Version 3.4.0

* Add Anima region attention support

* Added adaptation acknowledgement for Anima Attention couple implementation

* Address Anima attention cleanup feedback

README.md

@@ -293,3 +293,9 @@ git clone https://github.com/Acly/comfyui-tooling-nodes.git
 ```
 
 Restart ComfyUI and the nodes are functional.
+
+
+## Acknowledgements
+
+* Region nodes adapted from [laksjdjf/cgem156-ComfyUI](https://github.com/laksjdjf/cgem156-ComfyUI/blob/main/scripts/attention_couple/node.py)
+* Control nodes adapted from [kohya-ss/ComfyUI-Anima-LLLite](https://github.com/kohya-ss/ComfyUI-Anima-LLLite)

__init__.py

@@ -1,10 +1,12 @@
 from comfy_api.latest import ComfyExtension, io
-from . import api as api, nodes, tile, region, nsfw, translation, krita
+
+from . import api as api
+from . import control, krita, nodes, region, tile, translation
 
 
 class ExternalToolingNodes(ComfyExtension):
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
-        return [
+        node_list = [
             nodes.LoadImageCache,
             nodes.SaveImageCache,
             nodes.LoadImageBase64,
@@ -22,7 +24,6 @@ class ExternalToolingNodes(ComfyExtension):
             region.DefineRegion,
             region.ListRegionMasks,
             region.AttentionMask,
-            nsfw.NSFWFilter,
             translation.Translate,
             krita.KritaOutput,
             krita.KritaSendText,
@@ -32,7 +33,21 @@ class ExternalToolingNodes(ComfyExtension):
             krita.KritaMaskLayer,
             krita.Parameter,
             krita.KritaStyle,
+            krita.KritaStyleAndPrompt,
+            control.ControlApply,
+            control.ControlLoad,
         ]
+        try:  # see #66
+            from . import nsfw
+
+            node_list.append(nsfw.NSFWFilter)
+        except (ImportError, ModuleNotFoundError):
+            import traceback
+
+            print("[comfyui-tooling-nodes] WARNING: Could not import all nodes.")
+            traceback.print_exc()
+
+        return node_list
 
 
 async def comfy_entrypoint():

api.py

@@ -56,7 +56,11 @@ model_names = {
     "ACEStep": "ace-step",
     "Omnigen2": "omnigen2",
     "QwenImage": "qwen-image",
+    "QwenImage21": "qwen-image21",
+    "ErnieImage": "ernie-image",
     "Flux2": "flux2",
+    "Anima": "anima",
+    "Krea2": "krea2",
 }
 
 gguf_architectures = {
@@ -121,6 +125,9 @@ def inspect_safetensors(filename: str, model_type: str, is_checkpoint: bool):
                     raw_name = base_model.__class__.__name__
                     if raw_name == "SDXL":
                         model_type = base_model.model_type(cfg).name.lower().replace("_", "-")
+                    if raw_name == "Flux2":
+                        hidden_size = unet_config.get("hidden_size", 0)
+                        model_type = {3072: "klein-4b", 4096: "klein-9b"}.get(hidden_size, "dev")
 
             if not raw_name:
                 return {"base_model": "unknown"}
@@ -190,7 +197,6 @@ def inspect_gguf(filename: str, model_type: str):
         else:  # stable-diffusion.cpp, requires conversion. not handled for now
             return {"base_model": "flux", "is_inpaint": False}
 
-        # Detect Chroma (modified Flux)
         if arch_str == "flux" and any(
             t.name.startswith("distilled_guidance_layer")
             for t in itertools.islice(reader.tensors, 5)
@@ -204,10 +210,27 @@ def inspect_gguf(filename: str, model_type: str):
                     arch_str = "z-image"
                     break
 
+        # Detect Flux variants
+        result_type = None
+        if arch_str == "flux":
+            for t in reader.tensors:
+                if t.name.startswith("distilled_guidance_layer"):
+                    arch_str = "chroma"
+                    break
+                elif t.name == "double_stream_modulation_img.lin.weight":
+                    arch_str = "flux2"
+                    if t.shape[0] == 3072:
+                        result_type = "klein-4b"
+                    elif t.shape[0] == 4096:
+                        result_type = "klein-9b"
+                    break
+
         result = {
             "base_model": gguf_architectures.get(arch_str, arch_str),
             "is_inpaint": False,
         }
+        if result_type is not None:
+            result["type"] = result_type
         try:
             if file_type := reader.get_field("general.file_type"):
                 result["quant"] = file_type.contents().lower()
@@ -327,11 +350,11 @@ if _server is not None:
         except Exception as e:
             return web.json_response(dict(error=str(e)), status=500)
 
-    @_server.routes.put("/api/etn/image/{id}")
     async def put_image(request: web.Request):
         try:
             id = request.match_info.get("id", "")
             if id in image_cache:
+                await request.release()  # Consume and discard the data to avoid connection abort
                 return web.json_response(dict(status="cached"), status=200)
 
             content_type = request.headers.get("Content-Type", "application/octet-stream")
@@ -344,6 +367,17 @@ if _server is not None:
         except Exception as e:
             return web.json_response(dict(error=str(e)), status=500)
 
+    async def _put_image_expect_handler(request: web.Request):
+        if request.match_info.get("id", "") in image_cache:
+            # Skip "100 Continue" since we don't need the data, return 200 immediately.
+            return web.json_response(dict(status="cached"), status=200)
+        # otherwise run default aiohttp handler
+        return None
+
+    _server.app.router.add_route(
+        "PUT", "/api/etn/image/{id}", put_image, expect_handler=_put_image_expect_handler
+    )
+
     @_server.routes.put("/api/etn/upload/{folder_name}/{filename}")
     async def upload(request: web.Request):
         folder_name = request.match_info.get("folder_name", "")

control.py

@@ -0,0 +1,859 @@
+"""ControlNet-LLLite for Anima (DiT) — ComfyUI port (v2 architecture).
+
+Adapted from kohya-ss/ComfyUI-Anima-LLLite
+https://github.com/kohya-ss/ComfyUI-Anima-LLLite
+Apache-2.0 license
+
+Adapted from kohya-ss/sd-scripts. The on-disk weight format is the v2
+named-key format (per-module key prefix = lllite_name, shared encoder under
+``lllite_conditioning1.*``, depth embedding split per-module as
+``{name}.depth_embed``); legacy ``lllite_modules.*`` files are rejected.
+
+Differences vs. the sd-scripts reference (``networks/control_net_lllite_anima.py``):
+  * No dependency on ``library.utils`` — uses stdlib logging.
+  * Module discovery filters the LLM-Adapter sub-tree by class identity in
+    addition to the path-based check (ComfyUI ships two distinct ``Attention``
+    classes that share the bare class name).
+  * ``LLLiteModuleDiT`` keeps a ``restore()`` method (and an idempotent
+    ``apply_to()``); ComfyUI patches/unpatches the original Linear around
+    every sampler call via ``set_model_unet_function_wrapper``.
+  * Forward pass casts ``x`` and ``cond_emb`` to the LLLite parameter dtype
+    so autocast / mixed-precision flows that hand us a different dtype than
+    the LLLite weights still work.
+  * CFG batch-size and sequence-length mismatches fall back to identity
+    instead of asserting, so a slightly-off cond image cannot abort sampling.
+  * The training-side ``AnimaControlNetLLLiteWrapper`` is omitted; ComfyUI
+    integrates via ``model_function_wrapper`` in nodes.py instead.
+"""
+
+from __future__ import annotations
+
+from copy import copy
+import logging
+import os
+from dataclasses import dataclass
+from typing import Any
+
+import folder_paths
+import safetensors
+import safetensors.torch
+import torch
+import torch.nn.functional as F
+from comfy.model_patcher import ModelPatcher
+from comfy_api.latest import io
+from torch import nn
+
+logger = logging.getLogger("comfyui-tooling-nodes")
+
+
+# Class names of the modules that LLLite injects into. The LLM-Adapter uses
+# a different ``Attention`` class with the same bare name; we filter it by
+# path (``llm_adapter`` in the qualified name) and by the ``is_selfattn``
+# attribute presence.
