Release v2.0.0-rc.1 properly

Fixes #106

.github/workflows/publish.yml

@@ -7,15 +7,23 @@ on:
     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@main
+        uses: Comfy-Org/publish-node-action@v1
         with:
-          ## Add your own personal access token to your Github Repository secrets and reference it here.
           personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

.github/workflows/tests.yml

@@ -0,0 +1,18 @@
+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

@@ -0,0 +1,28 @@
+name: Run tests requiring ComfyUI
+on:
+  workflow_call:
+  workflow_dispatch:
+  push:
+    paths:
+      - prompt_control/nodes_lazy.py
+      - prompt_control/utils.py
+
+
+jobs:
+  run-graph-tests:
+    name: Run graph tests
+    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'
+      - run: pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+      - run: pip install -r requirements.txt -r ComfyUI/requirements.txt
+      - run: PYTHONPATH=ComfyUI python -m prompt_control.test_graph

Makefile

@@ -1,4 +1,4 @@
-all: format check
+all: format check test
 	@echo "Done"
 check:
 	find . -name "*.py" | xargs pyflakes
@@ -6,6 +6,9 @@ format:
 	find . -name "*.py" | xargs black -l 120
 
 test:
-	python -m prompt_control.test
+	python -m prompt_control.test_parser
+
+test_graph:
+	PYTHONPATH=../../ python -m prompt_control.test_graph
 
 .PHONY: check format all

README.md

@@ -2,11 +2,13 @@
 
 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.
 
+A `Basic Text to Image` template is included with the extension, and can be loaded from ComfyUI's template library.
+
 ## Prompt Control v2
 
 Prompt control has been almost completely rewritten. It now uses ComfyUI's lazy execution to build graphs from the text prompt at runtime. The generated graph is often exactly equivalent to a manually built workflow using native ComfyUI nodes. There are no more weird sampling hooks that could cause problems with other nodes
 
-Prompt Control also comes with `PCTextEncode`, which provides advanced text encoding with many additional features compared to ComfyUI's base `CLIPTextEncode`.
+Prompt Control comes with `PCTextEncode`, which provides advanced text encoding with many additional features compared to ComfyUI's base `CLIPTextEncode`.
 
 ### Removed features
 
@@ -14,10 +16,6 @@ Prompt Control also comes with `PCTextEncode`, which provides advanced text enco
 - LoRA block weight integration; ditto, for now.
 
 
-### Is it stable now?
-
-Unless I run into bugs or significant annoyances that require changing the interface, it probably won't change too much, but until I tag 2.0, everything can change.
-
 ### Everything broke, where are the old nodes?
 
 If you really need them, you can install the [legacy nodes](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.
@@ -30,6 +28,7 @@ See [features](#features) below. Things you can control via the prompt:
 - Prompt editing and filtering without noodle soup
 - LoRA loading and scheduling via ComfyUI's hook system
 - Masking, composition and area control (regional prompting)
+- Per-encoder prompts for models with multiple text encoders, such as SDXL and Flux
 - Prompt operations like `BREAK` and `AND`
 - Weight interpretation types (comfy, A1111, etc.)
 - Prompt masking with [cutoff](#cutoff)
@@ -39,10 +38,7 @@ See the [syntax documentation](doc/syntax.md)
 
 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](workflows/example-lazy.json?raw=1) shows LoRA scheduling and prompt editing and compares it with the same prompt implemented with built-in ComfyUI nodes.
-
-[Here](workflows/example-2pass.json?raw=1) is a two-pass workflow illustrating more features, including custom masks and filtering.
+[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.
 
 The tools in this repository combine well with the macro and wildcard functionality in [comfyui-utility-nodes](https://github.com/asagi4/comfyui-utility-nodes)
 
@@ -139,8 +135,6 @@ a (red:1.1) cat with (brown:0.9) spots and a long tail AND STYLE(A1111) an (old:
 ```
 Will interpret the first one using the default ComfyUI behaviour, the second prompt with A1111 and the last prompt with the default again
 
-For things (ie. the code imports) to work, the nodes must be cloned in a directory named exactly `ComfyUI_ADV_CLIP_emb`.
-
 ## Cutoff
 
 NOTE: Cutoff syntax might change at some point; it's pretty clunky.

doc/syntax.md

@@ -24,12 +24,20 @@ 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 equivalent to `[:cat:0.5]` meaning it switches from empty to `cat` at 0.5.
 
-**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
+### 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.
 
 ### 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
@@ -79,7 +87,6 @@ Might be useful with Jinja templating (see https://github.com/asagi4/comfyui-uti
 ```
 generates a LoRA schedule based on a sinewave
 
