Comments (5)
rinongal
commented on August 24, 2024
1
Are you working with the stable diffusion code or with the LDM code? The embedding merger has not been updated for the (still unofficial!) Stable Diffusion code. I'll probably get to it over the weekend.
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agcagc
commented on August 24, 2024
Ok thanks, I was using stable diffusion, other than that it works very well
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NathanielAyer
commented on August 24, 2024
I got this same error running merge_embeddings.py trying to merge two pt files produced by Textual Inversion, running on Amazon Linux 2. Same error in Windows 11.
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1blackbar
commented on August 24, 2024
For now wit this code you can use multiple embeddings without merge : https://github.com/AUTOMATIC1111/stable-diffusion-webui
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rinongal
commented on August 24, 2024
This should now be working. To merge SD checkpoints just add the -sd flag to the merge command. Feel free to re-open if you have more issues.
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Related Issues (20)
- cannot edit the image using new prompt HOT 1
- About hyperparameter optimization
- Different init_words have same loss_epoch curve HOT 3
- Cannot obtain the same results as the paper HOT 4
- Train Textual Inversion in alternative models of Stable Diffusion HOT 3
- Error when running Inversion script HOT 6
- Cuda ran out of memory while running main.py
- When will the Perfusion code be available?
- AttributeError: 'NoneType' object has no attribute 'load' HOT 2
- ValueError: The initializer token must be a single token. HOT 4
- A question about picture generation HOT 1
- what are the difference between models in ldm and first_stage_models? HOT 1
- How to set up to achieve the function of the photo in * style of #, one for learning content and one for learning style. HOT 1
- SD with style get bad result
- AttributeError: module 'main' has no attribute 'LearningRateMonitor'
- When I run it, I get the following error, HOT 1
- _pickle.UnpicklingError: invalid load key, '<'. HOT 7
- _pickle.UnpicklingError: invalid load key, '<'. HOT 2
- Request code about downstream tasks HOT 1
- when Release pre-trained embeddings?
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