+TARGET_ATTENTION_CLASS = "Attention"
+TARGET_MLP_CLASS = "GPT2FeedForward"
+LLM_ADAPTER_NAME = "llm_adapter"
+
+LLLITE_ARCH_VERSION = "2"
+
+
+# ----------------------------------------------------------------------------
+# target_layers: atomic specifiers and presets
+# ----------------------------------------------------------------------------
+
+ATOMIC_SPECIFIERS: tuple[str, ...] = (
+    "self_attn_q_pre",
+    "self_attn_kv_pre",
+    "cross_attn_q_pre",
+    "mlp_fc1_pre",
+)
+
+PRESETS: dict = {
+    "self_attn_q": ("self_attn_q_pre",),
+    "self_attn_qkv": ("self_attn_q_pre", "self_attn_kv_pre"),
+    "self_attn_qkv_cross_q": ("self_attn_q_pre", "self_attn_kv_pre", "cross_attn_q_pre"),
+}
+
+
+def parse_target_layers(spec: str) -> tuple[str, ...]:
+    """Resolve a ``target_layers`` spec to a canonical atomic tuple.
+
+    Accepts a preset name (``"self_attn_qkv"``) or a comma-separated list of
+    atomic specifiers (``"self_attn_q_pre,mlp_fc1_pre"``). Returns the atomics
+    in ``ATOMIC_SPECIFIERS`` order with duplicates removed.
+    """
+    if not isinstance(spec, str):
+        raise TypeError(f"target_layers must be str, got {type(spec).__name__}")
+    spec = spec.strip()
+    if not spec:
+        raise ValueError("target_layers spec is empty")
+
+    if spec in PRESETS:
+        parts = list(PRESETS[spec])
+    else:
+        parts = [p.strip() for p in spec.split(",") if p.strip()]
+        bad = [p for p in parts if p not in ATOMIC_SPECIFIERS]
+        if bad:
+            raise ValueError(
+                f"unknown target_layers atomic specifier(s): {bad}. "
+                f"valid atomic={list(ATOMIC_SPECIFIERS)}, presets={list(PRESETS)}"
+            )
+
+    return tuple(a for a in ATOMIC_SPECIFIERS if a in parts)
+
+
+# ----------------------------------------------------------------------------
+# Conditioning1 trunk (v2)
+# ----------------------------------------------------------------------------
+
+
+def _gn(channels: int) -> nn.GroupNorm:
+    g = 8
+    while g > 1 and channels % g != 0:
+        g //= 2
+    return nn.GroupNorm(g, channels)
+
+
+class _ResBlock(nn.Module):
+    def __init__(self, ch: int):
+        super().__init__()
+        self.norm1 = _gn(ch)
+        self.conv1 = nn.Conv2d(ch, ch, kernel_size=3, padding=1)
+        self.norm2 = _gn(ch)
+        self.conv2 = nn.Conv2d(ch, ch, kernel_size=3, padding=1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        h = self.conv1(F.silu(self.norm1(x)))
+        h = self.conv2(F.silu(self.norm2(h)))
+        return x + h
+
+
+ASPP_DEFAULT_DILATIONS: tuple[int, ...] = (1, 2, 4, 8)
+
+
+class _ASPP(nn.Module):
+    def __init__(self, ch: int, dilations: tuple[int, ...] = ASPP_DEFAULT_DILATIONS):
+        super().__init__()
+        assert len(dilations) >= 1, "ASPP needs at least one dilation"
+        branches = []
+        for d in dilations:
+            if d == 1:
+                conv = nn.Conv2d(ch, ch, kernel_size=1)
+            else:
+                conv = nn.Conv2d(ch, ch, kernel_size=3, padding=d, dilation=d)
+            branches.append(nn.Sequential(conv, _gn(ch), nn.SiLU()))
+        self.branches = nn.ModuleList(branches)
+
+        self.global_pool = nn.AdaptiveAvgPool2d(1)
+        self.global_conv = nn.Sequential(nn.Conv2d(ch, ch, kernel_size=1), _gn(ch), nn.SiLU())
+
+        n_branches = len(dilations) + 1
+        self.proj = nn.Sequential(nn.Conv2d(ch * n_branches, ch, kernel_size=1), _gn(ch), nn.SiLU())
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        h, w = x.shape[-2:]
+        outs = [b(x) for b in self.branches]
+        g = self.global_conv(self.global_pool(x))
+        g = F.interpolate(g, size=(h, w), mode="bilinear", align_corners=False)
+        outs.append(g)
+        return self.proj(torch.cat(outs, dim=1))
+
+
+class _Conditioning1(nn.Module):
+    def __init__(
+        self,
+        cond_dim: int,
+        cond_emb_dim: int,
+        n_resblocks: int,
+        use_aspp: bool = False,
+        aspp_dilations: tuple[int, ...] = ASPP_DEFAULT_DILATIONS,
+        cond_in_channels: int = 3,
+    ):
+        super().__init__()
+        assert cond_dim % 2 == 0, f"cond_dim must be even, got {cond_dim}"
+        assert cond_in_channels >= 1, f"cond_in_channels must be >= 1, got {cond_in_channels}"
+        ch_half = cond_dim // 2
+
+        self.cond_in_channels = cond_in_channels
+        self.conv1 = nn.Conv2d(cond_in_channels, ch_half, kernel_size=4, stride=4, padding=0)
+        self.norm1 = _gn(ch_half)
+        self.conv2 = nn.Conv2d(ch_half, ch_half, kernel_size=3, stride=1, padding=1)
+        self.norm2 = _gn(ch_half)
+        self.conv3 = nn.Conv2d(ch_half, cond_dim, kernel_size=4, stride=4, padding=0)
+        self.norm3 = _gn(cond_dim)
+
+        self.resblocks = nn.ModuleList([_ResBlock(cond_dim) for _ in range(n_resblocks)])
+        self.aspp = _ASPP(cond_dim, aspp_dilations) if use_aspp else None
+
+        self.proj = nn.Conv2d(cond_dim, cond_emb_dim, kernel_size=1)
+        self.out_norm = nn.LayerNorm(cond_emb_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        h = F.silu(self.norm1(self.conv1(x)))
+        h = F.silu(self.norm2(self.conv2(h)))
+        h = F.silu(self.norm3(self.conv3(h)))
+        for rb in self.resblocks:
+            h = rb(h)
+        if self.aspp is not None:
+            h = self.aspp(h)
+        h = self.proj(h)
+        b, c, hh, ww = h.shape
+        h = h.view(b, c, hh * ww).permute(0, 2, 1).contiguous()
+        h = self.out_norm(h)
+        return h
+
+
+# ----------------------------------------------------------------------------
+# LLLite module (v2: FiLM + SiLU + 5D path + depth embedding)
+# ----------------------------------------------------------------------------
+
+
+class LLLiteModuleDiT(nn.Module):
+    def __init__(
+        self,
+        name: str,
+        org_module: nn.Linear,
+        cond_emb_dim: int,
+        mlp_dim: int,
+        dropout: float | None = None,
+        multiplier: float = 1.0,
+    ):
+        super().__init__()
+        self.lllite_name = name
+        # Wrap in a list so the original Linear is not registered as a submodule
+        # and its weights stay out of state_dict.
+        self.org_module = [org_module]
+        self.cond_emb_dim = cond_emb_dim
+        self.mlp_dim = mlp_dim
+        self.dropout = dropout
+        self.multiplier = multiplier
+
+        in_dim = org_module.in_features
+
+        self.down = nn.Linear(in_dim, mlp_dim)
+        self.mid = nn.Linear(mlp_dim + cond_emb_dim, mlp_dim)
+
+        # FiLM: cond_local -> (gamma, beta), zero-init for identity at start.
+        self.cond_to_film = nn.Linear(cond_emb_dim, 2 * mlp_dim)
+        nn.init.zeros_(self.cond_to_film.weight)
+        nn.init.zeros_(self.cond_to_film.bias)
+
+        self.up = nn.Linear(mlp_dim, in_dim)
+        nn.init.zeros_(self.up.weight)
+        nn.init.zeros_(self.up.bias)
+
+        self.cond_emb: torch.Tensor | None = None
+        self.org_forward = None
+
+        # Set by the parent ControlNetLLLiteDiT after construction.
