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

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This commit is contained in:
Lincoln Stein
2023-03-24 17:45:02 -04:00
committed by GitHub
parent a97107bd90 bc01a96f9d
commit 9ceec40b76
29 changed files with 219 additions and 95 deletions
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1
invokeai/backend/generator/base.py
View File

@ -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,
)

40
invokeai/backend/model_management/convert_ckpt_to_diffusers.py
View File

@ -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

79
invokeai/backend/model_management/model_manager.py
View File

@ -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()

8
invokeai/backend/stable_diffusion/concepts_lib.py
View File

@ -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

1
invokeai/frontend/web/public/locales/ar.json
View File

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

1
invokeai/frontend/web/public/locales/de.json
View File

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

12
invokeai/frontend/web/public/locales/es.json
View File

@ -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.",

25
invokeai/frontend/web/public/locales/fr.json
View File

@ -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"
}
}

1
invokeai/frontend/web/public/locales/he.json
View File

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

14
invokeai/frontend/web/public/locales/it.json
View File

@ -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",

1
invokeai/frontend/web/public/locales/ko.json
View File

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

1
invokeai/frontend/web/public/locales/nl.json
View File

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

1
invokeai/frontend/web/public/locales/pl.json
View File

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

1
invokeai/frontend/web/public/locales/pt.json
View File

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

1
invokeai/frontend/web/public/locales/pt_BR.json
View File

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

1
invokeai/frontend/web/public/locales/ru.json
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@ -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",

1
invokeai/frontend/web/public/locales/uk.json
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@ -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",

1
invokeai/frontend/web/public/locales/zh_CN.json
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@ -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",

1
invokeai/frontend/web/public/locales/zh_Hant.json
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@ -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"
}
}