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
| { | |
| "source_repo": "ideogram-ai/ideogram-4-fp8", | |
| "target_repo": "WaveCut/ideogram-4-sdnq-uint4", | |
| "quantization": { | |
| "weights_dtype": "uint4", | |
| "group_size": 0, | |
| "use_svd": false, | |
| "use_dynamic_quantization": false, | |
| "use_stochastic_rounding": false, | |
| "dequantize_fp32": false, | |
| "add_skip_keys": false | |
| }, | |
| "components": { | |
| "transformer": { | |
| "file": "model/transformer/diffusion_pytorch_model.safetensors", | |
| "storage_mb": 4979.658447265625, | |
| "num_state_tensors": 880, | |
| "source_materialized_storage_mb": 17698.838134765625, | |
| "name": "quantize_transformer", | |
| "elapsed_seconds": 112.6355704489979, | |
| "gpu_before_mb": 561, | |
| "gpu_after_mb": 649, | |
| "gpu_peak_mb": 36525, | |
| "torch_peak_allocated_mb": 0.0, | |
| "torch_peak_reserved_mb": 0.0, | |
| "component": "transformer" | |
| }, | |
| "unconditional_transformer": { | |
| "file": "model/unconditional_transformer/diffusion_pytorch_model.safetensors", | |
| "storage_mb": 4979.658447265625, | |
| "num_state_tensors": 880, | |
| "source_materialized_storage_mb": 17698.838134765625, | |
| "name": "quantize_unconditional_transformer", | |
| "elapsed_seconds": 108.67946223300532, | |
| "gpu_before_mb": 649, | |
| "gpu_after_mb": 649, | |
| "gpu_peak_mb": 36525, | |
| "torch_peak_allocated_mb": 0.0, | |
| "torch_peak_reserved_mb": 0.0, | |
| "component": "unconditional_transformer" | |
| }, | |
| "text_encoder": { | |
| "storage_mb": 4097.525390625, | |
| "source_materialized_storage_mb": 14435.587890625, | |
| "num_state_tensors": 904, | |
| "name": "quantize_text_encoder", | |
| "elapsed_seconds": 102.32456034698407, | |
| "gpu_before_mb": 649, | |
| "gpu_after_mb": 649, | |
| "gpu_peak_mb": 24477, | |
| "torch_peak_allocated_mb": 0.0, | |
| "torch_peak_reserved_mb": 0.0, | |
| "component": "text_encoder" | |
| }, | |
| "vae": { | |
| "file": "model/vae/diffusion_pytorch_model.safetensors", | |
| "storage_mb": 50.18652153015137, | |
| "num_state_tensors": 395, | |
| "source_materialized_storage_mb": 160.30573844909668, | |
| "name": "quantize_vae", | |
| "elapsed_seconds": 2.675335832987912, | |
| "gpu_before_mb": 649, | |
| "gpu_after_mb": 649, | |
| "gpu_peak_mb": 861, | |
| "torch_peak_allocated_mb": 0.0, | |
| "torch_peak_reserved_mb": 0.0, | |
| "component": "vae" | |
| } | |
| }, | |
| "scaffolding": { | |
| "snapshot": "/root/.cache/huggingface/hub/models--ideogram-ai--ideogram-4-fp8/snapshots/ee79a7237b519f1402ceacf952f30c8a31ec5073" | |
| } | |
| } | |