Merge pull request #12846 from a666/deprecated-types
Fix some deprecated types
This commit is contained in:
AUTOMATIC1111
GitHub
commit
924642331b
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@ -29,7 +29,7 @@ from modules.sd_models import unload_model_weights, reload_model_weights, checkp
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from modules.sd_models_config import find_checkpoint_config_near_filename
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from modules.realesrgan_model import get_realesrgan_models
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from modules import devices
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from typing import Dict, List, Any
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from typing import Any
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import piexif
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import piexif.helper
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from contextlib import closing
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@ -221,15 +221,15 @@ class Api:
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self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
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self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
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self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
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self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
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self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
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self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem])
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self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
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self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem])
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self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
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self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
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self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
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self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
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self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=list[models.SamplerItem])
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self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=list[models.UpscalerItem])
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self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=list[models.LatentUpscalerModeItem])
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self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=list[models.SDModelItem])
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self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=list[models.SDVaeItem])
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self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=list[models.HypernetworkItem])
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self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=list[models.FaceRestorerItem])
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self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=list[models.RealesrganItem])
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self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=list[models.PromptStyleItem])
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self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
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self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
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self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
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@ -242,8 +242,8 @@ class Api:
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self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
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self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
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self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
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self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
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self.add_api_route("/sdapi/v1/extensions", self.get_extensions_list, methods=["GET"], response_model=List[models.ExtensionItem])
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self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=list[models.ScriptInfo])
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self.add_api_route("/sdapi/v1/extensions", self.get_extensions_list, methods=["GET"], response_model=list[models.ExtensionItem])
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if shared.cmd_opts.api_server_stop:
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self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
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@ -563,7 +563,7 @@ class Api:
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return options
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def set_config(self, req: Dict[str, Any]):
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def set_config(self, req: dict[str, Any]):
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checkpoint_name = req.get("sd_model_checkpoint", None)
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if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases:
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raise RuntimeError(f"model {checkpoint_name!r} not found")
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@ -1,12 +1,10 @@
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import inspect
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from pydantic import BaseModel, Field, create_model
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from typing import Any, Optional
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from typing_extensions import Literal
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from typing import Any, Optional, Literal
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from inflection import underscore
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
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from modules.shared import sd_upscalers, opts, parser
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from typing import Dict, List
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API_NOT_ALLOWED = [
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"self",
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@ -130,12 +128,12 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
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).generate_model()
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class TextToImageResponse(BaseModel):
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images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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parameters: dict
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info: str
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class ImageToImageResponse(BaseModel):
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images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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parameters: dict
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info: str
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@ -168,10 +166,10 @@ class FileData(BaseModel):
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name: str = Field(title="File name")
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class ExtrasBatchImagesRequest(ExtrasBaseRequest):
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imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
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imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
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class ExtrasBatchImagesResponse(ExtraBaseResponse):
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images: List[str] = Field(title="Images", description="The generated images in base64 format.")
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images: list[str] = Field(title="Images", description="The generated images in base64 format.")
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class PNGInfoRequest(BaseModel):
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image: str = Field(title="Image", description="The base64 encoded PNG image")
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@ -233,8 +231,8 @@ FlagsModel = create_model("Flags", **flags)
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class SamplerItem(BaseModel):
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name: str = Field(title="Name")
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aliases: List[str] = Field(title="Aliases")
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options: Dict[str, str] = Field(title="Options")
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aliases: list[str] = Field(title="Aliases")
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options: dict[str, str] = Field(title="Options")
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class UpscalerItem(BaseModel):
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name: str = Field(title="Name")
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@ -285,8 +283,8 @@ class EmbeddingItem(BaseModel):
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vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
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class EmbeddingsResponse(BaseModel):
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loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
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skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
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loaded: dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
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skipped: dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
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class MemoryResponse(BaseModel):
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ram: dict = Field(title="RAM", description="System memory stats")
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@ -304,14 +302,14 @@ class ScriptArg(BaseModel):
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minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
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maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
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step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI")
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choices: Optional[List[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
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choices: Optional[list[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
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class ScriptInfo(BaseModel):
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name: str = Field(default=None, title="Name", description="Script name")
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is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
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is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
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args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
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args: list[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
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class ExtensionItem(BaseModel):
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name: str = Field(title="Name", description="Extension name")
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@ -23,7 +23,7 @@ class Git(git.Git):
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)
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return self._parse_object_header(ret)
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def stream_object_data(self, ref: str) -> tuple[str, str, int, "Git.CatFileContentStream"]:
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def stream_object_data(self, ref: str) -> tuple[str, str, int, Git.CatFileContentStream]:
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# Not really streaming, per se; this buffers the entire object in memory.
