forked from Hitmare/Eris_api_tensor_patch
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parent
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commit
5299265293
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TRT- Patch from <https://gist.github.com/Saren-Arterius/2014d9799361711d25ff33740990a7b8>
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Modified for Linux by estro
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Copy files from `modules` folder into your `modules` folder. replace existing files.
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Original files for restoring can be found in the `#Original` folder
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Copy `api.py` from the previous folder into `modules/api` and enable `eris_TRTpatch` or `eris_imagelimit`
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::: warn
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If you use the TRTpatch with the Imagelimit you will need to create a **__Dynamic Tensor Profile__** with the following settings:
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`min/opt/max batch size: 1`
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`min/opt/max width: 256/576/1024`
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`min/opt/max height: 256/704/1024`
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`min/opt/max tokens: 75/75/500`\`
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Those settings are **__very critical__** to have the TRT Plugin working correctly
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:::
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import base64
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import io
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import os
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import time
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import datetime
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import uvicorn
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import ipaddress
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import requests
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import gradio as gr
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from threading import Lock
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from io import BytesIO
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from fastapi import APIRouter, Depends, FastAPI, Request, Response
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from fastapi.security import HTTPBasic, HTTPBasicCredentials
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from fastapi.exceptions import HTTPException
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from fastapi.responses import JSONResponse
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from fastapi.encoders import jsonable_encoder
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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, errors, restart, shared_items
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from modules.api import models
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from modules.shared import opts
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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 unload_model_weights, reload_model_weights, checkpoint_aliases
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from modules.sd_models_config import find_checkpoint_config_near_filename
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from modules.realesrgan_model import get_realesrgan_models
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from modules import devices
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from typing import Dict, List, Any
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import piexif
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import piexif.helper
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from contextlib import closing
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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 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 verify_url(url):
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"""Returns True if the url refers to a global resource."""
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import socket
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from urllib.parse import urlparse
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try:
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parsed_url = urlparse(url)
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domain_name = parsed_url.netloc
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host = socket.gethostbyname_ex(domain_name)
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for ip in host[2]:
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ip_addr = ipaddress.ip_address(ip)
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if not ip_addr.is_global:
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return False
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except Exception:
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return False
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return True
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def decode_base64_to_image(encoding):
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if encoding.startswith("http://") or encoding.startswith("https://"):
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if not opts.api_enable_requests:
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raise HTTPException(status_code=500, detail="Requests not allowed")
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if opts.api_forbid_local_requests and not verify_url(encoding):
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raise HTTPException(status_code=500, detail="Request to local resource not allowed")
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headers = {'user-agent': opts.api_useragent} if opts.api_useragent else {}
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response = requests.get(encoding, timeout=30, headers=headers)
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try:
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image = Image.open(BytesIO(response.content))
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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 image url") from e
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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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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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if image.mode == "RGBA":
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image = image.convert("RGB")
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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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rich_available = False
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try:
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if os.environ.get('WEBUI_RICH_EXCEPTIONS', None) is not None:
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import anyio # importing just so it can be placed on silent list
