Update app.py

app.py

@@ -7,30 +7,17 @@ import subprocess
 import time
 import requests
 import json
-import threading
 
 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"
+url = "http://54.91.63.201:80"
 
 print('=='*20)
 print(os.system("hostname -i"))
 
-
-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
-
-    return img
-
-def img2img_generate(url, source_img, prompt, steps=25, strength=0.75, seed=42, guidance_scale=7.5):
+def img2img_generate(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)
@@ -45,12 +32,18 @@ def img2img_generate(url, source_img, prompt, steps=25, strength=0.75, seed=42,
             "token": os.environ["access_token"]}
 
     start_time = time.time()
-    img = url_requests(url, data)
+    resp = requests.post(url, data=json.dumps(data))
+    img_str = json.loads(resp.text)["img_str"]
+    print("location: ", json.loads(resp.text)["ip"])
 
+    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
+    print("cost: ", time.time() - start_time)
     return img
 
 
-def txt2img_generate(url, prompt, steps=25, seed=42, guidance_scale=7.5):
+def txt2img_generate(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)
@@ -59,12 +52,20 @@ def txt2img_generate(url, prompt, steps=25, seed=42, guidance_scale=7.5):
         "steps": steps, "guidance_scale": guidance_scale, "seed": seed,
         "token": os.environ["access_token"]}
     start_time = time.time()
-    img = url_requests(url, data)
+    resp = requests.post(url, data=json.dumps(data))
 
+    img_str = json.loads(resp.text)["img_str"]
+    print("location: ", json.loads(resp.text)["ip"])
+
+    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
+    print("cost: ", time.time() - start_time)
     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**!
+This demo shows the accelerated inference performance of a Stable Diffusion model on **4th Gen Intel Xeon Scalable Processors (Sapphire Rapids)** on Amazon Web Services. Try it and generate photorealistic images from text!
+You may also want to try creating your own Stable Diffusion model with few-shot fine-tuning. Please refer to our <a href=\"https://medium.com/intel-analytics-software/personalized-stable-diffusion-with-few-shot-fine-tuning-on-a-single-cpu-f01a3316b13\">blog</a> and <a href=\"https://github.com/intel/neural-compressor/tree/master/examples/pytorch/diffusion_model/diffusers/textual_inversion\">code</a> available in <a href=\"https://github.com/intel/neural-compressor\">Intel Neural Compressor</a> and <a href=\"https://github.com/huggingface/diffusers\">Hugging Face Diffusers</a>.
 """
 
 css = '''
@@ -72,13 +73,12 @@ css = '''
     .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, #img_2, #img_3, #img_4{height:15rem}
 '''
 
 random_seed = random.randint(0, 2147483647)
 
 with gr.Blocks(css=css) as demo:
-    gr.Markdown("# Stable Diffusion Inference Demo Comparison")
+    gr.Markdown("# Stable Diffusion Inference Demo on 4th Gen Intel Xeon Scalable Processors")
     gr.Markdown(md)
 
     with gr.Tab("Text-to-Image"):
@@ -89,13 +89,11 @@ with gr.Blocks(css=css) as demo:
                 seed = gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1)
                 guidance_scale = gr.inputs.Slider(1.0, 20.0, label='Guidance Scale - how much the prompt will influence the results', default=7.5, step=0.1)
                 txt2img_button = gr.Button("Generate Image")
-                url_SPR_txt = gr.Textbox(label='url_SPR_txt', value="http://34.229.166.42:80", visible=False)
-                url_CLX_txt = gr.Textbox(label='url_CLX_txt', value="http://54.221.56.4:80", visible=False)
 
             with gr.Column():
-                result_image_1 = gr.Image(label="4th Gen Intel Xeon Scalable Processors (SPR)", elem_id="img_1")
-                result_image_2 = gr.Image(label="3rd Gen Intel Xeon Scalable Processors (ICX)", elem_id="img_2")
-    
+                result_image = gr.Image()
+
+
     with gr.Tab("Image-to-Image text-guided generation"):
         with gr.Row() as image_to_image:
             with gr.Column():
@@ -106,17 +104,13 @@ with gr.Blocks(css=css) as demo:
                 guidance_scale_2 = gr.inputs.Slider(1.0, 20.0, label='Guidance Scale - how much the prompt will influence the results', default=7.5, step=0.1)
                 strength = gr.inputs.Slider(0.0, 1.0, label='Strength - adding more noise to it the larger the strength', default=0.75, step=0.01)
                 img2img_button = gr.Button("Generate Image")
-                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="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)
+                result_image_2 = gr.Image()
+
+
+    txt2img_button.click(fn=txt2img_generate, inputs=[prompt, inference_steps, seed, guidance_scale], outputs=result_image, queue=False)
+    img2img_button.click(fn=img2img_generate, inputs=[source_img, prompt_2, inference_steps_2, strength, seed_2, guidance_scale_2], outputs=result_image_2, queue=False)
+
 
 demo.queue(default_enabled=False).launch(debug=True)