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README.md

@@ -11,7 +11,7 @@ tags: [text-to-image, diffusion, flow-matching, quantization, gguf, q4_k, ideogr
 
 A **GGUF Q4_K** (4.5 bits/weight) quantization of the Ideogram 4 DiT, 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). Quantization recipe + loader: see `recipe*.json`.
+> **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`).
 
 ## Why this one
 Q4_K is the **Pareto winner** on the quality-vs-memory frontier: at **10.4 GB** (the same
@@ -47,7 +47,9 @@ Files here:
 - `download_deps.py`, `usage.py` — setup + a minimal generation example.
 - `recipe-q4_k.json` — the exact quantization recipe / tensor layout.
 
-> `gguf_loader.py` is a **reference**: the dequant math is validated bit-exact; it loads only via this PyTorch
+> `gguf_loader.py` is a **reference**: the dequant math is validated bit-exact, but the
+> standalone loader hasn't been GPU-tested end-to-end yet — verify before production use.
+> This is **not** a llama.cpp / stable-diffusion.cpp file; it loads only via this PyTorch
 > path + the `ideogram4` pipeline.
 
 ## License