hoidhxd/SenseNova-U1.5-8B-GGUF-v2

SenseNova-U1.5-8B-MoT-Preview — GGUF Q4_0 (v2)

Q4_0 GGUF build of SenseNova-U1.5-8B-MoT-Preview, for running the model locally in ComfyUI on a consumer GPU.

v2 needs no patch file. The v1 repo required a hand-installed compatibility shim before it would load. This build keeps the offending tensors dense, so it works with the stock node as shipped. Same VRAM at runtime, ~900 MB more on disk. If you are on v1, switch.

File	SenseNova-U1.5-8B-MoT-Preview-Q4_0-v2.gguf
Size	10.13 GiB (10,875,444,576 bytes)
Source	16-shard BF16 release (~50 GB)
Fits	16 GB VRAM fully resident; 12 GB with layer offload
Shim required	No — verified on the stock node

The file must go in ComfyUI/models/gguf/, not ComfyUI/models/unet/ — see step 3.

Architecture: NEO-unify

SenseNova U1 is a native multimodal model — one graph handles text and pixels end to end.

• 🚫 No external text encoder (no CLIP, no T5).
• 🚫 No external VAE.

So the ComfyUI graph is just two nodes: a loader and a sampler. There is nothing else to wire up.

Quantization details

Converted directly from the BF16 safetensors. The type of every tensor is chosen by an explicit rule, not by a try/except fallback:

Rule	Type
1D tensors (bias, LayerNorm, RMSNorm) and anything ≤ 1024 params	F32
Tensors owned by a module that is not nn.Linear, plus the two FM embedders	F16
Conv kernels / anything whose last dim isn't a multiple of 32	F16
Everything else — the large 2D Linear weights	Q4_0

Of the 1116 tensors: 589 Q4_0, 11 F16, 516 F32. The Q4_0 set includes all 588 attention/MLP weights across the 42 layers and both _mot_gen branches plus lm_head, so image quality is unchanged from v1.

A *.plan.json manifest listing the exact type of every tensor is uploaded alongside the model.

What changed from v1

diffusers' GGUF quantizer only swaps nn.Linear for GGUFLinear. Every other module keeps the raw Q4_0 block bytes and crashes. v1 quantized five tensors it shouldn't have; v2 keeps them dense:

Tensor	Real module	Failure in v1
language_model.model.embed_tokens.weight	nn.Embedding	Embedding lookup returns rows of 4096 // 32 * 18 = 2304 instead of 4096 → RuntimeError: The size of tensor a (4096) must match the size of tensor b (2304)
fm_modules.timestep_embedder.mlp.{0,2}.weight	nn.Linear	modeling_fm_modules.py casts activations with t_freq.to(self.mlp[0].weight.dtype); on GGUFLinear that dtype is the storage dtype torch.uint8 → RuntimeError: mat1 and mat2 must have the same dtype, but got Byte and BFloat16
fm_modules.noise_scale_embedder.mlp.{0,2}.weight	nn.Linear	same as above

Six Conv2d kernels — patch_embedding (1024, 3, 16, 16), dense_embedding (4096, 1024, 2, 2) and fm_head.conv1/conv2 (…, 3, 3), each in both the vision tower and its _mot_gen twin — survived v1 only because their last dimension is the kernel size (16, 3 or 2), which isn't a multiple of 32: the quantizer raised and v1's except branch fell back to F16. That is exactly the "6 unaligned tensors" v1 reported. v2 declares them dense on purpose.

Note that dense_embedding is a 2×2 Conv2d that merges four vision patches into one LLM token (downsample_ratio: 0.5), despite the Linear-sounding name. lm_head is the only similarly-named tensor that really is an nn.Linear, and it stays Q4_0.

Cost: ~+900 MB on disk (the embedding is 622 M params: 350 MB at Q4_0 vs 1,245 MB at F16). Runtime VRAM is identical to v1 — v1's shim dequantized those same tensors to bf16 at load time anyway.

