{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Compel lets you re-weight different parts of text prompts to control how much influence each part has on the embedding tensor produced by diffusion models, using an intuitive syntax like `word++` or `word--`.","skillfed_tags":["prompt-engineering","diffusion-models","text-embedding"],"use_cases":["Emphasize specific objects or attributes in Stable Diffusion prompts by using ++ or -- syntax to control their visual weight in the generated image","Generate batches of images with different prompt weights applied consistently across multiple prompts and negative prompts","Combine style prompts with content prompts in SDXL or Flux, with independent weighting for each component","Integrate textual inversion embeddings into weighted prompts for fine-grained control over custom concepts","Debug tokenization and embedding behavior by accessing tokenization_info from the conditioning output"],"what_it_does":"Compel is a prompt weighting library for diffusion models that lets you control how much influence different parts of a text prompt have on the final image generation. Instead of treating a prompt as a flat string, you can use syntax like `word++` to upweight a concept or `word--` to downweight it, and these weights are applied to the embedding tensor before being passed to the diffusion pipeline. It works with Hugging Face's diffusers library and supports Stable Diffusion, SDXL, and Flux models, as well as negative prompts, style prompts, and batched generation.\n\nThe library handles the tokenization and embedding transformation internally, so you just pass your weighted prompt string and get back conditioning tensors ready for the pipeline. It also supports textual inversion embeddings and provides tokenization info for debugging. The main dependencies are torch, transformers, and diffusers\u2014all standard for generative AI work\u2014and it has no compiled dependencies, making installation straightforward.","worth_installing":"Yes. Compel is actively maintained, has no security vulnerabilities, uses a permissive license, and requires only standard dependencies. It solves a real problem\u2014fine-grained prompt control\u2014for anyone using diffusers-based image generation. Install it if you need to adjust the relative importance of different parts of your prompts; skip it if you're happy with flat prompt strings."},"id":"compel","links":{"html":"https://skillfed.io/packages/compel","md":"https://skillfed.io/packages/compel.md","pypi":"https://pypi.org/project/compel/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-30","license_spdx":null,"license_treatment":"permissive","name":"compel","python_support":"supports_current","summary":"A prompting enhancement library for transformers-type text embedding systems."},"popularity":{"monthly_downloads":131251,"position":11596,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.4.0"}