A collection of ComfyUI nodes for modern text and multimodal conditioning, image and mask processing, prompt presets, workflow parameters, loading, and general utilities. The encoder nodes track current ComfyUI Core behavior while retaining compatible legacy node IDs where practical.
The list below uses the canonical node IDs. Deprecated compatibility aliases remain registered for existing workflows but are not duplicated here.
UC_TextEncodeSystemPromptUC_TextEncodeLtxv2SystemPromptUC_WeightedTextEncodeSystemPromptUC_TextEncodeSystemEditAdvancedUC_TextEncodeGemmaSystemEditAdvancedUC_AdvancedVisualConditioningEncodeUC_AdvancedVisualConditioningEncodeTokenFusionUC_AdvancedMiniMaxH3ImageToVideoUC_AdvMiniMaxH3ImageToVideoTokenFusionUC_AdvancedMiniMaxH3ImageToVideoCombinedUC_AdvMiniMaxH3ImageToVideoCombinedTokenFusionUC_MiniMaxH3MediaConfigUC_MiniMaxH3FirstFrameReferencesUC_AdvancedVisConEncoderUC_AdvancedVisConEncoderTokenFusionUC_VisualConsensusConfigurationUC_AdvancedConsensusConfigurationUC_Krea2TokenAttentionWeightUC_Krea2TokenAttentionWeightTokenFusionUC_AttentionBiasTextEncodeUC_TextConsensusBlendConfigUC_VisualFusionConfigUC_ConditioningConsensusBlendUC_VLMInputEmbedsUC_Krea2LayerProbeUC_Krea2LayerAblatorUC_MiniMaxH3ClipProjectionPatcherUC_EncoderNodesGuide
UC_MiniMaxH3ClipProjectionPatcher projects a Qwen3-VL 4B or 8B text encoder into MiniMax H3's 32B conditioning space. Load the encoder with Core's Load CLIP node using type minimax, then connect it to the projection patcher.
Download one projection matching the encoder size into ComfyUI/models/clip_projections/:
- Qwen3-VL 4B v3.1
- Qwen3-VL 4B v3.1 with residual MLP
- Qwen3-VL 8B v3.1
- Qwen3-VL 8B v3.1 with residual MLP
Only one projection is used at a time. The control and obsolete/ files in the model repository are not normal generation models.
The optional MiniMax H3 Media Configurator controls separate Picture and Video timestamp syntax. Configured Video timestamps map one-to-one to already-selected images; without them, the Video input is a full 24 fps batch using ComfyUI Core H3 sampling, syntax, and native conditioning. vlm_resolution controls Picture token detail while vlm_video_resolution independently controls Video token detail. Video blocks remain outside Picture fusion. Audio and its MiniMax H3 audio VAE connect directly to each advanced main node and produce a standalone native audio reference plus Qwen Audio label.
Workflow JSON | API workflow JSON | Workflow overview | Reference images | Turbo LoRA used
This example uses eight chronological storyboard frames as 1024-resolution Qwen3-VL/DeepStack references. The prompt associates each ordered <Picture N> entry with a target timestamp. With ref_image_size set to none, the images provide visual-token conditioning without VAE reference encoding.
The workflow demonstrates strong subject, composition, and approximate timeline control without a native reference video. Its eight images reproduced the main framing and progression of a 12.25-second source sequence in seven sampling steps on a 16 GB GPU. Picture timestamps are prompt instructions, not fixed frame anchors, so results remain stochastic.
Extract the separately hosted reference-image ZIP into ComfyUI/input before loading either workflow.
The workflow uses Core's Create Video and Save Video nodes and requires no other custom-node collection.
For headless use, start ComfyUI with its API reachable, extract the reference ZIP, then run:
python workflows/UC_AdvancedMiniMaxH3ImageToVideo/run_api_workflow.py C:\path\to\reference-imagesThe standard-library runner uploads the eight images, substitutes the returned server filenames into the unchanged API workflow, queues it, waits for completion, and prints the saved-output metadata. Use --server http://host:8188 for another ComfyUI server and --seed N to override the workflow seed.
