reduce memory footprint
Browse files- app.py +161 -161
- models/region_diffusion_xl.py +11 -6
app.py
CHANGED
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@@ -260,45 +260,114 @@ def main():
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with gr.Row():
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gr.Markdown(help_text)
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with gr.Row():
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-
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[
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'{"ops":[{"insert":"
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'',
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0.3,
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0.3,
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0.5,
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3,
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None,
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],
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[
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'{"ops":[{"insert":"
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'',
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0.
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None,
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],
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[
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'{"ops":[{"insert":"A "},{"attributes":{"link":"Happy Kung fu panda art, elder, asian art, volumetric lighting, dramatic scene, ultra detailed, realism, chinese"},"insert":"panda"},{"insert":" standing on a cliff by a waterfall, wildlife photography, photograph, high quality, wildlife, f 1.8, soft focus, 8k, national geographic, award - winning photograph by nick nichols"}]}',
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'',
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5,
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0.3,
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None,
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],
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]
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-
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label='Footnote examples',
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inputs=[
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text_input,
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negative_prompt,
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@@ -319,55 +388,93 @@ def main():
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fn=generate,
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cache_examples=True,
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examples_per_page=20)
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# with gr.Row():
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#
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# [
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# '{"ops":[{"insert":"a
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# '
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#
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# 0.
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# 0
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# 0.
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#
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# 0
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# None,
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# ],
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# [
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# '{"ops":[{"insert":"a
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# '
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#
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# 0.
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# 0
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# 0
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# 6,
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# 0.5,
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# None,
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# ],
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# [
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# '{"ops":[{"insert":"
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# '
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#
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# 0.5,
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# 0.3,
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# 0.3,
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#
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# 0
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# None,
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# ],
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# [
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# '{"ops":[{"insert":"
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# '',
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#
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# 0.5,
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# 0.5,
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# 0.3,
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#
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# 0
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# None,
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# ],
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# ]
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-
# gr.Examples(examples=
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# label='Font
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# inputs=[
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# text_input,
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# negative_prompt,
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@@ -388,113 +495,6 @@ def main():
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# fn=generate,
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# cache_examples=True,
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# examples_per_page=20)
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-
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with gr.Row():
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style_examples = [
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[
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'{"ops":[{"insert":"a beautiful"},{"attributes":{"font":"mirza"},"insert":" garden"},{"insert":" with a "},{"attributes":{"font":"roboto"},"insert":"snow mountain"},{"insert":" in the background"}]}',
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'',
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10,
