix controlnet autoprocess not working when queue is running * feat(queue): add timestamps to queue status updates Also show execution time in queue list * feat(queue): change all execution-related events to use the `queue_id` as the room, also include `queue_item_id` in InvocationQueueItem This allows for much simpler handling of queue items. * feat(api): deprecate sessions router * chore(backend): tidy logging in `dependencies.py` * fix(backend): respect `use_memory_db` * feat(backend): add `config.log_sql` (enables sql trace logging) * feat: add invocation cache Supersedes #4574 The invocation cache provides simple node memoization functionality. Nodes that use the cache are memoized and not re-executed if their inputs haven't changed. Instead, the stored output is returned. ## Results This feature provides anywhere some significant to massive performance improvement. The improvement is most marked on large batches of generations where you only change a couple things (e.g. different seed or prompt for each iteration) and low-VRAM systems, where skipping an extraneous model load is a big deal. ## Overview A new `invocation_cache` service is added to handle the caching. There's not much to it. All nodes now inherit a boolean `use_cache` field from `BaseInvocation`. This is a node field and not a class attribute, because specific instances of nodes may want to opt in or out of caching. The recently-added `invoke_internal()` method on `BaseInvocation` is used as an entrypoint for the cache logic. To create a cache key, the invocation is first serialized using pydantic's provided `json()` method, skipping the unique `id` field. Then python's very fast builtin `hash()` is used to create an integer key. All implementations of `InvocationCacheBase` must provide a class method `create_key()` which accepts an invocation and outputs a string or integer key. ## In-Memory Implementation An in-memory implementation is provided. In this implementation, the node outputs are stored in memory as python classes. The in-memory cache does not persist application restarts. Max node cache size is added as `node_cache_size` under the `Generation` config category. It defaults to 512 - this number is up for discussion, but given that these are relatively lightweight pydantic models, I think it's safe to up this even higher. Note that the cache isn't storing the big stuff - tensors and images are store on disk, and outputs include only references to them. ## Node Definition The default for all nodes is to use the cache. The `@invocation` decorator now accepts an optional `use_cache: bool` argument to override the default of `True`. Non-deterministic nodes, however, should set this to `False`. Currently, all random-stuff nodes, including `dynamic_prompt`, are set to `False`. The field name `use_cache` is now effectively a reserved field name and possibly a breaking change if any community nodes use this as a field name. In hindsight, all our reserved field names should have been prefixed with underscores or something. ## One Gotcha Leaf nodes probably want to opt out of the cache, because if they are not cached, their outputs are not saved again. If you run the same graph multiple times, you only end up with a single image output, because the image storage side-effects are in the `invoke()` method, which is bypassed if we have a cache hit. ## Linear UI The linear graphs _almost_ just work, but due to the gotcha, we need to be careful about the final image-outputting node. To resolve this, a `SaveImageInvocation` node is added and used in the linear graphs. This node is similar to `ImagePrimitive`, except it saves a copy of its input image, and has `use_cache` set to `False` by default. This is now the leaf node in all linear graphs, and is the only node in those graphs with `use_cache == False` _and_ the only node with `is_intermedate == False`. ## Workflow Editor All nodes now have a footer with a new `Use Cache [ ]` checkbox. It defaults to the value set by the invocation in its python definition, but can be changed by the user. The workflow/node validation logic has been updated to migrate old workflows to use the new default values for `use_cache`. Users may still want to review the settings that have been chosen. In the event of catastrophic failure when running this migration, the default value of `True` is applied, as this is correct for most nodes. Users should consider saving their workflows after loading them in and having them updated. ## Future Enhancements - Callback A future enhancement would be to provide a callback to the `use_cache` flag that would be run as the node is executed to determine, based on its own internal state, if the cache should be used or not. This would be useful for `DynamicPromptInvocation`, where the deterministic behaviour is determined by the `combinatorial: bool` field. ## Future Enhancements - Persisted Cache Similar to how the latents storage is backed by disk, the invocation cache could be persisted to the database or disk. We'd need to be very careful about deserializing outputs, but it's perhaps worth exploring in the future. * fix(ui): fix queue list item width * feat(nodes): do not send the whole node on every generator progress * feat(ui): strip out old logic related to sessions Things like `isProcessing` are no longer relevant with queue. Removed them all & updated everything be appropriate for queue. May be a few little quirks I've missed... * feat(ui): fix up param collapse labels * feat(ui): click queue count to go to queue tab * tidy(queue): update comment, query format * feat(ui): fix progress bar when canceling * fix(ui): fix circular dependency * feat(nodes): bail on node caching logic if `node_cache_size == 0` * feat(nodes): handle KeyError on node cache pop * feat(nodes): bypass cache codepath if caches is disabled more better no do thing * fix(ui): reset api cache on connect/disconnect * feat(ui): prevent enqueue when no prompts generated * feat(ui): add queue controls to workflow editor * feat(ui): update floating buttons & other incidental UI tweaks * fix(ui): fix missing/incorrect translation keys * fix(tests): add config service to mock invocation services invoking needs access to `node_cache_size` to occur * optionally remove pause/resume buttons from queue UI * option to disable prepending * chore(ui): remove unused file * feat(queue): remove `order_id` entirely, `item_id` is now an autoinc pk --------- Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
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new_board_id = uuid_string()
sql_insert_board = f"INSERT INTO boards (board_id, board_name, created_at, updated_at) VALUES ('{new_board_id}', '{board_name}', '{board_date_string}', '{board_date_string}')"
self.cursor.execute(sql_insert_board)
self.connection.commit()
return new_board_id
def add_image_to_board(self, filename, board_id):
"""Add an image mapping to a board."""
