Create app.py

app.py

@@ -0,0 +1,269 @@
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, StableDiffusionPipeline
+import torch
+import cv2
+import numpy as np
+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 controlnet_aux import OpenposeDetector
+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
+HF_HUB_ENABLE_HF_TRANSFER=0
+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_controlnet"
+        tre='./tmpo/'+fnamo+'.json'
+        tra='./tmpo/'+fnamo+'_0.png'
+        trm='./tmpo/'+fnamo+'_1.png'
+        trv='./tmpo/'+fnamo+'_pose.png'
+        trh='./tmpo/'+fnamo+'_canny.png'
+        trg='./tmpo/'+fnamo+'_cann_im.png'
+        trq='./tmpo/'+fnamo+'_tilage.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,}
+        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
+            del trv
+            del trh
+            del trg
+            del trq
+        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,mput,neg_prompt,modil,stips,scaly,csal,csbl,nut,wei,hei,los,loca,gaul,progress=gr.Progress(track_tqdm=True)):
+    gc.collect()
+    adi=""
+    ldi=""
+    
+    openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
+    controlnet = [
+    ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float32),
+    ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float32),
+    ]
+    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:
+        pope = accelerator.prepare(StableDiffusionPipeline.from_pretrained(""+modil+"", use_safetensors=False,torch_dtype=torch.float32, safety_checker=None))
+        pipe = accelerator.prepare(StableDiffusionControlNetPipeline.from_pretrained(""+modil+"", use_safetensors=False,controlnet=controlnet,torch_dtype=torch.float32,safety_checker=None))
+    except:
+        gc.collect()
+        pope = accelerator.prepare(StableDiffusionPipeline.from_pretrained(""+modil+"", use_safetensors=True,torch_dtype=torch.float32, safety_checker=None))
+        pipe = accelerator.prepare(StableDiffusionControlNetPipeline.from_pretrained(""+modil+"", use_safetensors=True,controlnet=controlnet,torch_dtype=torch.float32,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:
+            pope.load_lora_weights(""+los+"", weight_name=""+str(crll(los))+"",)
+            pope.fuse_lora(fuse_unet=True,fuse_text_encoder=False)
+        except:
+            print("no can do")
+    else:
+        los=""
+        pope.unet.to(memory_format=torch.channels_last)
+        pope = accelerator.prepare(pope.to("cpu"))
+        pipe.unet.to(memory_format=torch.channels_last)
+        pipe = accelerator.prepare(pipe.to("cpu"))
+    gc.collect()
+    apol=[]
+    height=hei
+    width=wei
+    prompt=""+str(adi)+""+str(ldi)+""+prompt+""
+    negative_prompt=""+neg_prompt+""
+    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)
+    tilage = pope(prompt,num_inference_steps=5,height=height,width=width,generator=generator,cross_attention_kwargs={"scale": lora_scale}).images[0]
+    cannyimage = np.array(tilage)
+    low_threshold = 100
+    high_threshold = 200
+    fnamo=""+str(int(time.time()))+""
+    cannyimage = cv2.Canny(cannyimage, low_threshold, high_threshold)
+    cammyimage=Image.fromarray(cannyimage).save('./tmpo/'+fnamo+'_canny.png', 'PNG')
+    zero_start = cannyimage.shape[1] // 4
+    zero_end = zero_start + cannyimage.shape[1] // 2
+    cannyimage[:, zero_start:zero_end] = 0
+    cannyimage = cannyimage[:, :, None]
+    cannyimage = np.concatenate([cannyimage, cannyimage, cannyimage], axis=2)
+    canny_image = Image.fromarray(cannyimage)
+    pose_image = load_image(mput).resize((512, 512))
+    openpose_image = openpose(pose_image)
+    images = [openpose_image, canny_image]
+    omage=pipe([prompt]*2,images,num_inference_steps=stips,generator=generator,negative_prompt=[neg_prompt]*2,controlnet_conditioning_scale=[csal, csbl])
+    for i, imge in enumerate(omage["images"]):
+        apol.append(imge)
+        imge.save('./tmpo/'+fnamo+'_'+str(i)+'.png', 'PNG')
+    apol.append(openpose_image)
+    apol.append(cammyimage)
+    apol.append(canny_image)
+    apol.append(tilage)
+    openpose_image.save('./tmpo/'+fnamo+'_pose.png', 'PNG')
+    canny_image.save('./tmpo/'+fnamo+'_cann_im.png', 'PNG')
+    tilage.save('./tmpo/'+fnamo+'_tilage.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:
+    out=gr.Gallery(label="Generated Output Image", columns=1)
+    inut=gr.Textbox(label="Prompt")
+    mput=gr.Image(type="filepath")
+    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)
+        csal=gr.Slider(label="condition_scale_canny", value=0.5, minimum=0.1, step=0.1, maximum=1)
+        csbl=gr.Slider(label="condition_scale_pose", value=0.5, minimum=0.1, step=0.1, maximum=1)
+        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,mput,inet,inot,inyt,inat,csal,csbl,indt,inwt,inht,inlt,loca,gaul])
+
+iface.queue(max_size=1,api_open=False)
+iface.launch(max_threads=20,inline=False,show_api=False)