update app.py
Browse files1st modification for public
app.py
CHANGED
@@ -182,7 +182,7 @@ examples_context = [
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'in front of Eiffel tower'
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]
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examples_region1 = ['wearing red hat, high resolution, best quality'
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examples_region2 = ['smilling, wearing blue shirt, high resolution, best quality']
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with open('multi-concept/pose_data/pose.json') as f:
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@@ -199,62 +199,91 @@ css="""
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with gr.Blocks(css=css) as demo:
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gr.Markdown(f"""
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# Orthogonal Adaptation
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-
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""")
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with gr.Row():
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with gr.Column(elem_id="col-container"
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gr.Markdown(f"""
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###
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""")
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with gr.Row():
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region1_concept = gr.Dropdown(
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["Elsa", "Moana"],
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label="Character 1",
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info="Will add more characters later!"
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)
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region2_concept = gr.Dropdown(
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["Elsa", "Moana"],
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label="Character 2",
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info="Will add more characters later!"
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)
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region1_prompt = gr.Textbox(
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label="Region1 Prompt",
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show_label=False,
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max_lines=2,
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placeholder="Enter your regional prompt for character 1",
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container=False,
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)
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label="Region2 Prompt",
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show_label=False,
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max_lines=2,
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placeholder="Enter your regional prompt for character 2",
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container=False,
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)
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run_button = gr.Button("Run", scale=1)
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@@ -301,30 +330,15 @@ with gr.Blocks(css=css) as demo:
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step= 0.01,
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value=1.0,
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)
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with gr.Column(
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gr.Examples(
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label = 'Global Prompt example',
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examples = examples_context,
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inputs = [prompt]
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)
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with gr.Row():
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gr.Examples(
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label = 'Region1 Prompt example',
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examples = examples_region1,
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inputs = [region1_prompt]
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)
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gr.Examples(
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label = 'Region2 Prompt example',
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examples = [examples_region2],
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inputs = [region2_prompt]
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)
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result = gr.Image(label="Result", show_label=False)
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run_button.click(
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fn = generate,
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inputs = [region1_concept,
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'in front of Eiffel tower'
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]
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examples_region1 = ['wearing red hat, high resolution, best quality']
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examples_region2 = ['smilling, wearing blue shirt, high resolution, best quality']
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with open('multi-concept/pose_data/pose.json') as f:
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with gr.Blocks(css=css) as demo:
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gr.Markdown(f"""
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# Orthogonal Adaptation
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+
Describe your world with a **🪄 text prompt (global and local)** and choose two characters to merge.
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Select their **👯 poses (spatial conditions)** for regionally controllable sampling to generate a unique image using our model.
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Let your creativity run wild! (Currently running on : {power_device} )
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""")
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with gr.Row():
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with gr.Column(elem_id="col-container"):
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gr.Markdown(f"""
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### 🪄 Global and Region prompts
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""")
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with gr.Group():
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prompt = gr.Text(
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label="ContextPrompt",
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show_label=False,
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max_lines=1,
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placeholder="Enter your global context prompt",
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container=False,
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)
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with gr.Row():
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region1_concept = gr.Dropdown(
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["Elsa", "Moana"],
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label="Character 1",
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info="Will add more characters later!"
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)
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region2_concept = gr.Dropdown(
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["Elsa", "Moana"],
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label="Character 2",
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info="Will add more characters later!"
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)
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with gr.Row():
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region1_prompt = gr.Textbox(
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label="Region1 Prompt",
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show_label=False,
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max_lines=2,
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placeholder="Enter your regional prompt for character 1",
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container=False,
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)
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region2_prompt = gr.Textbox(
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label="Region2 Prompt",
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show_label=False,
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max_lines=2,
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placeholder="Enter your regional prompt for character 2",
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container=False,
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)
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gr.Examples(
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label = 'Global Prompt example',
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examples = examples_context,
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inputs = [prompt]
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)
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with gr.Row():
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gr.Examples(
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label = 'Region1 Prompt example',
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examples = examples_region1,
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inputs = [region1_prompt]
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)
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gr.Examples(
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label = 'Region2 Prompt example',
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examples = [examples_region2],
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inputs = [region2_prompt]
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)
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gr.Markdown(f"""
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### 👯 Spatial Condition
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""")
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with gr.Group():
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gallery = gr.Gallery(label = "Select pose for characters",
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value = [obj[1]for obj in pose_image_list],
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elem_id = [obj[0]for obj in pose_image_list],
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interactive=False, show_download_button=False,
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preview=True, height = 200, object_fit="scale-down")
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pose_image_id = gr.Textbox(visible=False)
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gallery.select(on_select, None, pose_image_id)
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run_button = gr.Button("Run", scale=1)
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step= 0.01,
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value=1.0,
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)
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with gr.Column():
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result = gr.Image(label="Result", show_label=False)
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gr.Markdown(f"""
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*Image generation may take longer for the first time you use a new combination of characters. <br />
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This is because the model needs to load weights for each concept involved.*
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""")
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run_button.click(
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fn = generate,
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inputs = [region1_concept,
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