Compel is a single-purpose, lightweight library that converts prompt strings to embedding tensors with some handy weighting and blending features.
To instantiate, pass a tokenizer and text_encoder (Transformer), typically from a StableDiffusion pipeline:
from diffusers import StableDiffusionPipeline
from compel import Compel
pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
compel = Compel(tokenizer=pipeline.tokenizer, text_encoder=pipeline.text_encoder)To generate conditioning tensors, call build_conditioning_tensor(prompt) or use the __call__ interface:
prompt = "a cat playing in the forest"
conditioning = compel(prompt)
# or: conditioning = compel.build_conditioning_tensor(prompt)
Typically with a Stable Diffusion workflow you'll also be using a negative prompt:
negative_prompt = "ugly, distorted, deformed"
negative_conditioning = compel(prompt)If you've disabled truncation or if your prompt contains .and() syntax, you'll need to ensure the conditioning and negative conditioning tensors are the same length:
[conditioning, negative_conditioning] = compel.pad_conditioning_tensors_to_same_length([conditioning, negative_conditioning])Finally, pass both of the tensors to your StableDiffusionPipeline, like this:
images = pipeline(prompt_embeds=conditioning, negative_propmt_embeds=negative_conditioning).images
images[0].save("image.jpg")Compel first parses the prompt into a Conjunction, then uses the structure of the Conjunction to construct a conditioning tensor for the prompt.
Weighting is applied using a combination of masked scaling of the parts of the conditioning tensor that correspond to the weighted terms in the prompt and, for weights < 1, a blend (lerp) with a version of the conditioning tensor where the negatively weighted terms have been removed.
Depending on model and CFG you can weight up to around 1.5 or 1.6 before things start to get weird.
For downweighting you can go all the way down to 0, at which point the downweighting terms completely disappear. The weight space is non-linear, and there seems to be an inflection point around 0.5 where the SD process suddenly changes how much attention it pays to the downweighted terms.
Blends are implemented as a mathematical lerp of the conditioning tensors. Sometimes they work the way you'd expect, more often they don't. Experiment and have fun.
Conjunctions work by concatenating prompts together. You'll need to use pad_conditioning_tensors_to_same_length if you want to combine conjunctions and negative prompts:
[conditioning, negative_conditioning] = compel.pad_conditioning_tensors_to_same_length([conditioning, negative_conditioning])Syntax features: Syntax
The Compel class: Compel
The prompt parser and structured prompt classes (Conjunction, FlattenedPrompt, Blend etc): PromptParser