Instructions to use Disty0/Z-Image-Turbo-SDNQ-uint4-svd-r32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Disty0/Z-Image-Turbo-SDNQ-uint4-svd-r32 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Disty0/Z-Image-Turbo-SDNQ-uint4-svd-r32", 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: 1,328 Bytes
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"_class_name": "ZImageTransformer2DModel",
"_diffusers_version": "0.36.0.dev0",
"_name_or_path": "Tongyi-MAI/Z-Image-Turbo",
"all_f_patch_size": [
1
],
"all_patch_size": [
2
],
"axes_dims": [
32,
48,
48
],
"axes_lens": [
1536,
512,
512
],
"cap_feat_dim": 2560,
"dim": 3840,
"in_channels": 16,
"n_heads": 30,
"n_kv_heads": 30,
"n_layers": 30,
"n_refiner_layers": 2,
"norm_eps": 1e-05,
"qk_norm": true,
"quantization_config": {
"add_skip_keys": true,
"dequantize_fp32": false,
"group_size": 0,
"is_integer": true,
"is_training": false,
"modules_dtype_dict": {},
"modules_to_not_convert": [
"all_x_embedder",
"cap_embedder",
"all_final_layer",
"t_embedder",
"layers.0.adaLN_modulation.0.weight"
],
"non_blocking": false,
"quant_conv": false,
"quant_method": "sdnq",
"quantization_device": "xpu",
"quantized_matmul_dtype": null,
"return_device": "cpu",
"svd_rank": 32,
"svd_steps": 8,
"use_grad_ckpt": true,
"use_quantized_matmul": false,
"use_quantized_matmul_conv": false,
"use_static_quantization": true,
"use_stochastic_rounding": false,
"use_svd": true,
"weights_dtype": "uint4"
},
"rope_theta": 256.0,
"t_scale": 1000.0
}
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