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
| { | |
| "bits": 4, | |
| "act_bits": 4, | |
| "data_type": "mx_fp", | |
| "act_data_type": "mx_fp", | |
| "group_size": 32, | |
| "act_group_size": 32, | |
| "sym": true, | |
| "act_sym": true, | |
| "act_dynamic": true, | |
| "enable_quanted_input": false, | |
| "static_attention_granularity": "tensor", | |
| "static_kv_granularity": "tensor", | |
| "autoround_version": "0.15.0", | |
| "block_name_to_quantize": "transformer_blocks,single_transformer_blocks", | |
| "quant_method": "auto-round", | |
| "packing_format": "auto_round:llm_compressor" | |
| } |