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
| { | |
| "_class_name": "Ideogram4SDNQPipeline", | |
| "_diffusers_version": "0.39.0.dev0", | |
| "_name_or_path": "ideogram-ai/debug-ideogram-v4", | |
| "scheduler": [ | |
| "diffusers", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "Qwen3VLTextModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "Qwen2Tokenizer" | |
| ], | |
| "transformer": [ | |
| "sdnq", | |
| "Ideogram4Transformer" | |
| ], | |
| "unconditional_transformer": [ | |
| "sdnq", | |
| "Ideogram4Transformer" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKLFlux2" | |
| ], | |
| "quantization": { | |
| "method": "SDNQ", | |
| "weights_dtype": "uint4", | |
| "source_repo": "ideogram-ai/ideogram-4-fp8" | |
| } | |
| } | |