Merge remote-tracking branch 'origin' into i18n-build-mode

.coveragerc

@@ -1,6 +0,0 @@
-[run]
-omit='.env/*'
-source='.'
-
-[report]
-show_missing = true

.github/CODEOWNERS

@@ -1,16 +1,16 @@
 # continuous integration
-/.github/workflows/ @mauwii @lstein
+/.github/workflows/ @mauwii @lstein @blessedcoolant
 
 # documentation
-/docs/ @lstein @mauwii @tildebyte
+/docs/ @lstein @mauwii @tildebyte @blessedcoolant
-/mkdocs.yml @lstein @mauwii
+/mkdocs.yml @lstein @mauwii @blessedcoolant
 
 # nodes
 /invokeai/app/ @Kyle0654 @blessedcoolant
 
 # installation and configuration
 /pyproject.toml @mauwii @lstein @blessedcoolant
-/docker/ @mauwii @lstein
+/docker/ @mauwii @lstein @blessedcoolant
 /scripts/ @ebr @lstein
 /installer/ @lstein @ebr
 /invokeai/assets @lstein @ebr

.github/workflows/build-container.yml

@@ -16,6 +16,9 @@ on:
       - 'v*.*.*'
   workflow_dispatch:
 
+permissions:
+  contents: write
+
 jobs:
   docker:
     if: github.event.pull_request.draft == false

.github/workflows/mkdocs-material.yml

@@ -5,6 +5,9 @@ on:
       - 'main'
       - 'development'
 
+permissions:
+    contents: write
+
 jobs:
   mkdocs-material:
     if: github.event.pull_request.draft == false

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

@@ -6,7 +6,6 @@ on:
       - '!pyproject.toml'
       - '!invokeai/**'
       - 'invokeai/frontend/web/**'
-      - '!invokeai/frontend/web/dist/**'
   merge_group:
   workflow_dispatch:
 

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

@@ -7,13 +7,11 @@ on:
       - 'pyproject.toml'
       - 'invokeai/**'
       - '!invokeai/frontend/web/**'
-      - 'invokeai/frontend/web/dist/**'
   pull_request:
     paths:
       - 'pyproject.toml'
       - 'invokeai/**'
       - '!invokeai/frontend/web/**'
-      - 'invokeai/frontend/web/dist/**'
     types:
       - 'ready_for_review'
       - 'opened'

.gitignore

@@ -63,6 +63,7 @@ pip-delete-this-directory.txt
 htmlcov/
 .tox/
 .nox/
+.coveragerc
 .coverage
 .coverage.*
 .cache
@@ -73,6 +74,7 @@ cov.xml
 *.py,cover
 .hypothesis/
 .pytest_cache/
+.pytest.ini
 cover/
 junit/
 

.pytest.ini

@@ -1,5 +0,0 @@
-[pytest]
-DJANGO_SETTINGS_MODULE = webtas.settings
-; python_files = tests.py test_*.py *_tests.py
-
-addopts = --cov=. --cov-config=.coveragerc --cov-report xml:cov.xml

README.md

@@ -139,7 +139,7 @@ not supported.
     _For Windows/Linux with an NVIDIA GPU:_
 
     ```terminal
-    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
     ```
 
     _For Linux with an AMD GPU:_

coverage/.gitignore

@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore

docs/contributing/LOCAL_DEVELOPMENT.md

@@ -0,0 +1,83 @@
+# Local Development
+
+If you are looking to contribute you will need to have a local development
+environment. See the
+[Developer Install](../installation/020_INSTALL_MANUAL.md#developer-install) for
+full details.
+
+Broadly this involves cloning the repository, installing the pre-reqs, and
+InvokeAI (in editable form). Assuming this is working, choose your area of
+focus.
+
+## Documentation
+
+We use [mkdocs](https://www.mkdocs.org) for our documentation with the
+[material theme](https://squidfunk.github.io/mkdocs-material/). Documentation is
+written in markdown files under the `./docs` folder and then built into a static
+website for hosting with GitHub Pages at
+[invoke-ai.github.io/InvokeAI](https://invoke-ai.github.io/InvokeAI).
+
+To contribute to the documentation you'll need to install the dependencies. Note
+the use of `"`.
+
+```zsh
+pip install ".[docs]"
+```
+
+Now, to run the documentation locally with hot-reloading for changes made.
+
+```zsh
+mkdocs serve
+```
+
+You'll then be prompted to connect to `http://127.0.0.1:8080` in order to
+access.
+
+## Backend
+
+The backend is contained within the `./invokeai/backend` folder structure. To
+get started however please install the development dependencies.
+
+From the root of the repository run the following command. Note the use of `"`.
+
+```zsh
+pip install ".[test]"
+```
+
+This in an optional group of packages which is defined within the
+`pyproject.toml` and will be required for testing the changes you make the the
+code.
+
+### Running Tests
+
+We use [pytest](https://docs.pytest.org/en/7.2.x/) for our test suite. Tests can
+be found under the `./tests` folder and can be run with a single `pytest`
+command. Optionally, to review test coverage you can append `--cov`.
+
+```zsh
+pytest --cov
+```
+
+Test outcomes and coverage will be reported in the terminal. In addition a more
+detailed report is created in both XML and HTML format in the `./coverage`
+folder. The HTML one in particular can help identify missing statements
+requiring tests to ensure coverage. This can be run by opening
+`./coverage/html/index.html`.
+
+For example.
+
+```zsh
+pytest --cov; open ./coverage/html/index.html
+```
+
+??? info "HTML coverage report output"
+
+    ![html-overview](../assets/contributing/html-overview.png)
+
+    ![html-detail](../assets/contributing/html-detail.png)
+
+## Front End
+
+<!--#TODO: get input from blessedcoolant here, for the moment inserted the frontend README via snippets extension.-->
+
+--8<-- "invokeai/frontend/web/README.md"

docs/features/INPAINTING.md

@@ -168,11 +168,15 @@ used by Stable Diffusion 1.4 and 1.5.
 After installation, your `models.yaml` should contain an entry that looks like
 this one:
 
-inpainting-1.5: weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
+```yml
-description: SD inpainting v1.5 config:
+inpainting-1.5:
-configs/stable-diffusion/v1-inpainting-inference.yaml vae:
+  weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
-models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt width: 512
+  description: SD inpainting v1.5
-height: 512
+  config: configs/stable-diffusion/v1-inpainting-inference.yaml
+  vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+  width: 512
+  height: 512
+```
 
 As shown in the example, you may include a VAE fine-tuning weights file as well.
 This is strongly recommended.

installer/templates/invoke.sh.in

@@ -24,9 +24,9 @@ if [ "$(uname -s)" == "Darwin" ]; then
     export PYTORCH_ENABLE_MPS_FALLBACK=1
 fi
 
-while true
-do
 if [ "$0" != "bash" ]; then
+  while true
+  do
     echo "Do you want to generate images using the"
     echo "1. command-line interface"
     echo "2. browser-based UI"
@@ -67,29 +67,29 @@ if [ "$0" != "bash" ]; then
             ;;
         7)
             invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
-	    ;;
+            ;;
-	8)
+        8)
-	    echo "Developer Console:"
+            echo "Developer Console:"
             file_name=$(basename "${BASH_SOURCE[0]}")
             bash --init-file "$file_name"
             ;;
         9)
-	    echo "Update:"
+            echo "Update:"
             invokeai-update
             ;;
         10)
             invokeai --help
             ;;
-	[qQ])
+        [qQ])
             exit 0
             ;;
         *)
             echo "Invalid selection"
             exit;;
     esac
+ done
 else # in developer console
     python --version
     echo "Press ^D to exit"
     export PS1="(InvokeAI) \u@\h \w> "
 fi
-done

invokeai/app/api/routers/sessions.py

@@ -270,3 +270,18 @@ async def invoke_session(
 
     ApiDependencies.invoker.invoke(session, invoke_all=all)
     return Response(status_code=202)
+
+
+@session_router.delete(
+    "/{session_id}/invoke",
+    operation_id="cancel_session_invoke",
+    responses={
+        202: {"description": "The invocation is canceled"}
+    },
+)
+async def cancel_session_invoke(
+    session_id: str = Path(description="The id of the session to cancel"),
+) -> None:
+    """Invokes a session"""
+    ApiDependencies.invoker.cancel(session_id)
+    return Response(status_code=202)

invokeai/app/invocations/generate.py

@@ -1,22 +1,19 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from datetime import datetime, timezone
+from functools import partial
-from typing import Any, Literal, Optional, Union
+from typing import 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
 
