Instructions to use WaveCut/ideogram-4-sdnq-uint4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/ideogram-4-sdnq-uint4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WaveCut/ideogram-4-sdnq-uint4", 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
| { | |
| "add_skip_keys": false, | |
| "dequantize_fp32": false, | |
| "dynamic_loss_threshold": null, | |
| "group_size": 0, | |
| "hadamard_group_size": 128, | |
| "is_integer": true, | |
| "is_training": false, | |
| "modules_dtype_dict": {}, | |
| "modules_quant_config": {}, | |
| "modules_to_not_convert": [ | |
| "encoder.down.0.block.0.norm1.weight", | |
| "encoder.down.0.block.0.norm2.weight", | |
| "encoder.down.0.block.1.norm1.weight", | |
| "encoder.down.0.block.1.norm2.weight", | |
| "encoder.down.1.block.0.norm1.weight", | |
| "encoder.down.1.block.0.norm2.weight", | |
| "encoder.down.1.block.1.norm1.weight", | |
| "encoder.down.1.block.1.norm2.weight", | |
| "encoder.down.2.block.0.norm1.weight", | |
| "encoder.down.2.block.0.norm2.weight", | |
| "encoder.down.2.block.1.norm1.weight", | |
| "encoder.down.2.block.1.norm2.weight", | |
| "encoder.down.3.block.0.norm1.weight", | |
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| "encoder.down.3.block.1.norm1.weight", | |
| "encoder.down.3.block.1.norm2.weight", | |
| "encoder.mid.block_1.norm1.weight", | |
| "encoder.mid.block_1.norm2.weight", | |
| "encoder.mid.attn_1.norm.weight", | |
| "encoder.mid.block_2.norm1.weight", | |
| "encoder.mid.block_2.norm2.weight", | |
| "encoder.norm_out.weight", | |
| "decoder.mid.block_1.norm1.weight", | |
| "decoder.mid.block_1.norm2.weight", | |
| "decoder.mid.attn_1.norm.weight", | |
| "decoder.mid.block_2.norm1.weight", | |
| "decoder.mid.block_2.norm2.weight", | |
| "decoder.up.0.block.0.norm1.weight", | |
| "decoder.up.0.block.0.norm2.weight", | |
| "decoder.up.0.block.1.norm1.weight", | |
| "decoder.up.0.block.1.norm2.weight", | |
| "decoder.up.0.block.2.norm1.weight", | |
| "decoder.up.0.block.2.norm2.weight", | |
| "decoder.up.1.block.0.norm1.weight", | |
| "decoder.up.1.block.0.norm2.weight", | |
| "decoder.up.1.block.1.norm1.weight", | |
| "decoder.up.1.block.1.norm2.weight", | |
| "decoder.up.1.block.2.norm1.weight", | |
| "decoder.up.1.block.2.norm2.weight", | |
| "decoder.up.2.block.0.norm1.weight", | |
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| "decoder.up.2.block.1.norm1.weight", | |
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| "decoder.up.2.block.2.norm1.weight", | |
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| "decoder.up.3.block.0.norm1.weight", | |
| "decoder.up.3.block.0.norm2.weight", | |
| "decoder.up.3.block.1.norm1.weight", | |
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| "decoder.up.3.block.2.norm1.weight", | |
| "decoder.up.3.block.2.norm2.weight", | |
| "decoder.norm_out.weight" | |
| ], | |
| "modules_to_not_use_matmul": [], | |
| "non_blocking": false, | |
| "quant_conv": true, | |
| "quant_embedding": false, | |
| "quant_method": "sdnq", | |
| "quantization_device": "cuda", | |
| "quantized_matmul_dtype": null, | |
| "return_device": "cpu", | |
| "sdnq_version": "0.1.9", | |
| "svd_rank": 32, | |
| "svd_steps": 8, | |
| "use_dynamic_quantization": false, | |
| "use_grad_ckpt": true, | |
| "use_hadamard": false, | |
| "use_quantized_matmul": false, | |
| "use_quantized_matmul_conv": false, | |
| "use_static_quantization": true, | |
| "use_stochastic_rounding": false, | |
| "use_svd": false, | |
| "weights_dtype": "uint4" | |
| } | |