v1.2.0

See #64

README.md

@@ -13,7 +13,7 @@ Things you can control via the prompt:
 - SDXL parameters
 - Other miscellaneous things
 
-[This example workflow](workflows/example.json?raw=1) implements a two-pass workflow illustrating most scheduling features.
+[This example workflow](workflows/example.json?raw=1) implements a two-pass workflow illustrating most scheduling features. (Note: the example still uses deprecated nodes; update pending).
 
 The tools in this repository combine well with the macro and wildcard functionality in [comfyui-utility-nodes](https://github.com/asagi4/comfyui-utility-nodes)
 
@@ -32,6 +32,7 @@ Then restart ComfyUI afterwards.
 
 I try to avoid behavioural changes that break old prompts, but they may happen occasionally.
 
+- 2024-12-03 ComfyUI merged support for model/conditioning hooks. There are two new nodes, `PCEncodeSchedule` and `PCLoraHooksFromSchedule` that can be used in combination with the hook nodes. Some functionality is still missing from them, but going forward, these nodes will be the only nodes supported; **I will not spend significant time fixing bugs in the old monkeypatched nodes anymore.**
 - 2024-02-02 The node will now automatically enable offloading LoRA backup weights to the CPU if you run out of memory during LoRA operations, even when `--highvram` is specified. This change persists until ComfyUI is restarted.
 - 2024-01-14 Multiple `CLIP_L` instances are now joined with a space separator instead of concatenated.
 - 2024-01-09 AITemplate support dropped. I don't recommend or test AITemplate anymore. Use Stable-Fast instead (see below for info)
@@ -57,7 +58,7 @@ a [large::0.1] [cat|dog:0.05] [<lora:somelora:0.5:0.6>::0.5]
 [in a park:in space:0.4]
 ```
 
-You can also use `a [b:c:0.3,0.7]` as a shortcut. The prompt be `a` until 0.3, `a b` until 0.7, and then `c`. `[a:0.1,0.4]` is equivalent to `[a::0.1,0.4]`
+You can also use `a [b:c:0.3,0.7]` as a shortcut. The prompt be `a` until 0.3, `a b` until 0.7, and then `a c`. `[a:0.1,0.4]` is equivalent to `[a::0.1,0.4]`
 
 ## LoRA loading
 
@@ -101,6 +102,8 @@ with `tags` `x,z` would result in the prompt `a blue cat running in space`
 
 ## 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.
 
 
@@ -215,6 +218,10 @@ gives you a mask that is a combination of 1, 2 and 3, where 1 and 3 are feathere
 The order of the `FEATHER` and `MASK` calls doesn't matter; you can have `FEATHER` before `MASK` or even interleave them.
 
 # Schedulable LoRAs
+
+Note: Use `PCLoraHooksFromSchedule`. It will work better.
+
+## 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.
@@ -225,6 +232,8 @@ You can also set the `PC_RETRY_ON_OOM` environment variable to any non-empty val
 
 ## LoRA Block Weight
 
+Note: Not supported by `PCEncodeSchedule` yet
+
 If you have [ComfyUI Inspire Pack](https://github.com/ltdrdata/ComfyUI-Inspire-Pack) installed, you can use its Lora Block Weight syntax, for example:
 
 ```
@@ -235,6 +244,8 @@ The syntax is the same as in the `ImpactWildcard` node, documented [here](https:
 
 # Other integrations
 ## Advanced CLIP encoding
+Note: `perp` is not supported by `PCEncodeSchedule`
+
 You can use the syntax `STYLE(weight_interpretation, normalization)` in a prompt to affect how prompts are interpreted.
 
 Without any extra nodes, only `perp` is available, which does the same as [ComfyUI_PerpWeight](https://github.com/bvhari/ComfyUI_PerpWeight) extension.
@@ -256,6 +267,8 @@ For things (ie. the code imports) to work, the nodes must be cloned in a directo
 
 ## Cutoff node integration
 
+Note: Not supported by `PCEncodeSchedule` yet.
+
 If you have [ComfyUI Cutoff](https://github.com/BlenderNeko/ComfyUI_Cutoff) cloned into your `custom_nodes`, you can use the `CUT` keyword to use cutoff functionality
 
 The syntax is
@@ -265,12 +278,16 @@ a group of animals, [CUT:white cat:white], [CUT:brown dog:brown:0.5:1.0:1.0:_]
 the parameters in the `CUT` section are `region_text:target_text:weight;strict_mask:start_from_masked:padding_token` of which only the first two are required.
 If `strict_mask`, `start_from_masked` or `padding_token` are specified in more than one section, the last one takes effect for the whole prompt
 
