Comments (3)
They have similar performance since I trained them with same datasets.
from auffusion.
Thanks for your attention! Because my auffusion_pipeline try to explore different combination of text encoder, therefore I try to hack the default StableDiffusionPipeline and add these variables. On the other hand, I trained another model and it can use the default StableDiffusionPipeline in notebook, you can try to use this model in Diffusers==0.25.1 and it might work.
PS: the attention-based method in notebook depend on Diffusers=0.18, therefore method except attention-based might work fine in your case.
from auffusion.
Thanks for your reply. This is helpful. I wonder what the performance gap between Auffusion-Full-no-adapter
and Auffusion-Full
.
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Related Issues (8)
- Can I control the duration of theText-guided style transfer's output audio? HOT 1
- Question about CLAP score evaluation HOT 2
- Key Differences with Riffusion? HOT 4
- pt_to_numpy in auffusion_pipeline.py has 'staticmethod' object is not callable error HOT 3
- the code for the audio-to-audio generation HOT 4
- `AttributeError: 'NoneType' object has no attribute 'shape'` when giving negative_prompt HOT 1
- About pre-trained VAE HOT 6
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from auffusion.