Re-enable these in the legacy node, the new one won't have them

.github/ISSUE_TEMPLATE/bug_report.md

@@ -20,7 +20,5 @@ A clear and concise description of what the bug is.
 Information needed to trigger the problem.
 If possible, attach a workflow to reproduce the problem
 
-If a workflow works, but isn't producing the correct output, please enable debug logging with the `PCSetLogLevel` node (from `promptcontrol/tools`) and run your workflow with debug logging enabled, and copy the outputs here.
-
 **Expected behavior**
 A description of what you expected to happen.

.github/workflows/publish.yml

@@ -1,29 +0,0 @@
-name: Publish to Comfy registry
-on:
-  workflow_dispatch:
-  push:
-    branches:
-      - master
-    paths:
-      - "pyproject.toml"
-
-permissions:
-  issues: write
-
-jobs:
-  tests:
-    uses: ./.github/workflows/tests.yml
-  tests_with_comfy:
-    uses: ./.github/workflows/tests_with_comfy.yml
-  publish-node:
-    name: Publish Custom Node to registry
-    runs-on: ubuntu-latest
-    if: ${{ github.repository_owner == 'asagi4' }}
-    needs: [tests, tests_with_comfy]
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-      - name: Publish Custom Node
-        uses: Comfy-Org/publish-node-action@v1
-        with:
-          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

.github/workflows/tests.yml

@@ -1,18 +0,0 @@
-name: Run parser tests
-on:
-  - workflow_call
-  - workflow_dispatch
-  - push
-
-jobs:
-  run-parser-tests:
-    name: Run parser tests
-    runs-on: ubuntu-latest
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-      - run: pip install -r requirements.txt
-      - run: python -m prompt_control.test_parser

.github/workflows/tests_with_comfy.yml

@@ -1,42 +0,0 @@
-name: Run tests requiring ComfyUI
-on:
-  workflow_call:
-  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 tests requiring ComfyUI
-    runs-on: ubuntu-latest
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-      - name: Check out ComfyUI
-        uses: actions/checkout@v4
-        with:
-          repository: comfyanonymous/ComfyUI
-          path: ComfyUI
-      - uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-          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

Makefile

@@ -1,25 +1,8 @@
-all: format check test
+all: format check
 	@echo "Done"
 check:
-	find . -name "*.py" | xargs pyflakes
+	pyflakes *.py */*.py */*/*.py
 format:
-	find . -name "*.py" | xargs black -l 120
-
-test:
-	python -m prompt_control.test_parser
-
-test_graph:
-	PYTHONPATH=../../ python -m prompt_control.test_graph
-
-test_encode:
-	PYTHONPATH=../../ python -m prompt_control.test_encode
-
-test_encode_both:
-	TEST_TE="clip_l t5" PYTHONPATH=../../ python -m prompt_control.test_encode
-
-test_heavy: test_graph test_encode_both
-
-manual_test:
-	PYTHONPATH=../../ python -im prompt_control.manual_test
+	black -l 120 *.py */*.py */*/*.py
 
 .PHONY: check format all

README.md

@@ -1,107 +1,9 @@
-# ComfyUI prompt control
+# ComfyUI prompt control (LEGACY VERSION)
 
-Control LoRA and prompt scheduling, advanced text encoding, regional prompting, and much more, through your text prompt. Generates dynamic graphs that are literally identical to handcrafted noodle soup.
+Go to https://github.com/asagi4/comfyui-prompt-control for the revised version of prompt control.
 
-Prompt Control comes with `PCTextEncode`, which provides advanced text encoding with many additional features compared to ComfyUI's base `CLIPTextEncode`.
-
-A `Basic Text to Image` template is included with the extension, and can be loaded from ComfyUI's template library.
-
-## What can it do?
-
-You can use text prompts to control the following:
-
-- A1111-style prompt scheduling and filtering without noodle soup.
-- 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 [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.
-
-[This workflow](example_workflows/Workflow%20Comparison.json?raw=1) shows LoRA scheduling and prompt editing and compares it with the same prompt implemented with built-in ComfyUI nodes. You can also find it in the template library.
-
-## Compatibility
-
-Prompt Control uses graph generation,  and tries to delegate functionality to core ComfyUI wherever possible, implementing any hooks and patches in a way that is maximally compatible. This means that it should just work in most cases, even with models and nodes not explicitly supported.
-
-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).
-
-You need to have `lark` installed in your Python environment for parsing to work (If you reuse A1111's venv, it'll already be there).
-
-If you use the portable version of ComfyUI on Windows with its embedded Python, you must open a terminal in the ComfyUI installation directory and run the command:
-```
-.\python_embeded\python.exe -m pip install lark
-```
-
-Then restart ComfyUI afterwards.
-
-# Core nodes
-
-**Note**: The documentation refers to the nodes with their internal names for consistency. The display name may change, but ComfyUI's search will always find the nodes with the internal name. `PCLazyTextEncode` and `PCLazyLoraLoader` are the main ones you'll want to use, also known as `PC: Schedule Prompt` and `PC: Schedule LoRas`.
-
-## PCLazyTextEncode and PCLazyTextEncodeAdvanced
-
-`PCLazyTextEncode` uses ComfyUI's lazy graph execution mechanism to generate a graph of `PCTextEncode` and `SetConditioningTimestepRange` nodes from a prompt with schedules. This has the advantage that if a part of the schedule doesn't change, ComfyUI's caching mechanism allows you to avoid re-encoding the non-changed part.
-
-for example, if you first encode `[cat:dog:0.1]` and later change that to `[cat:dog:0.5]`, no re-encoding takes place.
-
-for added fun, put `NODE(NodeClassName, textinputname)` in a prompt to generate a graph using **any other node** that's compatible. The node can't have required parameters besides a single CLIP parameter (which must be named `clip`) and the text prompt, and it must return a `CONDITIONING` as its first return value. The "default" values are `PCTextEncode` and `text`.
-
-For example, if you for some reason do not want the advanced features of `PCTextEncode`, use `NODE(CLIPTextEncode)` in the prompt and you'll still get scheduling with ComfyUI's regular TE node.
-
-The advanced node enables filtering the prompt for multi-pass workflows.
-
-## PCLazyLoraLoader and PCLazyLoraLoaderAdvanced
-
-This node reads LoRA expressions from the scheduled prompt and constructs a graph of `LoraLoader`s and `CreateHookLora`s as necessary to provide the necessary LoRA scheduling. Just use it in place of a `LoRALoader` and use the output normally.
-
-The Advanced node gives you access to the generated hooks. If you have `apply_hooks` set to true, you **do not** need to apply the `HOOKS`  output to a CLIP model separately; it's provided in case you want to use it elsewhere. The advanced node also enables filtering the prompt for multi-pass workflows.
-
-## PCTextEncode
-
-Encodes a single prompt with advanced (non-scheduling) syntax enabled. This is what actually does most of the work under the hood.
-
-Note: `PCTextEncode` **does not** ignore `<lora:...:1>` and will treat it as part of the prompt. To use a combined prompt for LoRAs and your input, use `PCLazyTextEncode` and `PCLazyLoraLoader`
-
-## PCAddMaskToCLIP
-
-This node attaches masks to a `CLIP` model so that they can be referred to when using the `IMASK` custom mask function of `PCTextEncode`.
-
-## PCSetTextEncodeSettings
-
-This node configures `PCTextEncode` default values for some functions by attaching the information to a `CLIP` model.
+These nodes exist only to reproduce old workflows. They are unmaintained
 
 # Known issues
 
-- 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.
+- ComfyUI's LoRA hooks are a bit slower than LoRALoader currently when the LoRA doesn't actually require scheduling. Hopefully this will improve upstream.

__init__.py

@@ -1,39 +1,46 @@
-"""
-@author: asagi4
-@title: ComfyUI Prompt Control
-@nickname: ComfyUI Prompt Control
-@description: Control LoRA and prompt scheduling, advanced text encoding, regional prompting, and much more, through your text prompt. Generates dynamic graphs that are literally identical to handcrafted noodle soup.
-"""
-
 import os
 import sys
 import logging
-import importlib
 
-
-log = logging.getLogger("comfyui-prompt-control")
-log.propagate = False
-if not log.handlers:
-    h = logging.StreamHandler(sys.stdout)
-    h.setFormatter(logging.Formatter("[PromptControl] %(levelname)s: %(message)s"))
-    log.addHandler(h)
-
-if os.environ.get("PROMPTCONTROL_DEBUG"):
-    log.setLevel(logging.DEBUG)
-else:
-    log.setLevel(logging.INFO)
-
-cache_hack = importlib.import_module(".prompt_control.cache_hack", package=__name__)
-cache_hack.init()
+from .prompt_control.legacy.node_clip import EditableCLIPEncode, ScheduleToCond
+from .prompt_control.legacy.node_lora import LoRAScheduler, ScheduleToModel, PCSplitSampling, PCWrapGuider
+from .prompt_control.legacy.node_other import (
+    PromptToSchedule,
+    FilterSchedule,
+    PCScheduleSettings,
+    PCScheduleAddMasks,
+    PCApplySettings,
+    PCPromptFromSchedule,
+)
+from .prompt_control.legacy.node_aio import PromptControlSimple
 
 NODE_CLASS_MAPPINGS = {}
 NODE_DISPLAY_NAME_MAPPINGS = {}
 
-WEB_DIRECTORY = "web"
+log = logging.getLogger("comfyui-prompt-control-legacy")
+log.propagate = False
+if not log.handlers:
+    h = logging.StreamHandler(sys.stdout)
+    h.setFormatter(logging.Formatter("[%(levelname)s] PromptControl (LEGACY VERSION): %(message)s"))
+    log.addHandler(h)
 
-nodes = ["base", "lazy", "tools", "hooks"]
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
 
-for node in nodes:
-    mod = importlib.import_module(f".prompt_control.nodes_{node}", package=__name__)
-    NODE_CLASS_MAPPINGS.update(mod.NODE_CLASS_MAPPINGS)
-    NODE_DISPLAY_NAME_MAPPINGS.update(mod.NODE_DISPLAY_NAME_MAPPINGS)
+
+NODE_CLASS_MAPPINGS.update(
+    {
+        "PromptControlSimple": PromptControlSimple,
+        "PromptToSchedule": PromptToSchedule,
+        "PCSplitSampling": PCSplitSampling,
+        "PCPromptFromSchedule": PCPromptFromSchedule,
+        "PCScheduleSettings": PCScheduleSettings,
+        "PCScheduleAddMasks": PCScheduleAddMasks,
+        "PCApplySettings": PCApplySettings,
+        "PCWrapGuider": PCWrapGuider,
+        "FilterSchedule": FilterSchedule,
+        "ScheduleToCond": ScheduleToCond,
+        "ScheduleToModel": ScheduleToModel,
+        "EditableCLIPEncode": EditableCLIPEncode,
+        "LoRAScheduler": LoRAScheduler,
+    }
+)

doc/attention_couple.md

@@ -1,40 +0,0 @@
-# Attention Couple
-
-NOTE: This is still considered an experimental feature, so the syntax may change.
-
-Attention Couple is an attention-based implementation of regional prompting. it is faster and often more flexible than latent-based masking.
-
-The implementation is based on the one by [pamparamm](https://github.com/pamparamm/ComfyUI-ppm.git), modified to use ComfyUI's hook system. This enables it to work with prompt scheduling.
-
-By default, the implementation produces slightly different results from Pamparamm's implementation because ComfyUI will only run the hook for conds that have it attached and can't batch negative conditionings.
-
-As a consequence of this, however, you can also use `COUPLE` in your negative prompt, and it will work correctly.
-
-To enable batching negative prompts, run your positive and negative prompt through the `PPCAttentionCoupleBatchNegative` node. This will make the outputs identical to pamparamm's implementation and will also improve performance. It will fall back to the default behaviour in cases where batching can't be done, so it should always be safe to use.
-
-
-## Syntax
-
-See also the main syntax documentation for `MASK` etc.
-
-### COUPLE: Trigger Attention Couple
-
-You can use `COUPLE` to attach attention-coupled prompts to a base prompt:
-
-`base_prompt COUPLE MASK(0 0.5) coupled prompt 1 with mask COUPLE IMASK(0) coupled prompt 2 with custom mask`
-
-as a shortcut, `COUPLE(maskparams)` is expanded to `COUPLE MASK(maskparams)`, so the above prompt can also be written as:
-
-`base_prompt COUPLE(0 0.5) coupled prompt 1 with mask COUPLE IMASK(0) coupled prompt 2 with custom mask`
-
-Behaviour:
-- If no mask is specified, an implicit `MASK()` is assumed.
-
-- For the base prompt, you can also use `FILL()` to automatically mask all parts not masked by coupled prompts
-
-- If the base prompt has weight set to zero (ie. ´:0` at the end), then the first coupled prompt with non-zero weight becomes the base prompt.
-
-For example:
-```
-dog FILL() COUPLE(0.5 1) cat
-```

doc/basic.md

@@ -1,206 +0,0 @@
-# Basic Prompt Syntax
-
-The syntax below documents the features of `PCTextEncode`
-
-## Combining prompts
-
-### AND
-
-`AND` can be used to create "prompt segments". By default, it works as if you had combined the different prompts with `ConditioningCombine`. 
-
-It is also used with regional prompting, see `MASK` and `COUPLE` below.
-
-Prompts can have a weight at the end:
-```
-cat :1 AND dog :2
-```
-`AND` is processed after schedule parsing, so you can change the weight mid-prompt: `cat:[1:2:0.5] AND dog`
-
-The weight defaults to 1. If a prompt's weight is set to 0, it's **skipped entirely.** This can be useful when scheduling to completely disable a prompt:
-
-```
-cat [\:0::0.5] AND dog
-```
-Note that the `:` needs to be escaped with a `\` or it will be interpreted as scheduling syntax.
-
-## 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`.
-
-- DEF macros are expanded
-- Scheduling is expanded, and for each scheduled prompt:
-  - The prompt is split by AND, and for each:
-    - Prompts are split by COUPLE. and for each:
-      - Most functions (like MASK) and cutoffs are evaluated
-      - prompts are split by `AVG()` or CAT
-        - the TE() function is evaluated to set per-encoder prompts
-        - BREAK is evaluated
-        - Everything else
-      - Prompts are combined with `ConditioningAverage` (for `AVG`) or `ConditioningConcat` (for `CAT`)
-    - If coupled prompts exist, the base cond is set up for attention coupling and returned
-  - Prompts split with `AND` are combined with `ConditioningCombine`
-- Each scheduled prompt is restricted to its effective range with  `ConditioningSetTimestepRange`
-
-## Functions
-
-There are some "functions" that can be included in a prompt to affect how it is interpreted.
-
-Functions have the form `FUNCNAME(param1, param2, ...)`. How parameters are interpreted is up to the function. 
-
-In general, function parameters will have default values that are used if the parameter is left empty.
-
-Note: Whitespace is usually *not* stripped from string parameters by default. Commas can be escaped with `\,`
-
-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.
-
-### 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.
-
-For some text encoders (like t5), this operation doesn't really make sense and BREAKs are simply ignored.
-
-### CAT
-
-`CAT` encodes each prompt separately before concatenating the resulting tensors into a single conditioning. It behaves identically to ComfyUI's `ConditioningConcat`.
-
-### AVG()
-
-`prompt1 AVG(weight) prompt2` encodes prompt1 and prompt2 separately, and then combines them using `ConditioningAverage`. The default for `weight` is `0.5`.
-
-`AVG` is processed before `BREAK` but after `AND`
-
-`p1 AVG() p2 AVG() p3` combines `p1` and `p2` first, then combines the result with `p3`.
-
-## Prompt weighting (also known as "Advanced CLIP Encode")
-
-### STYLE
-
-Use the syntax `STYLE(weight_interpretation, normalization)` in a prompt to affect how prompts are interpreted.
-
-The weight interpretations available are:
-  - comfy (default)
-  - comfy++
-  - compel
-  - down_weight
-  - A1111
-  - perp
-
-Normalizations are:
-  - none (default)
-  - length
-  - mean
-
-The normalization calculations are independent operations and you can combine them with `+`, eg `STYLE(A1111, length+mean)` or `STYLE(comfy, mean+length)`, or even something silly like `STYLE(perp, mean+length+mean+length)`
-
-The style can be specified separately for each AND:ed prompt, but the first prompt is special; later prompts will "inherit" it as default. For example:
-
-```
-STYLE(A1111) a (red:1.1) cat with (brown:0.9) spots and a long tail AND an (old:0.5) dog AND a (green:1.4) (balloon:1.1)
-```
-will interpret everything as A1111, but
-```
-a (red:1.1) cat with (brown:0.9) spots and a long tail AND STYLE(A1111) an (old:0.5) dog AND a (green:1.4) (balloon:1.1)
-```
-Will interpret the first one using the default ComfyUI behaviour, the second prompt with A1111 and the last prompt with the default again
-
-### SDXL: Configure SDXL prompting parameters
-
-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`.
-
-### TE: Per-encoder prompts for multi-encoder models
-
-You can specify per-encoder prompts using the `TE` function. The syntax is as follows:
-`TE(encoder_name=prompt)`. Whitespace surrounding the prompt and encoder name are ignored.
-
-For example:
-```
-TE(l=cat) TE(g = (dog:1.1)) TE(t5xxl=tiger)
-```
-The keys to use depend on what key ComfyUI uses for the encoder; for example `l` for CLIP L, `g` for CLIP G, and `t5xxl` for T5 XXL (Flux text encoder).
-
-Use `TE(help)` to print a help text listing available keys.
-
-Things to note:
-- If you set a prompt with `TE`, it will override the prompt outside the function for the specified text encoder.
-- Multiple instances of `TE` are joined with a space. That is, `TE(l=foo)TE(l=bar)` is the same as `TE(l=foo bar)`
-- `AND` and `BREAK` are processed before `TE`, so they do not do anything sensible; `TE(l=foo AND bar)` will parse as two prompts `TE(foo` and `bar)`. `SHIFT`, `SHUFFLE` and `OLDBREAK` do work, however.
-
-### SHUFFLE and SHIFT: Create prompt permutations
-
-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** To avoid breaking emphasis syntax, the functions ignore any separators inside parentheses
-
-For example:
-- `SHIFT(1) cat, dog, tiger, mouse` does a shift and results in `dog, tiger, mouse, cat`. (whitespace may vary)
-- `SHIFT(1,;) cat, dog ; tiger, mouse` results in `tiger, mouse, cat, dog`
-- `SHUFFLE() cat, dog, tiger, mouse` results in `cat, dog, mouse, tiger`
-- `SHUFFLE() SHIFT(1) cat, dog, tiger, mouse` results in `dog, mouse, tiger, cat`
-
-- `SHIFT(1) cat,dog BREAK tiger,mouse` results in `dog,cat BREAK tiger,mouse`
-- `SHIFT(1) cat, dog AND SHIFT(1) tiger, mouse` results in `dog, cat BREAK mouse, tiger`
-
-Whitespace is *not* stripped and may also be used as a joiner or separator
-- `SHIFT(1,, ) cat,dog` results in `dog cat`
-
-### NOISE: Add noise to a prompt
-
-The function `NOISE(weight, seed)` adds some random noise into the cond tensor. The seed is optional, and if not specified, the global RNG is used. `weight` should be between 0 and 1.
-
-The usefulness of this is questionable, but it wasn't difficult to implement, so here it is.
-
-## Regional prompting
-
-See [Regional prompting](/doc/regional_prompting.md)
-
-## Cutoff
-
-NOTE: Cutoff syntax might change at some point; it's pretty clunky.
-
-`PCTextEncode` reimplements cutoff from [ComfyUI Cutoff](https://github.com/BlenderNeko/ComfyUI_Cutoff).
-
-The syntax is
-```
-a group of animals, [CUT:white cat:white], [CUT:brown dog:brown:0.5:1.0:1.0:_]
-```
-You should read the prompt as `a group of animals, white cat, brown dog`, but CUT causes the tokens in `target_tokens` to be masked off from the base prompt in `region_text`, so that their effect can be isolated, and you're less likely to get brown cats or white dogs.
-
-Target tokens are treated individually, separated by space, for example, `[CUT:green apple, red apple, green leaf:green apple]` will mask *both* greens and the apple, giving you `+ +, red +, + leaf`. To mask out just `green apple`, use `[CUT:green apple, red apple:green_apple]` which will result in a masked prompt of `+ +, red apple`. Escape `_` with a `\`.
-
-the parameters in the `CUT` section are `region_text:target_tokens:weight;strict_mask:start_from_masked:padding_token` of which only the first two are required. The default values are `weight=1.0`, `strict_mask=1.0` `start_from_masked=1.0`, `padding_token=+`
-
-If `strict_mask`, `start_from_masked` or `padding_token` are specified in more than one CUT, the *last* one becomes the default for any CUTs afterwards that do not explicitly set the parameters. For example, in:
-
-`[CUT:white cat:white:0.5] and [CUT:black parrot, flying:black:1.0:0.5] and [CUT:green apple:green]`
-
-`white cat` will a weight of 0.5, and 1.0 for all parameters, and `black parrot` and `green apple` will *both* have a `strict_mask` parameter of 0.5.
-
-The parameters affect how the masked and unmasked prompts are combined to produce the final embedding. Just play around with them.
-
-## Miscellaneous
-- `<emb:xyz>` is alternative syntax for `embedding:xyz` to work around a syntax conflict with `[embedding:xyz:0.5]` which is parsed as a schedule that switches from `embedding` to `xyz`.
-
-# Experimental features
-
-> [!WARN]
-> These features are may change or disappear without warning
-
-## COUPLE: Attention couple
-
-See [here](/doc/attention_couple.md)
-
-## TE_WEIGHT
-
-For models using multiple text encoders, you can set weights per TE using the syntax `TE_WEIGHT(clipname=weight, clipname2=weight2, ...)` where `clipname` is one of the encoder names printed by `TE(help)`. For example with SDXL, try `TE_WEIGHT(g=0.25, l=0.75)`.
-
-The weights are applied as a multiplier to the TE output. You can also override pooled output multipliers using eg. `l_pooled`.
-
-To set a default value for all encoders, use `TE_WEIGHT(all=weight)`

doc/legacy.md

@@ -0,0 +1,44 @@
+# Legacy node documentation
+
+You really shouldn't be using these anymore
+
+## Old nodes
+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.
+
+## ScheduleToCond (deprecated)
+Produces a combined conditioning for the appropriate timesteps. From a schedule. Also applies LoRAs to the CLIP model according to the schedule.
+
+## ScheduleToModel (deprecated)
+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 (deprecated)
+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?)
+
+
+## PromptControlSimple (deprecated)
+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 of `PromptToSchedule` and `ScheduleToCond`
+- `LoRAScheduler`: A combination of `PromptToSchedule`, `FilterSchedule` and `ScheduleToModel`
+
+# Known issues
+
+- If you use LoRA scheduling in a workflow with `LoRALoader` nodes, you might get inconsistent results. For now, just avoid mixing `ScheduleToModel` or `LoRAScheduler` with `LoRALoader`. 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 with `STYLE:perp`
+- `PCSplitSampling` overrides ComfyUI's `BrownianTreeNoiseSampler` 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

doc/macros.md

@@ -1,60 +0,0 @@
-## 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"
-```

doc/regional_prompts.md

@@ -1,63 +0,0 @@
-# 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.

doc/schedules.md

@@ -1,110 +0,0 @@
-# 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.
-
-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

doc/syntax.md

@@ -0,0 +1,205 @@
+# Scheduling syntax
+
+Syntax is like A1111 for now, 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.
+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
+
+
+**Note:** As a special case, `[cat:0.5]` is like `[:cat:0.5]` meaning it switches from empty to `cat` at 0.5. Currently, `[:cat:0.5]` doesn't actually parse correctly, so you **must** use the shortcut form
+
+### Range expressions
+
+You can also use `a [during:after:0.3,0.7]` as a shortcut. The prompt be `a` until 0.3, `a during` until 0.7, and then `a after`. This form is equivalent to `[[during:after:0.7]:0.3]`
+For convenience, `[during:0.1,0.4]` is equivalent to `[during::0.1,0.4]`
+
+## 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`
+
+
+## LoRA Scheduling
+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.
+
+`PCLoraHooksFromSchedule` creates a properly scheduled `HOOKS` object from LoRA expressions included in the prompt. The older (deprecated) `ScheduleToModel` nodes will monkeypatch ComfyUI sampling and attempt to perform LoRA loading directly.
+
+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
+
+## Prompt interpolation
+
+Note: Not currently supported by `PCEncodeSchedule`
+
+`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.
+
+
+# Basic prompt syntax
+
+This syntax is also available in outside scheduled prompts, where applicable.
+
+## LoRA loading
+
+The A111-style syntax `<lora:loraname:weight>` can be used to load LoRAs via the prompt. See LoRA scheduling above.
+
+## Combining prompts, A1111-style
+
+- 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 the `ConditioningConcat` node.
+
+`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`
+
+
+## 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.
+
+### 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 as `CLIP_L(foo bar)`
+- Using `BREAK` isn't supported in it; it'll just parse as the plain word BREAK.
+- similarly, `AND` inside `CLIP_L` does not do anything sensible; `CLIP_L(foo AND bar)` will parse as two prompts `CLIP_L(foo` and `bar)`
+- `CLIP_L` and `SDXL` have no effect on SD 1.5.
+- The rest of the prompt becomes the `clip_g` prompt.
+- If there is no `CLIP_L` or `SDXL`, the prompts will work as with `CLIPTextEncode`.
+
+### 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 in `dog, tiger, mouse, cat`. (whitespace may vary)
+- `SHIFT(1,;) cat, dog ; tiger, mouse` results in `tiger, mouse, cat, dog`
+- `SHUFFLE() cat, dog, tiger, mouse` results in `cat, dog, mouse, tiger`
+- `SHUFFLE() SHIFT(1) cat, dog, tiger, mouse` results in `dog, mouse, tiger, cat`
+
+- `SHIFT(1) cat,dog BREAK tiger,mouse` results in `dog,cat BREAK tiger,mouse`
+- `SHIFT(1) cat, dog AND SHIFT(1) tiger, mouse` results in `dog, cat BREAK mouse, tiger`
+
+Whitespace is *not* stripped and may also be used as a joiner or separator
+- `SHIFT(1,, ) cat,dog` results in `dog 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.
+
+## Miscellaneous
+- `<emb:xyz>` is alternative syntax for `embedding:xyz` to work around a syntax conflict with `[embedding:xyz:0.5]` which is parsed as a schedule that switches from `embedding` to `xyz`.

