Instructions to use INCModel/Z-Image-Turbo-W4A16-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use INCModel/Z-Image-Turbo-W4A16-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("INCModel/Z-Image-Turbo-W4A16-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
Download transformer/quantization_config.json from INCModel/Z-Image-Turbo-W4A16-AutoRound: direct link, hf CLI and curl.
- Browser
- Download file 332 Bytes
-
https://huggingface.co/INCModel/Z-Image-Turbo-W4A16-AutoRound/resolve/main/transformer/quantization_config.json
- Command line
-
hf download hf://INCModel/Z-Image-Turbo-W4A16-AutoRound/transformer/quantization_config.json
-
curl -L -o quantization_config.json https://huggingface.co/INCModel/Z-Image-Turbo-W4A16-AutoRound/resolve/main/transformer/quantization_config.json
332 Bytes
| { | |
| "bits": 4, | |
| "data_type": "int", | |
| "group_size": 128, | |
| "sym": true, | |
| "batch_size": 1, | |
| "gradient_accumulate_steps": 8, | |
| "low_gpu_mem_usage": true, | |
| "autoround_version": "0.13.0", | |
| "block_name_to_quantize": "noise_refiner,context_refiner,layers", | |
| "quant_method": "auto-round", | |
| "packing_format": "auto_round:auto_gptq" | |
| } |