Text-to-Image
Diffusers
Safetensors
Flux2Pipeline
quantized
mxfp4
autoround
diffusion
autoquant-agent
Instructions to use INCModel3/FLUX.2-dev-MXFP4-RTN-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use INCModel3/FLUX.2-dev-MXFP4-RTN-AutoRound with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("INCModel3/FLUX.2-dev-MXFP4-RTN-AutoRound", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from INCModel3/FLUX.2-dev-MXFP4-RTN-AutoRound: direct link, hf CLI and curl.
- Browser
- Download file 2.06 kB
-
https://huggingface.co/INCModel3/FLUX.2-dev-MXFP4-RTN-AutoRound/resolve/main/README.md
- Command line
-
hf download hf://INCModel3/FLUX.2-dev-MXFP4-RTN-AutoRound/README.md
-
curl -L -o README.md https://huggingface.co/INCModel3/FLUX.2-dev-MXFP4-RTN-AutoRound/resolve/main/README.md
2.06 kB
| base_model: | |
| - black-forest-labs/FLUX.2-dev | |
| pipeline_tag: text-to-image | |
| license: other | |
| tags: | |
| - quantized | |
| - mxfp4 | |
| - autoround | |
| - diffusion | |
| - text-to-image | |
| - autoquant-agent | |
| # FLUX.2-dev-MXFP4-RTN-AutoRound | |
| ## Model Details | |
| This is a **MXFP4** (4-bit micro-scaling) quantization of [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev), a flagship text-to-image diffusion model. Generated by [AutoRound](https://github.com/intel/auto-round) with RTN (round-to-nearest, iters=0). | |
| - **Base model:** [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev) | |
| - **Quantization:** MXFP4 (W4A4), group_size=32 | |
| - **Method:** AutoRound RTN | |
| - **Model size:** ~62 GB (vs ~110 GB bf16) | |
| ## Quantization Details | |
| - **Scheme:** MXFP4 (data_type=mx_fp, bits=4, act_bits=4) | |
| - **Group size:** 32 | |
| - **Export format:** auto_round (vllm-omni compatible) | |
| - **Calibration:** coco2014, 28 steps, guidance 3.5 | |
| ## Evaluation | |
| Evaluated with vllm-omni diffusion harness (28 steps, guidance 3.5, 1024×1024, seed 42). | |
| | Benchmark | BF16 Baseline | MXFP4 Quantized | | |
| |---|---|---| | |
| | DrawBench CLIP | 32.48 | 32.44 | | |
| | DrawBench CLIP-IQA | 71.35 | 71.01 | | |
| | DrawBench ImageReward | 1.15 | 1.11 | | |
| | GenEval | 0.844 | 0.835 | | |
| MXFP4 quantization is nearly lossless vs the BF16 baseline (GenEval 0.835 vs 0.844, CLIP 32.44 vs 32.48). | |
| ## Usage | |
| ```python | |
| from vllm_omni.entrypoints.omni import Omni | |
| from vllm_omni.inputs.data import OmniDiffusionSamplingParams | |
| omni = Omni(model="INCModel3/FLUX.2-dev-MXFP4-RTN-AutoRound", mode="text-to-image") | |
| params = OmniDiffusionSamplingParams( | |
| height=1024, width=1024, seed=42, | |
| guidance_scale=3.5, num_inference_steps=28, num_outputs_per_prompt=1, | |
| ) | |
| out = omni.generate("a red bench in a park", sampling_params_list=[params]) | |
| ``` | |
| ## License | |
| Please follow the license of the original model [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev). | |
| _Produced with [autoquant-agent](https://github.com/) — agent-driven quantize + evaluate + self-heal._ | |