Update app.py

app.py

@@ -53,13 +53,13 @@ def download_image(url):
     response = requests.get(url)
     return PIL.Image.open(BytesIO(response.content)).convert("RGB")
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
+device = "cpu" #"cuda" if torch.cuda.is_available() else "cpu"
 
 model_id_or_path = "CompVis/stable-diffusion-v1-4"
 pipe = StableDiffusionInpaintingPipeline.from_pretrained(
     model_id_or_path,
     revision="fp16", 
-    torch_dtype=torch.float,
+    torch_dtype=torch.half, #float16
     use_auth_token=auth_token
 )
 
@@ -258,6 +258,7 @@ with image_blocks as demo:
             with gr.Box(elem_id="mask_radio").style(border=False):
                 radio = gr.Radio(["draw a mask above", "type what to mask below", "type what to keep"], value="draw a mask above", show_label=False, interactive=True).style(container=False)
                 word_mask = gr.Textbox(label = "What to find in your image", interactive=False, elem_id="word_mask", placeholder="Disabled").style(container=False)
+                img_res = gr.inputs.Dropdown("512*512", "256*256")
             prompt = gr.Textbox(label = 'Your prompt (what you want to add in place of what you are removing)')
             radio.change(fn=swap_word_mask, inputs=radio, outputs=word_mask,show_progress=False)
             radio.change(None, inputs=[], outputs=image_blocks, _js = """
@@ -278,10 +279,10 @@ with image_blocks as demo:
                 </div>
                 
                 
-            <div id="readme" class="Box-body readme blob js-code-block-container p-5 p-xl-6 gist-border-0">
+            <div id="readme" >
     <article class="markdown-body entry-content container-lg" itemprop="text"><h1 dir="auto"><a id="user-content-image-segmentation-using-text-and-image-prompts" class="anchor" aria-hidden="true" href="#image-segmentation-using-text-and-image-prompts"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>Image Segmentation Using Text and Image Prompts</h1>
 <p dir="auto">This repository contains the code used in the paper <a href="https://arxiv.org/abs/2112.10003" rel="nofollow">"Image Segmentation Using Text and Image Prompts"</a>.</p>
-<p dir="auto"><strong>The Paper has been accepted to CVPR 2022!</strong></p>
+
 <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/ThereforeGames/txt2mask/blob/main/repositories/clipseg/overview.png"><img src="/ThereforeGames/txt2mask/raw/main/repositories/clipseg/overview.png" alt="drawing" style="max-width: 100%;" height="200em"></a></p>
 <p dir="auto">The systems allows to create segmentation models without training based on:</p>
 <ul dir="auto">