app.py

from diffusers import DPMSolverMultistepScheduler
import gradio as gr
from PIL import Image
import cv2
import qrcode
import os, random, gc
import numpy as np
from transformers import pipeline
import PIL.Image
from diffusers.utils import load_image
from accelerate import Accelerator
from diffusers import StableDiffusionPipeline
import torch
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel

accelerator = Accelerator(cpu=True)
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",
    "sd-dreambooth-library/fashion",
    "DucHaiten/DucHaitenDreamWorld",
    "VegaKH/Ultraskin",
    "kandinsky-community/kandinsky-2-1",
    "MirageML/lowpoly-cyberpunk",
    "thehive/everyjourney-sdxl-0.9-finetuned",
    "plasmo/woolitize-768sd1-5",
    "plasmo/food-crit",
    "johnslegers/epic-diffusion-v1.1",
    "Fictiverse/ElRisitas",
    "robotjung/SemiRealMix",
    "herpritts/FFXIV-Style",
    "prompthero/linkedin-diffusion",
    "RayHell/popupBook-diffusion",
    "MirageML/lowpoly-world",
    "deadman44/SD_Photoreal_Merged_Models",
    "Conflictx/CGI_Animation",
    "johnslegers/epic-diffusion",
    "tilake/China-Chic-illustration",
    "wavymulder/modelshoot",
    "prompthero/openjourney-lora",
    "Fictiverse/Stable_Diffusion_VoxelArt_Model",
    "darkstorm2150/Protogen_v2.2_Official_Release",
    "hassanblend/HassanBlend1.5.1.2",
    "hassanblend/hassanblend1.4",
    "nitrosocke/redshift-diffusion",
    "prompthero/openjourney-v2",
    "nitrosocke/Arcane-Diffusion",
    "Lykon/DreamShaper",
    "wavymulder/Analog-Diffusion",
    "nitrosocke/mo-di-diffusion",
    "dreamlike-art/dreamlike-diffusion-1.0",
    "dreamlike-art/dreamlike-photoreal-2.0",
    "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",
    "digiplay/GhostMix",
    "ThePioneer/MISA",
    "TheLastBen/froggy-style-v21-768",
    "FloydianSound/Nixeu_Diffusion_v1-5",
    "kakaobrain/karlo-v1-alpha-image-variations",
    "digiplay/PotoPhotoRealism_v1",
    "ConsistentFactor/Aurora-By_Consistent_Factor",
    "rim0/quadruped_mechas",
    "Akumetsu971/SD_Samurai_Anime_Model",
    "Bojaxxx/Fantastic-Mr-Fox-Diffusion",
    "sd-dreambooth-library/original-character-cyclps",
    "AIArtsChannel/steampunk-diffusion",
]

controlnet = accelerator.prepare(ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float32))
def plex(qr_code_value, text, neg_prompt, modil, one, two, three):
    gc.collect()
    apol=[]
    modal=""+modil+""
    pipe = accelerator.prepare(StableDiffusionControlNetPipeline.from_pretrained(modal, controlnet=controlnet, torch_dtype=torch.float32, use_safetensors=False, safety_checker=None))
    pipe.unet.to(memory_format=torch.channels_last)
    pipe.scheduler = accelerator.prepare(DPMSolverMultistepScheduler.from_config(pipe.scheduler.config))
    pipe = pipe.to("cpu")
    negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
    prompt = text
    qr_code = qrcode.make(qr_code_value).resize((512, 512))
    rmage = load_image(qr_code)
    original = rmage.convert("RGB")
    original.thumbnail((512, 512))
    cannyimage = load_image(original).resize((512,512))
    cannyimage = np.array(cannyimage)
    pannyimage = load_image(original).resize((512,512))
    pannyimage = np.array(pannyimage)
    pannyimage = np.invert(pannyimage)
    pannyimage = Image.fromarray(pannyimage)
    low_threshold = 100
    high_threshold = 200
    cannyimage = cv2.Canny(cannyimage, low_threshold, high_threshold)
    cannyimage = cannyimage[:, :, None]
    cannyimage = np.concatenate([cannyimage, cannyimage, cannyimage], axis=2)
    cannyimage = Image.fromarray(cannyimage)
    images = [cannyimage]
    generator = torch.Generator(device="cpu").manual_seed(random.randint(1, 4836923))
    imzge = pipe(prompt,original,num_inference_steps=one,generator=generator,strength=two,negative_prompt=neg_prompt,controlnet_conditioning_scale=three,).images[0]
    apol.append(imzge)
    image = pipe([prompt]*2,images,num_inference_steps=one,generator=generator,strength=two,negative_prompt=[neg_prompt]*2,controlnet_conditioning_scale=three,)
    for i, imge in enumerate(image["images"]):
        apol.append(imge)

        img = load_image(imge)
        img.save('./image.png', 'PNG')
        img = img.resize((512, 512))
        img = img.convert("RGBA")
        img.save('./image.png', 'PNG')

        iog = load_image(original)
        iog.save('./imoge.png', 'PNG')
        iog = iog.resize((512, 512))
        iog = iog.convert("RGBA")
        iog.save('./imoge.png', 'PNG')
        doto = iog.getdata()
    
        new_data = []
        for item in doto:
            if item[0] in list(range(200, 256)):
                new_data.append((255, 255, 255, 0))
            else:
                new_data.append(item)
        iog.putdata(new_data)
        iog.save('./image.png', 'PNG')

        pixel_data1 = list(iog.getdata())
        pixel_data2 = list(img.getdata())

        new_pixel_data = [pixel if pixel[3] > 0 else pixel_data2[i] for i, pixel in enumerate(pixel_data1)]
        if i==1:
            new_imoge = Image.new("RGBA", img.size)
            new_imoge.putdata(new_pixel_data)
            new_imoge.save('./new_imoge.png', 'PNG')
            apol.append(new_imoge)
        else:
            new_image = Image.new("RGBA", img.size)
            new_image.putdata(new_pixel_data)
            new_image.save('./new_image.png', 'PNG')
            apol.append(new_image)
    apol.append(original)
    apol.append(cannyimage)
    apol.append(pannyimage)
    return apol

iface = gr.Interface(fn=plex, inputs=[gr.Textbox(label="QR Code URL"),gr.Textbox(label="prompt"),gr.Textbox(label="neg prompt"),gr.Dropdown(choices=models, label="some sd models", value=models[0], type="value"), gr.Slider(label="num inference steps", minimum=1, step=1, maximum=5, value=5), gr.Slider(label="prompt strength", minimum=0.01, step=0.01, maximum=0.99, value=0.20), gr.Slider(label="controlnet scale", minimum=0.01, step=0.01, maximum=0.99, value=0.80)], outputs=gr.Gallery(label="out", columns=2),description="Running on cpu, very slow! by JoPmt.")
iface.queue(max_size=1,api_open=False)
iface.launch(max_threads=1)