diffusers
LoRA
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diffusers | LoRA | |
---|---|---|
266 | 34 | |
22,543 | 9,046 | |
6.3% | 8.6% | |
9.9 | 5.4 | |
3 days ago | about 1 month ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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.
diffusers
- StableDiffusionSafetyChecker
- 🧨 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
LoRA
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DECT NR+: A technical dive into non-cellular 5G
This seems to be an order of magnitude better than LoRa (https://lora-alliance.org/ not https://arxiv.org/abs/2106.09685). LoRa doesn't have all the features this one does like OFDM, TDM, FDM, and HARQ. I didn't know there's spectrum dedicated for DECT use.
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Training LLMs Taking Too Much Time? Technique you need to know to train it faster
So to solve this, we tried researching into some optimization techniques and we found LoRA, Which stands for Low-Rank Adaptation of Large Language Models.
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OpenAI employee: GPT-4.5 rumor was a hallucination
> Anyone have any ideas / knowledge on how they deploy little incremental fixes to exploited jailbreaks, etc?
LoRa[1] would be my guess.
For detailed explanation I recommend the paper. But the short explanation is that it is a trick which lets you train a smaller, lower dimensional model which when you add to the original model it gets you the result you want.
1: https://arxiv.org/abs/2106.09685
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Can a LoRa be used on models other than Stable Diffusion?
LoRA was initially developed for large language models, https://arxiv.org/abs/2106.09685 (2021). It was later that people discovered that it worked REALLY well for diffusion models.
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StyleTTS2 – open-source Eleven Labs quality Text To Speech
Curious if we'll see a Civitai-style LoRA[1] marketplace for text-to-speech models.
1 = https://github.com/microsoft/LoRA
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Andreessen Horowitz Invests in Civitai, Which Profits from Nonconsensual AI Porn
From https://arxiv.org/abs/2106.09685:
> LoRA: Low-Rank Adaptation of Large Language Models
> An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example -- deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive. We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared to GPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times. LoRA performs on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3, despite having fewer trainable parameters, a higher training throughput, and, unlike adapters, no additional inference latency.
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Is supervised learning dead for computer vision?
Yes, your understanding is correct. However, instead of adding a head on top of the network, most fine-tuning is currently done with LoRA (https://github.com/microsoft/LoRA). This introduces low-rank matrices between different layers of your models, those are then trained using your training data while the rest of the models' weights are frozen.
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Run LLMs at home, BitTorrent‑style
Somewhat yes. See "LoRA": https://arxiv.org/abs/2106.09685
They're not composable in the sense that you can take these adaptation layers and arbitrarily combine them, but training different models while sharing a common base of weights is a solved problem.
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New LoRa RF distance record: 1336 km / 830 mi
With all the naive AI zealotry on HN can you really fault me?
They're referring to this:
https://arxiv.org/abs/2106.09685
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Open-source Fine-Tuning on Codebase with Refact
It's possible to fine-tune all parameters (called "full fine-tune"), but recently PEFT methods became popular. PEFT stands for Parameter-Efficient Fine-Tuning. There are several methods available, the most popular so far is LoRA (2106.09685) that can train less than 1% of the original weights. LoRA has one important parameter -- tensor size, called lora_r. It defines how much information LoRA can add to the network. If your codebase is small, the fine-tuning process will see the same data over and over again, many times in a loop. We found that for a smaller codebase small LoRA tensors work best because it won't overfit as much -- the tensors just don't have the capacity to fit the limited training set exactly. As the codebase gets bigger, tensors should become bigger as well. We also unfreeze token embeddings at a certain codebase size. To pick all the parameters automatically, we have developed a heuristic that calculates a score based on the source files it sees. This score is then used to determine the appropriate LoRA size, number of finetuning steps, and other parameters. We have tested this heuristic on several beta test clients, small codebases of several files, and large codebases like the Linux kernel (consisting of about 50,000 useful source files). If the heuristic doesn't work for you for whatever reason, you can set all the parameters yourself.
What are some alternatives?
stable-diffusion-webui - Stable Diffusion web UI
LyCORIS - Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion.
stable-diffusion - A latent text-to-image diffusion model
ComfyUI - The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
ControlNet - Let us control diffusion models!
invisible-watermark - python library for invisible image watermark (blind image watermark)
peft - 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
alpaca-lora - Instruct-tune LLaMA on consumer hardware
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.
LLaMA-Adapter - [ICLR 2024] Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters