diffusers_stablediff_conversion
diffusers
diffusers_stablediff_conversion | diffusers | |
---|---|---|
7 | 266 | |
53 | 22,881 | |
- | 3.8% | |
0.0 | 9.9 | |
about 1 year ago | 5 days ago | |
Python | Python | |
- | Apache License 2.0 |
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diffusers_stablediff_conversion
- New Release: Redshift Diffusion 768 trained on SD 2.0 - now available on HuggingFace
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How can I use trained styles in Automatic1111?
https://github.com/ratwithacompiler/diffusers_stablediff_conversion has a converter script.
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Is there any way to convert a ckpt file back into the original diffusers?
There are plenty of scripts such as this one to generate a single .ckpt file, but is there a way to do it the other way round?
- Help running the convention script to CKPT.
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Made a Hugginface Dreambooth models to .ckpt conversion script that needs testing
I just tried out the huggingface dreambooth colab and was annoyed that there's no way to use those as ckpts with all the open source tools so hacked this together: https://github.com/ratwithacompiler/diffusers_stablediff_conversion/blob/main/convert_diffusers_to_sd.py
- Automatic1111 with WORKING local textual inversion on 8GB 2090 Super !!!
diffusers
- StableDiffusionSafetyChecker
- ๐งจ diffusers 0.24.0 is out with Kandinsky 3.0, IP Adapters, and others
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What am I missing here? wheres the RND coming from?
I'm missing something about the random factor, from the sample code from https://github.com/huggingface/diffusers/blob/main/README.md
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T2IAdapter+ControlNet at the same time
Hey people, I noticed that combining these two methods in a single forward pass increases the controllability of the generation quite a bit. I was kind of puzzled that sometimes ControlNet yielded better results than T2IAdapter for some cases, and sometimes it was the other way around, so I decided to test both at the same time, and results were quite nice. Some visuals and more motivation here: https://github.com/huggingface/diffusers/issues/5847 And it was already merged here: https://github.com/huggingface/diffusers/pull/5869
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Won't you benchmark me?
Open Parti Prompts: The better way to evaluate diffusion models (repo)
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kohya_ss error. How do I solve this?
You have disabled the safety checker for by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .
- Making a ControlNet inpaint for sdxl
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Stable Diffusion Gets a Major Boost with RTX Acceleration
For developers, TensorRT support also exists for the diffusers library via community pipelines. [1] It's limited, but if you're only supporting a subset of features, it can help.
In general, these insane speed boosts comes at the cost of bleeding edge features.
[1] https://github.com/huggingface/diffusers/blob/28e8d1f6ec82a6...
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Mysterious weights when training UNET
I was training sdxl UNET base model, with the diffusers library, which was going great until around step 210k when the weights suddenly turned back to their original values and stayed that way. I also tried with the ema version, which didn't change at all. I also looked at the tensor's weight values directly which confirmed my suspicions.
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I Made Stable Diffusion XL Smarter by Finetuning It on Bad AI-Generated Images
Merging LoRAs is essentially taking a weighted average of the LoRA adapter weights. It's more common in other UIs.
diffusers is working on a PR for it: https://github.com/huggingface/diffusers/pull/4473
What are some alternatives?
diffusers - ๐ค Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch
stable-diffusion-webui - Stable Diffusion web UI
efficient-dreambooth - [Moved to: https://github.com/smy20011/dreambooth-docker]
stable-diffusion - A latent text-to-image diffusion model
fast-stable-diffusion - fast-stable-diffusion + DreamBooth
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) by way of Textual Inversion (https://arxiv.org/abs/2208.01618) for Stable Diffusion (https://arxiv.org/abs/2112.10752). Tweaks focused on training faces, objects, and styles.
invisible-watermark - python library for invisible image watermark (blind image watermark)
bin2ckpt - Convert Huggingface Pytorch checkpoint to Tensorflow checkpoint
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
sd-webui-additional-networks