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Merge branch 'main' into OMI

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Billy
2025-06-23 13:52:07 +10:00
parent e1157f343b 6a78739076
commit 62c3b01e4f
14 changed files with 611 additions and 17 deletions
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5
invokeai/app/invocations/baseinvocation.py
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@@ -582,6 +582,8 @@ def invocation(
fields: dict[str, tuple[Any, FieldInfo]] = {}
original_model_fields: dict[str, OriginalModelField] = {}
for field_name, field_info in cls.model_fields.items():
annotation = field_info.annotation
assert annotation is not None, f"{field_name} on invocation {invocation_type} has no type annotation."
@@ -589,7 +591,7 @@ def invocation(
f"{field_name} on invocation {invocation_type} has a non-dict json_schema_extra, did you forget to use InputField?"
)
cls._original_model_fields[field_name] = OriginalModelField(annotation=annotation, field_info=field_info)
original_model_fields[field_name] = OriginalModelField(annotation=annotation, field_info=field_info)
validate_field_default(cls.__name__, field_name, invocation_type, annotation, field_info)
@@ -676,6 +678,7 @@ def invocation(
docstring = cls.__doc__
new_class = create_model(cls.__qualname__, __base__=cls, __module__=cls.__module__, **fields) # type: ignore
new_class.__doc__ = docstring
new_class._original_model_fields = original_model_fields
InvocationRegistry.register_invocation(new_class)

5
invokeai/app/services/config/config_default.py
View File

@@ -24,7 +24,6 @@ from invokeai.frontend.cli.arg_parser import InvokeAIArgs
INIT_FILE = Path("invokeai.yaml")
DB_FILE = Path("invokeai.db")
LEGACY_INIT_FILE = Path("invokeai.init")
DEVICE = Literal["auto", "cpu", "cuda", "cuda:1", "mps"]
PRECISION = Literal["auto", "float16", "bfloat16", "float32"]
ATTENTION_TYPE = Literal["auto", "normal", "xformers", "sliced", "torch-sdp"]
ATTENTION_SLICE_SIZE = Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8]
@@ -93,7 +92,7 @@ class InvokeAIAppConfig(BaseSettings):
vram: DEPRECATED: This setting is no longer used. It has been replaced by `max_cache_vram_gb`, but most users will not need to use this config since automatic cache size limits should work well in most cases. This config setting will be removed once the new model cache behavior is stable.
lazy_offload: DEPRECATED: This setting is no longer used. Lazy-offloading is enabled by default. This config setting will be removed once the new model cache behavior is stable.
pytorch_cuda_alloc_conf: Configure the Torch CUDA memory allocator. This will impact peak reserved VRAM usage and performance. Setting to "backend:cudaMallocAsync" works well on many systems. The optimal configuration is highly dependent on the system configuration (device type, VRAM, CUDA driver version, etc.), so must be tuned experimentally.
device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `cuda:1`, `mps`
device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `mps`, `cuda:N` (where N is a device number)
precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`
sequential_guidance: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
attention_type: Attention type.<br>Valid values: `auto`, `normal`, `xformers`, `sliced`, `torch-sdp`
@@ -176,7 +175,7 @@ class InvokeAIAppConfig(BaseSettings):
pytorch_cuda_alloc_conf: Optional[str] = Field(default=None, description="Configure the Torch CUDA memory allocator. This will impact peak reserved VRAM usage and performance. Setting to \"backend:cudaMallocAsync\" works well on many systems. The optimal configuration is highly dependent on the system configuration (device type, VRAM, CUDA driver version, etc.), so must be tuned experimentally.")
# DEVICE
device: DEVICE = Field(default="auto", description="Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.")
device: str = Field(default="auto", description="Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `mps`, `cuda:N` (where N is a device number)", pattern=r"^(auto|cpu|mps|cuda(:\d+)?)$")
precision: PRECISION = Field(default="auto", description="Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.")
# GENERATION

2
invokeai/backend/model_manager/config.py
View File

@@ -296,7 +296,7 @@ class LoRAConfigBase(ABC, BaseModel):
from invokeai.backend.patches.lora_conversions.formats import flux_format_from_state_dict
sd = mod.load_state_dict(mod.path)
value = flux_format_from_state_dict(sd)
value = flux_format_from_state_dict(sd, mod.metadata())
mod.cache[key] = value
return value

