api.py

# import time

# from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
# from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
# from modules.sd_samplers import all_samplers
# from modules.extras import run_pnginfo
# import modules.shared as shared
# from modules import devices
# import uvicorn
# from fastapi import Body, APIRouter, HTTPException
# from fastapi.responses import JSONResponse
# from pydantic import BaseModel, Field, Json
# from typing import List
# import json
# import io
# import base64
# from PIL import Image

# sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)

# class TextToImageResponse(BaseModel):
#     images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
#     parameters: Json
#     info: Json

# class ImageToImageResponse(BaseModel):
#     images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
#     parameters: Json
#     info: Json

import time
import uvicorn
from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
from fastapi import APIRouter, HTTPException
import modules.shared as shared
from modules import devices
from modules.api.models import *
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
from modules.sd_samplers import all_samplers
from modules.extras import run_extras, run_pnginfo

# copy from wrap_gradio_gpu_call of webui.py
# because queue lock will be acquired in api handlers
# and time start needs to be set
# the function has been modified into two parts

def before_gpu_call():
    devices.torch_gc()

    shared.state.sampling_step = 0
    shared.state.job_count = -1
    shared.state.job_no = 0
    shared.state.job_timestamp = shared.state.get_job_timestamp()
    shared.state.current_latent = None
    shared.state.current_image = None
    shared.state.current_image_sampling_step = 0
    shared.state.skipped = False
    shared.state.interrupted = False
    shared.state.textinfo = None
    shared.state.time_start = time.time()

def after_gpu_call():
    shared.state.job = ""
    shared.state.job_count = 0

    devices.torch_gc()

def upscaler_to_index(name: str):
    try:
        return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
    except:
        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")

sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)

def setUpscalers(req: dict):
    reqDict = vars(req)
    reqDict['extras_upscaler_1'] = upscaler_to_index(req.upscaler_1)
    reqDict['extras_upscaler_2'] = upscaler_to_index(req.upscaler_2)
    reqDict.pop('upscaler_1')
    reqDict.pop('upscaler_2')
    return reqDict

class Api:
    def __init__(self, app, queue_lock):
        self.router = APIRouter()
        self.app = app
        self.queue_lock = queue_lock
        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
        self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
        self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
        self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
        self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
        self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"])

    def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
        sampler_index = sampler_to_index(txt2imgreq.sampler_index)

        if sampler_index is None:
            raise HTTPException(status_code=404, detail="Sampler not found")

        populate = txt2imgreq.copy(update={ # Override __init__ params
            "sd_model": shared.sd_model,
            "sampler_index": sampler_index[0],
            "do_not_save_samples": True,
            "do_not_save_grid": True
            }
        )
        p = StableDiffusionProcessingTxt2Img(**vars(populate))
        # Override object param
        before_gpu_call()
        with self.queue_lock:
            processed = process_images(p)
        after_gpu_call()

        b64images = list(map(encode_pil_to_base64, processed.images))

        return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())

    def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
        sampler_index = sampler_to_index(img2imgreq.sampler_index)

        if sampler_index is None:
            raise HTTPException(status_code=404, detail="Sampler not found")


        init_images = img2imgreq.init_images
        if init_images is None:
            raise HTTPException(status_code=404, detail="Init image not found")

        mask = img2imgreq.mask
        if mask:
            mask = decode_base64_to_image(mask)


        populate = img2imgreq.copy(update={ # Override __init__ params
            "sd_model": shared.sd_model,
            "sampler_index": sampler_index[0],
            "do_not_save_samples": True,
            "do_not_save_grid": True,
            "mask": mask
            }
        )
        p = StableDiffusionProcessingImg2Img(**vars(populate))

        imgs = []
        for img in init_images:
            img = decode_base64_to_image(img)
            imgs = [img] * p.batch_size

        p.init_images = imgs
        # Override object param
        before_gpu_call()
        with self.queue_lock:
            processed = process_images(p)
        after_gpu_call()

        b64images = list(map(encode_pil_to_base64, processed.images))

        if (not img2imgreq.include_init_images):
            img2imgreq.init_images = None
            img2imgreq.mask = None

        return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())

    def extras_single_image_api(self, req: ExtrasSingleImageRequest):
        reqDict = setUpscalers(req)

        reqDict['image'] = decode_base64_to_image(reqDict['image'])

        with self.queue_lock:
            result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", **reqDict)

        return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])

    def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
        reqDict = setUpscalers(req)

        def prepareFiles(file):
            file = decode_base64_to_file(file.data, file_path=file.name)
            file.orig_name = file.name
            return file

        reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
        reqDict.pop('imageList')

        with self.queue_lock:
            result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", **reqDict)

        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])

    def pnginfoapi(self, req: PNGInfoRequest):
        if(not req.image.strip()):
            return PNGInfoResponse(info="")

        result = run_pnginfo(decode_base64_to_image(req.image.strip()))

        return PNGInfoResponse(info=result[1])

    def progressapi(self):
        # copy from check_progress_call of ui.py

        if shared.state.job_count == 0:
            return ProgressResponse(progress=0, eta_relative=0, state=shared.state.js())

        # avoid dividing zero
        progress = 0.01

        if shared.state.job_count > 0:
            progress += shared.state.job_no / shared.state.job_count
        if shared.state.sampling_steps > 0:
            progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps

        time_since_start = time.time() - shared.state.time_start
        eta = (time_since_start/progress)
        eta_relative = eta-time_since_start

        progress = min(progress, 1)

        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.js())

    def launch(self, server_name, port):
        self.app.include_router(self.router)
        uvicorn.run(self.app, host=server_name, port=port)