feat(api): remove Flask app from global scope
This commit is contained in:
parent
943281feb5
commit
06c74a7a96
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@ -1,6 +1,7 @@
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.coverage
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coverage.xml
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*.log
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*.swp
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*.pyc
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@ -25,4 +25,4 @@ python3 -m onnx_web.convert \
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--token=${HF_TOKEN:-}
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echo "Launching API server..."
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flask --app=onnx_web.serve run --host=0.0.0.0
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flask --app='onnx_web.main:main()' run --host=0.0.0.0
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@ -24,4 +24,4 @@ python3 -m onnx_web.convert \
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--token=${HF_TOKEN:-}
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echo "Launching API server..."
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flask --app=onnx_web.serve run --host=0.0.0.0
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flask --app='onnx_web.main:main()' run --host=0.0.0.0
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@ -1,5 +1,10 @@
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from . import logging
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from .chain import correct_gfpgan, upscale_resrgan, upscale_stable_diffusion
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from .chain import (
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correct_codeformer,
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correct_gfpgan,
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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 get_latents_from_seed, load_pipeline, optimize_pipeline
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from .diffusion.run import (
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run_blend_pipeline,
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@ -13,3 +13,20 @@ from .source_txt2img import source_txt2img
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from .upscale_outpaint import upscale_outpaint
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from .upscale_resrgan import upscale_resrgan
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from .upscale_stable_diffusion import upscale_stable_diffusion
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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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"blend-mask": blend_mask,
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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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@ -4,9 +4,11 @@ from typing import Any, Optional, Tuple
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import numpy as np
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from diffusers import (
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DiffusionPipeline,
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OnnxRuntimeModel,
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StableDiffusionPipeline,
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DDIMScheduler,
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DDPMScheduler,
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DiffusionPipeline,
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DPMSolverMultistepScheduler,
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DPMSolverSinglestepScheduler,
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EulerAncestralDiscreteScheduler,
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@ -17,15 +19,13 @@ from diffusers import (
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KDPM2AncestralDiscreteScheduler,
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KDPM2DiscreteScheduler,
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LMSDiscreteScheduler,
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OnnxRuntimeModel,
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PNDMScheduler,
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StableDiffusionPipeline,
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)
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try:
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from diffusers import DEISMultistepScheduler
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except ImportError:
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from .stub_scheduler import StubScheduler as DEISMultistepScheduler
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from ..diffusion.stub_scheduler import StubScheduler as DEISMultistepScheduler
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from ..params import DeviceParams, Size
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from ..server import ServerContext
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@ -54,6 +54,10 @@ pipeline_schedulers = {
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}
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def get_pipeline_schedulers():
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return pipeline_schedulers
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def get_scheduler_name(scheduler: Any) -> Optional[str]:
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for k, v in pipeline_schedulers.items():
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if scheduler == v or scheduler == v.__name__:
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@ -137,13 +141,14 @@ def load_pipeline(
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server: ServerContext,
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pipeline: DiffusionPipeline,
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model: str,
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scheduler_type: Any,
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scheduler_name: str,
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device: DeviceParams,
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lpw: bool,
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inversion: Optional[str],
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):
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pipe_key = (pipeline, model, device.device, device.provider, lpw, inversion)
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scheduler_key = (scheduler_type, model)