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None,
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],
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[
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'{"ops":[{"insert":"a night"},{"attributes":{"font":"slabo"},"insert":" sky"},{"insert":" filled with stars above a turbulent"},{"attributes":{"font":"roboto"},"insert":" sea"},{"insert":" with giant waves"}]}',
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'',
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None,
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],
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]
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gr.Examples(examples=style_examples,
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label='Font style examples',
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inputs=[
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text_input,
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negative_prompt,
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num_segments,
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segment_threshold,
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inject_interval,
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inject_background,
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seed,
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color_guidance_weight,
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rich_text_input,
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],
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outputs=[
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plaintext_result,
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richtext_result,
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segments,
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token_map,
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],
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fn=generate,
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cache_examples=True,
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examples_per_page=20)
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with gr.Row():
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size_examples = [
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[
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'{"ops": [{"insert": "A pizza with "}, {"attributes": {"size": "60px"}, "insert": "pineapple"}, {"insert": " pepperoni, and mushroom on the top"}]}',
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'',
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],
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[
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'{"ops": [{"insert": "A pizza with pineapple, "}, {"attributes": {"size": "60px"}, "insert": "pepperoni"}, {"insert": ", and mushroom on the top"}]}',
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None,
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],
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[
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'{"ops": [{"insert": "A pizza with pineapple, pepperoni, and "}, {"attributes": {"size": "60px"}, "insert": "mushroom"}, {"insert": " on the top"}]}',
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'',
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None,
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],
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]
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gr.Examples(examples=size_examples,
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label='Font size examples',
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inputs=[
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text_input,
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negative_prompt,
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num_segments,
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segment_threshold,
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inject_interval,
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inject_background,
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seed,
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color_guidance_weight,
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rich_text_input,
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],
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outputs=[
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plaintext_result,
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richtext_result,
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segments,
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token_map,
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],
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fn=generate,
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cache_examples=True,
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examples_per_page=20)
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generate_button.click(fn=lambda: gr.update(visible=False), inputs=None, outputs=share_row, queue=False).then(
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fn=generate,
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inputs=[
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with gr.Row():
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gr.Markdown(help_text)
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+
# with gr.Row():
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# footnote_examples = [
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# [
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# '{"ops":[{"insert":"A close-up 4k dslr photo of a "},{"attributes":{"link":"A cat wearing sunglasses and a bandana around its neck."},"insert":"cat"},{"insert":" riding a scooter. Palm trees in the background."}]}',
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# '',
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# 9,
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# 0.3,
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# 0.3,
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# 0.5,
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# 3,
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# 0,
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# None,
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# ],
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# [
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# '{"ops":[{"insert":"A cozy "},{"attributes":{"link":"A charming wooden cabin with Christmas decoration, warm light coming out from the windows."},"insert":"cabin"},{"insert":" nestled in a "},{"attributes":{"link":"Towering evergreen trees covered in a thick layer of pristine snow."},"insert":"snowy forest"},{"insert":", and a "},{"attributes":{"link":"A cute snowman wearing a carrot nose, coal eyes, and a colorful scarf, welcoming visitors with a cheerful vibe."},"insert":"snowman"},{"insert":" stands in the yard."}]}',
