Merge branch 'main' into feat/use-custom-vaes

.coveragerc

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

.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

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"

invokeai/backend/generator/base.py

@@ -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,
                                          )

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
-                    )
+                    flat_ema_key_alt = "model_ema." + "".join(key.split(".")[2:])
+                    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 = {}
@@ -1050,6 +1060,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     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`
@@ -1086,12 +1097,13 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         verbosity = dlogging.get_verbosity()
         dlogging.set_verbosity_error()
 
-        checkpoint = (
-            torch.load(checkpoint_path)
-            if Path(checkpoint_path).suffix == ".ckpt"
-            else load_file(checkpoint_path)
-            
-        )
+        if Path(checkpoint_path).suffix == '.ckpt':
+            if scan_needed:
+                ModelManager.scan_model(checkpoint_path,checkpoint_path)
+            checkpoint = torch.load(checkpoint_path)
+        else:
+            checkpoint = load_file(checkpoint_path)
+
         cache_dir = global_cache_dir("hub")
         pipeline_class = (
             StableDiffusionGeneratorPipeline
@@ -1103,7 +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

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
@@ -282,13 +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(
@@ -359,6 +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()
@@ -378,9 +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(
@@ -431,10 +432,9 @@ 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
 
     def _load_ckpt_model(self, model_name, mconfig):
@@ -521,13 +521,14 @@ class ModelManager(object):
         if self._has_cuda():
             torch.cuda.empty_cache()
 
+    @classmethod
     def scan_model(self, model_name, checkpoint):
         """
-v        Apply picklescanner to the indicated checkpoint and issue a warning
+        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:
@@ -550,7 +551,7 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
                     print("### Exiting InvokeAI")
                     sys.exit()
         else:
-            print(">> Model scanned ok")
+            print("   | Model scanned ok")
 
     def import_diffuser_model(
         self,
@@ -669,7 +670,7 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
         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
@@ -677,15 +678,15 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
         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"),
@@ -696,13 +697,13 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
 
         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")
@@ -714,7 +715,7 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
                 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
             )
@@ -731,32 +732,33 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
             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 = (
-            torch.load(model_path)
-            if model_path.suffix == ".ckpt"
-            else safetensors.torch.load_file(model_path)
-        )
+        checkpoint = None
+        if model_path.suffix.endswith((".ckpt",".pt")):
+            self.scan_model(model_path,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"
@@ -764,7 +766,7 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
                 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"
@@ -792,6 +794,7 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
             model_description=description,
             original_config_file=model_config_file,
             commit_to_conf=commit_to_conf,
+            scan_needed=False,
         )
         return model_name
 
@@ -805,6 +808,7 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
             vae_path: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
@@ -843,11 +847,12 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
                 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,
@@ -858,7 +863,7 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
             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(
@@ -1084,7 +1089,7 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
             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
@@ -1096,7 +1101,7 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
                         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
@@ -1141,12 +1146,12 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
             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
@@ -1187,7 +1192,7 @@ v        Apply picklescanner to the indicated checkpoint and issue a warning
                     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/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"
     }
 }

pyproject.toml

@@ -38,7 +38,7 @@ 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",
@@ -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