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Stability AI

11 articles tagged with this keyword, sorted by most recent first.

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CIO AI 2026-07-02 23:00 UTC Score 25.0 USR-0125-20260702-global-ai-ne-35160020

生成AIはクリエイターの敵か味方か──アドビが示す「商業的に安全なAI」の条件(後編)

クリエイターの反発が始まった 生成AIが世界的に普及した2022〜2023年、クリエイティブの現場はかつてない混乱に包まれた。SNSにはジブリ風、手塚治虫風といった模倣イラストが溢れ、AIが生成したポートレートがコンテストで優勝するなど、従来の価値観を揺るがす出来事が相次いだ。だが、その熱狂の裏側で、プロのクリエイターたちは深刻な危機感を抱き始めていた。 自分の作風が無断で学習され、AIが似た絵を大量に生成する。しかも、それが商業利用されても責任の所在が不明確なままとなる。こうした不安は、ついに法廷闘争へと発展する。 2023年1月、米国のアーティストであるサラ・アンダーセン、ケリー・マッカーナン、カーラ・オルティスの3名が画像生成AI企業のStability AI、Midjourney、DeviantArtを相手取り、著作権侵害やパブリシティ権侵害で集団訴訟を提起した。 訴状では、AIがインターネット上の画像を無断で収集し、学習データとして利用していると主張され、しかも利用者がAIで作成した画像を使って原告になりすましていたという。 さらに、英国では大手ストックフォト企業のGetty Imagesが、Stability AIが同社の画像を無断利用したとして法的措置を検討していると発表した。生成画像の中にはGettyの透かし(ウォーターマーク)が残ったままのものまで存在し、同社は「自社の知的財産が大量に利用されている」と強く抗議した。 こうした訴訟は、単なる著作権問題にとどまらない。AIが生成したフェイク画像が社会不安を加速させた。実在しない爆発事故の写真がSNSで拡散され株価が急落した事件、政治家の偽画像が拡散され報道機関でさえ真偽判定に苦労する事例──こうした状況の中で、本物と偽物の境界を見失い始めていた。 2023年10月には、アンダーセンらの訴訟の一部が棄却される判断も出た。裁判所は、AI生成物が原告作品と「実質的に類似している」と証明できていない点を指摘し、MidjourneyとDeviantArtに対する訴えを退けた。しかし、Stability AIに対する訴訟は継続しており、AIと著作権をめぐる法廷闘争は今も続いている。 世界は、AIの恩恵と危険性が同時に噴き出す転換点に立たされていた。クリエイターの反発は単なる抵抗ではなく、創作の未来を守るための必然的な叫びだった。 なぜAIとクリエイターは対立するのか──依拠性という構造的問題 AIとクリエイターの対立は、単なる感情論ではない。根底には、AIの学習構造そのものが抱える依拠性の問題がある。ここでいう依拠性とは、AIが生成する画像が、どの作品や作家の作風にどれだけ依存しているかが不透明な状態を指す。 多くの生成AIは、インターネット上の膨大な画像を無差別に収集し、著作権の有無を問わず学習してしまう。つまり、誰かの作品が無断でAIの内部に取り込まれ、その作風が再現される可能性がある。 アドビ 執行役員 広報本部長の鈴木正義氏は「通常のAIは、ネット上の画像をワーッと学習してしまうのです。そこには第三者の著作物も含まれてしまう。だから依拠性の問題が起きる」と語る。 AIがどの作品を学習し、どの作家のスタイルを取り込み、どの程度似ているのか──そのプロセスがブラックボックス化しているため、クリエイターは「自分の作品が勝手に使われているのではないか」という不信感を抱く。 さらに、AIが生成した画像が商業利用されても、元のクリエイターに還元される仕組みが存在しない。AIは無断学習と無断再利用…

AI Stack Exchange 2024-07-06 09:34 UTC Score 18.0 AI-110-20240706-social-media-a0082591

How do you save a stable diffusion model locally for later us?

I am new to ML and plan to use KerasCV stabledifussion model to generate images from text. The example on the KerasCV website is straightforward but I could not find a way to save the model locally for later use. I also noticed that the library connects to hugging face to download encoder and diffusion model. Could you please point me to the right direction to do this locally? I would like all the model and its parameters to be local and I will be using it in a server. Also, if you have experience running such a model/server on the could, I would appreciate your guidance on the best approach wrt costs. Should I upload everything and store the whole data on the cloud or load it from hugging face? Which one would make more sense for cloud applications?

