819 lines
22 KiB
Python
819 lines
22 KiB
Python
import gc
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from functools import cmp_to_key
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from glob import glob
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from io import BytesIO
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from logging import getLogger
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from os import makedirs, path
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from typing import Dict, List, Tuple, Union
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import numpy as np
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import torch
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import yaml
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from diffusers.utils.logging import disable_progress_bar
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from flask import Flask, jsonify, make_response, request, send_from_directory, url_for
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from flask_cors import CORS
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from huggingface_hub.utils.tqdm import disable_progress_bars
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from jsonschema import validate
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from onnxruntime import get_available_providers
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from PIL import Image
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from .chain import (
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ChainPipeline,
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blend_img2img,
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blend_inpaint,
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correct_codeformer,
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correct_gfpgan,
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persist_disk,
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persist_s3,
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reduce_crop,
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reduce_thumbnail,
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source_noise,
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source_txt2img,
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upscale_outpaint,
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upscale_resrgan,
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upscale_stable_diffusion,
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)
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from .diffusion.load import pipeline_schedulers
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from .diffusion.run import (
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run_blend_pipeline,
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run_img2img_pipeline,
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run_inpaint_pipeline,
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run_txt2img_pipeline,
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run_upscale_pipeline,
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)
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from .image import ( # mask filters; noise sources
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mask_filter_gaussian_multiply,
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mask_filter_gaussian_screen,
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mask_filter_none,
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noise_source_fill_edge,
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noise_source_fill_mask,
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noise_source_gaussian,
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noise_source_histogram,
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noise_source_normal,
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noise_source_uniform,
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valid_image,
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)
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from .output import json_params, make_output_name
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from .params import (
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Border,
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DeviceParams,
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ImageParams,
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Size,
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StageParams,
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TileOrder,
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UpscaleParams,
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)
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from .server.device_pool import DevicePoolExecutor
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from .server.hacks import apply_patches
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from .utils import (
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ServerContext,
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base_join,
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get_and_clamp_float,
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get_and_clamp_int,
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get_from_list,
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get_from_map,
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get_not_empty,
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get_size,
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is_debug,
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)
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logger = getLogger(__name__)
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# config caching
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config_params: Dict[str, Dict[str, Union[float, int, str]]] = {}
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# pipeline params
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platform_providers = {
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"cpu": "CPUExecutionProvider",
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"cuda": "CUDAExecutionProvider",
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"directml": "DmlExecutionProvider",
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"rocm": "ROCMExecutionProvider",
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}
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noise_sources = {
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"fill-edge": noise_source_fill_edge,
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"fill-mask": noise_source_fill_mask,
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"gaussian": noise_source_gaussian,
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"histogram": noise_source_histogram,
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"normal": noise_source_normal,
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"uniform": noise_source_uniform,
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}
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mask_filters = {
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"none": mask_filter_none,
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"gaussian-multiply": mask_filter_gaussian_multiply,
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"gaussian-screen": mask_filter_gaussian_screen,
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}
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chain_stages = {
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"blend-img2img": blend_img2img,
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"blend-inpaint": blend_inpaint,