 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.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator
 from ...backend.stable_diffusion import PipelineIntermediateState
-from ...backend.util.util import image_to_dataURL
+from ..util.util import diffusers_step_callback_adapter, CanceledException
 
 SAMPLER_NAME_VALUES = Literal[
     tuple(InvokeAIGenerator.schedulers())
@@ -45,32 +42,26 @@ class TextToImageInvocation(BaseInvocation):
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
     def dispatch_progress(
-        self, context: InvocationContext, sample: Tensor, step: int
+        self, context: InvocationContext, intermediate_state: PipelineIntermediateState
     ) -> None:
-        # TODO: only output a preview image when requested
+        if (context.services.queue.is_canceled(context.graph_execution_state_id)):
-        image = Generator.sample_to_lowres_estimated_image(sample)
+            raise CanceledException
 
-        (width, height) = image.size
+        step = intermediate_state.step
-        width *= 8
+        if intermediate_state.predicted_original is not None:
-        height *= 8
+            # Some schedulers report not only the noisy latents at the current timestep,
+            # but also their estimate so far of what the de-noised latents will be.
+            sample = intermediate_state.predicted_original
+        else:
+            sample = intermediate_state.latents
         
-        dataURL = image_to_dataURL(image, image_format="JPEG")
+        diffusers_step_callback_adapter(sample, step, steps=self.steps, id=self.id, context=context)
-
-        context.services.events.emit_generator_progress(
-            context.graph_execution_state_id,
-            self.id,
-            {
-                "width": width,
-                "height": height,
-                "dataURL": dataURL
-            },
-            step,
-            self.steps,
-        )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        def step_callback(state: PipelineIntermediateState):
+        # def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, state.latents, state.step)
+        #     if (context.services.queue.is_canceled(context.graph_execution_state_id)):
+        #         raise CanceledException
+        #     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
@@ -79,7 +70,7 @@ class TextToImageInvocation(BaseInvocation):
         model= context.services.model_manager.get_model()
         outputs = Txt2Img(model).generate(
             prompt=self.prompt,
-            step_callback=step_callback,
+            step_callback=partial(self.dispatch_progress, context),
             **self.dict(
                 exclude={"prompt"}
             ),  # Shorthand for passing all of the parameters above manually
@@ -116,6 +107,22 @@ class ImageToImageInvocation(TextToImageInvocation):
         description="Whether or not the result should be fit to the aspect ratio of the input image",
     )
 
+    def dispatch_progress(
+        self, context: InvocationContext, intermediate_state: PipelineIntermediateState
+    ) -> None:  
+        if (context.services.queue.is_canceled(context.graph_execution_state_id)):
+            raise CanceledException
+
+        step = intermediate_state.step
+        if intermediate_state.predicted_original is not None:
+            # Some schedulers report not only the noisy latents at the current timestep,
+            # but also their estimate so far of what the de-noised latents will be.
+            sample = intermediate_state.predicted_original
+        else:
+            sample = intermediate_state.latents
+
+        diffusers_step_callback_adapter(sample, step, steps=self.steps, id=self.id, context=context)
+
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = (
             None
@@ -126,24 +133,23 @@ class ImageToImageInvocation(TextToImageInvocation):
         )
         mask = None
 
-        def step_callback(sample, step=0):
-            self.dispatch_progress(context, sample, 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?
         model = context.services.model_manager.get_model()
-        generator_output = next(
+        outputs = Img2Img(model).generate(
-            Img2Img(model).generate(
                 prompt=self.prompt,
                 init_image=image,
                 init_mask=mask,
-                step_callback=step_callback,
+                step_callback=partial(self.dispatch_progress, context),
                 **self.dict(
                     exclude={"prompt", "image", "mask"}
                 ),  # Shorthand for passing all of the parameters above manually
             )
-        )
+
+        # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
+        # each time it is called. We only need the first one.
+        generator_output = next(outputs)
 
         result_image = generator_output.image
 
@@ -173,6 +179,22 @@ class InpaintInvocation(ImageToImageInvocation):
         description="The amount by which to replace masked areas with latent noise",
     )
 
+    def dispatch_progress(
+        self, context: InvocationContext, intermediate_state: PipelineIntermediateState
+    ) -> None:  
+        if (context.services.queue.is_canceled(context.graph_execution_state_id)):
+            raise CanceledException
+
+        step = intermediate_state.step
+        if intermediate_state.predicted_original is not None:
+            # Some schedulers report not only the noisy latents at the current timestep,
+            # but also their estimate so far of what the de-noised latents will be.
+            sample = intermediate_state.predicted_original
+        else:
+            sample = intermediate_state.latents
+
+        diffusers_step_callback_adapter(sample, step, steps=self.steps, id=self.id, context=context)
+
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = (
             None
@@ -187,24 +209,23 @@ class InpaintInvocation(ImageToImageInvocation):
             else context.services.images.get(self.mask.image_type, self.mask.image_name)
         )
 
-        def step_callback(sample, step=0):
-            self.dispatch_progress(context, sample, 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?
-        manager = context.services.model_manager.get_model()
+        model = context.services.model_manager.get_model()
-        generator_output = next(
+        outputs = Inpaint(model).generate(
-            Inpaint(model).generate(
                 prompt=self.prompt,
-                init_image=image,
+                init_img=image,
-                mask_image=mask,
+                init_mask=mask,
-                step_callback=step_callback,
+                step_callback=partial(self.dispatch_progress, context),
                 **self.dict(
                     exclude={"prompt", "image", "mask"}
                 ),  # Shorthand for passing all of the parameters above manually
             )
-        )
+
+        # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
+        # each time it is called. We only need the first one.
+        generator_output = next(outputs)
 
         result_image = generator_output.image
 

invokeai/app/invocations/image.py

@@ -28,12 +28,28 @@ class ImageOutput(BaseInvocationOutput):
     image:      ImageField = Field(default=None, description="The output image")
     #fmt: on
 
+    class Config:
+        schema_extra = {
+            'required': [
+                'type',
+                'image',
+            ]
+        }
+
 class MaskOutput(BaseInvocationOutput):
     """Base class for invocations that output a mask"""
     #fmt: off
     type: Literal["mask"] = "mask"
     mask:      ImageField = Field(default=None, description="The output mask")
-    #fomt: on
+    #fmt: on
+
+    class Config:
+        schema_extra = {
+            'required': [
+                'type',
+                'mask',
+            ]
+        }
 
 # TODO: this isn't really necessary anymore
 class LoadImageInvocation(BaseInvocation):

invokeai/app/invocations/prompt.py

@@ -12,3 +12,11 @@ class PromptOutput(BaseInvocationOutput):
 
     prompt: str = Field(default=None, description="The output prompt")
     #fmt: on
+
+    class Config:
+        schema_extra = {
+            'required': [
+                'type',
+                'prompt',
+            ]
+        }

invokeai/app/services/graph.py

@@ -127,6 +127,13 @@ class NodeAlreadyExecutedError(Exception):
 class GraphInvocationOutput(BaseInvocationOutput):
     type: Literal["graph_output"] = "graph_output"
 
+    class Config:
+        schema_extra = {
+            'required': [
+                'type',
+                'image',
+            ]
+        }
 
 # TODO: Fill this out and move to invocations
 class GraphInvocation(BaseInvocation):
@@ -147,6 +154,13 @@ class IterateInvocationOutput(BaseInvocationOutput):
 
     item: Any = Field(description="The item being iterated over")
 
+    class Config:
+        schema_extra = {
+            'required': [
+                'type',
+                'item',
+            ]
+        }
 
 # TODO: Fill this out and move to invocations
 class IterateInvocation(BaseInvocation):
@@ -169,6 +183,13 @@ class CollectInvocationOutput(BaseInvocationOutput):
 
     collection: list[Any] = Field(description="The collection of input items")
 