example_workflows/Basic Text to Image.json

@@ -1,672 +0,0 @@
-{
-  "id": "e820c2fb-9502-45b7-a864-684757dddcdf",
-  "revision": 0,
-  "last_node_id": 18,
-  "last_link_id": 20,
-  "nodes": [
-    {
-      "id": 1,
-      "type": "CheckpointLoaderSimple",
-      "pos": [
-        -135,
-        -930
-      ],
-      "size": [
-        315,
-        98
-      ],
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "MODEL",
-          "type": "MODEL",
-          "slot_index": 0,
-          "links": [
-            2
-          ]
-        },
-        {
-          "name": "CLIP",
-          "type": "CLIP",
-          "slot_index": 1,
-          "links": [
-            3
-          ]
-        },
-        {
-          "name": "VAE",
-          "type": "VAE",
-          "slot_index": 2,
-          "links": [
-            18
-          ]
-        }
-      ],
-      "properties": {
-        "cnr_id": "comfy-core",
-        "ver": "0.3.18",
-        "Node name for S&R": "CheckpointLoaderSimple"
-      },
-      "widgets_values": [
-        "NoobAI-XL-Vpred-v1.0.safetensors"
-      ]
-    },
-    {
-      "id": 2,
-      "type": "PCLazyTextEncode",
-      "pos": [
-        555,
-        -720
-      ],
-      "size": [
-        252,
-        78
-      ],
-      "flags": {
-        "collapsed": true
-      },
-      "order": 7,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "clip",
-          "type": "CLIP",
-          "link": 5
-        },
-        {
-          "name": "text",
-          "type": "STRING",
-          "widget": {
-            "name": "text"
-          },
-          "link": 7
-        }
-      ],
-      "outputs": [
-        {
-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "slot_index": 0,
-          "links": [
-            12
-          ]
-        }
-      ],
-      "title": "PC: Schedule Prompt (positive)",
-      "properties": {
-        "cnr_id": "comfyui-prompt-control",
-        "ver": "2.0.0-beta.7",
-        "Node name for S&R": "PCLazyTextEncode"
-      },
-      "widgets_values": [
-        "STYLE(A1111) 1girl, [painting \\(medium\\), realistic,::0.2] fennec fox girl, animal ear fluff, [[purple:white pupils, purple:0.2] eyes:sparkling eyes:0.85], cargo pants, long sleeves, cardigan, winter, snow, steaming cup, coffee mug, [thermos,:0.1] [long hair,:0.25] [BREAK:0.3]\n[(masterpiece, best quality, newest, very awa,):0.1], night sky, full moon, star \\(sky\\),"
-      ],
-      "color": "#232",
-      "bgcolor": "#353"
-    },
-    {
-      "id": 3,
-      "type": "PCLazyLoraLoader",
-      "pos": [
-        257.5,
-        -745
-      ],
-      "size": [
-        210,
-        78
-      ],
-      "flags": {},
-      "order": 6,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "model",
-          "shape": 7,
-          "type": "MODEL",
-          "link": 2
-        },
-        {
-          "name": "clip",
-          "shape": 7,
-          "type": "CLIP",
-          "link": 3
-        },
-        {
-          "name": "text",
-          "type": "STRING",
-          "widget": {
-            "name": "text"
-          },
-          "link": 6
-        }
-      ],
-      "outputs": [
-        {
-          "name": "MODEL",
-          "type": "MODEL",
-          "slot_index": 0,
-          "links": [
-            17
-          ]
-        },
-        {
-          "name": "CLIP",
-          "type": "CLIP",
-          "slot_index": 1,
-          "links": [
-            5,
-            9
-          ]
-        }
-      ],
-      "properties": {
-        "cnr_id": "comfyui-prompt-control",
-        "ver": "2.0.0-beta.7",
-        "Node name for S&R": "PCLazyLoraLoader"
-      },
-      "widgets_values": [
-        "STYLE(A1111) 1girl, [painting \\(medium\\), realistic,::0.2] fennec fox girl, animal ear fluff, [[purple:white pupils, purple:0.2] eyes:sparkling eyes:0.85], cargo pants, long sleeves, cardigan, winter, snow, steaming cup, coffee mug, [thermos,:0.1] [long hair,:0.25] [BREAK:0.3]\n[(masterpiece, best quality, newest, very awa,):0.1], night sky, full moon, star \\(sky\\),"
-      ],
-      "color": "#223",
-      "bgcolor": "#335"
-    },
-    {
-      "id": 4,
-      "type": "KSampler",
-      "pos": [
-        930,
-        -780
-      ],
-      "size": [
-        315,
-        474
-      ],
-      "flags": {},
-      "order": 9,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "model",
-          "type": "MODEL",
-          "link": 17
-        },
-        {
-          "name": "positive",
-          "type": "CONDITIONING",
-          "link": 12
-        },
-        {
-          "name": "negative",
-          "type": "CONDITIONING",
-          "link": 13
-        },
-        {
-          "name": "latent_image",
-          "type": "LATENT",
-          "link": 14
-        }
-      ],
-      "outputs": [
-        {
-          "name": "LATENT",
-          "type": "LATENT",
-          "slot_index": 0,
-          "links": [
-            15
-          ]
-        }
-      ],
-      "properties": {
-        "cnr_id": "comfy-core",
-        "ver": "0.3.18",
-        "Node name for S&R": "KSampler"
-      },
-      "widgets_values": [
-        2,
-        "fixed",
-        25,
-        1.4000000000000001,
-        "euler_cfg_pp",
-        "simple",
-        1
-      ]
-    },
-    {
-      "id": 5,
-      "type": "PrimitiveNode",
-      "pos": [
-        -270,
-        -780
-      ],
-      "size": [
-        495,
-        225
-      ],
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "STRING",
-          "type": "STRING",
-          "widget": {
-            "name": "text"
-          },
-          "links": [
-            6,
-            7
-          ]
-        }
-      ],
-      "title": "Positive prompt (with LoRAs)",
-      "properties": {
-        "Run widget replace on values": false
-      },
-      "widgets_values": [
-        "STYLE(A1111) 1girl, [painting \\(medium\\), realistic,::0.2] fennec fox girl, animal ear fluff, [[purple:white pupils, purple:0.2] eyes:sparkling eyes:0.85], cargo pants, long sleeves, cardigan, winter, snow, steaming cup, coffee mug, [thermos,:0.1] [long hair,:0.25] [BREAK:0.3]\n[(masterpiece, best quality, newest, very awa,):0.1], night sky, full moon, star \\(sky\\),"
-      ],
-      "color": "#232",
-      "bgcolor": "#353"
-    },
-    {
-      "id": 6,
-      "type": "PrimitiveNode",
-      "pos": [
-        -270,
-        -510
-      ],
-      "size": [
-        480,
-        225
-      ],
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "STRING",
-          "type": "STRING",
-          "widget": {
-            "name": "text"
-          },
-          "slot_index": 0,
-          "links": [
-            8
-          ]
-        }
-      ],
-      "title": "Negative prompt",
-      "properties": {
-        "Run widget replace on values": false
-      },
-      "widgets_values": [
-        "chibi, [bad hands,low quality, worst quality,:0.05], simple background, blurry, sketch, unfinished, [holding two cups,no pupils,:0.1]"
-      ],
-      "color": "#322",
-      "bgcolor": "#533"
-    },
-    {
-      "id": 7,
-      "type": "PCLazyTextEncode",
-      "pos": [
-        555,
-        -675
-      ],
-      "size": [
-        252,
-        78
-      ],
-      "flags": {
-        "collapsed": true
-      },
-      "order": 8,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "clip",
-          "type": "CLIP",
-          "link": 9
-        },
-        {
-          "name": "text",
-          "type": "STRING",
-          "widget": {
-            "name": "text"
-          },
-          "link": 8
-        }
-      ],
-      "outputs": [
-        {
-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "slot_index": 0,
-          "links": [
-            13
-          ]
-        }
-      ],
-      "title": "PC: Schedule Prompt (negative)",
-      "properties": {
-        "cnr_id": "comfyui-prompt-control",
-        "ver": "2.0.0-beta.7",
-        "Node name for S&R": "PCLazyTextEncode"
-      },
-      "widgets_values": [
-        "chibi, [bad hands,low quality, worst quality,:0.05], simple background, blurry, sketch, unfinished, [holding two cups,no pupils,:0.1]"
-      ],
-      "color": "#322",
-      "bgcolor": "#533"
-    },
-    {
-      "id": 9,
-      "type": "EmptyLatentImage",
-      "pos": [
-        525,
-        -615
-      ],
-      "size": [
-        315,
-        106
-      ],
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "LATENT",
-          "type": "LATENT",
-          "links": [
-            14
-          ]
-        }
-      ],
-      "properties": {
-        "cnr_id": "comfy-core",
-        "ver": "0.3.18",
-        "Node name for S&R": "EmptyLatentImage"
-      },
-      "widgets_values": [
-        896,
-        1152,
-        1
-      ],
-      "color": "#432",
-      "bgcolor": "#653"
-    },
-    {
-      "id": 10,
-      "type": "VAEDecode",
-      "pos": [
-        1290,
-        -780
-      ],
-      "size": [
-        210,
-        46
-      ],
-      "flags": {},
-      "order": 10,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "samples",
-          "type": "LATENT",
-          "link": 15
-        },
-        {
-          "name": "vae",
-          "type": "VAE",
-          "link": 18
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "slot_index": 0,
-          "links": [
-            20
-          ]
-        }
-      ],
-      "properties": {
-        "cnr_id": "comfy-core",
-        "ver": "0.3.18",
-        "Node name for S&R": "VAEDecode"
-      },
-      "widgets_values": []
-    },
-    {
-      "id": 13,
-      "type": "MarkdownNote",
-      "pos": [
-        240,
-        -615
-      ],
-      "size": [
-        240,
-        105
-      ],
-      "flags": {},
-      "order": 4,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [],
-      "properties": {},
-      "widgets_values": [
-        "If you do not need LoRA scheduling, you can simply skip this node."
-      ],
-      "color": "#432",
-      "bgcolor": "#653"
-    },
-    {
-      "id": 15,
-      "type": "MarkdownNote",
-      "pos": [
-        240,
-        -450
-      ],
-      "size": [
-        600,
-        210
-      ],
-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [],
-      "properties": {},
-      "widgets_values": [
-        "`PC: Schedule prompt` will expand into instances of `PCTextEncode`. `PC: Schedule LoRAs` will expand into the required `LoRALoader`s and `CLIP` hooks required to schedule LoRAs in the prompt.\n\nYou can pass the same prompt to both nodes; `PC: Schedule Prompt` will simply ignore any `<lora:xyz:1>` elements, so they will not affect the prompt.\nSee the [full syntax available in the prompts](https://github.com/asagi4/comfyui-prompt-control/blob/master/doc/syntax.md) on GitHub"
-      ],
-      "color": "#432",
-      "bgcolor": "#653"
-    },
-    {
-      "id": 18,
-      "type": "SaveImage",
-      "pos": [
-        1290,
-        -690
-      ],
-      "size": [
-        270,
-        405
-      ],
-      "flags": {},
-      "order": 11,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 20
-        }
-      ],
-      "outputs": [],
-      "properties": {
-        "cnr_id": "comfy-core",
-        "ver": "0.3.18"
-      },
-      "widgets_values": [
-        "PromptControl"
-      ]
-    }
-  ],
-  "links": [
-    [
-      2,
-      1,
-      0,
-      3,
-      0,
-      "MODEL"
-    ],
-    [
-      3,
-      1,
-      1,
-      3,
-      1,
-      "CLIP"
-    ],
-    [
-      5,
-      3,
-      1,
-      2,
-      0,
-      "CLIP"
-    ],
-    [
-      6,
-      5,
-      0,
-      3,
-      2,
-      "STRING"
-    ],
-    [
-      7,
-      5,
-      0,
-      2,
-      1,
-      "STRING"
-    ],
-    [
-      8,
-      6,
-      0,
-      7,
-      1,
-      "STRING"
-    ],
-    [
-      9,
-      3,
-      1,
-      7,
-      0,
-      "CLIP"
-    ],
-    [
-      12,
-      2,
-      0,
-      4,
-      1,
-      "CONDITIONING"
-    ],
-    [
-      13,
-      7,
-      0,
-      4,
-      2,
-      "CONDITIONING"
-    ],
-    [
-      14,
-      9,
-      0,
-      4,
-      3,
-      "LATENT"
-    ],
-    [
-      15,
-      4,
-      0,
-      10,
-      0,
-      "LATENT"
-    ],
-    [
-      17,
-      3,
-      0,
-      4,
-      0,
-      "MODEL"
-    ],
-    [
-      20,
-      10,
-      0,
-      18,
-      0,
-      "IMAGE"
-    ],
-    [
-      18,
-      1,
-      2,
-      10,
-      1,
-      "VAE"
-    ]
-  ],
-  "groups": [],
-  "config": {},
-  "extra": {
-    "ds": {
-      "scale": 0.8,
-      "offset": [
-        591.75,
-        1235
-      ]
-    },
-    "linkExtensions": [
-      {
-        "id": 18,
-        "parentId": 1
-      }
-    ],
-    "reroutes": [
-      {
-        "id": 1,
-        "pos": [
-          1273.75,
-          -879.5
-        ],
-        "linkIds": [
-          18
-        ]
-      }
-    ]
-  },
-  "version": 0.4,
-  "models": [{
-    "name": "NoobAI-XL-Vpred-v1.0.safetensors",
-    "url": "https://huggingface.co/Laxhar/noobai-XL-Vpred-1.0/resolve/main/NoobAI-XL-Vpred-v1.0.safetensors",
-    "directory": "checkpoints"
-  }]
-}

prompt_control/adv_encode.py

@@ -1,392 +0,0 @@
-import torch
-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")
-
-
-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 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 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 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
-
-
-def perp_weight(weights, unweighted_embs, empty_embs):
-    unweighted, unweighted_pooled = unweighted_embs
-    zero, zero_pooled = empty_embs
-
-    weights = weights_like(weights, unweighted)
-
-    if zero.shape != unweighted.shape:
-        zero = zero.repeat(1, unweighted.shape[1] // zero.shape[1], 1)
-
-    perp = (
-        torch.mul(zero, unweighted).sum(dim=-1, keepdim=True) / (unweighted.norm(dim=-1, keepdim=True) ** 2)
-    ) * unweighted
-
-    over1 = weights.abs() > 1.0
-    result = unweighted + weights * perp
-    result[~over1] = (unweighted - (1 - weights) * perp)[~over1]
-    result[weights == 0.0] = zero[weights == 0.0]
-
-    # Not sure if this is an implementation bug or if this just doesn't make sense with T5
-    nans = result.isnan()
-    if nans.any():
-        log.warning("perp weight returned NaNs (known to happen with T5), replacing with 0")
-        result[nans] = 0.0
-
-    return result, unweighted_pooled
-
-
-def style_comfy(encoder, tokens, **kwargs):
-    tokens = encoder.without_word_ids(tokens)
-    return encoder.encode_fn(tokens)
-
-
-def style_a1111(encoder, tokens, **kwargs):
-    base_emb, pooled = 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
-
-
-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 = encoder.down_weight(
-        pos_tokens, encoder.weights(tokens), encoder.word_ids(tokens), weighted_emb, pooled
-    )
-    return weighted_emb, pooled
-
-
-def style_comfypp(encoder, tokens, **kwargs):
-    unweighted_tokens = encoder.unweighted(tokens)
-    base_emb, pooled_base = encoder.base_emb(tokens)
-    weighted_emb, tokens_down, _ = encoder.down_weight(
-        unweighted_tokens, encoder.weights(tokens), encoder.word_ids(tokens), base_emb, pooled_base
-    )
-    weights = encoder.weights(encoder.weighted_with(tokens, lambda w: w if w > 1.0 else 1.0))
-    embs, pooled = encoder.from_masked(
-        unweighted_tokens,
-        weights,
-        encoder.word_ids(tokens),
-        base_emb,
-        pooled_base,
-    )
-    weighted_emb += embs
-
-    return weighted_emb, pooled
-
-
-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)
-    weighted_emb, _, pooled = encoder.down_weight(
-        encoder.unweighted(tokens), weights, encoder.word_ids(tokens), base_emb, pooled_base
-    )
-
-    return weighted_emb, pooled
-
-
-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))
-
-
-def apply_negpip(encoder, emb, pooled, **kwargs):
-    original_tokens = kwargs["original_tokens"]
-    emb_negpip = torch.empty_like(emb).repeat(1, 2, 1)
-    emb_negpip[:, 0::2, :] = emb
-    emb_negpip[:, 1::2, :] = emb * weights_like(encoder.signs(original_tokens), emb)
-    return emb_negpip, pooled
-
-
-def norm_length(encoder, tokens, **kwargs):
-    word_ids = encoder.word_ids(tokens)
-    sums = dict(zip(*np.unique(word_ids, return_counts=True)))
-    sums[0] = 1
-    tokens = [[(t, _norm_mag(w, sums[id]) if id != 0 else 1.0, id) for (t, w, id) in x] for x in tokens]
-    return tokens
-
-
-def norm_mean(encoder, tokens, **kwargs):
-    weights = encoder.weights(tokens)
-    word_ids = encoder.word_ids(tokens)
-    delta = 1 - np.mean([w for x, y in zip(weights, word_ids) for w, id in zip(x, y) if id != 0])
-    tokens = [[(t, w if id == 0 else w + delta, id) for (t, w, id) in x] for x in tokens]
-    return tokens
-
-
-def norm_none(encoder, tokens, **kwargs):
-    return tokens
-
-
-class AdvancedEncoder:
-    STYLES = {
-        "A1111": style_a1111,
-        "comfy": style_comfy,
-        "comfy++": style_comfypp,
-        "compel": style_compel,
-        "down_weight": style_downweight,
-        "perp": style_perp,
-    }
-    NORMALIZATION_OPS = {
-        "none": norm_none,
-        "length": norm_length,
-        "mean": norm_mean,
-    }
-
-    @classmethod
-    def add_encoder(cls, name, fn):
-        cls.STYLES[name] = fn
-
-    def add_normalization_op(cls, name, fn):
-        cls.NORMALIZATION_OPS[name] = fn
-
-    @classmethod
-    def weighted_with(cls, tokens, fn=id, word_ids=True):
-        w = ([(t, fn(w), id) for t, w, id in x] for x in tokens)
-        if not word_ids:
-            w = cls.without_word_ids(w)
-        return list(w)
-
-    @classmethod
-    def unweighted(cls, tokens, word_ids=False):
-        return cls.weighted_with(tokens, fn=lambda w: 1.0, word_ids=word_ids)
-
-    @classmethod
-    def tokens_only(cls, tokens):
-        return list([t[0] for t in x] for x in tokens)
-
-    @classmethod
-    def weights(cls, tokens):
-        return list([t[1] for t in x] for x in tokens)
-
-    @classmethod
-    def word_ids(cls, tokens):
-        return list([t[2] for t in x] for x in tokens)
-
-    @classmethod
-    def signs(cls, tokens):
-        return list([copysign(1, t[1]) for t in x] for x in tokens)
-
-    @classmethod
-    def without_word_ids(cls, tokens):
-        return list([(t, w) for t, w, _ in x] for x in tokens)
-
-    def __init__(self, encode_fn, style, normalization, tokenizer, m_token="+", w_max=1.0, **extra_args):
-        self.encode_fn = encode_fn
-        self.preprocessors = []
-        self.postprocessors = []
-        self.tokenizer = tokenizer
-        self.extra_args = extra_args
-        self.m_token = tokenizer.tokenize_with_weights(m_token)[0][tokenizer.tokens_start]
-        self.max_length = tokenizer.max_length if tokenizer.pad_to_max_length else None
-        self.w_max = w_max
-
-        if style == "comfy++" and not self.max_length:
-            log.warning("comfy++ does not work with tokenizer %s, using default weighting", tokenizer)
-            style = "comfy"
-
-        norms = normalization.split("+")
-        assert style in self.STYLES, f"Invalid weight interpretation: {style}"
-        self.weight_fn = self.STYLES[style]
-        for n in norms:
-            n = n.strip()
-            assert n in self.NORMALIZATION_OPS, f"Invalid normalization: {normalization}"
-            self.preprocessors.append(self.NORMALIZATION_OPS[n])
-
-        negpip = extra_args.get("has_negpip")
-        if negpip:
-
-            def _encode(t):
-                emb, pooled = encode_fn(t)
-                return emb[:, 0::2, :], pooled
-
-            self.encode_fn = _encode
-            self.preprocessors.insert(0, lambda encoder, tokens, **kwargs: encoder.weighted_with(tokens, abs))
-            self.postprocessors.insert(0, apply_negpip)
-
-    def base_emb(self, tokens):
-        unweighted = self.unweighted(tokens)
-        return self.encode_fn(unweighted)
-
-    def down_weight(self, tokens, weights, word_ids, base_emb, pooled_base):
-        w, w_inv = np.unique(weights, return_inverse=True)
-
-        if np.sum(w < 1) == 0:
-            return (
-                base_emb,
-                tokens,
-                (
-                    base_emb[0, self.max_length - 1 : self.max_length, :]
-                    if (pooled_base is not None and self.max_length)
-                    else None
-                ),
-            )
-
-        masked_current = tokens
-        emblist = [base_emb]
-        for i in range(len(w)):
-            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)
-            emblist.append(masked)
-
-        embs = torch.cat(emblist)
-        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)
-        pooled = pooled_base
-        if pooled is not None and self.max_length:
-            pooled = weighted_emb[0, self.max_length - 1 : self.max_length, :]
-        return weighted_emb, masked_current, pooled
-
-    def from_masked(self, tokens, weights, word_ids, base_emb, pooled_base):
-        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), torch.zeros_like(pooled_base) if pooled_base is not None else None
-
-        weight_tensor = weights_like(weights, base_emb)
-
-        ws = []
-        masked_tokens = []
-        masks = []
-
-        # create prompts
-        for id, w in weight_dict.items():
-            masked, m = mask_word_id(tokens, word_ids, id, self.m_token)
-            masks.append(weights_like(m, base_emb))
-            masked_tokens.extend(masked)
-
-            ws.append(w)
-
-        # TODO: figure out how to get rid of this
-        embs = batched_clip_encode(masked_tokens, self.max_length, self.encode_fn, len(tokens))
-        masks = torch.cat(masks)
-
-        embs = base_emb.expand(embs.shape) - embs
-        if pooled_base is not None and self.max_length:
-            pooled = embs[0, self.max_length - 1 : self.max_length, :]
-            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)
-            pooled = pooled_base + pooled
-
-        if embs.shape[0] != masks.shape[0]:
-            embs = embs.repeat(masks.shape[0], 1, 1)
-        embs *= masks
-        embs = embs.sum(axis=0, keepdim=True)
-
-        return ((weight_tensor - 1) * embs), pooled
-
-    def __call__(self, tokens, apply_to_pooled=False, return_pooled=False):
-        normalized_tokens = tokens
-        for op in self.preprocessors:
-            normalized_tokens = op(self, normalized_tokens)
-
-        emb, pooled = 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
-
-
-def advanced_encode_from_tokens(
-    tokenized,
-    token_normalization,
-    weight_interpretation,
-    encode_func,
-    m_token="+",
-    w_max=1.0,
-    return_pooled=False,
-    apply_to_pooled=False,
-    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,
-        )

prompt_control/adv_encode_old.py

@@ -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

prompt_control/attention_couple_ppm.py

@@ -1,252 +0,0 @@
-# Lifted from https://github.com/pamparamm/ComfyUI-ppm/blob/c3e6b673ee2d424405dcb99aeed89f21943c89ac/nodes_ppm/attention_couple_ppm.py
-# Original implementation by laksjdjf, hako-mikan, Haoming02 licensed under GPL-3.0
-# https://github.com/laksjdjf/cgem156-ComfyUI/blob/1f5533f7f31345bafe4b833cbee15a3c4ad74167/scripts/attention_couple/node.py
-# https://github.com/Haoming02/sd-forge-couple/blob/e8e258e982a8d149ba59a4bc43b945467604311c/scripts/attention_couple.py
-import itertools
-import logging
-import math
-from typing import Any
-
-import torch
-import torch.nn.functional as F
-
-from comfy.hooks import EnumHookScope, HookGroup, TransformerOptionsHook, set_hooks_for_conditioning
-from comfy.model_patcher import ModelPatcher
-
-log = logging.getLogger("comfyui-prompt-control")
-
-
-def set_cond_attnmask(base_cond, extra_conds, fill=False):
-    hook = AttentionCoupleHook()
-    c = [base_cond[0][0], base_cond[0][1].copy()]
-    # hook uses these, remove them to avoid doing latent masking
-    c[1].pop("mask", None)
-    c[1].pop("strength", None)
-    c[1].pop("mask_strength", None)
-    c = [c]
-    c.extend(base_cond[1:])
-
-    hook.initialize_regions(base_cond[0], extra_conds, fill=fill)
-    group = HookGroup()
-    group.add(hook)
-
-    return set_hooks_for_conditioning(c, hooks=group, append_hooks=True)
-
-
-def get_mask(mask, batch_size, num_tokens, extra_options):
-    activations_shape = extra_options["activations_shape"]
-    size = activations_shape[-2:]
-
-    num_conds = mask.shape[0]
-    mask_downsample = F.interpolate(mask, size=size, mode="nearest")
-    mask_downsample_reshaped = mask_downsample.view(num_conds, num_tokens, 1).repeat_interleave(batch_size, dim=0)
-
-    return mask_downsample_reshaped
-
-
-class Proxy:
-    def __init__(self, function):
-        self.function = function
-
-    def to(self, *args, **kwargs):
-        self.function.__self__.to(*args, **kwargs)
-        return self
-
-    def __call__(self, *args, **kwargs):
-        return self.function(*args, *kwargs)
-
-
-class AttentionCoupleHook(TransformerOptionsHook):
-    COND_UNCOND_COUPLE_OPTION = "cond_or_uncond_hook_couple"
-    COND = 0
-    UNCOND = 1
-
-    def __init__(self):
-        super().__init__(hook_scope=EnumHookScope.HookedOnly)
-
-        self.transformers_dict = {
-            "patches": {
-                "attn2_output_patch": [Proxy(self.attn2_output_patch)],
-                "attn2_patch": [Proxy(self.attn2_patch)],
-            }
-        }
-        self.has_negpip = False
-
-        # calculate later. All clones must refer to the same kv dict
-        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]
-        self.conds: list[torch.Tensor] = [base_cond[0]] + [cond[0] for cond in conds]
-        base_mask = base_cond[1].get("mask", None)
-        masks = [cond[1].get("mask") * cond[1].get("mask_strength") for cond in conds]
-        if len(masks) < 1:
-            raise ValueError("Attention Couple hook makes no sense without masked conds")
-
-        if any(m is None for m in masks):
-            raise ValueError("All conds given to Attention Couple must have masks")
-
-        if any(m.shape != masks[0].shape for m in masks) or (
-            base_mask is not None and base_mask.shape != masks[0].shape
-        ):
-            largest_shape = max(m.shape for m in masks)
-            if base_mask is not None:
-                largest_shape = max(largest_shape, base_mask.shape)
-            log.warning("Attention Couple: Masks are irregularly shaped, resizing them all to match the largest")
-            for i in range(len(masks)):
-                masks[i] = F.interpolate(masks[i].unsqueeze(1), size=largest_shape[1:], mode="nearest-exact").squeeze(1)
-
-            if base_mask is not None:
-                base_mask = F.interpolate(base_mask.unsqueeze(1), size=largest_shape[1:], mode="nearest-exact").squeeze(
-                    1
-                )
-
-        if base_mask is None:
-            if not fill:
-                raise ValueError("You must specify a base mask when fill=False")
-            sum = torch.stack(masks, dim=0).sum(dim=0)
-            base_mask = torch.zeros_like(sum)
-            base_mask[sum <= 0] = 1.0
-
-        mask = [base_mask] + masks
-        mask = torch.stack(mask, dim=0)
-        if mask.sum(dim=0).min() <= 0 and not fill:
-            raise ValueError("Masks contain non-filled areas")
-
-        self.mask = mask / mask.sum(dim=0, keepdim=True)
-
-    def on_apply_hooks(self, model: ModelPatcher, transformer_options: dict[str, Any]):
-        if self.kv["k"] is None:
-            self.has_negpip = model.model_options.get("ppm_negpip", False)
-            log.debug("AttentionCouple has_negpip=%s", self.has_negpip)
-
-            # Skip the base cond here, which is always first
-            if self.has_negpip:
-                self.kv["k"] = [cond[:, 0::2] for cond in self.conds[1:]]
-                self.kv["v"] = [cond[:, 1::2] for cond in self.conds[1:]]
-            else:
-                self.kv["k"] = self.kv["v"] = self.conds[1:]
-
-        return super().on_apply_hooks(model, transformer_options)
-
-    def clone(self):
-        c: AttentionCoupleHook = super().clone()
-        c.mask = self.mask
-        c.conds = self.conds
-        c.kv = self.kv
-        c.has_negpip = self.has_negpip
-        c.base_strength = self.base_strength
-        c.strengths = self.strengths
-        c.num_conds = self.num_conds
-        return c
-
-    def to(self, *args, **kwargs):
-        self.conds = [c.to(*args, **kwargs) for c in self.conds]
-        self.mask = self.mask.to(*args, **kwargs)
-        if self.kv["k"] is not None:
-            self.kv["k"] = [c.to(*args, **kwargs) for c in self.kv["k"]]
-            self.kv["v"] = [c.to(*args, **kwargs) for c in self.kv["v"]]
-        return self
-
-    def attn2_patch(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, extra_options):
-        cond_or_uncond = extra_options["cond_or_uncond"]
-        cond_or_uncond_couple = extra_options[self.COND_UNCOND_COUPLE_OPTION] = list(cond_or_uncond)
-        num_chunks = len(cond_or_uncond)
-
-        # Cloning messes up the device sometimes
-        if self.kv["k"][0].device != k.device:
-            self.to(k)
-
-        conds_k = self.kv["k"]
-        conds_v = self.kv["v"]
-
-        lcm_tokens_k = math.lcm(k.shape[1], *(cond.shape[1] for cond in conds_k))
-        lcm_tokens_v = math.lcm(v.shape[1], *(cond.shape[1] for cond in conds_v))
-        q_chunks = q.chunk(num_chunks, dim=0)
-        k_chunks = k.chunk(num_chunks, dim=0)
-        v_chunks = v.chunk(num_chunks, dim=0)
-
-        bs = q.shape[0] // num_chunks
-
-        conds_k_tensor = conds_v_tensor = torch.cat(
-            [cond.repeat(bs, lcm_tokens_k // cond.shape[1], 1) * self.strengths[i] for i, cond in enumerate(conds_k)],
-            dim=0,
-        )
-        if self.has_negpip:
-            conds_v_tensor = torch.cat(
-                [
-                    cond.repeat(bs, lcm_tokens_v // cond.shape[1], 1) * self.strengths[i]
-                    for i, cond in enumerate(conds_v)
-                ],
-                dim=0,
-            )
-
-        qs, ks, vs = [], [], []
-        cond_or_uncond_couple.clear()
-
-        for i, cond_type in enumerate(cond_or_uncond):
-            q_target = q_chunks[i]
-            k_target = k_chunks[i].repeat(1, lcm_tokens_k // k.shape[1], 1)
-            v_target = v_chunks[i].repeat(1, lcm_tokens_v // v.shape[1], 1)
-            if cond_type == self.UNCOND:
-                qs.append(q_target)
-                ks.append(k_target)
-                vs.append(v_target)
-                cond_or_uncond_couple.append(self.UNCOND)
-            else:
-                qs.append(q_target.repeat(self.num_conds, 1, 1))
-                ks.append(
-                    torch.cat(
-                        [
-                            k_target * self.base_strength,
-                            conds_k_tensor,
-                        ],
-                        dim=0,
-                    )
-                )
-                vs.append(
-                    torch.cat(
-                        [
-                            v_target * self.base_strength,
-                            conds_v_tensor,
-                        ],
-                        dim=0,
-                    )
-                )
-                cond_or_uncond_couple.extend(itertools.repeat(self.COND, self.num_conds))
-
-        q = torch.cat(qs, dim=0)
-        k = torch.cat(ks, dim=0)
-        v = torch.cat(vs, dim=0)
-
-        return q, k, v
-
-    def attn2_output_patch(self, out, extra_options):
-        cond_or_uncond = extra_options[self.COND_UNCOND_COUPLE_OPTION]
-        bs = out.shape[0] // len(cond_or_uncond)
-        mask_downsample = get_mask(self.mask, bs, out.shape[1], extra_options)
-        outputs = []
-        cond_outputs = []
-        i_cond = 0
-        for i, cond_type in enumerate(cond_or_uncond):
-            pos, next_pos = i * bs, (i + 1) * bs
-
-            if cond_type == self.UNCOND:
-                outputs.append(out[pos:next_pos])
-            else:
-                pos_cond, next_pos_cond = i_cond * bs, (i_cond + 1) * bs
-                masked_output = out[pos:next_pos] * mask_downsample[pos_cond:next_pos_cond]
-                cond_outputs.append(masked_output)
-                i_cond += 1
-
-        if len(cond_outputs) > 0:
-            cond_output = torch.stack(cond_outputs).sum(0)
-            outputs.append(cond_output)
-
-        return torch.cat(outputs, dim=0)

