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Sleeping
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test
Browse files
app.py
CHANGED
@@ -90,22 +90,15 @@ def txt2img_generate(prompt, steps=25, seed=42, guidance_scale=7.5):
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except:
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print('No inference result. Please check server connection')
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return None
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# return [None, gr.update(visible=True), gr.update(visible=False)]
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img_byte = base64.b64decode(img_str)
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img_io = BytesIO(img_byte) # convert image to file-like object
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img = Image.open(img_io) # img is now PIL Image object
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print("elapsed time: ", time.time() - start_time)
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# if queue_size.isdigit():
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# if int(queue_size) > 4:
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# return [img, gr.update(visible=False), gr.update(visible=True)]
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# elif int(queue_size) <= 4:
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# return [img, gr.update(visible=True), gr.update(visible=False)]
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# else:
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# return [img, gr.update(visible=True), gr.update(visible=False)]
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return img
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md = """
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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! Please note that the demo is in **preview** under limited HW resources. We are committed to continue improving the demo and happy to hear your feedbacks. Thanks for your trying!
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You may also want to try creating your own Stable Diffusion 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>.
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@@ -127,8 +120,8 @@ css = '''
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.duplicate-button img{margin: 0}
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#mdStyle{font-size: 0.6rem}
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.generating.svelte-1w9161c { border: none }
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#txtGreenStyle {
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#txtOrangeStyle {
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'''
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random_seed = random.randint(0, 2147483647)
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@@ -137,8 +130,7 @@ with gr.Blocks(css=css) as demo:
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gr.Markdown("# Stable Diffusion Inference Demo on 4th Gen Intel Xeon Scalable Processors")
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gr.Markdown(md)
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textBoxOrange = gr.Textbox(set_msg, every=3, label='Real-time Jobs in Queue', elem_id='txtOrangeStyle', visible=False)
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with gr.Tab("Text-to-Image"):
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with gr.Row(visible=True) as text_to_image:
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@@ -169,10 +161,8 @@ with gr.Blocks(css=css) as demo:
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result_image_2 = gr.Image()
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txt2img_button.click(fn=txt2img_generate, inputs=[prompt, inference_steps, seed, guidance_scale], outputs=[result_image])
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# txt2img_button.click(fn=txt2img_generate, inputs=[prompt, inference_steps, seed, guidance_scale], outputs=[result_image, textBoxGreen, textBoxOrange])
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img2img_button.click(fn=img2img_generate, inputs=[source_img, prompt_2, inference_steps_2, strength, seed_2, guidance_scale_2], outputs=
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# img2img_button.click(fn=img2img_generate, inputs=[source_img, prompt_2, inference_steps_2, strength, seed_2, guidance_scale_2], outputs=[result_image_2, textBoxGreen, textBoxOrange])
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gr.Markdown("**Additional Test Configuration Details:**", elem_id='mdStyle')
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gr.Markdown(details, elem_id='mdStyle')
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@@ -180,4 +170,4 @@ with gr.Blocks(css=css) as demo:
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gr.Markdown("**Notices and Disclaimers:**", elem_id='mdStyle')
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gr.Markdown(legal, elem_id='mdStyle')
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demo.queue(max_size=int(os.environ["max_job_size"]), concurrency_count=int(os.environ["max_job_size"])).launch(debug=True, show_api=False)
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except:
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print('No inference result. Please check server connection')
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return None
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img_byte = base64.b64decode(img_str)
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img_io = BytesIO(img_byte) # convert image to file-like object
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img = Image.open(img_io) # img is now PIL Image object
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print("elapsed time: ", time.time() - start_time)
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return img
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+
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md = """
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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! Please note that the demo is in **preview** under limited HW resources. We are committed to continue improving the demo and happy to hear your feedbacks. Thanks for your trying!
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You may also want to try creating your own Stable Diffusion 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>.
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.duplicate-button img{margin: 0}
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#mdStyle{font-size: 0.6rem}
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.generating.svelte-1w9161c { border: none }
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#txtGreenStyle {2px solid #32ec48;}
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#txtOrangeStyle {2px solid #e77718;}
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'''
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random_seed = random.randint(0, 2147483647)
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gr.Markdown("# Stable Diffusion Inference Demo on 4th Gen Intel Xeon Scalable Processors")
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gr.Markdown(md)
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gr.Textbox(set_msg, every=3, label='Real-time Jobs in Queue', elem_id='txtOrangeStyle')
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with gr.Tab("Text-to-Image"):
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with gr.Row(visible=True) as text_to_image:
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result_image_2 = gr.Image()
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txt2img_button.click(fn=txt2img_generate, inputs=[prompt, inference_steps, seed, guidance_scale], outputs=[result_image])
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img2img_button.click(fn=img2img_generate, inputs=[source_img, prompt_2, inference_steps_2, strength, seed_2, guidance_scale_2], outputs=result_image_2)
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gr.Markdown("**Additional Test Configuration Details:**", elem_id='mdStyle')
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gr.Markdown(details, elem_id='mdStyle')
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gr.Markdown("**Notices and Disclaimers:**", elem_id='mdStyle')
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gr.Markdown(legal, elem_id='mdStyle')
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demo.queue(max_size=int(os.environ["max_job_size"]), concurrency_count=int(os.environ["max_job_size"])).launch(debug=True, show_api=False)
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