v2.1.2

Add a test for #133
#133 properly set function locations in get_function
2026-01-13 21:50:09 +02:00 · 2026-01-13 21:48:34 +02:00 · 2026-01-13 20:45:10 +02:00 · 2026-01-13 20:45:10 +02:00 · 2025-12-14 17:57:53 +02:00 · 2025-12-05 21:25:28 +02:00
33 changed files with 769 additions and 734 deletions
@@ -4,13 +4,17 @@ on:
  workflow_dispatch:
  push:
    paths:
+      - prompt_control/adv_encode.py
+      - prompt_control/attention_couple_ppm.py
      - prompt_control/nodes_lazy.py
+      - prompt_control/prompts.py
+      - prompt_control/parser.py
      - prompt_control/utils.py


 jobs:
  run-graph-tests:
-    name: Run graph tests
+    name: Run tests requiring ComfyUI
    runs-on: ubuntu-latest
    steps:
      - name: Check out code
@@ -23,6 +27,16 @@ jobs:
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
-      - run: pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
-      - run: pip install -r requirements.txt -r ComfyUI/requirements.txt
-      - run: PYTHONPATH=ComfyUI python -m prompt_control.test_graph
+          cache: pip
+      - name: install-torch
+        run: pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+      - name: install ComfyUI
+        run: pip install -r requirements.txt -r ComfyUI/requirements.txt
+      - name: Download clip_l.safetensors
+        run: curl -LO https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/clip_l.safetensors
+      - name: Force Comfy to use the CPU
+        run: sed -i "s/^cpu_state = CPUState.GPU/cpu_state = CPUState.CPU/g" ComfyUI/comfy/model_management.py
+      - name: Run graph tests
+        run: PYTHONPATH=ComfyUI python -m prompt_control.test_graph
+      - name: Run encoder tests (clip_l only)
+        run: PYTHONPATH=ComfyUI python -m prompt_control.test_encode
@@ -12,10 +12,10 @@ test_graph:
 	PYTHONPATH=../../ python -m prompt_control.test_graph

 test_encode:
-	PYTHONPATH=../../ python -m prompt_control.test_encode
+	PYTHONPATH=../../ python -m prompt_control.test_encode --verbose

 test_encode_both:
-	TEST_TE="clip_l t5" PYTHONPATH=../../ python -m prompt_control.test_encode
+	TEST_TE="clip_l t5" PYTHONPATH=../../ python -m prompt_control.test_encode --verbose

 test_heavy: test_graph test_encode_both

@@ -11,16 +11,16 @@ A `Basic Text to Image` template is included with the extension, and can be load
 You can use text prompts to control the following:

 - A1111-style prompt scheduling and filtering without noodle soup.
- LoRA loading and scheduling via ComfyUI's hook system
- Masking, composition and area control (regional prompting) with an implementation of [Attention Couple](/doc/attention_couple.md), also fully schedulable.
- Per-encoder prompts for models with multiple text encoders, such as SDXL and Flux
- Prompt combinators like `BREAK`, as well as `CAT`, `AVG()` and `AND` corresponding to ComfyUI's `ConditioningConcat`, `ConditioningAverage` and `ConditioningCombine` nodes.
- Different weight interpretation types (ComfyUI, A1111, compel, etc.)
- Prompt masking with an implementation of [cutoff](https://github.com/BlenderNeko/ComfyUI_Cutoff)
- Simple prompt macros with `DEF`
- And a bunch more
+- LoRA loading and [scheduling](/doc/schedules.md) via the prompt, using ComfyUI's hook system
+- Masking, composition and area control ([regional prompting](/doc/regional_prompts.md)) with an implementation of [Attention Couple](/doc/attention_couple.md), also fully schedulable.
+- [Advanced prompt encoding](/doc/basic.md)
+  - Per-encoder prompts for models with multiple text encoders, such as SDXL and Flux
+  - Prompt combinators like `BREAK`, as well as `CAT`, `AVG()` and `AND` corresponding to ComfyUI's `ConditioningConcat`, `ConditioningAverage` and `ConditioningCombine` nodes.
+  - Different weight interpretation types (ComfyUI, A1111, compel, etc.)
+  - Prompt masking with an implementation of [cutoff](https://github.com/BlenderNeko/ComfyUI_Cutoff)
+- Simple [prompt macros](/doc/macros.md) with `DEF`

-All features are fully schedulable unless otherwise stated. See the [syntax documentation](doc/syntax.md) for details on how to use each feature.
+All features are fully schedulable unless otherwise stated. See the [scheduling syntax documentation](doc/schedules.md) to get started.

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

@@ -32,21 +32,6 @@ Prompt Control uses graph generation,  and tries to delegate functionality to co

 If you encounter issues as a user or if you're a node developer and Prompt Control somehow breaks something, feel free to file a bug report.

-## 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
-
-### Removed features
-
- Prompt interpolation syntax; it was too cumbersome to maintain
- LoRA block weight integration; ditto, for now.
-
-### Everything broke, where are the old nodes?
-
-If you really need them, you can install the [legacy nodes](https://github.com/asagi4/comfyui-prompt-control-legacy). 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.
-
 ## Requirements

 For LoRA scheduling to work, you'll need at least version 0.3.7 of ComfyUI (0.3.36 of ComfyUI desktop).
@@ -100,8 +85,4 @@ This node configures `PCTextEncode` default values for some functions by attachi

 - ComfyUI's caching mechanism has an issue that makes it unnecessarily invalidate caches for certain inputs; you'll still get some benefit from the lazy nodes, but changing inputs that shouldn't affect downstream nodes (especially if using filtering) will still cause them to be recomputed because ComfyUI doesn't realize the inputs haven't changed.

-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.
-
 - Cutoff does not work with models that use non-CLIP text encoders, like Flux. This might be fixable, but it's uncertain if cutoff even makes sense for those models.
@@ -23,12 +23,11 @@ if os.environ.get("PROMPTCONTROL_DEBUG"):
 else:
    log.setLevel(logging.INFO)

-cache_hack = importlib.import_module(".prompt_control.cache_hack", package=__name__)
-cache_hack.init()
-
 NODE_CLASS_MAPPINGS = {}
 NODE_DISPLAY_NAME_MAPPINGS = {}

+WEB_DIRECTORY = "web"
+
 nodes = ["base", "lazy", "tools", "hooks"]

 for node in nodes:
@@ -15,7 +15,7 @@ To enable batching negative prompts, run your positive and negative prompt throu

 ## Syntax

-See also the main syntax documentation for `MASK` etc.
+See also the [regional prompting documentation](/doc/regional_prompts.md) for information about `MASK` etc.

 ### COUPLE: Trigger Attention Couple

@@ -38,3 +38,5 @@ For example:
 ```
 dog FILL() COUPLE(0.5 1) cat
 ```
+
+Note that because the generation still sees and diffuses the full latent, attention coupling is not guaranteed to perfectly limit the effect of your prompt to the masked area.
@@ -1,101 +1,6 @@
-# Prompt Control Syntax
+# Basic Prompt Syntax

-If you're viewing this on GitHub, I recommend opening the outline by clicking the button in the top right corner of the text view (it is annoyingly easy to miss).
-
-Scheduling syntax is similar to A1111, but only fractions are supported for steps. LoRAs are scheduled by including them in a scheduling expression.
-
-```
-a [large::0.1] [cat|dog:0.05] [<lora:somelora:0.5:0.6>::0.5]
-[in a park:in space:0.4]
-```
-
-## Scheduled prompts
-
-There are two forms of scheduled prompts.
-
-### Basic scheduling expressions
-Basic expressions take the form `[before:after:X]` where `X` is the switch point, a decimal number between 0.0 and 1.0 inclusive, representing 0 to 100% of timesteps. Either prompt can also be empty.
-For example:
-```
-a [red:blue:0.5] cat
-```
-switches from `a red cat` to `a blue cat` at 0.5. `before` and `after` can be arbitrary prompts (`after` can also be empty), including other scheduling expressions, allowing nesting:
-```
-a [red:[blue::0.7]:0.5] cat
-```
-
-switches from `a red cat` to `a blue cat` at 0.5 and to `a cat` at 0.7
-
-For convenience `[cat:0.5]` is equivalent to `[:cat:0.5]` meaning it switches from empty to `cat` at 0.5.
-
-### Range expressions
-
-The most general form of a schedule is a range expression: For example, in `[before:during:after:0.3,0.7]`, The prompt be `a before` until 0.3, `a during` until 0.7, and then `a after`. This form is equivalent to `[before:[during:after:0.7]:0.3]`
-
-For convenience, `[during:0.1,0.4]` is equivalent to `[:during::0.1,0.4]` and `[during:after:0.1,0.4]` is equivalent to `[:during:after:0.1,0.4]`.
-
-`[before:during:after:0.1]` is the same as `[before:during:after:0.1,1.0]` which is same as `[before:during:0.1]`
-
-
-### Using step numbers with the Advanced nodes
-
-If you provide a non-zero value to `num_steps` to the `Advanced` versions of the scheduling nodes, you will be able to use step numbers in prompts.
-
-For now, a value between 0 and 1.0 will be interpreted as a percentage if it contains a ., and as an absolute step otherwise.
-
-This is just syntactic sugar. Behind the scenes, the values are converted to percentages and have normal ComfyUI scheduling behaviour.
-
-## 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`
-
-The three prompt form `[a:b:c:TAG]` is parsed, but ignores `b` and is equivalent to `[a:c:TAG]`.
-
-## LoRA Scheduling
-When using the lazy graph building nodes, LoRAs can be scheduled by referring to them in a scheduling expression, like so:
-
-`<lora:fulllora:1> [<lora:partialora:1>::0.5]`
-
-This will schedule `fulllora` for the entire duration of the prompt and `partiallora` until half of sampling is complete.
-
-You can refer to LoRAs by using 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
-
-# Basic prompt syntax
-
-This syntax is also available in outside scheduled with the `PCTextEncode` node, where applicable.
+The syntax below documents the features of `PCTextEncode`

