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| from diffusers import AutoPipelineForText2Image, PNDMScheduler, StableDiffusionPipeline, EulerAncestralDiscreteScheduler | |
| 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(StableDiffusionPipeline.from_single_file("https://huggingface.co/lllyasviel/fav_models/fav/DreamShaper_8_pruned.safetensors", torch_dtype=torch.float32, variant=None, use_safetensors=True, safety_checker=None)) | |
| pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) | |
| pipe.unet.to(memory_format=torch.channels_last) | |
| pipe = accelerator.prepare(pipe.to("cpu")) | |
| def chdr(apol,prompt,modil,stips,fnamo,gaul): | |
| try: | |
| type="DrmShpr_SD" | |
| 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="DreamShaper_8_pruned" | |
| 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=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,inet,inyt,indt,inwt,inht,gaul]) | |
| iface.queue(max_size=1,api_open=False) | |
| iface.launch(max_threads=20,inline=False,show_api=False) |