Merge branch 'main' into stalker-modular_t2i_adapter

Author: RyanJDick

docker/Dockerfile

@@ -55,6 +55,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
 FROM node:20-slim AS web-builder
 ENV PNPM_HOME="/pnpm"
 ENV PATH="$PNPM_HOME:$PATH"
+RUN corepack use [email protected]
 RUN corepack enable
 
 WORKDIR /build

invokeai/app/api/routers/model_manager.py

@@ -6,7 +6,7 @@
 import traceback
 from copy import deepcopy
 from tempfile import TemporaryDirectory
-from typing import Any, Dict, List, Optional, Type
+from typing import List, Optional, Type
 
 from fastapi import Body, Path, Query, Response, UploadFile
 from fastapi.responses import FileResponse, HTMLResponse
@@ -430,13 +430,11 @@ async def delete_model_image(
 async def install_model(
     source: str = Query(description="Model source to install, can be a local path, repo_id, or remote URL"),
     inplace: Optional[bool] = Query(description="Whether or not to install a local model in place", default=False),
-    # TODO(MM2): Can we type this?
-    config: Optional[Dict[str, Any]] = Body(
-        description="Dict of fields that override auto-probed values in the model config record, such as name, description and prediction_type ",
-        default=None,
+    access_token: Optional[str] = Query(description="access token for the remote resource", default=None),
+    config: ModelRecordChanges = Body(
+        description="Object containing fields that override auto-probed values in the model config record, such as name, description and prediction_type ",
         example={"name": "string", "description": "string"},
     ),
-    access_token: Optional[str] = None,
 ) -> ModelInstallJob:
     """Install a model using a string identifier.
 
@@ -451,8 +449,9 @@ async def install_model(
        - model/name:fp16:path/to/model.safetensors
        - model/name::path/to/model.safetensors
 
-    `config` is an optional dict containing model configuration values that will override
-    the ones that are probed automatically.
+    `config` is a ModelRecordChanges object. Fields in this object will override
+    the ones that are probed automatically. Pass an empty object to accept
+    all the defaults.
 
     `access_token` is an optional access token for use with Urls that require
     authentication.
@@ -737,7 +736,7 @@ async def convert_model(
         # write the converted file to the convert path
         raw_model = converted_model.model
         assert hasattr(raw_model, "save_pretrained")
-        raw_model.save_pretrained(convert_path)
+        raw_model.save_pretrained(convert_path)  # type: ignore
         assert convert_path.exists()
 
         # temporarily rename the original safetensors file so that there is no naming conflict
@@ -750,12 +749,12 @@ async def convert_model(
         try:
             new_key = installer.install_path(
                 convert_path,
-                config={
-                    "name": original_name,
-                    "description": model_config.description,
-                    "hash": model_config.hash,
-                    "source": model_config.source,
-                },
+                config=ModelRecordChanges(
+                    name=original_name,
+                    description=model_config.description,
+                    hash=model_config.hash,
+                    source=model_config.source,
+                ),
             )
         except Exception as e:
             logger.error(str(e))

invokeai/app/invocations/denoise_latents.py

@@ -39,7 +39,7 @@
 from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import BaseModelType
 from invokeai.backend.model_patcher import ModelPatcher
-from invokeai.backend.stable_diffusion import PipelineIntermediateState, set_seamless
+from invokeai.backend.stable_diffusion import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.denoise_context import DenoiseContext, DenoiseInputs
 from invokeai.backend.stable_diffusion.diffusers_pipeline import (
     ControlNetData,
@@ -62,6 +62,7 @@
 from invokeai.backend.stable_diffusion.extensions.freeu import FreeUExt
 from invokeai.backend.stable_diffusion.extensions.preview import PreviewExt
 from invokeai.backend.stable_diffusion.extensions.rescale_cfg import RescaleCFGExt
+from invokeai.backend.stable_diffusion.extensions.seamless import SeamlessExt
 from invokeai.backend.stable_diffusion.extensions.t2i_adapter import T2IAdapterExt
 from invokeai.backend.stable_diffusion.extensions_manager import ExtensionsManager
 from invokeai.backend.stable_diffusion.schedulers import SCHEDULER_MAP
@@ -861,6 +862,10 @@ def step_callback(state: PipelineIntermediateState) -> None:
         if self.unet.freeu_config:
             ext_manager.add_extension(FreeUExt(self.unet.freeu_config))
 
