512 lines
14 KiB
Python
512 lines
14 KiB
Python
from diffusers import (
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# schedulers
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DDIMScheduler,
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DDPMScheduler,
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DPMSolverMultistepScheduler,
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DPMSolverSinglestepScheduler,
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EulerDiscreteScheduler,
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EulerAncestralDiscreteScheduler,
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HeunDiscreteScheduler,
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KDPM2AncestralDiscreteScheduler,
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KDPM2DiscreteScheduler,
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KarrasVeScheduler,
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LMSDiscreteScheduler,
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PNDMScheduler,
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)
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from flask import Flask, jsonify, request, send_from_directory, url_for
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from flask_cors import CORS
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from flask_executor import Executor
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from glob import glob
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from io import BytesIO
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from PIL import Image
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from onnxruntime import get_available_providers
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from os import makedirs, path, scandir
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from typing import Tuple
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from .image import (
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# mask filters
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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 sources
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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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)
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from .pipeline import (
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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 .upscale import (
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UpscaleParams,
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)
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from .utils import (
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is_debug,
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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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make_output_name,
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safer_join,
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BaseParams,
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Border,
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ServerContext,
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Size,
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)
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import gc
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import json
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import numpy as np
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# pipeline caching
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config_params = {}
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# pipeline params
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platform_providers = {
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'amd': 'DmlExecutionProvider',
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'cpu': 'CPUExecutionProvider',
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'cuda': 'CUDAExecutionProvider',
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'directml': 'DmlExecutionProvider',
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'nvidia': 'CUDAExecutionProvider',
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'rocm': 'ROCMExecutionProvider',
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}
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pipeline_schedulers = {
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'ddim': DDIMScheduler,
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'ddpm': DDPMScheduler,
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'dpm-multi': DPMSolverMultistepScheduler,
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'dpm-single': DPMSolverSinglestepScheduler,
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'euler': EulerDiscreteScheduler,
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'euler-a': EulerAncestralDiscreteScheduler,
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'heun': HeunDiscreteScheduler,
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'k-dpm-2-a': KDPM2AncestralDiscreteScheduler,
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'k-dpm-2': KDPM2DiscreteScheduler,
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'karras-ve': KarrasVeScheduler,
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'lms-discrete': LMSDiscreteScheduler,
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'pndm': PNDMScheduler,
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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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# Available ORT providers
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available_platforms = []
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# loaded from model_path
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diffusion_models = []
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correction_models = []
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upscaling_models = []
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def get_config_value(key: str, subkey: str = 'default'):
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return config_params.get(key).get(subkey)
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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[BaseParams, Size]:
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user = request.remote_addr
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# pipeline stuff
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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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provider = get_from_map(request.args, 'platform',
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platform_providers, get_config_value('platform'))
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scheduler = get_from_map(request.args, 'scheduler',
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pipeline_schedulers, get_config_value('scheduler'))
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# image params
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prompt = get_not_empty(request.args,
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'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, '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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steps = get_and_clamp_int(
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request.args, '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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height = get_and_clamp_int(
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request.args, '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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width = get_and_clamp_int(
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request.args, '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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seed = int(request.args.get('seed', -1))
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if seed == -1:
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seed = np.random.randint(np.iinfo(np.int32).max)
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print("request from %s: %s rounds of %s using %s on %s, %sx%s, %s, %s - %s" %
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(user, steps, scheduler.__name__, model_path, provider, width, height, cfg, seed, prompt))
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params = BaseParams(model_path, provider, scheduler, prompt,
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negative_prompt, cfg, steps, seed)
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size = Size(width, height)
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return (params, size)
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def border_from_request() -> Border:
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left = get_and_clamp_int(request.args, 'left', 0, get_config_value('width', 'max'), 0)
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right = get_and_clamp_int(request.args, 'right', 0, get_config_value('width', 'max'), 0)
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top = get_and_clamp_int(request.args, 'top', 0, get_config_value('height', 'max'), 0)
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bottom = get_and_clamp_int(request.args, 'bottom', 0, get_config_value('height', 'max'), 0)
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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_strength = get_and_clamp_float(
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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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scale=scale,
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outscale=outscale,
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faces=faces,
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platform='onnx',
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denoise=denoise,
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face_strength=face_strength,
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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 = [get_model_name(f) for f in glob(
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path.join(context.model_path, 'diffusion-*'))]
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diffusion_models.extend([
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get_model_name(f) for f in glob(path.join(context.model_path, 'stable-diffusion-*'))])
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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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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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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) as f:
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config_params = json.load(f)
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def load_platforms():
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global available_platforms
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providers = get_available_providers()
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available_platforms = [p for p in platform_providers if (
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platform_providers[p] in providers)]
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print('available acceleration platforms: %s' % (available_platforms))
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context = ServerContext.from_environ()