prompt_control/cache_hack.py

@@ -1,47 +0,0 @@
-import comfy_execution.caching
-from comfy_execution.graph_utils import is_link
-import nodes
-from os import environ
-
-import logging
-
-log = logging.getLogger("comfyui-prompt-control")
-
-
-include_unique_id_in_input = comfy_execution.caching.include_unique_id_in_input
-
-
-def promptcontrol_get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping):
-    if not dynprompt.has_node(node_id):
-        # This node doesn't exist -- we can't cache it.
-        return [float("NaN")]
-    node = dynprompt.get_node(node_id)
-    class_type = node["class_type"]
-    class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
-    inputs = node["inputs"]
-    if hasattr(class_def, "CACHE_KEY"):
-        inputs = getattr(class_def, "CACHE_KEY")(inputs)
-    signature = [class_type, self.is_changed_cache.get(node_id)]
-    if (
-        self.include_node_id_in_input()
-        or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT)
-        or include_unique_id_in_input(class_type)
-    ):
-        signature.append(node_id)
-    for key in sorted(inputs.keys()):
-        if is_link(inputs[key]):
-            (ancestor_id, ancestor_socket) = inputs[key]
-            ancestor_index = ancestor_order_mapping[ancestor_id]
-            signature.append((key, ("ANCESTOR", ancestor_index, ancestor_socket)))
-        else:
-            signature.append((key, inputs[key]))
-    return signature
-
-
-def init():
-    if environ.get("PROMPTCONTROL_ENABLE_CACHE_HACK") != "1":
-        return
-    log.warning("Enabling Prompt Control cache hack")
-    comfy_execution.caching.CacheKeySetInputSignature.get_immediate_node_signature = (
-        promptcontrol_get_immediate_node_signature
-    )

prompt_control/cutoff.py

@@ -1,229 +0,0 @@
-import torch
-import copy
-import re
-
-import numpy as np
-import logging
-
-log = logging.getLogger("comfyui-prompt-control")
-
-
-def replace_embeddings(max_token, prompt, replacements=None):
-    """Replaces embedding tensors in a token array and replaces them with increasing IDs past max_token"""
-
-    if replacements is None:
-        emb_lookup = []
-    else:
-        emb_lookup = replacements.copy()
-        max_token += len(emb_lookup)
-
-    def get_replacement(embedding):
-        for e, n in emb_lookup:
-            if torch.equal(embedding, e):
-                return n
-        return None
-
-    tokens = []
-    for x in prompt:
-        row = []
-        for i in range(len(x)):
-            emb = x[i][0]
-            if not torch.is_tensor(emb):
-                row.append(emb)
-            else:
-                n = get_replacement(emb)
-                if n is not None:
-                    row.append(n)
-                else:
-                    max_token += 1
-                    row.append(max_token)
-                    emb_lookup.append((emb, max_token))
-        tokens.append(row)
-    tokens = np.array(tokens)[:, 1:-1].reshape(-1)
-    return (tokens, emb_lookup)
-
-
-def unpad_prompt(pad_token, prompt):
-    res = np.trim_zeros(prompt, "b")
-    return np.trim_zeros(res - pad_token, "b") + pad_token
-
-
-def get_sublists(super_list, sub_list):
-    positions = []
-    for candidate_ind in (i for i, e in enumerate(super_list) if e == sub_list[0]):
-        if super_list[candidate_ind : candidate_ind + len(sub_list)] == sub_list:
-            positions.append(candidate_ind)
-    return positions
-
-
-def cutoff_add_region(
-    clip_regions, tokenizer, region_text, target_text, weight, strict_mask, start_from_masked, mask_token
-):
-    """Adds a cut region to the clip_regions dictionary. It is modified in place"""
-    base_tokens = clip_regions["base_tokens"]
-    region_outputs = []
-    target_outputs = []
-    if strict_mask is not None:
-        clip_regions["strict_mask"] = float(strict_mask)
-    if start_from_masked is not None:
-        clip_regions["start_from_masked"] = float(start_from_masked)
-    if mask_token is not None:
-        clip_regions["mask_token"] = tokenizer.tokenizer(mask_token)["input_ids"][1]
-    if weight is None:
-        weight = 1.0
-    else:
-        weight = float(weight)
-
-    region_text = region_text.strip()
-    target_text = target_text.strip()
-
-    strict_mask = clip_regions["strict_mask"]
-    start_from_masked = clip_regions["start_from_masked"]
-    mask_token = clip_regions["mask_token"]
-    log.info(f"CUT region {region_text=} {target_text=} {weight=} {strict_mask=} {start_from_masked=} {mask_token=}")
-
-    pad_token = tokenizer.end_token
-
-    prompt_tokens, emb_lookup = replace_embeddings(pad_token, base_tokens)
-
-    for rt in region_text.split("\n"):
-        region_tokens = tokenizer.tokenize_with_weights(rt)
-        region_tokens, _ = replace_embeddings(pad_token, region_tokens, emb_lookup)
-        region_tokens = unpad_prompt(pad_token, region_tokens).tolist()
-
-        # calc region mask
-        region_length = len(region_tokens)
-        regions = get_sublists(list(prompt_tokens), region_tokens)
-
-        region_mask = np.zeros(len(prompt_tokens))
-        for r in regions:
-            region_mask[r : r + region_length] = 1
-        region_mask = region_mask.reshape(-1, tokenizer.max_length - 2)
-        region_mask = np.pad(region_mask, pad_width=((0, 0), (1, 1)), mode="constant", constant_values=0)
-        region_mask = region_mask.reshape(1, -1)
-        region_outputs.append(region_mask)
-
-        # calc target mask
-        targets = []
-        for target in target_text.split(" "):
-            # deal with underscores
-            target = re.sub(r"(?<!\\)_", " ", target)
-            target = re.sub(r"\\_", "_", target)
-
-            target_tokens = tokenizer.tokenize_with_weights(target)
-            target_tokens, _ = replace_embeddings(pad_token, target_tokens, emb_lookup)
-            target_tokens = unpad_prompt(pad_token, target_tokens).tolist()
-
-            targets.extend([(x, len(target_tokens)) for x in get_sublists(region_tokens, target_tokens)])
-        targets = [(t_start + r, t_start + t_end + r) for r in regions for t_start, t_end in targets]
-
-        targets_mask = np.zeros(len(prompt_tokens))
-        for t_start, t_end in targets:
-            targets_mask[t_start:t_end] = 1
-        targets_mask = targets_mask.reshape(-1, tokenizer.max_length - 2)
-        targets_mask = np.pad(targets_mask, pad_width=((0, 0), (1, 1)), mode="constant", constant_values=0)
-        targets_mask = targets_mask.reshape(1, -1)
-        target_outputs.append(targets_mask)
-
-    # prepare output
-    region_mask_list = clip_regions["regions"].copy()
-    region_mask_list.extend(region_outputs)
-    target_mask_list = clip_regions["targets"].copy()
-    target_mask_list.extend(target_outputs)
-    weight_list = clip_regions["weights"].copy()
-    weight_list.extend([weight] * len(region_outputs))
-
-    clip_regions["regions"] = region_mask_list
-    clip_regions["targets"] = target_mask_list
-    clip_regions["weights"] = weight_list
-
-
-def create_masked_prompt(weighted_tokens, mask, mask_token):
-    mask_ids = list(zip(*np.nonzero(mask.reshape((len(weighted_tokens), -1)))))
-    new_prompt = copy.deepcopy(weighted_tokens)
-    for x, y in mask_ids:
-        new_prompt[x][y] = (mask_token,) + new_prompt[x][y][1:]
-    return new_prompt
-
-
-def process_cuts(encode, extra, tokens):
-    if not extra.get("cuts"):
-        return encode(tokens)
-
-    base = {
-        "base_tokens": tokens,
-        "regions": [],
-        "targets": [],
-        "weights": [],
-        "strict_mask": 1.0,
-        "start_from_masked": 1.0,
-        "mask_token": extra["tokenizer"].tokenizer("+")["input_ids"][1],
-    }
-
-    for cut in extra["cuts"]:
-        cutoff_add_region(base, extra["tokenizer"], *cut)
-
-    return encode_regions(base, encode, extra["tokenizer"])
-
-
-def debug_tokens(label, prompt, tokenizer):
-    log.debug("Tokens for %s", label)
-    for tokens in prompt:
-        tokens = (t for t in tokens if not torch.is_tensor(t[0]))
-        log.debug(" ".join(f"{x[0][0]} {x[1]}" for x in tokenizer.untokenize(tokens) if x[0][0] != tokenizer.end_token))
-
-
-def encode_regions(clip_regions, encode, tokenizer):
-    base_weighted_tokens = clip_regions["base_tokens"]
-    start_from_masked = clip_regions["start_from_masked"]
-    mask_token = clip_regions["mask_token"]
-    strict_mask = clip_regions["strict_mask"]
-
-    # calc base embedding
-    base_embedding_full, pool = encode(base_weighted_tokens)
-
-    # Avoid numpy value error and passthrough base embeddings if no regions are set.
-
-    # calc global target mask
-    global_target_mask = np.any(np.stack(clip_regions["targets"]), axis=0).astype(int)
-
-    # calc global region mask
-    global_region_mask = np.any(np.stack(clip_regions["regions"]), axis=0).astype(float)
-    regions_sum = np.sum(np.stack(clip_regions["regions"]), axis=0)
-    regions_normalized = np.divide(1, regions_sum, out=np.zeros_like(regions_sum), where=regions_sum != 0)
-
-    # mask base embeddings
-    base_masked_prompt = create_masked_prompt(base_weighted_tokens, global_target_mask, mask_token)
-    debug_tokens("base_masked", base_masked_prompt, tokenizer)
-    base_embedding_masked, _ = encode(base_masked_prompt)
-    base_embedding_start = base_embedding_full * (1 - start_from_masked) + base_embedding_masked * start_from_masked
-    base_embedding_outer = base_embedding_full * (1 - strict_mask) + base_embedding_masked * strict_mask
-
-    region_embeddings = []
-    for region, target, weight in zip(clip_regions["regions"], clip_regions["targets"], clip_regions["weights"]):
-        region_masking = torch.tensor(
-            regions_normalized * region * weight, dtype=base_embedding_full.dtype, device=base_embedding_full.device
-        ).unsqueeze(-1)
-
-        region_prompt = create_masked_prompt(base_weighted_tokens, global_target_mask - target, mask_token)
-        debug_tokens("region", region_prompt, tokenizer)
-        region_emb, _ = encode(region_prompt)
-        region_emb -= base_embedding_start
-        # NegPiP support:
-        if region_emb.shape[1] == 2 * region_masking.shape[1]:
-            region_masking = torch.repeat_interleave(region_masking, 2, dim=1)
-        region_emb *= region_masking
-
-        region_embeddings.append(region_emb)
-    region_embeddings = torch.stack(region_embeddings).sum(axis=0)
-
-    embeddings_final_mask = torch.tensor(
-        global_region_mask, dtype=base_embedding_full.dtype, device=base_embedding_full.device
-    ).unsqueeze(-1)
-    # NegPiP support:
-    if region_embeddings.shape[1] == 2 * embeddings_final_mask.shape[1]:
-        embeddings_final_mask = torch.repeat_interleave(embeddings_final_mask, 2, dim=1)
-
-    embeddings_final = base_embedding_start * embeddings_final_mask + base_embedding_outer * (1 - embeddings_final_mask)
-    embeddings_final += region_embeddings
-    return embeddings_final, pool

prompt_control/legacy/hijack.py

@@ -0,0 +1,160 @@
+from .utils import get_callback, unpatch_model
+import sys
+
+import logging
+import gc
+import comfy.model_management
+import os
+
+log = logging.getLogger("comfyui-prompt-control-legacy")
+
+
+def has_hijack(obj):
+    return hasattr(obj, "pc_hijack_done")
+
+
+def hijack(obj, attr, replacement):
+    setattr(obj, attr, replacement)
+    setattr(replacement, "pc_hijack_done", True)
+
+
+def hijack_sampler(module, function, is_custom):
+    mod = sys.modules[module]
+    orig_sampler = getattr(mod, function)
+    if has_hijack(orig_sampler):
+        return
+
+    from comfy.k_diffusion.sampling import BrownianTreeNoiseSampler
+
+    def pc_sample(*args, **kwargs):
+        model = args[0]
+        cb = get_callback(model)
+        BrownianTreeNoiseSampler.pc_reset(
+            model.model_options.get("pc_split_sampling"),
+            kwargs.get("force_full_denoise") or kwargs.get("denoise", 1.0) >= 1.0,
+        )
+        if cb:
+            try:
+                try:
+                    r = cb(orig_sampler, is_custom, *args, **kwargs)
+                except comfy.model_management.OOM_EXCEPTION:
+                    if not os.environ.get("PC_RETRY_ON_OOM"):
+                        raise
+                    log.error("Got OOM while sampling, freeing memory and retrying once...")
+                    unpatch_model(model)
+                    BrownianTreeNoiseSampler.pc_reset(False)
+                    gc.collect()
+                    comfy.model_management.soft_empty_cache()
+                    r = cb(orig_sampler, is_custom, *args, **kwargs)
+            except Exception:
+                log.error("Exception occurred during callback, unpatching model.")
+                unpatch_model(model)
+                BrownianTreeNoiseSampler.pc_reset(False)
+                raise
+        else:
+            r = orig_sampler(*args, **kwargs)
+        BrownianTreeNoiseSampler.pc_reset()
+        return r
+
+    hijack(mod, function, pc_sample)
+
+
+def hijack_ksampler(module, cls):
+    mod = sys.modules[module]
+    orig_sampler = getattr(mod, cls)
+    if has_hijack(orig_sampler):
+        return
+
+    class HijackedKSampler(orig_sampler):
+        def sample(
+            self,
+            noise,
+            positive,
+            negative,
+            cfg,
+            latent_image=None,
+            start_step=None,
+            last_step=None,
+            force_full_denoise=False,
+            denoise_mask=None,
+            sigmas=None,
+            callback=None,
+            disable_pbar=False,
+            seed=None,
+        ):
+            if sigmas is None:
+                sigmas = self.sigmas
+
+            from comfy.k_diffusion.sampling import BrownianTreeNoiseSampler
+
+            BrownianTreeNoiseSampler.set_global_sigmas(self.sigmas)
+
+            return super().sample(
+                noise,
+                positive,
+                negative,
+                cfg,
+                latent_image,
+                start_step,
+                last_step,
+                force_full_denoise,
+                denoise_mask,
+                sigmas,
+                callback,
+                disable_pbar,
+                seed,
+            )
+
+    hijack(mod, cls, HijackedKSampler)
+
+
+def hijack_browniannoisesampler(module, cls):
+    mod = sys.modules[module]
+    orig_sampler = getattr(mod, cls)
+    if has_hijack(orig_sampler):
+        return
+
+    class PCBrownianTreeNoiseSampler(orig_sampler):
+        global_instance = None
+        use_global_sigmas = False
+        global_sigmas = None
+        force_full_denoise = False
+
+        @classmethod
+        def pc_reset(cls, use_global_sigmas=False, force_full_denoise=False):
+            cls.global_instance = None
+            cls.global_sigmas = None
+            cls.use_global_sigmas = use_global_sigmas
+            cls.force_full_denoise = force_full_denoise
+
+        @classmethod
+        def set_global_sigmas(cls, sigmas):
+            if cls.global_sigmas is None and cls.use_global_sigmas:
+                cls.global_sigmas = (0 if cls.force_full_denoise else sigmas[sigmas > 0].min(), sigmas.max())
+                log.info(
+                    "Initializing BrownianTreeNoiseSampler instance with global sigmas %s, %s",
+                    cls.global_sigmas,
+                    cls.force_full_denoise,
+                )
+
+        def __init__(self, x, sigma_min, sigma_max, **kwargs):
+            if self.global_sigmas is not None:
+                sigma_min, sigma_max = self.global_sigmas
+            if not self.global_instance:
+                super().__init__(x, sigma_min, sigma_max, **kwargs)
+                PCBrownianTreeNoiseSampler.global_instance = self
+
+        def __call__(self, *args, **kwargs):
+            if self.global_instance and self != self.global_instance:
+                return self.global_instance(*args, **kwargs)
+            else:
+                return super().__call__(*args, **kwargs)
+
+    hijack(mod, cls, PCBrownianTreeNoiseSampler)
+
+
+def do_hijack():
+    hijack_browniannoisesampler("comfy.k_diffusion.sampling", "BrownianTreeNoiseSampler")
+    hijack_sampler("comfy.sample", "sample", False)
+    hijack_sampler("comfy.sample", "sample_custom", True)
+    hijack_ksampler("comfy.samplers", "KSampler")

prompt_control/legacy/node_aio.py

@@ -0,0 +1,49 @@
+from .node_clip import control_to_clip_common
+from .node_lora import schedule_lora_common
+from ..parser import parse_prompt_schedules
+
+
+class PromptControlSimple:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "clip": ("CLIP",),
+                "positive": ("STRING", {"multiline": True}),
+                "negative": ("STRING", {"multiline": True}),
+            },
+            "optional": {
+                "tags": ("STRING", {"default": ""}),
+                "start": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.1, "default": 0.0}),
+                "end": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.1, "default": 1.0}),
+            },
+        }
+
+    DEPRECATED = True
+    RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "MODEL", "CONDITIONING", "CONDITIONING")
+    RETURN_NAMES = ("model", "positive", "negative", "model_filtered", "pos_filtered", "neg_filtered")
+    CATEGORY = "promptcontrol/_legacy"
+    FUNCTION = "apply"
+
+    def apply(self, model, clip, positive, negative, tags="", start=0.0, end=1.0):
+        lora_cache = {}
+        cond_cache = {}
+        pos_sched = parse_prompt_schedules(positive)
+        pos_cond = pos_filtered = control_to_clip_common(clip, pos_sched, lora_cache, cond_cache)
+
+        neg_sched = parse_prompt_schedules(negative)
+        neg_cond = neg_filtered = control_to_clip_common(clip, neg_sched, lora_cache, cond_cache)
+
+        new_model = model_filtered = schedule_lora_common(model, pos_sched, lora_cache)
+
+        if [tags.strip(), start, end] != ["", 0.0, 1.0]:
+            pos_filtered = control_to_clip_common(
+                clip, pos_sched.with_filters(tags, start, end), lora_cache, cond_cache
+            )
+            neg_filtered = control_to_clip_common(
+                clip, neg_sched.with_filters(tags, start, end), lora_cache, cond_cache
+            )
+            model_filtered = schedule_lora_common(model, pos_sched.with_filters(tags, start, end), lora_cache)
+
+        return (new_model, pos_cond, neg_cond, model_filtered, pos_filtered, neg_filtered)