+        self.layer_idx: int = -1
+        self._depth_embeds_ref: list[nn.Parameter] = []
+
+    def apply_to(self):
+        if self.org_forward is None:
+            self.org_forward = self.org_module[0].forward
+            self.org_module[0].forward = self.forward
+
+    def restore(self):
+        if self.org_forward is not None:
+            self.org_module[0].forward = self.org_forward
+            self.org_forward = None
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # Input layouts:
+        #   self/cross attention q/k/v: (B, S, D) — already flattened in the Anima block
+        #   mlp.layer1:                 (B, T, H, W, D) — passed un-flattened
+        # Flatten the 5D case to 3D for the LLLite path and reshape on exit.
+        if self.multiplier == 0.0 or self.cond_emb is None:
+            return self.org_forward(x)
+
+        orig_shape = x.shape
+        is_5d = x.dim() == 5
+        if is_5d:
+            B, T, H, W, D = orig_shape
+            x = x.reshape(B, T * H * W, D)
+
+        cx = self.cond_emb  # (B_c, S, cond_emb_dim)
+
+        # Broadcast cond_emb to the runtime batch (CFG cond+uncond, multi-cond).
+        if x.shape[0] != cx.shape[0]:
+            if x.shape[0] % cx.shape[0] != 0:
+                return self.org_forward(x.reshape(orig_shape) if is_5d else x)
+            cx = cx.repeat(x.shape[0] // cx.shape[0], 1, 1)
+
+        if x.shape[1] != cx.shape[1]:
+            return self.org_forward(x.reshape(orig_shape) if is_5d else x)
+
+        # Run the LLLite mini-MLP in its own parameter dtype, then cast the
+        # correction back to ``x``'s dtype before adding. Robust to autocast
+        # flows where x and LLLite weights have different dtypes.
+        param_dtype = self.down.weight.dtype
+        x_proc = x if x.dtype == param_dtype else x.to(param_dtype)
+        if cx.dtype != param_dtype or cx.device != x.device:
+            cx = cx.to(device=x.device, dtype=param_dtype)
+
+        # Per-module depth embedding (zero-init so it's a no-op at train start).
+        if self._depth_embeds_ref:
+            depth_e = self._depth_embeds_ref[0][self.layer_idx]
+            if depth_e.dtype != param_dtype or depth_e.device != x.device:
+                depth_e = depth_e.to(device=x.device, dtype=param_dtype)
+            cond_local = cx + depth_e
+        else:
+            cond_local = cx
+
+        h = F.silu(self.down(x_proc))
+
+        gb = self.cond_to_film(cond_local)
+        gamma, beta = gb.chunk(2, dim=-1)
+
+        m = self.mid(torch.cat([cond_local, h], dim=-1))
+        m = m * (1 + gamma) + beta
+        m = F.silu(m)
+
+        if self.dropout is not None and self.training:
+            m = F.dropout(m, p=self.dropout)
+
+        out = self.up(m) * self.multiplier
+        if out.dtype != x.dtype:
+            out = out.to(x.dtype)
+
+        y = self.org_forward(x + out)
+
+        if is_5d:
+            # org Linear out_features may differ from in_features — recover with -1.
+            y = y.reshape(orig_shape[0], orig_shape[1], orig_shape[2], orig_shape[3], -1)
+        return y
+
+
+# ----------------------------------------------------------------------------
+# ControlNetLLLiteDiT
+# ----------------------------------------------------------------------------
+
+
+class ControlNetLLLiteDiT(nn.Module):
+    def __init__(
+        self,
+        dit: nn.Module,
+        cond_emb_dim: int = 32,
+        mlp_dim: int = 64,
+        target_layers: str = "self_attn_q",
+        dropout: float | None = None,
+        multiplier: float = 1.0,
+        cond_dim: int = 64,
+        cond_resblocks: int = 1,
+        use_aspp: bool = False,
+        aspp_dilations: tuple[int, ...] = ASPP_DEFAULT_DILATIONS,
+        cond_in_channels: int = 3,
+        inpaint_masked_input: bool = False,
+    ):
+        super().__init__()
+
+        atomics = parse_target_layers(target_layers)
+
+        self.cond_emb_dim = cond_emb_dim
+        self.mlp_dim = mlp_dim
+        self.target_layers = target_layers
+        self.target_atomics = atomics
+        self.dropout = dropout
+        self.multiplier = multiplier
+        self.cond_dim = cond_dim
+        self.cond_resblocks = cond_resblocks
+        self.use_aspp = use_aspp
+        self.aspp_dilations = tuple(aspp_dilations) if use_aspp else ()
+        # 4ch (RGB+mask) inpainting metadata. `inpaint_masked_input` records the training-time
+        # RGB-masking policy for cond_image preparation; it does not alter the forward pass here.
+        self.cond_in_channels = cond_in_channels
+        self.inpaint_masked_input = inpaint_masked_input
+
+        self.conditioning1 = _Conditioning1(
+            cond_dim,
+            cond_emb_dim,
+            cond_resblocks,
+            use_aspp=use_aspp,
+            aspp_dilations=aspp_dilations,
+            cond_in_channels=cond_in_channels,
+        )
+
+        modules = self._create_modules(dit, cond_emb_dim, mlp_dim, atomics, dropout, multiplier)
+        self.lllite_modules = nn.ModuleList(modules)
+
+        n = len(self.lllite_modules)
+        self.depth_embeds = nn.Parameter(torch.zeros(n, cond_emb_dim))
+        for i, m in enumerate(self.lllite_modules):
+            m.layer_idx = i
+            m._depth_embeds_ref = [self.depth_embeds]
+
+        aspp_info = f"aspp={'on' + str(list(self.aspp_dilations)) if use_aspp else 'off'}"
+        inpaint_info = (
+            f", inpaint=on(masked_input={inpaint_masked_input})" if cond_in_channels != 3 else ""
+        )
+        logger.info(
+            "ControlNet-LLLite (Anima v%s): created %d modules for target=%r "
+            "(atomics=%s), cond_in_channels=%d, cond_dim=%d, cond_resblocks=%d, %s, "
+            "cond_emb_dim=%d, mlp_dim=%d%s",
+            LLLITE_ARCH_VERSION,
+            n,
+            target_layers,
+            list(atomics),
+            cond_in_channels,
+            cond_dim,
+            cond_resblocks,
+            aspp_info,
+            cond_emb_dim,
+            mlp_dim,
+            inpaint_info,
+        )
+
+    @staticmethod
+    def _attn_atomic_match(is_self_attn: bool, child_name: str, atomics: tuple[str, ...]) -> bool:
+        if "output_proj" in child_name:
+            return False
+        if is_self_attn:
+            if child_name == "q_proj":
+                return "self_attn_q_pre" in atomics
+            if child_name in ("k_proj", "v_proj"):
+                return "self_attn_kv_pre" in atomics
+            return False
+        else:
+            if child_name == "q_proj":
+                return "cross_attn_q_pre" in atomics
+            return False  # cross_attn K,V live in text-embedding space
+
+    def _create_modules(
+        self,
+        dit: nn.Module,
+        cond_emb_dim: int,
+        mlp_dim: int,
+        atomics: tuple[str, ...],
+        dropout: float | None,
+        multiplier: float,
+    ) -> list[LLLiteModuleDiT]:
+        modules: list[LLLiteModuleDiT] = []
+        want_mlp_fc1 = "mlp_fc1_pre" in atomics
+        any_attn = any(
+            a in atomics for a in ("self_attn_q_pre", "self_attn_kv_pre", "cross_attn_q_pre")
+        )
+
+        for name, module in dit.named_modules():
+            if LLM_ADAPTER_NAME in name:
+                continue
+            cls = module.__class__.__name__
+
+            def _is_linear_like(module):
+                return (
+                hasattr(module, "in_features")
+                and hasattr(module, "out_features")
+                and callable(getattr(module, "forward", None))
+            )
+
+            if any_attn and cls == TARGET_ATTENTION_CLASS:
+                # The Anima-block Attention exposes is_selfattn; the LLM-Adapter
+                # Attention does not — skip the latter even if path filter misses.
