548 lines
19 KiB
Python
548 lines
19 KiB
Python
from flask import Flask, render_template,request, send_from_directory
|
|
from urllib import parse
|
|
from urllib import request as rq
|
|
import webbrowser
|
|
import os,base64,io, time, random
|
|
from PIL import Image, ImageFilter, ImageOps
|
|
try:
|
|
from signal import SIGKILL
|
|
except:
|
|
from signal import SIGABRT
|
|
import json, socket
|
|
|
|
def stopprocess():
|
|
pid = os.getpid()
|
|
try:
|
|
os.kill(int(pid), SIGKILL)
|
|
except:
|
|
os.kill(int(pid), SIGABRT)
|
|
return "none"
|
|
from pathlib import Path
|
|
import numpy as np
|
|
|
|
|
|
ip = ""
|
|
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
|
|
s.connect(("8.8.8.8", 80))
|
|
ip = s.getsockname()[0]
|
|
s.close()
|
|
|
|
tt = ""
|
|
imagesgen = []
|
|
genflag = False
|
|
sharedata = {
|
|
"savedata":"",
|
|
"imgs":{}
|
|
}
|
|
|
|
app = Flask(__name__)
|
|
|
|
image = ""
|
|
|
|
bs = 512
|
|
|
|
@app.route("/")
|
|
def index():
|
|
return render_template('index.html',byte_im="",w="",h="",bs=bs)
|
|
|
|
|
|
@app.route('/favicon.ico')
|
|
def favicon():
|
|
return send_from_directory(os.path.join(app.root_path, 'static'),
|
|
'favicon.ico', mimetype='image/vnd.microsoft.icon')
|
|
|
|
@app.route("/grabwfsmodels",methods=['GET', 'POST','DELETE'])
|
|
def grabwfsmodels():
|
|
models = ["Diffusers Models"]
|
|
path = Path('../../models/diffusers')
|
|
diffusers = [i for i in sorted(os.listdir(path)) if os.path.isdir(os.path.join(path,i))]
|
|
models+=diffusers
|
|
models+=["Checkpoints"]
|
|
path = Path('../../models/checkpoints')
|
|
ckpts = [i for i in sorted(os.listdir(path)) if i.endswith((".safetensors", ".ckpt"))]
|
|
models += ckpts
|
|
models+=["Unets"]
|
|
path = Path('../../models/unet')
|
|
unets = [i for i in sorted(os.listdir(path)) if i.endswith((".safetensors", ".ckpt",".gguf"))]
|
|
models+=unets
|
|
path = Path('./workflows')
|
|
wfs = sorted(os.listdir(path))
|
|
wfs = [i.replace(".json","") for i in wfs]
|
|
path = Path('../../models/loras')
|
|
loras = [i for i in sorted(os.listdir(path)) if i.endswith((".safetensors"))]
|
|
return{"models":models,"wfs":wfs,"loras":loras}
|
|
|
|
@app.route("/cancelgen",methods=['GET', 'POST','DELETE'])
|
|
def cancelgen():
|
|
global genflag
|
|
genflag = True
|
|
req = rq.Request("http://127.0.0.1:8188/interrupt",data={})
|
|
rq.urlopen(req)
|
|
return{}
|
|
|
|
@app.route("/saveimg",methods=['GET', 'POST','DELETE'])
|
|
def saveimg():
|
|
pixels = request.json["pixels"]
|
|
if pixels != "":
|
|
for i in range(0,len(pixels)):
|
|
for j in range(0,len(pixels[i])):
|
|
pixels[i][j] = tuple(pixels[i][j])
|
|
|
|
|
|
array = np.array(pixels, dtype=np.uint8)
|
|
image = Image.fromarray(array)
|
|