add_datetime_str = datetime.datetime.utcnow().isoformat()
sql_add_image_to_board = f"""INSERT INTO board_images (board_id, image_name, created_at, updated_at)
VALUES ('{board_id}', '{filename}', '{add_datetime_str}', '{add_datetime_str}')"""
self.cursor.execute(sql_add_image_to_board)
self.connection.commit()
def disconnect(self):
"""Disconnect from the db, cleaning up connections and cursors."""
if self.cursor is not None:
self.cursor.close()
if self.connection is not None:
self.connection.close()
def backup(self, timestamp_string):
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"""Take a backup of the database."""
if not os.path.exists(self.database_backup_dir):
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print(f"Database backup directory {self.database_backup_dir} does not exist -> creating...", end="")
os.makedirs(self.database_backup_dir)
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print("Done!")
database_backup_path = os.path.join(self.database_backup_dir, f"backup-{timestamp_string}-invokeai.db")
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print(f"Making DB Backup at {database_backup_path}...", end="")
shutil.copy2(self.database_path, database_backup_path)
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print("Done!")
class MediaImportProcessor:
"""Containing class for script functionality."""
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def __init__(self):
pass
board_name_id_map = {}
def get_import_file_list(self):
"""Ask the user for the import folder and scan for the list of files to return."""
while True:
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default = ""
while True:
import_dir = os.path.expanduser(
prompt(
"Inputs: Specify absolute path containing InvokeAI .png images to import: ",
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completer=PathCompleter(expanduser=True, only_directories=True),
default=default,
)
)
if len(import_dir) > 0 and Path(import_dir).is_dir():
break
default = import_dir
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recurse_directories = (
(prompt("Include files from subfolders recursively [yN]? ").strip() or "N").upper().startswith("N")
)
if recurse_directories:
is_recurse = False
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matching_file_list = glob.glob(import_dir + "/*.png", recursive=False)
else:
is_recurse = True
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matching_file_list = glob.glob(import_dir + "/**/*.png", recursive=True)
if len(matching_file_list) > 0:
return import_dir, is_recurse, matching_file_list
else:
print(f"The specific path {import_dir} exists, but does not contain .png files!")
def get_file_details(self, filepath):
"""Retrieve the embedded metedata fields and dimensions from an image file."""
with PIL.Image.open(filepath) as img:
img.load()
png_width, png_height = img.size
img_info = img.info
return img_info, png_width, png_height
def select_board_option(self, board_names, timestamp_string):
"""Allow the user to choose how a board is selected for imported files."""
while True:
print("\r\nOptions for board selection for imported images:")
print(f"1) Select an existing board name. (found {len(board_names)})")
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print("2) Specify a board name to create/add to.")
print("3) Create/add to board named 'IMPORT'.")
print(
f"4) Create/add to board named 'IMPORT' with the current datetime string appended (.e.g IMPORT_{timestamp_string})."
)
print(
"5) Create/add to board named 'IMPORT' with a the original file app_version appended (.e.g IMPORT_2.2.5)."
)
input_option = input("Specify desired board option: ")
# This was more elegant as a case statement, but not supported in python 3.9
if input_option == "1":
if len(board_names) < 1:
print("\r\nThere are no existing board names to choose from. Select another option!")
continue
board_name = self.select_item_from_list(
board_names, "board name", True, "Cancel, go back and choose a different board option."
)
if board_name is not None:
return board_name
elif input_option == "2":
while True:
board_name = input("Specify new/existing board name: ")
if board_name:
return board_name
elif input_option == "3":
return "IMPORT"
elif input_option == "4":
return f"IMPORT_{timestamp_string}"
elif input_option == "5":
return "IMPORT_APPVERSION"
def select_item_from_list(self, items, entity_name, allow_cancel, cancel_string):
"""A general function to render a list of items to select in the console, prompt the user for a selection and ensure a valid entry is selected."""