+                if not hasattr(module, "is_selfattn"):
+                    continue
+                is_self_attn = bool(module.is_selfattn)
+                for child_name, child in module.named_children():
+                    if not _is_linear_like(child):
+                        continue
+                    if not self._attn_atomic_match(is_self_attn, child_name, atomics):
+                        continue
+                    full_name = f"lllite_dit.{name}.{child_name}".replace(".", "_")
+                    modules.append(
+                        LLLiteModuleDiT(
+                            full_name, child, cond_emb_dim, mlp_dim, dropout, multiplier
+                        )
+                    )
+
+            elif want_mlp_fc1 and cls == TARGET_MLP_CLASS:
+                child = getattr(module, "layer1", None)
+                if not _is_linear_like(child):
+                    continue
+                full_name = f"lllite_dit.{name}.layer1".replace(".", "_")
+                modules.append(
+                    LLLiteModuleDiT(full_name, child, cond_emb_dim, mlp_dim, dropout, multiplier)
+                )
+
+        return modules
+
+    def set_cond_image(self, cond_image: torch.Tensor | None):
+        """cond_image: (B, 3, H*16, W*16) in [-1, 1]; ``None`` clears."""
+        if cond_image is None:
+            for m in self.lllite_modules:
+                m.cond_emb = None
+            return
+        cx = self.conditioning1(cond_image)  # (B, S, cond_emb_dim)
+        for m in self.lllite_modules:
+            m.cond_emb = cx
+
+    def clear_cond_image(self):
+        self.set_cond_image(None)
+
+    def set_multiplier(self, multiplier: float):
+        self.multiplier = multiplier
+        for m in self.lllite_modules:
+            m.multiplier = multiplier
+
+    def apply_to(self):
+        for m in self.lllite_modules:
+            m.apply_to()
+
+    def restore(self):
+        for m in self.lllite_modules:
+            m.restore()
+
+
+# ----------------------------------------------------------------------------
+# Save / load (named-key format; legacy lllite_modules.* is rejected)
+# ----------------------------------------------------------------------------
+
+_INTERNAL_MODULES_PREFIX = "lllite_modules."
+_INTERNAL_COND_PREFIX = "conditioning1."
+_INTERNAL_DEPTH_KEY = "depth_embeds"
+_SAVED_COND_PREFIX = "lllite_conditioning1."
+_SAVED_DEPTH_SUFFIX = ".depth_embed"
+
+
+def _from_saved_state_dict(lllite: ControlNetLLLiteDiT, weights_sd: dict) -> dict:
+    """Rewrite a v2 named-key state dict back to the internal layout."""
+    name_to_idx = {m.lllite_name: i for i, m in enumerate(lllite.lllite_modules)}
+    n_modules = len(name_to_idx)
+    out: dict = {}
+    depth_slices: dict = {}
+
+    for k, v in weights_sd.items():
+        if k.startswith(_SAVED_COND_PREFIX):
+            out[_INTERNAL_COND_PREFIX + k[len(_SAVED_COND_PREFIX) :]] = v
+            continue
+        if k.endswith(_SAVED_DEPTH_SUFFIX):
+            name = k[: -len(_SAVED_DEPTH_SUFFIX)]
+            if name in name_to_idx:
+                depth_slices[name_to_idx[name]] = v
+                continue
+        head, dot, tail = k.partition(".")
+        if dot and head in name_to_idx:
+            out[f"{_INTERNAL_MODULES_PREFIX}{name_to_idx[head]}.{tail}"] = v
+            continue
+        out[k] = v
+
+    if depth_slices:
+        missing = [i for i in range(n_modules) if i not in depth_slices]
+        if missing:
+            raise RuntimeError(f"depth_embed slices missing for module idx(es) {missing}")
+        out[_INTERNAL_DEPTH_KEY] = torch.stack([depth_slices[i] for i in range(n_modules)], dim=0)
+
+    return out
+
+
+def load_lllite_weights(lllite: ControlNetLLLiteDiT, file: str, strict: bool = False):
+    weights_sd = safetensors.torch.load_file(file)
+
+    if any(k.startswith(_INTERNAL_MODULES_PREFIX) for k in weights_sd):
+        raise RuntimeError(
+            f"weights at {file} appear to be in a legacy ControlNet-LLLite weight format "
+            f"(keys starting with '{_INTERNAL_MODULES_PREFIX}'). The current code uses a "
+            f"named-key format (per-module key prefix = lllite_name, e.g. "
+            f"'lllite_dit_blocks_0_self_attn_q_proj.down.weight'). Re-train with the current codebase."
+        )
+
+    converted = _from_saved_state_dict(lllite, weights_sd)
+    info = lllite.load_state_dict(converted, strict=strict)
+    logger.info("loaded LLLite weights from %s: %s", file, info)
+    return info
+
+
+def read_lllite_metadata(file: str) -> dict:
+    if os.path.splitext(file)[1] != ".safetensors":
+        raise RuntimeError(f"Must use .safetensors files, got {file}")
+
+    with safetensors.safe_open(file, framework="pt") as f:
+        return f.metadata() or {}
+
+
+# ----------------------------------------------------------------------------
+# ComfyUI nodes for Anima ControlNet-LLLite
+# ----------------------------------------------------------------------------
+
+
+def _get_inner_dit(model) -> torch.nn.Module:
+    """Reach the underlying Anima DiT (nn.Module) from a ComfyUI ModelPatcher."""
+    inner = getattr(model, "model", None)
+    if inner is None:
+        raise RuntimeError("Input MODEL has no .model attribute (not a ModelPatcher?)")
+    dit = getattr(inner, "diffusion_model", None)
+    if dit is None:
+        raise RuntimeError("MODEL.model has no .diffusion_model — not a UNet/DiT model?")
+    return dit
+
+
+def _target_cond_hw(latent_h: int, latent_w: int, patch_spatial: int = 2) -> tuple[int, int]:
+    """Return the (H, W) the cond image / mask must be resized to.
+
+    The LLLite ``conditioning1`` Conv has stride 16, so the cond image must be
+    sized to ``latent_HW * 8`` in input pixel space (= ``token_HW * 16`` after
+    DiT patchify with patch_spatial=2). The DiT internally pads the latent up
+    to a multiple of ``patch_spatial`` (see ``MiniTrainDIT.forward`` →
+    ``pad_to_patch_size``), so we mirror that rounding here — otherwise odd
+    latent dims (e.g. 1032 px → 129 latent) yield a token-count mismatch that
+    silently bypasses every LLLite module.
+    """
+    padded_h = ((latent_h + patch_spatial - 1) // patch_spatial) * patch_spatial
+    padded_w = ((latent_w + patch_spatial - 1) // patch_spatial) * patch_spatial
+    return padded_h * 8, padded_w * 8
+
+
+def _prepare_cond_image(
+    image: torch.Tensor,
+    latent_h: int,
+    latent_w: int,
+    device: torch.device,
+    dtype: torch.dtype,
+    patch_spatial: int = 2,
+) -> torch.Tensor:
+    """ComfyUI IMAGE (B,H,W,3) in [0,1] → (1,3,H*8,W*8) in [-1,1]."""
+    if image.ndim == 4 and image.shape[-1] == 3:
+        # (B, H, W, 3) -> (B, 3, H, W)
+        img = image.permute(0, 3, 1, 2).contiguous()
+    else:
+        raise ValueError(f"Unexpected cond image shape: {tuple(image.shape)} (expected B,H,W,3)")
+
+    img = img[:1]  # use first frame only
+    target_h, target_w = _target_cond_hw(latent_h, latent_w, patch_spatial)
+    if img.shape[-2] != target_h or img.shape[-1] != target_w:
+        img = F.interpolate(img, size=(target_h, target_w), mode="bicubic", align_corners=False)
+        img = img.clamp(0.0, 1.0)
+    img = img * 2.0 - 1.0
+    return img.to(device=device, dtype=dtype)
+
+
+def _prepare_mask(
+    mask: torch.Tensor,
+    latent_h: int,
+    latent_w: int,
+    device: torch.device,
+    dtype: torch.dtype,
+    patch_spatial: int = 2,
+) -> torch.Tensor:
+    """ComfyUI MASK (B,H,W) in [0,1] → (1,1,H*8,W*8) binarized at 0.5.