-
 # Basic prompt syntax
 
 This syntax is also available in outside scheduled prompts, where applicable.
@@ -98,7 +105,6 @@ 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. 
@@ -114,15 +120,23 @@ The nodes do not treat SDXL models specially, but there are some utilities that
 
 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)`.
+### Multiple text encoders: TE
+
+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:
-- 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`.
+- 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` inside `TE` does not do anything sensible; `TE(l=foo AND bar)` will parse as two prompts `TE(foo` and `bar)`. `BREAK`, `SHIFT` and `SHUFFLE` do work, however
 
 ### SHUFFLE and SHIFT
 
@@ -157,6 +171,8 @@ The function `NOISE(weight, seed)` adds some random noise into the prompt. The s
 
 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`
@@ -198,7 +214,44 @@ The order of the `FEATHER` and `MASK` calls doesn't matter; you can have `FEATHE
 
 # Experimental features
 
-Experimental features are unstable and may disappear or break without warning.
+Experimental features are unstable and may disappear or change without warning.
+
+## DEF
+
+You can define "prompt macros" by using `DEF`:
+```
+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]
+```
+
+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.
+
+Note that unspecified parameters will not be substituted:
+```
+DEF(mything=a $1 b $2)
+mything
+mything(A)
+```
+gives
+```
+a $1 b $2
+a A b $2
+```
+
+Macros are expanded before any other parsing takes place. The expansion continues until no further changes occur. Recursion will raise an error.
 
 ## Attention masking
 
@@ -206,4 +259,8 @@ Use `ATTN()` in combination with `MASK()` or `IMASK()` to enable attention maski
 
 ## 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 `g`, `l`, or `t5xxl`. For example with SDXL, try `TE_WEIGHT(g=0.25, l=0.75`). The weights are applied as a multiplier to the TE output.
+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)`

example_workflows/Basic Text to Image.json

@@ -0,0 +1,672 @@
+{
+  "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/nodes_lazy.py

@@ -2,7 +2,9 @@ import logging
 from .parser import parse_prompt_schedules
 from comfy_execution.graph_utils import GraphBuilder, is_link
 
-from .prompts import get_function
+from comfy_execution.graph import ExecutionBlocker
+
+from .utils import get_function
 
 log = logging.getLogger("comfyui-prompt-control")
 
@@ -85,11 +87,15 @@ def create_hook_nodes_for_lora(graph, path, info, existing_node, start_pct, end_
     return hook_node, next_keyframe
 
 
-def build_lora_schedule(graph, schedule, model, clip, apply_hooks=True, return_hooks=True):
+def build_lora_schedule(graph, schedule, model, clip, apply_hooks=True):
     # This gets rid of non-existent LoRAs
     consolidated = consolidate_schedule(schedule)
-    non_scheduled = find_nonscheduled_loras(consolidated)
-    model, clip = create_lora_loader_nodes(graph, model, clip, non_scheduled)
+    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
@@ -124,7 +130,7 @@ def build_lora_schedule(graph, schedule, model, clip, apply_hooks=True, return_h
             n.set_input("hooks_B", h.out(0))
             res = n
         res = res.out(0)
-        if apply_hooks:
+        if clip is not None and apply_hooks:
             n = graph.node("SetClipHooks")
             n.set_input("clip", clip)
             n.set_input("hooks", res)
@@ -132,13 +138,12 @@ def build_lora_schedule(graph, schedule, model, clip, apply_hooks=True, return_h
             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))
 
-    if return_hooks:
-        ret = (model, clip, res)
-    else:
-        ret = (model, clip)
+    ret = (model, clip, res)
 
     return {"result": ret, "expand": r}
 
@@ -149,16 +154,15 @@ class PCLazyLoraLoaderAdvanced:
     @classmethod
     def INPUT_TYPES(s):
         return {
-            "required": {
-                "text": ("STRING", {"multiline": True}),
+            "optional": {
                 "model": ("MODEL", {"rawLink": True}),
                 "clip": ("CLIP", {"rawLink": True}),
-            },
-            "optional": {
+                "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"},
         }
@@ -168,22 +172,23 @@ class PCLazyLoraLoaderAdvanced:
     CATEGORY = "promptcontrol"
     FUNCTION = "apply"
 
-    def apply(self, model, clip, text, unique_id, apply_hooks=True, tags="", start=0.0, end=1.0):
-        schedule = parse_prompt_schedules(text, filters=tags, start=start, end=end)
-        graph = GraphBuilder(f"PCLazyLoraLoaderAdvanced-{unique_id}")
-        return build_lora_schedule(graph, schedule, model, clip, apply_hooks=apply_hooks, return_hooks=True)
+    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(f"{unique_id}-")
+        r = build_lora_schedule(graph, schedule, model, clip, apply_hooks=apply_hooks)
+        return r
 
