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https://github.com/nod-ai/AMD-SHARK-Studio.git
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a886cba655
This commit adds adds `v_diffusion` model web visualization as a part of shark web. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com> Signed-off-by: Gaurav Shukla <gaurav@nod-labs.com>
46 lines
1.8 KiB
Python
46 lines
1.8 KiB
Python
from models.resnet50 import resnet_inf
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from models.albert_maskfill import albert_maskfill_inf
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from models.diffusion.v_diffusion import vdiff_inf
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import gradio as gr
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shark_web = gr.Blocks()
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with shark_web:
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with gr.Row():
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with gr.Column():
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with gr.Group():
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image = gr.Image(label="Image")
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label = gr.Label(label="Output")
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resnet = gr.Button("Recognize Image")
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resnet.click(resnet_inf, inputs=image, outputs=label)
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with gr.Column():
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with gr.Group():
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masked_text = gr.Textbox(
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label="Masked Text",
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placeholder="Give me a sentence with [MASK] to fill",
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)
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decoded_res = gr.Label(label="Decoded Results")
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albert_mask = gr.Button("Decode Mask")
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albert_mask.click(
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albert_maskfill_inf,
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inputs=masked_text,
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outputs=decoded_res,
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)
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with gr.Column():
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with gr.Group():
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prompt = gr.Textbox(
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label="Prompt", value="New York City, oil on canvas:5"
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)
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sample_count = gr.Number(label="Sample Count", value=1)
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batch_size = gr.Number(label="Batch Size", value=1)
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iters = gr.Number(label="Steps", value=2)
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v_diffusion = gr.Button("Generate image from prompt")
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generated_img = gr.Image(type="pil", shape=(100, 100))
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v_diffusion.click(
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vdiff_inf,
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inputs=[prompt, sample_count, batch_size, iters],
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outputs=generated_img,
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)
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shark_web.launch(share=True, server_port=8080)
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