6
invokeai/backend/model_manager/load/model_loaders/lora.py
View File

@@ -21,6 +21,10 @@ from invokeai.backend.model_manager.taxonomy import (
ModelType,
SubModelType,
)
from invokeai.backend.patches.lora_conversions.flux_aitoolkit_lora_conversion_utils import (
is_state_dict_likely_in_flux_aitoolkit_format,
lora_model_from_flux_aitoolkit_state_dict,
)
from invokeai.backend.patches.lora_conversions.flux_control_lora_utils import (
is_state_dict_likely_flux_control,
lora_model_from_flux_control_state_dict,
@@ -99,6 +103,8 @@ class LoRALoader(ModelLoader):
model = lora_model_from_flux_onetrainer_state_dict(state_dict=state_dict)
elif is_state_dict_likely_flux_control(state_dict=state_dict):
model = lora_model_from_flux_control_state_dict(state_dict=state_dict)
elif is_state_dict_likely_in_flux_aitoolkit_format(state_dict=state_dict):
model = lora_model_from_flux_aitoolkit_state_dict(state_dict=state_dict)
else:
raise ValueError("LoRA model is in unsupported FLUX format")
else:

1
invokeai/backend/model_manager/taxonomy.py
View File

@@ -138,6 +138,7 @@ class FluxLoRAFormat(str, Enum):
Kohya = "flux.kohya"
OneTrainer = "flux.onetrainer"
Control = "flux.control"
AIToolkit = "flux.aitoolkit"
AnyVariant: TypeAlias = Union[ModelVariantType, ClipVariantType, None]

63
invokeai/backend/patches/lora_conversions/flux_aitoolkit_lora_conversion_utils.py Normal file
View File

@@ -0,0 +1,63 @@
import json
from dataclasses import dataclass, field
from typing import Any
import torch
from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
from invokeai.backend.patches.layers.utils import any_lora_layer_from_state_dict
from invokeai.backend.patches.lora_conversions.flux_diffusers_lora_conversion_utils import _group_by_layer
from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
from invokeai.backend.util import InvokeAILogger
def is_state_dict_likely_in_flux_aitoolkit_format(state_dict: dict[str, Any], metadata: dict[str, Any] = None) -> bool:
if metadata:
try:
software = json.loads(metadata.get("software", "{}"))
except json.JSONDecodeError:
return False
return software.get("name") == "ai-toolkit"
# metadata got lost somewhere
return any("diffusion_model" == k.split(".", 1)[0] for k in state_dict.keys())
@dataclass
class GroupedStateDict:
transformer: dict[str, Any] = field(default_factory=dict)
# might also grow CLIP and T5 submodels
def _group_state_by_submodel(state_dict: dict[str, Any]) -> GroupedStateDict:
logger = InvokeAILogger.get_logger()
grouped = GroupedStateDict()
for key, value in state_dict.items():
submodel_name, param_name = key.split(".", 1)
match submodel_name:
case "diffusion_model":
grouped.transformer[param_name] = value
case _:
logger.warning(f"Unexpected submodel name: {submodel_name}")
return grouped
def _rename_peft_lora_keys(state_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
"""Renames keys from the PEFT LoRA format to the InvokeAI format."""
renamed_state_dict = {}
for key, value in state_dict.items():
renamed_key = key.replace(".lora_A.", ".lora_down.").replace(".lora_B.", ".lora_up.")
renamed_state_dict[renamed_key] = value
return renamed_state_dict
def lora_model_from_flux_aitoolkit_state_dict(state_dict: dict[str, torch.Tensor]) -> ModelPatchRaw:
state_dict = _rename_peft_lora_keys(state_dict)
by_layer = _group_by_layer(state_dict)
by_model = _group_state_by_submodel(by_layer)
layers: dict[str, BaseLayerPatch] = {}
for layer_key, layer_state_dict in by_model.transformer.items():
layers[FLUX_LORA_TRANSFORMER_PREFIX + layer_key] = any_lora_layer_from_state_dict(layer_state_dict)
return ModelPatchRaw(layers=layers)

