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import gradio
class Model:
def __init__(self, name, path="", prefix=""):
self.name = name
self.path = path
self.prefix = prefix
models = [
Model("Anime","models/DucHaiten/DucHaitenAnime","DucHaiten style"),
Model("dreamlike","models/dreamlike-art/dreamlike-diffusion-1.0","dreamlike style"),
#Model("Illuminati_Diffusion","models/IlluminatiAI/Illuminati_Diffusion_v1.0","photo realistic style"),
Model("CF25","models/gsdf/Counterfeit-V2.5","best quality"),
Model("Midjourney","models/prompthero/midjourney-v4-diffusion","midjourney style"),
Model("PhotoReal","models/dreamlike-art/dreamlike-photoreal-2.0","photoreal style"),
Model("MF Base","models/MyneFactory/MF-Base","booru style"),
Model("Anything","models/Linaqruf/anything-v3.0", "Anything style")
]
model1=[]
model2=[]
model3=[]
for i in range(len(models)):
model3.append(models[i].name)
model1.append(gradio.Interface.load(models[i].path))
model2.append(models[i].prefix)
def ProcessP(prompt, modelSelected):
print(prompt)
print(modelSelected)
if (modelSelected==''):
modelSelected = "Anything"
model_idx=model3.index(modelSelected)
prompt+=", in "+model2[model_idx]
image_return = model1[model_idx](prompt)
return image_return
sandbox = gradio.Interface(fn=ProcessP,
inputs=[gradio.Textbox(label="Enter Prompt:"), gradio.Dropdown(model3)],
outputs=[gradio.Image(label="Produced Image")],
title='AlStable Text to Image')
sandbox.queue(concurrency_count=20).launch(debug=True) |