use pipeline

app.py

@@ -1,22 +1,29 @@
 import gradio as gr
-from diffusers import StableDiffusionPipeline
-# from PIL import Image
+from PIL import Image
 
+from diffusers import (
+    StableDiffusionPipeline,
+    StableDiffusionImg2ImgPipeline,
+    StableDiffusionInpaintPipeline,
+)
 
 device="cuda"
+model_id = "IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1"
 
-# pipe_text2img = StableDiffusionPipeline.from_pretrained(model_id).to(device)
-io1_text2img = gr.Interface.load("models/IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1")
+pipe_text2img = StableDiffusionPipeline.from_pretrained(model_id).to(device)
+pipe_img2img = StableDiffusionImg2ImgPipeline(**pipe_text2img.components)
+pipe_inpaint = StableDiffusionInpaintPipeline(**pipe_text2img.components)
+
+# io1_text2img = gr.Interface.load("models/IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1")
 
 def resize(w_val,l_val,img):
   img = Image.open(img)
   img = img.resize((w_val,l_val), Image.Resampling.LANCZOS)
   return img
 
-
 def infer(prompt, guide, steps, width, height): 
-    
-    output = io1_text2img(prompt, guidance_scale=guide, num_inference_steps=steps, width=width, height=height)
+    output = pipe_text2img(prompt, guidance_scale=guide, num_inference_steps=steps, width=width, height=height)
+    # output = io1_text2img(prompt, guidance_scale=guide, num_inference_steps=steps, width=width, height=height)
     image = output.images[0]
     return image