ideogram4-transformer-*.gguf: the main text-guided diffusion transformer.
ideogram4-unconditional_transformer-*.gguf: the unconditional transformer used by CFG workflows.

These files are not a complete standalone Ideogram 4 package. Your workflow still needs the other runtime assets expected by Ideogram 4 in ComfyUI, such as the text or multimodal encoder components and VAE.

ComfyUI Support

Use these models with the ComfyUI nodes from molbal/ComfyUI-GGUF. Install that custom node repository into your ComfyUI custom_nodes folder, then restart ComfyUI.

Place the downloaded .gguf files in one of ComfyUI's diffusion model folders:

text

ComfyUI/models/diffusion_models/
ComfyUI/models/unet/

Load the files with Unet Loader (GGUF) or Unet Loader (GGUF/Advanced) in an Ideogram 4 workflow that accepts separate main and unconditional diffusion models.

Files

Each quant level is published for both the main transformer and the unconditional transformer.

Quant	Main transformer	Unconditional transformer	Approx. size per file
Q4_0	`ideogram4-transformer-q4_0.gguf`	`ideogram4-unconditional_transformer-q4_0.gguf`	5.64 GB
Q4_1	`ideogram4-transformer-q4_1.gguf`	`ideogram4-unconditional_transformer-q4_1.gguf`	6.21 GB
Q5_0	`ideogram4-transformer-q5_0.gguf`	`ideogram4-unconditional_transformer-q5_0.gguf`	6.77 GB
Q5_1	`ideogram4-transformer-q5_1.gguf`	`ideogram4-unconditional_transformer-q5_1.gguf`	7.33 GB
Q8_0	`ideogram4-transformer-q8_0.gguf`	`ideogram4-unconditional_transformer-q8_0.gguf`	10.14 GB

The main and unconditional models do not need to use the same quant level.

Suggested Pairings

Main transformer	Unconditional transformer	Notes
`q8_0`	`q8_0`	Highest precision GGUF pair in this repo.
`q8_0`	`q5_1`	Keeps the main transformer high precision while reducing memory on the unconditional side.
`q8_0`	`q4_1`	Larger quality bias toward the main transformer with lower CFG-side memory.
`q5_1`	`q4_1`	Balanced quality and size.
`q5_0`	`q4_1`	Lower memory starting point.
`q4_0`	`q4_0`	Smallest available GGUF pair.

Inference Measurements

Peak Memory

The chart below shows peak RAM and VRAM measured during Ideogram 4 inference with different main/unconditional quant pairings, including an NVFP4 baseline for comparison.

Relative Inference Speed

The chart below compares relative per-iteration inference speed across quant pairings, with the NVFP4 + NVFP4 run used as the 100% reference in the chart.

Ideogram 4 inference speed among various quantization combinations

Measurements were taken by u/molbal on Windows 11 with an AMD Ryzen 7 6800H CPU, 48 GB RAM, and an RTX 3080 Laptop GPU with 8 GB VRAM. Exact memory use and speed will vary by workflow, image size, sampler settings, ComfyUI version, and loaded auxiliary models.

Download

Download the two files you want to pair from the Files tab, or use the Hugging Face CLI. For example:

huggingface-cli download molbal/ideogram-4-gguf ideogram4-transformer-q5_1.gguf ideogram4-unconditional_transformer-q4_1.gguf --local-dir ComfyUI/models/diffusion_models

Compatibility Notes

These are non-K GGUF quantizations intended for PyTorch dequantization in ComfyUI. K-quants are not included because this ComfyUI loading path does not use fused quantized linear kernels.

If a workflow fails to load these files, update molbal/ComfyUI-GGUF and confirm that both the main and unconditional transformer files are present in a ComfyUI diffusion model folder.

License

These files are derived from ideogram-ai/ideogram-4-fp8 and follow the Ideogram 4 non-commercial license.