Awesome
ComfyUI prompt control
Nodes for LoRA and prompt scheduling that make basic operations in ComfyUI completely prompt-controllable.
LoRA and prompt scheduling should produce identical output to the equivalent ComfyUI workflow using multiple samplers or the various conditioning manipulation nodes. If you find situations where this is not the case, please report a bug.
What can it do?
Things you can control via the prompt:
- Prompt editing and filtering without multiple samplers
- LoRA loading and scheduling (including LoRA block weights)
- Prompt masking and area control, combining prompts and interpolation
- SDXL parameters
- Other miscellaneous things
This example workflow implements a two-pass workflow illustrating most scheduling features.
The tools in this repository combine well with the macro and wildcard functionality in comfyui-utility-nodes
Requirements
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.
Notable changes
I try to avoid behavioural changes that break old prompts, but they may happen occasionally.
- 2024-02-02 The node will now automatically enable offloading LoRA backup weights to the CPU if you run out of memory during LoRA operations, even when
--highvram
is specified. This change persists until ComfyUI is restarted. - 2024-01-14 Multiple
CLIP_L
instances are now joined with a space separator instead of concatenated. - 2024-01-09 AITemplate support dropped. I don't recommend or test AITemplate anymore. Use Stable-Fast instead (see below for info)
- 2024-01-08 Prompt control now enables in-place weight updates on the model. This shouldn't affect anything, but increases performance slightly. You can disable this by setting the environment variable
PC_NO_INPLACE_UPDATE
to any non-empty value. - 2023-12-28 MASK now uses ComfyUI's
mask_strength
attribute instead of calculating it on its own. This changes its behaviour slightly. - 2023-12-06: Removed
JinjaRender
,SimpleWildcard
,ConditioningCutoff
,CondLinearInterpolate
andStringConcat
. For the first two, see this repository for mostly-compatible implementations. - 2023-10-04:
STYLE:...
syntax changed toSTYLE(...)
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.
Currently there doesn't seem to be a good way to change this.
You can try using the PCSplitSampling
node to enable an alternative method of sampling.
Scheduling syntax
Syntax is like A1111 for now, but only fractions are supported for steps.
a [large::0.1] [cat|dog:0.05] [<lora:somelora:0.5:0.6>::0.5]
[in a park:in space:0.4]
You can also use a [b:c:0.3,0.7]
as a shortcut. The prompt be a
until 0.3, a b
until 0.7, and then a c
. [a:0.1,0.4]
is equivalent to [a::0.1,0.4]
LoRA loading
LoRAs can be loaded by referring to the filename without extension and subdirectories will also be searched. For example, <lora:cats:1>
. will match both cats.safetensors
and sd15/animals/cats.safetensors
. If there are multiple LoRAs with the same name, the first match will be loaded.
Alternatively, the name can include the full directory path relative to ComfyUI's search paths, without extension: <lora:XL/sdxllora:0.5>
. In this case, the full path must match.
If no match is found, the node will try to replace spaces with underscores and search again. That is, <lora:cats and dogs:1>
will find cats_and_dogs.safetensors
. This helps with some autocompletion scripts that replace underscores with spaces.
Finally, you can give the exact path (including the extension) as shown in LoRALoader
.
Alternating
Alternating syntax is [a|b:pct_steps]
, causing the prompt to alternate every pct_steps
. pct_steps
defaults to 0.1 if not specified. You can also have more than two options.
Sequences
The syntax [SEQ:a:N1:b:N2:c:N3]
is shorthand for [a:[b:[c::N3]:N2]:N1]
ie. it switches from a
to b
to c
to nothing at the specified points in sequence.
Might be useful with Jinja templating (see https://github.com/asagi4/comfyui-utility-nodes). For example:
[SEQ<% for x in steps(0.1, 0.9, 0.1) %>:<lora:test:<= sin(x*pi) + 0.1 =>>:<= x =><% endfor %>]
generates a LoRA schedule based on a sinewave
Tag selection
Using the FilterSchedule
node, in addition to step percentages, you can use a tag to select part of an input:
a large [dog:cat<lora:catlora:0.5>:SECOND_PASS]
Set the tags
parameter in the FilterSchedule
node to filter the prompt. If the tag matches any tag tags
(comma-separated), the second option is returned (cat
, in this case, with the LoRA). Otherwise, the first option is chosen (dog
, without LoRA).
the values in tags
are case-insensitive, but the tags in the input must be uppercase A-Z and underscores only, or they won't be recognized. That is, [dog:cat:hr]
will not work.
