Merge branch 'main' into tests

.github/ISSUE_TEMPLATE/BUG_REPORT.yml

@@ -65,6 +65,16 @@ body:
       placeholder: 8GB
     validations:
       required: false
+      
+  - type: input
+    id: version-number
+    attributes:
+      label: What version did you experience this issue on?
+      description: |
+        Please share the version of Invoke AI that you experienced the issue on. If this is not the latest version, please update first to confirm the issue still exists. If you are testing main, please include the commit hash instead.
+      placeholder: X.X.X
+    validations:
+      required: true
 
   - type: textarea
     id: what-happened

.github/workflows/close-inactive-issues.yml

@@ -24,3 +24,4 @@ jobs:
           days-before-pr-stale: -1
           days-before-pr-close: -1
           repo-token: ${{ secrets.GITHUB_TOKEN }}
+          operations-per-run: 500

.github/workflows/test-invoke-pip-skip.yml

@@ -1,12 +1,12 @@
 name: Test invoke.py pip
 on:
   pull_request:
-    paths-ignore:
-      - 'pyproject.toml'
-      - 'invokeai/**'
-      - 'invokeai/backend/**'
-      - 'invokeai/configs/**'
-      - 'invokeai/frontend/web/dist/**'
+    paths:
+      - '**'
+      - '!pyproject.toml'
+      - '!invokeai/**'
+      - 'invokeai/frontend/web/**'
+      - '!invokeai/frontend/web/dist/**'
   merge_group:
   workflow_dispatch:
 

.github/workflows/test-invoke-pip.yml

@@ -6,15 +6,13 @@ on:
     paths:
       - 'pyproject.toml'
       - 'invokeai/**'
-      - 'invokeai/backend/**'
-      - 'invokeai/configs/**'
+      - '!invokeai/frontend/web/**'
       - 'invokeai/frontend/web/dist/**'
   pull_request:
     paths:
       - 'pyproject.toml'
       - 'invokeai/**'
-      - 'invokeai/backend/**'
-      - 'invokeai/configs/**'
+      - '!invokeai/frontend/web/**'
       - 'invokeai/frontend/web/dist/**'
     types:
       - 'ready_for_review'

docs/features/TEXTUAL_INVERSION.md

@@ -17,7 +17,7 @@ notebooks.
 
 You will need a GPU to perform training in a reasonable length of
 time, and at least 12 GB of VRAM. We recommend using the [`xformers`
-library](../installation/070_INSTALL_XFORMERS) to accelerate the
+library](../installation/070_INSTALL_XFORMERS.md) to accelerate the
 training process further. During training, about ~8 GB is temporarily
 needed in order to store intermediate models, checkpoints and logs.
 

docs/installation/020_INSTALL_MANUAL.md

@@ -148,7 +148,7 @@ manager, please follow these steps:
     === "CUDA (NVidia)"
 
         ```bash
-        pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
+        pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
         ```
 
     === "ROCm (AMD)"