+
+    Returns the mask in ``{0.0, 1.0}`` (1 = inpaint area, 0 = keep). The caller
+    is responsible for the ``*2-1`` rescale before concat with RGB.
+    """
+    if mask.ndim == 3:
+        m = mask.unsqueeze(1)  # (B, 1, H, W)
+    elif mask.ndim == 4 and mask.shape[1] == 1:
+        m = mask
+    else:
+        raise ValueError(f"Unexpected mask shape: {tuple(mask.shape)} (expected B,H,W or B,1,H,W)")
+
+    m = m[:1]
+    target_h, target_w = _target_cond_hw(latent_h, latent_w, patch_spatial)
+    if m.shape[-2] != target_h or m.shape[-1] != target_w:
+        m = F.interpolate(m.float(), size=(target_h, target_w), mode="nearest")
+    m = (m >= 0.5).to(dtype=dtype)
+    return m.to(device=device)
+
+
+def _build_inpaint_cond_image(
+    rgb_pm1: torch.Tensor, mask01: torch.Tensor, masked_input: bool
+) -> torch.Tensor:
+    """rgb_pm1: (1,3,H,W) in [-1,1], mask01: (1,1,H,W) in {0,1}. Returns (1,4,H,W).
+
+    Mirrors ``_build_inpaint_cond_image`` in the sd-scripts training / inference
+    code: the mask channel is rescaled to ``[-1, +1]`` (matches the RGB range),
+    and if ``masked_input`` is set the RGB is zeroed where ``mask >= 0.5``.
+    """
+    if masked_input:
+        keep = (mask01 < 0.5).to(rgb_pm1.dtype)
+        rgb_pm1 = rgb_pm1 * keep
+    mask_pm1 = mask01.to(rgb_pm1.dtype) * 2.0 - 1.0
+    return torch.cat([rgb_pm1, mask_pm1], dim=1)
+
+
+ETNControlNet = io.Custom("ETN_CONTROL_NET")
+
+
+class ControlLoad(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_control_load",
+            display_name="Load ControlNet (tooling-nodes)",
+            description="Loads ControlNet weights. Currently only supports Anima LLLite weights.",
+            category="external_tooling",
+            inputs=[
+                io.Model.Input("model"),
+                io.Combo.Input("weights", folder_paths.get_filename_list("controlnet")),
+            ],
+            outputs=[
+                io.Model.Output("out_model", "model"),
+                ETNControlNet.Output("control_net"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model: ModelPatcher, weights: str):  # type: ignore[override]
+        weights_path = folder_paths.get_full_path("controlnet", weights)
+        if weights_path is None or not os.path.isfile(weights_path):
+            raise FileNotFoundError(f"LLLite weights not found: {weights}")
+
+        # Architecture is fully determined by the trained weights — read everything
+        # from metadata rather than exposing knobs that would just cause load errors.
+        meta = read_lllite_metadata(weights_path)
+        if "lllite.version" not in meta:
+            raise RuntimeError(
+                "Unrecognized model. This node currently only loads Anima LLLite weights."
+            )
+        ce_dim = int(meta.get("lllite.cond_emb_dim", 32))
+        m_dim = int(meta.get("lllite.mlp_dim", 64))
+        # v2 records the canonical atomic form under lllite.target_atomics; fall back
+        # to the legacy preset key, then to the v1 default.
+        tl = meta.get("lllite.target_atomics", meta.get("lllite.target_layers", "self_attn_q"))
+        cond_dim = int(meta.get("lllite.cond_dim", 64))
+        cond_resblocks = int(meta.get("lllite.cond_resblocks", 1))
+        use_aspp = str(meta.get("lllite.use_aspp", "false")).lower() == "true"
+        aspp_dilations_meta = meta.get("lllite.aspp_dilations")
+        if use_aspp and aspp_dilations_meta:
+            aspp_dilations = tuple(int(d) for d in aspp_dilations_meta.split(",") if d.strip())
+        else:
+            aspp_dilations = ASPP_DEFAULT_DILATIONS
+        cond_in_channels = int(meta.get("lllite.cond_in_channels", 3))
+        inpaint_masked_input = (
+            str(meta.get("lllite.inpaint_masked_input", "false")).lower() == "true"
+        )
+
+        lllite = ControlNetLLLiteDiT(
+            _get_inner_dit(model),
+            cond_emb_dim=ce_dim,
+            mlp_dim=m_dim,
+            target_layers=tl,
+            multiplier=1.0,
+            cond_dim=cond_dim,
+            cond_resblocks=cond_resblocks,
+            use_aspp=use_aspp,
+            aspp_dilations=aspp_dilations,
+            cond_in_channels=cond_in_channels,
+            inpaint_masked_input=inpaint_masked_input,
+        )
+        load_lllite_weights(lllite, weights_path, strict=False)
+        lllite.eval().requires_grad_(False)
+        return io.NodeOutput(model, lllite)
+
+
+class ControlApply(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_control_apply",
+            display_name="Apply ControlNet (tooling-nodes)",
+            description="Applies ControlNet conditioning. Currently only supports Anima LLLite weights.",
+            category="external_tooling",
+            inputs=[
+                io.Model.Input("model"),
+                ETNControlNet.Input("control_net"),
+                io.Image.Input("image"),
+                io.Mask.Input("mask", optional=True),
+                io.Float.Input("strength", default=1.0, min=-10.0, max=10.0, step=0.01),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001),
+            ],
+            outputs=[io.Model.Output("model")],
+        )
+
+    @classmethod
+    def execute(  # type: ignore[override]
+        cls,
+        model: ModelPatcher,
+        control_net: ControlNetLLLiteDiT,
+        image: torch.Tensor,
+        strength: float,
+        start_percent: float,
+        end_percent: float,
+        mask: torch.Tensor | None = None,
+    ):
+        dit = _get_inner_dit(model)
+        patch_spatial = int(getattr(dit, "patch_spatial", 2))
+
+        lllite = control_net
+        lllite.set_multiplier(strength)
+
+        # Mask / cond_in_channels consistency: 4ch weights need a MASK, 3ch weights ignore it.
+        if lllite.cond_in_channels == 4 and mask is None:
+            raise ValueError("ControlNet weights require a mask input (inpaint mode)")
+        if lllite.cond_in_channels != 4 and mask is not None:
+            mask = None
+
+        # Convert percent range -> sigma range (start_percent=0 → sigma_max).
+        model_sampling = model.get_model_object("model_sampling")
+        sigma_start = float(model_sampling.percent_to_sigma(start_percent))
+        sigma_end = float(model_sampling.percent_to_sigma(end_percent))
+
+        # Capture image / mask tensors (cloned to detach from any upstream caching)
+        src_image = image.detach().clone()
+        src_mask = mask.detach().clone() if mask is not None else None
+        is_inpaint = lllite.cond_in_channels == 4
+
+        # Cache for the per-resolution preprocessed cond image (avoids repeat resize)
+        cache: dict[str, Any] = {"cond_image_pp": None, "key": None, "lllite_loaded_to": None}
+
+        # Capture any previously-installed wrapper BEFORE we clone — model_options
+        # has a single "model_function_wrapper" slot, so without delegation a second
+        # wrapper-installing node would silently no-op the first. Mirrors the
+        # ChromaRadianceOptions pattern in comfy_extras/nodes_chroma_radiance.py.
+        old_wrapper = model.model_options.get("model_function_wrapper")
+
+        def _call_next(apply_model, input_x, timestep, c):
+            if old_wrapper is not None:
+                return old_wrapper(apply_model, {"input": input_x, "timestep": timestep, "c": c})
+            return apply_model(input_x, timestep, **c)
+
+        def wrapper(apply_model, args):
+            input_x = args["input"]
+            timestep = args["timestep"]
+            c = args["c"]
+
+            # Step-range gate: skip LLLite entirely when current sigma is outside
+            # [sigma_end, sigma_start]. percent_to_sigma maps 0.0 → sigma_max,
+            # 1.0 → sigma_min, so the active window is sigma_end <= sigma <= sigma_start.
