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from diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline |
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import torch |
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import cv2 |
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import numpy as np |
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from transformers import pipeline |
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import gradio as gr |
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from PIL import Image |
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from diffusers.utils import load_image |
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import os, random, gc, re, json, time, shutil, glob |
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import PIL.Image |
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import tqdm |
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from accelerate import Accelerator |
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from huggingface_hub import HfApi, InferenceClient, ModelCard, RepoCard, upload_folder, hf_hub_download, HfFileSystem |
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HfApi=HfApi() |
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HF_TOKEN=os.getenv("HF_TOKEN") |
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HF_HUB_DISABLE_TELEMETRY=1 |
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DO_NOT_TRACK=1 |
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HF_HUB_ENABLE_HF_TRANSFER=0 |
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accelerator = Accelerator(cpu=True) |
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InferenceClient=InferenceClient() |
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apol=[] |
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pope_prior = accelerator.prepare(KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float32)) |
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pope_prior.prior.to(memory_format=torch.channels_last) |
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pope_prior = pope_prior.to("cpu") |
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pope = accelerator.prepare(KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float32)) |
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pope.unet.to(memory_format=torch.channels_last) |
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pope = pope.to("cpu") |
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def chdr(apol,prompt,modil,stips,fnamo,gaul): |
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try: |
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type="KNDSK22_INTERP" |
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los="" |
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tre='./tmpo/'+fnamo+'.json' |
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tra='./tmpo/'+fnamo+'_0.png' |
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trm='./tmpo/'+fnamo+'_half.png' |
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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"] |
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flng=[itm[::-1] for itm in flng] |
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ptn = r"\b" + r"\b|\b".join(flng) + r"\b" |
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if re.search(ptn, prompt, re.IGNORECASE): |
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print("onon buddy") |
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else: |
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dobj={'img_name':fnamo,'model':modil,'lora':los,'prompt':prompt,'steps':stips,'type':type} |
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with open(tre, 'w') as f: |
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json.dump(dobj, f) |
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HfApi.upload_folder(repo_id="JoPmt/hf_community_images",folder_path="./tmpo",repo_type="dataset",path_in_repo="./",token=HF_TOKEN) |
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dobj={'img_name':fnamo,'model':modil,'lora':los,'prompt':prompt,'steps':stips,'type':type,'haed':gaul,} |
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with open(tre, 'w') as f: |
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json.dump(dobj, f) |
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HfApi.upload_folder(repo_id="JoPmt/Tst_datast_imgs",folder_path="./tmpo",repo_type="dataset",path_in_repo="./",token=HF_TOKEN) |
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try: |
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for pgn in glob.glob('./tmpo/*.png'): |
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os.remove(pgn) |
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for jgn in glob.glob('./tmpo/*.json'): |
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os.remove(jgn) |
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del tre |
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del tra |
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del trm |
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except: |
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print("cant") |
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except: |
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print("failed to make obj") |
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def plax(gaul,req: gr.Request): |
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gaul=str(req.headers) |
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return gaul |
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def plex(cook, img, neg_prompt, stips, prior_stps, itr_stps, one, two, three, nut, wit, het, gaul, progress=gr.Progress(track_tqdm=True)): |
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gc.collect() |
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apol=[] |
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modil="kandinsky-community/kandinsky-2-2-prior,kandinsky-community/kandinsky-2-2-decoder" |
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goof = load_image(img).resize((wit, het)) |
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prompt = cook |
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negative_prior_prompt = neg_prompt |
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nm=0 |
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fnamo=""+str(int(time.time()))+"" |
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if nut == 0: |
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nm = random.randint(1, 2147483616) |
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while nm % 32 != 0: |
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nm = random.randint(1, 2147483616) |
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else: |
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nm=nut |
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generator = torch.Generator(device="cpu").manual_seed(nm) |
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img_emb = pope_prior(prompt=prompt, guidance_scale=one, num_inference_steps=prior_stps, generator=generator) |
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negative_emb = pope_prior(prompt=negative_prior_prompt, guidance_scale=1, num_inference_steps=prior_stps) |
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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] |
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images_texts = [cook, goof, imags] |
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weights = [one, two, three] |
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primpt = "" |
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prior_out = pope_prior.interpolate(images_texts, weights, num_inference_steps=itr_stps) |
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imas = pope(**prior_out, height=het, width=wit, num_inference_steps=stips) |
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for i, imge in enumerate(imas["images"]): |
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apol.append(imge) |
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imge.save('./tmpo/'+fnamo+'_'+str(i)+'.png', 'PNG') |
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imags.save('./tmpo/'+fnamo+'_half.png', 'PNG') |
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apol.append(imags) |
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chdr(apol,prompt,modil,stips,fnamo,gaul) |
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return apol |
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def aip(ill,api_name="/run"): |
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return |
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def pit(ill,api_name="/predict"): |
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return |
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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: |
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out=gr.Gallery(label="Generated Output Image", columns=1) |
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inut=gr.Textbox(label="Prompt") |
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mput=gr.Image(label="drop", type="filepath") |
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gaul=gr.Textbox(visible=False) |
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btn=gr.Button("GENERATE") |
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with gr.Accordion("Advanced Settings", open=False): |
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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") |
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inyt=gr.Slider(label="Num inference steps",minimum=1,step=1,maximum=30,value=10) |
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ihop=gr.Slider(label="Num prior inference steps",minimum=1,step=1,maximum=10,value=5) |
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ihip=gr.Slider(label="Num prior interpolation steps",minimum=1,step=1,maximum=10,value=5) |
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inat=gr.Slider(label="Text Guide",minimum=0.01,step=0.01,maximum=0.99,value=0.5) |
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csal=gr.Slider(label="Your Image Guide",minimum=0.01,step=0.01,maximum=0.99,value=0.5) |
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csbl=gr.Slider(label="Generated Image Guide",minimum=0.01,step=0.01,maximum=0.99,value=0.3) |
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indt=gr.Slider(label="Manual seed (leave 0 for random)",minimum=0,step=32,maximum=2147483616,value=0) |
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inwt=gr.Slider(label="Width",minimum=256,step=32,maximum=1024,value=768) |
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inht=gr.Slider(label="Height",minimum=256,step=32,maximum=1024,value=768) |
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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]) |
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iface.queue(max_size=1,api_open=False) |
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iface.launch(max_threads=20,inline=False,show_api=False) |