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Merge pull request #3511 from bamarillo/master
[API][Feature] Add extras endpoints
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AUTOMATIC1111
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d5f31f1e14
@ -1,46 +1,37 @@
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from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
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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, HTTPException
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import modules.shared as shared
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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_pnginfo
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import modules.shared as shared
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import uvicorn
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from fastapi import Body, APIRouter, HTTPException
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from fastapi.responses import JSONResponse
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from pydantic import BaseModel, Field, Json
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from typing import List
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import json
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import io
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import base64
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from PIL import Image
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from modules.extras import run_extras
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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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class TextToImageResponse(BaseModel):
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images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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parameters: Json
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info: Json
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class ImageToImageResponse(BaseModel):
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images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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parameters: Json
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info: Json
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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"])
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self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"])
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def __base64_to_image(self, base64_string):
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# if has a comma, deal with prefix
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if "," in base64_string:
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base64_string = base64_string.split(",")[1]
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imgdata = base64.b64decode(base64_string)
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# convert base64 to PIL image
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return Image.open(io.BytesIO(imgdata))
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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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def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
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sampler_index = sampler_to_index(txt2imgreq.sampler_index)
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@ -60,15 +51,9 @@ class Api:
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with self.queue_lock:
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processed = process_images(p)
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b64images = []
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for i in processed.images:
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buffer = io.BytesIO()
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i.save(buffer, format="png")
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b64images.append(base64.b64encode(buffer.getvalue()))
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return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.js())
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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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@ -83,7 +68,7 @@ class Api:
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mask = img2imgreq.mask
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if mask:
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mask = self.__base64_to_image(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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@ -98,7 +83,7 @@ class Api:
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imgs = []
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for img in init_images:
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img = self.__base64_to_image(img)
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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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@ -106,20 +91,39 @@ class Api:
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with self.queue_lock:
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processed = process_images(p)
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b64images = []
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for i in processed.images:
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buffer = io.BytesIO()
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i.save(buffer, format="png")
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b64images.append(base64.b64encode(buffer.getvalue()))
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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=json.dumps(vars(img2imgreq)), info=processed.js())
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return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
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def extrasapi(self):
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raise NotImplementedError
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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):
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raise NotImplementedError
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@ -1,10 +1,10 @@
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from array import array
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from inflection import underscore
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from typing import Any, Dict, Optional
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from pydantic import BaseModel, Field, create_model
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
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import inspect
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from pydantic import BaseModel, Field, create_model
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from typing import Any, Optional
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from typing_extensions import Literal
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from inflection import underscore
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
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from modules.shared import sd_upscalers
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API_NOT_ALLOWED = [
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"self",
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@ -106,3 +106,46 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
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StableDiffusionProcessingImg2Img,
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[{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}]
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).generate_model()
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class TextToImageResponse(BaseModel):
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images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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parameters: dict
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info: str
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class ImageToImageResponse(BaseModel):
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images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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parameters: dict
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info: str
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class ExtrasBaseRequest(BaseModel):
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resize_mode: Literal[0, 1] = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.")
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show_extras_results: bool = Field(default=True, title="Show results", description="Should the backend return the generated image?")
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gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.")
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codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.")
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codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.")
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upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.")
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upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
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upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
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upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the choosen size?")
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upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
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upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
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extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
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class ExtraBaseResponse(BaseModel):
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html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
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class ExtrasSingleImageRequest(ExtrasBaseRequest):
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image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
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class ExtrasSingleImageResponse(ExtraBaseResponse):
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image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
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class FileData(BaseModel):
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data: str = Field(title="File data", description="Base64 representation of the file")
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name: str = Field(title="File name")
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class ExtrasBatchImagesRequest(ExtrasBaseRequest):
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imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
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class ExtrasBatchImagesResponse(ExtraBaseResponse):
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images: list[str] = Field(title="Images", description="The generated images in base64 format.")
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