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Update app_tsukasa_remote.py
Browse files- app_tsukasa_remote.py +7 -2
app_tsukasa_remote.py
CHANGED
@@ -317,7 +317,7 @@ yielding reasonably good results.
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</p>
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Overall, I'm confident that with a bit of experimentation, you can achieve
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The model should work well out of the box 90% of the time without the need for extensive tweaking.<br>
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However, here are some tips in case you encounter issues:
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</p>
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@@ -329,6 +329,10 @@ However, here are some tips in case you encounter issues:
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Ensure that your input closely matches your reference (audio or text prompt) in terms of tone,<br>
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non-verbal cues, duration, etc.
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</li>
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If your audio is too long but the input is too short, the speech rate will be slow, and vice versa.
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@@ -336,7 +340,8 @@ However, here are some tips in case you encounter issues:
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<li>
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Experiment with the <b>alpha</b>, <b>beta</b>, and <b>Intensity</b> parameters. The Diffusion<br>
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sampler is non-deterministic, so regenerate a few times if you're not satisfied with the output.
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</li>
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<li>
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</p>
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<p>
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+
Overall, I'm confident that with a bit of experimentation, you can achieve impressive results. <br>
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The model should work well out of the box 90% of the time without the need for extensive tweaking.<br>
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However, here are some tips in case you encounter issues:
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</p>
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Ensure that your input closely matches your reference (audio or text prompt) in terms of tone,<br>
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non-verbal cues, duration, etc.
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</li>
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<li>
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**Do not use very short inputs**
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</li>
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<li>
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If your audio is too long but the input is too short, the speech rate will be slow, and vice versa.
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<li>
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Experiment with the <b>alpha</b>, <b>beta</b>, and <b>Intensity</b> parameters. The Diffusion<br>
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sampler is non-deterministic, so regenerate a few times if you're not satisfied with the output. <br>
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don't use crazy values though! turn it off if it was bad by setting both alpha and beta to zero.
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</li>
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<li>
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