Remove obsolete frontend extensions

Support Anima LLLite v2 models

AGENTS.md

@@ -0,0 +1,272 @@
+# Advanced-ControlNet Contributor Guide
+
+This repository is expected to receive substantial AI-authored code. Treat this
+file as the implementation and verification contract for all changes, especially
+ports of new ControlNet families from ComfyUI.
+
+## Engineering Rules
+
+- Read the relevant ComfyUI implementation and this repository's equivalent
+  control path before editing. Do not design from a model card alone.
+- Make the smallest change that preserves vanilla ComfyUI behavior and adds the
+  established Advanced-ControlNet capabilities.
+- Reuse ComfyUI model classes, patchers, ops, model management, and loaders when
+  possible. Do not maintain a forked copy of core model code without a concrete
+  need.
+- Preserve existing node IDs, inputs, outputs, checkpoint locations, and saved
+  workflow compatibility. New node IDs generally use the `ACN_` prefix; keep
+  existing unprefixed IDs and the aliases in `nodes_deprecated.py` working.
+- Do not add dependencies unless the model cannot be supported with ComfyUI,
+  PyTorch, and the libraries already used by this repository.
+- Match the direct style of the surrounding file. Avoid one-use abstractions,
+  generic framework code, speculative fallbacks, and comments that restate the
+  code.
+- Use plain ASCII punctuation in code, comments, documentation, commit messages,
+  and PR descriptions.
+
+## Architecture Map
+
+- `adv_control/control.py`: standard ControlNet wrappers, conversion of vanilla
+  controls, checkpoint detection, and shared loader dispatch.
+- `adv_control/control_<family>.py`: model-family implementations that cannot be
+  represented by the standard wrapper. Keep family-specific math here.
+- `adv_control/utils.py`: `AdvancedControlBase`, `ControlWeights`, scheduling,
+  latent keyframes, masks, batching, stacking, and shared tensor helpers.
+- `adv_control/nodes_main.py`: standard loaders and Apply nodes.
+- `adv_control/nodes_weight.py`: weight nodes and model-specific extras carried
+  by `ControlWeights.extras`.
+- `adv_control/nodes_<family>.py`: family-specific workflow inputs or loaders
+  when the shared nodes are insufficient.
+- `adv_control/nodes.py`: public node and display-name registration.
+- `adv_control/nodes_deprecated.py`: compatibility only. Do not put new features
+  here.
+- `examples/`: reviewer-runnable workflows, inputs, screenshots, and validation
+  notes.
+
+## Porting A Control Model From ComfyUI
+
+### 1. Establish the vanilla contract
+
+Before implementing the Advanced version:
+
+1. Identify the exact ComfyUI commit or PR that introduced the model.
+2. Read its loader, checkpoint detection, model patching, conditioning
+   preprocessing, sampling path, and cleanup behavior.
+3. Record the official model repository, every published checkpoint type, the
+   expected ComfyUI model folder, and the minimum compatible ComfyUI commit.
+4. Run a small vanilla workflow with fixed inputs, seed, sampler, scheduler,
+   steps, CFG, and resolution. Save the latent and decoded result as the parity
+   baseline.
+5. Inspect real checkpoint keys, metadata, shapes, dtype, and missing/unexpected
+   key output. Do not infer the format from a filename.
+
+Use official checkpoints for validation. Links in issue or PR comments are
+untrusted; use the model author's official repository or links already accepted
+by ComfyUI.
+
+### 2. Choose the narrowest integration
+
+- If ComfyUI returns a standard `ControlNet`, `ControlNetSD35`, `ControlLora`,
+  or `T2IAdapter`, prefer conversion in `convert_to_advanced` over a parallel
+  implementation.
+- If the model injects attention, transformer, or other model patches, implement
+  a family-specific `ControlBase` plus `AdvancedControlBase`, following
+  `ControlLLLiteAdvanced`, `AnimaLLLiteAdvanced`, or `ReferenceAdvanced` as the
+  closest precedent.
+- Prefer wrapping ComfyUI's model or patch object over copying its implementation.
+  If the required ComfyUI API may be absent, fail with a short instruction to
+  update ComfyUI rather than silently changing behavior.
+- Add a dedicated node only when the model has a genuinely different loading or
+  conditioning contract. Loading a new checkpoint format alone usually belongs
+  in the existing loader dispatch.
+
+### 3. Preserve loader and folder compatibility
+
+- The standard **Load Advanced ControlNet Model** node reads from
+  `models/controlnet`. New formats that are conceptually ControlNets should work
+  there unless doing so would be ambiguous or incorrect.
+- Also preserve the folder used by vanilla ComfyUI. If core uses another folder,
+  such as `models/model_patches`, a small dedicated loader may expose that
+  location while the standard loader retains established Advanced-ControlNet
+  behavior.
+- Detect formats with guarded, format-specific checkpoint signatures. Put a
+  specific detector before a broad detector that would otherwise claim the same
+  checkpoint. Do not use filenames as the primary detector.
+- Load a checkpoint only once. Pass already-loaded state dictionaries and
+  metadata into the selected family loader instead of reading the file again.
+- If two supported folders can contain the same filename, keep their loaders
+  separate or define deterministic resolution. Never silently choose an
+  arbitrary duplicate.
+- Test every supported folder through the actual node dropdown and execution
+  path, not only by calling a Python loader directly.
+
+### 4. Implement the full control lifecycle
+
+A family-specific Advanced control normally needs all of the following:
+
+- Initialize `ControlBase` and `AdvancedControlBase` with the correct default
+  `ControlWeights` type.
+- Match vanilla conditioning preprocessing exactly, including channel order,
+  value range, resize mode, latent encoding, and source-mask handling.
+- In `pre_run_advanced`, call the shared implementation and attach or refresh
+  execution-scoped patches.
+- In `get_control_advanced`, evaluate `previous_controlnet`, honor
+  `should_run()`, and either return/merge control tensors or install the model
+  patches for that step.
+- Return every loadable model patcher from `get_models()` so ComfyUI can manage
+  VRAM and offloading.
+- Implement `copy()` using both ComfyUI's `copy_to()` and this repository's
+  `copy_to_advanced()`. Copies must not share mutable execution state that can
+  leak between conditioning branches or queued runs.
+- Clear prepared tensors, patch references, cached shapes, and other
+  execution-scoped state in `cleanup_advanced()`.
+- Use ComfyUI device, dtype, manual-cast, operations, and model-patcher APIs.
+  Do not hardcode CUDA, force float32, or move models manually when ComfyUI
+  already owns that lifecycle.
+- Preserve `previous_controlnet` behavior so same-family and mixed-family
+  controls can be stacked.
+
+## Advanced Feature Contract
+
+A port is not complete merely because default-strength generation works. Unless
+the model architecture makes a capability impossible, verify that it supports:
+
+- Apply-node strength and start/end percentage.
+- Timestep keyframes, including changing strength and inherited values.
+- Latent keyframes on a batch of at least two latents.
+- Apply-node effect masks and timestep-keyframe masks.
+- Default, universal/soft, and architecture-specific per-layer weights.
+- Weight overrides and model-specific weight extras.
+- Conditional/unconditional weighting when the selected weight node exposes it.
+- Stacking with another control, including correct `previous_controlnet` output.
+- Batched conditioning and sliding-context subset indexes where applicable.
+- Repeated execution, copying, cleanup, model offloading, and reload.
+
+Do not claim unsupported features in documentation. If an architecture cannot
+support a feature, document the reason and make incompatible weight types fail
+clearly through `compatible_weights`.
+
+### Masks and model-specific inputs
+
+- `mask_optional` on **Apply Advanced ControlNet** is always an effect mask. It
+  controls where this control influences generation.
+- A model's source mask, control-type selector, or other family-specific data is
+  not an effect mask. Do not overload `mask_optional` with a second meaning.
+- Do not add a model-specific input to the shared Apply node unless it is a
+  coherent capability needed by multiple model families.
+- Prefer a small family-specific extras node that stores auxiliary values in
+  `ControlWeights.extras`, then pass those weights through `weights_override`.
+  Define extras keys next to the model implementation rather than as unrelated
+  strings spread across nodes.
+- Validate required extras where they are first consumed and raise an actionable
+  error that names the exact nodes and connections needed to fix the workflow.
+- Apply effect masks at the actual injection representation. Attention-patch and
+  DiT controls may need token-space masks rather than the normal spatial control
+  tensor path.
+- Verify mask semantics with all-zero, all-one, and half-frame masks. All-zero
+  must equal no control and all-one must equal unmasked full control. Inspect the
+  multiplier at the injection site as well as the final image; global attention
+  can propagate influence outside directly controlled tokens.
+
+### Per-layer weights
+
+- Map custom weights to real architecture blocks in execution order. Confirm the
+  count from the loaded model, not from a model-card claim alone.
+- Default weights must reproduce vanilla output exactly.
+- Universal/soft weights must follow this repository's established progression
+  semantics. Implement a family-specific conversion only when the normal
+  `ControlWeights` layout does not represent the architecture.
+- Ordinary example workflows should use default weights. Do not connect an
+  advanced custom-weight node merely to demonstrate that it exists.
+
+## Required Validation
+
+Python import or compile checks are necessary but are not model validation. Use
+a real local ComfyUI installation, preferably managed by comfy-runner, with this
+repository linked as the custom node.
+
+### Vanilla parity
+
+For every published control type and materially different checkpoint format:
+
+1. Run vanilla ComfyUI and Advanced-ControlNet with identical model files,
+   conditioning, seed, sampler settings, and latent.
+2. Compare latent tensors before decode and decoded pixel arrays.
+3. Target maximum absolute latent difference `0.0` and identical pixels when
+   both paths implement the same math. If exact parity is impossible, explain
+   why and report a justified numerical tolerance plus image metrics.
+4. Confirm strength zero matches no control and strength one matches vanilla.
+5. Check logs for missing/unexpected keys, dtype/device errors, repeated model
+   loads, and cleanup failures.
+
+### Advanced behavior
+
+At minimum, execute focused workflows for:
+
+- A nontrivial start/end schedule or two timestep keyframes.
+- Batch size two with different latent-keyframe strengths.
+- All-zero, all-one, and half-frame effect masks.
+- Default weights and one nonuniform custom or soft-weight configuration.
+- Conditional/unconditional weighting when supported.
+- A stacked control path.
+- Missing required model-specific extras and the resulting readable error.
+- Both the vanilla model folder and any historical Advanced-ControlNet folder.
+- Re-queueing the same workflow to exercise copy and cleanup behavior.
+
+For model families with several control types, test every type. Do not assume
+that lineart, depth, pose, inpainting, union, or channel-count variants share the
+same conditioning contract.
+
+### Basic checks
+
+- Run `python -m compileall adv_control __init__.py` with the target ComfyUI
+  environment.
+- Parse every added workflow JSON.
+- Load each committed workflow in the real frontend, serialize it to an API
+  prompt, and execute that round-tripped prompt. This catches stale node IDs,
+  renamed inputs, invalid widgets, and missing model metadata.
+- Run `git diff --check`.
+
+There is currently no repository unit-test suite. Add focused tests when they
+can exercise pure detection, shape, mask, or scheduling logic without building
+a fake ComfyUI runtime. Do not add a large test framework solely for one port.
+
+## Examples and Review Evidence
+
+Every model-family port must be independently checkable by a reviewer:
+
+- Add one simple workflow for every public control type. Include required input
+  images or masks when licensing permits.
+- Keep ComfyUI's default node names. Use colored groups or regions to explain
+  branches; do not rename nodes, because reviewers need to identify their types.
+- Keep the normal workflows simple. Leave custom per-layer weights disconnected
+  unless a workflow specifically validates those advanced weights.
+- Include direct links to the official base model, encoder, VAE, and control
+  checkpoint repositories, plus the exact destination folder for each file.
+- Include a workflow screenshot and labeled output comparison in the PR. For
+  parity tests, show vanilla, Advanced-ControlNet, and an absolute-difference
+  result when practical.
+- Commit reusable workflows and small review images under `examples/<family>/`.
+  Do not commit model files, latent dumps, or large intermediate artifacts.
+- Document exact seeds/settings, expected numerical results, known limitations,
+  and reproduction steps in the example README and PR description.
+- If final-image interpretation is subtle, include the direct tensor-level
+  evidence needed to distinguish a real bug from model behavior.
+
+## Definition Of Done For A Model Port
+
+- [ ] The official checkpoint is detected without relying on its filename.
+- [ ] Vanilla and historical Advanced-ControlNet model folders are preserved.
+- [ ] Default output matches vanilla for every published control type.
+- [ ] Strength scheduling, keyframes, masks, weights, batching, and stacking are
+      tested or an architectural limitation is documented.
+- [ ] Effect masks are tested at the injection site and in decoded output.
+- [ ] Model-specific inputs use a narrow family boundary, not shared Apply-node
+      expansion.
+- [ ] `get_models`, `copy`, cleanup, dtype/device handling, and repeated runs are
+      verified.
+- [ ] Missing required inputs produce actionable errors.
+- [ ] Simple workflows, input assets, download links, screenshots, and exact
+      reproduction instructions are included.
+- [ ] Real frontend/API execution, compile checks, JSON parsing, and
+      `git diff --check` pass.