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import starlette # importing just so it can be placed on silent list
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from rich.console import Console
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console = Console()
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rich_available = True
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except Exception:
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pass
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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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def handle_exception(request: Request, e: Exception):
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err = {
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"error": type(e).__name__,
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"detail": vars(e).get('detail', ''),
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"body": vars(e).get('body', ''),
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"errors": str(e),
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}
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if not isinstance(e, HTTPException): # do not print backtrace on known httpexceptions
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message = f"API error: {request.method}: {request.url} {err}"
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if rich_available:
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print(message)
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console.print_exception(show_locals=True, max_frames=2, extra_lines=1, suppress=[anyio, starlette], word_wrap=False, width=min([console.width, 200]))
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else:
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errors.report(message, exc_info=True)
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return JSONResponse(status_code=vars(e).get('status_code', 500), content=jsonable_encoder(err))
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@app.middleware("http")
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async def exception_handling(request: Request, call_next):
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try:
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return await call_next(request)
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except Exception as e:
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return handle_exception(request, e)
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@app.exception_handler(Exception)
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async def fastapi_exception_handler(request: Request, e: Exception):
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return handle_exception(request, e)
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@app.exception_handler(HTTPException)
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async def http_exception_handler(request: Request, e: HTTPException):
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return handle_exception(request, e)
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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 = {}
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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=models.TextToImageResponse)
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self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=models.ImageToImageResponse)
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self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=models.ExtrasSingleImageResponse)
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self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=models.ExtrasBatchImagesResponse)
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self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=models.PNGInfoResponse)
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self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=models.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=models.OptionsModel)
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self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
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self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
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self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
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self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
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self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem])
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self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
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self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem])
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self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
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self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
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self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
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self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
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self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
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self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
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self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
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self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
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self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
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self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
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self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse)
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self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse)
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self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.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=models.ScriptsList)
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self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
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if shared.cmd_opts.api_server_stop:
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self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
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self.add_api_route("/sdapi/v1/server-restart", self.restart_webui, methods=["POST"])
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self.add_api_route("/sdapi/v1/server-stop", self.stop_webui, methods=["POST"])
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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 = [script.name for script in scripts.scripts_txt2img.scripts if script.name is not None]