docs/installation/060_INSTALL_PATCHMATCH.md

@@ -24,7 +24,7 @@ You need to have opencv installed so that pypatchmatch can be built:
 brew install opencv
 ```
 
-The next time you start `invoke`, after sucesfully installing opencv, pypatchmatch will be built.
+The next time you start `invoke`, after successfully installing opencv, pypatchmatch will be built.
 
 ## Linux
 
@@ -56,7 +56,7 @@ Prior to installing PyPatchMatch, you need to take the following steps:
 
 5. Confirm that pypatchmatch is installed. At the command-line prompt enter
    `python`, and then at the `>>>` line type
-   `from patchmatch import patch_match`: It should look like the follwing:
+   `from patchmatch import patch_match`: It should look like the following:
 
     ```py
     Python 3.9.5 (default, Nov 23 2021, 15:27:38)
@@ -108,4 +108,4 @@ Prior to installing PyPatchMatch, you need to take the following steps:
 
 [**Next, Follow Steps 4-6 from the Debian Section above**](#linux)
 
-If you see no errors, then you're ready to go!
+If you see no errors you're ready to go!

invokeai/app/api/dependencies.py

@@ -4,7 +4,8 @@ import os
 from argparse import Namespace
 
 from ...backend import Globals
-from ..services.generate_initializer import get_generate
+from ..services.model_manager_initializer import get_model_manager
+from ..services.restoration_services import RestorationServices
 from ..services.graph import GraphExecutionState
 from ..services.image_storage import DiskImageStorage
 from ..services.invocation_queue import MemoryInvocationQueue
@@ -37,18 +38,16 @@ class ApiDependencies:
     invoker: Invoker = None
 
     @staticmethod
-    def initialize(args, config, event_handler_id: int):
-        Globals.try_patchmatch = args.patchmatch
-        Globals.always_use_cpu = args.always_use_cpu
-        Globals.internet_available = args.internet_available and check_internet()
-        Globals.disable_xformers = not args.xformers
-        Globals.ckpt_convert = args.ckpt_convert
+    def initialize(config, event_handler_id: int):
+        Globals.try_patchmatch = config.patchmatch
+        Globals.always_use_cpu = config.always_use_cpu
+        Globals.internet_available = config.internet_available and check_internet()
+        Globals.disable_xformers = not config.xformers
+        Globals.ckpt_convert = config.ckpt_convert
 
         # TODO: Use a logger
         print(f">> Internet connectivity is {Globals.internet_available}")
 
-        generate = get_generate(args, config)
-
         events = FastAPIEventService(event_handler_id)
 
         output_folder = os.path.abspath(
@@ -61,7 +60,7 @@ class ApiDependencies:
         db_location = os.path.join(output_folder, "invokeai.db")
 
         services = InvocationServices(
-            generate=generate,
+            model_manager=get_model_manager(config),
             events=events,
             images=images,
             queue=MemoryInvocationQueue(),
@@ -69,6 +68,7 @@ class ApiDependencies:
                 filename=db_location, table_name="graph_executions"
             ),
             processor=DefaultInvocationProcessor(),
+            restoration=RestorationServices(config),
         )
 
         ApiDependencies.invoker = Invoker(services)

invokeai/app/api/routers/sessions.py

@@ -10,6 +10,7 @@ from pydantic.fields import Field
 from ...invocations import *
 from ...invocations.baseinvocation import BaseInvocation
 from ...services.graph import (
+    Edge,
     EdgeConnection,
     Graph,
     GraphExecutionState,
@@ -92,7 +93,7 @@ async def get_session(
 async def add_node(
     session_id: str = Path(description="The id of the session"),
     node: Annotated[
-        Union[BaseInvocation.get_invocations()], Field(discriminator="type")
+        Union[BaseInvocation.get_invocations()], Field(discriminator="type") # type: ignore
     ] = Body(description="The node to add"),
 ) -> str:
     """Adds a node to the graph"""
@@ -125,7 +126,7 @@ async def update_node(
     session_id: str = Path(description="The id of the session"),
     node_path: str = Path(description="The path to the node in the graph"),
     node: Annotated[
-        Union[BaseInvocation.get_invocations()], Field(discriminator="type")
+        Union[BaseInvocation.get_invocations()], Field(discriminator="type") # type: ignore
     ] = Body(description="The new node"),
 ) -> GraphExecutionState:
     """Updates a node in the graph and removes all linked edges"""
@@ -186,7 +187,7 @@ async def delete_node(
 )
 async def add_edge(
     session_id: str = Path(description="The id of the session"),
-    edge: tuple[EdgeConnection, EdgeConnection] = Body(description="The edge to add"),
+    edge: Edge = Body(description="The edge to add"),
 ) -> GraphExecutionState:
     """Adds an edge to the graph"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
@@ -228,9 +229,9 @@ async def delete_edge(
         return Response(status_code=404)
 
     try:
-        edge = (
-            EdgeConnection(node_id=from_node_id, field=from_field),
-            EdgeConnection(node_id=to_node_id, field=to_field),
+        edge = Edge(
+            source=EdgeConnection(node_id=from_node_id, field=from_field),
+            destination=EdgeConnection(node_id=to_node_id, field=to_field)
         )
         session.delete_edge(edge)
         ApiDependencies.invoker.services.graph_execution_manager.set(

invokeai/app/api_app.py

@@ -1,5 +1,4 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
 import asyncio
 from inspect import signature
 
@@ -53,11 +52,11 @@ config = {}
 # Add startup event to load dependencies
 @app.on_event("startup")
 async def startup_event():
-    args = Args()
-    config = args.parse_args()
+    config = Args()
+    config.parse_args()
 
     ApiDependencies.initialize(
-        args=args, config=config, event_handler_id=event_handler_id
+        config=config, event_handler_id=event_handler_id
     )
 
 
@@ -113,10 +112,8 @@ def custom_openapi():
         output_type_title = output_type_titles[output_type.__name__]
         invoker_schema = openapi_schema["components"]["schemas"][invoker_name]
         outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"}
-        if "additionalProperties" not in invoker_schema:
-            invoker_schema["additionalProperties"] = {}
 
-        invoker_schema["additionalProperties"]["outputs"] = outputs_ref
+        invoker_schema["output"] = outputs_ref
 
     app.openapi_schema = openapi_schema
     return app.openapi_schema

invokeai/app/cli_app.py

@@ -17,8 +17,9 @@ from .cli.commands import BaseCommand, CliContext, ExitCli, add_parsers, get_gra
 from .invocations import *
 from .invocations.baseinvocation import BaseInvocation
 from .services.events import EventServiceBase
-from .services.generate_initializer import get_generate
-from .services.graph import EdgeConnection, GraphExecutionState
+from .services.model_manager_initializer import get_model_manager
+from .services.restoration_services import RestorationServices
+from .services.graph import Edge, EdgeConnection, GraphExecutionState
 from .services.image_storage import DiskImageStorage
 from .services.invocation_queue import MemoryInvocationQueue
 from .services.invocation_services import InvocationServices
@@ -76,7 +77,7 @@ def get_command_parser() -> argparse.ArgumentParser:
 
 def generate_matching_edges(
     a: BaseInvocation, b: BaseInvocation
-) -> list[tuple[EdgeConnection, EdgeConnection]]:
+) -> list[Edge]:
     """Generates all possible edges between two invocations"""
     atype = type(a)
     btype = type(b)
@@ -93,24 +94,41 @@ def generate_matching_edges(
     matching_fields = matching_fields.difference(invalid_fields)
 
     edges = [
-        (
-            EdgeConnection(node_id=a.id, field=field),
-            EdgeConnection(node_id=b.id, field=field),
+        Edge(
+            source=EdgeConnection(node_id=a.id, field=field),
+            destination=EdgeConnection(node_id=b.id, field=field)
         )
         for field in matching_fields
     ]
     return edges
 
 
+class SessionError(Exception):
+    """Raised when a session error has occurred"""
+    pass
+
+
+def invoke_all(context: CliContext):
+    """Runs all invocations in the specified session"""
+    context.invoker.invoke(context.session, invoke_all=True)
+    while not context.get_session().is_complete():
+        # Wait some time
+        time.sleep(0.1)
+
+    # Print any errors
+    if context.session.has_error():
+        for n in context.session.errors:
+            print(
+                f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {context.session.errors[n]}"
+            )
+        
+        raise SessionError()
+
+
 def invoke_cli():
-    args = Args()
-    config = args.parse_args()
-
-    generate = get_generate(args, config)
-
-    # NOTE: load model on first use, uncomment to load at startup
-    # TODO: Make this a config option?
-    # generate.load_model()
+    config = Args()
+    config.parse_args()
+    model_manager = get_model_manager(config)
 
     events = EventServiceBase()
 
@@ -122,7 +140,7 @@ def invoke_cli():
     db_location = os.path.join(output_folder, "invokeai.db")
 
     services = InvocationServices(
-        generate=generate,
+        model_manager=model_manager,
         events=events,
         images=DiskImageStorage(output_folder),
         queue=MemoryInvocationQueue(),
@@ -130,11 +148,11 @@ def invoke_cli():
             filename=db_location, table_name="graph_executions"
         ),
         processor=DefaultInvocationProcessor(),
+        restoration=RestorationServices(config),
     )
 
     invoker = Invoker(services)
     session: GraphExecutionState = invoker.create_execution_state()
-
     parser = get_command_parser()
 
     # Uncomment to print out previous sessions at startup
@@ -151,8 +169,7 @@ def invoke_cli():
 
         try:
             # Refresh the state of the session
-            session = invoker.services.graph_execution_manager.get(session.id)
-            history = list(get_graph_execution_history(session))
+            history = list(get_graph_execution_history(context.session))
 
             # Split the command for piping
             cmds = cmd_input.split("|")
@@ -164,7 +181,7 @@ def invoke_cli():
                     raise InvalidArgs("Empty command")
 
                 # Parse args to create invocation
-                args = vars(parser.parse_args(shlex.split(cmd.strip())))
+                args = vars(context.parser.parse_args(shlex.split(cmd.strip())))
 
                 # Override defaults
                 for field_name, field_default in context.defaults.items():
@@ -176,16 +193,16 @@ def invoke_cli():
                 command = CliCommand(command=args)
 
                 # Run any CLI commands immediately
-                # TODO: this won't behave as expected if piping and using e.g. history,
-                # since invocations are gathered and then run together at the end.
-                # This is more efficient if the CLI is running against a distributed
-                # backend, so it's preferable not to change that behavior.
                 if isinstance(command.command, BaseCommand):
+                    # Invoke all current nodes to preserve operation order
+                    invoke_all(context)
+
+                    # Run the command
                     command.command.run(context)
                     continue
 
                 # Pipe previous command output (if there was a previous command)
-                edges = []
+                edges: list[Edge] = list()
                 if len(history) > 0 or current_id != start_id:
                     from_id = (
                         history[0] if current_id == start_id else str(current_id - 1)
@@ -193,7 +210,7 @@ def invoke_cli():
                     from_node = (
                         next(filter(lambda n: n[0].id == from_id, new_invocations))[0]
                         if current_id != start_id
-                        else session.graph.get_node(from_id)
+                        else context.session.graph.get_node(from_id)
                     )
                     matching_edges = generate_matching_edges(
                         from_node, command.command
@@ -203,23 +220,23 @@ def invoke_cli():
                 # Parse provided links
                 if "link_node" in args and args["link_node"]:
                     for link in args["link_node"]:
-                        link_node = session.graph.get_node(link)
+                        link_node = context.session.graph.get_node(link)
                         matching_edges = generate_matching_edges(
                             link_node, command.command
                         )
-                        matching_destinations = [e[1] for e in matching_edges]
-                        edges = [e for e in edges if e[1] not in matching_destinations]
+                        matching_destinations = [e.destination for e in matching_edges]
+                        edges = [e for e in edges if e.destination not in matching_destinations]
                         edges.extend(matching_edges)
 
                 if "link" in args and args["link"]:
                     for link in args["link"]:
-                        edges = [e for e in edges if e[1].node_id != command.command.id and e[1].field != link[2]]
+                        edges = [e for e in edges if e.destination.node_id != command.command.id and e.destination.field != link[2]]
                         edges.append(
-                            (
-                                EdgeConnection(node_id=link[1], field=link[0]),
-                                EdgeConnection(
+                            Edge(
+                                source=EdgeConnection(node_id=link[1], field=link[0]),
+                                destination=EdgeConnection(
                                     node_id=command.command.id, field=link[2]
-                                ),
+                                )
                             )
                         )
 
@@ -227,37 +244,24 @@ def invoke_cli():
 
                 current_id = current_id + 1
 
-            # Command line was parsed successfully
-            # Add the invocations to the session
-            for invocation in new_invocations:
-                session.add_node(invocation[0])
-                for edge in invocation[1]:
+                # Add the node to the session
+                context.session.add_node(command.command)
+                for edge in edges:
                     print(edge)
-                    session.add_edge(edge)
+                    context.session.add_edge(edge)
 
-            # Execute all available invocations
-            invoker.invoke(session, invoke_all=True)
-            while not session.is_complete():
-                # Wait some time
-                session = context.get_session()
-                time.sleep(0.1)
-
-            # Print any errors
-            if session.has_error():
-                for n in session.errors:
-                    print(
-                        f"Error in node {n} (source node {session.prepared_source_mapping[n]}): {session.errors[n]}"
-                    )
-
-                # Start a new session
-                print("Creating a new session")
-                session = invoker.create_execution_state()
-                context.session = session
+            # Execute all remaining nodes
+            invoke_all(context)
 
         except InvalidArgs:
             print('Invalid command, use "help" to list commands')
             continue
 
+        except SessionError:
+            # Start a new session
+            print("Session error: creating a new session")
+            context.session = context.invoker.create_execution_state()
+
         except ExitCli:
             break
 

invokeai/app/invocations/generate.py

@@ -4,6 +4,8 @@ from datetime import datetime, timezone
 from typing import Any, Literal, Optional, Union
 
 import numpy as np
+
+from torch import Tensor
 from PIL import Image
 from pydantic import Field
 from skimage.exposure.histogram_matching import match_histograms
@@ -12,12 +14,14 @@ from ..services.image_storage import ImageType
 from ..services.invocation_services import InvocationServices
 from .baseinvocation import BaseInvocation, InvocationContext
 from .image import ImageField, ImageOutput
+from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator, Generator
+from ...backend.stable_diffusion import PipelineIntermediateState
+from ...backend.util.util import image_to_dataURL
 
 SAMPLER_NAME_VALUES = Literal[
-    "ddim", "plms", "k_lms", "k_dpm_2", "k_dpm_2_a", "k_euler", "k_euler_a", "k_heun"
+    tuple(InvokeAIGenerator.schedulers())
 ]
 
-
 # Text to image
 class TextToImageInvocation(BaseInvocation):
     """Generates an image using text2img."""
@@ -41,35 +45,48 @@ class TextToImageInvocation(BaseInvocation):
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
     def dispatch_progress(
-        self, context: InvocationContext, sample: Any = None, step: int = 0
-    ) -> None:
+        self, context: InvocationContext, sample: Tensor, step: int
+    ) -> None:  
+        # TODO: only output a preview image when requested
+        image = Generator.sample_to_lowres_estimated_image(sample)
+
+        (width, height) = image.size
+        width *= 8
+        height *= 8
+
+        dataURL = image_to_dataURL(image, image_format="JPEG")
+
         context.services.events.emit_generator_progress(
             context.graph_execution_state_id,
             self.id,
+            {
+                "width": width,
+                "height": height,
+                "dataURL": dataURL
+            },
             step,
-            float(step) / float(self.steps),
+            self.steps,
         )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        def step_callback(sample, step=0):
-            self.dispatch_progress(context, sample, step)
+        def step_callback(state: PipelineIntermediateState):
+            self.dispatch_progress(context, state.latents, state.step)
 
         # Handle invalid model parameter
         # TODO: figure out if this can be done via a validator that uses the model_cache
         # TODO: How to get the default model name now?
-        if self.model is None or self.model == "":
-            self.model = context.services.generate.model_name
-
-        # Set the model (if already cached, this does nothing)
-        context.services.generate.set_model(self.model)
-
-        results = context.services.generate.prompt2image(
+        #       (right now uses whatever current model is set in model manager)
+        model= context.services.model_manager.get_model()
+        outputs = Txt2Img(model).generate(
             prompt=self.prompt,
             step_callback=step_callback,
             **self.dict(
                 exclude={"prompt"}
             ),  # Shorthand for passing all of the parameters above manually
         )
+        # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
+        # each time it is called. We only need the first one.
+        generate_output = next(outputs)
 
         # Results are image and seed, unwrap for now and ignore the seed
         # TODO: pre-seed?
@@ -78,7 +95,7 @@ class TextToImageInvocation(BaseInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-        context.services.images.save(image_type, image_name, results[0][0])
+        context.services.images.save(image_type, image_name, generate_output.image)
         return ImageOutput(
             image=ImageField(image_type=image_type, image_name=image_name)
         )
@@ -115,23 +132,20 @@ class ImageToImageInvocation(TextToImageInvocation):
         # Handle invalid model parameter
         # TODO: figure out if this can be done via a validator that uses the model_cache
         # TODO: How to get the default model name now?
-        if self.model is None or self.model == "":
-            self.model = context.services.generate.model_name
-
-        # Set the model (if already cached, this does nothing)
-        context.services.generate.set_model(self.model)
-
-        results = context.services.generate.prompt2image(
-            prompt=self.prompt,
-            init_img=image,
-            init_mask=mask,
-            step_callback=step_callback,
-            **self.dict(
-                exclude={"prompt", "image", "mask"}
-            ),  # Shorthand for passing all of the parameters above manually
+        model = context.services.model_manager.get_model()
+        generator_output = next(
+            Img2Img(model).generate(
+                prompt=self.prompt,
+                init_image=image,
+                init_mask=mask,
+                step_callback=step_callback,
+                **self.dict(
+                    exclude={"prompt", "image", "mask"}
+                ),  # Shorthand for passing all of the parameters above manually
+            )
         )
 
-        result_image = results[0][0]
+        result_image = generator_output.image
 
         # Results are image and seed, unwrap for now and ignore the seed
         # TODO: pre-seed?
@@ -145,7 +159,6 @@ class ImageToImageInvocation(TextToImageInvocation):
             image=ImageField(image_type=image_type, image_name=image_name)
         )
 
-
 class InpaintInvocation(ImageToImageInvocation):
     """Generates an image using inpaint."""
 
@@ -180,23 +193,20 @@ class InpaintInvocation(ImageToImageInvocation):
         # Handle invalid model parameter
         # TODO: figure out if this can be done via a validator that uses the model_cache
         # TODO: How to get the default model name now?
-        if self.model is None or self.model == "":
-            self.model = context.services.generate.model_name
-
-        # Set the model (if already cached, this does nothing)
-        context.services.generate.set_model(self.model)
-
-        results = context.services.generate.prompt2image(
-            prompt=self.prompt,
-            init_img=image,
-            init_mask=mask,
-            step_callback=step_callback,
-            **self.dict(
-                exclude={"prompt", "image", "mask"}
-            ),  # Shorthand for passing all of the parameters above manually
+        manager = context.services.model_manager.get_model()
+        generator_output = next(
+            Inpaint(model).generate(
+                prompt=self.prompt,
+                init_image=image,
+                mask_image=mask,
+                step_callback=step_callback,
+                **self.dict(
+                    exclude={"prompt", "image", "mask"}
+                ),  # Shorthand for passing all of the parameters above manually
+            )
         )
 
-        result_image = results[0][0]
+        result_image = generator_output.image
 
         # Results are image and seed, unwrap for now and ignore the seed
         # TODO: pre-seed?

invokeai/app/invocations/reconstruct.py

@@ -8,7 +8,6 @@ from ..services.invocation_services import InvocationServices
 from .baseinvocation import BaseInvocation, InvocationContext
 from .image import ImageField, ImageOutput
 
-
 class RestoreFaceInvocation(BaseInvocation):
     """Restores faces in an image."""
     #fmt: off
@@ -23,7 +22,7 @@ class RestoreFaceInvocation(BaseInvocation):
         image = context.services.images.get(
             self.image.image_type, self.image.image_name
         )
-        results = context.services.generate.upscale_and_reconstruct(
+        results = context.services.restoration.upscale_and_reconstruct(
             image_list=[[image, 0]],
             upscale=None,
             strength=self.strength,  # GFPGAN strength

invokeai/app/invocations/upscale.py

@@ -26,7 +26,7 @@ class UpscaleInvocation(BaseInvocation):
         image = context.services.images.get(
             self.image.image_type, self.image.image_name
         )
-        results = context.services.generate.upscale_and_reconstruct(
+        results = context.services.restoration.upscale_and_reconstruct(
             image_list=[[image, 0]],
             upscale=(self.level, self.strength),
             strength=0.0,  # GFPGAN strength

invokeai/app/services/events.py

@@ -1,7 +1,10 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Any, Dict
+from typing import Any, Dict, TypedDict
 
+ProgressImage = TypedDict(
+    "ProgressImage", {"dataURL": str, "width": int, "height": int}
+)
 
 class EventServiceBase:
     session_event: str = "session_event"
@@ -23,8 +26,9 @@ class EventServiceBase:
         self,
         graph_execution_state_id: str,
         invocation_id: str,
+        progress_image: ProgressImage | None,
         step: int,
-        percent: float,
+        total_steps: int,
     ) -> None:
         """Emitted when there is generation progress"""
         self.__emit_session_event(
@@ -32,8 +36,9 @@ class EventServiceBase:
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
                 invocation_id=invocation_id,
+                progress_image=progress_image,
                 step=step,
-                percent=percent,
+                total_steps=total_steps,
             ),
         )
 

invokeai/app/services/generate_initializer.py

@@ -1,255 +0,0 @@
-import os
-import sys
-import traceback
-from argparse import Namespace
-
-import invokeai.version
-from invokeai.backend import Generate, ModelManager
-
-from ...backend import Globals
-
-
-# TODO: most of this code should be split into individual services as the Generate.py code is deprecated
-def get_generate(args, config) -> Generate:
-    if not args.conf:
-        config_file = os.path.join(Globals.root, "configs", "models.yaml")
-        if not os.path.exists(config_file):
-            report_model_error(
-                args, FileNotFoundError(f"The file {config_file} could not be found.")
-            )
-
-    print(f">> {invokeai.version.__app_name__}, version {invokeai.version.__version__}")
-    print(f'>> InvokeAI runtime directory is "{Globals.root}"')
-
-    # these two lines prevent a horrible warning message from appearing
-    # when the frozen CLIP tokenizer is imported
-    import transformers  # type: ignore
-
-    transformers.logging.set_verbosity_error()
-    import diffusers
-
-    diffusers.logging.set_verbosity_error()
-
-    # Loading Face Restoration and ESRGAN Modules
-    gfpgan, codeformer, esrgan = load_face_restoration(args)
-
-    # normalize the config directory relative to root
-    if not os.path.isabs(args.conf):
-        args.conf = os.path.normpath(os.path.join(Globals.root, args.conf))
-
-    if args.embeddings:
-        if not os.path.isabs(args.embedding_path):
-            embedding_path = os.path.normpath(
-                os.path.join(Globals.root, args.embedding_path)
-            )
-        else:
-            embedding_path = args.embedding_path
-    else:
-        embedding_path = None
-
-    # migrate legacy models
-    ModelManager.migrate_models()
-
-    # load the infile as a list of lines
-    if args.infile:
-        try:
-            if os.path.isfile(args.infile):
-                infile = open(args.infile, "r", encoding="utf-8")
-            elif args.infile == "-":  # stdin
-                infile = sys.stdin
-            else:
-                raise FileNotFoundError(f"{args.infile} not found.")
-        except (FileNotFoundError, IOError) as e:
-            print(f"{e}. Aborting.")
-            sys.exit(-1)
-
-    # creating a Generate object:
-    try:
-        gen = Generate(
-            conf=args.conf,
-            model=args.model,
-            sampler_name=args.sampler_name,
-            embedding_path=embedding_path,
-            full_precision=args.full_precision,
-            precision=args.precision,
-            gfpgan=gfpgan,
-            codeformer=codeformer,
-            esrgan=esrgan,
-            free_gpu_mem=args.free_gpu_mem,
-            safety_checker=args.safety_checker,
-            max_loaded_models=args.max_loaded_models,
-        )
-    except (FileNotFoundError, TypeError, AssertionError) as e:
-        report_model_error(opt, e)
-    except (IOError, KeyError) as e:
-        print(f"{e}. Aborting.")
-        sys.exit(-1)
-
-    if args.seamless:
-        print(">> changed to seamless tiling mode")
-
-    # preload the model
-    try:
-        gen.load_model()
-    except KeyError:
-        pass
-    except Exception as e:
-        report_model_error(args, e)
-
-    # try to autoconvert new models
-    # autoimport new .ckpt files
-    if path := args.autoconvert:
-        gen.model_manager.autoconvert_weights(
-            conf_path=args.conf,
-            weights_directory=path,
-        )
-
-    return gen
-
-
-def load_face_restoration(opt):
-    try:
-        gfpgan, codeformer, esrgan = None, None, None
-        if opt.restore or opt.esrgan:
-            from invokeai.backend.restoration import Restoration
-
-            restoration = Restoration()
-            if opt.restore:
-                gfpgan, codeformer = restoration.load_face_restore_models(
-                    opt.gfpgan_model_path
-                )
-            else:
-                print(">> Face restoration disabled")
-            if opt.esrgan:
-                esrgan = restoration.load_esrgan(opt.esrgan_bg_tile)
-            else:
-                print(">> Upscaling disabled")
-        else:
-            print(">> Face restoration and upscaling disabled")
-    except (ModuleNotFoundError, ImportError):
-        print(traceback.format_exc(), file=sys.stderr)
-        print(">> You may need to install the ESRGAN and/or GFPGAN modules")
-    return gfpgan, codeformer, esrgan
-
-
-def report_model_error(opt: Namespace, e: Exception):
-    print(f'** An error occurred while attempting to initialize the model: "{str(e)}"')
-    print(
-        "** This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
-    )
-    yes_to_all = os.environ.get("INVOKE_MODEL_RECONFIGURE")
-    if yes_to_all:
-        print(
-            "** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
-        )
-    else:
-        response = input(
-            "Do you want to run invokeai-configure script to select and/or reinstall models? [y] "
-        )
-        if response.startswith(("n", "N")):
-            return
-
-    print("invokeai-configure is launching....\n")
-
-    # Match arguments that were set on the CLI
-    # only the arguments accepted by the configuration script are parsed
-    root_dir = ["--root", opt.root_dir] if opt.root_dir is not None else []
-    config = ["--config", opt.conf] if opt.conf is not None else []
-    previous_args = sys.argv
-    sys.argv = ["invokeai-configure"]
-    sys.argv.extend(root_dir)
-    sys.argv.extend(config)
-    if yes_to_all is not None:
-        for arg in yes_to_all.split():
-            sys.argv.append(arg)
-
-    from invokeai.frontend.install import invokeai_configure
-
-    invokeai_configure()
-    # TODO: Figure out how to restart
-    # print('** InvokeAI will now restart')
-    # sys.argv = previous_args
-    # main() # would rather do a os.exec(), but doesn't exist?
-    # sys.exit(0)
-
-
-# Temporary initializer for Generate until we migrate off of it
-def old_get_generate(args, config) -> Generate:
-    # TODO: Remove the need for globals
-    from invokeai.backend.globals import Globals
-
-    # alert - setting globals here
-    Globals.root = os.path.expanduser(
-        args.root_dir or os.environ.get("INVOKEAI_ROOT") or os.path.abspath(".")
-    )
-    Globals.try_patchmatch = args.patchmatch
-
-    print(f'>> InvokeAI runtime directory is "{Globals.root}"')
-
-    # these two lines prevent a horrible warning message from appearing
-    # when the frozen CLIP tokenizer is imported
-    import transformers
-
-    transformers.logging.set_verbosity_error()
-
-    # Loading Face Restoration and ESRGAN Modules
-    gfpgan, codeformer, esrgan = None, None, None
-    try:
-        if config.restore or config.esrgan:
-            from ldm.invoke.restoration import Restoration
-
-            restoration = Restoration()
-            if config.restore:
-                gfpgan, codeformer = restoration.load_face_restore_models(
-                    config.gfpgan_model_path
-                )
-            else:
-                print(">> Face restoration disabled")
-            if config.esrgan:
-                esrgan = restoration.load_esrgan(config.esrgan_bg_tile)
-            else:
-                print(">> Upscaling disabled")
-        else:
-            print(">> Face restoration and upscaling disabled")
-    except (ModuleNotFoundError, ImportError):
-        print(traceback.format_exc(), file=sys.stderr)
-        print(">> You may need to install the ESRGAN and/or GFPGAN modules")
-
-    # normalize the config directory relative to root
-    if not os.path.isabs(config.conf):
-        config.conf = os.path.normpath(os.path.join(Globals.root, config.conf))
-
-    if config.embeddings:
-        if not os.path.isabs(config.embedding_path):
-            embedding_path = os.path.normpath(
-                os.path.join(Globals.root, config.embedding_path)
-            )
-    else:
-        embedding_path = None
-
-    # TODO: lazy-initialize this by wrapping it
-    try:
-        generate = Generate(
-            conf=config.conf,
-            model=config.model,
-            sampler_name=config.sampler_name,
-            embedding_path=embedding_path,
-            full_precision=config.full_precision,
-            precision=config.precision,
-            gfpgan=gfpgan,
-            codeformer=codeformer,
-            esrgan=esrgan,
-            free_gpu_mem=config.free_gpu_mem,
-            safety_checker=config.safety_checker,
-            max_loaded_models=config.max_loaded_models,
-        )
-    except (FileNotFoundError, TypeError, AssertionError):
-        # emergency_model_reconfigure() # TODO?
-        sys.exit(-1)
-    except (IOError, KeyError) as e:
-        print(f"{e}. Aborting.")
-        sys.exit(-1)
-
-    generate.free_gpu_mem = config.free_gpu_mem
-
-    return generate

invokeai/app/services/graph.py

@@ -44,6 +44,11 @@ class EdgeConnection(BaseModel):
         return hash(f"{self.node_id}.{self.field}")
 
 
+class Edge(BaseModel):
+    source: EdgeConnection = Field(description="The connection for the edge's from node and field")
