Update app.py

app.py

@@ -5,8 +5,8 @@ from diffusers import AutoPipelineForInpainting, UNet2DConditionModel
 import diffusers
 from share_btn import community_icon_html, loading_icon_html, share_js
 
-unet = UNet2DConditionModel.from_pretrained("valhalla/sdxl-inpaint-ema", torch_dtype=torch.float16, revision="d5593e75323fa2a5285ebe02c1aba504a695bbf7")  # 50k
-pipe = AutoPipelineForInpainting.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = AutoPipelineForInpainting.from_pretrained("diffusers/inpainting-sdxl-0.1", torch_dtype=torch.float16, variant="fp16").to(device)
 
 def read_content(file_path: str) -> str:
     """read the content of target file
@@ -16,7 +16,7 @@ def read_content(file_path: str) -> str:
 
     return content
 
-def predict(dict, prompt="", guidance_scale=7.5, steps=20, strength=1.0, scheduler="EulerDiscreteScheduler"):
+def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=20, strength=1.0, scheduler="EulerDiscreteScheduler"):
 
     scheduler_class_name = scheduler.split("-")[0]
 
@@ -32,7 +32,7 @@ def predict(dict, prompt="", guidance_scale=7.5, steps=20, strength=1.0, schedul
     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, num_inference_steps=int(steps), strength=strength)
+    output = pipe(prompt = prompt, negative_prompt=negative_prompt, image=init_image, mask_image=mask, guidance_scale=guidance_scale, num_inference_steps=int(steps), strength=strength)
     
     return output.images[0], gr.update(visible=True)
 
@@ -89,8 +89,8 @@ with image_blocks as demo:
                         with gr.Row(mobile_collapse=False, 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=30, step=1, label="steps")
-                            strength = gr.Number(value=0.99, minimum=0.0, maximum=0.99, step=0.01, label="strength")
-    
+                            strength = gr.Number(value=0.99, minimum=0.01, maximum=0.99, 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(mobile_collapse=False, equal_height=True):
                             schedulers = ["DEISMultistepScheduler", "HeunDiscreteScheduler", "EulerDiscreteScheduler", "DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler-Karras", "DPMSolverMultistepScheduler-Karras-SDE"]
                             scheduler = gr.Dropdown(label="Schedulers", choices=schedulers, value="EulerDiscreteScheduler")
@@ -103,7 +103,7 @@ with image_blocks as demo:
                         share_button = gr.Button("Share to community", elem_id="share-btn",visible=True)
             
 
-    btn.click(fn=predict, inputs=[image, prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, share_btn_container])
+    btn.click(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.Examples(