Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import cv2
+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, UniPCMultistepScheduler
+from controlnet_aux import OpenposeDetector
+
+accelerator = Accelerator(cpu=True)
+openpose = accelerator.prepare(OpenposeDetector.from_pretrained("lllyasviel/ControlNet"))
+
+controlnet = [
+    accelerator.prepare(ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float32)),
+    accelerator.prepare(ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float32)),
+]
+
+models =[
+    "runwayml/stable-diffusion-v1-5",
+    "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",
+    "warp-ai/wuerstchen",
+    "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",
+]
+
+def plex(mput, prompt, neg_prompt, stips, modal_id, dula, blip, blop):
+    dula=dula ## shedulers todo
+    pope = accelerator.prepare(StableDiffusionPipeline.from_pretrained(modal_id, use_safetensors=False, safety_checker=None,torch_dtype=torch.float32))
+    pope.to("cpu")
+    pipe = accelerator.prepare(StableDiffusionControlNetPipeline.from_pretrained(modal_id, use_safetensors=False,controlnet=controlnet, safety_checker=None,torch_dtype=torch.float32))
+    pipe.to("cpu")
+    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+    generator = torch.Generator(device="cpu").manual_seed(16384)
+    
+    tilage = pope(prompt,num_inference_steps=5).images[0]
+    cannmage = tilage
+    cannyimage = np.array(cannmage)
+    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)
+    canny_image = Image.fromarray(cannyimage)  
+    pose_image = load_image(mput)
+    openpose_image = openpose(pose_image)
+    images = [openpose_image, canny_image]
+
+    imoge = pipe(
+        prompt,
+        images,
+        num_inference_steps=stips,
+        generator=generator,
+        negative_prompt=neg_prompt,
+        controlnet_conditioning_scale=[blip, blop],
+    ).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, type="value", label="select a model"), gr.Dropdown(choices=sdulers, 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()