+    class Config:
+        schema_extra = {
+            'required': [
+                'type',
+                'collection',
+            ]
+        }
 
 class CollectInvocation(BaseInvocation):
     """Collects values into a collection"""

invokeai/app/services/invocation_queue.py

@@ -2,6 +2,7 @@
 
 from abc import ABC, abstractmethod
 from queue import Queue
+import time
 
 
 # TODO: make this serializable
@@ -10,6 +11,7 @@ class InvocationQueueItem:
     graph_execution_state_id: str
     invocation_id: str
     invoke_all: bool
+    timestamp: float
 
     def __init__(
         self,
@@ -22,6 +24,7 @@ class InvocationQueueItem:
         self.graph_execution_state_id = graph_execution_state_id
         self.invocation_id = invocation_id
         self.invoke_all = invoke_all
+        self.timestamp = time.time()
 
 
 class InvocationQueueABC(ABC):
@@ -35,15 +38,44 @@ class InvocationQueueABC(ABC):
     def put(self, item: InvocationQueueItem | None) -> None:
         pass
 
+    @abstractmethod
+    def cancel(self, graph_execution_state_id: str) -> None:
+        pass
+
+    @abstractmethod
+    def is_canceled(self, graph_execution_state_id: str) -> bool:
+        pass
+
 
 class MemoryInvocationQueue(InvocationQueueABC):
     __queue: Queue
+    __cancellations: dict[str, float]
 
     def __init__(self):
         self.__queue = Queue()
+        self.__cancellations = dict()
 
     def get(self) -> InvocationQueueItem:
-        return self.__queue.get()
+        item = self.__queue.get()
+
+        while isinstance(item, InvocationQueueItem) \
+            and item.graph_execution_state_id in self.__cancellations \
+            and self.__cancellations[item.graph_execution_state_id] > item.timestamp:
+            item = self.__queue.get()
+
+        # Clear old items
+        for graph_execution_state_id in list(self.__cancellations.keys()):
+            if self.__cancellations[graph_execution_state_id] < item.timestamp:
+                del self.__cancellations[graph_execution_state_id]
+
+        return item
 
     def put(self, item: InvocationQueueItem | None) -> None:
         self.__queue.put(item)
+
+    def cancel(self, graph_execution_state_id: str) -> None:
+        if graph_execution_state_id not in self.__cancellations:
+            self.__cancellations[graph_execution_state_id] = time.time()
+
+    def is_canceled(self, graph_execution_state_id: str) -> bool:
+        return graph_execution_state_id in self.__cancellations

invokeai/app/services/invoker.py

@@ -51,6 +51,10 @@ class Invoker:
         self.services.graph_execution_manager.set(new_state)
         return new_state
     
+    def cancel(self, graph_execution_state_id: str) -> None:
+        """Cancels the given execution state"""
+        self.services.queue.cancel(graph_execution_state_id)
+
     def __start_service(self, service) -> None:
         # Call start() method on any services that have it
         start_op = getattr(service, "start", None)

invokeai/app/services/processor.py

@@ -4,7 +4,7 @@ from threading import Event, Thread
 from ..invocations.baseinvocation import InvocationContext
 from .invocation_queue import InvocationQueueItem
 from .invoker import InvocationProcessorABC, Invoker
-
+from ..util.util import CanceledException
 
 class DefaultInvocationProcessor(InvocationProcessorABC):
     __invoker_thread: Thread
@@ -58,6 +58,12 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                         )
                     )
 
+                    # Check queue to see if this is canceled, and skip if so
+                    if self.__invoker.services.queue.is_canceled(
+                        graph_execution_state.id
+                    ):
+                        continue
+
                     # Save outputs and history
                     graph_execution_state.complete(invocation.id, outputs)
 
@@ -76,6 +82,9 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                 except KeyboardInterrupt:
                     pass
 
+                except CanceledException:
+                    pass
+
                 except Exception as e:
                     error = traceback.format_exc()
 
@@ -96,6 +105,12 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
 
                     pass
                 
+                # Check queue to see if this is canceled, and skip if so
+                if self.__invoker.services.queue.is_canceled(
+                    graph_execution_state.id
+                ):
+                    continue
+
                 # Queue any further commands if invoking all
                 is_complete = graph_execution_state.is_complete()
                 if queue_item.invoke_all and not is_complete:

invokeai/app/util/util.py

@@ -0,0 +1,42 @@
+import torch
+from PIL import Image
+from ..invocations.baseinvocation import InvocationContext
+from ...backend.util.util import image_to_dataURL
+from ...backend.generator.base import Generator
+from ...backend.stable_diffusion import PipelineIntermediateState
+
+class CanceledException(Exception):
+    pass
+
+def fast_latents_step_callback(sample: torch.Tensor, step: int, steps: int, id: str, context: InvocationContext, ):
+    # 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,
+        id,
+        {
+            "width": width,
+            "height": height,
+            "dataURL": dataURL
+        },
+        step,
+        steps,
+    )
+
+def diffusers_step_callback_adapter(*cb_args, **kwargs):
+    """
+    txt2img gives us a Tensor in the step_callbak, while img2img gives us a PipelineIntermediateState.
+    This adapter grabs the needed data and passes it along to the callback function.
+    """
+    if isinstance(cb_args[0], PipelineIntermediateState):
+        progress_state: PipelineIntermediateState = cb_args[0]
+        return fast_latents_step_callback(progress_state.latents, progress_state.step, **kwargs)
+    else:
+        return fast_latents_step_callback(*cb_args, **kwargs)

invokeai/backend/generator/base.py

@@ -21,7 +21,7 @@ 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 typing import Callable, List, Iterator, Optional, Type
 from dataclasses import dataclass, field
 from diffusers.schedulers import SchedulerMixin as Scheduler
 
@@ -35,23 +35,23 @@ downsampling = 8
 
 @dataclass
 class InvokeAIGeneratorBasicParams:
-    seed: int=None
+    seed: Optional[int]=None
     width: int=512
     height: int=512
-    cfg_scale: int=7.5
+    cfg_scale: float=7.5
     steps: int=20
     ddim_eta: float=0.0
-    scheduler: int='ddim'
+    scheduler: str='ddim'
     precision: str='float16'
     perlin: float=0.0
-    threshold: int=0.0
+    threshold: float=0.0
     seamless: bool=False
     seamless_axes: List[str]=field(default_factory=lambda: ['x', 'y'])
-    h_symmetry_time_pct: float=None
+    h_symmetry_time_pct: Optional[float]=None
-    v_symmetry_time_pct: float=None
+    v_symmetry_time_pct: Optional[float]=None
     variation_amount: float = 0.0
     with_variations: list=field(default_factory=list)
-    safety_checker: SafetyChecker=None
+    safety_checker: Optional[SafetyChecker]=None
 
 @dataclass
 class InvokeAIGeneratorOutput:
@@ -61,10 +61,10 @@ class InvokeAIGeneratorOutput:
     and the model hash, as well as all the generate() parameters that went into
     generating the image (in .params, also available as attributes)
     '''
-    image: Image
+    image: Image.Image
     seed: int
     model_hash: str
-    attention_maps_images: List[Image]
+    attention_maps_images: List[Image.Image]
     params: Namespace
 
