507 lines
22 KiB
Python
507 lines
22 KiB
Python
import base64
|
|
import io
|
|
import time
|
|
import datetime
|
|
import uvicorn
|
|
from threading import Lock
|
|
from io import BytesIO
|
|
from gradio.processing_utils import decode_base64_to_file
|
|
from fastapi import APIRouter, Depends, FastAPI, HTTPException, Request, Response
|
|
from fastapi.security import HTTPBasic, HTTPBasicCredentials
|
|
from secrets import compare_digest
|
|
|
|
import modules.shared as shared
|
|
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui
|
|
from modules.api.models import *
|
|
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
|
|
from modules.extras import run_extras
|
|
from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
|
|
from modules.textual_inversion.preprocess import preprocess
|
|
from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
|
|
from PIL import PngImagePlugin,Image
|
|
from modules.sd_models import checkpoints_list, find_checkpoint_config
|
|
from modules.realesrgan_model import get_realesrgan_models
|
|
from modules import devices
|
|
from typing import List
|
|
|
|
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 one of these: {' , '.join([x.name for x in sd_upscalers])}")
|
|
|
|
def script_name_to_index(name, scripts):
|
|
try:
|
|
return [script.title().lower() for script in scripts].index(name.lower())
|
|
except:
|
|
raise HTTPException(status_code=422, detail=f"Script '{name}' not found")
|
|
|
|
def validate_sampler_name(name):
|
|
config = sd_samplers.all_samplers_map.get(name, None)
|
|
if config is None:
|
|
raise HTTPException(status_code=404, detail="Sampler not found")
|
|
|
|
return name
|
|
|
|
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
|
|
|
|
def decode_base64_to_image(encoding):
|
|
if encoding.startswith("data:image/"):
|
|
encoding = encoding.split(";")[1].split(",")[1]
|
|
return Image.open(BytesIO(base64.b64decode(encoding)))
|
|
|
|
def encode_pil_to_base64(image):
|
|
with io.BytesIO() as output_bytes:
|
|
|
|
# Copy any text-only metadata
|
|
use_metadata = False
|
|
metadata = PngImagePlugin.PngInfo()
|
|
for key, value in image.info.items():
|
|
if isinstance(key, str) and isinstance(value, str):
|
|
metadata.add_text(key, value)
|
|
use_metadata = True
|
|
|
|
image.save(
|
|
output_bytes, "PNG", pnginfo=(metadata if use_metadata else None)
|
|
)
|
|
bytes_data = output_bytes.getvalue()
|
|
return base64.b64encode(bytes_data)
|
|
|
|
def api_middleware(app: FastAPI):
|
|
@app.middleware("http")
|
|
async def log_and_time(req: Request, call_next):
|
|
ts = time.time()
|
|
res: Response = await call_next(req)
|
|
duration = str(round(time.time() - ts, 4))
|
|
res.headers["X-Process-Time"] = duration
|
|
endpoint = req.scope.get('path', 'err')
|
|
if shared.cmd_opts.api_log and endpoint.startswith('/sdapi'):
|
|
print('API {t} {code} {prot}/{ver} {method} {endpoint} {cli} {duration}'.format(
|
|
t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"),
|
|
code = res.status_code,
|
|
ver = req.scope.get('http_version', '0.0'),
|
|
cli = req.scope.get('client', ('0:0.0.0', 0))[0],
|
|
prot = req.scope.get('scheme', 'err'),
|
|
method = req.scope.get('method', 'err'),
|
|
endpoint = endpoint,
|
|
duration = duration,
|
|
))
|
|
return res
|
|
|
|
|
|
class Api:
|
|
def __init__(self, app: FastAPI, queue_lock: Lock):
|
|
if shared.cmd_opts.api_auth:
|
|
self.credentials = dict()
|
|
for auth in shared.cmd_opts.api_auth.split(","):
|
|
user, password = auth.split(":")
|
|
self.credentials[user] = password
|
|
|
|
self.router = APIRouter()
|
|
self.app = app
|
|
self.queue_lock = queue_lock
|
|
api_middleware(self.app)
|
|
self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
|
|
self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
|
|
self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
|
|
self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
|
|
self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
|
|
self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
|
|
self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
|
|
