Update app.py

app.py

@@ -12,17 +12,19 @@ import base64
 from io import BytesIO
 from PIL import Image
 
-url = "http://54.91.63.201:80"
+url = "http://107.23.90.209:80"
+
+# print('=='*20)
+# print(os.system("hostname -i"))
+
+def img2img_generate(source_img, prompt, steps=25, strength=0.75, seed=42, guidance_scale=7.5, hidden=""):
 
-print('=='*20)
-print(os.system("hostname -i"))
 
-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)
-    print(type(source_img))
+    print('image-to-image')
     print("prompt: ", prompt)
+    print("steps: ", steps)
     buffered = BytesIO()
     source_img.save(buffered, format="JPEG")
     img_b64 = base64.b64encode(buffered.getvalue())
@@ -33,19 +35,29 @@ def img2img_generate(source_img, prompt, steps=25, strength=0.75, seed=42, guida
 
     start_time = time.time()
     resp = requests.post(url, data=json.dumps(data))
-    img_str = json.loads(resp.text)["img_str"]
-    print("location: ", json.loads(resp.text)["ip"])
+
+    try:
+        img_str = json.loads(resp.text)["img_str"]
+        print("compute node: ", json.loads(resp.text)["ip"])
+    except:
+        print('no inference result. please check server connection')
+        return None
 
     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)
+    print("elapsed time: ", time.time() - start_time)
     return img
 
 
-def txt2img_generate(prompt, steps=25, seed=42, guidance_scale=7.5):
+def txt2img_generate(prompt, steps=25, seed=42, guidance_scale=7.5, hidden=""):
+
+    if hidden != os.environ["front_token"]:
+        return None
+
     # cpu info
     # print(subprocess.check_output(["cat /proc/cpuinfo | grep 'model name' |uniq"], stderr=subprocess.STDOUT).decode("utf8"))
+    print('text-to-image')
     print("prompt: ", prompt)
     print("steps: ", steps)
     data = {"prompt": prompt,
@@ -53,21 +65,33 @@ def txt2img_generate(prompt, steps=25, seed=42, guidance_scale=7.5):
         "token": os.environ["access_token"]}
     start_time = time.time()
     resp = requests.post(url, data=json.dumps(data))
-
-    img_str = json.loads(resp.text)["img_str"]
-    print("location: ", json.loads(resp.text)["ip"])
+    try:
+        img_str = json.loads(resp.text)["img_str"]
+        print("compute node: ", json.loads(resp.text)["ip"])
+    except:
+        print('no inference result. please check server connection')
+        return None
 
     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)
+    print("elapsed time: ", 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)** on Amazon Web Services. Try it and generate photorealistic images from text!
+This demo shows the accelerated inference performance of a Stable Diffusion model on **Intel Xeon Gold 64xx (4th Gen Intel Xeon Scalable Processors codenamed Sapphire Rapids)**. 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>.
 """
 
+legal = """
+Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex. Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates.  See backup for configuration details.  No product or component can be absolutely secure.
+© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.
+"""
+
+details = """
+4th Gen Intel Xeon Scalable Processor Inference. Test by Intel on 01/06/2023. 1 node, 1S, Intel(R) Xeon(R) Gold 64xx CPU @ 3.0GHz 32 cores and software with 512GB (8x64GB DDR5 4800 MT/s [4800 MT/s]), microcode 0x2a000080, HT on, Turbo on, Ubuntu 22.04.1 LTS, 5.15.0-1026-aws, 200G Amazon Elastic Block Store. Multiple nodes connected with Elastic Network Adapter (ENA). PyTorch Nightly build (2.0.0.dev20230105+cpu), Transformers 4.25.1, Diffusers 0.11.1, oneDNN v2.7.2.
+"""
+
 css = '''
     .instruction{position: absolute; top: 0;right: 0;margin-top: 0px !important}
     .arrow{position: absolute;top: 0;right: -110px;margin-top: -8px !important}
@@ -88,6 +112,7 @@ with gr.Blocks(css=css) as demo:
                 inference_steps = gr.inputs.Slider(1, 100, label='Inference Steps - increase the steps for better quality (e.g., avoiding black image) ', default=20, step=1)
                 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)
+                hidden = gr.Textbox(label='hidden', value=os.environ["front_token"], visible=False)
                 txt2img_button = gr.Button("Generate Image")
 
             with gr.Column():
@@ -98,19 +123,26 @@ with gr.Blocks(css=css) as demo:
         with gr.Row() as image_to_image:
             with gr.Column():
                 source_img = gr.Image(source="upload", type="pil", value="https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg")
+                # source_img = gr.Image(source="upload", type="pil")
                 prompt_2 = gr.inputs.Textbox(label='Prompt', default='A fantasy landscape, trending on artstation')
                 inference_steps_2 = gr.inputs.Slider(1, 100, label='Inference Steps - increase the steps for better quality (e.g., avoiding black image) ', default=20, step=1)
                 seed_2 = gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1)
                 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)
+                hidden_2 = gr.Textbox(label='hidden', value=os.environ["front_token"], visible=False)
                 img2img_button = gr.Button("Generate Image")
 
             with gr.Column():
                 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)
+    txt2img_button.click(fn=txt2img_generate, inputs=[prompt, inference_steps, seed, guidance_scale, hidden], 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, hidden_2], outputs=result_image_2, queue=False)
+
+    gr.Markdown("**Additional Test Configuration Details:**")
+    gr.Markdown(details)
 
+    gr.Markdown("**Notices and Disclaimers:**")
+    gr.Markdown(legal)
 
 demo.queue(default_enabled=False).launch(debug=True)