prompt_control/legacy/node_clip.py

@@ -0,0 +1,703 @@
+import logging
+import re
+import torch
+from ..parser import parse_prompt_schedules, parse_cuts
+from .utils import Timer, equalize, apply_loras_from_spec
+from ..utils import safe_float, get_function, parse_floats  # non-legacy
+from .perp_weight import perp_encode
+from comfy_extras.nodes_mask import FeatherMask, MaskComposite
+from node_helpers import conditioning_set_values
+
+
+log = logging.getLogger("comfyui-prompt-control-legacy")
+
+try:
+    from custom_nodes.ComfyUI_ADV_CLIP_emb.adv_encode import (
+        advanced_encode_from_tokens,
+        encode_token_weights_l,
+        encode_token_weights_g,
+        prepareXL,
+        encode_token_weights,
+    )
+
+    have_advanced_encode = True
+    AVAILABLE_STYLES = ["comfy", "A1111", "compel", "comfy++", "down_weight"]
+    AVAILABLE_NORMALIZATIONS = ["none", "mean", "length", "length+mean"]
+except ImportError:
+    have_advanced_encode = False
+    AVAILABLE_STYLES = ["comfy"]
+    AVAILABLE_NORMALIZATIONS = ["none"]
+
+try:
+    from custom_nodes.Vector_Sculptor_ComfyUI.nodes import vector_sculptor_tokens
+
+    can_sculpt = True
+    log.info("Vector sculptor extension detected, can use SCULPT()")
+except ImportError:
+    can_sculpt = False
+
+
+AVAILABLE_STYLES.append("perp")
+log.info("Use STYLE(weight_interpretation, normalization) at the start of a prompt to use advanced encodings")
+log.info("Weight interpretations available: %s", ",".join(AVAILABLE_STYLES))
+log.info("Normalization types available: %s", ",".join(AVAILABLE_NORMALIZATIONS))
+
+
+def linear_interpolate_cond(
+    start, end, from_step=0.0, to_step=1.0, step=0.1, start_at=None, end_at=None, prompt_start="N/A", prompt_end="N/A"
+):
+    count = min(len(start), len(end))
+    if len(start) != len(end):
+        log.info(
+            "Length of conds to interpolate does not match (start=%s != end=%s), interpolating up to %s.",
+            len(start),
+            len(end),
+            count,
+        )
+
+    all_res = []
+    for idx in range(count):
+        res = []
+        from_cond, to_cond = equalize(start[idx][0], end[idx][0])
+        from_pooled = start[idx][1].get("pooled_output")
+        to_pooled = end[idx][1].get("pooled_output")
+        start_at = start_at if start_at is not None else from_step
+        end_at = end_at if end_at is not None else to_step
+        total_steps = int(round((to_step - from_step) / step, 0))
+        num_steps = int(round((end_at - from_step) / step, 0))
+        start_on = int(round((start_at - from_step) / step, 0))
+        start_pct = start_at
+        log.debug(
+            f"interpolate_cond {idx=} {from_step=} {to_step=} {start_at=} {end_at=} {total_steps=} {num_steps=} {start_on=} {step=}"
+        )
+        x = 1 / (total_steps + 1)
+        for s in range(start_on, num_steps):
+            factor = round((s + 1) * x, 2)
+            new_cond = from_cond + (to_cond - from_cond) * factor
+            if from_pooled is not None and to_pooled is not None:
+                from_pooled, to_pooled = equalize(from_pooled, to_pooled)
+                new_pooled = from_pooled + (to_pooled - from_pooled) * factor
+            elif from_pooled is not None:
+                new_pooled = from_pooled
+
+            n = [new_cond, start[idx][1].copy()]
+            if new_pooled is not None:
+                n[1]["pooled_output"] = new_pooled
+            n[1]["start_percent"] = round(start_pct, 2)
+            n[1]["end_percent"] = min(round((start_pct + step), 2), 1.0)
+            start_pct += step
+            start_pct = round(start_pct, 2)
+            if prompt_start:
+                n[1]["prompt"] = f"linear:{round(1.0 - factor, 2)} / {factor}"
+            log.debug(
+                "Interpolating at step %s with factor %s (%s, %s)...",
+                s,
+                factor,
+                n[1]["start_percent"],
+                n[1]["end_percent"],
+            )
+            res.append(n)
+        if res:
+            res[-1][1]["end_percent"] = round(end_at, 2)
+            all_res.extend(res)
+    return all_res
+
+
+def get_control_points(schedule, steps, encoder):
+    assert len(steps) > 1
+    new_steps = set(steps)
+
+    for step in (s[0] for s in schedule if s[0] >= steps[0] and s[0] <= steps[-1]):
+        new_steps.add(step)
+    control_points = [(s, encoder(schedule.at_step(s)[1])) for s in new_steps]
+    log.debug("Actual control points for interpolation: %s (from %s)", new_steps, steps)
+    return sorted(control_points, key=lambda x: x[0])
+
+
+def linear_interpolator(control_points, step, start_pct, end_pct):
+    o_start, start = control_points[0]
+    o_end, _ = control_points[-1]
+    t_start = o_start
+    conds = []
+    for t_end, end in control_points[1:]:
+        if t_start < start_pct:
+            t_start, start = t_end, end
+            continue
+        if t_start >= end_pct:
+            break
+        cs = linear_interpolate_cond(start, end, o_start, o_end, step, start_at=t_start, end_at=end_pct)
+        if cs:
+            conds.extend(cs)
+        else:
+            break
+        t_start = t_end
+        start = end
+    return conds
+
+
+class ScheduleToCond:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {"clip": ("CLIP",), "prompt_schedule": ("PROMPT_SCHEDULE",)},
+        }
+
+    DEPRECATED = True
+    RETURN_TYPES = ("CONDITIONING",)
+    CATEGORY = "promptcontrol/_legacy"
+    FUNCTION = "apply"
+
+    def apply(self, clip, prompt_schedule):
+        with Timer("ScheduleToCond"):
+            r = (control_to_clip_common(clip, prompt_schedule),)
+        return r
+
+
+class EditableCLIPEncode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "clip": ("CLIP",),
+                "text": ("STRING", {"multiline": True}),
+            },
+            "optional": {"filter_tags": ("STRING", {"default": ""})},
+        }
+
+    DEPRECATED = True
+    RETURN_TYPES = ("CONDITIONING",)
+    CATEGORY = "promptcontrol/_legacy"
+    FUNCTION = "parse"
+
+    def parse(self, clip, text, filter_tags=""):
+        parsed = parse_prompt_schedules(text).with_filters(filter_tags)
+        return (control_to_clip_common(clip, parsed),)
+
+
+def get_sdxl(text, defaults):
+    # Defaults fail to parse and get looked up from the defaults dict
+    text, sdxl = get_function(text, "SDXL", ["none", "none", "none"])
+    if not sdxl:
+        return text, {}
+    args = sdxl[0]
+    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+")
+    cropw, croph = parse_floats(args[2], [d.get("sdxl_cwidth", 0), d.get("sdxl_cheight", 0)], split_re="\\s+")
+
+    opts = {
+        "width": int(w),
+        "height": int(h),
+        "target_width": int(tw),
+        "target_height": int(th),
+        "crop_w": int(cropw),
+        "crop_h": int(croph),
+    }
+    return text, opts
+
+
+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 = style.strip()
+    normalization = normalization.strip()
+    if style not in AVAILABLE_STYLES:
+        log.warning("Unrecognized prompt style: %s. Using %s", style, default_style)
+        style = default_style
+
+    if normalization not in AVAILABLE_NORMALIZATIONS:
+        log.warning("Unrecognized prompt normalization: %s. Using %s", normalization, default_normalization)
+        normalization = default_normalization
+
+    return style, normalization, text
+
+
+def encode_regions(clip, tokens, regions, weight_interpretation="comfy", token_normalization="none"):
+    from custom_nodes.ComfyUI_Cutoff.cutoff import CLIPSetRegion, finalize_clip_regions
+
+    clip_regions = {
+        "clip": clip,
+        "base_tokens": tokens,
+        "regions": [],
+        "targets": [],
+        "weights": [],
+    }
+
+    strict_mask = 1.0
+    start_from_masked = 1.0
+    mask_token = ""
+
+    for region in regions:
+        region_text, target_text, w, sm, sfm, mt = region
+        if w is not None:
+            w = safe_float(w, 0)
+        else:
+            w = 1.0
+        if sm is not None:
+            strict_mask = safe_float(sm, 1.0)
+        if sfm is not None:
+            start_from_masked = safe_float(sfm, 1.0)
+        if mt is not None:
+            mask_token = mt
+        log.info("Region: text %s, target %s, weight %s", region_text.strip(), target_text.strip(), w)
+        (clip_regions,) = CLIPSetRegion.add_clip_region(None, clip_regions, region_text, target_text, w)
+    log.info("Regions: mask_token=%s strict_mask=%s start_from_masked=%s", mask_token, strict_mask, start_from_masked)
+
+    (r,) = finalize_clip_regions(
+        clip_regions, mask_token, strict_mask, start_from_masked, token_normalization, weight_interpretation
+    )
+    cond, pooled = r[0][0], r[0][1].get("pooled_output")
+    return cond, pooled
+
+
+SHUFFLE_GEN = torch.Generator(device="cpu")
+
+
+def shuffle_chunk(shuffle, c):
+    func, shuffle = shuffle
+    shuffle_count = int(safe_float(shuffle[0], 0))
+    _, separator, joiner = shuffle
+    if separator == "default":
+        separator = ","
+
+    if not separator:
+        separator = ","
+
+    joiner = {
+        "default": ",",
+        "separator": separator,
+    }.get(joiner, joiner)
+
+    log.info("%s arg=%s sep=%s join=%s", func, shuffle_count, separator, joiner)
+    separated = c.split(separator)
+    if func == "SHIFT":
+        shuffle_count = shuffle_count % len(separated)
+        permutation = separated[shuffle_count:] + separated[:shuffle_count]
+    elif func == "SHUFFLE":
+        SHUFFLE_GEN.manual_seed(shuffle_count)
+        permutation = [separated[i] for i in torch.randperm(len(separated), generator=SHUFFLE_GEN)]
+    else:
+        # ??? should never get here
+        permutation = separated
+
+    permutation = [p for p in permutation if p.strip()]
+    if permutation != separated:
+        c = joiner.join(permutation)
+    return c
+
+
+def fix_word_ids(tokens):
+    """Fix word indexes. Tokenizing separately (when BREAKs exist) causes the indexes to restart which causes problems with some weighting algorithms that rely on them"""
+    for key in tokens:
+        max_idx = 0
+        for group in range(len(tokens[key])):
+            for i, token in enumerate(tokens[key][group]):
+                if len(token) < 3:
+                    # No need to fix ids when they don't exist
+                    return tokens
+                # Ignore zeros, they represent the padding token
+                if token[2] != 0 and token[2] < max_idx:
+                    tokens[key][group][i] = (token[0], token[1], token[2] + max_idx)
+            max_idx = max(max_idx, max(x for _, _, x in tokens[key][group]))
+    return tokens
+
+
+def encode_prompt(clip, text, default_style="comfy", default_normalization="none"):
+    style, normalization, text = get_style(text, default_style, default_normalization)
+    sculpts = []
+    if can_sculpt:
+        text, sculpts = get_function(text, "SCULPT", ["1.0", "forward", "none"])
+    text, regions = parse_cuts(text)
+    # defaults=None means there is no argument parsing at all
+    text, l_prompts = get_function(text, "CLIP_L", defaults=None)
+    chunks = re.split(r"\bBREAK\b", text)
+    token_chunks = []
+    need_word_ids = len(regions) > 0 or (have_advanced_encode and style != "perp")
+    for c in chunks:
+        c, shuffles = get_function(c.strip(), "(SHIFT|SHUFFLE)", ["0", "default", "default"], return_func_name=True)
+        r = c
+        for s in shuffles:
+            r = shuffle_chunk(s, r)
+        if r != c:
+            log.info("Shuffled prompt chunk to %s", r)
+            c = r
+        if sculpts:
+            w, method, norm = sculpts[0]
+            log.info("Using vector sculptor with method=%s norm=%s w=%s", method, norm, w)
+            w = safe_float(w, 1.0)
+            t = vector_sculptor_tokens(clip, c, method, norm, w)
+        else:
+            # Tokenizer returns padded results
+            t = clip.tokenize(c, return_word_ids=need_word_ids)
+        token_chunks.append(t)
+    tokens = token_chunks[0]
+
+    for key in tokens:
+        for c in token_chunks[1:]:
+            tokens[key].extend(c[key])
+
+    # Non-SDXL has only "l"
+    if "g" in tokens and l_prompts:
+        text_l = " ".join(l_prompts)
+        log.info("Encoded SDXL CLIP_L prompt: %s", text_l)
+        tokens["l"] = clip.tokenize(text_l, return_word_ids=need_word_ids)["l"]
+
+    if "g" in tokens and "l" in tokens and len(tokens["l"]) != len(tokens["g"]):
+        empty = clip.tokenize("", return_word_ids=need_word_ids)
+        while len(tokens["l"]) < len(tokens["g"]):
+            tokens["l"] += empty["l"]
+        while len(tokens["l"]) > len(tokens["g"]):
+            tokens["g"] += empty["g"]
+
+    tokens = fix_word_ids(tokens)
+
+    if len(regions) > 0:
+        return encode_regions(clip, tokens, regions, style, normalization)
+
+    if style == "perp":
+        if normalization != "none":
+            log.warning("Normalization is not supported with perp style weighting. Ignored '%s'", normalization)
+        return perp_encode(clip, tokens)
+
+    if "t5xxl" not in tokens and have_advanced_encode and not sculpts:
+        if "g" in tokens:
+            embs_l = None
+            embs_g = None
+            pooled = None
+            if "l" in tokens:
+                embs_l, _ = advanced_encode_from_tokens(
+                    tokens["l"],
+                    normalization,
+                    style,
+                    lambda x: encode_token_weights(clip, x, encode_token_weights_l),
+                    return_pooled=False,
+                )
+            if "g" in tokens:
+                embs_g, pooled = advanced_encode_from_tokens(
+                    tokens["g"],
+                    normalization,
+                    style,
+                    lambda x: encode_token_weights(clip, x, encode_token_weights_g),
+                    return_pooled=True,
+                    apply_to_pooled=False,
+                )
+            # Hardcoded clip_balance
+            return prepareXL(embs_l, embs_g, pooled, 0.5)
+        return advanced_encode_from_tokens(
+            tokens["l"],
+            normalization,
+            style,
+            lambda x: clip.encode_from_tokens({"l": x}, return_pooled=True),
+            return_pooled=True,
+            apply_to_pooled=True,
+        )
+    else:
+        return clip.encode_from_tokens(tokens, return_pooled=True)
+
+
+def get_area(text):
+    text, areas = get_function(text, "AREA", ["0 1", "0 1", "1"])
+    if not areas:
+        return text, None
+
+    args = areas[0]
+    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)
+
+    def is_pct(f):
+        return f >= 0.0 and f <= 1.0
+
+    def is_pixel(f):
+        return f == 0 or f > 1
+
+    if all(is_pct(v) for v in [h, w, y, x]):
+        area = ("percentage", h, w, y, x)
+    elif all(is_pixel(v) for v in [h, w, y, x]):
+        area = (int(h) // 8, int(w) // 8, int(y) // 8, int(x) // 8)
+    else:
+        raise Exception(
+            f"AREA specified with invalid size {x} {w}, {h} {y}. They must either all be percentages between 0 and 1 or positive integer pixel values excluding 1"
+        )
+
+    return text, (area, weight)
+
+
+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]
+    return text, (int(w), int(h))
+
+
+def make_mask(args, size, weight):
+    x1, x2 = parse_floats(args[0], [0.0, 1.0], split_re="\\s+")
+    y1, y2 = parse_floats(args[1], [0.0, 1.0], split_re="\\s+")
+
+    def is_pct(f):
+        return f >= 0.0 and f <= 1.0
+
+    def is_pixel(f):
+        return f == 0 or f > 1
+
+    if all(is_pct(v) for v in [x1, x2, y1, y2]):
+        w, h = size
+        xs = int(w * x1), int(w * x2)
+        ys = int(h * y1), int(h * y2)
+    elif all(is_pixel(v) for v in [x1, x2, y1, y2]):
+        w, h = size
+        xs = int(x1), int(x2)
+        ys = int(y1), int(y2)
+    else:
+        raise Exception(
+            f"MASK specified with invalid size {x1} {x2}, {y1} {y2}. They must either all be percentages between 0 and 1 or positive integer pixel values excluding 1"
+        )
+
+    mask = torch.full((h, w), 0, dtype=torch.float32, device="cpu")
+    mask[ys[0] : ys[1], xs[0] : xs[1]] = weight
+    mask = mask.unsqueeze(0)
+    log.info("Mask xs=%s, ys=%s, shape=%s, weight=%s", xs, ys, mask.shape, weight)
+    return mask
+
+
+def get_mask(text, size, input_masks):
+    """Parse MASK(x1 x2, y1 y2, weight), IMASK(i, weight) and FEATHER(left top right bottom)"""
+    # TODO: combine multiple masks
+    text, masks = get_function(text, "MASK", ["0 1", "0 1", "1", "multiply"])
+    text, imasks = get_function(text, "IMASK", ["0", "1", "multiply"])
+    text, feathers = get_function(text, "FEATHER", ["0 0 0 0"])
+    text, maskw = get_function(text, "MASKW", ["1.0"])
+    if not masks and not imasks:
+        return text, None, None
+
+    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]
+        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)
+    i = 0
+    for m in masks:
+        weight = safe_float(m[2], 1.0)
+        op = m[3]
+        nextmask = make_mask(m, size, weight)
+        if i < len(feathers):
+            nextmask = feather(feathers[i], nextmask)
+        i += 1
+        if mask is not None:
+            log.info("MaskComposite op=%s", op)
+            mask = MaskComposite().combine(mask, nextmask, 0, 0, op)[0]
+        else:
+            mask = nextmask
+
+    for idx, w, op in imasks:
+        idx = int(safe_float(idx, 0.0))
+        w = safe_float(w, 1.0)
+        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)
+        i += 1
+        if mask is not None:
+            mask = MaskComposite().combine(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)
+
+    return text, mask, totalweight
+
+
+def get_noise(text):
+    text, noises = get_function(
+        text,
+        "NOISE",
+        ["0.0", "none"],
+    )
+    if not noises:
+        return text, None, None
+    w = 0
+    # Only take seed from first noise spec, for simplicity
+    seed = safe_float(noises[0][1], "none")
+    if seed == "none":
+        gen = None
+    else:
+        gen = torch.Generator()
+        gen.manual_seed(int(seed))
+    for n in noises:
+        w += safe_float(n[0], 0.0)
+    return text, max(min(w, 1.0), 0.0), gen
+
+
+def apply_noise(cond, weight, gen):
+    if cond is None or not weight:
+        return cond
+
+    n = torch.randn(cond.size(), generator=gen).to(cond)
+
+    return cond * (1 - weight) + n * weight
+
+
+def do_encode(clip, text, defaults, masks):
+    # First style modifier applies to ANDed prompts too unless overridden
+    style, normalization, text = get_style(text)
+    text, mask_size = get_mask_size(text, defaults)
+
+    # Don't sum ANDs if this is in prompt
+    alt_method = "COMFYAND()" in text
+    text = text.replace("COMFYAND()", "")
+
+    prompts = [p.strip() for p in re.split(r"\bAND\b", text)]
+
+    p, sdxl_opts = get_sdxl(prompts[0], defaults)
+    prompts[0] = p
+
+    def weight(t):
+        opts = {}
+        m = re.search(r":(-?\d\.?\d*)(![A-Za-z]+)?$", t)
+        if not m:
+            return (1.0, opts, t)
+        w = float(m[1])
+        tag = m[2]
+        t = t[: m.span()[0]]
+        if tag == "!noscale":
+            opts["scale"] = 1
+
+        return w, opts, t
+
+    conds = []
+    res = []
+    scale = sum(abs(weight(p)[0]) for p in prompts if not ("AREA(" in p or "MASK(" in p))
+    for prompt in prompts:
+        prompt, mask, mask_weight = get_mask(prompt, mask_size, masks)
+        w, opts, prompt = weight(prompt)
+        text, noise_w, generator = get_noise(text)
+        if not w:
+            continue
+        prompt, area = get_area(prompt)
+        prompt, local_sdxl_opts = get_sdxl(prompt, defaults)
+        cond, pooled = encode_prompt(clip, prompt, style, normalization)
+        cond = apply_noise(cond, noise_w, generator)
+        pooled = apply_noise(pooled, noise_w, generator)
+
+        settings = {"prompt": prompt}
+        if alt_method:
+            settings["strength"] = w
+        settings.update(sdxl_opts)
+        settings.update(local_sdxl_opts)
+        if area:
+            settings["area"] = area[0]
+            settings["strength"] = area[1]
+            settings["set_area_to_bounds"] = False
+        if mask is not None:
+            settings["mask"] = mask
+            settings["mask_strength"] = mask_weight
+
+        if mask is not None or area or alt_method or local_sdxl_opts:
+            if pooled is not None:
+                settings["pooled_output"] = pooled
+            conds.append([cond, settings])
+        else:
+            s = opts.get("scale", scale)
+            res.append((cond, pooled, w / s))
+
+    sumconds = [r[0] * r[2] for r in res]
+    pooleds = [r[1] for r in res if r[1] is not None]
+
+    if len(res) > 0:
+        opts = sdxl_opts
+        if pooleds:
+            opts["pooled_output"] = sum(equalize(*pooleds))
+        sumcond = sum(equalize(*sumconds))
+        conds.append([sumcond, opts])
+    return conds
+
+
+def debug_conds(conds):
+    r = []
+    for i, c in enumerate(conds):
+        x = c[1].copy()
+        if "pooled_output" in x:
+            del x["pooled_output"]
+        r.append((i, x))
+    return r
+
+
+def control_to_clip_common(clip, schedules, lora_cache=None, cond_cache=None):
+    orig_clip = clip.clone()
+    current_loras = {}
+    if lora_cache is None:
+        lora_cache = {}
+    start_pct = 0.0
+    conds = []
+    cond_cache = cond_cache if cond_cache is not None else {}
+
+    def c_str(c):
+        r = [c["prompt"]]
+        loras = c["loras"]
+        for k in sorted(loras.keys()):
+            r.append(k)
+            r.append(loras[k]["weight_clip"])
+            for lbw, val in loras[k].get("lbw", {}).items():
+                r.append(lbw)
+                r.append(val)
+        return "".join(str(i) for i in r)
+
+    def encode(c):
+        nonlocal clip
+        nonlocal current_loras
+        prompt = c["prompt"]
+        loras = c["loras"]
+        cachekey = c_str(c)
+        cond = cond_cache.get(cachekey)
+        if cond is None:
+            if loras != current_loras:
+                _, clip = apply_loras_from_spec(loras, clip=orig_clip, cache=lora_cache, applied_loras=current_loras)
+                current_loras = loras
+            cond_cache[cachekey] = do_encode(clip, prompt, schedules.defaults, schedules.masks)
+        return cond_cache[cachekey]
+
+    for end_pct, c in schedules:
+        interpolations = [
+            i
+            for i in schedules.interpolations
+            if (start_pct >= i[0][0] and start_pct < i[0][-1]) or (end_pct > i[0][0] and start_pct < i[0][-1])
+        ]
+        new_start_pct = start_pct
+        if interpolations:
+            min_step = min(i[1] for i in interpolations)
+            for i in interpolations:
+                control_points, _ = i
+                interpolation_end_pct = min(control_points[-1], end_pct)
+                interpolation_start_pct = max(control_points[0], start_pct)
+
+                control_points = get_control_points(schedules, control_points, encode)
+                cs = linear_interpolator(control_points, min_step, interpolation_start_pct, interpolation_end_pct)
+                conds.extend(cs)
+                new_start_pct = max(new_start_pct, interpolation_end_pct)
+        start_pct = new_start_pct
+
+        if start_pct < end_pct:
+            cond = encode(c)
+            # Node functions return lists of cond
+            cond = conditioning_set_values(
+                cond, {"start_percent": round(start_pct, 2), "end_percent": round(end_pct, 2), "prompt": c["prompt"]}
+            )
+            conds.extend(cond)
+
+        start_pct = end_pct
+        log.debug("Conds at the end: %s", debug_conds(conds))
+
+    log.debug("Final cond info: %s", debug_conds(conds))
+    return conds

prompt_control/legacy/node_lora.py

@@ -0,0 +1,251 @@
+import logging
+import torch
+
+from .utils import unpatch_model, clone_model, set_callback, apply_loras_from_spec
+from ..parser import parse_prompt_schedules
+from .hijack import do_hijack
+from comfy.samplers import CFGGuider
+
+log = logging.getLogger("comfyui-prompt-control-legacy")
+
+
+def apply_lora_for_step(schedules, step, total_steps, state, original_model, lora_cache, patch=True):
+    # zero-indexed steps, 0 = first step, but schedules are 1-indexed
+    sched = schedules.at_step(step + 1, total_steps)
+    lora_spec = sched[1]["loras"]
+
+    if state["applied_loras"] != lora_spec:
+        log.debug("At step %s, applying lora_spec %s", step, lora_spec)
+        m, _ = apply_loras_from_spec(
+            lora_spec,
+            model=state["model"],
+            orig_model=original_model,
+            cache=lora_cache,
+            patch=patch,
+            applied_loras=state["applied_loras"],
+        )
+        state["model"] = m
+        state["applied_loras"] = lora_spec
+
+
+def schedule_lora_common(model, schedules, lora_cache=None):
+    do_hijack()
+    orig_model = clone_model(model)
+    orig_model.model_options["pc_schedules"] = schedules
+
+    if lora_cache is None:
+        lora_cache = {}
+
+    def sampler_cb(orig_sampler, is_custom, *args, **kwargs):
+        split_sampling = args[0].model_options.get("pc_split_sampling")
+        state = {}
+        if is_custom:
+            steps = len(args[4])
+            log.info(
+                "SamplerCustom detected, number of steps not available. LoRA schedules will be calculated based on the number of sigmas (%s)",
+                steps,
+            )
+        else:
+            log.debug("Normal sampler detected, using steps from parameter")
+            steps = args[2]
+        start_step = kwargs.get("start_step") or 0
+        # The model patcher may change if LoRAs are applied
+        state["model"] = args[0]
+        state["applied_loras"] = {}
+
+        orig_cb = kwargs["callback"]
+
+        def step_callback(*args, **kwargs):
+            current_step = args[0] + start_step
+            apply_lora_for_step(schedules, current_step, steps, state, orig_model, lora_cache, patch=True)
+            if orig_cb:
+                return orig_cb(*args, **kwargs)
+
+        kwargs["callback"] = step_callback
+
+        apply_lora_for_step(schedules, start_step, steps, state, orig_model, lora_cache, patch=True)
+
+        def filter_conds(conds, t, start_t, end_t):
+            r = []
+            for c in conds:
+                x = c[1].copy()
+                start_at = round(x["start_percent"], 2)
+                end_at = round(x["end_percent"], 2)
+                # Take any cond that has any effect before end_t, since the percentages may not perfectly match
+                if end_t > start_at and end_t <= end_at:
+                    del x["start_percent"]
+                    del x["end_percent"]
+                    r.append([c[0].clone(), x])
+                else:
+                    log.debug("Rejecting cond (%s, %s) between (%s, %s)", start_at, end_at, start_t, end_t)
+            if len(r) == 0:
+                log.error("No %s conds between (%s, %s); Try adjusting your steps", t, start_t, end_t)
+            return r
+
+        def get_steps(conds):
+            for c in conds:
+                yield round(c[1].get("end_percent", 0), 2)
+
+        if split_sampling:
+            actual_end_step = kwargs["last_step"] or steps
+            first_step = True
+            s = args[8]
+            all_steps = sorted(set(int(steps * i) for i in [1.0] + list(get_steps(args[6])) + list(get_steps(args[7]))))
+            for end_step in all_steps:
+                if end_step <= start_step:
+                    continue
+                start_t = round(start_step / steps, 2)
+                end_t = round(end_step / steps, 2)
+                new_kwargs = kwargs.copy()
+                new_args = list(args)
+                new_args[0] = state["model"]
+                new_args[6] = filter_conds(new_args[6], "positive", start_t, end_t)
+                new_args[7] = filter_conds(new_args[7], "negative", start_t, end_t)
+                new_args[8] = s
+                log.info("Sampling from %s to %s (total: %s)", start_step, end_step, actual_end_step)
+                new_kwargs["start_step"] = start_step
+                new_kwargs["last_step"] = end_step
+                if end_step >= min(steps, actual_end_step):
+                    new_kwargs["force_full_denoise"] = kwargs["force_full_denoise"]
+                else:
+                    new_kwargs["force_full_denoise"] = False
+
+                if not first_step:
+                    # disable_noise apparently does nothing currently, we need to override noise in args
+                    new_kwargs["disable_noise"] = True
+                    new_args[1] = torch.zeros_like(s)
+
+                s = orig_sampler(*new_args, **new_kwargs)
+                start_step = end_step
+                first_step = False
+        else:
+            args = list(args)
+            args[0] = state["model"]
+            s = orig_sampler(*args, **kwargs)
+
+        unpatch_model(state["model"])
+
+        return s
+
+    set_callback(orig_model, sampler_cb)
+
+    return orig_model
+
+
+class PCWrapGuider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "guider": ("GUIDER",),
+            },
+        }
+
+    DEPRECATED = True
+    CATEGORY = "promptcontrol/_legacy"
+    FUNCTION = "apply"
+    RETURN_TYPES = ("GUIDER",)
+
+    def apply(self, guider):
+        return (PCGuider(guider),)
+
+
+class PCGuider(CFGGuider):
+    def __init__(self, original_guider):
+        if "pc_schedules" not in original_guider.model_patcher.model_options:
+            raise ValueError(
+                "The guider passed to PCWrapGuider must contain a Model that has schedules applied. Use ScheduleToModel"
+            )
+        self.schedules = original_guider.model_patcher.model_options["pc_schedules"]
+        self.guider = original_guider
+        self.lora_cache = {}
+        # sets self.model_patcher
+        super().__init__(original_guider.model_patcher)
+
+    def sample(self, *args, **kwargs):
+        orig_cb = kwargs["callback"]
+        sigmas = args[3]
+        state = {"model": self.guider.model_patcher, "applied_loras": {}}
+
+        def step_callback(*args, **kwargs):
+            apply_lora_for_step(
+                self.schedules,
+                args[0],
+                len(sigmas),
+                state,
+                self.guider.model_patcher,
+                self.lora_cache,
+                patch=True,
+            )
+            if orig_cb:
+                return orig_cb(*args, **kwargs)
+
+        kwargs["callback"] = step_callback
+        apply_lora_for_step(
+            self.schedules, 0, len(sigmas), state, self.guider.model_patcher, self.lora_cache, patch=True
+        )
+        try:
+            r = self.guider.sample(*args, **kwargs)
+        finally:
+            unpatch_model(state["model"])
+        return r
+
+
+class ScheduleToModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "prompt_schedule": ("PROMPT_SCHEDULE",),
+            },
+        }
+
+    DEPRECATED = True
+    RETURN_TYPES = ("MODEL",)
+    CATEGORY = "promptcontrol/_legacy"
+    FUNCTION = "apply"
+
+    def apply(self, model, prompt_schedule):
+        return (schedule_lora_common(model, prompt_schedule),)
+
+
+class PCSplitSampling:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "split_sampling": (["enable", "disable"],),
+            },
+        }
+
+    DEPRECATED = True
+    RETURN_TYPES = ("MODEL",)
+    CATEGORY = "promptcontrol/_legacy"
+    FUNCTION = "apply"
+
+    def apply(self, model, split_sampling):
+        model = clone_model(model)
+        model.model_options["pc_split_sampling"] = split_sampling == "enable"
+        return (model,)
+
+
+class LoRAScheduler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "text": ("STRING", {"multiline": True}),
+            },
+        }
+
+    DEPRECATED = True
+    RETURN_TYPES = ("MODEL",)
+    CATEGORY = "promptcontrol/_legacy"
+    FUNCTION = "apply"
+
+    def apply(self, model, text):
+        schedules = parse_prompt_schedules(text)
+        return (schedule_lora_common(model, schedules),)