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scheduler_key = (scheduler_name, model)
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scheduler_type = get_pipeline_schedulers()[scheduler_name]
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cache_pipe = server.cache.get("diffusion", pipe_key)
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@ -0,0 +1,53 @@
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import gc
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from diffusers.utils.logging import disable_progress_bar
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from flask import Flask
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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 .server.api import register_api_routes
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from .server.static import register_static_routes
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from .server.config import get_available_platforms, load_models, load_params, load_platforms
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from .server.utils import check_paths
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from .server.context import ServerContext
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from .server.hacks import apply_patches
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from .utils import (
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is_debug,
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)
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from .worker import DevicePoolExecutor
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def main():
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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 is_debug():
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gc.set_debug(gc.DEBUG_STATS)
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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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pool = DevicePoolExecutor([p for p in get_available_platforms() if p.device != "any"])
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# register routes
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register_static_routes(app, context, pool)
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register_api_routes(app, context, pool)
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return app #, context, pool
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if __name__ == "__main__":
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# app, context, pool = main()
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app = main()
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app.run("0.0.0.0", 5000, debug=is_debug())
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# pool.join()
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@ -8,7 +8,6 @@ from typing import Any, List, Optional, Tuple
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from PIL import Image
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from .diffusion.load import get_scheduler_name
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from .params import Border, ImageParams, Param, Size, UpscaleParams
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from .server import ServerContext
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from .utils import base_join
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@ -44,7 +43,7 @@ def json_params(
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}
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json["params"]["model"] = path.basename(params.model)
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json["params"]["scheduler"] = get_scheduler_name(params.scheduler)
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json["params"]["scheduler"] = params.scheduler
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if border is not None:
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json["border"] = border.tojson()
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@ -71,7 +70,7 @@ def make_output_name(
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hash_value(sha, mode)
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hash_value(sha, params.model)
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hash_value(sha, params.scheduler.__name__)
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hash_value(sha, params.scheduler)
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hash_value(sha, params.prompt)
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hash_value(sha, params.negative_prompt)
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hash_value(sha, params.cfg)
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@ -148,7 +148,7 @@ class ImageParams:
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def __init__(
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self,
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model: str,
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scheduler: Any,
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scheduler: str,
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prompt: str,
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cfg: float,
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steps: int,
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@ -174,7 +174,7 @@ class ImageParams:
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def tojson(self) -> Dict[str, Optional[Param]]:
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return {
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"model": self.model,
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"scheduler": self.scheduler.__name__,
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"scheduler": self.scheduler,
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"prompt": self.prompt,
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"negative_prompt": self.negative_prompt,
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"cfg": self.cfg,
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@ -1,881 +0,0 @@
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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 import ServerContext, apply_patches
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from .transformers import run_txt2txt_pipeline
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from .utils import (
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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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from .worker import DevicePoolExecutor