README.md

@@ -12,7 +12,7 @@ ControlNet preprocessors are available through [comfyui_controlnet_aux](https://
 - Replicate ***"ControlNet is more important"*** feature from sd-webui-controlnet extension via ***uncond_multiplier*** on ***Soft Weights***
   - uncond_multiplier=0.0 gives identical results of auto1111's feature, but values between 0.0 and 1.0 can be used without issue to granularly control the setting.
 - ControlNet, T2IAdapter, and ControlLoRA support for sliding context windows
-- ControlLLLite support
+- ControlLLLite support, including Anima LLLite v2 and inpainting models
 - ControlNet++ support
 - CtrLoRA support
   - Relevant models linked on [CtrLoRA github page](https://github.com/xyfJASON/ctrlora)
@@ -83,6 +83,12 @@ Loads a ControlNet model and converts it into an Advanced version that supports
 ### Outputs
 - 🟪***CONTROL_NET***: loaded Advanced ControlNet
 
+## Anima LLLite v2
+
+Place Anima LLLite v2 files in `ComfyUI/models/model_patches` and load them with **Load Anima LLLite Model**. The regular Advanced ControlNet loader also recognizes these models when they are placed in `ComfyUI/models/controlnet`.
+
+Use the loaded model with **Apply Advanced ControlNet**. For the 4-channel inpainting model, pass the source mask through **Anima LLLite Extras** into the `cn_extras` input of a weights node; `mask_optional` on the Apply node remains the Advanced-ControlNet effect mask. **ControlNet Custom Weights [Anima]** provides one weight for each of Anima's 28 transformer blocks. Timestep keyframes, latent keyframes, soft weights, CFG/unconditional weighting, effect masks, and stacked controls work the same as with other Advanced-ControlNet models.
+
 ## Timestep Keyframe
 ![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/404f3cfe-5852-4eed-935b-37e32493d1b5)
 

__init__.py

@@ -1,11 +1,8 @@
 from .adv_control.nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
-from .adv_control import documentation
 from .adv_control.dinklink import init_dinklink
 from .adv_control.sampling import prepare_dinklink_acn_wrapper
 
-WEB_DIRECTORY = "./web"
-__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', "WEB_DIRECTORY"]
-documentation.format_descriptions(NODE_CLASS_MAPPINGS)
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
 
 init_dinklink()
 prepare_dinklink_acn_wrapper()

adv_control/control.py

@@ -13,7 +13,7 @@ from comfy.controlnet import ControlBase, ControlNet, ControlNetSD35, ControlLor
 from comfy.model_patcher import ModelPatcher
 
 from .control_sparsectrl import SparseControlNet, SparseSettings, SparseConst, InterfaceAnimateDiffModel, create_sparse_modelpatcher, load_sparsectrl_motionmodel
-from .control_lllite import LLLiteModule, LLLitePatch, load_controllllite
+from .control_lllite import LLLiteModule, LLLitePatch, load_anima_lllite, load_controllllite
 from .control_svd import svd_unet_config_from_diffusers_unet, SVDControlNet, svd_unet_to_diffusers
 from .utils import (AdvancedControlBase, TimestepKeyframeGroup, LatentKeyframeGroup, AbstractPreprocWrapper, ControlWeightType, ControlWeights, WeightTypeException, Extras,
                     manual_cast_clean_groupnorm, disable_weight_init_clean_groupnorm, WrapperConsts, prepare_mask_batch, get_properly_arranged_t2i_weights, load_torch_file_with_dict_factory,
@@ -510,7 +510,7 @@ class SparseCtrlAdvanced(ControlNetAdvanced):
 
 
 def load_controlnet(ckpt_path, timestep_keyframe: TimestepKeyframeGroup=None, model=None):
-    controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
+    controlnet_data, metadata = comfy.utils.load_torch_file(ckpt_path, safe_load=True, return_metadata=True)
     # from pathlib import Path
     # log_name = ckpt_path.split('\\')[-1]
     # with open(Path(__file__).parent.parent.parent / rf"keys_{log_name}.txt", "w") as afile:
@@ -519,6 +519,10 @@ def load_controlnet(ckpt_path, timestep_keyframe: TimestepKeyframeGroup=None, mo
     control = None
     # check if a non-vanilla ControlNet
     controlnet_type = ControlWeightType.DEFAULT
+    is_anima_lllite = (
+        "lllite_conditioning1.conv1.weight" in controlnet_data
+        and any(key.startswith("lllite_dit_blocks_") for key in controlnet_data)
+    )
     has_controlnet_key = False
     has_motion_modules_key = False
     has_temporal_res_block_key = False
@@ -548,7 +552,9 @@ def load_controlnet(ckpt_path, timestep_keyframe: TimestepKeyframeGroup=None, mo
     elif has_controlnet_key and has_temporal_res_block_key:
         controlnet_type = ControlWeightType.SVD_CONTROLNET
 
-    if controlnet_type != ControlWeightType.DEFAULT:
+    if is_anima_lllite:
+        control = load_anima_lllite(ckpt_path, controlnet_data=controlnet_data, metadata=metadata, timestep_keyframe=timestep_keyframe)
+    elif controlnet_type != ControlWeightType.DEFAULT:
         if controlnet_type == ControlWeightType.CONTROLLLLITE:
             control = load_controllllite(ckpt_path, controlnet_data=controlnet_data, timestep_keyframe=timestep_keyframe)
         elif controlnet_type == ControlWeightType.SPARSECTRL:
@@ -990,4 +996,3 @@ def load_svdcontrolnet(ckpt_path: str, controlnet_data: dict[str, Tensor]=None,
 
     control = SVDControlNetAdvanced(control_model, timestep_keyframes=timestep_keyframe, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
     return control
-

adv_control/control_lllite.py

@@ -10,14 +10,24 @@ import os
 import comfy.utils
 import comfy.ops
 import comfy.model_management
+import comfy.model_patcher
 from comfy.model_patcher import ModelPatcher
 from comfy.controlnet import ControlBase
 
+try:
+    import comfy.ldm.anima.lllite as comfy_anima_lllite
+except ImportError:
+    comfy_anima_lllite = None
+
 from .logger import logger
 from .utils import (AdvancedControlBase, TimestepKeyframeGroup, ControlWeights, broadcast_image_to_extend, extend_to_batch_size,
                     prepare_mask_batch)
 
 
+class AnimaLLLiteConst:
+    INPAINT_MASK = "anima_lllite_inpaint_mask"
+
+
 # based on set_model_patch code in comfy/model_patcher.py
 def set_model_patch(transformer_options, patch, name):
     to = transformer_options
@@ -371,6 +381,233 @@ class ControlLLLiteAdvanced(ControlBase, AdvancedControlBase):
         return c
 
