322 lines
8.7 KiB
Python
322 lines
8.7 KiB
Python
from logging import getLogger
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from typing import Tuple
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import numpy as np
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from flask import request
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from ..diffusers.load import get_available_pipelines, get_pipeline_schedulers
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from ..params import (
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Border,
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DeviceParams,
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HighresParams,
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ImageParams,
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Size,
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UpscaleParams,
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)
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from ..utils import (
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get_and_clamp_float,
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get_and_clamp_int,
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get_boolean,
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get_from_list,
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get_not_empty,
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)
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from .context import ServerContext
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from .load import (
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get_available_platforms,
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get_config_value,
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get_correction_models,
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get_highres_methods,
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get_network_models,
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get_upscaling_models,
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)
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from .utils import get_model_path
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logger = getLogger(__name__)
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def pipeline_from_request(
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server: ServerContext,
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default_pipeline: str = "txt2img",
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) -> Tuple[DeviceParams, ImageParams, Size]:
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user = request.remote_addr
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# platform stuff
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device = None
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device_name = request.args.get("platform")
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if device_name is not None and device_name != "any":
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for platform in get_available_platforms():
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if platform.device == device_name:
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device = platform
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# diffusion model
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model = get_not_empty(request.args, "model", get_config_value("model"))
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model_path = get_model_path(server, model)
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control = None
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control_name = request.args.get("control")
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for network in get_network_models():
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if network.name == control_name:
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control = network
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# pipeline stuff
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pipeline = get_from_list(
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request.args, "pipeline", get_available_pipelines(), default_pipeline
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)
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scheduler = get_from_list(request.args, "scheduler", get_pipeline_schedulers())
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if scheduler is None:
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scheduler = get_config_value("scheduler")
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# prompt does not come from config
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prompt = request.args.get("prompt", "")
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negative_prompt = request.args.get("negativePrompt", None)
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if negative_prompt is not None and negative_prompt.strip() == "":
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negative_prompt = None
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# image params
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batch = get_and_clamp_int(
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request.args,
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"batch",
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get_config_value("batch"),
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get_config_value("batch", "max"),
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get_config_value("batch", "min"),
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)
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cfg = get_and_clamp_float(
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request.args,
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"cfg",
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get_config_value("cfg"),
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get_config_value("cfg", "max"),
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get_config_value("cfg", "min"),
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)
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eta = get_and_clamp_float(
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request.args,
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"eta",
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get_config_value("eta"),
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get_config_value("eta", "max"),
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get_config_value("eta", "min"),
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)
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loopback = get_and_clamp_int(
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request.args,
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"loopback",
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get_config_value("loopback"),
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get_config_value("loopback", "max"),
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get_config_value("loopback", "min"),
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)
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steps = get_and_clamp_int(
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request.args,
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"steps",
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get_config_value("steps"),
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get_config_value("steps", "max"),
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get_config_value("steps", "min"),
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)
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height = get_and_clamp_int(
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request.args,
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"height",
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get_config_value("height"),
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get_config_value("height", "max"),
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get_config_value("height", "min"),
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)
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width = get_and_clamp_int(
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request.args,
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"width",
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get_config_value("width"),
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get_config_value("width", "max"),
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get_config_value("width", "min"),
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)
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tiled_vae = get_boolean(request.args, "tiledVAE", get_config_value("tiledVAE"))
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tiles = get_and_clamp_int(
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request.args,
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"tiles",
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get_config_value("tiles"),
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get_config_value("tiles", "max"),
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get_config_value("tiles", "min"),
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)
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overlap = get_and_clamp_float(
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request.args,
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"overlap",
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get_config_value("overlap"),
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get_config_value("overlap", "max"),
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get_config_value("overlap", "min"),
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)
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stride = get_and_clamp_int(
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request.args,
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"stride",
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get_config_value("stride"),
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get_config_value("stride", "max"),
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get_config_value("stride", "min"),
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)
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if stride > tiles:
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logger.info("limiting stride to tile size, %s > %s", stride, tiles)
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stride = tiles
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seed = int(request.args.get("seed", -1))
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if seed == -1:
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# this one can safely use np.random because it produces a single value
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seed = np.random.randint(np.iinfo(np.int32).max)
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logger.info(
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"request from %s: %s steps of %s using %s in %s on %s, %sx%s, %s, %s - %s",
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user,
