Create app.py

app.py

@@ -0,0 +1,105 @@
+from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
+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(StableDiffusionXLPipeline.from_pretrained("Lykon/dreamshaper-xl-lightning", torch_dtype=torch.bfloat16, use_safetensors=True, variant=None, safety_checker=False))
+pipe.scheduler = EulerDiscreteScheduler.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="Lkn_drmshpr_XL_lghtng"
+        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,}
+        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="Lykon/dreamshaper-xl-lightning"
+    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, denoising_end=1.0,num_inference_steps=stips, output_type="pil",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=2,step=1,maximum=10,value=4)
+        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)