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ComfyUI prompt control

Control LoRA and prompt scheduling, advanced text encoding, regional prompting, and much more, through your text prompt. Generates dynamic graphs that are literally identical to handcrafted noodle soup.

Prompt Control v2

Prompt control has been almost completely rewritten. It now uses ComfyUI's lazy execution to build graphs from the text prompt at runtime. The generated graph is often exactly equivalent to a manually built workflow using native ComfyUI nodes. There are no more weird sampling hooks that could cause problems with other nodes

Prompt Control also comes with PCTextEncode, which provides advanced text encoding with many additional features compared to ComfyUI's base CLIPTextEncode.

Removed features

Is it stable now?

Unless I run into bugs or significant annoyances that require changing the interface, it probably won't change too much, but until I tag 2.0, everything can change.

Everything broke, where are the old nodes?

If you really need them, you can install the legacy nodes. However, I will not fix bugs in those nodes, and I strongly recommend just migrating your workflows to the new nodes.

You can have both installed at the same time; none of the nodes conflict.

What can it do?

See features below. Things you can control via the prompt:

See the syntax documentation

If you find prompt scheduling inconvenient for some reason, PCTextEncode can be used as a drop-in replacement for CLIPTextEncode to get everything else.

This workflow shows LoRA scheduling and prompt editing and compares it with the same prompt implemented with built-in ComfyUI nodes.

Here is a two-pass workflow illustrating more features, including custom masks and filtering.

The tools in this repository combine well with the macro and wildcard functionality in comfyui-utility-nodes

Requirements

For LoRA scheduling to work, you'll need at least version 0.3.7 of ComfyUI (0.3.36 of ComfyUI desktop).

You need to have lark installed in your Python environment for parsing to work (If you reuse A1111's venv, it'll already be there).

If you use the portable version of ComfyUI on Windows with its embedded Python, you must open a terminal in the ComfyUI installation directory and run the command:

.\python_embeded\python.exe -m pip install lark

Then restart ComfyUI afterwards.

Core nodes

PCLazyTextEncode and PCLazyTextEncodeAdvanced

PCLazyTextEncode uses ComfyUI's lazy graph execution mechanism to generate a graph of PCTextEncode and SetConditioningTimestepRange nodes from a prompt with schedules. This has the advantage that if a part of the schedule doesn't change, ComfyUI's caching mechanism allows you to avoid re-encoding the non-changed part.

for example, if you first encode [cat:dog:0.1] and later change that to [cat:dog:0.5], no re-encoding takes place.

for added fun, put NODE(NodeClassName, textinputname) in a prompt to generate a graph using any other node that's compatible. The node can't have required parameters besides a single CLIP parameter (which must be named clip) and the text prompt, and it must return a CONDITIONING as its first return value. The "default" values are PCTextEncode and text.

For example, if you for some reason do not want the advanced features of PCTextEncode, use NODE(CLIPTextEncode) in the prompt and you'll still get scheduling with ComfyUI's regular TE node.

The advanced node enables filtering the prompt for multi-pass workflows.

PCLazyLoraLoader and PCLazyLoraLoaderAdvanced

This node reads LoRA expressions from the scheduled prompt and constructs a graph of LoraLoaders and CreateHookLoras as necessary to provide the necessary LoRA scheduling. Just use it in place of a LoRALoader and use the output normally.

The Advanced node gives you access to the generated hooks. If you have apply_hooks set to true, you do not need to apply the HOOKS output to a CLIP model separately; it's provided in case you want to use it elsewhere. The advanced node also enables filtering the prompt for multi-pass workflows.

PCTextEncode

Encodes a single prompt with advanced (non-scheduling) syntax enabled. This is what actually does most of the work under the hood.

Note: PCTextEncode does not ignore <lora:...:1> and will treat it as part of the prompt. To use a combined prompt for LoRAs and your input, use PCLazyTextEncode and PCLazyLoraLoader

PCAddMaskToCLIP

This node attaches masks to a CLIP model so that they can be referred to when using the IMASK custom mask function of PCTextEncode.

PCSetTextEncodeSettings

This node configures PCTextEncode default values for some functions by attaching the information to a CLIP model.

Features

Scheduling and LoRA loading

Prompt control provides a way to easily schedule different prompts and control LoRA loading.

See the syntax documentation

Note on how schedules work

ComfyUI does not use the step number to determine whether to apply conds; instead, it uses the sampler's timestep value which is affected by the scheduler you're using. This means that when the sampler scheduler isn't linear, the schedules generated by prompt control will not be either.

Advanced CLIP encoding

If you use PCTextEncode, advanced encodings are available automatically. Thanks to BlenderNeko for the original code.

Use the syntax STYLE(weight_interpretation, normalization) in a prompt to affect how prompts are interpreted.

The weight interpretations available are:

Normalizations are:

The normalization calculations are independent operations and you can combine them with +, eg STYLE(A1111, length+mean) or STYLE(comfy, mean+length), or even something silly like STYLE(perp, mean+length+mean+length)

The style can be specified separately for each AND:ed prompt, but the first prompt is special; later prompts will "inherit" it as default. For example:

STYLE(A1111) a (red:1.1) cat with (brown:0.9) spots and a long tail AND an (old:0.5) dog AND a (green:1.4) (balloon:1.1)

will interpret everything as A1111, but

a (red:1.1) cat with (brown:0.9) spots and a long tail AND STYLE(A1111) an (old:0.5) dog AND a (green:1.4) (balloon:1.1)

Will interpret the first one using the default ComfyUI behaviour, the second prompt with A1111 and the last prompt with the default again

For things (ie. the code imports) to work, the nodes must be cloned in a directory named exactly ComfyUI_ADV_CLIP_emb.

Cutoff

NOTE: Cutoff syntax might change at some point; it's pretty clunky.

PCTextEncode reimplements cutoff from ComfyUI Cutoff.

The syntax is

a group of animals, [CUT:white cat:white], [CUT:brown dog:brown:0.5:1.0:1.0:_]

You should read the prompt as a group of animals, white cat, brown dog, but CUT causes the tokens in target_tokens to be masked off from the base prompt in region_text, so that their effect can be isolated, and you're less likely to get brown cats or white dogs.

Target tokens are treated individually, separated by space, for example, [CUT:green apple, red apple, green leaf:green apple] will mask both greens and the apple, giving you + +, red +, + leaf. To mask out just green apple, use [CUT:green apple, red apple:green_apple] which will result in a masked prompt of + +, red apple. Escape _ with a \.

the parameters in the CUT section are region_text:target_tokens:weight;strict_mask:start_from_masked:padding_token of which only the first two are required. The default values are weight=1.0, strict_mask=1.0 start_from_masked=1.0, padding_token=+

If strict_mask, start_from_masked or padding_token are specified in more than one CUT, the last one becomes the default for any CUTs afterwards that do not explicitly set the parameters. For example, in:

[CUT:white cat:white:0.5] and [CUT:black parrot, flying:black:1.0:0.5] and [CUT:green apple:green]

white cat will a weight of 0.5, and 1.0 for all parameters, and black parrot and green apple will both have a strict_mask parameter of 0.5.

The parameters affect how the masked and unmasked prompts are combined to produce the final embedding. Just play around with them.

Known issues

If you want to enable a hack to fix this, set PROMPTCONTROL_ENABLE_CACHE_HACK=1 in your environment. Unset it to disable.

It's a purely optional performance optimization that allows Prompt Control nodes to override their cache keys in a way that should not interfere with other nodes. Note that the optimization only works if the text input to the lazy nodes is a constant (so either directly on the node or from a primitive); outputs from other nodes can't be optimized.