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
File size: 667 Bytes
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"_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"
}
}
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