 ## Combining prompts

@@ -118,6 +23,8 @@ cat [\:0::0.5] AND dog
 ```
 Note that the `:` needs to be escaped with a `\` or it will be interpreted as scheduling syntax.

+If `AND` is placed inside quotes (eg. `Text saying "CAT AND DOG"`) it will be treated as regular text.
+
 ## Note about processing order

 Prompt operators are processed in the following order, meaning that all features "below" another can be affected by the feature above it. That is, `BREAK` can go inside a `TE()` call, but not `AND` or `CAT`.
@@ -148,6 +55,8 @@ Note: Whitespace is usually *not* stripped from string parameters by default. Co

 Like `AND`, functions are parsed after regular scheduling syntax has been expanded, allowing things like `[AREA:MASK:0.3](...)`, in case that's somehow useful.

+like AND, if any function is placed inside quotes, it will *not* activate and is instead treated as regular text.
+
 ### BREAK
 The keyword `BREAK` causes the prompt to be tokenized in separate chunks, padding each chunk to the text encoder's maximum size before encoding.

@@ -252,52 +161,9 @@ The function `NOISE(weight, seed)` adds some random noise into the cond tensor.

 The usefulness of this is questionable, but it wasn't difficult to implement, so here it is.

-
 ## Regional prompting

-See also [Attention Couple](#attention-couple) below
-
-### 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.
-
-Multiple `MASK` or `IMASK` calls will be composited together using ComfyUI's `MaskComposite` node, using `op` as the `operation` parameter (defaulting to `multiply`).
-
-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 `PCAddMaskToCLIP`
-
-You can attach custom masks to a `CLIP` with the `PC: Attach Mask` nodes 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 the nodes multiple times *appends* masks 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 `PC: Configure PCTextEncode` 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: Mask operations
-
-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.
+See [Regional prompting](/doc/regional_prompting.md)

 ## Cutoff

@@ -328,68 +194,8 @@ The parameters affect how the masked and unmasked prompts are combined to produc

 # Experimental features

-Experimental features are unstable and may disappear or change without warning.
-
-## DEF: Lightweight prompt macros
-
-You can define "prompt macros" by using `DEF`.  Macros are expanded before any other parsing takes place. The expansion continues until no further changes occur. Recursion will raise an error.
-
-`PCLazyTextEncode` and `PCLazyLoraLoader` expand macros, but `PCTextEncode` **does not**. If you need to expand macros for a single prompt, use `PCMacroExpand`
-
-```
-DEF(MYMACRO=this is a prompt)
-[(MYMACRO:0.6):(MYMACRO:1.1):0.5]
-```
-is equivalent to
-```
-[(this is a prompt:0.5):(this is a prompt:1.1):0.5]
-```
-### Macro parameters
-It's also possible to give parameters to a macro:
-```
-DEF(MYMACRO=[(prompt $1:$2):(prompt $1:$3):$4])
-MYMACRO(test; 1.1; 0.7; 0.2)
-```
-gives
-```
-[(prompt test:1.1):(prompt test:0.7):0.2]
-```
-in this form, the variables $N (where N is any number corresponding to a positional parameter) will be replaced with the given parameter. The parameters must be separated with a semicolon, and can be empty.
-
-You can also optionally specify default values:
-
-```
-DEF(MACRO(example; 0; 1)=[$1:$2,$3])
-MACRO MACRO(test; 0.2)
-```
-gives
-```
-[example:0,1] [test:0.2,1]
-```
-
-```
-DEF(MACRO() = [a:$1:0.5])
-```
-sets the default value of `$1` to an empty string.
-
-### Unspecified parameters in macros
-
-Unspecified parameters (either via defaults or explicitly given) will not be substituted. Compare:
-
-```
-DEF(mything=a "$1" b "$2")
-mything
-mything()
-mything(A)
-```
-
-gives
-
-```
-a "$1" b "$2"
-a "" b "$2"
-a "A" b "$2"
-```
+> [!WARN]
+> These features are may change or disappear without warning

 ## COUPLE: Attention couple

@@ -0,0 +1,60 @@
+## DEF: Lightweight prompt macros
+
+You can define "prompt macros" by using `DEF`.  Macros are expanded before any other parsing takes place. The expansion continues until no further changes occur. Recursion will raise an error.
+
+`PCLazyTextEncode` and `PCLazyLoraLoader` expand macros, but `PCTextEncode` **does not**. If you need to expand macros for a single prompt, use `PCMacroExpand`
+
+```
+DEF(MYMACRO=this is a prompt)
+[(MYMACRO:0.6):(MYMACRO:1.1):0.5]
+```
+is equivalent to
+```
+[(this is a prompt:0.5):(this is a prompt:1.1):0.5]
+```
+### Macro parameters
+It's also possible to give parameters to a macro:
+```
+DEF(MYMACRO=[(prompt $1:$2):(prompt $1:$3):$4])
+MYMACRO(test; 1.1; 0.7; 0.2)
+```
+gives
+```
+[(prompt test:1.1):(prompt test:0.7):0.2]
+```
+in this form, the variables $N (where N is any number corresponding to a positional parameter) will be replaced with the given parameter. The parameters must be separated with a semicolon, and can be empty.
+
+You can also optionally specify default values:
+
+```
+DEF(MACRO(example; 0; 1)=[$1:$2,$3])
+MACRO MACRO(test; 0.2)
+```
+gives
+```
+[example:0,1] [test:0.2,1]
+```
+
+```
+DEF(MACRO() = [a:$1:0.5])
+```
+sets the default value of `$1` to an empty string.
+
+### Unspecified parameters in macros
+
+Unspecified parameters (either via defaults or explicitly given) will not be substituted. Compare:
+
+```
+DEF(mything=a "$1" b "$2")
+mything
+mything()
+mything(A)
+```
+
+gives
+
+```
+a "$1" b "$2"
+a "" b "$2"
+a "A" b "$2"
+```
@@ -0,0 +1,63 @@
+# Regional prompting
+
+This section documents the masking functionality of `PCTextEncode`
+
+See also [Attention Couple](/doc/attention_couple.md)
+
+Remember that when using the lazy nodes, prompt scheduling applies to masks as well, so you can change or enable/disable regional prompts at any point during sampling.
+
+## Behaviour
+
+For each prompt separated by `AND`, you can specify either latent masks or an area.
+
+- When masked, ComfyUI generates the model output using the **full latent** as the input, and then applies the mask to the output before adding it to your latent for the next step.
+- When an area is specified, ComfyUI generates a separate model output using the **part of the latent specified by the area** and then composites it into the full latent afterwards.
+- You can have *both* an AREA and a MASK specified, in which case the mask is applied to the latent specified by the AREA.
+
+For example, consider a 1024 by 1024 (width x height) generation:
+
+- `cat MASK(0 0.5, 0 1) AND dog MASK(0.5 1, 0 1)` generates two outputs at 1024x1024 for "dog" and "cat", then masks half of them off and adds the results together. The following step still see both the dog and the cat from the previous step, so they may blend slightly.
+
+- `cat AREA(0 0.5, 0 1) AND dog AREA(0.5 1, 0 1)` generates two completely separate outputs at **512**x1024 and then composites them together into the 1024x1024 latent. Because the areas do not overlap, the generation for `cat` will not see the output of `dog` and vice versa in subsequent steps as long as the area restriction is in effect.
+
+## 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.
+
+Multiple `MASK` or `IMASK` calls will be composited together using ComfyUI's `MaskComposite` node, using `op` as the `operation` parameter (defaulting to `multiply`).
+
+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 `PCAddMaskToCLIP`
+
+You can attach custom masks to a `CLIP` with the `PC: Attach Mask` nodes 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 the nodes multiple times *appends* masks rather than overriding existing ones, so if you need more than 4, you can just use it more than once.
+
+### Behaviour of multiple 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 `PC: Configure PCTextEncode` 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: Mask operations
+
+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.
@@ -0,0 +1,117 @@
+# Prompt Schedule Syntax
+
+> [!TIP]
+> If you're viewing this on GitHub, I recommend opening the outline by clicking the button in the top right corner of the text view (it is annoyingly easy to miss).
+
+> [!NOTE]
+> The syntax documented in this section is only available with the `PC: Schedule Prompt` and `PC: Schedule LoRAs` nodes and their advanced variants.
+
+Scheduling syntax is available with is similar to A1111, but only fractions are supported for steps. LoRAs are scheduled by including them in a scheduling expression.
+
+Besides the syntax documented below, the [basic syntax](/doc/basic.md) and [prompt macro](/doc/macros.md) features are also automatically available.
+
+```
+a [large::0.1] [cat|dog:0.05] [<lora:somelora:0.5:0.6>::0.5]
+[in a park:in space:0.4]
+```
+## Comments and escaping
+
+In schedules, any text on a line following a `#` is considered a comment and removed, including the `#` character.
+You can escape the following characters in places where they would otherwise conflict with syntax:
+
+- `#` with `\#`
+- `:` with `\:`
+- `\` with `\\`
+
+Escaping is only required if it would otherwise be considered syntax, that is `\o/` will be interpreted literally and the `\` does not need to be escaped, but in `[embedding:a:0.5]` you would need to escape the `:`.
+
+## Scheduled prompts
+
+There are two forms of scheduled prompts.
+
+### Basic scheduling expressions
+Basic expressions take the form `[before:after:X]` where `X` is the switch point, a decimal number between 0.0 and 1.0 inclusive, representing 0 to 100% of timesteps. Either prompt can also be empty.
+For example:
+```
+a [red:blue:0.5] cat
+```
+switches from `a red cat` to `a blue cat` at 0.5. `before` and `after` can be arbitrary prompts (`after` can also be empty), including other scheduling expressions, allowing nesting:
+```
+a [red:[blue::0.7]:0.5] cat
+```
+
+switches from `a red cat` to `a blue cat` at 0.5 and to `a cat` at 0.7
+
+For convenience `[cat:0.5]` is equivalent to `[:cat:0.5]` meaning it switches from empty to `cat` at 0.5.
+
+### Range expressions
+
+The most general form of a schedule is a range expression: For example, in `prompt [before:during:after:0.3,0.7]`, The prompt be `prompt before` until 0.3, `prompt during` until 0.7, and then `prompt after`. This form is equivalent to `prompt [before:[during:after:0.7]:0.3]`