self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
|
|
self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"])
|
|
self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
|
|
self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
|
|
self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
|
|
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
|
|
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
|
|
self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
|
|
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
|
|
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
|
|
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
|
|
self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
|
|
self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
|
|
self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
|
|
self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=EmbeddingsResponse)
|
|
self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
|
|
self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=CreateResponse)
|
|
self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=CreateResponse)
|
|
self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=PreprocessResponse)
|
|
self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse)
|
|
self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse)
|
|
|
|
def add_api_route(self, path: str, endpoint, **kwargs):
|
|
if shared.cmd_opts.api_auth:
|
|
return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs)
|
|
return self.app.add_api_route(path, endpoint, **kwargs)
|
|
|
|
def auth(self, credentials: HTTPBasicCredentials = Depends(HTTPBasic())):
|
|
if credentials.username in self.credentials:
|
|
if compare_digest(credentials.password, self.credentials[credentials.username]):
|
|
return True
|
|
|
|
raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
|
|
|
|
def get_script(self, script_name, script_runner):
|
|
if script_name is None:
|
|
return None, None
|
|
|
|
if not script_runner.scripts:
|
|
script_runner.initialize_scripts(False)
|
|
ui.create_ui()
|
|
|
|
script_idx = script_name_to_index(script_name, script_runner.selectable_scripts)
|
|
script = script_runner.selectable_scripts[script_idx]
|
|
return script, script_idx
|
|
|
|
def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
|
|
script, script_idx = self.get_script(txt2imgreq.script_name, scripts.scripts_txt2img)
|
|
|
|
populate = txt2imgreq.copy(update={ # Override __init__ params
|
|
"sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
|
|
"do_not_save_samples": True,
|
|
"do_not_save_grid": True
|
|
}
|
|
)
|
|
if populate.sampler_name:
|
|
populate.sampler_index = None # prevent a warning later on
|
|
|
|
args = vars(populate)
|
|
args.pop('script_name', None)
|
|
|
|
with self.queue_lock:
|
|
p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
|
|
|
|
shared.state.begin()
|
|
if script is not None:
|
|
p.outpath_grids = opts.outdir_txt2img_grids
|
|
p.outpath_samples = opts.outdir_txt2img_samples
|
|
p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args
|
|
processed = scripts.scripts_txt2img.run(p, *p.script_args)
|
|
else:
|
|
processed = process_images(p)
|
|
shared.state.end()
|
|
|
|
b64images = list(map(encode_pil_to_base64, processed.images))
|
|
|
|
return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
|
|
|
|
def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
|
|
init_images = img2imgreq.init_images
|
|
if init_images is None:
|
|
raise HTTPException(status_code=404, detail="Init image not found")
|
|
|
|
script, script_idx = self.get_script(img2imgreq.script_name, scripts.scripts_img2img)
|
|
|
|
mask = img2imgreq.mask
|
|
if mask:
|
|
mask = decode_base64_to_image(mask)
|
|
|
|
populate = img2imgreq.copy(update={ # Override __init__ params
|
|
"sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
|
|
"do_not_save_samples": True,
|
|
"do_not_save_grid": True,
|
|
"mask": mask
|
|
}
|
|
)
|
|
if populate.sampler_name:
|
|
populate.sampler_index = None # prevent a warning later on
|
|
|
|
args = vars(populate)
|
|
args.pop('include_init_images', None) # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