 
+class AnimaLLLiteAdvancedPatch:
+    def __init__(self, model_patch, control: 'AnimaLLLiteAdvanced'=None):
+        self.model_patch = model_patch
+        self.control = control
+
+    def set_control(self, control: 'AnimaLLLiteAdvanced') -> 'AnimaLLLiteAdvancedPatch':
+        self.control = control
+        return self
+
+    def clone_with_control(self, control: 'AnimaLLLiteAdvanced') -> 'AnimaLLLiteAdvancedPatch':
+        return AnimaLLLiteAdvancedPatch(self.model_patch, control)
+
+    def __call__(self, args):
+        if not self.control.should_run():
+            return args
+
+        x = args["x"]
+        if x.shape[2] != 1:
+            raise ValueError(f"Anima LLLite only supports T=1, got T={x.shape[2]}")
+
+        target_height = x.shape[-2] * 8
+        target_width = x.shape[-1] * 8
+        image = self.control.prepare_batched_tensor(self.control.cond_hint_original, x.shape[0])[:, :3]
+        image = comfy.utils.common_upscale(image, target_width, target_height, "bicubic", crop="center").clamp(0.0, 1.0)
+        image = image.to(device=x.device, dtype=x.dtype) * 2.0 - 1.0
+
+        if self.model_patch.model.cond_in_channels == 4:
+            mask = self.control.weights.extras.get(AnimaLLLiteConst.INPAINT_MASK)
+            if mask is None:
+                raise ValueError(
+                    "Anima LLLite inpainting models require an inpaint mask. Connect a MASK to Anima LLLite Extras, "
+                    "connect its cn_extras output to Default Weights, then connect CN_WEIGHTS to weights_override on Apply Advanced ControlNet."
+                )
+            if mask.ndim == 3:
+                mask = mask.unsqueeze(1)
+            if mask.ndim != 4 or mask.shape[1] != 1:
+                raise ValueError(f"Anima LLLite mask must have one channel, got shape {tuple(mask.shape)}")
+            if image.shape[0] > 1:
+                mask = self.control.prepare_batched_tensor(mask, image.shape[0], except_one=False)
+            mask = comfy.utils.common_upscale(mask.float(), target_width, target_height, "nearest-exact", crop="center")
+            if mask.shape[0] != image.shape[0]:
+                if image.shape[0] % mask.shape[0] != 0:
+                    raise ValueError(f"Anima LLLite mask batch {mask.shape[0]} cannot be broadcast to image batch {image.shape[0]}")
+                mask = mask.repeat(image.shape[0] // mask.shape[0], 1, 1, 1)
+            mask = (mask >= 0.5).to(device=x.device, dtype=x.dtype)
+            if self.model_patch.model.inpaint_masked_input:
+                image = image * (mask < 0.5).to(image.dtype)
+            image = torch.cat((image, mask * 2.0 - 1.0), dim=1)
+
+        cond_emb = self.model_patch.model.encode_conditioning(image)
+        multiplier, weight_mask = self.control.prepare_multiplier(args["img"])
+        args["transformer_options"]["model_patch_data"][self] = (cond_emb, multiplier, weight_mask)
+        return args
+
+    def to(self, device_or_dtype):
+        return self
+
+    def models(self):
+        return [self.model_patch]
+
+
+class AnimaLLLiteAdvancedAttentionPatch:
+    def __init__(self, patch: AnimaLLLiteAdvancedPatch, targets):
+        self.patch = patch
+        self.targets = targets
+
+    def __call__(self, q, k, v, pe=None, attn_mask=None, extra_options=None):
+        patch_data = extra_options["model_patch_data"].get(self.patch)
+        if patch_data is None:
+            return {"q": q, "k": k, "v": v, "pe": pe, "attn_mask": attn_mask}
+
+        cond_emb, multiplier, weight_mask = patch_data
+        block_index = extra_options["block_index"]
+        strength = self.patch.control.get_block_strength(block_index, multiplier, weight_mask)
+        values = {"q": q, "k": k, "v": v}
+        for value_name, target in self.targets.items():
+            values[value_name] = self.patch.model_patch.model.apply(values[value_name], cond_emb, block_index, target, strength)
+        return {"q": values["q"], "k": values["k"], "v": values["v"], "pe": pe, "attn_mask": attn_mask}
+
+
+class AnimaLLLiteAdvancedMLPPatch:
+    def __init__(self, patch: AnimaLLLiteAdvancedPatch):
+        self.patch = patch
+
+    def __call__(self, args):
+        patch_data = args["transformer_options"]["model_patch_data"].get(self.patch)
+        if patch_data is None:
+            return args
+
+        cond_emb, multiplier, weight_mask = patch_data
+        block_index = args["transformer_options"]["block_index"]
+        strength = self.patch.control.get_block_strength(block_index, multiplier, weight_mask)
+        args["x"] = self.patch.model_patch.model.apply(args["x"], cond_emb, block_index, "mlp_layer1", strength)
+        return args
+
+
+class AnimaLLLiteAdvanced(ControlBase, AdvancedControlBase):
+    def __init__(self, model_patch, timestep_keyframes: TimestepKeyframeGroup):
+        ControlBase.__init__(self)
+        AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.controllllite())
+        self.model_patch = model_patch
+        self.patch = AnimaLLLiteAdvancedPatch(model_patch, self)
+        self.patch_attn1 = AnimaLLLiteAdvancedAttentionPatch(
+            self.patch,
+            {"q": "self_attn_q_proj", "k": "self_attn_k_proj", "v": "self_attn_v_proj"},
+        )
+        self.patch_attn2 = AnimaLLLiteAdvancedAttentionPatch(self.patch, {"q": "cross_attn_q_proj"})
+        self.patch_mlp = AnimaLLLiteAdvancedMLPPatch(self.patch)
+
+    def prepare_batched_tensor(self, tensor: Tensor, target_batch: int, except_one=True) -> Tensor:
+        if self.sub_idxs is not None:
+            if tensor.shape[0] < self.full_latent_length:
+                tensor = extend_to_batch_size(tensor, self.full_latent_length)
+            tensor = tensor[self.sub_idxs]
+        if tensor.shape[0] != target_batch:
+            tensor = broadcast_image_to_extend(tensor, target_batch, self.batched_number, except_one=except_one)
+        return tensor
+
+    def prepare_effect_mask(self, mask: Tensor, img: Tensor) -> Tensor:
+        mask = self.prepare_batched_tensor(mask, img.shape[0], except_one=False)
+        mask = torch.nn.functional.interpolate(
+            mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).float(),
+            size=(img.shape[2], img.shape[3]),
+            mode="bilinear",
+        )
+        return mask.flatten(2).transpose(1, 2).to(device=img.device, dtype=img.dtype)
+
+    def prepare_multiplier(self, img: Tensor):
+        multiplier = self.strength * self._current_timestep_keyframe.strength
+        token_multiplier = None
+
+        masks = [self.mask_cond_hint_original, self._current_timestep_keyframe.mask_hint_orig]
+        for mask in masks:
+            if mask is not None:
+                mask_multiplier = self.prepare_effect_mask(mask, img)
+                token_multiplier = mask_multiplier if token_multiplier is None else token_multiplier * mask_multiplier
+
+        flat_img = img.flatten(1, 3)
+        if self.latent_keyframes is not None:
+            latent_multiplier = self.calc_latent_keyframe_mults(flat_img, self.batched_number)
+            token_multiplier = latent_multiplier if token_multiplier is None else token_multiplier * latent_multiplier
+
+        if self.weights.has_uncond_multiplier and self.cond_or_uncond is not None:
+            batch_multiplier = torch.ones((img.shape[0], 1, 1), dtype=img.dtype, device=img.device)
+            actual_length = img.shape[0] // self.batched_number
+            for idx, cond_type in enumerate(self.cond_or_uncond):
+                if cond_type == 1:
+                    batch_multiplier[actual_length * idx:actual_length * (idx + 1)] *= self.weights.uncond_multiplier
+            token_multiplier = batch_multiplier if token_multiplier is None else token_multiplier * batch_multiplier
+
+        weight_mask = None
+        if self.weights.weight_mask is not None:
+            weight_mask = self.prepare_effect_mask(self.weights.weight_mask, img)
+
+        if token_multiplier is not None:
+            multiplier = token_multiplier * multiplier
+        return multiplier, weight_mask
+
+    def get_block_strength(self, block_index: int, multiplier, weight_mask):
+        block_weight = 1.0
+        if self.weights.weight_type == "universal":
+            exponent = self.model_patch.model.block_count - block_index
+            if weight_mask is not None:
+                block_weight = torch.pow(weight_mask, exponent)
+            else:
+                block_weight = self.weights.base_multiplier ** exponent
+        elif self.weights.weights_input is not None and block_index < len(self.weights.weights_input):
+            block_weight = self.weights.weights_input[block_index]
+        return multiplier * block_weight
+
+    def pre_run_advanced(self, *args, **kwargs):
+        AdvancedControlBase.pre_run_advanced(self, *args, **kwargs)
+        self.patch.set_control(self)
+
+    def get_control_advanced(self, x_noisy: Tensor, t, cond, batched_number: int, transformer_options: dict):
+        control_prev = None
+        if self.previous_controlnet is not None:
+            control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number, transformer_options)
+        if not self.should_run():
+            return control_prev
+
+        set_model_patch(transformer_options, self.patch.set_control(self), "post_input")
+        set_model_attn1_patch(transformer_options, self.patch_attn1)
+        set_model_attn2_patch(transformer_options, self.patch_attn2)
+        set_model_patch(transformer_options, self.patch_mlp, "mlp_patch")
+        return control_prev
+
+    def get_models(self):
+        models = super().get_models()
+        models.append(self.model_patch)
+        return models
+
+    def copy(self):
+        copied = AnimaLLLiteAdvanced(self.model_patch, self.timestep_keyframes)
+        self.copy_to(copied)
+        self.copy_to_advanced(copied)
+        return copied
+
+
+def load_anima_lllite(ckpt_path: str, controlnet_data: dict[str, Tensor]=None, metadata=None, timestep_keyframe: TimestepKeyframeGroup=None):
+    if comfy_anima_lllite is None:
+        raise RuntimeError("Anima LLLite requires a newer version of ComfyUI. Please update ComfyUI.")
+    if controlnet_data is None or metadata is None:
+        loaded_data, loaded_metadata = comfy.utils.load_torch_file(ckpt_path, safe_load=True, return_metadata=True)
+        if controlnet_data is None:
+            controlnet_data = loaded_data
+        metadata = loaded_metadata
+
+    dtype = comfy.utils.weight_dtype(controlnet_data)
+    model = comfy_anima_lllite.AnimaLLLite(
+        controlnet_data,
+        metadata,
+        device=comfy.model_management.unet_offload_device(),
+        dtype=dtype,
+        operations=comfy.ops.manual_cast,
+    )
+    patcher_type = getattr(comfy.model_patcher, "CoreModelPatcher", ModelPatcher)
+    model_patcher = patcher_type(
+        model,
+        load_device=comfy.model_management.get_torch_device(),
+        offload_device=comfy.model_management.unet_offload_device(),
+    )
+    is_dynamic = getattr(model_patcher, "is_dynamic", lambda: False)()
+    model.load_state_dict(controlnet_data, assign=is_dynamic)
+    return AnimaLLLiteAdvanced(model_patcher, timestep_keyframe)
+
+
 def load_controllllite(ckpt_path: str, controlnet_data: dict[str, Tensor]=None, timestep_keyframe: TimestepKeyframeGroup=None):
     if controlnet_data is None:
         controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)