+
+For convenience, `[during:0.1,0.4]` is equivalent to `[:during::0.1,0.4]` and `[during:after:0.1,0.4]` is equivalent to `[:during:after:0.1,0.4]`.
+
+`[before:during:after:0.1]` is the same as `[before:during:after:0.1,1.0]` which is same as `[before:during:0.1]`
+
+
+### Using step numbers with the Advanced nodes
+
+If you provide a non-zero value to `num_steps` to the `Advanced` versions of the scheduling nodes, you will be able to use step numbers in prompts.
+
+For now, a value between 0 and 1.0 will be interpreted as a percentage if it contains a ., and as an absolute step otherwise.
+
+This is just syntactic sugar. Behind the scenes, the values are converted to percentages and have normal ComfyUI scheduling behaviour.
+
+## 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`
+
+The three prompt form `[a:b:c:TAG]` is parsed, but ignores `b` and is equivalent to `[a:c:TAG]`.
+
+## LoRA Scheduling
+When using the lazy graph building nodes, LoRAs can be scheduled by referring to them in a scheduling expression, like so:
+
+`<lora:fulllora:1> [<lora:partialora:1>::0.5]`
+
+This will schedule `fulllora` for the entire duration of the prompt and `partiallora` until half of sampling is complete.
+
+You can refer to LoRAs by using 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.
+
+You can also give the exact path (including the extension) as shown in `LoRALoader`.
+
+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, if none of the above produce a match, the search term will be split by whitespace and files that contain all of the parts in any order will be considered. If this returns only a single match, it will be loaded. For example, consider LoRAs:
+
+- `xl/red_cats.safetensors`
+- `flux/blue_cats.safetensors`
+- `flux/red_cats.safetensors`
+
+Then `<lora:cats xl:1>` would match the red cats LoRA, but `cats flux` would be ambiguous and not match.
+
+## 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
@@ -3,7 +3,6 @@ import numpy as np
 from math import copysign
 import logging
 import itertools
-from .adv_encode_old import old_advanced_encode_from_tokens

 log = logging.getLogger("comfyui-prompt-control")

@@ -26,7 +25,7 @@ def _grouper(n, iterable):
 def batched_clip_encode(tokens, length, encode_func, num_chunks):
    embs = []
    for e in _grouper(32, tokens):
-        enc, pooled = encode_func(e)
+        enc, pooled, *_ = encode_func(e)
        enc = enc.reshape((len(e), length, -1))
        embs.append(enc)

@@ -101,24 +100,24 @@ def style_comfy(encoder, tokens, **kwargs):


 def style_a1111(encoder, tokens, **kwargs):
-    base_emb, pooled = encoder.base_emb(tokens)
+    base_emb, pooled, *extra = encoder.base_emb(tokens)
    weighted_emb = base_emb * weights_like(encoder.weights(tokens), base_emb)
    weighted_emb = (base_emb.mean() / weighted_emb.mean()) * weighted_emb  # renormalize
-    return weighted_emb, pooled
+    return (weighted_emb, pooled) + tuple(extra)


 def style_compel(encoder, tokens, **kwargs):
    pos_tokens = encoder.weighted_with(tokens, lambda w: w if w > 1.0 else 1.0)
-    weighted_emb, pooled = encoder.encode_fn(pos_tokens)
+    weighted_emb, pooled, *extra = encoder.encode_fn(pos_tokens)
    weighted_emb, _, pooled = encoder.down_weight(
        pos_tokens, encoder.weights(tokens), encoder.word_ids(tokens), weighted_emb, pooled
    )
-    return weighted_emb, pooled
+    return (weighted_emb, pooled) + tuple(extra)


 def style_comfypp(encoder, tokens, **kwargs):
    unweighted_tokens = encoder.unweighted(tokens)
-    base_emb, pooled_base = encoder.base_emb(tokens)
+    base_emb, pooled_base, *extra = encoder.base_emb(tokens)
    weighted_emb, tokens_down, _ = encoder.down_weight(
        unweighted_tokens, encoder.weights(tokens), encoder.word_ids(tokens), base_emb, pooled_base
    )
@@ -132,23 +131,23 @@ def style_comfypp(encoder, tokens, **kwargs):
    )
    weighted_emb += embs

-    return weighted_emb, pooled
+    return (weighted_emb, pooled) + tuple(extra)


 def style_downweight(encoder, tokens, **kwargs):
    weights = scale_to_norm(encoder.weights(tokens), encoder.word_ids(tokens), encoder.w_max)
-    base_emb, pooled_base = encoder.base_emb(tokens)
+    base_emb, pooled_base, *extra = encoder.base_emb(tokens)
    weighted_emb, _, pooled = encoder.down_weight(
        encoder.unweighted(tokens), weights, encoder.word_ids(tokens), base_emb, pooled_base
    )

-    return weighted_emb, pooled
+    return (weighted_emb, pooled) + tuple(extra)


 def style_perp(encoder, tokens, **kwargs):
-    zero_emb, zero_pooled = encoder.encode_fn(encoder.tokenizer.tokenize_with_weights(""))
-    base_emb, pooled = encoder.base_emb(tokens)
-    return perp_weight(encoder.weights(tokens), (base_emb, pooled), (zero_emb, zero_pooled))
+    zero_emb, zero_pooled, *_ = encoder.encode_fn(encoder.tokenizer.tokenize_with_weights(""))
+    base_emb, pooled, *extra = encoder.base_emb(tokens)
+    return perp_weight(encoder.weights(tokens), (base_emb, pooled), (zero_emb, zero_pooled)) + tuple(extra)


 def apply_negpip(encoder, emb, pooled, **kwargs):
@@ -258,8 +257,8 @@ class AdvancedEncoder:
        if negpip:

            def _encode(t):
-                emb, pooled = encode_fn(t)
-                return emb[:, 0::2, :], pooled
+                emb, pooled, *extra = encode_fn(t)
+                return (emb[:, 0::2, :], pooled) + tuple(extra)

            self.encode_fn = _encode
            self.preprocessors.insert(0, lambda encoder, tokens, **kwargs: encoder.weighted_with(tokens, abs))
@@ -289,7 +288,7 @@ class AdvancedEncoder:
            if w[i] >= 1:
                continue
            masked_current = mask_inds(masked_current, np.where(w_inv == i)[0], self.m_token)
-            masked, _ = self.encode_fn(masked_current)
+            masked, _, *extra = self.encode_fn(masked_current)
            emblist.append(masked)

        embs = torch.cat(emblist)
@@ -349,16 +348,16 @@ class AdvancedEncoder:
        for op in self.preprocessors:
            normalized_tokens = op(self, normalized_tokens)

-        emb, pooled = self.weight_fn(self, normalized_tokens, original_tokens=tokens)
+        emb, pooled, *extra = self.weight_fn(self, normalized_tokens, original_tokens=tokens)

        for fn in self.postprocessors:
            emb, pooled = fn(self, emb, pooled, tokens=tokens, original_tokens=tokens)

-        if return_pooled:
-            if not apply_to_pooled:
-                _, pooled = self.base_emb(tokens)
-            return emb, pooled
-        return emb, None
+        if not return_pooled:
+            pooled = None
+        elif not apply_to_pooled:
+            _, pooled, *_ = self.base_emb(tokens)
+        return (emb, pooled) + tuple(extra)


 def advanced_encode_from_tokens(
@@ -373,20 +372,7 @@ def advanced_encode_from_tokens(
    tokenizer=None,
    **extra_args,
 ):
-    if "old+" not in weight_interpretation:
-        enc = AdvancedEncoder(
-            encode_func, weight_interpretation, token_normalization, tokenizer, m_token, w_max, **extra_args
-        )
-        return enc(tokenized, return_pooled=return_pooled, apply_to_pooled=apply_to_pooled)
-    else:
-        weight_interpretation = weight_interpretation.replace("old+", "")
-        log.warning("Using old implementation of %s", weight_interpretation)
-        return old_advanced_encode_from_tokens(
-            tokenized,
-            token_normalization,
-            weight_interpretation,
-            encode_func,
-            266,
-            return_pooled=return_pooled,
-            apply_to_pooled=apply_to_pooled,
-        )
+    enc = AdvancedEncoder(
+        encode_func, weight_interpretation, token_normalization, tokenizer, m_token, w_max, **extra_args
+    )
+    return enc(tokenized, return_pooled=return_pooled, apply_to_pooled=apply_to_pooled)
@@ -1,235 +0,0 @@
-import torch
-import numpy as np
-import logging
-import itertools
-
-log = logging.getLogger("comfyui-prompt-control")
-
-
-def _norm_mag(w, n):
-    d = w - 1
-    return 1 + np.sign(d) * np.sqrt(np.abs(d) ** 2 / n)
-    # return  np.sign(w) * np.sqrt(np.abs(w)**2 / n)
-
-
-def _grouper(n, iterable):
-    it = iter(iterable)
-    while True:
-        chunk = list(itertools.islice(it, n))
-        if not chunk:
-            return
-        yield chunk
-
-
-def batched_clip_encode(tokens, length, encode_func, num_chunks):
-    embs = []
-    for e in _grouper(32, tokens):
-        enc, pooled = encode_func(e)
-        enc = enc.reshape((len(e), length, -1))
-        embs.append(enc)
-
-    embs = torch.cat(embs)
-    embs = embs.reshape((len(tokens) // num_chunks, length * num_chunks, -1))
-    return embs
-
-
-def weights_like(weights, emb):
-    return torch.tensor(weights, dtype=emb.dtype, device=emb.device).reshape(1, -1, 1).expand(emb.shape)
-
-
-def divide_length(word_ids, weights):
-    sums = dict(zip(*np.unique(word_ids, return_counts=True)))
-    sums[0] = 1
-    weights = [[_norm_mag(w, sums[id]) if id != 0 else 1.0 for w, id in zip(x, y)] for x, y in zip(weights, word_ids)]
-    return weights
-
-
-def shift_mean_weight(word_ids, weights):
-    delta = 1 - np.mean([w for x, y in zip(weights, word_ids) for w, id in zip(x, y) if id != 0])
-    weights = [[w if id == 0 else w + delta for w, id in zip(x, y)] for x, y in zip(weights, word_ids)]
-    return weights
-
-
-def scale_to_norm(weights, word_ids, w_max):
-    top = np.max(weights)
-    w_max = min(top, w_max)
-    weights = [[w_max if id == 0 else (w / top) * w_max for w, id in zip(x, y)] for x, y in zip(weights, word_ids)]
-    return weights
-
-
-def mask_word_id(tokens, word_ids, target_id, mask_token):
-    new_tokens = [[mask_token if wid == target_id else t for t, wid in zip(x, y)] for x, y in zip(tokens, word_ids)]
-    mask = np.array(word_ids) == target_id
-    return (new_tokens, mask)
-
-
-def from_masked(tokens, weights, word_ids, base_emb, length, encode_func, m_token=266):
-    pooled_base = base_emb[0, length - 1 : length, :]
-    wids, inds = np.unique(np.array(word_ids).reshape(-1), return_index=True)
-    weight_dict = dict((id, w) for id, w in zip(wids, np.array(weights).reshape(-1)[inds]) if w != 1.0)
