Update app.py

app.py

@@ -13,36 +13,31 @@ import base64
 from io import BytesIO
 from PIL import Image
 
-
 url_SPR = "http://34.229.166.42:80"
 url_ICX = "http://54.221.56.4:80"
 
 print('=='*20)
 print(os.system("hostname -i"))
 
-def url_requests(url,data,sizeImg):
+
+def url_requests(url, data):
     resp = requests.post(url, data=json.dumps(data))
     img_str = json.loads(resp.text)["img_str"]
 
     img_byte = base64.b64decode(img_str)
     img_io = BytesIO(img_byte)  # convert image to file-like object
     img = Image.open(img_io)   # img is now PIL Image object
-    _img = img.resize((sizeImg))
-    return _img
-        
-        
 
+    return img
 
-def img2img_generate(source_img, prompt, steps=25, strength=0.25, seed=42, guidance_scale=15):
+def img2img_generate(url, source_img, prompt, steps=25, strength=0.75, seed=42, guidance_scale=7.5):
     # cpu info
     # print(subprocess.check_output(["cat /proc/cpuinfo | grep 'model name' |uniq"], stderr=subprocess.STDOUT).decode("utf8"))
     print('=*'*20)
     print(type(source_img))
     print("prompt: ", prompt)
     buffered = BytesIO()
-    
     source_img.save(buffered, format="JPEG")
-    print(source_img.size)
     img_b64 = base64.b64encode(buffered.getvalue())
 
     data = {"source_img": img_b64.decode(), "prompt": prompt, "steps": steps,
@@ -50,41 +45,34 @@ def img2img_generate(source_img, prompt, steps=25, strength=0.25, seed=42, guida
             "token": os.environ["access_token"]}
 
     start_time = time.time()
-    
-    img = url_requests(url, data, source_img.size)
-    
+    img = url_requests(url, data)
+
     return img
 
 
-def txt2img_generate(prompt, steps=25, seed=42, guidance_scale=7.5):
+def txt2img_generate(url, prompt, steps=25, seed=42, guidance_scale=7.5):
     # cpu info
     # print(subprocess.check_output(["cat /proc/cpuinfo | grep 'model name' |uniq"], stderr=subprocess.STDOUT).decode("utf8"))
     print("prompt: ", prompt)
     print("steps: ", steps)
     data = {"prompt": prompt,
-        "steps": steps, "guidance_scale": guidance_scale, "seed": seed}
+        "steps": steps, "guidance_scale": guidance_scale, "seed": seed,
+        "token": os.environ["access_token"]}
     start_time = time.time()
-    req_list = [ 
-        grequests.post(url, data=json.dumps(data)),
-        grequests.post(url, data=json.dumps(data)),
-    ]
-    img_list = url_requests(req_list)
-
-    return img_list
-md = '''
-'''
+    img = url_requests(url, data)
+
+    return img
+
+md = """
+This demo shows the accelerated inference performance of a Stable Diffusion model on **4th Gen Intel Xeon Scalable Processors (Sapphire Rapids)** vs. **3rd Gen Intel Xeon Scalable Processors (Ice Lake)** on Amazon Web Services. Try it and see up to **5x performance speedup** on **4th Gen Intel Xeon**!
+"""
 
 css = '''
     .instruction{position: absolute; top: 0;right: 0;margin-top: 0px !important}
     .arrow{position: absolute;top: 0;right: -110px;margin-top: -8px !important}
     #component-4, #component-3, #component-10{min-height: 0}
     .duplicate-button img{margin: 0}
-    img_1{height:5rem}
-    img_2{height:15rem}
-    img_3{height:15rem}
-    img_4{height:15rem}
-    img_5{height:15rem}
-    img_6{height:15rem}
+    #img_1, #img_2, #img_3, #img_4{height:15rem}
 '''
 
 random_seed = random.randint(0, 2147483647)
@@ -121,11 +109,14 @@ with gr.Blocks(css=css) as demo:
                 url_SPR = gr.Textbox(label='url_SPR', value="http://34.229.166.42:80", visible=False)
                 url_CLX = gr.Textbox(label='url_CLX', value="http://54.221.56.4:80", visible=False)
 
-            with gr.Column():
-                result_image_3 = gr.Image(label="Result01", elem_id="img_3")
-                result_image_4 = gr.Image(label="Result02", elem_id="img_4")
                 
-    txt2img_button.click(fn=txt2img_generate, inputs=[prompt, inference_steps, seed, guidance_scale], outputs=[result_image_1, result_image_2], queue=False)
+            with gr.Column():
+                result_image_3 = gr.Image(label="4th Gen Intel Xeon Scalable Processors (SPR)", elem_id="img_3")
+                result_image_4 = gr.Image(label="3rd Gen Intel Xeon Scalable Processors (ICX)", elem_id="img_4")
+    
+    txt2img_button.click(fn=txt2img_generate, inputs=[url_SPR_txt, prompt, inference_steps, seed, guidance_scale], outputs=result_image_1, queue=False)
+    txt2img_button.click(fn=txt2img_generate, inputs=[url_CLX_txt, prompt, inference_steps, seed, guidance_scale], outputs=result_image_2, queue=False)
     img2img_button.click(fn=img2img_generate, inputs=[url_SPR, source_img, prompt_2, inference_steps_2, strength, seed_2, guidance_scale_2], outputs=result_image_3, queue=False)
     img2img_button.click(fn=img2img_generate, inputs=[url_CLX, source_img, prompt_2, inference_steps_2, strength, seed_2, guidance_scale_2], outputs=result_image_4, queue=False)
-demo.queue(default_enabled=False).launch(debug=True)
+
+demo.queue(default_enabled=False).launch(debug=True)