app.py

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

accelerator = Accelerator(cpu=True)
MAX_SEED = np.iinfo(np.int32).max
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),
]

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",
    "nousr/robo-diffusion-2-base",
    "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",
    "FFusion/FFXL400",
    "digiplay/GhostMix",
    "ThePioneer/MISA",
    "TheLastBen/froggy-style-v21-768",
    "FloydianSound/Nixeu_Diffusion_v1-5",
    "diffusers/sdxl-instructpix2pix-768",
    "kakaobrain/karlo-v1-alpha-image-variations",
    "coreml-community/coreml-HassanBlend",
    "digiplay/PotoPhotoRealism_v1",
    "ConsistentFactor/Aurora-By_Consistent_Factor",
    "coreml/coreml-ghostmix-v11",
    "rim0/quadruped_mechas",
    "Akumetsu971/SD_Samurai_Anime_Model",
    "Bojaxxx/Fantastic-Mr-Fox-Diffusion",
    "sd-dreambooth-library/original-character-cyclps",
    "AIArtsChannel/steampunk-diffusion",
]

sdulers =[
    "UniPCMultistepScheduler",
    "DDIMScheduler",
    "DDPMScheduler",
    "DDIMInverseScheduler",
    "CMStochasticIterativeScheduler",
    "DEISMultistepScheduler",
    "DPMSolverMultistepInverse",
    "DPMSolverMultistepScheduler",
    "DPMSolverSDEScheduler",
    "DPMSolverSinglestepScheduler",
    "EulerAncestralDiscreteScheduler",
    "EulerDiscreteScheduler",
    "HeunDiscreteScheduler",
    "IPNDMScheduler",
    "KarrasVeScheduler",
    "KDPM2AncestralDiscreteScheduler",
    "KDPM2DiscreteScheduler",
    "LMSDiscreteScheduler",
    "PNDMScheduler",
    "RePaintScheduler",
    "ScoreSdeVeScheduler",
    "ScoreSdeVpScheduler",
    "VQDiffusionScheduler",
]

generator = torch.Generator(device="cpu").manual_seed(random.randint(0, MAX_SEED))

def plex(mput, prompt, neg_prompt, stips, modal_id, dula, blip, blop):
    modal_id = ""+modal_id+""
    dula=""+dula+"" ## shedulers todo
    pope = accelerator.prepare(StableDiffusionPipeline.from_pretrained(modal_id, use_safetensors=False, safety_checker=True,torch_dtype=torch.float32))
    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, safety_checker=True,torch_dtype=torch.float32))
    pipe.unet.to(memory_format=torch.channels_last)
    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
    pipe = accelerator.prepare(pipe.to("cpu"))
    
    tilage = pope(prompt,num_inference_steps=5,height=512,width=512,generator=generator).images[0]
    tilage.save('til.png', 'PNG')
    cannyimage = np.array(tilage)
    low_threshold = 100
    high_threshold = 200
    cannyimage = cv2.Canny(cannyimage, low_threshold, high_threshold)
    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,[openpose_image, canny_image],num_inference_steps=stips,negative_prompt=neg_prompt,controlnet_conditioning_scale=[blip, blop],height=512,width=512,generator=generator).images[0]
    return imoge

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=20, minimum=1, step=1, maximum=100), 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=0.05, step=0.05, maximum=0.95), gr.Slider(label="condition_scale_pose", value=0.5, minimum=0.05, step=0.05, maximum=0.95)], outputs=gr.Image(), title="Img2Img Guided Multi-Conditioned Canny/Pose Controlnet Selectable StableDiffusion Model Demo", description="by JoPmt.")
iface.launch()