adv_control/documentation.py

@@ -1,47 +0,0 @@
-from .logger import logger
-
-def image(src):
-    return f'<img src={src} style="width: 0px; min-width: 100%">'
-def video(src):
-    return f'<video src={src} autoplay muted loop controls controlslist="nodownload noremoteplayback noplaybackrate" style="width: 0px; min-width: 100%" class="VHS_loopedvideo">'
-def short_desc(desc):
-    return  f'<div id=VHS_shortdesc style="font-size: .8em">{desc}</div>'
-
-descriptions = {
-}
-
-sizes = ['1.4','1.2','1']
-def as_html(entry, depth=0):
-    if isinstance(entry, dict):
-        size = 0.8 if depth < 2 else 1
-        html = ''
-        for k in entry:
-            if k == "collapsed":
-                continue
-            collapse_single = k.endswith("_collapsed")
-            if collapse_single:
-                name = k[:-len("_collapsed")]
-            else:
-                name = k
-            collapse_flag = ' VHS_precollapse' if entry.get("collapsed", False) or collapse_single else ''
-            html += f'<div vhs_title=\"{name}\" style=\"display: flex; font-size: {size}em\" class=\"VHS_collapse{collapse_flag}\"><div style=\"color: #AAA; height: 1.5em;\">[<span style=\"font-family: monospace\">-</span>]</div><div style=\"width: 100%\">{name}: {as_html(entry[k], depth=depth+1)}</div></div>'
-        return html
-    if isinstance(entry, list):
-        html = ''
-        for i in entry:
-            html += f'<div>{as_html(i, depth=depth)}</div>'
-        return html
-    return str(entry)
-
-def format_descriptions(nodes):
-    for k in descriptions:
-        if k.endswith("_collapsed"):
-            k = k[:-len("_collapsed")]
-        nodes[k].DESCRIPTION = as_html(descriptions[k])
-    # undocumented_nodes = []
-    # for k in nodes:
-    #     if not hasattr(nodes[k], "DESCRIPTION"):
-    #         undocumented_nodes.append(k)
-    # if len(undocumented_nodes) > 0:
-    #     logger.info(f"Undocumented nodes: {undocumented_nodes}")
-

adv_control/nodes.py

@@ -1,10 +1,11 @@
 import comfy.sample
 
-from .nodes_main import (ControlNetLoaderAdvanced, DiffControlNetLoaderAdvanced,
+from .nodes_main import (ControlNetLoaderAdvanced, DiffControlNetLoaderAdvanced, AnimaLLLiteLoaderAdvanced,
                          AdvancedControlNetApply, AdvancedControlNetApplySingle)
 from .nodes_weight import (DefaultWeights, ScaledSoftMaskedUniversalWeights, ScaledSoftUniversalWeights,
                            SoftControlNetWeightsSD15, CustomControlNetWeightsSD15, CustomControlNetWeightsFlux,
-                           SoftT2IAdapterWeights, CustomT2IAdapterWeights, ExtrasMiddleMultNode)
+                           CustomControlNetWeightsAnima, SoftT2IAdapterWeights, CustomT2IAdapterWeights, ExtrasMiddleMultNode,
+                           AnimaLLLiteExtras)
 from .nodes_keyframes import (LatentKeyframeGroupNode, LatentKeyframeInterpolationNode, LatentKeyframeBatchedGroupNode, LatentKeyframeNode,
                               TimestepKeyframeNode, TimestepKeyframeInterpolationNode, TimestepKeyframeFromStrengthListNode)
 from .nodes_sparsectrl import SparseCtrlMergedLoaderAdvanced, SparseCtrlLoaderAdvanced, SparseIndexMethodNode, SparseSpreadMethodNode, RgbSparseCtrlPreprocessor, SparseWeightExtras
@@ -36,16 +37,19 @@ NODE_CLASS_MAPPINGS = {
     # Loaders
     "ACN_ControlNetLoaderAdvanced": ControlNetLoaderAdvanced,
     "ACN_DiffControlNetLoaderAdvanced": DiffControlNetLoaderAdvanced,
+    "ACN_AnimaLLLiteLoaderAdvanced": AnimaLLLiteLoaderAdvanced,
     # Weights
     "ACN_ScaledSoftControlNetWeights": ScaledSoftUniversalWeights,
     "ScaledSoftMaskedUniversalWeights": ScaledSoftMaskedUniversalWeights,
     "ACN_SoftControlNetWeightsSD15": SoftControlNetWeightsSD15,
     "ACN_CustomControlNetWeightsSD15": CustomControlNetWeightsSD15,
     "ACN_CustomControlNetWeightsFlux": CustomControlNetWeightsFlux,
+    "ACN_CustomControlNetWeightsAnima": CustomControlNetWeightsAnima,
     "ACN_SoftT2IAdapterWeights": SoftT2IAdapterWeights,
     "ACN_CustomT2IAdapterWeights": CustomT2IAdapterWeights,
     "ACN_DefaultUniversalWeights": DefaultWeights,
     "ACN_ExtrasMiddleMult": ExtrasMiddleMultNode,
+    "ACN_AnimaLLLiteExtras": AnimaLLLiteExtras,
     # SparseCtrl
     "ACN_SparseCtrlRGBPreprocessor": RgbSparseCtrlPreprocessor,
     "ACN_SparseCtrlLoaderAdvanced": SparseCtrlLoaderAdvanced,
@@ -93,16 +97,19 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     # Loaders
     "ACN_ControlNetLoaderAdvanced": "Load Advanced ControlNet Model 🛂🅐🅒🅝",
     "ACN_DiffControlNetLoaderAdvanced": "Load Advanced ControlNet Model (diff) 🛂🅐🅒🅝",
+    "ACN_AnimaLLLiteLoaderAdvanced": "Load Anima LLLite Model 🛂🅐🅒🅝",
     # Weights
     "ACN_ScaledSoftControlNetWeights": "Scaled Soft Weights 🛂🅐🅒🅝",
     "ScaledSoftMaskedUniversalWeights": "Scaled Soft Masked Weights 🛂🅐🅒🅝",
     "ACN_SoftControlNetWeightsSD15": "ControlNet Soft Weights [SD1.5] 🛂🅐🅒🅝",
     "ACN_CustomControlNetWeightsSD15": "ControlNet Custom Weights [SD1.5] 🛂🅐🅒🅝",
     "ACN_CustomControlNetWeightsFlux": "ControlNet Custom Weights [Flux] 🛂🅐🅒🅝",
+    "ACN_CustomControlNetWeightsAnima": "ControlNet Custom Weights [Anima] 🛂🅐🅒🅝",
     "ACN_SoftT2IAdapterWeights": "T2IAdapter Soft Weights 🛂🅐🅒🅝",
     "ACN_CustomT2IAdapterWeights": "T2IAdapter Custom Weights 🛂🅐🅒🅝",
     "ACN_DefaultUniversalWeights": "Default Weights 🛂🅐🅒🅝",
     "ACN_ExtrasMiddleMult": "Middle Weight Extras 🛂🅐🅒🅝",
+    "ACN_AnimaLLLiteExtras": "Anima LLLite Extras 🛂🅐🅒🅝",
     # SparseCtrl
     "ACN_SparseCtrlRGBPreprocessor": "RGB SparseCtrl 🛂🅐🅒🅝",
     "ACN_SparseCtrlLoaderAdvanced": "Load SparseCtrl Model 🛂🅐🅒🅝",

adv_control/nodes_deprecated.py

@@ -86,9 +86,6 @@ class ScaledSoftUniversalWeightsDeprecated:
                 "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                 "cn_extras": ("CN_WEIGHTS_EXTRAS",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
@@ -126,9 +123,6 @@ class SoftControlNetWeightsDeprecated:
                 "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                 "cn_extras": ("CN_WEIGHTS_EXTRAS",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     DEPRECATED = True
@@ -172,9 +166,6 @@ class CustomControlNetWeightsDeprecated:
                 "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                 "cn_extras": ("CN_WEIGHTS_EXTRAS",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     DEPRECATED = True
@@ -209,9 +200,6 @@ class SoftT2IAdapterWeightsDeprecated:
                 "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                 "cn_extras": ("CN_WEIGHTS_EXTRAS",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     DEPRECATED = True
@@ -244,9 +232,6 @@ class CustomT2IAdapterWeightsDeprecated:
                 "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                 "cn_extras": ("CN_WEIGHTS_EXTRAS",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     DEPRECATED = True
@@ -285,9 +270,6 @@ class AdvancedControlNetApplyDEPR:
                 "model_optional": ("MODEL",),
                 "vae_optional": ("VAE",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
 
     DEPRECATED = True
@@ -328,9 +310,6 @@ class AdvancedControlNetApplySingleDEPR:
                 "model_optional": ("MODEL",),
                 "vae_optional": ("VAE",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
 
     DEPRECATED = True
@@ -392,9 +371,6 @@ class DiffControlNetLoaderAdvancedDEPR:
             "optional": {
                 "tk_optional": ("TIMESTEP_KEYFRAME", ),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     DEPRECATED = True

adv_control/nodes_keyframes.py

@@ -26,9 +26,6 @@ class TimestepKeyframeNode:
                 "guarantee_steps": ("INT", {"default": 1, "min": 0, "max": BIGMAX}),
                 "mask_optional": ("MASK", ),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_NAMES = ("TIMESTEP_KF", )
@@ -85,9 +82,6 @@ class TimestepKeyframeInterpolationNode:
                 "mask_optional": ("MASK", ),
                 "print_keyframes": ("BOOLEAN", {"default": False}),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_NAMES = ("TIMESTEP_KF", )
@@ -146,9 +140,6 @@ class TimestepKeyframeFromStrengthListNode:
                 "mask_optional": ("MASK", ),
                 "print_keyframes": ("BOOLEAN", {"default": False}),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_NAMES = ("TIMESTEP_KF", )
@@ -205,9 +196,6 @@ class LatentKeyframeNode:
             "optional": {
                 "prev_latent_kf": ("LATENT_KEYFRAME", ),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
 