|
|
args.pop('script_name', None)
|
|
|
|
with self.queue_lock:
|
|
p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)
|
|
p.init_images = [decode_base64_to_image(x) for x in init_images]
|
|
|
|
shared.state.begin()
|
|
if script is not None:
|
|
p.outpath_grids = opts.outdir_img2img_grids
|
|
p.outpath_samples = opts.outdir_img2img_samples
|
|
p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args
|
|
processed = scripts.scripts_img2img.run(p, *p.script_args)
|
|
else:
|
|
processed = process_images(p)
|
|
shared.state.end()
|
|
|
|
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="", save_output=False, **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="", save_output=False, **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="")
|
|
|
|
image = decode_base64_to_image(req.image.strip())
|
|
if image is None:
|
|
return PNGInfoResponse(info="")
|
|
|
|
geninfo, items = images.read_info_from_image(image)
|
|
if geninfo is None:
|
|
geninfo = ""
|
|
|
|
items = {**{'parameters': geninfo}, **items}
|
|
|
|
return PNGInfoResponse(info=geninfo, items=items)
|
|
|
|
def progressapi(self, req: ProgressRequest = Depends()):
|
|
# copy from check_progress_call of ui.py
|
|
|
|
if shared.state.job_count == 0:
|
|
return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict())
|
|
|
|
# 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)
|
|
|
|
shared.state.set_current_image()
|
|
|
|
current_image = None
|
|
if shared.state.current_image and not req.skip_current_image:
|
|
current_image = encode_pil_to_base64(shared.state.current_image)
|
|
|
|
return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image)
|
|
|
|
def interrogateapi(self, interrogatereq: InterrogateRequest):
|
|
image_b64 = interrogatereq.image
|
|
if image_b64 is None:
|
|
raise HTTPException(status_code=404, detail="Image not found")
|
|
|
|
img = decode_base64_to_image(image_b64)
|
|
img = img.convert('RGB')
|
|
|
|
# Override object param
|
|
with self.queue_lock:
|
|
if interrogatereq.model == "clip":
|
|
processed = shared.interrogator.interrogate(img)
|
|
elif interrogatereq.model == "deepdanbooru":
|
|
processed = deepbooru.model.tag(img)
|
|
else:
|
|
raise HTTPException(status_code=404, detail="Model not found")
|
|
|
|
return InterrogateResponse(caption=processed)
|
|
|
|
def interruptapi(self):
|
|
shared.state.interrupt()
|
|
|
|
return {}
|
|
|
|
def skip(self):
|
|
shared.state.skip()
|
|
|
|
def get_config(self):
|
|
options = {}
|
|
for key in shared.opts.data.keys():
|
|
metadata = shared.opts.data_labels.get(key)
|
|
if(metadata is not None):
|
|
options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)})
|
|
else:
|
|
options.update({key: shared.opts.data.get(key, None)})
|
|
|
|
return options
|
|
|
|
def set_config(self, req: Dict[str, Any]):
|
|
for k, v in req.items():
|
|
shared.opts.set(k, v)
|
|
|
|
shared.opts.save(shared.config_filename)
|
|
return
|
|
|
|
def get_cmd_flags(self):
|
|
return vars(shared.cmd_opts)
|
|
|
|
def get_samplers(self):
|
|
return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
|
|
|
|
def get_upscalers(self):
|
|
upscalers = []
|
|
|
|
for upscaler in shared.sd_upscalers:
|
|
u = upscaler.scaler
|
|
upscalers.append({"name":u.name, "model_name":u.model_name, "model_path":u.model_path, "model_url":u.model_url})
|
|
|
|
return upscalers
|
|
|
|
def get_sd_models(self):
|
|
return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
|
|
|
|
def get_hypernetworks(self):
|
|
return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
|
|
|
|
def get_face_restorers(self):
|
|
return [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers]
|
|
|
|
def get_realesrgan_models(self):
|
|
return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)]
|
|
|
|
def get_prompt_styles(self):
|
|
styleList = []
|
|
for k in shared.prompt_styles.styles:
|
|
style = shared.prompt_styles.styles[k]
|
|
styleList.append({"name":style[0], "prompt": style[1], "negative_prompt": style[2]})
|
|
|
|
return styleList
|
|
|
|
def get_artists_categories(self):
|
|