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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()
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app = Flask(__name__)
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app.config['EXECUTOR_MAX_WORKERS'] = context.num_workers
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app.config['EXECUTOR_PROPAGATE_EXCEPTIONS'] = True
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CORS(app, origins=context.cors_origin)
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executor = Executor(app)
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if is_debug():
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gc.set_debug(gc.DEBUG_STATS)
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# TODO: these two use context
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def get_model_path(model: str):
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return safer_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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@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),
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'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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@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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@app.route('/api/settings/models')
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def list_models():
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return jsonify({
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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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@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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@app.route('/api/settings/params')
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def list_params():
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return jsonify(config_params)
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@app.route('/api/settings/platforms')
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def list_platforms():
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return jsonify(list(available_platforms))
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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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@app.route('/api/img2img', methods=['POST'])
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def img2img():
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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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params, size = pipeline_from_request()
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upscale = upscale_from_request()
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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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output = make_output_name(
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'img2img',
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params,
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size,
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extras=(strength,))
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print("img2img output: %s" % (output))
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source_image.thumbnail((size.width, size.height))
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executor.submit_stored(output, run_img2img_pipeline,
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context, params, output, upscale, source_image, strength)
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return jsonify({
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'output': output,
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'params': params.tojson(),
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'size': upscale.resize(size).tojson(),
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})
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@app.route('/api/txt2img', methods=['POST'])
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def txt2img():
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params, size = pipeline_from_request()
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upscale = upscale_from_request()
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output = make_output_name(
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'txt2img',
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params,
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size)
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print("txt2img output: %s" % (output))
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executor.submit_stored(
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output, run_txt2img_pipeline, context, params, size, output, upscale)
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return jsonify({
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'output': output,
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'params': params.tojson(),
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'size': upscale.resize(size).tojson(),
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})
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@app.route('/api/inpaint', methods=['POST'])
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def inpaint():
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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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mask_file = request.files.get('mask')
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mask_image = Image.open(BytesIO(mask_file.read())).convert('RGB')
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params, size = pipeline_from_request()
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expand = border_from_request()
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upscale = upscale_from_request()
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fill_color = get_not_empty(request.args, 'fillColor', 'white')
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mask_filter = get_from_map(request.args, 'filter', mask_filters, 'none')
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noise_source = get_from_map(
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request.args, 'noise', noise_sources, 'histogram')
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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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output = make_output_name(
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'inpaint',
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params,
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size,
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extras=(
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expand.left,
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expand.right,
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expand.top,
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expand.bottom,
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mask_filter.__name__,
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noise_source.__name__,
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strength,
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fill_color,
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)
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)
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print("inpaint output: %s" % output)
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source_image.thumbnail((size.width, size.height))
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mask_image.thumbnail((size.width, size.height))
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executor.submit_stored(
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output,
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run_inpaint_pipeline,
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context,
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params,
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size,
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output,
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upscale,
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source_image,
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mask_image,
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expand,
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noise_source,
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mask_filter,
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strength,
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fill_color)
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return jsonify({
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'output': output,
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'params': params.tojson(),
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'size': upscale.resize(size.add_border(expand)).tojson(),
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})
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@app.route('/api/upscale', methods=['POST'])
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def upscale():
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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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params, size = pipeline_from_request()
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upscale = upscale_from_request()
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output = make_output_name(
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'upscale',
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params,
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size)
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print("upscale output: %s" % (output))
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source_image.thumbnail((size.width, size.height))
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executor.submit_stored(output, run_upscale_pipeline,
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context, params, size, output, upscale, source_image)
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return jsonify({
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'output': output,
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'params': params.tojson(),
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'size': upscale.resize(size).tojson(),
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})
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@app.route('/api/ready')
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def ready():
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output_file = request.args.get('output', None)
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done = executor.futures.done(output_file)
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if done == True:
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executor.futures.pop(output_file)
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return jsonify({
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'ready': done,
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})
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@app.route('/output/<path:filename>')
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def output(filename: str):
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return send_from_directory(path.join('..', context.output_path), filename, as_attachment=False)
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