prompt_control/legacy/node_other.py

@@ -0,0 +1,159 @@
+import logging
+from ..parser import parse_prompt_schedules
+
+log = logging.getLogger("comfyui-prompt-control-legacy")
+
+
+class FilterSchedule:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {"prompt_schedule": ("PROMPT_SCHEDULE",)},
+            "optional": {
+                "tags": ("STRING", {"default": ""}),
+                "start": ("FLOAT", {"min": 0.00, "max": 1.00, "default": 0.0, "step": 0.01}),
+                "end": ("FLOAT", {"min": 0.00, "max": 1.00, "default": 1.0, "step": 0.01}),
+            },
+        }
+
+    DEPRECATED = True
+    RETURN_TYPES = ("PROMPT_SCHEDULE",)
+    CATEGORY = "promptcontrol/_legacy"
+    FUNCTION = "apply"
+
+    def apply(self, prompt_schedule, tags="", start=0.0, end=1.0):
+        p = prompt_schedule.with_filters(tags, start=start, end=end)
+        log.debug(
+            f"Filtered {prompt_schedule.parsed_prompt} with: ({tags}, {start}, {end}); the result is %s",
+            p.parsed_prompt,
+        )
+        return (p,)
+
+
+class PCApplySettings:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"prompt_schedule": ("PROMPT_SCHEDULE",), "settings": ("SCHEDULE_SETTINGS",)}}
+
+    DEPRECATED = True
+    RETURN_TYPES = ("PROMPT_SCHEDULE",)
+    CATEGORY = "promptcontrol/_legacy"
+    FUNCTION = "apply"
+
+    def apply(self, prompt_schedule, settings):
+        return (prompt_schedule.with_filters(defaults=settings),)
+
+
+class PCScheduleAddMasks:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {"prompt_schedule": ("PROMPT_SCHEDULE",)},
+            "optional": {
+                "mask1": ("MASK",),
+                "mask2": ("MASK",),
+                "mask3": ("MASK",),
+                "mask4": ("MASK",),
+            },
+        }
+
+    DEPRECATED = True
+    RETURN_TYPES = ("PROMPT_SCHEDULE",)
+    CATEGORY = "promptcontrol/_legacy"
+    FUNCTION = "apply"
+
+    def apply(self, prompt_schedule, mask1=None, mask2=None, mask3=None, mask4=None):
+        p = prompt_schedule.clone()
+        p.add_masks(mask1, mask2, mask3, mask4)
+        return (p,)
+
+
+class PCScheduleSettings:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {},
+            "optional": {
+                "steps": ("INT", {"default": 0, "min": 0, "max": 10000}),
+                "mask_width": ("INT", {"default": 512, "min": 64, "max": 4096 * 4}),
+                "mask_height": ("INT", {"default": 512, "min": 64, "max": 4096 * 4}),
+                "sdxl_width": ("INT", {"default": 1024, "min": 0, "max": 4096 * 4}),
+                "sdxl_height": ("INT", {"default": 1024, "min": 0, "max": 4096 * 4}),
+                "sdxl_target_w": ("INT", {"default": 1024, "min": 0, "max": 4096 * 4}),
+                "sdxl_target_h": ("INT", {"default": 1024, "min": 0, "max": 4096 * 4}),
+                "sdxl_crop_w": ("INT", {"default": 0, "min": 0, "max": 4096 * 4}),
+                "sdxl_crop_h": ("INT", {"default": 0, "min": 0, "max": 4096 * 4}),
+            },
+        }
+
+    DEPRECATED = True
+    RETURN_TYPES = ("SCHEDULE_SETTINGS",)
+    CATEGORY = "promptcontrol/_legacy"
+    FUNCTION = "apply"
+
+    def apply(
+        self,
+        steps=0,
+        mask_width=512,
+        mask_height=512,
+        sdxl_width=1024,
+        sdxl_height=1024,
+        sdxl_target_w=1024,
+        sdxl_target_h=1024,
+        sdxl_crop_w=0,
+        sdxl_crop_h=0,
+    ):
+        settings = {
+            "steps": steps,
+            "mask_width": mask_width,
+            "mask_height": mask_height,
+            "sdxl_width": sdxl_width,
+            "sdxl_height": sdxl_height,
+            "sdxl_twidth": sdxl_target_w,
+            "sdxl_theight": sdxl_target_h,
+            "sdxl_cwidth": sdxl_crop_w,
+            "sdxl_cheight": sdxl_crop_h,
+        }
+        return (settings,)
+
+
+class PCPromptFromSchedule:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt_schedule": ("PROMPT_SCHEDULE",),
+                "at": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01}),
+            },
+            "optional": {"tags": ("STRING", {"default": ""})},
+        }
+
+    DEPRECATED = True
+    RETURN_TYPES = ("STRING",)
+    CATEGORY = "promptcontrol/_legacy"
+    FUNCTION = "apply"
+
+    def apply(self, prompt_schedule, at, tags=""):
+        p = prompt_schedule.with_filters(tags, start=at, end=at).parsed_prompt[-1][1]
+        log.info("Prompt at %s:\n%s", at, p["prompt"])
+        log.info("LoRAs: %s", p["loras"])
+        return (p["prompt"],)
+
+
+class PromptToSchedule:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "text": ("STRING", {"multiline": True}),
+            },
+        }
+
+    DEPRECATED = True
+    RETURN_TYPES = ("PROMPT_SCHEDULE",)
+    CATEGORY = "promptcontrol/_legacy"
+    FUNCTION = "parse"
+
+    def parse(self, text, settings=None):
+        schedules = parse_prompt_schedules(text)
+        return (schedules,)

prompt_control/legacy/perp_weight.py

@@ -0,0 +1,70 @@
+import torch
+
+
+# Copied and adapted from https://github.com/bvhari/ComfyUI_PerpWeight/blob/main/clipperpweight.py
+def perp_encode(clip, tokens):
+    empty_tokens = clip.tokenize("")
+    sdxl_flag = "g" in tokens
+    empty_cond, empty_cond_pooled = clip.encode_from_tokens(empty_tokens, return_pooled=True)
+    unweighted_tokens = {}
+    for k in ["l", "g"]:
+        if k not in tokens:
+            continue
+        unweighted_tokens[k] = [[(t, 1.0) for t, _ in x] for x in tokens[k]]
+    unweighted_cond, unweighted_pooled = clip.encode_from_tokens(unweighted_tokens, return_pooled=True)
+    cond = torch.clone(unweighted_cond)
+
+    if sdxl_flag:
+        for i in range(unweighted_cond.shape[0]):
+            for j in range(unweighted_cond.shape[1]):
+                weight_l = tokens["l"][(j // 77)][(j % 77)][1]
+                if weight_l != 1.0:
+                    token_vector_l = unweighted_cond[i][j][:768]
+                    zero_vector_l = empty_cond[0][(j % 77)][:768]
+                    perp_l = (
+                        (torch.mul(zero_vector_l, token_vector_l).sum()) / (torch.norm(token_vector_l) ** 2)
+                    ) * token_vector_l
+                    if weight_l > 1.0:
+                        cond[i][j][:768] = token_vector_l + (weight_l * perp_l)
+                    elif (weight_l > 0.0) and (weight_l < 1.0):
+                        cond[i][j][:768] = token_vector_l - ((1 - weight_l) * perp_l)
+                    elif weight_l < 0.0:
+                        cond[i][j][:768] = token_vector_l + (weight_l * perp_l)
+                    elif weight_l == 0.0:
+                        cond[i][j][:768] = empty_cond[0][(j % 77)][:768]
+
+                weight_g = tokens["g"][(j // 77)][(j % 77)][1]
+                if weight_g != 1.0:
+                    token_vector_g = unweighted_cond[i][j][768:]
+                    zero_vector_g = empty_cond[0][(j % 77)][768:]
+                    perp_g = (
+                        (torch.mul(zero_vector_g, token_vector_g).sum()) / (torch.norm(token_vector_g) ** 2)
+                    ) * token_vector_g
+                    if weight_g > 1.0:
+                        cond[i][j][768:] = token_vector_g + (weight_g * perp_g)
+                    elif (weight_g > 0.0) and (weight_g < 1.0):
+                        cond[i][j][768:] = token_vector_g - ((1 - weight_g) * perp_g)
+                    elif weight_g < 0.0:
+                        cond[i][j][768:] = token_vector_g + (weight_g * perp_g)
+                    elif weight_g == 0.0:
+                        cond[i][j][768:] = empty_cond[0][(j % 77)][768:]
+    else:
+        tokens = tokens["l"]
+        for i in range(unweighted_cond.shape[0]):
+            for j in range(unweighted_cond.shape[1]):
+                weight = tokens[(j // 77)][(j % 77)][1]
+                if weight != 1.0:
+                    token_vector = unweighted_cond[i][j]
+                    zero_vector = empty_cond[0][(j % 77)]
+                    perp = (
+                        (torch.mul(zero_vector, token_vector).sum()) / (torch.norm(token_vector) ** 2)
+                    ) * token_vector
+                    if weight > 1.0:
+                        cond[i][j] = token_vector + (weight * perp)
+                    elif (weight > 0.0) and (weight < 1.0):
+                        cond[i][j] = token_vector - ((1 - weight) * perp)
+                    elif weight < 0.0:
+                        cond[i][j] = token_vector + (weight * perp)
+                    elif weight == 0.0:
+                        cond[i][j] = empty_cond[0][(j % 77)]
+    return cond, unweighted_pooled

prompt_control/legacy/utils.py

@@ -0,0 +1,265 @@
+from collections import namedtuple
+from os import environ
+from math import lcm
+import time
+import logging
+import torch
+
+from ..utils import lora_name_to_file, safe_float
+
+import nodes
+
+import comfy.model_management
+
+log = logging.getLogger("comfyui-prompt-control-legacy")
+
+FORCE_CPU_OFFLOAD = bool(environ.get("COMFYUI_PC_CPU_OFFLOAD"))
+
+
+# Minimal Modelpatcher that doesn't do anything, for LoRA loading when not
+# interested in either CLIP or unet
+class DummyModelPatcher:
+    class DummyTorchModel:
+        def __init__(self):
+            dummyconf = {
+                "num_res_blocks": [],
+                "channel_mult": [],
+                "transformer_depth": [],
+                "transformer_depth_output": [],
+                "transformer_depth_middle": 0,
+            }
+            self.model_config = namedtuple("DummyConfig", ["unet_config"])(dummyconf)
+
+        def state_dict(self):
+            return {}
+
+    def __init__(self):
+        self.model = self.DummyTorchModel()
+        self.cond_stage_model = self.DummyTorchModel()
+        self.weight_inplace_update = True
+        self.model_options = {}
+
+    def add_patches(self, patches, *args, **kwargs):
+        return []
+
+    def patch_model(self):
+        pass
+
+    def unpatch_model(self):
+        pass
+
+    def clone(self):
+        return self
+
+
+DUMMY_MODEL = DummyModelPatcher()
+
+
+def equalize(*tensors):
+    if all(t.shape[1] == tensors[0].shape[1] for t in tensors):
+        return tensors
+
+    x = lcm(*(t.shape[1] for t in tensors))
+
+    return (t.repeat(1, x // t.shape[1], 1) for t in tensors)
+
+
+def unpatch_model(model):
+    if model:
+        log.info("Unpatching model")
+        model.unpatch_model()
+
+
+def clone_model(model):
+    if not model:
+        return None
+    model = model.clone()
+    if not environ.get("PC_NO_INPLACE_UPDATE"):
+        model.weight_inplace_update = True
+    return model
+
+
+def add_patches(model, patches, weight):
+    model.add_patches(patches, weight)
+
+
+def patch_model(model, forget=False, orig=None):
+    global FORCE_CPU_OFFLOAD
+    try:
+        return _patch_model(model, forget, orig, FORCE_CPU_OFFLOAD)
+    except comfy.model_management.OOM_EXCEPTION:
+        FORCE_CPU_OFFLOAD = True
+        log.error("Ran out of memory while applying LoRAs, Forcing CPU offload from now on")
+        # Unpatch to restore partially applied weights
+        unpatch_model(model)
+        raise
+
+
+def _patch_model(model, forget=False, orig=None, offload_to_cpu=False):
+    if not model:
+        return None
+    if offload_to_cpu:
+        saved_offload = model.offload_device
+        model.offload_device = torch.device("cpu")
+    log.info(
+        "Patching model, model.load_device=%s model.model.device=%s cpu_offload=%s",
+        model.load_device,
+        model.model.device,
+        model.offload_device == torch.device("cpu"),
+    )
+    if orig:
+        model.backup = orig.backup
+    model.patch_model(device_to=model.load_device)
+    if offload_to_cpu:
+        model.offload_device = saved_offload
+    if forget:
+        model.patches = {}
+        model.object_patches = {}
+    return model
+
+
+def get_callback(model):
+    return model.model_options.get("prompt_control_callback")
+
+
+def set_callback(model, cb):
+    model.model_options["prompt_control_callback"] = cb
+
+
+# Hack to temporarily override printing to stdout to stop log spam
+def suppress_print(f):
+    def noop(*args):
+        pass
+
+    p = print
+    __builtins__["print"] = noop
+    rootlogger = logging.getLogger()
+    oldlevel = rootlogger.level
+    try:
+        rootlogger.setLevel(logging.ERROR)
+        x = f()
+    except BaseException:
+        __builtins__["print"] = p
+        rootlogger.setLevel(oldlevel)
+        raise
+    __builtins__["print"] = p
+    rootlogger.setLevel(oldlevel)
+    return x
+
+
+def load_lbw():
+    return nodes.NODE_CLASS_MAPPINGS.get("LoraLoaderBlockWeight //Inspire")
+
+
+def make_loader(filename, lbw):
+    if not lbw:
+        l = nodes.LoraLoader()
+
+        def loader(model, clip, model_weight, clip_weight, lbw):
+            return suppress_print(lambda: l.load_lora(model, clip, filename, model_weight, clip_weight))
+
+    else:
+        # This is already checked before calling make_loader
+        l = load_lbw()()
+
+        def loader(model, clip, model_weight, clip_weight, lbw):
+            spec = lbw["LBW"]
+            lbw_a = safe_float(lbw.get("A"), 4.0)
+            lbw_b = safe_float(lbw.get("B"), 1.0)
+            m = model or DUMMY_MODEL
+            c = clip or DUMMY_MODEL
+            m, c, _ = suppress_print(
+                lambda: l.doit(m, c, filename, model_weight, clip_weight, False, 0, lbw_a, lbw_b, "", spec)
+            )
+            if m is DUMMY_MODEL:
+                m = None
+            if c is DUMMY_MODEL:
+                c = None
+            return m, c
+
+    return loader
+
+
+def apply_loras_from_spec(
+    loraspec, model=None, clip=None, orig_model=None, orig_clip=None, patch=False, cache=None, applied_loras=None
+):
+    if applied_loras is None:
+        applied_loras = {}
+    actual_loraspec = {}
+    additive = True
+    for key in loraspec:
+        if key in applied_loras and applied_loras[key] == loraspec[key]:
+            continue
+        if key in applied_loras and applied_loras[key] != loraspec[key]:
+            additive = False
+        actual_loraspec[key] = loraspec[key]
+
+    for key in applied_loras:
+        if key not in loraspec:
+            actual_loraspec = loraspec
+            additive = False
+
+    backup_model = model
+    if not additive:
+        unpatch_model(model)
+        # Reset clip to unpatched
+        if clip:
+            clip = orig_clip or clip
+
+    if cache is None:
+        cache = {}
+    if not loraspec:
+        return model, clip
+
+    for name, params in actual_loraspec.items():
+        m, c = model, clip
+        w, w_clip = params["weight"], params["weight_clip"]
+        if w == 0:
+            m = None
+        if w_clip == 0:
+            c = None
+        if not w and not c:
+            continue
+
+        lbw = params.get("lbw")
+        if lbw and not load_lbw():
+            log.warning("LoraBlockWeight not available, ignoring LBW parameters")
+            lbw = None
+
+        # Cache the loader instance so that it doesn't reload the LoRA from disk all the time
+        cache_key = name, bool(lbw)
+        loader = cache.get(cache_key)
+        if not loader:
+            f = lora_name_to_file(name)
+            if not f:
+                log.warning("Lora %s not found", name)
+                continue
+            log.info("Loading LoRA: %s", f)
+            loader = make_loader(f, bool(lbw))
+            cache[cache_key] = loader
+
+        m, c = loader(m, c, w, w_clip, lbw)
+        model = m or model
+        clip = c or clip
+        if model:
+            log.info("Applying LoRA: %s:%s, LBW=%s, additive=%s", name, params["weight"], bool(lbw), additive)
+        if clip:
+            log.info("Applying CLIP LoRA: %s:%s, LBW=%s, additive=%s", name, params["weight_clip"], bool(lbw), additive)
+
+    # forget patches so we don't double-patch
+    model = patch_model(model, forget=True, orig=backup_model)
+    return model, clip
+
+
+class Timer:
+    def __init__(self, name):
+        self.name = name
+        self.start = None
+
+    def __enter__(self):
+        self.start = time.time()
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        elapsed = time.time() - self.start
+        if environ.get("PC_SHOW_TIMINGS"):
+            log.info("Executed %s in %s seconds", self.name, elapsed)

prompt_control/manual_test.py

@@ -1,46 +0,0 @@
-import main
-import nodes
-import prompt_control.adv_encode
-
-(l,) = nodes.CLIPLoader.load_clip(None, "clip_l.safetensors")
-(t5,) = nodes.CLIPLoader.load_clip(None, "t5base.safetensors")
-
-id(main)  # get rid of warning
-
-
-def adv(t, text, style="A1111", norm="none", new=True, **kwargs):
-    c = t.tokenize(text, return_word_ids=True)
-    if new:
-        style = "new+" + style
-    if t is t5:
-        te = t.patcher.model.t5base.encode_token_weights
-        token = t.tokenizer.clip_t5base
-        tok = c["t5base"]
-    else:
-        te = t.patcher.model.clip_l.encode_token_weights
-        token = t.tokenizer.clip_l
-        tok = c["l"]
-    return prompt_control.adv_encode.advanced_encode_from_tokens(tok, norm, style, te, tokenizer=token)
-
-
-def adv_all(t, text, styles=[], **kwargs):
-    r = []
-    for s in styles or prompt_control.adv_encode.AdvancedEncoder.STYLES:
-        print("Testing", s, kwargs)
-        r.append([s, adv(t, text, style=s, **kwargs)])
-    return r
-
-
-def replacenan(t):
-    t[t.isnan()] = 42.123321
-    return t
-
-
-def adv_equal(t, text, **kwargs):
-    old = adv_all(t, text, new=False, **kwargs)
-    new = adv_all(t, text, new=True, **kwargs)
-    r = {}
-    for i, o in enumerate(old):
-        n = new[i]
-        r[n[0]] = (replacenan(n[1][0]) == replacenan(o[1][0])).all()
-    return r

prompt_control/nodes_base.py

@@ -1,51 +0,0 @@
-import logging
-from .prompts import encode_prompt
-
-log = logging.getLogger("comfyui-prompt-control")
-
-
-class PCTextEncodeWithRange:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {"clip": ("CLIP",), "text": ("STRING", {"multiline": True})},
-            "optional": {
-                "start": ("FLOAT", {"min": 0.0, "max": 1.0, "default": 0.0, "step": 0.01}),
-                "end": ("FLOAT", {"min": 0.0, "max": 1.0, "default": 1.0, "step": 0.01}),
-            },
-        }
-
-    RETURN_TYPES = ("CONDITIONING",)
-    CATEGORY = "promptcontrol/tools"
-    FUNCTION = "apply"
-    DESCRIPTION = "Like PCTextEncode, but if you know the range you need for a prompt, can be slightly more efficient when you have LoRAs scheduled on a CLIP model"
-
-    def apply(self, clip, text, start=0.0, end=1.0):
-        log.debug("PCTextEncode: Encoding '%s'", text)
-        defaults = clip.patcher.model_options.get("x-promptcontrol.defaults", {})
-        masks = clip.patcher.model_options.get("x-promptcontrol.masks", None)
-        return (encode_prompt(clip, text, start, end, defaults, masks),)
-
-
-class PCTextEncode:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {"clip": ("CLIP",), "text": ("STRING", {"multiline": True})},
-        }
-
-    RETURN_TYPES = ("CONDITIONING",)
-    CATEGORY = "promptcontrol"
-    FUNCTION = "apply"
-    DESCRIPTION = "Encodes a prompt with extra goodies from Prompt Control. This node does *not* support scheduling"
-
-    def apply(self, clip, text):
-        return PCTextEncodeWithRange.apply(self, clip, text, 0.0, 1.0)
-
-
-NODE_CLASS_MAPPINGS = {"PCTextEncode": PCTextEncode, "PCTextEncodeWithRange": PCTextEncodeWithRange}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "PCTextEncode": "PC: Text Encode (no scheduling)",
-    "PCTextEncodeWithRange": "PC: Text Encode with Range (no scheduling)",
-}

prompt_control/nodes_hooks.py

@@ -1,133 +0,0 @@
-import logging
-
-import comfy.hooks
-import comfy.utils
-import folder_paths
-from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict
-
-from .attention_couple_ppm import AttentionCoupleHook
-from .parser import parse_prompt_schedules
-from .utils import consolidate_schedule
-
-log = logging.getLogger("comfyui-prompt-control")
-
-
-class PCLoraHooksFromText:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {"text": ("STRING",)},
-        }
-
-    RETURN_TYPES = ("HOOKS",)
-    OUTPUT_TOOLTIPS = ("set of hooks created from the prompt schedule",)
-    CATEGORY = "promptcontrol/v2"
-    FUNCTION = "apply"
-    EXPERIMENTAL = True
-
-    def apply(self, text):
-        prompt_schedule = parse_prompt_schedules(text)
-        consolidated = consolidate_schedule(prompt_schedule)
-        hooks = lora_hooks_from_schedule(consolidated, {})
-        return (hooks,)
-
-
-def lora_hooks_from_schedule(schedules, non_scheduled):
-    start_pct = 0.0
-    lora_cache = {}
-    all_hooks = []
-
-    def create_hook(loraspec, start_pct, end_pct, non_scheduled):
-        nonlocal lora_cache
-        hooks = []
-        hook_kf = comfy.hooks.HookKeyframeGroup()
-        for path, info in loras.items():
-            if non_scheduled.get(path) == info:
-                log.info("Skipping %s from hook, it's loaded directly on model", path)
-                continue
-            if path not in lora_cache:
-                lora_cache[path] = comfy.utils.load_torch_file(
-                    folder_paths.get_full_path("loras", path), safe_load=True
-                )
-            new_hook = comfy.hooks.create_hook_lora(
-                lora_cache[path], strength_model=info["weight"], strength_clip=info["weight_clip"]
-            )
-            # Set hook_ref so that identical hooks compare equal
-            new_hook.hooks[0].hook_ref = f"pc-{path}-{info['weight']}-{info['weight_clip']}"
-            hooks.append(new_hook)
-        if start_pct > 0.0:
-            kf = comfy.hooks.HookKeyframe(strength=0.0, start_percent=0.0)
-            hook_kf.add(kf)
-        kf = comfy.hooks.HookKeyframe(strength=1.0, start_percent=start_pct)
-        hook_kf.add(kf)
-        if end_pct < 1.0:
-            kf = comfy.hooks.HookKeyframe(strength=0.0, start_percent=end_pct)
-            hook_kf.add(kf)
-        hooks = comfy.hooks.HookGroup.combine_all_hooks(hooks)
-        if hooks:
-            hooks.set_keyframes_on_hooks(hook_kf=hook_kf)
-        return hooks
-
-    for end_pct, loras in schedules:
-        log.info("Creating LoRA hook from %s to %s: %s", start_pct, end_pct, loras)
-        hook = create_hook(loras, start_pct, end_pct, non_scheduled)
-        all_hooks.append(hook)
-        start_pct = end_pct
-
-    del lora_cache
-
-    all_hooks = [x for x in all_hooks if x]
-
-    if all_hooks:
-        hooks = comfy.hooks.HookGroup.combine_all_hooks(all_hooks)
-        return hooks
-
-
-class PCAttentionCoupleBatchNegative(ComfyNodeABC):
-    @classmethod
-    def INPUT_TYPES(cls) -> InputTypeDict:
-        return {
-            "required": {
-                "positive": (IO.CONDITIONING, {}),
-                "negative": (IO.CONDITIONING, {}),
-            },
-        }
-
-    RETURN_TYPES = (IO.CONDITIONING, IO.CONDITIONING)
-    RETURN_NAMES = ("positive", "negative")
-    CATEGORY = "promptcontrol/v2"
-    FUNCTION = "batch"
-    EXPERIMENTAL = True
-
-    # May cause side-effects?
-    # TODO: Support scheduling in negative prompt
-    def batch(self, positive, negative):
-        if len(negative) != 1:
-            log.warning("Batching scheduled negatives is not supported yet")
-            return (positive, negative)
-
-        negative_batch = []
-        for p in positive:
-            n = [negative[0][0], negative[0][1].copy()]
-            n_hook_group: comfy.hooks.HookGroup = n[1].get("hooks", comfy.hooks.HookGroup()).clone()
-            p_hook_group: comfy.hooks.HookGroup = p[1].get("hooks", comfy.hooks.HookGroup())
-            attn_couple = [hook for hook in p_hook_group.hooks if isinstance(hook, AttentionCoupleHook)]
-            for hook in attn_couple:
-                n_hook_group.add(hook)
-            n[1]["hooks"] = p_hook_group if n_hook_group.hooks == p_hook_group.hooks else n_hook_group
-            n[1]["start_percent"] = p[1].get("start_percent", 0.0)
-            n[1]["end_percent"] = p[1].get("end_percent", 1.0)
-            negative_batch.append(n)
-
-        return (positive, negative_batch)
-
-
-NODE_CLASS_MAPPINGS = {
-    "PCLoraHooksFromText": PCLoraHooksFromText,
-    "PCAttentionCoupleBatchNegative": PCAttentionCoupleBatchNegative,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "PCLoraHooksFromText": "PC: LoRA Hooks From Text (non-lazy)",
-    "PCAttentionCoupleBatchNegative": "PC: Attention Couple (batch negative)",
-}