sharedata["imgs"]["image"] = json.dumps(np.array(image).tolist())
|
|
sharedata["imgs"]["mask"] = json.dumps(np.array(image).tolist())
|
|
sharedata["imgs"]["reference"] = json.dumps(np.array(image).tolist())
|
|
wf = "saveimg.json"
|
|
def queue_prompt(prompt_workflow):
|
|
p = {"prompt": prompt_workflow}
|
|
data = json.dumps(p).encode('utf-8')
|
|
req = rq.Request("http://127.0.0.1:8188/prompt", data=data)
|
|
rq.urlopen(req)
|
|
path = Path('./workflows')
|
|
prompt_workflow = json.load(open(os.path.join(path,wf)))
|
|
for i in prompt_workflow:
|
|
if "seed" in prompt_workflow[i]['inputs']:
|
|
prompt_workflow[i]['inputs']['seed'] = random.randint(0,10000000000)
|
|
queue_prompt(prompt_workflow)
|
|
return{}
|
|
|
|
@app.route("/grabwfparams",methods=['GET', 'POST','DELETE'])
|
|
def grabwfparams():
|
|
wf = request.json["wf"]
|
|
wf +=".json"
|
|
path = Path('./workflows')
|
|
prompt_workflow = json.load(open(os.path.join(path,wf)))
|
|
for i in prompt_workflow:
|
|
if "steps" in prompt_workflow[i]["inputs"]:
|
|
params = [j for j in prompt_workflow[i]["inputs"] if any(x in str(type(prompt_workflow[i]["inputs"][j])) for x in ["int","float"])]
|
|
return{"params":params}
|
|
|
|
|
|
@app.route("/prepare",methods=['GET', 'POST','DELETE'])
|
|
def prep():
|
|
return{"exist":"no"}
|
|
|
|
@app.route("/getnodes",methods=['GET', 'POST','DELETE'])
|
|
def getNodes():
|
|
wf = request.json["wf"]+".json"
|
|
path = Path('./workflows')
|
|
prompt_workflow = json.load(open(os.path.join(path,wf)))
|
|
return{"prompt_workflow":prompt_workflow}
|
|
|
|
@app.route("/generate",methods=['GET', 'POST','DELETE'])
|
|
def generate():
|
|
wf = request.json["wf"]
|
|
gligparams = request.json["gligparams"]
|
|
wf+=".json"
|
|
model = request.json["model"]
|
|
lora = request.json["lora"]
|
|
params = request.json["params"]
|
|
def queue_prompt(prompt_workflow):
|
|
p = {"prompt": prompt_workflow}
|
|
data = json.dumps(p).encode('utf-8')
|
|
req = rq.Request("http://127.0.0.1:8188/prompt", data=data)
|
|
rq.urlopen(req)
|
|
path = Path('./workflows')
|
|
prompt_workflow = json.load(open(os.path.join(path,wf)))
|
|
if "gligen" in wf:
|
|
print(gligparams)
|
|
nn = int(list(prompt_workflow.keys())[-1])+1
|
|
for idx,i in enumerate(gligparams):
|
|
if idx<=len(gligparams)-2:
|
|
n = nn + int(idx)
|
|
prompt_workflow[str(n)] = {
|
|
"inputs":{},
|
|
"class_type":"",
|
|
"_meta":{}
|
|
}
|
|
prompt_workflow[str(n)]["inputs"]["text"] = i["text"]
|
|
prompt_workflow[str(n)]["inputs"]["width"] = i["width"]
|
|
prompt_workflow[str(n)]["inputs"]["height"] = i["height"]
|
|
prompt_workflow[str(n)]["inputs"]["x"] = i["x"]
|
|
prompt_workflow[str(n)]["inputs"]["y"] = i["y"]
|
|
if idx==0:
|
|