 
-class PCLazyLoraLoader:
-    CACHE_KEY = cache_key_lora
-
+class PCLazyLoraLoader(PCLazyLoraLoaderAdvanced):
     @classmethod
     def INPUT_TYPES(s):
         return {
-            "required": {
+            "optional": {
                 "model": ("MODEL", {"rawLink": True}),
                 "clip": ("CLIP", {"rawLink": True}),
-                "text": ("STRING", {"multiline": True}),
+                "text": ("STRING", {"multiline": True, "default": ""}),
             },
             "hidden": {"unique_id": "UNIQUE_ID"},
         }
@@ -192,14 +197,12 @@ class PCLazyLoraLoader:
         "MODEL",
         "CLIP",
     )
-    OUTPUT_TOOLTIPS = ("Returns a model and clip with LoRAs scheduled",)
     CATEGORY = "promptcontrol"
-    FUNCTION = "apply"
 
-    def apply(self, model, clip, text, unique_id):
-        graph = GraphBuilder(f"PCLazyLoraLoader-{unique_id}")
-        schedule = parse_prompt_schedules(text)
-        return build_lora_schedule(graph, schedule, model, clip, apply_hooks=True, return_hooks=False)
+    def apply(self, *args, **kwargs):
+        r = super().apply(*args, **kwargs)
+        r["result"] = r["result"][:2]
+        return r
 
 
 def build_scheduled_prompts(graph, schedules, clip):
@@ -235,31 +238,11 @@ def build_scheduled_prompts(graph, schedules, clip):
     return {"result": (node.out(0),), "expand": g}
 
 
-def cache_key_from_inputs(cachekey, text, tags="", start=0.0, end=1.0, **kwargs):
-    schedules = parse_prompt_schedules(text, filters=tags, start=start, end=end)
+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 PCLazyTextEncode:
-    CACHE_KEY = cache_key_prompt
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {"clip": ("CLIP", {"rawLink": True}), "text": ("STRING", {"multiline": True})},
-        }
-
-    RETURN_TYPES = ("CONDITIONING",)
-    OUTPUT_TOOLTIPS = ("A fully encoded and scheduled conditioning",)
-    CATEGORY = "promptcontrol"
-    FUNCTION = "apply"
-
-    def apply(self, clip, text):
-        schedules = parse_prompt_schedules(text)
-        graph = GraphBuilder()
-        return build_scheduled_prompts(graph, schedules, clip)
-
-
 class PCLazyTextEncodeAdvanced:
     CACHE_KEY = cache_key_prompt
 
@@ -271,6 +254,7 @@ class PCLazyTextEncodeAdvanced:
                 "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"},
         }
@@ -279,12 +263,23 @@ class PCLazyTextEncodeAdvanced:
     CATEGORY = "promptcontrol"
     FUNCTION = "apply"
 
-    def apply(self, clip, text, unique_id, tags="", start=0.1, end=1.0):
-        schedules = parse_prompt_schedules(text, filters=tags, start=start, end=end)
-        graph = GraphBuilder(f"PCLazyTextEncodeAdvanced-{unique_id}")
+    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(f"{unique_id}-")
         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,

prompt_control/nodes_tools.py

@@ -1,9 +1,71 @@
 import logging
 from .parser import parse_prompt_schedules
+from .nodes_lazy import NODE_CLASS_MAPPINGS as LAZY_NODES
+import json
+import folder_paths
+from pathlib import Path
+from comfy_execution.graph_utils import is_link
 