UC_AdvancedVisConEncoder runs two sequential stages: it first constructs a
complete spatially fused conditioning independently at every selected VLM
resolution, then passes those complete conditionings through the same consensus
mathematics as UC_ConditioningConsensusBlend. Spatial fusion and consensus
are not alternatives and are never crossfaded.
UC_AdvancedVisConEncoderTokenFusion is the additive token-first alternative.
At each lane and resolution it fuses per-source visual and DeepStack tokens,
runs one conditioning encode, then applies the same complete-conditioning
consensus across resolution samples. The original node remains unchanged.
Use UC_VisualConsensusConfiguration to combine one complete
UC_VisualFusionConfig with one UC_AdvancedConsensusConfiguration. Fusion
method off disables the spatial stage; consensus preset off disables the
cross-resolution consensus stage. Advanced Consensus Configuration inherits
the complete Text Consensus Blend Configurator contract and adds
resolution_samples plus a 32-aligned sample_offset.
block_size is specific to block-interleave. dither_ratio and
dither_secondary_pattern are specific to random-dither. Advanced Consensus
Configuration exposes resolution_samples and sample_offset. Offset defaults
to 32 and supports 32 through 512 in 32-unit steps. The configured sample
count is exact, so 1 remains one
resolution sample regardless of visual-source or batch-lane count. Original VLM
resolution supports one sample but cannot construct adjacent resolution
variants.
A batch in the only connected image socket behaves like its images were connected as separate visual sources. With multiple connected batched sockets, equal indices form independent lanes, singleton sockets broadcast, and all other batch lengths must match. Raw visual export uses the same spatial mask as the base-resolution conditioning fusion.
UC_Image_Color_NoiseUC_ExtractPrevalentColorsUC_ModifyMaskUC_SAM31CheckpointLoaderUC_MaskToBoundingBoxUC_ImageBlendByMaskUC_ImagePadUC_NoHaloLoHaloDownscaleUC_CropByMaskUC_StagedLayerCropsUC_ImageCropMergeUC_ExtractMaskUC_ExtractImageUC_ImageAndMaskResizeUC_ResizeMaskUC_BackgroundRemovalPreserveAlphaUC_FaceRemovalPreserveAlphaUC_UnifiedBackgroundReplaceUC_StagedLayeredBackgroundCompositeUC_StagedIndividualCompositesUC_StagedLayeredBackgroundCompositeOptionsUC_StagedMediaPipeFaceBackgroundCompositeUC_StagedMediaPipeFaceOptionsUC_LayeredBackgroundCompositeUC_MediaPipeFaceCompositeOptionsUC_MediaPipeFaceCompositeUC_ListToImageBatchUC_ImageMatchPropertiesUC_OpticalFlowCompositeUC_ImageInwardEdgeFillUC_ImageIterativeStretchFillUC_TextOverlayNodeUC_CompositeNodesGuideUC_LaMaInpaint
UC_StagedLayeredBackgroundComposite builds a scene from a background and ordered foreground sockets. Use run_staging to retain cutouts and populate the placement editor. Use run_staged to composite retained cutouts without loading models or evaluating foreground branches. Use full_run to restage and composite in one queue. foreground_0 is the backmost layer. Retained cutouts are held in server memory and must be recreated after restarting ComfyUI.
UC_StagedMediaPipeFaceBackgroundComposite detects faces in each foreground and adds them as independently placeable layers. The background and face options nodes contain removal, extraction, feathering, and blend settings. UC_StagedIndividualComposites provides the same ordinary foreground staging editor but returns one full-background image, placement mask, and box per included foreground without stacking them. UC_BackgroundRemovalPreserveAlpha directly returns source-resolution RGBA images and their soft alpha masks; existing RGBA inputs keep their supplied alpha without model execution. UC_FaceRemovalPreserveAlpha returns expanded face crops as RGBA images with matching alpha masks and transparent padding for differently sized batched crops.
UC_LoadLaMaModel loads Big LaMa .safetensors files from ComfyUI/models/lama through Unified Efficient Loader. Connect its LAMA_MODEL output to UC_LaMaInpaint. Device choices include ComfyUI's default device, CPU, and every visible GPU. Models are never downloaded automatically. Download Big LaMa or Anime/Manga Big LaMa, then place the selected file in ComfyUI/models/lama.