prompt_workflow[str(n)]["inputs"]["conditioning_to"] = ["6",0]
|
|
else:
|
|
prompt_workflow[str(n)]["inputs"]["conditioning_to"] = [str(n-1),0]
|
|
prompt_workflow[str(n)]["inputs"]["clip"] = ["4",1]
|
|
prompt_workflow[str(n)]["inputs"]["gligen_textbox_model"] = ["12",0]
|
|
prompt_workflow[str(n)]["class_type"] = "GLIGENTextBoxApply"
|
|
prompt_workflow[str(n)]["_meta"]["title"] = "GLIGENTextBoxApply"
|
|
prompt_workflow["6"]["inputs"]["text"] = gligparams[-1]["text"]
|
|
prompt_workflow["3"]["inputs"]["positive"] = [str(nn+int(len(gligparams)-2)),0]
|
|
if "areacomp" in wf:
|
|
print(gligparams)
|
|
nn = int(list(prompt_workflow.keys())[-1])+1
|
|
n = nn
|
|
for idx,i in enumerate(gligparams):
|
|
if idx<=len(gligparams)-2:
|
|
prompt_workflow[str(n)] = {
|
|
"inputs": {
|
|
"text": i["text"],
|
|
"clip": [
|
|
"4",
|
|
1
|
|
]
|
|
},
|
|
"class_type": "CLIPTextEncode",
|
|
"_meta": {
|
|
"title": "CLIP Text Encode (Prompt)"
|
|
}
|
|
}
|
|
n+=1
|
|
prompt_workflow[str(n)] = {
|
|
"inputs": {
|
|
"width": i["width"],
|
|
"height": i["height"],
|
|
"x": i["x"],
|
|
"y": i["y"],
|
|
"strength": 1,
|
|
"conditioning": [
|
|
str(n-1),
|
|
0
|
|
]
|
|
},
|
|
"class_type": "ConditioningSetArea",
|
|
"_meta": {
|
|
"title": "Conditioning (Set Area)"
|
|
}
|
|
}
|
|
n+=1
|
|
prompt_workflow[str(n)] = {
|
|
"inputs": {
|
|
"conditioning_1": [
|
|
str(n-1),
|
|
0
|
|
],
|
|
"conditioning_2": [
|
|
"6" if idx==0 else str(n-3),
|
|
0
|
|
]
|
|
},
|
|
"class_type": "ConditioningCombine",
|
|
"_meta": {
|
|
"title": "Conditioning (Combine)"
|
|
}
|
|
}
|
|
prompt_workflow["6"]["inputs"]["text"] = gligparams[-1]["text"]
|
|
prompt_workflow["3"]["inputs"]["positive"] = [str(n),0]
|
|
if "stickerize" not in wf:
|
|
for i in prompt_workflow:
|
|
if "seed" in prompt_workflow[i]['inputs']:
|
|
prompt_workflow[i]['inputs']['seed'] = random.randint(0,10000000000)
|
|
if "model_name" in prompt_workflow[i]['inputs'] and prompt_workflow[i]['class_type'] !='UpscaleModelLoader':
|
|
prompt_workflow[i]['inputs']['model_name'] = model
|
|
if "ckpt_name" in prompt_workflow[i]['inputs']:
|
|
prompt_workflow[i]['inputs']['ckpt_name'] = model
|
|
if "steps" in prompt_workflow[i]['inputs']:
|
|
for j in params:
|
|
prompt_workflow[i]['inputs'][j] = params[j]
|
|
else:
|
|
for i in prompt_workflow:
|
|
if "seed" in prompt_workflow[i]['inputs']:
|
|
prompt_workflow[i]['inputs']['seed'] = random.randint(0,10000000000)
|
|
changedparams = request.json["changedparams"]
|
|
print(changedparams)
|
|
for i in changedparams.keys():
|
|
for j in changedparams[i].keys():
|
|
prompt_workflow[i]['inputs'][j] = changedparams[i][j]
|
|
queue_prompt(prompt_workflow)
|
|
return{}
|
|
|
|
|
|
@app.route("/settaesd",methods=['GET', 'POST','DELETE'])