 # we are interposing a wrapper around the original Generator classes so that
@@ -92,8 +92,8 @@ class InvokeAIGenerator(metaclass=ABCMeta):
 
     def generate(self,
                  prompt: str='',
-                 callback: callable=None,
+                 callback: Optional[Callable]=None,
-                 step_callback: callable=None,
+                 step_callback: Optional[Callable]=None,
                  iterations: int=1,
                  **keyword_args,
                  )->Iterator[InvokeAIGeneratorOutput]:
@@ -154,6 +154,7 @@ class InvokeAIGenerator(metaclass=ABCMeta):
         for i in iteration_count:
             results = generator.generate(prompt,
                                          conditioning=(uc, c, extra_conditioning_info),
+                                         step_callback=step_callback,
                                          sampler=scheduler,
                                          **generator_args,
                                          )
@@ -205,10 +206,10 @@ class Txt2Img(InvokeAIGenerator):
 # ------------------------------------
 class Img2Img(InvokeAIGenerator):
     def generate(self,
-               init_image: Image | torch.FloatTensor,
+               init_image: Image.Image | torch.FloatTensor,
                strength: float=0.75,
                **keyword_args
-               )->List[InvokeAIGeneratorOutput]:
+               )->Iterator[InvokeAIGeneratorOutput]:
         return super().generate(init_image=init_image,
                                 strength=strength,
                                 **keyword_args
@@ -222,7 +223,7 @@ class Img2Img(InvokeAIGenerator):
 # 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,
+                 mask_image: Image.Image | torch.FloatTensor,
                  # Seam settings - when 0, doesn't fill seam
                  seam_size: int = 0,
                  seam_blur: int = 0,
@@ -235,7 +236,7 @@ class Inpaint(Img2Img):
                  inpaint_height=None,
                  inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF),
                  **keyword_args
-                 )->List[InvokeAIGeneratorOutput]:
+                 )->Iterator[InvokeAIGeneratorOutput]:
         return super().generate(
             mask_image=mask_image,
             seam_size=seam_size,
@@ -262,7 +263,7 @@ class Embiggen(Txt2Img):
             embiggen: list=None,
             embiggen_tiles: list = None,
             strength: float=0.75,
-            **kwargs)->List[InvokeAIGeneratorOutput]:
+            **kwargs)->Iterator[InvokeAIGeneratorOutput]:
         return super().generate(embiggen=embiggen,
                                 embiggen_tiles=embiggen_tiles,
                                 strength=strength,

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -372,22 +372,32 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
     unet_key = "model.diffusion_model."
     # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
     if sum(k.startswith("model_ema") for k in keys) > 100:
-        print(f"  | Checkpoint {path} has both EMA and non-EMA weights.")
+        print(f"   | Checkpoint {path} has both EMA and non-EMA weights.")
         if extract_ema:
-            print("  | Extracting EMA weights (usually better for inference)")
+            print("   | Extracting EMA weights (usually better for inference)")
             for key in keys:
                 if key.startswith("model.diffusion_model"):
                     flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
-                    unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
+                    flat_ema_key_alt = "model_ema." + "".join(key.split(".")[2:])
-                        flat_ema_key
+                    if flat_ema_key in checkpoint:
-                    )
+                        unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
+                            flat_ema_key
+                        )
+                    elif flat_ema_key_alt in checkpoint:
+                        unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
+                            flat_ema_key_alt
+                        )
+                    else:
+                        unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
+                            key
+                        )
         else:
             print(
-                "  | Extracting only the non-EMA weights (usually better for fine-tuning)"
+                "   | Extracting only the non-EMA weights (usually better for fine-tuning)"
             )
 
     for key in keys:
-        if key.startswith(unet_key):
+        if key.startswith("model.diffusion_model") and key in checkpoint:
             unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
 
     new_checkpoint = {}
@@ -1026,6 +1036,15 @@ def convert_open_clip_checkpoint(checkpoint):
 
     return text_model
 
+def replace_checkpoint_vae(checkpoint, vae_path:str):
+    if vae_path.endswith(".safetensors"):
+        vae_ckpt = load_file(vae_path)
+    else:
+        vae_ckpt = torch.load(vae_path, map_location="cpu")
+    state_dict = vae_ckpt['state_dict'] if "state_dict" in vae_ckpt else vae_ckpt
+    for vae_key in state_dict:
+        new_key = f'first_stage_model.{vae_key}'
+        checkpoint[new_key] = state_dict[vae_key]
 
 def load_pipeline_from_original_stable_diffusion_ckpt(
     checkpoint_path: str,
@@ -1038,8 +1057,10 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     extract_ema: bool = True,
     upcast_attn: bool = False,
     vae: AutoencoderKL = None,
+    vae_path: str = None,
     precision: torch.dtype = torch.float32,
     return_generator_pipeline: bool = False,
+    scan_needed:bool=True,
 ) -> Union[StableDiffusionPipeline, StableDiffusionGeneratorPipeline]:
     """
     Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml`
@@ -1067,6 +1088,8 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     :param precision: precision to use - torch.float16, torch.float32 or torch.autocast
     :param upcast_attention: Whether the attention computation should always be upcasted. This is necessary when
     running stable diffusion 2.1.
+    :param vae: A diffusers VAE to load into the pipeline.
+    :param vae_path: Path to a checkpoint VAE that will be converted into diffusers and loaded into the pipeline.
     """
 
     with warnings.catch_warnings():
@@ -1074,12 +1097,13 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         verbosity = dlogging.get_verbosity()
         dlogging.set_verbosity_error()
 
-        checkpoint = (
+        if Path(checkpoint_path).suffix == '.ckpt':
-            torch.load(checkpoint_path)
+            if scan_needed:
-            if Path(checkpoint_path).suffix == ".ckpt"
+                ModelManager.scan_model(checkpoint_path,checkpoint_path)
-            else load_file(checkpoint_path)
+            checkpoint = torch.load(checkpoint_path)
+        else:
+            checkpoint = load_file(checkpoint_path)
 
-        )
         cache_dir = global_cache_dir("hub")
         pipeline_class = (
             StableDiffusionGeneratorPipeline
@@ -1091,7 +1115,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         if "global_step" in checkpoint:
             global_step = checkpoint["global_step"]
         else:
-            print("  | global_step key not found in model")
+            print("   | global_step key not found in model")
             global_step = None
 
         # sometimes there is a state_dict key and sometimes not
@@ -1202,9 +1226,19 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
 
         unet.load_state_dict(converted_unet_checkpoint)
 
-        # Convert the VAE model, or use the one passed
+        # If a replacement VAE path was specified, we'll incorporate that into
-        if not vae:
+        # the checkpoint model and then convert it
-            print("  | Using checkpoint model's original VAE")
+        if vae_path:
+            print(f"   | Converting VAE {vae_path}")
+            replace_checkpoint_vae(checkpoint,vae_path)
+        # otherwise we use the original VAE, provided that
+        # an externally loaded diffusers VAE was not passed
+        elif not vae:
+            print("   | Using checkpoint model's original VAE")
+
+        if vae:
+            print("   | Using replacement diffusers VAE")
+        else:  # convert the original or replacement VAE
             vae_config = create_vae_diffusers_config(
                 original_config, image_size=image_size
             )
@@ -1214,8 +1248,6 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
 
             vae = AutoencoderKL(**vae_config)
             vae.load_state_dict(converted_vae_checkpoint)
-        else:
-            print("  | Using external VAE specified in config")
 
         # Convert the text model.
         model_type = pipeline_type

invokeai/backend/model_management/model_manager.py

@@ -34,7 +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 CUDA_DEVICE, CPU_DEVICE, ask_user, download_with_resume
+from ..util import CUDA_DEVICE, ask_user, download_with_resume
 
 class SDLegacyType(Enum):
     V1 = 1
@@ -45,9 +45,6 @@ class SDLegacyType(Enum):
     UNKNOWN = 99
 
 DEFAULT_MAX_MODELS = 2
-VAE_TO_REPO_ID = {  # hack, see note in convert_and_import()
-    "vae-ft-mse-840000-ema-pruned": "stabilityai/sd-vae-ft-mse",
-}
 
 class ModelManager(object):
     '''
@@ -285,13 +282,13 @@ class ModelManager(object):
             self.stack.remove(model_name)
         if delete_files:
             if weights:
-                print(f"** deleting file {weights}")
+                print(f"** Deleting file {weights}")
                 Path(weights).unlink(missing_ok=True)
             elif path:
-                print(f"** deleting directory {path}")
+                print(f"** Deleting directory {path}")
                 rmtree(path, ignore_errors=True)
             elif repo_id:
-                print(f"** deleting the cached model directory for {repo_id}")
+                print(f"** Deleting the cached model directory for {repo_id}")
                 self._delete_model_from_cache(repo_id)
 
     def add_model(
@@ -362,6 +359,7 @@ class ModelManager(object):
             raise NotImplementedError(
                 f"Unknown model format {model_name}: {model_format}"
             )
+        self._add_embeddings_to_model(model)
         
