semvec/pypi-packages huggingface dataset:
pubmedclient 0.2.0
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Requires Python: >=3.12

# PubMed Client

[PubMed](https://pubmed.ncbi.nlm.nih.gov/) is a database of over 37 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites.

PubMed Client is a simple open-source Python client for calling the NCBI PubMed API. 

At the moment, the SDK mostly wraps around Entrez 'E-Utilities' API.

See for more details on the E-Utilities API from PubMed: 

* [A General Introduction to the E-utilities](https://www.ncbi.nlm.nih.gov/books/NBK25497/)
* [The E-utilities In-Depth: Parameters, Syntax and More](https://www.ncbi.nlm.nih.gov/books/NBK25499/)

## Installation

```bash
# recommended uv, will add pubmesdk to your uv project
uv add pubmedclient

# or pip
pip install pubmedclient
```

## Usage

warning: without api key, you can only make 3 requests per second per IP.

```python
from pubmedclient import pubmedclient_client

# this is a thin wrapper around httpx
# with a few headers set for Entrez API
async with pubmedclient_client() as client:

    # get the name of all available databases via EInfo
    params = EInfoRequest()
    response = await einfo(client, params)
    print(response)

    # get info about pubmed database
    params = EInfoRequest(db="pubmed")
    response = await einfo(client, params)
    print(response)

    # search for articles about asthma in the pubmed database
    params = ESearchRequest(db="pubmed", term="asthma")
    response = await esearch(client, params)
    print(response)

    # fetch the abstract for one of the returned id
    params = EFetchRequest(db="pubmed", id="39737528", rettype="abstract", retmode="text")
    response = await efetch(client, params)
    print(response)
```

## Development

During development, setup the pip package in editable mode to resolve imports from the local package.

```bash
uv pip install -e .

# or pip
pip install -e .
```

## Contributing

The API coverage is far from complete.

We welcome contributions. Please feel free to open an issue or a PR.
caa-v22 0.1.5
Official implementation of CA-ToMe for stable diffusion models
Requires Python: <4.0,>=3.8

# CA-ToMe: Cached Adaptive Token Merging for Efficient Stable Diffusion

This repository contains the official implementation of CA-ToMe (Cached Adaptive Token Merging), a novel optimization technique for accelerating Stable Diffusion inference while maintaining high image quality.

## Overview

CA-ToMe builds upon Token Merging (ToMe) by introducing an adaptive caching mechanism that intelligently merges similar tokens at strategic timesteps during the diffusion process. This approach significantly reduces computational overhead while preserving the quality of generated images.

## Key Features

- **Adaptive Token Merging**: Dynamically merges similar tokens based on cosine similarity thresholds
- **Strategic Caching**: Pre-computed merge patterns at carefully selected timesteps
- **Multiple Configuration Options**: Various checkpoint configurations optimized for different performance-quality tradeoffs
- **Easy Integration**: Simple API to apply CA-ToMe to existing Stable Diffusion pipelines

## Installation