|
|
def settaesd():
|
|
global tt
|
|
tt = request.json["data"]
|
|
return {}
|
|
|
|
@app.route("/getGenStatus",methods=['GET', 'POST','DELETE'])
|
|
def getGenStatus():
|
|
global genflag, imagesgen
|
|
genflag = request.json["data"]
|
|
image = Image.open(request.json["data"]["genimg"])
|
|
buf = io.BytesIO()
|
|
image.save(buf, format='PNG')
|
|
byte_im = buf.getvalue()
|
|
byte_im = base64.b64encode(byte_im).decode('utf-8')
|
|
byte_im = f"data:image/png;base64,{byte_im}"
|
|
if len(imagesgen)<5:
|
|
imagesgen.append(byte_im)
|
|
else:
|
|
imagesgen.pop(0)
|
|
imagesgen.append(byte_im)
|
|
return {}
|
|
|
|
@app.route("/getImagesPaths",methods=['GET', 'POST','DELETE'])
|
|
def getImagesPaths():
|
|
global imagesgen
|
|
return {"imagesgen":imagesgen}
|
|
|
|
@app.route("/getSharedData",methods=['GET', 'POST','DELETE'])
|
|
def getSharedData():
|
|
global sharedata
|
|
return sharedata
|
|
|
|
|
|
@app.route("/savedata",methods=['GET', 'POST','DELETE'])
|
|
def savedata():
|
|
global image, tt, genflag
|
|
img2img = "inpaint"
|
|
taesd = request.json["taesd"]
|
|
savedata = request.json["savedata"]
|
|
if taesd == "false":
|
|
ff = int(savedata["ff"])
|
|
selectorsize = request.json["selectorsize"]
|
|
pixels = savedata["pxlsarray"]
|
|
if pixels != "":
|
|
for i in range(0,len(pixels)):
|
|
for j in range(0,len(pixels[i])):
|
|
pixels[i][j] = tuple(pixels[i][j])
|
|
|
|
|
|
array = np.array(pixels, dtype=np.uint8)
|
|
image = Image.fromarray(array)
|
|
sharedata["imgs"]["out"] = json.dumps(np.array(image).tolist())
|
|
left = int(float(savedata["crpdims"]["left"]))
|
|
top = int(float(savedata["crpdims"]["top"]))
|
|
right = int(float(savedata["crpdims"]["right"]))
|
|
bottom = int(float(savedata["crpdims"]["bottom"]))
|
|
croped = image.crop((left,top,right,bottom))
|
|
try:
|
|
left = int(float(savedata["crpdimsref"]["left"]))
|
|
top = int(float(savedata["crpdimsref"]["top"]))
|
|
right = int(float(savedata["crpdimsref"]["right"]))
|
|
bottom = int(float(savedata["crpdimsref"]["bottom"]))
|
|
if left==0 and right==0 and top ==0 and bottom==0:
|
|
ref = image
|
|
else:
|
|
ref = image.crop((left,top,right,bottom))
|
|
except:
|
|
ref = image
|
|
sharedata["imgs"]["reference"] = json.dumps(np.array(ref).tolist())
|
|
croped2 = croped.copy()
|
|
px = croped2.load()
|
|
for i in range(0,croped2.size[0]):
|
|
for j in range(0,croped2.size[1]):
|
|
try:
|
|
if px[i,j][3] <250:
|
|
px[i,j] = (255,255,255,255)
|
|
else:
|
|
px[i,j] = (0,0,0,255)
|
|
except:
|
|
px[i,j] = (0,0,0)
|
|
selectorsize = int(selectorsize)
|
|
bg = Image.new("RGB",(selectorsize,selectorsize),(0,0,0))
|
|
bg2 = Image.new("RGB",(selectorsize,selectorsize),(255,255,255))
|
|