 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": "DYNPROMPT",
+            },
+        }
+
+    @classmethod
+    def VALIDATE_INPUTS(self, input_types):
+        return True
+
+    OUTPUT_NODE = True
+    RETURN_TYPES = ()
+    CATEGORY = "promptcontrol/tools"
+    DESCRIPTION = "Saves the current expanded dynamic 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
+        )
+        p = {}
+        input_replace_map = {}
+        for node in prompt.all_node_ids():
+            n = prompt.get_node(node)
+            t = n["class_type"]
+            if t in LAZY_NODES:
+                expanded_prompt = LAZY_NODES[t]().apply(**n["inputs"], unique_id=node)
+                for k in expanded_prompt["expand"]:
+                    p[k] = expanded_prompt["expand"][k]
+                for i, _ in enumerate(expanded_prompt["result"]):
+                    input_replace_map[(node, i)] = [k, i]
+            else:
+                p[node] = n
+        for k in p:
+            for ik in p[k]["inputs"]:
+                x = p[k]["inputs"][ik]
+                if is_link(x) and tuple(x) in input_replace_map:
+                    p[k]["inputs"][ik] = input_replace_map[tuple(x)]
+        file = f"{filename}_{counter:05}_.json"
+        full_path = Path(full_output_folder) / file
+        with open(full_path, "w") as f:
+            log.info(f"Saving workflow to {full_path}")
+            json.dump(p, f)
+
+        return ()
+
+
 class PCSetLogLevel:
     @classmethod
     def INPUT_TYPES(s):
@@ -81,7 +143,6 @@ class PCSetPCTextEncodeSettings:
         return {
             "required": {"clip": ("CLIP",)},
             "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}),
@@ -101,7 +162,6 @@ class PCSetPCTextEncodeSettings:
     def apply(
         self,
         clip,
-        steps=0,
         mask_width=512,
         mask_height=512,
         sdxl_width=1024,
@@ -112,7 +172,6 @@ class PCSetPCTextEncodeSettings:
         sdxl_crop_h=0,
     ):
         settings = {
-            "steps": steps,
             "mask_width": mask_width,
             "mask_height": mask_height,
             "sdxl_width": sdxl_width,
@@ -156,6 +215,7 @@ NODE_CLASS_MAPPINGS = {
     "PCAddMaskToCLIPMany": PCAddMaskToCLIPMany,
     "PCSetLogLevel": PCSetLogLevel,
     "PCExtractScheduledPrompt": PCExtractScheduledPrompt,
+    "PCSaveExpandedWorkflow": PCSaveExpandedWorkflow,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -164,4 +224,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "PCAddMaskToCLIPMany": "PC: Attach Mask (multi)",
     "PCSetLogLevel": "PC: Configure Logging (for debug)",
     "PCExtractScheduledPrompt": "PC: Extract Scheduled Prompt",
+    "PCSaveExpandedWorkflow": "PC: Save Expanded Workflow (for debug)",
 }

prompt_control/parser.py

@@ -1,3 +1,4 @@
+# vim: sw=4 ts=4
 import lark
 import logging
 from math import ceil
@@ -102,13 +103,25 @@ def clamp(a, b, c):
     return min(max(a, b), c)
 
 
-def get_steps(tree):
-    res = [100]
+def get_steps(tree, num_steps):
+    res = [num_steps or 100]
 
     def tostep(s):
-        w = float(s) * 100
-        w = int(clamp(0, w, 100))
-        return w
+        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))
 
     class CollectSteps(lark.Visitor):
         def scheduled(self, tree):
@@ -130,15 +143,14 @@ def get_steps(tree):
         def sequence(self, tree):
             steps = tree.children[1::2]
             for i, steps in enumerate(steps):
-                w = float(tree.children[i * 2 + 1]) * 100
-                tree.children[i * 2 + 1] = clamp(0, w, 100)
+                w = tostep(tree.children[i * 2 + 1])
+                tree.children[i * 2 + 1] = w
                 res.append(w)
 
         def alternate(self, tree):
-            step_size = int(round(float(tree.children[-1] or 0.1), 2) * 100)
-            step_size = clamp(1, step_size, 100)
+            step_size = tostep(round(float(tree.children[-1] or 0.1), 2))
             tree.children[-1] = step_size
-            res.extend([x for x in range(step_size, 100, step_size)])
+            res.extend([x for x in range(step_size, num_steps or 100, step_size)])
 
     CollectSteps().visit(tree)
 
@@ -258,29 +270,31 @@ def at_step(step, filters, tree):
 
 
 class PromptSchedule(object):
-    def __init__(self, prompt, filters="", start=0.0, end=1.0):
+    # 0 num_steps means unconfigured
+    def __init__(self, prompt, filters="", start=0.0, end=1.0, num_steps=0):
         self.filters = filters
         self.start = start
         self.end = end
+        self.num_steps = num_steps
         self.prompt = prompt.strip()
         self.defaults = {}
         self.loaded_loras = {}
 
-        self.parsed_prompt = self._parse()
+        self.parsed_prompt = self._parse(num_steps)
 
     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):
+    def _parse(self, num_steps):
         filters = [x.strip() for x in self.filters.upper().split(",")]
         try:
             parsed = []
             tree = prompt_parser.parse(self.prompt)
-            steps = get_steps(tree)
+            steps = get_steps(tree, num_steps=num_steps)
 
             def f(x):
-                return round(x / 100, 2)
+                return round(x / (num_steps or 100), 2)
 
             for t in steps:
                 p = at_step(t, filters, tree)
@@ -308,7 +322,15 @@ class PromptSchedule(object):
         if len(res) == 0:
             res = [[1.0, parsed[-1][1]]]
 
-        return res
+        final = [res[0]]
+
+        # 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 clone(self):
         return self.with_filters()
@@ -322,6 +344,7 @@ class PromptSchedule(object):
             filters=ifspecified(filters, self.filters),
             start=ifspecified(start, self.start),
             end=ifspecified(end, self.end),
+            num_steps=self.num_steps,
         )
         return p
 