prompt_control/nodes_lazy.py

@@ -1,295 +0,0 @@
-import logging
-from .parser import parse_prompt_schedules
-from comfy_execution.graph_utils import GraphBuilder, is_link
-
-from comfy_execution.graph import ExecutionBlocker
-
-from .utils import get_function
-
-log = logging.getLogger("comfyui-prompt-control")
-
-from .utils import consolidate_schedule, find_nonscheduled_loras
-import json
-
-
-def _cache_key(cachekey, inputs):
-    out = inputs.copy()
-    text = inputs.get("text")
-    if text is not None and not is_link(text):
-        out["text"] = cache_key_from_inputs(cachekey, **inputs)
-    return out
-
-
-def cache_key_prompt(inputs):
-    return _cache_key("prompt", inputs)
-
-
-def cache_key_lora(inputs):
-    return _cache_key("loras", inputs)
-
-
-def create_lora_loader_nodes(graph, model, clip, loras):
-    for path, info in loras.items():
-        log.info("Creating LoraLoader for %s", path)
-        loader = graph.node("LoraLoader")
-        loader.set_input("model", model)
-        loader.set_input("clip", clip)
-        loader.set_input("strength_model", info["weight"])
-        loader.set_input("strength_clip", info["weight_clip"])
-        loader.set_input("lora_name", path)
-        model = loader.out(0)
-        clip = loader.out(1)
-    return model, clip
-
-
-def create_hook_nodes_for_lora(graph, path, info, existing_node, start_pct, end_pct):
-    prev_keyframe = None
-    next_keyframe = None
-    if not existing_node:
-        log.debug("Creating hook for %s, weight=%s, weight_clip=%s", path, info["weight"], info["weight_clip"])
-        hook_node = graph.node("CreateHookLora")
-        hook_node.set_input("lora_name", path)
-        hook_node.set_input("strength_model", info["weight"])
-        hook_node.set_input("strength_clip", info["weight_clip"])
-        prev_hook_kf = None
-        if start_pct > 0:
-            log.debug("Creating KF (0, %s) for %s", start_pct, path)
-            prev_keyframe = graph.node("CreateHookKeyframe")
-            prev_keyframe.set_input("strength_mult", 0.0)
-            prev_keyframe.set_input("start_percent", 0.0)
-            prev_hook_kf = prev_keyframe.out(0)
-    else:
-        log.debug("Hook already created for %s", path)
-        hook_node, prev_keyframe = existing_node
-        prev_hook_kf = prev_keyframe.out(0)
-
-    if (
-        prev_keyframe
-        and prev_keyframe.get_input("start_pct") == start_pct
-        and prev_keyframe.get_input("strength_mult") == 0.0
-    ):
-        next_keyframe = prev_keyframe
-        log.debug("Previous keyframe for %s starts at %s and has 0 strength, overriding", path, start_pct)
-    else:
-        log.debug("Creating keyframe for %s, start=%s ", path, start_pct)
-        next_keyframe = graph.node("CreateHookKeyframe")
-        next_keyframe.set_input("start_percent", start_pct)
-        next_keyframe.set_input("prev_hook_kf", prev_hook_kf)
-
-    next_keyframe.set_input("strength_mult", 1.0)
-    prev_hook_kf = next_keyframe.out(0)
-    if end_pct < 1.0:
-        log.debug("Creating end keyframe for %s, start=%s", path, end_pct)
-        next_keyframe = graph.node("CreateHookKeyframe")
-        next_keyframe.set_input("strength_mult", 0.0)
-        next_keyframe.set_input("start_percent", end_pct)
-        next_keyframe.set_input("prev_hook_kf", prev_hook_kf)
-    return hook_node, next_keyframe
-
-
-def build_lora_schedule(graph, schedule, model, clip, apply_hooks=True):
-    # This gets rid of non-existent LoRAs
-    consolidated = consolidate_schedule(schedule)
-    if model is not None:
-        non_scheduled = find_nonscheduled_loras(consolidated)
-        model, clip = create_lora_loader_nodes(graph, model, clip, non_scheduled)
-    else:
-        non_scheduled = {}
-        model = ExecutionBlocker("No model provided to PCLazyLoRALoader or PCLazyLoRALoaderAdvanced")
-
-    hook_nodes = {}
-    start_pct = 0.0
-
-    def key(lora, info):
-        return f"{lora}-{info['weight']}-{info['weight_clip']}"
-
-    for end_pct, loras in consolidated:
-        for lora, info in loras.items():
-            if non_scheduled.get(lora) == info:
-                continue
-            k = key(lora, info)
-            existing_node = hook_nodes.get(k)
-            hook_nodes[k] = create_hook_nodes_for_lora(graph, lora, info, existing_node, start_pct, end_pct)
-        start_pct = end_pct
-
-    hooks = []
-    # Attach the keyframe chain to the hook node
-    for hook, kfs in hook_nodes.values():
-        n = graph.node("SetHookKeyframes")
-        n.set_input("hooks", hook.out(0))
-        n.set_input("hook_kf", kfs.out(0))
-        hooks.append(n)
-
-    res = None
-    # Finally, combine all hooks and optionally apply
-    if len(hooks) > 0:
-        res = hooks[0]
-        for h in hooks[1:]:
-            n = graph.node("CombineHooks2")
-            n.set_input("hooks_A", res.out(0))
-            n.set_input("hooks_B", h.out(0))
-            res = n
-        res = res.out(0)
-        if clip is not None and apply_hooks:
-            n = graph.node("SetClipHooks")
-            n.set_input("clip", clip)
-            n.set_input("hooks", res)
-            n.set_input("apply_to_conds", True)
-            n.set_input("schedule_clip", True)
-            clip = n.out(0)
-
-    if clip is None:
-        clip = ExecutionBlocker("No clip model provided to PCLazyLoRALoader or PCLazyLoRALoaderAdvanced")
-    r = graph.finalize()
-    log.debug("LazyLoraLoader built graph: %s", json.dumps(r))
-
-    ret = (model, clip, res)
-
-    return {"result": ret, "expand": r}
-
-
-class PCLazyLoraLoaderAdvanced:
-    CACHE_KEY = cache_key_lora
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "optional": {
-                "model": ("MODEL", {"rawLink": True}),
-                "clip": ("CLIP", {"rawLink": True}),
-                "text": ("STRING", {"multiline": True, "default": ""}),
-                "apply_hooks": ("BOOLEAN", {"default": True}),
-                "tags": ("STRING", {"default": ""}),
-                "start": ("FLOAT", {"min": 0.0, "max": 1.0, "default": 0.0, "step": 0.01}),
-                "end": ("FLOAT", {"min": 0.0, "max": 1.0, "default": 1.0, "step": 0.01}),
-                "num_steps": ("INT", {"min": 0, "max": 10000, "default": 0, "step": 1}),
-            },
-            "hidden": {"unique_id": "UNIQUE_ID"},
-        }
-
-    RETURN_TYPES = ("MODEL", "CLIP", "HOOKS")
-    OUTPUT_TOOLTIPS = ("Returns a model and clip with LoRAs scheduled",)
-    CATEGORY = "promptcontrol"
-    FUNCTION = "apply"
-
-    def apply(
-        self, unique_id, model=None, clip=None, text="", apply_hooks=True, tags="", start=0.0, end=1.0, num_steps=0
-    ):
-        schedule = parse_prompt_schedules(text, filters=tags, start=start, end=end, num_steps=num_steps)
-        graph = GraphBuilder()
-        r = build_lora_schedule(graph, schedule, model, clip, apply_hooks=apply_hooks)
-        return r
-
-
-class PCLazyLoraLoader(PCLazyLoraLoaderAdvanced):
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "optional": {
-                "model": ("MODEL", {"rawLink": True}),
-                "clip": ("CLIP", {"rawLink": True}),
-                "text": ("STRING", {"multiline": True, "default": ""}),
-            },
-            "hidden": {"unique_id": "UNIQUE_ID"},
-        }
-
-    RETURN_TYPES = (
-        "MODEL",
-        "CLIP",
-    )
-    CATEGORY = "promptcontrol"
-
-    def apply(self, *args, **kwargs):
-        r = super().apply(*args, **kwargs)
-        r["result"] = r["result"][:2]
-        return r
-
-
-def build_scheduled_prompts(graph, schedules, clip):
-    nodes = []
-    start_pct = 0.0
-    for end_pct, c in schedules:
-        p = c["prompt"]
-        p, classnames = get_function(p, "NODE", ["PCTextEncode", "text"])
-        classname = "PCTextEncode"
-        paramname = "text"
-        if classnames:
-            classname = classnames[0][0]
-            paramname = classnames[0][1]
-        node = graph.node(classname)
-        node.set_input("clip", clip)
-        node.set_input(paramname, p)
-        timestep = graph.node("ConditioningSetTimestepRange")
-        timestep.set_input("conditioning", node.out(0))
-        timestep.set_input("start", start_pct)
-        timestep.set_input("end", end_pct)
-        nodes.append(timestep)
-        start_pct = end_pct
-    node = nodes[0]
-    for othernode in nodes[1:]:
-        combiner = graph.node("ConditioningCombine")
-        combiner.set_input("conditioning_1", node.out(0))
-        combiner.set_input("conditioning_2", othernode.out(0))
-        node = combiner
-
-    g = graph.finalize()
-    log.debug("Built graph: %s", json.dumps(g))
-
-    return {"result": (node.out(0),), "expand": g}
-
-
-def cache_key_from_inputs(cachekey, text, tags="", start=0.0, end=1.0, num_steps=0, **kwargs):
-    schedules = parse_prompt_schedules(text, filters=tags, start=start, end=end, num_steps=num_steps)
-    return [(pct, s[cachekey]) for pct, s in schedules]
-
-
-class PCLazyTextEncodeAdvanced:
-    CACHE_KEY = cache_key_prompt
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {"clip": ("CLIP", {"rawLink": True}), "text": ("STRING", {"multiline": True})},
-            "optional": {
-                "tags": ("STRING", {"default": ""}),
-                "start": ("FLOAT", {"min": 0.0, "max": 1.0, "default": 0.0, "step": 0.01}),
-                "end": ("FLOAT", {"min": 0.0, "max": 1.0, "default": 1.0, "step": 0.01}),
-                "num_steps": ("INT", {"min": 0, "max": 10000, "default": 0, "step": 1}),
-            },
-            "hidden": {"unique_id": "UNIQUE_ID"},
-        }
-
-    RETURN_TYPES = ("CONDITIONING",)
-    CATEGORY = "promptcontrol"
-    FUNCTION = "apply"
-
-    def apply(self, clip, text, unique_id, tags="", start=0.0, end=1.0, num_steps=0):
-        schedules = parse_prompt_schedules(text, filters=tags, start=start, end=end, num_steps=num_steps)
-        graph = GraphBuilder()
-        return build_scheduled_prompts(graph, schedules, clip)
-
-
-class PCLazyTextEncode(PCLazyTextEncodeAdvanced):
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {"clip": ("CLIP", {"rawLink": True}), "text": ("STRING", {"multiline": True})},
-            "hidden": {"unique_id": "UNIQUE_ID"},
-        }
-
-    CATEGORY = "promptcontrol"
-
-
-NODE_CLASS_MAPPINGS = {
-    "PCLazyTextEncode": PCLazyTextEncode,
-    "PCLazyTextEncodeAdvanced": PCLazyTextEncodeAdvanced,
-    "PCLazyLoraLoader": PCLazyLoraLoader,
-    "PCLazyLoraLoaderAdvanced": PCLazyLoraLoaderAdvanced,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "PCLazyTextEncode": "PC: Schedule Prompt",
-    "PCLazyTextEncodeAdvanced": "PC: Schedule prompt (Advanced)",
-    "PCLazyLoraLoader": "PC: Schedule LoRAs",
-    "PCLazyLoraLoaderAdvanced": "PC: Schedule LoRAs (Advanced)",
-}

prompt_control/nodes_tools.py

@@ -1,231 +0,0 @@
-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
-
-log = logging.getLogger("comfyui-prompt-control")
-
-
-class PCSaveExpandedWorkflow:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "any": ("*", {}),
-            },
-            "hidden": {
-                "prompt": "PROMPT",
-            },
-        }
-
-    @classmethod
-    def VALIDATE_INPUTS(self, input_types):
-        return True
-
-    OUTPUT_NODE = True
-    RETURN_TYPES = ()
-    CATEGORY = "promptcontrol/tools"
-    DESCRIPTION = "Expands lazy prompt control nodes in the prompt and saves the expanded prompt into a JSON file"
-
-    FUNCTION = "apply"
-
-    def apply(self, any, prompt):
-        full_output_folder, filename, counter, subfolder, prefix = folder_paths.get_save_image_path(
-            "pc_workflow_debug", self.output_dir
-        )
-        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(expanded, f)
-
-        return ()
-
-
-class PCSetLogLevel:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "clip": ("CLIP",),
-            },
-            "optional": {
-                "level": (["INFO", "DEBUG", "WARNING", "ERROR"], {"default": "INFO"}),
-            },
-        }
-
-    def apply(self, clip, level="INFO"):
-        log.setLevel(getattr(logging, level))
-        log.info("Set logging level to %s", level)
-        return (clip,)
-
-    RETURN_TYPES = ("CLIP",)
-    CATEGORY = "promptcontrol/tools"
-    DESCRIPTION = (
-        "A debug node to configure Prompt Control logging level. Pass a CLIP through it before you run any PC nodes"
-    )
-
-    FUNCTION = "apply"
-
-
-class PCAddMaskToCLIP:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {"clip": ("CLIP",)},
-            "optional": {
-                "mask": ("MASK",),
-            },
-        }
-
-    RETURN_TYPES = ("CLIP",)
-    CATEGORY = "promptcontrol/tools"
-    FUNCTION = "apply"
-    DESCRIPTION = "Attaches a mask to a CLIP object so that they can be referred to in a prompt using IMASK(). Using this node multiple times adds more masks rather than replacing existing ones."
-
-    def apply(self, clip, mask=None):
-        return PCAddMaskToCLIPMany().apply(clip, mask1=mask)
-
-
-class PCAddMaskToCLIPMany:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {"clip": ("CLIP",)},
-            "optional": {
-                "mask1": ("MASK",),
-                "mask2": ("MASK",),
-                "mask3": ("MASK",),
-                "mask4": ("MASK",),
-            },
-        }
-
-    RETURN_TYPES = ("CLIP",)
-    CATEGORY = "promptcontrol/tools"
-    FUNCTION = "apply"
-    DESCRIPTION = "Multi-input version of PCAddMaskToCLIP, for convenience"
-
-    def apply(self, clip, mask1=None, mask2=None, mask3=None, mask4=None):
-        clip = clip.clone()
-        current_masks = clip.patcher.model_options.get("x-promptcontrol.masks", [])
-        current_masks.extend(m for m in (mask1, mask2, mask3, mask4) if m is not None)
-        clip.patcher.model_options["x-promptcontrol.masks"] = current_masks
-        return (clip,)
-
-
-class PCSetPCTextEncodeSettings:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {"clip": ("CLIP",)},
-            "optional": {
-                "mask_width": ("INT", {"default": 512, "min": 64, "max": 4096 * 4}),
-                "mask_height": ("INT", {"default": 512, "min": 64, "max": 4096 * 4}),
-                "sdxl_width": ("INT", {"default": 1024, "min": 0, "max": 4096 * 4}),
-                "sdxl_height": ("INT", {"default": 1024, "min": 0, "max": 4096 * 4}),
-                "sdxl_target_w": ("INT", {"default": 1024, "min": 0, "max": 4096 * 4}),
-                "sdxl_target_h": ("INT", {"default": 1024, "min": 0, "max": 4096 * 4}),
-                "sdxl_crop_w": ("INT", {"default": 0, "min": 0, "max": 4096 * 4}),
-                "sdxl_crop_h": ("INT", {"default": 0, "min": 0, "max": 4096 * 4}),
-            },
-        }
-
-    RETURN_TYPES = ("CLIP",)
-    CATEGORY = "promptcontrol/tools"
-    FUNCTION = "apply"
-    DESCRIPTION = "Configures default values for PCTextEncode"
-
-    def apply(
-        self,
-        clip,
-        mask_width=512,
-        mask_height=512,
-        sdxl_width=1024,
-        sdxl_height=1024,
-        sdxl_target_w=1024,
-        sdxl_target_h=1024,
-        sdxl_crop_w=0,
-        sdxl_crop_h=0,
-    ):
-        settings = {
-            "mask_width": mask_width,
-            "mask_height": mask_height,
-            "sdxl_width": sdxl_width,
-            "sdxl_height": sdxl_height,
-            "sdxl_twidth": sdxl_target_w,
-            "sdxl_theight": sdxl_target_h,
-            "sdxl_cwidth": sdxl_crop_w,
-            "sdxl_cheight": sdxl_crop_h,
-        }
-        clip = clip.clone()
-        clip.patcher.model_options["x-promptcontrol.settings"] = settings
-        return (clip,)
-
-
-class PCExtractScheduledPrompt:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "text": ("STRING", {"multiline": True}),
-                "at": ("FLOAT", {"min": 0.0, "max": 1.0, "default": 1.0, "step": 0.01}),
-            },
-            "optional": {"tags": ("STRING", {"default": ""})},
-        }
-
-    RETURN_TYPES = ("STRING",)
-    CATEGORY = "promptcontrol/tools"
-    FUNCTION = "apply"
-    DESCRIPTION = "Parses the input prompt and returns the prompt scheduled at the specified point"
-
-    def apply(self, text, at, tags=""):
-        schedule = parse_prompt_schedules(text, filters=tags)
-        _, entry = schedule.at_step(at, total_steps=1)
-        prompt_text = entry.get("prompt", "")
-        return (prompt_text,)
-
-
-class PCMacroExpand:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "text": ("STRING", {"multiline": True}),
-            },
-        }
-
-    RETURN_TYPES = ("STRING",)
-    CATEGORY = "promptcontrol/tools"
-    FUNCTION = "apply"
-    DESCRIPTION = "Expands DEF macros in a string and returns the result"
-
-    def apply(self, text):
-        return (expand_macros(text),)
-
-
-NODE_CLASS_MAPPINGS = {
-    "PCSetPCTextEncodeSettings": PCSetPCTextEncodeSettings,
-    "PCAddMaskToCLIP": PCAddMaskToCLIP,
-    "PCAddMaskToCLIPMany": PCAddMaskToCLIPMany,
-    "PCSetLogLevel": PCSetLogLevel,
-    "PCExtractScheduledPrompt": PCExtractScheduledPrompt,
-    "PCSaveExpandedWorkflow": PCSaveExpandedWorkflow,
-    "PCMacroExpand": PCMacroExpand,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "PCSetPCTextEncodeSettings": "PC: Configure PCTextEncode",
-    "PCAddMaskToCLIP": "PC: Attach Mask",
-    "PCAddMaskToCLIPMany": "PC: Attach Mask (multi)",
-    "PCSetLogLevel": "PC: Configure Logging (for debug)",
-    "PCExtractScheduledPrompt": "PC: Extract Scheduled Prompt",
-    "PCSaveExpandedWorkflow": "PC: Save Expanded Workflow (for debug)",
-    "PCMacroExpand": "PC: Expand Macros",
-}

prompt_control/parser.py

@@ -1,70 +1,29 @@
-# vim: sw=4 ts=4
 import lark
 import logging
 from math import ceil
 
 logging.basicConfig()
-log = logging.getLogger("comfyui-prompt-control")
-import re
-
-from functools import lru_cache
-from .utils import get_function, find_closing_paren
+log = logging.getLogger("comfyui-prompt-control-legacy")
 
 if lark.__version__ == "0.12.0":
-    from sys import executable
-
-    x = "\n".join(
-        [
-            "Your lark package reports an ancient version (0.12.0) and will not work. If you have the 'lark-parser' package in your Python environment, remove that and *reinstall* lark!",
-            f"{executable} -m pip uninstall lark-parser lark",
-            f"{executable} -m pip install lark",
-        ]
-    )
+    x = "Your lark package reports an ancient version (0.12.0) and will not work. If you have the 'lark-parser' package in your Python environment, remove that and *reinstall* lark!"
     log.error(x)
     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 | interpolate | loraspec | PLAIN | /</ | />/ | WHITESPACE)+
 !emphasized: "(" prompt? ")"
         | "(" prompt ":" prompt ")"
         | "[" prompt "]"
-promptlist: ([prompt] ":")~1..3
-scheduled: "[" promptlist _WS? NUMBER ["," NUMBER] "]"
-        | "[" promptlist _WS? TAG "]"
-sequence.5:  "[SEQ" ":" [prompt] ":" NUMBER (":" [prompt] ":" NUMBER)* "]"
+scheduled: "[" [prompt ":"] [prompt] ":" _WS? NUMBER ["," NUMBER] "]"
+        | "[" [prompt ":"] [prompt] ":" _WS? TAG "]"
+sequence:  "[SEQ" ":" [prompt] ":" NUMBER (":" [prompt] ":" NUMBER)+ "]"
+interpolate.100: "[INT" ":" interp_prompts ":" interp_steps "]"
+interp_prompts: prompt (":" [prompt])+
+interp_steps: NUMBER ("," NUMBER)+ [":" NUMBER]
 alternate: "[" [prompt] ("|" [prompt])+ [":" NUMBER] "]"
 loraspec.99: "<lora:" FILENAME lora_weights [lora_block_weights] ">"
 lora_weights.1: (":" _WS? NUMBER)~1..2
@@ -80,7 +39,6 @@ TAG: /[A-Z_]+/
     lexer="dynamic",
 )
 
-
 cut_parser = lark.Lark(
     r"""
 !start: (prompt | /[][:()]/+)*
@@ -122,7 +80,7 @@ def parse_cuts(text):
 
 
 def flatten(x):
-    if type(x) in [str, tuple, int, type(None)] or isinstance(x, dict) and "type" in x:
+    if type(x) in [str, tuple] or isinstance(x, dict) and "type" in x:
         yield x
     else:
         for g in x:
@@ -134,25 +92,14 @@ def clamp(a, b, c):
     return min(max(a, b), c)
 
 
-def get_steps(tree, num_steps):
-    res = [num_steps or 100]
+def get_steps(tree):
+    res = [100]
+    interpolation_steps = []
 
     def tostep(s):
-        steps = num_steps or 100
-        if "." in str(s) or not num_steps:
-            w = float(s)
-            value = w * steps
-        else:
-            w = int(s)
-            value = w
-
-        if w > 1 and not num_steps:
-            log.warning(
-                "You haven't configured the number of steps for Prompt Control to use, %s will be clipped to 1.0", w
-            )
-            value = steps
-
-        return int(clamp(0, value, steps))
+        w = float(s) * 100
+        w = int(clamp(0, w, 100))
+        return w
 
     class CollectSteps(lark.Visitor):
         def scheduled(self, tree):
@@ -169,60 +116,55 @@ def get_steps(tree, num_steps):
             for i, _ in enumerate(tree.children[:-1]):
                 tree.children[i] = tostep(tree.children[i])
 
+            interpolation_steps.append((tuple(tree.children[:-1]), tree.children[-1]))
             res.extend(tree.children[:-1])
 
         def sequence(self, tree):
             steps = tree.children[1::2]
             for i, steps in enumerate(steps):
-                w = tostep(tree.children[i * 2 + 1])
-                tree.children[i * 2 + 1] = w
+                w = float(tree.children[i * 2 + 1]) * 100
+                tree.children[i * 2 + 1] = clamp(0, w, 100)
                 res.append(w)
 
         def alternate(self, tree):
-            step_size = tostep(round(float(tree.children[-1] or 0.1), 2))
+            step_size = int(round(float(tree.children[-1] or 0.1), 2) * 100)
+            step_size = clamp(1, step_size, 100)
             tree.children[-1] = step_size
-            res.extend([x for x in range(step_size, num_steps or 100, step_size)])
+            res.extend([x for x in range(step_size, 100, step_size)])
 
     CollectSteps().visit(tree)
 
-    return sorted(set(res))
+    return sorted(set(interpolation_steps)), sorted(set(res))
 
 
 def at_step(step, filters, tree):
     class AtStep(lark.Transformer):
         def scheduled(self, args):
-            before = None
-            during = None
-            after = None
             when_end = None
-            pl, when, *rest = args
-            if rest:
-                when_end = rest[0]
-
-            pl = list(pl)
-            if len(pl) == 1:
-                (during,) = pl  # [after:0.5] == [::after:0.5,0.5]
-                if when_end is None:
-                    when_end = when
-                    after = during
-            elif len(pl) == 2:
-                during, after = pl  # [during:after:0.5] = [before::after:0.5,0.5]
-                if when_end is None:
-                    when_end = when
-                    before = during
-            else:
-                before, during, after = pl  # [before:during:after:0.5,0.8]
-
+            before, after, when, *rest = args
             if isinstance(when, str):
                 return before or "" if when not in filters else after or ""
 
-            if when_end is None:
-                when_end = 1000_000
+            if rest:
+                when_end = rest[0]
+
+            if when_end is not None and step <= when and before is not None:
+                return ""
+
+            if when_end is not None and (step > when and step <= when_end):
+                # handle [a:0,1]
+                if before is None:
+                    return after or ""
+                return before or ""
+
+            if when_end is not None and step >= when_end:
+                # handle [a:0,1]
+                if before is None:
+                    return ""
+                return after or ""
 
             if step <= when:
                 return before or ""
-            if when < step <= when_end:
-                return during or ""
             else:
                 return after or ""
 
@@ -238,6 +180,24 @@ def at_step(step, filters, tree):
                     previous_step = s
             return ""
 
+        def interpolate(self, args):
+            prompts, starts = args
+            starts = starts[:-1]
+            prev_prompt = None
+            if step < starts[0]:
+                return prompts[0]
+            for i, x in enumerate(starts):
+                prev_prompt = prompts[i]
+                if x >= step:
+                    break
+            return prev_prompt
+
+        def interp_steps(self, args):
+            return list(args)
+
+        def interp_prompts(self, args):
+            return ["".join(flatten(a or [])) for a in args]
+
         def alternate(self, args):
             step_size = args[-1]
             idx = ceil(step / step_size)
@@ -271,7 +231,7 @@ def at_step(step, filters, tree):
             return {"prompt": p, "loras": loraspecs}
 
         def PLAIN(self, args):
-            return restore_escaped(args)
+            return args.replace("\\:", ":")
 
         def FILENAME(self, value):
             return str(value)
@@ -308,47 +268,68 @@ def at_step(step, filters, tree):
 
 
 class PromptSchedule(object):
-    # 0 num_steps means unconfigured
-    def __init__(self, prompt, filters="", start=0.0, end=1.0, num_steps=0):
+    def __init__(self, prompt, filters="", start=0.0, end=1.0, defaults=None, masks=None):
         self.filters = filters
         self.start = start
         self.end = end
-        self.num_steps = num_steps
-        # placeholder is restored on parse
-        self.prompt = remove_comments(escape_specials(prompt.strip()))
+        self.prompt = prompt.strip()
         self.defaults = {}
+        if defaults:
+            self.defaults = defaults
         self.loaded_loras = {}
 
-        self.parsed_prompt = self._parse(num_steps)
+        self.interpolations = None
+        self.parsed_prompt = None
+        self.interpolations, self.parsed_prompt = self._parse()
+        self.masks = masks
+        if masks is None:
+            self.masks = []
 
     def __iter__(self):
         # Filter out zero, it's only useful for interpolation
         return (x for x in self.parsed_prompt if x[0] != 0)
 
-    def _parse(self, num_steps):
+    def _parse(self):
         filters = [x.strip() for x in self.filters.upper().split(",")]
         try:
             parsed = []
+            interpolations = set()
             tree = prompt_parser.parse(self.prompt)
-            steps = get_steps(tree, num_steps=num_steps)
+            interpolation_steps, steps = get_steps(tree)
+            log.debug("Interpolation steps: %s", interpolation_steps)
 
             def f(x):
-                return round(x / (num_steps or 100), 2)
+                return round(x / 100, 2)
 
             for t in steps:
                 p = at_step(t, filters, tree)
+                for control_points, step in interpolation_steps:
+                    interp_start = None
+                    interp_end = None
+                    if t == control_points[-1]:
+                        interp_start = max(control_points[0], int(self.start * 100))
+                        interp_end = min(control_points[-1], int(self.end * 100))
+                        control_points = tuple(
+                            sorted(set(f(c) for c in control_points if c >= interp_start or c <= interp_end))
+                        )
+                    if interp_start is not None and interp_end is not None and interp_end > interp_start:
+                        interpolations.add((control_points, f(step)))
                 parsed.append([f(t), p])
 
         except lark.exceptions.LarkError as e:
             log.error("Prompt editing parse error: %s", e)
             parsed = [[1.0, {"prompt": self.prompt, "loras": {}}]]
-            raise
 
         # Tag filtering may return redundant prompts, so filter them out here
         res = []
+        prev_p = None
         prev_end = -1
 
         for end_at, p in parsed:
+            # Preserve prompt if it ends at the start of an interpolation, otherwise bump its end time
+            if p == prev_p and res[-1][0] not in [x[0][0] for x in interpolations]:
+                res[-1][0] = end_at
+                continue
             if end_at < self.start:
                 continue
             elif end_at <= self.end:
@@ -357,20 +338,18 @@ class PromptSchedule(object):
             elif end_at > self.end and prev_end < self.end:
                 res.append([end_at, p])
                 break
+            prev_p = p
 