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i2ilist = [script.name for script in scripts.scripts_img2img.scripts if script.name is not None]
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return models.ScriptsList(txt2img=t2ilist, img2img=i2ilist)
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def get_script_info(self):
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res = []
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|
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for script_list in [scripts.scripts_txt2img.scripts, scripts.scripts_img2img.scripts]:
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res += [script.api_info for script in script_list if script.api_info is not None]
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|
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||||||
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return res
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|
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||||||
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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
|
||||||
|
|
||||||
|
script_idx = script_name_to_index(script_name, script_runner.scripts)
|
||||||
|
return script_runner.scripts[script_idx]
|
||||||
|
|
||||||
|
def init_default_script_args(self, script_runner):
|
||||||
|
#find max idx from the scripts in runner and generate a none array to init script_args
|
||||||
|
last_arg_index = 1
|
||||||
|
for script in script_runner.scripts:
|
||||||
|
if last_arg_index < script.args_to:
|
||||||
|
last_arg_index = script.args_to
|
||||||
|
# None everywhere except position 0 to initialize script args
|
||||||
|
script_args = [None]*last_arg_index
|
||||||
|
script_args[0] = 0
|
||||||
|
|
||||||
|
# get default values
|
||||||
|
with gr.Blocks(): # will throw errors calling ui function without this
|
||||||
|
for script in script_runner.scripts:
|
||||||
|
if script.ui(script.is_img2img):
|
||||||
|
ui_default_values = []
|
||||||
|
for elem in script.ui(script.is_img2img):
|
||||||
|
ui_default_values.append(elem.value)
|
||||||
|
script_args[script.args_from:script.args_to] = ui_default_values
|
||||||
|
return script_args
|
||||||
|
|
||||||
|
def init_script_args(self, request, default_script_args, selectable_scripts, selectable_idx, script_runner):
|
||||||
|
script_args = default_script_args.copy()
|
||||||
|
# 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()
|
||||||
|
if selectable_scripts:
|
||||||
|
script_args[selectable_scripts.args_from:selectable_scripts.args_to] = request.script_args
|
||||||
|
script_args[0] = selectable_idx + 1
|
||||||
|
|
||||||
|
# Now check for always on scripts
|
||||||
|
if request.alwayson_scripts:
|
||||||
|
for alwayson_script_name in request.alwayson_scripts.keys():
|
||||||
|
alwayson_script = self.get_script(alwayson_script_name, script_runner)
|
||||||
|
if alwayson_script is None:
|
||||||
|
raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found")
|
||||||
|
# Selectable script in always on script param check
|
||||||
|
if alwayson_script.alwayson is False:
|
||||||
|
raise HTTPException(status_code=422, detail="Cannot have a selectable script in the always on scripts params")
|
||||||
|
# always on script with no arg should always run so you don't really need to add them to the requests
|
||||||
|
if "args" in request.alwayson_scripts[alwayson_script_name]:
|
||||||
|
# min between arg length in scriptrunner and arg length in the request
|
||||||
|
for idx in range(0, min((alwayson_script.args_to - alwayson_script.args_from), len(request.alwayson_scripts[alwayson_script_name]["args"]))):
|
||||||
|
script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx]
|
||||||
|
return script_args
|
||||||
|
|
||||||
|
def text2imgapi(self, txt2imgreq: models.StableDiffusionTxt2ImgProcessingAPI):
|
||||||
|
script_runner = scripts.scripts_txt2img
|
||||||
|
if not script_runner.scripts:
|
||||||
|
script_runner.initialize_scripts(False)
|
||||||
|
ui.create_ui()
|
||||||
|
if not self.default_script_arg_txt2img:
|
||||||
|
self.default_script_arg_txt2img = self.init_default_script_args(script_runner)
|
||||||
|
selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner)
|
||||||
|
|
||||||
|
populate = txt2imgreq.copy(update={ # Override __init__ params
|
||||||
|
"sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
|
||||||
|
"do_not_save_samples": not txt2imgreq.save_images,
|
||||||
|
"do_not_save_grid": not txt2imgreq.save_images,
|
||||||
|
})
|
||||||
|
if populate.sampler_name:
|
||||||
|
populate.sampler_index = None # prevent a warning later on
|
||||||
|
|
||||||
|
args = vars(populate)
|
||||||
|
args.pop('script_name', None)
|
||||||
|
args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
|
||||||
|
args.pop('alwayson_scripts', None)
|
||||||
|
|
||||||
|
script_args = self.init_script_args(txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner)
|
||||||
|
|
||||||
|
send_images = args.pop('send_images', True)
|
||||||
|
args.pop('save_images', None)
|
||||||
|
|
||||||
|
with self.queue_lock:
|
||||||
|
with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p:
|
||||||
|
p.is_api = True
|
||||||
|
p.scripts = script_runner
|
||||||
|
p.outpath_grids = opts.outdir_txt2img_grids
|
||||||
|
p.outpath_samples = opts.outdir_txt2img_samples
|
||||||
|
|
||||||
|
try:
|
||||||
|
shared.state.begin(job="scripts_txt2img")
|
||||||
|
if selectable_scripts is not None:
|
||||||
|
p.script_args = script_args
|
||||||
|
processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
|
||||||
|
else:
|
||||||
|
p.script_args = tuple(script_args) # Need to pass args as tuple here
|
||||||
|
processed = process_images(p)
|
||||||
|
finally:
|
||||||
|
shared.state.end()
|
||||||
|
shared.total_tqdm.clear()
|
||||||
|
|
||||||
|
b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
|
||||||
|
|
||||||
|
return models.TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
|
||||||
|
|
||||||
|
def img2imgapi(self, img2imgreq: models.StableDiffusionImg2ImgProcessingAPI):
|
||||||
|
init_images = img2imgreq.init_images
|
||||||
|
if init_images is None:
|
||||||
|
raise HTTPException(status_code=404, detail="Init image not found")
|
||||||
|
|
||||||
|
mask = img2imgreq.mask
|
||||||
|
if mask:
|
||||||
|
mask = decode_base64_to_image(mask)
|
||||||
|
|
||||||
|
script_runner = scripts.scripts_img2img
|
||||||
|
if not script_runner.scripts:
|
||||||
|
script_runner.initialize_scripts(True)
|
||||||
|
ui.create_ui()
|
||||||
|
if not self.default_script_arg_img2img:
|
||||||
|
self.default_script_arg_img2img = self.init_default_script_args(script_runner)
|
||||||
|
selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner)
|
||||||
|
|
||||||
|
populate = img2imgreq.copy(update={ # Override __init__ params
|
||||||
|
"sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
|
||||||
|
"do_not_save_samples": not img2imgreq.save_images,
|
||||||
|
"do_not_save_grid": not img2imgreq.save_images,
|
||||||
|
"mask": mask,
|
||||||
|
})
|
||||||
|
if populate.sampler_name:
|
||||||
|
populate.sampler_index = None # prevent a warning later on
|
||||||
|
|
||||||
|
args = vars(populate)
|
||||||
|
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.