@@ -336,7 +359,7 @@ class PromptSchedule(object):
         return len(self.parsed_prompt) - 1, self.parsed_prompt[-1]
 
 
-def replace_defs(text):
+def replace_def(text):
     text, defs = get_function(text, "DEF", defaults=None)
     res = text
     prevres = text
@@ -351,19 +374,37 @@ def replace_defs(text):
     while True:
         iterations += 1
         if iterations > 10:
-            log.error("Unable to resolve DEFs, make sure there are no cycles!")
+            raise ValueError("Unable to resolve DEFs, make sure there are no cycles!")
             return text
         for search, replace in replacements:
-            res = re.sub(rf"\b{re.escape(search)}\b", replace, res)
+            res = substitute_defcall(res, search, replace)
+            res = substitute_def(res, search, replace)
         if res == prevres:
             break
         prevres = res
-    if res != text:
+    if res.strip() != text.strip():
+        res = res.strip()
         log.info("DEFs expanded to: %s", res)
     return res
 
 
+def substitute_def(text, search, replace):
+    return re.sub(rf"\b{re.escape(search)}\b", replace, text)
+
+
+def substitute_defcall(text, search, replace):
+    text, defns = get_function(text, search, defaults=None, placeholder=f"DEFNCALL{search}")
+    for i, defn in enumerate(defns):
+        ph = f"\0DEFNCALL{search}{i}\0"
+        paramvals = [x.strip() for x in defn.split(";")]
+        r = replace
+        for i, v in enumerate(paramvals):
+            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 = replace_defs(prompt)
+    prompt = replace_def(prompt)
     return PromptSchedule(prompt, **kwargs)

prompt_control/prompts.py

@@ -138,6 +138,28 @@ def fix_word_ids(tokens):
     return tokens
 
 
+def tokenize_chunks(clip, text, need_word_ids):
+    chunks = re.split(r"\bBREAK\b", text)
+    token_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)
+            c = r
+        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])
+
+    return tokens
+
+
 def encode_prompt_segment(
     clip,
     text,
@@ -157,37 +179,46 @@ def encode_prompt_segment(
 
     # 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 = []
+    text, te_prompts = get_function(text, "TE", defaults=None)
     need_word_ids = True
-    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
-        t = clip.tokenize(c, return_word_ids=need_word_ids)
-        token_chunks.append(t)
-    tokens = token_chunks[0]
+    tokens = tokenize_chunks(clip, text, need_word_ids)
 
-    for key in tokens:
-        for c in token_chunks[1:]:
-            tokens[key].extend(c[key])
+    per_te_prompts = {}
+    if l_prompts:
+        log.warning("Note: CLIP_L is deprecated. Use TE(l=prompt) instead")
+        per_te_prompts["l"] = l_prompts
 
-    # 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"]
+    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 "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"]
+    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)[key]
+            log.info("Encoded prompt with TE '%s': %s", key, prompt)
+
+    maxlen = max(len(tokens[k]) for k in tokens)
+    empty = None
+    for k in tokens:
+        while len(tokens[k]) < maxlen:
+            if empty is None:
+                empty = clip.tokenize("", return_word_ids=need_word_ids)
+            tokens[k] += empty[k]
 
     tokens = fix_word_ids(tokens)
 
@@ -211,21 +242,28 @@ def apply_weights(output, te_name, spec):
     if te_name.startswith("clip_"):
         te_name = te_name[5:]
 
+    default = spec.get("all", None)
+
     if isinstance(output, tuple):
         out, pooled = output
-        if te_name in spec:
-            log.info("Weighting %s output by %s", te_name, spec[te_name])
-            out = out * spec[te_name]
         pkey = te_name + "_pooled"
-        if pkey in spec:
-            log.info("Weighting %s pooled output by %s", te_name, spec[pkey])
-            pooled = pooled * spec[pkey]
+        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
+            pooled = pooled * pooled_w
 
         return out, pooled
     else:
-        if te_name in spec:
-            log.info("Weighting %s output by %s", te_name, spec[te_name])
-            output = output * spec[te_name]
+        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
 