+    destination: EdgeConnection = Field(description="The connection for the edge's to node and field")
+
+
 def get_output_field(node: BaseInvocation, field: str) -> Any:
     node_type = type(node)
     node_outputs = get_type_hints(node_type.get_output_type())
@@ -194,7 +199,7 @@ class Graph(BaseModel):
     nodes: dict[str, Annotated[InvocationsUnion, Field(discriminator="type")]] = Field(
         description="The nodes in this graph", default_factory=dict
     )
-    edges: list[tuple[EdgeConnection, EdgeConnection]] = Field(
+    edges: list[Edge] = Field(
         description="The connections between nodes and their fields in this graph",
         default_factory=list,
     )
@@ -251,7 +256,7 @@ class Graph(BaseModel):
         except NodeNotFoundError:
             pass  # Ignore, not doesn't exist (should this throw?)
 
-    def add_edge(self, edge: tuple[EdgeConnection, EdgeConnection]) -> None:
+    def add_edge(self, edge: Edge) -> None:
         """Adds an edge to a graph
 
         :raises InvalidEdgeError: the provided edge is invalid.
@@ -262,7 +267,7 @@ class Graph(BaseModel):
         else:
             raise InvalidEdgeError()
 
-    def delete_edge(self, edge: tuple[EdgeConnection, EdgeConnection]) -> None:
+    def delete_edge(self, edge: Edge) -> None:
         """Deletes an edge from a graph"""
 
         try:
@@ -280,7 +285,7 @@ class Graph(BaseModel):
 
         # Validate all edges reference nodes in the graph
         node_ids = set(
-            [e[0].node_id for e in self.edges] + [e[1].node_id for e in self.edges]
+            [e.source.node_id for e in self.edges] + [e.destination.node_id for e in self.edges]
         )
         if not all((self.has_node(node_id) for node_id in node_ids)):
             return False
@@ -294,10 +299,10 @@ class Graph(BaseModel):
         if not all(
             (
                 are_connections_compatible(
-                    self.get_node(e[0].node_id),
-                    e[0].field,
-                    self.get_node(e[1].node_id),
-                    e[1].field,
+                    self.get_node(e.source.node_id),
+                    e.source.field,
+                    self.get_node(e.destination.node_id),
+                    e.destination.field,
                 )
                 for e in self.edges
             )
@@ -328,58 +333,58 @@ class Graph(BaseModel):
 
         return True
 
-    def _is_edge_valid(self, edge: tuple[EdgeConnection, EdgeConnection]) -> bool:
+    def _is_edge_valid(self, edge: Edge) -> bool:
         """Validates that a new edge doesn't create a cycle in the graph"""
 
         # Validate that the nodes exist (edges may contain node paths, so we can't just check for nodes directly)
         try:
-            from_node = self.get_node(edge[0].node_id)
-            to_node = self.get_node(edge[1].node_id)
+            from_node = self.get_node(edge.source.node_id)
+            to_node = self.get_node(edge.destination.node_id)
         except NodeNotFoundError:
             return False
 
         # Validate that an edge to this node+field doesn't already exist
-        input_edges = self._get_input_edges(edge[1].node_id, edge[1].field)
+        input_edges = self._get_input_edges(edge.destination.node_id, edge.destination.field)
         if len(input_edges) > 0 and not isinstance(to_node, CollectInvocation):
             return False
 
         # Validate that no cycles would be created
         g = self.nx_graph_flat()
-        g.add_edge(edge[0].node_id, edge[1].node_id)
+        g.add_edge(edge.source.node_id, edge.destination.node_id)
         if not nx.is_directed_acyclic_graph(g):
             return False
 
         # Validate that the field types are compatible
         if not are_connections_compatible(
-            from_node, edge[0].field, to_node, edge[1].field
+            from_node, edge.source.field, to_node, edge.destination.field
         ):
             return False
 
         # Validate if iterator output type matches iterator input type (if this edge results in both being set)
-        if isinstance(to_node, IterateInvocation) and edge[1].field == "collection":
+        if isinstance(to_node, IterateInvocation) and edge.destination.field == "collection":
             if not self._is_iterator_connection_valid(
-                edge[1].node_id, new_input=edge[0]
+                edge.destination.node_id, new_input=edge.source
             ):
                 return False
 
         # Validate if iterator input type matches output type (if this edge results in both being set)
-        if isinstance(from_node, IterateInvocation) and edge[0].field == "item":
+        if isinstance(from_node, IterateInvocation) and edge.source.field == "item":
             if not self._is_iterator_connection_valid(
-                edge[0].node_id, new_output=edge[1]
+                edge.source.node_id, new_output=edge.destination
             ):
                 return False
 
         # Validate if collector input type matches output type (if this edge results in both being set)
-        if isinstance(to_node, CollectInvocation) and edge[1].field == "item":
+        if isinstance(to_node, CollectInvocation) and edge.destination.field == "item":
             if not self._is_collector_connection_valid(
-                edge[1].node_id, new_input=edge[0]
+                edge.destination.node_id, new_input=edge.source
             ):
                 return False
 
         # Validate if collector output type matches input type (if this edge results in both being set)
-        if isinstance(from_node, CollectInvocation) and edge[0].field == "collection":
+        if isinstance(from_node, CollectInvocation) and edge.source.field == "collection":
             if not self._is_collector_connection_valid(
-                edge[0].node_id, new_output=edge[1]
+                edge.source.node_id, new_output=edge.destination
             ):
                 return False
 
@@ -438,15 +443,15 @@ class Graph(BaseModel):
                 # Remove the graph prefix from the node path
                 new_graph_node_path = (
                     new_node.id
-                    if "." not in edge[1].node_id
-                    else f'{edge[1].node_id[edge[1].node_id.rindex("."):]}.{new_node.id}'
+                    if "." not in edge.destination.node_id
+                    else f'{edge.destination.node_id[edge.destination.node_id.rindex("."):]}.{new_node.id}'
                 )
                 graph.add_edge(
-                    (
-                        edge[0],
-                        EdgeConnection(
-                            node_id=new_graph_node_path, field=edge[1].field
-                        ),
+                    Edge(
+                        source=edge.source,
+                        destination=EdgeConnection(
+                            node_id=new_graph_node_path, field=edge.destination.field
+                        )
                     )
                 )
 
@@ -454,51 +459,51 @@ class Graph(BaseModel):
                 # Remove the graph prefix from the node path
                 new_graph_node_path = (
                     new_node.id
-                    if "." not in edge[0].node_id
-                    else f'{edge[0].node_id[edge[0].node_id.rindex("."):]}.{new_node.id}'
+                    if "." not in edge.source.node_id
+                    else f'{edge.source.node_id[edge.source.node_id.rindex("."):]}.{new_node.id}'
                 )
                 graph.add_edge(
-                    (
-                        EdgeConnection(
-                            node_id=new_graph_node_path, field=edge[0].field
+                    Edge(
+                        source=EdgeConnection(
+                            node_id=new_graph_node_path, field=edge.source.field
                         ),
-                        edge[1],
+                        destination=edge.destination
                     )
                 )
 
     def _get_input_edges(
         self, node_path: str, field: Optional[str] = None
-    ) -> list[tuple[EdgeConnection, EdgeConnection]]:
+    ) -> list[Edge]:
         """Gets all input edges for a node"""
         edges = self._get_input_edges_and_graphs(node_path)
 
         # Filter to edges that match the field
-        filtered_edges = (e for e in edges if field is None or e[2][1].field == field)
+        filtered_edges = (e for e in edges if field is None or e[2].destination.field == field)
 
         # Create full node paths for each edge
         return [
-            (
-                EdgeConnection(
-                    node_id=self._get_node_path(e[0].node_id, prefix=prefix),
-                    field=e[0].field,
-                ),
-                EdgeConnection(
-                    node_id=self._get_node_path(e[1].node_id, prefix=prefix),
-                    field=e[1].field,
+            Edge(
+                source=EdgeConnection(
+                    node_id=self._get_node_path(e.source.node_id, prefix=prefix),
+                    field=e.source.field,
                 ),
+                destination=EdgeConnection(
+                    node_id=self._get_node_path(e.destination.node_id, prefix=prefix),
+                    field=e.destination.field,
+                )
             )
             for _, prefix, e in filtered_edges
         ]
 
     def _get_input_edges_and_graphs(
         self, node_path: str, prefix: Optional[str] = None
-    ) -> list[tuple["Graph", str, tuple[EdgeConnection, EdgeConnection]]]:
+    ) -> list[tuple["Graph", str, Edge]]:
         """Gets all input edges for a node along with the graph they are in and the graph's path"""
         edges = list()
 
         # Return any input edges that appear in this graph
         edges.extend(
-            [(self, prefix, e) for e in self.edges if e[1].node_id == node_path]
+            [(self, prefix, e) for e in self.edges if e.destination.node_id == node_path]
         )
 
         node_id = (
@@ -522,37 +527,37 @@ class Graph(BaseModel):
 
     def _get_output_edges(
         self, node_path: str, field: str
-    ) -> list[tuple[EdgeConnection, EdgeConnection]]:
+    ) -> list[Edge]:
         """Gets all output edges for a node"""
         edges = self._get_output_edges_and_graphs(node_path)
 
         # Filter to edges that match the field
-        filtered_edges = (e for e in edges if e[2][0].field == field)
+        filtered_edges = (e for e in edges if e[2].source.field == field)
 
         # Create full node paths for each edge
         return [
-            (
-                EdgeConnection(
-                    node_id=self._get_node_path(e[0].node_id, prefix=prefix),
-                    field=e[0].field,
-                ),
-                EdgeConnection(
-                    node_id=self._get_node_path(e[1].node_id, prefix=prefix),
-                    field=e[1].field,
+            Edge(
+                source=EdgeConnection(
+                    node_id=self._get_node_path(e.source.node_id, prefix=prefix),
+                    field=e.source.field,
                 ),
+                destination=EdgeConnection(
+                    node_id=self._get_node_path(e.destination.node_id, prefix=prefix),
+                    field=e.destination.field,
+                )
             )
             for _, prefix, e in filtered_edges
         ]
 
     def _get_output_edges_and_graphs(
         self, node_path: str, prefix: Optional[str] = None
-    ) -> list[tuple["Graph", str, tuple[EdgeConnection, EdgeConnection]]]:
+    ) -> list[tuple["Graph", str, Edge]]:
         """Gets all output edges for a node along with the graph they are in and the graph's path"""
         edges = list()
 
         # Return any input edges that appear in this graph
         edges.extend(
-            [(self, prefix, e) for e in self.edges if e[0].node_id == node_path]
+            [(self, prefix, e) for e in self.edges if e.source.node_id == node_path]
         )
 
         node_id = (
@@ -580,8 +585,8 @@ class Graph(BaseModel):
         new_input: Optional[EdgeConnection] = None,
         new_output: Optional[EdgeConnection] = None,
     ) -> bool:
-        inputs = list([e[0] for e in self._get_input_edges(node_path, "collection")])
-        outputs = list([e[1] for e in self._get_output_edges(node_path, "item")])
+        inputs = list([e.source for e in self._get_input_edges(node_path, "collection")])
+        outputs = list([e.destination for e in self._get_output_edges(node_path, "item")])
 
         if new_input is not None:
             inputs.append(new_input)
@@ -622,8 +627,8 @@ class Graph(BaseModel):
         new_input: Optional[EdgeConnection] = None,
         new_output: Optional[EdgeConnection] = None,
     ) -> bool:
-        inputs = list([e[0] for e in self._get_input_edges(node_path, "item")])
-        outputs = list([e[1] for e in self._get_output_edges(node_path, "collection")])
+        inputs = list([e.source for e in self._get_input_edges(node_path, "item")])
+        outputs = list([e.destination for e in self._get_output_edges(node_path, "collection")])
 
         if new_input is not None:
             inputs.append(new_input)
@@ -684,7 +689,7 @@ class Graph(BaseModel):
         # TODO: Cache this?
         g = nx.DiGraph()
         g.add_nodes_from([n for n in self.nodes.keys()])
-        g.add_edges_from(set([(e[0].node_id, e[1].node_id) for e in self.edges]))
+        g.add_edges_from(set([(e.source.node_id, e.destination.node_id) for e in self.edges]))
         return g
 
     def nx_graph_flat(
@@ -711,7 +716,7 @@ class Graph(BaseModel):
 
         # TODO: figure out if iteration nodes need to be expanded
 
-        unique_edges = set([(e[0].node_id, e[1].node_id) for e in self.edges])
+        unique_edges = set([(e.source.node_id, e.destination.node_id) for e in self.edges])
         g.add_edges_from(
             [
                 (self._get_node_path(e[0], prefix), self._get_node_path(e[1], prefix))
@@ -768,6 +773,24 @@ class GraphExecutionState(BaseModel):
         default_factory=dict,
     )
 
+    # Declare all fields as required; necessary for OpenAPI schema generation build.
+    # Technically only fields without a `default_factory` need to be listed here.
+    # See: https://github.com/pydantic/pydantic/discussions/4577
+    class Config:
+        schema_extra = {
+            'required': [
+                'id',
+                'graph',
+                'execution_graph',
+                'executed',
+                'executed_history',
+                'results',
+                'errors',
+                'prepared_source_mapping',
+                'source_prepared_mapping',
+            ]
+        }
+
     def next(self) -> BaseInvocation | None:
         """Gets the next node ready to execute."""
 
@@ -841,13 +864,13 @@ class GraphExecutionState(BaseModel):
             input_collection_prepared_node_id = next(
                 n[1]
                 for n in iteration_node_map
-                if n[0] == input_collection_edge[0].node_id
+                if n[0] == input_collection_edge.source.node_id
             )
             input_collection_prepared_node_output = self.results[
                 input_collection_prepared_node_id
             ]
             input_collection = getattr(
-                input_collection_prepared_node_output, input_collection_edge[0].field
+                input_collection_prepared_node_output, input_collection_edge.source.field
             )
             self_iteration_count = len(input_collection)
 
@@ -864,11 +887,11 @@ class GraphExecutionState(BaseModel):
         new_edges = list()
         for edge in input_edges:
             for input_node_id in (
-                n[1] for n in iteration_node_map if n[0] == edge[0].node_id
+                n[1] for n in iteration_node_map if n[0] == edge.source.node_id
             ):
-                new_edge = (
-                    EdgeConnection(node_id=input_node_id, field=edge[0].field),
-                    EdgeConnection(node_id="", field=edge[1].field),
+                new_edge = Edge(
+                    source=EdgeConnection(node_id=input_node_id, field=edge.source.field),
+                    destination=EdgeConnection(node_id="", field=edge.destination.field),
                 )
                 new_edges.append(new_edge)
 
@@ -893,9 +916,9 @@ class GraphExecutionState(BaseModel):
 
             # Add new edges to execution graph
             for edge in new_edges:
-                new_edge = (
-                    edge[0],
-                    EdgeConnection(node_id=new_node.id, field=edge[1].field),
+                new_edge = Edge(
+                    source=edge.source,
+                    destination=EdgeConnection(node_id=new_node.id, field=edge.destination.field),
                 )
                 self.execution_graph.add_edge(new_edge)
 
@@ -1043,26 +1066,26 @@ class GraphExecutionState(BaseModel):
         return self.execution_graph.nodes[next_node]
 
     def _prepare_inputs(self, node: BaseInvocation):
-        input_edges = [e for e in self.execution_graph.edges if e[1].node_id == node.id]
+        input_edges = [e for e in self.execution_graph.edges if e.destination.node_id == node.id]
         if isinstance(node, CollectInvocation):
             output_collection = [
-                getattr(self.results[edge[0].node_id], edge[0].field)
+                getattr(self.results[edge.source.node_id], edge.source.field)
                 for edge in input_edges
-                if edge[1].field == "item"
+                if edge.destination.field == "item"
             ]
             setattr(node, "collection", output_collection)
         else:
             for edge in input_edges:
-                output_value = getattr(self.results[edge[0].node_id], edge[0].field)
-                setattr(node, edge[1].field, output_value)
+                output_value = getattr(self.results[edge.source.node_id], edge.source.field)
+                setattr(node, edge.destination.field, output_value)
 
     # TODO: Add API for modifying underlying graph that checks if the change will be valid given the current execution state
-    def _is_edge_valid(self, edge: tuple[EdgeConnection, EdgeConnection]) -> bool:
+    def _is_edge_valid(self, edge: Edge) -> bool:
         if not self._is_edge_valid(edge):
             return False
 
         # Invalid if destination has already been prepared or executed
-        if edge[1].node_id in self.source_prepared_mapping:
+        if edge.destination.node_id in self.source_prepared_mapping:
             return False
 
         # Otherwise, the edge is valid
@@ -1089,17 +1112,17 @@ class GraphExecutionState(BaseModel):
             )
         self.graph.delete_node(node_path)
 
-    def add_edge(self, edge: tuple[EdgeConnection, EdgeConnection]) -> None:
-        if not self._is_node_updatable(edge[1].node_id):
+    def add_edge(self, edge: Edge) -> None:
+        if not self._is_node_updatable(edge.destination.node_id):
             raise NodeAlreadyExecutedError(
-                f"Destination node {edge[1].node_id} has already been prepared or executed and cannot be linked to"
+                f"Destination node {edge.destination.node_id} has already been prepared or executed and cannot be linked to"
             )
         self.graph.add_edge(edge)
 
-    def delete_edge(self, edge: tuple[EdgeConnection, EdgeConnection]) -> None:
-        if not self._is_node_updatable(edge[1].node_id):
+    def delete_edge(self, edge: Edge) -> None:
+        if not self._is_node_updatable(edge.destination.node_id):
             raise NodeAlreadyExecutedError(
-                f"Destination node {edge[1].node_id} has already been prepared or executed and cannot have a source edge deleted"
+                f"Destination node {edge.destination.node_id} has already been prepared or executed and cannot have a source edge deleted"
             )
         self.graph.delete_edge(edge)
 

invokeai/app/services/invocation_services.py

@@ -1,36 +1,39 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-from invokeai.backend import Generate
+from invokeai.backend import ModelManager
 
 from .events import EventServiceBase
 from .image_storage import ImageStorageBase
+from .restoration_services import RestorationServices
 from .invocation_queue import InvocationQueueABC
 from .item_storage import ItemStorageABC
 
-
 class InvocationServices:
     """Services that can be used by invocations"""
 
-    generate: Generate  # TODO: wrap Generate, or split it up from model?
     events: EventServiceBase
     images: ImageStorageBase
     queue: InvocationQueueABC
+    model_manager: ModelManager
+    restoration: RestorationServices
 
     # NOTE: we must forward-declare any types that include invocations, since invocations can use services
     graph_execution_manager: ItemStorageABC["GraphExecutionState"]
     processor: "InvocationProcessorABC"
 
     def __init__(
-        self,
-        generate: Generate,
-        events: EventServiceBase,
-        images: ImageStorageBase,
-        queue: InvocationQueueABC,
-        graph_execution_manager: ItemStorageABC["GraphExecutionState"],
-        processor: "InvocationProcessorABC",
+            self,
+            model_manager: ModelManager,
+            events: EventServiceBase,
+            images: ImageStorageBase,
+            queue: InvocationQueueABC,
+            graph_execution_manager: ItemStorageABC["GraphExecutionState"],
+            processor: "InvocationProcessorABC",
+            restoration: RestorationServices,
     ):
-        self.generate = generate
+        self.model_manager = model_manager
         self.events = events
         self.images = images
         self.queue = queue
         self.graph_execution_manager = graph_execution_manager
         self.processor = processor
+        self.restoration = restoration

invokeai/app/services/model_manager_initializer.py

@@ -0,0 +1,120 @@
+import os
+import sys
+import torch
+from argparse import Namespace
+from invokeai.backend import Args
+from omegaconf import OmegaConf
+from pathlib import Path
+
+import invokeai.version
+from ...backend import ModelManager
+from ...backend.util import choose_precision, choose_torch_device
+from ...backend import Globals
+
+# TODO: Replace with an abstract class base ModelManagerBase
+def get_model_manager(config: Args) -> ModelManager:
+    if not config.conf:
+        config_file = os.path.join(Globals.root, "configs", "models.yaml")
+        if not os.path.exists(config_file):
+            report_model_error(
+                config, FileNotFoundError(f"The file {config_file} could not be found.")
+            )
+
+    print(f">> {invokeai.version.__app_name__}, version {invokeai.version.__version__}")
+    print(f'>> InvokeAI runtime directory is "{Globals.root}"')
+
+    # these two lines prevent a horrible warning message from appearing
+    # when the frozen CLIP tokenizer is imported
+    import transformers  # type: ignore
+
+    transformers.logging.set_verbosity_error()
+    import diffusers
+
+    diffusers.logging.set_verbosity_error()
+
+    # normalize the config directory relative to root
+    if not os.path.isabs(config.conf):
+        config.conf = os.path.normpath(os.path.join(Globals.root, config.conf))
+
+    if config.embeddings:
+        if not os.path.isabs(config.embedding_path):
+            embedding_path = os.path.normpath(
+                os.path.join(Globals.root, config.embedding_path)
+            )
+        else:
+            embedding_path = config.embedding_path
+    else:
+        embedding_path = None
+
+    # migrate legacy models
+    ModelManager.migrate_models()
+
+    # creating the model manager
+    try:
+        device = torch.device(choose_torch_device())
+        precision = 'float16' if config.precision=='float16' \
+        else 'float32' if config.precision=='float32' \
+        else choose_precision(device)
+        
+        model_manager = ModelManager(
+            OmegaConf.load(config.conf),
+            precision=precision,
+            device_type=device,
+            max_loaded_models=config.max_loaded_models,
+            embedding_path = Path(embedding_path),
+        )
+    except (FileNotFoundError, TypeError, AssertionError) as e:
+        report_model_error(config, e)
+    except (IOError, KeyError) as e:
+        print(f"{e}. Aborting.")
+        sys.exit(-1)
+
+    # try to autoconvert new models
+    # autoimport new .ckpt files
+    if path := config.autoconvert:
+        model_manager.autoconvert_weights(
+            conf_path=config.conf,
+            weights_directory=path,
+        )
+
+    return model_manager
+
+def report_model_error(opt: Namespace, e: Exception):
+    print(f'** An error occurred while attempting to initialize the model: "{str(e)}"')
+    print(
+        "** This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
+    )
+    yes_to_all = os.environ.get("INVOKE_MODEL_RECONFIGURE")
+    if yes_to_all:
+        print(
+            "** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
+        )
+    else:
+        response = input(
+            "Do you want to run invokeai-configure script to select and/or reinstall models? [y] "
+        )
+        if response.startswith(("n", "N")):
+            return
+
+    print("invokeai-configure is launching....\n")
+
+    # Match arguments that were set on the CLI
+    # only the arguments accepted by the configuration script are parsed
+    root_dir = ["--root", opt.root_dir] if opt.root_dir is not None else []
+    config = ["--config", opt.conf] if opt.conf is not None else []
+    previous_config = sys.argv
+    sys.argv = ["invokeai-configure"]
+    sys.argv.extend(root_dir)
+    sys.argv.extend(config.to_dict())
+    if yes_to_all is not None:
+        for arg in yes_to_all.split():
+            sys.argv.append(arg)
+
+    from invokeai.frontend.install import invokeai_configure
+
+    invokeai_configure()
+    # TODO: Figure out how to restart
+    # print('** InvokeAI will now restart')
+    # sys.argv = previous_args
+    # main() # would rather do a os.exec(), but doesn't exist?
+    # sys.exit(0)

invokeai/app/services/restoration_services.py

@@ -0,0 +1,109 @@
+import sys
+import traceback
+import torch
+from ...backend.restoration import Restoration
+from ...backend.util import choose_torch_device, CPU_DEVICE, MPS_DEVICE
+
+# This should be a real base class for postprocessing functions,
+# but right now we just instantiate the existing gfpgan, esrgan
+# and codeformer functions.
+class RestorationServices:
+    '''Face restoration and upscaling'''
+    
+    def __init__(self,args):
+        try:
+            gfpgan, codeformer, esrgan = None, None, None
+            if args.restore or args.esrgan:
+                restoration = Restoration()
+                if args.restore:
+                    gfpgan, codeformer = restoration.load_face_restore_models(
+                        args.gfpgan_model_path
+                    )
+                else:
+                    print(">> Face restoration disabled")
+                    if args.esrgan:
+                        esrgan = restoration.load_esrgan(args.esrgan_bg_tile)
+                    else:
+                        print(">> Upscaling disabled")
+            else:
+                print(">> Face restoration and upscaling disabled")
+        except (ModuleNotFoundError, ImportError):
+            print(traceback.format_exc(), file=sys.stderr)
+            print(">> You may need to install the ESRGAN and/or GFPGAN modules")
+        self.device = torch.device(choose_torch_device())
+        self.gfpgan = gfpgan
+        self.codeformer = codeformer
+        self.esrgan = esrgan
+
+    # note that this one method does gfpgan and codepath reconstruction, as well as
+    # esrgan upscaling
+    # TO DO: refactor into separate methods
+    def upscale_and_reconstruct(
+        self,
+        image_list,
+        facetool="gfpgan",
+        upscale=None,
+        upscale_denoise_str=0.75,
+        strength=0.0,
+        codeformer_fidelity=0.75,
+        save_original=False,
+        image_callback=None,
+        prefix=None,
+    ):
+        results = []
+        for r in image_list:
+            image, seed = r
+            try:
+                if strength > 0:
+                    if self.gfpgan is not None or self.codeformer is not None:
+                        if facetool == "gfpgan":
+                            if self.gfpgan is None:
+                                print(
+                                    ">> GFPGAN not found. Face restoration is disabled."
+                                )
+                            else:
+                                image = self.gfpgan.process(image, strength, seed)
+                        if facetool == "codeformer":
+                            if self.codeformer is None:
+                                print(
+                                    ">> CodeFormer not found. Face restoration is disabled."
+                                )
+                            else:
+                                cf_device = (
+                                    CPU_DEVICE if self.device == MPS_DEVICE else self.device
+                                )
+                                image = self.codeformer.process(
+                                    image=image,
+                                    strength=strength,
+                                    device=cf_device,
+                                    seed=seed,
+                                    fidelity=codeformer_fidelity,
+                                )
+                    else:
+                        print(">> Face Restoration is disabled.")
+                if upscale is not None:
+                    if self.esrgan is not None:
+                        if len(upscale) < 2:
+                            upscale.append(0.75)
+                        image = self.esrgan.process(
+                            image,
+                            upscale[1],
+                            seed,
+                            int(upscale[0]),
+                            denoise_str=upscale_denoise_str,
+                        )
+                    else:
+                        print(">> ESRGAN is disabled. Image not upscaled.")
+            except Exception as e:
+                print(
+                    f">> Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
+                )
+
+            if image_callback is not None:
+                image_callback(image, seed, upscaled=True, use_prefix=prefix)
+            else:
+                r[0] = image
+
+            results.append([image, seed])
+
+        return results

invokeai/backend/__init__.py

@@ -2,6 +2,15 @@
 Initialization file for invokeai.backend
 """
 from .generate import Generate
+from .generator import (
+    InvokeAIGeneratorBasicParams,
+    InvokeAIGenerator,
+    InvokeAIGeneratorOutput,
+    Txt2Img,
+    Img2Img,
+    Inpaint
+)
 from .model_management import ModelManager
+from .safety_checker import SafetyChecker
 from .args import Args
 from .globals import Globals

invokeai/backend/args.py

@@ -490,7 +490,7 @@ class Args(object):
             "-z",
             type=int,
             default=6,
-            choices=range(0, 9),
+            choices=range(0, 10),
             dest="png_compression",
             help="level of PNG compression, from 0 (none) to 9 (maximum). Default is 6.",
         )
@@ -943,7 +943,6 @@ class Args(object):
             "--png_compression",
             "-z",
             type=int,
-            default=6,
             choices=range(0, 10),
             dest="png_compression",
             help="level of PNG compression, from 0 (none) to 9 (maximum). [6]",

invokeai/backend/generate.py

@@ -25,18 +25,19 @@ from accelerate.utils import set_seed
 from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.utils.import_utils import is_xformers_available
 from omegaconf import OmegaConf
+from pathlib import Path
 
 from .args import metadata_from_png
 from .generator import infill_methods
 from .globals import Globals, global_cache_dir
 from .image_util import InitImageResizer, PngWriter, Txt2Mask, configure_model_padding
 from .model_management import ModelManager
+from .safety_checker import SafetyChecker
 from .prompting import get_uc_and_c_and_ec
 from .prompting.conditioning import log_tokenization
 from .stable_diffusion import HuggingFaceConceptsLibrary
 from .util import choose_precision, choose_torch_device
 
-
 def fix_func(orig):
     if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
 
@@ -222,6 +223,7 @@ class Generate:
             self.precision,
             max_loaded_models=max_loaded_models,
             sequential_offload=self.free_gpu_mem,
+            embedding_path=Path(self.embedding_path),
         )
         # don't accept invalid models
         fallback = self.model_manager.default_model() or FALLBACK_MODEL_NAME
@@ -244,31 +246,8 @@ class Generate:
 
         # load safety checker if requested
         if safety_checker:
-            try:
-                print(">> Initializing NSFW checker")
-                from diffusers.pipelines.stable_diffusion.safety_checker import (
-                    StableDiffusionSafetyChecker,
-                )
-                from transformers import AutoFeatureExtractor
-
-                safety_model_id = "CompVis/stable-diffusion-safety-checker"
-                safety_model_path = global_cache_dir("hub")
-                self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(
-                    safety_model_id,
-                    local_files_only=True,
-                    cache_dir=safety_model_path,
-                )
-                self.safety_feature_extractor = AutoFeatureExtractor.from_pretrained(
-                    safety_model_id,
-                    local_files_only=True,
-                    cache_dir=safety_model_path,
-                )
-                self.safety_checker.to(self.device)
-            except Exception:
-                print(
-                    "** An error was encountered while installing the safety checker:"
-                )
-                print(traceback.format_exc())
