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628 lines
30 KiB
Python
628 lines
30 KiB
Python
import io
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import time
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import base64
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from io import BytesIO
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from typing import List
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from threading import Lock
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from secrets import compare_digest
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from fastapi import APIRouter, Depends, FastAPI
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from fastapi.security import HTTPBasic, HTTPBasicCredentials
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from fastapi.exceptions import HTTPException
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from PIL import PngImagePlugin,Image
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import piexif
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import piexif.helper
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import uvicorn
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import gradio as gr
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# from gradio.processing_utils import decode_base64_to_file # gradio 3.23
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from gradio_client.utils import decode_base64_to_file # gradio 3.28
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from modules import errors, shared, sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing
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from modules.api.models import * # pylint: disable=unused-wildcard-import, wildcard-import
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
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from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
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from modules.textual_inversion.preprocess import preprocess
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from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
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from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights
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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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errors.install()
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def upscaler_to_index(name: str):
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try:
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return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
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except Exception as e:
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raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in sd_upscalers])}") from e
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def script_name_to_index(name, scripts_list):
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try:
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return [script.title().lower() for script in scripts_list].index(name.lower())
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except Exception as e:
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raise HTTPException(status_code=422, detail=f"Script '{name}' not found") from e
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def validate_sampler_name(name):
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config = sd_samplers.all_samplers_map.get(name, None)
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if config is None:
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raise HTTPException(status_code=404, detail="Sampler not found")
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return name
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def setUpscalers(req: dict):
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reqDict = vars(req)
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reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None)
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reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None)
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return reqDict
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def decode_base64_to_image(encoding):
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if encoding.startswith("data:image/"):
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encoding = encoding.split(";")[1].split(",")[1]
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try:
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image = Image.open(BytesIO(base64.b64decode(encoding)))
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return image
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except Exception as e:
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raise HTTPException(status_code=500, detail="Invalid encoded image") from e
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def encode_pil_to_base64(image):
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with io.BytesIO() as output_bytes:
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if opts.samples_format.lower() == 'png':
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use_metadata = False
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encoded_metadata = PngImagePlugin.PngInfo()
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for k, v in image.info.items():
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if isinstance(k, str) and isinstance(v, str):
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encoded_metadata.add_text(k, v)
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use_metadata = True
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image.save(output_bytes, format="PNG", pnginfo=(encoded_metadata if use_metadata else None), quality=opts.jpeg_quality)
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elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"):
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parameters = image.info.get('parameters', None)
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exif_bytes = piexif.dump({
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"Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") }
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})
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if opts.samples_format.lower() in ("jpg", "jpeg"):
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image.save(output_bytes, format="JPEG", exif = exif_bytes, quality=opts.jpeg_quality)
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else:
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image.save(output_bytes, format="WEBP", exif = exif_bytes, quality=opts.jpeg_quality)
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else:
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raise HTTPException(status_code=500, detail="Invalid image format")
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bytes_data = output_bytes.getvalue()
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return base64.b64encode(bytes_data)
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class Api:
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def __init__(self, app: FastAPI, queue_lock: Lock):
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if shared.cmd_opts.api_auth:
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self.credentials = dict()
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for auth in shared.cmd_opts.api_auth.split(","):
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user, password = auth.split(":")
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self.credentials[user] = password