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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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correction_models: List[str] = []
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diffusion_models: List[str] = []
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inversion_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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inversion = request.args.get("inversion", None)
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inversion_path = None
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if inversion is not None and inversion.strip() != "":
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inversion_path = get_model_path(inversion)
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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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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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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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eta=eta,
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lpw=lpw,
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negative_prompt=negative_prompt,
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batch=batch,
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inversion=inversion_path,
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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)
|
||||
upscaling = get_from_list(request.args, "upscaling", upscaling_models)
|
||||
correction = get_from_list(request.args, "correction", correction_models)
|
||||
faces = get_not_empty(request.args, "faces", "false") == "true"
|
||||
face_outscale = get_and_clamp_int(request.args, "faceOutscale", 1, 4, 1)
|
||||
face_strength = get_and_clamp_float(request.args, "faceStrength", 0.5, 1.0, 0.0)
|
||||
upscale_order = request.args.get("upscaleOrder", "correction-first")
|
||||
|
||||
return UpscaleParams(
|
||||
upscaling,
|
||||
correction_model=correction,
|
||||
denoise=denoise,
|
||||
faces=faces,
|
||||
face_outscale=face_outscale,
|
||||
face_strength=face_strength,
|
||||
format="onnx",
|
||||
outscale=outscale,
|
||||
scale=scale,
|
||||
upscale_order=upscale_order,
|
||||
)
|
||||
|
||||
|
||||
def check_paths(context: ServerContext) -> None:
|
||||
if not path.exists(context.model_path):
|
||||
raise RuntimeError("model path must exist")
|
||||
|
||||
if not path.exists(context.output_path):
|
||||
makedirs(context.output_path)
|
||||
|
||||
|
||||
def get_model_name(model: str) -> str:
|
||||
base = path.basename(model)
|
||||
(file, _ext) = path.splitext(base)
|
||||
return file
|
||||
|
||||
|
||||
def load_models(context: ServerContext) -> None:
|
||||
global correction_models
|
||||
global diffusion_models
|
||||
global inversion_models
|
||||
global upscaling_models
|
||||
|
||||
diffusion_models = [
|
||||
get_model_name(f) for f in glob(path.join(context.model_path, "diffusion-*"))
|
||||
]
|
||||
diffusion_models.extend(
|
||||
[
|
||||
get_model_name(f)
|
||||
for f in glob(path.join(context.model_path, "stable-diffusion-*"))
|
||||
]
|
||||
)
|
||||
diffusion_models = list(set(diffusion_models))
|
||||
diffusion_models.sort()
|
||||
|
||||
correction_models = [
|
||||
get_model_name(f) for f in glob(path.join(context.model_path, "correction-*"))
|
||||
]
|
||||
correction_models = list(set(correction_models))
|
||||
correction_models.sort()
|
||||
|
||||
inversion_models = [
|
||||
get_model_name(f) for f in glob(path.join(context.model_path, "inversion-*"))
|
||||
]
|
||||
inversion_models = list(set(inversion_models))
|
||||
inversion_models.sort()
|
||||
|
||||
upscaling_models = [
|
||||
get_model_name(f) for f in glob(path.join(context.model_path, "upscaling-*"))
|
||||
]
|
||||
upscaling_models = list(set(upscaling_models))
|
||||
upscaling_models.sort()
|
||||
|
||||
|
||||
def load_params(context: ServerContext) -> None:
|
||||
global config_params
|
||||
params_file = path.join(context.params_path, "params.json")
|
||||
with open(params_file, "r") as f:
|
||||
config_params = yaml.safe_load(f)
|
||||
|
||||
if "platform" in config_params and context.default_platform is not None:
|
||||
logger.info(
|
||||
"Overriding default platform from environment: %s",
|
||||
context.default_platform,
|
||||
)
|
||||
config_platform = config_params.get("platform", {})
|
||||
config_platform["default"] = context.default_platform
|
||||
|
||||
|
||||
def load_platforms(context: ServerContext) -> None:
|
||||
global available_platforms
|
||||
|
||||
providers = list(get_available_providers())
|
||||
|
||||
for potential in platform_providers:
|
||||
if (
|
||||
platform_providers[potential] in providers
|
||||
and potential not in context.block_platforms
|
||||
):
|
||||
if potential == "cuda":
|
||||
for i in range(torch.cuda.device_count()):
|
||||
available_platforms.append(
|
||||
DeviceParams(
|
||||
potential,
|
||||
platform_providers[potential],
|
||||
{
|
||||
"device_id": i,
|
||||
},
|
||||
context.optimizations,
|
||||
)
|
||||
)
|
||||
else:
|
||||
available_platforms.append(
|
||||
DeviceParams(
|
||||
potential,
|
||||
platform_providers[potential],
|
||||
None,
|
||||
context.optimizations,
|
||||
)
|
||||
)
|
||||
|
||||
if context.any_platform:
|
||||
# the platform should be ignored when the job is scheduled, but set to CPU just in case
|
||||
available_platforms.append(
|
||||
DeviceParams(
|
||||
"any",
|
||||
platform_providers["cpu"],
|
||||
None,
|
||||
context.optimizations,
|
||||
)
|
||||
)
|
||||
|
||||
# make sure CPU is last on the list
|
||||
def any_first_cpu_last(a: DeviceParams, b: DeviceParams):
|
||||
if a.device == b.device:
|
||||
return 0
|
||||
|
||||
# any should be first, if it's available
|
||||
if a.device == "any":
|
||||
return -1
|
||||
|
||||
# cpu should be last, if it's available
|
||||
if a.device == "cpu":
|
||||
return 1
|
||||
|
||||
return -1
|
||||
|
||||
available_platforms = sorted(
|
||||
available_platforms, key=cmp_to_key(any_first_cpu_last)
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"available acceleration platforms: %s",
|
||||
", ".join([str(p) for p in available_platforms]),
|
||||
)
|
||||
|
||||
|
||||
context = ServerContext.from_environ()
|
||||
apply_patches(context)
|
||||
check_paths(context)
|
||||
load_models(context)
|
||||
load_params(context)
|
||||
load_platforms(context)
|
||||
|
||||
if not context.show_progress:
|
||||
disable_progress_bar()
|
||||
disable_progress_bars()
|
||||
|
||||
app = Flask(__name__)
|
||||
