Update app.py

app.py

@@ -1,32 +1,45 @@
 from diffusers import StableDiffusionPipeline
 import torch
-import gradio as gr
+import gradio
 import accelerate
 
-models=[
-"ItsJayQz/Marvel_WhatIf_Diffusion",
-"DGSpitzer/Cyberpunk-Anime-Diffusion",
-"wavymulder/portraitplus",
-"nitrosocke/classic-anim-diffusion",
-"22h/vintedois-diffusion-v0-1",
-"dreamlike-art/dreamlike-diffusion-1.0",
-"stabilityai/stable-diffusion-2-1"
+class Model:
+    def __init__(self, name, path="", prefix=""):
+        self.name = name
+        self.path = path
+        self.prefix = prefix
+
+models = [
+   Model("Marvel","models/ItsJayQz/Marvel_WhatIf_Diffusion", "whatif style"), 
+   Model("Cyberpunk Anime Diffusion", "models/DGSpitzer/Cyberpunk-Anime-Diffusion", "dgs illustration style"),
+   Model("Portrait plus", "models/wavymulder/portraitplus", "portrait+ style"),
+   Model("classic Disney", "models/nitrosocke/classic-anim-diffusion", "classic disney style"),
+   Model("vintedois", "models/22h/vintedois-diffusion-v0-1", "vintedois style"),
+   Model("dreamlike", "models/dreamlike-art/dreamlike-diffusion-1.0","dreamlike style"),
+   Model("SD21","models/stabilityai/stable-diffusion-2-1", "sd21 default style")
 ]
 
+model2=[]
+model3=[]
+
 for i in range(len(models)):
-     gr.Interface.load(f"models/{models[i]}",live=True,preprocess=True)
+    model3.append(models[i].name)
+    model2.append(models[i].prefix)
 
-def TextToImage(Prompt,model):
-  model_id = model
-  pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.get_default_dtype())
-  pipe = pipe.to("cpu")
-  prompt = Prompt
-  image = pipe(prompt).images[0]
-  return image
+def process1(prompt):
+    modelSelected==''
+    for i in range(len(models)):
+        if message.find(models[i].prefix)!=-1:
+            modelSelected=models[i].path
+            gradio.Interface.load(modelSelected)
+    if (modelSelected==''):
+        modelSelected = "models/stabilityai/stable-diffusion-2-1"
+        gradio.Interface.load(modelSelected)
+    image_return = modelSelected(prompt)
+    return image_return
 
-sandbox = gr.Interface(fn=TextToImage, 
-                        inputs=["text", gr.Dropdown(models)],
-                        outputs="image", 
+sandbox = gradio.Interface(fn=process1, 
+                        inputs=[gradio.Textbox(label="Enter Prompt:")],
+                        outputs=[gradio.Image(label="Produced Image")], 
                         title='AlStable Text to Image')
-
-sandbox.launch()
+sandbox.queue(concurrency_count=20).launch()