 

prompt_control/test.py

@@ -1,136 +0,0 @@
-import unittest
-from .parser import parse_prompt_schedules as parse
-
-
-class TestParser(unittest.TestCase):
-    def test_no_scheduling(self):
-        p = parse("This is a (basic:0.6) (prompt) with [no scheduling] features")
-        expected = [1.0, {"prompt": "This is a (basic:0.6) (prompt) with [no scheduling] features", "loras": {}}]
-        self.assertEqual(p.at_step(0), expected)
-        self.assertEqual(p.at_step(0.5), expected)
-        self.assertEqual(p.at_step(1), expected)
-
-    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]"
-        )
-        expected = [0.5, {"prompt": "This is a (basic:0.6) (prompt) with (very [simple]:1.1) features", "loras": {}}]
-        expected2 = [
-            0.8,
-            {"prompt": "This is a (basic:0.6) (prompt) with (very (basic:0.6):1.1) features", "loras": {}},
-        ]
-        expected3 = [1.0, {"prompt": "This is a (basic:0.6) (prompt) with (very (basic:0.6):1.1) ", "loras": {}}]
-        self.assertEqual(p.at_step(0), expected)
-        self.assertEqual(p.at_step(0.5), expected)
-        self.assertEqual(p.at_step(0.7), expected2)
-        self.assertEqual(p.at_step(1), expected3)
-
-    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 = [
-            1.0,
-            {
-                "prompt": "This is a (lora:0.6) (prompt) with [no scheduling] features  ",
-                "loras": {"foo": {"weight": 0.5, "weight_clip": 0.5}, "bar": {"weight": 0.5, "weight_clip": 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>"
-        )
-        expected = [
-            0.3,
-            {
-                "prompt": "This is a (lora:0.6) (prompt) with [scheduling] features  ",
-                "loras": {"foo": {"weight": 0.5, "weight_clip": 0.5}, "bar": {"weight": 0.5, "weight_clip": 1.0}},
-            },
-        ]
-        expected2 = [
-            1.0,
-            {
-                "prompt": "This is a (lora:0.6) (prompt) with [scheduling] features  ",
-                "loras": {"bar": {"weight": 1.0, "weight_clip": 1.2}},
-            },
-        ]
-        self.assertEqual(p.at_step(0.1), expected)
-        self.assertEqual(p.at_step(1), expected2)
-
-    def test_seq(self):
-        p = parse("This is a sequence of [SEQ:a:0.2::0.5:c:0.8]")
-        p2 = parse("This is a sequence of [[a:[c:0.5]:0.2]::0.8]")
-        prompts = {
-            0.2: "This is a sequence of a",
-            0.5: "This is a sequence of ",
-            0.8: "This is a sequence of c",
-            1.0: "This is a sequence of ",
-        }
-        self.assertEqual(p.parsed_prompt, p2.parsed_prompt)
-        for k in prompts:
-            self.assertEqual(p.at_step(k), [k, {"prompt": prompts[k], "loras": {}}])
-
-    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_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.assertEqual(
-            p.at_step(0.6), [0.7, {"prompt": "This prompt is ", "loras": {"cool": {"weight": 1.0, "weight_clip": 1.0}}}]
-        )
-        self.assertEqual(
-            p.at_step(0.7), [0.7, {"prompt": "This prompt is ", "loras": {"cool": {"weight": 1.0, "weight_clip": 1.0}}}]
-        )
-        p2 = p.with_filters(filters="hr, xyz")
-
-        # TODO: for some reason, this does not deduplicate
-        # self.assertEqual(p2.at_step(0), p2.at_step(1))
-        self.assertEqual(p2.at_step(0)[1]["prompt"], p2.at_step(1)[1]["prompt"])
-
-    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 in prompts:
-            self.assertEqual(p.at_step(k), [prompts[k][0], {"prompt": prompts[k][1], "loras": {}}])
-
-    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("[:[<lora:test:1>:c:0.5]:0.3]")
-        self.assertEqual(p.at_step(0), [0.3, {"prompt": "", "loras": {}}])
-        self.assertEqual(p.at_step(0.4), [0.5, {"prompt": "", "loras": {"test": {"weight": 1.0, "weight_clip": 1.0}}}])
-        self.assertEqual(p.at_step(1.0), [1.0, {"prompt": "c", "loras": {}}])
-
-        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 in prompts:
-            self.assertEqual(p.at_step(k), [prompts[k][0], {"prompt": prompts[k][1], "loras": {}}])
-
-
-if __name__ == "__main__":
-    unittest.main()