-
-    if len(weight_dict) == 0:
-        return torch.zeros_like(base_emb), base_emb[0, length - 1 : length, :]
-
-    weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device)
-    weight_tensor = weight_tensor.reshape(1, -1, 1).expand(base_emb.shape)
-
-    # m_token = (clip.tokenizer.end_token, 1.0) if  clip.tokenizer.pad_with_end else (0,1.0)
-    # TODO: find most suitable masking token here
-    m_token = (m_token, 1.0)
-
-    ws = []
-    masked_tokens = []
-    masks = []
-
-    # create prompts
-    for id, w in weight_dict.items():
-        masked, m = mask_word_id(tokens, word_ids, id, m_token)
-        masked_tokens.extend(masked)
-
-        m = torch.tensor(m, dtype=base_emb.dtype, device=base_emb.device)
-        m = m.reshape(1, -1, 1).expand(base_emb.shape)
-        masks.append(m)
-
-        ws.append(w)
-
-    # batch process prompts
-    embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens))
-    masks = torch.cat(masks)
-
-    embs = base_emb.expand(embs.shape) - embs
-    pooled = embs[0, length - 1 : length, :]
-
-    embs *= masks
-    embs = embs.sum(axis=0, keepdim=True)
-
-    pooled_start = pooled_base.expand(len(ws), -1)
-    ws = torch.tensor(ws).reshape(-1, 1).expand(pooled_start.shape)
-    pooled = (pooled - pooled_start) * (ws - 1)
-    pooled = pooled.mean(axis=0, keepdim=True)
-
-    return ((weight_tensor - 1) * embs), pooled_base + pooled
-
-
-def mask_inds(tokens, inds, mask_token):
-    clip_len = len(tokens[0])
-    inds_set = set(inds)
-    new_tokens = [
-        [mask_token if i * clip_len + j in inds_set else t for j, t in enumerate(x)] for i, x in enumerate(tokens)
-    ]
-    return new_tokens
-
-
-def down_weight(tokens, weights, word_ids, base_emb, length, encode_func):
-    w, w_inv = np.unique(weights, return_inverse=True)
-
-    if np.sum(w < 1) == 0:
-        return base_emb, tokens, base_emb[0, length - 1 : length, :]
-    # m_token = (clip.tokenizer.end_token, 1.0) if  clip.tokenizer.pad_with_end else (0,1.0)
-    # using the comma token as a masking token seems to work better than aos tokens for SD 1.x
-    m_token = (266, 1.0)
-
-    masked_tokens = []
-
-    masked_current = tokens
-    for i in range(len(w)):
-        if w[i] >= 1:
-            continue
-        masked_current = mask_inds(masked_current, np.where(w_inv == i)[0], m_token)
-        masked_tokens.extend(masked_current)
-
-    embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens))
-    embs = torch.cat([base_emb, embs])
-    w = w[w <= 1.0]
-    w_mix = np.diff([0] + w.tolist())
-    w_mix = torch.tensor(w_mix, dtype=embs.dtype, device=embs.device).reshape((-1, 1, 1))
-
-    weighted_emb = (w_mix * embs).sum(axis=0, keepdim=True)
-    return weighted_emb, masked_current, weighted_emb[0, length - 1 : length, :]
-
-
-def scale_emb_to_mag(base_emb, weighted_emb):
-    norm_base = torch.linalg.norm(base_emb)
-    norm_weighted = torch.linalg.norm(weighted_emb)
-    embeddings_final = (norm_base / norm_weighted) * weighted_emb
-    return embeddings_final
-
-
-# For verification
-def A1111_renorm(base_emb, weighted_emb):
-    embeddings_final = (base_emb.mean() / weighted_emb.mean()) * weighted_emb
-    return embeddings_final
-
-
-def from_zero(weights, base_emb):
-    weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device)
-    weight_tensor = weight_tensor.reshape(1, -1, 1).expand(base_emb.shape)
-    return base_emb * weight_tensor
-
-
-def old_advanced_encode_from_tokens(
-    tokenized,
-    token_normalization,
-    weight_interpretation,
-    encode_func,
-    m_token=266,
-    w_max=1.0,
-    return_pooled=False,
-    apply_to_pooled=False,
-    **extra_args,
-):
-    length = 77
-    tokens = [[t for t, _, _ in x] for x in tokenized]
-    weights = [[w for _, w, _ in x] for x in tokenized]
-    word_ids = [[wid for _, _, wid in x] for x in tokenized]
-
-    # weight normalization
-    # ====================
-
-    # distribute down/up weights over word lengths
-    if token_normalization.startswith("length"):
-        weights = divide_length(word_ids, weights)
-
-    # make mean of word tokens 1
-    if token_normalization.endswith("mean"):
-        weights = shift_mean_weight(word_ids, weights)
-
-    # weight interpretation
-    # =====================
-    pooled = None
-
-    if weight_interpretation in ["comfy", "perp"]:
-        weighted_tokens = [[(t, w) for t, w in zip(x, y)] for x, y in zip(tokens, weights)]
-        weighted_emb, pooled_base = encode_func(weighted_tokens)
-        pooled = pooled_base
-    else:
-        unweighted_tokens = [[(t, 1.0) for t, _, _ in x] for x in tokenized]
-        base_emb, pooled_base = encode_func(unweighted_tokens)
-
-    if weight_interpretation == "A1111":
-        weighted_emb = from_zero(weights, base_emb)
-        weighted_emb = A1111_renorm(base_emb, weighted_emb)
-        pooled = pooled_base
-
-    if weight_interpretation == "compel":
-        pos_tokens = [[(t, w) if w >= 1.0 else (t, 1.0) for t, w in zip(x, y)] for x, y in zip(tokens, weights)]
-        weighted_emb, _ = encode_func(pos_tokens)
-        weighted_emb, _, pooled = down_weight(pos_tokens, weights, word_ids, weighted_emb, length, encode_func)
-
-    if weight_interpretation == "comfy++":
-        weighted_emb, tokens_down, _ = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func)
-        weights = [[w if w > 1.0 else 1.0 for w in x] for x in weights]
-        # unweighted_tokens = [[(t,1.0) for t, _,_ in x] for x in tokens_down]
-        embs, pooled = from_masked(unweighted_tokens, weights, word_ids, base_emb, length, encode_func)
-        weighted_emb += embs
-
-    if weight_interpretation == "down_weight":
-        weights = scale_to_norm(weights, word_ids, w_max)
-        weighted_emb, _, pooled = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func)
-
-    if return_pooled:
-        if apply_to_pooled:
-            return weighted_emb, pooled
-        else:
-            return weighted_emb, pooled_base
-    return weighted_emb, None
@@ -76,10 +76,6 @@ class AttentionCoupleHook(TransformerOptionsHook):
        self.kv = {"k": None, "v": None}

    def initialize_regions(self, base_cond, conds, fill):
-        self._base_cond = base_cond
-        self._conds = conds
-        self._fill = fill
-
        self.num_conds = len(conds) + 1
        self.base_strength = base_cond[1].get("strength", 1.0)
        self.strengths = [cond[1].get("strength", 1.0) for cond in conds]
@@ -214,8 +214,7 @@ def build_scheduled_prompts(graph, schedules, clip):
        classname = "PCTextEncode"
        paramname = "text"
        if classnames:
-            classname = classnames[0][0]
-            paramname = classnames[0][1]
+            classname, paramname = classnames[0].args
        node = graph.node(classname)
        node.set_input("clip", clip)
        node.set_input(paramname, p)
@@ -1,10 +1,10 @@
 import logging
 from .parser import parse_prompt_schedules, expand_macros
 from .nodes_lazy import NODE_CLASS_MAPPINGS as LAZY_NODES
+from .utils import expand_graph
 import json
 import folder_paths
 from pathlib import Path
-from comfy_execution.graph_utils import is_link

 log = logging.getLogger("comfyui-prompt-control")

@@ -20,7 +20,7 @@ class PCSaveExpandedWorkflow:
                "any": ("*", {}),
            },
            "hidden": {
-                "prompt": "DYNPROMPT",
+                "prompt": "PROMPT",
            },
        }

@@ -31,7 +31,7 @@ class PCSaveExpandedWorkflow:
    OUTPUT_NODE = True
    RETURN_TYPES = ()
    CATEGORY = "promptcontrol/tools"
-    DESCRIPTION = "Saves the current expanded dynamic prompt into a JSON file"
+    DESCRIPTION = "Expands lazy prompt control nodes in the prompt and saves the expanded prompt into a JSON file"

    FUNCTION = "apply"

@@ -39,29 +39,12 @@ class PCSaveExpandedWorkflow:
        full_output_folder, filename, counter, subfolder, prefix = folder_paths.get_save_image_path(
            "pc_workflow_debug", self.output_dir
        )
-        p = {}
-        input_replace_map = {}
-        for node in prompt.all_node_ids():
-            n = prompt.get_node(node)
-            t = n["class_type"]
-            if t in LAZY_NODES:
-                expanded_prompt = LAZY_NODES[t]().apply(**n["inputs"], unique_id=node)
-                for k in expanded_prompt["expand"]:
-                    p[k] = expanded_prompt["expand"][k]
-                for i, _ in enumerate(expanded_prompt["result"]):
-                    input_replace_map[(node, i)] = [k, i]
-            else:
-                p[node] = n
-        for k in p:
-            for ik in p[k]["inputs"]:
-                x = p[k]["inputs"][ik]
-                if is_link(x) and tuple(x) in input_replace_map:
-                    p[k]["inputs"][ik] = input_replace_map[tuple(x)]
+        expanded = expand_graph(LAZY_NODES, prompt)
        file = f"{filename}_{counter:05}_.json"
        full_path = Path(full_output_folder) / file
        with open(full_path, "w") as f:
            log.info(f"Saving workflow to {full_path}")
-            json.dump(p, f)
+            json.dump(expanded, f)

        return ()

@@ -24,10 +24,40 @@ if lark.__version__ == "0.12.0":
    raise ImportError(x)


+ESCAPES = [
+    ("XxPCBackslashESCAPExX", "\\"),
+    ("XxPCColonESCAPExX", ":"),
+    ("XxPCCommentESCAPExX", "#"),
+]
+
+
+def escape_specials(string):
+    for ph, c in ESCAPES:
+        string = string.replace(rf"\{c}", ph)
+    return string
+
+
+def restore_escaped(string):
+    for ph, c in ESCAPES:
+        string = string.replace(ph, c)
+    return string
+
+
+def remove_comments(string):
+    r = []
+    for line in string.split("\n"):
+        comment = line.find("#")
+        if comment >= 0:
+            r.append(line[:comment])
+        else:
+            r.append(line)
+    return "\n".join(r)
+
+
 prompt_parser = lark.Lark(
    r"""
 !start: (prompt | /[][():|]/+)*
-prompt: (emphasized | embedding | scheduled | alternate | sequence | loraspec | PLAIN | /</ | />/ | WHITESPACE)+
+prompt: (emphasized | embedding | scheduled | alternate | sequence | loraspec | PLAIN | | /\\:/ | /</ | />/ | WHITESPACE)+
 !emphasized: "(" prompt? ")"
        | "(" prompt ":" prompt ")"
