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Create app.py
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app.py
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from diffusers import DPMSolverMultistepScheduler
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import gradio as gr
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from PIL import Image
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import cv2
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import qrcode
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import numpy as np
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from transformers import pipeline
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import PIL.Image
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from diffusers.utils import load_image
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from accelerate import Accelerator
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from diffusers import StableDiffusionPipeline
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import torch
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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
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accelerator = Accelerator(cpu=True)
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models =[
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"runwayml/stable-diffusion-v1-5",
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"prompthero/openjourney-v4",
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"CompVis/stable-diffusion-v1-4",
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"stabilityai/stable-diffusion-2-1",
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"stablediffusionapi/disney-pixal-cartoon",
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"stablediffusionapi/edge-of-realism",
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"MirageML/fantasy-scene",
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"wavymulder/lomo-diffusion",
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"sd-dreambooth-library/fashion",
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"DucHaiten/DucHaitenDreamWorld",
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"VegaKH/Ultraskin",
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"kandinsky-community/kandinsky-2-1",
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"MirageML/lowpoly-cyberpunk",
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"thehive/everyjourney-sdxl-0.9-finetuned",
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"plasmo/woolitize-768sd1-5",
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"plasmo/food-crit",
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"johnslegers/epic-diffusion-v1.1",
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"Fictiverse/ElRisitas",
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"robotjung/SemiRealMix",
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"herpritts/FFXIV-Style",
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"prompthero/linkedin-diffusion",
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"RayHell/popupBook-diffusion",
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"MirageML/lowpoly-world",
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"deadman44/SD_Photoreal_Merged_Models",
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"Conflictx/CGI_Animation",
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"johnslegers/epic-diffusion",
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"tilake/China-Chic-illustration",
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"wavymulder/modelshoot",
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"prompthero/openjourney-lora",
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"Fictiverse/Stable_Diffusion_VoxelArt_Model",
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"darkstorm2150/Protogen_v2.2_Official_Release",
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"hassanblend/HassanBlend1.5.1.2",
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"hassanblend/hassanblend1.4",
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"nitrosocke/redshift-diffusion",
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"prompthero/openjourney-v2",
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"nitrosocke/Arcane-Diffusion",
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"Lykon/DreamShaper",
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"wavymulder/Analog-Diffusion",
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"nitrosocke/mo-di-diffusion",
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"dreamlike-art/dreamlike-diffusion-1.0",
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"dreamlike-art/dreamlike-photoreal-2.0",
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"digiplay/RealismEngine_v1",
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"digiplay/AIGEN_v1.4_diffusers",
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"stablediffusionapi/dreamshaper-v6",
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"JackAnon/GorynichMix",
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"p1atdev/liminal-space-diffusion",
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"nadanainone/gigaschizonegs",
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"darkVOYAGE/dvMJv4",
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"lckidwell/album-cover-style",
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"axolotron/ice-cream-animals",
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"perion/ai-avatar",
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"digiplay/GhostMix",
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"ThePioneer/MISA",
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"TheLastBen/froggy-style-v21-768",
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"FloydianSound/Nixeu_Diffusion_v1-5",
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"kakaobrain/karlo-v1-alpha-image-variations",
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"digiplay/PotoPhotoRealism_v1",
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"ConsistentFactor/Aurora-By_Consistent_Factor",
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"rim0/quadruped_mechas",
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"Akumetsu971/SD_Samurai_Anime_Model",
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"Bojaxxx/Fantastic-Mr-Fox-Diffusion",
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"sd-dreambooth-library/original-character-cyclps",
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"AIArtsChannel/steampunk-diffusion",
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]
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controlnet = accelerator.prepare(ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float32))
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generator = torch.Generator(device="cpu").manual_seed(1)
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def plex(qr_code_value, text, neg_text, modil, one, two, three):
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apol=[]
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modal=""+modil+""
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pipe = accelerator.prepare(StableDiffusionControlNetPipeline.from_pretrained(modal, controlnet=controlnet, torch_dtype=torch.float32, use_safetensors=False, safety_checker=None))
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pipe.scheduler = accelerator.prepare(DPMSolverMultistepScheduler.from_config(pipe.scheduler.config))
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pipe = pipe.to("cpu")
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negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
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prompt = text
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qr_code = qrcode.make(qr_code_value).resize((512, 512))
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rmage = load_image(qr_code)
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original = rmage.convert("RGB")
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original.thumbnail((512, 512))
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cannyimage = load_image(original).resize((512,512))
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cannyimage = np.array(cannyimage)
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low_threshold = 100
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high_threshold = 200
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cannyimage = cv2.Canny(cannyimage, low_threshold, high_threshold)
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cannyimage = cannyimage[:, :, None]
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cannyimage = np.concatenate([cannyimage, cannyimage, cannyimage], axis=2)
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cannyimage = Image.fromarray(cannyimage)
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images = [cannyimage]
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image = pipe(
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[prompt]*2,
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images,
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num_inference_steps=one,
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generator=generator,
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strength=two
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negative_prompt=[neg_prompt]*2,
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controlnet_conditioning_scale=three,
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)
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for i, imge in enumerate(imoge["images"]):
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apol.append(imge)
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apol.append(original)
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apol.append(cannyimage)
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return apol
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iface = gr.Interface(fn=plex, inputs=[gr.Textbox(label="QR Code URL"),gr.Textbox(label="prompt"),gr.Textbox(label="neg prompt"),gr.Dopdown(choices=models, label="some sd models", value=models[0]), gr.Slider(label="num inference steps", minimum=1, step=1, maximum=5, value=5), gr.Slider(label="prompt strength", minimum=0.1, step=0.1, maximum=1, value=0.2), gr.Slider(label="controlnet scale", minimum=0.1, step=0.1, maximum=1, value=0.8)], outputs=gr.Gallery(label="out", columns=1))
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iface.queue(max=1)
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iface.launch()
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