     RETURN_NAMES = ("LATENT_KF", )
@@ -244,9 +232,6 @@ class LatentKeyframeGroupNode:
                 "latent_optional": ("LATENT", ),
                 "print_keyframes": ("BOOLEAN", {"default": False}),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_NAMES = ("LATENT_KF", )
@@ -366,9 +351,6 @@ class LatentKeyframeInterpolationNode:
                 "prev_latent_kf": ("LATENT_KEYFRAME", ),
                 "print_keyframes": ("BOOLEAN", {"default": False}),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
 
     RETURN_NAMES = ("LATENT_KF", )
@@ -439,9 +421,6 @@ class LatentKeyframeBatchedGroupNode:
                 "prev_latent_kf": ("LATENT_KEYFRAME", ),
                 "print_keyframes": ("BOOLEAN", {"default": False}),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
 
     RETURN_NAMES = ("LATENT_KF", )

adv_control/nodes_main.py

@@ -4,6 +4,7 @@ import folder_paths
 from comfy.model_patcher import ModelPatcher
 
 from .control import load_controlnet, convert_to_advanced, is_advanced_controlnet, is_sd3_advanced_controlnet
+from .control_lllite import load_anima_lllite
 from .utils import ControlWeights, LatentKeyframeGroup, TimestepKeyframeGroup, AbstractPreprocWrapper, BIGMAX
 
 from .logger import logger
@@ -45,9 +46,6 @@ class DiffControlNetLoaderAdvanced:
             "optional": {
                 "_tk_opt": ("TIMESTEP_KEYFRAME", ),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_TYPES = ("CONTROL_NET", )
@@ -65,6 +63,27 @@ class DiffControlNetLoaderAdvanced:
         return (controlnet,)
 
 
+class AnimaLLLiteLoaderAdvanced:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model_patch": (folder_paths.get_filename_list("model_patches"), ),
+            },
+            "optional": {
+                "timestep_kf": ("TIMESTEP_KEYFRAME", ),
+            },
+        }
+
+    RETURN_TYPES = ("CONTROL_NET", )
+    FUNCTION = "load_controlnet"
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/loaders"
+
+    def load_controlnet(self, model_patch, timestep_kf: TimestepKeyframeGroup=None):
+        model_patch_path = folder_paths.get_full_path_or_raise("model_patches", model_patch)
+        return (load_anima_lllite(model_patch_path, timestep_keyframe=timestep_kf),)
+
+
 class AdvancedControlNetApply:
     @classmethod
     def INPUT_TYPES(s):
@@ -85,9 +104,6 @@ class AdvancedControlNetApply:
                 "weights_override": ("CONTROL_NET_WEIGHTS", ),
                 "vae_optional": ("VAE",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
 
     RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
@@ -189,9 +205,6 @@ class AdvancedControlNetApplySingle:
                 "weights_override": ("CONTROL_NET_WEIGHTS", ),
                 "vae_optional": ("VAE",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
 
     RETURN_TYPES = ("CONDITIONING","MODEL",)

adv_control/nodes_plusplus.py

@@ -65,9 +65,6 @@ class PlusPlusInputNode:
                 "prev_plus_input": ("PLUS_INPUT",),
                 #"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": BIGMAX, "step": 0.01}),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_TYPES = ("PLUS_INPUT", )

adv_control/nodes_sparsectrl.py

@@ -134,9 +134,6 @@ class RgbSparseCtrlPreprocessor:
                 "vae": ("VAE", ),
                 "latent_size": ("LATENT", ),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
 
     RETURN_TYPES = ("IMAGE",)
@@ -169,9 +166,6 @@ class SparseWeightExtras:
                 "sparse_nonhint_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                 "sparse_mask_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_TYPES = ("CN_WEIGHTS_EXTRAS", )

adv_control/nodes_weight.py

@@ -1,6 +1,7 @@
 from torch import Tensor
 import torch
 from .utils import TimestepKeyframe, TimestepKeyframeGroup, ControlWeights, Extras, get_properly_arranged_t2i_weights, linear_conversion
+from .control_lllite import AnimaLLLiteConst
 from .logger import logger
 
 
@@ -14,9 +15,6 @@ class DefaultWeights:
             "optional": {
                 "cn_extras": ("CN_WEIGHTS_EXTRAS",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
@@ -45,9 +43,6 @@ class ScaledSoftMaskedUniversalWeights:
                 "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                 "cn_extras": ("CN_WEIGHTS_EXTRAS",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
@@ -81,9 +76,6 @@ class ScaledSoftUniversalWeights:
                 "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                 "cn_extras": ("CN_WEIGHTS_EXTRAS",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
@@ -120,9 +112,6 @@ class SoftControlNetWeightsSD15:
                 "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                 "cn_extras": ("CN_WEIGHTS_EXTRAS",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
@@ -165,9 +154,6 @@ class CustomControlNetWeightsSD15:
                 "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                 "cn_extras": ("CN_WEIGHTS_EXTRAS",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
@@ -216,9 +202,6 @@ class CustomControlNetWeightsFlux:
                 "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                 "cn_extras": ("CN_WEIGHTS_EXTRAS",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
@@ -238,6 +221,36 @@ class CustomControlNetWeightsFlux:
         return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
 
+class CustomControlNetWeightsAnima:
+    @classmethod
+    def INPUT_TYPES(s):
+        required = {
+            f"block_{index}": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001})
+            for index in range(28)
+        }
+        return {
+            "required": required,
+            "optional": {
+                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+            },
+        }
+
+    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
+    RETURN_NAMES = WEIGHTS_RETURN_NAMES
+    FUNCTION = "load_weights"
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
+
+    def load_weights(self, uncond_multiplier: float=1.0, cn_extras: dict[str]={}, **kwargs):
+        weights = [kwargs[f"block_{index}"] for index in range(28)]
+        control_weights = ControlWeights.controllllite(
+            weights_input=weights,
+            uncond_multiplier=uncond_multiplier,
+            extras=cn_extras,
+        )
+        return (control_weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=control_weights)))
+
+
 class SoftT2IAdapterWeights:
     @classmethod
     def INPUT_TYPES(s):
@@ -252,9 +265,6 @@ class SoftT2IAdapterWeights:
                 "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                 "cn_extras": ("CN_WEIGHTS_EXTRAS",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
@@ -283,9 +293,6 @@ class CustomT2IAdapterWeights:
                 "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
                 "cn_extras": ("CN_WEIGHTS_EXTRAS",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
@@ -312,9 +319,6 @@ class ExtrasMiddleMultNode:
             "optional": {
                 "cn_extras": ("CN_WEIGHTS_EXTRAS",),
             },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
         }
     
     RETURN_TYPES = ("CN_WEIGHTS_EXTRAS",)
@@ -327,3 +331,26 @@ class ExtrasMiddleMultNode:
         cn_extras = cn_extras.copy()
         cn_extras[Extras.MIDDLE_MULT] = middle_mult
         return (cn_extras,)
+
+
+class AnimaLLLiteExtras:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "inpaint_mask": ("MASK",),
+            },
+            "optional": {
+                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+            },
+        }
+
+    RETURN_TYPES = ("CN_WEIGHTS_EXTRAS",)
+    RETURN_NAMES = ("cn_extras",)
+    FUNCTION = "create_extras"
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/extras"
+
+    def create_extras(self, inpaint_mask: Tensor, cn_extras: dict[str]={}):
+        cn_extras = cn_extras.copy()
+        cn_extras[AnimaLLLiteConst.INPAINT_MASK] = inpaint_mask.clone()
+        return (cn_extras,)