        | "[" prompt "]"
@@ -241,7 +271,7 @@ def at_step(step, filters, tree):
            return {"prompt": p, "loras": loraspecs}

        def PLAIN(self, args):
-            return args.replace("\\:", ":")
+            return restore_escaped(args)

        def FILENAME(self, value):
            return str(value)
@@ -284,7 +314,8 @@ class PromptSchedule(object):
        self.start = start
        self.end = end
        self.num_steps = num_steps
-        self.prompt = prompt.strip()
+        # placeholder is restored on parse
+        self.prompt = remove_comments(escape_specials(prompt.strip()))
        self.defaults = {}
        self.loaded_loras = {}

@@ -373,9 +404,11 @@ def parse_search(search):
    args = ""
    name = search.strip()
    if arg_start > 0:
-        arg_end = find_closing_paren(search, arg_start)
+        arg_end = find_closing_paren(search, arg_start + 1)
+        if arg_end < 0:
+            arg_end = len(search)
        name = search[:arg_start].strip()
-        args = search[arg_start + 1 : arg_end - 1]
+        args = search[arg_start + 1 : arg_end]

    if not name:
        return None
@@ -394,7 +427,9 @@ def expand_macros(text):
    prevres = text
    replacements = []
    for d in defs:
-        r = d.split("=", 1)
+        if not d.args:
+            continue
+        r = d.args[0].split("=", 1)
        search = parse_search(r[0].strip())
        if not search or len(r) != 2:
            log.warning("Ignoring invalid DEF(%s)", d)
@@ -417,21 +452,16 @@ def expand_macros(text):
    return res


-def substitute_def(text, search, replace):
-    search, default_args = search
-    for i, v in enumerate(default_args):
-        replace = re.sub(rf"\${i+1}\b", v, replace)
-    return re.sub(rf"\b{re.escape(search)}\b", replace, text)
-
-
 def substitute_defcall(text, search, replace):
    name, default_args = search
-    text, defns = get_function(text, name, defaults=None, placeholder=f"DEFNCALL{name}")
-    for i, parameters in enumerate(defns):
-        ph = f"\0DEFNCALL{name}{i}\0"
+    text, defns = get_function(text, name, defaults=None, placeholder=f"DEFNCALL{name}", require_args=False)
+    for i, d in enumerate(defns):
+        ph = d.placeholder
+        assert ph is not None, "This is a bug"
+        parameters = d.args
        paramvals = []
-        if parameters is not None:
-            paramvals = [x.strip() for x in parameters.split(";")]
+        if parameters:
+            paramvals = [x.strip() for x in parameters[0].split(";")]
        r = replace
        for i, v in enumerate(paramvals):
            r = re.sub(rf"\${i+1}\b", v, r)
@@ -1,11 +1,23 @@
+from __future__ import annotations
 import logging
 import re
 import torch
+import math
 from functools import partial
 from comfy_extras.nodes_mask import FeatherMask, MaskComposite
 from nodes import ConditioningAverage

-from .utils import safe_float, get_function, split_by_function, parse_floats, smarter_split
+from .utils import (
+    safe_float,
+    get_function,
+    split_by_function,
+    parse_floats,
+    smarter_split,
+    call_node,
+    split_quotable,
+    FunctionSpec,
+    ComfyConditioning,
+)
 from .adv_encode import advanced_encode_from_tokens
 from .cutoff import process_cuts
 from .parser import parse_cuts
@@ -25,7 +37,7 @@ def get_sdxl(text, defaults):
    text, sdxl = get_function(text, "SDXL", ["none", "none", "none"])
    if not sdxl:
        return text, {}
-    args = sdxl[0]
+    args = sdxl[0].args
    d = defaults
    w, h = parse_floats(args[0], [d.get("sdxl_width", 1024), d.get("sdxl_height", 1024)], split_re="\\s+")
    tw, th = parse_floats(args[1], [d.get("sdxl_twidth", 1024), d.get("sdxl_theight", 1024)], split_re="\\s+")
@@ -46,7 +58,7 @@ def get_clipweights(text, existing_spec=None):
    text, spec = get_function(text, "TE_WEIGHT", defaults=None)
    if not spec:
        return existing_spec or {}, text
-    args = spec[0].strip()
+    args = spec[0].args[0].strip()
    res = {}
    for arg in args.split(","):
        try:
@@ -62,7 +74,7 @@ def get_style(text, default_style="comfy", default_normalization="none"):
    text, styles = get_function(text, "STYLE", [default_style, default_normalization])
    if not styles:
        return default_style, default_normalization, text
-    style, normalization = styles[0]
+    style, normalization = styles[0].args
    style = style.strip()
    normalization = normalization.strip()
    if style.replace("old+", "") not in AVAILABLE_STYLES:
@@ -77,8 +89,9 @@ def get_style(text, default_style="comfy", default_normalization="none"):
    return style, normalization, text


-def shuffle_chunk(shuffle, c):
-    func, shuffle = shuffle
+def shuffle_chunk(func_spec: FunctionSpec, c: str) -> str:
+    func = func_spec.name
+    shuffle = func_spec.args
    shuffle_count = int(safe_float(shuffle[0], 0))
    _, separator, joiner = shuffle
    if separator == "default":
@@ -128,11 +141,11 @@ def fix_word_ids(tokens):


 def tokenize_chunks(clip, text, need_word_ids, can_break):
-    chunks = re.split(r"\bBREAK\b", text)
+    chunks = list(split_quotable(text, r"\bBREAK\b"))
    token_chunks = []
    shuffled_chunks = []
    for c in chunks:
-        c, shuffles = get_function(c.strip(), "(SHIFT|SHUFFLE)", ["0", "default", "default"], return_func_name=True)
+        c, shuffles = get_function(c.strip(), "(SHIFT|SHUFFLE)", ["0", "default", "default"])
        r = c
        for s in shuffles:
            r = shuffle_chunk(s, r)
@@ -168,9 +181,10 @@ def tokenize(clip, text, can_break, empty_tokens):
    per_te_prompts = {}
    if l_prompts:
        log.warning("Note: CLIP_L is deprecated. Use TE(l=prompt) instead")
-        per_te_prompts["l"] = l_prompts
+        per_te_prompts["l"] = [x.args for x in l_prompts]

    for prompt in te_prompts:
+        prompt = prompt.args[0]
        if prompt.strip() == "help":
            log.info("Encoders available for TE: %s", ", ".join(tokens.keys()))
            continue
@@ -211,7 +225,7 @@ def encode_prompt_segment(
    default_style="comfy",
    default_normalization="none",
    clip_weights=None,
-) -> list[tuple[torch.Tensor, dict[str]]]:
+) -> list[ComfyConditioning]:
    style, normalization, text = get_style(text, default_style, default_normalization)
    clip_weights, text = get_clipweights(text, clip_weights)
    text, cuts = parse_cuts(text)
@@ -231,19 +245,18 @@ def encode_prompt_segment(

    # Chunks to ConditioningAverage:

-    text, averages = split_by_function(text, "AVG", ["0.5"])
+    text, averages = split_by_function(text, "AVG", ["0.5"], require_args=False)
    prompts_to_avg = []
-    for avg in averages:
-        w = safe_float(avg["args"][0], 0.5)
-        prompts_to_avg.append(text, w)
-        text = avg["text"]
+    for chunk, avg in averages:
+        w = safe_float(avg.args[0], 0.5)
+        prompts_to_avg.append((text, w))
+        text = chunk
    prompts_to_avg.append((text, 1.0))

    conds_to_avg = []
    for prompt, weight in prompts_to_avg:
        conds_to_cat = []
-        chunks = re.split(r"\bCAT\b", prompt)
-        for c in chunks:
+        for c in split_quotable(prompt, r"\bCAT\b"):
            tokens = tokenize(clip, c, can_break, empty)
            conds_to_cat.append(clip.encode_from_tokens_scheduled(tokens, add_dict=settings))

@@ -264,13 +277,22 @@ def encode_prompt_segment(
            w = next_w
            continue
        for i in range(len(base)):
-            (cond,) = ConditioningAverage.addWeighted(None, [base[i]], [cond[i]], w)
+            (cond,) = call_node(ConditioningAverage, [base[i]], [cond[i]], w)
            base[i] = cond[0]
        w = next_w

    return base


+def calc_w(tensor, w):
+    if math.isclose(w, 0):
+        return torch.zeros_like(tensor)
+    elif math.isclose(w, 1.0):
+        return tensor
+    else:
+        return tensor * w
+
+
 def apply_weights(output, te_name, spec):
    """Applies weights to TE outputs"""
    if not spec:
@@ -282,7 +304,7 @@ def apply_weights(output, te_name, spec):
    default = spec.get("all", None)

    if isinstance(output, tuple):
-        out, pooled = output
+        out, pooled, *extra = output
        pkey = te_name + "_pooled"
        if te_name in spec or pkey in spec or default is not None:
            w = spec.get(te_name, default)
@@ -292,16 +314,16 @@ def apply_weights(output, te_name, spec):
            if pooled_w is None:
                pooled_w = 1.0
            log.info("Weighting %s output by %s, pooled by %s", te_name, w, pooled_w)
-            out = out * w
+            out = calc_w(out, w)
            if pooled is not None:
-                pooled = pooled * pooled_w
+                pooled = calc_w(pooled, pooled_w)

-        return out, pooled
+        return (out, pooled) + tuple(extra)
    else:
        if te_name in spec or default is not None:
            w = spec.get(te_name, default)
            log.info("Weighting %s output by %s", te_name, w)
-            output = output * w
+            output = calc_w(output, w)
        return output


@@ -356,7 +378,7 @@ def get_area(text):
    if not areas:
        return text, None

-    args = areas[0]
+    args = areas[0].args
    x, w = parse_floats(args[0], [0.0, 1.0], split_re="\\s+")
    y, h = parse_floats(args[1], [0.0, 1.0], split_re="\\s+")
    weight = safe_float(args[2], 1.0)
@@ -383,7 +405,7 @@ def get_mask_size(text, defaults):
    text, sizes = get_function(text, "MASK_SIZE", ["512", "512"])
    if not sizes:
        return text, (defaults.get("mask_width", 512), defaults.get("mask_height", 512))
-    w, h = sizes[0]
+    w, h = sizes[0].args
    return text, (int(w), int(h))


@@ -429,46 +451,53 @@ def get_mask(text, size, input_masks):

    def feather(f, mask):
        l, t, r, b, *_ = [int(x) for x in parse_floats(f[0], [0, 0, 0, 0], split_re="\\s+")]
-        mask = FeatherMask().feather(mask, l, t, r, b)[0]
+        mask = call_node(FeatherMask, mask, l, t, r, b)[0]
        log.info("FeatherMask l=%s, t=%s, r=%s, b=%s", l, t, r, b)
        return mask

    mask = None
    totalweight = 1.0
    if maskw:
-        totalweight = safe_float(maskw[0][0], 1.0)
+        totalweight = safe_float(maskw[0].args[0], 1.0)
    i = 0
    for m in masks:
-        weight = safe_float(m[2], 1.0)
-        op = m[3]
-        nextmask = make_mask(m, size, weight)
+        weight = safe_float(m.args[2], 1.0)
+        op = m.args[3]
+        nextmask = make_mask(m.args, size, weight)
        if i < len(feathers):
-            nextmask = feather(feathers[i], nextmask)
+            nextmask = feather(feathers[i].args, nextmask)
        i += 1
        if mask is not None:
            log.info("MaskComposite op=%s", op)
-            mask = MaskComposite().combine(mask, nextmask, 0, 0, op)[0]
+            mask = call_node(MaskComposite, mask, nextmask, 0, 0, op)[0]
        else:
            mask = nextmask

-    for idx, w, op in imasks:
+    for im in imasks:
+        idx, w, op = im.args
        idx = int(safe_float(idx, 0.0))
        w = safe_float(w, 1.0)
+        if input_masks is None:
+            log.warn(
+                "IMASK requires you to attach custom masks to the CLIP object using PCAddMasksToClIP before using it"
+            )
+            input_masks = []
+
        if len(input_masks) < idx + 1:
            log.warn("IMASK index %s not found, ignoring...", idx)
            continue
        nextmask = input_masks[idx] * w
        if i < len(feathers):
-            nextmask = feather(feathers[i], nextmask)
+            nextmask = feather(feathers[i].args, nextmask)
        i += 1
        if mask is not None:
-            mask = MaskComposite().combine(mask, nextmask, 0, 0, op)[0]
+            mask = call_node(MaskComposite, mask, nextmask, 0, 0, op)[0]
        else:
            mask = nextmask

    # apply leftover FEATHER() specs to the whole
    for f in feathers[i:]:
-        mask = feather(f, mask)
+        mask = feather(f.args, mask)

    return text, mask, totalweight

@@ -483,14 +512,15 @@ def get_noise(text):
        return text, None, None
    w = 0
    # Only take seed from first noise spec, for simplicity
-    seed = safe_float(noises[0][1], "none")
+    seed = noises[0].args[0].strip()
    if seed == "none":
        gen = None
    else:
+        seed = safe_float(seed, 0)
        gen = torch.Generator()
        gen.manual_seed(int(seed))
    for n in noises:
-        w += safe_float(n[0], 0.0)
+        w += safe_float(n.args[0], 0.0)
    return text, max(min(w, 1.0), 0.0), gen


@@ -551,7 +581,7 @@ def encode_prompt(clip, text, start_pct, end_pct, defaults, masks):
    style, normalization, text = get_style(text)
    text, mask_size = get_mask_size(text, defaults)

-    prompts = [p.strip() for p in re.split(r"\bAND\b", text)]
+    prompts = list(split_quotable(text, r"\bAND\b"))

    p, sdxl_opts = get_sdxl(prompts[0], defaults)
    prompts[0] = p
@@ -573,9 +603,9 @@ def encode_prompt(clip, text, start_pct, end_pct, defaults, masks):
        return f"MASK({args})"

    for prompt in prompts:
-        base_prompt, attn_couple_prompts = split_by_function(prompt, "COUPLE", defaults=None)
+        base_prompt, attn_couple_prompts = split_by_function(prompt, "COUPLE", defaults=None, require_args=False)

-        prompts = [base_prompt] + [couple_mask(p["args"]) + p["text"] for p in attn_couple_prompts]
+        prompts = [base_prompt] + [couple_mask(f.args) + chunk for (chunk, f) in attn_couple_prompts]
        encoded = []
        for p in prompts:
            p, settings = process_settings(p, defaults, masks, mask_size, sdxl_opts)
@@ -1,6 +1,10 @@
 import unittest
+import unittest.mock as mock
 import numpy.testing as npt
 from os import environ
+import nodes
+import comfy_extras.nodes_mask
+from .nodes_base import PCTextEncode

 clips = []

@@ -10,10 +14,42 @@ logging.basicConfig()


 def run(f, *args):
-    return getattr(f, f.FUNCTION)(*args)
+    if hasattr(f, "execute"):
+        return f.execute(*args)
+    else:
+        return getattr(f, f.FUNCTION)(*args)


+@mock.patch("torch.cuda.current_device", lambda: "cpu")
 class TestEncode(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        global clips
+        print("Loading ComfyUI")
+        from comfy.sd import load_clip
+        from pathlib import Path
+
+        to_test = environ.get("TEST_TE", "clip_l").split()
+        model_dir = environ.get("COMFYUI_TE_DIR", ".")
+
+        te_root = Path(model_dir).resolve()
+
+        if "clip_l" in to_test:
+            clip_l = load_clip(
+                ckpt_paths=[str(te_root / "clip_l.safetensors")], clip_type="stable_diffusion", model_options={}
+            )
+            clips.append(("clip_l", clip_l))
+
+        if "t5" in to_test:
+            dual = load_clip(
+                [str(te_root / "clip_l.safetensors"), str(te_root / "t5xxl_fp16.safetensors")],
+                clip_type="flux",
+                model_options={},
+            )
+            clips.append(("clip_l+t5", dual))
+
+        print("Starting tests")
+
    def tensorsEqual(self, t1, t2):
        npt.assert_equal(t1.detach().numpy(), t2.detach().numpy())

@@ -30,6 +66,7 @@ class TestEncode(unittest.TestCase):
        pc = PCTextEncode()
        comfy = nodes.CLIPTextEncode()
        combine = nodes.ConditioningCombine()
+        average = nodes.ConditioningAverage()
        concat = nodes.ConditioningConcat()
        zeroout = nodes.ConditioningZeroOut()
        for k, clip in clips:
@@ -46,6 +83,17 @@ class TestEncode(unittest.TestCase):
                    c = c2  # Used in later tests
                    self.condEqual(c1, c2)

+                with self.subTest("Quotes"):
+                    (c1,) = run(pc, clip, 'Text saying "DOG MASK AND CAT COUPLE MASK(X)"')
+                    (c2,) = run(comfy, clip, 'Text saying "DOG MASK AND CAT COUPLE MASK(X)"')
+                    self.condEqual(c1, c2)
+
+                with self.subTest("Function cornercase"):
+                    (c1,) = run(pc, clip, "test SDXL function")
+                    (c2,) = run(comfy, clip, "test SDXL function")
+                    (c3,) = run(pc, clip, "test SDXL() function")
+                    self.condEqual(c1, c2)
+
                with self.subTest("Weights"):
                    (c1,) = run(pc, clip, "(test:1.2) (test:0.6)")
                    (c2,) = run(comfy, clip, "(test:1.2) (test:0.6)")
@@ -66,6 +114,36 @@ class TestEncode(unittest.TestCase):
                    (c2,) = run(zeroout, c)
                    self.condEqual(c1, c2)

+                with self.subTest("Average"):
+                    (c1,) = run(comfy, clip, "test1")
+                    (c2,) = run(comfy, clip, "test2")
+                    (c3,) = run(pc, clip, "test1 AVG() test2")
+                    (c4,) = run(pc, clip, "test1 AVG test2")
+                    (avg,) = run(average, c1, c2, 0.5)
+                    self.condEqual(avg, c3)
+                    self.condEqual(avg, c4)
+
+                with self.subTest("Average multi"):
+                    (c1,) = run(comfy, clip, "test1")
+                    (c2,) = run(comfy, clip, "test2")
+                    (c3,) = run(comfy, clip, "test3")
+                    (c4,) = run(pc, clip, "test1 AVG() test2 AVG() test3")
+                    (c5,) = run(pc, clip, "test1 AVG test2 AVG test3")
+                    (avg1,) = run(average, c1, c2, 0.5)
+                    (avg,) = run(average, avg1, c3, 0.5)
+                    self.condEqual(avg, c4)
+                    self.condEqual(avg, c5)
+
+    @unittest.expectedFailure
+    def test_failure(self):
+        pc = PCTextEncode()
+        comfy = nodes.CLIPTextEncode()
+        for k, clip in clips:
+            with self.subTest(k):
+                (c1,) = run(comfy, clip, "test SDXL function")
+                (c2,) = run(pc, clip, "test SDXL() function")
+                self.condEqual(c1, c2)
+
    def test_weight(self):
        pc = PCTextEncode()
        comfy = nodes.CLIPTextEncode()
@@ -127,31 +205,4 @@ class TestEncode(unittest.TestCase):


 if __name__ == "__main__":
-    print("Loading ComfyUI")
-    from comfy.sd import load_clip
-    import nodes
-    import comfy_extras.nodes_mask
-    from .nodes_base import PCTextEncode
-    from pathlib import Path
-
-    to_test = environ.get("TEST_TE", "clip_l").split()
-    model_path = environ.get("COMFYUI_MODEL_ROOT", ".")
-
-    te_root = (Path(model_path) / "text_encoders").resolve()
-
-    if "clip_l" in to_test:
-        clip_l = load_clip(
-            ckpt_paths=[str(te_root / "clip_l.safetensors")], clip_type="stable_diffusion", model_options={}
-        )
-        clips.append(("clip_l", clip_l))
-
-    if "t5" in to_test:
-        dual = load_clip(
-            [str(te_root / "clip_l.safetensors"), str(te_root / "t5xxl_fp16.safetensors")],
-            clip_type="flux",
-            model_options={},
-        )
-        clips.append(("clip_l+t5", dual))
-
-    print("Starting tests")
    unittest.main()
@@ -1,56 +0,0 @@
-import unittest
-import numpy.testing as npt
-
-clip_l = None
-dual = None
-
-
-def run(f, *args):
-    return getattr(f, f.FUNCTION)(*args)
-
-
-class TestEncode(unittest.TestCase):
-    def tensorsEqual(self, t1, t2):
-        npt.assert_equal(t1.detach().numpy(), t2.detach().numpy())
-
-    def condEqual(self, c1, c2, key=None, key_assert=None):
-        self.assertEqual(len(c1), len(c2))
-        for i in range(len(c1)):
-            a, b = c1[i], c2[i]
-            if key:
-                (key_assert or self.assertEqual)(a[1][key], b[1][key])
-            else:
-                self.tensorsEqual(a[0], b[0])
-
-    def test_styles(self):
-        pc = PCTextEncode()
-        for k, clip in [("l", clip_l), ("dual", dual)]:
-            for style in ["comfy++", "A1111", "comfy++", "compel", "down_weight"]:
-                with self.subTest(f"TE {k} style {style} does not fail when encoding weights"):
-                    for normalization in ["none", "mean", "length", "length+mean"]:
-                        with self.subTest(f"TE {k} style {style} normalization {normalization}"):
-                            (c,) = run(
-                                pc,
-                                clip,
-                                f"STYLE(old+{style}, {normalization}) this prompt has weights, (a:1.2) (b:1.2)",
-                            )
-                            (c2,) = run(
-                                pc,
