from diffusers import AmusedPipeline import torch 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=[] pipe = accelerator.prepare(AmusedPipeline.from_pretrained("amused/amused-512", variant=None, torch_dtype=torch.float32, use_safetensors=True)) pipe.vqvae.to(torch.float32) pipe.to("cpu") def chdr(apol,prompt,modil,stips,fnamo,gaul): try: type="aMused" los="" tre='./tmpo/'+fnamo+'.json' tra='./tmpo/'+fnamo+'_0.png' trm='./tmpo/'+fnamo+'_1.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(prompt,neg_prompt,stips,nut,wit,het,gaul,progress=gr.Progress(track_tqdm=True)): gc.collect() apol=[] modil="amused/amused-512" 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) image = pipe(prompt=[prompt]*2, negative_prompt=[neg_prompt]*2, generator=generator, num_inference_steps=stips,height=het,width=wit) for a, imze in enumerate(image["images"]): apol.append(imze) imze.save('./tmpo/'+fnamo+'_'+str(a)+'.png', 'PNG') 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") 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=20) 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=512,value=512) inht=gr.Slider(label="Height",minimum=256,step=32,maximum=512,value=512) btn.click(fn=plax,inputs=gaul,outputs=gaul).then(fn=plex, outputs=[out], inputs=[inut,inet,inyt,indt,inwt,inht,gaul]) iface.queue(max_size=1,api_open=False) iface.launch(max_threads=20,inline=False,show_api=False)