Workflow JSON | Workflow overview | Source assets
UC_AdjustedResolutionParametersUC_ResolutionSelectorExtendedUC_VideoResolutionSelectorUC_ImageScaleAndResolutionPickerUC_SwitchInverseNodeUC_SoftSwitchInverseNodeUC_IntegerRangeRandomUC_RandIntUC_StaticIntUC_StaticFloatUC_RandIntRangeUC_ColorConvertNodeUC_SeedClusterUC_FromSeedClusterUC_ExtractBoundingBoxUC_AdjustBoundingBoxUC_Ideogram4BoundingBoxCropUC_Ideogram4DebannerPatchUC_HighResolutionTileSplitUC_HighResolutionTileAccumulatorUC_HighResolutionTilingGuide
UC_SystemMessagePresetsUC_SystemMessageVideoPresetsUC_InstructPromptPresetsUC_InstructPromptVideoPresetsUC_BonusPromptPresetsUC_BonusPromptVideoPresetsUC_EditTargetPresetsUC_EditOpPresetsUC_CameraShotPresetsUC_VLMSysInstrPresetsUC_VLMSysInstrPresetsExperimentalUC_VLMSysInstrLegacyPresetsUC_VLMSysQueryAddPresetsUC_VLMSysQueryRawPresetsUC_VLMSysInstrAdvPresetsUC_VLMSysInstrAdvPresetsExperimentalUC_MiniMaxH3VLMSysInstrPresetsUC_MiniMaxH3VLMSysInstrPresetsExperimentalUC_MiniMaxH3VLMSysInstrAdvPresetsUC_MiniMaxH3VLMSysInstrAdvPresetsExperimentalUC_LegacyPromptPresetsUC_UnifiedPresets
UC_LoadImagePathUC_LoadImageDirectoryUC_LoadImageWithAlphaUC_SampleVideoFramesAsImagesUC_ImagesToVideoTimelineUC_VideoTimelineTextUC_LoraLoaderCLIPOnlyUC_LoadLaMaModelUC_TextGenerateUC_TextGenerateQwen35SystemPromptUC_EmbeddingDetokenizerAnalysisUC_ImageToVideoPromptUC_TagNormalizeCombineUC_FromListUC_GetJsonValueUC_MiniMaxH3CacheUC_MiniMaxH3SlaAttentionConfigUC_MiniMaxH3SpectrumUC_MiniMaxH3PDDAccUC_UnifiedAttentionPatcherUC_MarkdownPreviewUC_BoldFrakturTextStyleUC_UnBoldFrakturTextStyleUC_WordJoinerUC_UnWordJoinerUC_JSONMinifyRepairUC_StringUnescapeUC_TextConcatenateAutogrowUC_TextConcatenateListsAutogrowUC_Newline
Download the PDD Acc file matching the MiniMax H3 diffusion model:
Place the downloaded .safetensors file in ComfyUI/models/loras, or in the
configured external directory used by ComfyUI's loras model category. Restart
ComfyUI or refresh model files, then select it in UC_MiniMaxH3PDDAcc.
UC_UnifiedAttentionPatcher returns a cloned model with one selected attention
backend. Connect its model output in place of the original model. The
disabled mode returns the input model unchanged.
| Attention mode | Applies to | Optional runtime requirement | Behavior |
|---|---|---|---|
FlashAttention |
Attention calls without a mask | A compatible package providing flash_attn or flash_attn_interface |
Uses FlashAttention. allow_compile permits compilation after the initial run. |
SageAttention |
General model attention | sageattention; sageattn3 or sageattn3_per_block_mean additionally need sageattn3 |
Select a Sage kernel from sage_mode. allow_compile permits compilation after the initial run. |
Sparse / MiniMax H3 SLA |
MiniMax H3 self-attention only | CUDA and Triton | Routes each H3 attention block to selected key blocks while retaining dense attention where sparse routing is unsuitable. |
SLA uses its selected dense_backend for deliberate dense steps and sparse
fallbacks. auto retains the incoming ComfyUI-selected attention callable. No
attention package, model checkpoint, or LoRA is downloaded by this node.
h3_memory_optimizations is available only inside the SageAttention mode. It
requires a CUDA MiniMax H3 model and a compatible SageAttention installation.
