AgroNT is a DNA language model trained on primarily edible plant genomes. More specifically, AgroNT uses the transformer architecture with self-attention and a masked language modeling objective to leverage highly available genotype data from 48 different plant speices to learn general representations of nucleotide sequences. AgroNT contains 1 billion parameters and has a context window of 1024 tokens. AgroNt uses a non-overlapping 6-mer tokenizer to convert genomic nucletoide sequences to tokens. As a result the 1024 tokens correspond to approximately 6144 base pairs. Our pre-training dataset was built from (mostly) edible plants reference genomes contained in the Ensembl Plants database. The dataset consists of approximately 10.5 million genomic sequences across 48 different species. All reference genomes for each specie were assembled into a single fasta file. In this fasta file, all nucleotides other than A, T, C, G were replaced by N. A tokenizer was used to convert strings of letters into sequences of tokens. The tokenizer's alphabet consisted of the 4 6 = 4096 possible 6-mer combinations obtained by combining A, T, C, G, as well as five additional tokens representing standalone A, T, C, G, and N. It also included three special tokens: the pad [PAD], mask [MASK], and class [CLS] tokens. This resulted in a vocabulary of 4104 tokens. To tokenize an input sequence, the tokenizer started with a class token and then converted the sequence from left to right, matching 6-mer tokens when possible, or using the standalone tokens when necessary (for instance, when the letter N was present or if the sequence length was not a multiple of 6).
The Nucleotide Transformers are a collection of foundational language models that were pre-trained on DNA sequences from whole-genomes. Compared to other approaches, our models do not only integrate information from single reference genomes, but leverage DNA sequences from over 3,200 diverse human genomes, as well as 850 genomes from a wide range of species, including model and non-model organisms. Through robust and extensive evaluation, we show that these large models provide extremely accurate molecular phenotype prediction compared to existing methods Part of this collection is the **nucleotide-transformer-500m-human-ref**, a 500M parameters transformer pre-trained on the human reference genome. The model is made available both in Tensorflow and Pytorch. - **Repository:** Nucleotide Transformer - **Paper:** The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics
The Nucleotide Transformers are a collection of foundational language models that were pre-trained on DNA sequences from whole-genomes. Compared to other approaches, our models do not only integrate information from single reference genomes, but leverage DNA sequences from over 3,200 diverse human genomes, as well as 850 genomes from a wide range of species, including model and non-model organisms. Through robust and extensive evaluation, we show that these large models provide extremely accurate molecular phenotype prediction compared to existing methods Part of this collection is the **nucleotide-transformer-v2-500m-multi-species**, a 500m parameters transformer pre-trained on a collection of 850 genomes from a wide range of species, including model and non-model organisms. Until its next release, the `transformers` library needs to be installed from source with the following command in order to use the models:
The Nucleotide Transformers are a collection of foundational language models that were pre-trained on DNA sequences from whole-genomes. Compared to other approaches, our models do not only integrate information from single reference genomes, but leverage DNA sequences from over 3,200 diverse human genomes, as well as 850 genomes from a wide range of species, including model and non-model organisms. Through robust and extensive evaluation, we show that these large models provide extremely accurate molecular phenotype prediction compared to existing methods Part of this collection is the **nucleotide-transformer-v2-50m-multi-species**, a 50M parameters transformer pre-trained on a collection of 850 genomes from a wide range of species, including model and non-model organisms. Until its next release, the `transformers` library needs to be installed from source with the following command in order to use the models:
We have completed the training of the first version. The training was conducted with a total pixel count of `1024*1024` at multi-scale. We trained for 30k steps using a batch size of 8*8.
The training of union controlnet requires a significant amount of computational power. The current release is the first beta version checkpoint that maybe not been fully trained. The fully trainedbeta version is in the training process. We have conducted ablation studies that have demonstrated the validity of the code. The open-source release of the first beta version is solely to facilitate the rapid growth of the open-source community and the Flux ecosystem; it is common to encounter bad cases (please accept my apologies). It is worth noting that we have found that even a fully trained Union model may not perform as well as specialized models, such as pose control. However, as training progresses, the performance of the Union model will continue to approach that of specialized models.
This repository contains a IP-Adapter for FLUX.1-dev model released by researchers from InstantX Team, where image work just like text, so it may not be responsive or interfere with other text, but we do hope you enjoy this model, have fun and share your creative works with us on Twitter. This is a regular IP-Adapter, where the new layers are added into 38 single and 19 double blocks. We use google/siglip-so400m-patch14-384 to encode image for its superior performance, and adopt a simple MLPProjModel of 2 linear layers to project. The image token number is set to 128. The currently released model is trained on the 10M open source dataset with a batch size of 128 and 80K training steps. This model supports image reference, but is not for fine-grained style transfer or character consistency, which means that there exists a trade-off between content leakage and style transfer. We don't find similar properties in FLUX.1-dev (DiT-based) as in InstantStyle (UNet-based). It may take several attempts to get satisfied results. Furthermore, current released model may suffer from limited diversity, thus cannot cover some styles or concepts,