+            print(">> Initializing NSFW checker")
+            self.safety_checker = SafetyChecker(self.device)
         else:
             print(">> NSFW checker is disabled")
 
@@ -495,18 +474,6 @@ class Generate:
             torch.cuda.reset_peak_memory_stats()
 
         results = list()
-        init_image = None
-        mask_image = None
-
-        try:
-            if (
-                self.free_gpu_mem
-                and self.model.cond_stage_model.device != self.model.device
-            ):
-                self.model.cond_stage_model.device = self.model.device
-                self.model.cond_stage_model.to(self.model.device)
-        except AttributeError:
-            pass
 
         try:
             uc, c, extra_conditioning_info = get_uc_and_c_and_ec(
@@ -535,15 +502,6 @@ class Generate:
             generator.set_variation(self.seed, variation_amount, with_variations)
             generator.use_mps_noise = use_mps_noise
 
-            checker = (
-                {
-                    "checker": self.safety_checker,
-                    "extractor": self.safety_feature_extractor,
-                }
-                if self.safety_checker
-                else None
-            )
-
             results = generator.generate(
                 prompt,
                 iterations=iterations,
@@ -570,7 +528,7 @@ class Generate:
                 embiggen_strength=embiggen_strength,
                 inpaint_replace=inpaint_replace,
                 mask_blur_radius=mask_blur_radius,
-                safety_checker=checker,
+                safety_checker=self.safety_checker,
                 seam_size=seam_size,
                 seam_blur=seam_blur,
                 seam_strength=seam_strength,
@@ -952,18 +910,6 @@ class Generate:
         self.generators = {}
 
         set_seed(random.randrange(0, np.iinfo(np.uint32).max))
-        if self.embedding_path is not None:
-            print(f">> Loading embeddings from {self.embedding_path}")
-            for root, _, files in os.walk(self.embedding_path):
-                for name in files:
-                    ti_path = os.path.join(root, name)
-                    self.model.textual_inversion_manager.load_textual_inversion(
-                        ti_path, defer_injecting_tokens=True
-                    )
-            print(
-                f'>> Textual inversion triggers: {", ".join(sorted(self.model.textual_inversion_manager.get_all_trigger_strings()))}'
-            )
-
         self.model_name = model_name
         self._set_scheduler()  # requires self.model_name to be set first
         return self.model
@@ -1010,7 +956,7 @@ class Generate:
     ):
         results = []
         for r in image_list:
-            image, seed = r
+            image, seed, _ = r
             try:
                 if strength > 0:
                     if self.gfpgan is not None or self.codeformer is not None:

invokeai/backend/generator/__init__.py

@@ -1,5 +1,13 @@
 """
 Initialization file for the invokeai.generator package
 """
-from .base import Generator
+from .base import (
+    InvokeAIGenerator,
+    InvokeAIGeneratorBasicParams,
+    InvokeAIGeneratorOutput,
+    Txt2Img,
+    Img2Img,
+    Inpaint,
+    Generator,
+)
 from .inpaint import infill_methods

invokeai/backend/generator/base.py

@@ -4,11 +4,15 @@ including img2img, txt2img, and inpaint
 """
 from __future__ import annotations
 
+import itertools
+import dataclasses
+import diffusers
 import os
 import random
 import traceback
+from abc import ABCMeta
+from argparse import Namespace
 from contextlib import nullcontext
-from pathlib import Path
 
 import cv2
 import numpy as np
@@ -17,12 +21,258 @@ from PIL import Image, ImageChops, ImageFilter
 from accelerate.utils import set_seed
 from diffusers import DiffusionPipeline
 from tqdm import trange
+from typing import List, Iterator, Type
+from dataclasses import dataclass, field
+from diffusers.schedulers import SchedulerMixin as Scheduler
 
-import invokeai.assets.web as web_assets
+from ..image_util import configure_model_padding
 from ..util.util import rand_perlin_2d
+from ..safety_checker import SafetyChecker
+from ..prompting.conditioning import get_uc_and_c_and_ec
+from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
 
 downsampling = 8
-CAUTION_IMG = "caution.png"
+
+@dataclass
+class InvokeAIGeneratorBasicParams:
+    seed: int=None
+    width: int=512
+    height: int=512
+    cfg_scale: int=7.5
+    steps: int=20
+    ddim_eta: float=0.0
+    scheduler: int='ddim'
+    precision: str='float16'
+    perlin: float=0.0
+    threshold: int=0.0
+    seamless: bool=False
+    seamless_axes: List[str]=field(default_factory=lambda: ['x', 'y'])
+    h_symmetry_time_pct: float=None
+    v_symmetry_time_pct: float=None
+    variation_amount: float = 0.0
+    with_variations: list=field(default_factory=list)
+    safety_checker: SafetyChecker=None
+
+@dataclass
+class InvokeAIGeneratorOutput:
+    '''
+    InvokeAIGeneratorOutput is a dataclass that contains the outputs of a generation
+    operation, including the image, its seed, the model name used to generate the image
+    and the model hash, as well as all the generate() parameters that went into
+    generating the image (in .params, also available as attributes)
+    '''
+    image: Image
+    seed: int
+    model_hash: str
+    attention_maps_images: List[Image]
+    params: Namespace
+
+# we are interposing a wrapper around the original Generator classes so that
+# old code that calls Generate will continue to work.
+class InvokeAIGenerator(metaclass=ABCMeta):
+    scheduler_map = dict(
+        ddim=diffusers.DDIMScheduler,
+        dpmpp_2=diffusers.DPMSolverMultistepScheduler,
+        k_dpm_2=diffusers.KDPM2DiscreteScheduler,
+        k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
+        k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
+        k_euler=diffusers.EulerDiscreteScheduler,
+        k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
+        k_heun=diffusers.HeunDiscreteScheduler,
+        k_lms=diffusers.LMSDiscreteScheduler,
+        plms=diffusers.PNDMScheduler,
+    )
+
+    def __init__(self,
+                 model_info: dict,
+                 params: InvokeAIGeneratorBasicParams=InvokeAIGeneratorBasicParams(),
+                 ):
+        self.model_info=model_info
+        self.params=params
+
+    def generate(self,
+                 prompt: str='',
+                 callback: callable=None,
+                 step_callback: callable=None,
+                 iterations: int=1,
+                 **keyword_args,
+                 )->Iterator[InvokeAIGeneratorOutput]:
+        '''
+        Return an iterator across the indicated number of generations.
+        Each time the iterator is called it will return an InvokeAIGeneratorOutput
+        object. Use like this:
+
+           outputs = txt2img.generate(prompt='banana sushi', iterations=5)
+           for result in outputs:
+               print(result.image, result.seed)
+
+        In the typical case of wanting to get just a single image, iterations
+        defaults to 1 and do:
+
+           output = next(txt2img.generate(prompt='banana sushi')
+
+        Pass None to get an infinite iterator.
+
+           outputs = txt2img.generate(prompt='banana sushi', iterations=None)
+           for o in outputs:
+               print(o.image, o.seed)
+
+        '''
+        generator_args = dataclasses.asdict(self.params)
+        generator_args.update(keyword_args)
+
+        model_info = self.model_info
+        model_name = model_info['model_name']
+        model:StableDiffusionGeneratorPipeline = model_info['model']
+        model_hash = model_info['hash']
+        scheduler: Scheduler = self.get_scheduler(
+            model=model,
+            scheduler_name=generator_args.get('scheduler')
+        )
+        uc, c, extra_conditioning_info = get_uc_and_c_and_ec(prompt,model=model)
+        gen_class = self._generator_class()
+        generator = gen_class(model, self.params.precision)
+        if self.params.variation_amount > 0:
+            generator.set_variation(generator_args.get('seed'),
+                                    generator_args.get('variation_amount'),
+                                    generator_args.get('with_variations')
+                                    )
+
+        if isinstance(model, DiffusionPipeline):
+            for component in [model.unet, model.vae]:
+                configure_model_padding(component,
+                                        generator_args.get('seamless',False),
+                                        generator_args.get('seamless_axes')
+                                        )
+        else:
+            configure_model_padding(model,
+                                    generator_args.get('seamless',False),
+                                    generator_args.get('seamless_axes')
+                                    )
+
+        iteration_count = range(iterations) if iterations else itertools.count(start=0, step=1)
+        for i in iteration_count:
+            results = generator.generate(prompt,
+                                         conditioning=(uc, c, extra_conditioning_info),
+                                         step_callback=step_callback,
+                                         sampler=scheduler,
+                                         **generator_args,
+                                         )
+            output = InvokeAIGeneratorOutput(
+                image=results[0][0],
+                seed=results[0][1],
+                attention_maps_images=results[0][2],
+                model_hash = model_hash,
+                params=Namespace(model_name=model_name,**generator_args),
+            )
+            if callback:
+                callback(output)
+            yield output
+
+    @classmethod
+    def schedulers(self)->List[str]:
+        '''
+        Return list of all the schedulers that we currently handle.
+        '''
+        return list(self.scheduler_map.keys())
+
+    def load_generator(self, model: StableDiffusionGeneratorPipeline, generator_class: Type[Generator]):
+        return generator_class(model, self.params.precision)
+
+    def get_scheduler(self, scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
+        scheduler_class = self.scheduler_map.get(scheduler_name,'ddim')
+        scheduler = scheduler_class.from_config(model.scheduler.config)
+        # hack copied over from generate.py
+        if not hasattr(scheduler, 'uses_inpainting_model'):
+            scheduler.uses_inpainting_model = lambda: False
+        return scheduler
+
+    @classmethod
+    def _generator_class(cls)->Type[Generator]:
+        '''
+        In derived classes return the name of the generator to apply.
+        If you don't override will return the name of the derived
+        class, which nicely parallels the generator class names.
+        '''
+        return Generator
+
+# ------------------------------------
+class Txt2Img(InvokeAIGenerator):
+    @classmethod
+    def _generator_class(cls):
+        from .txt2img import Txt2Img
+        return Txt2Img
+
+# ------------------------------------
+class Img2Img(InvokeAIGenerator):
+    def generate(self,
+               init_image: Image | torch.FloatTensor,
+               strength: float=0.75,
+               **keyword_args
+               )->List[InvokeAIGeneratorOutput]:
+        return super().generate(init_image=init_image,
+                                strength=strength,
+                                **keyword_args
+                                )
+    @classmethod
+    def _generator_class(cls):
+        from .img2img import Img2Img
+        return Img2Img
+
+# ------------------------------------
+# Takes all the arguments of Img2Img and adds the mask image and the seam/infill stuff
+class Inpaint(Img2Img):
+    def generate(self,
+                 mask_image: Image | torch.FloatTensor,
+                 # Seam settings - when 0, doesn't fill seam
+                 seam_size: int = 0,
+                 seam_blur: int = 0,
+                 seam_strength: float = 0.7,
+                 seam_steps: int = 10,
+                 tile_size: int = 32,
+                 inpaint_replace=False,
+                 infill_method=None,
+                 inpaint_width=None,
+                 inpaint_height=None,
+                 inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF),
+                 **keyword_args
+                 )->List[InvokeAIGeneratorOutput]:
+        return super().generate(
+            mask_image=mask_image,
+            seam_size=seam_size,
+            seam_blur=seam_blur,
+            seam_strength=seam_strength,
+            seam_steps=seam_steps,
+            tile_size=tile_size,
+            inpaint_replace=inpaint_replace,
+            infill_method=infill_method,
+            inpaint_width=inpaint_width,
+            inpaint_height=inpaint_height,
+            inpaint_fill=inpaint_fill,
+            **keyword_args
+        )
+    @classmethod
+    def _generator_class(cls):
+        from .inpaint import Inpaint
+        return Inpaint
+
+# ------------------------------------
+class Embiggen(Txt2Img):
+    def generate(
+            self,
+            embiggen: list=None,
+            embiggen_tiles: list = None,
+            strength: float=0.75,
+            **kwargs)->List[InvokeAIGeneratorOutput]:
+        return super().generate(embiggen=embiggen,
+                                embiggen_tiles=embiggen_tiles,
+                                strength=strength,
+                                **kwargs)
+
+    @classmethod
+    def _generator_class(cls):
+        from .embiggen import Embiggen
+        return Embiggen
 
 
 class Generator:
@@ -44,7 +294,6 @@ class Generator:
         self.with_variations = []
         self.use_mps_noise = False
         self.free_gpu_mem = None
-        self.caution_img = None
 
     # this is going to be overridden in img2img.py, txt2img.py and inpaint.py
     def get_make_image(self, prompt, **kwargs):
@@ -64,10 +313,10 @@ class Generator:
     def generate(
         self,
         prompt,
-        init_image,
         width,
         height,
         sampler,
+        init_image=None,
         iterations=1,
         seed=None,
         image_callback=None,
@@ -76,7 +325,7 @@ class Generator:
         perlin=0.0,
         h_symmetry_time_pct=None,
         v_symmetry_time_pct=None,
-        safety_checker: dict = None,
+        safety_checker: SafetyChecker=None,
         free_gpu_mem: bool = False,
         **kwargs,
     ):
@@ -126,12 +375,13 @@ class Generator:
                         print("** An error occurred while getting initial noise **")
                         print(traceback.format_exc())
 
-                image = make_image(x_T)
+                # Pass on the seed in case a layer beneath us needs to generate noise on its own.
+                image = make_image(x_T, seed)
 
                 if self.safety_checker is not None:
-                    image = self.safety_check(image)
+                    image = self.safety_checker.check(image)
 
-                results.append([image, seed])
+                results.append([image, seed, attention_maps_images])
 
                 if image_callback is not None:
                     attention_maps_image = (
@@ -248,7 +498,8 @@ class Generator:
         matched_result.paste(init_image, (0, 0), mask=multiplied_blurred_init_mask)
         return matched_result
 
-    def sample_to_lowres_estimated_image(self, samples):
+    @staticmethod
+    def sample_to_lowres_estimated_image(samples):
         # origingally adapted from code by @erucipe and @keturn here:
         # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
 
@@ -289,19 +540,7 @@ class Generator:
             if self.variation_amount > 0:
                 random.seed()  # reset RNG to an actually random state, so we can get a random seed for variations
                 seed = random.randrange(0, np.iinfo(np.uint32).max)
-            return (seed, initial_noise)
-        else:
-            return (seed, None)
-
-    # returns a tensor filled with random numbers from a normal distribution
-    def get_noise(self, width, height):
-        """
-        Returns a tensor filled with random numbers, either form a normal distribution
-        (txt2img) or from the latent image (img2img, inpaint)
-        """
-        raise NotImplementedError(
-            "get_noise() must be implemented in a descendent class"
-        )
+        return (seed, initial_noise)
 
     def get_perlin_noise(self, width, height):
         fixdevice = "cpu" if (self.model.device.type == "mps") else self.model.device
@@ -362,53 +601,6 @@ class Generator:
 
         return v2
 
-    def safety_check(self, image: Image.Image):
-        """
-        If the CompViz safety checker flags an NSFW image, we
-        blur it out.
-        """
-        import diffusers
-
-        checker = self.safety_checker["checker"]
-        extractor = self.safety_checker["extractor"]
-        features = extractor([image], return_tensors="pt")
-        features.to(self.model.device)
-
-        # unfortunately checker requires the numpy version, so we have to convert back
-        x_image = np.array(image).astype(np.float32) / 255.0
-        x_image = x_image[None].transpose(0, 3, 1, 2)
-
-        diffusers.logging.set_verbosity_error()
-        checked_image, has_nsfw_concept = checker(
-            images=x_image, clip_input=features.pixel_values
-        )
-        if has_nsfw_concept[0]:
-            print(
-                "** An image with potential non-safe content has been detected. A blurred image will be returned. **"
-            )
-            return self.blur(image)
-        else:
-            return image
-
-    def blur(self, input):
-        blurry = input.filter(filter=ImageFilter.GaussianBlur(radius=32))
-        try:
-            caution = self.get_caution_img()
-            if caution:
-                blurry.paste(caution, (0, 0), caution)
-        except FileNotFoundError:
-            pass
-        return blurry
-
-    def get_caution_img(self):
-        path = None
-        if self.caution_img:
-            return self.caution_img
-        path = Path(web_assets.__path__[0]) / CAUTION_IMG
-        caution = Image.open(path)
-        self.caution_img = caution.resize((caution.width // 2, caution.height // 2))
-        return self.caution_img
-
     # this is a handy routine for debugging use. Given a generated sample,
     # convert it into a PNG image and store it at the indicated path
     def save_sample(self, sample, filepath):

invokeai/backend/generator/img2img.py

@@ -1,8 +1,10 @@
 """
 invokeai.backend.generator.img2img descends from .generator
 """
+from typing import Optional
 
 import torch
+from accelerate.utils import set_seed
 from diffusers import logging
 
 from ..stable_diffusion import (
@@ -61,10 +63,11 @@ class Img2Img(Generator):
             ),
         ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
 
-        def make_image(x_T):
+        def make_image(x_T: torch.Tensor, seed: int):
             # FIXME: use x_T for initial seeded noise
             # We're not at the moment because the pipeline automatically resizes init_image if
             # necessary, which the x_T input might not match.
+            # In the meantime, reset the seed prior to generating pipeline output so we at least get the same result.
             logging.set_verbosity_error()  # quench safety check warnings
             pipeline_output = pipeline.img2img_from_embeddings(
                 init_image,
@@ -73,6 +76,7 @@ class Img2Img(Generator):
                 conditioning_data,
                 noise_func=self.get_noise_like,
                 callback=step_callback,
+                seed=seed,
             )
             if (
                 pipeline_output.attention_map_saver is not None

invokeai/backend/generator/inpaint.py

@@ -159,6 +159,7 @@ class Inpaint(Img2Img):
         seam_size: int,
         seam_blur: int,
         prompt,
+        seed,
         sampler,
         steps,
         cfg_scale,
@@ -192,7 +193,7 @@ class Inpaint(Img2Img):
 
         seam_noise = self.get_noise(im.width, im.height)
 
-        result = make_image(seam_noise)
+        result = make_image(seam_noise, seed)
 
         return result
 
@@ -310,7 +311,7 @@ class Inpaint(Img2Img):
             uc, c, cfg_scale
         ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
 
-        def make_image(x_T):
+        def make_image(x_T: torch.Tensor, seed: int):
             pipeline_output = pipeline.inpaint_from_embeddings(
                 init_image=init_image,
                 mask=1 - mask,  # expects white means "paint here."
@@ -319,6 +320,7 @@ class Inpaint(Img2Img):
                 conditioning_data=conditioning_data,
                 noise_func=self.get_noise_like,
                 callback=step_callback,
+                seed=seed,
             )
 
             if (
@@ -341,6 +343,7 @@ class Inpaint(Img2Img):
                     seam_size,
                     seam_blur,
                     prompt,
+                    seed,
                     sampler,
                     seam_steps,
                     cfg_scale,

invokeai/backend/generator/txt2img.py

@@ -61,7 +61,7 @@ class Txt2Img(Generator):
             ),
         ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
 
-        def make_image(x_T) -> PIL.Image.Image:
+        def make_image(x_T: torch.Tensor, _: int) -> PIL.Image.Image:
             pipeline_output = pipeline.image_from_embeddings(
                 latents=torch.zeros_like(x_T, dtype=self.torch_dtype()),
                 noise=x_T,

invokeai/backend/generator/txt2img2img.py

@@ -64,7 +64,7 @@ class Txt2Img2Img(Generator):
             ),
         ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
 
-        def make_image(x_T):
+        def make_image(x_T: torch.Tensor, _: int):
             first_pass_latent_output, _ = pipeline.latents_from_embeddings(
                 latents=torch.zeros_like(x_T),
                 num_inference_steps=steps,

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -1075,9 +1075,10 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         dlogging.set_verbosity_error()
 
         checkpoint = (
-            load_file(checkpoint_path)
-            if Path(checkpoint_path).suffix == ".safetensors"
-            else torch.load(checkpoint_path)
+            torch.load(checkpoint_path)
+            if Path(checkpoint_path).suffix == ".ckpt"
+            else load_file(checkpoint_path)
+            
         )
         cache_dir = global_cache_dir("hub")
         pipeline_class = (
@@ -1274,7 +1275,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
                 tokenizer=tokenizer,
                 unet=unet.to(precision),
                 scheduler=scheduler,
-                safety_checker=safety_checker.to(precision),
+                safety_checker=None if return_generator_pipeline else safety_checker.to(precision),
                 feature_extractor=feature_extractor,
             )
         else:

invokeai/backend/model_management/model_manager.py

@@ -34,8 +34,7 @@ from picklescan.scanner import scan_file_path
 from invokeai.backend.globals import Globals, global_cache_dir
 
 from ..stable_diffusion import StableDiffusionGeneratorPipeline
-from ..util import CPU_DEVICE, ask_user, download_with_resume
-
+from ..util import CUDA_DEVICE, CPU_DEVICE, ask_user, download_with_resume
 
 class SDLegacyType(Enum):
     V1 = 1
@@ -51,23 +50,29 @@ VAE_TO_REPO_ID = {  # hack, see note in convert_and_import()
 }
 
 class ModelManager(object):
+    '''
+    Model manager handles loading, caching, importing, deleting, converting, and editing models.
+    '''
     def __init__(
-        self,
-        config: OmegaConf,
-        device_type: torch.device = CPU_DEVICE,
-        precision: str = "float16",
-        max_loaded_models=DEFAULT_MAX_MODELS,
-        sequential_offload=False,
+            self,
+            config: OmegaConf|Path,
+            device_type: torch.device = CUDA_DEVICE,
+            precision: str = "float16",
+            max_loaded_models=DEFAULT_MAX_MODELS,
+            sequential_offload=False,
+            embedding_path: Path=None,
     ):
         """
-        Initialize with the path to the models.yaml config file,
-        the torch device type, and precision. The optional
-        min_avail_mem argument specifies how much unused system
-        (CPU) memory to preserve. The cache of models in RAM will
-        grow until this value is approached. Default is 2G.
+        Initialize with the path to the models.yaml config file or
+        an initialized OmegaConf dictionary. Optional parameters
+        are the torch device type, precision, max_loaded_models,
+        and sequential_offload boolean. Note that the default device 
+        type and precision are set up for a CUDA system running at half precision.
         """
         # prevent nasty-looking CLIP log message
         transformers.logging.set_verbosity_error()
+        if not isinstance(config, DictConfig):
+            config = OmegaConf.load(config)
         self.config = config
         self.precision = precision
         self.device = torch.device(device_type)
@@ -76,6 +81,7 @@ class ModelManager(object):
         self.stack = []  # this is an LRU FIFO
         self.current_model = None
         self.sequential_offload = sequential_offload
+        self.embedding_path = embedding_path
 
     def valid_model(self, model_name: str) -> bool:
         """
@@ -84,12 +90,15 @@ class ModelManager(object):
         """
         return model_name in self.config
 
-    def get_model(self, model_name: str):
+    def get_model(self, model_name: str=None)->dict:
         """
         Given a model named identified in models.yaml, return
         the model object. If in RAM will load into GPU VRAM.
         If on disk, will load from there.
         """
+        if not model_name:
+            return self.get_model(self.current_model) if self.current_model else self.get_model(self.default_model())
+        
         if not self.valid_model(model_name):
             print(
                 f'** "{model_name}" is not a known model name. Please check your models.yaml file'
@@ -104,7 +113,7 @@ class ModelManager(object):
         if model_name in self.models:
             requested_model = self.models[model_name]["model"]
             print(f">> Retrieving model {model_name} from system RAM cache")
-            self.models[model_name]["model"] = self._model_from_cpu(requested_model)
+            requested_model.ready()
             width = self.models[model_name]["width"]
             height = self.models[model_name]["height"]
             hash = self.models[model_name]["hash"]
@@ -112,6 +121,7 @@ class ModelManager(object):
         else:  # we're about to load a new model, so potentially offload the least recently used one
             requested_model, width, height, hash = self._load_model(model_name)
             self.models[model_name] = {
+                "model_name": model_name,
                 "model": requested_model,
                 "width": width,
                 "height": height,
@@ -121,6 +131,7 @@ class ModelManager(object):
         self.current_model = model_name
         self._push_newest_model(model_name)
         return {
+            "model_name": model_name,
             "model": requested_model,
             "width": width,
             "height": height,
@@ -351,6 +362,7 @@ class ModelManager(object):
             raise NotImplementedError(
                 f"Unknown model format {model_name}: {model_format}"
             )
+        self._add_embeddings_to_model(model)
         
         # usage statistics
         toc = time.time()
@@ -499,7 +511,7 @@ class ModelManager(object):
 
         print(f">> Offloading {model_name} to CPU")
         model = self.models[model_name]["model"]
-        self.models[model_name]["model"] = self._model_to_cpu(model)
+        model.offload_all()
 
         gc.collect()
         if self._has_cuda():
@@ -557,7 +569,7 @@ class ModelManager(object):
         """
         model_name = model_name or Path(repo_or_path).stem
         model_description = (
-            model_description or f"Imported diffusers model {model_name}"
+            description or f"Imported diffusers model {model_name}"
         )
         new_config = dict(
             description=model_description,
@@ -720,9 +732,9 @@ class ModelManager(object):
 
         # another round of heuristics to guess the correct config file.
         checkpoint = (
-            safetensors.torch.load_file(model_path)
-            if model_path.suffix == ".safetensors"
-            else torch.load(model_path)
+            torch.load(model_path)
+            if model_path.suffix == ".ckpt"
+            else safetensors.torch.load_file(model_path)
         )
 
         # additional probing needed if no config file provided
@@ -1044,43 +1056,6 @@ class ModelManager(object):
             self.stack.remove(model_name)
         self.models.pop(model_name, None)
 
-    def _model_to_cpu(self, model):
-        if self.device == CPU_DEVICE:
-            return model
-
-        if isinstance(model, StableDiffusionGeneratorPipeline):
-            model.offload_all()
-            return model
-
-        model.cond_stage_model.device = CPU_DEVICE
-        model.to(CPU_DEVICE)
-
-        for submodel in ("first_stage_model", "cond_stage_model", "model"):
-            try:
-                getattr(model, submodel).to(CPU_DEVICE)
-            except AttributeError:
-                pass
-        return model
-
-    def _model_from_cpu(self, model):
-        if self.device == CPU_DEVICE:
-            return model
-
-        if isinstance(model, StableDiffusionGeneratorPipeline):
-            model.ready()
-            return model
-
-        model.to(self.device)
-        model.cond_stage_model.device = self.device
-
-        for submodel in ("first_stage_model", "cond_stage_model", "model"):
-            try:
-                getattr(model, submodel).to(self.device)
-            except AttributeError:
-                pass
-
-        return model
-
     def _pop_oldest_model(self):
         """
         Remove the first element of the FIFO, which ought
@@ -1098,6 +1073,19 @@ class ModelManager(object):
             self.stack.remove(model_name)
         self.stack.append(model_name)
 
+    def _add_embeddings_to_model(self, model: StableDiffusionGeneratorPipeline):
+        if self.embedding_path is not None:
+            print(f">> Loading embeddings from {self.embedding_path}")
+            for root, _, files in os.walk(self.embedding_path):
+                for name in files:
+                    ti_path = os.path.join(root, name)
+                    model.textual_inversion_manager.load_textual_inversion(
+                        ti_path, defer_injecting_tokens=True
+                    )
+            print(
+                f'>> Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}'
+            )
+            
     def _has_cuda(self) -> bool:
         return self.device.type == "cuda"
 

invokeai/backend/prompting/__init__.py

@@ -3,7 +3,6 @@ Initialization file for invokeai.backend.prompting
 """
 from .conditioning import (
     get_prompt_structure,
-    get_tokenizer,
     get_tokens_for_prompt_object,
     get_uc_and_c_and_ec,
     split_weighted_subprompts,

invokeai/backend/prompting/conditioning.py

@@ -7,7 +7,7 @@ get_uc_and_c_and_ec()           get the conditioned and unconditioned latent, an
 
 """
 import re
-from typing import Any, Optional, Union
+from typing import Optional, Union
 
 from compel import Compel
 from compel.prompt_parser import (
@@ -17,7 +17,6 @@ from compel.prompt_parser import (
     Fragment,
     PromptParser,
 )
-from transformers import CLIPTextModel, CLIPTokenizer
 
 from invokeai.backend.globals import Globals
 
@@ -25,36 +24,6 @@ from ..stable_diffusion import InvokeAIDiffuserComponent
 from ..util import torch_dtype
 
 
-def get_tokenizer(model) -> CLIPTokenizer:
-    # TODO remove legacy ckpt fallback handling
-    return (
-        getattr(model, "tokenizer", None)  # diffusers
-        or model.cond_stage_model.tokenizer
-    )  # ldm
-
-
-def get_text_encoder(model) -> Any:
-    # TODO remove legacy ckpt fallback handling
-    return getattr(
-        model, "text_encoder", None
-    ) or UnsqueezingLDMTransformer(  # diffusers
-        model.cond_stage_model.transformer
-    )  # ldm
-
-
-class UnsqueezingLDMTransformer:
-    def __init__(self, ldm_transformer):
-        self.ldm_transformer = ldm_transformer
-
-    @property
-    def device(self):
-        return self.ldm_transformer.device
-
-    def __call__(self, *args, **kwargs):
-        insufficiently_unsqueezed_tensor = self.ldm_transformer(*args, **kwargs)
-        return insufficiently_unsqueezed_tensor.unsqueeze(0)
-
-
 def get_uc_and_c_and_ec(
     prompt_string, model, log_tokens=False, skip_normalize_legacy_blend=False
 ):
@@ -64,13 +33,13 @@ def get_uc_and_c_and_ec(
         prompt_string
     )
 
-    tokenizer = get_tokenizer(model)
-    text_encoder = get_text_encoder(model)
+    tokenizer = model.tokenizer
     compel = Compel(
         tokenizer=tokenizer,
-        text_encoder=text_encoder,
+        text_encoder=model.text_encoder,
         textual_inversion_manager=model.textual_inversion_manager,
         dtype_for_device_getter=torch_dtype,
+        truncate_long_prompts=False
     )
 
     # get rid of any newline characters
@@ -82,12 +51,12 @@ def get_uc_and_c_and_ec(
     legacy_blend = try_parse_legacy_blend(
         positive_prompt_string, skip_normalize_legacy_blend
     )
-    positive_prompt: FlattenedPrompt | Blend
+    positive_prompt: Union[FlattenedPrompt, Blend]
     if legacy_blend is not None:
         positive_prompt = legacy_blend
     else:
         positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
-    negative_prompt: FlattenedPrompt | Blend = Compel.parse_prompt_string(
+    negative_prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(
         negative_prompt_string
     )
 
@@ -96,6 +65,7 @@ def get_uc_and_c_and_ec(
 
