ComfyUI-GGUF-Loader

GGUF Quantization support for native ComfyUI models — the CCTech Suite fork of city96/ComfyUI-GGUF.

These custom nodes provide support for model files stored in the GGUF format popularized by llama.cpp.

While quantization wasn't feasible for regular UNET models (conv2d), transformer/DiT models such as flux seem less affected by quantization. This allows running it in much lower bits per weight variable bitrate quants on low-end GPUs. For further VRAM savings, a node to load a quantized version of the T5 text encoder is also included.

Note: The "Force/Set CLIP Device" is NOT part of this node pack. Do not install it if you only have one GPU. Do not set it to cuda:0 then complain about OOM errors if you do not undestand what it is for. There is no need to copy the workflow above, just use your own workflow and replace the stock "Load Diffusion Model" with the "Unet Loader (GGUF)" node.

What this fork adds

Open upstream PRs backported, including newer architectures (LTX-2, Z-Image, Ideogram-4, Qwen3-VL / MiniMax-H3 text encoders). See PR_BACKPORT.md.
MiniMax-H3 fix: high-precision buffers such as adaln_t_table are kept in float32 through both conversion and loading. Stored as F16 they crash the sampler on the first step with expected dtype struct c10::Half for 'weight' but got dtype float.
Convenience pipelines: Scenema Audio and MiniMax Music 3 loaders/generators built on ComfyUI's native models.

Installation

Important

Make sure your ComfyUI is on a recent-enough version to support custom ops when loading the UNET-only.

To install the custom node normally, git clone this repository into your custom nodes folder (ComfyUI/custom_nodes) and install the only dependency for inference (pip install --upgrade gguf)

git clone https://github.com/ChrisColeTech/ComfyUI-GGUF-Loader

To install the custom node on a standalone ComfyUI release, open a CMD inside the "ComfyUI_windows_portable" folder (where your run_nvidia_gpu.bat file is) and use the following commands:

git clone https://github.com/ChrisColeTech/ComfyUI-GGUF-Loader ComfyUI/custom_nodes/ComfyUI-GGUF-Loader
.\python_embeded\python.exe -s -m pip install -r .\ComfyUI\custom_nodes\ComfyUI-GGUF-Loader\requirements.txt

On MacOS sequoia, torch 2.4.1 seems to be required, as 2.6.X nightly versions cause a "M1 buffer is not large enough" error. See this upstream issue for more information/workarounds.

Usage

Simply use the GGUF UNET loader found under the 🤖 CCTech/GGUF category. Place the .gguf model files in your ComfyUI/models/unet (or diffusion_models) folder.

LoRA loading is experimental but it should work with just the built-in LoRA loader node(s).

Pre-quantized models:

Initial support for quantizing T5 has also been added recently, these can be used using the various *CLIPLoader (gguf) nodes which can be used inplace of the regular ones. For the CLIP model, use whatever model you were using before for CLIP. The loader can handle both types of files - gguf and regular safetensors/bin.

t5_v1.1-xxl GGUF

See the instructions in the tools folder for how to create your own quants.

Nodes

The GGUF loaders live under 🤖 CCTech/GGUF; Scenema Audio under 🤖 CCTech/Scenema; MiniMax Music 3 under 🤖 CCTech/MiniMax Music; LTX-2.3 A/V under 🤖 CCTech/LTX-2.3; LTX-2.5 A/V under 🤖 CCTech/LTX-2.5; local LLM/VLM prompting under 🤖 CCTech/LM Studio; Qwen3-TTS under 🤖 CCTech/Qwen TTS; TripoSG image-to-3D under 🤖 CCTech/TripoSG; UniRig auto-rigging under 🤖 CCTech/UniRig; TRELLIS.2 / Pixal3D per-stage loaders under 🤖 CCTech/TRELLIS.2.

Node	Purpose
UNET Loader (GGUF)	drop-in replacement for `Load Diffusion Model`
UNET Loader (GGUF/Advanced)	same, with `dequant_dtype` / `patch_dtype` / `patch_on_device` exposed
CLIP Loader (GGUF)	single text encoder, `.gguf` or `safetensors`
Dual / Triple / Quadruple CLIP Loader (GGUF)	the same for models taking several encoders
Dual VAE Loader (video + audio)	two VAEs from one node, with the outputs named for their streams
Text Encoder + ClipProj Loader	loads a small encoder, GGUF included, and projects it into a large one's space
Scenema Models Loader	the Scenema Audio stack from your own folders — DiT, Gemma-3 text encoder (safetensors or GGUF), pipeline checkpoint, VAE encoder
Scenema VAE Encode (voice reference)	reference clip → audio latent for voice cloning
Scenema Audio Generate	expressive TTS with presets, scene/language, chunking and A2V voice cloning
Scenema Audio Voice Clone	standalone offline SeedVC identity transfer
MiniMax Music 3 Models Loader	loads the pruned AR conditioner, flow DiT, and DAV decoder from explicit dropdowns
MiniMax Music 3 Audio Generate	caption + structured lyrics → 44.1 kHz stereo music, including AR and DiT controls

Dual VAE Loader

MiniMax-H3 decodes video and audio through two separate VAEs, so every workflow ends up with a pair of VAELoader nodes side by side. This is that pair in one node, with outlets named video_vae and audio_vae. Loading is delegated to the stock loader, so taesd entries stay available and nothing behaves differently.

Text Encoder + ClipProj Loader

Loads a small text encoder and projects it into a large one's space in a single node — for MiniMax-H3, a Qwen3-VL-4B or -8B standing in for the 32B the model expects, which takes the text encoder from 15.7 GB to about 5.

The projection lives in clipproj.py, so there is no dependency on another node pack.¹ What you do need is a matrix, in ComfyUI/models/clip_projections/: mmh3-4b-* goes with a 4B and mmh3-8b-* with an 8B, and they are not interchangeable. The <control:...> entries are not projections but deliberate baselines — zero ignores your prompt entirely, identity copies the raw dimensions with no learning — and they run on any encoder. Run them first to see that the matrix is doing the work.

Differences from the upstream node:

It reads GGUF. Routing is by extension: .gguf through this pack's loader, everything else through ComfyUI's stock one — deliberately not through the GGUF path, which refuses scaled-fp8 checkpoints, and qwen3vl_*_fp8_scaled is the encoder most people have.
type defaults to auto and is checked against the file even when set by hand. Detection reads headers only, so a 10 GB encoder costs nothing to identify — and an mmproj file, which is the vision projector alone with no text model in it, is refused before the load instead of surfacing later as a missing-attribute error.
No device or mode widget. Those exist upstream for multi-GPU pinning, which its own README calls actively harmful on a single card: a pinned encoder holds 4–9 GB away from the diffusion model at every sampling step. This node uses ComfyUI's normal paging. If you do want an encoder pinned to a chosen card, install the upstream pack and use its ClipProj Device Loader → ClipProj Apply.

Scenema Audio

Ported from ScenemaAI/ComfyUI-ScenemaAudio (MIT), rebuilt on ComfyUI's native LTX-AV machinery and this pack's loaders with no HuggingFace runtime downloads. The SeedVC post-pass reuses Comfy's Whisper and BigVGAN implementations and includes only the checkpoint-specific architecture that Comfy core does not provide. Point the nodes at the same components the sidecar uses (from ScenemaAI/scenema-audio):

File	Folder	Dropdown
`scenema-audio-transformer-int8.safetensors` (or bf16, or a GGUF quant)	`models/diffusion_models` (unet)	transformer
`gemma-3-12b-it-*.gguf` (or a safetensors single-file Gemma-3 12B)	`models/text_encoders` (clip)	text encoder
`scenema-audio-pipeline.safetensors`	`models/vae`	pipeline
`scenema-audio-vae-encoder.safetensors`	`models/vae`	VAE encoder

The Models Loader outputs plain comfy MODEL / CLIP / VAE: the audio DiT loads as a comfy LTX-AV model with the video paths gated off (the comfy-native equivalent of the original nodes' audio-only monkey-patch), the Gemma text encoder runs as comfy's LTXAVTEModel — a GGUF stays quantized through this pack's ops, with the tokenizer rebuilt from the GGUF metadata — and the VAE is comfy's AudioVAE (encoder + decoder + BigVGAN vocoder with the 16 kHz → 48 kHz bandwidth extension, so output is 48 kHz stereo). The pipeline checkpoint also supplies the text projection and embeddings connectors. keep_loaded (on by default) caches the built model/clip/vae keyed on the four filename dropdowns, so re-queuing with the same selections skips rebuilding from disk — the DiT alone is 7-10 GB and the text encoder ~24 GB, and unlike LTXV23ModelsLoader's text-encoder cache, this loader previously cached nothing at all. Turn it off to force a genuine reload, e.g. after replacing a file on disk without renaming it.

Generate keeps the original node's behaviour: the <speak> XML prompt compiler (with [bracketed cues] and per-line action tags), the 12 production presets, scene/language dropdowns, pace, seed, automatic sentence-boundary chunking with A2V voice chaining between chunks, and per-chunk trim/normalize before concatenation. Optional ref_latent (from Scenema VAE Encode fed by LoadAudio) gives zero-shot A2V cloning. A final offline SeedVC pass provides fixed identity consistency for multi-chunk output or an explicit identity_reference; Scenema Audio Voice Clone exposes the same conversion independently. The pass is seeded from the workflow seed, and Generate keeps the unpolished audio if conversion fails or returns anything other than a same-shape, same-length, audible waveform. Place the seedvc, campplus, bigvgan, and whisper-small folders from ChrisColeTech/scenema-audio extras under models/scenema-audio/extras. Whisper word-match validation and the MelBandRoFormer SFX strip remain unported.

A CPU smoke test for the load paths lives at tools/smoke_scenema.py.

LTX-2.3 A/V

For LTX-2.3-family A/V checkpoints split into components — built for the 10Eros v1.4 distilled kit (distill LoRA pre-fused offline), works for any split with the same layout. One loader builds the whole stack on ComfyUI's native LTX-AV machinery:

File	Folder
`10Eros_v1.4_distilled-r72_Q4_K_M/Q6_K/Q8_0.gguf` or `..._fp8mixed.safetensors`	`models/diffusion_models` (unet)
`gemma-3-12b-*-Q4_K_M.gguf` (or a Gemma-3 12B safetensors)	`models/text_encoders`
`10Eros_v1.4_projections.safetensors` (text_embedding_projection)	`models/text_encoders`
`10Eros_v1.4_video_vae.safetensors`	`models/vae`
`10Eros_v1.4_audio_vae.safetensors`	`models/vae`

The GGUF DiTs carry their transformer config as a GGUF KV — no metadata sidecar to lose — so comfy builds the real LTX-2.3 geometry (48 layers, 9-row modulation tables, 4096/2048 embeddings connectors) instead of guessing the older LTX-2 layout. GGUF and fp8 weights stay quantized; the loader outputs plain comfy MODEL / CLIP / VAE plus the audio VAE, so everything composes with core nodes.