         # usage statistics
         toc = time.time()
@@ -381,9 +379,9 @@ class ModelManager(object):
 
         print(f">> Loading diffusers model from {name_or_path}")
         if using_fp16:
-            print("  | Using faster float16 precision")
+            print("   | Using faster float16 precision")
         else:
-            print("  | Using more accurate float32 precision")
+            print("   | Using more accurate float32 precision")
 
         # TODO: scan weights maybe?
         pipeline_args: dict[str, Any] = dict(
@@ -434,9 +432,7 @@ class ModelManager(object):
         # square images???
         width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor
         height = width
-
+        print(f"   | Default image dimensions = {width} x {height}")
-        print(f"  | Default image dimensions = {width} x {height}")
-        self._add_embeddings_to_model(pipeline)
         
         return pipeline, width, height, model_hash
 
@@ -457,15 +453,21 @@ class ModelManager(object):
 
         from . import load_pipeline_from_original_stable_diffusion_ckpt
 
-        self.offload_model(self.current_model)
+        try:
-        if vae_config := self._choose_diffusers_vae(model_name):
+            if self.list_models()[self.current_model]['status'] == 'active':
-            vae = self._load_vae(vae_config)
+                self.offload_model(self.current_model)
+        except Exception as e:
+            pass
+        
+        vae_path = None
+        if vae:
+            vae_path = vae if os.path.isabs(vae) else os.path.normpath(os.path.join(Globals.root, vae))
         if self._has_cuda():
             torch.cuda.empty_cache()
         pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
             checkpoint_path=weights,
             original_config_file=config,
-            vae=vae,
+            vae_path=vae_path,
             return_generator_pipeline=True,
             precision=torch.float16 if self.precision == "float16" else torch.float32,
         )
@@ -473,7 +475,6 @@ class ModelManager(object):
             pipeline.enable_offload_submodels(self.device)
         else:
             pipeline.to(self.device)
-
         return (
             pipeline,
             width,
@@ -512,18 +513,20 @@ class ModelManager(object):
         print(f">> Offloading {model_name} to CPU")
         model = self.models[model_name]["model"]
         model.offload_all()
+        self.current_model = None
 
         gc.collect()
         if self._has_cuda():
             torch.cuda.empty_cache()
 
+    @classmethod
     def scan_model(self, model_name, checkpoint):
         """
         Apply picklescanner to the indicated checkpoint and issue a warning
         and option to exit if an infected file is identified.
         """
         # scan model
-        print(f">> Scanning Model: {model_name}")
+        print(f"   | Scanning Model: {model_name}")
         scan_result = scan_file_path(checkpoint)
         if scan_result.infected_files != 0:
             if scan_result.infected_files == 1:
@@ -546,7 +549,7 @@ class ModelManager(object):
                     print("### Exiting InvokeAI")
                     sys.exit()
         else:
-            print(">> Model scanned ok")
+            print("   | Model scanned ok")
 
     def import_diffuser_model(
         self,
@@ -665,7 +668,7 @@ class ModelManager(object):
         print(f">> Probing {thing} for import")
 
         if thing.startswith(("http:", "https:", "ftp:")):
-            print(f"  | {thing} appears to be a URL")
+            print(f"   | {thing} appears to be a URL")
             model_path = self._resolve_path(
                 thing, "models/ldm/stable-diffusion-v1"
             )  # _resolve_path does a download if needed
@@ -673,15 +676,15 @@ class ModelManager(object):
         elif Path(thing).is_file() and thing.endswith((".ckpt", ".safetensors")):
             if Path(thing).stem in ["model", "diffusion_pytorch_model"]:
                 print(
-                    f"  | {Path(thing).name} appears to be part of a diffusers model. Skipping import"
+                    f"   | {Path(thing).name} appears to be part of a diffusers model. Skipping import"
                 )
                 return
             else:
-                print(f"  | {thing} appears to be a checkpoint file on disk")
+                print(f"   | {thing} appears to be a checkpoint file on disk")
                 model_path = self._resolve_path(thing, "models/ldm/stable-diffusion-v1")
 
         elif Path(thing).is_dir() and Path(thing, "model_index.json").exists():
-            print(f"  | {thing} appears to be a diffusers file on disk")
+            print(f"   | {thing} appears to be a diffusers file on disk")
             model_name = self.import_diffuser_model(
                 thing,
                 vae=dict(repo_id="stabilityai/sd-vae-ft-mse"),
@@ -692,13 +695,13 @@ class ModelManager(object):
 
         elif Path(thing).is_dir():
             if (Path(thing) / "model_index.json").exists():
-                print(f"  | {thing} appears to be a diffusers model.")
+                print(f"   | {thing} appears to be a diffusers model.")
                 model_name = self.import_diffuser_model(
                     thing, commit_to_conf=commit_to_conf
                 )
             else:
                 print(
-                    f"  |{thing} appears to be a directory. Will scan for models to import"
+                    f"   |{thing} appears to be a directory. Will scan for models to import"
                 )
                 for m in list(Path(thing).rglob("*.ckpt")) + list(
                     Path(thing).rglob("*.safetensors")
@@ -710,7 +713,7 @@ class ModelManager(object):
                 return model_name
 
         elif re.match(r"^[\w.+-]+/[\w.+-]+$", thing):
-            print(f"  | {thing} appears to be a HuggingFace diffusers repo_id")
+            print(f"   | {thing} appears to be a HuggingFace diffusers repo_id")
             model_name = self.import_diffuser_model(
                 thing, commit_to_conf=commit_to_conf
             )
@@ -727,32 +730,33 @@ class ModelManager(object):
             return
 
         if model_path.stem in self.config:  # already imported
-            print("  | Already imported. Skipping")
+            print("   | Already imported. Skipping")
             return model_path.stem
 
         # another round of heuristics to guess the correct config file.
-        checkpoint = (
+        checkpoint = None
-            torch.load(model_path)
+        if model_path.suffix.endswith((".ckpt",".pt")):
-            if model_path.suffix == ".ckpt"
+            self.scan_model(model_path,model_path)
-            else safetensors.torch.load_file(model_path)
+            checkpoint = torch.load(model_path)
-        )
+        else:
+            checkpoint = safetensors.torch.load_file(model_path)
 
         # additional probing needed if no config file provided
         if model_config_file is None:
             model_type = self.probe_model_type(checkpoint)
             if model_type == SDLegacyType.V1:
-                print("  | SD-v1 model detected")
+                print("   | SD-v1 model detected")
                 model_config_file = Path(
                     Globals.root, "configs/stable-diffusion/v1-inference.yaml"
                 )
             elif model_type == SDLegacyType.V1_INPAINT:
-                print("  | SD-v1 inpainting model detected")
+                print("   | SD-v1 inpainting model detected")
                 model_config_file = Path(
                     Globals.root, "configs/stable-diffusion/v1-inpainting-inference.yaml"
                 )
             elif model_type == SDLegacyType.V2_v:
                 print(
-                    "  | SD-v2-v model detected; model will be converted to diffusers format"
+                    "   | SD-v2-v model detected; model will be converted to diffusers format"
                 )
                 model_config_file = Path(
                     Globals.root, "configs/stable-diffusion/v2-inference-v.yaml"
@@ -760,7 +764,7 @@ class ModelManager(object):
                 convert = True
             elif model_type == SDLegacyType.V2_e:
                 print(
-                    "  | SD-v2-e model detected; model will be converted to diffusers format"
+                    "   | SD-v2-e model detected; model will be converted to diffusers format"
                 )
                 model_config_file = Path(
                     Globals.root, "configs/stable-diffusion/v2-inference.yaml"
@@ -788,18 +792,21 @@ class ModelManager(object):
             model_description=description,
             original_config_file=model_config_file,
             commit_to_conf=commit_to_conf,
+            scan_needed=False,
         )
         return model_name
 