It reduces the H3 attention path's peak memory use; selecting it for another
model raises an error rather than silently applying a different patch.
SLA is experimental and only patches MiniMax H3 models with 128-dimensional
attention heads. It does not modify ComfyUI Core files or model weights. The
minimax_h3_sla_config input on UC_UnifiedAttentionPatcher is optional.
Connect UC_MiniMaxH3SlaAttentionConfig to configure its documented controls;
omitting it uses their defaults.
- Main SLA controls:
sparsity: fraction of ordinary key blocks skipped. Start with the default0.90; compare output and speed against dense attention for each model, resolution, duration, and sampler.block_size: routing granularity. Smaller blocks retain finer temporal and audio detail at additional routing cost.dense_tail_steps: final sampler steps retained on dense attention for detail recovery. Set0to allow sparse routing at every eligible step.protect_reference_media:Offadds no visual-reference quota;Lightretains the best-scoring 15% of each reference block range;Heavy Enforcementretains every reference block.dense_backend:comfy_kitchen,auto,pytorch, or a value fromCUSTOM_SAGE_MODES. A selected unavailable backend raises an actionable error before sampling.
UC_MiniMaxH3SlaAttentionConfigcontrols:minimum_sequence_length: sequences below this threshold stay on the selected dense attention path.dense_steps: comma-separated zero-based steps or inclusive ranges kept dense, such as0,3-5. Default0preserves the first sampling step.protect_audio: preserves text and audio ranges in every sparse key selection.disable_fp16_accumulation: disables FP16/BF16 reduced-precision matmul accumulation for this SLA sampling run, then restores prior settings.stabilize_routing: biases near-cutoff block selection toward the prior sampling step. Use it only when motion detail is unstable; it retains a bounded routing history while sampling.
SLA calls stay dense when the call is masked, not MiniMax H3 packed
self-attention, uses an unsupported dtype/device, falls below the minimum
sequence length, falls in dense_steps or the configured dense tail, lacks
MiniMax H3 layout metadata, or when the sparse kernel fails. Each reason is
reported once in the ComfyUI console for that sampling run. A sparse-kernel
failure also disables further sparse attempts for the remainder of that run.
This preserves a usable model path when SLA cannot apply, but it also means a
run may receive less acceleration than its selected sparsity suggests.
Ideogram4SchedulerPresetUC_SigmaRescaleUC_DiscardPenultimateSigmaUC_SigmoidOffsetSchedulerUC_PowerShiftSchedulerUC_RadianceShiftSchedulerUC_SigmaCurveFromPointsSchedulerUC_SigmaCurvePchipScheduler
The migrated schedulers also register sigmoid_offset, power_shift,
radiance_shift, sigma_curve_from_points, and sigma_curve_pchip for Core
scheduler selectors. The Power Shift scheduler was inspired by
InverserSquaredScheduler.
UC_SigmaRescale maps an existing schedule to exact start and end sigma
values without changing its shape or number of steps.
The dedicated scheduler nodes do not include Core-style denoise controls or
optional penultimate-sigma controls. Connect UC_SigmaRescale after a
scheduler when setting image-to-image noise levels. Connect
UC_DiscardPenultimateSigma when the selected sampler requires penultimate
sigma removal. Radiance Shift performs its required compensated removal
internally. Sigmoid Offset retains its model-specific start_sigma
adjustment.
UC_LogicIFUC_LogicANDUC_LogicORUC_LogicNOTUC_LogicXORUC_MathAddUC_MathSubtractUC_MathMultiplyUC_MathDivideUC_MathPowerUC_MathFloorUC_MathCeilUC_MathRoundUC_MathModuloUC_MathAbsUC_MathSqrtUC_MathSinUC_MathCosUC_MathTanUC_MathMinUC_MathMaxUC_MathClampUC_MathNumberConvertUC_StringToNumberUC_NumberToStringUC_MathCompareUC_MathOperationUC_MathAspectRatio
These nodes replace the equivalent nodes from ComfyUI-LogicMath, ComfyUI_SigmoidOffsetScheduler, and ComfyUI_PowerShiftScheduler. Remove the standalone pack before accepting ComfyUI's workflow replacement prompt.