-## Stable-Fast
-
-The prompt control node works well with [ComfyUI_stable_fast](https://github.com/gameltb/ComfyUI_stable_fast). However, you should apply `ScheduleToModel` **after** applying `Apply StableFast Unet` to prevent constant recompilations.
-
 # Nodes
 
+## PCLoraHooksFromSchedule
+
+Creates a ComfyUI `HOOKS` object from a prompt schedule. Can be attached to a CLIP model to perform encoding and LoRA switching
+
+## PCEncodeSchedule
+
+Encodes all prompts in a schedule. Pass in a `CLIP` object with hooks attached for LoRA scheduling, then use the resulting `CONDITIONING` normally
+
 ## PromptToSchedule
 Parses a schedule from a text prompt. A schedule is essentially an array of `(valid_until, prompt)` pairs that the other nodes can use.
 
@@ -283,17 +300,6 @@ Always returns at least the last prompt in the schedule if everything would othe
 
 `start=0, end=0` returns the prompt at the start and `start=1.0, end=1.0` returns the prompt at the end.
 
-## ScheduleToCond
-Produces a combined conditioning for the appropriate timesteps. From a schedule. Also applies LoRAs to the CLIP model according to the schedule.
-
-## ScheduleToModel
-Produces a model that'll cause the sampler to reapply LoRAs at specific steps according to the schedule.
-
-This depends on a callback handled by a monkeypatch of the ComfyUI sampler function, so it might not work with custom samplers, but it shouldn't interfere with them either.
-
-## PCSplitSampling
-Causes sampling to be split into multiple sampler calls instead of relying on timesteps for scheduling. This makes the schedules more accurate, but seems to cause weird behaviour with SDE samplers. (Upstream bug?)
-
 ## PCScheduleSettings
 Returns an object representing **default values** for the `SDXL` function and allows configuring `MASK_SIZE` outside the prompt. You need to apply them to a schedule with `PCApplySettings`. Note that for the SDXL settings to apply, you still need to have `SDXL()` in the prompt.
 
@@ -311,7 +317,19 @@ LoRAs are *not* included in the text prompt, though they are logged.
 
 Attaches custom masks to a `PROMPT_SCHEDULE` that can then be used in a prompt.
 
-## PromptControlSimple
+## 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.

__init__.py

@@ -13,6 +13,7 @@ from .prompt_control.node_other import (
     PCPromptFromSchedule,
 )
 from .prompt_control.node_aio import PromptControlSimple
+from .prompt_control.node_hooks import PCLoraHooksFromSchedule, PCEncodeSchedule
 
 log = logging.getLogger("comfyui-prompt-control")
 log.propagate = False
@@ -43,4 +44,6 @@ NODE_CLASS_MAPPINGS = {
     "ScheduleToModel": ScheduleToModel,
     "EditableCLIPEncode": EditableCLIPEncode,
     "LoRAScheduler": LoRAScheduler,
+    "PCLoraHooksFromSchedule": PCLoraHooksFromSchedule,
+    "PCEncodeSchedule": PCEncodeSchedule,
 }

prompt_control/hijack.py

@@ -18,7 +18,7 @@ def hijack(obj, attr, replacement):
     setattr(replacement, "pc_hijack_done", True)
 
 
-def hijack_sampler(module, function):
+def hijack_sampler(module, function, is_custom):
     mod = sys.modules[module]
     orig_sampler = getattr(mod, function)
     if has_hijack(orig_sampler):
@@ -36,7 +36,7 @@ def hijack_sampler(module, function):
         if cb:
             try:
                 try:
-                    r = cb(orig_sampler, *args, **kwargs)
+                    r = cb(orig_sampler, is_custom, *args, **kwargs)
                 except comfy.model_management.OOM_EXCEPTION:
                     if not os.environ.get("PC_RETRY_ON_OOM"):
                         raise
@@ -45,7 +45,7 @@ def hijack_sampler(module, function):
                     BrownianTreeNoiseSampler.pc_reset(False)
                     gc.collect()
                     comfy.model_management.soft_empty_cache()
-                    r = cb(orig_sampler, *args, **kwargs)
+                    r = cb(orig_sampler, is_custom, *args, **kwargs)
             except Exception:
                 log.error("Exception occurred during callback, unpatching model.")
                 unpatch_model(model)
@@ -155,6 +155,6 @@ def hijack_browniannoisesampler(module, cls):
 