|
||||||
|
args.pop('script_name', None)
|
||||||
|
args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
|
||||||
|
args.pop('alwayson_scripts', None)
|
||||||
|
|
||||||
|
script_args = self.init_script_args(img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner)
|
||||||
|
|
||||||
|
send_images = args.pop('send_images', True)
|
||||||
|
args.pop('save_images', None)
|
||||||
|
|
||||||
|
with self.queue_lock:
|
||||||
|
with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p:
|
||||||
|
p.init_images = [decode_base64_to_image(x) for x in init_images]
|
||||||
|
p.is_api = True
|
||||||
|
p.scripts = script_runner
|
||||||
|
p.outpath_grids = opts.outdir_img2img_grids
|
||||||
|
p.outpath_samples = opts.outdir_img2img_samples
|
||||||
|
|
||||||
|
try:
|
||||||
|
shared.state.begin(job="scripts_img2img")
|
||||||
|
if selectable_scripts is not None:
|
||||||
|
p.script_args = script_args
|
||||||
|
processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
|
||||||
|
else:
|
||||||
|
p.script_args = tuple(script_args) # Need to pass args as tuple here
|
||||||
|
processed = process_images(p)
|
||||||
|
finally:
|
||||||
|
shared.state.end()
|
||||||
|
shared.total_tqdm.clear()
|
||||||
|
|
||||||
|
b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
|
||||||
|
|
||||||
|
if not img2imgreq.include_init_images:
|
||||||
|
img2imgreq.init_images = None
|
||||||
|
img2imgreq.mask = None
|
||||||
|
|
||||||
|
return models.ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
|
||||||
|
|
||||||
|
def extras_single_image_api(self, req: models.ExtrasSingleImageRequest):
|
||||||
|
reqDict = setUpscalers(req)
|
||||||
|
|
||||||
|
reqDict['image'] = decode_base64_to_image(reqDict['image'])
|
||||||
|
|
||||||
|
with self.queue_lock:
|
||||||
|
result = postprocessing.run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
|
||||||
|
|
||||||
|
return models.ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
|
||||||
|
|
||||||
|
def extras_batch_images_api(self, req: models.ExtrasBatchImagesRequest):
|
||||||
|
reqDict = setUpscalers(req)
|
||||||
|
|
||||||
|
image_list = reqDict.pop('imageList', [])
|
||||||
|
image_folder = [decode_base64_to_image(x.data) for x in image_list]
|
||||||
|
|
||||||
|
with self.queue_lock:
|
||||||
|
result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict)
|
||||||
|
|
||||||
|
return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
|
||||||
|
|
||||||
|
def pnginfoapi(self, req: models.PNGInfoRequest):
|
||||||
|
if(not req.image.strip()):
|
||||||
|
return models.PNGInfoResponse(info="")
|
||||||
|
|
||||||
|
image = decode_base64_to_image(req.image.strip())
|
||||||
|
if image is None:
|
||||||
|
return models.PNGInfoResponse(info="")
|
||||||
|
|
||||||
|
geninfo, items = images.read_info_from_image(image)
|
||||||
|
if geninfo is None:
|
||||||
|
geninfo = ""
|
||||||
|
|
||||||
|
items = {**{'parameters': geninfo}, **items}
|
||||||
|
|
||||||
|
return models.PNGInfoResponse(info=geninfo, items=items)
|
||||||
|
|
||||||
|
def progressapi(self, req: models.ProgressRequest = Depends()):
|
||||||
|
# copy from check_progress_call of ui.py
|
||||||
|
|
||||||
|
if shared.state.job_count == 0:
|
||||||
|
return models.ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo)
|
||||||
|
|
||||||
|
# avoid dividing zero
|
||||||
|
progress = 0.01
|
||||||
|
|
||||||
|
if shared.state.job_count > 0:
|
||||||
|
progress += shared.state.job_no / shared.state.job_count
|
||||||
|
if shared.state.sampling_steps > 0:
|
||||||
|
progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
|
||||||
|
|
||||||
|
time_since_start = time.time() - shared.state.time_start
|
||||||
|
eta = (time_since_start/progress)
|
||||||
|
eta_relative = eta-time_since_start
|
||||||
|
|
||||||
|
progress = min(progress, 1)
|
||||||
|
|
||||||
|
shared.state.set_current_image()
|
||||||
|
|
||||||
|
current_image = None
|
||||||
|
if shared.state.current_image and not req.skip_current_image:
|
||||||
|
current_image = encode_pil_to_base64(shared.state.current_image)
|
||||||
|
|
||||||
|
return models.ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)
|
||||||
|
|
||||||
|
def interrogateapi(self, interrogatereq: models.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 models.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]):
|
||||||
|