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self.router = APIRouter()
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self.app = app
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self.queue_lock = queue_lock
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self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
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self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
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self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
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self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
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self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
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self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
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self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
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self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
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self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"])
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self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=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=FlagsModel)
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self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
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self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
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self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
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self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
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self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
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self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
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self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
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self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=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/create/embedding", self.create_embedding, methods=["POST"], response_model=CreateResponse)
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self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=CreateResponse)
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self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=PreprocessResponse)
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self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse)
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self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse)
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self.add_api_route("/sdapi/v1/shutdown", self.shutdown, methods=["POST"])
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self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=MemoryResponse)
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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=ScriptsList)
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self.default_script_arg_txt2img = []
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self.default_script_arg_img2img = []
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def add_api_route(self, path: str, endpoint, **kwargs):
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if shared.cmd_opts.api_auth:
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return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs)
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return self.app.add_api_route(path, endpoint, **kwargs)
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def auth(self, credentials: HTTPBasicCredentials = Depends(HTTPBasic())):
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if credentials.username in self.credentials:
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if compare_digest(credentials.password, self.credentials[credentials.username]):
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return True
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raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
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def get_selectable_script(self, script_name, script_runner):
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if script_name is None or script_name == "":
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return None, None
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script_idx = script_name_to_index(script_name, script_runner.selectable_scripts)
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script = script_runner.selectable_scripts[script_idx]
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return script, script_idx
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def get_scripts_list(self):
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t2ilist = [str(title.lower()) for title in scripts.scripts_txt2img.titles]
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i2ilist = [str(title.lower()) for title in scripts.scripts_img2img.titles]
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return ScriptsList(txt2img = t2ilist, img2img = i2ilist)
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def get_script(self, script_name, script_runner):
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if script_name is None or script_name == "":
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return None, None
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script_idx = script_name_to_index(script_name, script_runner.scripts)
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return script_runner.scripts[script_idx]
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def init_default_script_args(self, script_runner):
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#find max idx from the scripts in runner and generate a none array to init script_args
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last_arg_index = 1
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for script in script_runner.scripts:
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if last_arg_index < script.args_to:
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last_arg_index = script.args_to
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# None everywhere except position 0 to initialize script args
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script_args = [None]*last_arg_index
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script_args[0] = 0
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# get default values
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with gr.Blocks(): # will throw errors calling ui function without this
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for script in script_runner.scripts:
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if script.ui(script.is_img2img):
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ui_default_values = []
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for elem in script.ui(script.is_img2img):
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ui_default_values.append(elem.value)
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script_args[script.args_from:script.args_to] = ui_default_values
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return script_args
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def init_script_args(self, p, request, default_script_args, selectable_scripts, selectable_script_idx, script_runner):
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script_args = default_script_args.copy()