CORS(app, origins=context.cors_origin)
|
||||
|
||||
# any is a fake device, should not be in the pool
|
||||
executor = DevicePoolExecutor([p for p in available_platforms if p.device != "any"])
|
||||
|
||||
if is_debug():
|
||||
gc.set_debug(gc.DEBUG_STATS)
|
||||
|
||||
|
||||
def ready_reply(ready: bool, progress: int = 0):
|
||||
return jsonify(
|
||||
{
|
||||
"progress": progress,
|
||||
"ready": ready,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def error_reply(err: str):
|
||||
response = make_response(
|
||||
jsonify(
|
||||
{
|
||||
"error": err,
|
||||
}
|
||||
)
|
||||
)
|
||||
response.status_code = 400
|
||||
return response
|
||||
|
||||
|
||||
def get_model_path(model: str):
|
||||
return base_join(context.model_path, model)
|
||||
|
||||
|
||||
def serve_bundle_file(filename="index.html"):
|
||||
return send_from_directory(path.join("..", context.bundle_path), filename)
|
||||
|
||||
|
||||
# routes
|
||||
|
||||
|
||||
@app.route("/")
|
||||
def index():
|
||||
return serve_bundle_file()
|
||||
|
||||
|
||||
@app.route("/<path:filename>")
|
||||
def index_path(filename):
|
||||
return serve_bundle_file(filename)
|
||||
|
||||
|
||||
@app.route("/api")
|
||||
def introspect():
|
||||
return {
|
||||
"name": "onnx-web",
|
||||
"routes": [
|
||||
{"path": url_from_rule(rule), "methods": list(rule.methods).sort()}
|
||||
for rule in app.url_map.iter_rules()
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
@app.route("/api/settings/masks")
|
||||
def list_mask_filters():
|
||||
return jsonify(list(mask_filters.keys()))
|
||||
|
||||
|
||||
@app.route("/api/settings/models")
|
||||
def list_models():
|
||||
return jsonify(
|
||||
{
|
||||
"correction": correction_models,
|
||||
"diffusion": diffusion_models,
|
||||
"inversion": inversion_models,
|
||||
"upscaling": upscaling_models,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
@app.route("/api/settings/noises")
|
||||
def list_noise_sources():
|
||||
return jsonify(list(noise_sources.keys()))
|
||||
|
||||
|
||||
@app.route("/api/settings/params")
|
||||
def list_params():
|
||||
return jsonify(config_params)
|
||||
|
||||
|
||||
@app.route("/api/settings/platforms")
|
||||
def list_platforms():
|
||||
return jsonify([p.device for p in available_platforms])
|
||||
|
||||
|
||||
@app.route("/api/settings/schedulers")
|
||||
def list_schedulers():
|
||||
return jsonify(list(pipeline_schedulers.keys()))
|
||||
|
||||
|
||||
@app.route("/api/img2img", methods=["POST"])
|
||||
def img2img():
|
||||
if "source" not in request.files:
|
||||
return error_reply("source image is required")
|
||||
|
||||
source_file = request.files.get("source")
|
||||
source = Image.open(BytesIO(source_file.read())).convert("RGB")
|
||||
|
||||
device, params, size = pipeline_from_request()
|
||||
upscale = upscale_from_request()
|
||||
|
||||
strength = get_and_clamp_float(
|
||||
request.args,
|
||||
"strength",
|
||||
get_config_value("strength"),
|
||||
get_config_value("strength", "max"),
|
||||
get_config_value("strength", "min"),
|
||||
)
|
||||
|
||||
output = make_output_name(context, "img2img", params, size, extras=(strength,))
|
||||
job_name = output[0]
|
||||
logger.info("img2img job queued for: %s", job_name)
|
||||
|
||||
source = valid_image(source, min_dims=size, max_dims=size)
|
||||
executor.submit(
|
||||
job_name,
|
||||
run_img2img_pipeline,
|
||||
context,
|
||||
params,
|
||||
output,
|
||||
upscale,
|
||||
source,
|
||||
strength,
|
||||
needs_device=device,
|
||||
)
|
||||
|
||||
return jsonify(json_params(output, params, size, upscale=upscale))
|
||||
|
||||
|
||||
@app.route("/api/txt2img", methods=["POST"])
|
||||
def txt2img():
|
||||
device, params, size = pipeline_from_request()
|
||||
upscale = upscale_from_request()
|
||||
|
||||
output = make_output_name(context, "txt2img", params, size)
|
||||
job_name = output[0]
|
||||
logger.info("txt2img job queued for: %s", job_name)
|
||||
|
||||
executor.submit(
|
||||
job_name,
|
||||
run_txt2img_pipeline,
|
||||
context,
|
||||
params,
|
||||
size,
|
||||
output,
|
||||
upscale,
|
||||
needs_device=device,
|
||||
)
|
||||
|
||||
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.open(BytesIO(source_file.read())).convert("RGB")
|
||||
|
||||
mask_file = request.files.get("mask")
|
||||
mask = 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")
|
||||
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__,
|
||||
fill_color,
|
||||
tile_order,
|
||||
),
|
||||
)
|
||||
job_name = output[0]
|
||||
logger.info("inpaint job queued for: %s", job_name)
|
||||
|
||||
source = valid_image(source, min_dims=size, max_dims=size)
|
||||
mask = valid_image(mask, min_dims=size, max_dims=size)
|
||||
executor.submit(
|
||||
job_name,
|
||||
run_inpaint_pipeline,
|
||||
context,
|
||||
params,
|
||||
size,
|
||||
output,
|
||||
upscale,
|
||||
source,
|
||||
mask,
|
||||
expand,
|
||||
noise_source,
|
||||
mask_filter,
|
||||
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.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)
|
||||
job_name = output[0]
|
||||
logger.info("upscale job queued for: %s", job_name)
|
||||
|
||||
source = valid_image(source, min_dims=size, max_dims=size)
|
||||
executor.submit(
|
||||
job_name,
|
||||
run_upscale_pipeline,
|
||||
context,
|
||||
params,
|
||||
size,
|
||||
output,
|
||||
upscale,
|
||||
source,
|
||||
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)
|
||||
job_name = output[0]
|
||||
|
||||
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.open(BytesIO(source_file.read())).convert("RGB")
|
||||
source = valid_image(source, max_dims=(size.width, size.height))
|
||||
kwargs["stage_source"] = source
|
||||
|
||||
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.open(BytesIO(mask_file.read())).convert("RGB")
|
||||
mask = valid_image(mask, max_dims=(size.width, size.height))
|
||||
kwargs["stage_mask"] = mask
|
||||
|
||||
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(
|
||||
job_name,
|
||||
pipeline,
|
||||
context,
|
||||
params,
|
||||
empty_source,
|
||||
output=output[0],
|
||||
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)
|
||||
job_name = output[0]
|
||||
logger.info("upscale job queued for: %s", job_name)
|
||||
|
||||
executor.submit(
|
||||
job_name,
|
||||
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/txt2txt", methods=["POST"])
|
||||
def txt2txt():
|
||||
device, params, size = pipeline_from_request()
|
||||
|
||||
output = make_output_name(context, "upscale", params, size)
|
||||
logger.info("upscale job queued for: %s", output)
|
||||
|
||||
executor.submit(
|
||||
output,
|
||||
run_txt2txt_pipeline,
|
||||
context,
|
||||
params,
|
||||