examples/anima_lllite_v2/README.md

@@ -0,0 +1,144 @@
+# Anima LLLite validation workflows
+
+This folder contains simple Advanced-ControlNet workflows for the two Anima
+LLLite v2 checkpoints: the five conditioning inputs documented for the v2
+any-test-like model and the v2 inpainting model. It also includes vanilla parity
+and effect-mask validation workflows.
+
+![Simple workflows for all six v2 examples](https://ampcode.com/attachments/3ef84a8ad6947c624cd82b5ac6fb9c664b224678c774a5eba79f4f7e6e8d5c3f.jpeg)
+
+![Control images and tested results for all six v2 examples](https://ampcode.com/attachments/addd1fdb85aa331cc622ef55a9397a996f34c5243e268a1b9bcc42ca39a25120.jpeg)
+
+## Simple v2 workflows
+
+| Type | Workflow | Input image | Checkpoint |
+| --- | --- | --- | --- |
+| Any - Grayscale A | [`anima_lllite_any_grayscale_a.json`](anima_lllite_any_grayscale_a.json) | [`anima_lllite_any_grayscale_a_control.png`](https://ampcode.com/attachments/911b31c414bcf5c9aeaa254356c89f04e1133ce2181d0414cd8b7e9a7fa7847c.png) | `anima-lllite-any-test-like-v2.safetensors` |
+| Any - Grayscale B | [`anima_lllite_any_grayscale_b.json`](anima_lllite_any_grayscale_b.json) | [`anima_lllite_any_grayscale_b_control.png`](https://ampcode.com/attachments/6950b3c8a47ff62311cc8ac147e2c8aa6aad9176abe7046db3a1bd6cf97cf705.png) | `anima-lllite-any-test-like-v2.safetensors` |
+| Any - Lineart | [`anima_lllite_any_lineart.json`](anima_lllite_any_lineart.json) | [`anima_lllite_any_lineart_control.png`](https://ampcode.com/attachments/338c2f07267d564ca9fb60bf79c229b5689509202734875f0cb674efb8d691b1.png) | `anima-lllite-any-test-like-v2.safetensors` |
+| Any - HED scribble | [`anima_lllite_any_hed_scribble.json`](anima_lllite_any_hed_scribble.json) | [`anima_lllite_any_hed_scribble_control.png`](https://ampcode.com/attachments/a52217f09c5852652667152cb901b98c3472c2e4cbf86a6cae53a36ec7e84192.png) | `anima-lllite-any-test-like-v2.safetensors` |
+| Any - PiDiNet scribble | [`anima_lllite_any_pidinet_scribble.json`](anima_lllite_any_pidinet_scribble.json) | [`anima_lllite_any_pidinet_scribble_control.png`](https://ampcode.com/attachments/57bc6b0d8cba0df17f92115917bfc6ccfce83107e56af624f0be1db32c402579.png) | `anima-lllite-any-test-like-v2.safetensors` |
+| Inpainting | [`anima_lllite_inpainting.json`](anima_lllite_inpainting.json) | [`anima_lllite_v2_control.png`](https://ampcode.com/attachments/3220b2f533a014619e3c0422eca8a3343e8488eb113795af5bae5f05d38a8ada.png) | `anima-lllite-inpainting-v2.safetensors` |
+
+Download the selected input image from the table and save it under the displayed
+filename in `ComfyUI/input`. Load its workflow and queue it unchanged. Results
+are saved under `ComfyUI/output/acn_anima_examples`. Binary inputs and
+screenshots are linked externally instead of being committed to this repository.
+
+These examples use default node names and do not connect the custom 28-layer
+Anima weights node. The inpainting workflow uses Anima LLLite Extras and
+Default Weights only because the model's source mask must be carried through
+`cn_extras`. Strength, start/end scheduling, effect masks, timestep keyframes,
+latent keyframes, and stacking remain available on the standard
+Advanced-ControlNet nodes.
+
+The model author trained the v2 any-test-like checkpoint on five conditioning
+types: HED scribble, PiDiNet scribble, Grayscale A, Grayscale B, and lineart,
+all with heavy augmentation. The examples above exercise each input type using
+that one v2 checkpoint instead of the lower-quality Preview3 depth, pose,
+lineart, and scribble checkpoints.
+
+The HED and PiDiNet controls were prepared with the
+[comfyui_controlnet_aux](https://github.com/Fannovel16/comfyui_controlnet_aux)
+HED and Scribble PiDiNet preprocessors. The HED soft-edge output was thresholded
+at 80 to make the documented white-on-black scribble representation. The model
+card names two grayscale generation patterns but does not define their
+individual construction; Grayscale A is the author's sample and Grayscale B
+demonstrates the documented inversion, contrast, and blur augmentation.
+
+## Requirements
+
+Official Hugging Face repositories:
+
+- [circlestone-labs/Anima](https://huggingface.co/circlestone-labs/Anima) - base model, text encoder, and VAE
+- [kohya-ss/Anima-LLLite](https://huggingface.co/kohya-ss/Anima-LLLite) - Anima LLLite control models
+
+Use ComfyUI commit `0f42ba514631` or later and place these files in the listed
+model folders. These are direct downloads from the official model repositories:
+
+| Download | ComfyUI model folder |
+| --- | --- |
+| [`anima-base-v1.0.safetensors`](https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/diffusion_models/anima-base-v1.0.safetensors?download=true) | `models/diffusion_models` |
+| [`qwen_3_06b_base.safetensors`](https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/text_encoders/qwen_3_06b_base.safetensors?download=true) | `models/text_encoders` |
+| [`qwen_image_vae.safetensors`](https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/vae/qwen_image_vae.safetensors?download=true) | `models/vae` |
+| [`anima-lllite-inpainting-v2.safetensors`](https://huggingface.co/kohya-ss/Anima-LLLite/resolve/main/anima-lllite-inpainting-v2.safetensors?download=true) | `models/model_patches` |
+| [`anima-lllite-any-test-like-v2.safetensors`](https://huggingface.co/kohya-ss/Anima-LLLite/resolve/main/anima-lllite-any-test-like-v2.safetensors?download=true) (3-channel any-type model) | `models/model_patches` |
+
+The included workflows use **Load Anima LLLite Model**, so their LLLite files
+belong in `models/model_patches`, matching vanilla ComfyUI. For compatibility
+with existing Advanced-ControlNet LLLite workflows, the files may instead be
+placed in `models/controlnet` and loaded with **Load Advanced ControlNet
+Model**. The standard loader automatically distinguishes Anima checkpoints
+from older SDXL LLLite checkpoints.
+
+## Run the inpainting comparison
+
+This validation workflow runs the inpainting model through vanilla ComfyUI and
+Advanced-ControlNet with identical inputs and sampling settings. It saves both
+decoded results, both latent tensors, and an absolute pixel-difference image.
+
+![Workflow showing the vanilla and Advanced-ControlNet branches](https://ampcode.com/attachments/f056cae89132c45bc133f456a2832a81255fee9c0782968a24adce2095b2fe1b.jpeg)
+
+![Bit-exact result comparison](https://ampcode.com/attachments/fcf073d1253ea721a2d46d34efde0e45ff6ccfb751fbd295a438ab459cc26037.jpeg)
+
+1. Download [`anima_lllite_v2_control.png`](https://ampcode.com/attachments/3220b2f533a014619e3c0422eca8a3343e8488eb113795af5bae5f05d38a8ada.png)
+   to `ComfyUI/input`.
+2. Load `anima_lllite_v2_inpaint_comparison.json` in ComfyUI.
+3. Queue the workflow without changing its settings.
+4. Inspect `ComfyUI/output/acn_anima_pr`.
+
+The control PNG contains a transparent edit region. ComfyUI's Load Image node
+provides that alpha channel as the source inpainting mask.
+
+The vanilla branch passes the mask directly to Apply Anima LLLite. The
+Advanced-ControlNet branch passes it through Anima LLLite Extras, the
+`cn_extras` input on Default Weights, and `weights_override` on Apply Advanced
+ControlNet.
+
+If an inpainting checkpoint reaches sampling without that source mask,
+Advanced-ControlNet raises an error that describes these connections instead
+of silently substituting an empty mask.
+
+With the included seed and settings, the expected results are:
+
+- Identical latent tensors with maximum and mean absolute differences of `0.0`.
+- Identical decoded PNG pixels.
+- A completely black `absolute_difference` image.
+
+## Run the any-type effect-mask comparison
+
+This workflow verifies the official 3-channel any-type checkpoint against
+vanilla ComfyUI and applies an Advanced-ControlNet effect mask to only the left
+half of the image. Its nodes retain their default names; the colored regions
+identify the comparison branches.
+
+![Any-type effect-mask workflow](https://ampcode.com/attachments/3a547b4914a84f0d578b0223149d3ed14e9b22542093e4bc51b6501aeb18f29f.jpeg)
+
+![Any-type parity and effect-mask results](https://ampcode.com/attachments/340c286b28d74d943f03a95d71208f2408c94934f0809fbb060cc774ae8c1b67.jpeg)
+
+1. Download [`anima_lllite_v2_any_control.png`](https://ampcode.com/attachments/db18d41c476324bdf5a5b6117476ed117cc590f8f48e193f6316d08b959bc49c.png)
+   and [`anima_lllite_v2_left_half_mask.png`](https://ampcode.com/attachments/e4fe3a88415357f29315c9afb124ee975a48dee8abccad1fe1903dd5d21b91b6.png)
+   to `ComfyUI/input`.
+2. Load `anima_lllite_v2_any_effect_mask.json` in ComfyUI.
+3. Queue the workflow without changing its settings.
+4. Inspect `ComfyUI/output/acn_anima_any_mask`.
+
+The expected results are:
+
+- Advanced-ControlNet full control and vanilla full control have identical
+  latent tensors and decoded pixels, with maximum absolute difference `0.0`.
+- A completely black effect mask produces the no-control latent exactly.
+- A completely white effect mask produces the unmasked full-control latent
+  exactly.
+- At the model token resolution, the included half mask is exactly `1.0` on
+  the left and `0.0` on the right. Direct LLLite injection is therefore zero
+  for every right-side token.
+- In the final image, the masked right side is closer to the no-control
+  baseline than full control: PSNR improves from `16.39` to `17.69`, and SSIM
+  improves from `0.554` to `0.600`.
+
+The final right half is not pixel-identical to the no-control image. This model
+patches self-attention Q, so controlled left-side tokens can influence
+right-side tokens through global self-attention and later diffusion steps.
+The effect mask guarantees local control injection, not hard image-space
+isolation after attention.