Sampling: CLIPTextEncode → LTX-2.3 Img/Audio to Video (one prep node: leave both optionals unconnected for txt2video, connect image for i2v, connect reference_audio for lip-synced audio-to-video — the official IA2V recipe, with the audio encoded and noise-mask-locked and the video length derived from the clip) → LTX-2.3 KSampler (distilled) or LTX-2.3 Two-Stage Sampler (base + refine). Both pass the exact LTX-2 distilled schedules (cfg 1.0, euler) and stamp frame_rate onto the conditioning for RoPE; the two-stage sampler additionally runs the official 3-step refine pass after a spatial ×2 latent upscale (LatentUpscaleModelLoader's output) in one node, splitting and rejoining the video/audio branches around the upscale model for you. Finish with LTX-2.3 AV Decode (video + audio VAE decode + mux, one fps) or, by hand, core LTXVSeparateAVLatent → VAE Decode + LTXVAudioVAEDecode → CreateVideo.

For an ID-LoRA talking-head pipeline (photo + reference voice → lip-synced video, e.g. the ltxv23_talking_head gallery workflow): stack a distilled LoRA (~0.5 strength) and an ID-LoRA (~1.0 strength) onto the model with core LoraLoaderModelOnly ×2, and set the reference voice with core LTXVReferenceAudio before the sampler — both are already correctly served by stock nodes, no CCTech wrapper needed.

LTX-2.3 Video to Video (IC-LoRA) is LTXV23ImgToVideo's superset, with the IC-LoRA task adapter loaded directly in this one node — no external LoraLoaderModelOnly wiring needed, no separate "is X attached" boolean toggles. model is now required in and (possibly patched) out; mode (t2v/i2v/v2v) declares which base behavior this call is and is validated against what's actually connected (i2v needs images, v2v needs video, t2v rejects either being connected) — images and video can still layer on top of each other exactly as before (a still for identity/framing plus a clip for the task transform) once the primary mode's requirement is satisfied.

Video-to-video via IC-LoRA (task adapters like beard removal, HDR grading, motion tracking — any LTX-2.3 IC-LoRA trained on whole-video reference conditioning) needs a genuinely different mechanism from images/reference_audio: the source video's own frames get VAE-encoded and appended as extra reference tokens the model cross-attends to at the same timeline position as what it's generating (comfy-core's keyframe_idxs RoPE mechanism), then cropped back off after sampling — not partially denoised, not held as a first frame. The official example workflows wire this by hand across ~10 nodes (ResizeImageMaskNode → GetImageSize → EmptyLTXVLatentVideo → LTXVConditioning → LTXAddVideoICLoRAGuide → VAEEncodeAudio → LTXVSetAudioRefTokens → LTXVConcatAVLatent); this node collapses all of that into one prep step. ic_lora (a dropdown over comfy's real loras folder, default none) picks the IC-LoRA task adapter and loads it onto model inside this node (comfy-core's real LoraLoaderModelOnly delegation, at ic_lora_strength) before injecting video's frames as the guide — none means video, if connected, is only used for length/frame_rate/original audio, ignored for guidance (useful for A/B-ing whether the IC-LoRA is doing anything). keep_original_audio (default on, only relevant with video) keeps the source clip's own audio unchanged in the output, same "generate video to match this fixed audio" mechanism reference_audio uses for a standalone reference clip. latent_downscale_factor only matters for the rare IC-LoRA trained on a downscaled reference grid — check the LoRA's model card (most, including every official example, use 1.0). Feed the outputs straight into the same LTX-2.3 KSampler (distilled) used above (it already samples any joint AV latent generically, no separate vid2vid sampler needed), then LTX-2.3 Crop Video Guide (strips the appended reference frames back off — a no-op if none were appended) before LTX-2.3 AV Decode.

Full-body person replacement (remove → pose-swap → blend, verified end-to-end on real GPU)

Replacing a person in a video while keeping their exact movements is a composite of three verified stages — no single LTX-2.3 LoRA does it in one pass:

Stage 1 — remove the original person. LTX-2.3 Remove Person (inpaint) ports Wan2GP's trained green-fill recipe: the per-frame mask is dilated 5px, painted chroma green #66FF00, green-padded at the tail, and appended as clean guide tokens at strength exactly 1.0 with ltx-2.3-22b-ic-lora-in-outpainting-0.9.safetensors (from DeepBeepMeep/LTX-2, loaded in-node) — the LoRA learned "green = regenerate"; the mask deliberately never touches the denoise mask. Sample with LTXV23KSampler (distilled (8 steps)), crop, decode, then LTX-2.3 Mask Blend composites the result back over the pristine originals (7-level Laplacian pyramid, soft low-res mask skirt, sanitize-source) so everything outside the mask stays pixel-identical. Masks come from comfy-core's own SAM3 nodes (SAM3_Detect with a CLIPTextEncode prompt like "woman", on the frames from GetVideoComponents) — download sam3.1_multiplex_fp16.safetensors from Comfy-Org/sam3 into models/checkpoints.

Stage 2 — motion-preserving swap (pose control). The mechanism that actually reproduces the original person's exact movements is pose control (a raw-clip guide keeps the source person's identity at any guide strength, and a green-silhouette fill carries position but not limb pose — both dead ends tested with real frames). Render the source clip as an OpenPose skeleton video (body+hands — ComfyUI-ControlNet-Nodes' OpenPose), then run LTXV23VidToVideo in i2v mode: images = a start frame with the NEW person already composited into the scene in the source's opening pose (the identity anchor, held at strength 1.0), video = the skeleton clip, ic_lora = ltx-2.3-22b-ic-lora-union-control-ref0.5.safetensors @ 1.0, and — critically — latent_downscale_factor = 2.0: the ref0.5 suffix means the LoRA was trained with its control video conditioned at half resolution, and a full-res skeleton guide makes the model reconstruct the skeleton instead of following it. Width/height must be divisible by 64 so the half-res guide lands on whole latents (the node errors clearly if not).

Stage 3 — restore the original background. SAM3_Detect ("woman") on the generated frames masks the new person; LTX-2.3 Mask Blend then composites her onto Stage 1's clean plate, so everything outside her silhouette is the original video's own pixels. LTXV23RemovePerson's optional start_image input is also available for anchor-based inpaint swaps (the control video opens on the provided frame verbatim, no green fill on frame 0).

Audio: wire the frame_rate outputs into every AV-decode/CreateVideo fps input (never hardcode - a 30fps source muxed at 24fps drifts audio progressively out of sync), and mux the ORIGINAL clip's waveform (GetVideoComponents on the source LoadVideo -> final CreateVideo's audio) into the final output rather than the decoded audio latent - keep_original_audio holds audio through the audio VAE for conditioning, but that round-trip (twice, in a two-stage graph) is a lossy reconstruction, not the original track. The installed workflow wires both correctly (verified: 30fps output matching a 30fps source, audio loudness profile identical to the original).

The installed workflow ltxv_v2v_test.json is the full 25-node composed graph (remove → pose-swap → background blend), verified end-to-end with frame-by-frame comparison: the reference-identity person performs the source's exact choreography in the original scene, with the original audio muxed in.

LTX-2.3 ID-LoRA Prompt Editor reviews and edits a captioner's generated [VISUAL]/[SPEECH]/[SOUNDS] block before it's used. Wire a captioner (e.g. LMStudioVisionPrompt) into its source socket; the three multiline boxes fill with the parsed fields after a run and stay directly editable. Type over any of them and the edit survives later runs; generate a genuinely new caption and all three refresh to it. Outputs tagged_prompt (the reassembled [VISUAL]: .../[SPEECH]: .../[SOUNDS]: ... string for CLIPTextEncode) plus visual_text/speech_text/sounds_text individually — wire speech_text straight into a TTS node's text input so what you typed is exactly what gets spoken.

The keep-the-edit-vs-refresh decision is made in Python by remembering the source this node last parsed (keyed by node id), not guessed from whether a box looks empty — and the paired web/ JS then writes the resolved values back unconditionally. Comfy-core has no widget that is auto-filled and editable and edit-preserving (its only populate-from-execution widget, TEXT_PREVIEW behind PreviewAny/SaveText, is hard-coded read-only), so this is genuinely custom. Note the parser stops each field at the next [TAG]: marker rather than end-of-string, so a middle section like [SPEECH] can't swallow [SOUNDS] after it.

A 5th output, speech_text_batch, splits [SPEECH] into one clip per non-blank line (a blank line is a separator, not an empty clip) and carries them as a real comfy list — OUTPUT_IS_LIST = (False, False, False, False, True) — rather than a delimited string. Pair it with LTX-2.3 Speech Batch Selector, which takes that list plus an index (negative counts from the end like Python; out-of-range clamps instead of erroring) and outputs the clip at that position plus count (the batch's total length, for driving a for-each loop) — INPUT_IS_LIST = True so it receives the whole batch in one call instead of comfy fanning out a separate call per clip.

LTX-2.3 ID-LoRA Assembler is the Editor's formatting step exposed standalone: three plain visual/speech/sounds STRING inputs in, one formatted [VISUAL]: .../[SPEECH]: .../[SOUNDS]: ... string out — no source, no parsing, no edit-state. For when you already have the three pieces from elsewhere (e.g. a clip picked via LTXV23SpeechBatchSelector, or hand-typed values) and just need them combined, rather than parsed apart from a captioner's raw output.

tools/smoke_ltx23.py validates every kit file's load path (both DiT formats, all three quants, TE + projections, both VAEs) without sampling. tools/smoke_id_lora_prompt_editor.py covers the parser, every state-machine transition (first run empty/filled, edit preserved, source changed, per-node isolation), the batch split, and the selector's indexing/clamping — no GPU required; the JS write-back itself needs a browser to confirm.