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print(f"Select a {entity_name.lower()} from the following list:")
index = 1
for item in items:
print(f"{index}) {item}")
index += 1
if allow_cancel:
print(f"{index}) {cancel_string}")
while True:
try:
option_number = int(input("Specify number of selection: "))
except ValueError:
continue
if allow_cancel and option_number == index:
return None
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if option_number >= 1 and option_number <= len(items):
return items[option_number - 1]
def import_image(self, filepath: str, board_name_option: str, db_mapper: DatabaseMapper, config: Config):
"""Import a single file by its path"""
parser = InvokeAIMetadataParser()
file_name = os.path.basename(filepath)
file_destination_path = os.path.join(config.outputs_path, file_name)
print("===============================================================================")
print(f"Importing {filepath}")
# check destination to see if the file was previously imported
if os.path.exists(file_destination_path):
print("File already exists in the destination, skipping!")
ImportStats.count_skipped_file_exists += 1
return
# check if file name is already referenced in the database
if db_mapper.does_image_exist(file_name):
print("A reference to a file with this name already exists in the database, skipping!")
ImportStats.count_skipped_db_exists += 1
return
# load image info and dimensions
img_info, png_width, png_height = self.get_file_details(filepath)
# parse metadata
destination_needs_meta_update = True
log_version_note = "(Unknown)"
if "invokeai_metadata" in img_info:
# for the latest, we will just re-emit the same json, no need to parse/modify
converted_field = None
latest_json_string = img_info.get("invokeai_metadata")
log_version_note = "3.0.0+"
destination_needs_meta_update = False
else:
if "sd-metadata" in img_info:
converted_field = parser.parse_meta_tag_sd_metadata(json.loads(img_info.get("sd-metadata")))
elif "invokeai" in img_info:
converted_field = parser.parse_meta_tag_invokeai(json.loads(img_info.get("invokeai")))
elif "dream" in img_info:
converted_field = parser.parse_meta_tag_dream(img_info.get("dream"))
elif "Dream" in img_info:
converted_field = parser.parse_meta_tag_dream(img_info.get("Dream"))
else:
converted_field = InvokeAIMetadata()
destination_needs_meta_update = False
print("File does not have metadata from known Invoke AI versions, add only, no update!")
# use the loaded img dimensions if the metadata didnt have them
if converted_field.width is None:
converted_field.width = png_width
if converted_field.height is None:
converted_field.height = png_height
log_version_note = converted_field.imported_app_version if converted_field else "NoVersion"
log_version_note = log_version_note or "NoVersion"
latest_json_string = converted_field.to_json()
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print(f"From Invoke AI Version {log_version_note} with dimensions {png_width} x {png_height}.")
# if metadata needs update, then update metdata and copy in one shot
if destination_needs_meta_update:
print("Updating metadata while copying...", end="")
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self.update_file_metadata_while_copying(
filepath, file_destination_path, "invokeai_metadata", latest_json_string
)
print("Done!")
else:
print("No metadata update necessary, copying only...", end="")
shutil.copy2(filepath, file_destination_path)
print("Done!")
# create thumbnail
print("Creating thumbnail...", end="")
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thumbnail_path = os.path.join(config.thumbnail_path, os.path.splitext(file_name)[0]) + ".webp"
thumbnail_size = 256, 256
with PIL.Image.open(filepath) as source_image:
source_image.thumbnail(thumbnail_size)
source_image.save(thumbnail_path, "webp")
print("Done!")
# finalize the dynamic board name if there is an APPVERSION token in it.
if converted_field is not None:
board_name = board_name_option.replace("APPVERSION", converted_field.imported_app_version or "NoVersion")
else:
board_name = board_name_option.replace("APPVERSION", "Latest")
# maintain a map of alrady created/looked up ids to avoid DB queries
print("Finding/Creating board...", end="")
if board_name in self.board_name_id_map:
board_id = self.board_name_id_map[board_name]
else:
board_id = db_mapper.get_board_id_with_create(board_name)
self.board_name_id_map[board_name] = board_id
print("Done!")
# add image to db
print("Adding image to database......", end="")
modified_time = datetime.datetime.utcfromtimestamp(os.path.getmtime(filepath))
db_mapper.add_new_image_to_database(file_name, png_width, png_height, latest_json_string, modified_time)
print("Done!")
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# add image to board
print("Adding image to board......", end="")
db_mapper.add_image_to_board(file_name, board_id)
print("Done!")