 def do_hijack():
     hijack_browniannoisesampler("comfy.k_diffusion.sampling", "BrownianTreeNoiseSampler")
-    hijack_sampler("comfy.sample", "sample")
-    hijack_sampler("comfy.sample", "sample_custom")
+    hijack_sampler("comfy.sample", "sample", False)
+    hijack_sampler("comfy.sample", "sample_custom", True)
     hijack_ksampler("comfy.samplers", "KSampler")

prompt_control/node_clip.py

@@ -286,6 +286,22 @@ def shuffle_chunk(shuffle, c):
     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 = []
@@ -296,6 +312,7 @@ def encode_prompt(clip, text, default_style="comfy", default_normalization="none
     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
@@ -311,28 +328,29 @@ def encode_prompt(clip, text, default_style="comfy", default_normalization="none
             t = vector_sculptor_tokens(clip, c, method, norm, w)
         else:
             # Tokenizer returns padded results
-            t = clip.tokenize(c, return_word_ids=len(regions) > 0 or (have_advanced_encode and style != "perp"))
+            t = clip.tokenize(c, return_word_ids=need_word_ids)
         token_chunks.append(t)
     tokens = token_chunks[0]
-    for c in token_chunks[1:]:
-        for key in tokens:
+
+    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=len(regions) > 0 or (have_advanced_encode and style != "perp")
-        )["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(text_l, return_word_ids=len(regions) > 0 or (have_advanced_encode and style != "perp"))
+        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)
 
@@ -341,7 +359,7 @@ def encode_prompt(clip, text, default_style="comfy", default_normalization="none
             log.warning("Normalization is not supported with perp style weighting. Ignored '%s'", normalization)
         return perp_encode(clip, tokens)
 
-    if have_advanced_encode and not sculpts:
+    if "t5xxl" not in tokens and have_advanced_encode and not sculpts:
         if "g" in tokens:
             embs_l = None
             embs_g = None