         # Always use the last prompt if everything was filtered
         if len(res) == 0:
             res = [[1.0, parsed[-1][1]]]
 
-        final = [res[0]]
+        return interpolations, res
 
-        # Clean up duplicates
-        for p in res[1:]:
-            if p[1] != final[-1][1]:
-                final.append(p)
-            else:
-                final[-1][0] = p[0]
-        return final
+    def add_masks(self, *masks):
+        for mask in masks:
+            if mask is not None:
+                self.masks.append(mask)
 
     def clone(self):
         return self.with_filters()
@@ -384,7 +363,8 @@ class PromptSchedule(object):
             filters=ifspecified(filters, self.filters),
             start=ifspecified(start, self.start),
             end=ifspecified(end, self.end),
-            num_steps=self.num_steps,
+            defaults=ifspecified(defaults, self.defaults),
+            masks=self.masks[:],
         )
         return p
 
@@ -398,83 +378,23 @@ class PromptSchedule(object):
                 return i, x
         return len(self.parsed_prompt) - 1, self.parsed_prompt[-1]
 
+    def interpolation_at(self, step, total_steps=1):
+        i, x = self.at_step_idx(step, total_steps)
+        for y in self.parsed_prompt[i:]:
+            step = min(y[0], 1.0)
+            if x[1]["prompt"] != y[1]["prompt"]:
+                return step, y
+        return 1.0, self.parsed_prompt[-1]
 
-def parse_search(search):
-    arg_start = search.find("(")
-    args = ""
-    name = search.strip()
-    if arg_start > 0:
-        arg_end = find_closing_paren(search, arg_start)
-        name = search[:arg_start].strip()
-        args = search[arg_start + 1 : arg_end - 1]
+    def load_loras(self, lora_cache=None):
+        from .utils import Timer, load_loras_from_schedule
 
-    if not name:
-        return None
-    args = args.strip()
-    # If using the form DEF(F()=$1) then the default value of $1 is the empty string
-    if arg_start > 0:
-        args = [a.strip() for a in args.split(";")]
-    else:
-        args = []
-    return name, args
+        if lora_cache is not None:
+            self.loaded_loras = lora_cache
+        with Timer("PromptSchedule.load_loras()"):
+            self.loaded_loras = load_loras_from_schedule(self.parsed_prompt, self.loaded_loras)
+        return self.loaded_loras
 
 
-def expand_macros(text):
-    text, defs = get_function(text, "DEF", defaults=None)
-    res = text
-    prevres = text
-    replacements = []
-    for d in defs:
-        r = d.split("=", 1)
-        search = parse_search(r[0].strip())
-        if not search or len(r) != 2:
-            log.warning("Ignoring invalid DEF(%s)", d)
-            continue
-        replacements.append((search, r[1].strip()))
-    iterations = 0
-    while True:
-        iterations += 1
-        if iterations > 10:
-            raise ValueError("Unable to resolve DEFs, make sure there are no cycles!")
-            return text
-        for search, replace in replacements:
-            res = substitute_defcall(res, search, replace)
-        if res == prevres:
-            break
-        prevres = res
-    if res.strip() != text.strip():
-        res = res.strip()
-        log.info("DEFs expanded to: %s", res)
-    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"
-        paramvals = []
-        if parameters is not None:
-            paramvals = [x.strip() for x in parameters.split(";")]
-        r = replace
-        for i, v in enumerate(paramvals):
-            r = re.sub(rf"\${i+1}\b", v, r)
-
-        for i, v in enumerate(default_args):
-            r = re.sub(rf"\${i+1}\b", v, r)
-
-        text = text.replace(ph, r)
-    return text
-
-
-@lru_cache
-def parse_prompt_schedules(prompt, **kwargs):
-    prompt = expand_macros(prompt)
-    return PromptSchedule(prompt, **kwargs)
+def parse_prompt_schedules(prompt):
+    return PromptSchedule(prompt)

prompt_control/prompts.py

@@ -1,616 +0,0 @@
-import logging
-import re
-import torch
-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 .adv_encode import advanced_encode_from_tokens
-from .cutoff import process_cuts
-from .parser import parse_cuts
-
-from .attention_couple_ppm import set_cond_attnmask
-
-log = logging.getLogger("comfyui-prompt-control")
-
-AVAILABLE_STYLES = ["comfy", "perp", "A1111", "compel", "comfy++", "down_weight"]
-AVAILABLE_NORMALIZATIONS = ["none", "mean", "length", "length+mean"]
-
-SHUFFLE_GEN = torch.Generator(device="cpu")
-
-
-def get_sdxl(text, defaults):
-    # Defaults fail to parse and get looked up from the defaults dict
-    text, sdxl = get_function(text, "SDXL", ["none", "none", "none"])
-    if not sdxl:
-        return text, {}
-    args = sdxl[0]
-    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+")
-    cropw, croph = parse_floats(args[2], [d.get("sdxl_cwidth", 0), d.get("sdxl_cheight", 0)], split_re="\\s+")
-
-    opts = {
-        "width": int(w),
-        "height": int(h),
-        "target_width": int(tw),
-        "target_height": int(th),
-        "crop_w": int(cropw),
-        "crop_h": int(croph),
-    }
-    return text, opts
-
-
-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()
-    res = {}
-    for arg in args.split(","):
-        try:
-            te, val = arg.strip().split("=")
-            te, val = te.strip(), float(val.strip())
-            res[te] = val
-        except ValueError:
-            log.warning("Invalid TE weight spec '%s', ignoring...", arg.strip())
-    return res, text
-
-
-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 = style.strip()
-    normalization = normalization.strip()
-    if style.replace("old+", "") not in AVAILABLE_STYLES:
-        log.warning("Unrecognized prompt style: %s. Using %s", style, default_style)
-
-    for part in normalization.split("+"):
-        if part not in AVAILABLE_NORMALIZATIONS:
-            log.warning("Unrecognized prompt normalization: %s. Using %s", normalization, default_normalization)
-            normalization = default_normalization
-            break
-
-    return style, normalization, text
-
-
-def shuffle_chunk(shuffle, c):
-    func, shuffle = shuffle
-    shuffle_count = int(safe_float(shuffle[0], 0))
-    _, separator, joiner = shuffle
-    if separator == "default":
-        separator = ","
-
-    if not separator:
-        separator = ","
-
-    joiner = {
-        "default": ",",
-        "separator": separator,
-    }.get(joiner, joiner)
-
-    log.debug("%s arg=%s sep=%s join=%s", func, shuffle_count, separator, joiner)
-    separated = smarter_split(separator, c)
-    log.debug("Prompt split into %s", separated)
-    if func == "SHIFT":
-        shuffle_count = shuffle_count % len(separated)
-        permutation = separated[shuffle_count:] + separated[:shuffle_count]
-    elif func == "SHUFFLE":
-        SHUFFLE_GEN.manual_seed(shuffle_count)
-        permutation = [separated[i] for i in torch.randperm(len(separated), generator=SHUFFLE_GEN)]
-    else:
-        # ??? should never get here
-        permutation = separated
-
-    permutation = [p for p in permutation if p.strip()]
-    if permutation != separated:
-        c = joiner.join(permutation)
-    return c
-
-
-def fix_word_ids(tokens):
-    """Fix word indexes. Tokenizing separately (when BREAKs exist) causes the indexes to restart which causes problems with some weighting algorithms that rely on them"""
-    for key in tokens:
-        max_idx = 0
-        for group in range(len(tokens[key])):
-            for i, token in enumerate(tokens[key][group]):
-                if len(token) < 3:
-                    # No need to fix ids when they don't exist
-                    return tokens
-                # Ignore zeros, they represent the padding token
-                if token[2] != 0 and token[2] < max_idx:
-                    tokens[key][group][i] = (token[0], token[1], token[2] + max_idx)
-            max_idx = max(max_idx, max(x for _, _, x in tokens[key][group]))
-    return tokens
-
-
-def tokenize_chunks(clip, text, need_word_ids, can_break):
-    chunks = re.split(r"\bBREAK\b", text)
-    token_chunks = []
-    shuffled_chunks = []
-    for c in chunks:
-        c, shuffles = get_function(c.strip(), "(SHIFT|SHUFFLE)", ["0", "default", "default"], return_func_name=True)
-        r = c
-        for s in shuffles:
-            r = shuffle_chunk(s, r)
-        if r != c:
-            log.info("Shuffled prompt chunk to %s", r)
-        shuffled_chunks.append(r)
-        t = clip.tokenize(c, return_word_ids=need_word_ids)
-        token_chunks.append(t)
-
-    tokens = token_chunks[0]
-    full_prompt = "".join(shuffled_chunks)
-    full_tokenized = tokens
-    if len(chunks) > 1:
-        full_tokenized = clip.tokenize(full_prompt, return_word_ids=need_word_ids)
-        for key in tokens:
-            if not can_break.get(key):
-                log.warning("BREAK does not make sense for %s, tokenizing as one chunk. Use CAT instead.", key)
-                tokens[key] = full_tokenized[key]
-                continue
-            for c in token_chunks[1:]:
-                tokens[key].extend(c[key])
-
-    return tokens
-
-
-def tokenize(clip, text, can_break, empty_tokens):
-    # defaults=None means there is no argument parsing at all
-    text, l_prompts = get_function(text, "CLIP_L", defaults=None)
-    text, te_prompts = get_function(text, "TE", defaults=None)
-    need_word_ids = True
-    tokens = tokenize_chunks(clip, text, need_word_ids, can_break)
-
-    per_te_prompts = {}
-    if l_prompts:
-        log.warning("Note: CLIP_L is deprecated. Use TE(l=prompt) instead")
-        per_te_prompts["l"] = l_prompts
-
-    for prompt in te_prompts:
-        if prompt.strip() == "help":
-            log.info("Encoders available for TE: %s", ", ".join(tokens.keys()))
-            continue
-        params = prompt.split("=", 1)
-        if len(params) != 2:
-            log.warning("Invalid TE call, ignoring: %s", prompt)
-            continue
-        te = params[0].strip()
-        prompt = params[1].strip()
-        if te not in tokens:
-            log.warning("Invalid TE call, no TE with key '%s', ignoring: %s", te)
-            log.info("Encoders available for TE: %s", ", ".join(tokens.keys()))
-            continue
-        l = per_te_prompts.get(te, [])
-        l.append(prompt)
-        per_te_prompts[te] = l
-
-    if per_te_prompts:
-        for key in per_te_prompts:
-            prompt = " ".join(per_te_prompts[key])
-            tokens[key] = tokenize_chunks(clip, prompt, need_word_ids, can_break)[key]
-            log.info("Encoded prompt with TE '%s': %s", key, prompt)
-
-    maxlen = max([0] + [len(tokens[k]) for k in tokens if can_break[k]])
-    for k in tokens:
-        if not can_break[k]:
-            continue
-        while len(tokens[k]) < maxlen:
-            tokens[k] += empty_tokens[k]
-
-    return fix_word_ids(tokens)
-
-
-def encode_prompt_segment(
-    clip,
-    text,
-    settings,
-    default_style="comfy",
-    default_normalization="none",
-    clip_weights=None,
-) -> list[tuple[torch.Tensor, dict[str]]]:
-    style, normalization, text = get_style(text, default_style, default_normalization)
-    clip_weights, text = get_clipweights(text, clip_weights)
-    text, cuts = parse_cuts(text)
-    extra = {}
-    if clip_weights:
-        extra["clip_weights"] = clip_weights
-    if cuts:
-        extra["cuts"] = cuts
-
-    empty = clip.tokenize("", return_word_ids=True)
-    can_break = {}
-    for k in empty:
-        tokenizer = getattr(clip.tokenizer, f"clip_{k}", getattr(clip.tokenizer, k, None))
-        can_break[k] = tokenizer and tokenizer.pad_to_max_length
-
-    clip = hook_te(clip, empty.keys(), style, normalization, extra)
-
-    # Chunks to ConditioningAverage:
-
-    text, averages = split_by_function(text, "AVG", ["0.5"])
-    prompts_to_avg = []
-    for avg in averages:
-        w = safe_float(avg["args"][0], 0.5)
-        prompts_to_avg.append((text, w))
-        text = avg["text"]
-    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:
-            tokens = tokenize(clip, c, can_break, empty)
-            conds_to_cat.append(clip.encode_from_tokens_scheduled(tokens, add_dict=settings))
-
-        base = conds_to_cat[0]
-        for cond in conds_to_cat[1:]:
-            assert len(cond) == len(base), "Conditioning length mismatch"
-            # Pooled gets ignored
-            for i in range(len(base)):
-                c1 = base[i][0]
-                c2 = cond[i][0]
-                base[i][0] = torch.cat((c1, c2), 1)
-        conds_to_avg.append((base, weight))
-
-    base, w = conds_to_avg[0]
-    for cond, next_w in conds_to_avg[1:]:
-        assert len(base) == len(cond), "Conditioning length mismatch"
-        if w == 1.0:
-            w = next_w
-            continue
-        for i in range(len(base)):
-            (cond,) = ConditioningAverage.addWeighted(None, [base[i]], [cond[i]], w)
-            base[i] = cond[0]
-        w = next_w
-
-    return base
-
-
-def apply_weights(output, te_name, spec):
-    """Applies weights to TE outputs"""
-    if not spec:
-        return output
-
-    if te_name.startswith("clip_"):
-        te_name = te_name[5:]
-
-    default = spec.get("all", None)
-
-    if isinstance(output, tuple):
-        out, pooled = 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)
-            pooled_w = spec.get(pkey, w)
-            if w is None:
-                w = 1.0
-            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
-            if pooled is not None:
-                pooled = pooled * pooled_w
-
-        return out, pooled
-    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
-        return output
-
-
-def make_patch(te_name, orig_fn, normalization, style, extra):
-    def encode(t):
-        r = advanced_encode_from_tokens(
-            t, normalization, style, orig_fn, return_pooled=True, apply_to_pooled=False, **extra
-        )
-        return apply_weights(r, te_name, extra.get("clip_weights"))
-
-    if "cuts" in extra:
-        return partial(process_cuts, encode, extra)
-    return encode
-
-
-def hook_te(clip, te_names, style, normalization, extra):
-    if style == "comfy" and normalization == "none" and not extra:
-        return clip
-    newclip = clip.clone()
-    for te_name in te_names:
-        tokenizer = getattr(clip.tokenizer, f"clip_{te_name}", getattr(clip.tokenizer, te_name, None))
-        if tokenizer:
-            x = extra.copy()
-            x["tokenizer"] = tokenizer
-            if not hasattr(clip.patcher.model, te_name):
-                te_name = "clip_" + te_name
-            if not hasattr(clip.patcher.model, te_name):
-                log.warning("TE model %s not found on model patcher. Skipping...", te_name)
-                continue
-
-            log.debug("Hooked into te=%s with style=%s, normalization=%s", te_name, style, normalization)
-            encode = clip.patcher.get_model_object(f"{te_name}.encode_token_weights")
-            x["has_negpip"] = clip.patcher.model_options.get("ppm_negpip", False)
-            newclip.patcher.add_object_patch(
-                f"{te_name}.encode_token_weights",
-                make_patch(
-                    te_name,
-                    encode,
-                    normalization,
-                    style,
-                    x,
-                ),
-            )
-        # 'g' and 'l' exist in these are clip_g and clip_l
-        else:
-            log.warning("Tokens contain items with key %s but no tokenizer found on object with that name.", te_name)
-    return newclip
-
-
-def get_area(text):
-    text, areas = get_function(text, "AREA", ["0 1", "0 1", "1"])
-    if not areas:
-        return text, None
-
-    args = areas[0]
-    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)
-
-    def is_pct(f):
-        return f >= 0.0 and f <= 1.0
-
-    def is_pixel(f):
-        return f == 0 or f > 1
-
-    if all(is_pct(v) for v in [h, w, y, x]):
-        area = ("percentage", h, w, y, x)
-    elif all(is_pixel(v) for v in [h, w, y, x]):
-        area = (int(h) // 8, int(w) // 8, int(y) // 8, int(x) // 8)
-    else:
-        raise Exception(
-            f"AREA specified with invalid size {x} {w}, {h} {y}. They must either all be percentages between 0 and 1 or positive integer pixel values excluding 1"
-        )
-
-    return text, (area, weight)
-
-
-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]
-    return text, (int(w), int(h))
-
-
-def make_mask(args, size, weight):
-    x1, x2 = parse_floats(args[0], [0.0, 1.0], split_re="\\s+")
-    y1, y2 = parse_floats(args[1], [0.0, 1.0], split_re="\\s+")
-
-    def is_pct(f):
-        return f >= 0.0 and f <= 1.0
-
-    def is_pixel(f):
-        return f == 0 or f > 1
-
-    if all(is_pct(v) for v in [x1, x2, y1, y2]):
-        w, h = size
-        xs = int(w * x1), int(w * x2)
-        ys = int(h * y1), int(h * y2)
-    elif all(is_pixel(v) for v in [x1, x2, y1, y2]):
-        w, h = size
-        xs = int(x1), int(x2)
-        ys = int(y1), int(y2)
-    else:
-        raise Exception(
-            f"MASK specified with invalid size {x1} {x2}, {y1} {y2}. They must either all be percentages between 0 and 1 or positive integer pixel values excluding 1"
-        )
-
-    mask = torch.full((h, w), 0, dtype=torch.float32, device="cpu")
-    mask[ys[0] : ys[1], xs[0] : xs[1]] = weight
-    mask = mask.unsqueeze(0)
-    log.debug("Mask xs=%s, ys=%s, shape=%s, weight=%s", xs, ys, mask.shape, weight)
-    return mask
-
-
-def get_mask(text, size, input_masks):
-    """Parse MASK(x1 x2, y1 y2, weight), IMASK(i, weight) and FEATHER(left top right bottom)"""
-    # TODO: combine multiple masks
-    text, masks = get_function(text, "MASK", ["0 1", "0 1", "1", "multiply"])
-    text, imasks = get_function(text, "IMASK", ["0", "1", "multiply"])
-    text, feathers = get_function(text, "FEATHER", ["0 0 0 0"])
-    text, maskw = get_function(text, "MASKW", ["1.0"])
-    if not masks and not imasks:
-        return text, None, None
-
-    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]
-        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)
-    i = 0
-    for m in masks:
-        weight = safe_float(m[2], 1.0)
-        op = m[3]
-        nextmask = make_mask(m, size, weight)
-        if i < len(feathers):
-            nextmask = feather(feathers[i], nextmask)
-        i += 1
-        if mask is not None:
-            log.info("MaskComposite op=%s", op)
-            mask = MaskComposite().combine(mask, nextmask, 0, 0, op)[0]
-        else:
-            mask = nextmask
-
-    for idx, w, op in imasks:
-        idx = int(safe_float(idx, 0.0))
-        w = safe_float(w, 1.0)
-        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)
-        i += 1
-        if mask is not None:
-            mask = MaskComposite().combine(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)
-
-    return text, mask, totalweight
-
-
-def get_noise(text):
-    text, noises = get_function(
-        text,
-        "NOISE",
-        ["0.0", "none"],
-    )
-    if not noises:
-        return text, None, None
-    w = 0
-    # Only take seed from first noise spec, for simplicity
-    seed = safe_float(noises[0][1], "none")
-    if seed == "none":
-        gen = None
-    else:
-        gen = torch.Generator()
-        gen.manual_seed(int(seed))
-    for n in noises:
-        w += safe_float(n[0], 0.0)
-    return text, max(min(w, 1.0), 0.0), gen
-
-
-def apply_noise(cond, weight, gen):
-    if cond is None or not weight:
-        return cond
-
-    n = torch.randn(cond.size(), generator=gen).to(cond)
-
-    return cond * (1 - weight) + n * weight
-
-
-def process_settings(prompt, defaults, masks, mask_size, sdxl_opts):
-    if "ATTN()" in prompt:
-        raise ValueError("ATTN() no longer works and has been replaced by COUPLE()")
-
-    def weight(t):
-        opts = {}
-        m = re.search(r":(-?\d\.?\d*)(![A-Za-z]+)?$", t.strip())
-        if not m:
-            return (None, opts, t)
-        w = float(m[1])
-        tag = m[2]
-        t = t[: m.span()[0]]
-        if tag == "!noscale":
-            opts["scale"] = 1
-
-        return w, opts, t
-
-    settings = {"prompt": prompt}
-
-    if "FILL()" in prompt:
-        prompt = prompt.replace("FILL()", "")
-        settings["x-promptcontrol.fill"] = True
-    prompt, mask, mask_weight = get_mask(prompt, mask_size, masks)
-    prompt, noise_w, generator = get_noise(prompt)
-    prompt, area = get_area(prompt)
-    prompt, local_sdxl_opts = get_sdxl(prompt, defaults)
-    # Get weight last so other syntax doesn't interfere with it
-    w, opts, prompt = weight(prompt)
-    if w is not None:
-        settings["strength"] = w
-    settings.update(sdxl_opts)
-    settings.update(local_sdxl_opts)
-    if area:
-        settings["area"] = area[0]
-        settings["strength"] = area[1]
-        settings["set_area_to_bounds"] = False
-    if mask is not None:
-        settings["mask"] = mask
-        settings["mask_strength"] = mask_weight
-
-    return prompt, settings
-
-
-def encode_prompt(clip, text, start_pct, end_pct, defaults, masks):
-    # First style modifier applies to ANDed prompts too unless overridden
-    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)]
-
-    p, sdxl_opts = get_sdxl(prompts[0], defaults)
-    prompts[0] = p
-
-    conds = []
-    # TODO: is this still needed?
-    # scale = sum(abs(weight(p)[0]) for p in prompts if not ("AREA(" in p or "MASK(" in p))
-
-    def ensure_mask(c):
-        if "mask" not in c[1]:
-            _, mask, _ = get_mask("MASK()", mask_size, masks)
-            c[1]["mask"] = mask
-            c[1]["mask_strength"] = 1.0
-        return c
-
-    def couple_mask(args):
-        if args is None:
-            return ""
-        return f"MASK({args})"
-
-    for prompt in prompts:
-        base_prompt, attn_couple_prompts = split_by_function(prompt, "COUPLE", defaults=None)
-
-        prompts = [base_prompt] + [couple_mask(p["args"]) + p["text"] for p in attn_couple_prompts]
-        encoded = []
-        for p in prompts:
-            p, settings = process_settings(p, defaults, masks, mask_size, sdxl_opts)
-            if settings.get("strength") == 0:  # weight is explicitly set to 0, skip
-                continue
-            settings["start_percent"] = start_pct
-            settings["end_percent"] = end_pct
-            x = encode_prompt_segment(clip, p, settings, style, normalization)
-            encoded.append(x)
-
-        assert all(
-            len(c) == len(encoded[0]) for c in encoded
-        ), "All encoded prompts didn't produce the same number of conds, I don't know what to do in this situation."
-
-        # each call to encode_prompt_segment can produce a number of conds based on any
-        # scheduled LoRA hooks on the clip model. Zip them together with coupled prompts
-        base_cond = []
-        for base_cond, *attention_couple in zip(*encoded):
-            s = base_cond[1]
-            # If there are LoRAs on the CLIP, we need to fix start_percent and end_percent on the new conds for things to work properly.
-            s["start_percent"] = s.get("clip_start_percent", s["start_percent"])
-            s["end_percent"] = s.get("clip_end_percent", s["end_percent"])
-            s.pop("clip_start_percent", None)
-            s.pop("clip_end_percent", None)
-            base_cond = [base_cond]
-            if attention_couple:
-                fill = base_cond[0][1].get("x-promptcontrol.fill")
-                if not fill:
-                    ensure_mask(base_cond[0])
-                # else, set_cond_attnmask will have the base mask fill any unspecified areas
-                base_cond = set_cond_attnmask(
-                    base_cond,
-                    [ensure_mask(c) for c in attention_couple],
-                    fill=fill,
-                )
-        conds.extend(base_cond)
-
-    return conds

prompt_control/test_encode.py

@@ -1,171 +0,0 @@
-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 = []
-
-import logging
-
-logging.basicConfig()
-
-
-def run(f, *args):
-    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())
-
-    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].get(key), b[1].get(key))
-            else:
-                self.tensorsEqual(a[0], b[0])
-
-    def test_basic_encode(self):
-        pc = PCTextEncode()
-        comfy = nodes.CLIPTextEncode()
-        combine = nodes.ConditioningCombine()
-        average = nodes.ConditioningAverage()
-        concat = nodes.ConditioningConcat()
-        zeroout = nodes.ConditioningZeroOut()
-        for k, clip in clips:
-            with self.subTest(k):
-                with self.subTest("No exceptions"):
-                    run(
-                        pc,
-                        clip,
-                        "test AND test (test:1.2) BREAK test AND TE_WEIGHT(all=0) SDXL() AND AREA(,,) test CAT test",
-                    )
-                with self.subTest("Basic"):
-                    (c1,) = run(pc, clip, "test")
-                    (c2,) = run(comfy, clip, "test")
-                    c = c2  # Used in later tests
-                    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)")
-                    self.condEqual(c1, c2)
-
-                with self.subTest("Concat"):
-                    (c1,) = run(pc, clip, "test CAT test")
-                    (c2,) = run(concat, c, c)
-                    self.condEqual(c1, c2)
-
-                with self.subTest("Combine"):
-                    (c1,) = run(pc, clip, "test AND test")
-                    (c2,) = run(combine, c, c)
-                    self.condEqual(c1, c2)
-
-                with self.subTest("Zero out"):
-                    (c1,) = run(pc, clip, "test TE_WEIGHT(all=0)")
-                    (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")
-                    (avg,) = run(average, c1, c2, 0.5)
-                    self.condEqual(avg, c3)
-
-    def test_weight(self):
-        pc = PCTextEncode()
-        comfy = nodes.CLIPTextEncode()
-        combine = nodes.ConditioningCombine()
-        strength = nodes.ConditioningSetAreaStrength()
-        for k, clip in clips:
-            (c,) = run(comfy, clip, "test")
-            (c2,) = run(strength, c, 0.5)
-            with self.subTest(f"Testing {k}"):
-                with self.subTest("Conditioning weights"):
-                    (a,) = run(pc, clip, "test :0.5 AND test :0.5")
-                    (b,) = run(combine, c2, c2)
-                    self.condEqual(a, b)
-                    self.condEqual(a, b, "strength")
-                with self.subTest("Weight == 0"):
-                    (a,) = run(pc, clip, "test :0.5 AND test :0 AND test")
-                    (b,) = run(combine, c2, c)
-                    self.condEqual(a, b)
-                    self.condEqual(a, b, "strength")
-
-    def test_attn_couple(self):
-        pc = PCTextEncode()
-        for k, clip in clips:
-            with self.subTest(f"Testing {k}"):
-                (c,) = run(pc, clip, "test COUPLE prompt1 AND test2 COUPLE prompt2")
-                (c2,) = run(pc, clip, "test COUPLE prompt1 COUPLE test2 COUPLE prompt2")
-                self.assertTrue(len(c) == 2)
-                self.assertTrue(len(c2) == 1)
-
-    def test_styles(self):
-        pc = PCTextEncode()
-        comfy = nodes.CLIPTextEncode()
-        for k, clip in clips:
-            (no_weights,) = run(comfy, clip, "this prompt has no weights")
-            for style in ["comfy", "A1111", "comfy++", "compel", "down_weight", "perp"]:
-                with self.subTest(f"TE {k} style {style} no weights equal comfy"):
-                    (c,) = run(pc, clip, "this prompt has no weights")
-                    self.condEqual(no_weights, c)
-                with self.subTest(f"TE {k} style {style} does not fail when encoding weights"):
-                    for normalization in ["none", "mean", "length", "mean+length", "length+mean"]:
-                        with self.subTest(f"TE {k} style {style} normalization {normalization}"):
-                            (c,) = run(
-                                pc,
-                                clip,
-                                f"STYLE({style}, {normalization}) (this prompt) (has weights:0.9), (a:1.2) (b:1.2)",
-                            )
-
-    def test_masks(self):
-        pc = PCTextEncode()
-        comfy = nodes.CLIPTextEncode()
-        solidmask = comfy_extras.nodes_mask.SolidMask()
-        setMask = nodes.ConditioningSetMask()
-        for k, clip in clips:
-            (c1,) = run(pc, clip, "test MASK()")
-            (c2,) = run(comfy, clip, "test")
-            (c2,) = run(setMask, c2, run(solidmask, 1.0, 512, 512)[0], "default", 1.0)
-            self.condEqual(c1, c2)
-            self.condEqual(c1, c2, "mask", self.tensorsEqual)
-
-
-if __name__ == "__main__":
-    unittest.main()