return shared.artist_db.cats
|
|
|
|
def get_artists(self):
|
|
return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
|
|
|
|
def get_embeddings(self):
|
|
db = sd_hijack.model_hijack.embedding_db
|
|
|
|
def convert_embedding(embedding):
|
|
return {
|
|
"step": embedding.step,
|
|
"sd_checkpoint": embedding.sd_checkpoint,
|
|
"sd_checkpoint_name": embedding.sd_checkpoint_name,
|
|
"shape": embedding.shape,
|
|
"vectors": embedding.vectors,
|
|
}
|
|
|
|
def convert_embeddings(embeddings):
|
|
return {embedding.name: convert_embedding(embedding) for embedding in embeddings.values()}
|
|
|
|
return {
|
|
"loaded": convert_embeddings(db.word_embeddings),
|
|
"skipped": convert_embeddings(db.skipped_embeddings),
|
|
}
|
|
|
|
def refresh_checkpoints(self):
|
|
shared.refresh_checkpoints()
|
|
|
|
def create_embedding(self, args: dict):
|
|
try:
|
|
shared.state.begin()
|
|
filename = create_embedding(**args) # create empty embedding
|
|
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used
|
|
shared.state.end()
|
|
return CreateResponse(info = "create embedding filename: {filename}".format(filename = filename))
|
|
except AssertionError as e:
|
|
shared.state.end()
|
|
return TrainResponse(info = "create embedding error: {error}".format(error = e))
|
|
|
|
def create_hypernetwork(self, args: dict):
|
|
try:
|
|
shared.state.begin()
|
|
filename = create_hypernetwork(**args) # create empty embedding
|
|
shared.state.end()
|
|
return CreateResponse(info = "create hypernetwork filename: {filename}".format(filename = filename))
|
|
except AssertionError as e:
|
|
shared.state.end()
|
|
return TrainResponse(info = "create hypernetwork error: {error}".format(error = e))
|
|
|
|
def preprocess(self, args: dict):
|
|
try:
|
|
shared.state.begin()
|
|
preprocess(**args) # quick operation unless blip/booru interrogation is enabled
|
|
shared.state.end()
|
|
return PreprocessResponse(info = 'preprocess complete')
|
|
except KeyError as e:
|
|
shared.state.end()
|
|
return PreprocessResponse(info = "preprocess error: invalid token: {error}".format(error = e))
|
|
except AssertionError as e:
|
|
shared.state.end()
|
|
return PreprocessResponse(info = "preprocess error: {error}".format(error = e))
|
|
except FileNotFoundError as e:
|
|
shared.state.end()
|
|
return PreprocessResponse(info = 'preprocess error: {error}'.format(error = e))
|
|
|
|
def train_embedding(self, args: dict):
|
|
try:
|
|
shared.state.begin()
|
|
apply_optimizations = shared.opts.training_xattention_optimizations
|
|
error = None
|
|
filename = ''
|
|
if not apply_optimizations:
|
|
sd_hijack.undo_optimizations()
|
|
try:
|
|
embedding, filename = train_embedding(**args) # can take a long time to complete
|
|
except Exception as e:
|
|
error = e
|
|
finally:
|
|
if not apply_optimizations:
|
|
sd_hijack.apply_optimizations()
|
|
shared.state.end()
|
|
return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error))
|
|
except AssertionError as msg:
|
|
shared.state.end()
|
|
return TrainResponse(info = "train embedding error: {msg}".format(msg = msg))
|
|
|
|
def train_hypernetwork(self, args: dict):
|
|
try:
|
|
shared.state.begin()
|
|
initial_hypernetwork = shared.loaded_hypernetwork
|
|
apply_optimizations = shared.opts.training_xattention_optimizations
|
|
error = None
|
|
filename = ''
|
|
if not apply_optimizations:
|
|
sd_hijack.undo_optimizations()
|
|
try:
|
|
hypernetwork, filename = train_hypernetwork(*args)
|
|
except Exception as e:
|
|
error = e
|
|
finally:
|
|
shared.loaded_hypernetwork = initial_hypernetwork
|
|
shared.sd_model.cond_stage_model.to(devices.device)
|
|
shared.sd_model.first_stage_model.to(devices.device)
|
|
if not apply_optimizations:
|
|
sd_hijack.apply_optimizations()
|
|
shared.state.end()
|
|
return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error))
|
|
except AssertionError as msg:
|
|
shared.state.end()
|
|
return TrainResponse(info = "train embedding error: {error}".format(error = error))
|
|
|
|
def launch(self, server_name, port):
|
|
self.app.include_router(self.router)
|
|
uvicorn.run(self.app, host=server_name, port=port)
|