7
invokeai/backend/patches/lora_conversions/formats.py
View File

@@ -1,4 +1,7 @@
from invokeai.backend.model_manager.taxonomy import FluxLoRAFormat
from invokeai.backend.patches.lora_conversions.flux_aitoolkit_lora_conversion_utils import (
is_state_dict_likely_in_flux_aitoolkit_format,
)
from invokeai.backend.patches.lora_conversions.flux_control_lora_utils import is_state_dict_likely_flux_control
from invokeai.backend.patches.lora_conversions.flux_diffusers_lora_conversion_utils import (
is_state_dict_likely_in_flux_diffusers_format,
@@ -11,7 +14,7 @@ from invokeai.backend.patches.lora_conversions.flux_onetrainer_lora_conversion_u
)
def flux_format_from_state_dict(state_dict):
def flux_format_from_state_dict(state_dict: dict, metadata: dict | None = None) -> FluxLoRAFormat | None:
if is_state_dict_likely_in_flux_kohya_format(state_dict):
return FluxLoRAFormat.Kohya
elif is_state_dict_likely_in_flux_onetrainer_format(state_dict):
@@ -20,5 +23,7 @@ def flux_format_from_state_dict(state_dict):
return FluxLoRAFormat.Diffusers
elif is_state_dict_likely_flux_control(state_dict):
return FluxLoRAFormat.Control
elif is_state_dict_likely_in_flux_aitoolkit_format(state_dict, metadata):
return FluxLoRAFormat.AIToolkit
else:
return None

1
invokeai/frontend/web/src/features/controlLayers/components/Toolbar/CanvasToolbarSaveToGalleryButton.tsx
View File

@@ -19,6 +19,7 @@ export const CanvasToolbarSaveToGalleryButton = memo(() => {
onClick={shift ? saveBboxToGallery : saveCanvasToGallery}
icon={<PiFloppyDiskBold />}
aria-label={shift ? t('controlLayers.saveBboxToGallery') : t('controlLayers.saveCanvasToGallery')}
colorScheme="invokeBlue"
tooltip={shift ? t('controlLayers.saveBboxToGallery') : t('controlLayers.saveCanvasToGallery')}
isDisabled={isBusy}
/>

10
invokeai/frontend/web/src/features/nodes/components/sidePanel/workflow/publish.ts
View File

@@ -122,11 +122,11 @@ const NODE_TYPE_PUBLISH_DENYLIST = [
'metadata_to_controlnets',
'metadata_to_ip_adapters',
'metadata_to_t2i_adapters',
'google_imagen3_generate',
'google_imagen3_edit',
'google_imagen4_generate',
'chatgpt_create_image',
'chatgpt_edit_image',
'google_imagen3_generate_image',
'google_imagen3_edit_image',
'google_imagen4_generate_image',
'chatgpt_4o_generate_image',
'chatgpt_4o_edit_image',
];
export const selectHasUnpublishableNodes = createSelector(selectNodes, (nodes) => {

7
invokeai/frontend/web/src/services/api/schema.ts
View File

@@ -12161,7 +12161,7 @@ export type components = {
* vram: DEPRECATED: This setting is no longer used. It has been replaced by `max_cache_vram_gb`, but most users will not need to use this config since automatic cache size limits should work well in most cases. This config setting will be removed once the new model cache behavior is stable.
* lazy_offload: DEPRECATED: This setting is no longer used. Lazy-offloading is enabled by default. This config setting will be removed once the new model cache behavior is stable.
* pytorch_cuda_alloc_conf: Configure the Torch CUDA memory allocator. This will impact peak reserved VRAM usage and performance. Setting to "backend:cudaMallocAsync" works well on many systems. The optimal configuration is highly dependent on the system configuration (device type, VRAM, CUDA driver version, etc.), so must be tuned experimentally.
* device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `cuda:1`, `mps`
* device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `mps`, `cuda:N` (where N is a device number)
* precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`
* sequential_guidance: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
* attention_type: Attention type.<br>Valid values: `auto`, `normal`, `xformers`, `sliced`, `torch-sdp`
@@ -12436,11 +12436,10 @@ export type components = {
pytorch_cuda_alloc_conf?: string | null;
/**
* Device
* @description Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.
* @description Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `mps`, `cuda:N` (where N is a device number)
* @default auto
* @enum {string}
*/
device?: "auto" | "cpu" | "cuda" | "cuda:1" | "mps";
device?: string;
/**
* Precision
* @description Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.

2
invokeai/version/invokeai_version.py
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@@ -1 +1 @@
__version__ = "5.14.0"
__version__ = "5.15.0"