from diffusers import DiffusionPipeline import torch import gradio as gr from PIL import Image import os, random, gc, re, json, time, shutil import PIL.Image import tqdm 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 accelerator = Accelerator(cpu=True) InferenceClient=InferenceClient() models =[] loris=[] apol=[] def hgfdm(models): models=models poi=InferenceClient.list_deployed_models() voi=poi["text-to-image"] for met in voi: pio=""+met+"" models.append(pio) return models 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,gaul): try: type="SD" fnamo=str(int(time.time())) flng=["yssup", "sllab", "stsaerb", "sinep", "selppin", "ssa", "tnuc", "mub", "kcoc", "kcid", "anigav", "dekan", "edun", "slatineg", "xes", "nrop", "stit", "ttub", "bojwolb", "noitartenep", "kcuf", "kcus", "kcil",] 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} tre='./tmpo/'+fnamo+'.json' 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,} tre='./tmpo/'+fnamo+'.json' 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) shutil.rmtree('./tmpo') 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,neg_prompt,modil,stips,scaly,nut,wei,hei,los,loca,gaul,progress=gr.Progress(track_tqdm=True)): gc.collect() adi="" ldi="" 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: pipe=accelerator.prepare(DiffusionPipeline.from_pretrained(""+modil+"",torch_dtype=torch.bfloat16, variant="fp16", use_safetensors=True, safety_checker=False)) except: try: pipe=accelerator.prepare(DiffusionPipeline.from_pretrained(""+modil+"",torch_dtype=torch.float32, variant="fp32", use_safetensors=True, safety_checker=False)) except: try: pipe=accelerator.prepare(DiffusionPipeline.from_pretrained(""+modil+"",torch_dtype=torch.bfloat16, variant="fp16", use_safetensors=False, safety_checker=False)) except: try: pipe=accelerator.prepare(DiffusionPipeline.from_pretrained(""+modil+"",torch_dtype=torch.float32, variant="fp32", use_safetensors=False, safety_checker=False)) except: try: pipe=accelerator.prepare(DiffusionPipeline.from_pretrained(""+modil+"",torch_dtype=torch.float, variant=None, use_safetensors=True, safety_checker=False)) except: try: pipe=accelerator.prepare(DiffusionPipeline.from_pretrained(""+modil+"",torch_dtype=torch.float, variant=None, use_safetensors=False, safety_checker=False)) except: print("no pipe") 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: pipe.load_lora_weights(""+los+"", weight_name=""+str(crll(los))+"",) pipe.fuse_lora(fuse_unet=True,fuse_text_encoder=False) except: print("no can do") else: los="" pipe.unet.to(memory_format=torch.channels_last) pipe.to("cpu") gc.collect() apol=[] 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) image = pipe(prompt=""+str(adi)+str(ldi)+prompt+"", negative_prompt=neg_prompt, generator=generator, num_inference_steps=stips, guidance_scale=scaly, width=wei, height=hei, cross_attention_kwargs={"scale": lora_scale}) for a, imze in enumerate(image["images"]): apol.append(imze) imze.save('./tmpo/'+str(int(time.time()))+'.png', 'PNG') chdr(apol,prompt,modil,los,stips,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) 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) 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, inet, inot, inyt, inat, indt, inwt, inht, inlt, loca, gaul]) iface.queue(max_size=1,api_open=False) iface.launch(max_threads=10,inline=False,show_api=False)