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import replicate |
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import gradio as gr |
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from io import BytesIO |
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import base64 |
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import os |
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illuse = replicate.Client(api_token=os.getenv('REPLICATE')) |
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model_name = "andreasjansson/illusion:75d51a73fce3c00de31ed9ab4358c73e8fc0f627dc8ce975818e653317cb919b" |
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example_image = "https://replicate.delivery/pbxt/hHJNV9QteKX8DK2ckkUeXsqbEIKNGFXU1fN0MJoizz3iPlOjA/output-0.png" |
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def generate(prompt, negative_prompt, qr_content, pattern_image, num_inference_steps, guidance_scale, width, height, seed, num_outputs, controlnet_conditioning_scale, border, qrcode_background): |
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try: |
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inputs = { |
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'prompt': prompt, |
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'negative_prompt': negative_prompt, |
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'qr_code_content': qr_content, |
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'num_inference_steps': num_inference_steps, |
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'guidance_scale': guidance_scale, |
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'width': width, |
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'height': height, |
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'seed': seed, |
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'num_outputs': num_outputs, |
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'controlnet_conditioning_scale': controlnet_conditioning_scale, |
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'border': border, |
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'qrcode_background': qrcode_background |
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} |
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if pattern_image is not None: |
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inputs['image'] = open(pattern_image, 'rb') |
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result = illuse.run( |
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model_name, |
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input=inputs |
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) |
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return result |
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except Exception as e: |
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print(e) |
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gr.Error(str(e)) |
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return |
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with gr.Blocks() as demo: |
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gr.Markdown(""" |
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# Illusion Diffusion Fast demo |
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## powered by replicate |
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""") |
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with gr.Row(): |
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with gr.Column(): |
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prompt = gr.Textbox(label="Prompt") |
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negative_prompt = gr.Textbox(label="Negative") |
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with gr.Row(): |
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qr_content = gr.Textbox(label="QR Code Content", placeholder="https://youtube.com/") |
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pattern_input = gr.Image(label="Pattern Image(if used QR Code Content wont be used)", type="filepath") |
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with gr.Accordion("Additional Settings", open=False): |
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with gr.Row(): |
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num_inference_steps = gr.Slider(label="num_inference_steps", minimum=20, maximum=100, step=1, value=50) |
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guidance_scale = gr.Slider(label="guidance_scale", minimum=0.1, maximum=30, step=0.01, value=7.5) |
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with gr.Row(): |
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width = gr.Slider(label='width', minimum=128, maximum=1024, step=8, value=768) |
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height = gr.Slider(label='height', minimum=128, maximum=1024, step=8, value=768) |
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with gr.Row(): |
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seed = gr.Number(label='seed', value=-1) |
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num_outputs = gr.Slider(label="num_outputs", minimum=1, maximum=4, step=1) |
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with gr.Row(): |
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controlnet_conditioning_scale = gr.Slider(label="controlnet_conditioning_scale", minimum=0, maximum=4, step=1, value=1) |
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border = gr.Slider(label="border", minimum=0, maximum=4, step=1, value=4) |
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qrcode_background = gr.Dropdown(label="qrcode_background", choices=['gray', 'white'], value='white') |
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run_btn = gr.Button("Run", variant="primary") |
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output = gr.Gallery([example_image]) |
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generation_event = run_btn.click(generate, inputs=[prompt, negative_prompt, qr_content, pattern_input, |
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num_inference_steps, guidance_scale, width, height, seed, |
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num_outputs, controlnet_conditioning_scale, border, |
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qrcode_background], outputs=output) |
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demo.launch(show_api=False) |
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