size,
|
||||
output,
|
||||
needs_device=device,
|
||||
)
|
||||
|
||||
return jsonify(json_params(output, params, size))
|
||||
|
||||
|
||||
@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:
|
||||
output = base_join(context.output_path, output_file)
|
||||
if path.exists(output):
|
||||
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
|
||||
)
|
|
@ -0,0 +1,477 @@
|
|||
from io import BytesIO
|
||||
from logging import getLogger
|
||||
from os import path
|
||||
|
||||
import yaml
|
||||
from flask import Flask, jsonify, make_response, request, url_for
|
||||
from jsonschema import validate
|
||||
from PIL import Image
|
||||
|
||||
from .context import ServerContext
|
||||
from .utils import wrap_route
|
||||
from ..worker.pool import DevicePoolExecutor
|
||||
|
||||
from .config import (
|
||||
get_available_platforms,
|
||||
get_config_params,
|
||||
get_config_value,
|
||||
get_correction_models,
|
||||
get_diffusion_models,
|
||||
get_inversion_models,
|
||||
get_mask_filters,
|
||||
get_noise_sources,
|
||||
get_upscaling_models,
|
||||
)
|
||||
from .params import border_from_request, pipeline_from_request, upscale_from_request
|
||||
|
||||
from ..chain import (
|
||||
CHAIN_STAGES,
|
||||
ChainPipeline,
|
||||
)
|
||||
from ..diffusion.load import get_pipeline_schedulers
|
||||
from ..diffusion.run import (
|
||||
run_blend_pipeline,
|
||||
run_img2img_pipeline,
|
||||
run_inpaint_pipeline,
|
||||
run_txt2img_pipeline,
|
||||
run_upscale_pipeline,
|
||||
)
|
||||
from ..image import ( # mask filters; noise sources
|
||||
valid_image,
|
||||
)
|
||||
from ..output import json_params, make_output_name
|
||||
from ..params import (
|
||||
Border,
|
||||
StageParams,
|
||||
TileOrder,
|
||||
UpscaleParams,
|
||||
)
|
||||
from ..transformers import run_txt2txt_pipeline
|
||||
from ..utils import (
|
||||
base_join,
|
||||
get_and_clamp_float,
|
||||
get_and_clamp_int,
|
||||
get_from_list,
|
||||
get_from_map,
|
||||
get_not_empty,
|
||||
get_size,
|
||||
)
|
||||
|
||||
logger = getLogger(__name__)
|
||||
|
||||
|
||||
def ready_reply(ready: bool, progress: int = 0):
|
||||
return jsonify(
|
||||
{
|
||||
"progress": progress,
|
||||
"ready": ready,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def error_reply(err: str):
|
||||
response = make_response(
|
||||
jsonify(
|
||||
{
|
||||
"error": err,
|
||||
}
|
||||
)
|
||||
)
|
||||
response.status_code = 400
|
||||
return response
|
||||
|
||||
|
||||
def url_from_rule(rule) -> str:
|
||||
options = {}
|
||||
for arg in rule.arguments:
|
||||
options[arg] = ":%s" % (arg)
|
||||
|
||||
return url_for(rule.endpoint, **options)
|
||||
|
||||
|
||||
def introspect(context: ServerContext, app: Flask):
|
||||
return {
|
||||
"name": "onnx-web",
|
||||
"routes": [
|
||||
{"path": url_from_rule(rule), "methods": list(rule.methods).sort()}
|
||||
for rule in app.url_map.iter_rules()
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
def list_mask_filters(context: ServerContext):
|
||||
return jsonify(list(get_mask_filters().keys()))
|
||||
|
||||
|
||||
def list_models(context: ServerContext):
|
||||
return jsonify(
|
||||
{
|
||||
"correction": get_correction_models(),
|
||||
"diffusion": get_diffusion_models(),
|
||||
"inversion": get_inversion_models(),
|
||||
"upscaling": get_upscaling_models(),
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def list_noise_sources(context: ServerContext):
|
||||
return jsonify(list(get_noise_sources().keys()))
|
||||
|
||||
|
||||
def list_params(context: ServerContext):
|
||||
return jsonify(get_config_params())
|
||||
|
||||
|
||||
def list_platforms(context: ServerContext):
|
||||
return jsonify([p.device for p in get_available_platforms()])
|
||||
|
||||
|
||||
def list_schedulers(context: ServerContext):
|
||||
return jsonify(list(get_pipeline_schedulers().keys()))
|
||||
|
||||
|
||||
def img2img(context: ServerContext, pool: DevicePoolExecutor):
|
||||
if "source" not in request.files:
|
||||
return error_reply("source image is required")
|
||||
|
||||
source_file = request.files.get("source")
|
||||
source = Image.open(BytesIO(source_file.read())).convert("RGB")
|
||||
|
||||
device, params, size = pipeline_from_request(context)
|
||||
upscale = upscale_from_request()
|
||||
|
||||
strength = get_and_clamp_float(
|
||||
request.args,
|
||||
"strength",
|
||||
get_config_value("strength"),
|
||||
get_config_value("strength", "max"),
|
||||
get_config_value("strength", "min"),
|
||||
)
|
||||
|
||||
output = make_output_name(context, "img2img", params, size, extras=(strength,))
|
||||
job_name = output[0]
|
||||
logger.info("img2img job queued for: %s", job_name)
|
||||
|
||||
source = valid_image(source, min_dims=size, max_dims=size)
|
||||
pool.submit(
|
||||
job_name,
|
||||
run_img2img_pipeline,
|
||||
context,
|
||||
params,
|
||||
output,
|
||||
upscale,
|
||||
source,
|
||||
strength,
|
||||
needs_device=device,
|
||||
)
|
||||
|
||||
return jsonify(json_params(output, params, size, upscale=upscale))
|
||||
|
||||
|
||||
def txt2img(context: ServerContext, pool: DevicePoolExecutor):
|
||||
device, params, size = pipeline_from_request(context)
|
||||
upscale = upscale_from_request()
|
||||
|
||||
output = make_output_name(context, "txt2img", params, size)
|
||||
job_name = output[0]
|
||||
logger.info("txt2img job queued for: %s", job_name)
|
||||
|
||||
pool.submit(
|
||||
job_name,
|
||||
run_txt2img_pipeline,
|
||||
context,
|
||||
params,
|
||||
size,
|
||||
output,
|
||||
upscale,
|
||||
needs_device=device,
|
||||
)
|
||||
|
||||
return jsonify(json_params(output, params, size, upscale=upscale))
|
||||
|
||||
|
||||
def inpaint(context: ServerContext, pool: DevicePoolExecutor):
|
||||
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.open(BytesIO(source_file.read())).convert("RGB")
|
||||
|
||||
mask_file = request.files.get("mask")
|
||||
mask = Image.open(BytesIO(mask_file.read())).convert("RGB")
|
||||
|
||||
device, params, size = pipeline_from_request(context)
|
||||
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", get_mask_filters(), "none")
|
||||
noise_source = get_from_map(request.args, "noise", get_noise_sources(), "histogram")
|
||||
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__,
|
||||
fill_color,
|
||||
tile_order,
|
||||
),
|
||||
)
|
||||
job_name = output[0]
|
||||
logger.info("inpaint job queued for: %s", job_name)
|
||||
|
||||
source = valid_image(source, min_dims=size, max_dims=size)
|
||||
mask = valid_image(mask, min_dims=size, max_dims=size)
|
||||
pool.submit(
|
||||
job_name,
|
||||
run_inpaint_pipeline,
|
||||
context,
|
||||
params,
|
||||
size,
|
||||
output,
|
||||
upscale,
|
||||