LTX-2.5 A/V

The same one-loader-plus-recipe-nodes treatment for LTX-2.5-family A/V checkpoints, under 🤖 CCTech/LTX-2.5 — a faithful mirror of ComfyUI's official video_ltx2_5_i2v workflow (verified end-to-end on real GPU, including the GGUF DiT path). LTX-2.5 is a different animal from 2.3 (128-channel /32-spatial /8-temporal video latents, a Gemma-4-12B text encoder with projection, its own VAE pair and trained schedules), which is why loading a 2.5 checkpoint in the 2.3 nodes errors — this family is the supported path.

LTX-2.5 Models Loader (LTXV25ModelsLoader): DiT (comfy-int8 safetensors or Q6_K/Q8_0 GGUF — GGUF stays quantized), the gemma4-12b-with-proj text encoder, video VAE, audio VAE → MODEL / CLIP / VAE / VAE, with the same single-entry encoder cache as the 2.3 loader. Validates the checkpoint really is an ltxav A/V model and that the two VAEs aren't swapped.
LTX-2.5 Img to Video (LTXV25ImgToVideo): prompts + the stage-1 init latent for t2v/i2v (mode validated against what's connected). width/height are the FINAL resolution — stage 1 samples at half (the official recipe's a/2 math) and the latent upscaler doubles it back. i2v runs core LTXVPreprocess (img_compression 18) then core LTXVImgToVideoInplace's exact in-place first-frame hold at image_strength 0.7 (the workflow's link-traced stage-1 value). One or more LoRAs chain externally through core LoraLoaderModelOnly nodes on the model wire — exactly the ltx23 convention and the official workflow's own wiring (LTX-2.3-ID-LoRA-TalkVid-3K @ 0.6 → LTX-2-Image2Vid-Adapter @ 0.6; cross-version 2.3 LoRAs apply cleanly to the 2.5 DiT, zero key mismatches).
LTX-2.5 Video to Video (IC-LoRA) (LTXV25VidToVideo): LTXV23VidToVideo's full surface, mirrored widget-for-widget onto the 2.5 recipe — mode (t2v/i2v/v2v), video + ic_lora/ic_lora_strength/guide_strength/latent_downscale_factor (the same named in-node IC-LoRA selector, guide tokens appended via core LTXVAddGuide on the stage-1 half-res grid), keep_original_audio and length_from_audio switches, and reference_audio (note the on-disk IC-LoRA weights are 2.3-trained; they load cleanly on 2.5, as with the ID-LoRAs). The one 2.5-specific widget is img_compression. Chain: prep → KSampler distilled → LTX-2.5 Crop Video Guide → Latent Upscale ×2 → KSampler refine → AV Decode.
LTX-2.5 Crop Video Guide (LTXV25CropVideoGuide): strips the appended guide/reference tokens back off after the stage-1 sampling pass (a no-op if none were appended) — crop BEFORE the latent upscale, exactly like the ltx23 chain.
LTX-2.5 KSampler (distilled) (LTXV25KSampler): the two official ManualSigmas schedules verbatim — distilled (8 steps) and refine (3 steps) — through core's real Guider_LTXAVDualCFG (separate video/audio CFG, both 1.0 official) with euler_ancestral. No step-count or scheduler widget to get wrong.
LTX-2.5 Latent Upscale x2 (LTXV25LatentUpscale): the official spatial ×2 latent upscaler on the video half (audio passes through), plus the refine-pass re-hold — wire the SAME first-frame image in and it's re-held at 1.0 on the upscaled latent (the workflow's second LTXVImgToVideoInplace), rebuilding the joint noise mask.
LTX-2.5 AV Decode (LTXV25AVDecode): tiled video VAE decode (the workflow's 512/64/64/16 defaults) + audio VAE decode + mux to VIDEO, one fps threaded through — wire the prep node's frame_rate output in.

The i2v chain: Models Loader → (any LoraLoaderModelOnly chain) → Img to Video (i2v, image) → KSampler distilled (8 steps) → Latent Upscale x2 (same image) → KSampler refine (3 steps) → AV Decode → core SaveVideo. The v2v chain inserts Crop Video Guide between the distilled pass and the upscale. LTXV25EmptyLatentAVBatch remains for hand-rolled graphs. tools/smoke_ltx25.py covers the geometry, both holds, both sigma lists, the VidToVideo surface (guide append/crop round-trip, the mode validation matrix, audio holds), the dual-CFG sampler composition, and the decode wiring on CPU.

LTX Reference / Face Identity (ported from the 10s dev pack)

Token-prefix reference conditioning for any ltxav model — listed under BOTH 🤖 CCTech/LTX-2.3 and 🤖 CCTech/LTX-2.5 with version-prefixed titles (LTX-2.3 Reference Conditioning ⚡ / LTX-2.5 …; same implementation, and note the Best-Face-ID LoRA the Reinforcer pairs with is 2.3-trained, so the 2.5 pairing is unverified cross-version territory) — a genuinely different mechanism from the IC-LoRA guide append: the reference image's latent is patchified and concatenated IN FRONT of the video token sequence inside the model's forward (frame-0 RoPE coordinates, adaLN rows extended to match, prefix stripped in unpatchify — the sampler never sees a shape change, no crop node exists or is needed). Zero new weights; per-instance clone-then-patch.

LTX Reference Conditioning is the whole plain-reference path in one node: the forward patch installs itself on the model instance (always take MODEL from this node's output), then the reference is VAE-encoded and attached (process_latent_in normalization, strength, position_mode reference/prefix_continuous, zero_ref_timesteps; strength 0 is a clean bypass that also clears prior reference state). A batched IMAGE plus start_frame/num_frames selects a multi-frame reference window (defaults = single image).
LTX Face Identity Reinforcer composes the full Best-Face-ID recipe in one node (it likewise installs the forward patch itself): face detection (YuNet→MediaPipe→Haar), auto face crop with bbox tracking, soft/hard spatial gating, and the trained source_id 2 / phase_scale 1 rotary phase tag. Pair it with Best_FaceID_v1.0_LoRA.safetensors (Alissonerdx/LTX-Best-Face-ID) @ 1.0 on the model — the phase tag is that LoRA's trained convention and does little without it.

Two-stage graphs: feed the reference/reinforcer MODEL to the BASE sampler only - wire the refine/second-stage sampler from the model BEFORE these nodes. Reference attention during a re-noise pass smears face colors onto the subject (verified live at a x2 refine); identity is established at the base pass and survives the refine on its own.

GPU-verified A/B (same seed, t2v): conditioning alone visibly pulls identity toward the reference photo; reinforcer + Best-Face-ID locks it closest; strength 0 / disabled are bitwise no-ops (CPU-proven). Works with joint AV latents (this pack's prep nodes) — the video half is unbound automatically. tools/smoke_ltx_reference.py covers the rotation math, adaLN extension, per-instance install, injection round-trip, and face gating on CPU.

Preprocessors

This repo keeps only the minimum preprocessing this pack's own img2img nodes actually derive internally: Depth Anything V2 and plain cv2.Canny — the two preprocessors Krea2Img2Img/QwenImageImg2Img/Flux Klein img2img's control_mode="auto_depth"/"auto_canny" have always used. Nothing beyond that is registered as its own node here.

For the full ControlNet-aux-style preprocessor set (normal maps, soft edges, MLSD, lineart variants, OpenPose — 11 nodes total, each a real architecture+inference port from comfyui_controlnet_aux, Apache-2.0), install the standalone ComfyUI-ControlNet-Nodes package and wire its output into control_image/control_source_image yourself with control_mode="manual" — same pattern this pack already uses for any control type it doesn't auto-derive. That package also documents the two license caveats (Soft Edge PiDiNet's research-use note, OpenPose's CMU noncommercial-research-only license) in full.

Krea2 Depth Map, Flux Klein Depth Map, and Qwen-Image Canny still work exactly as before — they're the same Depth Anything V2 / cv2.Canny implementation, kept under their original historical names (not exposed as generic DepthMap/Canny nodes here, to avoid a name collision with ComfyUI-ControlNet-Nodes if both packs are installed). Depth Anything V2 auto-downloads its own weights from HuggingFace on first use into the real ComfyUI install's models/depth_anything_v2/ folder — nothing is ever vendored as a weight file in this repo.

control_mode="none" is available on all three img2img nodes (Krea2Img2Img, QwenImageImg2Img, Flux Klein img2img): it skips control-image attachment/auto-derivation, even if a Control LoRA/qwen_control/control_source_image is connected upstream, instead of the previous behavior where a loaded Control LoRA or connected qwen_control forced every generation to either auto-derive or manually supply a control image — there was no way to just turn control off for one call without physically rewiring the graph. none fixes that: toggle it via the dropdown instead. control_mode only ever governs the dedicated control-source input (control_image/control_source_image) on all three nodes — it never touches images (or Krea2Img2Img/QwenImageImg2Img's own img2img target), which is always attached when connected regardless of control_mode.

tools/smoke_preprocessors.py covers Depth Map/Canny's shape/dtype contract and confirms this module registers no nodes of its own — 4/4, no GPU.

Krea2 Control

Krea2 is natively detected by ComfyUI core (comfy.sd.load_diffusion_model_state_dict picks it up via unet_config.image_model == "krea2", comfy.sd.CLIPType.KREA2 selects its Qwen3-VL-4B text encoder) — there's no bespoke sampling algorithm or conditioning format to reimplement, unlike LTX-2.3. Only two things needed building: a GGUF-aware loader, and the Control LoRA mechanism, which has no comfy-native equivalent at all.

File	Folder	Dropdown
Krea2 diffusion model (`.safetensors` or GGUF)	`models/diffusion_models` (unet)	unet_name
Krea2 text encoder, Qwen3-VL-4B (`.safetensors` or GGUF)	`models/text_encoders` (clip)	clip_name
Krea2 VAE	`models/vae`	vae_name

Krea2 Model Loader is a thin convenience loader — MODEL/CLIP/VAE by name, matching ZImageLoader's shape (no per-checkpoint surgery needed, unlike Scenema, since comfy already knows the architecture).

