ComfyUI-ClarkAirSana
Run Clark Air Sana 1.6B (ternary ~1.58-bit, GemLite INT2 kernels) natively under ComfyUI's KSampler.
Nodes (6)
Your words become conditioning here — and yes, you need two of them
The 2 GB text encoder that makes Sana understand English instead of tags
A blank 32-channel canvas Sana can actually paint on
495 MB of Sana that still runs under the boring old KSampler
Latents in, pixels out
The 1.2 GB codec that turns Sana's dense latents back into pictures
ComfyUI-ClarkAirSana
Run Clark Air Sana 1.6B — ternary (~1.58-bit) weights on real GemLite INT2 CUDA kernels — natively in ComfyUI under the standard KSampler. Self-contained: the Sana model, the Gemma text encoder, and the DC-AE VAE are all included, so there is no other custom node to install.
Quick start
- In ComfyUI-Manager, install Clark Air Sana and restart ComfyUI. (That's the only node pack you need. The Python deps install automatically.)
- Download the model → put it in
ComfyUI/models/clark_air_sana/: clark_air_sana_gemlite_comfy.safetensors (495 MB). - ⬇ Download the workflow: example_workflow.json → drag it onto the ComfyUI canvas → press Queue.
The Gemma text encoder and DC-AE VAE download themselves on the first run.
Requirements
- NVIDIA CUDA GPU. GemLite INT2 runs on Triton kernels, so Linux or WSL2 is the smooth
path. On native Windows you need
triton-windows(the deps file installs it automatically, but Triton on native Windows is community-supported and may not work on every setup — WSL2 is recommended). - Python deps (installed for you from
requirements.txt):gemlite,bitsandbytes,triton,transformers,diffusers,accelerate,timm,einops,safetensors,numpy. If your ComfyUI is the portable build, the Manager handles this; to do it by hand use the bundled Python:.\python_embeded\python.exe -s -m pip install -r ComfyUI\custom_nodes\ComfyUI-ClarkAirSana\requirements.txt.
Nodes
All provided by this pack (category ClarkAir/Sana):
| Node | Output | Role | |---|---|---| | Clark Air Sana Loader | MODEL | the GemLite INT2 ternary transformer | | Clark Air Gemma Loader / Encode | CONDITIONING | Gemma-2 text encoder (4-bit by default) | | Clark Air DC-AE VAE Loader / Decode | IMAGE | DC-AE latents → pixels | | Clark Air Sana Empty Latent | LATENT | 32-channel Sana latent |
Workflow
example_workflow.json (drag onto the canvas) wires:
Clark Air Gemma Loader ─┬─ Gemma Encode (positive) ─┐
└─ Gemma Encode (negative) ─┤
Clark Air Sana Loader ───────── MODEL ──────────────┼─ KSampler ─ Clark Air VAE Decode ─ SaveImage
Clark Air DC-AE VAE Loader ──── VAE ─────────────────┘ │
Clark Air Sana Empty Latent ─── LATENT ───────────────────┘
KSampler: euler / normal, 20 steps, cfg 4.5, 512×512 (verified end-to-end through ComfyUI's
KSampler). example_workflow_api.json is the same graph in API format.
Footprint (~3.2 GB)
| Component | Download |
|---|---|
| transformer (this repo) | 495 MB (ternary, GemLite INT2 + FP8 islands) |
| Gemma text encoder | ~2.1 GB (unsloth/gemma-2-2b-it-bnb-4bit, 4-bit; switch to Efficient-Large-Model/gemma-2-2b-it for fp16) |
| DC-AE VAE | 1.2 GB (loaded bf16) |
Notes
- Keep the transformer GPU-resident. GemLite holds packed codes as buffers that ComfyUI's
lowvram weight-streaming can't move; the loader pins the ~0.5 GB trunk on the GPU. Avoid
--lowvramfor it (Gemma/VAE are fine). - Works offline. Once the Gemma encoder and DC-AE VAE have been fetched once, the nodes load
them straight from the local cache with no network check, so a queue never stalls on
huggingface.cowhen you are offline. - The Sana model code under
sana/is vendored from ComfyUI_ExtraModels (Apache-2.0), which adapts NVlabs/Sana — seeNOTICE. This pack bundles it so it runs with no external node dependency.