Instructions to use changwangss/FLUX.2-dev-MXFP8-RTN-FullEncoder-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use changwangss/FLUX.2-dev-MXFP8-RTN-FullEncoder-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("changwangss/FLUX.2-dev-MXFP8-RTN-FullEncoder-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: 621 Bytes
6ec783c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"quant_method": "auto-round",
"packing_format": "auto_round:llm_compressor",
"bits": 8,
"group_size": 32,
"sym": true,
"data_type": "mx_fp",
"iters": 0,
"model_free": true,
"autoround_version": "0.17.0",
"enable_quanted_input": false,
"block_name_to_quantize": "language_model.model.layers,vision_tower.transformer.layers",
"act_bits": 8,
"act_data_type": "mx_fp",
"act_dynamic": true,
"act_group_size": 32,
"act_sym": true,
"extra_config": {
"language_model.lm_head": {
"bits": 16,
"data_type": "float",
"act_bits": 16,
"act_data_type": "float"
}
}
} |