JoPmt commited on
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Create app.py

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  1. app.py +269 -0
app.py ADDED
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+ from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, StableDiffusionPipeline
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+ import torch
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+ import cv2
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+ import numpy as np
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+ from transformers import pipeline
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+ import gradio as gr
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+ from PIL import Image
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+ from diffusers.utils import load_image
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+ import os, random, gc, re, json, time, shutil, glob
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+ import PIL.Image
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+ import tqdm
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+ from controlnet_aux import OpenposeDetector
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+ from accelerate import Accelerator
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+ from huggingface_hub import HfApi, list_models, InferenceClient, ModelCard, RepoCard, upload_folder, hf_hub_download, HfFileSystem
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+ HfApi=HfApi()
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+ HF_TOKEN=os.getenv("HF_TOKEN")
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+ HF_HUB_DISABLE_TELEMETRY=1
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+ DO_NOT_TRACK=1
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+ HF_HUB_ENABLE_HF_TRANSFER=0
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+ accelerator = Accelerator(cpu=True)
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+ InferenceClient=InferenceClient()
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+
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+ models =[]
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+ loris=[]
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+ apol=[]
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+
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+ def hgfdm(models):
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+ models=models
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+ poi=InferenceClient.list_deployed_models()
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+ voi=poi["text-to-image"]
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+ for met in voi:
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+ pio=""+met+""
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+ models.append(pio)
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+ return models
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+
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+ def smdls(models):
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+ models=models
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+ mtlst=HfApi.list_models(filter="diffusers:StableDiffusionPipeline",limit=500,full=True,)
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+ if mtlst:
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+ for nea in mtlst:
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+ vmh=""+str(nea.id)+""
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+ models.append(vmh)
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+ return models
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+
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+ def sldls(loris):
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+ loris=loris
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+ ltlst=HfApi.list_models(filter="stable-diffusion",search="lora",limit=500,full=True,)
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+ if ltlst:
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+ for noa in ltlst:
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+ lmh=""+str(noa.id)+""
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+ loris.append(lmh)
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+ return loris
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+
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+ def chdr(apol,prompt,modil,los,stips,fnamo,gaul):
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+ try:
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+ type="SD_controlnet"
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+ tre='./tmpo/'+fnamo+'.json'
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+ tra='./tmpo/'+fnamo+'_0.png'
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+ trm='./tmpo/'+fnamo+'_1.png'
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+ trv='./tmpo/'+fnamo+'_pose.png'
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+ trh='./tmpo/'+fnamo+'_canny.png'
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+ trg='./tmpo/'+fnamo+'_cann_im.png'
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+ trq='./tmpo/'+fnamo+'_tilage.png'
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+ flng=["yssup", "sllab", "stsaerb", "sinep", "selppin", "ssa", "tnuc", "mub", "kcoc", "kcid", "anigav", "dekan", "edun", "slatineg", "xes", "nrop", "stit", "ttub", "bojwolb", "noitartenep", "kcuf", "kcus", "kcil", "elttil", "gnuoy", "thgit", "lrig", "etitep", "dlihc", "yxes"]
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+ flng=[itm[::-1] for itm in flng]
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+ ptn = r"\b" + r"\b|\b".join(flng) + r"\b"
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+ if re.search(ptn, prompt, re.IGNORECASE):
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+ print("onon buddy")
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+ else:
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+ dobj={'img_name':fnamo,'model':modil,'lora':los,'prompt':prompt,'steps':stips,'type':type}
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+ with open(tre, 'w') as f:
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+ json.dump(dobj, f)
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+ HfApi.upload_folder(repo_id="JoPmt/hf_community_images",folder_path="./tmpo",repo_type="dataset",path_in_repo="./",token=HF_TOKEN)
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+ dobj={'img_name':fnamo,'model':modil,'lora':los,'prompt':prompt,'steps':stips,'type':type,'haed':gaul,}
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+ with open(tre, 'w') as f:
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+ json.dump(dobj, f)
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+ HfApi.upload_folder(repo_id="JoPmt/Tst_datast_imgs",folder_path="./tmpo",repo_type="dataset",path_in_repo="./",token=HF_TOKEN)
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+ try:
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+ for pgn in glob.glob('./tmpo/*.png'):
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+ os.remove(pgn)
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+ for jgn in glob.glob('./tmpo/*.json'):
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+ os.remove(jgn)
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+ del tre
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+ del tra
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+ del trm
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+ del trv
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+ del trh
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+ del trg
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+ del trq
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+ except:
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+ print("cant")
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+ except:
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+ print("failed to umake obj")
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+
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+ def crll(dnk):
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+ lix=""
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+ lotr=HfApi.list_files_info(repo_id=""+dnk+"",repo_type="model")
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+ for flre in list(lotr):
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+ fllr=[]
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+ gar=re.match(r'.+(\.pt|\.ckpt|\.bin|\.safetensors)$', flre.path)
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+ yir=re.search(r'[^/]+$', flre.path)
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+ if gar:
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+ fllr.append(""+str(yir.group(0))+"")
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+ lix=""+fllr[-1]+""
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+ else:
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+ lix=""
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+ return lix
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+
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+ def plax(gaul,req: gr.Request):
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+ gaul=str(req.headers)
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+ return gaul
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+
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+ def plex(prompt,mput,neg_prompt,modil,stips,scaly,csal,csbl,nut,wei,hei,los,loca,gaul,progress=gr.Progress(track_tqdm=True)):
114
+ gc.collect()
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+ adi=""
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+ ldi=""
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+
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+ openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
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+ controlnet = [
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+ ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float32),
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+ ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float32),
122
+ ]
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+ try:
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+ crda=ModelCard.load(""+modil+"")
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+ card=ModelCard.load(""+modil+"").data.to_dict().get("instance_prompt")
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+ cerd=ModelCard.load(""+modil+"").data.to_dict().get("custom_prompt")
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+ cird=ModelCard.load(""+modil+"").data.to_dict().get("lora_prompt")
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+ mtch=re.search(r'(?:(?<=trigger words:)|(?<=trigger:)|(?<=You could use)|(?<=You should use))\s*(.*?)\s*(?=to trigger)', crda.text, re.IGNORECASE)
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+ moch=re.search(r'(?:(?<=trigger words:)|(?<=trigger:)|(?<=You could use)|(?<=You should use))\s*([^.]*)', crda.text, re.IGNORECASE)
130
+ if moch:
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+ adi+=""+str(moch.group(1))+", "
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+ else:
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+ print("no floff trigger")
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+ if mtch:
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+ adi+=""+str(mtch.group(1))+", "
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+ else:
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+ print("no fluff trigger")
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+ if card:
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+ adi+=""+str(card)+", "
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+ else:
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+ print("no instance")
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+ if cerd:
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+ adi+=""+str(cerd)+", "
144
+ else:
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+ print("no custom")
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+ if cird:
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+ adi+=""+str(cird)+", "
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+ else:
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+ print("no lora")
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+ except:
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+ print("no card")
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+ try:
153
+ pope = accelerator.prepare(StableDiffusionPipeline.from_pretrained(""+modil+"", use_safetensors=False,torch_dtype=torch.float32, safety_checker=None))
154
+ pipe = accelerator.prepare(StableDiffusionControlNetPipeline.from_pretrained(""+modil+"", use_safetensors=False,controlnet=controlnet,torch_dtype=torch.float32,safety_checker=None))
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+ except:
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+ gc.collect()
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+ pope = accelerator.prepare(StableDiffusionPipeline.from_pretrained(""+modil+"", use_safetensors=True,torch_dtype=torch.float32, safety_checker=None))
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+ pipe = accelerator.prepare(StableDiffusionControlNetPipeline.from_pretrained(""+modil+"", use_safetensors=True,controlnet=controlnet,torch_dtype=torch.float32,safety_checker=None))
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+ if los:
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+ try:
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+ lrda=ModelCard.load(""+los+"")
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+ lard=ModelCard.load(""+los+"").data.to_dict().get("instance_prompt")
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+ lerd=ModelCard.load(""+los+"").data.to_dict().get("custom_prompt")
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+ lird=ModelCard.load(""+los+"").data.to_dict().get("stable-diffusion")
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+ ltch=re.search(r'(?:(?<=trigger words:)|(?<=trigger:)|(?<=You could use)|(?<=You should use))\s*(.*?)\s*(?=to trigger)', lrda.text, re.IGNORECASE)
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+ loch=re.search(r'(?:(?<=trigger words:)|(?<=trigger:)|(?<=You could use)|(?<=You should use))\s*([^.]*)', lrda.text, re.IGNORECASE)
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+ if loch and lird:
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+ ldi+=""+str(loch.group(1))+", "
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+ else:
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+ print("no lloff trigger")
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+ if ltch and lird:
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+ ldi+=""+str(ltch.group(1))+", "
173
+ else:
174
+ print("no lluff trigger")
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+ if lard and lird:
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+ ldi+=""+str(lard)+", "
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+ else:
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+ print("no instance")
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+ ldi+=""
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+ if lerd and lird:
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+ ldi+=""+str(lerd)+", "
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+ else:
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+ print("no custom")
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+ ldi+=""
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+ except:
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+ print("no trigger")
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+ try:
188
+ pope.load_lora_weights(""+los+"", weight_name=""+str(crll(los))+"",)
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+ pope.fuse_lora(fuse_unet=True,fuse_text_encoder=False)
190
+ except:
191
+ print("no can do")
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+ else:
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+ los=""
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+ pope.unet.to(memory_format=torch.channels_last)
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+ pope = accelerator.prepare(pope.to("cpu"))
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+ pipe.unet.to(memory_format=torch.channels_last)
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+ pipe = accelerator.prepare(pipe.to("cpu"))
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+ gc.collect()
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+ apol=[]
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+ height=hei
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+ width=wei
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+ prompt=""+str(adi)+""+str(ldi)+""+prompt+""
203
+ negative_prompt=""+neg_prompt+""
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+ lora_scale=loca
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+ if nut == 0:
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+ nm = random.randint(1, 2147483616)
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+ while nm % 32 != 0:
208
+ nm = random.randint(1, 2147483616)
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+ else:
210
+ nm=nut
211
+ generator = torch.Generator(device="cpu").manual_seed(nm)
212
+ tilage = pope(prompt,num_inference_steps=5,height=height,width=width,generator=generator,cross_attention_kwargs={"scale": lora_scale}).images[0]
213
+ cannyimage = np.array(tilage)
214
+ low_threshold = 100
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+ high_threshold = 200
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+ fnamo=""+str(int(time.time()))+""
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+ cannyimage = cv2.Canny(cannyimage, low_threshold, high_threshold)
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+ cammyimage=Image.fromarray(cannyimage).save('./tmpo/'+fnamo+'_canny.png', 'PNG')
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+ zero_start = cannyimage.shape[1] // 4
220
+ zero_end = zero_start + cannyimage.shape[1] // 2
221
+ cannyimage[:, zero_start:zero_end] = 0
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+ cannyimage = cannyimage[:, :, None]
223
+ cannyimage = np.concatenate([cannyimage, cannyimage, cannyimage], axis=2)
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+ canny_image = Image.fromarray(cannyimage)
225
+ pose_image = load_image(mput).resize((512, 512))
226
+ openpose_image = openpose(pose_image)
227
+ images = [openpose_image, canny_image]
228
+ omage=pipe([prompt]*2,images,num_inference_steps=stips,generator=generator,negative_prompt=[neg_prompt]*2,controlnet_conditioning_scale=[csal, csbl])
229
+ for i, imge in enumerate(omage["images"]):
230
+ apol.append(imge)
231
+ imge.save('./tmpo/'+fnamo+'_'+str(i)+'.png', 'PNG')
232
+ apol.append(openpose_image)
233
+ apol.append(cammyimage)
234
+ apol.append(canny_image)
235
+ apol.append(tilage)
236
+ openpose_image.save('./tmpo/'+fnamo+'_pose.png', 'PNG')
237
+ canny_image.save('./tmpo/'+fnamo+'_cann_im.png', 'PNG')
238
+ tilage.save('./tmpo/'+fnamo+'_tilage.png', 'PNG')
239
+ chdr(apol,prompt,modil,los,stips,fnamo,gaul)
240
+ return apol
241
+
242
+ def aip(ill,api_name="/run"):
243
+ return
244
+ def pit(ill,api_name="/predict"):
245
+ return
246
+
247
+ with gr.Blocks(theme=random.choice([gr.themes.Monochrome(),gr.themes.Base.from_hub("gradio/seafoam"),gr.themes.Base.from_hub("freddyaboulton/dracula_revamped"),gr.themes.Glass(),gr.themes.Base(),]),analytics_enabled=False) as iface:
248
+ out=gr.Gallery(label="Generated Output Image", columns=1)
249
+ inut=gr.Textbox(label="Prompt")
250
+ mput=gr.Image(type="filepath")
251
+ gaul=gr.Textbox(visible=False)
252
+ inot=gr.Dropdown(choices=smdls(models),value=random.choice(models), type="value")
253
+ btn=gr.Button("GENERATE")
254
+ with gr.Accordion("Advanced Settings", open=False):
255
+ inlt=gr.Dropdown(choices=sldls(loris),value=None, type="value")
256
+ inet=gr.Textbox(label="Negative_prompt", value="low quality, bad quality,")
257
+ inyt=gr.Slider(label="Num inference steps",minimum=1,step=1,maximum=30,value=20)
258
+ inat=gr.Slider(label="Guidance_scale",minimum=1,step=1,maximum=20,value=7)
259
+ csal=gr.Slider(label="condition_scale_canny", value=0.5, minimum=0.1, step=0.1, maximum=1)
260
+ csbl=gr.Slider(label="condition_scale_pose", value=0.5, minimum=0.1, step=0.1, maximum=1)
261
+ loca=gr.Slider(label="Lora scale",minimum=0.1,step=0.1,maximum=0.9,value=0.5)
262
+ indt=gr.Slider(label="Manual seed (leave 0 for random)",minimum=0,step=32,maximum=2147483616,value=0)
263
+ inwt=gr.Slider(label="Width",minimum=512,step=32,maximum=1024,value=512)
264
+ inht=gr.Slider(label="Height",minimum=512,step=32,maximum=1024,value=512)
265
+
266
+ btn.click(fn=plax,inputs=gaul,outputs=gaul).then(fn=plex, outputs=[out], inputs=[inut,mput,inet,inot,inyt,inat,csal,csbl,indt,inwt,inht,inlt,loca,gaul])
267
+
268
+ iface.queue(max_size=1,api_open=False)
269
+ iface.launch(max_threads=20,inline=False,show_api=False)