     def convert_and_import(
-        self,
+            self,
-        ckpt_path: Path,
+            ckpt_path: Path,
-        diffusers_path: Path,
+            diffusers_path: Path,
-        model_name=None,
+            model_name=None,
-        model_description=None,
+            model_description=None,
-        vae=None,
+            vae:dict=None,
-        original_config_file: Path = None,
+            vae_path:Path=None,
-        commit_to_conf: Path = None,
+            original_config_file: Path = None,
+            commit_to_conf: Path = None,
+            scan_needed: bool=True,
     ) -> str:
         """
         Convert a legacy ckpt weights file to diffuser model and import
@@ -827,18 +834,23 @@ class ModelManager(object):
         try:
             # By passing the specified VAE to the conversion function, the autoencoder
             # will be built into the model rather than tacked on afterward via the config file
-            vae_model = self._load_vae(vae) if vae else None
+            vae_model=None
+            if vae:
+                vae_model=self._load_vae(vae)
+                vae_path=None
             convert_ckpt_to_diffusers(
                 ckpt_path,
                 diffusers_path,
                 extract_ema=True,
                 original_config_file=original_config_file,
                 vae=vae_model,
+                vae_path=vae_path,
+                scan_needed=scan_needed,
             )
             print(
-                f"  | Success. Optimized model is now located at {str(diffusers_path)}"
+                f"   | Success. Optimized model is now located at {str(diffusers_path)}"
             )
-            print(f"  | Writing new config file entry for {model_name}")
+            print(f"   | Writing new config file entry for {model_name}")
             new_config = dict(
                 path=str(diffusers_path),
                 description=model_description,
@@ -849,7 +861,7 @@ class ModelManager(object):
             self.add_model(model_name, new_config, True)
             if commit_to_conf:
                 self.commit(commit_to_conf)
-            print(">> Conversion succeeded")
+            print("   | Conversion succeeded")
         except Exception as e:
             print(f"** Conversion failed: {str(e)}")
             print(
@@ -879,36 +891,6 @@ class ModelManager(object):
 
         return search_folder, found_models
 
-    def _choose_diffusers_vae(
-        self, model_name: str, vae: str = None
-    ) -> Union[dict, str]:
-        # In the event that the original entry is using a custom ckpt VAE, we try to
-        # map that VAE onto a diffuser VAE using a hard-coded dictionary.
-        # I would prefer to do this differently: We load the ckpt model into memory, swap the
-        # VAE in memory, and then pass that to convert_ckpt_to_diffuser() so that the swapped
-        # VAE is built into the model. However, when I tried this I got obscure key errors.
-        if vae:
-            return vae
-        if model_name in self.config and (
-            vae_ckpt_path := self.model_info(model_name).get("vae", None)
-        ):
-            vae_basename = Path(vae_ckpt_path).stem
-            diffusers_vae = None
-            if diffusers_vae := VAE_TO_REPO_ID.get(vae_basename, None):
-                print(
-                    f">> {vae_basename} VAE corresponds to known {diffusers_vae} diffusers version"
-                )
-                vae = {"repo_id": diffusers_vae}
-            else:
-                print(
-                    f'** Custom VAE "{vae_basename}" found, but corresponding diffusers model unknown'
-                )
-                print(
-                    '** Using "stabilityai/sd-vae-ft-mse"; If this isn\'t right, please edit the model config'
-                )
-                vae = {"repo_id": "stabilityai/sd-vae-ft-mse"}
-        return vae
-
     def _make_cache_room(self) -> None:
         num_loaded_models = len(self.models)
         if num_loaded_models >= self.max_loaded_models:
@@ -1105,7 +1087,7 @@ class ModelManager(object):
             with open(hashpath) as f:
                 hash = f.read()
             return hash
-        print("  | Calculating sha256 hash of model files")
+        print("   | Calculating sha256 hash of model files")
         tic = time.time()
         sha = hashlib.sha256()
         count = 0
@@ -1117,7 +1099,7 @@ class ModelManager(object):
                         sha.update(chunk)
         hash = sha.hexdigest()
         toc = time.time()
-        print(f"  | sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
+        print(f"   | sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
         with open(hashpath, "w") as f:
             f.write(hash)
         return hash
@@ -1162,12 +1144,12 @@ class ModelManager(object):
             local_files_only=not Globals.internet_available,
         )
 
-        print(f"  | Loading diffusers VAE from {name_or_path}")
+        print(f"   | Loading diffusers VAE from {name_or_path}")
         if using_fp16:
             vae_args.update(torch_dtype=torch.float16)
             fp_args_list = [{"revision": "fp16"}, {}]
         else:
-            print("  | Using more accurate float32 precision")
+            print("   | Using more accurate float32 precision")
             fp_args_list = [{}]
 
         vae = None
@@ -1208,7 +1190,7 @@ class ModelManager(object):
                     hashes_to_delete.add(revision.commit_hash)
         strategy = cache_info.delete_revisions(*hashes_to_delete)
         print(
-            f"** deletion of this model is expected to free {strategy.expected_freed_size_str}"
+            f"** Deletion of this model is expected to free {strategy.expected_freed_size_str}"
         )
         strategy.execute()
 

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/web/invoke_ai_web_server.py

@@ -1022,7 +1022,7 @@ class InvokeAIWebServer:
                     "RGB"
                 )
 
-            def image_progress(sample, step):
+            def image_progress(intermediate_state: PipelineIntermediateState):
                 if self.canceled.is_set():
                     raise CanceledException
 
@@ -1030,6 +1030,14 @@ class InvokeAIWebServer:
                 nonlocal generation_parameters
                 nonlocal progress
 
+                step = intermediate_state.step
+                if intermediate_state.predicted_original is not None:
+                    # Some schedulers report not only the noisy latents at the current timestep,
+                    # but also their estimate so far of what the de-noised latents will be.
+                    sample = intermediate_state.predicted_original
+                else:
+                    sample = intermediate_state.latents
+
                 generation_messages = {
                     "txt2img": "common.statusGeneratingTextToImage",
                     "img2img": "common.statusGeneratingImageToImage",
@@ -1302,16 +1310,9 @@ class InvokeAIWebServer:
 
                 progress.set_current_iteration(progress.current_iteration + 1)
 
-            def diffusers_step_callback_adapter(*cb_args, **kwargs):
-                if isinstance(cb_args[0], PipelineIntermediateState):
-                    progress_state: PipelineIntermediateState = cb_args[0]
-                    return image_progress(progress_state.latents, progress_state.step)
-                else:
-                    return image_progress(*cb_args, **kwargs)
-
             self.generate.prompt2image(
                 **generation_parameters,
-                step_callback=diffusers_step_callback_adapter,
+                step_callback=image_progress,
                 image_callback=image_done,
             )
 

invokeai/frontend/CLI/CLI.py

@@ -772,16 +772,10 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
             original_config_file = Path(model_info["config"])
             model_name = model_name_or_path
             model_description = model_info["description"]
-            vae = model_info["vae"]
+            vae_path = model_info.get("vae")
         else:
             print(f"** {model_name_or_path} is not a legacy .ckpt weights file")
             return
-        if vae_repo := invokeai.backend.model_management.model_manager.VAE_TO_REPO_ID.get(
-            Path(vae).stem
-        ):
-            vae_repo = dict(repo_id=vae_repo)
-        else:
-            vae_repo = None
         model_name = manager.convert_and_import(
             ckpt_path,
             diffusers_path=Path(
@@ -790,7 +784,7 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
             model_name=model_name,
             model_description=model_description,
             original_config_file=original_config_file,
-            vae=vae_repo,
+            vae_path=vae_path,
         )
     else:
         try:

invokeai/frontend/web/index.d.ts

@@ -1,4 +1,6 @@
 import React, { PropsWithChildren } from 'react';
+import { IAIPopoverProps } from '../web/src/common/components/IAIPopover';
+import { IAIIconButtonProps } from '../web/src/common/components/IAIIconButton';
 
 export {};
 
@@ -50,9 +52,27 @@ declare module '@invoke-ai/invoke-ai-ui' {
   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 };
+export {
+  ThemeChanger,
+  InvokeAiLogoComponent,
+  IAIPopover,
+  IAIIconButton,
+  SettingsModal,
+};
 export = Invoke;

invokeai/frontend/web/package.json

@@ -6,7 +6,6 @@
     "prepare": "cd ../../../ && husky install invokeai/frontend/web/.husky",
     "dev": "concurrently \"vite dev\" \"yarn run theme:watch\"",
     "build": "yarn run lint && vite build",
-    "build:package": "vite build --mode=package",
     "preview": "vite preview",
     "lint:madge": "madge --circular src/main.tsx",
     "lint:eslint": "eslint --max-warnings=0 .",