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# '',
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# 12,
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# 0.4,
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# 0.3,
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# 0.5,
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# 3,
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# 0,
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# None,
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# ],
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# [
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# '{"ops":[{"insert":"A "},{"attributes":{"link":"Happy Kung fu panda art, elder, asian art, volumetric lighting, dramatic scene, ultra detailed, realism, chinese"},"insert":"panda"},{"insert":" standing on a cliff by a waterfall, wildlife photography, photograph, high quality, wildlife, f 1.8, soft focus, 8k, national geographic, award - winning photograph by nick nichols"}]}',
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# '',
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# 5,
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# 0.3,
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# 0,
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# 0.1,
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# 4,
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# 0,
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# None,
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# ],
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# ]
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# gr.Examples(examples=footnote_examples,
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# label='Footnote examples',
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# inputs=[
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# text_input,
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# negative_prompt,
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# num_segments,
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# segment_threshold,
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# inject_interval,
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# inject_background,
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# seed,
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# color_guidance_weight,
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# rich_text_input,
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# ],
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# outputs=[
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# plaintext_result,
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# richtext_result,
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# segments,
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# token_map,
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# ],
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# fn=generate,
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# cache_examples=True,
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# examples_per_page=20)
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with gr.Row():
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color_examples = [
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# [
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# '{"ops":[{"insert":"a beautifule girl with big eye, skin, and long "},{"attributes":{"color":"#04a704"},"insert":"hair"},{"insert":", t-shirt, bursting with vivid color, intricate, elegant, highly detailed, photorealistic, digital painting, artstation, illustration, concept art."}]}',
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# 'lowres, had anatomy, bad hands, cropped, worst quality',
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# 11,
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# 0.5,
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# 0.3,
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# 0.3,
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# 6,
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# 0.5,
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# None,
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# ],
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# [
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# '{"ops":[{"insert":"a beautifule girl with big eye, skin, and long "},{"attributes":{"color":"#ff5df1"},"insert":"hair"},{"insert":", t-shirt, bursting with vivid color, intricate, elegant, highly detailed, photorealistic, digital painting, artstation, illustration, concept art."}]}',
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# 'lowres, had anatomy, bad hands, cropped, worst quality',
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# 11,
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# 0.5,
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# 0.3,
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# 0.3,
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# 6,
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# 0.5,
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# None,
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# ],
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[
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'{"ops":[{"insert":"a beautifule girl with big eye, skin, and long "},{"attributes":{"color":"#999999"},"insert":"hair"},{"insert":", t-shirt, bursting with vivid color, intricate, elegant, highly detailed, photorealistic, digital painting, artstation, illustration, concept art."}]}',
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'lowres, had anatomy, bad hands, cropped, worst quality',
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11,
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None,
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],
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[
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'{"ops":[{"insert":"a Gothic "},{"attributes":{"color":"#FD6C9E"},"insert":"church"},{"insert":" in a the sunset with a beautiful landscape in the background."}]}',
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'',
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10,
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0.5,
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None,
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],
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]
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gr.Examples(examples=color_examples,
