181 lines
6.8 KiB
Python
181 lines
6.8 KiB
Python
import time
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import uvicorn
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from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
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from fastapi import APIRouter, Depends, HTTPException
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import modules.shared as shared
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from modules import devices
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from modules.api.models import *
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
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from modules.sd_samplers import all_samplers
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from modules.extras import run_extras, run_pnginfo
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def upscaler_to_index(name: str):
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try:
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return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
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except:
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raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
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sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
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def setUpscalers(req: dict):
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reqDict = vars(req)
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reqDict['extras_upscaler_1'] = upscaler_to_index(req.upscaler_1)
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reqDict['extras_upscaler_2'] = upscaler_to_index(req.upscaler_2)
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reqDict.pop('upscaler_1')
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reqDict.pop('upscaler_2')
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return reqDict
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class Api:
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def __init__(self, app, queue_lock):
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self.router = APIRouter()
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self.app = app
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self.queue_lock = queue_lock
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self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
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self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
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self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
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self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
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self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
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self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
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def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
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sampler_index = sampler_to_index(txt2imgreq.sampler_index)
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if sampler_index is None:
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raise HTTPException(status_code=404, detail="Sampler not found")
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populate = txt2imgreq.copy(update={ # Override __init__ params
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"sd_model": shared.sd_model,
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"sampler_index": sampler_index[0],
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"do_not_save_samples": True,
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"do_not_save_grid": True
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}
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)
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p = StableDiffusionProcessingTxt2Img(**vars(populate))
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# Override object param
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shared.state.begin()
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with self.queue_lock:
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processed = process_images(p)
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shared.state.end()
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b64images = list(map(encode_pil_to_base64, processed.images))
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return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
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def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
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sampler_index = sampler_to_index(img2imgreq.sampler_index)
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if sampler_index is None:
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raise HTTPException(status_code=404, detail="Sampler not found")
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init_images = img2imgreq.init_images
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if init_images is None:
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raise HTTPException(status_code=404, detail="Init image not found")
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mask = img2imgreq.mask
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if mask:
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mask = decode_base64_to_image(mask)
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populate = img2imgreq.copy(update={ # Override __init__ params
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"sd_model": shared.sd_model,
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"sampler_index": sampler_index[0],
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"do_not_save_samples": True,
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"do_not_save_grid": True,
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"mask": mask
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}
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)
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p = StableDiffusionProcessingImg2Img(**vars(populate))
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imgs = []
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for img in init_images:
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img = decode_base64_to_image(img)
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imgs = [img] * p.batch_size
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p.init_images = imgs
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shared.state.begin()
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with self.queue_lock:
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processed = process_images(p)
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shared.state.end()
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b64images = list(map(encode_pil_to_base64, processed.images))
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if (not img2imgreq.include_init_images):
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img2imgreq.init_images = None
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img2imgreq.mask = None
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return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
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def extras_single_image_api(self, req: ExtrasSingleImageRequest):
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reqDict = setUpscalers(req)
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reqDict['image'] = decode_base64_to_image(reqDict['image'])
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with self.queue_lock:
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result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", **reqDict)
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return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
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def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
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reqDict = setUpscalers(req)
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def prepareFiles(file):
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file = decode_base64_to_file(file.data, file_path=file.name)
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file.orig_name = file.name
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return file
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reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
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reqDict.pop('imageList')
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with self.queue_lock:
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result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", **reqDict)
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return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
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def pnginfoapi(self, req: PNGInfoRequest):
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if(not req.image.strip()):
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return PNGInfoResponse(info="")
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result = run_pnginfo(decode_base64_to_image(req.image.strip()))
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return PNGInfoResponse(info=result[1])
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def progressapi(self, req: ProgressRequest = Depends()):
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# copy from check_progress_call of ui.py
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if shared.state.job_count == 0:
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return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict())
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# avoid dividing zero
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progress = 0.01
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if shared.state.job_count > 0:
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progress += shared.state.job_no / shared.state.job_count
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if shared.state.sampling_steps > 0:
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progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
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time_since_start = time.time() - shared.state.time_start
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eta = (time_since_start/progress)
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eta_relative = eta-time_since_start
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progress = min(progress, 1)
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current_image = None
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if shared.state.current_image and not req.skip_current_image:
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current_image = encode_pil_to_base64(shared.state.current_image)
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return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image)
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def launch(self, server_name, port):
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self.app.include_router(self.router)
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uvicorn.run(self.app, host=server_name, port=port)
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