+        ### seamless
+        if self.unet.seamless_axes:
+            ext_manager.add_extension(SeamlessExt(self.unet.seamless_axes))
+
         # context for loading additional models
         with ExitStack() as exit_stack:
             # later should be smth like:
@@ -944,7 +949,7 @@ def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             ExitStack() as exit_stack,
             unet_info.model_on_device() as (model_state_dict, unet),
             ModelPatcher.apply_freeu(unet, self.unet.freeu_config),
-            set_seamless(unet, self.unet.seamless_axes),  # FIXME
+            SeamlessExt.static_patch_model(unet, self.unet.seamless_axes),  # FIXME
             # Apply the LoRA after unet has been moved to its target device for faster patching.
             ModelPatcher.apply_lora_unet(
                 unet,

invokeai/app/invocations/latents_to_image.py

@@ -24,7 +24,7 @@
 from invokeai.app.invocations.model import VAEField
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.stable_diffusion import set_seamless
+from invokeai.backend.stable_diffusion.extensions.seamless import SeamlessExt
 from invokeai.backend.stable_diffusion.vae_tiling import patch_vae_tiling_params
 from invokeai.backend.util.devices import TorchDevice
 
@@ -59,7 +59,7 @@ def invoke(self, context: InvocationContext) -> ImageOutput:
 
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
-        with set_seamless(vae_info.model, self.vae.seamless_axes), vae_info as vae:
+        with SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes), vae_info as vae:
             assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
             latents = latents.to(vae.device)
             if self.fp32:

invokeai/app/invocations/spandrel_image_to_image.py

@@ -23,7 +23,7 @@
 from invokeai.backend.tiles.utils import TBLR, Tile
 
 
-@invocation("spandrel_image_to_image", title="Image-to-Image", tags=["upscale"], category="upscale", version="1.2.0")
+@invocation("spandrel_image_to_image", title="Image-to-Image", tags=["upscale"], category="upscale", version="1.3.0")
 class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Run any spandrel image-to-image model (https://github.com/chaiNNer-org/spandrel)."""
 
@@ -36,16 +36,6 @@ class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     tile_size: int = InputField(
         default=512, description="The tile size for tiled image-to-image. Set to 0 to disable tiling."
     )
-    scale: float = InputField(
-        default=4.0,
-        gt=0.0,
-        le=16.0,
-        description="The final scale of the output image. If the model does not upscale the image, this will be ignored.",
-    )
-    fit_to_multiple_of_8: bool = InputField(
-        default=False,
-        description="If true, the output image will be resized to the nearest multiple of 8 in both dimensions.",
-    )
 
     @classmethod
     def scale_tile(cls, tile: Tile, scale: int) -> Tile:
@@ -152,6 +142,47 @@ def upscale_image(
 
         return pil_image
 
+    @torch.inference_mode()
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        # Images are converted to RGB, because most models don't support an alpha channel. In the future, we may want to
+        # revisit this.
+        image = context.images.get_pil(self.image.image_name, mode="RGB")
+
+        # Load the model.
+        spandrel_model_info = context.models.load(self.image_to_image_model)
+
+        # Do the upscaling.
+        with spandrel_model_info as spandrel_model:
+            assert isinstance(spandrel_model, SpandrelImageToImageModel)
+
+            # Upscale the image
+            pil_image = self.upscale_image(image, self.tile_size, spandrel_model, context.util.is_canceled)
+
+        image_dto = context.images.save(image=pil_image)
+        return ImageOutput.build(image_dto)
+
+
+@invocation(
+    "spandrel_image_to_image_autoscale",
+    title="Image-to-Image (Autoscale)",
+    tags=["upscale"],
+    category="upscale",
+    version="1.0.0",
+)
+class SpandrelImageToImageAutoscaleInvocation(SpandrelImageToImageInvocation):
+    """Run any spandrel image-to-image model (https://github.com/chaiNNer-org/spandrel) until the target scale is reached."""
+
+    scale: float = InputField(
+        default=4.0,
+        gt=0.0,
+        le=16.0,
+        description="The final scale of the output image. If the model does not upscale the image, this will be ignored.",
+    )
+    fit_to_multiple_of_8: bool = InputField(
+        default=False,
+        description="If true, the output image will be resized to the nearest multiple of 8 in both dimensions.",
+    )
+
     @torch.inference_mode()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         # Images are converted to RGB, because most models don't support an alpha channel. In the future, we may want to