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# position 0 in script_arg is the idx+1 of the selectable script that is going to be run when using scripts.scripts_*2img.run()
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if selectable_scripts:
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# TODO this can corrupt values for other scripts
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script_args[selectable_scripts.args_from:selectable_scripts.args_to] = request.script_args
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script_args[0] = selectable_script_idx + 1
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# Now check for always on scripts
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if request.alwayson_scripts and (len(request.alwayson_scripts) > 0):
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for alwayson_script_name in request.alwayson_scripts.keys():
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alwayson_script = self.get_script(alwayson_script_name, script_runner)
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if alwayson_script is None:
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raise HTTPException(status_code=422, detail=f"Always on script not found: {alwayson_script_name}")
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if not alwayson_script.alwayson:
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raise HTTPException(status_code=422, detail=f"Selectable script cannot be in always on params: {alwayson_script_name}")
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if "args" in request.alwayson_scripts[alwayson_script_name]:
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# TODO this can corrupt values for other scripts
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script_args[alwayson_script.args_from:alwayson_script.args_to] = request.alwayson_scripts[alwayson_script_name]["args"]
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p.per_script_args[alwayson_script.title()] = request.alwayson_scripts[alwayson_script_name]["args"]
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return script_args
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def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
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script_runner = scripts.scripts_txt2img
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if not script_runner.scripts:
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script_runner.initialize_scripts(False)
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ui.create_ui()
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if not self.default_script_arg_txt2img:
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self.default_script_arg_txt2img = self.init_default_script_args(script_runner)
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selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner)
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populate = txt2imgreq.copy(update={ # Override __init__ params
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"sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
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"do_not_save_samples": not txt2imgreq.save_images,
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"do_not_save_grid": not txt2imgreq.save_images,
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})
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if populate.sampler_name:
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populate.sampler_index = None # prevent a warning later on
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args = vars(populate)
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args.pop('script_name', None)
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args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
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args.pop('alwayson_scripts', None)
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send_images = args.pop('send_images', True)
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args.pop('save_images', None)
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with self.queue_lock:
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p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
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p.scripts = script_runner
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p.outpath_grids = opts.outdir_grids or opts.outdir_txt2img_grids
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p.outpath_samples = opts.outdir_samples or opts.outdir_txt2img_samples
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shared.state.begin()
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script_args = self.init_script_args(p, txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner)
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if selectable_scripts is not None:
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processed = scripts.scripts_txt2img.run(p, *script_args) # Need to pass args as list here
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else:
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p.script_args = tuple(script_args) # Need to pass args as tuple here
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processed = process_images(p)
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shared.state.end()
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b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
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return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
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def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
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init_images = img2imgreq.init_images
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if init_images is None:
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raise HTTPException(status_code=404, detail="Init image not found")
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mask = img2imgreq.mask
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if mask:
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mask = decode_base64_to_image(mask)
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script_runner = scripts.scripts_img2img
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if not script_runner.scripts:
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script_runner.initialize_scripts(True)
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ui.create_ui()
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if not self.default_script_arg_img2img:
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self.default_script_arg_img2img = self.init_default_script_args(script_runner)
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selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner)
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populate = img2imgreq.copy(update={ # Override __init__ params
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"sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
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"do_not_save_samples": not img2imgreq.save_images,
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"do_not_save_grid": not img2imgreq.save_images,
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"mask": mask,
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})
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if populate.sampler_name:
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populate.sampler_index = None # prevent a warning later on
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args = vars(populate)