For example, a prompt
a [black:blue:X] [cat:dog:Y] [walking:running:Z] in space
with tags
x,z
would result in the prompt a blue cat running in space
Prompt interpolation
a red [INT:dog:cat:0.2,0.8:0.05]
will attempt to interpolate the tensors for a red dog
and a red cat
between the specified range in as many steps of 0.05 as will fit.
SDXL
The nodes do not treat SDXL models specially, but there are some utilities that enable SDXL specific functionality.
You can use the function SDXL(width height, target_width target_height, crop_w crop_h)
to set SDXL prompt parameters. SDXL()
is equivalent to SDXL(1024 1024, 1024 1024, 0 0)
unless the default values have been overridden by PCScheduleSettings
.
To set the clip_l
prompt, as with CLIPTextEncodeSDXL
, use the function CLIP_L(prompt text goes here)
.
Things to note:
- Multiple instances of
CLIP_L
are joined with a space. That is,CLIP_L(foo)CLIP_L(bar)
is the same asCLIP_L(foo bar)
- Using
BREAK
isn't supported in it; it'll just parse as the plain word BREAK. - similarly,
AND
insideCLIP_L
does not do anything sensible;CLIP_L(foo AND bar)
will parse as two promptsCLIP_L(foo
andbar)
CLIP_L
andSDXL
have no effect on SD 1.5.- The rest of the prompt becomes the
clip_g
prompt. - If there is no
CLIP_L
orSDXL
, the prompts will work as withCLIPTextEncode
.
Other syntax:
-
<emb:xyz>
is alternative syntax forembedding:xyz
to work around a syntax conflict with[embedding:xyz:0.5]
which is parsed as a schedule that switches fromembedding
toxyz
. -
The keyword
BREAK
causes the prompt to be tokenized in separate chunks, which results in each chunk being individually padded to the text encoder's maximum token length. This is mostly equivalent to theConditioningConcat
node.
Combining prompts
AND
can be used to combine prompts. You can also use a weight at the end. It does a weighted sum of each prompt,
cat :1 AND dog :2
The weight defaults to 1 and are normalized so that a:2 AND b:2
is equal to a AND b
. AND
is processed after schedule parsing, so you can change the weight mid-prompt: cat:[1:2:0.5] AND dog
if there is COMFYAND()
in the prompt, the behaviour of AND
will change to work like ConditioningCombine
, but in practice this seems to be just slower while producing the same output.
Functions
There are some "functions" that can be included in a prompt to do various things.
Functions have the form FUNCNAME(param1, param2, ...)
. How parameters are interpreted is up to the function.
Note: Whitespace is not stripped from string parameters by default. Commas can be escaped with \,
Like AND
, these functions are parsed after regular scheduling syntax has been expanded, allowing things like [AREA:MASK:0.3](...)
, in case that's somehow useful.
SHUFFLE and SHIFT
Default parameters: SHUFFLE(seed=0, separator=,, joiner=,)
, SHIFT(steps=0, separator=,, joiner=,)
SHIFT
moves elements to the left by steps
. The default is 0 so SHIFT()
does nothing
SHUFFLE
generates a random permutation with seed
as its seed.
These functions are applied to each prompt chunk after BREAK
, AND
etc. have been parsed. The prompt is split by separator
, the operation is applied, and it's then joined back by joiner
.
Multiple instances of these functions are applied in the order they appear in the prompt.
NOTE: These functions are not smart about syntax and will break emphasis if the separator occurs inside parentheses. I might fix this at some point, but for now, keep this in mind.
For example:
-
SHIFT(1) cat, dog, tiger, mouse
does a shift and results indog, tiger, mouse, cat
. (whitespace may vary) -
SHIFT(1,;) cat, dog ; tiger, mouse
results intiger, mouse, cat, dog
-
SHUFFLE() cat, dog, tiger, mouse
results incat, dog, mouse, tiger
-
SHUFFLE() SHIFT(1) cat, dog, tiger, mouse
results indog, mouse, tiger, cat
-
SHIFT(1) cat,dog BREAK tiger,mouse
results indog,cat BREAK tiger,mouse
-
SHIFT(1) cat, dog AND SHIFT(1) tiger, mouse
results indog, cat BREAK mouse, tiger
Whitespace is not stripped and may also be used as a joiner or separator
SHIFT(1,, ) cat,dog
results indog cat
NOISE
The function NOISE(weight, seed)
adds some random noise into the prompt. The seed is optional, and if not specified, the global RNG is used. weight
should be between 0 and 1.