     c, options = compel.build_conditioning_tensor_for_prompt_object(positive_prompt)
     uc, _ = compel.build_conditioning_tensor_for_prompt_object(negative_prompt)
+    [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
 
     tokens_count = get_max_token_count(tokenizer, positive_prompt)
 
@@ -116,12 +86,12 @@ def get_prompt_structure(
     legacy_blend = try_parse_legacy_blend(
         positive_prompt_string, skip_normalize_legacy_blend
     )
-    positive_prompt: FlattenedPrompt | Blend
+    positive_prompt: Union[FlattenedPrompt, Blend]
     if legacy_blend is not None:
         positive_prompt = legacy_blend
     else:
         positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
-    negative_prompt: FlattenedPrompt | Blend = Compel.parse_prompt_string(
+    negative_prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(
         negative_prompt_string
     )
 
@@ -129,7 +99,7 @@ def get_prompt_structure(
 
 
 def get_max_token_count(
-    tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=True
+    tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=False
 ) -> int:
     if type(prompt) is Blend:
         blend: Blend = prompt
@@ -245,7 +215,7 @@ def log_tokenization_for_prompt_object(
             )
 
 
-def log_tokenization_for_text(text, tokenizer, display_label=None):
+def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_too_long=False):
     """shows how the prompt is tokenized
     # usually tokens have '</w>' to indicate end-of-word,
     # but for readability it has been replaced with ' '
@@ -260,11 +230,11 @@ def log_tokenization_for_text(text, tokenizer, display_label=None):
         token = tokens[i].replace("</w>", " ")
         # alternate color
         s = (usedTokens % 6) + 1
-        if i < tokenizer.model_max_length:
+        if truncate_if_too_long and i >= tokenizer.model_max_length:
+            discarded = discarded + f"\x1b[0;3{s};40m{token}"
+        else:
             tokenized = tokenized + f"\x1b[0;3{s};40m{token}"
             usedTokens += 1
-        else:  # over max token length
-            discarded = discarded + f"\x1b[0;3{s};40m{token}"
 
     if usedTokens > 0:
         print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')

invokeai/backend/safety_checker.py

@@ -0,0 +1,82 @@
+'''
+SafetyChecker class - checks images against the StabilityAI NSFW filter
+and blurs images that contain potential NSFW content.
+'''
+import diffusers
+import numpy as np
+import torch
+import traceback
+from diffusers.pipelines.stable_diffusion.safety_checker import (
+    StableDiffusionSafetyChecker,
+)
+from pathlib import Path
+from PIL import Image, ImageFilter
+from transformers import AutoFeatureExtractor
+
+import invokeai.assets.web as web_assets
+from .globals import global_cache_dir
+from .util import CPU_DEVICE
+
+class SafetyChecker(object):
+    CAUTION_IMG = "caution.png"
+    
+    def __init__(self, device: torch.device):
+        path = Path(web_assets.__path__[0]) / self.CAUTION_IMG
+        caution = Image.open(path)
+        self.caution_img = caution.resize((caution.width // 2, caution.height // 2))
+        self.device = device
+        
+        try:
+            safety_model_id = "CompVis/stable-diffusion-safety-checker"
+            safety_model_path = global_cache_dir("hub")
+            self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(
+                safety_model_id,
+                local_files_only=True,
+                cache_dir=safety_model_path,
+            )
+            self.safety_feature_extractor = AutoFeatureExtractor.from_pretrained(
+                safety_model_id,
+                local_files_only=True,
+                cache_dir=safety_model_path,
+            )
+        except Exception:
+            print(
+                "** An error was encountered while installing the safety checker:"
+            )
+            print(traceback.format_exc())
+
+    def check(self, image: Image.Image):
+        """
+        Check provided image against the StabilityAI safety checker and return
+
+        """
+
+        self.safety_checker.to(self.device)
+        features = self.safety_feature_extractor([image], return_tensors="pt")
+        features.to(self.device)
+        
+        # unfortunately checker requires the numpy version, so we have to convert back
+        x_image = np.array(image).astype(np.float32) / 255.0
+        x_image = x_image[None].transpose(0, 3, 1, 2)
+
+        diffusers.logging.set_verbosity_error()
+        checked_image, has_nsfw_concept = self.safety_checker(
+            images=x_image, clip_input=features.pixel_values
+        )
+        self.safety_checker.to(CPU_DEVICE) # offload
+        if has_nsfw_concept[0]:
+            print(
+                "** An image with potential non-safe content has been detected. A blurred image will be returned. **"
+            )
+            return self.blur(image)
+        else:
+            return image
+
+    def blur(self, input):
+        blurry = input.filter(filter=ImageFilter.GaussianBlur(radius=32))
+        try:
+            if caution := self.caution_img:
+                blurry.paste(caution, (0, 0), caution)
+        except FileNotFoundError:
+            pass
+        return blurry

invokeai/backend/stable_diffusion/concepts_lib.py

@@ -6,7 +6,6 @@ The interface is through the Concepts() object.
 """
 import os
 import re
-import traceback
 from typing import Callable
 from urllib import error as ul_error
 from urllib import request
@@ -15,7 +14,6 @@ from huggingface_hub import (
     HfApi,
     HfFolder,
     ModelFilter,
-    ModelSearchArguments,
     hf_hub_url,
 )
 
@@ -84,7 +82,7 @@ class HuggingFaceConceptsLibrary(object):
         """
         if not concept_name in self.list_concepts():
             print(
-                f"This concept is not a local embedding trigger, nor is it a HuggingFace concept. Generation will continue without the concept."
+                f"{concept_name} is not a local embedding trigger, nor is it a HuggingFace concept. Generation will continue without the concept."
             )
             return None
         return self.get_concept_file(concept_name.lower(), "learned_embeds.bin")
@@ -236,7 +234,7 @@ class HuggingFaceConceptsLibrary(object):
         except ul_error.HTTPError as e:
             if e.code == 404:
                 print(
-                    f"This concept is not known to the Hugging Face library. Generation will continue without the concept."
+                    f"Concept {concept_name} is not known to the Hugging Face library. Generation will continue without the concept."
                 )
             else:
                 print(
@@ -246,7 +244,7 @@ class HuggingFaceConceptsLibrary(object):
             return False
         except ul_error.URLError as e:
             print(
-                f"ERROR: {str(e)}. This may reflect a network issue. Generation will continue without the concept."
+                f"ERROR while downloading {concept_name}: {str(e)}. This may reflect a network issue. Generation will continue without the concept."
             )
             os.rmdir(dest)
             return False

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -9,6 +9,7 @@ from typing import Any, Callable, Generic, List, Optional, Type, TypeVar, Union
 
 import einops
 import PIL.Image
+from accelerate.utils import set_seed
 import psutil
 import torch
 import torchvision.transforms as T
@@ -54,16 +55,6 @@ class PipelineIntermediateState:
     attention_map_saver: Optional[AttentionMapSaver] = None
 
 
-# copied from configs/stable-diffusion/v1-inference.yaml
-_default_personalization_config_params = dict(
-    placeholder_strings=["*"],
-    initializer_wods=["sculpture"],
-    per_image_tokens=False,
-    num_vectors_per_token=1,
-    progressive_words=False,
-)
-
-
 @dataclass
 class AddsMaskLatents:
     """Add the channels required for inpainting model input.
@@ -175,7 +166,7 @@ def image_resized_to_grid_as_tensor(
     :param normalize: scale the range to [-1, 1] instead of [0, 1]
     :param multiple_of: resize the input so both dimensions are a multiple of this
     """
-    w, h = trim_to_multiple_of(*image.size)
+    w, h = trim_to_multiple_of(*image.size, multiple_of=multiple_of)
     transformation = T.Compose(
         [
             T.Resize((h, w), T.InterpolationMode.LANCZOS),
@@ -290,10 +281,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
         unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
         scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offsensive or harmful.
+            Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
         feature_extractor ([`CLIPFeatureExtractor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
@@ -436,11 +427,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         """
         Ready this pipeline's models.
 
-        i.e. pre-load them to the GPU if appropriate.
+        i.e. preload them to the GPU if appropriate.
         """
         self._model_group.ready()
 
-    def to(self, torch_device: Optional[Union[str, torch.device]] = None):
+    def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings=False):
         # overridden method; types match the superclass.
         if torch_device is None:
             return self
@@ -690,6 +681,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         callback: Callable[[PipelineIntermediateState], None] = None,
         run_id=None,
         noise_func=None,
+        seed=None,
     ) -> InvokeAIStableDiffusionPipelineOutput:
         if isinstance(init_image, PIL.Image.Image):
             init_image = image_resized_to_grid_as_tensor(init_image.convert("RGB"))
@@ -703,6 +695,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             device=self._model_group.device_for(self.unet),
             dtype=self.unet.dtype,
         )
+        if seed is not None:
+            set_seed(seed)
         noise = noise_func(initial_latents)
 
         return self.img2img_from_latents_and_embeddings(
@@ -731,9 +725,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             device=self._model_group.device_for(self.unet),
         )
         result_latents, result_attention_maps = self.latents_from_embeddings(
-            initial_latents,
-            num_inference_steps,
-            conditioning_data,
+            latents=initial_latents if strength < 1.0 else torch.zeros_like(
+                initial_latents, device=initial_latents.device, dtype=initial_latents.dtype
+            ),
+            num_inference_steps=num_inference_steps,
+            conditioning_data=conditioning_data,
             timesteps=timesteps,
             noise=noise,
             run_id=run_id,
@@ -779,6 +775,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         callback: Callable[[PipelineIntermediateState], None] = None,
         run_id=None,
         noise_func=None,
+        seed=None,
     ) -> InvokeAIStableDiffusionPipelineOutput:
         device = self._model_group.device_for(self.unet)
         latents_dtype = self.unet.dtype
@@ -802,6 +799,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         init_image_latents = self.non_noised_latents_from_image(
             init_image, device=device, dtype=latents_dtype
         )
+        if seed is not None:
+            set_seed(seed)
         noise = noise_func(init_image_latents)
 
         if mask.dim() == 3:
@@ -831,9 +830,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         try:
             result_latents, result_attention_maps = self.latents_from_embeddings(
-                init_image_latents,
-                num_inference_steps,
-                conditioning_data,
+                latents=init_image_latents if strength < 1.0 else torch.zeros_like(
+                    init_image_latents, device=init_image_latents.device, dtype=init_image_latents.dtype
+                ),
+                num_inference_steps=num_inference_steps,
+                conditioning_data=conditioning_data,
                 noise=noise,
                 timesteps=timesteps,
                 additional_guidance=guidance,
@@ -911,20 +912,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             device=self._model_group.device_for(self.unet),
         )
 
-    @property
-    def cond_stage_model(self):
-        return self.embeddings_provider
-
-    @torch.inference_mode()
-    def _tokenize(self, prompt: Union[str, List[str]]):
-        return self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-
     @property
     def channels(self) -> int:
         """Compatible with DiffusionWrapper"""
@@ -936,11 +923,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         return super().decode_latents(latents)
 
     def debug_latents(self, latents, msg):
+        from invokeai.backend.image_util import debug_image
         with torch.inference_mode():
-            from ldm.util import debug_image
-
             decoded = self.numpy_to_pil(self.decode_latents(latents))
-            for i, img in enumerate(decoded):
-                debug_image(
-                    img, f"latents {msg} {i+1}/{len(decoded)}", debug_status=True
-                )
+        for i, img in enumerate(decoded):
+            debug_image(
+                img, f"latents {msg} {i+1}/{len(decoded)}", debug_status=True
+            )

invokeai/backend/util/util.py

@@ -3,6 +3,9 @@ import math
 import multiprocessing as mp
 import os
 import re
+import io
+import base64
+
 from collections import abc
 from inspect import isfunction
 from pathlib import Path
@@ -364,3 +367,16 @@ def url_attachment_name(url: str) -> dict:
 def download_with_progress_bar(url: str, dest: Path) -> bool:
     result = download_with_resume(url, dest, access_token=None)
     return result is not None
+
+
+def image_to_dataURL(image: Image.Image, image_format: str = "PNG") -> str:
+    """
+    Converts an image into a base64 image dataURL.
+    """
+    buffered = io.BytesIO()
+    image.save(buffered, format=image_format)
+    mime_type = Image.MIME.get(image_format.upper(), "image/" + image_format.lower())
+    image_base64 = f"data:{mime_type};base64," + base64.b64encode(
+        buffered.getvalue()
+    ).decode("UTF-8")
+    return image_base64

invokeai/backend/web/invoke_ai_web_server.py

@@ -29,7 +29,6 @@ from ..image_util import PngWriter, retrieve_metadata
 from ...frontend.merge.merge_diffusers import merge_diffusion_models
 from ..prompting import (
     get_prompt_structure,
-    get_tokenizer,
     get_tokens_for_prompt_object,
 )
 from ..stable_diffusion import PipelineIntermediateState
@@ -1274,7 +1273,7 @@ class InvokeAIWebServer:
                     None
                     if type(parsed_prompt) is Blend
                     else get_tokens_for_prompt_object(
-                        get_tokenizer(self.generate.model), parsed_prompt
+                        self.generate.model.tokenizer, parsed_prompt
                     )
                 )
                 attention_maps_image_base64_url = (

invokeai/frontend/web/.eslintrc.js

@@ -35,6 +35,7 @@ module.exports = {
       { varsIgnorePattern: '^_', argsIgnorePattern: '^_' },
     ],
     'prettier/prettier': ['error', { endOfLine: 'auto' }],
+    '@typescript-eslint/ban-ts-comment': 'warn',
   },
   settings: {
     react: {

invokeai/frontend/web/dist/assets/App-06f6cac1.css

@@ -1 +0,0 @@
-.ltr-image-gallery-css-transition-enter{transform:translate(150%)}.ltr-image-gallery-css-transition-enter-active{transform:translate(0);transition:all .12s ease-out}.ltr-image-gallery-css-transition-exit{transform:translate(0)}.ltr-image-gallery-css-transition-exit-active{transform:translate(150%);transition:all .12s ease-out}.rtl-image-gallery-css-transition-enter{transform:translate(-150%)}.rtl-image-gallery-css-transition-enter-active{transform:translate(0);transition:all .12s ease-out}.rtl-image-gallery-css-transition-exit{transform:translate(0)}.rtl-image-gallery-css-transition-exit-active{transform:translate(-150%);transition:all .12s ease-out}.ltr-parameters-panel-transition-enter{transform:translate(-150%)}.ltr-parameters-panel-transition-enter-active{transform:translate(0);transition:all .12s ease-out}.ltr-parameters-panel-transition-exit{transform:translate(0)}.ltr-parameters-panel-transition-exit-active{transform:translate(-150%);transition:all .12s ease-out}.rtl-parameters-panel-transition-enter{transform:translate(150%)}.rtl-parameters-panel-transition-enter-active{transform:translate(0);transition:all .12s ease-out}.rtl-parameters-panel-transition-exit{transform:translate(0)}.rtl-parameters-panel-transition-exit-active{transform:translate(150%);transition:all .12s ease-out}

invokeai/frontend/web/dist/assets/App-08e5c546.css

@@ -0,0 +1 @@
+.ltr-image-gallery-css-transition-enter{transform:translate(150%)}.ltr-image-gallery-css-transition-enter-active{transform:translate(0);transition:all .12s ease-out}.ltr-image-gallery-css-transition-exit{transform:translate(0)}.ltr-image-gallery-css-transition-exit-active{transform:translate(150%);transition:all .12s ease-out}.rtl-image-gallery-css-transition-enter{transform:translate(-150%)}.rtl-image-gallery-css-transition-enter-active{transform:translate(0);transition:all .12s ease-out}.rtl-image-gallery-css-transition-exit{transform:translate(0)}.rtl-image-gallery-css-transition-exit-active{transform:translate(-150%);transition:all .12s ease-out}

invokeai/frontend/web/dist/index.html

@@ -12,7 +12,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-b928084d.js"></script>
+    <script type="module" crossorigin src="./assets/index-f7f41e1f.js"></script>
     <link rel="stylesheet" href="./assets/index-5483945c.css">
   </head>
 

invokeai/frontend/web/dist/locales/en.json

@@ -1,4 +1,26 @@
 {
+    "accessibility": {
+        "modelSelect": "Model Select",
+        "invokeProgressBar": "Invoke progress bar",
+        "reset": "Reset",
+        "uploadImage": "Upload Image",
+        "previousImage": "Previous Image",
+        "nextImage": "Next Image",
+        "useThisParameter": "Use this parameter",
+        "copyMetadataJson": "Copy metadata JSON",
+        "exitViewer": "ExitViewer",
+        "zoomIn": "Zoom In",
+        "zoomOut": "Zoom Out",
+        "rotateCounterClockwise": "Rotate Counter-Clockwise",
+        "rotateClockwise": "Rotate Clockwise",
+        "flipHorizontally": "Flip Horizontally",
+        "flipVertically": "Flip Vertically",
+        "modifyConfig": "Modify Config",
+        "toggleAutoscroll": "Toggle autoscroll",
+        "toggleLogViewer": "Toggle Log Viewer",
+        "showGallery": "Show Gallery",
+        "showOptionsPanel": "Show Options Panel"
+    },
     "common": {
         "hotkeysLabel": "Hotkeys",
         "themeLabel": "Theme",
@@ -27,10 +49,11 @@
         "langSimplifiedChinese": "简体中文",
         "langUkranian": "Украї́нська",
         "langSpanish": "Español",
-        "text2img": "Text To Image",
+        "txt2img": "Text To Image",
         "img2img": "Image To Image",
         "unifiedCanvas": "Unified Canvas",
         "nodes": "Nodes",
+        "postprocessing": "Post Processing",
         "nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.",
         "postProcessing": "Post Processing",
         "postProcessDesc1": "Invoke AI offers a wide variety of post processing features. Image Upscaling and Face Restoration are already available in the WebUI. You can access them from the Advanced Options menu of the Text To Image and Image To Image tabs. You can also process images directly, using the image action buttons above the current image display or in the viewer.",
@@ -41,6 +64,8 @@
         "trainingDesc2": "InvokeAI already supports training custom embeddings using Textual Inversion using the main script.",
         "upload": "Upload",
         "close": "Close",
+        "cancel": "Cancel",
+        "accept": "Accept",
         "load": "Load",
         "back": "Back",
         "statusConnected": "Connected",
@@ -310,6 +335,7 @@
         "addNewModel": "Add New Model",
         "addCheckpointModel": "Add Checkpoint / Safetensor Model",
         "addDiffuserModel": "Add Diffusers",
+        "scanForModels": "Scan For Models",
         "addManually": "Add Manually",
         "manual": "Manual",
         "name": "Name",
@@ -574,7 +600,7 @@
         "autoSaveToGallery": "Auto Save to Gallery",
         "saveBoxRegionOnly": "Save Box Region Only",
         "limitStrokesToBox": "Limit Strokes to Box",
-        "showCanvasDebugInfo": "Show Canvas Debug Info",
+        "showCanvasDebugInfo": "Show Additional Canvas Info",
         "clearCanvasHistory": "Clear Canvas History",
         "clearHistory": "Clear History",
         "clearCanvasHistoryMessage": "Clearing the canvas history leaves your current canvas intact, but irreversibly clears the undo and redo history.",

invokeai/frontend/web/dist/locales/es.json

@@ -63,7 +63,14 @@
         "back": "Atrás",
         "statusConvertingModel": "Convertir el modelo",
         "statusModelConverted": "Modelo adaptado",
-        "statusMergingModels": "Fusionar modelos"
+        "statusMergingModels": "Fusionar modelos",
+        "oceanTheme": "Océano",
+        "langPortuguese": "Portugués",
+        "langKorean": "Coreano",
+        "langHebrew": "Hebreo",
+        "pinOptionsPanel": "Pin del panel de opciones",
+        "loading": "Cargando",
+        "loadingInvokeAI": "Cargando invocar a la IA"
     },
     "gallery": {
         "generations": "Generaciones",
@@ -363,7 +370,6 @@
         "convertToDiffusersHelpText6": "¿Desea transformar este modelo?",
         "convertToDiffusersSaveLocation": "Guardar ubicación",
         "v1": "v1",
-        "v2": "v2",
         "statusConverting": "Adaptar",
         "modelConverted": "Modelo adaptado",
         "sameFolder": "La misma carpeta",
@@ -386,14 +392,19 @@
         "modelMergeAlphaHelp": "Alfa controla la fuerza de mezcla de los modelos. Los valores alfa más bajos reducen la influencia del segundo modelo.",
         "modelMergeInterpAddDifferenceHelp": "En este modo, el Modelo 3 se sustrae primero del Modelo 2. La versión resultante se mezcla con el Modelo 1 con la tasa alfa establecida anteriormente. La versión resultante se mezcla con el Modelo 1 con la tasa alfa establecida anteriormente.",
         "ignoreMismatch": "Ignorar discrepancias entre modelos seleccionados",
-        "modelMergeHeaderHelp1": "Puede combinar hasta tres modelos diferentes para crear una mezcla que se adapte a sus necesidades.",
+        "modelMergeHeaderHelp1": "Puede unir hasta tres modelos diferentes para crear una combinación que se adapte a sus necesidades.",
         "inverseSigmoid": "Sigmoideo inverso",
         "weightedSum": "Modelo de suma ponderada",
         "sigmoid": "Función sigmoide",
         "allModels": "Todos los modelos",
         "repo_id": "Identificador del repositorio",
         "pathToCustomConfig": "Ruta a la configuración personalizada",
-        "customConfig": "Configuración personalizada"
+        "customConfig": "Configuración personalizada",
+        "v2_base": "v2 (512px)",
+        "none": "ninguno",
+        "pickModelType": "Elige el tipo de modelo",
+        "v2_768": "v2 (768px)",
+        "addDifference": "Añadir una diferencia"
     },
     "parameters": {
         "images": "Imágenes",
@@ -589,5 +600,27 @@
         "betaDarkenOutside": "Oscurecer fuera",
         "betaLimitToBox": "Limitar a caja",
         "betaPreserveMasked": "Preservar área enmascarada"
+    },
+    "accessibility": {
+        "invokeProgressBar": "Activar la barra de progreso",
+        "modelSelect": "Seleccionar modelo",
+        "reset": "Reiniciar",
+        "uploadImage": "Cargar imagen",
+        "previousImage": "Imagen anterior",
+        "nextImage": "Siguiente imagen",
+        "useThisParameter": "Utiliza este parámetro",
+        "copyMetadataJson": "Copiar los metadatos JSON",
+        "exitViewer": "Salir del visor",
+        "zoomIn": "Acercar",
+        "zoomOut": "Alejar",
+        "rotateCounterClockwise": "Girar en sentido antihorario",
+        "rotateClockwise": "Girar en sentido horario",
+        "flipHorizontally": "Voltear horizontalmente",
+        "flipVertically": "Voltear verticalmente",
+        "modifyConfig": "Modificar la configuración",
+        "toggleAutoscroll": "Activar el autodesplazamiento",
+        "toggleLogViewer": "Alternar el visor de registros",
+        "showGallery": "Mostrar galería",
+        "showOptionsPanel": "Mostrar el panel de opciones"
     }
 }

invokeai/frontend/web/dist/locales/fr.json

@@ -45,7 +45,9 @@
         "statusUpscaling": "Mise à échelle",
         "statusUpscalingESRGAN": "Mise à échelle (ESRGAN)",
         "statusLoadingModel": "Chargement du modèle",
-        "statusModelChanged": "Modèle changé"
+        "statusModelChanged": "Modèle changé",
+        "discordLabel": "Discord",
+        "githubLabel": "Github"
     },
     "gallery": {
         "generations": "Générations",

invokeai/frontend/web/dist/locales/he.json

@@ -92,7 +92,6 @@
         "modelThree": "מודל 3",
         "mergedModelName": "שם מודל ממוזג",
         "v1": "v1",
-        "v2": "v2",
         "invokeRoot": "תיקיית InvokeAI",
         "customConfig": "תצורה מותאמת אישית",
         "pathToCustomConfig": "נתיב לתצורה מותאמת אישית",

invokeai/frontend/web/dist/locales/it.json

@@ -63,7 +63,14 @@
         "langSimplifiedChinese": "Cinese semplificato",
         "langDutch": "Olandese",
         "statusModelConverted": "Modello Convertito",
-        "statusConvertingModel": "Conversione Modello"
+        "statusConvertingModel": "Conversione Modello",
+        "langKorean": "Coreano",
+        "langPortuguese": "Portoghese",
+        "pinOptionsPanel": "Blocca il pannello Opzioni",
+        "loading": "Caricamento in corso",
+        "oceanTheme": "Oceano",
+        "langHebrew": "Ebraico",
+        "loadingInvokeAI": "Caricamento Invoke AI"
     },
     "gallery": {
         "generations": "Generazioni",
@@ -361,7 +368,6 @@
         "convertToDiffusersHelpText5": "Assicurati di avere spazio su disco sufficiente. I modelli generalmente variano tra 4 GB e 7 GB di dimensioni.",
         "convertToDiffusersHelpText6": "Vuoi convertire questo modello?",
         "convertToDiffusersSaveLocation": "Ubicazione salvataggio",
-        "v2": "v2",
         "inpainting": "v1 Inpainting",
         "customConfig": "Configurazione personalizzata",
         "statusConverting": "Conversione in corso",
@@ -393,7 +399,12 @@
         "customSaveLocation": "Ubicazione salvataggio personalizzata",
         "weightedSum": "Somma pesata",
         "sigmoid": "Sigmoide",
-        "inverseSigmoid": "Sigmoide inverso"
+        "inverseSigmoid": "Sigmoide inverso",
+        "v2_base": "v2 (512px)",
+        "v2_768": "v2 (768px)",
+        "none": "niente",
+        "addDifference": "Aggiungi differenza",
+        "pickModelType": "Scegli il tipo di modello"
     },
     "parameters": {
         "images": "Immagini",
@@ -589,5 +600,27 @@
         "betaDarkenOutside": "Oscura all'esterno",
         "betaLimitToBox": "Limita al rettangolo",
         "betaPreserveMasked": "Conserva quanto mascherato"
+    },
+    "accessibility": {
+        "modelSelect": "Seleziona modello",
+        "invokeProgressBar": "Barra di avanzamento generazione",
+        "uploadImage": "Carica immagine",
+        "previousImage": "Immagine precedente",
+        "nextImage": "Immagine successiva",
+        "useThisParameter": "Usa questo parametro",
+        "reset": "Reimposta",
+        "copyMetadataJson": "Copia i metadati JSON",
+        "exitViewer": "Esci dal visualizzatore",
+        "zoomIn": "Zoom avanti",
+        "zoomOut": "Zoom Indietro",
+        "rotateCounterClockwise": "Ruotare in senso antiorario",
+        "rotateClockwise": "Ruotare in senso orario",
+        "flipHorizontally": "Capovolgi orizzontalmente",
+        "toggleLogViewer": "Attiva/disattiva visualizzatore registro",
+        "showGallery": "Mostra la galleria immagini",
+        "showOptionsPanel": "Mostra il pannello opzioni",
+        "flipVertically": "Capovolgi verticalmente",
+        "toggleAutoscroll": "Attiva/disattiva lo scorrimento automatico",
+        "modifyConfig": "Modifica configurazione"
     }
 }

invokeai/frontend/web/dist/locales/nl.json

@@ -302,7 +302,7 @@
         "name": "Naam",
         "nameValidationMsg": "Geef een naam voor je model",
         "description": "Beschrijving",
-        "descriptionValidationMsg": "Voeg een beschrijving toe voor je model",
+        "descriptionValidationMsg": "Voeg een beschrijving toe voor je model.",
         "config": "Configuratie",
         "configValidationMsg": "Pad naar het configuratiebestand van je model.",
         "modelLocation": "Locatie model",
@@ -364,7 +364,6 @@
         "convertToDiffusersHelpText5": "Zorg ervoor dat je genoeg schijfruimte hebt. Modellen nemen gewoonlijk ongeveer 4 - 7 GB ruimte in beslag.",
         "convertToDiffusersSaveLocation": "Bewaarlocatie",
         "v1": "v1",
-        "v2": "v2",
         "inpainting": "v1-inpainting",
         "customConfig": "Eigen configuratie",
         "pathToCustomConfig": "Pad naar eigen configuratie",

invokeai/frontend/web/dist/locales/pt.json

@@ -63,6 +63,560 @@
         "statusGeneratingOutpainting": "Geração de Ampliação",
         "statusGenerationComplete": "Geração Completa",
         "statusMergingModels": "Mesclando Modelos",
-        "statusMergedModels": "Modelos Mesclados"
+        "statusMergedModels": "Modelos Mesclados",
+        "oceanTheme": "Oceano",
+        "pinOptionsPanel": "Fixar painel de opções",
+        "loading": "A carregar",
+        "loadingInvokeAI": "A carregar Invoke AI",
+        "langPortuguese": "Português"
+    },
+    "gallery": {
+        "galleryImageResetSize": "Resetar Imagem",
+        "gallerySettings": "Configurações de Galeria",
+        "maintainAspectRatio": "Mater Proporções",
+        "autoSwitchNewImages": "Trocar para Novas Imagens Automaticamente",
+        "pinGallery": "Fixar Galeria",
+        "singleColumnLayout": "Disposição em Coluna Única",
+        "allImagesLoaded": "Todas as Imagens Carregadas",
+        "loadMore": "Carregar Mais",
+        "noImagesInGallery": "Sem Imagens na Galeria",
+        "generations": "Gerações",
+        "showGenerations": "Mostrar Gerações",
+        "uploads": "Enviados",
+        "showUploads": "Mostrar Enviados",
+        "galleryImageSize": "Tamanho da Imagem"
+    },
+    "hotkeys": {
+        "generalHotkeys": "Atalhos Gerais",
+        "galleryHotkeys": "Atalhos da Galeria",
+        "toggleViewer": {
+            "title": "Ativar Visualizador",
+            "desc": "Abrir e fechar o Visualizador de Imagens"
+        },
+        "maximizeWorkSpace": {
+            "desc": "Fechar painéis e maximixar área de trabalho",
+            "title": "Maximizar a Área de Trabalho"
+        },
+        "changeTabs": {
+            "title": "Mudar Guias",
+            "desc": "Trocar para outra área de trabalho"
+        },
+        "consoleToggle": {
+            "desc": "Abrir e fechar console",
+            "title": "Ativar Console"
+        },
+        "setPrompt": {
+            "title": "Definir Prompt",
+            "desc": "Usar o prompt da imagem atual"
+        },
+        "sendToImageToImage": {
+            "desc": "Manda a imagem atual para Imagem Para Imagem",
+            "title": "Mandar para Imagem Para Imagem"
+        },
+        "previousImage": {
+            "desc": "Mostra a imagem anterior na galeria",
+            "title": "Imagem Anterior"
+        },
+        "nextImage": {
+            "title": "Próxima Imagem",
+            "desc": "Mostra a próxima imagem na galeria"
+        },
+        "decreaseGalleryThumbSize": {
+            "desc": "Diminui o tamanho das thumbs na galeria",
+            "title": "Diminuir Tamanho da Galeria de Imagem"
+        },
+        "selectBrush": {
+            "title": "Selecionar Pincel",
+            "desc": "Seleciona o pincel"
+        },
+        "selectEraser": {
+            "title": "Selecionar Apagador",
+            "desc": "Seleciona o apagador"
+        },
+        "decreaseBrushSize": {
+            "title": "Diminuir Tamanho do Pincel",
+            "desc": "Diminui o tamanho do pincel/apagador"
+        },
+        "increaseBrushOpacity": {
+            "desc": "Aumenta a opacidade do pincel",
+            "title": "Aumentar Opacidade do Pincel"
+        },
+        "moveTool": {
+            "title": "Ferramenta Mover",
+            "desc": "Permite navegar pela tela"
+        },
+        "decreaseBrushOpacity": {
+            "desc": "Diminui a opacidade do pincel",
+            "title": "Diminuir Opacidade do Pincel"
+        },
+        "toggleSnap": {
+            "title": "Ativar Encaixe",
+            "desc": "Ativa Encaixar na Grade"
+        },
+        "quickToggleMove": {
+            "title": "Ativar Mover Rapidamente",
+            "desc": "Temporariamente ativa o modo Mover"
+        },
+        "toggleLayer": {
+            "title": "Ativar Camada",
+            "desc": "Ativa a seleção de camada de máscara/base"
+        },
+        "clearMask": {
+            "title": "Limpar Máscara",
+            "desc": "Limpa toda a máscara"
+        },
+        "hideMask": {
+            "title": "Esconder Máscara",
+            "desc": "Esconde e Revela a máscara"
+        },
+        "mergeVisible": {
+            "title": "Fundir Visível",
+            "desc": "Fundir todas as camadas visíveis das telas"
+        },
+        "downloadImage": {
+            "desc": "Descarregar a tela atual",
+            "title": "Descarregar Imagem"
+        },
+        "undoStroke": {
+            "title": "Desfazer Traço",
+            "desc": "Desfaz um traço de pincel"
+        },
+        "redoStroke": {
+            "title": "Refazer Traço",
+            "desc": "Refaz o traço de pincel"
+        },
+        "keyboardShortcuts": "Atalhos de Teclado",
+        "appHotkeys": "Atalhos do app",
+        "invoke": {
+            "title": "Invocar",
+            "desc": "Gerar uma imagem"
+        },
+        "cancel": {
+            "title": "Cancelar",
+            "desc": "Cancelar geração de imagem"
+        },
+        "focusPrompt": {
+            "title": "Foco do Prompt",
+            "desc": "Foco da área de texto do prompt"