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"correct-codeformer": correct_codeformer,
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"correct-gfpgan": correct_gfpgan,
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"persist-disk": persist_disk,
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"persist-s3": persist_s3,
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"reduce-crop": reduce_crop,
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"reduce-thumbnail": reduce_thumbnail,
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"source-noise": source_noise,
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"source-txt2img": source_txt2img,
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"upscale-outpaint": upscale_outpaint,
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"upscale-resrgan": upscale_resrgan,
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"upscale-stable-diffusion": upscale_stable_diffusion,
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}
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# Available ORT providers
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available_platforms: List[DeviceParams] = []
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# loaded from model_path
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diffusion_models: List[str] = []
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correction_models: List[str] = []
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upscaling_models: List[str] = []
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def get_config_value(key: str, subkey: str = "default", default=None):
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return config_params.get(key, {}).get(subkey, default)
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def url_from_rule(rule) -> str:
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options = {}
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for arg in rule.arguments:
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options[arg] = ":%s" % (arg)
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return url_for(rule.endpoint, **options)
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def pipeline_from_request() -> 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 available_platforms:
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if platform.device == device_name:
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device = platform
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# pipeline stuff
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lpw = get_not_empty(request.args, "lpw", "false") == "true"
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model = get_not_empty(request.args, "model", get_config_value("model"))
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model_path = get_model_path(model)
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scheduler = get_from_map(
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request.args, "scheduler", pipeline_schedulers, get_config_value("scheduler")
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)
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# image params
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prompt = get_not_empty(request.args, "prompt", get_config_value("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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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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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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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 rounds of %s using %s on %s, %sx%s, %s, %s - %s",
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user,
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steps,
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scheduler.__name__,
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model_path,
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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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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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lpw=lpw,
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negative_prompt=negative_prompt,
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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, "left", 0, get_config_value("width", "max"), 0
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)
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right = get_and_clamp_int(
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request.args, "right", 0, get_config_value("width", "max"), 0
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)
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top = get_and_clamp_int(
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request.args, "top", 0, get_config_value("height", "max"), 0
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)
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bottom = get_and_clamp_int(
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request.args, "bottom", 0, get_config_value("height", "max"), 0
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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(request.args, "denoise", 0.5, 1.0, 0.0)
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scale = get_and_clamp_int(request.args, "scale", 1, 4, 1)
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outscale = get_and_clamp_int(request.args, "outscale", 1, 4, 1)
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upscaling = get_from_list(request.args, "upscaling", upscaling_models)
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correction = get_from_list(request.args, "correction", 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(request.args, "faceOutscale", 1, 4, 1)
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face_strength = get_and_clamp_float(request.args, "faceStrength", 0.5, 1.0, 0.0)
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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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)
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def check_paths(context: ServerContext):
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if not path.exists(context.model_path):
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raise RuntimeError("model path must exist")
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if not path.exists(context.output_path):
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makedirs(context.output_path)
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def get_model_name(model: str) -> str:
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base = path.basename(model)
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(file, _ext) = path.splitext(base)
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return file
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def load_models(context: ServerContext):
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global diffusion_models
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global correction_models
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global upscaling_models
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diffusion_models = [
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get_model_name(f) for f in glob(path.join(context.model_path, "diffusion-*"))
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]
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diffusion_models.extend(
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[
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get_model_name(f)