+            sigma = float(timestep.max().item())
+            if not (sigma_end <= sigma <= sigma_start):
+                return _call_next(apply_model, input_x, timestep, c)
+
+            # Anima latent shape: (B, C, T, H, W) — take spatial dims from the tail.
+            latent_h, latent_w = int(input_x.shape[-2]), int(input_x.shape[-1])
+            device = input_x.device
+            dtype = input_x.dtype
+
+            # Move LLLite to the runtime device/dtype lazily.
+            tag = (device, dtype)
+            if cache["lllite_loaded_to"] != tag:
+                lllite.to(device=device, dtype=dtype)
+                cache["lllite_loaded_to"] = tag
+                cache["cond_image_pp"] = None  # invalidate
+
+            key = (latent_h, latent_w, device, dtype)
+            if cache["key"] != key or cache["cond_image_pp"] is None:
+                rgb = _prepare_cond_image(
+                    src_image, latent_h, latent_w, device, dtype, patch_spatial
+                )
+                if is_inpaint:
+                    assert src_mask is not None, "Cannot use inpaint control-net without a mask"
+                    mk = _prepare_mask(src_mask, latent_h, latent_w, device, dtype, patch_spatial)
+                    cache["cond_image_pp"] = _build_inpaint_cond_image(
+                        rgb, mk, lllite.inpaint_masked_input
+                    )
+                else:
+                    cache["cond_image_pp"] = rgb
+                cache["key"] = key
+
+            lllite.set_multiplier(strength)
+            lllite.set_cond_image(cache["cond_image_pp"])
+            lllite.apply_to()
+            try:
+                return _call_next(apply_model, input_x, timestep, c)
+            finally:
+                lllite.restore()
+                lllite.clear_cond_image()
+
+        m = model.clone()
+        m.set_model_unet_function_wrapper(wrapper)
+        return (m,)

krita.py

@@ -1,15 +1,16 @@
 import sys
-import torch
-import numpy as np
 from enum import Enum
 from pathlib import Path
 from typing import Any, NamedTuple
-from PIL import Image
 
-import server
 import comfy.samplers
+import numpy as np
+import server
+import torch
 from comfy.comfy_types.node_typing import IO
 from comfy_api.latest import io
+from PIL import Image
+
 from .nodes import SendImageWebSocket
 
 
@@ -102,7 +103,7 @@ class KritaOutput(io.ComfyNode):
         )
 
     @classmethod
-    def execute(
+    def execute(  # type: ignore
         cls,
         images: torch.Tensor,
         x: int = 0,
@@ -141,7 +142,7 @@ class KritaSendText(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, value: Any, name: str, type: str):
+    def execute(cls, value: Any, name: str, type: str):  # type: ignore
         mime = {
             "text": "text/plain",
             "markdown": "text/markdown",
@@ -169,12 +170,13 @@ class KritaCanvas(io.ComfyNode):
                 io.Int.Output(display_name="width"),
                 io.Int.Output(display_name="height"),
                 io.Int.Output(display_name="seed"),
+                io.Mask.Output(display_name="mask"),
             ],
         )
 
     @classmethod
-    def execute(cls):
+    def execute(cls, **kwargs):
-        return io.NodeOutput(_placeholder_image(), 512, 512, 0)
+        return io.NodeOutput(_placeholder_image(), 512, 512, 0, torch.ones(1, 512, 512))
 
 
 class SelectionContext(Enum):
@@ -236,7 +238,7 @@ class KritaImageLayer(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, name: str):
+    def execute(cls, name: str):  # type: ignore
         return io.NodeOutput(_placeholder_image(), torch.ones(1, 512, 512))
 
 
@@ -254,7 +256,7 @@ class KritaMaskLayer(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, name: str):
+    def execute(cls, name: str):  # type: ignore
         return io.NodeOutput(torch.ones(1, 512, 512))
 
 
@@ -289,7 +291,7 @@ class Parameter(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, name: str, type: str, default, min=0.0, max=1.0):
+    def execute(cls, name: str, type: str, default, min=0.0, max=1.0):  # type: ignore
         if type == "number":
             return io.NodeOutput(float(default))
         elif type == "number (integer)":
@@ -326,5 +328,37 @@ class KritaStyle(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, name: str, sampler_preset: str):
+    def execute(cls, name: str, sampler_preset: str):  # type: ignore
+        raise NotImplementedError("This workflow must be started from Krita!")
+
+
+class KritaStyleAndPrompt(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ETN_KritaStyleAndPrompt",
+            display_name="Krita Style & Prompt",
+            category="krita",
+            inputs=[
+                io.Combo.Input("sampler_preset", options=["auto", "regular", "live"]),
+            ],
+            outputs=[
+                io.Model.Output(display_name="model (with loras)"),
+                io.Clip.Output(display_name="clip"),
+                io.Vae.Output(display_name="vae"),
+                io.String.Output(display_name="positive prompt (evaluated)"),
+                io.String.Output(display_name="negative prompt (evaluated)"),
+                io.Combo.Output(
+                    display_name="sampler name", options=comfy.samplers.KSampler.SAMPLERS
+                ),
+                io.Combo.Output(
+                    display_name="scheduler", options=comfy.samplers.KSampler.SCHEDULERS
+                ),
+                io.Int.Output(display_name="steps"),
+                io.Float.Output(display_name="guidance"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, name: str, sampler_preset: str):  # type: ignore
         raise NotImplementedError("This workflow must be started from Krita!")

nodes.py

@@ -1,20 +1,21 @@
 from __future__ import annotations
+
+import base64
+import time
 from copy import copy
 from dataclasses import dataclass
-import time
+from io import BytesIO
 from typing import NamedTuple
 from uuid import uuid4
-from PIL import Image
+
 import numpy as np
-import base64
 import torch
 import torch.nn.functional as F
-from io import BytesIO
-from server import PromptServer, BinaryEventTypes
-
 from comfy.clip_vision import ClipVisionModel
 from comfy.sd import StyleModel
 from comfy_api.latest import io
+from PIL import Image
+from server import BinaryEventTypes, PromptServer
 
 
 class LoadImageBase64(io.ComfyNode):
@@ -29,14 +30,14 @@ class LoadImageBase64(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, image: str):
+    def execute(cls, image: str):  # type: ignore
         _strip_prefix(image, "data:image/png;base64,")
         imgdata = base64.b64decode(image)
         img = Image.open(BytesIO(imgdata))
 
         if "A" in img.getbands():
             mask = np.array(img.getchannel("A")).astype(np.float32) / 255.0
-            mask = torch.from_numpy(mask)
+            mask = torch.from_numpy(mask)[None,]
         else:
             mask = None
 
@@ -59,7 +60,7 @@ class LoadMaskBase64(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, mask: str):
+    def execute(cls, mask: str):  # type: ignore
         _strip_prefix(mask, "data:image/png;base64,")
         imgdata = base64.b64decode(mask)
         img = Image.open(BytesIO(imgdata))
@@ -85,7 +86,7 @@ class SendImageWebSocket(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, images: torch.Tensor, format: str):
+    def execute(cls, images: torch.Tensor, format: str):  # type: ignore
         results = []
         for tensor in images:
             array = 255.0 * tensor.cpu().numpy()
@@ -198,12 +199,13 @@ class LoadImageCache(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, id: str):
+    def execute(cls, id: str):  # type: ignore
         image_data, content_type = image_cache.get(id, extend=True)
         if image_data is None:
             raise ValueError(f"Image with ID {id} not found in cache.")