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

@@ -8,7 +8,6 @@
         "darkTheme": "داكن",
         "lightTheme": "فاتح",
         "greenTheme": "أخضر",
-        "text2img": "نص إلى صورة",
         "img2img": "صورة إلى صورة",
         "unifiedCanvas": "لوحة موحدة",
         "nodes": "عقد",

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

@@ -7,7 +7,6 @@
         "darkTheme": "Dunkel",
         "lightTheme": "Hell",
         "greenTheme": "Grün",
-        "text2img": "Text zu Bild",
         "img2img": "Bild zu Bild",
         "nodes": "Knoten",
         "langGerman": "Deutsch",

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

@@ -8,7 +8,6 @@
         "darkTheme": "Oscuro",
         "lightTheme": "Claro",
         "greenTheme": "Verde",
-        "text2img": "Texto a Imagen",
         "img2img": "Imagen a Imagen",
         "unifiedCanvas": "Lienzo Unificado",
         "nodes": "Nodos",
@@ -70,7 +69,11 @@
         "langHebrew": "Hebreo",
         "pinOptionsPanel": "Pin del panel de opciones",
         "loading": "Cargando",
-        "loadingInvokeAI": "Cargando invocar a la IA"
+        "loadingInvokeAI": "Cargando invocar a la IA",
+        "postprocessing": "Tratamiento posterior",
+        "txt2img": "De texto a imagen",
+        "accept": "Aceptar",
+        "cancel": "Cancelar"
     },
     "gallery": {
         "generations": "Generaciones",
@@ -404,7 +407,8 @@
         "none": "ninguno",
         "pickModelType": "Elige el tipo de modelo",
         "v2_768": "v2 (768px)",
-        "addDifference": "Añadir una diferencia"
+        "addDifference": "Añadir una diferencia",
+        "scanForModels": "Buscar modelos"
     },
     "parameters": {
         "images": "Imágenes",
@@ -574,7 +578,7 @@
         "autoSaveToGallery": "Guardar automáticamente en galería",
         "saveBoxRegionOnly": "Guardar solo región dentro de la caja",
         "limitStrokesToBox": "Limitar trazos a la caja",
-        "showCanvasDebugInfo": "Mostrar información de depuración de lienzo",
+        "showCanvasDebugInfo": "Mostrar la información adicional del lienzo",
         "clearCanvasHistory": "Limpiar historial de lienzo",
         "clearHistory": "Limpiar historial",
         "clearCanvasHistoryMessage": "Limpiar el historial de lienzo también restablece completamente el lienzo unificado. Esto incluye todo el historial de deshacer/rehacer, las imágenes en el área de preparación y la capa base del lienzo.",

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

@@ -8,7 +8,6 @@
         "darkTheme": "Sombre",
         "lightTheme": "Clair",
         "greenTheme": "Vert",
-        "text2img": "Texte en image",
         "img2img": "Image en image",
         "unifiedCanvas": "Canvas unifié",
         "nodes": "Nœuds",
@@ -47,7 +46,19 @@
         "statusLoadingModel": "Chargement du modèle",
         "statusModelChanged": "Modèle changé",
         "discordLabel": "Discord",
-        "githubLabel": "Github"
+        "githubLabel": "Github",
+        "accept": "Accepter",
+        "statusMergingModels": "Mélange des modèles",
+        "loadingInvokeAI": "Chargement de Invoke AI",
+        "cancel": "Annuler",
+        "langEnglish": "Anglais",
+        "statusConvertingModel": "Conversion du modèle",
+        "statusModelConverted": "Modèle converti",
+        "loading": "Chargement",
+        "pinOptionsPanel": "Épingler la page d'options",
+        "statusMergedModels": "Modèles mélangés",
+        "txt2img": "Texte vers image",
+        "postprocessing": "Post-Traitement"
     },
     "gallery": {
         "generations": "Générations",
@@ -518,5 +529,15 @@
         "betaDarkenOutside": "Assombrir à l'extérieur",
         "betaLimitToBox": "Limiter à la boîte",
         "betaPreserveMasked": "Conserver masqué"
+    },
+    "accessibility": {
+        "uploadImage": "Charger une image",
+        "reset": "Réinitialiser",
+        "nextImage": "Image suivante",
+        "previousImage": "Image précédente",
+        "useThisParameter": "Utiliser ce paramètre",
+        "zoomIn": "Zoom avant",
+        "zoomOut": "Zoom arrière",
+        "showOptionsPanel": "Montrer la page d'options"
     }
 }

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

@@ -125,7 +125,6 @@
         "langSimplifiedChinese": "סינית",
         "langUkranian": "אוקראינית",
         "langSpanish": "ספרדית",
-        "text2img": "טקסט לתמונה",
         "img2img": "תמונה לתמונה",
         "unifiedCanvas": "קנבס מאוחד",
         "nodes": "צמתים",

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

@@ -8,7 +8,6 @@
         "darkTheme": "Scuro",
         "lightTheme": "Chiaro",
         "greenTheme": "Verde",
-        "text2img": "Testo a Immagine",
         "img2img": "Immagine a Immagine",
         "unifiedCanvas": "Tela unificata",
         "nodes": "Nodi",
@@ -70,7 +69,11 @@
         "loading": "Caricamento in corso",
         "oceanTheme": "Oceano",
         "langHebrew": "Ebraico",
-        "loadingInvokeAI": "Caricamento Invoke AI"
+        "loadingInvokeAI": "Caricamento Invoke AI",
+        "postprocessing": "Post Elaborazione",
+        "txt2img": "Testo a Immagine",
+        "accept": "Accetta",
+        "cancel": "Annulla"
     },
     "gallery": {
         "generations": "Generazioni",
@@ -404,7 +407,8 @@
         "v2_768": "v2 (768px)",
         "none": "niente",
         "addDifference": "Aggiungi differenza",
-        "pickModelType": "Scegli il tipo di modello"
+        "pickModelType": "Scegli il tipo di modello",
+        "scanForModels": "Cerca modelli"
     },
     "parameters": {
         "images": "Immagini",
@@ -574,7 +578,7 @@
         "autoSaveToGallery": "Salvataggio automatico nella Galleria",
         "saveBoxRegionOnly": "Salva solo l'area di selezione",
         "limitStrokesToBox": "Limita i tratti all'area di selezione",
-        "showCanvasDebugInfo": "Mostra informazioni di debug della Tela",
+        "showCanvasDebugInfo": "Mostra ulteriori informazioni sulla Tela",
         "clearCanvasHistory": "Cancella cronologia Tela",
         "clearHistory": "Cancella la cronologia",
         "clearCanvasHistoryMessage": "La cancellazione della cronologia della tela lascia intatta la tela corrente, ma cancella in modo irreversibile la cronologia degli annullamenti e dei ripristini.",
@@ -612,7 +616,7 @@
         "copyMetadataJson": "Copia i metadati JSON",
         "exitViewer": "Esci dal visualizzatore",
         "zoomIn": "Zoom avanti",
-        "zoomOut": "Zoom Indietro",
+        "zoomOut": "Zoom indietro",
         "rotateCounterClockwise": "Ruotare in senso antiorario",
         "rotateClockwise": "Ruotare in senso orario",
         "flipHorizontally": "Capovolgi orizzontalmente",

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

@@ -11,7 +11,6 @@
         "langArabic": "العربية",
         "langEnglish": "English",
         "langDutch": "Nederlands",
-        "text2img": "텍스트->이미지",
         "unifiedCanvas": "통합 캔버스",
         "langFrench": "Français",
         "langGerman": "Deutsch",

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

@@ -8,7 +8,6 @@
         "darkTheme": "Donker",
         "lightTheme": "Licht",
         "greenTheme": "Groen",
-        "text2img": "Tekst naar afbeelding",
         "img2img": "Afbeelding naar afbeelding",
         "unifiedCanvas": "Centraal canvas",
         "nodes": "Knooppunten",

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

@@ -8,7 +8,6 @@
         "darkTheme": "Ciemny",
         "lightTheme": "Jasny",
         "greenTheme": "Zielony",
-        "text2img": "Tekst na obraz",
         "img2img": "Obraz na obraz",
         "unifiedCanvas": "Tryb uniwersalny",
         "nodes": "Węzły",

