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Update app.py
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app.py
CHANGED
@@ -22,14 +22,14 @@ def download_image(url):
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response = requests.get(url)
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return Image.open(BytesIO(response.content)).convert("RGB")
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def
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url = "http://engine.
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payload = {
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"file": image_base64_file,
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"mask_file": mask_base64_file,
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"
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}
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response = requests.post(url, json=payload, headers=auth_headers)
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response = response.json()
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@@ -47,7 +47,7 @@ def predict(dict):
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mask_base64_file = convert_mask_image_to_base64_string(mask)
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mask_type = "manual"
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gen_img =
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return gen_img
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@@ -102,12 +102,12 @@ div#share-btn-container > div {flex-direction: row;background: black;align-items
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image_blocks = gr.Blocks(css=css, elem_id="total-container")
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with image_blocks as demo:
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with gr.Column(elem_id="col-container"):
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gr.Markdown("## BRIA
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gr.HTML('''
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<p style="margin-bottom: 10px; font-size: 94%">
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This demo showcases the BRIA
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The pipeline comprises multiple components, including <a href="https://huggingface.co/briaai/BRIA-2.3" target="_blank">briaai/BRIA-2.3</a>,
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<a href="https://huggingface.co/briaai/BRIA-2.3-ControlNet-
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and <a href="https://huggingface.co/briaai/BRIA-2.3-FAST-LORA" target="_blank">briaai/BRIA-2.3-FAST-LORA</a>, all trained on licensed data.<br>
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This ensures full legal liability coverage for copyright and privacy infringement.<br>
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Notes:<br>
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@@ -115,21 +115,22 @@ with image_blocks as demo:
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- For multiple masks, results are better if all masks are included in inference.<br>
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</p>
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''')
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with gr.Row():
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with gr.Column():
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image = gr.ImageEditor(sources=["upload"], layers=False, transforms=[],
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brush=gr.Brush(colors=["#000000"], color_mode="fixed"),
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)
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with gr.Row(elem_id="prompt-container", equal_height=True):
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with gr.Column():
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btn = gr.Button("
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with gr.Column():
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image_out = gr.Image(label="Output", elem_id="output-img")
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# Button click will trigger the inpainting function (
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btn.click(fn=predict, inputs=[image], outputs=[image_out], api_name='run')
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gr.HTML(
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"""
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@@ -140,4 +141,4 @@ with image_blocks as demo:
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"""
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)
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image_blocks.queue(max_size=25,api_open=False).launch(show_api=False)
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response = requests.get(url)
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return Image.open(BytesIO(response.content)).convert("RGB")
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def gen_fill_api_call(image_base64_file, mask_base64_file, prompt):
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url = "http://engine.int.bria-api.com/v1/gen_fill"
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payload = {
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"file": image_base64_file,
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"mask_file": mask_base64_file,
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"prompt": prompt,
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}
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response = requests.post(url, json=payload, headers=auth_headers)
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response = response.json()
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mask_base64_file = convert_mask_image_to_base64_string(mask)
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mask_type = "manual"
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gen_img = gen_fill_api_call(image_base64_file, mask_base64_file, mask_type)
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return gen_img
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image_blocks = gr.Blocks(css=css, elem_id="total-container")
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with image_blocks as demo:
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with gr.Column(elem_id="col-container"):
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gr.Markdown("## BRIA Generative Fill API")
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gr.HTML('''
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<p style="margin-bottom: 10px; font-size: 94%">
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This demo showcases the BRIA Generative Fill capability, which allows users to remove specific elements or objects from images.<br>
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The pipeline comprises multiple components, including <a href="https://huggingface.co/briaai/BRIA-2.3" target="_blank">briaai/BRIA-2.3</a>,
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<a href="https://huggingface.co/briaai/BRIA-2.3-ControlNet-Generative-Fill" target="_blank">briaai/BRIA-2.3-ControlNet-Generative-Fill</a>,
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and <a href="https://huggingface.co/briaai/BRIA-2.3-FAST-LORA" target="_blank">briaai/BRIA-2.3-FAST-LORA</a>, all trained on licensed data.<br>
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This ensures full legal liability coverage for copyright and privacy infringement.<br>
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Notes:<br>
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- For multiple masks, results are better if all masks are included in inference.<br>
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</p>
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''')
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with gr.Row():
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with gr.Column():
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image = gr.ImageEditor(sources=["upload"], layers=False, transforms=[],
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brush=gr.Brush(colors=["#000000"], color_mode="fixed"),
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)
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prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...")
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with gr.Row(elem_id="prompt-container", equal_height=True):
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with gr.Column():
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btn = gr.Button("Fill!", elem_id="run_button")
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with gr.Column():
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image_out = gr.Image(label="Output", elem_id="output-img")
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# Button click will trigger the inpainting function (now with prompt included)
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btn.click(fn=predict, inputs=[image, prompt], outputs=[image_out], api_name='run')
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gr.HTML(
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"""
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"""
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)
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image_blocks.queue(max_size=25, api_open=False).launch(show_api=False)
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