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DiT-XL-2-256

facebook

We train latent diffusion models, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by Gflops. We find that DiTs with higher Gflops---through increased transformer depth/width or increased number of input tokens---consistently have lower FID. In addition to good scalability properties, our DiT-XL/2 models outperform all prior diffusion models on the class-conditional ImageNet 512×512 and 256×256 benchmarks, achieving a state-of-the-art FID of 2.27 on the latter.

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DiT-XL-2-512

facebook

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**Model Description:** Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. `dpr-ctx_encoder-multiset-base` is the context encoder trained using the Natural Questions (NQ) dataset, TriviaQA, WebQuestions (WQ), and CuratedTREC (TREC). `dpr-ctx_encoder-multiset-base`, `dpr-question_encoder-multiset-base`, and `dpr-reader-multiset-base` can be used for the task of open-domain question answering. The model should not be used to intentionally create hostile or alienating environments for people. In addition, the set of DPR models was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out-of-scope for the abilities of this model.

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- Model Details - How To Get Started With the Model - Uses - Risks, Limitations and Biases - Training - Evaluation - Environmental Impact - Technical Specifications - Citation Information - Model Card Authors **Model Description:** Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. `dpr-ctx_encoder-single-nq-base` is the Context Encoder trained using the Natural Questions (NQ) dataset (Lee et al., 2019; Kwiatkowski et al., 2019). - **Developed by:** See GitHub repo for model developers - **Model Type:** BERT-based encoder - **Language(s):** CC-BY-NC-4.0, also see Code of Conduct - **License:** English - **Related Models:** - `dpr-question-encoder-single-nq-base` - `dpr-reader-single-nq-base` - `dpr-ctx_encoder-multiset-base` - `dpr-question_encoder-multiset-base` - `dpr-reader-multiset-base` - **Resources for more information:** - Research Paper - GitHub Repo - Hugging Face DPR docs - BERT Base Uncased Model Card

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- Model Details - How To Get Started With the Model - Uses - Risks, Limitations and Biases - Training - Evaluation - Environmental Impact - Technical Specifications - Citation Information - Model Card Authors **Model Description:** Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. `dpr-question_encoder-single-nq-base` is the question encoder trained using the Natural Questions (NQ) dataset (Lee et al., 2019; Kwiatkowski et al., 2019). - **Developed by:** See GitHub repo for model developers - **Model Type:** BERT-based encoder - **Language(s):** CC-BY-NC-4.0, also see Code of Conduct - **License:** English - **Related Models:** - `dpr-ctx_encoder-single-nq-base` - `dpr-reader-single-nq-base` - `dpr-ctx_encoder-multiset-base` - `dpr-question_encoder-multiset-base` - `dpr-reader-multiset-base` - **Resources for more information:** - Research Paper - GitHub Repo - Hugging Face DPR docs - BERT Base Uncased Model Card

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