MOSS-Image downloads: every file, with sizes
The official weights, the Comfy-Org single files, the community quantizations and the turbo LoRAs, as listed on Hugging Face on 18 Oct 2026. Start with the picker; the full list is below it.
Which files fit your GPU?
Pick your graphics card memory. These are the SFT files; swap sft for base in the name for Base.
24 GB or more · RTX 3090 / 4090 / 5090, A6000
| File | Size | Folder in ComfyUI/models |
|---|---|---|
| moss_image_sft_bf16.safetensors | 10.2 GB | diffusion_models |
| qwen3.5_9b_int8_convrot.safetensors | 9.75 GB | text_encoders |
| flux2-vae.safetensors | 0.34 GB | vae |
| Total download | 20.2 GB |
Full-precision transformer. A 5090 user reports 10–12 s for a 1024×1024 image at 30 steps in BF16.
16 GB · RTX 4060 Ti 16G / 4070 Ti Super / 4080
| File | Size | Folder in ComfyUI/models |
|---|---|---|
| moss_image_sft_int8_convrot.safetensors | 5.41 GB | diffusion_models |
| qwen3.5_9b_int8_convrot.safetensors | 9.75 GB | text_encoders |
| flux2-vae.safetensors | 0.34 GB | vae |
| Total download | 15.5 GB |
INT8 transformer, the one ComfyUI's own template loads, with the higher-precision INT8 text encoder. The 15.5 GB total does not have to fit at once: ComfyUI loads the text encoder for the prompt and the transformer for sampling.
12 GB · RTX 3060 12G / 4070 / 5070
| File | Size | Folder in ComfyUI/models |
|---|---|---|
| moss_image_sft_int8_convrot.safetensors | 5.41 GB | diffusion_models |
| qwen3.5_9b_w4a8_gptq.safetensors | 7.34 GB | text_encoders |
| flux2-vae_small.safetensors | 0.25 GB | vae |
| Total download | 13.0 GB |
Exactly the files in ComfyUI's MOSS-Image template. A 3060 12 GB tester measured a 9.7 GB peak with an INT8 transformer and about 137 s per 1-megapixel image at 50 steps.
8 GB · RTX 3070 / 4060 / 2080
| File | Size | Folder in ComfyUI/models |
|---|---|---|
| moss_image_sft_int4_convrot.safetensors | 3.04 GB | diffusion_models |
| qwen3.5_9b_w4a8_gptq.safetensors | 7.34 GB | text_encoders |
| flux2-vae_small.safetensors | 0.25 GB | vae |
| Total download | 10.6 GB |
Community INT4 transformer (needs a ComfyUI build with INT4 ConvRot). The other 8 GB route is the Q4_K_S GGUF, which its author ran on an RTX 3070 in low-VRAM mode; see the download page. The 7.3 GB text encoder will spill into system RAM, so have at least 16 GB of it.
6 GB or less · GTX 1660 / RTX 2060 / laptops
| File | Size | Folder in ComfyUI/models |
|---|---|---|
| MOSS-Image-SFT-Q3_K_M.gguf | 2.89 GB | diffusion_models |
| qwen3.5_9b_w4a8_gptq.safetensors | 7.34 GB | text_encoders |
| flux2-vae_small.safetensors | 0.25 GB | vae |
| Total download | 10.5 GB |
Thin ice: one user says 4-bit quants run on 6 GB with the text encoder offloaded to the CPU, nobody has posted numbers yet, and the GGUF needs the experimental ComfyUI-MOSS-GGUF nodes. The online demo is the easier way.
Mac (Apple Silicon) · M-series, through mflux
| File | Size | Folder in ComfyUI/models |
|---|---|---|
| vanch007/MOSS-Image-SFT-mlx-bf16 | 5.17 GB | mflux model |
| Total download | 5.17 GB |
A community MLX port (transformer 4.92 GB + VAE 0.25 GB) run with mflux-generate-moss-image, 25 steps and guidance 3 in its example. It needs an mflux build with MOSS-Image support; check the model card.
No GPU · browser only
Use a hosted demo: the official Space (50 steps, free with a Hugging Face account quota) or the 8-step distilled Space, which is much faster.
Official weights (Diffusers format)
For the Python pipeline, the Gradio app and the official ComfyUI plugin's HF Loader node. Each repo is about 27 GB: transformer 10.2 GB, Qwen3.5-9B text encoder 16.8 GB, VAE 0.25 GB.
| Download | Size | What it is | Where it goes |
|---|---|---|---|
| OpenMOSS-Team/MOSS-Image-SFT | 27.2 GB | Fine-tuned from Base on curated data. The one to generate with. | Point the HF Loader's model_path at the downloaded folder |
| OpenMOSS-Team/MOSS-Image-Base | 27.2 GB | No post-training or CFG distillation. For LoRA training and research. | Same |
| ModelScope mirrors | Same files, faster from mainland China. | Same |
Comfy-Org single files (native ComfyUI)
Load with the stock Load Diffusion Model, Load CLIP (type moss_image) and Load VAE nodes. Needs a ComfyUI build from 16 Oct 2026 or later.
| Download | Size | What it is | Where it goes |
|---|---|---|---|
| moss_image_sft_bf16.safetensors | 10.2 GB | SFT transformer, full BF16 | models/diffusion_models |
| moss_image_sft_int8_convrot.safetensors | 5.41 GB | SFT transformer, INT8. ComfyUI template default | models/diffusion_models |
| moss_image_base_bf16.safetensors | 10.2 GB | Base transformer, full BF16 | models/diffusion_models |
| moss_image_base_int8_convrot.safetensors | 5.41 GB | Base transformer, INT8 | models/diffusion_models |
| qwen3.5_9b_int8_convrot.safetensors | 9.75 GB | Text encoder, INT8 | models/text_encoders |
| qwen3.5_9b_w4a8_gptq.safetensors | 7.34 GB | Text encoder, 4-bit weights. ComfyUI template default | models/text_encoders |
| flux2-vae.safetensors | 0.34 GB | FLUX.2 VAE | models/vae |
| flux2-vae_small.safetensors | 0.25 GB | FLUX.2 VAE, small. ComfyUI template default | models/vae |
Community quantizations
Smaller transformers for less VRAM. All Apache 2.0, all marked experimental by their authors. Use the Comfy-Org text encoder and VAE with them.
| Download | Size | What it is | Where it goes |
|---|---|---|---|
| toxicdog INT4 ConvRot (SFT) | 3.04 GB | W4A4 transformer; re-uploaded with better scales after the first version showed streaks | models/diffusion_models; needs ComfyUI with INT4 ConvRot |
| realrebelai GGUFs (SFT and Base) | 2.25 GB | Q2_K 2.25 GB up to Q8_0 5.71 GB, plus W4A8 safetensors 3.33 GB. Base Q4_K_S tested on an RTX 3070 8 GB | Not the city96 GGUF loader alone: needs ComfyUI-MOSS-GGUF + ComfyUI-GGUF + the official plugin. Comfy-Org says GGUF will not be supported natively |
| WaveCut OrbitQuant W4A8 (SFT) | 3.03 GB | Transformer 3.03 GB with a 5.51 GB text encoder | See the model card |
| vanch007 MLX BF16 (SFT) | 5.17 GB | Apple Silicon port for mflux | mflux-generate-moss-image |
Faster sampling (turbo)
Distilled to 4–10 steps with no CFG. Details and settings on the turbo page.
| Download | Size | What it is | Where it goes |
|---|---|---|---|
| lodestones 8-step DMD LoRA | 1.19 GB | lora.safetensors at strength 1, or lora_latest.safetensors at 0.7. 8 steps, CFG 1 | models/loras, LoraLoaderModelOnly after the SFT model |
| toxicdog SFT Turbo INT8 | 5.41 GB | SFT with the 8-step distillation merged in. 8 steps, CFG 1, euler / simple | models/diffusion_models |
| toxicdog SFT Turbo INT8 AIO | 15.5 GB | The turbo transformer with text encoder and VAE in one checkpoint | models/checkpoints |
| darask0 4-step adapter (rapid-moss) | 1.19 GB | 4 steps, no CFG. 0.81 s per image on an H100 | Use its bundled native workflow; a plain LoRA loader merges it wrongly |
Fixes for SFT banding
SFT often paints faint vertical stripes over skies, fog and plain walls.
| Download | Size | What it is | Where it goes |
|---|---|---|---|
| MATLOWAI DeBand LoRA L37-41 | 1.13 GB | Main fix, strength 1.0. Add the L32-36 “extra” LoRA too if stripes remain | models/loras, LoraLoaderModelOnly |
| MATLOWAI DeBand INT8 (merged) | 5.41 GB | Ready-made files: DeBand (25 steps), DeBand+ and DeBand Fast (10 steps) | models/diffusion_models |
| Kijai experimental SFT LoRAs | 1.16 GB | Blocks 21–49 rank 512 at strength −0.5 removes stripes and the overblown SFT look | models/loras |
Online demos
Nothing to install.
| Download | Size | What it is | Where it goes |
|---|---|---|---|
| Official Space | The Gradio app from the repo, with the 20 example prompts | Browser | |
| 8-step DMD Space | The distilled model, much faster | Browser |
Speed and memory people have measured
Nobody has published a full VRAM table yet, so these are individual reports. Each one links to where it was posted.
| GPU | Setup | Size | Time per image | VRAM | Source |
|---|---|---|---|---|---|
| RTX 3060 12 GB | INT8 transformer, 50 steps, CFG 3 | 1 MP | 137 s | 9.7 GB peak | r/StableDiffusion test, 35 prompts |
| RTX 3060 12 GB | INT8 + 8-step DMD, CFG 1 | 1 MP | ~24 s | same test | |
| RTX 3060 12 GB | INT8 transformer, 50 steps | 2.1 MP | 340 s | same test | |
| RTX 3060 12 GB | 30 steps | ~1 MP | ~2 min 25 s | release thread | |
| RTX 3070 8 GB | Base Q4_K_S GGUF, low-VRAM mode | works | quant author's smoke test | ||
| RTX 5090 | BF16, 30 steps | 1024×1024 | 10–12 s | Comfy-Org weights thread | |
| RTX 5090 | BF16, 30 steps | 4K | ~4 min | Comfy-Org weights thread | |
| H100 | SFT, 50 steps, CFG 3 (transformer only) | 1024×1024 | 16.2 s | 4-step adapter card | |
| H100 | 8-step DMD (transformer only) | 1024×1024 | 1.54 s | 4-step adapter card | |
| H100 | 4-step adapter (transformer only) | 1024×1024 | 0.81 s | 4-step adapter card |
Which version should I download?
For making pictures, SFT. For training a LoRA, Base: the model card says Base keeps its training information intact precisely so the community can fine-tune it, and the developers ask you to keep the text encoder frozen. The comparison is on the Base vs SFT page.
If you use ComfyUI, take the Comfy-Org single files, not the official Diffusers folders: they load with stock nodes and the INT8 transformer is half the size. The official folders are for the Python pipeline, the Gradio app and the official plugin; the ComfyUI page explains the two routes.