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label='Font color examples',
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inputs=[
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text_input,
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negative_prompt,
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fn=generate,
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cache_examples=True,
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examples_per_page=20)
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+
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# with gr.Row():
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# style_examples = [
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# [
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# '{"ops":[{"insert":"a beautiful"},{"attributes":{"font":"mirza"},"insert":" garden"},{"insert":" with a "},{"attributes":{"font":"roboto"},"insert":"snow mountain"},{"insert":" in the background"}]}',
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# '',
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# 10,
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# 0.6,
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# 0,
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# 0.4,
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# 5,
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# 0,
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# None,
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# ],
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# [
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# '{"ops":[{"insert":"a night"},{"attributes":{"font":"slabo"},"insert":" sky"},{"insert":" filled with stars above a turbulent"},{"attributes":{"font":"roboto"},"insert":" sea"},{"insert":" with giant waves"}]}',
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# '',
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# 2,
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# 0.6,
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# 0,
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# 0,
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# 6,
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# 0.5,
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# None,
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# ],
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# ]
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# gr.Examples(examples=style_examples,
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# label='Font style examples',
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# inputs=[
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# text_input,
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+
# negative_prompt,
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+
# num_segments,
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+
# segment_threshold,
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+
# inject_interval,
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+
# inject_background,
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+
# seed,
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+
# color_guidance_weight,
|
| 428 |
+
# rich_text_input,
|
| 429 |
+
# ],
|
| 430 |
+
# outputs=[
|
| 431 |
+
# plaintext_result,
|
| 432 |
+
# richtext_result,
|
| 433 |
+
# segments,
|
| 434 |
+
# token_map,
|
| 435 |
+
# ],
|
| 436 |
+
# fn=generate,
|
| 437 |
+
# cache_examples=True,
|
| 438 |
+
# examples_per_page=20)
|
| 439 |
+
|
| 440 |
+
# with gr.Row():
|
| 441 |
+
# size_examples = [
|
| 442 |
# [
|
| 443 |
+
# '{"ops": [{"insert": "A pizza with "}, {"attributes": {"size": "60px"}, "insert": "pineapple"}, {"insert": " pepperoni, and mushroom on the top"}]}',
|
| 444 |
+
# '',
|
| 445 |
+
# 5,
|
|
|
|
| 446 |
# 0.3,
|
| 447 |
+
# 0,
|
| 448 |
+
# 0,
|
| 449 |
+
# 3,
|
| 450 |
+
# 1,
|
| 451 |
+
# None,
|
| 452 |
+
# ],
|
| 453 |
+
# [
|
| 454 |
+
# '{"ops": [{"insert": "A pizza with pineapple, "}, {"attributes": {"size": "60px"}, "insert": "pepperoni"}, {"insert": ", and mushroom on the top"}]}',
|
| 455 |
+
# '',
|
| 456 |
+
# 5,
|
| 457 |
# 0.3,
|
| 458 |
+
# 0,
|
| 459 |
+
# 0,
|
| 460 |
+
# 3,
|
| 461 |
+
# 1,
|
| 462 |
# None,
|
| 463 |
# ],
|
| 464 |
# [
|
| 465 |
+
# '{"ops": [{"insert": "A pizza with pineapple, pepperoni, and "}, {"attributes": {"size": "60px"}, "insert": "mushroom"}, {"insert": " on the top"}]}',
|
| 466 |
# '',
|
| 467 |
+
# 5,
|
|
|
|
|
|
|
| 468 |
# 0.3,
|
| 469 |
+
# 0,
|
| 470 |
+
# 0,
|
| 471 |
+
# 3,
|
| 472 |
+
# 1,
|
| 473 |
# None,
|
| 474 |
# ],
|
| 475 |
# ]
|
| 476 |
+
# gr.Examples(examples=size_examples,
|
| 477 |
+
# label='Font size examples',
|
| 478 |
# inputs=[
|
| 479 |
# text_input,
|
| 480 |
# negative_prompt,
|
|
|
|
| 495 |
# fn=generate,
|
| 496 |
# cache_examples=True,
|
| 497 |
# examples_per_page=20)
|
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|
| 498 |
generate_button.click(fn=lambda: gr.update(visible=False), inputs=None, outputs=share_row, queue=False).then(
|
| 499 |
fn=generate,
|
| 500 |
inputs=[
|
models/region_diffusion_xl.py
CHANGED
|
@@ -846,12 +846,16 @@ class RegionDiffusionXL(DiffusionPipeline, FromSingleFileMixin):
|
|
| 846 |
# apply guidance
|
| 847 |
if use_guidance and t < text_format_dict['guidance_start_step']:
|
| 848 |
with torch.enable_grad():
|
|
|
|
|
|
|
| 849 |
if not latents.requires_grad:
|
| 850 |
latents.requires_grad = True
|
| 851 |
# import ipdb;ipdb.set_trace()
|
| 852 |
-
latents_0 = self.predict_x0(latents, noise_pred, t).to(dtype=latents.dtype)
|
|
|
|
| 853 |
latents_inp = latents_0 / self.vae.config.scaling_factor
|
| 854 |
-
imgs = self.vae.
|
|
|
|
| 855 |
imgs = (imgs / 2 + 0.5).clamp(0, 1)
|
| 856 |
loss_total = 0.
|
| 857 |
for attn_map, rgb_val in zip(text_format_dict['color_obj_atten'], text_format_dict['target_RGB']):
|
|
@@ -863,6 +867,7 @@ class RegionDiffusionXL(DiffusionPipeline, FromSingleFileMixin):
|
|
| 863 |
loss_total.backward()
|
| 864 |
latents = (
|
| 865 |
latents - latents.grad * text_format_dict['color_guidance_weight'] * text_format_dict['color_obj_atten_all']).detach().clone().to(dtype=prompt_embeds.dtype)
|
|
|
|
| 866 |
|
| 867 |
# apply background injection
|
| 868 |
if i == int(inject_background * len(self.scheduler.timesteps)) and inject_background > 0:
|
|
@@ -1023,7 +1028,7 @@ class RegionDiffusionXL(DiffusionPipeline, FromSingleFileMixin):
|
|
| 1023 |
PyTorch Forward hook to save outputs at each forward pass.
|
| 1024 |
"""
|
| 1025 |
if 'attn1' in name:
|
| 1026 |
-
modified_args = (args[0], self.self_attention_maps_cur[name])
|
| 1027 |
return modified_args
|
| 1028 |
# cross attention injection
|
| 1029 |
# elif 'attn2' in name:
|
|
@@ -1039,7 +1044,7 @@ class RegionDiffusionXL(DiffusionPipeline, FromSingleFileMixin):
|
|
| 1039 |
PyTorch Forward hook to save outputs at each forward pass.
