542 lines
24 KiB
Python
542 lines
24 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
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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.extras import run_extras
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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, find_checkpoint_config
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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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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'] = upscaler_to_index(req.upscaler_1)
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reqDict['extras_upscaler_2'] = upscaler_to_index(req.upscaler_2)
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reqDict.pop('upscaler_1')
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reqDict.pop('upscaler_2')
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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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return Image.open(BytesIO(base64.b64decode(encoding)))
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def encode_pil_to_base64(image):
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with io.BytesIO() as output_bytes:
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# Copy any text-only metadata
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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(
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output_bytes, "PNG", pnginfo=(metadata if use_metadata else None)
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)
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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/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
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self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
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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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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_script(self, script_name, script_runner):
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if script_name is None:
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return None, None
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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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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 text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
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script, script_idx = self.get_script(txt2imgreq.script_name, scripts.scripts_txt2img)
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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": True,
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"do_not_save_grid": True
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}
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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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with self.queue_lock:
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p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
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shared.state.begin()
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if script is not None:
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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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p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args
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processed = scripts.scripts_txt2img.run(p, *p.script_args)
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else:
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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))
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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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script, script_idx = self.get_script(img2imgreq.script_name, scripts.scripts_img2img)
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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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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": True,
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"do_not_save_grid": True,
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"mask": mask
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}
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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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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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shared.state.begin()
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if script is not None:
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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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p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args
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processed = scripts.scripts_img2img.run(p, *p.script_args)
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else:
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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))
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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 = 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 = 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)
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def interrogateapi(self, interrogatereq: InterrogateRequest):
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image_b64 = interrogatereq.image
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if image_b64 is None:
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raise HTTPException(status_code=404, detail="Image not found")
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img = decode_base64_to_image(image_b64)
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img = img.convert('RGB')
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# Override object param
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with self.queue_lock:
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if interrogatereq.model == "clip":
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processed = shared.interrogator.interrogate(img)
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elif interrogatereq.model == "deepdanbooru":
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processed = deepbooru.model.tag(img)
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else:
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raise HTTPException(status_code=404, detail="Model not found")
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return InterrogateResponse(caption=processed)
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def interruptapi(self):
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shared.state.interrupt()
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return {}
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def skip(self):
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shared.state.skip()
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def get_config(self):
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options = {}
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for key in shared.opts.data.keys():
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metadata = shared.opts.data_labels.get(key)
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if(metadata is not None):
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options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)})
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else:
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options.update({key: shared.opts.data.get(key, None)})
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return options
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def set_config(self, req: Dict[str, Any]):
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for k, v in req.items():
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shared.opts.set(k, v)
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shared.opts.save(shared.config_filename)
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return
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def get_cmd_flags(self):
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return vars(shared.cmd_opts)
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def get_samplers(self):
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return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
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def get_upscalers(self):
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upscalers = []
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for upscaler in shared.sd_upscalers:
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u = upscaler.scaler
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upscalers.append({"name":u.name, "model_name":u.model_name, "model_path":u.model_path, "model_url":u.model_url})
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return upscalers
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def get_sd_models(self):
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return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
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def get_hypernetworks(self):
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return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
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def get_face_restorers(self):
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return [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers]
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def get_realesrgan_models(self):
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return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)]
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def get_prompt_styles(self):
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styleList = []
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for k in shared.prompt_styles.styles:
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style = shared.prompt_styles.styles[k]
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styleList.append({"name":style[0], "prompt": style[1], "negative_prompt": style[2]})
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return styleList
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def get_artists_categories(self):
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return shared.artist_db.cats
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def get_artists(self):
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return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
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def get_embeddings(self):
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db = sd_hijack.model_hijack.embedding_db
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def convert_embedding(embedding):
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return {
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"step": embedding.step,
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"sd_checkpoint": embedding.sd_checkpoint,
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"sd_checkpoint_name": embedding.sd_checkpoint_name,
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"shape": embedding.shape,
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"vectors": embedding.vectors,
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}
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def convert_embeddings(embeddings):
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return {embedding.name: convert_embedding(embedding) for embedding in embeddings.values()}
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return {
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"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()
|
|
initial_hypernetwork = shared.loaded_hypernetwork
|
|
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.loaded_hypernetwork = initial_hypernetwork
|
|
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)
|