 
         img = Image.open(BytesIO(image_data))
+
         w, h = img.size
         c = len(img.getbands())
         normalized = np.array(img).astype(np.float32) / 255.0
@@ -237,7 +239,7 @@ class SaveImageCache(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, images: torch.Tensor, format: str):
+    def execute(cls, images: torch.Tensor, format: str):  # type: ignore
         results = []
         for tensor in images:
             array = 255.0 * tensor.cpu().numpy()
@@ -284,7 +286,7 @@ class ApplyMaskToImage(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, image: torch.Tensor, mask: torch.Tensor):
+    def execute(cls, image: torch.Tensor, mask: torch.Tensor):  # type: ignore
         out = to_bchw(image)
         if out.shape[1] == 3:  # Assuming RGB images
             out = torch.cat([out, torch.ones_like(out[:, :1, :, :])], dim=1)
@@ -300,7 +302,7 @@ class ApplyMaskToImage(io.ComfyNode):
         # Apply each mask in the batch to its corresponding image's alpha channel
         for i in range(out.shape[0]):
             alpha = mask[i] if is_mask_batch else mask[0]
-            out[i, 3, :, :] = alpha
+            out[i, 3, :, :] *= alpha
 
         return (to_bhwc(out),)
 
@@ -329,7 +331,7 @@ class ReferenceImage(io.ComfyNode):
         )
 
     @classmethod
-    def execute(
+    def execute(  # type: ignore
         cls,
         image: torch.Tensor,
         weight: float,
@@ -359,7 +361,7 @@ class ApplyReferenceImages(io.ComfyNode):
         )
 
     @classmethod
-    def execute(
+    def execute(  # type: ignore
         cls,
         conditioning: list[list],
         clip_vision: ClipVisionModel,

nsfw.py

@@ -1,5 +1,4 @@
 from __future__ import annotations
-from weakref import ref as WeakRef
 from pathlib import Path
 from tqdm import tqdm
 import torch
@@ -39,6 +38,10 @@ class CLIPSafetyChecker(PreTrainedModel):
         self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)
         self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False)
 
+        # Model requires post_init after transformers v4.57.3
+        if hasattr(self, "post_init"):
+            self.post_init()
+
     def forward(self, clip_input, images: Tensor, sensitivity: float):
         with torch.no_grad():
             image_batch = self.vision_model(clip_input)[1]

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-tooling-nodes"
 description = "Provides nodes and server API extensions geared towards using ComfyUI as a backend for external tools."
-version = "3.1.0"
+version = "3.4.0"
 license = { file = "LICENSE" }
 
 [project.urls]
@@ -13,7 +13,7 @@ line-length = 100
 preview = true
 
 [tool.ruff.lint]
-ignore = ["E741"]
+ignore = ["E741", "BLE001"]
 
 [tool.black]
 line-length = 100

region.py

@@ -1,16 +1,34 @@
+# Adapted from https://github.com/pamparamm/ComfyUI-ppm
 # Adapted from https://github.com/laksjdjf/cgem156-ComfyUI/blob/main/scripts/attention_couple/node.py
 # by @laksjdjf
 
 from __future__ import annotations
-from typing import NamedTuple
+from functools import partial
+from typing import Any, NamedTuple
 import torch
 import torch.nn.functional as F
 import math
 from torch import Tensor, Size
+import comfy.model_management
+import comfy.patcher_extension
 from comfy.model_patcher import ModelPatcher
+from comfy.model_base import Anima, CosmosPredict2
+from comfy.ldm.cosmos.predict2 import Attention as CosmosAttention
+from comfy.sampler_helpers import convert_cond
+from comfy.samplers import process_conds
 from comfy_api.latest import io
 
 
+COND = 0
+UNCOND = 1
+ANIMA_COUPLE_WRAPPER_KEY = "etn_attention_mask_anima"
+ANIMA_COUPLE_PATCH_KEY = "etn_attention_mask_patch"
+CONDS_COUPLE_KEY = "etn_couple_conds"
+COND_UNCOND_COUPLE_KEY = "etn_couple_cond_or_uncond"
+COUPLE_ACTIVE_KEY = "etn_couple_active"
+NUM_TOKENS_COUPLE_KEY = "etn_couple_num_tokens"
+
+
 def downsample_mask(mask: Tensor, batch: int, target_size: int, original_shape: Size) -> Tensor:
     h, w = original_shape[2], original_shape[3]
     hm, wm = mask.shape[2], mask.shape[3]
@@ -34,6 +52,12 @@ def downsample_mask(mask: Tensor, batch: int, target_size: int, original_shape:
     return result
 
 
+def reshape_mask(mask: Tensor, size: tuple[int, int], batch: int, target_size: int) -> Tensor:
+    result = F.interpolate(mask, size=size, mode="nearest")
+    result = result.view(mask.shape[0], target_size, 1)
+    return result.repeat_interleave(batch, dim=0)
+
+
 def lcm(a: int, b: int):
     return a * b // math.gcd(a, b)
 
@@ -145,6 +169,7 @@ class AttentionMaskPatch:
         mask_sum = mask.sum(dim=0, keepdim=True)
         assert mask_sum.sum() > 0, "There are areas that are zero in all masks."
         self.mask = mask / mask_sum
+        self.region_conds = [r.conditioning for r in region_list]
         self.conds = [r.conditioning[0][0] for r in region_list]
         self.num_tokens = [cond.shape[1] for cond in self.conds]
         self.num_conds = len(region_list)
@@ -153,6 +178,8 @@ class AttentionMaskPatch:
     @staticmethod
     def apply(model: ModelPatcher, regions: Region):
         patch = AttentionMaskPatch(regions.preprocess())
+        if _is_anima_couple_model(model):
+            return patch.apply_anima(model)
 
         def attn2_patch(q: Tensor, k: Tensor, v: Tensor, extra_options: dict):
             assert k.mean() == v.mean(), "k and v must be the same."
@@ -221,3 +248,189 @@ class AttentionMaskPatch:
         new_model.set_model_attn2_output_patch(attn2_output_patch)
         new_model.set_attachments("etn_attention_mask", patch)
         return new_model
+
+    def apply_anima(self, model: ModelPatcher):
+        new_model = model.clone()
+        _patch_cosmos_attention(new_model)
+
+        device = comfy.model_management.get_torch_device()
+        conds_converted = [convert_cond(cond)[0] for cond in self.region_conds]
+        new_model.add_wrapper_with_key(
+            comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE,
+            ANIMA_COUPLE_WRAPPER_KEY,
+            _anima_couple_sample_wrapper(conds_converted, device),
+        )
+        new_model.add_wrapper_with_key(
+            comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL,
+            ANIMA_COUPLE_WRAPPER_KEY,
+            _anima_couple_diffusion_wrapper(self),
+        )
+        new_model.set_attachments("etn_attention_mask", self)
+        return new_model
+
+
+def _is_anima_couple_model(model: ModelPatcher) -> bool:
+    model_type = type(model.model)
+    return issubclass(model_type, (Anima, CosmosPredict2))
+
+
+def _anima_couple_sample_wrapper(conds_converted: list, device):
+    def sample_wrapper(executor, *args, **kwargs):
+        if len(conds_converted) > 0:
+            guider = args[0]
+            extra_options: dict[str, Any] = args[2]
+            seed: int = extra_options["seed"]
+            noise: Tensor = args[4]
+            latent_image: Tensor = args[5]
+            denoise_mask: Tensor | None = args[6]
+
+            conds_processed = process_conds(
+                guider.inner_model,
+                noise,
+                {"positive": conds_converted},
+                device,
+                latent_image,
+                denoise_mask,
+                seed,
+                latent_shapes=[latent_image.shape],
+            )["positive"]
+
+            conds_couple = [cond["model_conds"]["c_crossattn"].cond for cond in conds_processed]
+
+            model_options: dict[str, Any] = extra_options["model_options"]
+            transformer_options: dict[str, Any] = model_options.get("transformer_options", {}).copy()
+            transformer_options[CONDS_COUPLE_KEY] = conds_couple