examples/anima_lllite_v2/anima_lllite_any_grayscale_a.json

@@ -0,0 +1,941 @@
+{
+  "id": "00000000-0000-0000-0000-000000000000",
+  "revision": 0,
+  "last_node_id": 12,
+  "last_link_id": 26,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "UNETLoader",
+      "pos": [
+        50,
+        100
+      ],
+      "size": [
+        270,
+        82
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "unet_name",
+          "name": "unet_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "unet_name"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "weight_dtype",
+          "name": "weight_dtype",
+          "type": "COMBO",
+          "widget": {
+            "name": "weight_dtype"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "MODEL",
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            20
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "UNETLoader",
+        "models": [
+          {
+            "name": "anima-base-v1.0.safetensors",
+            "url": "https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/diffusion_models/anima-base-v1.0.safetensors",
+            "directory": "diffusion_models"
+          }
+        ]
+      },
+      "widgets_values": [
+        "anima-base-v1.0.safetensors",
+        "default"
+      ]
+    },
+    {
+      "id": 2,
+      "type": "CLIPLoader",
+      "pos": [
+        50,
+        250
+      ],
+      "size": [
+        270,
+        106
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "clip_name",
+          "name": "clip_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "clip_name"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "type",
+          "name": "type",
+          "type": "COMBO",
+          "widget": {
+            "name": "type"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "device",
+          "name": "device",
+          "shape": 7,
+          "type": "COMBO",
+          "widget": {
+            "name": "device"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "CLIP",
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            14,
+            15
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPLoader",
+        "models": [
+          {
+            "name": "qwen_3_06b_base.safetensors",
+            "url": "https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/text_encoders/qwen_3_06b_base.safetensors",
+            "directory": "text_encoders"
+          }
+        ]
+      },
+      "widgets_values": [
+        "qwen_3_06b_base.safetensors",
+        "qwen_image",
+        "default"
+      ]
+    },
+    {
+      "id": 3,
+      "type": "CLIPTextEncode",
+      "pos": [
+        360,
+        190
+      ],
+      "size": [
+        400,
+        200
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "clip",
+          "name": "clip",
+          "type": "CLIP",
+          "link": 14
+        },
+        {
+          "localized_name": "text",
+          "name": "text",
+          "type": "STRING",
+          "widget": {
+            "name": "text"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "CONDITIONING",
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            16
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "anime illustration of a female knight in ornate silver plate armor holding a sword, side profile in a dense forest, detailed"
+      ]
+    },
+    {
+      "id": 4,
+      "type": "CLIPTextEncode",
+      "pos": [
+        360,
+        440
+      ],
+      "size": [
+        400,
+        200
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "clip",
+          "name": "clip",
+          "type": "CLIP",
+          "link": 15
+        },
+        {
+          "localized_name": "text",
+          "name": "text",
+          "type": "STRING",
+          "widget": {
+            "name": "text"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "CONDITIONING",
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            17
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        ""
+      ]
+    },
+    {
+      "id": 5,
+      "type": "EmptySD3LatentImage",
+      "pos": [
+        50,
+        470
+      ],
+      "size": [
+        270,
+        106
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "width",
+          "name": "width",
+          "type": "INT",
+          "widget": {
+            "name": "width"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "height",
+          "name": "height",
+          "type": "INT",
+          "widget": {
+            "name": "height"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "batch_size",
+          "name": "batch_size",
+          "type": "INT",
+          "widget": {
+            "name": "batch_size"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "LATENT",
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            23
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptySD3LatentImage"
+      },
+      "widgets_values": [
+        512,
+        512,
+        1
+      ]
+    },
+    {
+      "id": 6,
+      "type": "LoadImage",
+      "pos": [
+        50,
+        650
+      ],
+      "size": [
+        282.798828125,
+        314
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "image",
+          "name": "image",
+          "type": "COMBO",
+          "widget": {
+            "name": "image"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "choose file to upload",
+          "name": "upload",
+          "type": "IMAGEUPLOAD",
+          "widget": {
+            "name": "upload"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "IMAGE",
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            19
+          ]
+        },
+        {
+          "localized_name": "MASK",
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "anima_lllite_any_grayscale_a_control.png",
+        "image"
+      ]
+    },
+    {
+      "id": 7,
+      "type": "VAELoader",
+      "pos": [
+        360,
+        700
+      ],
+      "size": [
+        270,
+        58
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "vae_name",
+          "name": "vae_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "vae_name"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "VAE",
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            25
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAELoader",
+        "models": [
+          {
+            "name": "qwen_image_vae.safetensors",
+            "url": "https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/vae/qwen_image_vae.safetensors",
+            "directory": "vae"
+          }
+        ]
+      },
+      "widgets_values": [
+        "qwen_image_vae.safetensors"
+      ]
+    },
+    {
+      "id": 8,
+      "type": "ACN_AnimaLLLiteLoaderAdvanced",
+      "pos": [
+        860,
+        120
+      ],
+      "size": [
+        315,
+        58
+      ],
+      "flags": {},
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examples/anima_lllite_v2/anima_lllite_any_hed_scribble.json

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examples/anima_lllite_v2/anima_lllite_any_pidinet_scribble.json

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+          "widget": {
+            "name": "strength"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "start_percent",
+          "name": "start_percent",
+          "type": "FLOAT",
+          "widget": {
+            "name": "start_percent"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "end_percent",
+          "name": "end_percent",
+          "type": "FLOAT",
+          "widget": {
+            "name": "end_percent"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "positive",
+          "name": "positive",
+          "type": "CONDITIONING",
+          "links": [
+            21
+          ]
+        },
+        {
+          "localized_name": "negative",
+          "name": "negative",
+          "type": "CONDITIONING",
+          "links": [
+            22
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ACN_AdvancedControlNetApply_v2"
+      },
+      "widgets_values": [
+        1,
+        0,
+        1
+      ]
+    },
+    {
+      "id": 10,
+      "type": "KSampler",
+      "pos": [
+        1580,
+        110
+      ],
+      "size": [
+        270,
+        262
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "model",
+          "name": "model",
+          "type": "MODEL",
+          "link": 20
+        },
+        {
+          "localized_name": "positive",
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 21
+        },
+        {
+          "localized_name": "negative",
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 22
+        },
+        {
+          "localized_name": "latent_image",
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 23
+        },
+        {
+          "localized_name": "seed",
+          "name": "seed",
+          "type": "INT",
+          "widget": {
+            "name": "seed"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "steps",
+          "name": "steps",
+          "type": "INT",
+          "widget": {
+            "name": "steps"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "cfg",
+          "name": "cfg",
+          "type": "FLOAT",
+          "widget": {
+            "name": "cfg"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "sampler_name",
+          "name": "sampler_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "sampler_name"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "scheduler",
+          "name": "scheduler",
+          "type": "COMBO",
+          "widget": {
+            "name": "scheduler"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "denoise",
+          "name": "denoise",
+          "type": "FLOAT",
+          "widget": {
+            "name": "denoise"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "LATENT",
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            24
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        82000,
+        "fixed",
+        12,
+        1,
+        "euler",
+        "simple",
+        1
+      ]
+    },
+    {
+      "id": 11,
+      "type": "VAEDecode",
+      "pos": [
+        1940,
+        140
+      ],
+      "size": [
+        140,
+        46
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "samples",
+          "name": "samples",
+          "type": "LATENT",
+          "link": 24
+        },
+        {
+          "localized_name": "vae",
+          "name": "vae",
+          "type": "VAE",
+          "link": 25
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "IMAGE",
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            26
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 12,
+      "type": "SaveImage",
+      "pos": [
+        2190,
+        110
+      ],
+      "size": [
+        270,
+        58
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "images",
+          "name": "images",
+          "type": "IMAGE",
+          "link": 26
+        },
+        {
+          "localized_name": "filename_prefix",
+          "name": "filename_prefix",
+          "type": "STRING",
+          "widget": {
+            "name": "filename_prefix"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "images",
+          "name": "images",
+          "type": "IMAGE",
+          "links": null
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        "acn_anima_examples/any_pidinet_scribble"
+      ]
+    }
+  ],
+  "links": [
+    [
+      14,
+      2,
+      0,
+      3,
+      0,
+      "CLIP"
+    ],
+    [
+      15,
+      2,
+      0,
+      4,
+      0,
+      "CLIP"
+    ],
+    [
+      16,
+      3,
+      0,
+      9,
+      0,
+      "CONDITIONING"
+    ],
+    [
+      17,
+      4,
+      0,
+      9,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      18,
+      8,
+      0,
+      9,
+      2,
+      "CONTROL_NET"
+    ],
+    [
+      19,
+      6,
+      0,
+      9,
+      3,
+      "IMAGE"
+    ],
+    [
+      20,
+      1,
+      0,
+      10,
+      0,
+      "MODEL"
+    ],
+    [
+      21,
+      9,
+      0,
+      10,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      22,
+      9,
+      1,
+      10,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      23,
+      5,
+      0,
+      10,
+      3,
+      "LATENT"
+    ],
+    [
+      24,
+      10,
+      0,
+      11,
+      0,
+      "LATENT"
+    ],
+    [
+      25,
+      7,
+      0,
+      11,
+      1,
+      "VAE"
+    ],
+    [
+      26,
+      11,
+      0,
+      12,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [
+    {
+      "id": 20,
+      "title": "SHARED INPUTS",
+      "bounding": [
+        10,
+        35,
+        740,
+        900
+      ],
+      "color": "#3f789e",
+      "font_size": 32,
+      "flags": {}
+    },
+    {
+      "id": 21,
+      "title": "ANIMA LLLITE V2 ANY - PIDINET SCRIBBLE",
+      "bounding": [
+        810,
+        35,
+        1660,
+        900
+      ],
+      "color": "#6f4d86",
+      "font_size": 32,
+      "flags": {}
+    }
+  ],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 1,
+      "offset": [
+        0,
+        0
+      ]
+    },
+    "info": {
+      "name": "Anima LLLite v2 any - PiDiNet scribble",
+      "author": "Kosinkadink",
+      "description": "Advanced-ControlNet example for Anima LLLite v2 any with PiDiNet scribble conditioning."
+    }
+  },
+  "version": 0.4
+}

web/js/autosize.js

@@ -1,53 +0,0 @@
-import { app } from '../../../scripts/app.js'
-
-function addResizeHook(node, padding, useOldMin=false) {
-    let origOnCreated = node.onNodeCreated
-    node.onNodeCreated = function() {
-        let r = origOnCreated?.apply(this, arguments)
-        let size = this.computeSize();
-        size[0] += padding || 0;
-        if (useOldMin) {
-            //equal to LiteGraph.NODE_WIDTH*1.5*1.5
-            size[0] = Math.max(size[0], 315)
-        }
-        this.setSize(size);
-        return r
-    }
-}
-
-app.registerExtension({
-    name: "AdvancedControlNet.autosize",
-    async beforeRegisterNodeDef(nodeType, nodeData, app) {
-        //since python_module is based off folder path,
-        //it could be changed by users and should only be used as fallback
-        if (nodeData?.name?.startsWith("ACN_")
-            || nodeData.python_module == 'custom_nodes.ComfyUI-Advanced-ControlNet') {
-            if (nodeData?.input?.hidden?.autosize) {
-                addResizeHook(nodeType.prototype, nodeData.input.hidden.autosize[1]?.padding)
-            } else if (!nodeData?.input?.optional?.autosize) {
-                addResizeHook(nodeType.prototype, 0, true)
-            }
-        }
-    },
-    async getCustomWidgets() {
-        return {
-            ACNAUTOSIZE(node, inputName, inputData) {
-                let w = {
-                    name : inputName,
-                    type : "ACN.AUTOSIZE",
-                    value : "",
-                    options : {"serialize": false},
-                    computeSize : function(width) {
-                        return [0, -4];
-                    }
-                }
-                if (!node.widgets) {
-                    node.widgets = []
-                }
-                node.widgets.push(w)
-                addResizeHook(node, inputData[1].padding);
-                return w;
-            }
-        }
-    }
-});

