app.py

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()
safety_checker=None

models =[
    "runwayml-stable-diffusion-v1-5-ov",
    "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)