+            transformer_options[NUM_TOKENS_COUPLE_KEY] = [cond.shape[1] for cond in conds_couple]
+            model_options["transformer_options"] = transformer_options
+
+        return executor(*args, **kwargs)
+
+    return sample_wrapper
+
+
+def _anima_couple_diffusion_wrapper(patch: AttentionMaskPatch):
+    def diffusion_wrapper(executor, *args, **kwargs):
+        anima_model = executor.class_obj
+        x: Tensor = args[0]
+        transformer_options: dict[str, Any] = kwargs.get("transformer_options", {}).copy()
+        patch_spatial = getattr(anima_model, "patch_spatial", 1)
+
+        activations_shape = list(x.shape)
+        activations_shape[-2] = activations_shape[-2] // patch_spatial
+        activations_shape[-1] = activations_shape[-1] // patch_spatial
+
+        transformer_options["activations_shape"] = activations_shape
+        transformer_options[ANIMA_COUPLE_PATCH_KEY] = patch
+        kwargs["transformer_options"] = transformer_options
+
+        return executor(*args, **kwargs)
+
+    return diffusion_wrapper
+
+
+def pre_cross_attention(
+    patch: AttentionMaskPatch,
+    transformer_options: dict,
+    x: Tensor,
+    context: Tensor,
+    rope_emb: Tensor | None,
+) -> tuple[Tensor, Tensor, Tensor | None, dict]:
+    transformer_options = transformer_options.copy()
+    if CONDS_COUPLE_KEY not in transformer_options:
+        transformer_options[COND_UNCOND_COUPLE_KEY] = list(transformer_options["cond_or_uncond"])
+        transformer_options[COUPLE_ACTIVE_KEY] = False
+        return x, context, rope_emb, transformer_options
+
+    conds: list[Tensor] = transformer_options[CONDS_COUPLE_KEY]
+    num_tokens_c: list[int] = transformer_options[NUM_TOKENS_COUPLE_KEY]
+    cond_or_uncond = transformer_options["cond_or_uncond"]
+
+    num_chunks = len(cond_or_uncond)
+    batch = x.shape[0] // num_chunks
+    x_chunks = x.chunk(num_chunks, dim=0)
+    c_chunks = context.chunk(num_chunks, dim=0)
+    lcm_tokens_c = lcm_for_list(num_tokens_c + [context.shape[1]])
+    conds_c_tensor = torch.cat(
+        [cond.repeat(batch, lcm_tokens_c // num_tokens_c[i], 1) for i, cond in enumerate(conds)],
+        dim=0,
+    )
+
+    xs, cs = [], []
+    cond_or_uncond_couple = []
+    for i, cond_type in enumerate(cond_or_uncond):
+        x_target = x_chunks[i]
+        c_target = c_chunks[i].repeat(1, lcm_tokens_c // context.shape[1], 1)
+        if cond_type == UNCOND:
+            xs.append(x_target)
+            cs.append(c_target)
+            cond_or_uncond_couple.append(UNCOND)
+        else:
+            xs.append(x_target.repeat(patch.num_conds, 1, 1))
+            cs.append(conds_c_tensor)
+            cond_or_uncond_couple.extend([COND] * patch.num_conds)
+
+    transformer_options[COND_UNCOND_COUPLE_KEY] = cond_or_uncond_couple
+    transformer_options[COUPLE_ACTIVE_KEY] = True
+
+    return torch.cat(xs, dim=0), torch.cat(cs, dim=0), rope_emb, transformer_options
+
+
+def cross_attention_output(patch: AttentionMaskPatch, transformer_options: dict, out: Tensor):
+    cond_or_uncond = transformer_options[COND_UNCOND_COUPLE_KEY]
+    size = tuple(transformer_options["activations_shape"][-2:])
+    batch = out.shape[0] // len(cond_or_uncond)
+    mask = patch.mask.to(out.device, dtype=out.dtype)
+    mask_downsample = reshape_mask(mask, size, batch, out.shape[1])
+
+    outputs = []
+    cond_outputs = []
+    i_cond = 0
+    for i, cond_type in enumerate(cond_or_uncond):
+        pos, next_pos = i * batch, (i + 1) * batch
+        if cond_type == UNCOND:
+            outputs.append(out[pos:next_pos])
+        else:
+            pos_cond, next_pos_cond = i_cond * batch, (i_cond + 1) * batch
+            cond_outputs.append(out[pos:next_pos] * mask_downsample[pos_cond:next_pos_cond])
+            i_cond += 1
+
+    if len(cond_outputs) > 0:
+        outputs.append(torch.stack(cond_outputs).sum(0))
+
+    return torch.cat(outputs, dim=0)
+
+
+def _patch_cosmos_attention(model_patcher: ModelPatcher):
+    cosmos_model = model_patcher.get_model_object("diffusion_model")
+    for block_name, block in (
+        (n, b)
+        for n, b in cosmos_model.named_modules()
+        if ("cross_attn" in n or "self_attn" in n) and isinstance(b, CosmosAttention)
+    ):
+        patch_name = f"diffusion_model.{block_name}.forward"
+        if patch_name not in model_patcher.object_patches:
+            model_patcher.add_object_patch(patch_name, partial(_cosmos_attention_forward_patched, block))
+
+
+def _cosmos_attention_forward_patched(
+    self,
+    x: Tensor,
+    context: Tensor | None = None,
+    rope_emb: Tensor | None = None,
+    transformer_options: dict | None = None,
+) -> Tensor:
+    transformer_options = transformer_options if transformer_options is not None else {}
+    patch: AttentionMaskPatch | None = transformer_options.get(ANIMA_COUPLE_PATCH_KEY)
+
+    if context is not None and patch is not None:
+        x, context, rope_emb, transformer_options = pre_cross_attention(
+            patch, transformer_options, x, context, rope_emb
+        )
+
+    q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb)
+    output = self.compute_attention(q, k, v, transformer_options=transformer_options)
+
+    if context is not None and patch is not None and transformer_options.get(COUPLE_ACTIVE_KEY, False):
+        output = cross_attention_output(patch, transformer_options, output)
+
+    return output

tile.py

@@ -9,9 +9,13 @@ IntArray = npt.NDArray[np.int_]
 
 
 class TileLayout:
-    def __init__(self, image: Tensor, min_tile_size: int, padding: int, blending: int):
+    def __init__(
-        assert all([x % 8 == 0 for x in image.shape[-3:-1]]), "Image size must be divisible by 8"
+        self, image: Tensor, min_tile_size: int, padding: int, blending: int, multiple: int
-        assert min_tile_size % 8 == 0, "Tile size must be divisible by 8"
+    ):
+        assert all([x % multiple == 0 for x in image.shape[-3:-1]]), (
+            "Image size must be divisible by multiple"
+        )
+        assert min_tile_size % multiple == 0, "Tile size must be divisible by multiple"
         assert blending <= padding, "Blending must be smaller than padding"
 
         self.image_size: IntArray = np.array(image.shape[-3:-1])
@@ -21,7 +25,7 @@ class TileLayout:
 
         image_size_with_overlap = self.image_size + (self.tile_count - 1) * 2 * padding
         tile_size = np.ceil(image_size_with_overlap / self.tile_count)
-        self.tile_size: IntArray = (np.ceil(tile_size / 8) * 8).astype(int)
+        self.tile_size: IntArray = (np.ceil(tile_size / multiple) * multiple).astype(int)
 
     def size(self, coord: IntArray):
         return self.end(coord) - self.start(coord)
@@ -84,13 +88,14 @@ class CreateTileLayout(io.ComfyNode):
                 io.Int.Input("min_tile_size", default=512, min=64, max=8192, step=8),
                 io.Int.Input("padding", default=32, min=0, max=8192, step=8),
                 io.Int.Input("blending", default=8, min=0, max=256, step=8),
+                io.Int.Input("multiple", default=8, min=1, max=1024, step=1),
             ],
             outputs=[io.Custom("TileLayout").Output(display_name="layout")],
         )
 
     @classmethod
-    def execute(cls, image: Tensor, min_tile_size: int, padding: int, blending: int):
+    def execute(cls, image: Tensor, min_tile_size: int, padding: int, blending: int, multiple: int):
-        return io.NodeOutput(TileLayout(image, min_tile_size, padding, blending))
+        return io.NodeOutput(TileLayout(image, min_tile_size, padding, blending, multiple))
 
 
 class ExtractImageTile(io.ComfyNode):