MOSS-Image for ComfyUI

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Independent DiT (UNET), text encoder and VAE files for ComfyUI MOSS-Image T8. Supports MOSS-Image Base and SFT with the original OpenMOSS inference pipeline. This is a community repackaging: tensor names, shapes, dtypes and raw data are preserved; configuration is embedded in safetensors metadata.

Files

Component File Size
Base DiT, BF16 diffusion_models/moss-image-base-bf16.safetensors 10.16 GB
SFT DiT, BF16 diffusion_models/moss-image-sft-bf16.safetensors 10.16 GB
Shared full Qwen3.5 TE, BF16 text_encoders/moss-image-qwen35-bf16.safetensors 16.79 GB
Shared FLUX.2 VAE, FP32 vae/moss-image-vae.safetensors 0.25 GB

Download each selected weight together with its matching .assets directory. These contain integrity manifests and the TE tokenizer. Base and SFT use the same TE and VAE; both variants together total about 37.4 GB. Components are never merged into one checkpoint.

Keep tokenizer/ limited to its three shipped files. Extra files and subdirectories are rejected by the loaders.

Quick start

  1. Install ComfyUI MOSS-Image T8 through ComfyUI Manager (publisher t8star, package moss-image-t8) or follow the manual installation instructions.
  2. From the ComfyUI directory, download models into models/ with the directory structure preserved:
hf download t8star/Moss-Image-Comfy --local-dir models --include "diffusion_models/*" --include "text_encoders/*" --include "vae/*"
  1. Restart ComfyUI and drag a SFT workflow PNG or Base workflow PNG onto the canvas. JSON workflows are also available.

For SFT only, replace "diffusion_models/*" with "diffusion_models/moss-image-sft-bf16.safetensors*".

Defaults: 1024×1024, 50 steps, CFG 3, shift 5, seed 42, empty negative prompt, unconditional skip layer 22 (zero-based). TE/DiT use BF16 and VAE decoding uses FP32. Both variants passed actual ComfyUI canvas generation at these settings on a 24 GB RTX 5090 Laptop GPU. See validation.

SFT generated example, with embedded workflow

Provenance and integrity

All 1,936 tensors were compared byte for byte with the source shards, including the complete TE vision weights. Configurations and tokenizer files were checked against the pinned upstream content IDs. Each .assets/manifest.json records the full weight SHA256 and provenance; SHA256SUMS covers all distributed weights and assets.

Models and pipeline by OpenMOSS; ComfyUI integration and repackaging by T8star. Apache-2.0; see NOTICE. This repository contains component files for the linked integration; use its dedicated loaders and official-pipeline sampler.

Links

Bilibili · YouTube · API · Free gallery · Online AI apps · ComfyUI bundle · Hugging Face

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