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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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models =[ |
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"runwayml/stable-diffusion-v1-5", |
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"prompthero/openjourney-v4", |
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"CompVis/stable-diffusion-v1-4", |
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"stabilityai/stable-diffusion-2-1", |
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"stablediffusionapi/edge-of-realism", |
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"MirageML/fantasy-scene", |
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"wavymulder/lomo-diffusion", |
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"sd-dreambooth-library/fashion", |
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"DucHaiten/DucHaitenDreamWorld", |
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"VegaKH/Ultraskin", |
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"kandinsky-community/kandinsky-2-1", |
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"MirageML/lowpoly-cyberpunk", |
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"thehive/everyjourney-sdxl-0.9-finetuned", |
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"plasmo/woolitize-768sd1-5", |
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"plasmo/food-crit", |
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"johnslegers/epic-diffusion-v1.1", |
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"Fictiverse/ElRisitas", |
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"robotjung/SemiRealMix", |
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"herpritts/FFXIV-Style", |
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"prompthero/linkedin-diffusion", |
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"RayHell/popupBook-diffusion", |
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"MirageML/lowpoly-world", |
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"deadman44/SD_Photoreal_Merged_Models", |
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"johnslegers/epic-diffusion", |
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"tilake/China-Chic-illustration", |
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"wavymulder/modelshoot", |
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"prompthero/openjourney-lora", |
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"Fictiverse/Stable_Diffusion_VoxelArt_Model", |
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"darkstorm2150/Protogen_v2.2_Official_Release", |
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"hassanblend/HassanBlend1.5.1.2", |
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"hassanblend/hassanblend1.4", |
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"nitrosocke/redshift-diffusion", |
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"prompthero/openjourney-v2", |
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"nitrosocke/Arcane-Diffusion", |
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"Lykon/DreamShaper", |
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"wavymulder/Analog-Diffusion", |
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"nitrosocke/mo-di-diffusion", |
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"dreamlike-art/dreamlike-diffusion-1.0", |
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"dreamlike-art/dreamlike-photoreal-2.0", |
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"digiplay/RealismEngine_v1", |
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"digiplay/AIGEN_v1.4_diffusers", |
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"stablediffusionapi/dreamshaper-v6", |
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"p1atdev/liminal-space-diffusion", |
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"nadanainone/gigaschizonegs", |
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"lckidwell/album-cover-style", |
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"axolotron/ice-cream-animals", |
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"perion/ai-avatar", |
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"digiplay/GhostMix", |
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"ThePioneer/MISA", |
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"TheLastBen/froggy-style-v21-768", |
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"FloydianSound/Nixeu_Diffusion_v1-5", |
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"kakaobrain/karlo-v1-alpha-image-variations", |
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"digiplay/PotoPhotoRealism_v1", |
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"ConsistentFactor/Aurora-By_Consistent_Factor", |
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"rim0/quadruped_mechas", |
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"Akumetsu971/SD_Samurai_Anime_Model", |
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"Bojaxxx/Fantastic-Mr-Fox-Diffusion", |
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"sd-dreambooth-library/original-character-cyclps", |
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] |
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loris=[] |
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apol=[] |
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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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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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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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try: |
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for pxn in glob.glob('./tmpo/*.png'): |
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os.remove(pxn) |
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except: |
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print("lou") |
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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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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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def plax(gaul,req: gr.Request): |
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gaul=str(req.headers) |
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return gaul |
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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)): |
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gc.collect() |
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adi="" |
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ldi="" |
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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), |
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] |
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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) |
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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)+", " |
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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: |
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pope = accelerator.prepare(StableDiffusionPipeline.from_pretrained(""+modil+"", use_safetensors=False,torch_dtype=torch.float32, safety_checker=None)) |
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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))+", " |
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else: |
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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: |
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pope.load_lora_weights(""+los+"", weight_name=""+str(crll(los))+"",) |
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pope.fuse_lora(fuse_unet=True,fuse_text_encoder=False) |
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except: |
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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+"" |
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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: |
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nm = random.randint(1, 2147483616) |
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else: |
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nm=nut |
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generator = torch.Generator(device="cpu").manual_seed(nm) |
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tilage = pope(prompt,num_inference_steps=5,height=height,width=width,generator=generator,cross_attention_kwargs={"scale": lora_scale}).images[0] |
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cannyimage = np.array(tilage) |
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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 |
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zero_end = zero_start + cannyimage.shape[1] // 2 |
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cannyimage[:, zero_start:zero_end] = 0 |
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cannyimage = cannyimage[:, :, None] |
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cannyimage = np.concatenate([cannyimage, cannyimage, cannyimage], axis=2) |
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canny_image = Image.fromarray(cannyimage) |
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pose_image = load_image(mput).resize((512, 512)) |
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openpose_image = openpose(pose_image) |
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images = [openpose_image, canny_image] |
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omage=pipe([prompt]*2,images,num_inference_steps=stips,generator=generator,negative_prompt=[neg_prompt]*2,controlnet_conditioning_scale=[csal, csbl]) |
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for i, imge in enumerate(omage["images"]): |
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apol.append(imge) |
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imge.save('./tmpo/'+fnamo+'_'+str(i)+'.png', 'PNG') |
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apol.append(openpose_image) |
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apol.append(cammyimage) |
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apol.append(canny_image) |
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apol.append(tilage) |
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openpose_image.save('./tmpo/'+fnamo+'_pose.png', 'PNG') |
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canny_image.save('./tmpo/'+fnamo+'_cann_im.png', 'PNG') |
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tilage.save('./tmpo/'+fnamo+'_tilage.png', 'PNG') |
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chdr(apol,prompt,modil,los,stips,fnamo,gaul) |
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return apol |
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def aip(ill,api_name="/run"): |
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return |
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def pit(ill,api_name="/predict"): |
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return |
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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: |
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out=gr.Gallery(label="Generated Output Image", columns=1) |
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inut=gr.Textbox(label="Prompt") |
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mput=gr.Image(type="filepath") |
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gaul=gr.Textbox(visible=False) |
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inot=gr.Dropdown(choices=smdls(models),value=random.choice(models), type="value") |
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btn=gr.Button("GENERATE") |
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with gr.Accordion("Advanced Settings", open=False): |
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inlt=gr.Dropdown(choices=sldls(loris),value=None, type="value") |
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inet=gr.Textbox(label="Negative_prompt", value="low quality, bad quality,") |
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inyt=gr.Slider(label="Num inference steps",minimum=1,step=1,maximum=30,value=20) |
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inat=gr.Slider(label="Guidance_scale",minimum=1,step=1,maximum=20,value=7) |
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csal=gr.Slider(label="condition_scale_canny", value=0.5, minimum=0.1, step=0.1, maximum=1) |
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csbl=gr.Slider(label="condition_scale_pose", value=0.5, minimum=0.1, step=0.1, maximum=1) |
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loca=gr.Slider(label="Lora scale",minimum=0.1,step=0.1,maximum=0.9,value=0.5) |
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indt=gr.Slider(label="Manual seed (leave 0 for random)",minimum=0,step=32,maximum=2147483616,value=0) |
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inwt=gr.Slider(label="Width",minimum=512,step=32,maximum=1024,value=512) |
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inht=gr.Slider(label="Height",minimum=512,step=32,maximum=1024,value=512) |
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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]) |
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iface.queue(max_size=1,api_open=False) |
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iface.launch(max_threads=20,inline=False,show_api=False) |