+        },
+        "toggleOptions": {
+            "title": "Ativar Opções",
+            "desc": "Abrir e fechar o painel de opções"
+        },
+        "pinOptions": {
+            "title": "Fixar Opções",
+            "desc": "Fixar o painel de opções"
+        },
+        "closePanels": {
+            "title": "Fechar Painéis",
+            "desc": "Fecha os painéis abertos"
+        },
+        "unifiedCanvasHotkeys": "Atalhos da Tela Unificada",
+        "toggleGallery": {
+            "title": "Ativar Galeria",
+            "desc": "Abrir e fechar a gaveta da galeria"
+        },
+        "setSeed": {
+            "title": "Definir Seed",
+            "desc": "Usar seed da imagem atual"
+        },
+        "setParameters": {
+            "title": "Definir Parâmetros",
+            "desc": "Usar todos os parâmetros da imagem atual"
+        },
+        "restoreFaces": {
+            "title": "Restaurar Rostos",
+            "desc": "Restaurar a imagem atual"
+        },
+        "upscale": {
+            "title": "Redimensionar",
+            "desc": "Redimensionar a imagem atual"
+        },
+        "showInfo": {
+            "title": "Mostrar Informações",
+            "desc": "Mostrar metadados de informações da imagem atual"
+        },
+        "deleteImage": {
+            "title": "Apagar Imagem",
+            "desc": "Apaga a imagem atual"
+        },
+        "toggleGalleryPin": {
+            "title": "Ativar Fixar Galeria",
+            "desc": "Fixa e desafixa a galeria na interface"
+        },
+        "increaseGalleryThumbSize": {
+            "title": "Aumentar Tamanho da Galeria de Imagem",
+            "desc": "Aumenta o tamanho das thumbs na galeria"
+        },
+        "increaseBrushSize": {
+            "title": "Aumentar Tamanho do Pincel",
+            "desc": "Aumenta o tamanho do pincel/apagador"
+        },
+        "fillBoundingBox": {
+            "title": "Preencher Caixa Delimitadora",
+            "desc": "Preenche a caixa delimitadora com a cor do pincel"
+        },
+        "eraseBoundingBox": {
+            "title": "Apagar Caixa Delimitadora",
+            "desc": "Apaga a área da caixa delimitadora"
+        },
+        "colorPicker": {
+            "title": "Selecionar Seletor de Cor",
+            "desc": "Seleciona o seletor de cores"
+        },
+        "showHideBoundingBox": {
+            "title": "Mostrar/Esconder Caixa Delimitadora",
+            "desc": "Ativa a visibilidade da caixa delimitadora"
+        },
+        "saveToGallery": {
+            "title": "Gravara Na Galeria",
+            "desc": "Grava a tela atual na galeria"
+        },
+        "copyToClipboard": {
+            "title": "Copiar para a Área de Transferência",
+            "desc": "Copia a tela atual para a área de transferência"
+        },
+        "resetView": {
+            "title": "Resetar Visualização",
+            "desc": "Reseta Visualização da Tela"
+        },
+        "previousStagingImage": {
+            "title": "Imagem de Preparação Anterior",
+            "desc": "Área de Imagem de Preparação Anterior"
+        },
+        "nextStagingImage": {
+            "title": "Próxima Imagem de Preparação Anterior",
+            "desc": "Próxima Área de Imagem de Preparação Anterior"
+        },
+        "acceptStagingImage": {
+            "title": "Aceitar Imagem de Preparação Anterior",
+            "desc": "Aceitar Área de Imagem de Preparação Anterior"
+        }
+    },
+    "modelManager": {
+        "modelAdded": "Modelo Adicionado",
+        "modelUpdated": "Modelo Atualizado",
+        "modelEntryDeleted": "Entrada de modelo excluída",
+        "description": "Descrição",
+        "modelLocationValidationMsg": "Caminho para onde o seu modelo está localizado.",
+        "repo_id": "Repo ID",
+        "vaeRepoIDValidationMsg": "Repositório Online do seu VAE",
+        "width": "Largura",
+        "widthValidationMsg": "Largura padrão do seu modelo.",
+        "height": "Altura",
+        "heightValidationMsg": "Altura padrão do seu modelo.",
+        "findModels": "Encontrar Modelos",
+        "scanAgain": "Digitalize Novamente",
+        "deselectAll": "Deselecionar Tudo",
+        "showExisting": "Mostrar Existente",
+        "deleteConfig": "Apagar Config",
+        "convertToDiffusersHelpText6": "Deseja converter este modelo?",
+        "mergedModelName": "Nome do modelo mesclado",
+        "alpha": "Alpha",
+        "interpolationType": "Tipo de Interpolação",
+        "modelMergeHeaderHelp1": "Pode mesclar até três modelos diferentes para criar uma mistura que atenda às suas necessidades.",
+        "modelMergeHeaderHelp2": "Apenas Diffusers estão disponíveis para mesclagem. Se deseja mesclar um modelo de checkpoint, por favor, converta-o para Diffusers primeiro.",
+        "modelMergeInterpAddDifferenceHelp": "Neste modo, o Modelo 3 é primeiro subtraído do Modelo 2. A versão resultante é mesclada com o Modelo 1 com a taxa alpha definida acima.",
+        "nameValidationMsg": "Insira um nome para o seu modelo",
+        "descriptionValidationMsg": "Adicione uma descrição para o seu modelo",
+        "config": "Configuração",
+        "modelExists": "Modelo Existe",
+        "selectAndAdd": "Selecione e Adicione Modelos Listados Abaixo",
+        "noModelsFound": "Nenhum Modelo Encontrado",
+        "v2_768": "v2 (768px)",
+        "inpainting": "v1 Inpainting",
+        "customConfig": "Configuração personalizada",
+        "pathToCustomConfig": "Caminho para configuração personalizada",
+        "statusConverting": "A converter",
+        "modelConverted": "Modelo Convertido",
+        "ignoreMismatch": "Ignorar Divergências entre Modelos Selecionados",
+        "addDifference": "Adicionar diferença",
+        "pickModelType": "Escolha o tipo de modelo",
+        "safetensorModels": "SafeTensors",
+        "cannotUseSpaces": "Não pode usar espaços",
+        "addNew": "Adicionar Novo",
+        "addManually": "Adicionar Manualmente",
+        "manual": "Manual",
+        "name": "Nome",
+        "configValidationMsg": "Caminho para o ficheiro de configuração do seu modelo.",
+        "modelLocation": "Localização do modelo",
+        "repoIDValidationMsg": "Repositório Online do seu Modelo",
+        "updateModel": "Atualizar Modelo",
+        "availableModels": "Modelos Disponíveis",
+        "load": "Carregar",
+        "active": "Ativado",
+        "notLoaded": "Não carregado",
+        "deleteModel": "Apagar modelo",
+        "deleteMsg1": "Tem certeza de que deseja apagar esta entrada do modelo de InvokeAI?",
+        "deleteMsg2": "Isso não vai apagar o ficheiro de modelo checkpoint do seu disco. Pode lê-los, se desejar.",
+        "convertToDiffusers": "Converter para Diffusers",
+        "convertToDiffusersHelpText1": "Este modelo será convertido ao formato 🧨 Diffusers.",
+        "convertToDiffusersHelpText2": "Este processo irá substituir a sua entrada de Gestor de Modelos por uma versão Diffusers do mesmo modelo.",
+        "convertToDiffusersHelpText3": "O seu ficheiro de ponto de verificação no disco NÃO será excluído ou modificado de forma alguma. Pode adicionar o seu ponto de verificação ao Gestor de modelos novamente, se desejar.",
+        "convertToDiffusersSaveLocation": "Local para Gravar",
+        "v2_base": "v2 (512px)",
+        "mergeModels": "Mesclar modelos",
+        "modelOne": "Modelo 1",
+        "modelTwo": "Modelo 2",
+        "modelThree": "Modelo 3",
+        "mergedModelSaveLocation": "Local de Salvamento",
+        "merge": "Mesclar",
+        "modelsMerged": "Modelos mesclados",
+        "mergedModelCustomSaveLocation": "Caminho Personalizado",
+        "invokeAIFolder": "Pasta Invoke AI",
+        "inverseSigmoid": "Sigmóide Inversa",
+        "none": "nenhum",
+        "modelManager": "Gerente de Modelo",
+        "model": "Modelo",
+        "allModels": "Todos os Modelos",
+        "checkpointModels": "Checkpoints",
+        "diffusersModels": "Diffusers",
+        "addNewModel": "Adicionar Novo modelo",
+        "addCheckpointModel": "Adicionar Modelo de Checkpoint/Safetensor",
+        "addDiffuserModel": "Adicionar Diffusers",
+        "vaeLocation": "Localização VAE",
+        "vaeLocationValidationMsg": "Caminho para onde o seu VAE está localizado.",
+        "vaeRepoID": "VAE Repo ID",
+        "addModel": "Adicionar Modelo",
+        "search": "Procurar",
+        "cached": "Em cache",
+        "checkpointFolder": "Pasta de Checkpoint",
+        "clearCheckpointFolder": "Apagar Pasta de Checkpoint",
+        "modelsFound": "Modelos Encontrados",
+        "selectFolder": "Selecione a Pasta",
+        "selected": "Selecionada",
+        "selectAll": "Selecionar Tudo",
+        "addSelected": "Adicione Selecionado",
+        "delete": "Apagar",
+        "formMessageDiffusersModelLocation": "Localização dos Modelos Diffusers",
+        "formMessageDiffusersModelLocationDesc": "Por favor entre com ao menos um.",
+        "formMessageDiffusersVAELocation": "Localização do VAE",
+        "formMessageDiffusersVAELocationDesc": "Se não provido, InvokeAI irá procurar pelo ficheiro VAE dentro do local do modelo.",
+        "convert": "Converter",
+        "convertToDiffusersHelpText4": "Este é um processo único. Pode levar cerca de 30 a 60s, a depender das especificações do seu computador.",
+        "convertToDiffusersHelpText5": "Por favor, certifique-se de que tenha espaço suficiente no disco. Os modelos geralmente variam entre 4GB e 7GB de tamanho.",
+        "v1": "v1",
+        "sameFolder": "Mesma pasta",
+        "invokeRoot": "Pasta do InvokeAI",
+        "custom": "Personalizado",
+        "customSaveLocation": "Local de salvamento personalizado",
+        "modelMergeAlphaHelp": "Alpha controla a força da mistura dos modelos. Valores de alpha mais baixos resultam numa influência menor do segundo modelo.",
+        "sigmoid": "Sigmóide",
+        "weightedSum": "Soma Ponderada"
+    },
+    "parameters": {
+        "width": "Largura",
+        "seed": "Seed",
+        "hiresStrength": "Força da Alta Resolução",
+        "negativePrompts": "Indicações negativas",
+        "general": "Geral",
+        "randomizeSeed": "Seed Aleatório",
+        "shuffle": "Embaralhar",
+        "noiseThreshold": "Limite de Ruído",
+        "perlinNoise": "Ruído de Perlin",
+        "variations": "Variatções",
+        "seedWeights": "Pesos da Seed",
+        "restoreFaces": "Restaurar Rostos",
+        "faceRestoration": "Restauração de Rosto",
+        "type": "Tipo",
+        "denoisingStrength": "A força de remoção de ruído",
+        "scale": "Escala",
+        "otherOptions": "Outras Opções",
+        "seamlessTiling": "Ladrilho Sem Fronteira",
+        "hiresOptim": "Otimização de Alta Res",
+        "imageFit": "Caber Imagem Inicial No Tamanho de Saída",
+        "codeformerFidelity": "Fidelidade",
+        "seamSize": "Tamanho da Fronteira",
+        "seamBlur": "Desfoque da Fronteira",
+        "seamStrength": "Força da Fronteira",
+        "seamSteps": "Passos da Fronteira",
+        "tileSize": "Tamanho do Ladrilho",
+        "boundingBoxHeader": "Caixa Delimitadora",
+        "seamCorrectionHeader": "Correção de Fronteira",
+        "infillScalingHeader": "Preencimento e Escala",
+        "img2imgStrength": "Força de Imagem Para Imagem",
+        "toggleLoopback": "Ativar Loopback",
+        "symmetry": "Simetria",
+        "promptPlaceholder": "Digite o prompt aqui. [tokens negativos], (upweight)++, (downweight)--, trocar e misturar estão disponíveis (veja docs)",
+        "sendTo": "Mandar para",
+        "openInViewer": "Abrir No Visualizador",
+        "closeViewer": "Fechar Visualizador",
+        "usePrompt": "Usar Prompt",
+        "deleteImage": "Apagar Imagem",
+        "initialImage": "Imagem inicial",
+        "showOptionsPanel": "Mostrar Painel de Opções",
+        "strength": "Força",
+        "upscaling": "Redimensionando",
+        "upscale": "Redimensionar",
+        "upscaleImage": "Redimensionar Imagem",
+        "scaleBeforeProcessing": "Escala Antes do Processamento",
+        "invoke": "Invocar",
+        "images": "Imagems",
+        "steps": "Passos",
+        "cfgScale": "Escala CFG",
+        "height": "Altura",
+        "sampler": "Amostrador",
+        "imageToImage": "Imagem para Imagem",
+        "variationAmount": "Quntidade de Variatções",
+        "scaledWidth": "L Escalada",
+        "scaledHeight": "A Escalada",
+        "infillMethod": "Método de Preenchimento",
+        "hSymmetryStep": "H Passo de Simetria",
+        "vSymmetryStep": "V Passo de Simetria",
+        "cancel": {
+            "immediate": "Cancelar imediatamente",
+            "schedule": "Cancelar após a iteração atual",
+            "isScheduled": "A cancelar",
+            "setType": "Definir tipo de cancelamento"
+        },
+        "sendToImg2Img": "Mandar para Imagem Para Imagem",
+        "sendToUnifiedCanvas": "Mandar para Tela Unificada",
+        "copyImage": "Copiar imagem",
+        "copyImageToLink": "Copiar Imagem Para a Ligação",
+        "downloadImage": "Descarregar Imagem",
+        "useSeed": "Usar Seed",
+        "useAll": "Usar Todos",
+        "useInitImg": "Usar Imagem Inicial",
+        "info": "Informações"
+    },
+    "settings": {
+        "confirmOnDelete": "Confirmar Antes de Apagar",
+        "displayHelpIcons": "Mostrar Ícones de Ajuda",
+        "useCanvasBeta": "Usar Layout de Telas Beta",
+        "enableImageDebugging": "Ativar Depuração de Imagem",
+        "useSlidersForAll": "Usar deslizadores para todas as opções",
+        "resetWebUIDesc1": "Reiniciar a interface apenas reinicia o cache local do broswer para imagens e configurações lembradas. Não apaga nenhuma imagem do disco.",
+        "models": "Modelos",
+        "displayInProgress": "Mostrar Progresso de Imagens Em Andamento",
+        "saveSteps": "Gravar imagens a cada n passos",
+        "resetWebUI": "Reiniciar Interface",
+        "resetWebUIDesc2": "Se as imagens não estão a aparecer na galeria ou algo mais não está a funcionar, favor tentar reiniciar antes de postar um problema no GitHub.",
+        "resetComplete": "A interface foi reiniciada. Atualize a página para carregar."
+    },
+    "toast": {
+        "uploadFailed": "Envio Falhou",
+        "uploadFailedMultipleImagesDesc": "Várias imagens copiadas, só é permitido uma imagem de cada vez",
+        "uploadFailedUnableToLoadDesc": "Não foj possível carregar o ficheiro",
+        "downloadImageStarted": "Download de Imagem Começou",
+        "imageNotLoadedDesc": "Nenhuma imagem encontrada a enviar para o módulo de imagem para imagem",
+        "imageLinkCopied": "Ligação de Imagem Copiada",
+        "imageNotLoaded": "Nenhuma Imagem Carregada",
+        "parametersFailed": "Problema ao carregar parâmetros",
+        "parametersFailedDesc": "Não foi possível carregar imagem incial.",
+        "seedSet": "Seed Definida",
+        "upscalingFailed": "Redimensionamento Falhou",
+        "promptNotSet": "Prompt Não Definido",
+        "tempFoldersEmptied": "Pasta de Ficheiros Temporários Esvaziada",
+        "imageCopied": "Imagem Copiada",
+        "imageSavedToGallery": "Imagem Salva na Galeria",
+        "canvasMerged": "Tela Fundida",
+        "sentToImageToImage": "Mandar Para Imagem Para Imagem",
+        "sentToUnifiedCanvas": "Enviada para a Tela Unificada",
+        "parametersSet": "Parâmetros Definidos",
+        "parametersNotSet": "Parâmetros Não Definidos",
+        "parametersNotSetDesc": "Nenhum metadado foi encontrado para essa imagem.",
+        "seedNotSet": "Seed Não Definida",
+        "seedNotSetDesc": "Não foi possível achar a seed para a imagem.",
+        "promptSet": "Prompt Definido",
+        "promptNotSetDesc": "Não foi possível achar prompt para essa imagem.",
+        "faceRestoreFailed": "Restauração de Rosto Falhou",
+        "metadataLoadFailed": "Falha ao tentar carregar metadados",
+        "initialImageSet": "Imagem Inicial Definida",
+        "initialImageNotSet": "Imagem Inicial Não Definida",
+        "initialImageNotSetDesc": "Não foi possível carregar imagem incial"
+    },
+    "tooltip": {
+        "feature": {
+            "prompt": "Este é o campo de prompt. O prompt inclui objetos de geração e termos estilísticos. Também pode adicionar peso (importância do token) no prompt, mas comandos e parâmetros de CLI não funcionarão.",
+            "other": "Essas opções ativam modos alternativos de processamento para o Invoke. 'Seamless tiling' criará padrões repetidos na saída. 'High resolution' é uma geração em duas etapas com img2img: use essa configuração quando desejar uma imagem maior e mais coerente sem artefatos. Levará mais tempo do que o txt2img usual.",
+            "seed": "O valor da semente afeta o ruído inicial a partir do qual a imagem é formada. Pode usar as sementes já existentes de imagens anteriores. 'Limiar de ruído' é usado para mitigar artefatos em valores CFG altos (experimente a faixa de 0-10) e o Perlin para adicionar ruído Perlin durante a geração: ambos servem para adicionar variação às suas saídas.",
+            "imageToImage": "Image to Image carrega qualquer imagem como inicial, que é então usada para gerar uma nova junto com o prompt. Quanto maior o valor, mais a imagem resultante mudará. Valores de 0.0 a 1.0 são possíveis, a faixa recomendada é de 0.25 a 0.75",
+            "faceCorrection": "Correção de rosto com GFPGAN ou Codeformer: o algoritmo detecta rostos na imagem e corrige quaisquer defeitos. Um valor alto mudará mais a imagem, a resultar em rostos mais atraentes. Codeformer com uma fidelidade maior preserva a imagem original às custas de uma correção de rosto mais forte.",
+            "seamCorrection": "Controla o tratamento das emendas visíveis que ocorrem entre as imagens geradas no canvas.",
+            "gallery": "A galeria exibe as gerações da pasta de saída conforme elas são criadas. As configurações são armazenadas em ficheiros e acessadas pelo menu de contexto.",
+            "variations": "Experimente uma variação com um valor entre 0,1 e 1,0 para mudar o resultado para uma determinada semente. Variações interessantes da semente estão entre 0,1 e 0,3.",
+            "upscale": "Use o ESRGAN para ampliar a imagem imediatamente após a geração.",
+            "boundingBox": "A caixa delimitadora é a mesma que as configurações de largura e altura para Texto para Imagem ou Imagem para Imagem. Apenas a área na caixa será processada.",
+            "infillAndScaling": "Gira os métodos de preenchimento (usados em áreas mascaradas ou apagadas do canvas) e a escala (útil para tamanhos de caixa delimitadora pequenos)."
+        }
+    },
+    "unifiedCanvas": {
+        "emptyTempImagesFolderMessage": "Esvaziar a pasta de ficheiros de imagem temporários também reseta completamente a Tela Unificada. Isso inclui todo o histórico de desfazer/refazer, imagens na área de preparação e a camada base da tela.",
+        "scaledBoundingBox": "Caixa Delimitadora Escalada",
+        "boundingBoxPosition": "Posição da Caixa Delimitadora",
+        "next": "Próximo",
+        "accept": "Aceitar",
+        "showHide": "Mostrar/Esconder",
+        "discardAll": "Descartar Todos",
+        "betaClear": "Limpar",
+        "betaDarkenOutside": "Escurecer Externamente",
+        "base": "Base",
+        "brush": "Pincel",
+        "showIntermediates": "Mostrar Intermediários",
+        "showGrid": "Mostrar Grade",
+        "clearCanvasHistoryConfirm": "Tem certeza que quer limpar o histórico de tela?",
+        "boundingBox": "Caixa Delimitadora",
+        "canvasDimensions": "Dimensões da Tela",
+        "canvasPosition": "Posição da Tela",
+        "cursorPosition": "Posição do cursor",
+        "previous": "Anterior",
+        "betaLimitToBox": "Limitar á Caixa",
+        "layer": "Camada",
+        "mask": "Máscara",
+        "maskingOptions": "Opções de Mascaramento",
+        "enableMask": "Ativar Máscara",
+        "preserveMaskedArea": "Preservar Área da Máscara",
+        "clearMask": "Limpar Máscara",
+        "eraser": "Apagador",
+        "fillBoundingBox": "Preencher Caixa Delimitadora",
+        "eraseBoundingBox": "Apagar Caixa Delimitadora",
+        "colorPicker": "Seletor de Cor",
+        "brushOptions": "Opções de Pincel",
+        "brushSize": "Tamanho",
+        "move": "Mover",
+        "resetView": "Resetar Visualização",
+        "mergeVisible": "Fundir Visível",
+        "saveToGallery": "Gravar na Galeria",
+        "copyToClipboard": "Copiar para a Área de Transferência",
+        "downloadAsImage": "Descarregar Como Imagem",
+        "undo": "Desfazer",
+        "redo": "Refazer",
+        "clearCanvas": "Limpar Tela",
+        "canvasSettings": "Configurações de Tela",
+        "snapToGrid": "Encaixar na Grade",
+        "darkenOutsideSelection": "Escurecer Seleção Externa",
+        "autoSaveToGallery": "Gravar Automaticamente na Galeria",
+        "saveBoxRegionOnly": "Gravar Apenas a Região da Caixa",
+        "limitStrokesToBox": "Limitar Traços à Caixa",
+        "showCanvasDebugInfo": "Mostrar Informações de Depuração daTela",
+        "clearCanvasHistory": "Limpar o Histórico da Tela",
+        "clearHistory": "Limpar Históprico",
+        "clearCanvasHistoryMessage": "Limpar o histórico de tela deixa a sua tela atual intacta, mas limpa de forma irreversível o histórico de desfazer e refazer.",
+        "emptyTempImageFolder": "Esvaziar a Pasta de Ficheiros de Imagem Temporários",
+        "emptyFolder": "Esvaziar Pasta",
+        "emptyTempImagesFolderConfirm": "Tem certeza que quer esvaziar a pasta de ficheiros de imagem temporários?",
+        "activeLayer": "Camada Ativa",
+        "canvasScale": "Escala da Tela",
+        "betaPreserveMasked": "Preservar Máscarado"
+    },
+    "accessibility": {
+        "invokeProgressBar": "Invocar barra de progresso",
+        "reset": "Repôr",
+        "nextImage": "Próxima imagem",
+        "useThisParameter": "Usar este parâmetro",
+        "copyMetadataJson": "Copiar metadados JSON",
+        "zoomIn": "Ampliar",
+        "zoomOut": "Reduzir",
+        "rotateCounterClockwise": "Girar no sentido anti-horário",
+        "rotateClockwise": "Girar no sentido horário",
+        "flipVertically": "Espelhar verticalmente",
+        "modifyConfig": "Modificar config",
+        "toggleAutoscroll": "Alternar rolagem automática",
+        "showGallery": "Mostrar galeria",
+        "showOptionsPanel": "Mostrar painel de opções",
+        "uploadImage": "Enviar imagem",
+        "previousImage": "Imagem anterior",
+        "flipHorizontally": "Espelhar horizontalmente",
+        "toggleLogViewer": "Alternar visualizador de registo"
     }
 }

invokeai/frontend/web/dist/locales/pt_BR.json

@@ -63,7 +63,10 @@
         "statusMergingModels": "Mesclando Modelos",
         "statusMergedModels": "Modelos Mesclados",
         "langRussian": "Russo",
-        "langSpanish": "Espanhol"
+        "langSpanish": "Espanhol",
+        "pinOptionsPanel": "Fixar painel de opções",
+        "loadingInvokeAI": "Carregando Invoke AI",
+        "loading": "Carregando"
     },
     "gallery": {
         "generations": "Gerações",
@@ -358,7 +361,6 @@
         "convertToDiffusersHelpText6": "Você deseja converter este modelo?",
         "convertToDiffusersSaveLocation": "Local para Salvar",
         "v1": "v1",
-        "v2": "v2",
         "inpainting": "v1 Inpainting",
         "customConfig": "Configuração personalizada",
         "pathToCustomConfig": "Caminho para configuração personalizada",
@@ -381,7 +383,19 @@
         "allModels": "Todos os Modelos",
         "repoIDValidationMsg": "Repositório Online do seu Modelo",
         "convert": "Converter",
-        "convertToDiffusersHelpText2": "Este processo irá substituir sua entrada de Gerenciador de Modelos por uma versão Diffusers do mesmo modelo."
+        "convertToDiffusersHelpText2": "Este processo irá substituir sua entrada de Gerenciador de Modelos por uma versão Diffusers do mesmo modelo.",
+        "mergedModelCustomSaveLocation": "Caminho Personalizado",
+        "mergedModelSaveLocation": "Local de Salvamento",
+        "interpolationType": "Tipo de Interpolação",
+        "ignoreMismatch": "Ignorar Divergências entre Modelos Selecionados",
+        "invokeAIFolder": "Pasta Invoke AI",
+        "weightedSum": "Soma Ponderada",
+        "sigmoid": "Sigmóide",
+        "inverseSigmoid": "Sigmóide Inversa",
+        "modelMergeHeaderHelp1": "Você pode mesclar até três modelos diferentes para criar uma mistura que atenda às suas necessidades.",
+        "modelMergeInterpAddDifferenceHelp": "Neste modo, o Modelo 3 é primeiro subtraído do Modelo 2. A versão resultante é mesclada com o Modelo 1 com a taxa alpha definida acima.",
+        "modelMergeAlphaHelp": "Alpha controla a força da mistura dos modelos. Valores de alpha mais baixos resultam em uma influência menor do segundo modelo.",
+        "modelMergeHeaderHelp2": "Apenas Diffusers estão disponíveis para mesclagem. Se você deseja mesclar um modelo de checkpoint, por favor, converta-o para Diffusers primeiro."
     },
     "parameters": {
         "images": "Imagems",
@@ -441,7 +455,22 @@
         "info": "Informações",
         "deleteImage": "Apagar Imagem",
         "initialImage": "Imagem inicial",
-        "showOptionsPanel": "Mostrar Painel de Opções"
+        "showOptionsPanel": "Mostrar Painel de Opções",
+        "vSymmetryStep": "V Passo de Simetria",
+        "hSymmetryStep": "H Passo de Simetria",
+        "symmetry": "Simetria",
+        "copyImage": "Copiar imagem",
+        "negativePrompts": "Indicações negativas",
+        "hiresStrength": "Força da Alta Resolução",
+        "denoisingStrength": "A força de remoção de ruído",
+        "imageToImage": "Imagem para Imagem",
+        "cancel": {
+            "setType": "Definir tipo de cancelamento",
+            "isScheduled": "Cancelando",
+            "schedule": "Cancelar após a iteração atual",
+            "immediate": "Cancelar imediatamente"
+        },
+        "general": "Geral"
     },
     "settings": {
         "models": "Modelos",
@@ -454,7 +483,8 @@
         "resetWebUI": "Reiniciar Interface",
         "resetWebUIDesc1": "Reiniciar a interface apenas reinicia o cache local do broswer para imagens e configurações lembradas. Não apaga nenhuma imagem do disco.",
         "resetWebUIDesc2": "Se as imagens não estão aparecendo na galeria ou algo mais não está funcionando, favor tentar reiniciar antes de postar um problema no GitHub.",
-        "resetComplete": "A interface foi reiniciada. Atualize a página para carregar."
+        "resetComplete": "A interface foi reiniciada. Atualize a página para carregar.",
+        "useSlidersForAll": "Usar deslizadores para todas as opções"
     },
     "toast": {
         "tempFoldersEmptied": "Pasta de Arquivos Temporários Esvaziada",
@@ -546,5 +576,20 @@
         "betaDarkenOutside": "Escurecer Externamente",
         "betaLimitToBox": "Limitar Para a Caixa",
         "betaPreserveMasked": "Preservar Máscarado"
+    },
+    "tooltip": {
+        "feature": {
+            "seed": "O valor da semente afeta o ruído inicial a partir do qual a imagem é formada. Você pode usar as sementes já existentes de imagens anteriores. 'Limiar de ruído' é usado para mitigar artefatos em valores CFG altos (experimente a faixa de 0-10), e o Perlin para adicionar ruído Perlin durante a geração: ambos servem para adicionar variação às suas saídas.",
+            "gallery": "A galeria exibe as gerações da pasta de saída conforme elas são criadas. As configurações são armazenadas em arquivos e acessadas pelo menu de contexto.",
+            "other": "Essas opções ativam modos alternativos de processamento para o Invoke. 'Seamless tiling' criará padrões repetidos na saída. 'High resolution' é uma geração em duas etapas com img2img: use essa configuração quando desejar uma imagem maior e mais coerente sem artefatos. Levará mais tempo do que o txt2img usual.",
+            "boundingBox": "A caixa delimitadora é a mesma que as configurações de largura e altura para Texto para Imagem ou Imagem para Imagem. Apenas a área na caixa será processada.",
+            "upscale": "Use o ESRGAN para ampliar a imagem imediatamente após a geração.",
+            "seamCorrection": "Controla o tratamento das emendas visíveis que ocorrem entre as imagens geradas no canvas.",
+            "faceCorrection": "Correção de rosto com GFPGAN ou Codeformer: o algoritmo detecta rostos na imagem e corrige quaisquer defeitos. Um valor alto mudará mais a imagem, resultando em rostos mais atraentes. Codeformer com uma fidelidade maior preserva a imagem original às custas de uma correção de rosto mais forte.",
+            "prompt": "Este é o campo de prompt. O prompt inclui objetos de geração e termos estilísticos. Você também pode adicionar peso (importância do token) no prompt, mas comandos e parâmetros de CLI não funcionarão.",
+            "infillAndScaling": "Gerencie os métodos de preenchimento (usados em áreas mascaradas ou apagadas do canvas) e a escala (útil para tamanhos de caixa delimitadora pequenos).",
+            "imageToImage": "Image to Image carrega qualquer imagem como inicial, que é então usada para gerar uma nova junto com o prompt. Quanto maior o valor, mais a imagem resultante mudará. Valores de 0.0 a 1.0 são possíveis, a faixa recomendada é de 0.25 a 0.75",
+            "variations": "Experimente uma variação com um valor entre 0,1 e 1,0 para mudar o resultado para uma determinada semente. Variações interessantes da semente estão entre 0,1 e 0,3."
+        }
     }
 }

invokeai/frontend/web/dist/locales/ru.json

@@ -46,7 +46,15 @@
         "statusLoadingModel": "Загрузка модели",
         "statusModelChanged": "Модель изменена",
         "githubLabel": "Github",
-        "discordLabel": "Discord"
+        "discordLabel": "Discord",
+        "statusMergingModels": "Слияние моделей",
+        "statusModelConverted": "Модель сконвертирована",
+        "statusMergedModels": "Модели объединены",
+        "pinOptionsPanel": "Закрепить панель настроек",
+        "loading": "Загрузка",
+        "loadingInvokeAI": "Загрузка Invoke AI",
+        "back": "Назад",
+        "statusConvertingModel": "Конвертация модели"
     },
     "gallery": {
         "generations": "Генерации",
@@ -323,7 +331,30 @@
         "deleteConfig": "Удалить конфигурацию",
         "deleteMsg1": "Вы точно хотите удалить модель из InvokeAI?",
         "deleteMsg2": "Это не удалит файл модели с диска. Позже вы можете добавить его снова.",
-        "repoIDValidationMsg": "Онлайн-репозиторий модели"
+        "repoIDValidationMsg": "Онлайн-репозиторий модели",
+        "convertToDiffusersHelpText5": "Пожалуйста, убедитесь, что у вас достаточно места на диске. Модели обычно занимают 4 – 7 Гб.",
+        "invokeAIFolder": "Каталог InvokeAI",
+        "ignoreMismatch": "Игнорировать несоответствия между выбранными моделями",
+        "addCheckpointModel": "Добавить модель Checkpoint/Safetensor",
+        "formMessageDiffusersModelLocationDesc": "Укажите хотя бы одно.",
+        "convertToDiffusersHelpText3": "Файл модели на диске НЕ будет удалён или изменён. Вы сможете заново добавить его в Model Manager при необходимости.",
+        "vaeRepoID": "ID репозитория VAE",
+        "mergedModelName": "Название объединенной модели",
+        "checkpointModels": "Checkpoints",
+        "allModels": "Все модели",
+        "addDiffuserModel": "Добавить Diffusers",
+        "repo_id": "ID репозитория",
+        "formMessageDiffusersVAELocationDesc": "Если не указано, InvokeAI будет искать файл VAE рядом с моделью.",
+        "convert": "Преобразовать",
+        "convertToDiffusers": "Преобразовать в Diffusers",
+        "convertToDiffusersHelpText1": "Модель будет преобразована в формат 🧨 Diffusers.",
+        "convertToDiffusersHelpText4": "Это единоразовое действие. Оно может занять 30—60 секунд в зависимости от характеристик вашего компьютера.",
+        "convertToDiffusersHelpText6": "Вы хотите преобразовать эту модель?",
+        "statusConverting": "Преобразование",
+        "modelConverted": "Модель преобразована",
+        "invokeRoot": "Каталог InvokeAI",
+        "modelsMerged": "Модели объединены",
+        "mergeModels": "Объединить модели"
     },
     "parameters": {
         "images": "Изображения",
@@ -503,5 +534,8 @@
         "betaDarkenOutside": "Затемнить снаружи",
         "betaLimitToBox": "Ограничить выделением",
         "betaPreserveMasked": "Сохранять маскируемую область"
+    },
+    "accessibility": {
+        "modelSelect": "Выбор модели"
     }
 }

invokeai/frontend/web/dist/locales/zh_Hant.json

@@ -1 +1,39 @@
-{}
+{
+    "common": {
+        "nodes": "節點",
+        "img2img": "圖片轉圖片",
+        "langSimplifiedChinese": "簡體中文",
+        "statusError": "錯誤",
+        "statusDisconnected": "已中斷連線",
+        "statusConnected": "已連線",
+        "back": "返回",
+        "load": "載入",
+        "close": "關閉",
+        "langEnglish": "英語",
+        "settingsLabel": "設定",
+        "upload": "上傳",
+        "langArabic": "阿拉伯語",
+        "greenTheme": "綠色",
+        "lightTheme": "淺色",
+        "darkTheme": "深色",
+        "discordLabel": "Discord",
+        "nodesDesc": "使用Node生成圖像的系統正在開發中。敬請期待有關於這項功能的更新。",
+        "reportBugLabel": "回報錯誤",
+        "githubLabel": "GitHub",
+        "langKorean": "韓語",
+        "langPortuguese": "葡萄牙語",
+        "hotkeysLabel": "快捷鍵",
+        "languagePickerLabel": "切換語言",
+        "langDutch": "荷蘭語",
+        "langFrench": "法語",
+        "langGerman": "德語",
+        "langItalian": "義大利語",
+        "langJapanese": "日語",
+        "langPolish": "波蘭語",
+        "langBrPortuguese": "巴西葡萄牙語",
+        "langRussian": "俄語",
+        "langSpanish": "西班牙語",
+        "text2img": "文字到圖像",
+        "unifiedCanvas": "統一畫布"
+    }
+}

invokeai/frontend/web/index.d.ts

@@ -1,3 +1,7 @@
+import React, { PropsWithChildren } from 'react';
+import { IAIPopoverProps } from '../web/src/common/components/IAIPopover';
+import { IAIIconButtonProps } from '../web/src/common/components/IAIIconButton';
+
 export {};
 
 declare module 'redux-socket.io-middleware';
@@ -39,3 +43,36 @@ declare global {
   }
   /* eslint-enable @typescript-eslint/no-explicit-any */
 }
+
+declare module '@invoke-ai/invoke-ai-ui' {
