Update app.py

app.py

@@ -2,32 +2,28 @@ import gradio as gr
 from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
 import torch
 
+model_id = "Fazzie/PokemonGAI"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+print("load model successfully!")
+pipe = pipe.to(device)
 
 def imagen(inputs):
 
     prompt = inputs
     negative_prompt = 'pixelated, distorted'
 
-    model_id = "Fazzie/PokemonGAI"
-
-
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    if torch.cuda.is_available():
-        pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16")  
-    else:
-        pipe = StableDiffusionPipeline.from_pretrained(model_id)
-
-    print("load model successfully!")
-    pipe = pipe.to(device)
-    
-    # pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-
     print("start inference")
     image = pipe(prompt=inputs, negative_prompt=negative_prompt).images[0]
     print("finish inference!")
     return image
-
+    
 demo = gr.Interface(fn=imagen, inputs=[gr.Textbox(label = 'Prompt')], outputs="image")
-demo.launch()
+
+if __name__ == "__main__":
+    demo.launch()
+
+image.save("1.png")
 
 # gr.Interface.load("models/Fazzie/PokemonGAI").launch()