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Update app.py
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app.py
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@@ -2,7 +2,10 @@ import gradio as gr
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import os
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title="Prompt Converter"
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description="
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stable_diffusion = gr.Blocks.load(name="spaces/runwayml/stable-diffusion-v1-5")
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clip_interrogator_2 = gr.Blocks.load(name="spaces/fffiloni/CLIP-Interrogator-2")
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@@ -25,7 +28,7 @@ def infer(prompt):
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#print(result)
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return result[0]
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prompt_input = gr.Textbox(lines=4, label="Input Stable Diffusion
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prompt_output = gr.Textbox(lines=4, label="Converted Stable Diffusion
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gr.Interface(fn=infer, inputs=[prompt_input], outputs=[prompt_output],title=title,description=description).queue(max_size=10).launch(enable_queue=True)
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import os
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title="Prompt Converter"
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description="""
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Stable Diffusion 2 uses OpenCLIP ViT-H model trained LAION dataset so it knows different things than the OpenAI ViT-L we're all used to prompting.
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<br />This demo Convert a v1.x stable diffusion prompt to a stable diffusion 2.x prompt, by generating an image through RunwayML Stable Diffusion 1.5, then Interrogate the resulting image through CLIP Interrogator 2 to give you a Stable Diffusion 2 equivalent prompt.
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"""
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stable_diffusion = gr.Blocks.load(name="spaces/runwayml/stable-diffusion-v1-5")
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clip_interrogator_2 = gr.Blocks.load(name="spaces/fffiloni/CLIP-Interrogator-2")
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#print(result)
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return result[0]
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prompt_input = gr.Textbox(lines=4, label="Input v1.x Stable Diffusion prompt")
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prompt_output = gr.Textbox(lines=4, label="Converted v2.x Stable Diffusion prompt")
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gr.Interface(fn=infer, inputs=[prompt_input], outputs=[prompt_output],title=title,description=description).queue(max_size=10).launch(enable_queue=True)
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