Update app.py

app.py

@@ -2,13 +2,13 @@ import gradio as gr
 import torch
 import numpy as np
 from PIL import Image
-
+from diffusers.models import AutoencoderKL
 from diffusers import AutoPipelineForInpainting, UNet2DConditionModel
 import diffusers
 from share_btn import community_icon_html, loading_icon_html, share_js
 
-
-pipe = AutoPipelineForInpainting.from_pretrained("SG161222/Realistic_Vision_V5.0_noVAE")
+vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
+pipe = AutoPipelineForInpainting.from_pretrained("SG161222/Realistic_Vision_V5.0_noVAE",vae=vae)
 
 def read_content(file_path: str) -> str:
     """read the content of target file
@@ -18,7 +18,7 @@ def read_content(file_path: str) -> str:
 
     return content
 
-def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=30, strength=1.0, scheduler="DPMSolverMultistepScheduler-Karras"):
+def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=30, strength=0.8, scheduler="DPMSolverMultistepScheduler-Karras"):
     if negative_prompt == "":
         negative_prompt = None
     scheduler_class_name = scheduler.split("-")[0]
@@ -30,7 +30,7 @@ def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=30, s
         add_kwargs["algorithm_type"] = "sde-dpmsolver++"
 
     scheduler = getattr(diffusers, scheduler_class_name)
-    pipe.scheduler = scheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler", **add_kwargs)
+    pipe.scheduler = scheduler.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", subfolder="scheduler", **add_kwargs)
     
     init_image = dict["image"]
     mask = dict["mask"]
@@ -91,7 +91,7 @@ with image_blocks as demo:
                         with gr.Row(equal_height=True):
                             guidance_scale = gr.Number(value=7.5, minimum=1.0, maximum=20.0, step=0.1, label="guidance_scale")
                             steps = gr.Number(value=20, minimum=10, maximum=100, step=1, label="steps")
-                            strength = gr.Number(value=0.99, minimum=0.01, maximum=1.0, step=0.01, label="strength")
+                            strength = gr.Number(value=0.8, minimum=0.01, maximum=1.0, step=0.01, label="strength")
                             negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt", info="what you don't want to see in the image")
                         with gr.Row(equal_height=True):
                             schedulers = ["DEISMultistepScheduler", "HeunDiscreteScheduler", "EulerDiscreteScheduler", "DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler-Karras", "DPMSolverMultistepScheduler-Karras-SDE"]
@@ -102,20 +102,12 @@ with image_blocks as demo:
                     with gr.Group(elem_id="share-btn-container", visible=False) as share_btn_container:
                         community_icon = gr.HTML(community_icon_html)
                         loading_icon = gr.HTML(loading_icon_html)
-                        share_button = gr.Button("Share to community", elem_id="share-btn",visible=True)
+                        share_button = gr.Button("Share to community", elem_id="share-btn",visible=False)
             
 
     btn.click(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, share_btn_container], api_name='run')
     prompt.submit(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, share_btn_container])
     share_button.click(None, [], [], _js=share_js)
 
-    gr.HTML(
-        """
-            <div class="footer">
-                <p>Model by <a href="https://huggingface.co/diffusers" style="text-decoration: underline;" target="_blank">Diffusers</a> - Gradio Demo by 🤗 Hugging Face
-                </p>
-            </div>
-        """
-    )
 
 image_blocks.queue(max_size=25,api_open=True).launch(show_api=True)