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Fix some deprecated types

This commit is contained in:
a666 2023-08-25 01:58:19 -06:00
parent 84d41e49b3
commit b6c1a1bbbf
7 changed files with 34 additions and 38 deletions
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26
modules/api/api.py
View File

@ -29,7 +29,7 @@ from modules.sd_models import unload_model_weights, reload_model_weights, checkp
from modules.sd_models_config import find_checkpoint_config_near_filename
from modules.realesrgan_model import get_realesrgan_models
from modules import devices
from typing import Dict, List, Any
from typing import Any
import piexif
import piexif.helper
from contextlib import closing
@ -221,15 +221,15 @@ class Api:
self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem])
self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem])
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=list[models.SamplerItem])
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=list[models.UpscalerItem])
self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=list[models.LatentUpscalerModeItem])
self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=list[models.SDModelItem])
self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=list[models.SDVaeItem])
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=list[models.HypernetworkItem])
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=list[models.FaceRestorerItem])
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=list[models.RealesrganItem])
self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=list[models.PromptStyleItem])
self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
@ -242,8 +242,8 @@ class Api:
self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
self.add_api_route("/sdapi/v1/extensions", self.get_extensions_list, methods=["GET"], response_model=List[models.ExtensionItem])
self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=list[models.ScriptInfo])
self.add_api_route("/sdapi/v1/extensions", self.get_extensions_list, methods=["GET"], response_model=list[models.ExtensionItem])
if shared.cmd_opts.api_server_stop:
self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
@ -563,7 +563,7 @@ class Api:
return options
def set_config(self, req: Dict[str, Any]):
def set_config(self, req: dict[str, Any]):
checkpoint_name = req.get("sd_model_checkpoint", None)
if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases:
raise RuntimeError(f"model {checkpoint_name!r} not found")

24
modules/api/models.py
View File

@ -1,12 +1,10 @@
import inspect
from pydantic import BaseModel, Field, create_model
from typing import Any, Optional
from typing_extensions import Literal
from typing import Any, Optional, Literal
from inflection import underscore
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
from modules.shared import sd_upscalers, opts, parser
from typing import Dict, List
API_NOT_ALLOWED = [
"self",
@ -130,12 +128,12 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
).generate_model()
class TextToImageResponse(BaseModel):
images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
parameters: dict
info: str
class ImageToImageResponse(BaseModel):
images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
parameters: dict
info: str
@ -168,10 +166,10 @@ class FileData(BaseModel):
name: str = Field(title="File name")
class ExtrasBatchImagesRequest(ExtrasBaseRequest):
imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
class ExtrasBatchImagesResponse(ExtraBaseResponse):
images: List[str] = Field(title="Images", description="The generated images in base64 format.")
images: list[str] = Field(title="Images", description="The generated images in base64 format.")
class PNGInfoRequest(BaseModel):
image: str = Field(title="Image", description="The base64 encoded PNG image")
@ -233,8 +231,8 @@ FlagsModel = create_model("Flags", **flags)
class SamplerItem(BaseModel):
name: str = Field(title="Name")
aliases: List[str] = Field(title="Aliases")
options: Dict[str, str] = Field(title="Options")
aliases: list[str] = Field(title="Aliases")
options: dict[str, str] = Field(title="Options")
class UpscalerItem(BaseModel):
name: str = Field(title="Name")
@ -285,8 +283,8 @@ class EmbeddingItem(BaseModel):
vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
class EmbeddingsResponse(BaseModel):
loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
loaded: dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
skipped: dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
class MemoryResponse(BaseModel):
ram: dict = Field(title="RAM", description="System memory stats")
@ -304,14 +302,14 @@ class ScriptArg(BaseModel):
minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI")
choices: Optional[List[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
choices: Optional[list[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
class ScriptInfo(BaseModel):
name: str = Field(default=None, title="Name", description="Script name")
is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
args: list[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
class ExtensionItem(BaseModel):
name: str = Field(title="Name", description="Extension name")

2
modules/gitpython_hack.py
View File

@ -23,7 +23,7 @@ class Git(git.Git):
)
return self._parse_object_header(ret)
def stream_object_data(self, ref: str) -> tuple[str, str, int, "Git.CatFileContentStream"]:
def stream_object_data(self, ref: str) -> tuple[str, str, int, Git.CatFileContentStream]:
# Not really streaming, per se; this buffers the entire object in memory.
# Shouldn't be a problem for our use case, since we're only using this for
# object headers (commit objects).

7
modules/prompt_parser.py
View File

@ -2,7 +2,6 @@ from __future__ import annotations
import re
from collections import namedtuple
from typing import List
import lark
# 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]"
@ -240,14 +239,14 @@ def get_multicond_prompt_list(prompts: SdConditioning | list[str]):
class ComposableScheduledPromptConditioning:
def __init__(self, schedules, weight=1.0):
self.schedules: List[ScheduledPromptConditioning] = schedules
self.schedules: list[ScheduledPromptConditioning] = schedules
self.weight: float = weight
class MulticondLearnedConditioning:
def __init__(self, shape, batch):
self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS
self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None, use_old_scheduling=False) -> MulticondLearnedConditioning:
@ -278,7 +277,7 @@ class DictWithShape(dict):
return self["crossattn"].shape
def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
param = c[0][0].cond
is_dict = isinstance(param, dict)

6
modules/script_callbacks.py
View File

@ -1,7 +1,7 @@
import inspect
import os
from collections import namedtuple
from typing import Optional, Dict, Any
from typing import Optional, Any
from fastapi import FastAPI
from gradio import Blocks
@ -255,7 +255,7 @@ def image_grid_callback(params: ImageGridLoopParams):
report_exception(c, 'image_grid')
def infotext_pasted_callback(infotext: str, params: Dict[str, Any]):
def infotext_pasted_callback(infotext: str, params: dict[str, Any]):
for c in callback_map['callbacks_infotext_pasted']:
try:
c.callback(infotext, params)
@ -446,7 +446,7 @@ def on_infotext_pasted(callback):
"""register a function to be called before applying an infotext.
The callback is called with two arguments:
- infotext: str - raw infotext.
- result: Dict[str, any] - parsed infotext parameters.
- result: dict[str, any] - parsed infotext parameters.
"""
add_callback(callback_map['callbacks_infotext_pasted'], callback)

4
modules/sub_quadratic_attention.py
View File

@ -15,7 +15,7 @@ import torch
from torch import Tensor
from torch.utils.checkpoint import checkpoint
import math
from typing import Optional, NamedTuple, List
from typing import Optional, NamedTuple
def narrow_trunc(
@ -97,7 +97,7 @@ def _query_chunk_attention(
)
return summarize_chunk(query, key_chunk, value_chunk)
chunks: List[AttnChunk] = [
chunks: list[AttnChunk] = [
chunk_scanner(chunk) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
]
acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks)))

3
modules/ui.py
View File

@ -1338,7 +1338,6 @@ checkpoint: <a id="sd_checkpoint_hash">N/A</a>
def setup_ui_api(app):
from pydantic import BaseModel, Field
from typing import List
class QuicksettingsHint(BaseModel):
name: str = Field(title="Name of the quicksettings field")
@ -1347,7 +1346,7 @@ def setup_ui_api(app):
def quicksettings_hint():
return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]
app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])
app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=list[QuicksettingsHint])
app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])