prompt_control/node_hooks.py

@@ -0,0 +1,508 @@
+import logging
+import re
+import torch
+from .utils import safe_float, get_function, parse_floats, lora_name_to_file
+from comfy_extras.nodes_mask import FeatherMask, MaskComposite
+import comfy.utils
+import comfy.hooks
+import folder_paths
+
+log = logging.getLogger("comfyui-prompt-control")
+
+AVAILABLE_STYLES = ["comfy"]
+AVAILABLE_NORMALIZATIONS = ["none"]
+
+have_advanced_encode = False
+try:
+    import custom_nodes.ComfyUI_ADV_CLIP_emb.adv_encode as adv_encode
+
+    have_advanced_encode = True
+    AVAILABLE_STYLES.extend(["A1111", "compel", "comfy++", "down_weight"])
+    AVAILABLE_NORMALIZATIONS.extend(["mean", "length", "length+mean"])
+except ImportError:
+    pass
+
+
+class PCLoraHooksFromSchedule:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {"prompt_schedule": ("PROMPT_SCHEDULE",)},
+        }
+
+    RETURN_TYPES = ("HOOKS",)
+    OUTPUT_TOOLTIPS = ("set of hooks created from the prompt schedule",)
+    CATEGORY = "promptcontrol/_unstable"
+    FUNCTION = "apply"
+
+    def apply(self, prompt_schedule):
+        return (lora_hooks_from_schedule(prompt_schedule),)
+
+
+class PCEncodeSchedule:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {"clip": ("CLIP",), "prompt_schedule": ("PROMPT_SCHEDULE",)},
+        }
+
+    RETURN_TYPES = ("CONDITIONING",)
+    CATEGORY = "promptcontrol/_unstable"
+    FUNCTION = "apply"
+
+    def apply(self, clip, prompt_schedule):
+        return (encode_schedule(clip, prompt_schedule),)
+
+
+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_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 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, settings, default_style="comfy", default_normalization="none"
+) -> list[tuple[torch.Tensor, dict[str]]]:
+    style, normalization, text = get_style(text, default_style, default_normalization)
+    # 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 = have_advanced_encode or style == "comfy" and normalization == "none"
+    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])
+
+    # 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)
+
+    newclip = clip
+    if have_advanced_encode:
+        newclip = clip.clone()
+        if hasattr(clip.patcher.model, "clip_g"):
+            newclip.patcher.add_object_patch(
+                "clip_g.encode_token_weights",
+                encoder_patch(style, normalization, clip.patcher.get_model_object("clip_g.encode_token_weights")),
+            )
+        if hasattr(clip.patcher.model, "clip_l"):
+            newclip.patcher.add_object_patch(
+                "clip_l.encode_token_weights",
+                encoder_patch(style, normalization, clip.patcher.get_model_object("clip_l.encode_token_weights")),
+            )
+
+    return newclip.encode_from_tokens_scheduled(tokens, add_dict=settings)
+
+
+def encoder_patch(style, normalization, orig_fn):
+    if not have_advanced_encode or style == "comfy" and normalization == "none":
+        return orig_fn
+    else:
+        log.debug("Encoding with style=%s, normalization=%s", style, normalization)
+        return lambda t: adv_encode.advanced_encode_from_tokens(
+            t, normalization, style, orig_fn, return_pooled=True, apply_to_pooled=False
+        )
+
+
+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, 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
+
+    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 = []
+    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)
+        settings = {"prompt": prompt}
+        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
+
+        settings["start_percent"] = start_pct
+        settings["end_percent"] = end_pct
+        x = encode_prompt(clip, prompt, settings, style, normalization)
+        conds.extend(x)
+
+    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 lora_hooks_from_schedule(schedules):
+    start_pct = 0.0
+
+    lora_cache = {}
+
+    all_hooks = []
+
+    prev_loras = {}
+
+    def create_hook(loraspec, start_pct, end_pct):
+        nonlocal lora_cache
+        hooks = []
+        hook_kf = comfy.hooks.HookKeyframeGroup()
+        for lora, info in loras.items():
+            path = lora_name_to_file(lora)
+            if not 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
+
+    consolidated = []
+
+    prev_loras = {}
+    for end_pct, c in reversed(list(schedules)):
+        loras = c["loras"]
+        if loras != prev_loras:
+            consolidated.append((end_pct, loras))
+        prev_loras = loras
+    consolidated = reversed(consolidated)
+
+    for end_pct, loras in consolidated:
+        log.info("Creating LoRA hook from %s to %s: %s", start_pct, end_pct, loras)
+        hook = create_hook(loras, start_pct, end_pct)
+        all_hooks.append(hook)
+        start_pct = end_pct
+
+    del lora_cache
+
+    all_hooks = [x for x in all_hooks if x is not None]
+
+    if all_hooks:
+        hooks = comfy.hooks.HookGroup.combine_all_hooks(all_hooks)
+        return hooks
+
+
+def encode_schedule(clip, schedules):
+    start_pct = 0.0
+    conds = []
+    for end_pct, c in schedules:
+        if start_pct < end_pct:
+            prompt = c["prompt"]
+            cond = do_encode(clip, prompt, start_pct, end_pct, schedules.defaults, schedules.masks)
+            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/node_lora.py

@@ -1,6 +1,5 @@
 import logging
 import torch
-import inspect
 
 from .utils import unpatch_model, clone_model, set_callback, apply_loras_from_spec
 from .parser import parse_prompt_schedules
@@ -37,17 +36,17 @@ def schedule_lora_common(model, schedules, lora_cache=None):
     if lora_cache is None:
         lora_cache = {}
 
-    def sampler_cb(orig_sampler, *args, **kwargs):
+    def sampler_cb(orig_sampler, is_custom, *args, **kwargs):
         split_sampling = args[0].model_options.get("pc_split_sampling")
         state = {}
-        # For custom samplers, sigmas is not a keyword argument. Do the check this way to fall back to old behaviour if other hijacks exist.
-        if "sigmas" in inspect.getfullargspec(orig_sampler).args:
+        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

prompt_control/utils.py

@@ -173,10 +173,15 @@ def _patch_model(model, forget=False, orig=None, offload_to_cpu=False):
     if offload_to_cpu:
         saved_offload = model.offload_device
         model.offload_device = torch.device("cpu")
-    log.info("Patching model, cpu_offload=%s", 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()
+    model.patch_model(device_to=model.load_device)
     if offload_to_cpu:
         model.offload_device = saved_offload
     if forget:

pyproject.toml

@@ -1,8 +1,8 @@
 [project]
 name = "comfyui-prompt-control"
 description = "Nodes for convenient prompt editing, making many common operations prompt-controllable"
-version = "1.1.0"
-license = "LICENSE"
+version = "1.2.0"
+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"]