| 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,fnamo,gaul): | |
| try: | |
| type="SD" | |
| tre='./tmpo/'+fnamo+'.json' | |
| tra='./tmpo/'+fnamo+'.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",] | |
| 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,} | |
| 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: | |
| del tre | |
| del tra | |
| except: | |
| print("cant") | |
| 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=None)) or accelerator.prepare(DiffusionPipeline.from_pretrained(""+modil+"",torch_dtype=torch.float32,variant="fp32",use_safetensors=True,safety_checker=None)) or accelerator.prepare(DiffusionPipeline.from_pretrained(""+modil+"",torch_dtype=torch.bfloat16,variant="fp16",use_safetensors=False,safety_checker=None)) | |
| except: | |
| gc.collect() | |
| pipe = accelerator.prepare(DiffusionPipeline.from_pretrained(""+modil+"",torch_dtype=torch.float32,variant="fp32",use_safetensors=False,safety_checker=None)) or accelerator.prepare(DiffusionPipeline.from_pretrained(""+modil+"",torch_dtype=torch.float,variant=None,use_safetensors=True,safety_checker=None)) or accelerator.prepare(DiffusionPipeline.from_pretrained(""+modil+"",torch_dtype=torch.float,variant=None,use_safetensors=False,safety_checker=None)) | |
| 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) | |
| fnamo=""+str(int(time.time()))+"" | |
| imze.save('./tmpo/'+fnamo+'.png', 'PNG') | |
| chdr(apol,prompt,modil,los,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) | |
| 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) |