MASK, IMASK and AREA
You can use MASK(x1 x2, y1 y2, weight, op)
to specify a region mask for a prompt. The values are specified as a percentage with a float between 0
and 1
, or as absolute pixel values (these can't be mixed). 1
will be interpreted as a percentage instead of a pixel value.
Similarly, you can use AREA(x1 x2, y1 y2, weight)
to specify an area for the prompt (see ComfyUI's area composition examples). The area is calculated by ComfyUI relative to your latent size.
Custom masks: IMASK and PCScheduleAddMasks
You can attach custom masks to a PROMPT_SCHEDULE
with the PCScheduleAddMasks
node and then refer to those masks in the prompt using IMASK(index, weight, op)
. Indexing starts from zero, so 0 is the first attached mask etc. PCSCheduleAddMasks
ignores empty inputs, so if you only add a mask to the mask4
input, it will still have index 0.
Applying PCScheduleAddMasks
multiple times appends masks to a schedule rather than overriding existing ones, so if you need more than 4, you can just use it more than once.
Behaviour of masks
If multiple MASK
s are specified, they are combined together with ComfyUI's MaskComposite
node, with op
specifying the operation to use (default multiply
). In this case, the combined mask weight can be set with MASKW(weight)
(defaults to 1.0).
Masks assume a size of (512, 512)
, unless overridden with PCScheduleSettings
and pixel values will be relative to that. ComfyUI will scale the mask to match the image resolution. You can change it manually by using MASK_SIZE(width, height)
anywhere in the prompt,
These are handled per AND
-ed prompt, so in prompt1 AND MASK(...) prompt2
, the mask will only affect prompt2.
The default values are MASK(0 1, 0 1, 1)
and you can omit unnecessary ones, that is, MASK(0 0.5, 0.3)
is MASK(0 0.5, 0.3 1, 1)
Note that because the default values are percentages, MASK(0 256, 64 512)
is valid, but MASK(0 200)
will raise an error.
Masking does not affect LoRA scheduling unless you set unet weights to 0 for a LoRA.
FEATHER
When you use MASK
or IMASK
, you can also call FEATHER(left top right bottom)
to apply feathering using ComfyUI's FeatherMask
node. The values are in pixels and default to 0
.
If multiple masks are used, FEATHER
is applied before compositing in the order they appear in the prompt, and any leftovers are applied to the combined mask. If you want to skip feathering a mask while compositing, just use FEATHER()
with no arguments.
For example:
MASK(1) MASK(2) MASK(3) FEATHER(1) FEATHER() FEATHER(3) weirdmask FEATHER(4)
gives you a mask that is a combination of 1, 2 and 3, where 1 and 3 are feathered before compositing and then FEATHER(4)
is applied to the composite.
The order of the FEATHER
and MASK
calls doesn't matter; you can have FEATHER
before MASK
or even interleave them.
Schedulable LoRAs
The ScheduleToModel
node patches a model so that when sampling, it'll switch LoRAs between steps. You can apply the LoRA's effect separately to CLIP conditioning and the unet (model).
Swapping LoRAs often can be quite slow without the --highvram
switch because ComfyUI will shuffle things between the CPU and GPU. When things stay on the GPU, it's quite fast.
If you run out of VRAM during a LoRA swap, the node will attempt to save VRAM by enabling CPU offloading for future generations even in highvram mode. This persists until ComfyUI is restarted.
You can also set the PC_RETRY_ON_OOM
environment variable to any non-empty value to automatically retry sampling once if VRAM runs out.
LoRA Block Weight
If you have ComfyUI Inspire Pack installed, you can use its Lora Block Weight syntax, for example:
a prompt <lora:cars:1:LBW=SD-OUTALL;A=1.0;B=0.0;>
The ;
is optional if there is only 1 parameter.