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args.pop('include_init_images', None) # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
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args.pop('script_name', None)
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args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
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args.pop('alwayson_scripts', None)
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send_images = args.pop('send_images', True)
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args.pop('save_images', None)
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with self.queue_lock:
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p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)
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p.init_images = [decode_base64_to_image(x) for x in init_images]
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p.scripts = script_runner
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p.outpath_grids = opts.outdir_img2img_grids
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p.outpath_samples = opts.outdir_img2img_samples
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shared.state.begin()
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script_args = self.init_script_args(p, img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner)
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if selectable_scripts is not None:
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processed = scripts.scripts_img2img.run(p, *script_args) # Need to pass args as list here
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else:
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p.script_args = tuple(script_args) # Need to pass args as tuple here
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processed = process_images(p)
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shared.state.end()
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b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
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if not img2imgreq.include_init_images:
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img2imgreq.init_images = None
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img2imgreq.mask = None
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return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
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def extras_single_image_api(self, req: ExtrasSingleImageRequest):
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reqDict = setUpscalers(req)
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reqDict['image'] = decode_base64_to_image(reqDict['image'])
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with self.queue_lock:
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result = postprocessing.run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
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return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
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def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
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reqDict = setUpscalers(req)
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def prepareFiles(file):
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file = decode_base64_to_file(file.data, file_path=file.name)
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file.orig_name = file.name
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return file
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reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
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reqDict.pop('imageList')
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with self.queue_lock:
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result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
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return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
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def pnginfoapi(self, req: PNGInfoRequest):
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if not req.image.strip():
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return PNGInfoResponse(info="")
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image = decode_base64_to_image(req.image.strip())
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if image is None:
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return PNGInfoResponse(info="")
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geninfo, items = images.read_info_from_image(image)
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if geninfo is None:
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geninfo = ""
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items = {**{'parameters': geninfo}, **items}
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return PNGInfoResponse(info=geninfo, items=items)
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def progressapi(self, req: ProgressRequest = Depends()):
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# copy from check_progress_call of ui.py
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if shared.state.job_count == 0:
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return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo)
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# avoid dividing zero
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progress = 0.01
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if shared.state.job_count > 0:
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progress += shared.state.job_no / shared.state.job_count
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if shared.state.sampling_steps > 0:
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progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
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time_since_start = time.time() - shared.state.time_start
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eta = time_since_start / progress
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eta_relative = eta-time_since_start
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progress = min(progress, 1)
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shared.state.set_current_image()
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current_image = None
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if shared.state.current_image and not req.skip_current_image:
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current_image = encode_pil_to_base64(shared.state.current_image)
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return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)
|
|
|
|
def interrogateapi(self, interrogatereq: InterrogateRequest):
|
|
image_b64 = interrogatereq.image
|
|
if image_b64 is None:
|
|
raise HTTPException(status_code=404, detail="Image not found")
|
|
|
|
img = decode_base64_to_image(image_b64)
|
|
img = img.convert('RGB')
|
|
|
|
# Override object param
|
|
with self.queue_lock:
|
|
if interrogatereq.model == "clip":
|
|
processed = shared.interrogator.interrogate(img)
|
|
elif interrogatereq.model == "deepdanbooru":
|
|
processed = deepbooru.model.tag(img)
|
|
else:
|
|
raise HTTPException(status_code=404, detail="Model not found")
|
|
|
|
return InterrogateResponse(caption=processed)
|
|
|
|
def interruptapi(self):
|
|
shared.state.interrupt()
|
|
|
|
return {}
|
|
|
|
def unloadapi(self):
|
|
unload_model_weights()
|
|
|
|
return {}
|
|
|
|
def reloadapi(self):
|
|
reload_model_weights()
|
|
|
|
return {}
|
|
|
|
def skip(self):
|
|
shared.state.skip()
|
|
|
|
def get_config(self):
|
|
options = {}
|
|
for k in shared.opts.data.keys():
|
|
if shared.opts.data_labels.get(k) is not None:
|
|