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steps,
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scheduler,
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model_path,
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pipeline,
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device or "any device",
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width,
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height,
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cfg,
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seed,
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prompt,
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)
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params = ImageParams(
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model_path,
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pipeline,
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scheduler,
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prompt,
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cfg,
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steps,
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seed,
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eta=eta,
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negative_prompt=negative_prompt,
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batch=batch,
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control=control,
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loopback=loopback,
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tiled_vae=tiled_vae,
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tiles=tiles,
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overlap=overlap,
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stride=stride,
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)
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size = Size(width, height)
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return (device, params, size)
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def border_from_request() -> Border:
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left = get_and_clamp_int(
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request.args,
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"left",
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get_config_value("left"),
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get_config_value("left", "max"),
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get_config_value("left", "min"),
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)
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right = get_and_clamp_int(
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request.args,
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"right",
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get_config_value("right"),
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get_config_value("right", "max"),
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get_config_value("right", "min"),
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)
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top = get_and_clamp_int(
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request.args,
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"top",
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get_config_value("top"),
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get_config_value("top", "max"),
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get_config_value("top", "min"),
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)
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bottom = get_and_clamp_int(
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request.args,
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"bottom",
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get_config_value("bottom"),
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get_config_value("bottom", "max"),
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get_config_value("bottom", "min"),
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)
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return Border(left, right, top, bottom)
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def upscale_from_request() -> UpscaleParams:
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denoise = get_and_clamp_float(
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request.args,
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"denoise",
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get_config_value("denoise"),
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get_config_value("denoise", "max"),
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get_config_value("denoise", "min"),
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)
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scale = get_and_clamp_int(
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request.args,
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"scale",
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get_config_value("scale"),
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get_config_value("scale", "max"),
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get_config_value("scale", "min"),
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)
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outscale = get_and_clamp_int(
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request.args,
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"outscale",
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get_config_value("outscale"),
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get_config_value("outscale", "max"),
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get_config_value("outscale", "min"),
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)
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upscaling = get_from_list(request.args, "upscaling", get_upscaling_models())
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correction = get_from_list(request.args, "correction", get_correction_models())
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faces = get_not_empty(request.args, "faces", "false") == "true"
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face_outscale = get_and_clamp_int(
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request.args,
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"faceOutscale",
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get_config_value("faceOutscale"),
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get_config_value("faceOutscale", "max"),
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get_config_value("faceOutscale", "min"),
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)
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face_strength = get_and_clamp_float(
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request.args,
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"faceStrength",
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get_config_value("faceStrength"),
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get_config_value("faceStrength", "max"),
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get_config_value("faceStrength", "min"),
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)
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upscale_order = request.args.get("upscaleOrder", "correction-first")
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return UpscaleParams(
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upscaling,
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correction_model=correction,
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denoise=denoise,
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faces=faces,
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face_outscale=face_outscale,
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face_strength=face_strength,
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format="onnx",
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outscale=outscale,
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scale=scale,
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upscale_order=upscale_order,
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)
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def highres_from_request() -> HighresParams:
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iterations = get_and_clamp_int(
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request.args,
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"highresIterations",
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get_config_value("highresIterations"),
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get_config_value("highresIterations", "max"),
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get_config_value("highresIterations", "min"),
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)
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method = get_from_list(request.args, "highresMethod", get_highres_methods())
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scale = get_and_clamp_int(
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request.args,
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"highresScale",
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get_config_value("highresScale"),
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get_config_value("highresScale", "max"),
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get_config_value("highresScale", "min"),
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)
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steps = get_and_clamp_int(
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request.args,
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"highresSteps",
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get_config_value("highresSteps"),
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get_config_value("highresSteps", "max"),
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get_config_value("highresSteps", "min"),
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)
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strength = get_and_clamp_float(
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request.args,
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"highresStrength",
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get_config_value("highresStrength"),
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get_config_value("highresStrength", "max"),
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get_config_value("highresStrength", "min"),
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)
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return HighresParams(
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scale,
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steps,
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strength,
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method=method,
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iterations=iterations,
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)
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