Added custom GGUF loader note to README.md

README.md

@@ -9,9 +9,16 @@ tags: [text-to-image, diffusion, flow-matching, quantization, gguf, q4_k, ideogr
 
 # Ideogram 4 — GGUF Q4_K (Transformer Lab)
 
-A **GGUF Q4_K** (4.5 bits/weight) quantization of the Ideogram 4 DiT, for consumer GPUs.
+A **GGUF Q4_K** (4.5 bits/weight) quantization of the Ideogram 4 DiT, sized for consumer GPUs.
 
-> **Note:** this checkpoint is the **quantized DiT only** (both CFG branches). To run it you also need the **Qwen3-VL text encoder and VAE** from the base repo [`ideogram-ai/ideogram-4-fp8`](https://huggingface.co/ideogram-ai/ideogram-4-fp8) and the custom inference code at [`github.com/ideogram-oss/ideogram4`](https://github.com/ideogram-oss/ideogram4). The quantization recipe and loader are included **in this repo** (`recipe-q4_k.json`, `gguf_loader.py`).
+> :warning: **Not a llama.cpp / stable-diffusion.cpp file.** Despite the `.gguf` extension, this
+> loads **only** via the included PyTorch `gguf_loader.py` + the `ideogram4` pipeline. It is
+> **not** compatible with llama.cpp, stable-diffusion.cpp, Ollama, etc.
+
+> ℹ️ **Quantized DiT only.** This checkpoint is the DiT (both CFG branches). To generate you
+> also need the **Qwen3-VL text encoder and VAE** from the base repo [`ideogram-ai/ideogram-4-fp8`](https://huggingface.co/ideogram-ai/ideogram-4-fp8)
+> and the custom inference code at [`github.com/ideogram-oss/ideogram4`](https://github.com/ideogram-oss/ideogram4).
+> The quantization recipe and loader are included **in this repo** (`recipe-q4_k.json`, `gguf_loader.py`).
 
 ## Why this one
 Q4_K is the **Pareto winner** on the quality-vs-memory frontier: at **10.4 GB** (the same