source,
|
||||
mask,
|
||||
expand,
|
||||
noise_source,
|
||||
mask_filter,
|
||||
fill_color,
|
||||
tile_order,
|
||||
needs_device=device,
|
||||
)
|
||||
|
||||
return jsonify(json_params(output, params, size, upscale=upscale, border=expand))
|
||||
|
||||
|
||||
def upscale(context: ServerContext, pool: DevicePoolExecutor):
|
||||
if "source" not in request.files:
|
||||
return error_reply("source image is required")
|
||||
|
||||
source_file = request.files.get("source")
|
||||
source = Image.open(BytesIO(source_file.read())).convert("RGB")
|
||||
|
||||
device, params, size = pipeline_from_request(context)
|
||||
upscale = upscale_from_request()
|
||||
|
||||
output = make_output_name(context, "upscale", params, size)
|
||||
job_name = output[0]
|
||||
logger.info("upscale job queued for: %s", job_name)
|
||||
|
||||
source = valid_image(source, min_dims=size, max_dims=size)
|
||||
pool.submit(
|
||||
job_name,
|
||||
run_upscale_pipeline,
|
||||
context,
|
||||
params,
|
||||
size,
|
||||
output,
|
||||
upscale,
|
||||
source,
|
||||
needs_device=device,
|
||||
)
|
||||
|
||||
return jsonify(json_params(output, params, size, upscale=upscale))
|
||||
|
||||
|
||||
def chain(context: ServerContext, pool: DevicePoolExecutor):
|
||||
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(context)
|
||||
output = make_output_name(context, "chain", params, size)
|
||||
job_name = output[0]
|
||||
|
||||
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.open(BytesIO(source_file.read())).convert("RGB")
|
||||
source = valid_image(source, max_dims=(size.width, size.height))
|
||||
kwargs["stage_source"] = source
|
||||
|
||||
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.open(BytesIO(mask_file.read())).convert("RGB")
|
||||
mask = valid_image(mask, max_dims=(size.width, size.height))
|
||||
kwargs["stage_mask"] = mask
|
||||
|
||||
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))
|
||||
pool.submit(
|
||||
job_name,
|
||||
pipeline,
|
||||
context,
|
||||
params,
|
||||
empty_source,
|
||||
output=output[0],
|
||||
size=size,
|
||||
needs_device=device,
|
||||
)
|
||||
|
||||
return jsonify(json_params(output, params, size))
|
||||
|
||||
|
||||
def blend(context: ServerContext, pool: DevicePoolExecutor):
|
||||
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(context)
|
||||
upscale = upscale_from_request()
|
||||
|
||||
output = make_output_name(context, "upscale", params, size)
|
||||
job_name = output[0]
|
||||
logger.info("upscale job queued for: %s", job_name)
|
||||
|
||||
pool.submit(
|
||||
job_name,
|
||||
run_blend_pipeline,
|
||||
context,
|
||||
params,
|
||||
size,
|
||||
output,
|
||||
upscale,
|
||||
sources,
|
||||
mask,
|
||||
needs_device=device,
|
||||
)
|
||||
|
||||
return jsonify(json_params(output, params, size, upscale=upscale))
|
||||
|
||||
|
||||
def txt2txt(context: ServerContext, pool: DevicePoolExecutor):
|
||||
device, params, size = pipeline_from_request(context)
|
||||
|
||||
output = make_output_name(context, "upscale", params, size)
|
||||
logger.info("upscale job queued for: %s", output)
|
||||
|
||||
pool.submit(
|
||||
output,
|
||||
run_txt2txt_pipeline,
|
||||
context,
|
||||
params,
|
||||
size,
|
||||
output,
|
||||
needs_device=device,
|
||||
)
|
||||
|
||||
return jsonify(json_params(output, params, size))
|
||||
|
||||
|
||||
def cancel(context: ServerContext, pool: DevicePoolExecutor):
|
||||
output_file = request.args.get("output", None)
|
||||
|
||||
cancel = pool.cancel(output_file)
|
||||
|
||||
return ready_reply(cancel)
|
||||
|
||||
|
||||
def ready(context: ServerContext, pool: DevicePoolExecutor):
|
||||
output_file = request.args.get("output", None)
|
||||
|
||||
done, progress = pool.done(output_file)
|
||||
|
||||
if done is None:
|
||||
output = base_join(context.output_path, output_file)
|
||||
if path.exists(output):
|
||||
return ready_reply(True)
|
||||
|
||||
return ready_reply(done, progress=progress)
|
||||
|
||||
|
||||
def status(context: ServerContext, pool: DevicePoolExecutor):
|
||||
return jsonify(pool.status())
|
||||
|
||||
|
||||
def register_api_routes(app: Flask, context: ServerContext, pool: DevicePoolExecutor):
|
||||
return [
|
||||
app.route("/api")(wrap_route(introspect, context, app=app)),
|
||||
app.route("/api/settings/masks")(wrap_route(list_mask_filters, context)),
|
||||
app.route("/api/settings/models")(wrap_route(list_models, context)),
|
||||
app.route("/api/settings/noises")(wrap_route(list_noise_sources, context)),
|
||||
app.route("/api/settings/params")(wrap_route(list_params, context)),
|
||||
app.route("/api/settings/platforms")(wrap_route(list_platforms, context)),
|
||||
app.route("/api/settings/schedulers")(wrap_route(list_schedulers, context)),
|
||||
app.route("/api/img2img", methods=["POST"])(wrap_route(img2img, context, pool=pool)),
|
||||
app.route("/api/txt2img", methods=["POST"])(wrap_route(txt2img, context, pool=pool)),
|
||||
app.route("/api/txt2txt", methods=["POST"])(wrap_route(txt2txt, context, pool=pool)),
|
||||
app.route("/api/inpaint", methods=["POST"])(wrap_route(inpaint, context, pool=pool)),
|
||||
app.route("/api/upscale", methods=["POST"])(wrap_route(upscale, context, pool=pool)),
|
||||
app.route("/api/chain", methods=["POST"])(wrap_route(chain, context, pool=pool)),
|
||||
app.route("/api/blend", methods=["POST"])(wrap_route(blend, context, pool=pool)),
|
||||
app.route("/api/cancel", methods=["PUT"])(wrap_route(cancel, context, pool=pool)),
|
||||
app.route("/api/ready")(wrap_route(ready, context, pool=pool)),
|
||||
app.route("/api/status")(wrap_route(status, context, pool=pool)),
|
||||
]
|
|
@ -0,0 +1,224 @@
|
|||
from functools import cmp_to_key
|
||||
from glob import glob
|
||||
from logging import getLogger
|
||||
from os import path
|
||||
from typing import Dict, List, Optional, Union
|
||||
|
||||
import torch
|
||||
import yaml
|
||||
from onnxruntime import get_available_providers
|
||||
|
||||
from .context import ServerContext
|
||||
from ..image import ( # mask filters; noise sources
|
||||
mask_filter_gaussian_multiply,
|
||||
mask_filter_gaussian_screen,
|
||||
mask_filter_none,
|
||||
noise_source_fill_edge,
|
||||
noise_source_fill_mask,
|
||||
noise_source_gaussian,
|
||||
noise_source_histogram,
|
||||
noise_source_normal,
|
||||
noise_source_uniform,
|
||||
)
|
||||
from ..params import (
|
||||
DeviceParams,
|
||||
)
|
||||
|
||||
logger = getLogger(__name__)
|
||||
|
||||
# config caching
|
||||
config_params: Dict[str, Dict[str, Union[float, int, str]]] = {}
|
||||
|
||||
# pipeline params
|
||||
platform_providers = {
|
||||
"cpu": "CPUExecutionProvider",
|
||||
"cuda": "CUDAExecutionProvider",
