Update app.py

app.py

@@ -12,11 +12,12 @@ from torch import autocast
 import cv2
 from matplotlib import pyplot as plt
 from torchvision import transforms
-from diffusers import DiffusionPipeline
+from diffusers import DiffusionPipeline, UNet2DModel
 
 from share_btn import community_icon_html, loading_icon_html, share_js
 
-pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16).to("cuda")
+unet = UNet2DModel.from_pretrained("valhalla/sdxl-inpaint-ema")
+pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
 
 def read_content(file_path: str) -> str:
     """read the content of target file
@@ -26,10 +27,13 @@ def read_content(file_path: str) -> str:
 
     return content
 
-def predict(dict, prompt=""):
-    init_image = dict["image"].convert("RGB").resize((512, 512))
-    mask = dict["mask"].convert("RGB").resize((512, 512))
-    output = pipe(prompt = prompt, image=init_image, mask_image=mask,guidance_scale=7.5)
+def predict(dict, prompt="", guidance_scale, steps, strength):
+    
+    init_image = dict["image"].convert("RGB").resize((1024, 1024))
+    mask = dict["mask"].convert("RGB").resize((1024, 1024))
+    
+    output = pipe(prompt = prompt, image=init_image, mask_image=mask, guidance_scale=guidance_scale, steps=int(steps), strength=strength)
+    
     return output.images[0], gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
 
 
@@ -82,6 +86,10 @@ with image_blocks as demo:
                     image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Upload").style(height=400)
                     with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
                         prompt = gr.Textbox(placeholder = 'Your prompt (what you want in place of what is erased)', show_label=False, elem_id="input-text")
+                        guidance_scale = gradio.Number(value=7.5, minimum=1.0, maximum=20.0)
+                        steps = gradio.Number(value=20, minimum=10, maximum=50)
+                        strength = gradio.Number(value=0.0, minimum=0.0, maximum=0.0)
+                        
                         btn = gr.Button("Inpaint!").style(
                             margin=False,
                             rounded=(False, True, True, False),
@@ -95,7 +103,7 @@ with image_blocks as demo:
                         share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
             
 
-            btn.click(fn=predict, inputs=[image, prompt], outputs=[image_out, community_icon, loading_icon, share_button])
+            btn.click(fn=predict, inputs=[image, prompt, guidance_scale, steps, strength], outputs=[image_out, community_icon, loading_icon, share_button])
             share_button.click(None, [], [], _js=share_js)