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# Shouldn't be a problem for our use case, since we're only using this for
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# object headers (commit objects).
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@ -2,7 +2,6 @@ from __future__ import annotations
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import re
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from collections import namedtuple
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from typing import List
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import lark
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# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
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@ -240,14 +239,14 @@ def get_multicond_prompt_list(prompts: SdConditioning | list[str]):
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class ComposableScheduledPromptConditioning:
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def __init__(self, schedules, weight=1.0):
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self.schedules: List[ScheduledPromptConditioning] = schedules
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self.schedules: list[ScheduledPromptConditioning] = schedules
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self.weight: float = weight
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class MulticondLearnedConditioning:
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def __init__(self, shape, batch):
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self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS
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self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
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self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
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def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None, use_old_scheduling=False) -> MulticondLearnedConditioning:
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@ -278,7 +277,7 @@ class DictWithShape(dict):
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return self["crossattn"].shape
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def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
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def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
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param = c[0][0].cond
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is_dict = isinstance(param, dict)
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@ -1,7 +1,7 @@
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import inspect
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import os
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from collections import namedtuple
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from typing import Optional, Dict, Any
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from typing import Optional, Any
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from fastapi import FastAPI
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from gradio import Blocks
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@ -258,7 +258,7 @@ def image_grid_callback(params: ImageGridLoopParams):
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report_exception(c, 'image_grid')
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def infotext_pasted_callback(infotext: str, params: Dict[str, Any]):
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def infotext_pasted_callback(infotext: str, params: dict[str, Any]):
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for c in callback_map['callbacks_infotext_pasted']:
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try:
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c.callback(infotext, params)
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@ -449,7 +449,7 @@ def on_infotext_pasted(callback):
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"""register a function to be called before applying an infotext.
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The callback is called with two arguments:
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- infotext: str - raw infotext.
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- result: Dict[str, any] - parsed infotext parameters.
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- result: dict[str, any] - parsed infotext parameters.
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"""
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add_callback(callback_map['callbacks_infotext_pasted'], callback)
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@ -15,7 +15,7 @@ import torch
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from torch import Tensor
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from torch.utils.checkpoint import checkpoint
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import math
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from typing import Optional, NamedTuple, List
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from typing import Optional, NamedTuple
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def narrow_trunc(
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@ -97,7 +97,7 @@ def _query_chunk_attention(
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)
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return summarize_chunk(query, key_chunk, value_chunk)
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chunks: List[AttnChunk] = [
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chunks: list[AttnChunk] = [
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chunk_scanner(chunk) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
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]
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acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks)))
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@ -1348,7 +1348,6 @@ checkpoint: <a id="sd_checkpoint_hash">N/A</a>
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def setup_ui_api(app):
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from pydantic import BaseModel, Field
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from typing import List
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class QuicksettingsHint(BaseModel):
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name: str = Field(title="Name of the quicksettings field")
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@ -1357,7 +1356,7 @@ def setup_ui_api(app):
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def quicksettings_hint():
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return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]
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app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])
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app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=list[QuicksettingsHint])
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app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
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