ImportStats.count_imported += 1
if log_version_note in ImportStats.count_imported_by_version:
ImportStats.count_imported_by_version[log_version_note] += 1
else:
ImportStats.count_imported_by_version[log_version_note] = 1
def update_file_metadata_while_copying(self, filepath, file_destination_path, tag_name, tag_value):
"""Perform a metadata update with save to a new destination which accomplishes a copy while updating metadata."""
with PIL.Image.open(filepath) as target_image:
existing_img_info = target_image.info
metadata = PIL.PngImagePlugin.PngInfo()
# re-add any existing invoke ai tags unless they are the one we are trying to add
for key in existing_img_info:
if key != tag_name and key in ("dream", "Dream", "sd-metadata", "invokeai", "invokeai_metadata"):
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metadata.add_text(key, existing_img_info[key])
metadata.add_text(tag_name, tag_value)
target_image.save(file_destination_path, pnginfo=metadata)
def process(self):
"""Begin main processing."""
print("===============================================================================")
print("This script will import images generated by earlier versions of")
print("InvokeAI into the currently installed root directory:")
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print(f" {app_config.root_path}")
print("If this is not what you want to do, type ctrl-C now to cancel.")
# load config
print("===============================================================================")
print("= Configuration & Settings")
config = Config()
config.find_and_load()
db_mapper = DatabaseMapper(config.database_path, config.database_backup_dir)
db_mapper.connect()
import_dir, is_recurse, import_file_list = self.get_import_file_list()
ImportStats.count_source_files = len(import_file_list)
board_names = db_mapper.get_board_names()
board_name_option = self.select_board_option(board_names, config.TIMESTAMP_STRING)
print("\r\n===============================================================================")
print("= Import Settings Confirmation")
print()
print(f"Database File Path : {config.database_path}")
print(f"Outputs/Images Directory : {config.outputs_path}")
print(f"Import Image Source Directory : {import_dir}")
print(f" Recurse Source SubDirectories : {'Yes' if is_recurse else 'No'}")
print(f"Count of .png file(s) found : {len(import_file_list)}")
print(f"Board name option specified : {board_name_option}")
print(f"Database backup will be taken at : {config.database_backup_dir}")
print("\r\nNotes about the import process:")
print("- Source image files will not be modified, only copied to the outputs directory.")
print("- If the same file name already exists in the destination, the file will be skipped.")
print("- If the same file name already has a record in the database, the file will be skipped.")
print("- Invoke AI metadata tags will be updated/written into the imported copy only.")
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print(
"- On the imported copy, only Invoke AI known tags (latest and legacy) will be retained (dream, sd-metadata, invokeai, invokeai_metadata)"
)
print(
"- A property 'imported_app_version' will be added to metadata that can be viewed in the UI's metadata viewer."
)
print(
"- The new 3.x InvokeAI outputs folder structure is flat so recursively found source imges will all be placed into the single outputs/images folder."
)
while True:
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should_continue = prompt("\nDo you wish to continue with the import [Yn] ? ").lower() or "y"
if should_continue == "n":
print("\r\nCancelling Import")
return
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elif should_continue == "y":
print()
break
db_mapper.backup(config.TIMESTAMP_STRING)
print()
ImportStats.time_start = datetime.datetime.utcnow()
for filepath in import_file_list:
try:
self.import_image(filepath, board_name_option, db_mapper, config)
except sqlite3.Error as sql_ex:
print(f"A database related exception was found processing {filepath}, will continue to next file. ")
print("Exception detail:")
print(sql_ex)
ImportStats.count_file_errors += 1
except Exception as ex:
print(f"Exception processing {filepath}, will continue to next file. ")
print("Exception detail:")
print(ex)
ImportStats.count_file_errors += 1
print("\r\n===============================================================================")
print(f"= Import Complete - Elpased Time: {ImportStats.get_elapsed_time_string()}")
print()
print(f"Source File(s) : {ImportStats.count_source_files}")
print(f"Total Imported : {ImportStats.count_imported}")
print(f"Skipped b/c file already exists on disk : {ImportStats.count_skipped_file_exists}")
print(f"Skipped b/c file already exists in db : {ImportStats.count_skipped_db_exists}")
print(f"Errors during import : {ImportStats.count_file_errors}")
if ImportStats.count_imported > 0:
print("\r\nBreakdown of imported files by version:")
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for key, version in ImportStats.count_imported_by_version.items():
print(f" {key:20} : {version}")
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def main():
try:
processor = MediaImportProcessor()
processor.process()
except KeyboardInterrupt:
print("\r\n\r\nUser cancelled execution.")
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if __name__ == "__main__":
main()