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
File size: 489 Bytes
59fb46e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"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"
} |