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for f in glob(path.join(context.model_path, "stable-diffusion-*"))
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]
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)
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diffusion_models = list(set(diffusion_models))
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diffusion_models.sort()
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correction_models = [
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get_model_name(f) for f in glob(path.join(context.model_path, "correction-*"))
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]
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correction_models = list(set(correction_models))
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correction_models.sort()
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upscaling_models = [
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get_model_name(f) for f in glob(path.join(context.model_path, "upscaling-*"))
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]
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upscaling_models = list(set(upscaling_models))
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upscaling_models.sort()
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def load_params(context: ServerContext):
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global config_params
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params_file = path.join(context.params_path, "params.json")
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with open(params_file, "r") as f:
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config_params = yaml.safe_load(f)
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if "platform" in config_params and context.default_platform is not None:
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logger.info(
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"Overriding default platform from environment: %s",
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context.default_platform,
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)
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config_platform = config_params.get("platform", {})
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config_platform["default"] = context.default_platform
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def load_platforms(context: ServerContext):
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global available_platforms
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providers = list(get_available_providers())
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for potential in platform_providers:
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if (
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platform_providers[potential] in providers
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and potential not in context.block_platforms
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):
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if potential == "cuda":
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for i in range(torch.cuda.device_count()):
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available_platforms.append(
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DeviceParams(
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potential,
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platform_providers[potential],
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{
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"device_id": i,
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},
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)
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)
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else:
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available_platforms.append(
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DeviceParams(potential, platform_providers[potential])
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)
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if context.any_platform:
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# the platform should be ignored when the job is scheduled, but set to CPU just in case
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available_platforms.append(DeviceParams("any", platform_providers["cpu"]))
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# make sure CPU is last on the list
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def any_first_cpu_last(a: DeviceParams, b: DeviceParams):
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if a.device == b.device:
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return 0
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# any should be first, if it's available
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if a.device == "any":
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return -1
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# cpu should be last, if it's available
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if a.device == "cpu":
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return 1
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return -1
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available_platforms = sorted(
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available_platforms, key=cmp_to_key(any_first_cpu_last)
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)
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logger.info(
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"available acceleration platforms: %s",
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", ".join([str(p) for p in available_platforms]),
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)
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context = ServerContext.from_environ()
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apply_patches(context)
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check_paths(context)
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load_models(context)
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load_params(context)
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load_platforms(context)
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if not context.show_progress:
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disable_progress_bar()
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disable_progress_bars()
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app = Flask(__name__)
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CORS(app, origins=context.cors_origin)
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# any is a fake device, should not be in the pool
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executor = DevicePoolExecutor([p for p in available_platforms if p.device != "any"])
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if is_debug():
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gc.set_debug(gc.DEBUG_STATS)
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def ready_reply(ready: bool, progress: int = 0):
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return jsonify(
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{
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"progress": progress,
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"ready": ready,
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}
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)
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def error_reply(err: str):
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response = make_response(
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jsonify(
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{