prompt_control/test_encode_refactor.py

@@ -1,71 +0,0 @@
-import unittest
-import numpy.testing as npt
-from os import environ
-
-clips = []
-
-
-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 clips:
-            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")
-    from comfy.sd import load_clip
-    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()

prompt_control/test_graph.py

@@ -1,244 +0,0 @@
-import unittest
-import unittest.mock as mock
-import logging
-
-log = logging.getLogger("comfyui-prompt-control")
-
-
-def reset_graphbuilder_state():
-    from comfy_execution.graph_utils import GraphBuilder
-
-    GraphBuilder.set_default_prefix("UID", 0, 0)
-
-
-def find_file(name):
-    names = {"test": "test.safetensors", "other": "some/other.safetensors"}
-    return names.get(name)
-
-
-def loraloader(text, adv=False, **kwargs):
-    from .nodes_lazy import PCLazyLoraLoader, PCLazyLoraLoaderAdvanced
-
-    reset_graphbuilder_state()
-    if adv:
-        cls = PCLazyLoraLoader
-    else:
-        cls = PCLazyLoraLoaderAdvanced
-    model = [0, 1]
-    clip = [0, 0]
-    return cls().apply(unique_id="UID", model=model, clip=clip, text=text, **kwargs)
-
-
-def te(text, adv=False, **kwargs):
-    from .nodes_lazy import PCLazyTextEncode, PCLazyTextEncodeAdvanced
-
-    if adv:
-        cls = PCLazyTextEncode
-    else:
-        cls = PCLazyTextEncodeAdvanced
-    reset_graphbuilder_state()
-    clip = [0, 0]
-    return cls().apply(clip=clip, text=text, unique_id="UID", **kwargs)
-
-
-@mock.patch("prompt_control.utils.lora_name_to_file", find_file)
-@mock.patch("torch.cuda.current_device", lambda: "cpu")
-class GraphTests(unittest.TestCase):
-    maxDiff = 4096
-
-    def test_textencode(self):
-        for p in ["test", "[test:0.2] test", "[test[test::0.5]]<lora:test:1>"]:
-            r1 = te(p)
-            r2 = te(p, adv=True)
-            with self.subTest(f"Expansion: {p}"):
-                self.assertEqual(r1, r2)
-
-        reset_graphbuilder_state()
-        with self.subTest("Expansion: LoRA"):
-            r = te("test<lora:test:1>")
-            self.assertEqual(
-                r,
-                {
-                    "result": (["UID.0.0.2", 0],),
-                    "expand": {
-                        "UID.0.0.1": {"class_type": "PCTextEncode", "inputs": {"clip": [0, 0], "text": "test"}},
-                        "UID.0.0.2": {
-                            "class_type": "ConditioningSetTimestepRange",
-                            "inputs": {"conditioning": ["UID.0.0.1", 0], "start": 0.0, "end": 1.0},
-                        },
-                    },
-                },
-            )
-        with self.subTest("Expansion: LoRA with schedule"):
-            r = te("simple [test:0.1,0.5] prompt<lora:test:1>")
-            self.assertEqual(
-                r,
-                {
-                    "result": (["UID.0.0.8", 0],),
-                    "expand": {
-                        "UID.0.0.1": {
-                            "class_type": "PCTextEncode",
-                            "inputs": {"clip": [0, 0], "text": "simple  prompt"},
-                        },
-                        "UID.0.0.2": {
-                            "class_type": "ConditioningSetTimestepRange",
-                            "inputs": {"conditioning": ["UID.0.0.1", 0], "start": 0.0, "end": 0.1},
-                        },
-                        "UID.0.0.3": {
-                            "class_type": "PCTextEncode",
-                            "inputs": {"clip": [0, 0], "text": "simple test prompt"},
-                        },
-                        "UID.0.0.4": {
-                            "class_type": "ConditioningSetTimestepRange",
-                            "inputs": {"conditioning": ["UID.0.0.3", 0], "start": 0.1, "end": 0.5},
-                        },
-                        "UID.0.0.5": {
-                            "class_type": "PCTextEncode",
-                            "inputs": {"clip": [0, 0], "text": "simple  prompt"},
-                        },
-                        "UID.0.0.6": {
-                            "class_type": "ConditioningSetTimestepRange",
-                            "inputs": {"conditioning": ["UID.0.0.5", 0], "start": 0.5, "end": 1.0},
-                        },
-                        "UID.0.0.7": {
-                            "class_type": "ConditioningCombine",
-                            "inputs": {"conditioning_1": ["UID.0.0.2", 0], "conditioning_2": ["UID.0.0.4", 0]},
-                        },
-                        "UID.0.0.8": {
-                            "class_type": "ConditioningCombine",
-                            "inputs": {"conditioning_1": ["UID.0.0.7", 0], "conditioning_2": ["UID.0.0.6", 0]},
-                        },
-                    },
-                },
-            )
-
-    @mock.patch("prompt_control.utils.lora_name_to_file", find_file)
-    def test_loraloader(self):
-        with self.assertLogs(log, level="WARNING") as cm:
-            result = loraloader("prompt here <lora:nonexistent:1.0:0.5>")["expand"]
-            result_adv = loraloader("prompt here <lora:nonexistent:1.0:0.5>", adv=True)["expand"]
-        self.assertIn("LoRA 'nonexistent' not found", cm.output[0])
-        self.assertEqual(result, {})
-        self.assertEqual(result_adv, {})
-
-        result = loraloader("<lora:test:1>")["expand"]
-        result2 = loraloader("prompt here <lora:test:1.0:0.5><lora:test:0:0.5>")["expand"]
-        result3 = loraloader("prompt here <lora:test:1.0:0.5><lora:test:0:0.5>", adv=True)["expand"]
-        self.assertEqual(result, result2)
-        self.assertEqual(result2, result3)
-        self.assertEqual(
-            result,
-            {
-                "UID.0.0.1": {
-                    "class_type": "LoraLoader",
-                    "inputs": {
-                        "model": [0, 1],
-                        "clip": [0, 0],
-                        "strength_model": 1.0,
-                        "strength_clip": 1.0,
-                        "lora_name": "test.safetensors",
-                    },
-                }
-            },
-        )
-        result = loraloader("<lora:test:1><lora:other:0.5>")["expand"]
-        self.assertEqual(
-            result,
-            {
-                "UID.0.0.1": {
-                    "class_type": "LoraLoader",
-                    "inputs": {
-                        "model": [0, 1],
-                        "clip": [0, 0],
-                        "strength_model": 1.0,
-                        "strength_clip": 1.0,
-                        "lora_name": "test.safetensors",
-                    },
-                },
-                "UID.0.0.2": {
-                    "class_type": "LoraLoader",
-                    "inputs": {
-                        "model": ["UID.0.0.1", 0],
-                        "clip": ["UID.0.0.1", 1],
-                        "strength_model": 0.5,
-                        "strength_clip": 0.5,
-                        "lora_name": "some/other.safetensors",
-                    },
-                },
-            },
-        )
-
-        result = loraloader("prompt here <lora:test:1.0:0.5>")["expand"]
-        self.assertEqual(
-            result,
-            {
-                "UID.0.0.1": {
-                    "class_type": "LoraLoader",
-                    "inputs": {
-                        "model": [0, 1],
-                        "clip": [0, 0],
-                        "strength_model": 1.0,
-                        "strength_clip": 0.5,
-                        "lora_name": "test.safetensors",
-                    },
-                }
-            },
-        )
-
-        result = loraloader("prompt [<lora:test:0.5>:0.5]")["expand"]
-        result2 = loraloader("prompt [<lora:test:0.5>:0.5]", adv=True)["expand"]
-        self.assertEqual(result, result2)
-        expected = {
-            "UID.0.0.1": {
-                "class_type": "CreateHookLora",
-                "inputs": {"lora_name": "test.safetensors", "strength_model": 0.5, "strength_clip": 0.5},
-            },
-            "UID.0.0.2": {
-                "class_type": "CreateHookKeyframe",
-                "inputs": {"strength_mult": 0.0, "start_percent": 0.0},
-            },
-            "UID.0.0.3": {
-                "class_type": "CreateHookKeyframe",
-                "inputs": {
-                    "start_percent": 0.5,
-                    "prev_hook_kf": ["UID.0.0.2", 0],
-                    "strength_mult": 1.0,
-                },
-            },
-            "UID.0.0.4": {
-                "class_type": "SetHookKeyframes",
-                "inputs": {"hooks": ["UID.0.0.1", 0], "hook_kf": ["UID.0.0.3", 0]},
-            },
-            "UID.0.0.5": {
-                "class_type": "SetClipHooks",
-                "inputs": {
-                    "clip": [0, 0],
-                    "hooks": ["UID.0.0.4", 0],
-                    "apply_to_conds": True,
-                    "schedule_clip": True,
-                },
-            },
-        }
-        self.assertEqual(result, expected)
-        result2 = loraloader("prompt [<lora:test:0.5>:0.5]", adv=True, start=0.6)["expand"]
-        self.assertEqual(
-            result2,
-            {
-                "UID.0.0.1": {
-                    "class_type": "LoraLoader",
-                    "inputs": {
-                        "model": [0, 1],
-                        "clip": [0, 0],
-                        "strength_model": 0.5,
-                        "strength_clip": 0.5,
-                        "lora_name": "test.safetensors",
-                    },
-                }
-            },
-        )
-        result2 = loraloader("prompt [<lora:test:0.5>:0.5]", end=0.5)["expand"]
-        self.assertEqual(result2, {})
-
-
-if __name__ == "__main__":
-    unittest.main()

prompt_control/test_parser.py

@@ -1,240 +0,0 @@
-import unittest
-from .parser import parse_prompt_schedules as parse, expand_macros
-
-
-def prompt(until, text, *loras):
-    loras = {lora: {"weight": unet, "weight_clip": te} for lora, unet, te in loras}
-    return [until, {"prompt": text, "loras": loras}]
-
-
-class TestParser(unittest.TestCase):
-    def assertPrompt(self, p, at, until, text, *loras):
-        self.assertEqual(p.at_step(at), prompt(until, text, *loras))
-
-    def test_no_scheduling(self):
-        p = parse("This is a (basic:0.6) (prompt) with [no scheduling] features")
-        expected = prompt(1.0, "This is a (basic:0.6) (prompt) with [no scheduling] features")
-        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]"]],
-            [parse(p) for p in ["[before:during:after:0.1]", "[before:during:after:0.1,1.0]", "[before:during:0.1]"]],
-            [parse(p) for p in ["[a:0.1,0.5]", "[[a:0.1]::0.5]", "[:a::0.1,0.5]", "[a::0.1,0.5]"]],
-            [parse(p) for p in ["[a:b:0.5]", "[a::b:0.5,0.5]"]],
-            [parse(p) for p in ["[a::0.5]", "[a:::0.5,0.5]"]],
-        ]
-        for group in eqs:
-            for p in group[1:]:
-                with self.subTest(p):
-                    self.assertEqual(group[0].parsed_prompt, p.parsed_prompt)
-
-    def test_basic(self):
-        p = parse(
-            "This is a (basic:0.6) (prompt) with (very [[simple]:(basic:0.6):0.5]:1.1) [features::0.8][ and this is ignored:1]"
-        )
-        self.assertPrompt(p, 0, 0.5, "This is a (basic:0.6) (prompt) with (very [simple]:1.1) features")
-        self.assertPrompt(p, 0.5, 0.5, "This is a (basic:0.6) (prompt) with (very [simple]:1.1) features")
-        self.assertPrompt(p, 0.7, 0.8, "This is a (basic:0.6) (prompt) with (very (basic:0.6):1.1) features")
-        self.assertPrompt(p, 1.0, 1.0, "This is a (basic:0.6) (prompt) with (very (basic:0.6):1.1) ")
-
-    def test_lora(self):
-        p = parse("This is a (lora:0.6) (prompt) with [no scheduling] features <lora:foo:0.5> <lora:bar:0.5:1.0>")
-        expected = prompt(
-            1.0, "This is a (lora:0.6) (prompt) with [no scheduling] features  ", ("foo", 0.5, 0.5), ("bar", 0.5, 1.0)
-        )
-        self.assertEqual(p.at_step(0), expected)
-        self.assertEqual(p.at_step(0.5), expected)
-        self.assertEqual(p.at_step(1), expected)
-
-    def test_scheduled_lora(self):
-        p = parse(
-            "This is a (lora:0.6) (prompt) with [scheduling] features [<lora:foo:0.5>:<lora:bar:0.5:0.2>:0.3] <lora:bar:0.5:1.0>"
-        )
-        self.assertPrompt(
-            p,
-            0.1,
-            0.3,
-            "This is a (lora:0.6) (prompt) with [scheduling] features  ",
-            ("foo", 0.5, 0.5),
-            ("bar", 0.5, 1.0),
-        )
-        self.assertPrompt(p, 0.5, 1.0, "This is a (lora:0.6) (prompt) with [scheduling] features  ", ("bar", 1.0, 1.2))
-
-    def test_seq(self):
-        p = parse("This is a sequence of [SEQ:a:0.2::0.5:c:0.8][SEQ: and x:0.8]")
-        p2 = parse("This is a sequence of [[a:[c:0.5]:0.2]::0.8][ and x::0.8]")
-        prompts = {
-            0.2: "This is a sequence of a and x",
-            0.5: "This is a sequence of  and x",
-            0.8: "This is a sequence of c and x",
-            1.0: "This is a sequence of ",
-        }
-        self.assertEqual(p.parsed_prompt, p2.parsed_prompt)
-        for k, v in prompts.items():
-            self.assertPrompt(p, k, k, v)
-
-    def test_shortcuts_scheduling(self):
-        p = parse("A schedule [a:0.1,0.7] b")
-        p2 = parse("A schedule [[a:0.1]::0.7] b")
-        p3 = parse("A schedule [a:b:0.5,0.8]")
-        p4 = parse("A schedule [[a:0.5]:b:0.8]")
-        self.assertEqual(p.parsed_prompt, p2.parsed_prompt)
-        self.assertEqual(p3.parsed_prompt, p4.parsed_prompt)
-
-    def test_range(self):
-        p = parse("test [excluded::excluded2:0.1,0.4] test")
-        self.assertPrompt(p, 0, 0.1, "test excluded test")
-        self.assertPrompt(p, 0.2, 0.4, "test  test")
-        self.assertPrompt(p, 0.45, 1.0, "test excluded2 test")
-        p = parse("test [[:included::0.2,0.8]|[excluded::excluded2:0.4,0.9]:0.1] test")
-        self.assertPrompt(p, 0, 0.1, "test  test")
-        self.assertPrompt(p, 0.25, 0.3, "test included test")
-        self.assertPrompt(p, 0.15, 0.2, "test excluded test")
-        self.assertPrompt(p, 0.25, 0.3, "test included test")
-        self.assertPrompt(p, 0.55, 0.6, "test  test")
-        self.assertPrompt(p, 0.95, 1.0, "test excluded2 test")
-
-    def test_nested(self):
-        p = parse(
-            "This [prompt is [SEQ:[crazy:weird:0.2] stuff:0.5:<lora:cool:1>:0.7:nesting:1.0]:completely ignored with tags:HR]"
-        )
-        prompts = {
-            0.2: (0.2, "This prompt is crazy stuff"),
-            0.3: (0.5, "This prompt is weird stuff"),
-            0.5: (0.5, "This prompt is weird stuff"),
-            0.8: (1.0, "This prompt is nesting"),
-        }
-        for k in prompts:
-            self.assertEqual(p.at_step(k), [prompts[k][0], {"prompt": prompts[k][1], "loras": {}}])
-
-        self.assertPrompt(p, 0.6, 0.7, "This prompt is ", ("cool", 1.0, 1.0))
-        self.assertPrompt(p, 0.7, 0.7, "This prompt is ", ("cool", 1.0, 1.0))
-        p2 = p.with_filters(filters="hr, xyz")
-
-        self.assertEqual(p2.at_step(0), p2.at_step(1))
-
-    def test_def(self):
-        p = parse("DEF(X=0.5) [a:b:X] DEF(test = [c:X]) test test")
-        prompts = {
-            0.2: (0.5, "a   "),
-            0.6: (1.0, "b  c c"),
-        }
-        for k, v in prompts.items():
-            self.assertPrompt(p, k, v[0], v[1])
-
-        p = parse("DEF(X=[($1):($1:$2):$2])X(test;0.7)")
-        p2 = parse("[(test):(test:0.7):0.7]")
-        with self.subTest("parameters"):
-            self.assertEqual(p.parsed_prompt, p2.parsed_prompt)
-
-        p = parse("DEF(X=[($1):($1:$2):$2])DEF(Y=X(test;$1))Y(0.7) Y(0.5)")
-        p2 = parse("[(test):(test:0.7):0.7] [(test):(test:0.5):0.5]")
-        with self.subTest("two functions"):
-            self.assertEqual(p.parsed_prompt, p2.parsed_prompt)
-
-        p = expand_macros("DEF(X(a;b)=$1 $2 $3 d)X(A) X(A;B;C)")
-        with self.subTest("defaults"):
-            self.assertEqual(p, "A b $3 d A B C d")
-
-        p = expand_macros("DEF(MACRO()=[empty:$1:$2])MACRO MACRO(;) MACRO(;0.5) MACRO(a;0.5)")
-        with self.subTest("Empty default for $1"):
-            self.assertEqual(p, "[empty::$2] [empty::] [empty::0.5] [empty:a:0.5]")
-
-        p = expand_macros("DEF(X=$1)DEF(Y()=$1)[X Y][X() Y()][X(1) Y(1)]")
-        with self.subTest("defaults, DEF=X vs DEF=X()"):
-            self.assertEqual(p, "[$1 ][ ][1 1]")
-
-        p = parse("DEF(test(1)=prompt $1)DEF(test2((a); (test))=[$1:$2:0.5])test test2")
-        p2 = parse("prompt 1 [(a):(prompt 1):0.5]")
-        with self.subTest("defaults, nested parens"):
-            self.assertEqual(p.parsed_prompt, p2.parsed_prompt)
-
-        with self.assertRaises(ValueError) as c:
-            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]")
-        self.assertEqual(p.parsed_prompt, p2.parsed_prompt)
-
-        p = parse("test [[a:[b<lora:test:0.5>:0.6]:0.5]:HR]")
-        p2 = parse("test [:[a:[:b<lora:test:0.5>:0.6]:0.5]:HR]")
-        self.assertEqual(p.parsed_prompt, p2.parsed_prompt)
-
-        pf = p.with_filters(filters="hr")
-        self.assertEqual(pf.parsed_prompt, p2.with_filters(filters="hr").parsed_prompt)
-        self.assertPrompt(pf, 0, 0.5, "test a")
-        self.assertPrompt(pf, 0.55, 0.6, "test ")
-        self.assertPrompt(pf, 0.8, 1.0, "test b", ("test", 0.5, 0.5))
-
-        p = parse("[:[<lora:test:1>:c:0.5]:0.3]")
-        self.assertPrompt(p, 0, 0.3, "")
-        self.assertPrompt(p, 0.4, 0.5, "", ("test", 1.0, 1.0))
-        self.assertPrompt(p, 1.0, 1.0, "c")
-
-        p = parse("an [<emb:foo>:<emb:bar>:0.5]")
-        prompts = {
-            0.2: (0.5, "an embedding:foo"),
-            0.8: (1.0, "an embedding:bar"),
-        }
-        for k, v in prompts.items():
-            self.assertPrompt(p, k, v[0], v[1])
-
-    def test_alternating(self):
-        p = parse("[cat|dog|tiger]")
-        p2 = parse("[cat|dog|tiger:0.1]")
-        p3 = parse("[cat|[dog|wolf]|tiger]")
-        p4 = parse("[cat|[dog:wolf<lora:canine:1>:0.5]:0.2]")
-
-        self.assertEqual(p.parsed_prompt, p2.parsed_prompt)
-        for i, x in enumerate(["cat", "wolf", "tiger", "cat", "dog", "tiger", "cat", "wolf", "tiger", "cat"]):
-            step = round((i * 0.1) + 0.1, 2)
-            with self.subTest(step):
-                self.assertPrompt(p3, step, step, x)
-
-        for i, x in enumerate([["cat"], ["dog"], ["cat"], ["wolf", ("canine", 1.0, 1.0)], ["cat"]]):
-            step = round((i * 0.2) + 0.2, 2)
-            with self.subTest(step):
-                self.assertPrompt(p4, step, step, *x)
-        self.assertPrompt(p4, 0.7, 0.8, "wolf", ("canine", 1.0, 1.0))
-
-
-if __name__ == "__main__":
-    unittest.main()

prompt_control/utils.py

@@ -1,78 +1,10 @@
 from pathlib import Path
 import re
 import logging
-import copy
 
-# Allow testing
-try:
-    from folder_paths import get_filename_list
-except ImportError:
+import folder_paths
 
-    def get_filename_list(x):
-        raise NotImplementedError("How did you get here?")
-
-
-log = logging.getLogger("comfyui-prompt-control")
-
-
-def consolidate_schedule(prompt_schedule):
-    prev_loras = {}
-    not_found = []
-    consolidated = []
-    for end_pct, c in reversed(list(prompt_schedule)):
-        loras = {}
-        for k, v in c["loras"].items():
-            if k in not_found:
-                continue
-            path = lora_name_to_file(k)
-            if path is None:
-                not_found.append(k)
-                continue
-            loras[path] = v
-
-        if loras != prev_loras:
-            consolidated.append((end_pct, loras))
-        prev_loras = loras
-    for k in not_found:
-        log.warning("LoRA '%s' not found, ignoring...", k)
-    return list(reversed(consolidated))
-
-
-def find_nonscheduled_loras(consolidated_schedule):
-    consolidated_schedule = list(consolidated_schedule)
-    if not consolidated_schedule:
-        return {}
-    last_end, candidate_loras = consolidated_schedule[0]
-    to_remove = set()
-    for candidate, weights in candidate_loras.items():
-        for end, loras in consolidated_schedule[1:]:
-            last_end = end
-            if loras.get(candidate) != weights:
-                to_remove.add(candidate)
-    # No candidates if the schedule does not span full time
-    if last_end < 1.0:
-        return {}
-    return {k: v for (k, v) in candidate_loras.items() if k not in to_remove}
-
-
-def smarter_split(separator, string):
-    """Does not break () when splitting"""
-    splits = []
-    prev = 0
-    stack = 0
-    escape = False
-    for idx, x in enumerate(string):
-        if x == "(" and not escape:
-            stack += 1
-        elif x == ")" and not escape:
-            stack = max(0, stack - 1)
-        elif x == separator and stack == 0:
-            splits.append(string[prev:idx])
-            prev = idx + 1
-        escape = x == "\\"
-
-    splits.append(string[prev : idx + 1])
-    return splits
+log = logging.getLogger("comfyui-prompt-control-legacy")
 
 
 def find_closing_paren(text, start):
@@ -88,65 +20,26 @@ def find_closing_paren(text, start):
     return len(text)
 
 
-def get_function(text, func, defaults, return_func_name=False, placeholder="", return_dict=False):
-    rex = re.compile(rf"\b{func}\b", re.MULTILINE)
+def get_function(text, func, defaults, return_func_name=False):
+    rex = re.compile(rf"\b{func}\(", re.MULTILINE)
     instances = []
     match = rex.search(text)
-    count = 0
     while match:
         # Match start, content start
-        start, at_paren = match.span()
-        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)
-            args = parse_strings(text[after_first_paren:end], defaults)
-            end += 1
-        else:
-            end = at_paren
-            args = None
-        ph = None
-        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:
+        start, after_first_paren = match.span()
+        funcname = text[start : after_first_paren - 1]
+        end = find_closing_paren(text, after_first_paren)
+        args = parse_strings(text[after_first_paren:end], defaults)
+        if 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:]
+        text = text[:start] + text[end + 1 :]
         match = rex.search(text)
-        count += 1
     return text, instances
 
 
-def split_by_function(text, func, defaults=None):
-    """
-    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.
-    """
-    text, functions = get_function(text, func, defaults, return_dict=True)
-    chunks = []
-    prev = 0
-    for f in functions:
-        chunks.append(text[prev : f["position"]])
-        prev = f["position"]
-    chunks.append(text[prev:])
-    for i, f in enumerate(functions):
-        f["text"] = chunks[i + 1]
-    return chunks[0], functions
-
-
 def parse_args(strings, arg_spec, strip=True):
     args = [s[1] for s in arg_spec]
     for i, spec in list(enumerate(arg_spec))[: len(strings)]:
@@ -185,7 +78,7 @@ def safe_float(f, default):
 
 
 def lora_name_to_file(name):
-    filenames = get_filename_list("loras")
+    filenames = folder_paths.get_filename_list("loras")
     # Return exact matches as is
     if name in filenames:
         return name
@@ -196,38 +89,3 @@ def lora_name_to_file(name):
             if p.name == n or str(p) == n:
                 return f
     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

pyproject.toml

@@ -1,15 +1,16 @@
 [project]
-name = "comfyui-prompt-control"
-description = "Provides nodes for prompt editing and LoRA scheduling, advanced regional prompting (including attention masking) and more, all controlled through your text prompt"
-version = "2.0.0-rc.9"
+name = "comfyui-prompt-control-legacy"
+description = "Legacy prompt control nodes. These exist only to allow old workflows to run"
+version = "1.2.1"
 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"]
 
 [project.urls]
-Repository = "https://github.com/asagi4/comfyui-prompt-control"
+Repository = "https://github.com/asagi4/comfyui-prompt-control-legacy"
+#  Used by Comfy Registry https://comfyregistry.org
 
 [tool.comfy]
 PublisherId = "asagi4"
-DisplayName = "ComfyUI Prompt Control"
+DisplayName = "ComfyUI Prompt Control (LEGACY VERSION)"
 Icon = ""

tools/expand_graph.py

@@ -1,13 +0,0 @@
-#!/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))

web/docs/PCAddMaskToCLIP.md

@@ -1,3 +0,0 @@
-# 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`.

web/docs/PCAddMaskToCLIPMany.md

@@ -1 +0,0 @@
-PCAddMaskToCLIP.md

web/docs/PCAttentionCoupleBatchNegative.md

@@ -1,7 +0,0 @@
-# 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)

web/docs/PCLazyLoraLoader.md

@@ -1,7 +0,0 @@
-# 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)

web/docs/PCLazyLoraLoaderAdvanced.md

@@ -1 +0,0 @@
-PCLazyLoraLoader.md

web/docs/PCLazyTextEncode.md

@@ -1,7 +0,0 @@
-# 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)

web/docs/PCLazyTextEncodeAdvanced.md

@@ -1 +0,0 @@
-PCLazyTextEncode.md

web/docs/PCLoraHooksFromText.md

@@ -1,5 +0,0 @@
-# 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`.

web/docs/PCMacroExpand.md

@@ -1,5 +0,0 @@
-# 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.

web/docs/PCSetPCTextEncodeSettings.md

@@ -1,7 +0,0 @@
-# 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.

web/docs/PCTextEncode.md

@@ -1,5 +0,0 @@
-# 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)