invokeai/app/services/model_install/model_install_base.py

@@ -3,7 +3,7 @@
 
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Union
+from typing import List, Optional, Union
 
 from pydantic.networks import AnyHttpUrl
 
@@ -12,7 +12,7 @@
 from invokeai.app.services.events.events_base import EventServiceBase
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_install.model_install_common import ModelInstallJob, ModelSource
-from invokeai.app.services.model_records import ModelRecordServiceBase
+from invokeai.app.services.model_records import ModelRecordChanges, ModelRecordServiceBase
 from invokeai.backend.model_manager import AnyModelConfig
 
 
@@ -64,15 +64,15 @@ def event_bus(self) -> Optional["EventServiceBase"]:
     def register_path(
         self,
         model_path: Union[Path, str],
-        config: Optional[Dict[str, Any]] = None,
+        config: Optional[ModelRecordChanges] = None,
     ) -> str:
         """
         Probe and register the model at model_path.
 
         This keeps the model in its current location.
 
         :param model_path: Filesystem Path to the model.
-        :param config: Dict of attributes that will override autoassigned values.
+        :param config: ModelRecordChanges object that will override autoassigned model record values.
         :returns id: The string ID of the registered model.
         """
 
@@ -92,7 +92,7 @@ def unconditionally_delete(self, key: str) -> None:
     def install_path(
         self,
         model_path: Union[Path, str],
-        config: Optional[Dict[str, Any]] = None,
+        config: Optional[ModelRecordChanges] = None,
     ) -> str:
         """
         Probe, register and install the model in the models directory.
@@ -101,22 +101,22 @@ def install_path(
         the models directory handled by InvokeAI.
 
         :param model_path: Filesystem Path to the model.
-        :param config: Dict of attributes that will override autoassigned values.
+        :param config: ModelRecordChanges object that will override autoassigned model record values.
         :returns id: The string ID of the registered model.
         """
 
     @abstractmethod
     def heuristic_import(
         self,
         source: str,
-        config: Optional[Dict[str, Any]] = None,
+        config: Optional[ModelRecordChanges] = None,
         access_token: Optional[str] = None,
         inplace: Optional[bool] = False,
     ) -> ModelInstallJob:
         r"""Install the indicated model using heuristics to interpret user intentions.
 
         :param source: String source
-        :param config: Optional dict. Any fields in this dict
+        :param config: Optional ModelRecordChanges object. Any fields in this object
          will override corresponding autoassigned probe fields in the
          model's config record as described in `import_model()`.
         :param access_token: Optional access token for remote sources.
@@ -147,7 +147,7 @@ def heuristic_import(
     def import_model(
         self,
         source: ModelSource,
-        config: Optional[Dict[str, Any]] = None,
+        config: Optional[ModelRecordChanges] = None,
     ) -> ModelInstallJob:
         """Install the indicated model.
 

invokeai/app/services/model_install/model_install_common.py

@@ -2,13 +2,14 @@
 import traceback
 from enum import Enum
 from pathlib import Path
-from typing import Any, Dict, Literal, Optional, Set, Union
+from typing import Literal, Optional, Set, Union
 
 from pydantic import BaseModel, Field, PrivateAttr, field_validator
 from pydantic.networks import AnyHttpUrl
 from typing_extensions import Annotated
 
 from invokeai.app.services.download import DownloadJob, MultiFileDownloadJob
+from invokeai.app.services.model_records import ModelRecordChanges
 from invokeai.backend.model_manager import AnyModelConfig, ModelRepoVariant
 from invokeai.backend.model_manager.config import ModelSourceType
 from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
@@ -133,8 +134,9 @@ class ModelInstallJob(BaseModel):
     id: int = Field(description="Unique ID for this job")
     status: InstallStatus = Field(default=InstallStatus.WAITING, description="Current status of install process")
     error_reason: Optional[str] = Field(default=None, description="Information about why the job failed")
-    config_in: Dict[str, Any] = Field(
-        default_factory=dict, description="Configuration information (e.g. 'description') to apply to model."
+    config_in: ModelRecordChanges = Field(
+        default_factory=ModelRecordChanges,
+        description="Configuration information (e.g. 'description') to apply to model.",
     )
     config_out: Optional[AnyModelConfig] = Field(
         default=None, description="After successful installation, this will hold the configuration object."