Krea2 Control LoRA Loader loads any Krea2 LoRA from models/loras and patches it onto a MODEL — it auto-detects which of two unrelated mechanisms the file actually needs, the same auto-detect-and-dispatch approach Qwen-Image ControlNet Loader uses for Qwen-Image's own two ControlNet formats, so you don't need to know in advance which loader a given file requires:

Widened-projection Control LoRAs (e.g. depth-control-lora.safetensors) ship with the DiT's first input-projection layer widened — trained to accept image tokens concatenated with control tokens — plus small LoRA-rank patches on the attention blocks. Detected by shape-matching an expanded first weight against the live model. The loader patches the block weights through the normal ModelPatcher machinery (so offload/low-VRAM handling still applies), and registers a DIFFUSION_MODEL wrapper plus a PatcherInjection that swap the widened projection in only for the duration of each forward call — image tokens still pass through the model's original first layer during that swap (summed with the control contribution), so an ordinary LoRA on the base model keeps working. The projection is restored immediately after each forward call, so removing this node leaves the base model untouched. Ported essentially verbatim from the local comfyui-krea2-controlnet-main pack (no LICENSE file shipped; its README credits Tanmaypatil123/Krea-2-controlnet for documenting the reference pipeline and Patil/Krea-2-depth-controlnet for the public depth LoRA weights) — this is correctness-critical low-level ModelPatcher plumbing validated against a working pack. Use control_mode/control_image on Krea2 img2img after this.
Ordinary in-context LoRAs (e.g. nynxz/NK2E's krea2_canny-v0.1.safetensors) have no widened projection at all (confirmed by inspecting its actual tensor keys: plain lora_down/lora_up/alpha, nothing else). Detected by the absence of that expanded weight, and applied via comfy.sd.load_lora_for_models — the same call stock LoraLoaderModelOnly makes internally, no wrapper or injection needed since there's no runtime control-token swap to perform. Krea2Img2Img has no dedicated reference-conditioning input for this kind anymore — install ComfyUI-Flux-Reference-Tools and wire its reference-conditioning node onto model instead (works on any Flux-family model, not just Krea2).

Krea2 Depth Map turns a source photo into a depth map standalone — for hand-building a graph, or feeding something other than Krea2 img2img. Runs Depth Anything V2 (DINOv2 encoder + DPT decoder head), ported from Fannovel16/comfyui_controlnet_aux (Apache-2.0) and consolidated into vendor/depth_anything_v2.py per this pack's flat-file convention. Weights (ckpt_name: vits/vitb/vitl/vitg) auto-download from HuggingFace on first use into models/depth_anything_v2/, same pattern as Qwen3-TTS Models Loader — nothing extra to install. This is now an alias for the shared Depth Map (Depth Anything V2) node under 🤖 CCTech/Preprocessors (see that section above) — same node, kept registered under its original name so saved workflows keep working. For the common case (depth Control LoRA), you don't need this node at all — see below.

Krea2 img2img + Krea2 ControlNet img2img are two separate, standalone nodes — split so a plain img2img graph never has to look at control-related sockets, and vice versa (see Flux Klein below for the same split and the two real bugs that motivated it). Pick whichever matches what you're doing; neither depends on the other.

Krea2 img2img — model, clip, vae, prompt, negative_prompt, strength, width, height, plus optional images (one or more source photos, batch-aware — leave unconnected for txt2img). No control-related inputs at all. images is real img2img — VAE-encoded, then partially denoised at strength (comfy's own img2img convention: noise added onto the encoded latent proportional to 1 - strength, sampled from there). It's batch-aware: a batch of N photos naturally becomes N independent img2img generations, since vae.encode()/KSampler already process a batched latent as N parallel runs — no special-casing needed.

identity_edit (optional, default off) switches this same node to drive the Krea 2 Identity Edit LoRA in place — no separate Krea2 Identity Edit nodes needed. When on, images stops being an img2img source and becomes the LoRA's source photo: it's injected as in-context tokens (the same model-forward wrapper Krea2 Identity Edit (source patch) uses) and grounds prompt/negative_prompt on that image through Qwen3-VL (the same path Krea2 Identity Edit (grounded encode) uses) instead of plain text encoding. strength is ignored when identity_edit=True — the target always starts at full noise, since source preservation now comes from the injected context rather than partial denoising, and mixing both fights the LoRA's trained recipe. This toggle only adds the driving mechanism — the LoRA itself still needs to be loaded upstream (e.g. LoraLoaderModelOnly); with identity_edit=True and no such LoRA loaded, the extra context tokens are simply inert. ref_boost/grounding_px mirror the standalone nodes' same-named dials. For the advanced cases this toggle doesn't cover (dual-reference, ref_boost_mask, fit_mode="crop (legacy)", a custom system_prompt), use the standalone Krea2 Identity Edit (source patch)/Krea2 Identity Edit (grounded encode) nodes instead — they share the exact same underlying implementation, just with the full dial set exposed.

Krea2 ControlNet img2img — everything Krea2 img2img has, plus control_image for the loaded Control LoRA. control_mode (on the widened-projection path) picks how the control signal gets produced, since nothing in a LoRA file says what type it is:

auto_depth (default) — derives a depth map from images automatically, using the same Depth Anything V2 model as Krea2 Depth Map.
auto_canny — derives a canny edge map from images automatically (plain cv2.Canny, no model, no download) — for a canny checkpoint that is a widened-projection Control LoRA.
manual — no automatic derivation; connect control_image yourself. Use this for any widened-projection Control LoRA the two auto modes don't cover (pose/lineart/normal — install ComfyUI-ControlNet-Nodes for those preprocessors).
none — skip control attachment entirely, even if a Control LoRA is loaded upstream. Previously a loaded Control LoRA forced every generation to either auto-derive or manually supply control_image — there was no way to just turn control off for one call without physically disconnecting or removing the loader. none fixes that.

Connecting control_image explicitly always overrides auto-derivation, in any mode — including none, which only skips auto-derivation/the require-a-control-image guard, not an explicitly wired control_image.

It VAE-encodes images and the resolved control image, attaches the control latent to the model, and CLIP-encodes your prompt — outputs model, positive, negative, latent, denoise straight into a stock KSampler. If a widened-projection Control LoRA is loaded and neither control_image nor usable images is available to derive one from, it raises immediately instead of silently sampling a half-configured model — the same guarantee the original pack's separate Apply node existed for. The reverse (control_image connected but no Control LoRA loaded) is not an error — there's nothing to attach it to, so it's simply ignored with a warning, so you can leave a preprocessor chain wired in while toggling the LoRA loader on/off.

Minimal graph (plain): Krea2 Model Loader → Krea2 img2img (prompt typed directly into this node, Load Image → images) → Krea2 KSampler (or stock KSampler) → VAE Decode. With ControlNet: swap in Krea2 ControlNet img2img, with Krea2 Control LoRA Loader between the model loader and it.

Krea2 KSampler is a drop-in for stock KSampler with one extra option, denoise_mode, mirroring Z-Image KSampler/Qwen-Image KSampler. Krea2 shares the exact same ModelSamplingFlux/shift=1.15 setup as Qwen-Image (literally the same shift value, copy-pasted alongside the Qwen-Image-family config in comfy's own supported_models.py), so the same comfy-vs-diffusers denoise-slicing discrepancy applies — verified against a real loaded Krea2 model, not assumed: at 9 steps, denoise 0.9, comfy starts at sigma ≈0.9660 vs ≈0.9619 under the diffusers-style slice. denoise_mode="comfy" (default) is unchanged stock behavior; "diffusers" matches diffusers-pipeline img2img exactly.

Krea2 Identity Edit (source patch) + Krea2 Identity Edit (grounded encode) are a faithful port of comfyui-krea2edit (Apache-2.0, lbouaraba) — the nodes behind the separately-licensed Krea 2 Identity Edit LoRA (krea2_identity_edit_v1_2.safetensors, huggingface.co/conradlocke/krea2-identity-edit, Krea 2 Community License Agreement — weights are NOT bundled or vendored here, download separately). This is a genuinely different mechanism from Krea2 img2img's control_image path — comfy's native Krea2 forward only ever builds [text | target], with no built-in way to prepend a clean source-image block, so the Identity Edit LoRA needs the diffusion model's forward wrapped directly to rebuild the sequence as [text | source(frame=1) | target(frame=0)], exactly matching how it was trained (ai-toolkit's predict_velocity_edit). Confirmed by reading comfy/ldm/krea2/model.py's real SingleStreamDiT directly — attribute-for-attribute, the port matches. Ported via comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL — a real comfy ModelPatcher extension point (the same mechanism Krea2ControlLoRALoader's own wrapper already uses in this file, and Klein's Identity Feature Transfer hooks use elsewhere in this pack) — not a from-scratch sampler; CFGGuider/sampler_post_cfg_function stays fully intact.

Krea2 Identity Edit (source patch) takes model + source_latent (a VAEEncode of the photo being edited) and wraps the model's forward so that source gets prepended as clean in-context tokens at RoPE frame 1. vae + source_image (recommended) enables the blur-proof pixel-space path — the node fits the raw image to the target grid in pixel space instead of resizing an already-VAE-encoded latent. target_latent (recommended whenever using the pixel path) — wire the same latent that feeds KSampler.latent_image, so the source gets VAE-encoded during this node's own execution instead of on the first sampling step (skipping it can silently evict part of the resident diffusion model mid-sampling on VRAM-tight setups, streaming weights from CPU for the rest of the run — the console tells you which path you got). source_latent_b/source_image_b add a second reference (RoPE frame 2) for two-input edits (e.g. person + scene). ref_boost/ref_boost_a are reference-fidelity dials — how hard the model locks onto the reference's appearance (1.0 = neutral, model-specific optimum is usually higher). fit_mode="fit" (default, training-matched, needs vae+source_image) resamples the source to the target grid at a centered offset; "crop (legacy)" is the older v1/v1.1 geometry.

Krea2 Identity Edit (grounded encode) encodes the prompt together with the source image through Qwen3-VL's own vision path — the semantic half of the training recipe the source-patch node's VAE tokens don't cover (appearance vs. scene semantics like "the man on the left"). Both nodes are required for correct results — a stock CLIPTextEncode never lets the model see the image while reading the instruction, and quality drops sharply, especially for scene-referential edits. image/image_b (optional — leave unconnected to fall back to plain text-only encoding, matching the LoRA's own trained unconditional for the negative prompt at CFG>1) mirror the patch node's dual-reference order (scene, then subject). grounding_px (default 768, trained range 384–768) caps the resolution fed to the vision encoder — lower for stronger edit adherence, higher for stronger identity/likeness.

