lingua-py
diffusers
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lingua-py | diffusers | |
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8 | 266 | |
881 | 22,543 | |
- | 6.3% | |
7.4 | 9.9 | |
27 days ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
lingua-py
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Typos โ automatic language recognition and error detection in Word and Excel documents
แ โ Recognition of 75 languages
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The hand-picked selection of the best Python libraries and tools of 2022
Hi u/dekked_, perhaps you want to add my natural language detection library Lingua to the NLP section of the long tail. It is pretty unique among the natural language detection libraries for Python because it is able to detect multiple languages in mixed-language text. It is also one of the most accurate libraries when detecting the language of short text. I would very much appreciate if you added my library to your list.
- Lingua 1.2.0 - The most accurate natural language detection library for Python - now with support for detecting multiple languages in mixed-language text.
- Lingua 1.1.0 - The most accurate natural language detection library for Python
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Lingua-Go, the most accurate language detection for Go
I've compared the Python implementation of Lingua with fasttext. Lingua performs clearly better. Look here: https://github.com/pemistahl/lingua-py#4-how-good-is-it
- Lingua 1.0.1: The most accurate natural language detection library for Python - previously for Python >= 3.9, now compatible with Python >= 3.7
- Announcing Lingua 1.0.0: The most accurate natural language detection library for Python, suitable for long and short text alike
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?
lingua-rs - The most accurate natural language detection library for Rust, suitable for short text and mixed-language text
stable-diffusion-webui - Stable Diffusion web UI
langid.py - Stand-alone language identification system
stable-diffusion - A latent text-to-image diffusion model
cld3
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
LSTM_langid - Source code for the Apple reproduction
invisible-watermark - python library for invisible image watermark (blind image watermark)
lingua-go - The most accurate natural language detection library for Go, suitable for short text and mixed-language text
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
cld2 - Compact Language Detector 2
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.