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

@@ -20,7 +20,6 @@
         "langSpanish": "Espanhol",
         "langRussian": "Русский",
         "langUkranian": "Украї́нська",
-        "text2img": "Texto para Imagem",
         "img2img": "Imagem para Imagem",
         "unifiedCanvas": "Tela Unificada",
         "nodes": "Nós",

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

@@ -8,7 +8,6 @@
         "darkTheme": "Noite",
         "lightTheme": "Dia",
         "greenTheme": "Verde",
-        "text2img": "Texto Para Imagem",
         "img2img": "Imagem Para Imagem",
         "unifiedCanvas": "Tela Unificada",
         "nodes": "Nódulos",

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

@@ -8,7 +8,6 @@
         "darkTheme": "Темная",
         "lightTheme": "Светлая",
         "greenTheme": "Зеленая",
-        "text2img": "Изображение из текста (text2img)",
         "img2img": "Изображение в изображение (img2img)",
         "unifiedCanvas": "Универсальный холст",
         "nodes": "Ноды",

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

@@ -8,7 +8,6 @@
         "darkTheme": "Темна",
         "lightTheme": "Світла",
         "greenTheme": "Зелена",
-        "text2img": "Зображення із тексту (text2img)",
         "img2img": "Зображення із зображення (img2img)",
         "unifiedCanvas": "Універсальне полотно",
         "nodes": "Вузли",

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

@@ -8,7 +8,6 @@
         "darkTheme": "暗色",
         "lightTheme": "亮色",
         "greenTheme": "绿色",
-        "text2img": "文字到图像",
         "img2img": "图像到图像",
         "unifiedCanvas": "统一画布",
         "nodes": "节点",

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

@@ -33,7 +33,6 @@
         "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/common/components/IAIPopover.tsx

@@ -8,7 +8,7 @@ import {
 } from '@chakra-ui/react';
 import { memo, ReactNode } from 'react';
 
-type IAIPopoverProps = PopoverProps & {
+export type IAIPopoverProps = PopoverProps & {
   triggerComponent: ReactNode;
   triggerContainerProps?: BoxProps;
   children: ReactNode;

invokeai/frontend/web/src/exports.tsx

@@ -2,6 +2,15 @@ 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 };
+export {
+  InvokeAiLogoComponent,
+  ThemeChanger,
+  IAIPopover,
+  IAIIconButton,
+  SettingsModal,
+};

invokeai/frontend/web/src/features/lightbox/components/ReactPanZoomButtons.tsx

@@ -34,7 +34,7 @@ const ReactPanZoomButtons = ({
       <IAIIconButton
         icon={<BiZoomIn />}
         aria-label={t('accessibility.zoomIn')}
-        tooltip="Zoom In"
+        tooltip={t('accessibility.zoomIn')}
         onClick={() => zoomIn()}
         fontSize={20}
       />
@@ -42,7 +42,7 @@ const ReactPanZoomButtons = ({
       <IAIIconButton
         icon={<BiZoomOut />}
         aria-label={t('accessibility.zoomOut')}
-        tooltip="Zoom Out"
+        tooltip={t('accessibility.zoomOut')}
         onClick={() => zoomOut()}
         fontSize={20}
       />
@@ -50,7 +50,7 @@ const ReactPanZoomButtons = ({
       <IAIIconButton
         icon={<BiRotateLeft />}
         aria-label={t('accessibility.rotateCounterClockwise')}
-        tooltip="Rotate Counter-Clockwise"
+        tooltip={t('accessibility.rotateCounterClockwise')}
         onClick={rotateCounterClockwise}
         fontSize={20}
       />
@@ -58,7 +58,7 @@ const ReactPanZoomButtons = ({
       <IAIIconButton
         icon={<BiRotateRight />}
         aria-label={t('accessibility.rotateClockwise')}
-        tooltip="Rotate Clockwise"
+        tooltip={t('accessibility.rotateClockwise')}
         onClick={rotateClockwise}
         fontSize={20}
       />
@@ -66,7 +66,7 @@ const ReactPanZoomButtons = ({
       <IAIIconButton
         icon={<MdFlip />}
         aria-label={t('accessibility.flipHorizontally')}
-        tooltip="Flip Horizontally"
+        tooltip={t('accessibility.flipHorizontally')}
         onClick={flipHorizontally}
         fontSize={20}
       />
@@ -74,7 +74,7 @@ const ReactPanZoomButtons = ({
       <IAIIconButton
         icon={<MdFlip style={{ transform: 'rotate(90deg)' }} />}
         aria-label={t('accessibility.flipVertically')}
-        tooltip="Flip Vertically"
+        tooltip={t('accessibility.flipVertically')}
         onClick={flipVertically}
         fontSize={20}
       />
@@ -82,7 +82,7 @@ const ReactPanZoomButtons = ({
       <IAIIconButton
         icon={<BiReset />}
         aria-label={t('accessibility.reset')}
-        tooltip="Reset"
+        tooltip={t('accessibility.reset')}
         onClick={() => {
           resetTransform();
           reset();

invokeai/frontend/web/vite.config.ts

@@ -1,4 +1,3 @@
-import path from 'path';
 import react from '@vitejs/plugin-react-swc';
 import { visualizer } from 'rollup-plugin-visualizer';
 import { defineConfig, PluginOption } from 'vite';
@@ -58,26 +57,6 @@ export default defineConfig(({ mode }) => {
         // sourcemap: true, // this can be enabled if needed, it adds ovwer 15MB to the commit
       },
     };
-  } else if (mode === 'package') {
-    return {
-      ...common,
-      build: {
-        ...common.build,
-        lib: {
-          entry: path.resolve(__dirname, 'src/exports.tsx'),
-          name: 'InvokeAI UI',
-          fileName: (format) => `invoke-ai-ui.${format}.js`,
-        },
-        rollupOptions: {
-          external: ['react', 'react-dom'],
-          output: {
-            globals: {
-              react: 'React',
-            },
-          },
-        },
-      },
-    };
   } else {
     return {
       ...common,

pyproject.toml

@@ -38,16 +38,16 @@ dependencies = [
   "albumentations",
   "click",
   "clip_anytorch",          # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
-  "compel==1.0.1",
+  "compel==1.0.4",
   "datasets",
   "diffusers[torch]~=0.14",
   "dnspython==2.2.1",
   "einops",
   "eventlet",
   "facexlib",
-  "fastapi==0.85.0",
+  "fastapi==0.94.1",
-  "fastapi-events==0.6.0",
+  "fastapi-events==0.8.0",
-  "fastapi-socketio==0.0.9",
+  "fastapi-socketio==0.0.10",
   "flask==2.1.3",
   "flask_cors==3.0.10",
   "flask_socketio==5.3.0",
@@ -75,7 +75,7 @@ dependencies = [
   "torchvision>=0.14.1",
   "torchmetrics",
   "transformers~=4.26",
-  "uvicorn[standard]==0.20.0",
+  "uvicorn[standard]==0.21.1",
   "windows-curses; sys_platform=='win32'",
 ]
 
@@ -139,8 +139,24 @@ version = { attr = "invokeai.version.__version__" }
 "invokeai.configs" = ["*.example", "**/*.yaml", "*.txt"]
 "invokeai.frontend.web.dist" = ["**"]
 
+#=== Begin: PyTest and Coverage
 [tool.pytest.ini_options]
-addopts = "-p pytest_cov --junitxml=junit/test-results.xml --cov-report=term:skip-covered --cov=ldm/invoke --cov=backend --cov-branch"
+addopts = "--cov-report term --cov-report html --cov-report xml"
+[tool.coverage.run]
+branch = true
+source = ["invokeai"]
+omit = ["*tests*", "*migrations*", ".venv/*", "*.env"]
+[tool.coverage.report]
+show_missing = true
+fail_under = 85  # let's set something sensible on Day 1 ...
+[tool.coverage.json]
+output = "coverage/coverage.json"
+pretty_print = true
+[tool.coverage.html]
+directory = "coverage/html"
+[tool.coverage.xml]
+output = "coverage/index.xml"
+#=== End: PyTest and Coverage
 
 [flake8]
 max-line-length = 120