Minimal graph: LoadImage → VAEEncode → Krea2 Identity Edit (source patch)'s source_latent (model from Krea2ModelLoader → stock LoraLoaderModelOnly with the Identity Edit LoRA @ 1.0) → stock KSampler. Two Krea2 Identity Edit (grounded encode) instances (same source image, one with your instruction, one empty) feed KSampler's positive/negative. EmptySD3LatentImage feeds both KSampler.latent_image and (recommended) the patch node's target_latent.

tools/smoke_krea2.py covers the tensor-prep helpers (grayscale/normalize/invert/resize), the Krea2ControlInputProjection forward math (image-only fallback, and image+control summation), the widened-projection-vs-ordinary-LoRA auto-detection, Krea2 ControlNet img2img's guard rails, auto_depth/auto_canny derivation from images, manual-override precedence, Krea2 img2img's plain-node behavior (txt2img/img2img latent shapes, a multi-image batch producing a correctly-batched latent, zero control-related inputs), Krea2 KSampler's two denoise modes, Krea2 Identity Edit (source patch)'s wrapper registration and the pixel-path pre-encode timing (target_latent present vs. absent, dual-reference pre-encoding), krea2_edit_forward's [text | source | target] concatenation round-tripping to the original shape against a synthetic DiT, and Krea2 Identity Edit (grounded encode)'s image-grounded tokenize call (images=/llama_template= kwargs, vision-block count, grounding_px downscaling) — 39/39, no GPU. Loading real GGUF/LoRA weights end-to-end through the same Krea2ControlLoRALoader for both the widened-projection depth LoRA and the ordinary canny LoRA, the Depth Anything V2 port's load_state_dict(strict=True) against the real HuggingFace checkpoint, the new images slot's INPUT_TYPES shape, the diffusers-mode sigma math against that same real model, and — for the Edit nodes — every comfy API called (SingleStreamDiT's real attributes, the WrappersMP.DIFFUSION_MODEL wrapper actually bridging through comfy's real sampler_helpers.prepare_model_patcher into transformer_options, Krea2Tokenizer's real images=/llama_template= kwargs) were all verified separately against the actual portable ComfyUI environment.

Qwen-Image ControlNet

Unlike Krea2's Control LoRA, Qwen-Image ControlNet needed no algorithm ported at all — every format in circulation is already native to ComfyUI core:

Format	Example file	Comfy mechanism
InstantX / Union	`*-InstantX-ControlNet-Union.safetensors`	`comfy.controlnet.load_controlnet_state_dict()` → a real `ControlNet` object, attaches to CONDITIONING (same as any classic ControlNet)
Qwen-Image-Fun ControlNet	—	same dispatcher, also a `ControlNet` object
DiffSynth patches (canny/depth/inpaint)	`qwen_image_{canny,depth,inpaint}_diffsynth_controlnet.safetensors`	`comfy_extras.nodes_model_patch.ModelPatchLoader` → a `MODEL_PATCH`, attaches to MODEL via `DiffSynthCnetPatch` — the same mechanism this pack's `nodes_zimage.py` already uses for Z-Image's ControlNet

Qwen-Image Model Loader is the same thin GGUF-aware convenience loader as Krea2ModelLoader/ZImageLoader — MODEL/CLIP/VAE by name (type="qwen_image" for CLIP).

Qwen-Image ControlNet Loader loads a checkpoint from models/model_patches or models/controlnet and auto-detects which of the two mechanisms above it needs, checking only the DiffSynth signature (controlnet_blocks.0.y_rms.weight, the only real MODEL_PATCH format) and falling through to comfy.controlnet.load_controlnet_state_dict() for everything else — InstantX/Union and Qwen-Image-Fun both land there, since Fun is a real ControlNet architecture despite its name, not a model patch (comfy's own dispatcher already checks the Fun signature — control_blocks.0.after_proj.weight + control_img_in.weight — internally). It's a dispatcher, not a reimplementation. Outputs a QWEN_IMAGE_CONTROL wrapper tagging which attachment point the loaded checkpoint needs.

Qwen-Image Canny is the standalone version of control_mode="auto_canny" (below) — plain cv2.Canny edge detection, no model, no download. Same role Krea2 Depth Map plays for depth: wire it in explicitly to preview the edge map before it goes into control_image, or reuse it elsewhere, instead of it happening invisibly inside Qwen-Image img2img. This is now an alias for the shared Canny node under 🤖 CCTech/Preprocessors.

Qwen-Image img2img + Qwen-Image ControlNet img2img are two separate, standalone nodes, same split as Krea2/Klein — a plain img2img graph never has to look at control-related sockets, and vice versa.

Qwen-Image img2img — model, clip, vae, prompt, negative_prompt, strength, width, height, plus optional images (one or more source photos, batch-aware). No control-related inputs at all. images is real img2img — VAE-encoded, then partially denoised at strength. It's batch-aware: a batch of N photos naturally becomes N independent img2img generations, since vae.encode()/KSampler already process a batched latent as N parallel runs. This is also the only mechanism this node has for "edit this photo" — for a Qwen-Image-Edit checkpoint, connect the photo here with a real strength so the model is actually denoising from it.

Qwen-Image ControlNet img2img — everything Qwen-Image img2img has, plus qwen_control + control_image and mask. It routes to whichever attachment the loaded checkpoint needs automatically — DiffSynthCnetPatch on a cloned MODEL for DiffSynth patches, or the same .set_cond_hint()/.set_previous_controlnet() calls stock ControlNetApplyAdvanced makes, applied to CONDITIONING, for InstantX/Union/Fun — so you never need to know which mechanism a given checkpoint uses. Unlike Krea2 (one control type: depth), Qwen-Image checkpoints span several different preprocessing needs this pack can't detect from the file, so control_mode picks how control_image gets produced:

manual (default) — connect control_image yourself, matching whichever checkpoint you loaded (or install ComfyUI-ControlNet-Nodes for normal/soft-edge/line/pose preprocessors this pack doesn't auto-derive).
auto_canny — derives a canny edge map from images automatically (plain cv2.Canny, no model, no download).
auto_depth — derives a depth map from images automatically, using the same Depth Anything V2 model as Krea2 Depth Map.
none — skip control attachment entirely, even if qwen_control is connected. Previously a connected qwen_control forced every generation to either auto-derive or manually supply control_image — there was no way to just turn control off for one call. none fixes that.

Connecting control_image explicitly always overrides auto-derivation, in any mode. control_image is required even for an inpaint checkpoint (outside none mode) — mask only refines the region, it doesn't replace it; there's no photo-only way to auto-derive a mask. Same guard rails as before the split: qwen_control with nothing usable to attach (and control_mode not none) raises; control_image with no qwen_control is ignored with a warning.

Graph (plain): Qwen-Image Model Loader → Qwen-Image img2img (prompt typed directly into this node, Load Image → images) → stock KSampler → VAE Decode. With ControlNet: swap in Qwen-Image ControlNet img2img, alongside Qwen-Image ControlNet Loader → qwen_control, with images doing double duty as the control_mode derivation source (or your own control map via control_image).

Not ported: Lotus depth estimation (a diffusion-based depth model some Qwen-Image InstantX workflows use as their depth preprocessor) — it's a different architecture from Depth Anything V2, and a separate job (preprocessor, not ControlNet). Krea2 Depth Map's Depth Anything V2 covers the same role for now.

Qwen-Image KSampler is a drop-in for stock KSampler with one extra option, denoise_mode. Qwen-Image has no bespoke sampling code in comfy at all — it shares ModelSamplingFlux (shift=1.15) with the rest of the Flux family — but comfy's own denoise-slicing convention (KSampler.set_steps: re-expand to int(steps/denoise) steps, take the tail) provably diverges from the diffusers img2img convention (compute the schedule at steps, slice from t_start = steps - round(steps*denoise)), the same class of discrepancy Z-Image KSampler was built for. Verified against Qwen-Image's actual shift value on a real loaded model, not assumed: at 9 steps, denoise 0.9, comfy starts at sigma ≈0.9660 vs ≈0.9619 under the diffusers-style slice — smaller than Z-Image's measured gap (0.9643 vs 0.9567) but the identical mechanism. denoise_mode="comfy" (default) is unchanged stock behavior; "diffusers" matches the original pipeline's img2img exactly. This is a compatibility switch, not a quality fix — small in magnitude, worth having for exact parity.

tools/smoke_qwen_image.py covers the control-dispatch helper (conditioning stamping, hint/strength passthrough), Qwen-Image ControlNet img2img's guard rails and both control attachment paths (model_patch → cloned MODEL; controlnet → CONDITIONING, MODEL left untouched), Qwen-Image img2img's plain-node latent shapes and a multi-image batch producing a correctly-batched latent, Qwen-Image Canny's output shape, and Qwen-Image KSampler's two denoise modes — 14/14, no GPU. The format auto-detection, both control attachment mechanisms, the new images slot's INPUT_TYPES shape, and the diffusers-mode sigma math were all verified separately against the real files/real loaded model in the actual portable ComfyUI environment.

Flux Klein

FLUX.2 Klein is natively detected by comfy core (unet_config.image_model == "flux2", matched by comfy/supported_models.py) — like Krea2 and Qwen-Image, this needed only a thin GGUF-aware convenience loader plus a real port of the genuinely Klein-specific tooling from ComfyUI-Flux2Klein-Enhancer (MIT License, capitan01R) — none of it is vendored or reimplemented from scratch, it's a faithful port of that pack's own mechanisms onto stock comfy MODEL/CONDITIONING objects.

This pack keeps only the 5 nodes that actually need Klein-specific structure (Klein's own empty-latent shape, its Qwen3-layer-stacked conditioning, or its own CLIP tokenizer internals). The other 9 nodes originally built as part of this port turned out to be architecturally generic to any Flux-family model (Flux.1, Kontext, Klein) — they only read comfy's own shared reference_latents/attn1_patch/sampler_post_cfg_function mechanisms, nothing Klein-only — so they were extracted, renamed, and moved to the standalone ComfyUI-Flux-Reference-Tools package: FluxMultiReferenceLatent, FluxColorAnchor, FluxDetailController, FluxTextEnhancer, FluxMaskRefController, FluxRefLatentController, FluxTextRefBalance, FluxRefLatentWeight, FluxIdentityGuidance. Install that package for 3+ reference chaining, color/identity anchoring, or per-reference K/V weighting — on Klein or any other Flux-family model. This was a breaking change for any saved workflow using those 9 nodes' old Klein-branded type names directly (no in-repo alias is possible for a node that now lives in a different package).

Flux Klein Model Loader is the same thin GGUF-aware convenience loader as Krea2ModelLoader/QwenImageModelLoader — MODEL/CLIP/VAE by name (clip_type=CLIPType.FLUX2, matching core's CLIPLoader type dropdown).

