ComfyUI-Anima-LLLite

ComfyUI custom node for ControlNet-LLLite for Anima (DiT-based).

LLLite is a lightweight ControlNet variant that injects a low-rank correction into the attention (and optionally MLP) projections of the DiT. This node loads weights trained with kohya-ss/sd-scripts and applies them to the ComfyUI Anima model at inference time.

This is intended as a minimal reference implementation. Community nodes with extra features (per-step scheduling, multi-cond, region masks, per-block selection, …) are welcome and can build on this codebase.

Install

Clone into ComfyUI/custom_nodes/:

cd ComfyUI/custom_nodes
git clone <this-repo> ComfyUI-Anima-LLLite

Place LLLite weights (.safetensors) under ComfyUI/models/controlnet/.

Node

Apply Anima ControlNet-LLLite (loaders category)

Input	Type	Notes
model	MODEL	Anima checkpoint
lllite_name	filename	from models/controlnet/
image	IMAGE	control image (any resolution; auto-resized to latent×8)
strength	FLOAT	LLLite multiplier (default 1.0)
start_percent	FLOAT	start of the active sampling window (0.0 = sigma_max)
end_percent	FLOAT	end of the active sampling window (1.0 = sigma_min)
preserve_wrapper	BOOLEAN	if a previous node already installed a model_function_wrapper, delegate to it from inside this node's wrapper so they cascade instead of overwriting (default True)
mask (optional)	MASK	required when the weights are 4-channel (inpaint); white = inpaint area, black = keep

Output: patched MODEL.

All architectural parameters (cond_emb_dim, mlp_dim, cond_dim, cond_resblocks, use_aspp, aspp_dilations, target_layers, cond_in_channels, inpaint_masked_input) are baked into the trained weights and read automatically from the safetensors metadata — they are not exposed as node inputs because changing them would just cause load errors.

Inpainting (4-channel) weights

If the loaded weights were trained with --lllite_cond_in_channels=4 (inpaint mode), the node also requires a MASK input. The mask convention matches the sd-scripts training/inference code:

• white (1.0) = inpaint area (the region the model should fill)
• black (0.0) = keep

The mask is resized to match the control image (nearest-neighbor), binarized at 0.5, and concatenated with the RGB control image as a 4th channel (rescaled to [-1, +1] to match the RGB range). When the weights were trained with --lllite_inpaint_masked_input, the RGB channels are also zeroed inside the mask region before concatenation — this is read from the weights metadata and applied automatically.

3-channel weights continue to work with no mask input; if a mask is connected to a 3-channel LLLite the node logs a warning and ignores it. A 4-channel weight without a mask raises a ValueError rather than silently substituting an empty mask.

Cascading multiple LLLite nodes

Multiple Apply Anima ControlNet-LLLite nodes can be chained on the same MODEL to apply more than one LLLite at the same time (e.g. a pose LLLite and a depth LLLite, or several inpaint LLLites with different conditions). Just feed the output MODEL of one node into the model input of the next.

ComfyUI's model_options has a single model_function_wrapper slot, so two nodes that each install a wrapper would normally cause the outer one to silently overwrite the inner one. To avoid this, each node captures the previously-installed wrapper before cloning and delegates to it from inside its own wrapper, so wrappers stack instead of replacing each other. This is controlled by the preserve_wrapper toggle (default True); turn it off to get the legacy single-wrapper behavior, or to deliberately replace an upstream wrapper.

Notes when cascading:

• Each node has its own start_percent / end_percent window — when a node's window is inactive it is a pass-through to the next wrapper, so you can stage different LLLites across different parts of the schedule.
• strength is per-node and they add independently in the LLLite output; there is no global normalization, so very high combined strengths can over-saturate the same way a single high strength would.
• The pattern is compatible with other well-behaved wrapper-installing nodes (e.g. ChromaRadianceOptions) provided they follow the same delegate-to-previous-wrapper convention.

How it works

• Discovers the LLLite target Linears under each Anima DiT block according to the target_atomics recorded in the weight metadata:

  • self_attn_q_pre → self-attention q_proj
  • self_attn_kv_pre → self-attention k_proj + v_proj
  • cross_attn_q_pre → cross-attention q_proj
  • mlp_fc1_pre → GPT2FeedForward.layer1 (the MLP fc1)

  The LLM-Adapter sub-tree and output_proj are always skipped.

• On each sampling step the wrapper monkey-patches those Linears with the LLLite forward, runs apply_model, and restores the originals — so the patch never leaks across model clones.

• The control image is resized to latent_HW * 8 once per resolution, then embedded by the v2 conditioning1 trunk (Conv 4×4 s=4 → Conv 3×3 s=1 → Conv 4×4 s=4 with GroupNorm/SiLU, optional ResBlocks, optional ASPP, 1×1 projection, LayerNorm) to a per-token feature map matching the DiT token grid. For 4-channel (inpaint) weights, the mask is resized with nearest-neighbor, binarized, rescaled to [-1, +1], and concatenated as the 4th channel of the cond image before this trunk.

• Each LLLite module applies the v2 forward: down → silu → concat with (cond + per-module depth-embedding) → mid → FiLM(γ, β) → silu → up, added to the original Linear input.

• CFG is supported: cond_emb is broadcast to match the runtime batch size.

Weight format (v2, named keys)

The state dict is in the v2 self-describing format. Each LLLite module's weights are prefixed by the target Linear's full path, so the file alone identifies which Linear each tensor belongs to:

lllite_conditioning1.{conv1,conv2,conv3,norm1,...,proj,out_norm,...}.{weight,bias}
lllite_dit_blocks_{i}_self_attn_q_proj.down.{weight,bias}
lllite_dit_blocks_{i}_self_attn_q_proj.mid.{weight,bias}
lllite_dit_blocks_{i}_self_attn_q_proj.cond_to_film.{weight,bias}
lllite_dit_blocks_{i}_self_attn_q_proj.up.{weight,bias}
lllite_dit_blocks_{i}_self_attn_q_proj.depth_embed
...

The expected safetensors metadata keys are:

• lllite.version (= "2")
• lllite.cond_emb_dim, lllite.mlp_dim
• lllite.cond_dim, lllite.cond_resblocks
• lllite.use_aspp, lllite.aspp_dilations
• lllite.target_atomics (canonical, comma-separated atomic specifiers) — falls back to lllite.target_layers for older v2 snapshots
• lllite.cond_in_channels ("3" standard / "4" inpaint; defaults to 3)
• lllite.inpaint_masked_input ("true" / "false"; defaults to false, only meaningful when cond_in_channels=4)

Legacy weights with lllite_modules.{i}.* keys (the pre-v2 format) are rejected on load. Re-train against the current sd-scripts codebase.

Credits

• LLLite design and Anima training implementation: kohya-ss
• Adapted for ComfyUI from sd-scripts
• ComfyUI port written collaboratively with Claude (Anthropic, claude-opus-4-7)