options.update({k: shared.opts.data.get(k, shared.opts.data_labels.get(k).default)})
|
|
else:
|
|
options.update({k: shared.opts.data.get(k, None)})
|
|
|
|
return options
|
|
|
|
def set_config(self, req: Dict[str, Any]):
|
|
for k, v in req.items():
|
|
shared.opts.set(k, v)
|
|
|
|
shared.opts.save(shared.config_filename)
|
|
return
|
|
|
|
def get_cmd_flags(self):
|
|
return vars(shared.cmd_opts)
|
|
|
|
def get_samplers(self):
|
|
return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
|
|
|
|
def get_upscalers(self):
|
|
return [
|
|
{
|
|
"name": upscaler.name,
|
|
"model_name": upscaler.scaler.model_name,
|
|
"model_path": upscaler.data_path,
|
|
"model_url": None,
|
|
"scale": upscaler.scale,
|
|
}
|
|
for upscaler in shared.sd_upscalers
|
|
]
|
|
|
|
def get_sd_models(self):
|
|
return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in checkpoints_list.values()]
|
|
|
|
def get_hypernetworks(self):
|
|
return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
|
|
|
|
def get_face_restorers(self):
|
|
return [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers]
|
|
|
|
def get_realesrgan_models(self):
|
|
return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)]
|
|
|
|
def get_prompt_styles(self):
|
|
styleList = []
|
|
for k in shared.prompt_styles.styles:
|
|
style = shared.prompt_styles.styles[k]
|
|
styleList.append({"name":style[0], "prompt": style[1], "negative_prompt": style[2]})
|
|
|
|
return styleList
|
|
|
|
def get_embeddings(self):
|
|
db = sd_hijack.model_hijack.embedding_db
|
|
|
|
def convert_embedding(embedding):
|
|
return {
|
|
"step": embedding.step,
|
|
"sd_checkpoint": embedding.sd_checkpoint,
|
|
"sd_checkpoint_name": embedding.sd_checkpoint_name,
|
|
"shape": embedding.shape,
|
|
"vectors": embedding.vectors,
|
|
}
|
|
|
|
def convert_embeddings(embeddings):
|
|
return {embedding.name: convert_embedding(embedding) for embedding in embeddings.values()}
|
|
|
|
return {
|
|
"loaded": convert_embeddings(db.word_embeddings),
|
|
"skipped": convert_embeddings(db.skipped_embeddings),
|
|
}
|
|
|
|
def refresh_checkpoints(self):
|
|
shared.refresh_checkpoints()
|
|
|
|
def create_embedding(self, args: dict):
|
|
try:
|
|
shared.state.begin()
|
|
filename = create_embedding(**args) # create empty embedding
|
|
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used
|
|
shared.state.end()
|
|
return CreateResponse(info = f"create embedding filename: {filename}")
|
|
except AssertionError as e:
|
|
shared.state.end()
|
|
return TrainResponse(info = f"create embedding error: {e}")
|
|
|
|
def create_hypernetwork(self, args: dict):
|
|
try:
|
|
shared.state.begin()
|
|
filename = create_hypernetwork(**args) # create empty embedding # pylint: disable=E1111
|
|
shared.state.end()
|
|
return CreateResponse(info = f"create hypernetwork filename: {filename}")
|
|
except AssertionError as e:
|
|
shared.state.end()
|
|
return TrainResponse(info = f"create hypernetwork error: {e}")
|
|
|
|
def preprocess(self, args: dict):
|
|
try:
|
|
shared.state.begin()
|
|
preprocess(**args) # quick operation unless blip/booru interrogation is enabled
|
|
shared.state.end()
|
|
return PreprocessResponse(info = 'preprocess complete')
|
|
except KeyError as e:
|
|
shared.state.end()
|
|
return PreprocessResponse(info = f"preprocess error: invalid token: {e}")
|
|
except AssertionError as e:
|
|
shared.state.end()
|
|
return PreprocessResponse(info = f"preprocess error: {e}")
|
|
except FileNotFoundError as e:
|
|
shared.state.end()
|
|
return PreprocessResponse(info = f'preprocess error: {e}')
|
|
|
|
def train_embedding(self, args: dict):
|
|
try:
|
|
shared.state.begin()
|
|
apply_optimizations = False
|
|
error = None
|
|
filename = ''
|
|
if not apply_optimizations:
|
|
sd_hijack.undo_optimizations()
|
|
try:
|
|
_embedding, filename = train_embedding(**args) # can take a long time to complete
|
|
except Exception as e:
|
|
error = e
|
|
finally:
|
|
if not apply_optimizations:
|
|
sd_hijack.apply_optimizations()
|
|
shared.state.end()
|
|
return TrainResponse(info = f"train embedding complete: filename: {filename} error: {error}")
|
|
except AssertionError as msg:
|
|
shared.state.end()
|
|
return TrainResponse(info = f"train embedding error: {msg}")
|
|
|
|
def train_hypernetwork(self, args: dict):
|
|
try:
|
|
shared.state.begin()
|
|
shared.loaded_hypernetworks = []
|
|
apply_optimizations = False
|
|
error = None
|
|
filename = ''
|
|
if not apply_optimizations:
|
|
sd_hijack.undo_optimizations()
|
|
try:
|
|
_hypernetwork, filename = train_hypernetwork(**args)
|
|
except Exception as e:
|
|
error = e
|
|
finally:
|
|
shared.sd_model.cond_stage_model.to(devices.device)
|
|
shared.sd_model.first_stage_model.to(devices.device)
|
|
if not apply_optimizations:
|
|
sd_hijack.apply_optimizations()
|
|
shared.state.end()
|
|
return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
|
|
except AssertionError:
|
|
shared.state.end()
|
|
return TrainResponse(info=f"train embedding error: {error}")
|
|
|
|
def shutdown(self):
|
|
shared.log.info('Shutdown request received')
|
|
# from modules.shared import demo
|
|
# demo.close()
|
|
# time.sleep(0.5)
|
|
# import sys
|
|
# sys.exit(0)
|
|
import os
|
|
os._exit(0)
|
|
|
|
def get_memory(self):
|
|
try:
|
|
import os
|
|
import psutil
|
|
process = psutil.Process(os.getpid())
|
|
res = process.memory_info() # only rss is cross-platform guaranteed so we dont rely on other values
|
|
ram_total = 100 * res.rss / process.memory_percent() # and total memory is calculated as actual value is not cross-platform safe
|
|
ram = { 'free': ram_total - res.rss, 'used': res.rss, 'total': ram_total }
|
|
except Exception as err:
|
|
ram = { 'error': f'{err}' }
|
|
try:
|
|
import torch
|
|
if shared.cmd_opts.use_ipex():
|
|
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
|
|
system = { 'free': (torch.xpu.get_device_properties("xpu").total_memory - torch.xpu.memory_allocated()), 'used': torch.xpu.memory_allocated(), 'total': torch.xpu.get_device_properties("xpu").total_memory }
|
|
s = dict(torch.xpu.memory_stats("xpu"))
|
|
allocated = { 'current': s['allocated_bytes.all.current'], 'peak': s['allocated_bytes.all.peak'] }
|
|
reserved = { 'current': s['reserved_bytes.all.current'], 'peak': s['reserved_bytes.all.peak'] }
|
|
active = { 'current': s['active_bytes.all.current'], 'peak': s['active_bytes.all.peak'] }
|
|
inactive = { 'current': s['inactive_split_bytes.all.current'], 'peak': s['inactive_split_bytes.all.peak'] }
|
|
warnings = { 'retries': s['num_alloc_retries'], 'oom': s['num_ooms'] }
|
|
cuda = {
|
|
'system': system,
|
|
'active': active,
|
|
'allocated': allocated,
|
|
'reserved': reserved,
|
|
'inactive': inactive,
|
|
'events': warnings,
|
|
}
|
|
elif torch.cuda.is_available():
|
|
s = torch.cuda.mem_get_info()
|
|
system = { 'free': s[0], 'used': s[1] - s[0], 'total': s[1] }
|
|
s = dict(torch.cuda.memory_stats(shared.device))
|
|
allocated = { 'current': s['allocated_bytes.all.current'], 'peak': s['allocated_bytes.all.peak'] }
|
|
reserved = { 'current': s['reserved_bytes.all.current'], 'peak': s['reserved_bytes.all.peak'] }
|
|
active = { 'current': s['active_bytes.all.current'], 'peak': s['active_bytes.all.peak'] }
|
|
inactive = { 'current': s['inactive_split_bytes.all.current'], 'peak': s['inactive_split_bytes.all.peak'] }
|
|
warnings = { 'retries': s['num_alloc_retries'], 'oom': s['num_ooms'] }
|
|
cuda = {
|
|
'system': system,
|
|
'active': active,
|
|
'allocated': allocated,
|
|
'reserved': reserved,
|
|
'inactive': inactive,
|
|
'events': warnings,
|
|
}
|
|
else:
|
|
cuda = { 'error': 'unavailable' }
|
|
except Exception as err:
|
|
cuda = { 'error': f'{err}' }
|
|
return MemoryResponse(ram = ram, cuda = cuda)
|
|
|
|
def launch(self, server_name, port):
|
|
self.app.include_router(self.router)
|
|
uvicorn.run(self.app, host=server_name, port=port)
|