|
| 1040 |
"""
|
| 1041 |
modified_args = (args[0], args[1],
|
| 1042 |
-
self.self_attention_maps_cur[name])
|
| 1043 |
return modified_args
|
| 1044 |
for name, module in self.unet.named_modules():
|
| 1045 |
leaf_name = name.split('.')[-1]
|
|
@@ -1077,7 +1082,7 @@ class RegionDiffusionXL(DiffusionPipeline, FromSingleFileMixin):
|
|
| 1077 |
# activations[name] = out[1][1].detach()
|
| 1078 |
else:
|
| 1079 |
assert out[1][1].shape[-1] != 77
|
| 1080 |
-
activations[name] = out[1][1].detach()
|
| 1081 |
|
| 1082 |
def save_resnet_activations(activations, name, module, inp, out):
|
| 1083 |
r"""
|
|
@@ -1087,7 +1092,7 @@ class RegionDiffusionXL(DiffusionPipeline, FromSingleFileMixin):
|
|
| 1087 |
# out[1] - residual hidden feature
|
| 1088 |
# import ipdb;ipdb.set_trace()
|
| 1089 |
assert out[1].shape[-1] == 64
|
| 1090 |
-
activations[name] = out[1].detach()
|
| 1091 |
attention_dict = collections.defaultdict(list)
|
| 1092 |
for name, module in self.unet.named_modules():
|
| 1093 |
leaf_name = name.split('.')[-1]
|
|
|
|
| 846 |
# apply guidance
|
| 847 |
if use_guidance and t < text_format_dict['guidance_start_step']:
|
| 848 |
with torch.enable_grad():
|
| 849 |
+
self.unet.to(device='cpu')
|
| 850 |
+
torch.cuda.empty_cache()
|
| 851 |
if not latents.requires_grad:
|
| 852 |
latents.requires_grad = True
|
| 853 |
# import ipdb;ipdb.set_trace()
|
| 854 |
+
# latents_0 = self.predict_x0(latents, noise_pred, t).to(dtype=latents.dtype)
|
| 855 |
+
latents_0 = self.predict_x0(latents, noise_pred, t).to(dtype=torch.bfloat16)
|
| 856 |
latents_inp = latents_0 / self.vae.config.scaling_factor
|
| 857 |
+
imgs = self.vae.to(dtype=latents_inp.dtype).decode(latents_inp).sample
|
| 858 |
+
# imgs = self.vae.decode(latents_inp.to(dtype=torch.float32)).sample
|
| 859 |
imgs = (imgs / 2 + 0.5).clamp(0, 1)
|
| 860 |
loss_total = 0.
|
| 861 |
for attn_map, rgb_val in zip(text_format_dict['color_obj_atten'], text_format_dict['target_RGB']):
|
|
|
|
| 867 |
loss_total.backward()
|
| 868 |
latents = (
|
| 869 |
latents - latents.grad * text_format_dict['color_guidance_weight'] * text_format_dict['color_obj_atten_all']).detach().clone().to(dtype=prompt_embeds.dtype)
|
| 870 |
+
self.unet.to(device=latents.device)
|
| 871 |
|
| 872 |
# apply background injection
|
| 873 |
if i == int(inject_background * len(self.scheduler.timesteps)) and inject_background > 0:
|
|
|
|
| 1028 |
PyTorch Forward hook to save outputs at each forward pass.
|
| 1029 |
"""
|
| 1030 |
if 'attn1' in name:
|
| 1031 |
+
modified_args = (args[0], self.self_attention_maps_cur[name].to(args[0].device))
|
| 1032 |
return modified_args
|
| 1033 |
# cross attention injection
|
| 1034 |
# elif 'attn2' in name:
|
|
|
|
| 1044 |
PyTorch Forward hook to save outputs at each forward pass.
|
| 1045 |
"""
|
| 1046 |
modified_args = (args[0], args[1],
|
| 1047 |
+
self.self_attention_maps_cur[name].to(args[0].device))
|
| 1048 |
return modified_args
|
| 1049 |
for name, module in self.unet.named_modules():
|
| 1050 |
leaf_name = name.split('.')[-1]
|
|
|
|
| 1082 |
# activations[name] = out[1][1].detach()
|
| 1083 |
else:
|
| 1084 |
assert out[1][1].shape[-1] != 77
|
| 1085 |
+
activations[name] = out[1][1].detach().cpu()
|
| 1086 |
|
| 1087 |
def save_resnet_activations(activations, name, module, inp, out):
|
| 1088 |
r"""
|
|
|
|
| 1092 |
# out[1] - residual hidden feature
|
| 1093 |
# import ipdb;ipdb.set_trace()
|
| 1094 |
assert out[1].shape[-1] == 64
|
| 1095 |
+
activations[name] = out[1].detach().cpu()
|
| 1096 |
attention_dict = collections.defaultdict(list)
|
| 1097 |
for name, module in self.unet.named_modules():
|
| 1098 |
leaf_name = name.split('.')[-1]
|