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| from diffusers import AutoPipelineForText2Image | |
| import torch | |
| import gradio as gr | |
| from PIL import Image | |
| import os, random | |
| from diffusers.utils import load_image | |
| from accelerate import Accelerator | |
| accelerator = Accelerator() | |
| models =[ | |
| "prompthero/midjourney-v4-diffusion", | |
| "nitrosocke/classic-anim-diffusion", | |
| "stablediffusionapi/disney-pixal-cartoon", | |
| "stablediffusionapi/edge-of-realism", | |
| "sd-dreambooth-library/original-character-cyclps", | |
| "AIArtsChannel/steampunk-diffusion", | |
| "nitrosocke/mo-di-diffusion", | |
| "MirageML/fantasy-scene", | |
| "wavymulder/lomo-diffusion", | |
| "sd-dreambooth-library/fashion", | |
| "DucHaiten/DucHaitenDreamWorld", | |
| "VegaKH/Ultraskin", | |
| "kandinsky-community/kandinsky-2-1", | |
| "plasmo/woolitize-768sd1-5", | |
| "plasmo/food-crit", | |
| "johnslegers/epic-diffusion-v1.1", | |
| "robotjung/SemiRealMix", | |
| "prompthero/linkedin-diffusion", | |
| "RayHell/popupBook-diffusion", | |
| "MirageML/lowpoly-world", | |
| "warp-ai/wuerstchen", | |
| "deadman44/SD_Photoreal_Merged_Models", | |
| "johnslegers/epic-diffusion", | |
| "wavymulder/modelshoot", | |
| "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", | |
| "dreamlike-art/dreamlike-diffusion-1.0", | |
| "dreamlike-art/dreamlike-photoreal-2.0", | |
| "digiplay/RealismEngine_v1", | |
| "digiplay/AIGEN_v1.4_diffusers", | |
| "stablediffusionapi/dreamshaper-v6", | |
| "axolotron/ice-cream-animals", | |
| "TheLastBen/froggy-style-v21-768", | |
| "FloydianSound/Nixeu_Diffusion_v1-5", | |
| "digiplay/PotoPhotoRealism_v1", | |
| ] | |
| ###bor = len(models) | |
| ###current = random.randint(1, bor) | |
| def plex(modil,prompt,neg_prompt): | |
| pipe = accelerator.prepare(AutoPipelineForText2Image.from_pretrained(""+modil+"", torch_dtype=torch.float32)) | |
| pipe = accelerator.prepare(pipe.to("cpu")) | |
| image = pipe(prompt=prompt, negative_prompt=neg_prompt,num_inference_steps=10).images[0] | |
| return image | |
| iface = gr.Interface(fn=plex,inputs=[gr.Dropdown(choices=models, type="value", value=models[0]), gr.Textbox(label="Prompt"), gr.Textbox(label="negative_prompt", value="low quality, bad quality")],outputs=gr.Image(label="Generated Output Image"), title="AutoPipelineForText2Image_SD_Multi",description="AutoPipelineForText2Image_SD_Multi") | |
| iface.queue(max_size=1,api_open=False) | |
| iface.launch(max_threads=1) |