Compel

A text prompt weighting and blending library for transformers-type text embedding systems, by @damian0815.

With a flexible and intuitive syntax, you can re-weight different parts of a prompt string and thus re-weight the different parts of the embeddning tensor produced from the string.

Tested and developed against Hugging Face's StableDiffusionPipeline but it should work with any diffusers-based system that uses an Tokenizer and a Text Encoder of some kind.

Adapted from the InvokeAI prompting code (also by @damian0815). For now, the syntax is fully documented here.

Note that cross-attention control .swap() is currently ignored by Compel, but you can use it by calling build_conditioning_tensor_for_prompt_object() yourself, and implementing cross-attention control in your diffusion loop.

Installation

pip install compel

Demo

see compel-demo.ipynb

Quickstart

with Hugging Face diffusers >=0.12:

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)

# upweight "ball"
prompt = "a cat playing with a ball++ in the forest"
conditioning = compel.build_conditioning_tensor(prompt)
# or: conditioning = compel([prompt])

# generate image
images = pipeline(prompt_embeds=conditioning, num_inference_steps=20).images
images[0].save("image.jpg")

For batched input, use the call interface to compel:

import torch

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)

prompts = ["a cat playing with a ball++ in the forest", "a dog playing with a ball in the forest"]
prompt_embeds = compel(prompts)
images = pipeline(prompt_embeds=prompt_embeds).images

images[0].save("image0.jpg")
images[1].save("image1.jpg")

Changelog

1.1.0 - support for parsing withLora/useLora on parse_prompt_string().

• Compel.parse_prompt_string() now returns a Conjunction
• any appearances of withLora(name[, weight]) or useLora(name[, weight]) anywhere in the prompt string will be parsed to LoraWeight instances, and returned on the outermost Conjunction returned by parse_prompt_string().

1.0.5 - fix incorrect parsing when passing invalid (auto1111) syntax that has a float

also fix test case for default swap parameters

1.0.4 - fix embeddings for empty swap target (eg cat.swap("")) when truncation is disabled

1.0.3 - better defaults for .swap (damian0815#8)

1.0.2 - fix padding for non-truncated batched embeddings (damian0815#9)

1.0.1 - fix for InvokeAI's --free_gpu_mem option

1.0.0 - new downweighting algorithm

Downweighting now works by applying an attention mask to remove the downweighted tokens, rather than literally removing them from the sequence. This behaviour is the default, but the old behaviour can be re-enabled by passing downweight_mode=DownweightMode.REMOVE on init of the Compel instance.

Formerly, downweighting a token worked by both multiplying the weighting of the token's embedding, and doing an inverse-weighted blend with a copy of the token sequence that had the downweighted tokens removed. The intuition is that as weight approaches zero, the tokens being downweighted should be actually removed from the sequence. However, removing the tokens resulted in the positioning of all downstream tokens becoming messed up. The blend ended up blending a lot more than just the tokens in question.

As of v1.0.0, taking advice from @keturn and @bonlime (damian0815#7) the procedure is by default different. Downweighting still involves a blend but what is blended is a version of the token sequence with the downweighted tokens masked out, rather than removed. This correctly preserves positioning embeddings of the other tokens.

Also a bugfix: fix black images on weight 0 (invoke-ai/InvokeAI#2832)

0.1.10 - add support for prompts longer than the model's max token length.

To enable, initialize Compel with truncate_long_prompts=False (default is True). Prompts that are longer than the model's max_token_length will be chunked and padded out to an integer multiple of max_token_length.

Note that even if you don't use a negative prompt, you'll need to build a conditioning tensor for a negative prompt of at least "", and use compel.pad_conditioning_tensors_to_same_length(), otherwise the you'll get an error about mismatched conditioning tensor lengths:

compel = Compel(..., truncate_long_prompts=False)
prompt = "a cat playing with a ball++ in the forest, amazing, exquisite, stunning, masterpiece, skilled, powerful, incredible, amazing, trending on gregstation, greg, greggy, greggs greggson, greggy mcgregface, ..." # very long prompt
conditioning = compel.build_conditioning_tensor(prompt)
negative_prompt = "" # it's necessary to create an empty prompt - it can also be very long, if you want
negative_conditioning = compel.build_conditioning_tensor(negative_prompt)
[conditioning, negative_conditioning] = compel.pad_conditioning_tensors_to_same_length([conditioning, negative_conditioning])

0.1.9 - broken

0.1.8 - downgrade Python min version to 3.7

0.1.7 - InvokeAI compatibility