Create app.py

app.py

@@ -0,0 +1,207 @@
+from diffusers import DiffusionPipeline
+import torch
+import gradio as gr
+from PIL import Image
+import os, random, gc, re, json, time
+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)
+    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.float, variant=None, use_safetensors=True, safety_checker=None))
+    except:
+        pipe=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)
+        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)