-                                clip,
-                                f"STYLE({style}, {normalization}) this prompt has weights, (a:1.2) (b:1.2)",
-                            )
-                            self.condEqual(c, c2)
-
-
-if __name__ == "__main__":
-    print("Loading ComfyUI")
-    import main
-
-    id(main)  # get rid of flake warning
-    import nodes
-    from .nodes_base import PCTextEncode
-
-    (clip_l,) = nodes.CLIPLoader().load_clip("clip_l.safetensors")
-    (dual,) = nodes.DualCLIPLoader().load_clip("clip_l.safetensors", "clip_g.safetensors", "sdxl")
-    print("Starting tests")
-    unittest.main()
@@ -18,6 +18,13 @@ class TestParser(unittest.TestCase):
        self.assertEqual(p.at_step(0.5), expected)
        self.assertEqual(p.at_step(1), expected)

+    def test_quote(self):
+        p = parse('This is a text with a "QUOTED DEF(X=Y)"')
+        expected = prompt(1.0, 'This is a text with a "QUOTED DEF(X=Y)"')
+        self.assertEqual(p.at_step(0), expected)
+        self.assertEqual(p.at_step(0.5), expected)
+        self.assertEqual(p.at_step(1), expected)
+
    def test_equivalences(self):
        eqs = [
            [parse(p) for p in ["[a:0.1]", "[:a:0.1]", "[:a:0,0.1]", "[:a::0.1,1.0]", "[:a::0.1]"]],
@@ -156,6 +163,39 @@ class TestParser(unittest.TestCase):
            expand_macros("DEF(X=recurse Y) DEF(Y=recurse X) X")
        self.assertTrue("Unable to resolve DEFs" in str(c.exception))

+    def test_escapes(self):
+        p = parse(r"[a:\:a:0.5] :\[a:b:0.5]")
+        self.assertPrompt(p, 0, 0.5, r"a :\[a:b:0.5]")
+        self.assertPrompt(p, 0.55, 1, r":a :\[a:b:0.5]")
+
+        p = parse(r"[embedding\:a:embedding\:b:0.1,0.5]")
+        self.assertPrompt(p, 0.15, 0.5, r"embedding:a")
+        self.assertPrompt(p, 0.55, 1, r"embedding:b")
+
+        p = parse(r"[embedding\:a:embedding\:b:embedding\:c:0.1,0.5]")
+        self.assertPrompt(p, 0.0, 0.1, r"embedding:a")
+        self.assertPrompt(p, 0.15, 0.5, r"embedding:b")
+        self.assertPrompt(p, 0.55, 1, r"embedding:c")
+
+        p = parse(r"[a\:b\\:c:0.5]")
+        self.assertPrompt(p, 0.0, 0.5, "a:b\\")
+        self.assertPrompt(p, 0.55, 1, r"c")
+
+        p = parse(r"[a:\#b:0.5]")
+        self.assertPrompt(p, 0.0, 0.5, "a")
+        self.assertPrompt(p, 0.55, 1, "#b")
+
+    def test_comments(self):
+        p = parse("this is a # comment")
+        self.assertPrompt(p, 0, 1.0, "this is a ")
+        p = parse("this is a [comment#:scheduled:0.6]")
+        self.assertPrompt(p, 0, 1.0, "this is a [comment")
+        p = parse(r"this is a [comment\#:scheduled:0.6]")
+        self.assertPrompt(p, 0, 0.6, "this is a comment#")
+        self.assertPrompt(p, 0.65, 1.0, "this is a scheduled")
+        p = parse("#this is a comment\nthis is a prompt")
+        self.assertPrompt(p, 0, 1.0, "\nthis is a prompt")
+
    def test_misc(self):
        p = parse("[[a:c:0.5]:0.7]")
        p2 = parse("[:[a:c:0.5]:0.7]")
@@ -1,19 +1,48 @@
+from __future__ import annotations
 from pathlib import Path
 import re
 import logging
+import copy
+
+from dataclasses import dataclass
+from typing import Any, TypeAlias, Iterator, TypeVar, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import torch  # flakes8: noqa
+
+FunctionArgs: TypeAlias = list[str]
+ComfyConditioning: TypeAlias = tuple["torch.Tensor", dict[str, Any]]
+
+
+@dataclass
+class FunctionSpec:
+    name: str
+    args: FunctionArgs
+    position: int
+    placeholder: str | None
+

 # Allow testing
 try:
    from folder_paths import get_filename_list
 except ImportError:

-    def get_filename_list(x):
-        raise NotImplementedError("How did you get here?")
+    def get_filename_list(folder_name) -> list[str]:
+        return []


 log = logging.getLogger("comfyui-prompt-control")


+def call_node(cls, *args, **kwargs):
+    if hasattr(cls, "execute"):
+        # v3 node
+        return cls.execute(*args, **kwargs)
+    else:
+        func = getattr(cls(), cls.FUNCTION)
+        return func(*args, **kwargs)
+
+
 def consolidate_schedule(prompt_schedule):
    prev_loras = {}
    not_found = []
@@ -54,10 +83,11 @@ def find_nonscheduled_loras(consolidated_schedule):
    return {k: v for (k, v) in candidate_loras.items() if k not in to_remove}


-def smarter_split(separator, string):
+def smarter_split(separator: str, string: str) -> list[str]:
    """Does not break () when splitting"""
    splits = []
    prev = 0
+    idx = 0
    stack = 0
    escape = False
    for idx, x in enumerate(string):
@@ -74,7 +104,7 @@ def smarter_split(separator, string):
    return splits


-def find_closing_paren(text, start):
+def find_closing_paren(text: str, start: int) -> int:
    stack = 1
    for i, char in enumerate(text[start:]):
        if char == ")":
@@ -83,90 +113,126 @@ def find_closing_paren(text, start):
            stack += 1
        if stack == 0:
            return start + i
-    # Implicit closing paren after end
-    return len(text)
+    return -1


-def get_function(text, func, defaults, return_func_name=False, placeholder="", return_dict=False):
-    rex = re.compile(rf"\b{func}\b", re.MULTILINE)
-    instances = []
+def find_function_spans(
+    text: str, func: str, require_args: bool, defaults: FunctionArgs | None
+) -> Iterator[tuple[int, int, str, FunctionArgs]]:
+    if require_args:
+        rex = re.compile(rf"\b{func}\(", re.MULTILINE)
+    else:
+        rex = re.compile(rf"\b{func}\b", re.MULTILINE)
+
+    idx = 0
    match = rex.search(text)
-    count = 0
    while match:
-        # Match start, content start
        start, at_paren = match.span()
+        if require_args:
+            at_paren = at_paren - 1
        funcname = text[start:at_paren]
        after_first_paren = at_paren + 1
        if text[at_paren:after_first_paren] == "(":
            end = find_closing_paren(text, after_first_paren)
+            if end < 0:
+                continue
            args = parse_strings(text[after_first_paren:end], defaults)
            end += 1
        else:
            end = at_paren
-            args = None
+            args = defaults or []
+        yield idx + start, idx + end, funcname, args
+        idx = idx + end
+        text = text[end:]
+        match = rex.search(text)
+
+
+def get_function(
+    text: str, func: str, defaults: list[str] | None, placeholder: str = "", require_args: bool = True
+) -> tuple[str, list[FunctionSpec]]:
+    spans = [x.span() for x in re.finditer(r'".+?"', text)]
+    instances = []
+    count = 0
+    chunks = []
+    current = 0
+    skipped = 0
+    for start, end, funcname, args in find_function_spans(text, func, require_args, defaults):
        ph = None
+        if spans_include(spans, start, end):
+            continue
        if placeholder:
            ph = f"\0{placeholder}{count}\0"
-        if return_dict:
-            instances.append(
-                {
-                    "name": funcname,
-                    "args": args,
-                    "position": start,
-                    "placeholder": ph,
-                }
-            )
-        elif return_func_name:
-            instances.append((funcname, args))
-        else:
-            instances.append(args)
-
-        if placeholder:
-            text = text[:start] + f"\0{placeholder}{count}\0" + text[end:]
-        else:
-            text = text[:start] + text[end:]
-        match = rex.search(text)
+        instances.append(FunctionSpec(funcname, args, start - skipped, ph))
+        skipped += end - start
+        chunks.append(text[current:start] + (ph or ""))
+        current = end
        count += 1
+    chunks.append(text[current:])
+    text = "".join(chunks)
    return text, instances


-def split_by_function(text, func, defaults=None):
+def spans_include(spans: list[tuple[int, int]], s: int, e: int) -> bool:
+    return any((s > a and e < b) for a, b in spans)
+
+
+def split_quotable(text: str, regexp: str) -> Iterator[str]:
+    start_from = 0
+    spans = [x.span() for x in re.finditer(r'".+?"', text)]
+    for x in re.finditer(regexp, text):
+        s, e = x.span()
+        if not spans_include(spans, s, e):
+            yield text[start_from:s].strip()
+            start_from = e
+    yield text[start_from:].strip()
+
+
+def split_by_function(
+    text: str, func: str, defaults: list[str] | None = None, require_args: bool = True
+) -> tuple[str, list[tuple[str, FunctionSpec]]]:
    """
-    Splits a string by function calls, returning the text preceding the first call and a list of dictionaries with a "text" key with the prompt before the next split or until hthe end of the text.
+    Splits a string by function calls, returning the leftover text along with a list of functions with their associated text chunk.
    """
-    text, functions = get_function(text, func, defaults, return_dict=True)
+    text, functions = get_function(text, func, defaults, require_args=require_args)
    chunks = []
    prev = 0
    for f in functions:
-        chunks.append(text[prev : f["position"]])
-        prev = f["position"]
+        chunks.append(text[prev : f.position])
+        prev = f.position
    chunks.append(text[prev:])
+    r = []
    for i, f in enumerate(functions):
-        f["text"] = chunks[i + 1]
-    return chunks[0], functions
+        r.append((chunks[i + 1], f))
+    return chunks[0], r


-def parse_args(strings, arg_spec, strip=True):
+T = TypeVar("T")
+
+
+def parse_args(strings: list[str], arg_spec: list[tuple[Any, T]], strip: bool = True) -> list[T]:
    args = [s[1] for s in arg_spec]
    for i, spec in list(enumerate(arg_spec))[: len(strings)]:
        try:
            if strip:
                strings[i] = strings[i].strip()
-            args[i] = spec[0](strings[i])
+            f = spec[0]
+            args[i] = f(strings[i])
        except ValueError:
            pass
    return args


-def parse_floats(string, defaults, split_re=","):
+def parse_floats(string: str, defaults: list[float], split_re: str = ",") -> list[float]:
    spec = [(float, d) for d in defaults]
    return parse_args(re.split(split_re, string.strip()), spec)


-def parse_strings(string, defaults, split_re=r"(?<!\\),", replace=(r"\,", ",")):
+def parse_strings(