|
||||
"directml": "DmlExecutionProvider",
|
||||
"rocm": "ROCMExecutionProvider",
|
||||
}
|
||||
noise_sources = {
|
||||
"fill-edge": noise_source_fill_edge,
|
||||
"fill-mask": noise_source_fill_mask,
|
||||
"gaussian": noise_source_gaussian,
|
||||
"histogram": noise_source_histogram,
|
||||
"normal": noise_source_normal,
|
||||
"uniform": noise_source_uniform,
|
||||
}
|
||||
mask_filters = {
|
||||
"none": mask_filter_none,
|
||||
"gaussian-multiply": mask_filter_gaussian_multiply,
|
||||
"gaussian-screen": mask_filter_gaussian_screen,
|
||||
}
|
||||
|
||||
|
||||
# Available ORT providers
|
||||
available_platforms: List[DeviceParams] = []
|
||||
|
||||
# loaded from model_path
|
||||
correction_models: List[str] = []
|
||||
diffusion_models: List[str] = []
|
||||
inversion_models: List[str] = []
|
||||
upscaling_models: List[str] = []
|
||||
|
||||
|
||||
def get_config_params():
|
||||
return config_params
|
||||
|
||||
|
||||
def get_available_platforms():
|
||||
return available_platforms
|
||||
|
||||
|
||||
def get_correction_models():
|
||||
return correction_models
|
||||
|
||||
|
||||
def get_diffusion_models():
|
||||
return diffusion_models
|
||||
|
||||
|
||||
def get_inversion_models():
|
||||
return inversion_models
|
||||
|
||||
|
||||
def get_upscaling_models():
|
||||
return upscaling_models
|
||||
|
||||
|
||||
def get_mask_filters():
|
||||
return mask_filters
|
||||
|
||||
|
||||
def get_noise_sources():
|
||||
return noise_sources
|
||||
|
||||
|
||||
def get_config_value(key: str, subkey: str = "default", default=None):
|
||||
return config_params.get(key, {}).get(subkey, default)
|
||||
|
||||
|
||||
def get_model_name(model: str) -> str:
|
||||
base = path.basename(model)
|
||||
(file, _ext) = path.splitext(base)
|
||||
return file
|
||||
|
||||
|
||||
def load_models(context: ServerContext) -> None:
|
||||
global correction_models
|
||||
global diffusion_models
|
||||
global inversion_models
|
||||
global upscaling_models
|
||||
|
||||
diffusion_models = [
|
||||
get_model_name(f) for f in glob(path.join(context.model_path, "diffusion-*"))
|
||||
]
|
||||
diffusion_models.extend(
|
||||
[
|
||||
get_model_name(f)
|
||||
for f in glob(path.join(context.model_path, "stable-diffusion-*"))
|
||||
]
|
||||
)
|
||||
diffusion_models = list(set(diffusion_models))
|
||||
diffusion_models.sort()
|
||||
|
||||
correction_models = [
|
||||
get_model_name(f) for f in glob(path.join(context.model_path, "correction-*"))
|
||||
]
|
||||
correction_models = list(set(correction_models))
|
||||
correction_models.sort()
|
||||
|
||||
inversion_models = [
|
||||
get_model_name(f) for f in glob(path.join(context.model_path, "inversion-*"))
|
||||
]
|
||||
inversion_models = list(set(inversion_models))
|
||||
inversion_models.sort()
|
||||
|
||||
upscaling_models = [
|
||||
get_model_name(f) for f in glob(path.join(context.model_path, "upscaling-*"))
|
||||
]
|
||||
upscaling_models = list(set(upscaling_models))
|
||||
upscaling_models.sort()
|
||||
|
||||
|
||||
def load_params(context: ServerContext) -> None:
|
||||
global config_params
|
||||
params_file = path.join(context.params_path, "params.json")
|
||||
with open(params_file, "r") as f:
|
||||
config_params = yaml.safe_load(f)
|
||||
|
||||
if "platform" in config_params and context.default_platform is not None:
|
||||
logger.info(
|
||||
"Overriding default platform from environment: %s",
|
||||
context.default_platform,
|
||||
)
|
||||
config_platform = config_params.get("platform", {})
|
||||
config_platform["default"] = context.default_platform
|
||||
|
||||
|
||||
def load_platforms(context: ServerContext) -> None:
|
||||
global available_platforms
|
||||
|
||||
providers = list(get_available_providers())
|
||||
|
||||
for potential in platform_providers:
|
||||
if (
|
||||
platform_providers[potential] in providers
|
||||
and potential not in context.block_platforms
|
||||
):
|
||||
if potential == "cuda":
|
||||
for i in range(torch.cuda.device_count()):
|
||||
available_platforms.append(
|
||||
DeviceParams(
|
||||
potential,
|
||||
platform_providers[potential],
|
||||
{
|
||||
"device_id": i,
|
||||
},
|
||||
context.optimizations,
|
||||
)
|
||||
)
|
||||
else:
|
||||
available_platforms.append(
|
||||
DeviceParams(
|
||||
potential,
|
||||
platform_providers[potential],
|
||||
None,
|
||||
context.optimizations,
|
||||
)
|
||||
)
|
||||
|
||||
if context.any_platform:
|
||||
# the platform should be ignored when the job is scheduled, but set to CPU just in case
|
||||
available_platforms.append(
|
||||
DeviceParams(
|
||||
"any",
|
||||
platform_providers["cpu"],
|
||||
None,
|
||||
context.optimizations,
|
||||
)
|
||||
)
|
||||
|
||||
# make sure CPU is last on the list
|
||||
def any_first_cpu_last(a: DeviceParams, b: DeviceParams):
|
||||
if a.device == b.device:
|
||||
return 0
|
||||
|
||||
# any should be first, if it's available
|
||||
if a.device == "any":
|
||||
return -1
|
||||
|
||||
# cpu should be last, if it's available
|
||||
if a.device == "cpu":
|
||||
return 1
|
||||
|
||||
return -1
|
||||
|
||||
available_platforms = sorted(
|
||||
available_platforms, key=cmp_to_key(any_first_cpu_last)
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"available acceleration platforms: %s",
|
||||
", ".join([str(p) for p in available_platforms]),
|
||||
)
|
||||
|
|
@ -0,0 +1,183 @@
|
|||
from logging import getLogger
|
||||
from typing import Tuple
|
||||
|
||||
import numpy as np
|
||||
from flask import request
|
||||
|
||||
from .context import ServerContext
|
||||
|
||||
from .config import get_available_platforms, get_config_value, get_correction_models, get_upscaling_models
|
||||
from .utils import get_model_path
|
||||
|
||||
from ..diffusion.load import pipeline_schedulers
|
||||
from ..params import (
|
||||
Border,
|
||||
DeviceParams,
|
||||
ImageParams,
|
||||
Size,
|
||||
UpscaleParams,
|
||||
)
|
||||
from ..utils import (
|
||||
get_and_clamp_float,
|
||||
get_and_clamp_int,
|
||||
get_from_list,
|
||||
get_not_empty,
|
||||
)
|
||||
|
||||
logger = getLogger(__name__)
|
||||
|
||||
|
||||
def pipeline_from_request(context: ServerContext) -> Tuple[DeviceParams, ImageParams, Size]:
|
||||
user = request.remote_addr
|
||||
|
||||
# platform stuff
|
||||
device = None
|
||||
device_name = request.args.get("platform")
|
||||
|
||||
if device_name is not None and device_name != "any":
|
||||
for platform in get_available_platforms():
|
||||
if platform.device == device_name:
|
||||
device = platform
|
||||
|
||||
# pipeline stuff
|
||||
lpw = get_not_empty(request.args, "lpw", "false") == "true"
|
||||
model = get_not_empty(request.args, "model", get_config_value("model"))