add = (int(float(savedata["additionaldims"]["left"])),int(float(savedata["additionaldims"]["top"])),int(float(selectorsize-savedata["additionaldims"]["right"])),int(float(selectorsize-savedata["additionaldims"]["bottom"])))
|
|
bg.paste(croped,(add))
|
|
bg2.paste(croped2,(add))
|
|
|
|
|
|
|
|
###########################
|
|
toparr = []
|
|
leftarr = []
|
|
rightarr = []
|
|
bottomarr = []
|
|
|
|
topleftarr = []
|
|
toprightarr = []
|
|
bottomleftarr = []
|
|
bottomrightarr = []
|
|
whitepix = 0
|
|
px = bg2.load()
|
|
for i in range(0,bg2.size[0]):
|
|
for j in range(0,bg2.size[1]):
|
|
if px[i,j][0] == 255:
|
|
whitepix+=1
|
|
try:
|
|
if px[i,j][0] == 255 and 0<i<bg.size[0]-1 and 0<j<bg.size[1]-1:
|
|
if px[i,j+1][0] <255:
|
|
rightarr.append([i,j])
|
|
if px[i,j-1][0] <255:
|
|
leftarr.append([i,j])
|
|
|
|
if px[i+1,j][0] <255:
|
|
bottomarr.append([i,j])
|
|
|
|
if px[i-1,j][0] <255:
|
|
toparr.append([i,j])
|
|
|
|
if px[i-1,j-1][0] <255:
|
|
topleftarr.append([i,j])
|
|
if px[i-1,j+1][0] <255:
|
|
toprightarr.append([i,j])
|
|
|
|
if px[i+1,j+1][0] <255:
|
|
bottomrightarr.append([i,j])
|
|
|
|
if px[i+1,j-1][0] <255:
|
|
bottomleftarr.append([i,j])
|
|
|
|
except:
|
|
continue
|
|
|
|
|
|
for i in range(0,len(toparr)):
|
|
for k in range(0,ff):
|
|
try:
|
|
px[toparr[i][0]-k,toparr[i][1]] = (255,255,255)
|
|
except:
|
|
continue
|
|
|
|
for i in range(0,len(leftarr)):
|
|
for k in range(0,ff):
|
|
try:
|
|
px[leftarr[i][0],leftarr[i][1]-k] = (255,255,255)
|
|
except:
|
|
continue
|
|
|
|
for i in range(0,len(rightarr)):
|
|
for k in range(0,ff):
|
|
try:
|
|
px[rightarr[i][0],rightarr[i][1]+k] = (255,255,255)
|
|
except:
|
|
continue
|
|
|
|
for i in range(0,len(bottomarr)):
|
|
for k in range(0,ff):
|
|
try:
|
|
px[bottomarr[i][0]+k,bottomarr[i][1]] = (255,255,255)
|
|
except:
|
|
continue
|
|
|
|
|
|
|
|
for i in range(0,len(topleftarr)):
|
|
for k in range(0,ff):
|
|
try:
|
|
px[topleftarr[i][0]-k,topleftarr[i][1]-k] = (255,255,255)
|
|
except:
|
|
continue
|
|
|
|
for i in range(0,len(toprightarr)):
|
|
for k in range(0,ff):
|
|
try:
|
|
px[toprightarr[i][0]-k,toprightarr[i][1]+k] = (255,255,255)
|
|
except:
|
|
continue
|
|
|
|
for i in range(0,len(bottomleftarr)):
|
|
for k in range(0,ff):
|
|
try:
|
|
px[bottomleftarr[i][0]+k,bottomleftarr[i][1]-k] = (255,255,255)
|
|
except:
|
|
continue
|
|
|
|
for i in range(0,len(bottomrightarr)):
|
|
for k in range(0,ff):
|
|
try:
|
|
px[bottomrightarr[i][0]+k,bottomrightarr[i][1]+k] = (255,255,255)
|
|
except:
|
|
continue
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
########################
|
|
|
|
|
|
|
|
sharedata["imgs"]["image"] = json.dumps(np.array(bg).tolist())
|
|
if whitepix == 0:
|
|
