Comments (4)
Hello, thanks for the implementation! It works very well.
As a suggestion, it may be helpful to provide code that works with the broader Github community. While the Diffusers library does allow for better ease of use and a more streamlined experience, it can possibly hinder the freedom to use across similar implementations due to how their library works.
I reproduced cross attention control based on the stable diffusion repository referring to this repository. However, I'm not sure the codes reproduced properly.
Refer to my repository: https://github.com/sunwoo76/CrossAttentionControl-stablediffusion
@bloc97, your repository, and comments were good guidance to me. Could you check my implementation too? Thanks :)
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I believe the easiest way for someone to get started is to simply download the diffusers library and run this jupyter notebook. While it does require the diffusers library, it is not using any of the pre-implemented pipelines, diffusers is only used to download and load the neural network with schedulers, all of the diffusion logic is in the jupyter notebook.
If you are using another library, simply replace the unet
, vae
and scheduler
variables with the ones from another library. I might be wrong but I think the cross attention modules should be implemented similarly, but maybe you have to modify slightly the code injection as the variable names might be different.
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@sunwoo76 Awesome! I didn't look very deeply in the code but it looks good at first glance from reading the README. I think showing a visualization of the attention maps is a very good feature!
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Hello, thanks for the implementation! It works very well.
As a suggestion, it may be helpful to provide code that works with the broader Github community. While the Diffusers library does allow for better ease of use and a more streamlined experience, it can possibly hinder the freedom to use across similar implementations due to how their library works.I reproduced cross attention control based on the stable diffusion repository referring to this repository. However, I'm not sure the codes reproduced properly. Refer to my repository: https://github.com/sunwoo76/CrossAttentionControl-stablediffusion
@bloc97, your repository, and comments were good guidance to me. Could you check my implementation too? Thanks :)
Amazing work!
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Related Issues (20)
- negative weighting HOT 1
- how to prevent promp 1 from being distorted HOT 1
- Question about the code in CrossAttention_Release.ipynb HOT 1
- Did you get a same result? HOT 1
- AttributeError: 'dict' object has no attribute 'sample' HOT 2
- Can't run the notebook in Google Colab, some issues with versions. HOT 2
- Notebook error HOT 3
- Add direct target editing to the notebook HOT 1
- How to make image inversion more precise? HOT 8
- please add InverseCrossAttention to colab
- Better support for prompt_edit_token_weights parsing
- Implementing Dreambooth weights HOT 2
- The differences from the official implementation? HOT 1
- About the finite difference gradient descent method HOT 1
- Question about original google implementation with stable diffusion HOT 3
- About terms["nll"]
- Why LMSDiscreteScheduler? HOT 1
- Relating to the recent paper about 'Self-guidance' method HOT 1
- An observation HOT 3
- Optmised for 6 GB? HOT 1
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