4cbbb881ee
…to free VRAM. New Action buttons in the settings to manually free and reload checkpoints, essentially juggling models between RAM and VRAM.
627 lines
29 KiB
Python
627 lines
29 KiB
Python
import base64
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import io
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import time
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import datetime
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import uvicorn
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from threading import Lock
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from io import BytesIO
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from gradio.processing_utils import decode_base64_to_file
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from fastapi import APIRouter, Depends, FastAPI, HTTPException, Request, Response
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from fastapi.security import HTTPBasic, HTTPBasicCredentials
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from secrets import compare_digest
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import modules.shared as shared
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from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing
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from modules.api.models 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 PIL import PngImagePlugin,Image
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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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from typing import List
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import piexif
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import piexif.helper
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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:
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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])}")
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def script_name_to_index(name, scripts):
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try:
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return [script.title().lower() for script in scripts].index(name.lower())
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except:
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raise HTTPException(status_code=422, detail=f"Script '{name}' not found")
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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 err:
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raise HTTPException(status_code=500, detail="Invalid encoded image")
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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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metadata = PngImagePlugin.PngInfo()
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for key, value in image.info.items():
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if isinstance(key, str) and isinstance(value, str):
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metadata.add_text(key, value)
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use_metadata = True
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image.save(output_bytes, format="PNG", pnginfo=(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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def api_middleware(app: FastAPI):
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@app.middleware("http")
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async def log_and_time(req: Request, call_next):
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ts = time.time()
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res: Response = await call_next(req)
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duration = str(round(time.time() - ts, 4))
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res.headers["X-Process-Time"] = duration
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endpoint = req.scope.get('path', 'err')
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if shared.cmd_opts.api_log and endpoint.startswith('/sdapi'):
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print('API {t} {code} {prot}/{ver} {method} {endpoint} {cli} {duration}'.format(
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t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"),
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code = res.status_code,
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ver = req.scope.get('http_version', '0.0'),
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cli = req.scope.get('client', ('0:0.0.0', 0))[0],
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prot = req.scope.get('scheme', 'err'),
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method = req.scope.get('method', 'err'),
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endpoint = endpoint,
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duration = duration,
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))
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return res
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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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api_middleware(self.app)
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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/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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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_script_args(self, request, selectable_scripts, selectable_idx, 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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# 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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script_args[selectable_scripts.args_from:selectable_scripts.args_to] = request.script_args
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script_args[0] = selectable_idx + 1
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else:
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# when [0] = 0 no selectable script to run
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script_args[0] = 0
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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 == None:
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raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found")
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# Selectable script in always on script param check
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if alwayson_script.alwayson == False:
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raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params")
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# always on script with no arg should always run so you don't really need to add them to the requests
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if "args" in request.alwayson_scripts[alwayson_script_name]:
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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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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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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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script_args = self.init_script_args(txt2imgreq, selectable_scripts, selectable_script_idx, script_runner)
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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_txt2img_grids
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p.outpath_samples = opts.outdir_txt2img_samples
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shared.state.begin()
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if selectable_scripts != None:
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p.script_args = script_args
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processed = scripts.scripts_txt2img.run(p, *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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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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script_args = self.init_script_args(img2imgreq, selectable_scripts, selectable_script_idx, script_runner)
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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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if selectable_scripts != None:
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p.script_args = script_args
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processed = scripts.scripts_img2img.run(p, *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)
|
|
|
|
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 key in shared.opts.data.keys():
|
|
metadata = shared.opts.data_labels.get(key)
|
|
if(metadata is not None):
|
|
options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)})
|
|
else:
|
|
options.update({key: shared.opts.data.get(key, 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 = "create embedding filename: {filename}".format(filename = filename))
|
|
except AssertionError as e:
|
|
shared.state.end()
|
|
return TrainResponse(info = "create embedding error: {error}".format(error = e))
|
|
|
|
def create_hypernetwork(self, args: dict):
|
|
try:
|
|
shared.state.begin()
|
|
filename = create_hypernetwork(**args) # create empty embedding
|
|
shared.state.end()
|
|
return CreateResponse(info = "create hypernetwork filename: {filename}".format(filename = filename))
|
|
except AssertionError as e:
|
|
shared.state.end()
|
|
return TrainResponse(info = "create hypernetwork error: {error}".format(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 = "preprocess error: invalid token: {error}".format(error = e))
|
|
except AssertionError as e:
|
|
shared.state.end()
|
|
return PreprocessResponse(info = "preprocess error: {error}".format(error = e))
|
|
except FileNotFoundError as e:
|
|
shared.state.end()
|
|
return PreprocessResponse(info = 'preprocess error: {error}'.format(error = e))
|
|
|
|
def train_embedding(self, args: dict):
|
|
try:
|
|
shared.state.begin()
|
|
apply_optimizations = shared.opts.training_xattention_optimizations
|
|
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 = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error))
|
|
except AssertionError as msg:
|
|
shared.state.end()
|
|
return TrainResponse(info = "train embedding error: {msg}".format(msg = msg))
|
|
|
|
def train_hypernetwork(self, args: dict):
|
|
try:
|
|
shared.state.begin()
|
|
shared.loaded_hypernetworks = []
|
|
apply_optimizations = shared.opts.training_xattention_optimizations
|
|
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="train embedding complete: filename: {filename} error: {error}".format(filename=filename, error=error))
|
|
except AssertionError as msg:
|
|
shared.state.end()
|
|
return TrainResponse(info="train embedding error: {error}".format(error=error))
|
|
|
|
def get_memory(self):
|
|
try:
|
|
import os, 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 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)
|