Flux Klein img2img + Flux Klein ControlNet img2img are two separate, standalone nodes: model, clip, vae, prompt, negative_prompt, batch_size, width, height, plus optional images — outputs model, positive, negative, latent. There is no partial-denoise img2img path here at all — every real Klein edit example starts from a pure-noise EmptyFlux2LatentImage ([batch_size, 128, height // 16, width // 16], confirmed via comfy core's own EmptyFlux2LatentImage — not the generic 4-channel/8-downscale placeholder used elsewhere in this pack, since comfy's fix_empty_latent_channels only auto-corrects channel count, not the spatial downscale ratio unless downscale_ratio_spacial is explicitly passed). No denoise output either — it would always be a constant 1.0, which is already stock KSampler's own default denoise value, so it would carry zero information; leave KSampler's denoise input disconnected.

images — one or more RAW reference photos, always attached to reference_latents on positive+negative when connected. This is a single IMAGE socket but batch-aware: if the incoming tensor has N images stacked in the batch dimension, each gets its own VAEEncode + reference_latents append — combine multiple photos upstream with a stock Batch Images node before wiring the result in here. This is Klein's real editing mechanism, confirmed by tracing the actual node graph inside the real shipped example workflows (single-reference "Image Edit (Flux.2 Klein 9B)" and dual-reference "9B-base", image_flux2_klein_image_edit_9b_base.json): it never partially denoises the edited photo, it always starts from pure noise and drives the whole edit off reference images VAE-encoded and attached to positive and negative conditioning as reference_latents, plus a text instruction — a 2-image batch here reproduces the dual-reference subgraph's own two sequential ReferenceLatent calls per conditioning branch exactly.

Flux Klein ControlNet img2img — everything Flux Klein img2img has, plus control_source_image: a photo to turn into a controlnet-style map before attaching it. Klein has no real ControlNet/Control-LoRA of its own, so "using it as a controlnet image" just means running it through this pack's preprocessor and feeding the result through the same reference_latents mechanism as images, appended after them. control_mode picks the preprocessor and applies only to this slot: manual (default) attaches it raw (e.g. if you already computed your own map elsewhere), auto_depth runs it through this pack's Depth Anything V2 first — reproducing the example workflow's own trick of feeding a reference through AIO_Preprocessor (set to MiDaS-DepthMapPreprocessor) before encoding it, the one mode CONFIRMED meaningful to Klein's own training — auto_canny does the same with plain cv2.Canny, mechanically valid but unverified for Klein specifically, and none skips control_source_image attachment entirely even if it's connected, for toggling it off without rewiring. This is the same minimum set Krea2 ControlNet img2img/Qwen-Image ControlNet img2img use — for normal maps/soft edges/lines/pose, install ComfyUI-ControlNet-Nodes and wire its output into control_source_image yourself with control_mode=manual. depth_ckpt_name picks the Depth Anything V2 model size (downloads on first use, same as Krea2 Depth Map).

This split exists because of a real bug, caught by the user against a real edit test: an earlier revision had a single reference_image slot doing double duty as both the raw identity/content reference and the thing control_mode decided how to preprocess. That meant control_mode="none" — meant only to skip auto-depth/auto-canny preprocessing — silently skipped the entire reference attachment too, even with reference_image genuinely connected: a coherent image would still come out (the model just fell back to prompt-only generation), with no error, making it look like the reference mechanism itself was broken rather than a dropdown state. First fixed by splitting the one node's inputs into images/control_source_image (so control_mode could no longer reach images), then split further into two separate standalone nodes entirely — a plain img2img node can no longer show a control-related dropdown at all, closing the whole class of confusion by construction, not just this one bug.

width/height are a pixel budget, not an exact literal size, confirmed by tracing the actual node graph inside the real shipped example workflow: whenever images (or, on the ControlNet node, control_source_image) is connected, the real canvas is re-derived from that photo's own aspect ratio at the same total-pixel budget (_scale_to_megapixels, an aspect-preserving port of comfy core's own ImageScaleToTotalPixels math, rounded to the nearest multiple of 16 to stay aligned with Flux.2's /16 latent downscale) — exactly mirroring the example workflow's own ImageScaleToTotalPixels → GetImageSize → EmptyFlux2LatentImage chain, which never trusts an independent user-typed size when a photo is connected. Pure txt2img (nothing connected) still uses width/height literally.

Flux Klein Depth Map is that same Depth Anything V2 detector exposed as its own standalone node — image in, depth map out — mirroring Krea2 Depth Map's existing explicit-node convention. It's the same shared Depth Map (Depth Anything V2) implementation under its historical Klein-branded name (see Preprocessors above).

Flux Klein Identity Feature Transfer is a near-verbatim port of the source pack's flagship IdentityFeatureTransferFinal — multi-reference identity-preserving feature transfer, using only stock ModelPatcher hooks (set_model_attn1_output_patch, always; set_model_attn1_patch, only when mask_behavior="zero_unmasked_tokens" and a mask is wired). Both hooks fire generically from comfy's own comfy/ldm/flux/layers.py DoubleStreamBlock/SingleStreamBlock forward passes — shared Flux/Kontext/Klein code — and read four extra_options keys (reference_image_num_tokens, block_index, block_type, img_slice) that comfy's own model code already populates every forward call. The transfer does per-image centering of generated vs. reference features, normalized similarity matching with a configurable floor, temperature-controlled reference pooling, and confidence-gated pull at scheduled double/single blocks, all ported as-is: preset (HARD_LOCK/MID_LOCK/SOFT_LOCK/custom), reference_index/reference_indices, similarity_floor, softmax_temperature, mask_threshold, double_blocks/single_blocks schedule strings, optional sigmas (per-step strength decay), debug, mask_behavior, and up to eight subject_mask_1..8 inputs. Stays in this repo because its default schedules/presets hardcode block counts (8 double / 24 single) tuned specifically for the Klein 9B layout — genuinely Klein-specific, not generalizable without redesigning the preset system.

Known caveat, carried over from the source rather than fixed speculatively: those hardcoded block counts are never read from the live model. Out-of-range indices clamp harmlessly on a different-sized checkpoint (e.g. the 4B klein-base variant), but a preset may end up applying strength to the wrong semantic blocks — use preset="custom" with your own schedule strings for non-9B checkpoints.

Flux Klein Enhancer — scalar/whitening ops on the active-token conditioning region, plus a Klein-specific per-Qwen3-layer scale (Klein conditioning stacks 3 hidden-layer slices along the embed dim; early_layer_scale/mid_layer_scale/late_layer_scale target them individually). Genuinely Klein-specific — the 3-layer-stack assumption is meaningless on any other model's conditioning shape.

Flux Klein Sectioned Encoder — tokenizes a front/mid/end sectioned prompt (three text boxes, or one combined_prompt with [FRONT]/[MID]/[END] markers) and stamps real per-section token ranges as conditioning metadata by reaching into the CLIP's own HF tokenizer (clip.tokenizer.qwen3_4b.tokenizer / .qwen3_8b.tokenizer depending on which Klein text-encoder variant is loaded — confirmed against a real loaded Klein CLIP object, not assumed). The one node in this repo with a genuine Klein-CLIP-internals dependency. Its klein_sections metadata is also read (optionally) by FluxDetailController in the separate ComfyUI-Flux-Reference-Tools package, if you have both installed.

Explicitly not ported: Flux2KleinKSamplerExperimental — confirmed to reimplement Euler sampling from scratch (manual forward-pass loop, hand-rolled CFG, local shift/schedule math) instead of going through comfy.samplers/CFGGuider, which means it bypasses comfy's sampler_post_cfg_function pipeline entirely. Within the source pack itself this makes it strictly less compatible than stock KSampler — pairing it with the source's own Color Anchor or Identity Guidance nodes would silently do nothing. Use stock KSampler for Klein. Also not ported: the source pack's own superseded IdentityFeatureTransfer/Advanced/V3 nodes, kept there only for that pack's backward compatibility.

Graph (plain): Flux Klein Model Loader → Flux Klein img2img (prompt typed directly into this node, images for Klein's real edit mechanism) → optionally Flux Klein Identity Feature Transfer on model → stock KSampler → VAE Decode. With ControlNet: swap in Flux Klein ControlNet img2img. For 3+ references, more control types, color/identity anchoring, or per-reference weighting, install ComfyUI-Flux-Reference-Tools and/or ComfyUI-ControlNet-Nodes and chain their nodes in — see each package's own README for graph examples.

tools/smoke_flux_klein.py covers all 7 nodes (FluxKleinModelLoader, FluxKleinImg2Img, FluxKleinControlNetImg2Img, Flux2KleinDepthMap alias, Flux2KleinIdentityFeatureTransfer, Flux2KleinEnhancer, Flux2KleinSectionedEncoder) — Flux Klein img2img's plain-node latent shape (real Flux.2 128-channel shape, no denoise output, batch repeat), its _scale_to_megapixels canvas-sizing helper (aspect ratio preserved, hits the requested megapixel budget, rounds to 16), the canvas correctly deriving from a connected non-square images input instead of the square widget defaults, Flux Klein ControlNet img2img's images taking sizing priority over control_source_image when both are connected, a 2-image batch in images attaching 2 separate reference_latents (one per image, not one batched encode), images and control_source_image combining correctly, its control_source_image/control_mode attachment (manual attaches raw, auto_depth/auto_canny each derive for real, none skips ONLY control_source_image, control_mode's option list matches the minimum set exactly), Flux Klein Depth Map's delegation to the shared depth helper, Flux Klein Identity Feature Transfer's hook registration and similarity-pull math on a synthetic attention tensor, Enhancer's no-op passthrough and active-region scaling, and Sectioned Encoder's klein_sections emission with a fake HF tokenizer and its no-tokenizer fallback — 26/26, no GPU. Native Flux2 detection, CLIPType.FLUX2, the real clip.tokenizer.qwen3_4b.tokenizer attribute path, the canvas-sizing fix, and both nodes' INPUT_TYPES (plain has zero control keys, ControlNet is a strict superset) were all verified separately against the actual portable ComfyUI environment, the user's real flux-2-klein-9b GGUF checkpoint, a real loaded Klein CLIP object, and the real Flux.2 VAE.

