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Runtime error
| 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) |