+    string: str, defaults: FunctionArgs | None, split_re: str = r"(?<!\\),", replace: tuple[str, str] = (r"\,", ",")
+) -> FunctionArgs:
    if defaults is None:
-        return string
-    spec = [(lambda x: x, d) for d in defaults]
+        return [string]
+    spec = [(str, d) for d in defaults]
    splits = re.split(split_re, string)
    if replace:
        f, t = replace
@@ -174,7 +240,7 @@ def parse_strings(string, defaults, split_re=r"(?<!\\),", replace=(r"\,", ",")):
    return parse_args(splits, spec, strip=False)


-def safe_float(f, default):
+def safe_float(f: Any, default: float) -> float:
    if f is None:
        return default
    try:
@@ -183,7 +249,7 @@ def safe_float(f, default):
        return default


-def lora_name_to_file(name):
+def lora_name_to_file(name: str) -> str | None:
    filenames = get_filename_list("loras")
    # Return exact matches as is
    if name in filenames:
@@ -194,4 +260,45 @@ def lora_name_to_file(name):
            p = Path(f).with_suffix("")
            if p.name == n or str(p) == n:
                return f
+    # Finally, try to find unique match from parts
+    parts = name.split()
+    search = [f for f in filenames if all(p in f for p in parts)]
+    if len(search) == 1:
+        return search[0]
+
    return None
+
+
+def map_inputs(input_map, inputs):
+    new_inputs = {}
+    for k in inputs:
+        key = inputs[k]
+        new_inputs[k] = key
+        if isinstance(key, list):
+            key = tuple(key)
+            x = input_map.get(key, inputs[k])
+            new_inputs[k] = x
+    return new_inputs
+
+
+def expand_graph(node_mappings, graph):
+    input_map = {}
+    new_graph = copy.deepcopy(graph)
+    for k in graph:
+        data = graph[k]
+        if not isinstance(data, dict) or "class_type" not in data or data["class_type"] not in node_mappings:
+            continue
+        node = node_mappings[data["class_type"]]()
+        inputs = map_inputs(input_map, data["inputs"].copy())
+        inputs["unique_id"] = k
+        fn = getattr(node, getattr(node, "FUNCTION"))
+        expansion = fn(**inputs)
+        for i, v in enumerate(expansion["result"]):
+            input_map[(k, i)] = v
+        del new_graph[k]
+        new_graph.update(expansion["expand"])
+
+    for k in new_graph:
+        data = new_graph[k]
+        data["inputs"] = map_inputs(input_map, data["inputs"])
+    return new_graph
@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-prompt-control"
-description = "Nodes for convenient prompt editing, making many common operations prompt-controllable"
-version = "2.0.0-rc.8"
+description = "Provides nodes for prompt editing and LoRA scheduling, advanced regional prompting (including attention masking) and more, all controlled through your text prompt"
+version = "2.1.2"
 license = { file = "LICENSE" }
 # some lark versions older than 1.1.9 apparently have a bug that breaks things, see https://github.com/asagi4/comfyui-prompt-control/issues/35
 dependencies = ["lark >= 1.1.9"]
@@ -0,0 +1,13 @@
+#!/usr/bin/env python3
+from prompt_control.utils import expand_graph
+from prompt_control.nodes_lazy import NODE_CLASS_MAPPINGS as LN
+import json
+import sys
+
+
+# Needs ComfyUI in Python path
+# Usage: PYTHONPATH=../..:. python tools/expand_graph < graph_in_api_format.json > out.json
+if __name__ == "__main__":
+    graph = json.load(sys.stdin)
+    new = expand_graph(LN, graph)
+    print(json.dumps(new))
@@ -0,0 +1,3 @@
+# PC: Attach Mask
+
+Attaches custom masks to a CLIP object so that they can be referred to in prompts using `PCTextEncode` or `PC: Schedule prompt`.
@@ -0,0 +1 @@
+PCAddMaskToCLIP.md
@@ -0,0 +1,7 @@
+# PC: Attention Couple (batch negative)
+
+This node applies an optimization that re-enables negative cond batching when Attention Couple is in use.
+
+It improves performance when negative prompts are not scheduled, but slightly affects outputs and is not required for Attention Couple to work.
+
+Simply add it to your workflow and pass in your positive and negative prompts. It is always safe to use, as it will not do anything when it detects that the optimization can't be applied (eg. when negative prompts contain schedules)
@@ -0,0 +1,7 @@
+# PC: Schedule LoRAs
+
+This node is the core of Prompt Control. It evaluates a prompt schedule and dynamically expands into a scheduled workflow consisting of necessary calls to `LoRALoader` and `Create Hook LoRA` (for scheduled LoRAs).
+
+You can use it in place or in addition to your usual `LoRA Loader` nodes; just pass in a text prompt containing your LoRA schedule (it can be shared with `PC: Schedule Prompt`). Then connect your MODEL output as usual and the CLIP output to your `PC: Schedule Prompt` nodes.
+
+For documentation on syntax, for now see the [documentation on GitHub](https://github.com/asagi4/comfyui-prompt-control/blob/master/doc/schedules.md)
@@ -0,0 +1 @@
+PCLazyLoraLoader.md
@@ -0,0 +1,7 @@
+# PC: Schedule Prompt
+
+This node is the core of Prompt Control. It evaluates a prompt schedule and dynamically expands into a scheduled workflow consisting of calls to `PCTextEncode`, `SetConditioningTimesteps` and other necessary nodes.
+
+To use it, simply replace your usual `CLIP Text Encode` nodes with `PC: Schedule Prompt` nodes. For LoRA Loading, you should use `PC: Schedule LoRAs` in place (or in addition to) of your usual LoRA Loader node.
+
+For documentation on syntax, for now see the [documentation on GitHub](https://github.com/asagi4/comfyui-prompt-control/blob/master/doc/schedules.md)
@@ -0,0 +1 @@
+PCLazyTextEncode.md
@@ -0,0 +1,5 @@
+# PC: LoRA Hooks from Text (non-lazy)
+
+Creates cond hooks from a LoRA schedule, if you want to apply them manually for some reason.
+
+You should not need to use this. Use `PC: Schedule LoRAs`.
@@ -0,0 +1,5 @@
+# PC: Expand Macros
+
+Expands [prompt macros](https://github.com/asagi4/comfyui-prompt-control/blob/master/doc/macros.md)
+
+You should not need to use this directly. Use `PC: Schedule Prompt` instead.
@@ -0,0 +1,7 @@
+# PC: Configure PCTextEncode
+
+Configures a CLIP object with new default values used by `PCTextEncode`. Apply it before everything else.
+
+This is needed if you want to do scheduling with steps instead of denoising percentages, but otherwise it's completely optional.
+
+Note that steps are simply syntactic sugar for percentages and may not correspond to actual steps depending on the scheduler used.
@@ -0,0 +1,5 @@
+# PC: Text Encode (no scheduling)
+
+This node encodes text using some special syntax for advanced features. You should rarely need to use this node directly, and instead use `PC: Schedule Prompt` which uses this node under the hood.
+
+For documentation on syntax, see the [documentation on GitHub](https://github.com/asagi4/comfyui-prompt-control/blob/master/doc/basic.md)
Author	SHA1	Message	Date
asagi4	d7be7bc29e	v2.1.2	2026-01-13 21:50:09 +02:00
asagi4	329d4cf95f	Add a test for #133	2026-01-13 21:48:34 +02:00
asagi4	c52ace71aa	#133 properly set function locations in get_function	2026-01-13 20:45:10 +02:00
asagi4	4806cf5959	refactor get_function to make it more consistent	2026-01-13 20:45:10 +02:00
asagi4	a0ab709f50	v2.1.1	2025-12-14 17:57:53 +02:00
asagi4	66fef1ffe8	Avoid splitting AND, CAT and others when inside quotes See #132	2025-12-05 21:25:28 +02:00
asagi4	3341e9f81e	Fix TE_WEIGHT failing with some encoders See #131 Prompt weighting and attention couple will probably not work, but this should prevent exceptions.	2025-12-03 16:07:31 +02:00
asagi4	aa6b4608f0	Clarify attention couple docs a bit and add a warning if IMASK is used without attaching custom masks See #108	2025-12-01 13:19:29 +02:00
asagi4	d1cc60b00a	v2.1.0	2025-11-20 20:20:22 +02:00
asagi4	efe8939250	Fix issue with T5 encoding sometimes returning NaNs in test	2025-11-20 20:14:40 +02:00
asagi4	39b353b916	Fix tests for #130	2025-11-20 20:14:37 +02:00
asagi4	485bc7f2ab	Prepare for v3 conversion of imported nodes, see #12 This should prevent things from breaking, but it needs a bit of testing.	2025-11-20 15:29:33 +02:00
asagi4	1d03ded9dd	Find LoRAs with partial match	2025-11-20 15:23:51 +02:00
asagi4	94a4d076e0	Remove unused attributes	2025-08-30 13:46:00 +03:00
asagi4	228dc4b22b	Remove the old advanced encoding implementation	2025-08-27 20:27:30 +03:00
asagi4	db523e1f16	Remove dead code	2025-08-27 20:23:41 +03:00
asagi4	6b08c7a90e	v2.0.1	2025-08-27 20:20:13 +03:00
asagi4	76142c4b7e	Fix #127	2025-08-27 20:15:30 +03:00
asagi4	1d84fdaf9e	Release 2.0.0	2025-08-19 19:53:34 +03:00
asagi4	1c50ae5297	Disable the cache hack for now	2025-08-19 19:53:23 +03:00
asagi4	51618289e7	v2.0.0-rc.9	2025-06-21 13:16:57 +03:00
asagi4	cea1e5b30f	Initial embedded documentation	2025-06-21 13:15:58 +03:00
asagi4	55f0574ac7	Clarification	2025-06-21 12:22:05 +03:00
asagi4	167689cb8b	Add some explanations, see #121	2025-06-21 12:18:01 +03:00
asagi4	2be3abed44	Fix graph expansion node	2025-06-21 11:32:51 +03:00
asagi4	f86abb0816	Add tool for expanding lazy graphs	2025-06-21 11:19:40 +03:00
asagi4	a3537a5b2f	Some progress...	2025-06-13 21:55:17 +03:00
asagi4	af7e4542d1	Let's just bruteforce it	2025-06-13 21:49:59 +03:00
asagi4	f37f14b2a2	Does this work?	2025-06-13 21:33:51 +03:00
asagi4	7b8231d36b	...	2025-06-13 21:16:50 +03:00
asagi4	9b5e15fde3	Forgot to import mock	2025-06-13 21:06:14 +03:00
asagi4	d97d30074f	Encoder tests need a CPU mock too for CI	2025-06-13 21:03:52 +03:00
asagi4	b2eb9b88ba	Try running encoder tests in CI	2025-06-13 20:59:37 +03:00
asagi4	c9459e39f9	Fix #120 and add a test	2025-06-13 09:08:04 +03:00
asagi4	7507b2b55f	Also strip comments if they're at the start of a line	2025-06-12 23:30:54 +03:00
asagi4	cf1efecf4c	Fix minor mistake in doc	2025-06-12 23:12:13 +03:00
asagi4	ffcf94bcaa	Syntax	2025-06-12 23:05:44 +03:00
asagi4	ec8c40355c	Split documentation	2025-06-12 23:03:39 +03:00
asagi4	6e538e0abc	Fix markdown syntax	2025-06-12 22:42:45 +03:00
asagi4	25c44a1fbb	Documentation	2025-06-12 22:41:31 +03:00
asagi4	72d5490498	Add support for commenting out things with # You can escape it with \# Fixes #105	2025-06-12 22:26:34 +03:00
asagi4	3de4538326	Test cleanup	2025-06-12 21:38:56 +03:00
asagi4	f61af15d52	Update the description a bit	2025-06-10 19:05:52 +03:00