+  declare class ThemeChanger extends React.Component<ThemeChangerProps> {
+    public constructor(props: ThemeChangerProps);
+  }
+
+  declare class InvokeAiLogoComponent extends React.Component<InvokeAILogoComponentProps> {
+    public constructor(props: InvokeAILogoComponentProps);
+  }
+
+  declare class IAIPopover extends React.Component<IAIPopoverProps> {
+    public constructor(props: IAIPopoverProps);
+  }
+
+  declare class IAIIconButton extends React.Component<IAIIconButtonProps> {
+    public constructor(props: IAIIconButtonProps);
+  }
+
+  declare class SettingsModal extends React.Component<SettingsModalProps> {
+    public constructor(props: SettingsModalProps);
+  }
+}
+
+declare function Invoke(props: PropsWithChildren): JSX.Element;
+
+export {
+  ThemeChanger,
+  InvokeAiLogoComponent,
+  IAIPopover,
+  IAIIconButton,
+  SettingsModal,
+};
+export = Invoke;

invokeai/frontend/web/package.json

@@ -8,7 +8,7 @@
     "build": "yarn run lint && vite build",
     "preview": "vite preview",
     "lint:madge": "madge --circular src/main.tsx",
-    "lint:eslint": "eslint --max-warnings=0",
+    "lint:eslint": "eslint --max-warnings=0 .",
     "lint:prettier": "prettier --check .",
     "lint:tsc": "tsc --noEmit",
     "lint": "yarn run lint:eslint && yarn run lint:prettier && yarn run lint:tsc && yarn run lint:madge",
@@ -36,6 +36,7 @@
   },
   "dependencies": {
     "@chakra-ui/anatomy": "^2.1.1",
+    "@chakra-ui/cli": "^2.3.0",
     "@chakra-ui/icons": "^2.0.17",
     "@chakra-ui/react": "^2.5.1",
     "@chakra-ui/styled-system": "^2.6.1",
@@ -52,6 +53,7 @@
     "i18next-http-backend": "^2.1.1",
     "konva": "^8.4.2",
     "lodash": "^4.17.21",
+    "patch-package": "^6.5.1",
     "re-resizable": "^6.9.9",
     "react": "^18.2.0",
     "react-colorful": "^5.6.1",
@@ -72,7 +74,6 @@
     "uuid": "^9.0.0"
   },
   "devDependencies": {
-    "@chakra-ui/cli": "^2.3.0",
     "@fontsource/inter": "^4.5.15",
     "@types/dateformat": "^5.0.0",
     "@types/react": "^18.0.28",
@@ -92,7 +93,6 @@
     "husky": "^8.0.3",
     "lint-staged": "^13.1.2",
     "madge": "^6.0.0",
-    "patch-package": "^6.5.1",
     "postinstall-postinstall": "^2.1.0",
     "prettier": "^2.8.4",
     "rollup-plugin-visualizer": "^5.9.0",

invokeai/frontend/web/public/locales/en.json

@@ -1,4 +1,26 @@
 {
+    "accessibility": {
+        "modelSelect": "Model Select",
+        "invokeProgressBar": "Invoke progress bar",
+        "reset": "Reset",
+        "uploadImage": "Upload Image",
+        "previousImage": "Previous Image",
+        "nextImage": "Next Image",
+        "useThisParameter": "Use this parameter",
+        "copyMetadataJson": "Copy metadata JSON",
+        "exitViewer": "ExitViewer",
+        "zoomIn": "Zoom In",
+        "zoomOut": "Zoom Out",
+        "rotateCounterClockwise": "Rotate Counter-Clockwise",
+        "rotateClockwise": "Rotate Clockwise",
+        "flipHorizontally": "Flip Horizontally",
+        "flipVertically": "Flip Vertically",
+        "modifyConfig": "Modify Config",
+        "toggleAutoscroll": "Toggle autoscroll",
+        "toggleLogViewer": "Toggle Log Viewer",
+        "showGallery": "Show Gallery",
+        "showOptionsPanel": "Show Options Panel"
+    },
     "common": {
         "hotkeysLabel": "Hotkeys",
         "themeLabel": "Theme",
@@ -27,10 +49,11 @@
         "langSimplifiedChinese": "简体中文",
         "langUkranian": "Украї́нська",
         "langSpanish": "Español",
-        "text2img": "Text To Image",
+        "txt2img": "Text To Image",
         "img2img": "Image To Image",
         "unifiedCanvas": "Unified Canvas",
         "nodes": "Nodes",
+        "postprocessing": "Post Processing",
         "nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.",
         "postProcessing": "Post Processing",
         "postProcessDesc1": "Invoke AI offers a wide variety of post processing features. Image Upscaling and Face Restoration are already available in the WebUI. You can access them from the Advanced Options menu of the Text To Image and Image To Image tabs. You can also process images directly, using the image action buttons above the current image display or in the viewer.",
@@ -41,6 +64,8 @@
         "trainingDesc2": "InvokeAI already supports training custom embeddings using Textual Inversion using the main script.",
         "upload": "Upload",
         "close": "Close",
+        "cancel": "Cancel",
+        "accept": "Accept",
         "load": "Load",
         "back": "Back",
         "statusConnected": "Connected",
@@ -310,6 +335,7 @@
         "addNewModel": "Add New Model",
         "addCheckpointModel": "Add Checkpoint / Safetensor Model",
         "addDiffuserModel": "Add Diffusers",
+        "scanForModels": "Scan For Models",
         "addManually": "Add Manually",
         "manual": "Manual",
         "name": "Name",
@@ -574,7 +600,7 @@
         "autoSaveToGallery": "Auto Save to Gallery",
         "saveBoxRegionOnly": "Save Box Region Only",
         "limitStrokesToBox": "Limit Strokes to Box",
-        "showCanvasDebugInfo": "Show Canvas Debug Info",
+        "showCanvasDebugInfo": "Show Additional Canvas Info",
         "clearCanvasHistory": "Clear Canvas History",
         "clearHistory": "Clear History",
         "clearCanvasHistoryMessage": "Clearing the canvas history leaves your current canvas intact, but irreversibly clears the undo and redo history.",

invokeai/frontend/web/public/locales/es.json

@@ -63,7 +63,14 @@
         "back": "Atrás",
         "statusConvertingModel": "Convertir el modelo",
         "statusModelConverted": "Modelo adaptado",
-        "statusMergingModels": "Fusionar modelos"
+        "statusMergingModels": "Fusionar modelos",
+        "oceanTheme": "Océano",
+        "langPortuguese": "Portugués",
+        "langKorean": "Coreano",
+        "langHebrew": "Hebreo",
+        "pinOptionsPanel": "Pin del panel de opciones",
+        "loading": "Cargando",
+        "loadingInvokeAI": "Cargando invocar a la IA"
     },
     "gallery": {
         "generations": "Generaciones",
@@ -385,14 +392,19 @@
         "modelMergeAlphaHelp": "Alfa controla la fuerza de mezcla de los modelos. Los valores alfa más bajos reducen la influencia del segundo modelo.",
         "modelMergeInterpAddDifferenceHelp": "En este modo, el Modelo 3 se sustrae primero del Modelo 2. La versión resultante se mezcla con el Modelo 1 con la tasa alfa establecida anteriormente. La versión resultante se mezcla con el Modelo 1 con la tasa alfa establecida anteriormente.",
         "ignoreMismatch": "Ignorar discrepancias entre modelos seleccionados",
-        "modelMergeHeaderHelp1": "Puede combinar hasta tres modelos diferentes para crear una mezcla que se adapte a sus necesidades.",
+        "modelMergeHeaderHelp1": "Puede unir hasta tres modelos diferentes para crear una combinación que se adapte a sus necesidades.",
         "inverseSigmoid": "Sigmoideo inverso",
         "weightedSum": "Modelo de suma ponderada",
         "sigmoid": "Función sigmoide",
         "allModels": "Todos los modelos",
         "repo_id": "Identificador del repositorio",
         "pathToCustomConfig": "Ruta a la configuración personalizada",
-        "customConfig": "Configuración personalizada"
+        "customConfig": "Configuración personalizada",
+        "v2_base": "v2 (512px)",
+        "none": "ninguno",
+        "pickModelType": "Elige el tipo de modelo",
+        "v2_768": "v2 (768px)",
+        "addDifference": "Añadir una diferencia"
     },
     "parameters": {
         "images": "Imágenes",
@@ -588,5 +600,27 @@
         "betaDarkenOutside": "Oscurecer fuera",
         "betaLimitToBox": "Limitar a caja",
         "betaPreserveMasked": "Preservar área enmascarada"
+    },
+    "accessibility": {
+        "invokeProgressBar": "Activar la barra de progreso",
+        "modelSelect": "Seleccionar modelo",
+        "reset": "Reiniciar",
+        "uploadImage": "Cargar imagen",
+        "previousImage": "Imagen anterior",
+        "nextImage": "Siguiente imagen",
+        "useThisParameter": "Utiliza este parámetro",
+        "copyMetadataJson": "Copiar los metadatos JSON",
+        "exitViewer": "Salir del visor",
+        "zoomIn": "Acercar",
+        "zoomOut": "Alejar",
+        "rotateCounterClockwise": "Girar en sentido antihorario",
+        "rotateClockwise": "Girar en sentido horario",
+        "flipHorizontally": "Voltear horizontalmente",
+        "flipVertically": "Voltear verticalmente",
+        "modifyConfig": "Modificar la configuración",
+        "toggleAutoscroll": "Activar el autodesplazamiento",
+        "toggleLogViewer": "Alternar el visor de registros",
+        "showGallery": "Mostrar galería",
+        "showOptionsPanel": "Mostrar el panel de opciones"
     }
 }

invokeai/frontend/web/public/locales/it.json

@@ -63,7 +63,14 @@
         "langSimplifiedChinese": "Cinese semplificato",
         "langDutch": "Olandese",
         "statusModelConverted": "Modello Convertito",
-        "statusConvertingModel": "Conversione Modello"
+        "statusConvertingModel": "Conversione Modello",
+        "langKorean": "Coreano",
+        "langPortuguese": "Portoghese",
+        "pinOptionsPanel": "Blocca il pannello Opzioni",
+        "loading": "Caricamento in corso",
+        "oceanTheme": "Oceano",
+        "langHebrew": "Ebraico",
+        "loadingInvokeAI": "Caricamento Invoke AI"
     },
     "gallery": {
         "generations": "Generazioni",
@@ -392,7 +399,12 @@
         "customSaveLocation": "Ubicazione salvataggio personalizzata",
         "weightedSum": "Somma pesata",
         "sigmoid": "Sigmoide",
-        "inverseSigmoid": "Sigmoide inverso"
+        "inverseSigmoid": "Sigmoide inverso",
+        "v2_base": "v2 (512px)",
+        "v2_768": "v2 (768px)",
+        "none": "niente",
+        "addDifference": "Aggiungi differenza",
+        "pickModelType": "Scegli il tipo di modello"
     },
     "parameters": {
         "images": "Immagini",
@@ -588,5 +600,27 @@
         "betaDarkenOutside": "Oscura all'esterno",
         "betaLimitToBox": "Limita al rettangolo",
         "betaPreserveMasked": "Conserva quanto mascherato"
+    },
+    "accessibility": {
+        "modelSelect": "Seleziona modello",
+        "invokeProgressBar": "Barra di avanzamento generazione",
+        "uploadImage": "Carica immagine",
+        "previousImage": "Immagine precedente",
+        "nextImage": "Immagine successiva",
+        "useThisParameter": "Usa questo parametro",
+        "reset": "Reimposta",
+        "copyMetadataJson": "Copia i metadati JSON",
+        "exitViewer": "Esci dal visualizzatore",
+        "zoomIn": "Zoom avanti",
+        "zoomOut": "Zoom Indietro",
+        "rotateCounterClockwise": "Ruotare in senso antiorario",
+        "rotateClockwise": "Ruotare in senso orario",
+        "flipHorizontally": "Capovolgi orizzontalmente",
+        "toggleLogViewer": "Attiva/disattiva visualizzatore registro",
+        "showGallery": "Mostra la galleria immagini",
+        "showOptionsPanel": "Mostra il pannello opzioni",
+        "flipVertically": "Capovolgi verticalmente",
+        "toggleAutoscroll": "Attiva/disattiva lo scorrimento automatico",
+        "modifyConfig": "Modifica configurazione"
     }
 }

invokeai/frontend/web/public/locales/pt.json

@@ -63,6 +63,560 @@
         "statusGeneratingOutpainting": "Geração de Ampliação",
         "statusGenerationComplete": "Geração Completa",
         "statusMergingModels": "Mesclando Modelos",
-        "statusMergedModels": "Modelos Mesclados"
+        "statusMergedModels": "Modelos Mesclados",
+        "oceanTheme": "Oceano",
+        "pinOptionsPanel": "Fixar painel de opções",
+        "loading": "A carregar",
+        "loadingInvokeAI": "A carregar Invoke AI",
+        "langPortuguese": "Português"
+    },
+    "gallery": {
+        "galleryImageResetSize": "Resetar Imagem",
+        "gallerySettings": "Configurações de Galeria",
+        "maintainAspectRatio": "Mater Proporções",
+        "autoSwitchNewImages": "Trocar para Novas Imagens Automaticamente",
+        "pinGallery": "Fixar Galeria",
+        "singleColumnLayout": "Disposição em Coluna Única",
+        "allImagesLoaded": "Todas as Imagens Carregadas",
+        "loadMore": "Carregar Mais",
+        "noImagesInGallery": "Sem Imagens na Galeria",
+        "generations": "Gerações",
+        "showGenerations": "Mostrar Gerações",
+        "uploads": "Enviados",
+        "showUploads": "Mostrar Enviados",
+        "galleryImageSize": "Tamanho da Imagem"
+    },
+    "hotkeys": {
+        "generalHotkeys": "Atalhos Gerais",
+        "galleryHotkeys": "Atalhos da Galeria",
+        "toggleViewer": {
+            "title": "Ativar Visualizador",
+            "desc": "Abrir e fechar o Visualizador de Imagens"
+        },
+        "maximizeWorkSpace": {
+            "desc": "Fechar painéis e maximixar área de trabalho",
+            "title": "Maximizar a Área de Trabalho"
+        },
+        "changeTabs": {
+            "title": "Mudar Guias",
+            "desc": "Trocar para outra área de trabalho"
+        },
+        "consoleToggle": {
+            "desc": "Abrir e fechar console",
+            "title": "Ativar Console"
+        },
+        "setPrompt": {
+            "title": "Definir Prompt",
+            "desc": "Usar o prompt da imagem atual"
+        },
+        "sendToImageToImage": {
+            "desc": "Manda a imagem atual para Imagem Para Imagem",
+            "title": "Mandar para Imagem Para Imagem"
+        },
+        "previousImage": {
+            "desc": "Mostra a imagem anterior na galeria",
+            "title": "Imagem Anterior"
+        },
+        "nextImage": {
+            "title": "Próxima Imagem",
+            "desc": "Mostra a próxima imagem na galeria"
+        },
+        "decreaseGalleryThumbSize": {
+            "desc": "Diminui o tamanho das thumbs na galeria",
+            "title": "Diminuir Tamanho da Galeria de Imagem"
+        },
+        "selectBrush": {
+            "title": "Selecionar Pincel",
+            "desc": "Seleciona o pincel"
+        },
+        "selectEraser": {
+            "title": "Selecionar Apagador",
+            "desc": "Seleciona o apagador"
+        },
+        "decreaseBrushSize": {
+            "title": "Diminuir Tamanho do Pincel",
+            "desc": "Diminui o tamanho do pincel/apagador"
+        },
+        "increaseBrushOpacity": {
+            "desc": "Aumenta a opacidade do pincel",
+            "title": "Aumentar Opacidade do Pincel"
+        },
+        "moveTool": {
+            "title": "Ferramenta Mover",
+            "desc": "Permite navegar pela tela"
+        },
+        "decreaseBrushOpacity": {
+            "desc": "Diminui a opacidade do pincel",
+            "title": "Diminuir Opacidade do Pincel"
+        },
+        "toggleSnap": {
+            "title": "Ativar Encaixe",
+            "desc": "Ativa Encaixar na Grade"
+        },
+        "quickToggleMove": {
+            "title": "Ativar Mover Rapidamente",
+            "desc": "Temporariamente ativa o modo Mover"
+        },
+        "toggleLayer": {
+            "title": "Ativar Camada",
+            "desc": "Ativa a seleção de camada de máscara/base"
+        },
+        "clearMask": {
+            "title": "Limpar Máscara",
+            "desc": "Limpa toda a máscara"
+        },
+        "hideMask": {
+            "title": "Esconder Máscara",
+            "desc": "Esconde e Revela a máscara"
+        },
+        "mergeVisible": {
+            "title": "Fundir Visível",
+            "desc": "Fundir todas as camadas visíveis das telas"
+        },
+        "downloadImage": {
+            "desc": "Descarregar a tela atual",
+            "title": "Descarregar Imagem"
+        },
+        "undoStroke": {
+            "title": "Desfazer Traço",
+            "desc": "Desfaz um traço de pincel"
+        },
+        "redoStroke": {
+            "title": "Refazer Traço",
+            "desc": "Refaz o traço de pincel"
+        },
+        "keyboardShortcuts": "Atalhos de Teclado",
+        "appHotkeys": "Atalhos do app",
+        "invoke": {
+            "title": "Invocar",
+            "desc": "Gerar uma imagem"
+        },
+        "cancel": {
+            "title": "Cancelar",
+            "desc": "Cancelar geração de imagem"
+        },
+        "focusPrompt": {
+            "title": "Foco do Prompt",
+            "desc": "Foco da área de texto do prompt"
+        },
+        "toggleOptions": {
+            "title": "Ativar Opções",
+            "desc": "Abrir e fechar o painel de opções"
+        },
+        "pinOptions": {
+            "title": "Fixar Opções",
+            "desc": "Fixar o painel de opções"
+        },
+        "closePanels": {
+            "title": "Fechar Painéis",
+            "desc": "Fecha os painéis abertos"
+        },
+        "unifiedCanvasHotkeys": "Atalhos da Tela Unificada",
+        "toggleGallery": {
+            "title": "Ativar Galeria",
+            "desc": "Abrir e fechar a gaveta da galeria"
+        },
+        "setSeed": {
+            "title": "Definir Seed",
+            "desc": "Usar seed da imagem atual"
+        },
+        "setParameters": {
+            "title": "Definir Parâmetros",
+            "desc": "Usar todos os parâmetros da imagem atual"
+        },
+        "restoreFaces": {
+            "title": "Restaurar Rostos",
+            "desc": "Restaurar a imagem atual"
+        },
+        "upscale": {
+            "title": "Redimensionar",
+            "desc": "Redimensionar a imagem atual"
+        },
+        "showInfo": {
+            "title": "Mostrar Informações",
+            "desc": "Mostrar metadados de informações da imagem atual"
+        },
+        "deleteImage": {
+            "title": "Apagar Imagem",
+            "desc": "Apaga a imagem atual"
+        },
+        "toggleGalleryPin": {
+            "title": "Ativar Fixar Galeria",
+            "desc": "Fixa e desafixa a galeria na interface"
+        },
+        "increaseGalleryThumbSize": {
+            "title": "Aumentar Tamanho da Galeria de Imagem",
+            "desc": "Aumenta o tamanho das thumbs na galeria"
+        },
+        "increaseBrushSize": {
+            "title": "Aumentar Tamanho do Pincel",
+            "desc": "Aumenta o tamanho do pincel/apagador"
+        },
+        "fillBoundingBox": {
+            "title": "Preencher Caixa Delimitadora",
+            "desc": "Preenche a caixa delimitadora com a cor do pincel"
+        },
+        "eraseBoundingBox": {
+            "title": "Apagar Caixa Delimitadora",
+            "desc": "Apaga a área da caixa delimitadora"
+        },
+        "colorPicker": {
+            "title": "Selecionar Seletor de Cor",
+            "desc": "Seleciona o seletor de cores"
+        },
+        "showHideBoundingBox": {
+            "title": "Mostrar/Esconder Caixa Delimitadora",
+            "desc": "Ativa a visibilidade da caixa delimitadora"
+        },
+        "saveToGallery": {
+            "title": "Gravara Na Galeria",
+            "desc": "Grava a tela atual na galeria"
+        },
+        "copyToClipboard": {
+            "title": "Copiar para a Área de Transferência",
+            "desc": "Copia a tela atual para a área de transferência"
+        },
+        "resetView": {
+            "title": "Resetar Visualização",
+            "desc": "Reseta Visualização da Tela"
+        },
+        "previousStagingImage": {
+            "title": "Imagem de Preparação Anterior",
+            "desc": "Área de Imagem de Preparação Anterior"
+        },
+        "nextStagingImage": {
+            "title": "Próxima Imagem de Preparação Anterior",
+            "desc": "Próxima Área de Imagem de Preparação Anterior"
+        },
+        "acceptStagingImage": {
+            "title": "Aceitar Imagem de Preparação Anterior",
+            "desc": "Aceitar Área de Imagem de Preparação Anterior"
+        }
+    },
+    "modelManager": {
+        "modelAdded": "Modelo Adicionado",
+        "modelUpdated": "Modelo Atualizado",
+        "modelEntryDeleted": "Entrada de modelo excluída",
+        "description": "Descrição",
+        "modelLocationValidationMsg": "Caminho para onde o seu modelo está localizado.",
+        "repo_id": "Repo ID",
+        "vaeRepoIDValidationMsg": "Repositório Online do seu VAE",
+        "width": "Largura",
+        "widthValidationMsg": "Largura padrão do seu modelo.",
+        "height": "Altura",
+        "heightValidationMsg": "Altura padrão do seu modelo.",
+        "findModels": "Encontrar Modelos",
+        "scanAgain": "Digitalize Novamente",
+        "deselectAll": "Deselecionar Tudo",
+        "showExisting": "Mostrar Existente",
+        "deleteConfig": "Apagar Config",
+        "convertToDiffusersHelpText6": "Deseja converter este modelo?",
+        "mergedModelName": "Nome do modelo mesclado",
+        "alpha": "Alpha",
+        "interpolationType": "Tipo de Interpolação",
+        "modelMergeHeaderHelp1": "Pode mesclar até três modelos diferentes para criar uma mistura que atenda às suas necessidades.",
+        "modelMergeHeaderHelp2": "Apenas Diffusers estão disponíveis para mesclagem. Se deseja mesclar um modelo de checkpoint, por favor, converta-o para Diffusers primeiro.",
+        "modelMergeInterpAddDifferenceHelp": "Neste modo, o Modelo 3 é primeiro subtraído do Modelo 2. A versão resultante é mesclada com o Modelo 1 com a taxa alpha definida acima.",
+        "nameValidationMsg": "Insira um nome para o seu modelo",
+        "descriptionValidationMsg": "Adicione uma descrição para o seu modelo",
+        "config": "Configuração",
+        "modelExists": "Modelo Existe",
+        "selectAndAdd": "Selecione e Adicione Modelos Listados Abaixo",
+        "noModelsFound": "Nenhum Modelo Encontrado",
+        "v2_768": "v2 (768px)",
+        "inpainting": "v1 Inpainting",
+        "customConfig": "Configuração personalizada",
+        "pathToCustomConfig": "Caminho para configuração personalizada",
+        "statusConverting": "A converter",
+        "modelConverted": "Modelo Convertido",
+        "ignoreMismatch": "Ignorar Divergências entre Modelos Selecionados",
+        "addDifference": "Adicionar diferença",
+        "pickModelType": "Escolha o tipo de modelo",
+        "safetensorModels": "SafeTensors",
+        "cannotUseSpaces": "Não pode usar espaços",
+        "addNew": "Adicionar Novo",
+        "addManually": "Adicionar Manualmente",
+        "manual": "Manual",
+        "name": "Nome",
+        "configValidationMsg": "Caminho para o ficheiro de configuração do seu modelo.",
+        "modelLocation": "Localização do modelo",
+        "repoIDValidationMsg": "Repositório Online do seu Modelo",
+        "updateModel": "Atualizar Modelo",
+        "availableModels": "Modelos Disponíveis",
+        "load": "Carregar",
+        "active": "Ativado",
+        "notLoaded": "Não carregado",
+        "deleteModel": "Apagar modelo",
+        "deleteMsg1": "Tem certeza de que deseja apagar esta entrada do modelo de InvokeAI?",
+        "deleteMsg2": "Isso não vai apagar o ficheiro de modelo checkpoint do seu disco. Pode lê-los, se desejar.",
+        "convertToDiffusers": "Converter para Diffusers",
+        "convertToDiffusersHelpText1": "Este modelo será convertido ao formato 🧨 Diffusers.",
+        "convertToDiffusersHelpText2": "Este processo irá substituir a sua entrada de Gestor de Modelos por uma versão Diffusers do mesmo modelo.",
+        "convertToDiffusersHelpText3": "O seu ficheiro de ponto de verificação no disco NÃO será excluído ou modificado de forma alguma. Pode adicionar o seu ponto de verificação ao Gestor de modelos novamente, se desejar.",
+        "convertToDiffusersSaveLocation": "Local para Gravar",
+        "v2_base": "v2 (512px)",
+        "mergeModels": "Mesclar modelos",
+        "modelOne": "Modelo 1",
+        "modelTwo": "Modelo 2",
+        "modelThree": "Modelo 3",
+        "mergedModelSaveLocation": "Local de Salvamento",
+        "merge": "Mesclar",
+        "modelsMerged": "Modelos mesclados",
+        "mergedModelCustomSaveLocation": "Caminho Personalizado",
+        "invokeAIFolder": "Pasta Invoke AI",
+        "inverseSigmoid": "Sigmóide Inversa",
+        "none": "nenhum",
+        "modelManager": "Gerente de Modelo",
+        "model": "Modelo",
+        "allModels": "Todos os Modelos",
+        "checkpointModels": "Checkpoints",
+        "diffusersModels": "Diffusers",
+        "addNewModel": "Adicionar Novo modelo",
+        "addCheckpointModel": "Adicionar Modelo de Checkpoint/Safetensor",
+        "addDiffuserModel": "Adicionar Diffusers",
+        "vaeLocation": "Localização VAE",
+        "vaeLocationValidationMsg": "Caminho para onde o seu VAE está localizado.",
+        "vaeRepoID": "VAE Repo ID",
+        "addModel": "Adicionar Modelo",
+        "search": "Procurar",
+        "cached": "Em cache",
+        "checkpointFolder": "Pasta de Checkpoint",
+        "clearCheckpointFolder": "Apagar Pasta de Checkpoint",
+        "modelsFound": "Modelos Encontrados",
+        "selectFolder": "Selecione a Pasta",
+        "selected": "Selecionada",
+        "selectAll": "Selecionar Tudo",
+        "addSelected": "Adicione Selecionado",
+        "delete": "Apagar",
+        "formMessageDiffusersModelLocation": "Localização dos Modelos Diffusers",
+        "formMessageDiffusersModelLocationDesc": "Por favor entre com ao menos um.",
+        "formMessageDiffusersVAELocation": "Localização do VAE",
+        "formMessageDiffusersVAELocationDesc": "Se não provido, InvokeAI irá procurar pelo ficheiro VAE dentro do local do modelo.",
+        "convert": "Converter",
+        "convertToDiffusersHelpText4": "Este é um processo único. Pode levar cerca de 30 a 60s, a depender das especificações do seu computador.",
+        "convertToDiffusersHelpText5": "Por favor, certifique-se de que tenha espaço suficiente no disco. Os modelos geralmente variam entre 4GB e 7GB de tamanho.",
+        "v1": "v1",
+        "sameFolder": "Mesma pasta",
+        "invokeRoot": "Pasta do InvokeAI",
+        "custom": "Personalizado",
+        "customSaveLocation": "Local de salvamento personalizado",
+        "modelMergeAlphaHelp": "Alpha controla a força da mistura dos modelos. Valores de alpha mais baixos resultam numa influência menor do segundo modelo.",
+        "sigmoid": "Sigmóide",
+        "weightedSum": "Soma Ponderada"
+    },
+    "parameters": {
+        "width": "Largura",
+        "seed": "Seed",
+        "hiresStrength": "Força da Alta Resolução",
+        "negativePrompts": "Indicações negativas",
+        "general": "Geral",
+        "randomizeSeed": "Seed Aleatório",
+        "shuffle": "Embaralhar",
+        "noiseThreshold": "Limite de Ruído",
+        "perlinNoise": "Ruído de Perlin",
+        "variations": "Variatções",
+        "seedWeights": "Pesos da Seed",
+        "restoreFaces": "Restaurar Rostos",
+        "faceRestoration": "Restauração de Rosto",
+        "type": "Tipo",
+        "denoisingStrength": "A força de remoção de ruído",
+        "scale": "Escala",
+        "otherOptions": "Outras Opções",
+        "seamlessTiling": "Ladrilho Sem Fronteira",
+        "hiresOptim": "Otimização de Alta Res",
+        "imageFit": "Caber Imagem Inicial No Tamanho de Saída",
+        "codeformerFidelity": "Fidelidade",
+        "seamSize": "Tamanho da Fronteira",
+        "seamBlur": "Desfoque da Fronteira",
+        "seamStrength": "Força da Fronteira",
+        "seamSteps": "Passos da Fronteira",
+        "tileSize": "Tamanho do Ladrilho",
+        "boundingBoxHeader": "Caixa Delimitadora",
+        "seamCorrectionHeader": "Correção de Fronteira",
+        "infillScalingHeader": "Preencimento e Escala",
+        "img2imgStrength": "Força de Imagem Para Imagem",
+        "toggleLoopback": "Ativar Loopback",
+        "symmetry": "Simetria",
+        "promptPlaceholder": "Digite o prompt aqui. [tokens negativos], (upweight)++, (downweight)--, trocar e misturar estão disponíveis (veja docs)",
+        "sendTo": "Mandar para",
+        "openInViewer": "Abrir No Visualizador",
+        "closeViewer": "Fechar Visualizador",
+        "usePrompt": "Usar Prompt",
+        "deleteImage": "Apagar Imagem",
+        "initialImage": "Imagem inicial",
+        "showOptionsPanel": "Mostrar Painel de Opções",
+        "strength": "Força",
+        "upscaling": "Redimensionando",
+        "upscale": "Redimensionar",
+        "upscaleImage": "Redimensionar Imagem",
+        "scaleBeforeProcessing": "Escala Antes do Processamento",
+        "invoke": "Invocar",
+        "images": "Imagems",
+        "steps": "Passos",
+        "cfgScale": "Escala CFG",
+        "height": "Altura",
+        "sampler": "Amostrador",
+        "imageToImage": "Imagem para Imagem",
+        "variationAmount": "Quntidade de Variatções",
+        "scaledWidth": "L Escalada",
+        "scaledHeight": "A Escalada",
+        "infillMethod": "Método de Preenchimento",
+        "hSymmetryStep": "H Passo de Simetria",
+        "vSymmetryStep": "V Passo de Simetria",
+        "cancel": {
+            "immediate": "Cancelar imediatamente",
+            "schedule": "Cancelar após a iteração atual",
+            "isScheduled": "A cancelar",
+            "setType": "Definir tipo de cancelamento"
+        },
+        "sendToImg2Img": "Mandar para Imagem Para Imagem",
+        "sendToUnifiedCanvas": "Mandar para Tela Unificada",
+        "copyImage": "Copiar imagem",
+        "copyImageToLink": "Copiar Imagem Para a Ligação",
+        "downloadImage": "Descarregar Imagem",
+        "useSeed": "Usar Seed",
+        "useAll": "Usar Todos",
+        "useInitImg": "Usar Imagem Inicial",
+        "info": "Informações"
+    },
+    "settings": {
+        "confirmOnDelete": "Confirmar Antes de Apagar",
+        "displayHelpIcons": "Mostrar Ícones de Ajuda",
+        "useCanvasBeta": "Usar Layout de Telas Beta",
+        "enableImageDebugging": "Ativar Depuração de Imagem",
+        "useSlidersForAll": "Usar deslizadores para todas as opções",
+        "resetWebUIDesc1": "Reiniciar a interface apenas reinicia o cache local do broswer para imagens e configurações lembradas. Não apaga nenhuma imagem do disco.",
+        "models": "Modelos",
+        "displayInProgress": "Mostrar Progresso de Imagens Em Andamento",
+        "saveSteps": "Gravar imagens a cada n passos",
+        "resetWebUI": "Reiniciar Interface",
+        "resetWebUIDesc2": "Se as imagens não estão a aparecer na galeria ou algo mais não está a funcionar, favor tentar reiniciar antes de postar um problema no GitHub.",
+        "resetComplete": "A interface foi reiniciada. Atualize a página para carregar."
+    },
+    "toast": {
+        "uploadFailed": "Envio Falhou",
+        "uploadFailedMultipleImagesDesc": "Várias imagens copiadas, só é permitido uma imagem de cada vez",
+        "uploadFailedUnableToLoadDesc": "Não foj possível carregar o ficheiro",
+        "downloadImageStarted": "Download de Imagem Começou",
+        "imageNotLoadedDesc": "Nenhuma imagem encontrada a enviar para o módulo de imagem para imagem",
+        "imageLinkCopied": "Ligação de Imagem Copiada",
+        "imageNotLoaded": "Nenhuma Imagem Carregada",
+        "parametersFailed": "Problema ao carregar parâmetros",
+        "parametersFailedDesc": "Não foi possível carregar imagem incial.",
+        "seedSet": "Seed Definida",
+        "upscalingFailed": "Redimensionamento Falhou",
+        "promptNotSet": "Prompt Não Definido",
+        "tempFoldersEmptied": "Pasta de Ficheiros Temporários Esvaziada",
+        "imageCopied": "Imagem Copiada",
+        "imageSavedToGallery": "Imagem Salva na Galeria",
+        "canvasMerged": "Tela Fundida",
+        "sentToImageToImage": "Mandar Para Imagem Para Imagem",
+        "sentToUnifiedCanvas": "Enviada para a Tela Unificada",
+        "parametersSet": "Parâmetros Definidos",
+        "parametersNotSet": "Parâmetros Não Definidos",
+        "parametersNotSetDesc": "Nenhum metadado foi encontrado para essa imagem.",
+        "seedNotSet": "Seed Não Definida",
+        "seedNotSetDesc": "Não foi possível achar a seed para a imagem.",
+        "promptSet": "Prompt Definido",
+        "promptNotSetDesc": "Não foi possível achar prompt para essa imagem.",
+        "faceRestoreFailed": "Restauração de Rosto Falhou",
+        "metadataLoadFailed": "Falha ao tentar carregar metadados",
+        "initialImageSet": "Imagem Inicial Definida",
+        "initialImageNotSet": "Imagem Inicial Não Definida",
+        "initialImageNotSetDesc": "Não foi possível carregar imagem incial"
+    },
+    "tooltip": {
+        "feature": {
+            "prompt": "Este é o campo de prompt. O prompt inclui objetos de geração e termos estilísticos. Também pode adicionar peso (importância do token) no prompt, mas comandos e parâmetros de CLI não funcionarão.",
+            "other": "Essas opções ativam modos alternativos de processamento para o Invoke. 'Seamless tiling' criará padrões repetidos na saída. 'High resolution' é uma geração em duas etapas com img2img: use essa configuração quando desejar uma imagem maior e mais coerente sem artefatos. Levará mais tempo do que o txt2img usual.",
+            "seed": "O valor da semente afeta o ruído inicial a partir do qual a imagem é formada. Pode usar as sementes já existentes de imagens anteriores. 'Limiar de ruído' é usado para mitigar artefatos em valores CFG altos (experimente a faixa de 0-10) e o Perlin para adicionar ruído Perlin durante a geração: ambos servem para adicionar variação às suas saídas.",
+            "imageToImage": "Image to Image carrega qualquer imagem como inicial, que é então usada para gerar uma nova junto com o prompt. Quanto maior o valor, mais a imagem resultante mudará. Valores de 0.0 a 1.0 são possíveis, a faixa recomendada é de 0.25 a 0.75",
+            "faceCorrection": "Correção de rosto com GFPGAN ou Codeformer: o algoritmo detecta rostos na imagem e corrige quaisquer defeitos. Um valor alto mudará mais a imagem, a resultar em rostos mais atraentes. Codeformer com uma fidelidade maior preserva a imagem original às custas de uma correção de rosto mais forte.",
+            "seamCorrection": "Controla o tratamento das emendas visíveis que ocorrem entre as imagens geradas no canvas.",
+            "gallery": "A galeria exibe as gerações da pasta de saída conforme elas são criadas. As configurações são armazenadas em ficheiros e acessadas pelo menu de contexto.",