border = Image.new("RGB",(bg2.size[0],bg2.size[1]),(0,0,0))
|
|
bg2 = ImageOps.invert(bg2)
|
|
bg2 = bg2.resize((bg2.size[0]-ff*2,bg2.size[1]-ff*2))
|
|
border.paste(bg2,(ff,ff))
|
|
bg2 = border
|
|
img2img = "img2img"
|
|
bg2 = bg2.filter(ImageFilter.BoxBlur(ff-int(ff/2)))
|
|
if request.json["savedata"]["mskarray"]=="":
|
|
sharedata["imgs"]["mask"] = json.dumps(np.array(bg2).tolist())
|
|
else:
|
|
pixels = savedata["mskarray"]
|
|
if pixels != "":
|
|
for i in range(0,len(pixels)):
|
|
for j in range(0,len(pixels[i])):
|
|
pixels[i][j] = tuple(pixels[i][j])
|
|
|
|
|
|
array = np.array(pixels, dtype=np.uint8)
|
|
maskimage = Image.fromarray(array)
|
|
sharedata["imgs"]["out"] = json.dumps(np.array(image).tolist())
|
|
left = int(float(savedata["crpdims"]["left"]))
|
|
top = int(float(savedata["crpdims"]["top"]))
|
|
right = int(float(savedata["crpdims"]["right"]))
|
|
bottom = int(float(savedata["crpdims"]["bottom"]))
|
|
cropedmsk = maskimage.crop((left,top,right,bottom))
|
|
cropedmsk = cropedmsk.filter(ImageFilter.BoxBlur(ff-int(ff/2)))
|
|
sharedata["imgs"]["mask"] = json.dumps(np.array(cropedmsk).tolist())
|
|
width = int(float(savedata["additionaldims"]["left"])) + int(float(savedata["additionaldims"]["right"]))
|
|
height = int(float(savedata["additionaldims"]["top"])) + int(float(savedata["additionaldims"]["bottom"]))
|
|
|
|
data = {
|
|
"savedata":savedata
|
|
}
|
|
json_object = json.dumps(data, indent=4)
|
|
sharedata["savedata"] = savedata
|
|
taesds = "no"
|
|
else:
|
|
img2img = request.json["img2img"]
|
|
if img2img=="":
|
|
img2img = "inpaint"
|
|
flag = False
|
|
realflag = flag
|
|
if genflag:
|
|
try:
|
|
image = Image.open(genflag["genimg"])
|
|
except:
|
|
pass
|
|
tt = ""
|
|
genflag = False
|
|
flag = True
|
|
if flag:
|
|
taesds = "no"
|
|
realflag = flag
|
|
flag = False
|
|
try:
|
|
width = image.size[0]
|
|
height = image.size[1]
|
|
buf = io.BytesIO()
|
|
image.save(buf, format='PNG')
|
|
byte_im = buf.getvalue()
|
|
byte_im = base64.b64encode(byte_im).decode('utf-8')
|
|
byte_im = f"data:image/png;base64,{byte_im}"
|
|
except:
|
|
byte_im = ""
|
|
width = ""
|
|
height = ""
|
|
|
|
else:
|
|
try:
|
|
width = tt["width"]
|
|
height = tt["height"]
|
|
byte_im = tt["img"]
|
|
taesds = "no"
|
|
except:
|
|
taesds = "yes"
|
|
try:
|
|
return {"img":byte_im,"width":width,"height":height,"data":savedata,"flag":str(realflag),"taesd":taesds,"img2img":img2img}
|
|
except:
|
|
byte_im = ""
|
|
width = ""
|
|
height = ""
|
|
return {"img":byte_im,"width":width,"height":height,"data":savedata,"flag":str(realflag),"taesd":taesds,"img2img":img2img}
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
webbrowser.open("http://localhost:5000")
|
|
app.run(debug=False)
|
|
|