molbal/ideogram-4-gguf

text-to-image model by molbal

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Quantized GGUF diffusion transformer weights for Ideogram 4, converted from the original FP8 release for use with ComfyUI GGUF loader nodes. This repository contains GGUF files for the two Ideogram 4 diffusion components: 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. Use these models with the ComfyUI nodes from molbal/ComfyUI-GGUF. Install that custom node repository into your ComfyUI customnodes folder, then restart ComfyUI. Place the downloaded .gguf files in one of ComfyUI's diffusion model folders: 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. Each quant level is published for both the main transformer and the unconditional transformer. The main and unconditional models do not need to use the…

molbal/ideogram-4-gguf on Hugging Face A short preview, not the full document.

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Inside the original model card — Document outline
  1. Ideogram 4 GGUF
  2. ComfyUI Support
  3. Files
  4. Suggested Pairings
  5. Inference Measurements
  6. Peak Memory
  7. Relative Inference Speed
  8. Download
  9. Compatibility Notes
  10. License

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Model overview

molbal/ideogram-4-gguf is a text-to-image model repository published by molbal on Hugging Face. A library was not reported.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task
text-to-image
Library
Not reported
Recent downloads (30 days)
68,746
Cumulative likes
10
Hugging Face trending score
0
Reported safetensors parameters
Not reported
Architecture
Not reported
License
other
Access
Not gated by Hugging Face
Created
2026-06-12T17:10:42.000Z
Last modified
2026-06-15T20:08:14.000Z

Compatibility and lineage

Language coverage was not reported.

Reported base models: ideogram-ai/ideogram-4-fp8

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Model card and usage instructions

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Source and freshness

Source: Hugging Face Hub. Metadata observed 2026-09-21T06:36:49.979Z. Daily imports are snapshots, not real-time monitoring.

Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.

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