checkpoint_name = req.get("sd_model_checkpoint", None)
|
||||||
|
if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases:
|
||||||
|
raise RuntimeError(f"model {checkpoint_name!r} not found")
|
||||||
|
|
||||||
|
for k, v in req.items():
|
||||||
|
shared.opts.set(k, v, is_api=True)
|
||||||
|
|
||||||
|
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_latent_upscale_modes(self):
|
||||||
|
return [
|
||||||
|
{
|
||||||
|
"name": upscale_mode,
|
||||||
|
}
|
||||||
|
for upscale_mode in [*(shared.latent_upscale_modes or {})]
|
||||||
|
]
|
||||||
|
|
||||||
|
def get_sd_models(self):
|
||||||
|
import modules.sd_models as sd_models
|
||||||
|
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 sd_models.checkpoints_list.values()]
|
||||||
|
|
||||||
|
def get_sd_vaes(self):
|
||||||
|
import modules.sd_vae as sd_vae
|
||||||
|
return [{"model_name": x, "filename": sd_vae.vae_dict[x]} for x in sd_vae.vae_dict.keys()]
|
||||||
|
|
||||||
|
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):
|
||||||
|
with self.queue_lock:
|
||||||
|
shared.refresh_checkpoints()
|
||||||
|
|
||||||
|
def refresh_vae(self):
|
||||||
|
with self.queue_lock:
|
||||||
|
shared_items.refresh_vae_list()
|
||||||
|
|
||||||
|
def create_embedding(self, args: dict):
|
||||||
|
try:
|
||||||
|
shared.state.begin(job="create_embedding")
|
||||||
|
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
|
||||||
|
return models.CreateResponse(info=f"create embedding filename: {filename}")
|
||||||
|
except AssertionError as e:
|
||||||
|
return models.TrainResponse(info=f"create embedding error: {e}")
|
||||||
|
finally:
|
||||||
|
shared.state.end()
|
||||||
|
|
||||||
|
|
||||||
|
def create_hypernetwork(self, args: dict):
|
||||||
|
try:
|
||||||
|
shared.state.begin(job="create_hypernetwork")
|
||||||
|
filename = create_hypernetwork(**args) # create empty embedding
|
||||||
|
return models.CreateResponse(info=f"create hypernetwork filename: {filename}")
|
||||||
|
except AssertionError as e:
|
||||||
|
return models.TrainResponse(info=f"create hypernetwork error: {e}")
|
||||||
|
finally:
|
||||||
|
shared.state.end()
|
||||||
|
|
||||||
|
def preprocess(self, args: dict):
|
||||||
|
try:
|
||||||
|
shared.state.begin(job="preprocess")
|
||||||
|
preprocess(**args) # quick operation unless blip/booru interrogation is enabled
|
||||||
|
shared.state.end()
|
||||||
|
return models.PreprocessResponse(info='preprocess complete')
|
||||||
|
except KeyError as e:
|
||||||
|
return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
|
||||||
|
except Exception as e:
|
||||||
|
return models.PreprocessResponse(info=f"preprocess error: {e}")
|
||||||
|
finally:
|
||||||
|
shared.state.end()
|
||||||
|
|
||||||
|
def train_embedding(self, args: dict):
|
||||||
|
try:
|
||||||
|
shared.state.begin(job="train_embedding")
|
||||||
|
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()
|
||||||
|
return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
|
||||||
|
except Exception as msg:
|
||||||
|
return models.TrainResponse(info=f"train embedding error: {msg}")
|
||||||
|
finally:
|
||||||
|
shared.state.end()
|
||||||
|
|
||||||
|
def train_hypernetwork(self, args: dict):
|
||||||
|
try:
|
||||||
|
shared.state.begin(job="train_hypernetwork")
|
||||||
|
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 models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
|
||||||
|
except Exception as exc:
|
||||||
|
return models.TrainResponse(info=f"train embedding error: {exc}")
|
||||||
|
finally:
|
||||||
|
shared.state.end()
|
||||||
|
|
||||||
|
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 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 models.MemoryResponse(ram=ram, cuda=cuda)
|
||||||
|
|
||||||
|
def launch(self, server_name, port, root_path):
|
||||||
|
self.app.include_router(self.router)
|
||||||
|
uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path)
|
||||||
|
|
||||||
|
def kill_webui(self):
|
||||||
|
restart.stop_program()
|
||||||
|
|
||||||
|
def restart_webui(self):
|
||||||
|
if restart.is_restartable():
|
||||||
|
restart.restart_program()
|
||||||
|
return Response(status_code=501)
|
||||||
|
|
||||||
|
def stop_webui(request):
|
||||||
|
shared.state.server_command = "stop"
|
||||||
|
return Response("Stopping.")
|
||||||
|
|
||||||
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue