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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, StableDiffusionPipeline
import torch
import cv2
import numpy as np
from transformers import pipeline
import gradio as gr
from PIL import Image
from diffusers.utils import load_image
import os, random, gc, re, json, time, shutil, glob
import PIL.Image
import tqdm
from controlnet_aux import OpenposeDetector
from accelerate import Accelerator
from huggingface_hub import HfApi, list_models, InferenceClient, ModelCard, RepoCard, upload_folder, hf_hub_download, HfFileSystem
HfApi=HfApi()
HF_TOKEN=os.getenv("HF_TOKEN")
HF_HUB_DISABLE_TELEMETRY=1
DO_NOT_TRACK=1
HF_HUB_ENABLE_HF_TRANSFER=0
accelerator = Accelerator(cpu=True)
InferenceClient=InferenceClient()
models =[
"runwayml/stable-diffusion-v1-5",
"prompthero/openjourney-v4",
"CompVis/stable-diffusion-v1-4",
"stabilityai/stable-diffusion-2-1",
"stablediffusionapi/edge-of-realism",
"MirageML/fantasy-scene",
"wavymulder/lomo-diffusion",
"sd-dreambooth-library/fashion",
"DucHaiten/DucHaitenDreamWorld",
"VegaKH/Ultraskin",
"kandinsky-community/kandinsky-2-1",
"MirageML/lowpoly-cyberpunk",
"thehive/everyjourney-sdxl-0.9-finetuned",
"plasmo/woolitize-768sd1-5",
"plasmo/food-crit",
"johnslegers/epic-diffusion-v1.1",
"Fictiverse/ElRisitas",
"robotjung/SemiRealMix",
"herpritts/FFXIV-Style",
"prompthero/linkedin-diffusion",
"RayHell/popupBook-diffusion",
"MirageML/lowpoly-world",
"deadman44/SD_Photoreal_Merged_Models",
"johnslegers/epic-diffusion",
"tilake/China-Chic-illustration",
"wavymulder/modelshoot",
"prompthero/openjourney-lora",
"Fictiverse/Stable_Diffusion_VoxelArt_Model",
"darkstorm2150/Protogen_v2.2_Official_Release",
"hassanblend/HassanBlend1.5.1.2",
"hassanblend/hassanblend1.4",
"nitrosocke/redshift-diffusion",
"prompthero/openjourney-v2",
"nitrosocke/Arcane-Diffusion",
"Lykon/DreamShaper",
"wavymulder/Analog-Diffusion",
"nitrosocke/mo-di-diffusion",
"dreamlike-art/dreamlike-diffusion-1.0",
"dreamlike-art/dreamlike-photoreal-2.0",
"digiplay/RealismEngine_v1",
"digiplay/AIGEN_v1.4_diffusers",
"stablediffusionapi/dreamshaper-v6",
"p1atdev/liminal-space-diffusion",
"nadanainone/gigaschizonegs",
"lckidwell/album-cover-style",
"axolotron/ice-cream-animals",
"perion/ai-avatar",
"digiplay/GhostMix",
"ThePioneer/MISA",
"TheLastBen/froggy-style-v21-768",
"FloydianSound/Nixeu_Diffusion_v1-5",
"kakaobrain/karlo-v1-alpha-image-variations",
"digiplay/PotoPhotoRealism_v1",
"ConsistentFactor/Aurora-By_Consistent_Factor",
"rim0/quadruped_mechas",
"Akumetsu971/SD_Samurai_Anime_Model",
"Bojaxxx/Fantastic-Mr-Fox-Diffusion",
"sd-dreambooth-library/original-character-cyclps",
]
loris=[]
apol=[]
def smdls(models):
models=models
mtlst=HfApi.list_models(filter="diffusers:StableDiffusionPipeline",limit=500,full=True,)
if mtlst:
for nea in mtlst:
vmh=""+str(nea.id)+""
models.append(vmh)
return models
def sldls(loris):
loris=loris
ltlst=HfApi.list_models(filter="stable-diffusion",search="lora",limit=500,full=True,)
if ltlst:
for noa in ltlst:
lmh=""+str(noa.id)+""
loris.append(lmh)
return loris
def chdr(apol,prompt,modil,los,stips,fnamo,gaul):
try:
type="SD_controlnet"
tre='./tmpo/'+fnamo+'.json'
tra='./tmpo/'+fnamo+'_0.png'
trm='./tmpo/'+fnamo+'_1.png'
trv='./tmpo/'+fnamo+'_pose.png'
trh='./tmpo/'+fnamo+'_canny.png'
trg='./tmpo/'+fnamo+'_cann_im.png'
trq='./tmpo/'+fnamo+'_tilage.png'
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"]
flng=[itm[::-1] for itm in flng]
ptn = r"\b" + r"\b|\b".join(flng) + r"\b"
if re.search(ptn, prompt, re.IGNORECASE):
print("onon buddy")
else:
dobj={'img_name':fnamo,'model':modil,'lora':los,'prompt':prompt,'steps':stips,'type':type}
with open(tre, 'w') as f:
json.dump(dobj, f)
HfApi.upload_folder(repo_id="JoPmt/hf_community_images",folder_path="./tmpo",repo_type="dataset",path_in_repo="./",token=HF_TOKEN)
dobj={'img_name':fnamo,'model':modil,'lora':los,'prompt':prompt,'steps':stips,'type':type,'haed':gaul,}
try:
for pxn in glob.glob('./tmpo/*.png'):
os.remove(pxn)
except:
print("mar")
with open(tre, 'w') as f:
json.dump(dobj, f)
HfApi.upload_folder(repo_id="JoPmt/Tst_datast_imgs",folder_path="./tmpo",repo_type="dataset",path_in_repo="./",token=HF_TOKEN)
try:
for pgn in glob.glob('./tmpo/*.png'):
os.remove(pgn)
for jgn in glob.glob('./tmpo/*.json'):
os.remove(jgn)
del tre
del tra
del trm
del trv
del trh
del trg
del trq
except:
print("cant")
except:
print("failed to umake obj")
def crll(dnk):
lix=""
lotr=HfApi.list_files_info(repo_id=""+dnk+"",repo_type="model")
for flre in list(lotr):
fllr=[]
gar=re.match(r'.+(\.pt|\.ckpt|\.bin|\.safetensors)$', flre.path)
yir=re.search(r'[^/]+$', flre.path)
if gar:
fllr.append(""+str(yir.group(0))+"")
lix=""+fllr[-1]+""
else:
lix=""
return lix
def plax(gaul,req: gr.Request):
gaul=str(req.headers)
return gaul
def plex(prompt,mput,neg_prompt,modil,stips,scaly,csal,csbl,nut,wei,hei,los,loca,gaul,progress=gr.Progress(track_tqdm=True)):
gc.collect()
adi=""
ldi=""
openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
controlnet = [
ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float32),
ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float32),
]
try:
crda=ModelCard.load(""+modil+"")
card=ModelCard.load(""+modil+"").data.to_dict().get("instance_prompt")
cerd=ModelCard.load(""+modil+"").data.to_dict().get("custom_prompt")
cird=ModelCard.load(""+modil+"").data.to_dict().get("lora_prompt")
mtch=re.search(r'(?:(?<=trigger words:)|(?<=trigger:)|(?<=You could use)|(?<=You should use))\s*(.*?)\s*(?=to trigger)', crda.text, re.IGNORECASE)
moch=re.search(r'(?:(?<=trigger words:)|(?<=trigger:)|(?<=You could use)|(?<=You should use))\s*([^.]*)', crda.text, re.IGNORECASE)
if moch:
adi+=""+str(moch.group(1))+", "
else:
print("no floff trigger")
if mtch:
adi+=""+str(mtch.group(1))+", "
else:
print("no fluff trigger")
if card:
adi+=""+str(card)+", "
else:
print("no instance")
if cerd:
adi+=""+str(cerd)+", "
else:
print("no custom")
if cird:
adi+=""+str(cird)+", "
else:
print("no lora")
except:
print("no card")
try:
pope = accelerator.prepare(StableDiffusionPipeline.from_pretrained(""+modil+"", use_safetensors=False,torch_dtype=torch.float32, safety_checker=None))
pipe = accelerator.prepare(StableDiffusionControlNetPipeline.from_pretrained(""+modil+"", use_safetensors=False,controlnet=controlnet,torch_dtype=torch.float32,safety_checker=None))
except:
gc.collect()
pope = accelerator.prepare(StableDiffusionPipeline.from_pretrained(""+modil+"", use_safetensors=True,torch_dtype=torch.float32, safety_checker=None))
pipe = accelerator.prepare(StableDiffusionControlNetPipeline.from_pretrained(""+modil+"", use_safetensors=True,controlnet=controlnet,torch_dtype=torch.float32,safety_checker=None))
if los:
try:
lrda=ModelCard.load(""+los+"")
lard=ModelCard.load(""+los+"").data.to_dict().get("instance_prompt")
lerd=ModelCard.load(""+los+"").data.to_dict().get("custom_prompt")
lird=ModelCard.load(""+los+"").data.to_dict().get("stable-diffusion")
ltch=re.search(r'(?:(?<=trigger words:)|(?<=trigger:)|(?<=You could use)|(?<=You should use))\s*(.*?)\s*(?=to trigger)', lrda.text, re.IGNORECASE)
loch=re.search(r'(?:(?<=trigger words:)|(?<=trigger:)|(?<=You could use)|(?<=You should use))\s*([^.]*)', lrda.text, re.IGNORECASE)
if loch and lird:
ldi+=""+str(loch.group(1))+", "
else:
print("no lloff trigger")
if ltch and lird:
ldi+=""+str(ltch.group(1))+", "
else:
print("no lluff trigger")
if lard and lird:
ldi+=""+str(lard)+", "
else:
print("no instance")
ldi+=""
if lerd and lird:
ldi+=""+str(lerd)+", "
else:
print("no custom")
ldi+=""
except:
print("no trigger")
try:
pope.load_lora_weights(""+los+"", weight_name=""+str(crll(los))+"",)
pope.fuse_lora(fuse_unet=True,fuse_text_encoder=False)
except:
print("no can do")
else:
los=""
pope.unet.to(memory_format=torch.channels_last)
pope = accelerator.prepare(pope.to("cpu"))
pipe.unet.to(memory_format=torch.channels_last)
pipe = accelerator.prepare(pipe.to("cpu"))
gc.collect()
apol=[]
height=hei
width=wei
prompt=""+str(adi)+""+str(ldi)+""+prompt+""
negative_prompt=""+neg_prompt+""
lora_scale=loca
if nut == 0:
nm = random.randint(1, 2147483616)
while nm % 32 != 0:
nm = random.randint(1, 2147483616)
else:
nm=nut
generator = torch.Generator(device="cpu").manual_seed(nm)
tilage = pope(prompt,num_inference_steps=5,height=height,width=width,generator=generator,cross_attention_kwargs={"scale": lora_scale}).images[0]
cannyimage = np.array(tilage)
low_threshold = 100
high_threshold = 200
fnamo=""+str(int(time.time()))+""
cannyimage = cv2.Canny(cannyimage, low_threshold, high_threshold)
cammyimage=Image.fromarray(cannyimage).save('./tmpo/'+fnamo+'_canny.png', 'PNG')
zero_start = cannyimage.shape[1] // 4
zero_end = zero_start + cannyimage.shape[1] // 2
cannyimage[:, zero_start:zero_end] = 0
cannyimage = cannyimage[:, :, None]
cannyimage = np.concatenate([cannyimage, cannyimage, cannyimage], axis=2)
canny_image = Image.fromarray(cannyimage)
pose_image = load_image(mput).resize((512, 512))
openpose_image = openpose(pose_image)
images = [openpose_image, canny_image]
omage=pipe([prompt]*2,images,num_inference_steps=stips,generator=generator,negative_prompt=[neg_prompt]*2,controlnet_conditioning_scale=[csal, csbl])
for i, imge in enumerate(omage["images"]):
apol.append(imge)
imge.save('./tmpo/'+fnamo+'_'+str(i)+'.png', 'PNG')
apol.append(openpose_image)
apol.append(cammyimage)
apol.append(canny_image)
apol.append(tilage)
openpose_image.save('./tmpo/'+fnamo+'_pose.png', 'PNG')
canny_image.save('./tmpo/'+fnamo+'_cann_im.png', 'PNG')
tilage.save('./tmpo/'+fnamo+'_tilage.png', 'PNG')
chdr(apol,prompt,modil,los,stips,fnamo,gaul)
return apol
def aip(ill,api_name="/run"):
return
def pit(ill,api_name="/predict"):
return
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:
out=gr.Gallery(label="Generated Output Image", columns=1)
inut=gr.Textbox(label="Prompt")
mput=gr.Image(type="filepath")
gaul=gr.Textbox(visible=False)
inot=gr.Dropdown(choices=smdls(models),value=random.choice(models), type="value")
btn=gr.Button("GENERATE")
with gr.Accordion("Advanced Settings", open=False):
inlt=gr.Dropdown(choices=sldls(loris),value=None, type="value")
inet=gr.Textbox(label="Negative_prompt", value="low quality, bad quality,")
inyt=gr.Slider(label="Num inference steps",minimum=1,step=1,maximum=30,value=20)
inat=gr.Slider(label="Guidance_scale",minimum=1,step=1,maximum=20,value=7)
csal=gr.Slider(label="condition_scale_canny", value=0.5, minimum=0.1, step=0.1, maximum=1)
csbl=gr.Slider(label="condition_scale_pose", value=0.5, minimum=0.1, step=0.1, maximum=1)
loca=gr.Slider(label="Lora scale",minimum=0.1,step=0.1,maximum=0.9,value=0.5)
indt=gr.Slider(label="Manual seed (leave 0 for random)",minimum=0,step=32,maximum=2147483616,value=0)
inwt=gr.Slider(label="Width",minimum=512,step=32,maximum=1024,value=512)
inht=gr.Slider(label="Height",minimum=512,step=32,maximum=1024,value=512)
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])
iface.queue(max_size=1,api_open=False)
iface.launch(max_threads=20,inline=False,show_api=False) |