prompt_control/test_graph.py

@@ -0,0 +1,215 @@
+import unittest
+import unittest.mock as mock
+import logging
+
+log = logging.getLogger("comfyui-prompt-control")
+
+
+def find_file(name):
+    names = {"test": "test.safetensors", "other": "some/other.safetensors"}
+    return names.get(name)
+
+
+def apply(cls, text, **kwargs):
+    model = [0, 1]
+    clip = [0, 0]
+    return cls().apply(unique_id="UID", model=model, clip=clip, text=text, **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):
+        clip = [0, 0]
+        from .nodes_lazy import PCLazyTextEncode, PCLazyTextEncodeAdvanced
+
+        for p in ["test", "[test:0.2] test", "[test[test::0.5]]<lora:test:1>"]:
+            r1 = PCLazyTextEncode().apply(clip, p, "UID")
+            r2 = PCLazyTextEncodeAdvanced().apply(clip, p, "UID")
+            self.assertEqual(r1, r2)
+
+        r = PCLazyTextEncode().apply(clip, "test<lora:test:1>", "UID")
+        self.assertEqual(
+            r,
+            {
+                "result": (["UID-2", 0],),
+                "expand": {
+                    "UID-1": {"class_type": "PCTextEncode", "inputs": {"clip": [0, 0], "text": "test"}},
+                    "UID-2": {
+                        "class_type": "ConditioningSetTimestepRange",
+                        "inputs": {"conditioning": ["UID-1", 0], "start": 0.0, "end": 1.0},
+                    },
+                },
+            },
+        )
+        r = PCLazyTextEncode().apply(clip, "simple [test:0.1,0.5] prompt<lora:test:1>", "UID")
+        self.assertEqual(
+            r,
+            {
+                "result": (["UID-8", 0],),
+                "expand": {
+                    "UID-1": {"class_type": "PCTextEncode", "inputs": {"clip": [0, 0], "text": "simple  prompt"}},
+                    "UID-2": {
+                        "class_type": "ConditioningSetTimestepRange",
+                        "inputs": {"conditioning": ["UID-1", 0], "start": 0.0, "end": 0.1},
+                    },
+                    "UID-3": {"class_type": "PCTextEncode", "inputs": {"clip": [0, 0], "text": "simple test prompt"}},
+                    "UID-4": {
+                        "class_type": "ConditioningSetTimestepRange",
+                        "inputs": {"conditioning": ["UID-3", 0], "start": 0.1, "end": 0.5},
+                    },
+                    "UID-5": {"class_type": "PCTextEncode", "inputs": {"clip": [0, 0], "text": "simple  prompt"}},
+                    "UID-6": {
+                        "class_type": "ConditioningSetTimestepRange",
+                        "inputs": {"conditioning": ["UID-5", 0], "start": 0.5, "end": 1.0},
+                    },
+                    "UID-7": {
+                        "class_type": "ConditioningCombine",
+                        "inputs": {"conditioning_1": ["UID-2", 0], "conditioning_2": ["UID-4", 0]},
+                    },
+                    "UID-8": {
+                        "class_type": "ConditioningCombine",
+                        "inputs": {"conditioning_1": ["UID-7", 0], "conditioning_2": ["UID-6", 0]},
+                    },
+                },
+            },
+        )
+
+    @mock.patch("prompt_control.utils.lora_name_to_file", find_file)
+    def test_loraloader(self):
+        from .nodes_lazy import PCLazyLoraLoader, PCLazyLoraLoaderAdvanced
+
+        model = [0, 1]
+        clip = [0, 0]
+        with self.assertLogs(log, level="WARNING") as cm:
+            result = apply(PCLazyLoraLoader, "prompt here <lora:nonexistent:1.0:0.5>")["expand"]
+            result_adv = apply(PCLazyLoraLoaderAdvanced, "prompt here <lora:nonexistent:1.0:0.5>")["expand"]
+        self.assertIn("LoRA 'nonexistent' not found", cm.output[0])
+        self.assertEqual(result, {})
+        self.assertEqual(result_adv, {})
+
+        result = apply(PCLazyLoraLoader, "<lora:test:1>")["expand"]
+        result2 = apply(PCLazyLoraLoader, "prompt here <lora:test:1.0:0.5><lora:test:0:0.5>")["expand"]
+        result3 = apply(PCLazyLoraLoaderAdvanced, "prompt here <lora:test:1.0:0.5><lora:test:0:0.5>")["expand"]
+        self.assertEqual(result, result2)
+        self.assertEqual(result2, result3)
+        self.assertEqual(
+            result,
+            {
+                "UID-1": {
+                    "class_type": "LoraLoader",
+                    "inputs": {
+                        "model": [0, 1],
+                        "clip": [0, 0],
+                        "strength_model": 1.0,
+                        "strength_clip": 1.0,
+                        "lora_name": "test.safetensors",
+                    },
+                }
+            },
+        )
+        result = apply(PCLazyLoraLoader, "<lora:test:1><lora:other:0.5>")["expand"]
+        self.assertEqual(
+            result,
+            {
+                "UID-1": {
+                    "class_type": "LoraLoader",
+                    "inputs": {
+                        "model": [0, 1],
+                        "clip": [0, 0],
+                        "strength_model": 1.0,
+                        "strength_clip": 1.0,
+                        "lora_name": "test.safetensors",
+                    },
+                },
+                "UID-2": {
+                    "class_type": "LoraLoader",
+                    "inputs": {
+                        "model": ["UID-1", 0],
+                        "clip": ["UID-1", 1],
+                        "strength_model": 0.5,
+                        "strength_clip": 0.5,
+                        "lora_name": "some/other.safetensors",
+                    },
+                },
+            },
+        )
+
+        result = apply(PCLazyLoraLoader, "prompt here <lora:test:1.0:0.5>")["expand"]
+        self.assertEqual(
+            result,
+            {
+                "UID-1": {
+                    "class_type": "LoraLoader",
+                    "inputs": {
+                        "model": [0, 1],
+                        "clip": [0, 0],
+                        "strength_model": 1.0,
+                        "strength_clip": 0.5,
+                        "lora_name": "test.safetensors",
+                    },
+                }
+            },
+        )
+
+        result = apply(PCLazyLoraLoader, "prompt [<lora:test:0.5>:0.5]")["expand"]
+        result2 = apply(PCLazyLoraLoaderAdvanced, "prompt [<lora:test:0.5>:0.5]")["expand"]
+        self.assertEqual(result, result2)
+        expected = {
+            "UID-1": {
+                "class_type": "CreateHookLora",
+                "inputs": {"lora_name": "test.safetensors", "strength_model": 0.5, "strength_clip": 0.5},
+            },
+            "UID-2": {
+                "class_type": "CreateHookKeyframe",
+                "inputs": {"strength_mult": 0.0, "start_percent": 0.0},
+            },
+            "UID-3": {
+                "class_type": "CreateHookKeyframe",
+                "inputs": {
+                    "start_percent": 0.5,
+                    "prev_hook_kf": ["UID-2", 0],
+                    "strength_mult": 1.0,
+                },
+            },
+            "UID-4": {
+                "class_type": "SetHookKeyframes",
+                "inputs": {"hooks": ["UID-1", 0], "hook_kf": ["UID-3", 0]},
+            },
+            "UID-5": {
+                "class_type": "SetClipHooks",
+                "inputs": {
+                    "clip": [0, 0],
+                    "hooks": ["UID-4", 0],
+                    "apply_to_conds": True,
+                    "schedule_clip": True,
+                },
+            },
+        }
+        self.assertEqual(result, expected)
+        result2 = apply(PCLazyLoraLoaderAdvanced, "prompt [<lora:test:0.5>:0.5]", start=0.6)["expand"]
+        self.assertEqual(
+            result2,
+            {
+                "UID-1": {
+                    "class_type": "LoraLoader",
+                    "inputs": {
+                        "model": [0, 1],
+                        "clip": [0, 0],
+                        "strength_model": 0.5,
+                        "strength_clip": 0.5,
+                        "lora_name": "test.safetensors",
+                    },
+                }
+            },
+        )
+        result2 = PCLazyLoraLoaderAdvanced().apply(model, clip, "prompt [<lora:test:0.5>:0.5]", "UID", end=0.5)[
+            "expand"
+        ]
+        self.assertEqual(result2, {})
+
+
+if __name__ == "__main__":
+    unittest.main()