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Using this model in ComfyUI

Tested on Windows 11 + RTX 5060 Ti 16 GB, ComfyUI with a Python 3.13 venv. Linux is the same apart from paths.

Throughout, <ComfyUI> is your ComfyUI root (e.g. D:\ComfyUI) and <python> is the interpreter ComfyUI itself runs on — not your system Python. For a portable build that is <ComfyUI>\..\python_embeded\python.exe; for a venv install, <ComfyUI>\venv\Scripts\python.exe (Windows) or <ComfyUI>/venv/bin/python (Linux).

1. Install the custom nodes

Install ComfyUI-SenseNova-U1 through ComfyUI Manager, or clone it:

git clone https://github.com/OpenSenseNova/ComfyUI-SenseNova-U1 <ComfyUI>/custom_nodes/ComfyUI-SenseNova-U1

2. Install the runtime and the GGUF extra

The nodes need the sensenova-u1 runtime package plus the GGUF dependencies. Install both into ComfyUI's Python:

<python> -m pip install -r <ComfyUI>/custom_nodes/ComfyUI-SenseNova-U1/requirements.txt

<python> -m pip install "gguf>=0.10.0" "diffusers>=0.30.0" accelerate transformers

requirements.txt pulls sensenova-u1 from a GitHub release tarball, which is intentional — a git+https install would drag in hundreds of MB of evaluation submodules.

3. Put the GGUF where the node actually looks

ComfyUI/models/unet/ does not work. The SenseNova U1 Local Loader scans exactly two folder names, gguf and diffusion_models, and diffusion_models filters on ComfyUI's supported_pt_extensions, which does not include .gguf. Anything in unet/ is invisible to the node and the dropdown comes up empty.

Download into <ComfyUI>/models/gguf/:

hf download hoidhxd/SenseNova-U1.5-8B-GGUF-v2 SenseNova-U1.5-8B-MoT-Preview-Q4_0-v2.gguf --local-dir <ComfyUI>/models/gguf

If you already have the file elsewhere (a different drive, say) don't copy 10 GiB around — register the directory in <ComfyUI>/extra_model_paths.yaml under the key gguf:

ai_models:
    base_path: C:/Users/Admin/ai/models
    gguf: SenseNova-U1.5-8B-GGUF-v2

The key name is what matters. ComfyUI gives an unrecognised folder name an empty extension set, and an empty set means "no filter" — which is why .gguf files surface under gguf but not under diffusion_models.

Restart ComfyUI after adding files; the dropdown is built at startup.

4. Get the config and tokenizer

The GGUF holds weights only. The loader still needs the config and tokenizer from the base repo — but not the 50 GB of safetensors:

hf download sensenova/SenseNova-U1.5-8B-MoT-Preview --local-dir <somewhere>/SenseNova-U1.5-8B-MoT-Preview --include "*.json" "*.txt"

That yields ~5 MB:

config.json  added_tokens.json  special_tokens_map.json
tokenizer_config.json  vocab.json  merges.txt
model.safetensors.index.json

This directory is what you type into the loader's model_path.

5. Coming from v1? Remove the shim

If you previously installed <ComfyUI>/custom_nodes/sensenova_u1_embed_fix/, delete that folder. Leaving it in place is not fatal — it only touches tensors that are already dense here, so it becomes a no-op — but it is dead weight and will confuse the next person to read your install.

Fresh installs: skip this step. There is nothing to install.

6. Build the workflow

Two nodes, one link:

[SenseNova U1 Local Loader] --u1_model--> [SenseNova U1 Local Text to Image] --images--> [Save Image]

SenseNova U1 Local Loader

Input	Value
model_path	the config/tokenizer directory from step 4
sensenova_u1_src	leave as-is (auto-resolved)
device	cuda
dtype	bfloat16
attn_backend	auto
device_map	none — must be none when a GGUF is selected
max_memory	empty
vram_mode	full on 16 GB, balanced on 12 GB
gguf_checkpoint	SenseNova-U1.5-8B-MoT-Preview-Q4_0-v2.gguf

vram_mode replaced the old prefetch_count input:

• full — every weight stays on the GPU. Fastest, ~2× the offload modes.
• balanced — asynchronous layer prefetch, overlaps host→device copies with compute. Use this on 12 GB.
• low — synchronous one-layer-at-a-time swap. Smallest footprint, slowest.

device_map is for splitting across multiple GPUs and is mutually exclusive with vram_mode; leave it none for single-GPU use.

SenseNova U1 Local Text to Image

Input	Default	Notes
prompt	—	plain text, no encoder node
resolution	2048x2048|1:1	native sizes only, see below
cfg_scale	4.0
cfg_norm	none	global / channel / cfg_zero_star
timestep_shift	3.0	sampler schedule shift
cfg_interval_start / _end	0.0 / 1.0	window where CFG applies
num_steps	50	16 is fine for drafts
batch_size	1
seed	—
think_mode	false	model reasons before drawing; text on the think_text output

U1.5 samples only at its own native resolutions. Pick the aspect ratio you want and downscale afterwards if you need a specific pixel size:

Ratio	Pixels	Ratio	Pixels
1:1	2048×2048	2:1	2880×1440
16:9	2720×1536	1:2	1440×2880
9:16	1536×2720	3:1	3456×1152
3:2	2496×1664	1:3	1152×3456
2:3	1664×2496	4:3	2368×1760
3:4	1760×2368

Example prompt:

A cinematic, dynamic shot of a terrified old man frantically running away from a massive, shadowy monster in a dark, foggy forest, high contrast, 8k resolution, photorealistic.

Also available: SenseNova U1 Local Image Edit (image + instruction) and SenseNova U1 Local Interleave (alternating text and images). Ready-made graphs ship in the node's example_workflows/ folder.

7. VRAM and timing

Measured on an RTX 5060 Ti 16 GB with this v2 file on the stock node — no shim installed:

Run	Steps	Size	Wall time
t2i, full, includes loading the 10.13 GiB file	16	2048×2048	179 s
edit, balanced, 2.1 MP, includes reload	8	1440×1440	219 s

At vram_mode=full the weights sit at 10.13 GiB allocated after load; sampling 2048² runs at 11.84 GiB steady with a peak of 12.19 GiB allocated (12.7 GiB reserved by the allocator). The edit run in balanced mode peaked at just 4.85 GiB allocated, with the weights streamed from host memory.

batch_size=2 at 2048² peaked at ~14.2 GiB in our v1 tests, about as far as a 16 GB card goes — go balanced beyond that.

Image editing needs more room than generation. The edit node runs the source image and the generated one through the model together; at full with the node's stock 4.19 MP target it OOMs on 16 GB (11.81 GiB weights plus a 2.27 GiB allocation). Use vram_mode=balanced and lower the megapixel target to ~2.1 for editing.

Every run above includes a model reload, because each changed something in the loader's cache key. Changing vram_mode, model_path, dtype, device_map or the GGUF selection forces a full reload — keep them stable between generations and only the first run pays the load cost.

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Troubleshooting

Symptom	Cause	Fix
gguf_checkpoint dropdown is empty	file is in models/unet/	move it to models/gguf/ (step 3), restart
tensor a (4096) ... tensor b (2304)	you are loading the v1 file	use this v2 file, or install v1's shim
got Byte and BFloat16	you are loading the v1 file	as above
No module named 'sensenova_u1'	deps went into the wrong Python	reinstall with ComfyUI's interpreter (step 2)
OOM while editing	vram_mode=full + 4.19 MP	balanced, ~2.1 MP
Dropdown lists the file but load hangs	device_map is not none	set device_map=none

Download

hf download hoidhxd/SenseNova-U1.5-8B-GGUF-v2 SenseNova-U1.5-8B-MoT-Preview-Q4_0-v2.gguf --local-dir .

License

Inherits the license of the base model, sensenova/SenseNova-U1.5-8B-MoT-Preview.