web/js/documentation.js

@@ -1,293 +0,0 @@
-import { app } from '../../../scripts/app.js'
-
-function chainCallback(object, property, callback) {
-    if (object == undefined) {
-        //This should not happen.
-        console.error("Tried to add callback to non-existant object")
-        return;
-    }
-    if (property in object && object[property]) {
-        const callback_orig = object[property]
-        object[property] = function () {
-            const r = callback_orig.apply(this, arguments);
-            callback.apply(this, arguments);
-            return r
-        };
-    } else {
-        object[property] = callback;
-    }
-}
-var helpDOM;
-function initHelpDOM() {
-    let parentDOM = document.createElement("div");
-    document.body.appendChild(parentDOM)
-    parentDOM.appendChild(helpDOM)
-    helpDOM.className = "litegraph";
-    let scrollbarStyle = document.createElement('style');
-    scrollbarStyle.innerHTML = `
-    <style id="scroll-properties">
-            * {
-                scrollbar-width: 6px;
-                scrollbar-color: #0003  #0000;
-            }
-            ::-webkit-scrollbar {
-                background: transparent;
-                width: 6px;
-            }
-            ::-webkit-scrollbar-thumb {
-                background: #0005;
-                border-radius: 20px
-            }
-            ::-webkit-scrollbar-button {
-                display: none;
-            }
-            .VHS_loopedvideo::-webkit-media-controls-mute-button {
-                display:none;
-            }
-            .VHS_loopedvideo::-webkit-media-controls-fullscreen-button {
-                display:none;
-            }
-        </style>
-    `
-    parentDOM.appendChild(scrollbarStyle)
-    chainCallback(app.canvas, "onDrawForeground", function (ctx, visible_rect){
-        let n = helpDOM.node
-        if (!n || !n?.graph) {
-            parentDOM.style['left'] = '-5000px'
-            return
-        }
-        //draw : function(ctx, node, widgetWidth, widgetY, height) {
-        //update widget position, even if off screen
-        const transform = ctx.getTransform();
-        const scale = app.canvas.ds.scale;//gets the litegraph zoom
-        //calculate coordinates with account for browser zoom
-        const bcr = app.canvas.canvas.getBoundingClientRect()
-        const x = transform.e*scale/transform.a + bcr.x;
-        const y = transform.f*scale/transform.a + bcr.y;
-        //TODO: text reflows at low zoom. investigate alternatives
-        Object.assign(parentDOM.style, {
-            left: (x+(n.pos[0] + n.size[0]+15)*scale) + "px",
-            top: (y+(n.pos[1]-LiteGraph.NODE_TITLE_HEIGHT)*scale) + "px",
-            width: "400px",
-            minHeight: "100px",
-            maxHeight: "600px",
-            overflowY: 'scroll',
-            transformOrigin: '0 0',
-            transform: 'scale(' + scale + ',' + scale +')',
-            fontSize: '18px',
-            backgroundColor: LiteGraph.NODE_DEFAULT_BGCOLOR,
-            boxShadow: '0 0 10px black',
-            borderRadius: '4px',
-            padding: '3px',
-            zIndex: 3,
-            position: "absolute",
-            display: 'inline',
-        });
-    });
-    function setCollapse(el, doCollapse) {
-        if (doCollapse) {
-            el.children[0].children[0].innerHTML = '+'
-            Object.assign(el.children[1].style, {
-                color: '#CCC',
-                overflowX: 'hidden',
-                width: '0px',
-                minWidth: 'calc(100% - 20px)',
-                textOverflow: 'ellipsis',
-                whiteSpace: 'nowrap',
-            })
-            for (let child of el.children[1].children) {
-                if (child.style.display != 'none'){
-                    child.origDisplay = child.style.display
-                }
-                child.style.display = 'none'
-            }
-        } else {
-            el.children[0].children[0].innerHTML = '-'
-            Object.assign(el.children[1].style, {
-                color: '',
-                overflowX: '',
-                width: '100%',
-                minWidth: '',
-                textOverflow: '',
-                whiteSpace: '',
-            })
-            for (let child of el.children[1].children) {
-                child.style.display = child.origDisplay
-            }
-        }
-    }
-    helpDOM.collapseOnClick = function() {
-        let doCollapse = this.children[0].innerHTML == '-'
-        setCollapse(this.parentElement, doCollapse)
-    }
-    helpDOM.selectHelp = function(name, value) {
-        //attempt to navigate to name in help
-        function collapseUnlessMatch(items,t) {
-            var match = items.querySelector('[vhs_title="' + t + '"]')
-            if (!match) {
-                for (let i of items.children) {
-                    if (i.innerHTML.slice(0,t.length+5).includes(t)) {
-                        match = i
-                        break
-                    }
-                }
-            }
-            if (!match) {
-                return null
-            }
-            //For longer documentation items with fewer collapsable elements,
-            //scroll to make sure the entirety of the selected item is visible
-            //This has the unfortunate side effect of trying to scroll the main
-            //window if the documentation windows is forcibly offscreen,
-            //but it's easy to simply scroll the main window back and seems to
-            //have no visual side effects
-            match.scrollIntoView(false)
-            window.scrollTo(0,0)
-            for (let i of items.querySelectorAll('.VHS_collapse')) {
-                if (i.contains(match)) {
-                    setCollapse(i, false)
-                } else {
-                    setCollapse(i, true)
-                }
-            }
-            return match
-        }
-        let target = collapseUnlessMatch(helpDOM, name)
-        if (target && value) {
-            collapseUnlessMatch(target, value)
-        }
-    }
-
-    helpDOM.addHelp = function(node, nodeType, description) {
-        if (!description) {
-            return
-        }
-        //Pad computed size for the clickable question mark
-        let originalComputeSize = node.computeSize
-        node.computeSize = function() {
-            let size = originalComputeSize.apply(this, arguments)
-            if (!this.title) {
-                return size
-            }
-            let title_width = this.title.length * 0.6 * LiteGraph.NODE_TEXT_SIZE
-            size[0] = Math.max(size[0], title_width + LiteGraph.NODE_TITLE_HEIGHT)
-            return size
-        }
-
-        node.description = description
-        chainCallback(node, "onDrawForeground", function (ctx) {
-            //draw question mark
-            ctx.save()
-            ctx.font = 'bold 20px Arial'
-            ctx.fillText("?", this.size[0]-17, -8)
-            ctx.restore()
-        })
-        chainCallback(node, "onMouseDown", function (e, pos, canvas) {
-            //On click would be preferred, but this'll be good enough
-            if (pos[1] < 0 && pos[0] + LiteGraph.NODE_TITLE_HEIGHT > this.size[0]) {
-                //corner question mark clicked
-                if (helpDOM.node == this) {
-                    helpDOM.node = undefined
-                } else {
-                    helpDOM.node = this;
-                    helpDOM.innerHTML = this.description || "no help provided ".repeat(20)
-                    for (let e of helpDOM.querySelectorAll('.VHS_collapse')) {
-                        e.children[0].onclick = helpDOM.collapseOnClick
-                        e.children[0].style.cursor = 'pointer'
-                    }
-                    for (let e of helpDOM.querySelectorAll('.VHS_precollapse')) {
-                        setCollapse(e, true)
-                    }
-                }
-                return true
-            }
-        })
-        let timeout = null
-        chainCallback(node, "onMouseMove", function (e, pos, canvas) {
-            if (timeout) {
-                clearTimeout(timeout)
-                timeout = null
-            }
-            if (helpDOM.node != this) {
-                return
-            }
-            timeout = setTimeout(() => {
-                let n = this
-                if (pos[0] > 0 && pos[0] < n.size[0]
-                    && pos[1] > 0 && pos[1] < n.size[1]) {
-                    //TODO: provide help specific to element clicked
-                    let inputRows = Math.max(n.inputs.length, n.outputs.length)
-                    if (pos[1] < LiteGraph.NODE_SLOT_HEIGHT * inputRows) {
-                        let row = Math.floor((pos[1] - 7) / LiteGraph.NODE_SLOT_HEIGHT)
-                        if (pos[0] < n.size[0]/2) {
-                            if (row < n.inputs.length) {
-                                helpDOM.selectHelp(n.inputs[row].name)
-                            }
-                        } else {
-                            if (row < n.outputs.length) {
-                                helpDOM.selectHelp(n.outputs[row].name)
-                            }
-                        }
-                    } else {
-                        //probably widget, but widgets have variable height.
-                        let basey = LiteGraph.NODE_SLOT_HEIGHT * inputRows + 6
-                        for (let w of n.widgets) {
-                            if (w.y) {
-                                basey = w.y
-                            }
-                            let wheight = LiteGraph.NODE_WIDGET_HEIGHT+4
-                            if (w.computeSize) {
-                                wheight = w.computeSize(n.size[0])[1]
-                            }
-                            if (pos[1] < basey + wheight) {
-                                helpDOM.selectHelp(w.name, w.value)
-                                break
-                            }
-                            basey += wheight
-                        }
-                    }
-                }
-            }, 500)
-        })
-        chainCallback(node, "onMouseLeave", function (e, pos, canvas) {
-            if (timeout) {
-                clearTimeout(timeout)
-                timeout = null
-            }
-        });
-    }
-}
-
-
-
-app.registerExtension({
-    name: "AdvancedControlNet.documentation",
-    async init() {
-        if (app.VHSHelp) {
-            helpDOM = app.VHSHelp
-        } else {
-            helpDOM = document.createElement("div");
-            initHelpDOM()
-            app.VHSHelp = helpDOM
-        }
-    },
-    async beforeRegisterNodeDef(nodeType, nodeData, app) {
-        // NOTE: May need manual adjusting for the few non-namespaced nodes
-        if(nodeData?.name?.startsWith("ACN_") && nodeData.description) {
-            let description = nodeData.description
-            let el = document.createElement("div")
-            el.innerHTML = description
-            if (!el.children.length) {
-                //Is plaintext. Do minor convenience formatting
-                let chunks = description.split('\n')
-                nodeData.description = chunks[0]
-                description = chunks.join('<br>')
-            } else {
-                nodeData.description = el.querySelector('#VHS_shortdesc')?.innerHTML || el.children[1]?.firstChild?.innerHTML
-            }
-            chainCallback(nodeType.prototype, "onNodeCreated", function () {
-                helpDOM.addHelp(this, nodeType, description)
-            })
-        }
-    },
-});