+            "variations": "Experimente uma variação com um valor entre 0,1 e 1,0 para mudar o resultado para uma determinada semente. Variações interessantes da semente estão entre 0,1 e 0,3.",
+            "upscale": "Use o ESRGAN para ampliar a imagem imediatamente após a geração.",
+            "boundingBox": "A caixa delimitadora é a mesma que as configurações de largura e altura para Texto para Imagem ou Imagem para Imagem. Apenas a área na caixa será processada.",
+            "infillAndScaling": "Gira os métodos de preenchimento (usados em áreas mascaradas ou apagadas do canvas) e a escala (útil para tamanhos de caixa delimitadora pequenos)."
+        }
+    },
+    "unifiedCanvas": {
+        "emptyTempImagesFolderMessage": "Esvaziar a pasta de ficheiros de imagem temporários também reseta completamente a Tela Unificada. Isso inclui todo o histórico de desfazer/refazer, imagens na área de preparação e a camada base da tela.",
+        "scaledBoundingBox": "Caixa Delimitadora Escalada",
+        "boundingBoxPosition": "Posição da Caixa Delimitadora",
+        "next": "Próximo",
+        "accept": "Aceitar",
+        "showHide": "Mostrar/Esconder",
+        "discardAll": "Descartar Todos",
+        "betaClear": "Limpar",
+        "betaDarkenOutside": "Escurecer Externamente",
+        "base": "Base",
+        "brush": "Pincel",
+        "showIntermediates": "Mostrar Intermediários",
+        "showGrid": "Mostrar Grade",
+        "clearCanvasHistoryConfirm": "Tem certeza que quer limpar o histórico de tela?",
+        "boundingBox": "Caixa Delimitadora",
+        "canvasDimensions": "Dimensões da Tela",
+        "canvasPosition": "Posição da Tela",
+        "cursorPosition": "Posição do cursor",
+        "previous": "Anterior",
+        "betaLimitToBox": "Limitar á Caixa",
+        "layer": "Camada",
+        "mask": "Máscara",
+        "maskingOptions": "Opções de Mascaramento",
+        "enableMask": "Ativar Máscara",
+        "preserveMaskedArea": "Preservar Área da Máscara",
+        "clearMask": "Limpar Máscara",
+        "eraser": "Apagador",
+        "fillBoundingBox": "Preencher Caixa Delimitadora",
+        "eraseBoundingBox": "Apagar Caixa Delimitadora",
+        "colorPicker": "Seletor de Cor",
+        "brushOptions": "Opções de Pincel",
+        "brushSize": "Tamanho",
+        "move": "Mover",
+        "resetView": "Resetar Visualização",
+        "mergeVisible": "Fundir Visível",
+        "saveToGallery": "Gravar na Galeria",
+        "copyToClipboard": "Copiar para a Área de Transferência",
+        "downloadAsImage": "Descarregar Como Imagem",
+        "undo": "Desfazer",
+        "redo": "Refazer",
+        "clearCanvas": "Limpar Tela",
+        "canvasSettings": "Configurações de Tela",
+        "snapToGrid": "Encaixar na Grade",
+        "darkenOutsideSelection": "Escurecer Seleção Externa",
+        "autoSaveToGallery": "Gravar Automaticamente na Galeria",
+        "saveBoxRegionOnly": "Gravar Apenas a Região da Caixa",
+        "limitStrokesToBox": "Limitar Traços à Caixa",
+        "showCanvasDebugInfo": "Mostrar Informações de Depuração daTela",
+        "clearCanvasHistory": "Limpar o Histórico da Tela",
+        "clearHistory": "Limpar Históprico",
+        "clearCanvasHistoryMessage": "Limpar o histórico de tela deixa a sua tela atual intacta, mas limpa de forma irreversível o histórico de desfazer e refazer.",
+        "emptyTempImageFolder": "Esvaziar a Pasta de Ficheiros de Imagem Temporários",
+        "emptyFolder": "Esvaziar Pasta",
+        "emptyTempImagesFolderConfirm": "Tem certeza que quer esvaziar a pasta de ficheiros de imagem temporários?",
+        "activeLayer": "Camada Ativa",
+        "canvasScale": "Escala da Tela",
+        "betaPreserveMasked": "Preservar Máscarado"
+    },
+    "accessibility": {
+        "invokeProgressBar": "Invocar barra de progresso",
+        "reset": "Repôr",
+        "nextImage": "Próxima imagem",
+        "useThisParameter": "Usar este parâmetro",
+        "copyMetadataJson": "Copiar metadados JSON",
+        "zoomIn": "Ampliar",
+        "zoomOut": "Reduzir",
+        "rotateCounterClockwise": "Girar no sentido anti-horário",
+        "rotateClockwise": "Girar no sentido horário",
+        "flipVertically": "Espelhar verticalmente",
+        "modifyConfig": "Modificar config",
+        "toggleAutoscroll": "Alternar rolagem automática",
+        "showGallery": "Mostrar galeria",
+        "showOptionsPanel": "Mostrar painel de opções",
+        "uploadImage": "Enviar imagem",
+        "previousImage": "Imagem anterior",
+        "flipHorizontally": "Espelhar horizontalmente",
+        "toggleLogViewer": "Alternar visualizador de registo"
     }
 }

invokeai/frontend/web/public/locales/pt_BR.json

@@ -63,7 +63,10 @@
         "statusMergingModels": "Mesclando Modelos",
         "statusMergedModels": "Modelos Mesclados",
         "langRussian": "Russo",
-        "langSpanish": "Espanhol"
+        "langSpanish": "Espanhol",
+        "pinOptionsPanel": "Fixar painel de opções",
+        "loadingInvokeAI": "Carregando Invoke AI",
+        "loading": "Carregando"
     },
     "gallery": {
         "generations": "Gerações",

invokeai/frontend/web/public/locales/ru.json

@@ -46,7 +46,15 @@
         "statusLoadingModel": "Загрузка модели",
         "statusModelChanged": "Модель изменена",
         "githubLabel": "Github",
-        "discordLabel": "Discord"
+        "discordLabel": "Discord",
+        "statusMergingModels": "Слияние моделей",
+        "statusModelConverted": "Модель сконвертирована",
+        "statusMergedModels": "Модели объединены",
+        "pinOptionsPanel": "Закрепить панель настроек",
+        "loading": "Загрузка",
+        "loadingInvokeAI": "Загрузка Invoke AI",
+        "back": "Назад",
+        "statusConvertingModel": "Конвертация модели"
     },
     "gallery": {
         "generations": "Генерации",
@@ -323,7 +331,30 @@
         "deleteConfig": "Удалить конфигурацию",
         "deleteMsg1": "Вы точно хотите удалить модель из InvokeAI?",
         "deleteMsg2": "Это не удалит файл модели с диска. Позже вы можете добавить его снова.",
-        "repoIDValidationMsg": "Онлайн-репозиторий модели"
+        "repoIDValidationMsg": "Онлайн-репозиторий модели",
+        "convertToDiffusersHelpText5": "Пожалуйста, убедитесь, что у вас достаточно места на диске. Модели обычно занимают 4 – 7 Гб.",
+        "invokeAIFolder": "Каталог InvokeAI",
+        "ignoreMismatch": "Игнорировать несоответствия между выбранными моделями",
+        "addCheckpointModel": "Добавить модель Checkpoint/Safetensor",
+        "formMessageDiffusersModelLocationDesc": "Укажите хотя бы одно.",
+        "convertToDiffusersHelpText3": "Файл модели на диске НЕ будет удалён или изменён. Вы сможете заново добавить его в Model Manager при необходимости.",
+        "vaeRepoID": "ID репозитория VAE",
+        "mergedModelName": "Название объединенной модели",
+        "checkpointModels": "Checkpoints",
+        "allModels": "Все модели",
+        "addDiffuserModel": "Добавить Diffusers",
+        "repo_id": "ID репозитория",
+        "formMessageDiffusersVAELocationDesc": "Если не указано, InvokeAI будет искать файл VAE рядом с моделью.",
+        "convert": "Преобразовать",
+        "convertToDiffusers": "Преобразовать в Diffusers",
+        "convertToDiffusersHelpText1": "Модель будет преобразована в формат 🧨 Diffusers.",
+        "convertToDiffusersHelpText4": "Это единоразовое действие. Оно может занять 30—60 секунд в зависимости от характеристик вашего компьютера.",
+        "convertToDiffusersHelpText6": "Вы хотите преобразовать эту модель?",
+        "statusConverting": "Преобразование",
+        "modelConverted": "Модель преобразована",
+        "invokeRoot": "Каталог InvokeAI",
+        "modelsMerged": "Модели объединены",
+        "mergeModels": "Объединить модели"
     },
     "parameters": {
         "images": "Изображения",
@@ -503,5 +534,8 @@
         "betaDarkenOutside": "Затемнить снаружи",
         "betaLimitToBox": "Ограничить выделением",
         "betaPreserveMasked": "Сохранять маскируемую область"
+    },
+    "accessibility": {
+        "modelSelect": "Выбор модели"
     }
 }

invokeai/frontend/web/public/locales/zh_Hant.json

@@ -19,6 +19,21 @@
         "discordLabel": "Discord",
         "nodesDesc": "使用Node生成圖像的系統正在開發中。敬請期待有關於這項功能的更新。",
         "reportBugLabel": "回報錯誤",
-        "githubLabel": "GitHub"
+        "githubLabel": "GitHub",
+        "langKorean": "韓語",
+        "langPortuguese": "葡萄牙語",
+        "hotkeysLabel": "快捷鍵",
+        "languagePickerLabel": "切換語言",
+        "langDutch": "荷蘭語",
+        "langFrench": "法語",
+        "langGerman": "德語",
+        "langItalian": "義大利語",
+        "langJapanese": "日語",
+        "langPolish": "波蘭語",
+        "langBrPortuguese": "巴西葡萄牙語",
+        "langRussian": "俄語",
+        "langSpanish": "西班牙語",
+        "text2img": "文字到圖像",
+        "unifiedCanvas": "統一畫布"
     }
 }

invokeai/frontend/web/src/app/App.tsx

@@ -9,34 +9,53 @@ import useToastWatcher from 'features/system/hooks/useToastWatcher';
 
 import FloatingGalleryButton from 'features/ui/components/FloatingGalleryButton';
 import FloatingParametersPanelButtons from 'features/ui/components/FloatingParametersPanelButtons';
-import { Box, Grid } from '@chakra-ui/react';
-import { APP_HEIGHT, APP_PADDING, APP_WIDTH } from 'theme/util/constants';
+import { Box, Flex, Grid, Portal, useColorMode } from '@chakra-ui/react';
+import { APP_HEIGHT, APP_WIDTH } from 'theme/util/constants';
+import ImageGalleryPanel from 'features/gallery/components/ImageGalleryPanel';
+import Lightbox from 'features/lightbox/components/Lightbox';
+import { useAppSelector } from './storeHooks';
+import { PropsWithChildren, useEffect } from 'react';
 
 keepGUIAlive();
 
-const App = () => {
+const App = (props: PropsWithChildren) => {
   useToastWatcher();
 
+  const currentTheme = useAppSelector((state) => state.ui.currentTheme);
+  const { setColorMode } = useColorMode();
+
+  useEffect(() => {
+    setColorMode(['light'].includes(currentTheme) ? 'light' : 'dark');
+  }, [setColorMode, currentTheme]);
+
   return (
     <Grid w="100vw" h="100vh">
+      <Lightbox />
       <ImageUploader>
         <ProgressBar />
         <Grid
           gap={4}
-          p={APP_PADDING}
+          p={4}
           gridAutoRows="min-content auto"
           w={APP_WIDTH}
           h={APP_HEIGHT}
         >
-          <SiteHeader />
-          <InvokeTabs />
+          {props.children || <SiteHeader />}
+          <Flex gap={4} w="full" h="full">
+            <InvokeTabs />
+            <ImageGalleryPanel />
+          </Flex>
         </Grid>
         <Box>
           <Console />
         </Box>
       </ImageUploader>
-      <FloatingParametersPanelButtons />
-      <FloatingGalleryButton />
+      <Portal>
+        <FloatingParametersPanelButtons />
+      </Portal>
+      <Portal>
+        <FloatingGalleryButton />
+      </Portal>
     </Grid>
   );
 };

invokeai/frontend/web/src/app/ThemeLocaleProvider.tsx

@@ -9,6 +9,15 @@ import { greenTeaThemeColors } from 'theme/colors/greenTea';
 import { invokeAIThemeColors } from 'theme/colors/invokeAI';
 import { lightThemeColors } from 'theme/colors/lightTheme';
 import { oceanBlueColors } from 'theme/colors/oceanBlue';
+import '@fontsource/inter/100.css';
+import '@fontsource/inter/200.css';
+import '@fontsource/inter/300.css';
+import '@fontsource/inter/400.css';
+import '@fontsource/inter/500.css';
+import '@fontsource/inter/600.css';
+import '@fontsource/inter/700.css';
+import '@fontsource/inter/800.css';
+import '@fontsource/inter/900.css';
 
 type ThemeLocaleProviderProps = {
   children: ReactNode;

invokeai/frontend/web/src/app/constants.ts

@@ -31,18 +31,14 @@ export const DIFFUSERS_SAMPLERS: Array<string> = [
 ];
 
 // Valid image widths
-export const WIDTHS: Array<number> = [
-  64, 128, 192, 256, 320, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960,
-  1024, 1088, 1152, 1216, 1280, 1344, 1408, 1472, 1536, 1600, 1664, 1728, 1792,
-  1856, 1920, 1984, 2048,
-];
+export const WIDTHS: Array<number> = Array.from(Array(65)).map(
+  (_x, i) => i * 64
+);
 
 // Valid image heights
-export const HEIGHTS: Array<number> = [
-  64, 128, 192, 256, 320, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960,
-  1024, 1088, 1152, 1216, 1280, 1344, 1408, 1472, 1536, 1600, 1664, 1728, 1792,
-  1856, 1920, 1984, 2048,
-];
+export const HEIGHTS: Array<number> = Array.from(Array(65)).map(
+  (_x, i) => i * 64
+);
 
 // Valid upscaling levels
 export const UPSCALING_LEVELS: Array<{ key: string; value: number }> = [

invokeai/frontend/web/src/app/store.ts

@@ -57,10 +57,13 @@ const galleryBlacklist = [
   'currentImage',
   'currentImageUuid',
   'shouldAutoSwitchToNewImages',
-  'shouldHoldGalleryOpen',
   'intermediateImage',
 ].map((blacklistItem) => `gallery.${blacklistItem}`);
 
+const lightboxBlacklist = ['isLightboxOpen'].map(
+  (blacklistItem) => `lightbox.${blacklistItem}`
+);
+
 const rootReducer = combineReducers({
   generation: generationReducer,
   postprocessing: postprocessingReducer,
@@ -75,7 +78,12 @@ const rootPersistConfig = getPersistConfig({
   key: 'root',
   storage,
   rootReducer,
-  blacklist: [...canvasBlacklist, ...systemBlacklist, ...galleryBlacklist],
+  blacklist: [
+    ...canvasBlacklist,
+    ...systemBlacklist,
+    ...galleryBlacklist,
+    ...lightboxBlacklist,
+  ],
   debounce: 300,
 });
 

invokeai/frontend/web/src/common/components/GuideIcon.tsx

@@ -1,5 +1,6 @@
 import { Box, forwardRef, Icon } from '@chakra-ui/react';
 import { Feature } from 'app/features';
+import { memo } from 'react';
 import { IconType } from 'react-icons';
 import { MdHelp } from 'react-icons/md';
 import GuidePopover from './GuidePopover';
@@ -19,4 +20,4 @@ const GuideIcon = forwardRef(
   )
 );
 
-export default GuideIcon;
+export default memo(GuideIcon);

invokeai/frontend/web/src/common/components/GuidePopover.tsx

@@ -11,7 +11,7 @@ import { Feature, useFeatureHelpInfo } from 'app/features';
 import { useAppSelector } from 'app/storeHooks';
 import { systemSelector } from 'features/system/store/systemSelectors';
 import { SystemState } from 'features/system/store/systemSlice';
-import { ReactElement } from 'react';
+import { memo, ReactElement } from 'react';
 
 type GuideProps = {
   children: ReactElement;
@@ -30,7 +30,7 @@ const GuidePopover = ({ children, feature }: GuideProps) => {
   if (!shouldDisplayGuides) return null;
 
   return (
-    <Popover trigger="hover">
+    <Popover trigger="hover" isLazy>
       <PopoverTrigger>
         <Box>{children}</Box>
       </PopoverTrigger>
@@ -46,4 +46,4 @@ const GuidePopover = ({ children, feature }: GuideProps) => {
   );
 };
 
-export default GuidePopover;
+export default memo(GuidePopover);

invokeai/frontend/web/src/common/components/IAIAlertDialog.tsx

@@ -8,7 +8,8 @@ import {
   forwardRef,
   useDisclosure,
 } from '@chakra-ui/react';
-import { cloneElement, ReactElement, ReactNode, useRef } from 'react';
+import { cloneElement, memo, ReactElement, ReactNode, useRef } from 'react';
+import { useTranslation } from 'react-i18next';
 import IAIButton from './IAIButton';
 
 type Props = {
@@ -22,10 +23,12 @@ type Props = {
 };
 
 const IAIAlertDialog = forwardRef((props: Props, ref) => {
+  const { t } = useTranslation();
+
   const {
-    acceptButtonText = 'Accept',
+    acceptButtonText = t('common.accept'),
     acceptCallback,
-    cancelButtonText = 'Cancel',
+    cancelButtonText = t('common.cancel'),
     cancelCallback,
     children,
     title,
@@ -56,6 +59,7 @@ const IAIAlertDialog = forwardRef((props: Props, ref) => {
         isOpen={isOpen}
         leastDestructiveRef={cancelRef}
         onClose={onClose}
+        isCentered
       >
         <AlertDialogOverlay>
           <AlertDialogContent>
@@ -79,4 +83,4 @@ const IAIAlertDialog = forwardRef((props: Props, ref) => {
     </>
   );
 });
-export default IAIAlertDialog;
+export default memo(IAIAlertDialog);

invokeai/frontend/web/src/common/components/IAIButton.tsx

@@ -5,7 +5,7 @@ import {
   Tooltip,
   TooltipProps,
 } from '@chakra-ui/react';
-import { ReactNode } from 'react';
+import { memo, ReactNode } from 'react';
 
 export interface IAIButtonProps extends ButtonProps {
   tooltip?: string;
@@ -25,4 +25,4 @@ const IAIButton = forwardRef((props: IAIButtonProps, forwardedRef) => {
   );
 });
 
-export default IAIButton;
+export default memo(IAIButton);

invokeai/frontend/web/src/common/components/IAICheckbox.tsx

@@ -1,5 +1,5 @@
 import { Checkbox, CheckboxProps } from '@chakra-ui/react';
-import type { ReactNode } from 'react';
+import { memo, ReactNode } from 'react';
 
 type IAICheckboxProps = CheckboxProps & {
   label: string | ReactNode;
@@ -14,4 +14,4 @@ const IAICheckbox = (props: IAICheckboxProps) => {
   );
 };
 
-export default IAICheckbox;
+export default memo(IAICheckbox);

invokeai/frontend/web/src/common/components/IAIColorPicker.tsx

@@ -1,4 +1,5 @@
 import { chakra, ChakraProps } from '@chakra-ui/react';
+import { memo } from 'react';
 import { RgbaColorPicker } from 'react-colorful';
 import { ColorPickerBaseProps, RgbaColor } from 'react-colorful/dist/types';
 
@@ -35,4 +36,4 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
   );
 };
 
-export default IAIColorPicker;
+export default memo(IAIColorPicker);

invokeai/frontend/web/src/common/components/IAIForm.tsx

@@ -0,0 +1,8 @@
+import { chakra } from '@chakra-ui/react';
+
+/**
+ * Chakra-enabled <form />
+ */
+const IAIForm = chakra.form;
+
+export default IAIForm;

invokeai/frontend/web/src/common/components/IAIForms/IAIFormErrorMessage.tsx

@@ -0,0 +1,15 @@
+import { FormErrorMessage, FormErrorMessageProps } from '@chakra-ui/react';
+import { ReactNode } from 'react';
+
+type IAIFormErrorMessageProps = FormErrorMessageProps & {
+  children: ReactNode | string;
+};
+
+export default function IAIFormErrorMessage(props: IAIFormErrorMessageProps) {
+  const { children, ...rest } = props;
+  return (
+    <FormErrorMessage color="error.400" {...rest}>
+      {children}
+    </FormErrorMessage>
+  );
+}

invokeai/frontend/web/src/common/components/IAIForms/IAIFormHelperText.tsx

@@ -0,0 +1,15 @@
+import { FormHelperText, FormHelperTextProps } from '@chakra-ui/react';
+import { ReactNode } from 'react';
+
+type IAIFormHelperTextProps = FormHelperTextProps & {
+  children: ReactNode | string;
+};
+
+export default function IAIFormHelperText(props: IAIFormHelperTextProps) {
+  const { children, ...rest } = props;
+  return (
+    <FormHelperText margin={0} color="base.400" {...rest}>
+      {children}
+    </FormHelperText>
+  );
+}

invokeai/frontend/web/src/common/components/IAIForms/IAIFormItemWrapper.tsx

@@ -0,0 +1,23 @@
+import { Flex } from '@chakra-ui/react';
+import { ReactElement } from 'react';
+
+export function IAIFormItemWrapper({
+  children,
+}: {
+  children: ReactElement | ReactElement[];
+}) {
+  return (
+    <Flex
+      sx={{
+        flexDirection: 'column',
+        padding: 4,
+        rowGap: 4,
+        borderRadius: 'base',
+        width: 'full',
+        bg: 'base.900',
+      }}
+    >
+      {children}
+    </Flex>
+  );
+}

invokeai/frontend/web/src/common/components/IAIIconButton.tsx

@@ -5,15 +5,17 @@ import {
   Tooltip,
   TooltipProps,
 } from '@chakra-ui/react';
+import { memo } from 'react';
 
 export type IAIIconButtonProps = IconButtonProps & {
+  role?: string;
   tooltip?: string;
   tooltipProps?: Omit<TooltipProps, 'children'>;
   isChecked?: boolean;
 };
 
 const IAIIconButton = forwardRef((props: IAIIconButtonProps, forwardedRef) => {
-  const { tooltip = '', tooltipProps, isChecked, ...rest } = props;
+  const { role, tooltip = '', tooltipProps, isChecked, ...rest } = props;
 
   return (
     <Tooltip
@@ -26,6 +28,7 @@ const IAIIconButton = forwardRef((props: IAIIconButtonProps, forwardedRef) => {
     >
       <IconButton
         ref={forwardedRef}
+        role={role}
         aria-checked={isChecked !== undefined ? isChecked : undefined}
         {...rest}
       />
@@ -33,4 +36,5 @@ const IAIIconButton = forwardRef((props: IAIIconButtonProps, forwardedRef) => {
   );
 });
 
-export default IAIIconButton;
+IAIIconButton.displayName = 'IAIIconButton';
+export default memo(IAIIconButton);

invokeai/frontend/web/src/common/components/IAIInput.tsx

@@ -5,7 +5,7 @@ import {
   Input,
   InputProps,
 } from '@chakra-ui/react';
-import { ChangeEvent } from 'react';
+import { ChangeEvent, memo } from 'react';
 
 interface IAIInputProps extends InputProps {
   label?: string;
@@ -15,7 +15,7 @@ interface IAIInputProps extends InputProps {
   formControlProps?: Omit<FormControlProps, 'isInvalid' | 'isDisabled'>;
 }
 
-export default function IAIInput(props: IAIInputProps) {
+const IAIInput = (props: IAIInputProps) => {
   const {
     label = '',
     isDisabled = false,
@@ -34,4 +34,6 @@ export default function IAIInput(props: IAIInputProps) {
       <Input {...rest} />
     </FormControl>
   );
-}
+};
+
+export default memo(IAIInput);

invokeai/frontend/web/src/common/components/IAINumberInput.tsx

@@ -16,7 +16,7 @@ import {
 } from '@chakra-ui/react';
 import { clamp } from 'lodash';
 
-import { FocusEvent, useEffect, useState } from 'react';
+import { FocusEvent, memo, useEffect, useState } from 'react';
 
 const numberStringRegex = /^-?(0\.)?\.?$/;
 
@@ -139,4 +139,4 @@ const IAINumberInput = (props: Props) => {
   );
 };
 
-export default IAINumberInput;
+export default memo(IAINumberInput);

invokeai/frontend/web/src/common/components/IAIOption.tsx

@@ -0,0 +1,18 @@
+import { useToken } from '@chakra-ui/react';
+import { ReactNode } from 'react';
+
+type IAIOptionProps = {
+  children: ReactNode | string | number;
+  value: string | number;
+};
+
+export default function IAIOption(props: IAIOptionProps) {
+  const { children, value } = props;
+  const [base800, base200] = useToken('colors', ['base.800', 'base.200']);
+
+  return (
+    <option value={value} style={{ background: base800, color: base200 }}>
+      {children}
+    </option>
+  );
+}

invokeai/frontend/web/src/common/components/IAIPopover.tsx

@@ -6,9 +6,9 @@ import {
   PopoverProps,
   PopoverTrigger,
 } from '@chakra-ui/react';
-import { ReactNode } from 'react';
+import { memo, ReactNode } from 'react';
 
-type IAIPopoverProps = PopoverProps & {
+export type IAIPopoverProps = PopoverProps & {
   triggerComponent: ReactNode;
   triggerContainerProps?: BoxProps;
   children: ReactNode;
@@ -35,4 +35,4 @@ const IAIPopover = (props: IAIPopoverProps) => {
   );
 };
 
-export default IAIPopover;
+export default memo(IAIPopover);

invokeai/frontend/web/src/common/components/IAISelect.tsx

@@ -6,7 +6,8 @@ import {
   Tooltip,
   TooltipProps,
 } from '@chakra-ui/react';
-import { MouseEvent } from 'react';
+import { memo, MouseEvent } from 'react';
+import IAIOption from './IAIOption';
 
 type IAISelectProps = SelectProps & {
   label?: string;
@@ -37,13 +38,13 @@ const IAISelect = (props: IAISelectProps) => {
         <Select {...rest}>
           {validValues.map((opt) => {
             return typeof opt === 'string' || typeof opt === 'number' ? (
-              <option key={opt} value={opt}>
+              <IAIOption key={opt} value={opt}>
                 {opt}
-              </option>
+              </IAIOption>
             ) : (
-              <option key={opt.value} value={opt.value}>
+              <IAIOption key={opt.value} value={opt.value}>
                 {opt.key}
-              </option>
+              </IAIOption>
             );
           })}
         </Select>
@@ -52,4 +53,4 @@ const IAISelect = (props: IAISelectProps) => {
   );
 };
 
-export default IAISelect;
+export default memo(IAISelect);

invokeai/frontend/web/src/common/components/IAISimpleMenu.tsx

@@ -11,7 +11,7 @@ import {
   IconButtonProps,
   ButtonProps,
 } from '@chakra-ui/react';
-import { MouseEventHandler, ReactNode } from 'react';
+import { memo, MouseEventHandler, ReactNode } from 'react';
 import { MdArrowDropDown, MdArrowDropUp } from 'react-icons/md';
 
 interface IAIMenuItem {
@@ -31,7 +31,7 @@ interface IAIMenuProps {
   menuItemProps?: MenuItemProps;
 }
 
-export default function IAISimpleMenu(props: IAIMenuProps) {
+const IAISimpleMenu = (props: IAIMenuProps) => {
   const {
     menuType = 'icon',
     iconTooltip,
@@ -68,6 +68,7 @@ export default function IAISimpleMenu(props: IAIMenuProps) {
           <MenuButton
             as={menuType === 'icon' ? IconButton : Button}
             tooltip={iconTooltip}
+            aria-label={iconTooltip}
             icon={isOpen ? <MdArrowDropUp /> : <MdArrowDropDown />}
             paddingX={0}
             paddingY={menuType === 'regular' ? 2 : 0}
@@ -82,4 +83,6 @@ export default function IAISimpleMenu(props: IAIMenuProps) {
       )}
     </Menu>
   );
-}
+};
+
+export default memo(IAISimpleMenu);

invokeai/frontend/web/src/common/components/IAISlider.tsx

@@ -25,7 +25,8 @@ import {
 } from '@chakra-ui/react';
 import { clamp } from 'lodash';
 
-import { FocusEvent, useEffect, useMemo, useState } from 'react';
+import { useTranslation } from 'react-i18next';
+import { FocusEvent, memo, useEffect, useMemo, useState } from 'react';
 import { BiReset } from 'react-icons/bi';
 import IAIIconButton, { IAIIconButtonProps } from './IAIIconButton';
 
@@ -61,7 +62,7 @@ export type IAIFullSliderProps = {
   sliderIAIIconButtonProps?: IAIIconButtonProps;
 };
 
-export default function IAISlider(props: IAIFullSliderProps) {
+const IAISlider = (props: IAIFullSliderProps) => {
   const [showTooltip, setShowTooltip] = useState(false);
   const {
     label,
@@ -96,6 +97,8 @@ export default function IAISlider(props: IAIFullSliderProps) {
     ...rest
   } = props;
 
+  const { t } = useTranslation();
+
   const [localInputValue, setLocalInputValue] = useState<
     string | number | undefined
   >(String(value));
@@ -171,16 +174,22 @@ export default function IAISlider(props: IAIFullSliderProps) {
             <>
               <SliderMark
                 value={min}
-                insetInlineStart={0}
-                sx={{ insetInlineStart: 'unset !important' }}
+                // insetInlineStart={0}
+                sx={{
+                  insetInlineStart: '0 !important',
+                  insetInlineEnd: 'unset !important',
+                }}
                 {...sliderMarkProps}
               >
                 {min}
               </SliderMark>
               <SliderMark
                 value={max}
-                insetInlineEnd={0}
-                sx={{ insetInlineStart: 'unset !important' }}
+                // insetInlineEnd={0}
+                sx={{
+                  insetInlineStart: 'unset !important',
+                  insetInlineEnd: '0 !important',
+                }}
                 {...sliderMarkProps}
               >
                 {max}
@@ -234,7 +243,7 @@ export default function IAISlider(props: IAIFullSliderProps) {
         {withReset && (
           <IAIIconButton
             size="sm"
-            aria-label="Reset"
+            aria-label={t('accessibility.reset')}
             tooltip="Reset"
             icon={<BiReset />}
             onClick={handleResetDisable}
@@ -245,4 +254,6 @@ export default function IAISlider(props: IAIFullSliderProps) {
       </HStack>
     </FormControl>
   );
-}
+};
+
+export default memo(IAISlider);

invokeai/frontend/web/src/common/components/IAISwitch.tsx

@@ -6,6 +6,7 @@ import {
   Switch,
   SwitchProps,
 } from '@chakra-ui/react';
+import { memo } from 'react';
 
 interface Props extends SwitchProps {
   label?: string;
@@ -44,4 +45,4 @@ const IAISwitch = (props: Props) => {
   );
 };
 
-export default IAISwitch;
+export default memo(IAISwitch);

invokeai/frontend/web/src/common/components/ImageUploader.tsx

@@ -3,10 +3,11 @@ import { ImageUploaderTriggerContext } from 'app/contexts/ImageUploaderTriggerCo
 import { useAppDispatch, useAppSelector } from 'app/storeHooks';
 import useImageUploader from 'common/hooks/useImageUploader';
 import { uploadImage } from 'features/gallery/store/thunks/uploadImage';
-import { tabDict } from 'features/ui/components/InvokeTabs';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
+import { ResourceKey } from 'i18next';
 import {
   KeyboardEvent,
+  memo,
   ReactNode,
   useCallback,
   useEffect,
@@ -134,7 +135,7 @@ const ImageUploader = (props: ImageUploaderProps) => {
   const overlaySecondaryText = ['img2img', 'unifiedCanvas'].includes(
     activeTabName
   )
-    ? ` to ${tabDict[activeTabName as keyof typeof tabDict].tooltip}`
+    ? ` to ${String(t(`common.${activeTabName}` as ResourceKey))}`
     : ``;
 
   return (
@@ -161,4 +162,4 @@ const ImageUploader = (props: ImageUploaderProps) => {
   );
 };
 
-export default ImageUploader;
+export default memo(ImageUploader);

invokeai/frontend/web/src/common/components/ImageUploaderIconButton.tsx

@@ -1,14 +1,16 @@
 import { ImageUploaderTriggerContext } from 'app/contexts/ImageUploaderTriggerContext';
 import { useContext } from 'react';
+import { useTranslation } from 'react-i18next';
 import { FaUpload } from 'react-icons/fa';
 import IAIIconButton from './IAIIconButton';
 
 const ImageUploaderIconButton = () => {
+  const { t } = useTranslation();
   const openImageUploader = useContext(ImageUploaderTriggerContext);
 
   return (
     <IAIIconButton
-      aria-label="Upload Image"
+      aria-label={t('accessibility.uploadImage')}
       tooltip="Upload Image"
       icon={<FaUpload />}
       onClick={openImageUploader || undefined}

invokeai/frontend/web/src/component.tsx

@@ -0,0 +1,38 @@
+import React, { lazy, PropsWithChildren } from 'react';
+import { Provider } from 'react-redux';
+import { PersistGate } from 'redux-persist/integration/react';
+import { store } from './app/store';
+import { persistor } from './persistor';
+import '@fontsource/inter/100.css';
+import '@fontsource/inter/200.css';
+import '@fontsource/inter/300.css';
+import '@fontsource/inter/400.css';
+import '@fontsource/inter/500.css';
+import '@fontsource/inter/600.css';
+import '@fontsource/inter/700.css';
+import '@fontsource/inter/800.css';
+import '@fontsource/inter/900.css';
+
+import Loading from './Loading';
+
+// Localization
+import './i18n';
+
+const App = lazy(() => import('./app/App'));
+const ThemeLocaleProvider = lazy(() => import('./app/ThemeLocaleProvider'));
+
+export default function Component(props: PropsWithChildren) {
+  return (
+    <React.StrictMode>
+      <Provider store={store}>
+        <PersistGate loading={<Loading />} persistor={persistor}>
+          <React.Suspense fallback={<Loading showText />}>
+            <ThemeLocaleProvider>
+              <App>{props.children}</App>
+            </ThemeLocaleProvider>
+          </React.Suspense>
+        </PersistGate>
+      </Provider>
+    </React.StrictMode>
+  );
+}

invokeai/frontend/web/src/exports.tsx

@@ -0,0 +1,16 @@
+import Component from './component';
+
+import InvokeAiLogoComponent from './features/system/components/InvokeAILogoComponent';
+import ThemeChanger from './features/system/components/ThemeChanger';
+import IAIPopover from './common/components/IAIPopover';
+import IAIIconButton from './common/components/IAIIconButton';
+import SettingsModal from './features/system/components/SettingsModal/SettingsModal';
+
+export default Component;
+export {
+  InvokeAiLogoComponent,
+  ThemeChanger,
+  IAIPopover,
+  IAIIconButton,
+  SettingsModal,
+};

invokeai/frontend/web/src/features/canvas/components/IAICanvasGrid.tsx

@@ -1,5 +1,6 @@
 // Grid drawing adapted from https://longviewcoder.com/2021/12/08/konva-a-better-grid/
 
+import { useToken } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { RootState } from 'app/store';
 import { useAppSelector } from 'app/storeHooks';
@@ -22,13 +23,6 @@ const selector = createSelector(
   }
 );
 
-const gridLinesColor = {
-  dark: 'rgba(255, 255, 255, 0.2)',
-  green: 'rgba(255, 255, 255, 0.2)',
-  light: 'rgba(0, 0, 0, 0.2)',
-  ocean: 'rgba(136, 148, 184, 0.2)',
-};
-
 const IAICanvasGrid = () => {
   const currentTheme = useAppSelector(
     (state: RootState) => state.ui.currentTheme
@@ -37,6 +31,8 @@ const IAICanvasGrid = () => {
     useAppSelector(selector);
   const [gridLines, setGridLines] = useState<ReactNode[]>([]);
 
+  const [gridLineColor] = useToken('colors', ['gridLineColor']);
+
   const unscale = useCallback(
     (value: number) => {
       return value / stageScale;
@@ -45,9 +41,6 @@ const IAICanvasGrid = () => {
   );
 
   useLayoutEffect(() => {
-    const gridLineColor =
-      gridLinesColor[currentTheme as keyof typeof gridLinesColor];
-
     const { width, height } = stageDimensions;
     const { x, y } = stageCoordinates;
 
@@ -112,7 +105,14 @@ const IAICanvasGrid = () => {
     ));
 
     setGridLines(xLines.concat(yLines));
-  }, [stageScale, stageCoordinates, stageDimensions, currentTheme, unscale]);
+  }, [
+    stageScale,
+    stageCoordinates,
+    stageDimensions,
+    currentTheme,
+    unscale,
+    gridLineColor,
+  ]);
 
   return <Group>{gridLines}</Group>;
 };

invokeai/frontend/web/src/features/canvas/components/IAICanvasStatusText.tsx

@@ -104,7 +104,7 @@ const IAICanvasStatusText = () => {
         margin: 1,
         borderRadius: 'base',
         pointerEvents: 'none',
-        bg: 'blackAlpha.500',
+        bg: 'base.800',
       }}
     >
       <Box

invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasMaskOptions.tsx

@@ -104,7 +104,6 @@ const IAICanvasMaskOptions = () => {
 
   return (
     <IAIPopover
-      trigger="hover"
       triggerComponent={
         <ButtonGroup>
           <IAIIconButton

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

@@ -88,7 +88,7 @@ const IAICanvasSettingsButtonPopover = () => {
 
   return (
     <IAIPopover
-      trigger="hover"
+      isLazy={false}
       triggerComponent={
         <IAIIconButton
           tooltip={t('unifiedCanvas.canvasSettings')}

invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasToolChooserOptions.tsx

@@ -219,7 +219,6 @@ const IAICanvasToolChooserOptions = () => {
         onClick={handleSelectColorPickerTool}
       />
       <IAIPopover
-        trigger="hover"
         triggerComponent={
           <IAIIconButton
             aria-label={t('unifiedCanvas.brushOptions')}