Chip Huyen Blog 2023-10-10 00:00 UTC Score 53.0 USR-0111-20231010-ai-specialis-f4a68771

Multimodality and Large Multimodal Models (LMMs)

For a long time, each ML model operated in one data mode – text (translation, language modeling), image (object detection, image classification), or audio (speech recognition). However, natural intelligence is not limited to just a single modality. Humans can read, talk, and see. We listen to music to relax and watch out for strange noises to detect danger. Being able to work with multimodal data is essential for us or any AI to operate in the real world. OpenAI noted in their GPT-4V system card that “ incorporating additional modalities (such as image inputs) into LLMs is viewed by some as a key frontier in AI research and development .” Incorporating additional modalities to LLMs (Large Language Models) creates LMMs (Large Multimodal Models). Not all multimodal systems are LMMs. For example, text-to-image models like Midjourney, Stable Diffusion, and Dall-E are multimodal but don’t have a language model component. Multimodal can mean one or more of the following: Input and output are of different modalities (e.g. text-to-image, image-to-text) Inputs are multimodal (e.g. a system that can process both text and images) Outputs are multimodal (e.g. a system that can generate both text and images) This post covers multimodal systems in general, including LMMs. It consists of 3 parts. Part 1 covers the context for multimodality, including why multimodal, different data modalities, and types of multimodal tasks. Part 2 discusses the fundamentals of a multimodal system, using the…

AI Stack Exchange 2023-05-18 16:07 UTC Score 32.0 AI-110-20230518-social-media-d1399981

Should I be layer freezing when fine-tuning an LLM?

I've had it in my head that generally speaking, it's better to freeze layers when fine-tuning an LLM, as per this quote from HuggingFace's article : PEFT approaches only fine-tune a small number of (extra) model parameters while freezing most parameters of the pretrained LLMs, thereby greatly decreasing the computational and storage costs. This also overcomes the issues of catastrophic forgetting, a behaviour observed during the full finetuning of LLMs. PEFT approaches have also shown to be better than fine-tuning in the low-data regimes and generalize better to out-of-domain scenarios. It can be applied to various modalities, e.g., image classification and stable diffusion dreambooth. I think what I might be confused by is what is meant by the "(extra)" part. It led me to try fine-tuning a BERT model in PyTorch by freezing all parameters except for the final feed-forward of the transformer responsible for sequence classification: for param in model.parameters(): param.requires_grad = False for param in model.classifier.parameters(): param.requires_grad = True However, this caused my model to get significantly worse evaluation metrics on my test set than before I did this. This lead me to the following conclusions: My dataset of ~100K datapoints is not of a "low-data regime" and therefore doesn't benefit from PEFT? But doesn't it say this generalizes better to "out-of-domain scenarios"? How do I know the particular seq classification I'm doing with BERT is out-of-domain? Bec…

Anyscale Blog 2023-03-28 00:00 UTC Score 53.0 USR-0085-20230328-ai-specialis-931dd4cd

Faster stable diffusion fine-tuning with Ray AIR

*This is part 3 of our generative AI blog series that dives into a concrete example of how you can use Ray to scale the training of generative AI models. To learn more using Ray to productionize generative model workloads, see [part 1](https://www.anyscale.com/blog/ray-common-production-challenges-for-generative-ai-infrastructure). To learn about how Ray empowers LLM frameworks such as Alpa, see [part 2](https://www.anyscale.com/blog/training-175b-parameter-language-models-at-1000-gpu-scale-with-alpa-and-ray).*

Jay Alammar Blog 2023-01-01 00:00 UTC Score 41.0 USR-0113-20230101-ai-specialis-60429c7c

Remaking Old Computer Graphics With AI Image Generation

Can AI Image generation tools make re-imagined, higher-resolution versions of old video game graphics? Over the last few days, I used AI image generation to reproduce one of my childhood nightmares. I wrestled with Stable Diffusion, Dall-E and Midjourney to see how these commercial AI generation tools can help retell an old visual story - the intro cinematic to an old video game (Nemesis 2 on the MSX). This post describes the process and my experience in using these models/services to retell a story in higher fidelity graphics. Meet Dr. Venom This fine-looking gentleman is the villain in a video game. Dr. Venom appears in the intro cinematic of Nemesis 2, a 1987 video game. This image, in particular, comes at a dramatic reveal in the cinematic. Let’s update these graphics with visual generative AI tools and see how they compare and where each succeeds and fails. Remaking Old Computer graphics with AI Image Generation Here’s a side-by-side look at the panels from the original cinematic (left column) and the final ones generated by the AI tools (right column): This figure does not show the final Dr. Venom graphic because I want you to witness it as I had, in the proper context and alongside the appropriate music. You can watch that here:

Jay Alammar Blog 2022-10-04 00:00 UTC Score 33.0 USR-0113-20221004-ai-specialis-dafdda9c

The Illustrated Stable Diffusion

Translations: Chinese, Vietnamese. (V2 Nov 2022: Updated images for more precise description of forward diffusion. A few more images in this version) AI image generation is the most recent AI capability blowing people’s minds (mine included). The ability to create striking visuals from text descriptions has a magical quality to it and points clearly to a shift in how humans create art. The release of Stable Diffusion is a clear milestone in this development because it made a high-performance model available to the masses (performance in terms of image quality, as well as speed and relatively low resource/memory requirements). After experimenting with AI image generation, you may start to wonder how it works. This is a gentle introduction to how Stable Diffusion works. Stable Diffusion is versatile in that it can be used in a number of different ways. Let’s focus at first on image generation from text only (text2img). The image above shows an example text input and the resulting generated image (The actual complete prompt is here). Aside from text to image, another main way of using it is by making it alter images (so inputs are text + image).