| --- |
| license: apache-2.0 |
| base_model: OpenMOSS-Team/MOSS-Image-SFT |
| base_model_relation: quantized |
| pipeline_tag: text-to-image |
| library_name: diffusers |
| language: |
| - en |
| - pl |
| tags: |
| - quantization |
| - torchao |
| - diffusion-transformer |
| - moss-image-sft |
| - nvfp4 |
| --- |
| |
| # MOSS-Image-SFT — NVFP4-Core |
|
|
| ## Release status |
|
|
| Experimental checkpoint — **further testing required**. Packed NVFP4 weights and saved-shard integrity were verified; native NVFP4 full 50-step inference, performance and image fidelity have **not** been validated. Short fallback / weight-reconstruction tests do not demonstrate native FP4 speed. |
|
|
| **Benchmark interpretation:** The [image gallery and measurements](https://huggingface.co/datasets/WiktorMatuszek/moss-zimage-quantization-benchmarks) are **reference examples for visual inspection**, not a required reproducibility package or guarantee of quality, speed, or VRAM usage on another setup. |
|
|
| **Original MOSS-Image-SFT DiT with genuinely packed NVFP4 storage weights.** The model is quantized from the original SFT checkpoint; there is **no** extra training, LoRA, distillation or architecture change. |
|
|
| | Property | This checkpoint | |
| |---|---| |
| | Quantization | Packed E2M1 4-bit weights, FP8 block scales and FP32 global scale | |
| | Coverage | **228 / 269** transformer Linear projections (precision-sensitive edge layers retained in BF16) | |
| | Stored DiT weights | **3.706 GiB**, **60.9%** smaller than original 9.467 GiB BF16 DiT | |
| | Checkpoint files | Three TorchAO/PyTorch `.bin` weight shards; **not** generic safetensors | |
| | Upstream revision | [OpenMOSS-Team/MOSS-Image-SFT](https://huggingface.co/OpenMOSS-Team/MOSS-Image-SFT), `fc5de0d56fc910cbca6f6625a70f5f582b26deb1` | |
| | Integrity | All 463 original state keys matched; shards reloaded and SHA-256 verified | |
|
|
| This repo contains the **transformer only**. The original Qwen text encoder, tokenizer, FP32 VAE, scheduler and pipeline code are loaded from the pinned upstream revision as needed. The stored DiT size is **not** total RAM/VRAM or whole-pipeline model size. |
|
|
| ## Evidence and measured limits |
|
|
| **Weights:** all 228 quantized Linear weight matrices compared with the original BF16 tensors: parameter-weighted MSE **1.98e-05**, SQNR **20.47 dB**, cosine **0.995515**. These are *weight-space errors*, not generated-image correctness. Detailed [per-layer measurements](benchmark/weight_quantization_metrics.json), [quantization receipt](QUANTIZATION_RECEIPT.json), [checkpoint integrity](COMPLETE.json). |
|
|
| **Original SFT50 image study:** **Not measured** with the original 50-step pipeline and native runtime in the recorded SM80 benchmark. Checkpoint weight verification does not imply that image quality or GPU execution passed. |
|
|
| **Runtime qualification:** native NVFP4 execution did not pass the original SFT50 kernel check in the recorded SM80 setup. A distinct short fallback reconstructed weights for higher-precision computation; it is **not native FP4 speed evidence**, nor a quality-accepted 50-step run. See [kernel and image study](https://huggingface.co/datasets/WiktorMatuszek/moss-zimage-quantization-benchmarks). |
|
|
| ## Use with original SFT inference |
|
|
| Install a CUDA-compatible PyTorch for your GPU, followed by the repository requirements. The conversion environment was PyTorch 2.7.0+cu128 / TorchAO 0.12.0+cu128; compatibility with packed tensor subclasses and native kernels is device dependent. |
|
|
| ```bash |
| hf download WiktorMatuszek/MOSS-Image-SFT-NVFP4-Core --local-dir ./moss-quant |
| cd moss-quant |
| python -m pip install -r requirements.txt |
| |
| # Check all real saved shards before using the model |
| python generate.py --verify-only |
| |
| # Original MOSS-SFT: 50 denoising steps, CFG 3, 1024px |
| python generate.py --prompt-file example_layout.json --output image.png |
| ``` |
|
|
| The launcher downloads the **unchanged missing upstream components**, not duplicate original BF16 DiT weights. Optional [Qwen FP8 encoder](https://huggingface.co/WiktorMatuszek/MOSS-Image-SFT-Qwen3.5-FP8-WO) / [Flux2 VAE FP16](https://huggingface.co/WiktorMatuszek/MOSS-Image-SFT-VAE-FP16) can be selected with `--encoder-repo` and `--vae-repo`; that combination needs its own end-to-end quality validation. |
|
|
| ## Files and limitations |
|
|
| `transformer/` contains the quantized checkpoint; `generate.py`, `run_quantized_sft.py`, `compact_components.py` contain the strict pinned loader; `QUANTIZATION_RECEIPT.json` and `COMPLETE.json` record checksums. Frozen prompt suites are archived once in the [research dataset](https://huggingface.co/datasets/WiktorMatuszek/moss-zimage-quantization-benchmarks/tree/main/research/moss50-20261021), not copied into this model repo. |
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| Pixel similarity does **not** prove correct Polish spelling, photorealism, aesthetic quality or native-kernel support. Test speed and GPU memory on your intended stack. The full cross-format gallery, including explicitly missing measurements, is maintained **once** in the [benchmark dataset](https://huggingface.co/datasets/WiktorMatuszek/moss-zimage-quantization-benchmarks). |
|
|
| **Attribution:** independently quantized from [OpenMOSS-Team/MOSS-Image-SFT](https://huggingface.co/OpenMOSS-Team/MOSS-Image-SFT) at `fc5de0d56fc910cbca6f6625a70f5f582b26deb1`; original Apache-2.0 license in [LICENSE](LICENSE). Not an official OpenMOSS-Team release. |
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|
