from diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline 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 accelerate import Accelerator from huggingface_hub import HfApi, 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() apol=[] pope_prior = accelerator.prepare(KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float32)) pope_prior.prior.to(memory_format=torch.channels_last) pope_prior = pope_prior.to("cpu") pope = accelerator.prepare(KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float32)) pope.unet.to(memory_format=torch.channels_last) pope = pope.to("cpu") def chdr(apol,prompt,modil,stips,fnamo,gaul): try: type="KNDSK22_INTERP" los="" tre='./tmpo/'+fnamo+'.json' tra='./tmpo/'+fnamo+'_0.png' trm='./tmpo/'+fnamo+'_half.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("lou") 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 except: print("cant") except: print("failed to make obj") def plax(gaul,req: gr.Request): gaul=str(req.headers) return gaul def plex(cook, img, neg_prompt, stips, prior_stps, itr_stps, one, two, three, nut, wit, het, gaul, progress=gr.Progress(track_tqdm=True)): gc.collect() apol=[] modil="kandinsky-community/kandinsky-2-2-prior,kandinsky-community/kandinsky-2-2-decoder" goof = load_image(img).resize((wit, het)) prompt = cook negative_prior_prompt = neg_prompt nm=0 fnamo=""+str(int(time.time()))+"" 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) img_emb = pope_prior(prompt=prompt, guidance_scale=one, num_inference_steps=prior_stps, generator=generator) negative_emb = pope_prior(prompt=negative_prior_prompt, guidance_scale=1, num_inference_steps=prior_stps) imags = pope(image_embeds=img_emb.image_embeds,negative_image_embeds=negative_emb.image_embeds,num_inference_steps=stips,generator=generator,height=het,width=wit).images[0] images_texts = [cook, goof, imags] weights = [one, two, three] primpt = "" prior_out = pope_prior.interpolate(images_texts, weights, num_inference_steps=itr_stps) imas = pope(**prior_out, height=het, width=wit, num_inference_steps=stips) for i, imge in enumerate(imas["images"]): apol.append(imge) imge.save('./tmpo/'+fnamo+'_'+str(i)+'.png', 'PNG') imags.save('./tmpo/'+fnamo+'_half.png', 'PNG') apol.append(imags) chdr(apol,prompt,modil,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: ##iface.description="Running on cpu, very slow! by JoPmt." out=gr.Gallery(label="Generated Output Image", columns=1) inut=gr.Textbox(label="Prompt") mput=gr.Image(label="drop", type="filepath") gaul=gr.Textbox(visible=False) btn=gr.Button("GENERATE") with gr.Accordion("Advanced Settings", open=False): inet=gr.Textbox(label="Negative_prompt", value="lowres,text,bad quality,low quality,jpeg artifacts,ugly,bad hands,bad face,blurry,bad eyes,watermark,signature") inyt=gr.Slider(label="Num inference steps",minimum=1,step=1,maximum=30,value=10) ihop=gr.Slider(label="Num prior inference steps",minimum=1,step=1,maximum=10,value=5) ihip=gr.Slider(label="Num prior interpolation steps",minimum=1,step=1,maximum=10,value=5) inat=gr.Slider(label="Text Guide",minimum=0.01,step=0.01,maximum=0.99,value=0.5) csal=gr.Slider(label="Your Image Guide",minimum=0.01,step=0.01,maximum=0.99,value=0.5) csbl=gr.Slider(label="Generated Image Guide",minimum=0.01,step=0.01,maximum=0.99,value=0.3) indt=gr.Slider(label="Manual seed (leave 0 for random)",minimum=0,step=32,maximum=2147483616,value=0) inwt=gr.Slider(label="Width",minimum=256,step=32,maximum=1024,value=768) inht=gr.Slider(label="Height",minimum=256,step=32,maximum=1024,value=768) btn.click(fn=plax,inputs=gaul,outputs=gaul).then(fn=plex, outputs=[out], inputs=[inut,mput,inet,inyt,ihop,ihip,inat,csal,csbl,indt,inwt,inht,gaul]) iface.queue(max_size=1,api_open=False) iface.launch(max_threads=20,inline=False,show_api=False)