LM Studio

LM Studio Vision Prompt, under 🤖 CCTech/LM Studio, is a local drop-in for a cloud vision-prompt node such as comfy-core's GeminiNode: optional image + prompt + system_prompt in, one STRING out, so it slots into an existing workflow (e.g. ltxv23_talking_head's photo-to-prompt step) without touching anything downstream. It talks to LM Studio's local OpenAI-compatible server (LM Studio > Developer > Start Server; base_url defaults to http://localhost:1234/v1 and is editable per node). Leave model blank to auto-use whatever's currently loaded in LM Studio (a live /v1/models call at run time, so it stays correct across model switches and across whichever base_url this node points at), or type an exact model id to pin one. A connection failure raises a clear "is the server running?" error rather than a bare traceback. Needs the optional requests dependency (pip install requests, already in requirements.txt).

tools/smoke_lmstudio.py mocks the HTTP calls and validates payload assembly, response parsing and error handling — no GPU or running server required.

Qwen3-TTS

Qwen3-TTS Models Loader + Qwen3-TTS Custom Voice, under 🤖 CCTech/Qwen TTS, are a local port of the flybirdxx/ComfyUI-Qwen-TTS pack's FB_Qwen3TTSCustomVoice node (used for the reference voice in the ltxv23_talking_head workflow), wrapping the qwen-tts pip package's own Qwen3TTSModel directly — the model itself is a transformers checkpoint plus a separate codec/vocoder submodel, not this repo's GGUF-quantization territory, so there's nothing to port at the weights level.

The models-folder layout matches DarioFT/ComfyUI-Qwen3-TTS's convention rather than inventing a new one: pick a repo_id from the loader's dropdown (CustomVoice/VoiceDesign/Base × 1.7B/0.6B) and it downloads once into models/Qwen3-TTS/<folder_name>/ (the speech tokenizer/codec lives inside that same repo, no separate download) — an existing HuggingFace/ModelScope cache copy is migrated in place instead of re-downloading if found. Every load after the first is fully offline. To pre-fetch by hand instead:

huggingface-cli download Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --local-dir ComfyUI/models/Qwen3-TTS/Qwen3-TTS-12Hz-1.7B-CustomVoice

The loader always loads with local_files_only=True — no network access once the files are on disk. speaker is a built-in named voice baked into the checkpoint (e.g. "Dylan"); an unknown name raises an error listing every valid one. instruct is real model-native conditioning on the 1.7B checkpoint, but the 0.6B checkpoint silently drops it upstream — this node logs a warning instead of reproducing that silence. Needs the optional qwen-tts dependency (already in requirements.txt).

Six more nodes ported from the same pack's newer revision round out the family. These are faithful ports of the flybirdxx surfaces: each model-using node loads its own checkpoint in-node (model_choice 0.6B/1.7B, device, precision, attention widgets — including sage_attn and custom_model_path on Voice Clone), exactly like the source pack, so there is no upstream model wiring to get wrong:

Qwen3-TTS Voice Clone — clone a reference voice onto new text. Connect ref_audio (any Comfy AUDIO, stereo is downmixed, sub-1024-sample clips zero-padded) or a reusable voice_clone_prompt; the prompt wins when both are connected. Optional instruct styling, x_vector_only, and custom_model_path for fine-tuned checkpoints. Loads a *-Base checkpoint.
Qwen3-TTS Voice Clone Prompt — extract the reusable VOICE_CLONE_PROMPT features from reference audio once (same *-Base family Voice Clone uses), then reuse them across any number of generations without re-encoding the audio.
Qwen3-TTS Voice Design — describe a voice in text (instruct, e.g. "a deep male voice with a calm tone") and speak the target text with it. Loads the *-VoiceDesign checkpoint (1.7B only — 0.6B is rejected, matching upstream).
Qwen3-TTS Role Bank — collect up to 8 named VOICE_CLONE_PROMPTs into one bank for multi-role dialogue workflows (slots without a connected prompt are skipped).
Qwen3-TTS Save Voice / Qwen3-TTS Load Speaker — persist a prompt under models/qwen-tts/voices/ (the source pack's layout, so saved voices are interchangeable with it) as <name>.qvp + .json (transcript) + .wav preview, and load it back (fast-loading the cached .qvp and restoring the transcript; the audio output works standalone as a Voice Clone ref_audio when no .qvp exists). The wav write/read prefers torchaudio/soundfile and falls back to a stdlib 16-bit PCM path, so the round-trip needs no audio backend installed.

Under the hood the in-node loading reuses this pack's plumbing: models are searched in models/qwen-tts/ (the source pack's tree, also registered for extra_model_paths.yaml) then models/Qwen3-TTS/, downloaded from HuggingFace when found in neither, and a single-slot cache means two nodes with the same config share weights. The Qwen3-TTS Models Loader + Qwen3-TTS Custom Voice pair above is unchanged and still works alongside them.

tools/smoke_qwen_tts.py stubs folder_paths and qwen_tts.Qwen3TTSModel and validates path discovery, loader kwargs/caching, the generate node's seeding/AUDIO-dict/unload logic, the new nodes' in-node model resolution (local search, cache reuse/eviction, attention fallback), audio conversion/kwargs, and the save/load round-trip — no GPU or real weights required. The full chain (CustomVoice reference speech → prompt extraction → save/load → clone from the saved prompt → role bank → voice design) is verified end-to-end against real checkpoints on every port.

TripoSG (image → 3D mesh)

A pure-PyTorch port of VAST-AI-Research/TripoSG (MIT) — no diffusers, no transformers, no hub downloads — taken from the owner's parity-checked POC, split at its own stage seams so each step can be inspected or swapped. The output is ComfyUI core's MESH, so Save 3D Model (SaveGLB), Create 3D File (from Mesh), Preview 3D and every 3d/mesh node (Decimate Mesh for a face budget, Fill Holes, Unwrap Mesh, …) take it directly.

Node	What it does (POC defaults)
TripoSG Model Loader	the split checkpoint: 1.5B rectified-flow DiT, SDF VAE (decoder), DINOv2 image encoder; `dtype` auto = float16 on CUDA
TripoSG RMBG Loader	BriaRMBG-1.4, run in float32
TripoSG Prepare Image	the POC's foreground crop: use the image's alpha when it is meaningful (connect LoadImage's `mask`), else RMBG-1.4 → Otsu threshold → small-blob cleanup; composite on white, centred square crop, 10% padding. `remove_background` off treats the whole frame as the subject
TripoSG Sampler	DINOv2 tokens → 50 rectified-flow steps, CFG 7.0 (unconditional = zero tokens), 2048 shape tokens, seeded
TripoSG Decode Mesh	SDF VAE on a hierarchical octree (dense 2⁸, refined to 2⁹ near the surface, bounds ±1.005) → Lewiner marching cubes → a watertight, untextured MESH

Place the files from ChrisColeTech/TripoSG under ComfyUI/models/triposg/ (both dropdowns scan that folder; extra_model_paths key triposg):

HF path	Folder
`split/TripoSG/` (transformer/, vae/, image_encoder_dinov2/, feature_extractor_dinov2/, scheduler/, model_index.json — 7.9 GB)	`models/triposg/TripoSG/`
`split/RMBG-1.4/` (config.json, model.safetensors — 176 MB)	`models/triposg/RMBG-1.4/` (an `rmbg-1.4` folder inside the TripoSG folder is found too)

huggingface-cli download ChrisColeTech/TripoSG --include "split/TripoSG/*" "split/RMBG-1.4/*" --local-dir hf_triposg

then move hf_triposg/split/TripoSG and hf_triposg/split/RMBG-1.4 into models/triposg/. The split/TripoSG-scribble variant (text + scribble conditioning) is a different pipeline and is not listed. Needs scikit-image (marching cubes, in requirements.txt). ≈10 GB VRAM at float16; meshes are dense (hundreds of thousands of faces) — add core Decimate Mesh before saving if you need a budget. Textures are not part of TripoSG (the owner's sidecar paints with a separate Hunyuan3D-Paint stage, which neither ComfyUI core nor this pack ships); core's UV/texture nodes can bake one onto the mesh.

tools/smoke_triposg.py runs against the real ComfyUI modules on CPU: node registration, the alpha/RMBG preprocessing paths, an analytic-sphere decode through core SaveGLB / MeshToFile3D, seeded CFG sampling on a tiny DiT, and — with the weights — strict key/shape checks of every component against the checkpoint headers (--full loads them for real). The split stages were checked bit-for-bit against the POC pipeline on CPU (same seed: identical vertices and faces).

UniRig (auto-rigging)

Rigs any triangle mesh — TripoSG's output directly — into a skinned GLB: autoregressive skeleton prediction, then per-vertex skin weights. The model code is PozzettiAndrea/ComfyUI-UniRig's inference package (GPL-3.0, from VAST-AI-Research/UniRig, MIT), run the way the owner's POC runs it: no Blender, no spconv/cumm, no torch_scatter/torch_cluster, no flash-attn and no transformers — the skeleton decoder is a pure-PyTorch OPT-350m + beam sampler, and the skinning network's sparse convolutions use a pure-PyTorch SubMConv3d (checked against real spconv: max abs error 1e-5). Ids and types are CCTech-prefixed, so ComfyUI-UniRig can stay installed alongside.

Node	What it does (POC defaults)
UniRig Skeleton Model Loader / UniRig Skin Model Loader	`skeleton.safetensors` / `skin.safetensors`; `precision` fp32 (POC) or bf16/fp16/auto
UniRig Load Mesh	a GLB/glTF/OBJ/STL from ComfyUI's `input/` or `output/` folder → MESH (e.g. `3d/triposg_00001_.glb` from SaveGLB)
UniRig Extract Skeleton	decimate above 50k faces, normalise to [-1, 1], 2048 seeded surface samples → Michelangelo encoder → 15-beam sampling (temperature 1.5, top-k 5, top-p 0.95, repetition penalty 3.0). `skeleton_template` mixamo = the 52-bone humanoid named `mixamorig:` (Mixamo-animation compatible), vroid = same bones as `J_Bip_`, articulationxl = a free-form skeleton for creatures and objects. Outputs the decimated MESH the rig binds to, the skeleton, and a bone listing
UniRig Apply Skinning	voxel geodesic prior (grid 196) + PTv3/Michelangelo/bone attention → per-vertex weights, then UniRig's reskin edge diffusion (1 pass) and 0.03 threshold
UniRig Rig to GLB	a skinned bind-pose GLB (`FILE_3D_GLB`): joint node tree, inverse bind matrices, top-4 weights per vertex — for `Save 3D Model`, `Save3DAdvanced`, `Preview 3D`

Place skeleton.safetensors (1.4 GB) and skin.safetensors (1.5 GB) from ChrisColeTech/unirig in ComfyUI/models/unirig/ — the same folder ComfyUI-UniRig uses, so an existing download is reused. Needs trimesh (skinning voxelization), addict and einops; fast-simplification gives the POC's exact decimation, without it core ComfyUI's QEM decimator is used. The seed also seeds the beam sampler and PTv3's point shuffling, so a re-run with the same seed gives the same rig.

tools/smoke_unirig.py (real ComfyUI modules, CPU) checks registration, MESH batch slicing, the sparse convolution against a dense conv3d, the GLB writer round trip through core Get3DComponents, and both real loaders; --rig mesh.obj runs the whole chain on CPU. Example graphs (API format) are in example_workflows/: image → TripoSG → SaveGLB, saved GLB → UniRig → rigged GLB, and the two chained.

