Update app.py

app.py

@@ -1,25 +1,30 @@
-import gradio as gr
-from PIL import Image
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, StableDiffusionPipeline
+import torch
 import cv2
-import os, random
 import numpy as np
 from transformers import pipeline
-import PIL.Image
+import gradio as gr
+from PIL import Image
 from diffusers.utils import load_image
-from accelerate import Accelerator
-from diffusers import StableDiffusionPipeline
-import torch
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
+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 =[
     "runwayml/stable-diffusion-v1-5",
     "prompthero/openjourney-v4",
     "CompVis/stable-diffusion-v1-4",
     "stabilityai/stable-diffusion-2-1",
-    "stablediffusionapi/disney-pixal-cartoon",
     "stablediffusionapi/edge-of-realism",
     "MirageML/fantasy-scene",
     "wavymulder/lomo-diffusion",
@@ -39,7 +44,6 @@ models =[
     "RayHell/popupBook-diffusion",
     "MirageML/lowpoly-world",
     "deadman44/SD_Photoreal_Merged_Models",
-    "Conflictx/CGI_Animation",
     "johnslegers/epic-diffusion",
     "tilake/China-Chic-illustration",
     "wavymulder/modelshoot",
@@ -59,10 +63,8 @@ models =[
     "digiplay/RealismEngine_v1",
     "digiplay/AIGEN_v1.4_diffusers",
     "stablediffusionapi/dreamshaper-v6",
-    "JackAnon/GorynichMix",
     "p1atdev/liminal-space-diffusion",
     "nadanainone/gigaschizonegs",
-    "darkVOYAGE/dvMJv4",
     "lckidwell/album-cover-style",
     "axolotron/ice-cream-animals",
     "perion/ai-avatar",
@@ -77,82 +79,242 @@ models =[
     "Akumetsu971/SD_Samurai_Anime_Model",
     "Bojaxxx/Fantastic-Mr-Fox-Diffusion",
     "sd-dreambooth-library/original-character-cyclps",
-    ##"AIArtsChannel/steampunk-diffusion",
 ]
+loris=[]
+apol=[]
 
-sdulers =[
-    "UniPCMultistepScheduler",
-    "DDIMScheduler",
-    "DDPMScheduler",
-    "DDIMInverseScheduler",
-    "CMStochasticIterativeScheduler",
-    "DEISMultistepScheduler",
-    "DPMSolverMultistepInverse",
-    "DPMSolverMultistepScheduler",
-    "DPMSolverSDEScheduler",
-    "DPMSolverSinglestepScheduler",
-    "EulerAncestralDiscreteScheduler",
-    "EulerDiscreteScheduler",
-    "HeunDiscreteScheduler",
-    "IPNDMScheduler",
-    "KarrasVeScheduler",
-    "KDPM2AncestralDiscreteScheduler",
-    "KDPM2DiscreteScheduler",
-    "LMSDiscreteScheduler",
-    "PNDMScheduler",
-    "RePaintScheduler",
-    "ScoreSdeVeScheduler",
-    "ScoreSdeVpScheduler",
-    "VQDiffusionScheduler",
-]
+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
 
-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),
-]
+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
 
-generator = torch.Generator(device="cpu").manual_seed(random.randint(1, 83647))
+def plax(gaul,req: gr.Request):
+    gaul=str(req.headers)
+    return gaul
 
-def plex(mput, prompt, neg_prompt, stips, modal_id, dula, blip, blop):
-    apol = []
-    modal_id = ""+modal_id+""
-    dula = ""+dula+""
-    pope = accelerator.prepare(StableDiffusionPipeline.from_pretrained(modal_id, use_safetensors=False,torch_dtype=torch.float32, safety_checker=None))
-    pope.unet.to(memory_format=torch.channels_last)
-    pope = accelerator.prepare(pope.to("cpu"))
-    pipe = accelerator.prepare(StableDiffusionControlNetPipeline.from_pretrained(modal_id, use_safetensors=False,controlnet=controlnet,torch_dtype=torch.float32,safety_checker=None))
-    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-    pipe.unet.to(memory_format=torch.channels_last)
-    pipe = accelerator.prepare(pipe.to("cpu"))
+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=""
     
-    tilage = pope(prompt,num_inference_steps=5,height=512,width=512,generator=generator).images[0]
-    ##tilage.save('./til.png', 'PNG')
+    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)
-    ##canny_image.save('./can.png', 'PNG')
     pose_image = load_image(mput).resize((512, 512))
-    ##pose_image.save('./pos.png', 'PNG')
     openpose_image = openpose(pose_image)
-    ##openpose_image.save('./fin.png','PNG')
     images = [openpose_image, canny_image]
-    imoge = pipe([prompt]*2,images,num_inference_steps=stips,generator=generator,negative_prompt=[neg_prompt]*2,controlnet_conditioning_scale=[blip, blop])
-    for i, imge in enumerate(imoge["images"]):
+    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(cannyimage)
+    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
 
-iface = gr.Interface(fn=plex,inputs=[gr.Image(type="filepath"), gr.Textbox(label="prompt"), gr.Textbox(label="neg_prompt", value="monochrome, lowres, bad anatomy, worst quality, low quality"), gr.Slider(label="infer_steps", value=5, minimum=1, step=1, maximum=5), gr.Dropdown(choices=models, value=models[0], type="value", label="select a model"), gr.Dropdown(choices=sdulers, value=sdulers[0], type="value", label="schedulrs"), gr.Slider(label="condition_scale_canny", value=0.5, minimum=0.1, step=0.1, maximum=1), gr.Slider(label="condition_scale_pose", value=0.5, minimum=0.1, step=0.1, maximum=1)], outputs=gr.Gallery(columns=2,rows=3), title="Img2Img Guided Multi-Conditioned Canny/Pose Controlnet Selectable StableDiffusion Model Demo", description="by JoPmt.")
+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")
+    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=1)
+iface.launch(max_threads=20,inline=False,show_api=False)