The syntax is the same as in the ImpactWildcard
node, documented here
Other integrations
Advanced CLIP encoding
You can use the syntax STYLE(weight_interpretation, normalization)
in a prompt to affect how prompts are interpreted.
Without any extra nodes, only perp
is available, which does the same as ComfyUI_PerpWeight extension.
If you have Advanced CLIP Encoding nodes cloned into your custom_nodes
, more options will be available.
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 node integration
If you have ComfyUI Cutoff cloned into your custom_nodes
, you can use the CUT
keyword to use cutoff functionality
The syntax is
a group of animals, [CUT:white cat:white], [CUT:brown dog:brown:0.5:1.0:1.0:_]
the parameters in the CUT
section are region_text:target_text:weight;strict_mask:start_from_masked:padding_token
of which only the first two are required.
If strict_mask
, start_from_masked
or padding_token
are specified in more than one section, the last one takes effect for the whole prompt
Stable-Fast
The prompt control node works well with ComfyUI_stable_fast. However, you should apply ScheduleToModel
after applying Apply StableFast Unet
to prevent constant recompilations.
Nodes
PromptToSchedule
Parses a schedule from a text prompt. A schedule is essentially an array of (valid_until, prompt)
pairs that the other nodes can use.
FilterSchedule
Filters a schedule according to its parameters, removing any changes that do not occur within [start, end)
.
The node also does tag filtering if any tags are specified.
Always returns at least the last prompt in the schedule if everything would otherwise be filtered.
start=0, end=0
returns the prompt at the start and start=1.0, end=1.0
returns the prompt at the end.
ScheduleToCond
Produces a combined conditioning for the appropriate timesteps. From a schedule. Also applies LoRAs to the CLIP model according to the schedule.
ScheduleToModel
Produces a model that'll cause the sampler to reapply LoRAs at specific steps according to the schedule.
This depends on a callback handled by a monkeypatch of the ComfyUI sampler function, so it might not work with custom samplers, but it shouldn't interfere with them either.
PCSplitSampling
Causes sampling to be split into multiple sampler calls instead of relying on timesteps for scheduling. This makes the schedules more accurate, but seems to cause weird behaviour with SDE samplers. (Upstream bug?)
PCScheduleSettings
Returns an object representing default values for the SDXL
function and allows configuring MASK_SIZE
outside the prompt. You need to apply them to a schedule with PCApplySettings
. Note that for the SDXL settings to apply, you still need to have SDXL()
in the prompt.
The "steps" parameter currently does nothing; it's for future features.
PCApplySettings
Applies the give default values from PCScheduleSettings
to a schedule
PCPromptFromSchedule
Extracts a text prompt from a schedule; also logs it to the console. LoRAs are not included in the text prompt, though they are logged.
PCScheduleAddMasks
Attaches custom masks to a PROMPT_SCHEDULE
that can then be used in a prompt.
PromptControlSimple
This node exists purely for convenience. It's a combination of PromptToSchedule
, ScheduleToCond
, ScheduleToModel
and FilterSchedule
such that it provides as output a model, positive conds and negative conds, both with and without any specified filters applied.
This makes it handy for quick one- or two-pass workflows.
Older nodes
EditableCLIPEncode
: A combination ofPromptToSchedule
andScheduleToCond
LoRAScheduler
: A combination ofPromptToSchedule
,FilterSchedule
andScheduleToModel
Known issues
- If you use LoRA scheduling in a workflow with
LoRALoader
nodes, you might get inconsistent results. For now, just avoid mixingScheduleToModel
orLoRAScheduler
withLoRALoader
. See https://github.com/asagi4/comfyui-prompt-control/issues/36 - Workflows using
SamplerCustom
will calculate LoRA schedules based on the number of sigmas given to the sampler instead of the number of steps, since that information isn't available. CUT
does not work withSTYLE:perp
PCSplitSampling
overrides ComfyUI'sBrownianTreeNoiseSampler
noise sampling behaviour so that each split segment doesn't add crazy amounts of noise to the result with some samplers.- Split sampling may have weird behaviour if your step percentages go below 1 step.
- Interpolation is probably buggy and will likely change behaviour whenever code gets refactored.
- If execution is interrupted and LoRA scheduling is used, your models might be left in an undefined state until you restart ComfyUI