TRELLIS.2 and Pixal3D (image → PBR-textured mesh, per-stage files)

ComfyUI core runs TRELLIS.2 and Pixal3D natively in pure PyTorch (Trellis2/Pixal3D Conditioning, Shape/Upsample/Texture stages, the TRELLIS VAE decoders and the 3d/texturing nodes), but its loaders take Comfy-Org's merged single files. These nodes, under 🤖 CCTech/TRELLIS.2, load the per-stage checkpoints as the owner publishes them — safetensors or GGUF (GGUF stays quantized through this pack's ops) — into core's own MODEL / VAE / CLIP_VISION / BACKGROUND_REMOVAL / MOGE_MODEL objects, so everything downstream is core. The tensors are identical; only the key prefixes differ (every file was checked key-for-key against core's modules). No CUDA extension wheels (flex_gemm, o_voxel, cumesh, flash-attn) are needed.

Node	What it does
TRELLIS.2 / Pixal3D Model Loader (per-stage)	sparse-structure, shape-512, shape-1024 and (optional) texture-1024 flow DiTs → one core MODEL. TRELLIS.2 vs Pixal3D is read from the weights (Pixal3D's projection heads); mixing the two is refused
TRELLIS.2 / Pixal3D VAE Loader (per-stage)	structure + shape decoders → shape VAE; texture decoder → texture VAE
TRELLIS.2 / Pixal3D DINOv3 Loader	`dinov3-vitl16` (Hugging Face format) → CLIP_VISION; add the NAF upsampler file for Pixal3D
TRELLIS.2 / Pixal3D BiRefNet Loader	the extras `birefnet` → core Remove Background
Pixal3D MoGe-2 Loader	`moge-2-vitl/model.pt` → core MoGe Inference → MoGe Geometry To FOV (Pixal3D's camera)
TRELLIS.2 / Pixal3D Prepare Image	the owner's sidecar preprocessing: 1024 cap, alpha > 0.8 box, centred square crop (TRELLIS.2 1.0×, Pixal3D 1.1×), subject on black
TRELLIS.2 Sparse Structure Cleanup	the sidecar's structure post-pass (fill enclosed holes, keep the shell), between VAE Decode Structure and Shape Stage

example_workflows/trellis2_image_to_textured_glb_api.json and pixal3d_image_to_textured_glb_api.json are the owner's sidecar runs rebuilt from these loaders and core's stages: 1024 cascade, 12 steps per stage, guidance 7.5 / rescale 0.7 / shift 5 for the structure, 7.5 / 0.5 for shape, 1.0 for texture (each with its guidance interval), then fill holes → narrow-band remesh at 1024 → decimate (TRELLIS.2 500k / Pixal3D 400k faces) → UV unwrap → bake base colour, metallic and roughness from the voxel field (2048 / 4096 px) → SaveGLB.

Files (folders models/trellis2/ and models/pixal3d/, registered for extra_model_paths.yaml under the same keys; dropdowns show trellis2/… and pixal3d/… paths):

From	Put under	Used by
ChrisColeTech/TRELLIS.2-4B-FP16 `fp16/` (`ss_flow_img_dit_1_3B_64`, `slat_flow_img2shape_dit_1_3B_512/1024`, `slat_flow_imgshape2tex_dit_1_3B_1024`, `shape_dec_next_dc_f16c32`, `tex_dec_next_dc_f16c32`), `microsoft/ckpts/ss_dec_conv3d_16l8_fp16`, `facebook/` (DINOv3)	`models/trellis2/` (any sub-folders)	TRELLIS.2
ChrisColeTech/Pixal3D `ckpts/` (safetensors) or ChrisColeTech/Pixal3D-GGUF `split/{Sparse,shape,texture,decoder}`	`models/pixal3d/`	Pixal3D
either repo's `extras/{birefnet,dinov3-vitl16,moge-2-vitl}`	`models/pixal3d/extras/`	both (BiRefNet), Pixal3D (DINOv3, MoGe)
`naf_release.pth` (valeoai/NAF; torch hub caches it in `~/.cache/torch/hub/checkpoints/`)	`models/pixal3d/naf/`	Pixal3D Conditioning

Pixal3D needs the NAF upsampler; without it the DINOv3 loader still works for TRELLIS.2. tex_512 checkpoints are not offered (both pipelines run the 1024 cascade). Shape encoders (*_enc_*) are not used.

tools/smoke_trellis2.py (real ComfyUI modules, CPU) checks the nodes, the two vendored stages, and every per-stage file against core's modules key by key from the headers alone; --full loads all of it for real through the nodes and runs BiRefNet, --forward adds a GGUF DiT forward.

MiniMax Music 3

The two-node convenience pipeline composes ComfyUI's native MiniMax Music 3 implementation; no model code is vendored. Download Comfy-Org/MiniMax-Music-3 and place its split files as follows:

File	Folder
`minimax_music3_text_encoder_pruned_int8_convrot.safetensors` (or a prepared GGUF)	`models/text_encoders`
`minimax_music3_dit_int8_convrot.safetensors` (or fp16/bf16/GGUF)	`models/diffusion_models`
`minimax_music3_dav.safetensors`	`models/vae`

The loader returns standard comfy MODEL / CLIP / VAE objects. Native ConvRot INT8 safetensors use Comfy's optimized mixed-precision path; GGUF remains packed through this pack's operations. Generate exposes caption, section-tagged lyrics, maximum duration (up to 360 seconds), seed, AR CFG/top-k, Euler/simple steps and DiT CFG. Duration is an upper bound: the autoregressive model can end a musically complete song earlier. DAV decoding switches to tiled mode automatically for long outputs and returns 44.1 kHz stereo AUDIO.

tools/smoke_minimax_music3.py validates the three real checkpoint load paths without generating a song.

Audio Stem Split lives in the same 🤖 CCTech/MiniMax Music category — separates any Comfy AUDIO (a MiniMax Music 3 generation or anything else) into an acapella and an instrumental with MelBandRoformer (public MelBandRoformer_fp16 checkpoint; only the inference architecture is ported, staged through Comfy model management like every other model in this pack). Useful standalone, and as a cleaner input for downstream captioning or remixing — an isolated stem gives an audio-language model a much cleaner read on the vocal or the arrangement than the full mix.

Credits

Fork of city96/ComfyUI-GGUF (Apache-2.0) — the loader, the quantization tooling and the custom ops are theirs; the backported PRs are their authors', listed in PR_BACKPORT.md. Report bugs in this fork here, not upstream.

clipproj.py is ported from nicolab28/ComfyUI-ClipProj (MIT, LICENSE-ClipProj); the matrices are theirs too, on Hugging Face. GGUF comes from llama.cpp. The Scenema Audio nodes are ported from ScenemaAI/ComfyUI-ScenemaAudio (MIT). TripoSG (vendor/triposg) is a pure-PyTorch port of VAST-AI-Research/TripoSG (MIT), with the RMBG-1.4 architecture from briaai/RMBG-1.4 (its weights carry Bria's own licence). vendor/unirig is PozzettiAndrea/ComfyUI-UniRig's inference package and stays GPL-3.0 (its LICENSE); UniRig itself is VAST-AI-Research/UniRig (MIT). vendor/trellis2 carries two stages from the owner's sidecar ports of microsoft/TRELLIS.2 and TencentARC/Pixal3D (both MIT); the networks themselves are ComfyUI core's. SeedVC inference is adapted from billwuhao/ComfyUI_Seed-VC and Plachtaa/seed-vc (Apache-2.0).

Model weights stay under their own licences — MiniMax-H3's is a custom one, worth reading before any use.

Ported from ComfyUI-ClipProj by nicolab28 (MIT, LICENSE-ClipProj), and checked against it: identical conditioning to the bit. The matrices are theirs as well — NicoLab28/ClipProj-MiniMax-H3. ↩

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
example_workflows		example_workflows
nodes		nodes
ops		ops
scripts/fp8_to_gguf		scripts/fp8_to_gguf
tests		tests
tools		tools
vendor		vendor
web		web
.gitignore		.gitignore
AGENTS.md		AGENTS.md
LICENSE		LICENSE
PR_BACKPORT.md		PR_BACKPORT.md
README.md		README.md
__init__.py		__init__.py
loader.py		loader.py
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt

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ComfyUI-GGUF-Loader

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Dual VAE Loader

Text Encoder + ClipProj Loader

Scenema Audio

LTX-2.3 A/V

Full-body person replacement (remove → pose-swap → blend, verified end-to-end on real GPU)

LTX-2.5 A/V

LTX Reference / Face Identity (ported from the 10s dev pack)

Preprocessors

Krea2 Control

Qwen-Image ControlNet

Flux Klein

LM Studio

Qwen3-TTS

TripoSG (image → 3D mesh)

UniRig (auto-rigging)

TRELLIS.2 and Pixal3D (image → PBR-textured mesh, per-stage files)

MiniMax Music 3

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ComfyUI-GGUF-Loader

What this fork adds

Installation

Usage

Nodes

Dual VAE Loader

Text Encoder + ClipProj Loader

Scenema Audio

LTX-2.3 A/V

Full-body person replacement (remove → pose-swap → blend, verified end-to-end on real GPU)

LTX-2.5 A/V

LTX Reference / Face Identity (ported from the 10s dev pack)

Preprocessors

Krea2 Control

Qwen-Image ControlNet

Flux Klein

LM Studio

Qwen3-TTS

TripoSG (image → 3D mesh)

UniRig (auto-rigging)

TRELLIS.2 and Pixal3D (image → PBR-textured mesh, per-stage files)

MiniMax Music 3

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