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"error": err,
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}
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)
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)
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response.status_code = 400
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return response
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def get_model_path(model: str):
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return base_join(context.model_path, model)
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def serve_bundle_file(filename="index.html"):
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return send_from_directory(path.join("..", context.bundle_path), filename)
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# routes
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@app.route("/")
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def index():
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return serve_bundle_file()
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@app.route("/<path:filename>")
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def index_path(filename):
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return serve_bundle_file(filename)
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|
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@app.route("/api")
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def introspect():
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return {
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"name": "onnx-web",
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"routes": [
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{"path": url_from_rule(rule), "methods": list(rule.methods).sort()}
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for rule in app.url_map.iter_rules()
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],
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}
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|
|
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@app.route("/api/settings/masks")
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def list_mask_filters():
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return jsonify(list(mask_filters.keys()))
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|
|
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@app.route("/api/settings/models")
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def list_models():
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return jsonify(
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{
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"diffusion": diffusion_models,
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"correction": correction_models,
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"upscaling": upscaling_models,
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}
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)
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|
|
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@app.route("/api/settings/noises")
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def list_noise_sources():
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return jsonify(list(noise_sources.keys()))
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|
|
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@app.route("/api/settings/params")
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def list_params():
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return jsonify(config_params)
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|
|
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@app.route("/api/settings/platforms")
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def list_platforms():
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return jsonify([p.device for p in available_platforms])
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|
|
|
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@app.route("/api/settings/schedulers")
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def list_schedulers():
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return jsonify(list(pipeline_schedulers.keys()))
|
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|
|
|
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@app.route("/api/img2img", methods=["POST"])
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def img2img():
|
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if "source" not in request.files:
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return error_reply("source image is required")
|
|
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source_file = request.files.get("source")
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source_image = Image.open(BytesIO(source_file.read())).convert("RGB")
|
|
|
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device, params, size = pipeline_from_request()
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upscale = upscale_from_request()
|
|
|
|
strength = get_and_clamp_float(
|
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request.args,
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|
"strength",
|
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get_config_value("strength"),
|
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get_config_value("strength", "max"),
|
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get_config_value("strength", "min"),
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)
|
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|
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output = make_output_name(context, "img2img", params, size, extras=(strength,))
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logger.info("img2img job queued for: %s", output)
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|
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source_image = valid_image(source_image, min_dims=size, max_dims=size)
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executor.submit(
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output,
|
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run_img2img_pipeline,
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context,
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params,
|
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output,
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upscale,
|
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source_image,
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strength,
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needs_device=device,
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)
|
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return jsonify(json_params(output, params, size, upscale=upscale))
|
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|
|
|
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@app.route("/api/txt2img", methods=["POST"])
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def txt2img():
|
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device, params, size = pipeline_from_request()
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|
upscale = upscale_from_request()
|
|
|
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output = make_output_name(context, "txt2img", params, size)
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logger.info("txt2img job queued for: %s", output)
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|
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executor.submit(
|
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output,
|
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run_txt2img_pipeline,
|