|
||||
model_path = get_model_path(context, model)
|
||||
scheduler = get_from_list(
|
||||
request.args, "scheduler", pipeline_schedulers.keys()
|
||||
)
|
||||
|
||||
if scheduler is None:
|
||||
scheduler = get_config_value("scheduler")
|
||||
|
||||
inversion = request.args.get("inversion", None)
|
||||
inversion_path = None
|
||||
if inversion is not None and inversion.strip() != "":
|
||||
inversion_path = get_model_path(context, inversion)
|
||||
|
||||
# image params
|
||||
prompt = get_not_empty(request.args, "prompt", get_config_value("prompt"))
|
||||
negative_prompt = request.args.get("negativePrompt", None)
|
||||
|
||||
if negative_prompt is not None and negative_prompt.strip() == "":
|
||||
negative_prompt = None
|
||||
|
||||
batch = get_and_clamp_int(
|
||||
request.args,
|
||||
"batch",
|
||||
get_config_value("batch"),
|
||||
get_config_value("batch", "max"),
|
||||
get_config_value("batch", "min"),
|
||||
)
|
||||
cfg = get_and_clamp_float(
|
||||
request.args,
|
||||
"cfg",
|
||||
get_config_value("cfg"),
|
||||
get_config_value("cfg", "max"),
|
||||
get_config_value("cfg", "min"),
|
||||
)
|
||||
eta = get_and_clamp_float(
|
||||
request.args,
|
||||
"eta",
|
||||
get_config_value("eta"),
|
||||
get_config_value("eta", "max"),
|
||||
get_config_value("eta", "min"),
|
||||
)
|
||||
steps = get_and_clamp_int(
|
||||
request.args,
|
||||
"steps",
|
||||
get_config_value("steps"),
|
||||
get_config_value("steps", "max"),
|
||||
get_config_value("steps", "min"),
|
||||
)
|
||||
height = get_and_clamp_int(
|
||||
request.args,
|
||||
"height",
|
||||
get_config_value("height"),
|
||||
get_config_value("height", "max"),
|
||||
get_config_value("height", "min"),
|
||||
)
|
||||
width = get_and_clamp_int(
|
||||
request.args,
|
||||
"width",
|
||||
get_config_value("width"),
|
||||
get_config_value("width", "max"),
|
||||
get_config_value("width", "min"),
|
||||
)
|
||||
|
||||
seed = int(request.args.get("seed", -1))
|
||||
if seed == -1:
|
||||
# this one can safely use np.random because it produces a single value
|
||||
seed = np.random.randint(np.iinfo(np.int32).max)
|
||||
|
||||
logger.info(
|
||||
"request from %s: %s rounds of %s using %s on %s, %sx%s, %s, %s - %s",
|
||||
user,
|
||||
steps,
|
||||
scheduler,
|
||||
model_path,
|
||||
device or "any device",
|
||||
width,
|
||||
height,
|
||||
cfg,
|
||||
seed,
|
||||
prompt,
|
||||
)
|
||||
|
||||
params = ImageParams(
|
||||
model_path,
|
||||
scheduler,
|
||||
prompt,
|
||||
cfg,
|
||||
steps,
|
||||
seed,
|
||||
eta=eta,
|
||||
lpw=lpw,
|
||||
negative_prompt=negative_prompt,
|
||||
batch=batch,
|
||||
inversion=inversion_path,
|
||||
)
|
||||
size = Size(width, height)
|
||||
return (device, params, size)
|
||||
|
||||
|
||||
def border_from_request() -> Border:
|
||||
left = get_and_clamp_int(
|
||||
request.args, "left", 0, get_config_value("width", "max"), 0
|
||||
)
|
||||
right = get_and_clamp_int(
|
||||
request.args, "right", 0, get_config_value("width", "max"), 0
|
||||
)
|
||||
top = get_and_clamp_int(
|
||||
request.args, "top", 0, get_config_value("height", "max"), 0
|
||||
)
|
||||
bottom = get_and_clamp_int(
|
||||
request.args, "bottom", 0, get_config_value("height", "max"), 0
|
||||
)
|
||||
|
||||
return Border(left, right, top, bottom)
|
||||
|
||||
|
||||
def upscale_from_request() -> UpscaleParams:
|
||||
denoise = get_and_clamp_float(request.args, "denoise", 0.5, 1.0, 0.0)
|
||||
scale = get_and_clamp_int(request.args, "scale", 1, 4, 1)
|
||||
outscale = get_and_clamp_int(request.args, "outscale", 1, 4, 1)
|
||||
upscaling = get_from_list(request.args, "upscaling", get_upscaling_models())
|
||||
correction = get_from_list(request.args, "correction", get_correction_models())
|
||||
faces = get_not_empty(request.args, "faces", "false") == "true"
|
||||
face_outscale = get_and_clamp_int(request.args, "faceOutscale", 1, 4, 1)
|
||||
face_strength = get_and_clamp_float(request.args, "faceStrength", 0.5, 1.0, 0.0)
|
||||
upscale_order = request.args.get("upscaleOrder", "correction-first")
|
||||
|
||||
return UpscaleParams(
|
||||
upscaling,
|
||||
correction_model=correction,
|
||||
denoise=denoise,
|
||||
faces=faces,
|
||||
face_outscale=face_outscale,
|
||||
face_strength=face_strength,
|
||||
format="onnx",
|
||||
outscale=outscale,
|
||||
scale=scale,
|
||||
upscale_order=upscale_order,
|
||||
)
|
|
@ -0,0 +1,34 @@
|
|||
from os import path
|
||||
|
||||
from flask import Flask, send_from_directory
|
||||
|
||||
from .utils import wrap_route
|
||||
from .context import ServerContext
|
||||
from ..worker.pool import DevicePoolExecutor
|
||||
|
||||
|
||||
def serve_bundle_file(context: ServerContext, filename="index.html"):
|
||||
return send_from_directory(path.join("..", context.bundle_path), filename)
|
||||
|
||||
|
||||
# non-API routes
|
||||
def index(context: ServerContext):
|
||||
return serve_bundle_file(context)
|
||||
|
||||
|
||||
def index_path(context: ServerContext, filename: str):
|
||||
return serve_bundle_file(context, filename)
|
||||
|
||||
|
||||
def output(context: ServerContext, filename: str):
|
||||
return send_from_directory(
|
||||
path.join("..", context.output_path), filename, as_attachment=False
|
||||
)
|
||||
|
||||
|
||||
def register_static_routes(app: Flask, context: ServerContext, pool: DevicePoolExecutor):
|
||||
return [
|
||||
app.route("/")(wrap_route(index, context)),
|
||||
app.route("/<path:filename>")(wrap_route(index_path, context)),
|
||||
app.route("/output/<path:filename>")(wrap_route(output, context)),
|
||||
]
|
|
@ -0,0 +1,32 @@
|
|||
from os import makedirs, path
|
||||
from typing import Callable, Dict, List, Tuple
|
||||
from functools import partial, update_wrapper
|
||||
|
||||
from flask import Flask
|
||||
|
||||
from onnx_web.utils import base_join
|
||||
from onnx_web.worker.pool import DevicePoolExecutor
|
||||
|
||||
from .context import ServerContext
|
||||
|
||||
|
||||
def check_paths(context: ServerContext) -> None:
|
||||
if not path.exists(context.model_path):
|
||||
raise RuntimeError("model path must exist")
|
||||
|
||||
if not path.exists(context.output_path):
|
||||
makedirs(context.output_path)
|
||||
|
||||
|
||||
def get_model_path(context: ServerContext, model: str):
|
||||
return base_join(context.model_path, model)
|
||||
|
||||
|
||||
def register_routes(app: Flask, context: ServerContext, pool: DevicePoolExecutor, routes: List[Tuple[str, Dict, Callable]]):
|
||||
pass
|
||||
|
||||
|
||||
def wrap_route(func, *args, **kwargs):
|
||||
partial_func = partial(func, *args, **kwargs)
|
||||
update_wrapper(partial_func, func)
|
||||
return partial_func
|
Loading…
Reference in New Issue