prompt_control/test_parser.py

@@ -0,0 +1,160 @@
+import unittest
+from .parser import parse_prompt_schedules as parse
+
+
+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_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_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]")
+        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]")
+        self.assertEqual(p.parsed_prompt, p2.parsed_prompt)
+
+        with self.assertRaises(ValueError) as c:
+            parse("DEF(X=recurse Y) DEF(Y=recurse X) X")
+        self.assertTrue("Unable to resolve DEFs" in str(c.exception))
+
+    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)
+            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)
+            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

@@ -87,10 +87,11 @@ def find_closing_paren(text, start):
     return len(text)
 
 
-def get_function(text, func, defaults, return_func_name=False):
+def get_function(text, func, defaults, return_func_name=False, placeholder=""):
     rex = re.compile(rf"\b{func}\(", re.MULTILINE)
     instances = []
     match = rex.search(text)
+    count = 0
     while match:
         # Match start, content start
         start, after_first_paren = match.span()
@@ -102,8 +103,12 @@ def get_function(text, func, defaults, return_func_name=False):
         else:
             instances.append(args)
 
-        text = text[:start] + text[end + 1 :]
+        if placeholder:
+            text = text[:start] + f"\0{placeholder}{count}\0" + text[end + 1 :]
+        else:
+            text = text[:start] + text[end + 1 :]
         match = rex.search(text)
+        count += 1
     return text, instances
 
 

pyproject.toml

@@ -1,14 +1,13 @@
 [project]
 name = "comfyui-prompt-control"
 description = "Nodes for convenient prompt editing, making many common operations prompt-controllable"
-version = "2.0.0-beta.6"
+version = "2.0.0-rc.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"
-#  Used by Comfy Registry https://comfyregistry.org
 
 [tool.comfy]
 PublisherId = "asagi4"