|
context,
|
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params,
|
|
size,
|
|
output,
|
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upscale,
|
|
needs_device=device,
|
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)
|
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|
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return jsonify(json_params(output, params, size, upscale=upscale))
|
|
|
|
|
|
@app.route("/api/inpaint", methods=["POST"])
|
|
def inpaint():
|
|
if "source" not in request.files:
|
|
return error_reply("source image is required")
|
|
|
|
if "mask" not in request.files:
|
|
return error_reply("mask image is required")
|
|
|
|
source_file = request.files.get("source")
|
|
source_image = Image.open(BytesIO(source_file.read())).convert("RGB")
|
|
|
|
mask_file = request.files.get("mask")
|
|
mask_image = Image.open(BytesIO(mask_file.read())).convert("RGB")
|
|
|
|
device, params, size = pipeline_from_request()
|
|
expand = border_from_request()
|
|
upscale = upscale_from_request()
|
|
|
|
fill_color = get_not_empty(request.args, "fillColor", "white")
|
|
mask_filter = get_from_map(request.args, "filter", mask_filters, "none")
|
|
noise_source = get_from_map(request.args, "noise", noise_sources, "histogram")
|
|
strength = get_and_clamp_float(
|
|
request.args,
|
|
"strength",
|
|
get_config_value("strength"),
|
|
get_config_value("strength", "max"),
|
|
get_config_value("strength", "min"),
|
|
)
|
|
tile_order = get_from_list(
|
|
request.args, "tileOrder", [TileOrder.grid, TileOrder.kernel, TileOrder.spiral]
|
|
)
|
|
|
|
output = make_output_name(
|
|
context,
|
|
"inpaint",
|
|
params,
|
|
size,
|
|
extras=(
|
|
expand.left,
|
|
expand.right,
|
|
expand.top,
|
|
expand.bottom,
|
|
mask_filter.__name__,
|
|
noise_source.__name__,
|
|
strength,
|
|
fill_color,
|
|
tile_order,
|
|
),
|
|
)
|
|
logger.info("inpaint job queued for: %s", output)
|
|
|
|
source_image = valid_image(source_image, min_dims=size, max_dims=size)
|
|
mask_image = valid_image(mask_image, min_dims=size, max_dims=size)
|
|
executor.submit(
|
|
output,
|
|
run_inpaint_pipeline,
|
|
context,
|
|
params,
|
|
size,
|
|
output,
|
|
upscale,
|
|
source_image,
|
|
mask_image,
|
|
expand,
|
|
noise_source,
|
|
mask_filter,
|
|
strength,
|
|
fill_color,
|
|
tile_order,
|
|
needs_device=device,
|
|
)
|
|
|
|
return jsonify(json_params(output, params, size, upscale=upscale, border=expand))
|
|
|
|
|
|
@app.route("/api/upscale", methods=["POST"])
|
|
def upscale():
|
|
if "source" not in request.files:
|
|
return error_reply("source image is required")
|
|
|
|
source_file = request.files.get("source")
|
|
source_image = Image.open(BytesIO(source_file.read())).convert("RGB")
|
|
|
|
device, params, size = pipeline_from_request()
|
|
upscale = upscale_from_request()
|
|
|
|
output = make_output_name(context, "upscale", params, size)
|
|
logger.info("upscale job queued for: %s", output)
|
|
|
|
source_image = valid_image(source_image, min_dims=size, max_dims=size)
|
|
executor.submit(
|
|
output,
|
|
run_upscale_pipeline,
|
|
context,
|
|
params,
|
|
size,
|
|
output,
|
|
upscale,
|
|
source_image,
|
|
needs_device=device,
|
|
)
|
|
|
|
return jsonify(json_params(output, params, size, upscale=upscale))
|
|
|
|
|
|
@app.route("/api/chain", methods=["POST"])
|
|
def chain():
|
|
logger.debug(
|
|
"chain pipeline request: %s, %s", request.form.keys(), request.files.keys()
|
|
)
|
|
body = request.form.get("chain") or request.files.get("chain")
|
|
if body is None:
|
|
return error_reply("chain pipeline must have a body")
|
|
|
|
data = yaml.safe_load(body)
|
|
with open("./schemas/chain.yaml", "r") as f:
|
|
schema = yaml.safe_load(f.read())
|
|
|
|
logger.debug("validating chain request: %s against %s", data, schema)
|
|
validate(data, schema)
|
|
|
|
# get defaults from the regular parameters
|
|
device, params, size = pipeline_from_request()
|
|
output = make_output_name(context, "chain", params, size)
|
|
|
|
pipeline = ChainPipeline()
|
|
for stage_data in data.get("stages", []):
|
|
callback = chain_stages[stage_data.get("type")]
|
|
kwargs = stage_data.get("params", {})
|
|
logger.info("request stage: %s, %s", callback.__name__, kwargs)
|
|
|
|
stage = StageParams(
|
|
stage_data.get("name", callback.__name__),
|
|
tile_size=get_size(kwargs.get("tile_size")),
|
|
outscale=get_and_clamp_int(kwargs, "outscale", 1, 4),
|
|
)
|
|
|
|
if "border" in kwargs:
|
|
border = Border.even(int(kwargs.get("border")))
|
|
kwargs["border"] = border
|
|
|
|
if "upscale" in kwargs:
|
|
upscale = UpscaleParams(kwargs.get("upscale"))
|
|
kwargs["upscale"] = upscale
|
|
|
|
stage_source_name = "source:%s" % (stage.name)
|
|
stage_mask_name = "mask:%s" % (stage.name)
|
|
|
|
if stage_source_name in request.files:
|
|
logger.debug(
|
|
"loading source image %s for pipeline stage %s",
|
|
stage_source_name,
|
|
stage.name,
|
|
)
|
|
source_file = request.files.get(stage_source_name)
|
|
source_image = Image.open(BytesIO(source_file.read())).convert("RGB")
|
|
source_image = valid_image(source_image, max_dims=(size.width, size.height))
|
|
kwargs["source_image"] = source_image
|
|
|
|
if stage_mask_name in request.files:
|
|
logger.debug(
|
|
"loading mask image %s for pipeline stage %s",
|
|
stage_mask_name,
|
|
stage.name,
|
|
)
|
|
mask_file = request.files.get(stage_mask_name)
|
|
mask_image = Image.open(BytesIO(mask_file.read())).convert("RGB")
|
|
mask_image = valid_image(mask_image, max_dims=(size.width, size.height))
|
|
kwargs["mask_image"] = mask_image
|
|
|
|
pipeline.append((callback, stage, kwargs))
|
|
|
|
logger.info("running chain pipeline with %s stages", len(pipeline.stages))
|
|
|
|
# build and run chain pipeline
|
|
empty_source = Image.new("RGB", (size.width, size.height))
|
|
executor.submit(
|
|
output,
|
|
pipeline,
|
|
context,
|
|
params,
|
|
empty_source,
|
|
output=output,
|
|
size=size,
|
|
needs_device=device,
|
|
)
|
|
|
|
return jsonify(json_params(output, params, size))
|
|
|
|
|
|
@app.route("/api/blend", methods=["POST"])
|
|
def blend():
|
|
if "mask" not in request.files:
|
|
return error_reply("mask image is required")
|
|
|
|
mask_file = request.files.get("mask")
|
|
mask = Image.open(BytesIO(mask_file.read())).convert("RGBA")
|
|
mask = valid_image(mask)
|
|
|
|
max_sources = 2
|
|
sources = []
|
|
|
|
for i in range(max_sources):
|
|
source_file = request.files.get("source:%s" % (i))
|
|
source = Image.open(BytesIO(source_file.read())).convert("RGBA")
|
|
source = valid_image(source, mask.size, mask.size)
|
|
sources.append(source)
|
|
|
|
device, params, size = pipeline_from_request()
|
|
upscale = upscale_from_request()
|
|
|
|
output = make_output_name(context, "upscale", params, size)
|
|
logger.info("upscale job queued for: %s", output)
|
|
|
|
executor.submit(
|
|
output,
|
|
run_blend_pipeline,
|
|
context,
|
|
params,
|
|
size,
|
|
output,
|
|
upscale,
|
|
sources,
|
|
mask,
|
|
needs_device=device,
|
|
)
|
|
|
|
return jsonify(json_params(output, params, size, upscale=upscale))
|
|
|
|
|
|
@app.route("/api/cancel", methods=["PUT"])
|
|
def cancel():
|
|
output_file = request.args.get("output", None)
|
|
|
|
cancel = executor.cancel(output_file)
|
|
|
|
return ready_reply(cancel)
|
|
|
|
|
|
@app.route("/api/ready")
|
|
def ready():
|
|
output_file = request.args.get("output", None)
|
|
|
|
done, progress = executor.done(output_file)
|
|
|
|
if done is None:
|
|
file = base_join(context.output_path, output_file)
|
|
if path.exists(file):
|
|
return ready_reply(True)
|
|
|
|
return ready_reply(done, progress=progress)
|
|
|
|
|
|
@app.route("/api/status")
|
|
def status():
|
|
return jsonify(executor.status())
|
|
|
|
|
|
@app.route("/output/<path:filename>")
|
|
def output(filename: str):
|
|
return send_from_directory(
|
|
path.join("..", context.output_path), filename, as_attachment=False
|
|
)
|